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This study presents a mathematical model to determine the optimal site and the size of plug‐in hybrid electric vehicles charging stations (CSs) in the distribution networks. The presented optimal planning is done along with considering the rate of customers' participation in demand response programs (DRPs) at the presence of some uncertainties associated with the load values and electricity market price. In order to model the load, time‐of‐use DRP is used. The proposed model involves distribution system manager (DSM) benefit maximising derived from the appropriate use of CS for charging and discharging vehicle batteries, reliability improvement and supplying network's load demand at peak times as the objective function, technical limitation as constraint and the siting and sizing of electric vehicles CS as optimisation variables. The genetic algorithm method with embedded Monte Carlo simulation is used in order to solve the optimisation problem and is applied to the 9‐bus and 33‐bus networks. The test results indicate that not only does the appropriate planning have an economic benefit for DSM, but also voltage profile and power supply reliability for customers can be improved and active power losses can also be reduced. [ABSTRACT FROM AUTHOR]

This study proposes long‐term dynamic planning of distributed generation (DG) units in distribution networks considering active management (AM). In the AM operation mode, the distribution network equipment is controlled in real time based on the real‐time measurements of system parameters (voltage and current). The proposed model determines the optimal size, location and time of investment of DG units. The uncertainty of energy price and rate of load growth is handled using a scenario‐based modelling approach. To demonstrate the advantages of dynamic DG planning, its results are compared with those of static model. Also the results of optimal DG planning in active network are compared with those of the passive operation mode. In addition, a technical evaluation and comparison between conventional, passive and active networks have been performed. The proposed model is successfully applied to the 33‐bus radial distribution network. The results indicate that using AM, the cost function and losses are decreased effectively compared to the passive management. Computation of the optimal DG planning in AM environment is formulated as a mixed integer non‐linear problem which can be solved using commercial optimisation packages like as GAMS. [ABSTRACT FROM AUTHOR]

In the near future, millions of electric vehicles (EVs), especially plug‐in hybrid EVs (PHEVs), will be integrated into the electrical system. Such an increase in the demand for energy will require optimised management of distribution networks. This study presents a new formulation to determine the optimal size and location of PHEV charging stations to maximise the distribution system manager (DSM) benefit. A two‐stage procedure has been considered to optimise PHEV charging station planning. In the first stage, the candidate locations and their size are determined based on: (i) the sum of benefits from the appropriate use of charging station for charging and discharging of vehicles and (ii) the benefit of providing spinning reserve and supplying the network's load demand at peak times by DSM. In the next stage, an improved framework is proposed to classify charging stations into efficient and inefficient sets characterised by non‐financial indices. The proposed method is data envelopment analysis efficiency and three indices, including active power loss, voltage profile improvement and CO2 emission. The suggested model is successfully applied to a test system with nine buses. Simulation results show that appropriate planning results in economic benefits as well as improvements in active power losses and the voltage profile. [ABSTRACT FROM AUTHOR]

One of the main purposes of distribution network operators is increasing the participation of distributed generations (DGs). The DGs contribute in reduction of power losses, improvement of voltage profile and reliability and the postponement of network reinforcement costs. New control methods are required for DG management in order to provide more flexibility in distribution networks. In this study, the authors discuss a specific aspect of active management (AM), which is an effective approach for increasing the penetration of DG units and their management. AM is based on using the control and communication links for better operation of DGs. The effect of AM on the DG integrated distribution network in an uncertain environment is assessed. Fuzzy technique is implemented for modelling the uncertainty of loads, generation of DGs, purchased power from the grid and electricity price. By considering an uncertain environment, in this study, the effect of AM on decreasing the risk of technical issues (active loss and voltage profile) in distribution network is evaluated. [ABSTRACT FROM AUTHOR]