Electric vehicles (EVs) have a very high potential and sustainability in the future transportation sector due to their less pollution and low operating cost. The uncoordinated charging of these EVs may push the power delivery system towards lower performance and even at the risk of damaging the power system equipment. The proper scheduling and coordination of EVs with the system may lessen the negative impact. In this paper, a multi-objective scheduling approach for individual candidate-based EVs is proposed to relieve the distress in power delivery in the best possible way. Minimization of average demand deviation and system unbalance reduction are selected as the objectives to restore the power delivery performance. The proposed approach has been verified on a realistic three-phase power distribution infrastructure. A simple linear regression model is employed to predict the near future number of EVs in the test region. An optimizitaion algorithm based on particle swarm optimization (PSO) technique is operated to find the finest solution satisfying EV charging constraints. The stochastic nature of load demands at the node points of the test network is analyzed with the help of a probabilistic demand model and it is further incorporated into the optimization framework. It is possible to reduce the system’s average demand deviation and unbalance level by 53.74% and 51.36%, respectively, with the proposed EV scheduling approach. A comparative study with the existing methods further claims the efficacy of the proposed approach.

—Real-world distribution networks are generally unbalanced due to unbalanced loads and asymmetric line parameters. Proper allocation of distributed generation (DG) units in unbalanced radial distribution networks to extract their maximum potential benefits is still quite difficult. Converter-coupled DG units have individual phase real and reactive power regulating ability and provide voltage unbalance compensation. This article proposes a new technique for the suitable allocation and capacity of converter-based multiple DG units considering the unbalanced operation of distribution networks. The objective function contains power loss minimization, multiphase voltage stability augmentation, and reduction of voltage unbalances among different phases in the system. A complex multi-objective optimization problem has been synthesized with the help of a Fast and Flexible Radial Power Flow (FFRPF) and solved by employing a Weighted aggregation (WA) based particle swarm optimization (PSO) approach. The performance of the proposed technique is validated on 19-bus, 25-bus, and 34-bus unbalanced radial distribution networks that consider various power factor modes of the converter-coupled DG units. The analysis of the simulation results indicates a significant enhancement in the power delivery efficiency in all the unbalanced test systems

The present power distribution infrastructure faces significantly high demand in most of the time hours of the day. Thus, it needs to manage power intelligently in those high-demand hours. Optimal allocation of battery energy storage system (BESS) can foster power management in the distribution system by exchanging energy with it. However, BESS should be strategically allocated to achieve maximum power delivery efficiency with lower investment. BESS units also need to be strategically scheduled to charge in light load hours and discharged in peak demand hours. The present study proposes a strategic planning model that can minimize the cost of BESS allocation and improve the distribution network’s technical performance. The cost function is framed considering battery life, investment in BESS, and investment in the inverter. The distribution system’s performance is realized with a typical network performance index (NPI). The NPI is synthesized from voltage deviation of buses and change in total active power losses in the network. A multiobjective optimization algorithm-based solution technique is presented to comprehend the planning model’s outcome in the most suitable location, size, and battery scheduling scenario. The proposed planning approach has been tested on a 28-bus Indian power distribution network. The result of techno-economic optimization confirms a good trade-off solution with 37.5% of network performance improvement with respect to the base configuration and INR 39.06LAC of total investment. The proposed approach has been verified with the existing BESS allocation model, demonstrating its efficacy.