Multi-agent power generation optimal scheduling method based on reinforcement learning

Abstract

The invention provides a multi-agent power generation optimization scheduling method based on reinforcement learning, and the method comprises the steps: building a multi-agent complementary optimization model through taking the maximum total operation benefit as a target; constructing a multi-agent game model based on the established multi-agent complementary optimization model, obtaining a localoptimal strategy under mutual coordination of the agents according to a Nash game theory, and constructing a local optimal strategy set; solving an optimization problem by utilizing a Q learning algorithm to obtain global optimum, namely an optimal strategy set pi *. according to the method, a multi-agent optimization problem of a complex system can be converted into a state-action value functionconvergence problem according to a thought of combining a Nash game and a Q learning algorithm, an optimal optimization scheme is obtained, the optimization scheduling complexity is reduced, model-free optimization can be realized, and the purposes of energy conservation and high efficiency are achieved.

Description

technical field [0001] The invention relates to a multi-agent power generation optimization scheduling method based on reinforcement learning. Background technique [0002] Due to the variety of distributed energy resources involved in the multi-agent system, and the strong randomness between the power generation side and the load side, the optimal scheduling of the multi-agent system presents the characteristics of multiple constraints and strong uncertainty. The traditional centralized optimal scheduling method has the problem of unsatisfactory effect. [0003] The above problems should be considered and solved in the process of multi-agent optimal scheduling. Contents of the invention [0004] The purpose of the present invention is to provide a multi-agent power generation optimization scheduling method based on reinforcement learning to solve the problem of unsatisfactory effects in the traditional centralized optimization scheduling method in the prior art. [0005...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a multi-agent power generation optimization scheduling method based on reinforcement learning to solve the problem of unsatisfactory effects in the traditional centralized optimization scheduling method in the prior art

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