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Summary The advancement and deployment of electric vehicle (EV) technologies are considered as an emergent solution to meet the current and future energy crises. The electrification of transportation systems is a promising approach to green the transportation systems and to reduce the issues of climate change. This paper investigates the present status, latest deployment, and challenging issues in the implementation of EV infrastructure, charging power levels, in conjunction with several charging power topologies, and analyzes EV impacts and prospects in society. In this study, the on‐board and off‐board categories of charging systems with unidirectional and bidirectional power flow comparison are addressed. Moreover, an extensive analysis of unidirectional and bidirectional chargers is presented. Unidirectional charging offers hardware limitation and reduces the interconnection issues. Bidirectional charging provides the fundamental feature of vehicle‐to‐grid technology. Furthermore, the beneficial and harmful impacts of EVs are categorized with remedial measures for harmful impacts and prolific benefits for beneficial impacts.

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Abstract:In grid advancement, energy storage systems are playing an important role in lowering the cost, reducing infrastructural investment, ensuring reliability and increasing operational capability. The storage system can provide stabilization services and is pivotal for backup power for emergencies. With a continuous rise in fuel prices and increasing environmental issues, the energy from renewable resources is gaining more popularity. The main drawbacks of some renewable sources are their intermittent energy generation and uncertain source availability, which has increased interest in energy storage systems (ESSs). This paper investigates the economic feasibility when ESSs are introduced in the electric grid with an expansion of a storage system as well as more percentage of the renewable energy integration and less percentage of fuel consumption by conventional power sources. The Artificial Neural Network is implemented to validate the forecasted load model. The uncertainties associated with the renewable energy system are handled by a chance-constrained model and solved by a genetic algorithm (GA) in MATLAB; selection criteria of GA for optimization process is also discussed in detail. The effectivity of the proposed methodology is verified by applying it to a case that lies in the western region of China.

Large‐scale integration of electric vehicles (EVs) into residential distribution networks (RDNs) is an evolving issue of paramount significance for utility operators. Similarly, electric load forecasting is an operational process permitting the utilities to manage demand issues for optimal energy utilization. Unbalanced voltages prevent the effective and reliable operation of RDNs. This study implements a novel framework to examine risks associated with RDNs by applying a residential forecasting model with a stochastic model of EVs charging pattern. Diversified EV loads require a stochastic approach to predict EVs charging demand; consequently, a probabilistic model is developed to account for several realistic aspects comprising charging time, battery capacity, driving mileage, state‐of‐charge, travelling frequency, charging power, and time‐of‐use mechanism under peak and off‐peak charging strategies. Peak‐day forecast of various households is obtained in summer and winter by implementing an optimum nonlinear auto‐regressive neural‐network (NN) with time‐varying external input vectors (NARX). Outputs of the EV stochastic model and residential forecasting model obtained from Monte‐Carlo simulations and the NARX‐NN model, respectively, are utilized to evaluate power quality parameters of RDNs. Performance specifications of RDNs including voltage unbalance factor (VUF) and voltage behavior are assessed in context to EV charging scenarios with various charging power levels under different penetration levels.

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.

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Summary 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.