Research on Bidding Strategy of Thermal Power Companies in Electricity Market Based on Multi-Agent Deep Deterministic Policy Gradient

With the continuous improvement of new energy penetration in the power system, the price of the spot market of power frequently fluctuates greatly, which damages the income of a large number of thermal power enterprises. In order to lock in the profit, thermal power enterprises should turn the main...

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Veröffentlicht in:	IEEE access Jg. 9; S. 81750 - 81764
Hauptverfasser:	Liu, Dunnan, Gao, Yuan, Wang, Weiye, Dong, Zhixin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms bidding strategy Clearing Companies Continuous improvement Electric power systems Electricity market Electricity pricing Energy conversion efficiency Game theory Games Machine learning multi agent deep deterministic policy gradient algorithm multi agent reinforcement learning Multi-agent systems Multiagent systems Numerical models Power markets Profits Reinforcement learning Strategy Thermoelectricity
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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