| Summary: | Microgrid energy management is a typical discrete non-linear optimization problem that is usually solved by off-line optimization, day-ahead demand-side management, and long-term dynamic optimization scheduling strategy. However, due to the intermittent distributed generation and time-varying load in microgrids, more attention should be paid to the real-time optimal scheduling of the overall operation of energy to ensure the dynamic balance of supply and demand in microgrids. Combining demand-side response with real-time power price, this paper applies the strategy to microgrid energy management and proposes a distributed energy real-time management model of microgrid based on demand-side response function. A deep adaptive dynamic programming optimization algorithm is also proposed for the model. The real-time interaction between microgrid operators and users is realized. The closed-loop feedback control structure of the proposed model ensures the real-time optimization control strategy. Therefore, the proposed energy management model and control strategy can realize intra-day dispatching in microgrids. The real-time performance and effectiveness of the proposed energy management model and control strategy are also verified by numerical simulation. Finally, since the proposed model is approximate, whether the solution obtained by the algorithm is the optimal or satisfactory solution of the optimization strategy set is a lack of theoretical support. Therefore, according to the approximation theorem of bounded rationality, the application conditions of the model in power markets are proposed. It is proved that the proposed model meets the application conditions, and is a specific application of bounded rationality approaching complete rationality in the power market. It is also proved that the best solution is involved in the satisfactory solution set of the model. Thus, the control strategy is a rational and feasible optimal management control strategy, which provides a theoretical basis for its further implementation.
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