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100% Clean Electricity by 2035 Study

An NREL study shows there are multiple pathways to 100% clean electricity by 2035 that would produce significant benefits exceeding the additional power system costs.

For the study, funded by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy, NREL modeled technology deployment, costs, benefits, and challenges to decarbonize the U.S. power sector by 2035, evaluating a range of future scenarios to achieve a net-zero power grid by 2035.

The exact technology mix and costs will be determined by research and development, among other factors, over the next decade. The results are published in Examining Supply-Side Options To Achieve 100% Clean Electricity by 2035 .

Scenario Approach

To examine what it would take to achieve a net-zero U.S. power grid by 2035, NREL leveraged decades of research on high-renewable power systems, from the Renewable Electricity Futures Study , to the Storage Futures Study , to the Los Angeles 100% Renewable Energy Study , to the Electrification Futures Study , and more.

NREL used its publicly available flagship  Regional Energy Deployment System   capacity expansion model to study supply-side scenarios representing a range of possible pathways to a net-zero power grid by 2035—from the most to the least optimistic availability and costs of technologies.

The scenarios apply a carbon constraint to:

• Achieve 100% clean electricity by 2035 under accelerated demand electrification
• Reduce economywide, energy-related emissions by 62% in 2035 relative to 2005 levels—a steppingstone to economywide decarbonization by 2050.

For each scenario, NREL modeled the least-cost option to maintain safe and reliable power during all hours of the year.

Key Findings

Technology deployment must rapidly scale up.

In all modeled scenarios, new clean energy technologies are deployed at an unprecedented scale and rate to achieve 100% clean electricity by 2035. As modeled, wind and solar energy provide 60%–80% of generation in the least-cost electricity mix in 2035, and the overall generation capacity grows to roughly three times the 2020 level by 2035—including a combined 2 terawatts of wind and solar.

To achieve those levels would require rapid and sustained growth in installations of solar and wind generation capacity. If there are challenges with siting and land use to be able to deploy this new generation capacity and associated transmission, nuclear capacity helps make up the difference and more than doubles today’s installed capacity by 2035.

Across the four scenarios, 5–8 gigawatts of new hydropower and 3–5 gigawatts of new geothermal capacity are also deployed by 2035. Diurnal storage (2–12 hours of capacity) also increases across all scenarios, with 120–350 gigawatts deployed by 2035 to ensure demand for electricity is met during all hours of the year.

Seasonal storage becomes important when clean electricity makes up about 80%–95% of generation and there is a multiday to seasonal mismatch of variable renewable supply and demand. Across the scenarios, seasonal capacity in 2035 ranges about 100–680 gigawatts.

Significant additional research is needed to understand the manufacturing and supply chain associated with the unprecedent deployment envisioned in the scenarios.

Significant Additional Transmission Capacity

In all scenarios, significant transmission is also added in many locations, mostly to deliver energy from wind-rich regions to major load centers in the eastern United States. As modeled, the total transmission capacity in 2035 is one to almost three times today’s capacity, which would require between 1,400 and 10,100 miles of new high-capacity lines per year, assuming new construction starts in 2026.

Climate and Health Benefits of Decarbonization Offset the Costs

NREL finds in all modeled scenarios the health and climate benefits associated with fewer emissions offset the power system costs to get to 100% clean electricity.

Decarbonizing the power grid by 2035 could total $330 billion to $740 billion in additional power system costs, depending on restrictions on new transmission and other infrastructure development. However, there is substantial reduction in petroleum use in transportation and natural gas in buildings and industry by 2035. As a result, up to 130,000 premature deaths are avoided by 2035, which could save between $390 billion to $400 billion in avoided mortality costs.

When factoring in the avoided cost of damage from floods, drought, wildfires, and hurricanes due to climate change, the United States could save over an additional $1.2 trillion—totaling an overall net benefit to society ranging from $920 billion to $1.2 trillion.

Necessary Actions To Achieve 100% Clean Electricity

The transition to a 100% clean electricity U.S. power system will require more than reduced technology costs. Several key actions will need to take place in the coming decade:

• Dramatic acceleration of electrification and increased efficiency in demand
• New energy infrastructure installed rapidly throughout the country
• Expanded clean technology manufacturing and the supply chain
• Continued research, development, demonstration, and deployment to bring emerging technologies to the market.

Failing to achieve any of the key actions could increase the difficulty of realizing the scenarios outlined in the study.

Study Resources

Full report, supporting materials.

Download the technical report, Examining Supply-Side Options To Achieve 100% Clean Electricity by 2035 .

Download the report overview infographic and a 1-slide summary brief deck or a 10-slide summary brief deck .

Paul Denholm

Principal Energy Analyst

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Renewable Energy Policy in Cities: Selected Case Studies

Browse by theme.

• Dezhou, China   which has actively supported the establishment of renewable energy industries with the Dezhou Economic Development Zone for solar technology
• Chemnitz, Germany  where the local government enabled the formulation of strategies to use renewable sources and in 2008 developed the Integrated Climate Protection Programme (Integriertes Klimaschutzprogramm).
• Belo Horizonte, Brazil which has reduced greenhouse gas (GHG) emission substantially and, since 2007, turning a closed landfill site into a waste-to-energy facility.
• Austin, US where the GreenChoice Program active since 2001 has stimulated the initial demand for renewable-based electricity, facilitating municipal and commu­nity procurement of renewable energy.
• Sydney, Australia, and Nagpur, India where energy efficiency and renewable energy have reduced emissions from public street lights.
• Sao Paolo, Brazil where a local regulation requires new residential, commercial and industrial buildings to install solar water heating systems (SWH) to cover at least 40% of the energy used for heating water.
• Malmo, Sweden which set targets significantly more ambitious than either the European Union target for Sweden (49% by 2020) or the national plan (50% by 2020), so that the city is expected to be climate neutral with municipal operations run on 100% renewable energy by 2030.

Additional analyses

100% renewable energy scenarios: supporting ambitious policy targets, green hydrogen for sustainable industrial development: a policy toolkit for developing countries, north africa: policies and finance for renewable energy, renewable energy market analysis: mano river union region, the breakthrough agenda report 2022, related content.

Record Growth in Renewables, but Progress Needs to be Equitable

Innovative Policymaking is Crucial to Drive Green Hydrogen Market and Ensure its Sustainable Production

Renewables Jobs Nearly Doubled in Past Decade, Soared to 13.7 Million in 2022

G7 Communiqué Echoes IRENA's Call for Rapid Deployment of Renewables

Under IRENA's ETAF, Masdar and AIIB Reach Financial Close on 3 Uzbek Solar Projects

Ørsted’s renewable-energy transformation

To stop climate change, companies in every industry must rapidly reduce their carbon emissions. That is no easy task, but a few businesses show it can be done. Ørsted, an energy company based in Denmark, stands out as an example. Twelve years ago, when it was called DONG Energy, the company earned most of its revenues by selling heat and power, 85 percent of which came from coal. Then, in 2009, management announced a major strategic shift: the company would seek to generate 85 percent of heat and power from renewable sources by 2040.

Ørsted invested aggressively in offshore wind and phased out coal. By 2019, it had become the world’s largest producer of offshore-wind energy. The company also raised its renewable-generation share to 86 percent—hitting its target 21 years ahead of schedule. In an interview with McKinsey, the CEO of Ørsted’s offshore-wind business, Martin Neubert, tells the story of the company’s transformation: the strategic decision that started it all, the changes it went through, and the outlook for the future. (The remarks below have been condensed and edited for clarity.)

McKinsey: Back in 2008, DONG Energy was a profitable and stable conventional-energy company. How did the idea of pivoting to renewables come up?

Martin Neubert: At that time, DONG Energy was largely a domestic Danish energy company. Eighty-five percent of our power and heat production was powered by coal, and 15 percent by renewables. For us, one key factor supporting the decision to rethink our strategy in favor of renewables was the failed attempt to develop a 1,600-megawatt coal-fired power plant project, called Lubmin, in Northeast Germany.

We had made substantial investments in this greenfield project during the more than six years we spent trying to develop it. And while the project was supported by the German federal government, we experienced strong local opposition against the idea of building a coal-fired power plant on the Mecklenburg-Vorpommern coastline. This was the first clear sign telling us that the world was beginning to move in a different direction, and we concluded that there was no sustainable way of realizing the project. Also, in 2009, the global renewable-energy agenda was positioned strongly at the United Nations COP15 [15th Conference of the Parties] climate summit in Copenhagen, supported both by the Danish government and by our board of directors.

McKinsey: How did management assess the company’s position and its ability to shift toward renewables?

Martin Neubert: In 2008–09, we formulated a new strategy and vision called 85/15, stating that we wanted to change our generation mix from 85 percent conventional, 15 percent renewable to 85 percent renewable, 15 percent conventional. The 85/15 split, which was decided on by executive management, reflected the ambition to conduct a complete turnaround of our generation mix. It also took into account that DONG Energy had spent three decades establishing itself as a company focused on the generation of conventional fuels. So the expectation was that such a turnaround would have to be completed within one generation, or the equivalent of 30 years.

At the time, I don’t think anyone thought we would turn our generation mix upside down within only ten years. But that was not the discussion then. Instead, we discussed what our future growth areas should be: areas where we had critical mass, where we had the right competences, and where we could differentiate ourselves. It became clear that one was wind power, which three of the six companies that merged to become DONG Energy in 2006 had already pursued.

Onshore wind was well established. We had a sizeable portfolio of projects in Poland and Sweden, and we had been involved in projects in Spain and Greece. As for offshore wind, we had early-stage operating projects in Denmark and the United Kingdom and large-scale development projects. That gave us critical mass in wind when we formulated our vision.

We also had a team of 50 or 60 people working on renewable-energy projects. Some had spent their careers on these technologies, particularly onshore wind. That gave us substantial in-house expertise, backed by a clear understanding of what it would take to develop wind power, technology-wise.

The 85/15 split, which was decided on by executive management, reflected the ambition to conduct a complete turnaround of our generation mix. It also took into account that DONG Energy had spent three decades establishing itself as a company focused on the generation of conventional fuels. So the expectation was that such a turnaround would have to be completed within one generation, or the equivalent of 30 years.

McKinsey: Back then, the technology landscape for offshore wind looked very different from what it looks like now. How did that factor into your thinking?

Martin Neubert:  At the time, no offshore-wind projects bigger than 160 megawatts had been built. So we had to ask how we could build large-scale offshore-wind projects in a different way. Could we move from building one highly customized offshore-wind project every two or three years to building one or two more standardized projects every year? What would it take to go from handcrafting to serial production?

Answering that question involved a 360-degree review: the supply chain, our competencies, the financing models. We concluded that we could not do it alone. One challenge was installation. The installation companies in the market were small. We found a considerable risk that they could go bankrupt during a project. That led us to acquire A2SEA as an installation supplier.

We would also need strong partnerships with suppliers of turbines, foundations, and cables. Turbines were a particular issue. Since no purpose-built installation vessels existed, we reasoned that we would benefit from working with a manufacturer on the design, layout, and funding of second-generation installation vessels. Siemens quickly realized that offshore wind could develop into a large industry. We entered a partnership with them, which included the delivery of 500 3.6-megawatt turbines. At the time, it was one of the largest energy agreements Siemens had ever made.

McKinsey: How did executives and staff react to the decision to take the company in a new direction?

Martin Neubert: There was internal pressure to keep DONG Energy the same. It wasn’t unexpected, because we had spent three decades turning the company into a traditional fossil-fuel company. Fossil fuels were our core competence and the focus of our growth strategy. Our employees also perceived that we were the world’s best at running coal-fired power plants, and a benchmark for the industry. The skepticism was broad and profound.

Ultimately, though, internal skepticism receded. In 2012, when Henrik Poulsen had just joined as CEO, our portfolio of assets and activities had high exposure to gas and gas-fired power plants. As gas prices dropped in the United States, vast amounts of surplus American coal ended up in Europe, where it replaced gas as the preferred fuel for power generation. That caused us financial difficulties, which made it easier for people to accept the new focus on offshore wind and on the exploration and production of oil and gas, and the moves to divest noncore businesses.

We began implementing the new strategy by establishing a wind-power business unit. I think those of us who were asked to join this business unit saw it as the beginning of an interesting journey. A group of strong European utilities was active in UK offshore wind at the time. We all thought that something big was going on and that the UK would be the right place to pursue offshore-wind projects at industrial scale.

That proved to be the case when the UK government strengthened its support for offshore wind to help make these projects financially viable. If that hadn’t happened, I’m not sure that we would have progressed as fast as we did.

McKinsey: Getting into offshore wind required a multiyear effort to sell holdings and build up new assets. How did management secure the necessary capital even as the company was exiting businesses that were reliable sources of cash?

Martin Neubert: We had multiple new projects in the UK that needed funding. One model would have involved financing them with external debt and then divesting once the projects were operational. But raising debt for each project would not have worked well with our group-level funding strategy. Another approach, partnering with electric utilities, would have been too complicated, because these companies had their own asset portfolios and strategies.

We needed financial partners that could deliver capital and manage their investments while relying upon our experience constructing and operating offshore-wind projects. One structural issue, however, was that we did not want to use project financing, whereas many of our financial investors preferred or were even required to leverage their investment via project financing.

This led us to develop the “farm down” model, in which we could fund our half of a project on our balance sheet and partners could use project financing to fund the rest. With farm-downs happening before commissioning, we provided investors with turnkey project offerings, which would protect them from risks we can manage best, including development, construction, and operating risks. That model resonated with the Danish pension funds, and later with Dutch and Canadian pension funds and other investors.

Had we not developed the farm-down model, we couldn’t have funded all these projects in Europe. And the structure that we innovated became widely used in the industry.

Powering up sustainable energy

McKinsey: What organizational changes took place as Ørsted’s portfolio shifted toward renewables?

Martin Neubert: By 2012, our wind-power business unit had grown to hundreds of employees. But it was still working like a start-up. To support new projects, we added whatever resources were needed, which led to inefficiencies. We lacked a proper organizational structure and operating model.

Correcting that was one of the key accomplishments of my predecessor, Samuel Leupold. He introduced our first real operating model, establishing global functions, clear project governance, and a product-line organization that systematically reduced the cost of offshore-wind electricity by eliminating ad hoc or project-specific sourcing and procurement.

During the past three years, Ørsted has also cultivated a “one company” approach spanning our business units. For example, we have established a management-team forum, consisting of all EVPs and SVPs, who meet four times a year to talk about our strategy and strategic enablers such as talent and digital. That forum facilitates open discussions to break silos, align our approach, and build a strong network among senior leaders. In addition, we have reestablished our leadership-forum meetings for our top 400 leaders.

McKinsey: Ørsted has made significant moves in recent years. Can you talk about those, and the rationale for them?

Martin Neubert: The strategic steps we’ve taken during the past three to five years have focused on turning Ørsted into a global renewable-energy major. The first step was divesting our oil and gas business, which concentrated our business almost entirely on renewables. We also invested in the conversion of our domestic heat and power plants, enabling them to move away from coal toward biomass. As a result, we will exit coal in 2023, and our power generation will be carbon neutral in 2025.

In 2016, we completed our IPO, and DONG Energy, which we were still called at the time, became a publicly listed company. The IPO provided us with the flexibility and access to equity that we need to fund growth. The IPO also gave institutional and retail investors an opportunity to take part in our green transition, while sharpening our profile as a renewable pure-play.

Within the past couple of years, we have reentered the onshore-wind market and moved into solar PV [photovoltaic] and storage solutions. These moves will help diversify our technology mix so we can better meet the demands of our customers. What’s important to note is that we are moving into these technologies at scale. North America, for example, is a large market for onshore wind and storage solutions, and we are investing there. Everything we do reflects our vision to create a world that runs entirely on green energy. And while offshore wind has the potential to power the world, we’re convinced that a broader technology mix will support the growth of our company even better.

Within the past couple of years, we have reentered the onshore-wind market and moved into solar PV and storage solutions. These moves will help diversify our technology mix so we can better meet the demands of our customers. Everything we do reflects our vision to create a world that runs entirely on green energy.

McKinsey: Ørsted’s transformation into an offshore-wind leader has been complete for some time. What opportunities do you see for growth in that market?

Martin Neubert:  Our ambition is to remain the global leader in offshore wind. In the past two to three years, offshore wind has expanded from a predominantly European market to a global market. We’ve been a first mover as that shift has occurred. We were the first European developer that went into large-scale offshore wind in the US. We were also the first foreign offshore-wind developer to enter Taiwan. Within a few years, we have developed sizable project portfolios in both markets.

To support our growth, we recently reorganized our offshore-wind business and established four new regions. Moving closer to different markets is important for navigating their development. It also helps with commercial matters like owning wind farms. At the same time, we want to keep the scale advantages, leverage, and standards that our global operations and EPC [engineering, procurement, construction] functions deliver, and so they work closely with our regions.

McKinsey: New horizons for change in the energy sector are coming into view. How does management keep working hard to ensure that Ørsted remains a leader in offshore wind, while challenging itself to gain a strong position in the energy industry’s next evolutionary phase?

Martin Neubert: We ask ourselves that regularly. And I have been asked many times, by investors, by the media, and by people within our organization, if we are at risk, considering that bottom-fixed offshore wind is our bread and butter. We value our global leadership position in offshore wind, and we want to retain that. Obviously, we don’t want to miss out on major developments—for example, in floating offshore wind. But we must respond as the needs of our customers change.

The ability to reinvent ourselves has proven to be key. In 2006, DONG Energy consisted of some oil and gas licenses. Then it reinvented itself through the merger of six domestic energy companies. A few years later, the company reinvented itself again by establishing a wind-power business unit that became a global leader within a few years. Scanning new horizons and spotting new business areas are essential to Ørsted’s strategy and our ambition to become a global renewable-energy major.

Martin Neubert is executive vice president and CEO of offshore wind at Ørsted. This interview was conducted by Christer Tryggestad , a senior partner in McKinsey’s Oslo office.

This article was edited by Josh Rosenfield, an executive editor in the New York office.

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• Original article
• Open access
• Published: 05 September 2016

Smart energy systems for smart city districts: case study Reininghaus District

• Stephan Maier 1  

Energy, Sustainability and Society volume  6 , Article number:  23 ( 2016 ) Cite this article

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Dense settlement structures in cities have high demands of energy. Usually, these demands exceed the local resource availability. Individually developed supply options to cover these demands differ from place to place and can also vary within the boundaries of a city. In a common sense of European governance, cities are pushed to save energy, increase renewables and reduce import dependency on fossil fuels. There are many innovative concepts and technologies available to tackle these needs. The paper provides a comprehensive methodology for planning and assessing the development of ‘smart’ energy systems leading to complex energy provision technology networks using different on-site as well as off-site resources.

The use of the P-graph (process-graph) method allows the optimisation of energy systems by using different energy sources for heating, storing and cooling. This paper discusses this method in the development of an urban brown field, the premises of the Reininghaus District , a former brewery in the city of Graz in Austria. The case study is interesting as it combines on-site energy sources (e.g. solar heat and photovoltaic) with nearby industrial waste heat and cooling at different temperatures and grid-based resources such as existing district heating, natural gas, and electricity. The case study also includes the competition between centralised technologies (e.g. large scale combined heat and power and heat pumps with district heating grids) and decentralised technologies (e.g. small scale combined heat and power, single building gas boilers, solar collectors, etc. in buildings).

Ecological assessment with the Energetic Long-Term Analysis of Settlement Structures (ELAS) calculator provides an evaluation of the ecological impact of the developed energy systems.

Different scenarios based on two building standards OIB (low energy house standard) and NZE (passive house standard) as well as different prices for key energy resources were developed for an urban development concept for the Reininghaus District. The results of these scenarios show a very wide spectrum of structures of the energy system with strong variations often caused by small changes in cost or prices. The optimisation shows that small changes in the setup of the price/cost structure can cause dramatic differences in the optimal energy system to supply a smart city district. However, decentralised systems with low-temperature waste heat and decentralised heat pumps in the building groups show the financially most feasible and, compared to alternatives, most ecological way to supply the new buildings.

Conclusions

The planning process for the development of the Reininghaus District is a complex and therefore lengthy process and shall be concretised over the next decades. Optimal energy technology networks and scenarios resulting from the application of the described methods support the framework energy plan. The accumulated knowledge can be used to form smart energy supply solutions as an integral part for the discussion of the stakeholders (investors, city department) to guide the forming of their action plan through the development of the city quarter.

Smart city approaches

Cities are the fastest growing form of settlement worldwide requiring sustainable energy systems to deal with their increasing density and size [ 1 ]. Although urban population growth in developed countries (0.5 %) is projected to be below population growth in less developed countries (2.3 %) from 2007 to 2025, there is a general shift from rural to urban areas; 60 % or 5 billion of the global population (8.4 billion people) will live in cities by 2030 [ 2 ]. This growth implicates a growing resource demand for buildings, infrastructure and energy supply in urban areas. City authorities are challenged worldwide because the demand for additional living and working space is rising.

The European Union provides different initiatives as well as funds and regulates by law how European cities should deal with these challenges in order to become smart cities [ 3 ]. The term ‘smart city’ was only developed quite recently. An exact approach to the definition of an optimal interpretation of ‘smart development’ and what ‘smart’ means for the city and its inhabitants is controversial. Every smart city design has a different focus on what ‘smart’ or ‘smarter city’ means and how to proceed with their specific development [ 4 ]. In this context, de Jong et al. gave a good overview about attributes which in the course of time have been attached to the word ‘city’ to name urban planning-related activities of researchers, decision makers and city planners, with definitions like liveable , green , intelligent , low carbon , sustainable , digital , information , knowledge , resilient , eco and ubiquitous [ 5 ]. They come to the conclusion that so far, most articles use the word sustainable city and since 2009, it seems to be replaced by the term smart city . From the organisational perspective, a differentiation of term smart cities was classified into the following two hierarchically counter-directed approaches [ 6 ]:

Top-down smart cities are usually initiated by city institutions, information and communication technology (ICT) and/or research facilities, and it is a straight forward planning concept.

Bottom-up smart cities are usually modelled by local inhabitants, and an innovative potential, societal knowledge and networks is used by the cities themselves to design the city.

A categorisation of a smart city so far is in many cases very technology-oriented putting expansion, improvement and integration of information and communication technologies in front of the attempt to smarten a city. However, with the difficulties that implementations of new technologies can pose also other aspects were added to the discussion. Some of the terms and ideas which are currently used in this relation are categorised [ 7 ]:

General improvement of urban energy and planning concepts

Environmental sustainability (sustainable resource use)

Social sustainability (realising social inclusion of different kinds of urban residents in public services, citizen democratisation/cultural and societal empowerment)

Higher quality of life through technical improvements in telecommunication infrastructure/administration/networks/living/mobility

Economic development/efficiency

Integrating private sector, business-oriented urban development

High-tech/creative industries in long-term growth

Social/relational capital in city development

Batty et al. classify the wide spectrum of terms and ideas which come up with the concept of ‘smart city’ and present and divide them into six functions [ 8 ]:

Smart economy (competitiveness)

Smart people (social and human capital)

Smart governance (participation)

Smart mobility (transport and ICT)

Smart environment (natural resources)

Smart living (quality of life)

Apart from the general observation of factual issues, it is the procedural setting which creates momentum in city development. Inside the functional framework, stakeholders can be identified as drivers in different institutions. Depending on their field of expertise, they have to deal with diverse contextual issues. In this relation, it is important to know which actor plays a role to which extent and how he or she is influencing the whole system of a city. In the Austrian research context, Saringer-Bory et al. categorise Smart City Actors in the SmartCityAkteursmatrix (smart city actors matrix) [ 9 ] as follows:

Institutional category (public science, government, non-governmental organisation (NGO), private businesses, etc.)

Field of expertise (spatial planning, architecture, urbanism, energy planning, mobility, climate research, social research, etc.)

Keywords (energy standard, consumption, resources, renovation, insulation, networks, logistics, etc.)

In this respect, technological expansions of city infrastructure do not simultaneously imply improvements neither regarding sustainability issues nor the reduction of energy demand and increase in quality of life, wealth and benefit for the whole community [ 10 ]. There may be the risk that a smart city development is interpreted one-sidedly from a technical-business perspective only, and social and environmental requirements can be missed again. Smart city planners must not forget that just letting grow new businesses to produce smart technologies can have rebound effects so that social and environmental requirements of a sustainable urban development can be missed again. Environmental savings and social justice can be outweighed by an additional implementation of technologies whose main goal is to increase quality of life.

From an ecological perspective, the European regulatory states clear legal framework conditions for the communities of the European Union. One goal of the united European governmental efforts is to reduce greenhouse gas emissions by 20 % below the 1990 level by 2020 [ 11 ] and achieve a 40 % reduction by 2030 [ 12 ]. According to the European Parliament, buildings account for 40 % of the total energy consumption and 36 % of CO 2 emissions in the EU [ 13 ]. Low energy buildings and zero energy buildings could contribute to energy savings and emission reductions [ 14 ]. Hence, the EU passed the Energy Performance of Buildings Directive 2010 [ 15 ] and in the Energy Efficiency Directive 2012 stating that all public buildings by 2018 and all new buildings by 2020 must be nearly zero energy buildings. The laws clearly state that smart initiatives must include more than just adding smart grid technologies and technical process automation to the existing infrastructure. Holistic and sustainable system thinking needs to permeate widely through different levels of society. This requires a critical discussion on sustainable development and innovation itself and how theoretical sustainable concepts can be realised [ 16 ].

New approaches to build a framework to optimise new city quarters or rehabilitate existing urban quarters provide a holistic view to find smart, economically feasible energy systems with social and ecological benefits for the society. Different examples of attempts to build frameworks to push innovative planning concepts can be discovered in often closely related developments. These developments reveal a basic tendency of trying to push spatial planning closer together with energy planning. In Austria, the OEROK ( Oesterreichische Raumordnungskonferenz /Austrian Conference on Spatial Planning) is an institution that was established by the federal government, the federal provinces and municipalities of Austria to coordinate spatial development on a national level. The OEROK started collecting and disseminating expert knowledge while raising awareness on the importance of Energieraumplanung (integrated spatial and energy planning) [ 17 ], as it is defined in the Oesterreichisches Raumentwicklungskonzept— OEREK [ 18 ]. The situation in Switzerland is similar to Austria: the programme EnergieSchweiz [ 19 ] provides a statement of requirements named Energierichtplanung (energy planning framework). In Germany, the term Energienutzungsplanung (energy utilisation planning), which is an energy usage plan to systematically develop future energy systems for municipalities according to specific local situations and renewable energy systems, first appeared in Bavaria in 2012 [ 20 ].

Across European approaches, an integration of energy and spatial planning is at different stages of development. Best practise examples and instruments for future-oriented and resource-efficient energy and spatial planning have already been defined and discussed by various authors [ 21 ]. An integrated spatial and energy planning process needs to integrate a reduction in the dependence on fossil fuels and an extended use of urban energy resources in comparison to the current state. Renewable energy technologies utilising industrial waste heat can be considered as they can provide positive impacts on local economy and reduce ecological burdens as well [ 22 ].

Planning and implementation of smart urban energy systems, however, involves a wide spectrum of stakeholders: from city administration to developers to energy providers to current as well as future inhabitants. The planning discourse between these stakeholders can be supported by reliable and comprehensive methods to design and evaluate complex energy systems. Such decision support methods

Provide answers to the different perceptions of the economic framework for the development of smart energy systems brought to the table by various stakeholders by creating reliable scenarios

Allow comparison of the scenarios by guaranteeing optimal energy systems generated by using different resource options and economic frameworks

Provide comprehensive ecological evaluation of the scenarios along with thorough economic and technical specification to enable a holistic planning process

An approach to support stakeholders and policy makers with a selection and an application of different methods for an integrated spatial and energy planning is provided by Stoeglehner et al. in a study which gives an overview about tools which cover analysis of energy savings, energy efficiency, renewable energy, spatial planning, mobility and evaluation and optimisation of planning schemes [ 23 ].

Smart energy planning

Energy planning that leads to ‘smart’ urban solutions requires integration of energy design into spatial planning and urban planning. This means that the design of new settlements, as well as the refurbishment of existing city quarters, requires an interdisciplinary planning approach that takes spatial and mobility planning, energy systems design, building and infrastructure design and the evaluation of ecological impacts into account [ 24 ]. Innovative approaches must therefore be applied. Electricity supply in urban areas has moved to the centre of debate on how to supply urban areas with renewable energy [ 25 ]. Heat integration and heat storage, the integration of industrial waste heat and solar thermal energy in supply networks have also become major aspects of smart city development [ 26 ]. The case study region of Reininghaus District has already been in the focus of scientific endeavours to merge all these aspects [ 27 ]. The following tools and methods have been applied to the framework energy plan for the Reininghaus District allowing for systemic energy system design and holistic ecological evaluation.

For the investigated area, total energy demands are needed as important basic information to optimise the energy technology network. A further downscale to the building level is essential to focus on the level of a city quarter. In Austria, the OIB-standard, set by the Austrian Institute of Construction Engineering [ 28 ], is the minimum requirement regulated by Austrian law. The maximum energy consumption is continuously being lowered to reach EU regulation goals within the next few years. To constantly decrease energy consumption per building, energy demand levels permitted by the current Austrian Low Energy Building Standard (OIB-standard) and the Nearly Zero Emission Buildings Standard (nZEB or NZE-standard) are under ongoing discussion. In the interest to guarantee a better readability in this work, the phrases ‘NZE-standard’ and ‘OIB-standard’ will be consistently used. To avoid misunderstandings, each part of the phrases is connected by a hyphen. Improvements which can be applied to save energy in buildings can be reached, for instance with energy efficiency measurements such as optimised insulation building retrofit [ 29 ]. The focus of the optimisation of a whole city quarter’s energy technology network needs to be extended from single house level to building groups. This is particularly important because small-scale modelling differs greatly from large-scale optimisation. Examples of how settlements could be designed and supplied with renewable energy often applied single technology approaches and partly hybrid renewable supply systems [ 30 ].

These approaches clearly highlight the importance and requirement for developing strategies and concepts to help city stakeholders with their burden of finding an optimal and secure future energy supply. Even though this paper is concerned with the challenges posed by these concepts, it also discusses an approach that is generally applicable to the selection of spatial planning processes. The paper considers the optimisation of renewable and fossil technologies along with specific conditions of the optimised city district, such as resource availability, competition and market prices.

Research problem

The research questions posed in this case study is the integration of available local energy sources into the energy system in order to meet future Reininghaus District demands. Three companies, which produce waste heat at different temperature levels, are located close to or even directly in the area: Steel plant Marienhütte Stahlwerk , which is already connected to the district heating grid of Graz, produces heat of approximately 80 °C, Linde Gas heat of around 20 °C and STAMAG Malzfabrik of around 20 °C. Additional waste heat in large amounts is available from the steel plant at a temperature of 30 °C. A cooling stream of 10 °C (which could also be used for upgrading in a heat pump) is available from groundwater wells left over from the former brewery within the district premises.

For a more precise approximation of different local differences, the Reininghaus District was split into 17 groups of buildings as they will be described in the case study description in chapter 3. After defining possible energy demands for the planned groups of buildings, the availability of local energy (e.g. waste heat, geothermal energy and rooftops for solar energy) and existing energy infrastructure to supply Reininghaus District was modelled. The district can easily be connected to the existing district heating grid of the city, currently supplied mainly by three large gas-, coal- and oil-fired combined heat and power (CHP) plants. The gas-fired plant (250 MW thermal power) is located within the city, and the coal (230 MW thermal power) and the oil-fired plant (230 MW thermal power) are located 20 km south of Graz [ 31 ]. Approximately 90 % of the district heat in Graz is generated from fossil fuels. Natural gas and electricity grid connection is also possible for the district. The model must provide consistent, economically optimal structures of the network between all possible sources, distribution within the quarters, conversion technologies and consumption. It takes seasonal variations into account. Subsequently, the optimal structures generated for different economical frameworks (scenarios) are ecologically evaluated.

Many approaches focus on smart energy system design and smart energy systems as a priori 100 % renewable systems (e.g. Lund et al. [ 32 ]). To keep the door open to compare existing energy regimes and infrastructure like fossil energy systems, the pre-definition of a specific target resource system was avoided in this work. The aim of this methodological framework is to provide information about optimal technology networks and the ecological and socio-economic evaluation of different options for future city developments. It consists of the Process Network Synthesis (PNS) and, inter alia, the tool Energetic Long-term Assessment of Settlement Structures (ELAS), the tool applying the Sustainable Process Index (SPI). In order to achieve the research goals raised in the framework plan to find smart and sustainable energy systems for cities, the PNS was chosen. With this method, it is possible to model complex systems and find optimal energy systems before studying the relevant matters in depth with the help of a modelling or design process. On the other hand, ELAS and SPI can deal with interdisciplinary issues of complex settlement structures and a comprehensive ecological evaluation. Process cycles of various energy systems can so be ecologically evaluated and provide usable information and a practical model for a stakeholder process.

Process Network Synthesis (PNS)

One method in particular that has proven its worth in planning tasks like integrated spatial and energy planning is PNS [ 33 ]. This method has been developed in the framework of process technology [ 34 ]. The mathematical structure behind has been discussed in several publications [ 35 ]. It uses a directed bipartite graph (p-graph or process-graph) method to describe process networks and employs combinatorial rules to find all feasible network solutions using all possible resources, intermediates and products as well as all relevant technologies processing these mass and energy flows (superstructure). Software employing this method (PNS Studio) is freely accessible from www.p-graph.com [ 36 ]. All data concerning flows and cost of technologies, pipes and transport can be provided in predefined material and operating units input tables of PNS Studio. Moreover, parameters like the required and maximum flows, lower bound and upper bound of capacity constraints for operating units can be set. Flows are split into resources, intermediates and products. Flows can then be set as input and output flows in the operating units to display conversion and production interdependencies between the technologies considered.

Figure  1 shows how the user-defined maximum technology structure is then the starting point for a rigorous evaluation of all feasible process networks linking resources to the desired products. The first step is the generation of a pre-optimisation of a feasible maximal process network (maximum structure) with the maximum structure generator (MSG), and in a second step, the optimal process network (optimum structure) is generated using a branch-and-bound optimisation included in the solver of PNS Studio.

Maximum structure and optimum structure of a technology network

In addition to its long tradition in process industry applications, PNS has also been successfully applied to regional resource utilisation problems [ 37 ]. A special software tool using the PNS method (RegiOpt) was recently developed [ 38 ] to provide a preliminary design for smart regional energy systems based on renewable resources and is freely available on the internet [ 39 ].

In this study, an in-depth modelling is carried out to generate scenarios for the optimal energy supply of the Reininghaus District using PNS. This method allows the optimisation of local energy and material demands and supplies situations represented in energy technology networks. Seasonal variations in resource provision and consumptions are treated by a multiperiodic option of the PNS [ 40 ].

ELAS calculator

ELAS was developed to analyse urban structures ranging from single houses to whole settlement structures regarding their energy situation and in particular their ecological performance. The calculator allows the evaluation of single buildings as well as whole settlements. It can be applied to existing structures as well as planning tasks and also allows the evaluation of refurbishing and extension plans to existing settlements [ 41 ].

The calculator takes site-specific data of residential settlements into account. This consists of energy consumption and supply, mobility induced by the location of the settlement as well as the distances to service provision. It uses a life cycle approach to the evaluation and accounts for the ecological impact of construction, use and disposal of all buildings and energy infrastructure in a settlement, such as roads, wastewater drains and lighting of public space.

Results of the ELAS calculator contain accumulated energy demand, ecological footprint (calculated with the SPI method) [ 42 ], CO 2 life cycle emissions and regional economic impact (turn over, value added, imports, jobs created or lost) of the settlements. The Sustainable Process Index is an ecological footprint method. It has recently been described and discussed in its international methodical context [ 43 ]. The SPI has been applied in several fields such as ecological evaluation of agricultural products [ 44 ] or collectively shown in the evaluation of energy technology systems based on renewable resources [ 45 ]. The ELAS calculator is an online tool that runs SPI evaluations in the background (along with other technical parameters and statistical variables) [ 46 ]. The ELAS calculator allows users to provide specific data via a GUI (graphical user interface).

ELAS provides municipalities a basis for sustainable energy supply and appropriate policy decisions or gives an overview of individual energy consumption and its economic and ecological effects. The tool is freely available online.

The result of the optimal structure of PNS obtained for a particular set of economic boundary conditions (a scenario) is used as input for ELAS evaluation. Amounts of resources (in this case, energy flows for the supply of the city quarter) resulting from PNS are one input for the assessment with ELAS calculator. In addition, the input parameters for ELAS are the site-specific data, building standards, infrastructure, induced mobility and energy, construction and mobility costs. Results are the ecological footprint (SPI), energy demand and CO 2 . The total sequence proceeds as shown in Fig.  2 .

Methodical framework

Case study description

Graz, the capital of the Austrian Federal State of Styria is a middle-sized city (approximately 280,000 inhabitants by 2016 [ 47 ]) that is estimated to reach approximately 325,000 inhabitants by 2030, which would result in an increasing demand of living and working space [ 48 ]. In order to meet the growing population’s needs, the city planning department is required to densify the city preferably in a vertical manner. A horizontal extension should be excluded from spatial planning. According to the City Government, a horizontal densification is allowed for new buildings only. Following this urban areas already dedicated as building land are currently under heavy exploitation. Some of the existing building ensembles have been designated a UNESCO heritage site, whereby they are subordinated by law according to the Dachlandschaftsverordnung [ 49 ]. This law protects the urban roofscape, particularly in central parts of the city. These old buildings must be conserved, meaning an addition of storeys is not permitted there.

Most of the Reininghaus District is green- and brownfield, measuring an area of 110 ha. Reininghaus District is located 1.8 km west of the historical city centre, and approximately 12,000 inhabitants are predicted to live there after completion of all developments. The development of this new city quarter is carried out by an interdisciplinary team, consisting of scientists, architects, developers and experts from various stakeholders. For the part of the scientific support, a consortium of Graz University of Technology is integrated in the project Energy City Graz, which is a sub-project of the flagship project Energy City Graz (ECR). The Reininghaus project is funded by the Austrian state research and technology programmes Building of Tomorrow and City of Tomorrow [ 50 ].

The case study carried out in this project is an integral part of the framework energy plan City Graz Reininghaus [ 51 ]. This framework plan covers:

The concept of a self-sufficient, in terms of energy, city district, the initiation and supervision of the development process for the energy-optimised and sustainable city district Graz-Reininghaus, the phrasing of guidelines, recommendations and a checklist for future energy optimised city developments in Graz and Styria

The development of specific energy values in private legal contracts between the city of Graz and future investors, promoted by incentive systems like bonus cubage and higher housing density for buildings

Concepts for the integration of the energy values in suitable manner in local plans and regulations (City Development Concept STEK Graz, City-District-Development-Concept Graz-Reininghaus and development plans for the city quarters on location)

Graz city officials have announced the intention of using the plan to define an innovative framework for the development of the Reininghaus District [ 52 ]. The outcome of the complete framework energy plan includes possible energy technology network solutions as well as architectural, mobility, environmental and infrastructural guidelines.

The forming of the optimal building characteristics by an interdisciplinary team played an important role to provide a basic model and let heat and electric experts calculate energy needs of the new quarter. Generally, a perimeter block development was chosen as a basic building concept for all groups of buildings. To guarantee individuality for the specification of the building characteristics for each group of buildings, an architectural competition was performed for the first groups and will be announced for the other groups of buildings over the next years or decades. Depending on side-related issues, the cubature and spatial planning aspects of each building and group of buildings were adapted related to the expected context (mixture of utilisation including open green areas, mobility, air quality issues, wind, shadowing effects, etc.). In coordination with the framework plan, superordinate goals such as for the whole district Reininghaus could so be followed. Apart from the energy technology network solutions, the other guidelines are not part of this work. Information directly important to form the context of this work is provided here. A detailed description of it can be found in the framework energy plan.

For the framework energy plan, the city district Reininghaus was divided into 17 groups of buildings consolidated in three geographical districts: North, East and South as shown in Fig.  3 . The group of buildings feature homogenous parameters, like energy standards for buildings, functional mixes, load profiles for heat and cooling, defined in the university consortium, which serves as the guideline for development. They are individual components in the model described in this study. Within these groups of buildings, average distribution distances for linking buildings to energy grids are assumed. For the larger geographical districts, it is necessary to define the overall structure of distribution grids for natural gas, district heating, electricity and wastewater collection systems as well.

Three geographical subareas N, E and S including 17 groups of city buildings

This paper presents a model that identifies the optimal technological network as well as locations for energy provision installations, which

Provide all groups of buildings with heat and electricity

Integrate locally available resources while generating the highest possible value added for the whole energy provision system of the total district Reininghaus

Take resource, investment and infrastructure costs as well as limitations of (and possible competition for) resources into account

This model may be used to develop consistent optimised scenarios for different economic framework configurations and evaluates them economically and ecologically. The scenarios serve as a solid basis for negotiations between the various stakeholders, whose interactions will shape the district’s future smart energy system. The goal of using this model is to provide stakeholders and decision makers with a factual basis for a discourse on the development and implementation of a smart energy system in this brownfield development project.

The energy aspects of the framework plan for the Reininghaus District brownfield development is based on the following premises:

The framework plan includes options for OIB-standard buildings (52.6 kWh/y.m 2 heating and 4.6 kWh/y.m 2 cooling demand) and NZE-standard buildings (11.2 kWh/y . m 2 heating and 11.4 kWh/y . m 2 cooling demand) as a basis for the calculation of the energy demand of the group of buildings.

Load profiles for heating and cooling are taken into account.

Existing sources of industrial waste heat shall be taken into consideration for optimal energy systems.

Additional infrastructure to utilise waste heat (e.g. distribution pipes) is accounted for.

A scenario presuming autarky (at least for cooling and heating) has to be provided for reference.

The model generating the scenarios forming the basis for the stakeholder discourse takes capacity limits and qualities of local energy sources into consideration.

The framework plan is the result of an intensive discourse between stakeholders in the Reininghaus District development, taking long-term trends in the economic and environmental framework for supplying and using energy in this district into account and was set as a requirement by the City of Graz and the Austrian Federal State of Styria provided to investors who want to develop the district.

Table  1 provides an overview of the existing heating/cooling sources in the district. Photovoltaic (PV) panels installed on roofs can supply a total of 1760 MWh/year; however, they compete with thermal solar collectors for roof space. The electricity grid can provide electricity with no practical capacity limit for the district. The natural gas grid is also assumed to cover any load necessary to supply the district. The maximum capacities and temperature levels of the waste heat and renewable energy sources range from 800 to 100,000 MWh/year and from 10 °C to almost 80 °C.

Definition of the maximal energy technology system

Besides quality and limitation of energy sources, the model must also consider seasonal variations in energy demand, in particular for heating and cooling. Respective demands were provided during the project by the Institute of Electrical Power Systems and the Institute of Thermal Engineering of Graz University of Technology and are shown in Figs.  4 and 5 as load functions for heating, cooling, warm water and electricity for the assumed scenarios of building standards for the district. In OIB and NZE standard cases, an electricity demand of 30 GWh/year (excluding electricity for providing heat via heat pumps) is assumed. The entire district requires 45.7 GWh/year of heat (warm water and heating) and 3.2 GWh/year of cooling for the OIB-standard scenario.

Load variation over time for the district assuming low energy house standard (OIB) in MWh [ 53 ]

Load variation over time for the district assuming passive house standard (NZE) in MWh [ 54 ]

For the NZE-standard scenario, the entire heat demand is with 16.6 GWh/year lower than the OIB-standard demand. On the other hand, NZE-buildings need 4.8 GWh/year more energy for cooling.

NZE-standard buildings can reduce heat demand by 63 % however with an increasing demand for cooling in summer (2.5 times more energy compared to OIB-standard) because the different construction needs more ventilation for cooling.

In order to align energy provision with these load profiles, three periods were defined to represent the time-dependent demand of the district. Months with similar energy demand levels were merged to three periods winter, midterm and summer (see Table  2 ).

The economic framework for the district’s development consists of the end-consumer prices and the cost of obtaining energy from different sources and the feed-in tariffs into distribution grids for surplus energy provided in the district. Additional file 1 : Table S1 provides energy prices and cost of this economic framework for the baseline scenario NZE. The prices and costs in this table are not fixed but subject to stakeholder negotiations and market price developments. The scenarios provided in the ‘ Results ’ section show how changes in this economic framework of prices and costs shape the complex structure of energy provision for the Reininghaus District.

Investment costs of the technologies (taking different sizes of installations into account) as well as costs of grid infrastructure within the district are based on industry information and experiences from other projects. They are summarised in Additional file 1 and remain constant in all scenarios.

There is a large portfolio of technologies that may be applied to provide heating, cooling and electricity for the Reininghaus District. This ranges from simply connecting the group of buildings to existing distribution grids for electricity or heat, to the utilisation of local heat sources to upgrade waste heat with heat pumps, or the collection of solar energy via thermal collectors or PV panels, to name just a few. Many of these technologies can be applied decentrally, meaning at the building site, or centrally, meaning at the site of the source. These options differ by the size of the installations and the distribution system (and distribution losses) as well. Low-temperature waste heat from an industrial source in the form of a water flow can, for example, be the input to a large heat pump at the source, which increases the temperature of the waste heat to the temperature level necessary for residential heating, subsequently distributing hot water via a district heating grid to the buildings. It can, however, also be distributed directly via a grid to the buildings and may locally be upgraded in smaller on-site heat pumps to heat the buildings. Investment costs and distribution losses will vary considerably for these cases and may also vary for different groups of buildings, given the differences in the length of the distribution pipes. This central/decentral option also applies to gas burners and CHP technologies based on gas. Lower prices for coal could influence the optimal energy technology system but a specific barrier to burning solid particles are air-quality restrictions within the city of Graz due to geographically related inversion problems with too high particulate accumulation.

Figure  6 shows the ‘maximum technology system’ of all suggested technologies used in the model with the exception of distribution grids. These distribution systems connect each sub-quarter with all energy sources available and are modelled in detail. This technology portfolio is the basis for the generation of scenarios provided in the subsequent chapter.

Maximum technology system for the energy system in detail.

Scenario section

The following describes the scenarios, generated with the PNS based on different economic framework conditions. They provide an example of the broad variation of structures for the energy system for the district, caused by often quite small differences in costs and relative prices of the energy sources in question. The total area of all roofs of the buildings was assumed for three kinds of use: 7 % for solar thermal, 1 % for photovoltaic panels (for energy installation) and 50 % for green planted roofs (for ecological cooling effects), and the remaining 42 % were not open to any purpose. The assumption for energy installations was set on a very low level for the following reasons. On the one hand, climate geographical analysis during the project showed that too much sealing would heat up passing air mass and so further deteriorate the micro-climate in Graz. On the other hand, architectural and constructional characteristic (especially different heights and alignments of buildings cause shadowing effects) as well as the Dachlandschaftsverordnung reduce the potential for solar use. This low energy potential was taken in order to be on the safe side with the assumptions.

The time frame for the realisation of the Smart City district Reininghaus is divided into two different segments. A first part consisting of two building blocks (see quarters 1, 4 and 4a in Fig.  3 ) will be realised within the next years while the remainder of the district will require a longer time to be realised. Therefore, the modelling was also split with one model containing only the two building blocks that will be realised soon and the comprehensive model that includes all building blocks. This arrangement also allows to analyse if the optimum of a subset of buildings leads to qualitatively different solutions than if the whole district is subject to the optimum. In all cases, the optimum was carried out for both OIB and NZE standards.

In a last step, the framework of all scenario groups underwent more specific changes. This was important for gaining an overview of how price changes in global market prices or considerations about autonomy and technical changes summarised below would influence the optimal energy technology system. The following parameter classes were defined:

Identification of sharp cost limits of available waste heat (reducing or increasing actual prices until technology structures start to switch)

Identification of cost limits for natural gas, existing district heat, cold water well, etc. are not economic anymore (reducing or increasing actual prices until technologies start to switch)

Autonomous heating and cooling supply options of Reininghaus

Each of the scenarios was also ecologically evaluated with ELAS calculator.

Table  3 describes scenario parameters. Value added per year is in this relation the main output of each optimal scenario and is the result of the total income from selling energy minus the sum of annualised investment cost, operating cost and resource cost. The optimal scenarios were calculated for the case of supplying all quarters of the case study area Reininghaus simultaneously. Results show that value added ranges, depending on changing of parameters, from 245,000 €/year to 641,000 €/year for the whole Reininghaus District and 17,000 €/year to 193,000 €/year for the subset of quarters 1, 4 and 4a. Cost variations were made starting from the basic purchase cost for energy from Additional file 1 : Table S1. Prices for selling the energy to the end-consumers were set as fixed market prices.

The following graphs are all based on the maximum technology system as defined in Fig.  6 . Sources and technologies that are part of the optimal structure under the designated economic framework are circled. Red circles mean heating, blue circles cooling and green circles identify technologies and sources used for electricity provision in the district. Broken circles represent sources and technologies that are only used for some groups of buildings, or only service a smaller part of the demand, supporting the main sources of energy. The baseline scenario and the scenarios with changed natural gas cost, changed district heat cost and heating autonomy will be shown here in further detail.

Figures  7 and 8 show the energy supply structure for the base line scenario for purchase cost, selling prices and feed-in tariffs defined in Additional file 1 : Table S1. The results of these two figures stand for all quarters.

Optimum energy technology system for base line scenario all quarters (OIB-standard)

Optimum energy technology system for base line scenario all quarters (NZE-standard)

Result overview

These figures show that under current economic conditions, providing heat with decentral natural gas burners in the buildings is optimal, with support from industrial waste heat. In the OIB-standard case (Fig.  7 ), gas furnaces and high temperature industrial waste heat from the steel plant Marienhütte Stahlwerk share the main burden of heat provision (of which 10 % comes from the industrial waste heat). Low-temperature industrial waste heat from the companies Marienhütte Stahlwerk and STAMAG Malzfabrik provide only a small fraction of the heat demand for quarters close to this source, with the waste heat from Marienhütte Stahlwerk being up-graded in de-centralized heat pumps, the STAMAG Malzfabrik waste heat will be used in a central heat pump at the site of the company. Cooling is provided by district cooling from the cold water sources located at the Erber estates. Electricity comes mainly from the grid, with local PV contributing 7 % of the demand. The picture is quite similar for the NZE-standard case (Fig.  8 ), with the exception that the share of the district heat covered by high-temperature waste heat from Marienhütte Stahlwerk is much larger at 30 %. Low-temperature heat from this steel plant will not be used. Cooling now will be provided by de-central air conditioners.

Results of structures for quarters 1, 4 and 4a show the same energy technology network but different amounts of energy described in Table  3 .

Figure  9 represents how dramatic changes in the overall structure can become if the cost for a certain source is changed even slightly. A 4 % (NZE-standard) to 16 % (OIB-standard) increase in the gas price will change the supply structure for the NZE-standard case from that given in Fig.  8 to that in Fig.  9 . Gas will no longer contribute to supplying heat to the district. It will be replaced by high-temperature industrial waste heat from the Marienhütte Stahlwerk, supported by de-central heat pumps fed by low-temperature waste heat from the Marienhütte Stahlwerk and Linde Gas. In this scenario, solar thermal collectors will also contribute to cover the heat demand, reducing the available area for PV panels to supply electricity.

Optimum energy technology system in the scenario natural gas all quarters with reduced gas price (OIB-standard and NZE-standard case)

The existing district heating system will only become part of the energy solution for the Reininghaus District if the cost per unit of energy is reduced by approximately 23 % (OIB-standard) to 26 % in the NZE-standard case.

Then, the optimal energy supply structure will change as seen in Fig.  10 . Although the main share of heat is still provided by de-centralized gas furnaces, a third of the group of buildings will be connected to the municipal district heating system. Small contributions for buildings close to the sources will be made by high-temperature waste heat from the steel plant Marienhütte Stahlwerk and by low-temperature waste heat from Linde Gas, upgraded to a central heat pump at the site of the company.

Optimum energy technology system in the scenario district heat all quarters with reduced district heating cost (OIB-standard and NZE-standard case)

Finally, the question of only supplying heat and cooling from local sources will be addressed in Fig.  11 . Full-energy autonomy is not a viable option for the district, as PV area is restricted to roofs of buildings (even with more intensive area use). This means that under this assumption, it will always be necessary to import electricity. Figure  9 already indicated that the heat load and as cooling may be supplied by local energy sources only (if electricity is not restricted). Figure  11 shows the most optimal energy system for autonomy regarding heat. In this case, the structures are quite similar for the OIB-standard and NZE-standard case. The main burden of heat supply will be taken by the high-temperature waste heat from the steel plant Marienhütte Stahlwerk, supported by de-central heat pumps fed by low-temperature waste heat from Marienhütte Stahlwerk and Linde Gas. This scenario requires an increase of imported electricity to power the heat pumps. It is interesting that the reduction of profit for this scenario is minor, with 13 % for the OIB-standard case and only 9 % for the NZE-standard case.

Optimum energy technology system for scenario heating autonomy all quarters (OIB-standard and NZE-standard case)

Apart from different amounts of energy, described in Table  3 , results of structures for quarters 1, 4 and 4a only show different energy technology networks. In this case (as well for OIB-standard as NZE-standard), instead of gas burners, the use of waste heat in combination with decentral heat pumps is the main part of the solution.

Within the optimal scenario where resources, infrastructure (a.o. pipes) and cost/prices are included, marginal prices of resources were calculated. Cost for energy on the local energy market can be tested for volatility. Natural gas becomes uneconomic at a purchase price more than 51 (NZE-standard) or 57 euro (OIB-standard) per MWh, existing district heat becomes economic up to a purchase price of 50 (NZE-standard) and 51 euro (OIB-standard) per MWh (Table  4 ) and cooling water from deep water wells for cooling processes becomes economic between 37 (NZE-standard) and 41 euro (OIB-standard) per MWh.

High resource costs of waste heat and cooling water favours decentralized technologies. These are primarily natural gas burners, solar thermal plants, heat pumps, photovoltaic systems, air conditioning and to a lesser extent cooling water and waste heat.

The scenario with the highest economic revenue considering both economic performance and low ecological impact leads to a scenario that is completely supplied by the locally available waste heat and cooling water. Total costs of approximately 2.9 million euros per year and product revenue around 3.6 million euros per year can be achieved in this way (Fig.  12 ). A total revenue of about 0.6 million euros per year could so be created with an optimal energy technology network in the OIB-standard case.

Total cost and revenue in Euro per year (OIB-standard case)

For a yearly-based calculation, the individual life cycle for all energy technologies has been taken into account. The resource cost can exceed investment and operating cost many times over (91 % resource cost of total cost, see Fig.  13 ).

Cost allocation in % per year (OIB-standard case)

All scenarios that are generated by optimising economic profit via the PNS model are also evaluated ecologically with the ELAS calculator. The results of this tool provide a broader picture than just the ecological impact of the energy system, also including the life cycle impact (evaluated with SPI) of buildings and urban infrastructure. By replacing fossil fuels with the use of waste heat, the environmental pressure decreases in both cases to a very low value compared to electricity (Fig.  14 ). Electricity remains constant because it is already supplied to a high extent with renewables, and the potential for local electricity production with photovoltaic systems was highly limited because of the respective scenario setting (PV in competition with solar and green biomass on roofs).

Comparison of ecological impact of scenarios evaluated with SPI for relation of heat to electricity, using the ELAS calculator (absolute)

Figure  15 provides a comparison of the ecological impact with the part of the energy system highlighted. The baseline optimum scenarios are diagrammed between two extreme scenarios. The advantage of using local waste energy sources is clearly visible by comparing the ‘gas free’ scenario and the heating autarky scenario with the others. The ecological performance of the NZE and OIB-standard overlap. In comparison to the 110 ha Reininghaus District, the ecological footprint of the OIB-standard is 4000 (100 % waste heat scenario) to 8000 times (100 % natural gas scenario) bigger. For the NZE-standard, it needs 5000 (100 % waste heat scenario) to 6000 (100 % natural gas scenario) times this area.

Comparison of total ecological impact of scenarios evaluated with SPI, using the ELAS calculator

The electricity demand is covered by the current Austrian mix (78.6 % renewable sources) and cannot be influenced much locally. But the ecological pressure for the heat production can be reduced by more than 60 % as Fig.  16 shows.

Comparison of ecological impact of scenarios evaluated with SPI for relation of heat to electricity, using the ELAS calculator (in %)

Due to less heat demand, NZE-buildings are already on a much lower level than OIB-buildings (about one third) with corresponding ecological pressure. Independently of the building standards, the substitution of fossil fuels in heat supply can reduce the ecological pressure in both cases.

Energy supply of the Reininghaus District is just a part of the total ecological footprint; therefore, it is suggested that other important factors be discussed further (e.g. mobility). Possible changes of ecological pressure due to heating and cooling (OIB-standard in comparison to NZE-standard) are strongly dependent to the technology network. The existing district heat network of Graz originates from approximately 90 % coal and natural gas supply, which implicates a high ecological pressure. This high value can be reduced if locally available waste heat is used (not including the internal energy demands of the companies Marienhütte Stahlwerk, Linde Gas and STAMAG Malzfabrik themselves). The import dependency on fossil fuels for direct energy supply for building demands can be reduced by almost 100 %.

In the search for and planning of a smart economy, optimal energy systems have been found with PNS. Results show that with current prices, decentral natural gas burners supported by industrial waste heat are the economically most feasible energy system. The testing of price variabilities in the scenarios showed that with an increase of the natural gas purchase price (16 % OIB-standard) and a slight increase (4 % NZE-standard), the option of burning gas is no longer a part of the energy system. Instead, an additional use of industrial waste heat in combination with de-central heat pumps is then financially feasible. To become financially feasible, the district heat purchase price must drop by 9 % in the OIB-standard case and by 26 % in the NZE-standard case, or alternatively, the price of fossils must increase. Depending on further parameters such as density of energy demand, also, solar thermal collectors can become financially feasible. The very cost-intensive energy system of the already existing district heat on location is not financially feasible at the current high price level.

Not less important than the financial issue in the definition of the background section, the need for a smart and, as a synonym for a far-reaching concept, a sustainable city was mentioned. This implies that an ecological evaluation of the discussed scenarios of energy technology systems can be supportive in the discussion and scaling of a well-balanced socio-ecological economic process.

After the process optimisation and the ecological evaluation, resource, financial and ecological aspects were overlapped to find the scenario which is most ecologically friendly at the lowest costs. In this consideration, decentralised systems with low temperature waste heat and decentralised heat pumps show the financially most feasible (revenue more than 640,000 €/year) and, compared to alternatives, most ecological way (more than 60 % reduction potential of the ecological pressures of heat) to supply the new quarter. This scenario has the highest financial revenue when energy cost for purchasing the cooling water at 10 °C from deep water wells is available at very low costs. This seemed to be a realistic option due to the fact that the investor and owner of the deep water wells was interested in independent implementation alternatives to provide a decentral energy system solution. A use of the local waste heat can reduce import dependency on fossil fuels by almost 100 % of the final energy consumption.

The general planning process for the Reininghaus District is still in progress. The scenario results were used to inform the involved stakeholders about how the ecological footprint could potentially be reduced and how financial issues of different developments of energy prices and energy technology supply options could be handled. This basic research about ecological aspects of optimal energy technology supply for the Reininghaus District further was compiled with other information collected in the project from project partners. This information consists of analyses concerning microclimate, city planning, construction materials and further modelling of concrete scenarios for the spatial planning and energy supply of the Reininghaus District. The framework energy plan City Graz Reininghaus can now evolve into a comprehensive guideline for an integrated city planning.

This work did not cover all discussed sustainable city or smart city aspects since it laid the focus on the application of the described methodology for the use in urban energy planning and environmental sustainability concepts. In this case, the field of application of the discussed methodology can be described as a top-down smart city approach initiated by institutions of all administrative levels of the state (municipality, province and federal ministry) and research facilities. From the perspective of a general smart city concept categorised into six smart city functions, the discussed methodology mainly considered the smart economy and smart environment functions.

However, it allowed to model the specific local context of a city quarter considering many factors in relation to urban energy planning like, e.g. available concerning energy consumption, available local and imported resources, energy standards, existing infrastructure, local stakeholder interventions, etc. Most of them were mentioned as keywords in the smart city actors matrix in the ‘ Background ’ section. With this contextual setting, the financial feasibility of well-established technologies could be identified by testing the elasticities of energy cost.

Applying PNS for resource and technology systems for the energy supply of city districts helped to discover optimal energy systems out of a setting of complex supply and demand options. In the case of the smart city district Reininghaus, Process Network Synthesis has been applied to guarantee that only optimal scenarios representing different economic boundary conditions considering cost variations for energy services and energy sources are compared.

The results of the scenario generation with the PNS method show that even small changes in the setup of the price/cost structure for cooling, heating and electricity can cause dramatic differences in the optimal energy system to supply a smart city district. Shifts between fossil and renewable systems already happen when cost for natural gas rise slightly (NZE-standard case). A shift to an integration of heat from the existing district heat system would need a significant reduction of the price for district heat. Integrating industrial waste heat may allow to cover the heat demand for buildings with NZE-standard and even OIB-standard with local sources at reasonable economic cost. Electricity supply however cannot be covered within the restriction of only applying PV panels on rooftops.

Besides the economic optimum, ecological performance is a major issue in planning smart cities. A comparison of the ecologic life cycle performance, including construction and operation of buildings as well as urban infrastructure and mobility, clearly indicates that energy plays a key role in the ecological impact of settlements. Using the comprehensive evaluation method of Sustainable Process Index (integrated in the ELAS-calculator), roughly two thirds of the ecological impact in the case of a smart city district within a middle-sized city can be attributed to different forms of energy supply (most of it for domestic heat purposes). Using local energy sources instead of imported fossil fuels, in particular industrial waste heat, can considerably reduce the ecological impact. In addition, this exposes a potential to stimulate regional value creation when reducing the import dependency on fossil fuels.

Applying the selected methods could provide useful information to give some answers to the challenges and research questions posed in the beginning of this case study. An integration of available local energy sources into an optimal energy system in order to meet future energy demands could be discussed with the generation of scenarios for a new development of a city district. These scenarios contain optimal solutions for energy systems considering resource limits and expectable changes of market prices. The analysis of settlement structures of the scenarios helped to find out the impact level for each energy system option from an ecological point of view. An energy system solution could be found with the lowest ecological impact and the highest financial revenue.

Abbreviations

°C, degrees Celsius; CHP, combined heat and power; €/year, currency of European Euros per year; ELAS, Energetic Long-term Analysis of Settlement Structures; Erber, investor, owning the estates where water wells are located in Reininghaus; GWh, gigawatt hours; GWh/year, gigawatt hours per year; ICT, information and communication technology; ID, identification number; kWh, kilowatt hour; Linde Gas, company for gas products; Marienhütte Stahlwerk, name of an industrial steel company; MWh, megawatt hour(s); MWh/year, megawatt hour(s) per year; NGO, non-governmental organisation; NZE-standard, Nearly Zero Emission standard (In the interest to guarantee a better readability in this work, the phrase ‘NZE-standard’ will be consistently used. To avoid misunderstandings, each part of the phrase is connected by a hyphen.); OIB-standard, Oesterreichisches Institut für Bautechnik (Austrian Institute for Construction Engineering), compulsory building standards (In the interest to guarantee a better readability in this work, the phrase ‘OIB-standard’ will be consistently used. To avoid misunderstandings, each part of the phrase is connected by a hyphen.); OEROK, Austrian Conference on Spatial Planning/Oesterreichische Raumordnungskonferenz; PNS, process network synthesis; PV, photovoltaic; STAMAG Malzfabrik, Stadtlauer Malzfabrik Aktiengesellschaft (malthouse); var., various

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Acknowledgements

The author would like to thank for the funding by the BMVIT Building of the Future and City of the Future, municipality of Graz and the Land Steiermark, which made it possible to work in a new dimension of interdisciplinary teamwork between different departments throughout the University of Technology Graz and the Karl-Franzens-University Graz. Interdisciplinary hurdles within the project team were largely overcome thanks to a very constructive and open atmosphere for discussions.

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The author has contributed to the Smart City project Energy City Graz Reininghaus. SM has written, read and approved the manuscript.

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SM, born in 1983 in St. Andrä, Austria graduated in environmental system sciences from the University of Graz. He is currently working at the Institute for Process and Particle Engineering, Graz University of Technology. His research is focused on energy technology system optimisation in regions and rural and urban areas, holistic urban energy system planning and ecological evaluation.

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When the wind doesn’t blow and the sun doesn’t shine, renewable power producers have to rely on stored energy. Battery energy storage helps suppliers through peak demand times and increases power grid stability. However, current power markets often do not incentivize storage, which has different costs and physical constraints than non-renewables generators.

The United States, China, Australia, and the United Kingdom have all successfully developed renewable energy storage systems. Sun et al. conducted a study of these countries to determine the policies and market mechanisms that could help other countries promote their own energy storage deployments.

“Energy storage development is an essential regulating resource for future intermittent renewables with high penetration to the grid,” said author Huihong Yuan. “We conducted this study in the hope that it can provide useful references for energy storage development in various countries in terms of policy and market-based development.”

The researchers studied the installed capacity and development plans, and analyzed the impact of energy storage policies and business models. From their analysis, the researchers summarized the challenges in each country and proposed targeted solutions.

“We found that the market-oriented development of energy storage has made the best progress in the United States at present,” Yuan said. “I believe that further analyzing the reason for this progress would be helpful to other countries looking to develop energy storage.”

The researchers hope their results will be a useful reference for policymakers, investors, and operators in countries developing energy storage in conjunction with renewables. The researchers intend to continue studying the economics of energy storage, which remains a major challenge to development.

Source: “Development status, policy and market mechanisms for battery energy storage in the US, China, Australia and the UK,” by Jin Sun, Jing Liu, Yangguang Wang, Huihong Yuan, and Ze Yan, Journal of Renewable and Sustainable Energy (2023). The article can be accessed at https://doi.org/10.1063/5.0146184 .

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Is renewable energy sustainable? Potential relationships between renewable energy production and the Sustainable Development Goals

• Jing Tian   ORCID: orcid.org/0000-0002-5223-7494 1 ,
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Given the key role renewable energy plays in averting the impending climate crisis, assessments of the sustainability of renewable energy systems (RESs) are often heavily skewed towards their environmental benefits, such as reductions in carbon emissions. However, RES projects also have the potential to actively harm progress towards other aspects of sustainability, particularly when hidden within the energy generation process. Given the growing understanding of the ’dark side‘ of renewables, we must ask the question: Is renewable energy sustainable? To gain a better understanding of this issue, we analyzed the degree of alignment of seven aspects of the renewable energy production process with the Sustainable Development Goals (SDGs) and their targets for six renewable energy types categorizing the relationships as either enablers or inhibitors. This information makes it possible for decision- and policy- makers to move beyond carbon tunnel vision to consider the wider impacts of RESs on sustainable development.

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Modelling six sustainable development transformations in Australia and their accelerators, impediments, enablers, and interlinkages

Introduction.

Achieving net zero carbon emissions is the holy grail of climate change policies, with the transition to renewable energy sources often considered the hero in this quest. While the need to transition to renewables is unquestioned, the myopic pursuit of achieving net zero emissions has resulted in ’carbon tunnel vision 1 ‘ (i.e., a focus on the ability of renewables to reduce carbon emissions at the expense of the consideration of wider impacts), as a consequence of which the broader environmental, social and economic impacts (both positive and negative) of the transition are generally ignored. This means that we are now in treacherous territory, as the switch to renewables to address the current climate crisis could unwittingly create a cascade of other problems for future generations. Consequently, there is a need to better understand the potential positive and negative impacts of renewable energy systems so that we can ensure that the transition to renewables can occur in a sustainable manner.

In order to meet this need, we present a high-level overview of the potential enabling (positive) and inhibiting (negative) relationships between renewable energy systems (RESs) and the United Nation’s Sustainable Development Goals (SDGs) 2 , based on a review of the literature (see Fig. 1 caption for details and definitions). We pay particular attention to how these relationships vary for different types of renewable energy systems (biomass, hydropower, solar, geothermal, wind, wave & tidal 3 ) and how the various aspects of the renewable energy production process affect the environmental, social and economic elements of sustainability as characterized by the SDGs 4 . This enables us to obtain a better understanding of (i) the degree of sustainability of renewable energy systems, (ii) the impacts of adopting carbon tunnel vision, and (iii) what we need to do to broaden our vision to achieve more sustainable outcomes.

SDGs are grouped according to the categories of social, environmental and economic factors based on the Wedding Cake Model 52 . Specific targets recognized in the 2030 Agenda for Sustainable Development 2 (excluding government implementation targets) are grouped under each associated SDG and ordered clockwise. As was done in previous papers 53 , connections shown in green in ( a ) indicate a renewable energy project can potentially enable achieving a SDG target (this is equivalent to the concepts of reinforcing 54 providing synergies 55 and accomplishing 53 SDG targets). Connections shown in orange in ( b ) indicate a renewable energy project can potentially inhibit progress towards a SDG target (this is equivalent to the concepts of undermining 54 , providing trade-offs 55 and inhibiting 53 progress). Full results of the assessment for each target can be found in the Supplementary Information . Note that SDGs 4, 5, and 10 are excluded from this study since no direct relationships with quantitative indicators could be identified in literature. Given that SDG 16 and SDG 17 are at the heart of the SDG synergies, serving as fundamental interconnections to all other goals 56 , they are also excluded from our study. This is an original figure that was produced by the authors using AutoCAD.

How sustainable are renewable energy systems?

While the transition from fossil fuels to renewable energy sources is strongly associated with positive impacts on climate action (SDG 13), there can also be a number of inhibiting relationships with this SDG (Fig. 1b ). Such cases primarily involve the flaring (i.e., burning) of greenhouse gas, leading to emissions during certain types of renewable energy production (e.g., the generation of carbon emissions 5 and the leakage of methane during transportation and storage 6 for biomass production; the release of greenhouse gases when drilling for geothermal energy 7 ; and disturbing deep underwater sediments (e.g., particles settled at the bottom of water bodies) during the operation of hydropower plants 8 ). More importantly, renewable energy systems can also have potential enabling and inhibiting relationships with a number of other SDGs within the environmental category, including life below water (SDG 14), life on land (SDG 15) and clean water and sanitation (SDG 6).

Impacts related to life below water (SDG 14) are primarily associated with the production of wave and tidal power, with potential enabling relationships including enhancing the protection of coastal areas, as the installation of barriers and turbines can contribute to nutrient accumulation for coral protection 1 , 9 , and potential inhibiting relationships including threats to marine life, such as the harming of bird populations by offshore wind farms 10 , 11 . For life on land (SDG 15), potential enabling relationships include the repurposing of natural land, such as establishing wind and solar farms on degraded land 12 , whereas potential inhibiting relationships include the degradation of land quality when biomass contributes to soil erosion and degradation through the use of energy crops and the collection of crop residuals 13 . Regarding clean water and sanitation (SDG 6), potential enabling relationships include improved water-use efficiency 14 , 15 and potential inhibiting relationships relate to the reduced availability of drinking water, such as the contamination of underground aquifers from geothermal exploration, the tainting of potable surface water as a result of the leakage of biomass feedstock, and the allocation of significant water resources for hydropower infrastucture 16 , 17 .

In addition to their impact on the production of affordable and clean energy (SDG 7), renewable energy systems can also affect a range of other SDGs in the social category, including no poverty (SDG 1), zero hunger (SDG 2), good health and well-being (SDG 3), and sustainable cities and communities (SDG 11). However, in contrast to SDG 7, where renewable energy systems solely act as enablers, for these other SDGs, they can act as both inhibitors and enablers. For example, in relation to no poverty (SDG 1), potential inhibiting relationships stem from the intermittency of wind and solar energy sources 18 , while enablers could relate to the improvement of living standards through the provision of usable energy 19 . As far as zero hunger (SDG 2) is concerned, potential inhibiting relationships include the reduction of land availability for food production due to renewable energy installations 13 , with potential enabling relationships pertaining to the integration of RESs into agricultural farms (e.g., shading crops with solar panels) 20 , which has the potential to enhance resilience and productivity within the agriculture sector. Regarding good health and well-being (SDG 3), inhibiting relationships could include illnesses caused by harmful chemicals inadvertently released into the air and water, such as hazardous wastewater from geothermal energy production 21 , while potential enabling relationships include the prevention of respiratory infections and disease related to carbon pollution 22 . Finally, in relation to sustainable cities and communities (SDG 11), inhibiting relationships could arise from the environmental impact of RESs on modern cities, such as foul odours from biomass conversion, alterations in the microclimate caused by wind turbines and hydropower dams 23 and light pollution from solar panels 24 . In contrast, potential enabling relationships might relate to reduced damage to heritage land compared with that caused by the exploitation of conventional energy sources 12 , 25 .

RESs also have potential enabling and inhibiting relationships with various economic SDGs, including decent work and economic growth (SDG 8), industry, innovation and infrastructure (SDG 9) and responsible consumption and production (SDG 12). In relation to decent work (SDG 8), potential enabling relationships include the provision of decent work opportunities within emerging RES projects 26 , while inhibiting relationships relate to the likely reduction in job availability in the fossil fuel industry 27 , 28 . As far as industry, innovation and infrastructure (SDG 9) is concerned, potential enabling relationships include decreased carbon intensity through soil carbon sequestration and CO 2 recycling, while inhibiting relationships could relate to bioenergy and hydropower, for which energy sources require transportation, potentially increasing carbon intensity 29 . With regard to responsible consumption and production (SDG 12), enabling relationships could include improved management of natural resources, where waste and recyclable materials as waste can be utilized as a bioenergy source 30 , whereas potential inhibiting relationships include encroachment on natural resources and the generation of hazardous waste 15 , 21 .

What is the impact of carbon tunnel vision?

In order to obtain a more holistic and comprehensive understanding of the impact carbon tunnel vision has on broader aspects of sustainability, the relationships in Fig. 1 are decomposed by renewable energy type and aspect of the energy production process (Fig. 2 ). The different types of renewables considered include biomass, hydropower, solar, geothermal, wind, and wave & tidal, as these are the most commonly used sources, given current technologies. The aspects of the renewable energy production process considered include source selection, conversion and associated operational requirements, re-use, waste production, storage and transmission & distribution (Fig. 3 ), as these can differ for different types of RESs and include lesser-known elements of the renewable energy supply chain that often receive diminished attention. In the absence of this more nuanced understanding, it is easy to underestimate both the negative and positive sustainability impacts of renewable energy production on SDGs, making it more difficult to escape the currently adopted carbon tunnel vision, as detailed in subsequent sections.

SDG targets are presented by a single value and are divided into three principal spheres—social, economic, and environmental—which are depicted on the vertical axis. The horizontal axis categorizes the six renewable energy types. Within each type, the seven aspects of the energy production process (see Fig. 3 ) are presented in two rows, where connections are shown between a SDG, renewable energy type and aspect of the renewable energy production process. A green index color represents ‘enablers,’ while the orange index color signifies ‘inhibitors’. A lack of highlighting indicates the absence of identified evidence from literature, although it is important to note that this does not necessarily imply the absence of a relationship per se, just that this was outside of the boundary of consideration used here (more details are provided in the Supplementary Information ). This is an original figure that was produced by the authors using the Microsoft Excel Spreadsheet Software.

These aspects are presented within the context of the operational input-process-output concept. Source selection is considered as the first aspect, noting that the storing of potential energy is where impacts emerge—there are no direct impacts from renewable energy types with kinetic energy sources. The process of converting the source into energy can influence SDGs, both through the conversion process itself (i.e., plant location) and the associated operational requirements. After the completion of the renewable energy production process step and before the generation of the output, by-products can either be re-used elsewhere or go to waste. The production outputs can be divided into two parts: storage for local use and operational support, and transmission and distribution for grid connection or delivery. This is an original figure that was produced by the authors using Microsoft PowerPoint.

Underestimation of negative sustainability impacts

As can be seen from Fig. 2 , one of the major impacts of adopting carbon tunnel vision is that, by solely focusing on climate action (SDG 13) and the production of affordable and clean energy (SDG 7), the vast majority of inhibiting relationships between renewable energy production and the SDGs (i.e., the orange cells in Fig. 2 ) are ignored, which is likely to result in a distorted view of the sustainability of RESs. However, it should be noted that the focus on net zero emissions might not be the only reason for the lack of consideration of the potentially negative impacts of renewables on sustainability. This is because inhibiting relationships are primarily associated with the less well-known and understood aspects of the renewable energy production process (such as conversion and associated operational requirements, re-use and the generation of waste), rather than the more well-known and better understood processes (such as those associated with source selection, storage and transmission & distribution).

These potentially negative impacts affect a range of SDGs (Fig. 2 ). For example, operational requirements of renewable energy projects can have a negative impact on SDG 2 (zero hunger) because the development of RESs competes with the agricultural sector for natural resources such as water and minerals, along with land use 15 . This is particularly the case for bioenergy, as energy farming may occupy agriculturally viable land 13 , 16 . The conversion process and storage of energy can have a negative impact on SDG 11 (sustainable cities and communities), as renewable energy plants and storage facilities can unintentionally encroach on cultural and heritage lands, especially sacred lands of First Nations people (i.e., for indigenous peoples who are the earliest known inhabitants of an area), posing a potential infringement on indigenous rights 25 , 31 . Similarly, the conversion process can have a negative impact on SDG 15 (life on land), as renewable energy facilities are likely to cause damage to the biodiversity of surrounding areas (i.e. natural wildlife) 32 , 33 .

In most cases, the inhibiting relationships between the aspects of the renewable energy production process and the SDGs are specific to a particular renewable energy type. For example, the storage component of the source selection step (Fig. 3 ) can negatively impact SDG 12 (responsible consumption and production) in the case of biomass and hydropower. For the former, this is because the feedstock required for bioenergy production necessitates the use of storage facilities, like warehouses or hubs for biomass storage and pre-processing 34 , thereby increasing material resource use and land occupation. For the latter, this is because the storage of water required for hydropower production necessitates the use of dams or reservoirs for storage and collection, potentially altering and using surrounding natural resources 21 , 35 . In contrast, this is not the case for solar, wind and wave & tidal energy (Fig. 3 ).

Similarly, the conversion process (Fig. 3 ) can result in an inhibitive relationship with SDG 14 (life below water) for hydropower, wind and wave & tidal. For hydropower, this is due to the potential to artificially alter aquatic ecosystems and redirect the flow of rivers 21 , 35 . For wind power, this is because of the potential contribution of offshore wind farms to biofouling and the generation of underwater noise 36 , whereas for wave & tidal power, tidal barriers can modify the flow of water and wave patterns 1 , 9 . However, the same does not apply to biomass, solar, or geothermal. This demonstrates that particular care must be taken to understand the inhibiting factors for different renewable energy types in order to obtain a comprehensive understanding of their impact on sustainability.

Underestimation of positive sustainability impacts

Figure 2 also highlights that another significant impact of adopting carbon tunnel vision by only considering SDG 13 (climate action) is the lack of consideration of a large number of the other positive SDG impacts of renewable energy production, which is also likely to result in a distorted assessment of the sustainability of RESs. As can be seen in Fig. 2 , all types of RESs exhibit potentially enabling relationships with all of the social (i.e., SDGs 1 - 3, 7, 11) and economic (i.e., SDGs 8, 9, 12) aspects of sustainability. In addition, the components of the renewable energy production process where these occur are generally the same. For example, for SDG 1 (Target 1.5: build resilience to environmental, economic and social disasters), there is a potentially enabling relationship with source selection, transmission & distribution, and storage. This is because renewable energy can directly assist individuals in impoverished conditions by providing them access to electricity, thereby reducing their risk of suffering from local disasters 37 . For SDG 2 (zero hunger), there is a potentially enabling relationship with transmission and storage, attributable to the efficiency and advanced integrated farming techniques that can be enhanced when food production is paired with RESs 38 . Similarly, for SDG 3 (good health and well-being), there is a potential enabling relationship from using renewable energy (conversion, transmission & distribution and storage), as this can reduce the risk of cardiovascular diseases caused by air pollution (PM2.5, PM10) 22 , as well as chronic respiratory disease resulting from the burning of traditional energy sources like coal and fuel 39 . For SDG 15 (life on land), there is a potentially enabling relationship with the conversion process, as renewable energy plants do not require further deforestation for installation and can repurpose degraded land, such as deserts or areas suffering from soil erosion 12 .

However, some of these enabling relationships only apply to specific combinations of renewable energy type and aspects of the energy production process. For example, biomass and hydropower can have a positive impact on SDG 6 (clean water and sanitation) and SDG 11 (sustainable cities and communities) because they are able to use municipal wastewater as one of their energy sources 30 , 40 , thereby purifying water and reusing it as a product or by-product 41 . Additionally, bioenergy, geothermal energy and hydropower can have a positive impact on SDG 12 (responsible consumption and production), as bioenergy production can result in the generation of fertilizer as a by-product, thereby reducing material usage and promoting recycling 42 , 43 , hydropower can supply clean water to downstream areas 44 , and geothermal energy can provide heating/irrigation water for direct applications such as greenhouse farming 45 .

How do we broaden our vision?

As highlighted in the previous sections, while renewable energy sources are a strong enabler of climate action, as well as a number of other SDGs, they can also have a range of negative social, environmental and economic impacts. Consequently, there are several significant conclusions to draw that affect how we should think about climate policy:

Ignoring the potential negative impacts of RESs in the singular pursuit of net zero carbon emissions has the potential to result in disastrous consequences and the perverse outcome that solutions intended to increase the sustainability of humankind actually have the opposite effect. We need to heed the lessons of history to avoid another “hole in the ozone layer” by trying to “fix” a specific issue without considering all potential consequences in an integrated fashion. For policy makers, this can be combated by more cross-agency participation in the management of renewable energy zones and planning, so that trade-offs of a proposed solution can be more apparent.

RESs have enabling relationships with a much broader range of SDGs, not just climate action (SDG 13) and affordable and clean energy (SDG 7), which, if ignored, can significantly underestimate their positive impact on sustainability. This includes the potential to improve the living conditions of communities through the creation of employment opportunities, improved access to resources or reduced health risks, as well as through supporting the biodiversity of the surrounding environment. While there is mounting political pressure to deliver on decarbonization targets, these synergies are at risk of not being capitalized on, and the multiple benefits of implementing renewable energy projects need to be framed in a more holistic way.

By identifying the potential inhibiting and enabling relationships between RESs and the SDGs, this paper provides a blueprint for sustainability assessments that will enable us to broaden our vision beyond considering the impacts of renewables on net-zero emissions to considering the full range of sustainability impacts, allowing for more structured conversations to occur within project management and policy development. This includes an awareness of all potential negative and positive impacts of different types of renewables on different elements of sustainability, as well as for which aspect(s) of the renewable energy production process they occur. Such awareness is especially important for the aspects for which management decisions determine whether sustainability impacts are enabling or inhibiting. For example, the conversion process can have both positive and negative impacts on SDG 11 (sustainable cities and communities), depending on how the government and local society manage their strategy for the preservation, protection, and conservation of all cultural and natural heritage. Similarly, operation and transmission & distribution can have both positive and negative impacts on SDG 8 (decent work and economic growth), depending on the degree to which renewable energy sources are able to promote GDP growth 46 and create more job opportunities with fair pay 47 . To further the ability for renewable energy projects to be more sustainable, future work on this topic should focus on ways to quantity the impact renewable energy projects can have on the SDGs identified, to allow for more direct comparisons for decision makers 48 , 49 , and policy makers alike 50 , 51 .

The enabling and inhibiting relationships between renewable energy sources and the SDGs identified in this paper provide a step toward the information needed to develop climate policy and associated action plans that ensure that we can achieve net zero emissions by implementing RESs in a sustainable manner. This will enable us to address the climate crisis in a manner that avoids mistakes of the past and creates a positive future for our planet.

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Acknowledgements

The authors would like to thank the Future Fuels Cooperative Research Centre for providing funding for this work through project RP1.2-04. The authors would also like to thank the anonymous reviewers of this paper, whose comments have improved its quality significantly.

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Tian, J., Anthony Culley, S., Maier, H.R. et al. Is renewable energy sustainable? Potential relationships between renewable energy production and the Sustainable Development Goals. npj Clim. Action 3 , 35 (2024). https://doi.org/10.1038/s44168-024-00120-6

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DOI : https://doi.org/10.1038/s44168-024-00120-6

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Case study: reducing heating energy consumption in a high tunnel greenhouse with renewable energy and microclimate control by bench-top root-zone heating, bench covers, and under-bench insulation

• Open access
• Published: 11 May 2024
• Volume 5 , article number  86 , ( 2024 )

Cite this article

You have full access to this open access article

• Hei-Young Kim   ORCID: orcid.org/0009-0005-8490-9565 1 ,
• Ok-Youn Yu 2 &
• Jeremy Ferrell 2  

Appalachian State University's Nexus project designed an efficient greenhouse heating system that integrated renewable energy and root zone heating technology to reduce the greenhouse heating energy burden on local farmers and installed it at local cooperative farms. This study analyzed 5 years of data from 2018 to 2022 to investigate the energy savings and microclimate control effectiveness of the Nexus heating system installed at Springhouse Farm in North Carolina, USA. By varying bench cover materials, bottom insulation, and the number of loops of root zone tubing, the different soil temperatures required for plant types and growth stages were achieved with a single temperature controller. A root zone heating fluid of 32.2 ℃ satisfactorily maintained the germination soil between 20 and 25 ℃ in March 2019 with an average outside temperature of 4.8 ℃ and an average low temperature of − 0.4 ℃. Growing soil maintained an average temperature of 15 ℃ with bottom insulation and an average of 11–12 ℃ without bottom insulation. Compared to the conventional heating system (a forced-air propane unit heater alone), weather-adjusted propane consumption (propane usage divided by heating degree days) was reduced by 65% with the Nexus system alone and 45% with the Nexus system and unit heater together. It shows that the Nexus system has significantly reduced greenhouse heating energy consumption and maintained productive conditions. The renewable energy fraction ranged only 9–13% of the total thermal energy used due to the high inlet temperature entering the solar thermal collector. This can be improved by separating the heat storage and backup heat source.

Avoid common mistakes on your manuscript.

1 Introduction

Research conducted by the Appalachian Sustainable Agriculture Project (ASAP) revealed that demand for locally grown produce exceeds current spending by 260% in western North Carolina (NC) [ 1 ]. This unmet demand for locally grown food indicates that the potential exists for increasing rural farmers’ income in the region [ 1 , 2 ]. However, regional barriers, including a relatively short growing season and mountainous terrain, hinder this benefit by limiting the availability of locally grown produce along with consistency and access [ 3 , 4 ]. Many farmers in the region are small-scale, family-owned, and struggle to maintain profitability with limited resources, resulting in low farming income and high rates of off-farm income sources [ 4 , 5 , 6 ]. In Southern Central Appalachia, which includes the area this study focuses on, 64% of farms had an average net loss of $10,734 per farm in 2017 [ 4 ].

Growing season extension through greenhouse production has been proposed as a key solution that simultaneously meets the demand for productivity improvement and local produce in the region [ 1 , 2 ]. High tunnels’ relatively inexpensive installation cost ($32–47/m 2 ) attracts small-scale farmers, so they are commonly used for growing season extension either by passive means or with active heating and ventilation systems [ 7 ]. However, the high heat loss rate (U-value: 6.4 W/m 2 ·K) of polyethylene, the main material of the high tunnel, does not assure its envelope thermal insulation [ 8 , 9 ]. In addition, the requisite energy costs exclude many rural farmers from being able to afford a heated greenhouse [ 10 , 11 ]. Therefore, farmers in this region, with the late frost/freeze and cold snaps in spring, struggle to balance the economic viability of high tunnel crop production with energy costs.

Heating energy is one of the main overhead costs in greenhouse production in temperate climate regions and thus serves as a key factor in increasing agricultural productivity [ 12 , 13 ]. Forced-air unit heaters are chosen by many growers because they are easy to install and have low initial costs, but the heated air rises upward, increasing the temperature difference between the inside and outside of the greenhouse ceiling, causing large heat loss [ 13 ]. Additionally, unit heaters are frequently installed high up in the greenhouse to secure cultivation space, and energy is wasted by heating the entire greenhouse rather than the plant areas.

Local heating and temperature control are efficient ways to save energy. Because each organ of a plant has a different sensitivity to heat, uniform temperature control of the entire greenhouse is not necessary [ 14 ]. Root zone heating (RZH) has been suggested as an effective heating method that simultaneously enhances plant health and saves heating energy [ 14 ]. Maintaining root zone temperatures within the optimal range, even at low greenhouse air temperatures, promotes root growth, activity, and nutrient uptake, leading to improved aboveground biomass and increased yields with reduced disease risk in studies of tomato crops [ 15 , 16 ]. On the other hand, because root growth is sensitive to temperature, low root zone temperature restricts the growth of the entire plant even when optimal greenhouse air temperatures are provided [ 14 , 17 , 18 ].

Compared to conventional heating systems that heat air, RZH lowers the temperature difference between the greenhouse surface and the outside air, resulting in reduced conductive heat loss through the greenhouse plastic [ 19 ]. As warm roots allow lower ambient temperature, nighttime air temperature can be lowered by 2.8 to 5.6 ℃ when using RZH [ 13 , 15 , 20 ]. Several RZH methods, such as carbon crystal electrothermal film, hydronic tubing system, electric heating mats, and heated hydroponic system, have been investigated by researchers, demonstrating that RZH effectively controls soil temperature and provides a favorable environment [ 15 , 16 , 21 , 22 ].

Another effort to save greenhouse energy is the application of renewable energy. Due to the rising cost, depletion, and negative environmental impact of fossil fuels, interest in the application of renewable energy in greenhouse production has increased, and technologies such as solar thermal systems and earth-air heat exchangers (EAHE) have been investigated for greenhouse [ 11 , 23 , 24 ]. These technologies include heat exchangers and heat storage such as soil, water, or phase change materials to increase greenhouse air temperature and maintain the appropriate temperature required for crops during the night [ 11 , 23 , 25 ]. Mehmet Esen [ 11 ] conducted an experimental study by designing a heating system that integrated biogas, solar thermal energy, and ground source heat pump under the winter climate conditions of eastern Turkey, and presented its potential as an efficient heating system [ 11 ]. However, most studies in greenhouse heating have been conducted in experimental greenhouses for short periods (usually less than a year) or through simulations, and few studies have been conducted on commercial greenhouses to demonstrate the effectiveness of their system design in a real production environment. In addition, only a few studies have investigated RZH performance in terms of greenhouse energy saving [ 14 ].

Appalachian State University’s sustainable energy project, the Nexus, developed an unique greenhouse heating system by integrating renewable energy and RZH to enhance energy savings and installed it at two local cooperative farms [ 26 ]. The heating system collects thermal energy from a solar thermal collector and a small-scale pyrolysis system (biochar kiln) and efficiently delivers heat to crops in a greenhouse through RZH system. We designed a biochar kiln and integrated it into the Nexus system. This innovative approach allows us to harness woody biomass, which is abundant in our region, for dual purposes: producing biochar as a valuable soil amendment and supplying heat energy during periods when solar energy is unavailable. [ 26 ]. This system was designed to be practical and capable of being added to an existing greenhouse.

The purpose of this study is to investigate the performance of the Nexus greenhouse heating system at Springhouse Farm in North Carolina, USA on microclimate conditions in terms of soil temperature and energy savings over 5 years of operation. The effectiveness of the under-bench insulation and bench covers installed in reducing heat loss from RZH was explored. Energy savings (propane consumption) were compared considering weather conditions each year. This study holds significant importance as it conducted a long-term evaluation of the RZH system, which integrates renewable energy within a commercial greenhouse. The evaluation focused on energy savings, with adjustments made based on weather conditions.

2 Research questions

To evaluate the system in terms of energy savings and productive microclimate conditions, the following questions were established:

How efficient are RZH systems compared to conventional forced air heating?

What is the share of renewable energy in the energy saved through the Nexus pilot system?

Can the heating system with improved bench covers maintain productive growing conditions without running forced air heating?

How does the heat loss, which depends on the material of the bench cover, affect the soil temperature?

How does under-bench insulation affect soil temperature?

3 Methodology

3.1 description of greenhouse at springhouse farm and nexus heating system.

Springhouse Farm has a 6.1 m by 9.1 m high tunnel greenhouse consisting of double polyethylene (PE) film and air-inflated between the layers. Inside the greenhouse, there are four growing benches and one germination bench. The greenhouse is used primarily for germination and propagation with work beginning in late January to early March. Before the proposed heating system was installed, they used a propane forced-air unit heater and electric heat mats for early germination.

In late 2017, the Nexus team designed and installed a greenhouse heating system to reduce propane consumption through renewable energy and efficient RZH heat distribution [ 26 ]. The system includes a solar collector, a biochar kiln, a food dehydrator, heat storage, and an RZH system (Fig.  1 ).

Nexus pilot system at Springhouse farm, Vilas, NC. a solar thermal collector and food dehydrator; b biochar kiln; c main plumbing and water heater (heat storage)

A heat transfer fluid, 50% propylene glycol–water solution, flows through the system to collect heat and deliver it to the plant rooting zone inside the greenhouse. The thermal collection components include a 30 evacuated-tube-solar thermal collector and a biochar kiln with a built-in heat exchanger. A differential controller controls circulation pumps to collect heat from renewable sources (e.g., sun or biomass) and delivers the heat to the heat storage, a 151.4-L propane water heater located inside the greenhouse. The propane water heater is a storage of heat collected from renewable energy system and is also used as a backup heat source.

The stored heat is distributed to crops on a germination bench and four growing benches through the RZH system (Figs.  2 and 3 ). All benches are 1.2 m wide and 2.4 m long. Christenbury’s report was referred for the RZH piping design of the Nexus system [ 20 ]. Manifold and main pipe were built with 25 mm, 19 mm, and 13 mm PVC pipes and fittings. The 25 mm main pipe was buried 0.3-m-deep in the ground. To distribute heat to the crop root zone (soil) on the benches, 6.35 mm PE drip irrigation tubing was installed due to its ease of acquisition and installation. The RZH tubing and the benches are connected in parallel to provide even flow rate (Fig.  3 ).

a RZH tubing on growing benches; b RZH thermal image

Schematic of the Nexus heating system at Springhouse Farm [ 26 ]

For sufficient heat transfer, the velocity must be kept below 2 m/s [ 20 ]. Flow rate is the product of velocity and pipe cross-sectional area, so a flow rate of 0.53 l/min or less in 6.35 mm PE tubing (4.32 mm ID) provides adequate heat to the soil. Based on the overall piping design, the total flow rate and friction loss were calculated to select a pump of an appropriate size for RZH circulation. Through preliminary experiments, we found that 37.8 ℃ water through 9 loops (4.9 m per loop) of 6.35 mm PE drip irrigation tubing on a 1.2 m by 2.4 m bench, the temperature of the soil in seedling trays were maintained at 12.8–15.6 ℃ evenly. Based on it, 9 loops of PE tubing were placed on each growing bench and 18 loops on the germination bench for a higher soil temperature.

The RZH circulation pump is regulated by the thermostat that senses the germination soil temperature. A mixing valve mixes the higher-temperature fluid from the heat storage (a 151.4-L propane water heater) with the lower-temperature fluid from the RZH return pipe to discharge the 32.2 ℃ mixed fluid to the RZH supply pipe. When all the heat collected from the renewable energy system is used up and the temperature of the fluid in the heat storage (water heater) drops below 32.2 ℃, the propane gas burner of the water heater ignites to maintain the temperature of the fluid above 32.2 ℃. The water heater has only two setpoints, 32.2 ℃ and 48.9 ℃, and it was set to the lower temperature of 32.2 ℃. During the warm season when heating is not needed in the greenhouse, the collected heat bypasses the water heater and is instead dumped into the food dehydrator where the heat can be used to dry food (Fig.  3 ).

3.2 Weather indicators and energy (propane gas) savings

Weather and propane gas usage data from 2016 through 2022 were compared. To compare each year’s weather conditions, heating degree days (HDDs) were calculated, and the number of days with freezing temperatures (below 0 ℃) was recorded. The HDDs is a measure of how much the outside temperature is below a certain level (base temperature) in degrees for given days and used to estimate the demand of heating energy in a building [ 27 ]. It is relative to a base temperature and obtained by subtracting the average temperature of the day from a base temperature:

where, HDDs is the sum of the differences between average temperature of day n (T n-a ) and a base temperature (T base ) over given days. In this study, the unit heater’s setpoint temperature of 12.8 ℃ in 2016 and 2017 before using the Nexus system was set as a base temperature. All the HDDs from the day the greenhouse heating started to the end of May were summed to measure how cold the temperature was during the heating system operation. Because the weather varies every year as well as the period of the greenhouse heating, we compared the total propane consumption of each year by dividing it by HDDs.

3.3 Overall renewable energy gains

The overall heat gain from the Nexus system’s collection components (the solar thermal collector and the biochar kiln) was calculated in kJs from 2018 through 2022. All the heat energy gained from the solar collector and the biochar kiln is stored in the water heater (heat storage). Therefore, the temperature rise of the water heater during daytime (or biochar kiln operation) was used to calculate the amount of renewable energy gained. We looked at the days with no solar energy gained, such as cloudy and rainy days, and then averaged the water heater temperature on those days. This was the baseline water heater temperature, whereby only propane was used to heat the fluid.

The difference between the baseline temperature and the maximum water heater temperature of each day is the temperature rise by solar radiation or biochar kiln burn. Therefore, the thermal energy gained from the renewable energy system can be calculated by multiplying the total degrees of rise, mass, and specific heat of 50% propylene glycol–water solution. Total heat gains for renewable energy system can be calculated as follows:

where, Q total is total heat gains for renewable energy system; m is a mass of 50% propylene glycol–water solution; C P50 is the specific heat of the solution; t n,max is maximum temperature of the water heater of day n; and t 0 is a baseline temperature of the water heater. We turned off the propane water heater when the daily low temperature outside was constantly above 10 ℃: May 8 in 2018, May 16 in 2019, May 14 in 2020, May 27 in 2021, and May 25 in 2022. After turning off the water heater, the water heater is not a backup heat source, and the soil is heated only with renewable energy at night. The minimum temperature of the water heater on each day was the baseline temperature after turning off the water heater.

The amount of thermal energy generated by a biochar kiln is influenced by many variables such as moisture content of the combustion chamber and biomass, so we refrained from using the biochar kiln during the greenhouse heating period to focus on the system’s solar energy collection for the reliable results.

3.4 Heat loss reduction with a lower U-value bench cover

Bench covers were used to trap heat. The bench cover used in 2018 was made of 0.15 mm thick polyethylene film. In 2019, we upgraded the bench covers to reduce heat loss (Fig.  4 ). We built zippered growing bench covers using 0.15 mm-thick polyethylene film for easy opening and closing, and they were placed over gable frames made of PVC pipes. The germination bench cover was constructed using wood frames and four 0.6 m by 1.2 m, 8 mm double wall polycarbonate panels. U-value, the overall heat transfer coefficient, of an 8 mm double wall polycarbonate panel is 3.03 W/m 2 ·K, which is smaller than half of the U-value of single polyethylene film, 6.4 W/m 2 ·K [ 8 , 9 ].

Upgraded bench covers in 2019: a germination bench with polycarbonate cover; b growing bench with zippered polyethylene cover

The effect of the improved germination bench cover was evaluated by three methods: (1) the germination soil temperature, (2) the ignition time interval of the propane water heater, and (3) temperature difference between germination soil and greenhouse air. We compared the germination bench soil temperatures in March 2018 and March 2019. The temperature data collected every minute for March each year were converted into a daily distribution and depicted in one chart.

Since the RZH system is controlled by the germination soil temperature, we anticipated that the lower U-value of the upgraded germination cover would result in longer intervals between propane ignition in the water heater. We recorded the number of minutes that it took from one peak temperature to the next in the water heater between midnight and 8 a.m. on cold nights in March 2018 and March 2019.

3.5 Under-bench insulation

Heat transfer occurs through conduction, convection, and radiation and can be reduced by insulation and reflective film. Different insulators were installed under the RZH tubing (Fig.  5 ) of each bench using extruded polystyrene board (XPS) and reflective bubble insulation. XPS is a common insulation material for buildings that provides good resistance to thermal conduction and convection. The R-value, a measure of insulation ability, of the 1-inch XPS used in the project is rated 0.88 K m 2 /W [ 16 ]. The R-value of reflective bubble insulation varies depending on the installation location and conditions [ 28 ]. It has a small R-value in the material itself with air bubble wrap, but the level of insulation can be increased by minimizing convection and radiation with its low-emittance and almost leak-free reflective surface [ 29 ]. In addition, since there are reflective surfaces on both sides of the air layer, another reflective airspace can be formed when installed, which can enhance the overall insulation [ 28 , 29 ]. Reflective surfaces reflect heat radiated from surrounding heat sources. Thus, the reflective bubble insulation installed below the RZH tubing reflects radiant heat toward the plants located above the tubing.

Growing bench insulation: a bench 1 with XPS and reflective bubble insulation; b bench 2 without insulation; c bench 3 with reflective bubble insulation; d bench 4 with XPS

The tubing on growing bench 1 is placed over both XPS and reflective bubble insulation, while bench 3 and bench 4 contain only reflective bubble insulation and XPS, respectively. Bench 2 has no insulation.

4 Results and discussion

4.1 setpoints of control systems.

Table 1 summarizes the setpoint temperatures of the unit heater, the RZH system, and the ventilation fan used from 2016 to 2022 to maintain the appropriate growing conditions at Springhouse Farm. In 2016 and 2017, before the Nexus system was installed, the unit heater was set to 12.8 ℃. RZH allows for lower greenhouse air temperature, we reduced unit heater setpoints [ 13 , 15 , 20 ]. After the Nexus system was installed, the unit heater was not operated (2019 and 2020) or was set to 8.9 ℃ (2018, 2021 and 2022), which is 3.9 ℃ lower than previous settings.

4.2 Weather indicators and energy (propane gas) savings

Table 2 shows each year’s HDDs the number of days when the outside temperature is below 0 ℃, and propane usage. Because the weather varies every year as well as the period of the greenhouse heating, the total propane consumption of each year was divided by HDDs (propane usage/HDDs) to compare the weather-adjusted propane gas consumption.

Since 2018, the year when the Nexus system started operation, weather-adjusted propane gas consumption has decreased significantly compared to 2016 and 2017. The average weather-adjusted propane gas consumption was 1.72 for the unit heater only (2016 and 2017), 0.6 for the Nexus system only (2019 and 2020), and 0.95 for both uses (2018, 2021, and 2022). It decreased by more than 65% when only using the Nexus system and by more than 45% when using both the Nexus system and the unit heater.

4.3 Overall renewable energy gains

Table 3 shows the properties of 50% propylene glycol–water solution and the renewable energy harvest (kJ) during the operation of the heating system. The renewable energy fraction, which represents the share of renewable energy in total thermal energy consumption, varies based on several factors. These factors include solar irradiation, the renewable energy collection period, and propane consumption, all of which are influenced by the weather conditions during the year. On average, this fraction ranged from 9 to 13% annually.

4.4 Heat loss reduction with a lower U-value bench cover

4.4.1 germination soil temperature.

Figure  6 plots the daily temperature distribution of germination soil in March 2018 and March 2019. The average outside temperature and average low temperature were 3.9 ℃ and − 0.2 ℃ in March 2018 and 4.8 ℃ and − 0.4 ℃ in March 2019, respectively.

Comparison of daily temperature distribution of germination soil according to differences in bench covering materials (polyethylene in 2018 and polycarbonate double wall in 2019)

The RZH system is controlled by the germination soil temperature. It turns on when the germination soil temperature falls below the low bound of the controller and turns off when it reaches the high bound. Each year’s RZH setpoint temperatures are shown in Table  1 . To germinate various types of crops such as tomatoes, peppers, beans, peas, cucumbers, etc. on the germination table, the farmer requested to maintain soil temperature above 20 ℃ during the night. Despite the setpoint temperatures in 2018 being set at 23.3 ℃/25.6 ℃ (low bound/high bound), it frequently dropped below 20 ℃ due to heat loss through polyethylene film cover. In 2019, the overall germination soil temperature remained above 20 ℃ with the upgraded cover made of 8 mm double wall polycarbonate panels even with lower setpoints at 22.2 ℃/23.9 ℃ (low bound/high bound).

Note that the midday soil temperature is affected by the ventilation fan, as the temperature inside the greenhouse rises rapidly during the day. Because the farmer set the ventilation fan temperature to 32.2 ℃ in 2018 and 23.9 ℃ in 2019, the midday soil temperature in 2018 was relatively high.

4.4.2 Propane water heater ignition time intervals

For design simplicity, we designed a propane water heater as a renewable energy storage and backup heat source. In-tank water heaters heat and store fluid by igniting a propane burner to maintain the setpoint temperature. Therefore, the ignition time interval of the burner is an indicator of how much propane (backup heat) is consumed.

The ignition intervals of the propane burners were recorded to evaluate the effect of the upgraded covers on energy savings. The ignition interval is the number of minutes that it takes from one peak temperature to the next in the propane water heater between midnight and 8 a.m. Table 4 presents the comparison of the ignition intervals of the propane burner on cold nights in 2018 and 2019. Compared to 2018, the average intervals were longer in 2019 despite lower outside temperatures, meaning that the upgraded covers can maintain the germination bench temperature above 20 ℃ while burning less propane.

4.4.3 Temperature difference between soil and greenhouse air

Figure  7 plots how much the temperature of the germination soil remained higher than the air inside the greenhouse overnight (midnight to 7 a.m.). Two nights with similar outside temperatures were compared: March 7, 2018, and March 25, 2019. The average outside temperature and greenhouse air temperature during this period were − 0.9 ℃ and 5.3 ℃ on March 7, 2018, and 0.1 ℃ and 6.7 ℃ on March 25, 2019, respectively. On both days, the unit heater was turned off. On average, the germination soil was 16.5 ℃ higher than the greenhouse air with the ungraded covers, while it was 13.7 ℃ higher with the polyethylene film cover.

Temperature differences between the germination soil and greenhouse interior on two nights (Mar 7, 2018 and Mar 25, 2019) when the outside temperatures were similar

4.5 Effects of under-bench insulation on soil temperature

Nighttime (midnight to 7 a.m.) soil temperature on the growing benches varied depending on whether an insulator was installed under the RZH tubing. Figure  8 shows average soil temperatures of the growing benches in March of 2018 and 2019. The average outside temperature and average low temperature were 3.9 ℃ and − 0.2 ℃ in March 2018, and 4.8 ℃ and − 0.4 ℃ in March 2019, respectively. In 2018, the unit heater was set to 8.9 degrees, and the unit heater was not used in 2019.

Average soil temperatures on growing benches: a March in 2018; b March in 2019

In 2018, the average nighttime soil temperatures of benches 1, 2, and 3 were 15.4 ℃, 13 ℃, and 14.7 ℃, respectively. In 2019, they were 15 ℃, 11,5 ℃, and 15,4 ℃, respectively. The nighttime soil temperatures with no bottom insulation (bench 2) presented the lowest average temperature both with the unit heater running in 2018 and without running in 2019, compared to the other two benches with insulation (Fig.  8 ). Bench 2, which is open below the RZH tubing, undergoes convective heat loss due to the inflow of relatively cold greenhouse air and radiant heat transfer in the downward direction, resulting in greater heat loss compared to the other two benches where the bottom of the tubing is closed with insulation.

Single insulation (bench 1) and multi-layer insulation (bench 3) performed similarly. The thermal resistance of the single bubble insulation is assumed to be sufficient to prevent downward conductive heat transfer from the RZH tubing. On both benches, bottom insulation prevents the inflow of cold greenhouse air, reducing convective heat loss. The reflective surface reflects the radiant heat coming down from the RZH tubing and directs it towards the soil.

Running the unit heater did not significantly affect the nighttime soil temperature on benches with bottom insulation (benches 1&3). On the other hand, bench 2 (no insulation) had lower temperatures without the unit heater running in 2019 than in 2018 when greenhouse air temperature was maintained above 8.9 ℃ by the unit heater. Benches 2 and 3, where the convective inflow of greenhouse air is blocked by bottom insulation, are less affected by greenhouse air temperature. Note that bench 4 with XPS is excluded in this study since bench 4 was occasionally under different conditions according to the farmer’s needs.

4.6 Effect of bench covers on soil temperature

The bench cover forms a small greenhouse within the greenhouse, increasing the energy efficiency of the RZH system at night. Figure  9 compares the nighttime temperature difference between the soil and the greenhouse air on March 6 and 29, 2018. The average nighttime air temperature inside the greenhouse was 11.9 ℃ on both days. The benches were heated with RZH, but the unit heater was not running. Bench 1 was covered on both days. Benches 2 and 3 were covered on March 6 but uncovered on March 29.

Temperature differences between soil and air inside the greenhouse on Mar 6, 2018, and Mar 29, 2018: a bench 1—covered on both days; b bench 2—covered on Mar 6 but uncovered on Mar 29; c bench 3—covered on Mar 6 but uncovered on Mar 29

The average temperature differences on covered bench 1 (XPS and reflective insulation) remained close on both days: 5.1 ℃ and 5.3 ℃. The soil temperature on bench 2 (no insulation) was on average 3.1 ℃ higher than the greenhouse air temperature when it was covered, while it was only 1.1 ℃ higher when it was not covered. Likewise, the soil temperature on bench 3 (reflective insulation) was on average 4.6 ℃ higher when covered but only 2.2 ℃ higher when uncovered. Because the bench cover prevents heat loss from the soil, the temperature difference between the soil and the air inside the greenhouse is greater when the bench is covered.

4.7 Temperature distributions during nighttime

The RZH, covers, and insulation on the benches create a microclimate of small greenhouses within the larger greenhouse. Figure  10 shows the temperatures of both the air and soil on benches 1 and 2, the greenhouse air, and the outside air during the nighttime (midnight to 7 a.m.) in March 2018 (from the 9th to the 31st) and March 2019 (from 1 to 31st). In 2018, data after the unit heater thermostat was replaced on March 8th was analyzed. The sensors were placed 0.2 m above the bench RZH tubing and 1.8 m above the ground, both inside and outside the greenhouse, to measure air temperature every minute. In 2018, the unit heater was set to 8.9 ℃, while it remained unused in 2019.

Nighttime soil and air temperature distributions in March 2018 a and March 2019 b

In 2018, the average nighttime temperatures for the soil and air on benches 1 and 2, the greenhouse air, and the outside were 15.4 ℃, 15 ℃, 13 ℃, 12.3 ℃, 10.7 ℃, and 2.6 ℃, respectively. In 2019, these temperatures were 15 ℃, 12.3 ℃, 11.5 ℃, 10.5 ℃, 6.8 ℃, and 0.8 ℃, respectively. Notably, in 2019, when the unit heater was not used, the greenhouse air was influenced by the outside temperature. It increases the temperature difference between the bench cover surface and the greenhouse air, leading to increased conductive heat loss through the covers. As a result, the average nighttime bench temperatures were lower, and the temperature distribution was wider compared to 2018, except for the soil temperature of bench 1 with bottom insulation.

5 Conclusion

Appalachian State University’s Nexus Project has developed a sustainable greenhouse heating system that integrates renewable energy (solar thermal energy and biomass energy) and RZH as a solution to increase profitability. The Nexus system was installed and demonstrated at a local cooperative farm, Springhouse Farm in North Carolina, U.S.A. System performance was studied by analyzing data for 5 years (from 2018 to 2022). The system has been successfully operated without major defects while maintaining productive microclimate conditions. Because this study was conducted in a commercial greenhouse, we were unable to conduct quantitative studies comparing crop biomass or yield under rigorous experimental conditions, but feedback from Springhouse farmers supports that conditions were for healthy crop production: " The greenhouse has performed wonderfully over the years since changing our system. Our plant health and vitality have increased. I attribute it to the benefits associated with bottom heating. We sell some of our plant starts and the feedback has been very positive. Most customers comment that our plants surpass other big box store-bought plants in health and productivity. Saving money on propane is just an added bonus!".

The conclusion and recommendations drawn from this study are summarized as follows:

The Nexus system significantly reduced greenhouse heating energy consumption. Compared to the conventional heating system (a forced-air propane unit heater alone), weather-adjusted propane consumption (propane usage/HDDs) was reduced by 65% when using the Nexus system alone and 45% when using the Nexus system and unit heater together.

The double-wall polycarbonate cover, which has lower thermal conductivity, maintained the temperature of the germination soil stably compared to the polyethylene film cover. Analysis of propane water heater ignition intervals confirmed that the upgraded germination bench cover reduced overall fuel consumption as RZH was controlled by the temperature of the germination soil.

Under-bench insulation helps reduce heat transfer between RZH tubing and greenhouse air through the bench bottom. However, the soil temperatures of the growing bench with single-layer reflective bubble insulation and the bench with multi-layer insulation with added XPS were similar when 32.2 ℃ fluid flowed through RZH tubing.

The combination of RZH, bench covers, and insulation creates a microclimate on small greenhouses within the larger greenhouse. This microclimate remains warm overnight, effectively reducing energy consumption. However, when the temperature inside the greenhouse was not maintained with the unit heater, heat loss through the bench cover increases, leading to more fluctuations in soil and air temperatures.

Varying the cover material, the presence of bottom insulation, and the number of RZH tubing loops resulted in different nighttime soil temperatures. When the soil temperature of the germination bench (double-wall polycarbonate cover, bottom insulation, and 18 loops) was maintained at 20–25 ℃, the growing bench (polyethylene film cover, insulation, and 9 loops) had about 15 ℃. On the growing bench without insulation, the soil was maintained at 11–12 ℃ in March 2019 with an average outside temperature of 4.8 ℃ and low temperature of − 0.4 ℃. The RZH benches at different temperatures can be filled with plants suited to those temperatures.

The bench covers helped the soil temperature higher. When uncovered, the soil temperature was only 1 to 2 ℃ higher than the greenhouse air, but when covered, the soil temperature was higher by 3 to 5 ℃.

The share of renewable energy used to heat the greenhouse ranged from 9 to 13% of the total thermal energy used. The amount of solar energy actually collected through the Nexus system was less than 50% of the harvestable solar energy in the region calculated through modeling. This is a limitation caused by the design of the system where heat storage is also used as a backup energy source, keeping the collector inlet temperature high. It can be improved by separating the backup heat source from heat storage (e.g., adding an on-demand water heater as a backup).

Parallel arrangement of RZH tubing loops and benches ensures uniform fluid flow rates on each bench. Scale-up can be achieved by adding identical RZH units to larger greenhouses.

Data availability

All data supporting the findings of this study are provided within the paper, and the corresponding raw data is available upon request.

Code availability

Not applicable.

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Acknowledgements

The authors would like to thank local farmers for their assistance with this research, particularly Ms. Amy Fiedler, owner of Springhouse Farm. The information contained in this paper is part of the research projects entitled “Demonstration of root zone heating supported by the developed greenhouse heating system” sponsored by the USDA Southern SARE On-Farm research program (Project number OS18-123) and “Promoting Biomass Greenhouse Heating Systems” sponsored by the Bioenergy Research Initiative—North Carolina Department of Agriculture and Consumer Services (Contract 17-078-4003). The authors thank all of the sponsors.

The information contained in this paper is part of the research projects sponsored by the USDA Southern SARE On-Farm research program (Project number OS18-123) and the Bioenergy Research Initiative—North Carolina Department of Agriculture and Consumer Services (Contract 17-078-4003).

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All authors contributed to conceptualization, investigation, and design. H.K. performed methodology, data curation, analysis, visualization, and writing (original draft). O.Y. performed funding acquisition, project administration, supervision, methodology, and writing (review and editing). J.F. performed project administration, supervision, and writing (review and editing). All authors read and approved the final manuscript.

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Kim, HY., Yu, OY. & Ferrell, J. Case study: reducing heating energy consumption in a high tunnel greenhouse with renewable energy and microclimate control by bench-top root-zone heating, bench covers, and under-bench insulation. Discov Sustain 5 , 86 (2024). https://doi.org/10.1007/s43621-024-00276-5

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Published : 11 May 2024

DOI : https://doi.org/10.1007/s43621-024-00276-5

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Case studies of renewable thermal energy, a report to the renewable thermal collaborative by the center for climate and energy solutions.

This series of case studies showcases successful outcomes from the use of renewable thermal technologies at several different large companies and in a major city. It also provides some understanding of the potential benefits and challenges when considering different renewable heating and cooling technologies. In each of the case studies, significant cost and emissions savings were generated by investments in renewable thermal solutions.

One key theme across all of the case studies was that each organization had clearly established sustainability goals that supported a renewable approach. Other factors that facilitated implementation of renewable thermal solutions included high and volatile fossil fuel costs or the phaseout of older capital investments, which offered an opportunity to review renewable options for heating and cooling needs.

Another common theme shared by each of the case studies was the availability of a local resource. This makes the projects more difficult to replicate since local circumstances can greatly vary the project economics or viability of a certain technology for a given application. However, facilities co-located with each other may offer expanded possibilities for renewable thermal solutions.

Most projects included in this report were self-financed and achieved their expected return on investment. However, one technology facing more economic barriers is renewable natural gas (RNG). RNG projects in the United States have been stalled due to low domestic natural gas prices. In the RNG case study included in this report, the market for the renewable fuel standard program was used to mitigate this cost barrier, but broader federal programs may be needed to help support RNG over the long term. The introduction of a thermal renewable energy certificate could also make tracking and claims easier and more standardized for these types of projects.

The Renewable Thermal Collaborative (RTC) is facilitated by the  Center for Climate and Energy Solutions ,  David Gardiner and Associates , and  World Wildlife Fund . The goal of the RTC is to raise awareness and build greater supply and demand for renewable thermal options. Increasing the availability and cost competitiveness of these solutions is key to deploying them at scale. With greater scale, more organizations in the industrial and commercial sectors will be able to make dramatic cuts in their carbon emissions.

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