Micro-grid dispatch is facing a great challenge since the system security and economic cost are often time-varying with different requirements. In this paper, a novel multiple time-scale dispatch model is firstly constructed with considering load voltage and frequency dynamics of islanding awareness, which is limited with rate-of-change of frequency (RoCoF) of micro-grid. Based on this model, micro-grid power dispatch is converted into a multi-objective optimization problem (MOP) with taking economic cost, voltage deviation and frequency stability into consideration. To address this problem, a penalty-based boundary intersection (PBI) based multi-objective optimization approach is developed with an elite learning technique. In the above approach, a developed deep deterministic policy gradient (DDPG) is proposed to learn evolutionary parameters with grid based alternating weight vectors, which can lead to better convergence ability and diversity distribution. Simulation results show that the proposed optimization strategy can properly achieve minimum economic cost with simultaneously satisfying voltage and frequency stability, which provides a viable and promising way for tackling with micro-grid dispatch problem.

Privacy disclosures and malevolent data intrusions targeting adversarial agents pose significant menaces to cyber-physical systems, a reality that extends to the intricate realm of micro-grid energy management. This paper proposes a homomorphic encryption based resilient distributed algorithm with an event-triggered mechanism to address this problem. Due to the potential information disclosure issue, exchange information is encrypted to an arbitrary neighbor and decrypted with a private key to protect agents. Considering the potential security attacks on adversary agents, an event-trigger based resilient distributed optimization with trusted agents (ETRDO-T) is proposed. It ensures the convergence of distributed algorithms, as well as relives the communication burden caused by homomorphic encryption. The simulation results, it can be seen that even under data attacks from malicious nodes, this method can effectively protect privacy information in information exchange while ensuring the convergence of energy management.

To address the false data injection (FDI) and denial of service (DoS) attack, this article proposes an event-trigger-based resilient distributed energy management approach for cyber–physical system of smart grid. Here, an event-trigger-based resilient consensus algorithm (ERCA) is proposed with the attack identification and compensation mechanism. The event-triggered mechanism is improved within distributed optimization combined with reliable acknowledgment (ACK) signals technique to mitigate the impact of data loss or transmission delay, and trust nodes-based compensation approach is proposed during resilient coordinated optimization for state correction to ensure the stability and security of power grid system. The optimality and convergence of the proposed method are proved theoretically that the proposed method can approximate to optimal solution well and achieve consensus by ensuring the proactive involvement of all participants under coordinated cyber attack. According to those obtained simulation results, it reveals that the proposed algorithm can effectively solve the energy management issue under coordinated DoS and FDI attack.

With consideration of false data injection (FDI) on the demand side, it brings a great challenge for the optimal defensive strategy with the security issue, voltage stability, power flow, and economic cost indexes. This article proposes a Takagi-Sugeuo-Kang (TSK) fuzzy system-based reinforcement learning approach for the resilient optimal defensive strategy of interconnected microgrids. Due to FDI uncertainty of the system load, TSK-based deep deterministic policy gradient (DDPG) is proposed to learn the actor network and the critic network, where multiple indexes' assessment occurs in the critic network, and the security switching control strategy is made in the actor network. Alternating direction method of multipliers (ADMM) method is improved for policy gradient with online coordination between the actor network and the critic network learning, and its convergence and optimality are proved properly. On the basis of security switching control strategy, the penalty-based boundary intersection (PBI)-based multiobjective optimization method is utilized to solve economic cost and emission issues simultaneously with considering voltage stability and rate-of-change of frequency (RoCoF) limits. According to simulation results, it reveals that the proposed resilient optimal defensive strategy can be a viable and promising alternative for tackling uncertain attack problems on interconnected microgrids.

Due to uncertainty and dynamic characteristics from intermittent energy and load demand response (DR), the optimal operation of the hybrid energy system is a great challenge. This article proposes an event-triggered multiagent coordinated optimization strategy with two-layered architecture. First, the price-bidding-based DR model is proposed with different stakeholders, and it also deduces the optimal bidding price with the Nash equilibrium theory. Then, four agents are designed to control different kinds of energy resources: agent 1 mainly analyzes the uncertainty or randomness caused by intermittent power, agent 2 takes charge of the dynamic economic dispatch (DED) within thermal units, agent 3 manages the optimal scheduling of energy storage, and agent 4 mainly undertakes the load-shifting strategy from consumers. In the upper-layer level, all agents coordinate together to ensure the stability of the hybrid energy system with an event-triggered mechanism, and the intelligent control approach mainly depends on switching on/off power generators or curtailing system load, and the consensus algorithm is utilized to optimize the subsystem problem in the lower-layer level. Furthermore, the simulation results can further verify the efficiency of the proposed method, and it also reveals that the event-triggered multiagent optimization strategy can be a promising way to solve the hybrid energy system problem.