An adaptive approach for distribution system reconfiguration and charging management of plug-in electric vehicles (PEV) is presented in this study. A stochastic model predictive control is applied to stochastically, adaptively, and dynamically reconfigure the system, manage the incidental charging pattern of PEVs, and deal with the variable and uncertain power of renewable energy sources. The objective function of problem is minimizing daily operation cost of system. Herein, the geography of area is considered and the behavior of PEVs' drivers (based on their income level) is modeled with respect to the value of incentive and their hourly distance from each charging station. It is shown that behavioral model of drivers is able to affect the optimal results of problem. The simulation results demonstrate the competence of the proposed approach for cost reduction and making the problem outputs robust with respect to prediction errors.

In this study, the structure of an electrical distribution network, which includes several electrical distribution feeders, is adaptively and dynamically reconfigured to minimise the daily operation cost of system that is under control of a local distribution company. Herein, for the first time, stochastic model predictive control (MPC) concept is applied in the problem to have dynamic and adaptability futures in the optimisation problem and to deal with the uncertainty and variability issues of renewable energy resources. The daily operation cost of distribution system comprises hourly energy loss cost of electrical feeders and switching cost of initially open and close switches installed on each feeder. The numerical study shows the existence of notable potential in reconfiguration of distribution system for energy loss and operation cost reduction. In addition, it is demonstrated that applying MPC in the optimal reconfiguration of distribution system results in better consequences in the presence of variable power of renewables. Moreover, it is proven that application of MPC in the problem increases the robustness and resiliency of optimisation procedure with respect to the prediction errors due to dynamic and adaptability characteristics of MPC.

The planning and operation problems of parking lots of plug-in electric vehicles (PEVs) are studied in this paper. Herein, each distribution company (DISCO) allocates the parking lots to the electrical feeders to minimize the power loss and expected energy not supplied of the system, and consequently minimize the total cost of the planning problem over the given time horizon. In addition, the generation company (GENCO) manages the charging time of PEVs parked in the parking lots to defer the more expensive and pollutant generation units, and as a result maximize its daily profit. In both planning and operation problems, the behavioral model of PEVs' drivers are modeled with respect to the value of incentive and their distance from the parking lots. To achieve the realistic results in the planning problem of each DISCO, several economic and technical factors including yearly inflation and interest rates, hourly and daily variations of the load demand, yearly load growth of the system, and yearly growth rate of PEVs' application are considered. The optimization problems of each DISCO and GENCO are solved applying quantum-inspired simulated annealing algorithm and genetic algorithm, respectively. It is demonstrated that the behavioral model of drivers, their driving patterns, and even the type of PEVs can remarkably affect the outcomes of planning and operation problems. It is shown that the optimal allocation of parking lots can minimize every DISCO's planning cost and optimal charging management of PEVs can increase the GENCO's daily profit.

In this study, reliability and economic‐driven capacitor allocation problem in electrical distribution system is investigated for a definite planning horizon considering several technical and economic aspects. Also, the system reliability modelling is performed based on a new approach, which is considering values of reactive component of currents flowing through branches of the feeder and applying different feeder's failure rate models. The aim of this study is minimising total cost of the system including system power loss cost, system risk cost, investment cost and maintenance cost. Herein, in order to achieve practical outcomes, inflation and interest rates as the economic factors considered in the planning, and yearly load growth, hourly and daily varying load, and customers’ load types are applied in the modelling, and also load model‐based power flow is run rather than simple power flow. In this study, it is demonstrated that considering unreal model for the system load types or ignoring dependence of system reliability on the reactive component of current can significantly affect the results of the capacitor placement problem and also it can impose extra cost to the local distribution company.

In this paper, price-controlled energy management is investigated in a bi-level optimization framework, that is, energy scheduling problem of smart homes (SHs) and generation scheduling and unit commitment (UC) problems of a generation company (GENCO). SHs as the responsive customers (respect to the energy management) include a variety of sources such as photovoltaic (PV) panels, diesel generator, and battery as an energy storage. In addition, SHs are able to transact electricity with the GENCO through the power system. In this paper, the goal of GENCO is to design an optimal energy management scheme (optimal price of electricity) to maximize its daily profit. Herein, each SH reacts to the energy management scheme and reschedules its energy resources to minimize its daily operation cost. In this paper, a scenario-based stochastic optimization approach is applied in the energy scheduling problem of an SH to address the variability and uncertainty issues of the PV panels. Also, a combination of genetic algorithm (GA) and linear programming is applied as the optimization tool for the energy scheduling problem of an SH. Moreover, lambda-iteration economic dispatch and GA techniques are applied to solve the generation scheduling and UC problems of the GENCO, respectively. The numerical study demonstrates that in order to reach the maximum profit of GENCO, the energy management must be optimally designed and implemented; otherwise, the energy management scheme may result in detriment. Moreover, it is shown that each SH is able to get benefit from the energy management scheme and minimize its daily operation cost.

Investigating optimal value of the power factor for the optimally allocated and sized solar parking lots of the plug‐in electric vehicles (PEVs) based on minimum total cost of the problem over the given planning period is the aim of this study. Covering a parking lot with photovoltaic (PV) panels can keep the PEVs cool, charge their batteries for free, and generate extra energy and deliver it to the grid. Moreover, the inverter installed in the charging station of a parking lot is capable of generating or absorbing reactive power by changing the operating mode of the inverter without any degradation of the PEV's battery. Therefore, the PEVs and the solar parking lots can be applied as the auxiliary sources of active and reactive powers by the local distribution company (DISCO). In this study, in order to achieve realistic results, the economic aspects and technical factors of the system, and also the behavioural model of the PEVs fleet are taken into consideration in the planning problem. Moreover, the stochastic nature of the PEVs behaviour and power of the installed PV panels are modelled in the problem. Herein, the planning problem is solved applying quantum annealing (QA).