Multi-Agent System for Electric Vehicle Charging Scheduling in Parking Lots

Abstract

As the number of electric vehicles (EVs) increases, massive numbers of EVs have started to gather in commercial parking lots to charge and discharge, which may significantly impact the operation of the grid.There may also be a deviation in the departure time of charged and discharged EVs in commercial parking lots.This deviation can lead to insufficient battery energy when the EVs leave the parking lot. This study uses the simulation software AnyLogic to build a commercial parking lot multi-agent simulation model, and the agentbased model can fully reflect the autonomy of individual EVs. Based on this simulation model, we propose an EV scheduling algorithm. The algorithm contains two main agents. The first is the power distribution center agent (PDCA), which is used to coordinate the energy output of photovoltaic (PV), energy storage system (ESS), and distribution station (DS) to solve the problem of grid overload. The second is the scheduling center agent (SCA), which is used to solve the insufficient battery energy problem due to EVs' random departures.The SCA includes two stages. In the first stage, a priority scheduling algorithm is proposed to emphasize the fairness of EV charging. In the second stage, a genetic algorithm is used to accurately determine the time interval between charging and discharging to ensure the maximum benefit of EV owner. Finally, simulation experiments are conducted in AnyLogic, and the results demonstrate the superiority of the algorithm over the classical algorithm.

Parallel Algorithm on Multicore Processor and Graphics Processing Unit for the Optimization of Electric Vehicle Recharge Scheduling

“…Each thread also computes the electricity cost to recharge its associated vehicle. Three parallel reduction primitives are used to compute the charge rate excess term from Equation (10), the transformer limit excess term from Equation (12), and the overall cost for the parking lot. Finally, the fitness for the candidate solutions is computed by thread 0 of each block using the aggregated values from the parallel reductions.…”

“…They can be used where classic and heuristic methods fail to perform adequately. Examples of metaheuristics for the EV charging schedule are particle swarm optimization (PSO) [10,11], and the genetic algorithm (GA) [12,13], although several others have been used. In fact, metaheuristics are probably the most commonly used methods for the EV charge scheduling problem.…”