A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Wind-solar Coupling

Tim Mareda; Ludovic Gaudard; Franco Romerio

doi:10.1109/JAS.2017.7510523

Volume 4 Issue 2

Apr. 2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(2): 260-272

Tim Mareda, Ludovic Gaudard and Franco Romerio, "A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Wind-solar Coupling," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 260-272, Apr. 2017. doi: 10.1109/JAS.2017.7510523

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Tim Mareda, Ludovic Gaudard and Franco Romerio, "A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Wind-solar Coupling," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 260-272, Apr. 2017. doi: 10.1109/JAS.2017.7510523

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A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Wind-solar Coupling

doi: 10.1109/JAS.2017.7510523

1.
Institute for Environmental Sciences, University of Geneva, Boulevard Carl-Vogt 66 CH-1205 Geneva, Switzerland
2.
Geneva School of Economics and Management (GSEM), University of Geneva, Uni Mail, Bd du Pont-d'Arve 40, CH-1211 Genève 4, Switzerland

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Author Bio:
Ludovic Gaudard is associate researcher at the University of Geneva (Institute for Environmental Sciences), postdoctor at the Imperial College London (Center for environmental policy) and visiting fellow at the London School of Economics (Grantham Research Institute on Climate Change and the Environment). He holds a Ph. D. `es sciences in interdisciplinary studies (economics and environmental sciences), a masteros degree in environmental sciences and a bacheloros degree in physics. His current research focuses on the management of risk and uncertainty in power plant investments. The most important drivers are investigated and the implications for market regulation and energy policy are highlighted.(e-mail: ludovic.gaudard@unige.ch)

Franco Romerio leads the group "energy, policy and economics" at the University of Geneva (Geneva School of Economics and Management, and Institute for Environmental Sciences). He holds a Ph. D. degree in economics and a master degree in history both from the University of Geneva, as well as a postgraduate degree in energy from the Federal Institute of Technology in Lausanne (EPFL). He has written numerous books and articles, and has sat on several commissions both for public bodies and electricity companies. In particular, between 1998 and 2009, he was member of the board of directors of the Azienda Elettrica Ticinese (AET), which is a public electricity company with an annual turnover of approximately 1 billion. He teaches energy economics and policy at the University of Geneva and at the EPFL.(e-mail: franco.romerio@unige.ch)
Corresponding author: Tim Mareda holds a bachelor degree in physics and a master degree in environmental sciences, completed within the research group "Energy, policy and economics" at Geneva University, Switzerland. He is currently working as a scientific collaborator and teaching assistant at Geneva Institute for environmental science (ISE-unige) and pursuing a Ph. D. thesis on energy systems. His research is focusing on paradigm shifts in the power sector, notably looking into electricity storage dynamics, integration of distributed and intermittent generation, demandresponse mechanisms and "smart-grid" concepts. His current research interests include geographical information systems, complex system modeling, and the integration of physical and socio-economical dimensions in the energy sector. Corresponding author of this paper.(e-mail: tim.mareda@unige.ch)
Received Date: 2015-10-22
Accepted Date: 2016-02-28

Abstract

Abstract

The transitional path towards a highly renewable power system based on wind and solar energy sources is investigated considering their intermittent and spatially distributed characteristics. Using an extensive weather-driven simulation of hourly power mismatches between generation and load, we explore the interplay between geographical resource complementarity and energy storage strategies. Solar and wind resources are considered at variable spatial scales across Europe and related to the Swiss load curve, which serve as a typical demand side reference. The optimal spatial distribution of renewable units is further assessed through a parameterized optimization method based on a genetic algorithm. It allows us to explore systematically the effective potential of combined integration strategies depending on the sizing of the system, with a focus on how overall performance is affected by the definition of network boundaries. Upper bounds on integration schemes are provided considering both renewable penetration and needed reserve power capacity. The quantitative trade-off between grid extension, storage and optimal wind-solar mix is highlighted. This paper also brings insights on how optimal geographical distribution of renewable units evolves as a function of renewable penetration and grid extent.
- Energy optimization,
- grid integration,
- genetic algorithm,
- optimal spatial distribution,
- power system modeling

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References(31)

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A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Wind-solar Coupling

doi: 10.1109/JAS.2017.7510523

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