The wind-photovoltaic (PV) hybrid renewable energy system (HRES), which consists of permanent-magnet synchronous generators (PMSG) and PV arrays, is becoming a cost-effective electric source for powering islanded areas. However, high penetration of renewables makes the power system vulnerable to transient voltage faults, which undermines the stability of the future inverter-dominated grid. To address this issue, a coordinative low-voltage-ride-through (LVRT) control scheme is proposed for the operation of the wind-PV HRES in this article. This control scheme will exploit the maximum energy inertia of the HRES for incorporating the power imbalance between the faulted grid and the renewable generators. An optimization problem is formulated to maximize the renewable energy harvesting within the operational and environmental limitations. To cope with different working conditions, four control processes are coordinated in an optimized manner during the LVRT period: 1) adaptive dc-link voltage control; 2) PMSG rotating speed control; 3) PV energy curtailment control; and 4) blade pitch angle control. Besides, this control scheme applies a direct output control that can generate stable and accurate current as per grid code requirements. The results of the hardware-in-the-loop (HIL) experiment and the MATLAB/Simulink simulation are provided to verify the effectiveness of the proposed control scheme.

In this paper, a novel machine learning based data-driven pricing method is proposed for sharing rooftop photovoltaic (PV) generation and energy storage (ES) in an electrically interconnected residential building cluster (RBC). In the studied problem, the energy sharing process is modeled by the leader-followers Stackelberg game where the owner of the rooftop PV system is responsible for pricing self-generated PV energy and operating ES devices. Meanwhile, local electricity consumers in the RBC choose their energy consumption with the given internal electricity prices. To track the stochastic rooftop PV panel outputs, the long short-term memory (LSTM) network based rolling-horizon prediction function is developed to dynamically predict future trends of PV generation. With system information, the predicted information is fed into a Q-learning based decision-making process to find near-optimal pricing strategies. The simulation results verify the effectiveness of the proposed approach in solving energy sharing problems with partial or uncertain information.

A novel concurrent control scheme of optimal re/active power regulation in a wind farm is proposed in this letter, of which the objective is to minimize the detrimental shortfall of active power during low-voltage-fault events. Distinguished from the existing works, the proposed control scheme incorporates an efficient optimization module into the wind turbine controllers. Specifically, it features the merit that the practical feasibility of tracking the resultant references can be ensured via considering multiple constraints in presence of complex wake interactions. Case studies on a permanent-magnet-synchronous-generator based wind farm built in a joint DIgSILENT-MATLAB platform are carried out to demonstrate the effectiveness of the proposed control scheme.

The large-scale integration of renewable energy sources in the power system can render instability issues, which degrades the power quality of the voltage supply. To address this issue, the electric springs (ES), which are elastic load demand control technologies, have been reported. Since it was proposed in 2011, many literatures on the applications, circuit topologies, control designs and the stability analysis of the ES have been reported. Based on the reported literatures, a comprehensive review of AC and DC ES technologies has been performed in this paper. The research outcomes are systematically summarized. The trend of the potential development of ES technologies is envisioned.

Biology‐inspired neural computing is a potential candidate for the implementation of next‐generation intelligent systems. Memristor is a passive electrical element with resistance‐switching dynamics. Owing to its natural advantages of non‐volatility, nanoscale geometries, and variable conductance, memristor can effectively simulate the synaptic connecting strength between the neurones in the multilayer neural networks. This study presents a kind of memristor synapse‐based multilayer neural network hardware architecture with a suitable training methodology. Specifically, a novel dual‐complementary metal–oxide–semiconductor/memristor synaptic circuit is presented, which is capable of performing the negative, zero, and positive synaptic weights via controlling the direction of current passing through the memristors. Then, the neurone circuit synthesised with multiple synaptic circuits and an activation unit is further designed, which can be utilised to constitute a compact multilayer neural network with fully connected configuration. Also, a hardware‐friendly chip‐in‐the‐loop training method is provided during the network training phase. For the verification purpose, the presented neural network is applied for the realisation of single image super‐resolution reconstruction.

For the conventional configuration of the single-phase electric springs, the electrolytic capacitor (E-cap) is required for buffering the double-line-frequency DC-link power. This demands large capacitance and constant average DC-link voltage for achieving sufficiently low voltage ripples of the E-cap, which renders low efficiency and poor reliability of the ES. To address these issues, a cascaded voltage control scheme is proposed in this paper. The proposed control scheme enables large fluctuations of the DC-link voltage so that the film capacitor (F-cap), which is of smaller capacitance and higher reliability, can be applied. Besides, the proposed control scheme can adaptively adjust the average DC-link voltage for achieving the minimum power loss of the ES. The quasi-steady-state and steady-state models of the electric-spring-based smart load are developed. The optimum average DC-link voltage for achieving the minimum power loss is analytically derived. The functionality and loss reduction capability of the proposed controller are verified through hardware experiments and simulations.