Abstract
The efficient and flexible design of renewable power plants is key to increase competitiveness of clean technologies and accomplish climate targets. However, renewable power plants that deliver energy only when the renewable source is available produce large fluctuations and increase cost of integration in the wider electricity system. Optimised design of renewable power plants with energy storage increases reliability and decreases integration cost of sustainable technologies. Here we show a two-stage multi-objective optimisation framework to optimise the design and the operation of power plants that combine two or more generation technologies and energy storage, with the aim of producing firm or dispatchable electricity. With the optimisation framework it is possible to handle different technologies to design a sustainable, cost competitive, flexible and dispatchable power plant. Besides, the post-optimisation analysis handles other key performance indicators and provides detailed information that improves the decision making.
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References
Denholm, P., Hand, M.: Grid flexibility and storage required to achieve very high penetration of variable renewable electricity. Energy Policy. 39, 1817–1830 (2011). https://doi.org/10.1016/j.enpol.2011.01.019
Bravo, R., Friedrich, D.: Two-stage optimisation of hybrid solar power plants. Solar Energy 164, 187–199 (2018). https://doi.org/10.1016/j.solener.2018.01.078
International Energy Agency – IEA: Technology Roadmap Solar thermal electricity (2014). www.iea.org
Tezer, T., Yaman, R., Yaman, G.: Evaluation of approaches used for optimization of stand-alone hybrid renewable energy systems. Renew. Sustain. Energy Rev. 73 840–853 (2017). https://doi.org/10.1016/J.RSER.2017.01.118
Stojiljkovic, M.M.: Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics. Energy 137, 1231–1251 (2017). https://doi.org/10.1016/j.energy.2017.06.037
Hart, W.E., Watson, J.-P., Woodruff, D.L.: Pyomo: modeling and solving mathematical programs in Python. Math. Prog. Comp. 3(3), 219–260 (2011)
Fortin, F.-A., De Rainville, F.-M., Gardner, M.-A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)
IBM ILOG CPLEX Optimizer. CPLEX User’s Manual, Version 12 Release 7 (2016)
National Renewable Energy Laboratory. Golden, CO. https://sam.nrel.gov/content/downloads. System Advisor Model Version 2017.9.5. Accessed 31 Oct 2016
Coordinador Electrico Nacional. Las Condes, Santiago, Chile. https://www.coordinador.cl/sistema-informacion-publica/
Ministerio de Energia Chile, Departamento de Geofisica, Universidad de Chile, Santiago, Chile. http://www.minenergia.cl/exploradorsolar/
Acknowledgements
Ruben Bravo is supported by a PhD Scholarship from Becas Chile, National Commission for Scientific and Technological Research, CONICYT-CHILE.
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Bravo, R., Friedrich, D. (2019). Two-Stage, Multi-objective Optimisation Framework for an Efficient Pathway to Decarbonise the Power Sector. In: Rodrigues, H., et al. EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization. EngOpt 2018. Springer, Cham. https://doi.org/10.1007/978-3-319-97773-7_122
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DOI: https://doi.org/10.1007/978-3-319-97773-7_122
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