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

A multi-agent, optimal scheduling technology, applied in machine learning, data processing applications, system integration technology, etc., can solve problems such as unsatisfactory results

Active Publication Date: 2020-01-24
NANJING UNIV OF POSTS & TELECOMM
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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 reinforce

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

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Embodiment

[0062] A multi-agent power generation optimization scheduling method based on reinforcement learning, such as figure 1 , including the following steps,

[0063] S1. With the goal of maximizing the total operation benefit, establish a complementary optimization model of multi-agents;

[0064] Under the multi-agent system, there are many types of distributed energy sources and the coupling is complex, which makes the optimal scheduling of multi-agents present the characteristics of multi-objective, multi-constraint, and strong uncertainty. With the goal of maximizing power generation efficiency, considering the power balance constraints, Thermal power balance constraints, energy operation constraints, energy output ramp rate constraints, thermal energy storage heat storage constraints, etc., establish a multi-agent optimal scheduling model.

[0065] Total operating benefit:

[0066] Total power generation revenue:

[0067]

[0068] Start-up and stop costs of energy suppl...

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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...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06G06N20/00
CPCG06Q10/04G06Q10/0631G06Q50/06G06N20/00Y04S10/50
Inventor 张慧峰李金洧岳东
Owner NANJING UNIV OF POSTS & TELECOMM
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