Placement of Public Fast-Charging Station and Solar Distributed Generation with Battery Energy Storage in Distribution Network Considering Uncertainties and Traffic Congestion

In this paper, a sustainable solution for the allocation of Public Fast-Charging Stations (PFCSs) and Solar Distributed Generations (SDGs) along with Battery Energy Storages (BESs) and its scheduling is proposed. The solution is obtained with minimization of Energy loss, voltage deviation index, and investment as well as operation maintenance costs of PFCS, SDG, BES, considering battery degradation. Moreover, the associate relevant factors such as; number of charging ports, capacities of the PFCS and EV flow captured by the PFCS are evaluated. Two-stage optimization is employed to getting the solutions. The first stage of optimization deals with PFCS's location, SDG's locations with sizes and BES scheduling. On the other hand, second stage looks after the assignment of EVs to the apt PFCSs considering the shortest distances with traffic congestions in view of reducing energy consumption of the EVs. As a test case, a 33 node radial distribution network is chosen with the corresponding traffic network. The allocation problem is solved by using Harris Hawks Optimization (HHO) and Grey Wolf Optimizer (GWO). Four other established optimization techniques are used to authenticate the solutions. The EV assignment problem is tackled by Integer Linear Programming (ILP). 2m Point Estimation Method (2m PEM) is applied to deal with the uncertainties associated with EVs, traffic flow and SDG.

Automated summary

This paper proposes a sustainable solution for the allocation of Public Fast-Charging Stations (PFCS), Solar Distributed Generations (SDGs), and Battery Energy Storages (BES) that considers traffic and energy consumption. Two-stage optimization is used to address the location, size, and scheduling of PFCS, SDG, and BES, as well as the assignment of electric vehicles (EVs) to the appropriate PFCS to minimize energy consumption. The allocation problem is solved using Harris Hawks Optimization (HHO) and Grey Wolf Optimizer (GWO), with the EV assignment problem being tackled by Integer Linear Programming (ILP). The approach also deals with uncertainties associated with EVs, traffic flow, and SDG using the 2m Point Estimation Method (2m PEM).