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Recently, the penetration of photovoltaic (PV) units and plug-in electric vehicles (PEVs) has been quickly increased worldwide. Due to the intermittent nature of PV and the stochastic nature of PEVs, several operation problems can be noticed in distribution systems, including excessive energy losses and voltage violations. In this paper, an optimization-based algorithm is proposed to accurately determine the optimal locations and capacities of multiple PV units in the presence of PEVs to minimize energy losses while considering various system constraints. The proposed algorithm considers the uncertainty of PV and loads, and the stochastic nature of PEVs. Furthermore, the operational constraints of PEVs are incorporated in the optimization model: 1) arrival and departure times, 2) initial state of charge (SOC), 3) minimum preset state of charge by the owner, and 4) the time-of-use electricity tariff, and 5) different charging control schemes. The optimal PV planning model is formulated as a two-layer optimization problem that ensures an optimal PV allocation while optimizing PEV charging simultaneously. A two-layer metaheuristic method is developed to solve the optimization model considering annual datasets of the studied distribution systems. The results demonstrate the efficacy of the proposed algorithm. Index Terms-Distribution systems; photovoltaic; plug-in electric vehicle; energy losses; optimal allocation. I. INTRODUCTION S the annual demand on electricity grows, the use of distributed energy resources (DER) in power distribution systems has remarkably increased throughout the world. Photovoltaic (PV) is one of the most promising DER types. Indeed, the connection of PV units to distribution systems has several benefits to various entities, such as utility, owner, and final user. It is a fact that PV units with their active/reactive power control functionalities can improve the reliability of the power supply, enhance voltage profile, enhance power quality, and minimize energy losses [1]-[4]. Nevertheless, the

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Recently, the integration of inverter-based wind turbine generation systems (WTGS) and plug-in electric vehicles (PEV) has remarkably been expanded into distribution systems throughout the world. These distributed resources could have various technical benefits to the grid. However, they are also associated with potential operation problems due to their stochastic nature, such as high power losses and voltage deviations. An optimizationbased approach is introduced in this paper to properly allocate multiple WTGS in distribution systems in the presence of PEVs. The proposed approach considers 1) uncertainty models of WTGS, PEV, and loads, 2) DSTATCOM functionality of WTGS, and 3) various system constraints. Besides, the realistic operational requirements of PEVs are addressed, including initial and preset conditions of their state of charge (SOC), arriving and departing times, and various controlled/uncontrolled charging schemes. The WTGS planning paradigm is established as a bi-level optimization problem which guarantees the optimal integration of multiple WTGS, besides optimized PEV charging in a simultaneous manner. For this purpose, a bi-level metaheuristic algorithm is developed for solving the planning model. Intensive simulations and comparisons with various approaches on the 69-bus distribution system interconnected with four PEV charging stations are deeply presented considering annual datasets. The results reveal the effectiveness of the proposed approach.

Optimal inclusion of a photovoltaic system and wind energy resources in electrical grids is a strenuous task due to the continuous variation of their output powers and stochastic nature. Thus, it is mandatory to consider the variations of the Renewable energy resources (RERs) for efficient energy management in the electric system. The aim of the paper is to solve the energy management of a micro-grid (MG) connected to the main power system considering the variations of load demand, photovoltaic (PV), and wind turbine (WT) under deterministic and probabilistic conditions. The energy management problem is solved using an efficient algorithm, namely equilibrium optimizer (EO), for a multi-objective function which includes cost minimization, voltage profile improvement, and voltage stability improvement. The simulation results reveal that the optimal installation of a grid-connected PV unit and WT can considerably reduce the total cost and enhance system performance. In addition to that, EO is superior to both whale optimization algorithm (WOA) and sine cosine algorithm (SCA) in terms of the reported objective function.

Recently, the use of renewable energy sources (RES) and electric vehicles (EVs) has been rapidly increased worldwide. As a result of the highly fluctuating nature of RES, the charging and discharging rates of EVs significantly have to be increased, and so the lifespan of EV batteries decreases. In this paper, an optimization-based method is proposed to smooth voltage fluctuations due to various RES types by optimally controlling the charging and discharging power of EVs and the reactive power of the RES inverters. To extend the lifespan of the EV battery, EV power fluctuations and their minimum preset state of charge (SOC) are considered in the proposed optimization model. For this purpose, a new multi-objective function is formulated, including 1) voltage fluctuations, 2) EV power fluctuations, and 3) the deviation of SOC of EVs from their minimum desired level. The use of the hull moving average (HMA) is proposed to mitigate voltage fluctuations, which eliminates the lag problem of the widely used moving average methods. The gravitational search algorithm (GSA) is utilized to accurately solve the optimization model. The simulation results demonstrate the effectiveness of the proposed method to smooth voltage fluctuations while considering degradation and charging plan of EV batteries.

Integrating renewable energy resources (RERs) has become the head of concern of the modern power system to diminish the dependence of using conventional energy resources. However, intermittent, weather dependent, and stochastic natural are the main features of RESs which lead to increasing the uncertainty of the power system. This paper addresses the optimal reactive power dispatch (ORPD) problem using an improved version of the lightning attachment procedure optimization (LAPO), considering the uncertainties of the wind and solar RERs as well as load demand. The improved lightning attachment procedure optimization (ILAPO) is proposed to boost the searching capability and avoid stagnation of the traditional LAPO. ILAPO is based on two improvements: i) Levy flight to enhance the exploration process, ii) Spiral movement of the particles to improve the exploitation process of the LAPO. The scenario-based method is used to generate a set of scenarios captured from the uncertainties of solar irradiance and wind speed as well as load demand. The proposed ILAPO algorithm is employed to, optimally, dispatch the reactive power in the presence of RERs. The power losses and the total voltage deviations are used as objective functions to be minimized. The proposed algorithm is validated using IEEE 30-bus system under deterministic and probabilistic conditions. The obtained results verified the efficacy of the proposed ILAPO for ORPD solution compared with the traditional LAPO and other reported optimization algorithms. INDEX TERMS Optimal reactive power dispatch, renewable energy, lightning attachment procedure optimization, power losses, uncertainty.