• Energy Research

The anticipation of large‐scale electric vehicles (EVs) charging and discharging load can bring security and reliability challenges to the power system. As a smart load, EV requires an intelligently designed scheduling and pricing algorithm that takes into account the stochastic EVs’ user behavior, grid charging capacity, battery characteristics, and real‐time electricity price variations. Herein, a multiobjective comprehensive stand‐alone solution is proposed considering a dynamic pricing model to intelligently regulate EVs charging/discharging schedule. An improved optimal forecasting approach is utilized to precisely predict the load variations by utilizing historical load and weather data. The proposed alternative heuristic charging strategy optimally configures solution indices and provides a tradeoff between considered evaluation parameters taken from the perspective of both power suppliers and EV users, thus mitigating the effect of uncontrolled charging introduced by stochastic charge–discharge activities. The objective is to shift the peak hours’ load to nonpeak hours with a reduction in average‐to‐peak ratio, minimize charging cost, and maximize the availability of charging capacity for pledging traveling plans determined by EV users. Different EV penetrations are tested to validate the performance of the proposed solution under massive EV integration, with a driving pattern obtained from the Beijing National Travel Survey.

Abstract The large-scale penetration of renewable generation, the ever-increasing load, and aging of the transmission infrastructure all pose excessive stress on secure power system operation due to increasing congestion levels of the network. This arises the need for system operators to exploit the inherent flexibility in the system by using new cost-effective transmission topologies to better utilize the existing transmission assets. Dynamic Line Rating (DLR) and Optimal Transmission Switching (OTS) technologies both provide flexible ways to boost power system performance and maximize the use of existing infrastructure. In this paper, OTS and DLR are co-optimized in a stochastic unit commitment (SUC) formulation to characterize its influence on the scheduling of power system operation in the presence of renewable energy resources. Based on the predicted meteorological parameters and loading, the optimization problem i.e. Flexible Transmission Dynamic Line Rating (FTDLR) determines when and which line should be upgraded to adopt DLR and which line should be removed from the network. The inherent flexibility in transmission system is utilized by anticipating the capacity of transmission lines using DLR in co-ordination with reconfiguring network topology to relieve system congestions and reduce system operating cost. The flexibility obtained from the current transmission system can help in postponing the need for building new transmission corridors and deepen the integration of renewable generation. Numerical results establish that the enforcement of DLR and the practice of OTS are complementary, which can help minimize the system operating cost, wind curtailment, and network congestion.

SummaryNetwork overloading and electric power shortfalls occur due to large variations in anticipated renewable electric power and electric loads, which may cause system overheating or network failure. A large portion of residential loads (RLs) comprises of electric vehicles (EVs); therefore, by using in‐system electric vehicle batteries (EVBs), above mentioned issues can be resolved. A lot of research has been done in this domain but the stochastic nature of this system has not been fully explored yet. In this article, an intelligent charging mechanism of EVs has been proposed for RLs. EVBs are charged during off‐peak hours and then batteries are utilized as an independent power source during peak hours to decrease stress on the power network. Vehicle‐to‐grid charging and discharging scenarios are considered, to optimize the rate of charging. A novel stochastic model predictive control (SMPC) framework, based on smart charging/discharging of EVBs has been proposed in this article. SMPC deals with the uncertain variations in the projected generation and demand of electric power, due to variable renewable energy and high electric loads, respectively. It deals with the discrepancy of supply, demand, and energy prices. Using SMPC, complex stochastic problems can be transformed into a simpler optimization problem, making it computation efficient and suitable for real‐time applications. To relax constraints on power demand, a worst case scenario technique has been used which reduces the computational cost of the system. Simulation results depict that controlled charging and discharging of EVBs in power grid network help reduce overshoots and undershoots of electric power demand in a smart community. Comparison of the proposed strategy has been done with benchmark algorithms, to show the competency of the proposed strategy. This study provides insight of a smart community microgrid operation and resultant trade‐offs to manage the demand and supply of electricity.

The integration of distributed generation (DG) into distribution networks introduces uncertainties that can substantially affect network reliability. It is crucial to implement appropriate measures to maintain reliability parameters within acceptable limits and ensure a stable power supply for consumers. This paper aims to optimize the location, size, and number of DG units to minimize active power losses and improve distribution System (DS) reliability while considering system operational constraints. To achieve this objective, multiple tests are conducted, and the particle swarm optimization (PSO) technique is implemented. The simulation studies are performed using the ETAP software 19.0.1 version, while the PSO algorithm is implemented in MATLAB R2018a. ETAP enables a comprehensive evaluation of the DG system’s performance, providing valuable insights into its effectiveness in reducing power losses and enhancing system reliability. The PSO algorithm in MATLAB ensures accurate optimization, facilitating the identification of the optimal DG unit location and size. This study uses a modified IEEE-13 bus unbalanced radial DS as the test system, assessing the effects of photovoltaic (PV) and wind DG units under various scenarios and penetration levels. The results demonstrate that the optimal DG unit location and size of either a single PV or wind DG unit significantly reduce power losses, improve DS reliability, and enable effective load sharing with the substation. Moreover, this study analyzes the impact of DG unit uncertainty on system performance. The findings underscore the potential of optimized DG integration to enhance DS efficiency and reliability in the presence of renewable energy sources.

Power systems are being transformed to enhance the sustainability. This paper contributes to the knowledge regarding the operational process of future power networks by developing a realistic and stochastic charging model of electric vehicles (EVs). Large-scale integration of EVs into residential distribution networks (RDNs) is an evolving issue of paramount significance for utility operators. Unbalanced voltages prevent effective and reliable operation of RDNs. Diversified EV loads require a stochastic approach to predict EVs charging demand, consequently, a probabilistic model is developed to account several realistic aspects comprising charging time, battery capacity, driving mileage, state-of-charge, traveling frequency, charging power, and time-of-use mechanism under peak and off-peak charging strategies. An attempt is made to examine risks associated with RDNs by applying a stochastic model of EVs charging pattern. The output of EV stochastic model obtained from Monte-Carlo simulations is utilized to evaluate the power quality parameters of RDNs. The equipment capability of RDNs must be evaluated to determine the potential overloads. Performance specifications of RDNs including voltage unbalance factor, voltage behavior, domestic transformer limits and feeder losses are assessed in context to EV charging scenarios with various charging power levels at different penetration levels. Moreover, the impact assessment of EVs on RDNs is found to majorly rely on the type and location of a power network.