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Algorithms 2017, 10(2), 53; doi:10.3390/a10020053

Application of Gradient Descent Continuous Actor-Critic Algorithm for Bilateral Spot Electricity Market Modeling Considering Renewable Power Penetration

1
School of Economics and Management, North China Electric Power University, Beijing 102206, China
2
School of Business Administration, China University of Petroleum-Beijing, Beijing 102249, China
*
Author to whom correspondence should be addressed.
Academic Editor: Hans Kellerer
Received: 2 March 2017 / Revised: 28 April 2017 / Accepted: 3 May 2017 / Published: 10 May 2017
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Abstract

The bilateral spot electricity market is very complicated because all generation units and demands must strategically bid in this market. Considering renewable resource penetration, the high variability and the non-dispatchable nature of these intermittent resources make it more difficult to model and simulate the dynamic bidding process and the equilibrium in the bilateral spot electricity market, which makes developing fast and reliable market modeling approaches a matter of urgency nowadays. In this paper, a Gradient Descent Continuous Actor-Critic algorithm is proposed for hour-ahead bilateral electricity market modeling in the presence of renewable resources because this algorithm can solve electricity market modeling problems with continuous state and action spaces without causing the “curse of dimensionality” and has low time complexity. In our simulation, the proposed approach is implemented on an IEEE 30-bus test system. The adequate performance of our proposed approach—such as reaching Nash Equilibrium results after enough iterations of training are tested and verified, and some conclusions about the relationship between increasing the renewable power output and participants’ bidding strategy, locational marginal prices, and social welfare—is also evaluated. Moreover, the comparison of our proposed approach with the fuzzy Q-learning-based electricity market approach implemented in this paper confirms the superiority of our proposed approach in terms of participants’ profits, social welfare, average locational marginal prices, etc. View Full-Text
Keywords: bidding strategy; bilateral spot electricity market; renewable resources; Gradient Descent Continuous Actor-Critic (GDCAC) algorithm; reinforcement learning bidding strategy; bilateral spot electricity market; renewable resources; Gradient Descent Continuous Actor-Critic (GDCAC) algorithm; reinforcement learning
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Zhao, H.; Wang, Y.; Zhao, M.; Sun, C.; Tan, Q. Application of Gradient Descent Continuous Actor-Critic Algorithm for Bilateral Spot Electricity Market Modeling Considering Renewable Power Penetration. Algorithms 2017, 10, 53.

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