This paper proposes a distributed fractional-order predefined-time sliding mode controller (DFOPTSMC) to adjust the external energy storage to improve the transient stability of the power system with time delay and input saturation. The time delay is able to worsen the performance of the power system, and even bring about instability. Based on the Artstein transform theorem, the model of the power system with communication delay is transformed into a model without delay. Meanwhile, the output of the external energy storage should be limited in practical power systems. The saturation compensation system is proposed to remedy the influence of input saturation of the external energy storage. Therefore, a DFOPTSMC considering communication delay and input saturation is proposed to adjust the energy storage device, which has fast response ability and strong regulation capability, to increase transient stability of the power system. The predefined-time stability of the power system under the DFOPTSMC is analyzed through the Lyapunov function. Finally, simulations are taken to verify that the proposed DFOPTSMC is able to raise the transient stability of the power system as well as ensure that the power system returns to stability region under a large delay margin.

The frequency and voltage restoration along with power sharing is imperative for the safe, reliable and stable operation of the islanded AC microgrids (MGs). In this paper, a novel distributed fractional-order predefined-time sliding mode controller (DFOPTSMC) is proposed as a secondary controller for the islanded AC MGs. The proposed DFOPTSMC can ensure that frequency and estimated average voltage (EAV) converge to reference value along with accurate power sharing within a setting predefined time. The fractional calculus algorithm is implemented to design the DFOPTSMC, which increases the degree of freedom of parameter adjustment and greatly enhances the flexibility of the DFOPTSMC. In addition, a distributed predefined-time observer is presented to achieve the EAV within a setting predefined time. The upper bound convergence time of the islanded AC MGs under the proposed DFOPTSMC is directly equal to a tunable parameter, thus overcoming the disadvantages of finite-time control and fixed-time control. The Lyapunov function is applied to present rigorous proof to analyze the stability of the islanded AC MGs under the proposed DFOPTSMC. An islanded AC MG is taken into simulation on the Matlab/Simulink 2021a platform and the experimental platform to test and verify the effectiveness and superior performance of the proposed DFOPTSMC. The results indicate that the proposed DFOPTSMC can significantly enhance the dynamic performances of the islanded AC MGs.

Aiming at the problem of frequency and voltage deviation caused by load switching in islanded shipboard microgrids (MGs) under complex sea conditions, this paper proposes robust distributed fixed-time fault-tolerant controllers (RDFTFTCs) for the islanded shipboard MG with actuator fault to adjust frequency and voltage as we as achieve accurate power sharing. The actuator fault with biased fault and partial loss of effectiveness fault can affect the performance of the controller, resulting in poor power quality and even instability for the islanded shipboard MG. Therefore, fixed-time observers are proposed to estimate the disturbances caused by the actuator faults and the estimation errors can converge to the origin point within a fixed time. In addition, the estimated average voltages (EAVs) of all distributed generators (DGs) are obtained through distributed fixed-time observers (DFTOs). Furthermore, the RDFTFTCs are proposed to adjust the frequency and voltage, and obtain accurate power sharing within a fixed time independent of the initial operating states of the islanded shipboard MG. The fixed-time stability of the islanded shipboard MG is proven through the Lyapunov function under the proposed RDFTFTCs. The performances of the proposed RDFTFTCs for the islanded shipboard MGs with faster convergence speed and smaller overshoot than that under existing control methods in different circumstances are validated through the simulations in the Matlab/Simulink.

In the residential demand response area, currently the incentive-based method (e.g., direct load control, DLC) may impair users’ comfort and autonomy, while the price-based method can hardly guarantee users’ engagements. This paper proposes an edge-cloud integrated demand response framework to achieve an effect-predictable residential demand response without harming users’ benefits. First, we combine the cloud-computing resource (cloud) and the home-installed smart thermostats (edges) to formulate an efficient, cost-effective, and data-secured infrastructure to implement the demand response program. Then, we model the demand response problem between the load aggregator and its served residential users as a bi-level optimization problem, and the key is for the load aggregator to find the optimal incentive strategy. To solve this problem, we introduce an RL algorithm, i.e., Continuous Action Reinforcement Learning Automata, to quickly obtain the optimal incentive strategy under an incomplete information scenario. Simulation results based on 136 real-world residential users in Austin area demonstrate that the proposed CEI-DR framework can increase the social welfare by about $8.6/h compared to the traditional DLC method during a normal DR event.

This article proposes a novel distributed predefined-time sliding mode controller (DPTSMC) to gain exceptional performances of power system under input saturation. Firstly, considering the saturation of excitation input to avoid the overvoltage of the coil, an auxiliary system is taken to compensate the effect of the excitation saturation. Then, the sliding mode control (SMC) and the predefined-time stability theory are taken to devise the DPTSMC to make the power system stable in a predefined time, which has an upper limit and is directly equivalent to an adjustable parameter, thus satisfying the high requirement of the power system for expected convergence time. Meanwhile, the estimated upper bound convergence time of the power system under the proposed DPTSMC is less conservative than some existing fixed-time control strategies. Moreover, the distributed control is taken to devise the DPTSMC and the controller can receive the real-time information from the phasor measurement units (PMU). Hence, the central controller is not required and the communication network is simple, thus releasing the pressure in complicated calculation and communication, and gaining rapid and coordinated response capacity. Next, the stabilization of the power system under the DPTSMC within a predefined upper limited time is proved through the Lyapunov function. Finally, the exceptional performance of the proposed DPTSMC than the existing control methods is verified through the simulation in the IEEE 39-bus 10-machine New England power system under different operation scenarios.

Integration of distributed energy resources, such as photovoltaics, has expanded rapidly within power distribution networks in recent years. Existing management architectures face great challenges in balancing data security and communication efficiency. To address this issue, the paper proposes a cloud-edge collaborative framework that caters to managing multiple distributed energy resources. Firstly, a two-stage structural design method is introduced to determine the optimal configuration of edge nodes for variable reduction. Secondly, a credit-based data interaction scheme considering inherent uncertainty of distributed energy resources is proposed to ensure trustworthy cloud-edge collaborative optimizations. The above work is expected to facilitate the in-depth application of cloud-edge collaboration in the energy scheduling field.