Reliability based Min-Max Regret Stochastic Optimization Model for Capacity Market with Renewable Energy and Practice in China
Abstract
Capacity market is a long-term clearing model to coordinate the traditional thermal generation and the renewable energy generation, which minimizes the total capacity cost of traditional generation, while satisfying the operational constraints and reliability requirement. Furthermore, to address the uncertainties in the long-term optimal decision, min-max regret is employed to find the optimal solution under the worst regret, generating several representable scenarios that can help generation companies to understand how these scenarios would impact on the future generation planning. Finally, a decomposition method is proposed to solve this reliability based min-max regret stochastic optimization model by bisection. The test results on the practical NW grid in China show the effectiveness of the proposed model.
Recommended Citation
T. Ding et al., "Reliability based Min-Max Regret Stochastic Optimization Model for Capacity Market with Renewable Energy and Practice in China," IEEE Transactions on Sustainable Energy, vol. 10, no. 4, pp. 2065 - 2074, Institute of Electrical and Electronics Engineers (IEEE), Oct 2019.
The definitive version is available at https://doi.org/10.1109/TSTE.2018.2878224
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Center for Research in Energy and Environment (CREE)
Keywords and Phrases
Commerce; Electric load management; Gas generators; Optimization; Random processes; Reliability; Renewable energy resources; Stochastic programming; Stochastic systems; Capacity markets; Indexes; Load modeling; Minmax regret; Reliability Evaluation; Renewable energies; Renewable energy source; Stochastic models; Generators; Min-max regret; Renewable energy sources; Stochastic processes
International Standard Serial Number (ISSN)
1949-3029; 1949-3037
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Oct 2019
Comments
This work was supported in part by National Key Research and Development Program of China (2016YFB0901900), in part by National Natural Science Foundation of China (Grant 51607137 and U1766215), in part by China Postdoctoral Science Foundation (2017T100748) and in part by the Faculty of EIT Mid-Career Research Program and the University Bridging Funding.