Land Use Potential in Energy System Optimization Models: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Daniele Mosso
,
Luca Rajteri
,
Laura Savoldi

In energy system optimization models (ESOMs), land use aspects can be integrated at the cost of a finer spatial resolution and a more detailed characterization of land, tailored to regional constraints and specificities. 

  • energy system optimization models
  • land use
  • spatially explicit energy planning

1. Introduction

The sharp rise in temperatures from pre-industrial levels caused by climate change is leading to a paradigm shift in the use of energy throughout all sectors of the economy [1]. The typical mitigation strategy applied by most international authorities is represented by the reduction of the greenhouse gas emission footprint for all energy-intensive sectors [2]. To do that, the decarbonization practice in any sector requires the replacement of its primary inputs with carbon-free alternatives, changing both production processes and involved technologies [3]. Among all the others, the power sector is to be the major decarbonization player in the next decades [4]. Indeed, to further decarbonize the electricity sector and reach a net zero energy system by 2050, a mix of increasingly affordable and mature variable renewable energy source (VRES) technologies, mainly solar photovoltaic (PV) and wind turbine (WT), will need to be deployed [5]. These are characterized by intensive land use, especially photovoltaic, wind [6], and biofuel [7]. Now that the shift towards renewable energy sources is expected to increase, worldwide competition for land and its energy policy implications have not adequately been addressed [8].
In this context, the importance of informed energy models plays a crucial role. Several tools are available to evaluate the possible energy system evolution considering the expected energy transition with different sectorial coverage, time horizon and time steps, spatial scales, and modelling methods [9]. For instance, energy system optimization models (ESOMs) are characterized by a detailed technoeconomic description of the main technologies (or processes) belonging to the most energy-intensive sectors of the system. For this reason, they are typically used to suggest possible optimal future evolution of the energy and technology mix over the long run, according to alternative socioeconomic and policy scenarios [10]. They are optimization models evaluating the minimum-cost configuration of the system [11], according to the studied scenario and the technology modules included in the model. Because of such features, ESOMs have been widely used to assess the effects of decarbonization strategies or innovative technologies on several sectors of the economy, focusing on several sectors (i.e., transport [12], industry [13], hydrogen [14]) and regions (e.g., Belgium [15], US [16], EU [17], world [18][19]). In the transition from a fossil-based to a renewable-based energy system there are, however, new challenges that traditional ESOMs are not yet able to address [20].
Methodologically, many complexities exist concerning the use of space for power plant installation. Starting from the data gathering, the evaluation of accessible land resources is often overestimated during the initial assessment phase. The energy potential of a specific site is subject to a multitude of constraints, encompassing administrative, technical, and economic factors, which collectively impinge upon the availability of land resources within a given region [21]. This necessitates a rigorous process for the identification of appropriate sites, commonly referred to as “land eligibility” (LE). A major challenge in this process is the issue of comparability across different assessment tools, surrounded by the absence of standardized data sources [22]. Finally, the optimal land allocation strategy remains unaddressed in ESOMs. Notably, the siting of a plant should encompass a comprehensive evaluation of all the potential multi-sectoral use of a given site. This evaluation extends beyond mere energy production to include other significant uses such as agriculture and afforestation [23]. This critical dimension, situated within the broader land–energy nexus, calls for a thorough appraisal of land value alongside the identification of sector-specific trade-offs [24]. Presently, this aspect is not integrated into dedicated ESOMs [25] but rather belongs to other methodologies such as integrated assessment models (IAMs) [26] and the analytical hierarchy process (AHP) [27].

2. Benefits and Challenges of Spatially Explicit ESOMs

Enhancing spatial resolution in energy modelling is crucial for a deeper understanding of technology costs, timing, and generation mix [27]. Indeed, this approach encompasses factors affecting demand, supply-side elements, and technological characterization of ESOMs [27]. In this context, geographical information systems (GISs) emerge as a tool that yields promising results in calculating weather potentials, notably for wind (including both offshore and onshore) and solar energy at very high resolution [28]. For example, a study [29] demonstrated that up to 47% of the yearly averaged wind power could be used as baseload power, thanks to a local GIS-based analysis. These approaches are advantageous not only for determining the optimal locations of VRES plants based on meteorological conditions [30] but also for characterizing entire regions or technologies from a technoeconomic point of view, serving as an input for ESOM. To clarify the importance of this practice, a study using a mixed-integer linear programming (MILP) model for heat decarbonization identified spatial resolution as a key variable in influencing scenario results, alongside demand, costs, and efficiency [29], by performing a global sensitivity analysis. Concerning the benefit (or adverse side effect) of varying spatial resolution, Stolten et al. [31] have already demonstrated the goodness of this practice. In their work, they used region clustering based on energy potential characteristics and found that increasing spatial resolution improves model accuracy. However, they also noted a saturation effect of this benefit at higher resolutions and emphasized the importance of considering both time and spatial resolution to increase accuracy. A remarkable limitation of the study is the spatial scope given the focus on the whole European area. Indeed, as confirmed by Frysztacki et al. [32][33], modelling a fully renewable European electricity system, even at a resolution of one node per country is insufficient to retrieve reliable capacity expansion suggestions. Other attempts at a lower spatial scale have been conducted. Downscaling to the national model, a comprehensive review on the topic of spatial resolution in ESOMs is performed in [20] by analyzing 36 multi-sectoral ESOMs from 22 countries, with varying levels of spatial and temporal resolution. The analysis demonstrates to what extent higher spatial resolution impacts the outcomes of energy system analysis. They observed that fine-grained spatial resolution in ESOMs provides significant added value for regions with heterogeneous renewable potential or higher variability in energy services. As spatially resolved models can significantly alter the scenario outcomes, particularly in scenarios with high shares of variable renewable energy sources, disaggregating renewable resources tends to reduce costs. At smaller spatial scales, however, a lack of relevant works is highlighted.

3. Land Availability and Potential Assessment

Incorporating land use and spatial explicitness into ESOMs at the local scale necessitates a comprehensive assessment phase. This assessment phase involves first a detailed analysis of land eligibility (LE) for VRES installations and then the VRES potential estimation [31].
The LE analysis focuses on identifying land that is unsuitable for renewable energy projects due to various limitations. Technical constraints encompass existing renewable energy facilities and areas with limited natural wind or solar resources [21]. Regulatory and environmental restrictions, considering local community concerns regarding land usage, can also curtail the available land for renewable energy projects [21]. It is crucial to consider all these limitations when evaluating the trade-offs and challenges related to land availability for renewable energy projects. A pertinent example at the European level underscores this point: to meet the targets for wind and photovoltaic solar capacity, substantial land area is required. For instance, in France, Germany, and Italy, which are expected to host approximately 50% of the EU’s renewable energy installations, achieving the 2040 renewable capacity goals would require an additional 23,000 to 35,000 square kilometers of land. This area is roughly equivalent to the size of Belgium [34]. This underscores the need for comprehensive land eligibility assessments to realistically achieve renewable energy targets. Examples of LE analyses in the literature are common, as analyzed in the review of Ryberg et al. [21], covering more than 50 works. However, Ryberg concludes that, despite this attention from the community, inconsistencies between studies have prevented a collective understanding of how different criteria influence land availability. In response to that, a major attempt to unify the way LE is evaluated is performed in the GLAES tool (Geospatial Land Availability for Energy Systems) [21]. However, there is a significant gap in the current research: the application and validation of the GLAES framework on a smaller scale has not been explored. Validating GLAES at a small scale is crucial to confirm its reliability and flexibility in different, often more complex local environments.
For the VRES potential assessment, several raw data sources are available and have been listed in a rigorous analysis in [35]. In this study, a repository of all the well-established sources classified by temporal and spatial resolution is proposed, encompassing all the existing renewable energy sources. In addition, Maclaurin et al. [36] developed The Renewable Energy Potential (reV) model, a platform for the detailed assessment of renewable energy resources and their geospatial intersection with grid infrastructure and land use characteristics. Moreover, a major recent attempt exists to incorporate all these VRES potential estimations in a unique versatile tool [37]. Such a framework, called “at-lite”, retrieves global historical weather data, and converts it into power generation potentials and time series for VRES technologies like wind and solar power. These efforts, despite their robustness, often lack the necessary precision at a granular spatial scale. This limitation is significant when considering the intricacies of local environments and the specific demands of smaller regions. Consequently, there is a pressing need for the integration of LE and VRES assessments at a more detailed local level.

4. The Problem of Optimal Siting

Up to this point, the purpose of the increased spatial resolution and better land characterization is to provide better planning solutions, generally reflected in minor system costs. But there is another area where land-specific consideration may help. Notably, together with the cost, the problem of optimal siting of renewable energy must also be addressed [38], to make, for instance, ESOMs capable of providing site-specific insights about plant siting. There is a wide range of research papers that have attempted to extract the optimum location of renewable energy facilities. In [39], a multi-attribute decision-making (MADM) approach and evaluation for ideal site selection for wind power plants was developed. MADM is a process for evaluating and comparing options based on multiple criteria or attributes [40]. In another study [41], the authors developed a novel framework for determining the optimal location for constructing PV farms, focusing on environmental sustainability. They employed an AHP that, like MADM, in energy decision-making aids in prioritizing various energy solutions based on multiple criteria like cost, efficiency, and environmental impact [42]. The discussed methodology excels in identifying optimal locations for renewable energy facilities by leveraging site-specific characteristics like regional potential. However, those analyses often lack integration with broader energy systems, biasing the optimal land management choice. Indeed, the superior suitability of a site for VRES installation does not necessarily imply that deploying VRES is the optimal use for that site. Alternative land uses, such as afforestation or land-use change, may offer greater effectiveness in systemic decarbonization perspective. Therefore, incorporating these optimal siting methodologies in ESOMs becomes relevant also for a more comprehensive approach to energy planning, ensuring that site selection not only focuses on local potential but also aligns with wider system efficiency and sustainability goals.

5. Land–Energy Nexus

A final point emerging from the literature is related to the sectorial trade-offs between the energy and the land use-related sectors [24]. As decarbonization policies are developed, conflicts between sectors are leading to competing demands for land [43]. Renewable energy projects, as well as afforestation for carbon sequestration, often compete with agricultural land uses, thus emphasizing the need for integrated planning that considers both energy requirements and sustainable land management [44]. This nexus has an impact both on the economic and the emission side [45] of the energy planning process. A global study using an IAM highlighted the economic aspect of land use in energy planning, revealing that solar energy yields are higher over croplands, potentially leading to land use competition [45]. However, the study also presents agrivoltaics as a solution to this challenge. Agrivoltaics, combining agriculture and solar energy on the same land, can alleviate the competition for land by enabling simultaneous agricultural production and energy generation [45]. From an emission perspective, a study [8] reveals that land cover changes, both direct and indirect, can cause a net release of carbon ranging from 0 to 50 g CO2/kWh, depending on various factors like region, solar technology efficiency, and land management practices in solar parks. Since the significance in capturing those aspects is demonstrated by the above-mentioned literature, comprehensive ESOMs should include them. Nevertheless, an extensive review states that more work is needed to effectively consider policy trade-offs between the land and energy sector in models, especially from an economic and carbon balance point of view [46]. In particular, ESOMs currently lack representation of land and its related properties, such as crop yields and carbon sequestration potentials, essential for the abovementioned trade-off estimations [47]. Their integration is crucial for comprehensive land-centric perspectives on carbon capture and mitigation strategies [47].

This entry is adapted from the peer-reviewed paper 10.3390/su16041644

References

  1. Fawzy, S.; Osman, A.I.; Doran, J.; Rooney, D.W. Strategies for mitigation of climate change: A review. Environ. Chem. Lett. 2020, 18, 2069–2094.
  2. VijayaVenkataRaman, S.; Iniyan, S.; Goic, R. A review of climate change, mitigation and adaptation. Renew. Sustain. Energy Rev. 2012, 16, 878–897.
  3. Gabrielli, P.; Rosa, L.; Gazzani, M.; Meys, R.; Bardow, A.; Mazzotti, M.; Sansavini, G. Net-zero emissions chemical industry in a world of limited resources. One Earth 2023, 6, 682–704.
  4. Martins, F.; Moura, P.; de Almeida, A.T. The Role of Electrification in the Decarbonization of the Energy Sector in Portugal. Energies 2022, 15, 1759.
  5. International Renewable Energy Agency (IRENA). Electricity Storage and Renewables: Costs and Markets to 2030. 2017. Available online: https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2017/Oct/IRENA_Electricity_Storage_Costs_2017.pdf (accessed on 26 June 2023).
  6. Lovering, J.; Swain, M.; Blomqvist, L.; Hernandez, R.R. Land-use intensity of electricity production and tomorrow’s energy landscape. PLoS ONE 2022, 17, e0270155.
  7. Merfort, L.; Bauer, N.; Humpenöder, F.; Klein, D.; Strefler, J.; Popp, A.; Luderer, G.; Kriegler, E. State of global land regulation inadequate to control biofuel land-use-Chang. emissions. Nat. Clim. Chang. 2023, 13, 610–612.
  8. Van de Ven, D.-J.; Capellan-Peréz, I.; Arto, I.; Cazcarro, I.; de Castro, C.; Patel, P.; Gonzalez-Eguino, M. The potential land requirements and related land use change emissions of solar energy. Sci. Rep. 2021, 11, 2907.
  9. Chang, M.; Thellufsen, J.Z.; Zakeri, B.; Pickering, B.; Pfenninger, S.; Lund, H.; Østergaard, P.A. Trends in tools and approaches for modelling the energy transition. Appl. Energy 2021, 290, 116731.
  10. Prina, M.G.; Manzolini, G.; Moser, D.; Nastasi, B.; Sparber, W. Classification and challenges of bottom-up energy system models—A review. Renew. Sustain. Energy Rev. 2020, 129, 109917.
  11. Loulou, R.; Goldstein, G.; Kanudia, A.; Lettila, A.; Remme, U. Documentation for the TIMES Model: Part I. 2016. Available online: https://iea-etsap.org/docs/Documentation_for_the_TIMES_Model-Part-I_July-2016.pdf (accessed on 17 October 2022).
  12. Lerede, D.; Bustreo, C.; Gracceva, F.; Lechón, Y.; Savoldi, L. Analysis of the Effects of Electrification of the Road Transport Sector on the Possible Penetration of Nuclear Fusion in the Long-Term European Energy Mix. Energies 2020, 13, 3634.
  13. Lerede, D.; Bustreo, C.; Gracceva, F.; Saccone, M.; Savoldi, L. Techno-economic and environmental characterization of industrial technologies for transparent bottom-up energy modeling. Renew. Sustain. Energy Rev. 2021, 140, 110742.
  14. Balbo, A.; Colucci, G.; Nicoli, M.; Savoldi, L. Exploring the Role of Hydrogen to Achieve the Italian Decarbonization Targets Using an Open-Source Energy System Optimization Model. 2023, pp. 89–100. Available online: https://publications.waset.org/10013040/exploring-the-role-of-hydrogen-to-achieve-the-italian-decarbonization-targets-using-an-open-source-energy-system-optimization-model (accessed on 25 April 2023).
  15. Limpens, G.; Jeanmart, H.; Maréchal, F. Belgian Energy Transition: What Are the Options? Energies 2020, 13, 261.
  16. Eshraghi, H.; de Queiroz, A.R.; DeCarolis, J.F. US Energy-Related Greenhouse Gas Emissions in the Absence of Federal Climate Policy. Environ. Sci. Technol. 2018, 52, 9595–9604.
  17. Barnes, T.; Shivakumar, A.; Brinkerink, M.; Niet, T. OSeMOSYS Global, an open-source, open data global electricity system model generator. Sci. Data 2022, 9, 1–13.
  18. Gago Da Camara Simoes, S.; Nijs, W.; Ruiz Castello, P.; Sgobbi, A.; Radu, D.; Bolat, P.; Thiel, C.; Peteves, E. The JRC-EU-TIMES Model—Assessing the Long-Term Role of the SET Plan Energy Technologies; Publications Office of the European Union: Luxembourg, 2013.
  19. Lerede, D.; Nicoli, M.; Savoldi, L.; Trotta, A. Analysis of the possible contribution of different nuclear fusion technologies to the global energy transition. Energy Strat. Rev. 2023, 49, 101144.
  20. Aryanpur, V.; O’Gallachoir, B.; Dai, H.; Chen, W.; Glynn, J. A review of spatial resolution and regionalisation in national-scale energy systems optimisation models. Energy Strat. Rev. 2021, 37, 100702.
  21. Ryberg, D.S.; Robinius, M.; Stolten, D. Evaluating Land Eligibility Constraints of Renewable Energy Sources in Europe. Energies 2018, 11, 1246.
  22. Risch, S.; Maier, R.; Du, J.; Pflugradt, N.; Stenzel, P.; Kotzur, L.; Stolten, D. Potentials of Renewable Energy Sources in Germany and the Influence of Land Use Datasets. Energies 2022, 15, 5536.
  23. Ramos, E.P.; Sridharan, V.; Alfstad, T.; Niet, T.; Shivakumar, A.; Howells, M.I.; Rogner, H.; Gardumi, F. Climate, Land, Energy and Water systems interactions—From key concepts to model implementation with OSeMOSYS. Environ. Sci. Policy 2022, 136, 696–716.
  24. Lamhamedi, B.E.H.; de Vries, W.T. An Exploration of the Land–(Renewable) Energy Nexus. Land 2022, 11, 767.
  25. Chen, Y.-K.; Kirkerud, J.G.; Bolkesjø, T.F. Balancing GHG mitigation and land-use conflicts: Alternative Northern European energy system scenarios. Appl. Energy 2022, 310, 118557.
  26. Bacca, E.J.M.; Stevanović, M.; Bodirsky, B.L.; Karstens, K.; Chen, D.M.-C.; Leip, D.; Müller, C.; Minoli, S.; Heinke, J.; Jägermeyr, J.; et al. Uncertainty in land-use adaptation persists despite crop model projections showing lower impacts under high warming. Commun. Earth Environ. 2023, 4, 284.
  27. Moksnes, N.; Howells, M.; Usher, W. Increasing spatial and temporal resolution in energy system optimisation model—The case of Kenya. Energy Strat. Rev. 2024, 51, 101263.
  28. Martínez-Gordón, R.; Morales-España, G.; Sijm, J.; Faaij, A. A review of the role of spatial resolution in energy systems modelling: Lessons learned and applicability to the North Sea region. Renew. Sustain. Energy Rev. 2021, 141, 110857.
  29. Yliruka, M.I.; Moret, S.; Jalil-Vega, F.; Hawkes, A.D.; Shah, N. The Trade-Off between Spatial Resolution and Uncertainty in Energy System Modelling. Comput. Aided Chem. Eng. 2022, 49, 2035–2040.
  30. Archer, C.L.; Jacobson, M.Z. Supplying Baseload Power and Reducing Transmission Requirements by Interconnecting Wind Farms. J. Appl. Meteorol. Clim. 2007, 46, 1701–1717.
  31. Patil, S.; Kotzur, L.; Stolten, D. Advanced Spatial and Technological Aggregation Scheme for Energy System Models. Energies 2022, 15, 9517.
  32. Frysztacki, M.M.; Hörsch, J.; Hagenmeyer, V.; Brown, T. The strong effect of network resolution on electricity system models with high shares of wind and solar. Appl. Energy 2021, 291, 116726.
  33. Frysztacki, M.M.; Hagenmeyer, V.; Brown, T. Inverse methods: How feasible are spatially low-resolved capacity expansion modelling results when disaggregated at high spatial resolution? Energy 2023, 281, 128133.
  34. McKinsey. Land: A Crucial Resource for Europe’s Energy Transition. Available online: https://www.mckinsey.com/industries/electric-power-and-natural-gas/our-insights/land-a-crucial-resource-for-the-energy-transition (accessed on 8 November 2023).
  35. Stucchi, L.; Aiello, M.; Gargiulo, A.; Brovelli, M.A. Copernicus and the energy challenge. ISPRS Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 2021, XLVI-4/W2, 189–196.
  36. Maclaurin, G.; Grue, N.; Lopez, A.; Heimiller, D.; Rossol, M.; Buster, G.; Williams, T. The Renewable Energy Potential (reV) Model: A Geospatial Platform for Technical Potential and Supply Curve Modeling. 2021. Available online: https://www.nrel.gov/gis/renewable-energy-potential.html (accessed on 1 February 2023).
  37. Hofmann, F.; Hampp, J.; Neumann, F.; Brown, T.; Hörsch, J. atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series. J. Open Source Softw. 2021, 6, 3294.
  38. Dominguez, O.D.M.; Kasmaei, M.P.; Lavorato, M.; Mantovani, J.R.S. Optimal siting and sizing of renewable energy sources, storage devices, and reactive support devices to obtain a sustainable electrical distribution systems. Energy Syst. 2018, 9, 529–550.
  39. Attaullah; Ashraf, S.; Rehman, N.; Khan, A.; Naeem, M.; Park, C. A wind power plant site selection algorithm based on q-rung orthopair hesitant fuzzy rough Einstein aggregation information. Sci. Rep. 2022, 12, 5443.
  40. Zanakis, S.H.; Solomon, A.; Wishart, N.; Dublish, S. Multi-attribute decision making: A simulation comparison of select methods. Eur. J. Oper. Res. 1998, 7, 507–529.
  41. Türk, S.; Koç, A.; Şahin, G. Multi-criteria of PV solar site selection problem using GIS-intuitionistic fuzzy based approach in Erzurum province/Turkey. Sci. Rep. 2021, 11, 5034.
  42. Topuz, M.; Deniz, M. Application of GIS and AHP for land use suitability analysis: Case of Demirci district (Turkey). Humanit. Soc. Sci. Commun. 2023, 10, 115.
  43. Jones, S.M.; Smith, A.C.; Leach, N.; Henrys, P.; Atkinson, P.M.; Harrison, P.A. Pathways to achieving nature-positive and carbon–neutral land use and food systems in Wales. Reg. Environ. Chang. 2023, 23, 37.
  44. Caldera, U.; Breyer, C. Afforesting arid land with renewable electricity and desalination to mitigate climate change. Nat. Sustain. 2023, 6, 526–538.
  45. Adeh, E.H.; Good, S.P.; Calaf, M.; Higgins, C.W. Solar PV Power Potential is Greatest Over Croplands. Sci. Rep. 2019, 9, 11442.
  46. Niet, T.; Fraser, S.; Arianpoo, N.; Kuling, K.; Wright, A.; Wright, A.S. Embedding the United Nations Sustainable Development Goals into Systems Analysis—Expanding the Food-Energy-Water Nexus. 2020. Available online: https://assets.researchsquare.com/files/rs-52249/v1_stamped.pdf?c=1597093325 (accessed on 30 December 2023).
  47. Zhou, J.; Ding, Q.; Zou, Z.; Deng, J.; Xu, C.; Yang, W. Land suitability evaluation of large-scale photovoltaic plants using structural equation models. Resour. Conserv. Recycl. 2023, 198, 107179.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
Video Production Service
Feedback