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Distribution system planners and operators have increasingly exposed great attention to maximizing the penetration of renewable energy resources (RERs), and electric vehicles (EVs) toward modern microgrids. Accordingly, intensive operational and economic problems are expected in such microgrids. Specifically, the operators need to meet the increased demand for EVs and increase the dependence on RERs. The charging strategy for EVs and the RER penetration level may result in increased power loss, thermal loading, voltage deviation, and overall system cost. To address these concerns, this paper proposed an optimal planning approach for allocating EV charging stations with controllable charging and hybrid RERs within multi‐microgrids, where the charging strategy in the proposed planning approach contributed to improving power quality and overall system cost, where the voltage deviation, energy not supplied, total cost have been reduced to 26.03%, 49.57%, and 70.45%, respectively. The simulation results are compared with different optimization techniques to verify the effectiveness of the proposed algorithm. The proposed simultaneous allocation approach of EV charging stations and RERs can reduce operating costs for RERs and conventional stations while increasing the charging stations' capacity. [ABSTRACT FROM AUTHOR]

• A novel approach is proposed for maximizing photovoltaic hosting capacity. • The reactive power sources and transformer taps are considered. • The stochastic nature of electric vehicles is considered. • A double-layer metaheuristic optimizer is developed. • IEEE 69-bus distribution system is used for simulation. The interest in maximizing the hosting capacity of photovoltaics is recently being enlarged globally. This paper proposes a novel stochastic approach for maximizing the hosting capacity of photovoltaics in distribution systems. The proposed approach is based on a coordinated management scheme of control devices in distribution systems, i.e. transformer taps and VAr sources. It also considers the promising electrical vehicles with their stochastic nature and comprehensive model, including the arriving and departing times, and initial and preset conditions of their batteries state of charge. Further, the planning model of photovoltaics considers the reactive power support of the photovoltaic inverter based on the recently released IEEE 1547:2018 standard. Compared to existing approaches, the unique merit of the proposed approach is its ability to maximize the hosting capacity of photovoltaics by simultaneous optimization of the different control variables. To accurately solve this stochastic optimization model, a double-layer metaheuristic optimizer is developed for maximizing the hosting capacity of photovoltaics and addressing all constraints. The inner level of the optimizer optimizes the charging/discharging power of electric vehicles, transformer taps, and reactive power support while the outer one maximizes the sizes of photovoltaics. To assess the effectiveness of the proposed approach, various scenarios are performed on the IEEE 69-bus distribution system. The proposed approach can maximize the hosting capacity of photovoltaics while optimally managing transformers, VAr sources, and electric vehicles in a coordinated manner. [ABSTRACT FROM AUTHOR]

Achieving the goal of sustainable development is dependent on the widespread integration of renewable energy sources, energy storage systems (ESSs), and electric vehicles (EVs). However, a continuous increase in the penetration of such elements would bring more complexities to the distribution network. Accordingly, this paper presents a unified planning model comprising renewable energy-based distributed generation (DG), ESS, and electric vehicle charging stations (EVCSs). In this regard, a Latin Hypercube Sampling method is utilized to generate multi-scenario for describing the uncertainty of renewable energy and load demand. The stochastic EV charging behaviors are represented by various probability density functions (PDF). In addition, an exploitable capacity loss of ESS and EV batteries is calculated by the battery degradation model based on the depth of discharge (DOD). Furthermore, the battery degradation cost is incorporated into the objective of the planning model to identify the optimal decision for candidate assets. A piecewise linearization approach is introduced to convert the problem into a mix-integer linear programming (MILP) model. Numerical results demonstrate that the exploitable capacity loss of batteries plays a key role in asset planning and provides potential contributions to the optimal decisions of the distribution network. In the meantime, by considering battery degradation in the optimization model, the sustainability and lifetime of the battery can be preserved. • Uncertainties include the renewable energy, load demand, and behaviors of electric vehicles are considered. • Renewable energy-based DG, ESS, and EVCS are involved in the unified planning model. • The degradation model is proposed to analyze the exploitable capacity loss of ESS and EV battery. • The enhancement of investment decision while maintaining the lifetime of battery is achieved. [ABSTRACT FROM AUTHOR]

The penetration of renewable energy sources (RES) has been increased throughout the world. The main characteristic of RESs is that their generating powers are intermittent and unpredictable. This paper presents an interval optimization method to optimally schedule electric vehicles (EV) with considering the uncertainty of RES generation and loads. For this purpose, the RES generation (including photovoltaic and wind power) and loads are considered as interval parameters, and the charging/discharging power of EV is expressed as an interval variable to be optimally computed. The capability of RES inverters to regulate voltages is also considered in the interval optimization model. The objective function is to minimize the network active power losses and total voltage magnitude deviation with considering overall system constraints. The proposed method is tested on a 33-bus distribution system with uncertain RESs and loads, and the optimal day-ahead scheduling of EV is performed. Different case studies are carried out to test the effectiveness of the proposed method. It is demonstrated that the proposed interval optimization method can accurately represent the uncertain problem, and it provides further information compared with the deterministic optimization. [ABSTRACT FROM AUTHOR]

• Optimization-based method is proposed for voltage fluctuations smoothing due to RES. • The reactive power capability of RES inverters is considered. • The fluctuations of EV powers and their minimum SOC at departure time are considered. • Hull moving average (HMA) is used to mitigate voltage fluctuations. • IEEE 90-bus and IEEE 33-bus distribution systems are used for simulation. 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. [ABSTRACT FROM AUTHOR]