Optimal generalized bayesian nash equilibrium of frequency-constrained electricity market in the presence of renewable energy sources

Rayati, M ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/TSTE.2018.2886077
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2020
  4. Abstract:
  5. In this paper, the problem of frequency-constrained electricity market (FCEM) is modeled in the presence of renewable energy sources (RESs) and price-maker players by using equilibrium problem with equilibrium constraints formulation. It is worth mentioning that due to the intermittency of RESs, the FCEM problem in the presence of price-maker players becomes more imperative as stability of power system frequency is a public good and the free-rider problem arises. Moreover, as players of FCEM do not know their rivals' objective functions, the problem is modeled based on Bayesian game theory. The FCEM problem is converted into a game of complete but imperfect information under the common prior assumption. Here, an optimal generalized Bayesian Nash equilibrium (OGBNE) for the FCEM problem is presented, from which price-maker players do not have any incentive to deviate while power system dynamic is improved simultaneously. The effectiveness of proposed OGBNE compared with other specific dispatches is evaluated by implementing it on an IEEE 30-bus test system in the case study section. Results verify the superiority and appropriateness of the proposed OGBNE dispatch. © 2010-2012 IEEE
  6. Keywords:
  7. Equilibrium problem with equilibrium constraints (EPEC) ; Frequency-constrained electricity market (FCEM) ; Renewable energy sources (RESs) ; Commerce ; Computation theory ; Electric industry ; Electric utilities ; Electromagnetic wave emission ; Energy policy ; Gas generators ; Natural resources ; Power markets ; Renewable energy resources ; Bayes method ; Bayesian game ; Equilibrium problem with equilibrium constraint (EPEC) ; Games ; Nash equilibria ; Renewable energy source ; Game theory
  8. Source: IEEE Transactions on Sustainable Energy ; Volume 11, Issue 1 , 2020 , Pages 136-144
  9. URL: https://ieeexplore.ieee.org/document/8571295