Demand response model: A cooperative-competitive multi-agent reinforcement learning approach

Salazar, Eduardo J.; Rosero, Veronica; Gabrielski, Jawana; Samper, Mauricio E.

doi:10.1016/j.engappai.2024.108273

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2024

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Cited by 5 publications

References 28 publications

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Cooperative price-based demand response program for multiple aggregators based on multi-agent reinforcement learning and Shapley-value

Fraija,

Henao,

Agbossou

et al. 2024

Sustainable Energy, Grids and Networks

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Cooperative price-based demand response program for multiple aggregators based on multi-agent reinforcement learning and Shapley-value

Fraija,

Henao,

Agbossou

et al. 2024

Sustainable Energy, Grids and Networks

View full text Add to dashboard Cite

A Power Load Forecasting Model Based on CNNLSTM-Attention

Zhang,

Cao,

Zhou

2024

2024 6th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT)

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Hierarchical Coordinated Predictive Control of Multiagent Systems for Process Industries

Cheng,

Yang,

Xiao

2024

Applied Sciences

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Focusing on the requirements for efficient and accurate control in large-scale process industries with integrated “distributed-decentralized” characteristics, a novel hierarchical coordinated predictive control strategy for process industries is proposed with a multiagent system as the computational paradigm. This approach comprehensively considers the overall state of the system, the interactions of control actions among agents, the constraints of processes and energy consumption to solve the problems of poor flexibility of agent decision-making in the narrow consensus strategy and strong interaction of parts of the system. The proposed hierarchical control strategy requires each agent to perform three tasks at each time step. First, each agent iteratively obtains a consistent basis for closed-loop prediction in a distributed way. Then, each agent independently proposes a control scheme and determines its own priority by playing games based on the economic performance of the scheme. Next, each agent calculates its own optimal dynamic predictive control sequence in order of priority based on the system’s dynamic process model. Finally, by considering the temperature-control process of heating an alumina ceramic block in a high-power microwave reactor with six microwave sources, the effectiveness of the proposed hierarchical coordinated predictive control strategy is verified under different communication topologies by comparing it with the centralized model predictive control strategy.

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Demand response model: A cooperative-competitive multi-agent reinforcement learning approach

Cited by 5 publications

References 28 publications

Cooperative price-based demand response program for multiple aggregators based on multi-agent reinforcement learning and Shapley-value

Cooperative price-based demand response program for multiple aggregators based on multi-agent reinforcement learning and Shapley-value

A Power Load Forecasting Model Based on CNNLSTM-Attention

Hierarchical Coordinated Predictive Control of Multiagent Systems for Process Industries

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