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By increasing the number of electric vehicles (EVs) in the power system, it is necessary to manage their interaction with the grid such that to minimize the costs of the owners as well as to maximize the profit of their serving entities. Therefore, in this paper, a stochastic bi-level decision making problem for the participation of EV aggregator in a competitive environment is presented, with considering various sources of uncertainty. The sources of uncertainty for the participation of aggregators in the electricity market include day-ahead (DA) and balancing market prices as well as rivals' suggested prices and charging and discharging EVs demand that are modeled with time series. In the proposed bi-level program, the goal of the first level is to maximize the aggregator's profit in interaction with the network, and the goal of the second level is to minimize the payments of the owners. Since the objective function of the first and second levels are in contradiction with each other, with using KKT optimization conditions and the duality theory, the proposed two-level problem has become a linear single-level problem. Finally, the proposed program is implemented in typical test system and the results show that by using this model for the aggregator's decision making, it can offer proper charge and discharge price signals to the EV owners to attract the them in a competitive market and also to maximize its profit.

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This paper presents a model for distribution network optimization considering a high penetration of photovoltaic (PV) sources and electric vehicle charging stations (EVCSs) based on on-load tap changing transformers (OLTC) and step voltage regulator (SVR), shunt capacitor (SC), and shunt reactor (ShR). The purpose is to prevent overvoltage due to power injection by PV sources and voltage drop due to EV charging in distribution networks. The proposed model is solved using a new hybrid algorithm called PSO-GA. Relevant studies show that with the increasing number of PSO replications, particle population variability is easily eliminated and placed in local optimization. The idea of ​​combining GA is based on the PSO introduced in this study. Crossover and mutations of GA are performed on the PSO population, which is useful for improving the overall optimal ability of particles and causing the algorithm to deviate from the local optimal point. Two different IEEE standard test networks are tested under different load scenarios to analyze the proposed model. The results reveal the performance of the proposed model.
 

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Climate change and environmental instability have made the supply chain of the electricity industry face a serious challenge. Calculating the productivity of the three-stage sustainable supply chain of this industry during consecutive years is the main problem of this research. The method of this research is of an applied type with the aim of developing data coverage analysis models, which is quantitative in terms of variables and retrospective in terms of cross-sectional time. In this article, a new model based on data envelopment analysis is presented, which is able to combine with Malmquist's productivity index, in addition to determining the efficiency score, the productivity level in the three-stage supply chain of electricity production areas in measure the country of Iran during the years 1398 to 1400 and show the amount of progress or regression, according to which, out of 16 districts under study, 5 districts have progressed consecutively.

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With the increasing penetration of renewable energy sources such as wind and photovoltaic generation in future microgrids, challenges arise due to variable weather conditions. In this paper, a model is proposed to optimize the performance of microgrids under the worst-case scenario of renewable energy source failures using a bi-level optimization. In the upper-level problem, optimization is carried out in terms of energy loss reduction, load shedding in the load management program, as well as optimal charging and discharging of energy storage systems. The lower-level problem considers maximizing the utilization of renewable energy. A bi-level optimization solution method is proposed, which involves binary variables at both levels and is solved using a non-dominated sorting genetic algorithm (NSGA-II). The proposed model and algorithm are implemented using the Julia programming language. The performance of the model is examined under various conditions using a 33-bus microgrid, and the optimization results demonstrate the optimal performance of the microgrid under the worst-case scenario of renewable energy source failures.
 

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The development of microgrids is progressing due to smart loads, renewable energy sources, energy storage systems and also the presence of electric vehicles (EV). The presence of such devices in microgrids may cause inconsistency in the microgrid, which leads to increased losses and changes in the voltage of microgrid buses. In this paper, a mixed integer quadratic programming (MIQP) model is presented for microgrid energy management in the presence of smart loads, renewable energy sources, electric vehicles and energy storage systems. Also, to prevent voltage changes and reduce losses, the Distributed Flexible Alternating Transmission System (D-FACTS) device has been used. A scenario-based multi-objective function is proposed to reduce power losses and voltage deviations, reduce power outages of renewable sources, and reduce environmental pollution caused by distributed generation with fossil fuel (DG) and finally reduce the microgrid load definitively to reduce the vulnerability of the system. In this paper, an innovative evolutionary algorithm called learner performance-based behavior (LPB) algorithm is proposed. The proposed model is implemented on a 33-bus microgrid and the results show that the proposed energy management with demand side management can reduce energy loss by 9% and voltage deviation by 10%.