Energy system modelling; multi-objective optimisation; multi-sectoral planning; near-optimality; necessary condition; suboptimal space
Abstract :
[en] In the energy transition context, restructuring energy systems and making informed decisions on the optimal energy mix and technologies is crucial. Energy system optimisation models (ESOMs) are commonly used for this purpose. However, their focus on cost minimisation limits their usefulness in addressing other factors like environmental sustainability and social equity. Moreover, by searching for only one global optimum, they overlook diverse alternative solutions. This paper aims to overcome these limitations by exploring near-optimal spaces in multi-objective optimisation problems, providing valuable insights for decision-makers. The authors extend the concepts of epsilon-optimality and necessary conditions to multi-objective problems. They apply this methodology to a case study of the Belgian energy transition in 2035 while considering both cost and energy invested as objectives. The results reveal opportunities to reduce dependence on endogenous resources while requiring substantial reliance on exogenous resources. They demonstrate the versatility of potential exogenous resources and provide insights into objective trade-offs. This paper represents a pioneering application of the proposed methodology to a real-world problem, highlighting the added value of near-optimal solutions in multi-objective optimisation. Future work could address limitations, such as approximating the epsilon-optimal space, investigating parametric uncertainty, and extending the approach to other case studies and objectives, enhancing its applicability in energy system planning and decision-making.
Research center :
Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège
Disciplines :
Energy
Author, co-author :
Dubois, Antoine ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Dumas, Jonathan; Réseau de transport d'électricité [FR] > Recherche et Développement
Thiran, Paolo; UCL - Catholic University of Louvain [BE] > Institute of Mechanics
Limpens, Gauthier; UCL - Catholic University of Louvain [BE] > Institute of Mechanics
Ernst, Damien ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Language :
English
Title :
Multi-objective near-optimal necessary conditions for multi-sectoral planning
Publication date :
15 November 2023
Journal title :
Applied Energy
ISSN :
0306-2619
eISSN :
1872-9118
Publisher :
Elsevier, London, United Kingdom
Volume :
350
Peer reviewed :
Peer Reviewed verified by ORBi
Development Goals :
7. Affordable and clean energy
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
DeCarolis, J., Daly, H., Dodds, P., Keppo, I., Li, F., McDowall, W., Pye, S., Strachan, N., Trutnevyte, E., Usher, W., Winning, M., Yeh, S., Zeyringer, M., Formalizing best practice for energy system optimization modelling. Appl Energy 194 (2017), 184–198, 10.1016/j.apenergy.2017.03.001.
Pfenninger, S., Hawkes, A., Keirstead, J., Energy systems modeling for twenty-first century energy challenges. Renew Sustain Energy Rev 33 (2014), 74–86, 10.1016/j.rser.2014.02.003.
Fishbone, L.G., Abilock, H., MARKAL, a linear-programming model for energy systems analysis: Technical description of the BNL version. Int J Energy Res 5:4 (1981), 353–375, 10.1002/er.4440050406.
Schrattenholzer, L., The energy supply model MESSAGE. 1981 URL https://pure.iiasa.ac.at/id/eprint/1542/.
Quoilin, S., Hidalgo Gonzalez, I., Zucker, A., Modelling future EU power systems under high shares of renewables: The Dispa-SET 2.1 open-source model. 2017 URL https://orbi.uliege.be/handle/2268/205771.
Yue, X., Pye, S., DeCarolis, J., Li, F.G., Rogan, F., Gallachóir, B.Ó., A review of approaches to uncertainty assessment in energy system optimization models. Energy Strategy Rev 21 (2018), 204–217, 10.1016/j.esr.2018.06.003.
Fazlollahi, S., Mandel, P., Becker, G., Maréchal, F., Methods for multi-objective investment and operating optimization of complex energy systems. Energy 45:1 (2012), 12–22, 10.1016/j.energy.2012.02.046.
Trutnevyte, E., Does cost optimization approximate the real-world energy transition?. Energy 106 (2016), 182–193, 10.1016/j.energy.2016.03.038.
Fujino, J., Hibino, G., Ehara, T., Matsuoka, Y., Masui, T., Kainuma, M., Back-casting analysis for 70% emission reduction in Japan by 2050. Clim Policy 8:sup1 (2008), S108–S124, 10.3763/cpol.2007.0491.
Hughes, N., Strachan, N., Methodological review of UK and international low carbon scenarios. Energy Policy 38:10 (2010), 6056–6065, 10.1016/j.enpol.2010.05.061.
Becerra-López, H.R., Golding, P., Multi-objective optimization for capacity expansion of regional power-generation systems: Case study of far west Texas. Energy Convers Manage 49:6 (2008), 1433–1445, 10.1016/j.enconman.2007.12.021.
Fonseca, J.D., Commenge, J.-M., Camargo, M., Falk, L., Gil, I.D., Multi-criteria optimization for the design and operation of distributed energy systems considering sustainability dimensions. Energy, 214, 2021, 118989, 10.1016/j.energy.2020.118989.
Fonseca, J.D., Commenge, J.-M., Camargo, M., Falk, L., Gil, I.D., Sustainability analysis for the design of distributed energy systems: A multi-objective optimization approach. Appl Energy, 290, 2021, 116746, 10.1016/j.apenergy.2021.116746.
Jing, R., Zhu, X., Zhu, Z., Wang, W., Meng, C., Shah, N., Li, N., Zhao, Y., A multi-objective optimization and multi-criteria evaluation integrated framework for distributed energy system optimal planning. Energy Convers Manage 166 (2018), 445–462, 10.1016/j.enconman.2018.04.054.
DeCarolis, J.F., Using modeling to generate alternatives (MGA) to expand our thinking on energy futures. Energy Econ 33:2 (2011), 145–152, 10.1016/j.eneco.2010.05.002.
Brill, E.D., Chang, S.-Y., Hopkins, L.D., Modeling to generate alternatives: The HSJ approach and an illustration using a problem in land use planning. Manage Sci 28:3 (1982), 221–235, 10.1287/mnsc.28.3.221.
DeCarolis, J., Babaee, S., Li, B., Kanungo, S., Modelling to generate alternatives with an energy system optimization model. Environ Model Softw 79 (2016), 300–310, 10.1016/j.envsoft.2015.11.019.
Price, J., Keppo, I., Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models. Appl Energy 195 (2017), 356–369, 10.1016/j.apenergy.2017.03.065.
Li, F.G., Trutnevyte, E., Investment appraisal of cost-optimal and near-optimal pathways for the UK electricity sector transition to 2050. Appl Energy 189 (2017), 89–109, 10.1016/j.apenergy.2016.12.047.
Pedersen, T.T., Victoria, M., Rasmussen, M.G., Andresen, G.B., Modeling all alternative solutions for highly renewable energy systems. Energy, 2021, 121294, 10.1016/j.energy.2021.121294.
Grochowicz, A., van Greevenbroek, K., Benth, F.E., Zeyringer, M., Intersecting near-optimal spaces: European power systems with more resilience to weather variability. Energy Econ, 118, 2023, 106496, 10.1016/j.eneco.2022.106496.
Neumann, F., Brown, T., The near-optimal feasible space of a renewable power system model. Electr Power Syst Res, 190, 2021, 106690, 10.1016/j.epsr.2020.106690.
Dubois, A., Ernst, D., Computing necessary conditions for near-optimality in capacity expansion planning problems. Electr Power Syst Res, 211, 2022, 108343, 10.1016/j.epsr.2022.108343.
Dubois, A., Dumas, J., Limpens, G., Thiran, P., Multi-objective near-optimal necessary conditions for multi-sectoral planning - code. 2023, 10.5281/zenodo.7665440.
Dubois, A., Dumas, J., Limpens, G., Thiran, P., Multi-objective near-optimal necessary conditions for multi-sectoral planning - dataset. 2023, 10.5281/zenodo.7665340.
Ehrgott, M., Multicriteria optimization. 2005, Springer Science & Business Media, 10.1007/978-3-662-22199-0.
Alarcon-Rodriguez, A., Ault, G., Galloway, S., Multi-objective planning of distributed energy resources: A review of the state-of-the-art. Renew Sustain Energy Rev 14:5 (2010), 1353–1366, 10.1016/j.rser.2010.01.006.
European Commission, A., European Green Deal. 2021 URL https://ec.europa.eu/clima/eu-action/european-green-deal_en, [accessed 08-June-2022].
International Energy Agency, A., IEA - Energy Statistics Data Browser. 2022 URL https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, [accessed 17-June-2022].
Commission, E., for Climate Action, D.-G., for Energy, D.-G., for Mobility, D.-G., Transport, C., Zampara, M., Obersteiner, M., Evangelopoulou, S., De Vita, A., Winiwarter, W., Witzke, H., Tsani, S., Kesting, M., Paroussos, L., Höglund-Isaksson, L., Papadopoulos, D., Capros, P., Kannavou, M., Siskos, P., Tasios, N., Kouvaritakis, N., Karkatsoulis, P., Fragkos, P., Gomez-Sanabria, A., Petropoulos, A., Havlík, P., Frank, S., Purohit, P., Fragiadakis, K., Forsell, N., Gusti, M., Nakos, C., EU reference scenario 2016 : energy, transport and GHG emissions : trends to 2050. 2016, Publications Office, 10.2833/001137.
Dupont, E., Germain, M., Jeanmart, H., Estimate of the societal energy return on investment (EROI). Biophys Econ Sustain 6:1 (2021), 1–14, 10.1007/s41247-021-00084-9.
Brockway, P.E., Owen, A., Brand-Correa, L.I., Hardt, L., Estimation of global final-stage energy-return-on-investment for fossil fuels with comparison to renewable energy sources. Nat Energy 4:7 (2019), 612–621, 10.1038/s41560-019-0425-z.
Dumas, J., Dubois, A., Thiran, P., Jacques, P., Contino, F., Cornélusse, B., Limpens, G., The energy return on investment of whole-energy systems: Application to Belgium. Biophys Econ Sustain, 7(4), 2022, 12, 10.1007/s41247-022-00106-0.
European Commission - Eurostat, J., Glossary: Final Energy Consumption. 2022 URL https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Final_energy_consumption, [accessed 17-June-2022].
European Commission - Eurostat, Z., Eurostat - Population density. 2022 URL https://ec.europa.eu/eurostat/databrowser/view/tps00003/default/table, [accessed 17-June-2022].
Belgian Offshore Platform, Z., Belgian Offshore Platform. 2022 URL https://www.belgianoffshoreplatform.be/en/, [accessed 26-Jul-2022].
Limpens, G., Jeanmart, H., Maréchal, F., Belgian energy transition: What are the options?. Energies, 13(1), 2020, 10.3390/en13010261.
Limpens, G., Moret, S., Jeanmart, H., Maréchal, F., EnergyScope TD: A novel open-source model for regional energy systems. Appl Energy, 255, 2019, 113729, 10.1016/j.apenergy.2019.113729.
Contino, F., Moret, S., Limpens, G., Jeanmart, H., Whole-energy system models: The advisors for the energy transition. Prog Energy Combust Sci, 81, 2020, 100872, 10.1016/j.pecs.2020.100872.
Limpens G, Coppitters D, Rixhon X, Contino F, Jeanmart H. The impact of uncertainties on the Belgian energy system: Application of the polynomial chaos expansion to the energyscope model. In: ECOS 2020 - proceedings of the 33rd international conference on efficiency, cost, optimization, simulation and environmental impact of energy systems. 2020, p. 724–33, URL.
Rixhon, X., Limpens, G., Coppitters, D., Jeanmart, H., Contino, F., The role of electrofuels under uncertainties for the belgian energy transition. Energies, 14(13), 2021, 10.3390/en14134027.
Colla, M., Blondeau, J., Jeanmart, H., Optimal use of lignocellulosic biomass for the energy transition, including the non-energy demand: The case of the belgian energy system. Front Energy Res, 10, 2022, 10.3389/fenrg.2022.802327.
Limpens G, Jeanmart H. System LCOE: Applying a whole-energy system model to estimate the integration costs of photovoltaic. In: Proceedings of the ECOS2021—the 34th international conference, Taormina, Italy, June. 2021, p. 28–2, URL.
Li, X., Damartzis, T., Stadler, Z., Moret, S., Meier, B., Friedl, M., Maréchal, F., Decarbonization in complex energy systems: A study on the feasibility of carbon neutrality for Switzerland in 2050. Front Energy Res, 8, 2020, 10.3389/fenrg.2020.549615.
Guevara, E., Babonneau, F., de Mello, T.H., Moret, S., A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty. Appl Energy, 271, 2020, 115005, 10.1016/j.apenergy.2020.115005.
Borasio, M., Moret, S., Deep decarbonisation of regional energy systems: A novel modelling approach and its application to the Italian energy transition. Renew Sustain Energy Rev, 153, 2022, 111730, 10.1016/j.rser.2021.111730.
Muyldermans, B., Nève, G., Multi-criteria optimisation of an energy system and application to the Belgian case. Master's thesis, 2021, UCL - Ecole polytechnique de Louvain URL http://hdl.handle.net/2078.1/thesis:33139.
Wernet, G., Bauer, C., Steubing, B., Reinhard, J., Moreno-Ruiz, E., Weidema, B., The ecoinvent database version 3 (part i): overview and methodology. Int J Life Cycl Assess 21:9 (2016), 1218–1230, 10.1007/s11367-016-1087-8.
Orban, A., Energy return on investment of electrofuels. Master's thesis, 2022, ULiège URL https://matheo.uliege.be/handle/2268.2/15914.
Nacken, L., Krebs, F., Fischer, T., Hoffmann, C., Integrated renewable energy systems for Germany–A model-based exploration of the decision space. 2019 16th International Conference on the European Energy Market, EEM, 2019, IEEE, 1–8, 10.1109/EEM.2019.8916442.
Limpens, G., Generating energy transition pathways: application to Belgium. (Ph.D. thesis), 2021, UCL-Université Catholique de Louvain URL http://hdl.handle.net/2078.1/249196.
