Journal
IEEE ACCESS
Volume 9, Issue -, Pages 63059-63072Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3075247
Keywords
Optimization; Microgrids; Heuristic algorithms; Stochastic processes; Power systems; Real-time systems; Convex functions; Distributed architecture; distributed optimization; Monte Carlo tree search; multi-agent deep reinforcement learning; optimal power flow
Categories
Funding
- Canada First Research Excellence Fund (CFREF) under the Future Energy Systems Research Initiative at the University of Alberta
- Natural Science and Engineering Research Council (NSERC) of Canada [RGPIN-2017-05866]
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This article introduces a distributed optimal power flow algorithm for optimal integration of stochastic distributed energy resources in power distribution systems, reducing computational complexity of linear programming approach and accelerating optimization process through the use of machine learning algorithms.
This article develops a special decomposition methodology for the traditional optimal power flow which facilitates optimal integration of stochastic distributed energy resources in power distribution systems. The resulting distributed optimal power flow algorithm reduces the computational complexity of the conventional linear programming approach while avoiding the challenges associated with the stochastic nature of the energy resources and loads. It does so using machine learning algorithms employed for two crucial tasks. First, two proposed algorithms, Dynamic Distributed Multi-Microgrid and Monte Carlo Tree Search based Reinforcement Learning, constitute dynamic microgrids of network nodes to confirm the electric power transaction optimality. Second, the optimal distributed energy resources are obtained by the proposed deep reinforcement learning method named Multi Leader-Follower Actors under Centralized Critic. It accelerates conventional linear programming approach by considering a reduced set of resources and their constraints. The proposed method is demonstrated through a real-time balancing electricity market constructed over the IEEE 123-bus system and enhanced using price signals based on distribution locational marginal prices. This application clearly shows the ability of the new approach to effectively coordinate multiple distribution system entities while maintaining system security constraints.
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