Virtual synchronous generator (VSG) control has received much attention for interfacing renewable energy sources, thus creating a low-inertia microgrid. However, proportional reactive power-sharing and voltage accuracy is still a serious concern due to unequal feeder impedances between VSG controlled DGs and the point of power coupling. This article proposes a virtual variable impedance (VVI) based VSG control technique to mitigate the reactive power sharing error by improving the line-impedance ratio among multiple DGs. The proposed VVI control is designed to estimate the value of variable virtual impedance by establishing an exponential relationship with the inverter's output reactive power. The reactive power communication method is also introduced to identify the proportion of reactive power share being injected by the DG. The effectiveness of the proposed VVI control is further analyzed through the small-signal stability analysis and Lyapunov stability analysis of a multi-VSG system. Finally, the proposed controller is tested in MATLAB/Simulink software and on an experimental setup that includes inverter hardware and dSPACE simulator. Results have shown that the proposed method allow only 0.026% of reactive power sharing error as compared to the conventional droop control and state-of-the-art virtual capacitor-based droop control that experiences 2.86% and 2.8% error, respectively.

The advancement in electrical load forecasting techniques with new algorithms offers reliable solutions to operators for operational cost reduction, optimum use of available resources, effective power management, and a reliable planning process. The focus is to develop a comprehensive understanding regarding the forecast accuracy generated by employing a state of the art optimal autoregressive neural network (NARX) for multiple, nonlinear, dynamic, and exogenous time varying input vectors. Other classical computational methods such as a bagged regression tree (BRT), an autoregressive and moving average with external inputs (ARMAX), and a conventional feedforward artificial neural network are implemented for comparative error assessment. The training of the applied method is realized in a closed loop by feeding back the predicted results obtained from the open loop model, which made the implemented model more robust when compared with conventional forecasting approaches. The recurrent nature of the applied model reduces its dependency on the external data and a produced mean absolute percentage error (MAPE) below 1%. Subsequently, more precision in handling daily grid operations with an average improvement of 16%–20% in comparison with existing computational techniques is achieved. The network is further improved by proposing a lightning search algorithm (LSA) for optimized NARX network parameters and an exponential weight decay (EWD) technique to control the input error weights.

A large-scale power system breakdown in the United Kingdom caused blackouts in several important cities, losing about 3.2 percent of the load and affecting nearly 1 million power users on 9 August 2019. On the basis of the accident investigation report provided by the UK National Grid, the specific reasons for the sub-synchronous oscillation of Hornsea wind farm were analyzed. The Hornsea wind power system model was established by MATLAB simulation software to reproduce the accident. To solve this problem, based on the positive and negative sequence decomposition, the control strategy of grid-side converter of doubly-fed induction generator is improved to control the positive sequence voltage of the generator terminal, which can quickly recover the voltage by compensating the reactive power at the grid side. Consequently, the influence of the fault is weakened on the Hornsea wind farm system, and the sub-synchronous oscillation of the system is suppressed. The simulation results verify the effectiveness of the proposed control strategy in suppressing the sub-synchronous oscillation of weak AC wind power system after being applied to doubly-fed induction generator, which serves as a reference for studying similar problems of offshore wind power.

The droop control strategies can realise an autonomous power allocation among virtual synchronous generators (VSGs) that can provide the recompenses of reduced system complexity and enhanced reliability. The stability of a low‐inertia microgrid can be improved by implementing a VSG, designed for the coordination between multiple variable electric vehicle (EV) based charging loads. This study mainly investigates two control schemes. Firstly, the instantaneous contribution from EV charging control for any disturbance that can provide adequate damping and inertia to a low‐inertia microgrid without degradation of the battery. Secondly, the adjustment of Q – V droop control is suggested by correcting the excitation voltage of a VSG. This method can reduce the influence of line impedances and power ratings on reactive power sharing in a multi‐VSGs system. Additionally, four different active and reactive power control modes of VSG are discussed to emphasise EV charging and discharging control through the VSG controller. These modes are explained through circuit and vector diagrams in direct and quadratic coordinates. The efficacy of the proposed strategies and their influence on power sharing is theoretically demonstrated and analysed. Finally, the theoretical results are validated through extensive simulation and experimental verification.

In order to cope with the challenge of reducing CO₂ emissions concerning climate change, many countries are increasing their renewable energy sources (RES) shares in their power system networks. However, the existing transmission network is not planned to accommodate such a large‐scale integration of RES. Therefore, the need for new transmission topologies to better utilize the existing transmission infrastructure is more demanding to be practically implemented. In this paper, optimal transmission switching (OTS) is incorporated in a generic two‐stage stochastic unit commitment (SUC) problem with high penetration of wind power generation. The problem is modeled as a mixed integer programming problem (MIP). To reduce the computational complexity for solving the MIP, a warm‐start strategy is proposed to assist CPLEX in finding the optimal solution in a reasonable amount of time. Numerical tests are conducted on the modified IEEE 6‐bus and 118‐bus systems to validate the effectiveness of the optimization problem incorporating OTS in SUC problem. Using the standard IEEE 118‐bus system, an extensive economic and computational analysis is performed to characterize OTS effect on total system operating cost, sensitivity to variation in load, and transmission line congestion. Test results validate that system operating cost can be reduced up to 8% as compared with generic SUC problem without transmission switching.