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The ever-increasing requirements of demand response dynamics, competition among different stakeholders, and information privacy protection intensify the challenge of the optimal operation of microgrids. To tackle the above problems, this article proposes a three-stage optimization strategy with a deep reinforcement learning (DRL)-based distributed privacy optimization. In the upper layer of the model, the rule-based deep deterministic policy gradient (DDPG) algorithm is proposed to optimize the load migration problem with demand response, which enhances dynamic characteristics with the interaction between electricity prices and consumer behavior. Due to the competition among different stakeholders and the information privacy requirement in the middle layer of the model, a potential game-based distributed privacy optimization algorithm is improved to seek Nash equilibriums (NEs) with encoded exchange information by a distributed privacy-preserving optimization algorithm, which can ensure the convergence as well as protect privacy information of each stakeholder. In the lower layer of the model of each stakeholder, economic cost and emission rate are both taken as operation objectives, and a gradient descent-based multiobjective optimization method is employed to approach this objective. The simulation results confirm that the proposed three-stage optimization strategy can be a viable and efficient way for the optimal operation of microgrids.

When there is a sudden load disturbance in an islanded microgrid, the peer-to-peer control model requires the energy resource to maintain a margin of generation, resulting in a relatively limited regulation range, that is, voltage/frequency sometimes requires additional control to maintain stability. A “source–storage–load” coordinated master–slave control strategy is proposed in this study to address the aforementioned issues. The system voltage and frequency will be stable as long as the output frequency and voltage of the master resource are stable. Furthermore, it can fully utilize the power supply capacity of resources to support the supply–demand balance. The following tasks are included in the proposed strategy: 1) to improve the operational security in the face of load disruption, a source–storage–load coordinated control method based on the “ramping speed” ratio is proposed, which can quickly restore the balance of supply and demand; 2) to improve the communication reliability in the face of interruption, a channel planning method is proposed, which can address the communication interruption problem by constructing an internal network among source–storage–load; and 3) to improve the mode switching stability of resources subjected to external disturbance, the external disturbance suppression and stability analysis involved in the regulation process are completed using sliding-mode control and small signal model methods. Related case studies are carried out to verify the effectiveness of the proposed strategies.

The ever-increasing false data injection (FDI) attack on the demand side brings great challenges to the energy management of interconnected microgrids. To address those aspects, this article proposes a resilient optimal defensive strategy with the distributed deep reinforcement learning (DRL) approach. To evaluate the FDI attack on demand response (DR), an online evaluation approach with the recursive least-square (RLS) method is proposed to evaluate the extent of supply security or voltage stability of the microgrids system is affected by the FDI attack. On the basis of evaluated security confidence, a distributed actor network learning approach is proposed to deduce optimal network weight, which can generate an optimal defensive scheme to ensure the economic and security issue of the microgrids system. From the methodology’s view, it can also enhance the autonomy of each microgrid as well as accelerate DRL efficiency. According to those simulation results, it can reveal that the proposed method can evaluate FDI attack impact well and an improved distributed DRL approach can be a viable and promising way for the optimal defense of microgrids against the FDI attack on the demand side.