Optimal Cooperative Charging Strategy for a Smart Charging Station of Electric Vehicles

Abstract

This paper proposes a novel cooperative charging strategy for a smart charging station in the dynamic electricity pricing environment, which helps electric vehicles (EVs) to economically accomplish the charging task by the given deadlines. This strategy allows EVs to share their battery-stored energy with each other under the coordination of an aggregator, so that more flexibility is given to the aggregator for better scheduling. Mathematically, the scheduling problem is formulated as a constrained mixed-integer linear program (MILP) to capture the discrete nature of the battery states, i.e., charging, idle and discharging. Then, an efficient algorithm is proposed to solve the MILP by means of dual decomposition and Benders decomposition. At last, the algorithm can be implemented in a distributed fashion, which makes it scalable and thus suitable for large-scale scheduling problems. Numerical results validate our theoretical analysis.

Charging plug-in electric vehicles as a mixed-integer aggregative game

“…Since δ c i and δ d i already satisfy the inequalities in (10) and (11), for all t ∈ T , the constraint in (7) reads as…”

“…However, most of the models adopted in the literature do not describe the intrinsic discrete operations of each PEV, e.g., being plugged-in or plugged-out from one of the available charging points, in g2v or in v2g mode [11], [12], [13]. The selfish nature of each PEV, which pursues an economic, possibly profitable, usage of the charging station, together with the presence of both continuous (amount of energy charged/discharged) and discrete decision variables (operating mode) over a certain prediction horizon, motivates us to model the charge scheduling coordination of the PEVs as a collection of inter-dependent mixed-integer optimization problems, where the coupling constraints arise in aggregative form ( §II).…”

Dynamic Pricing for Electric Vehicle Charging—A Literature Review

“…You et al [96] describe such a pricing scheme for charging stations with bidirectional charging capability. They assume that the charging provider purchases energy at real-time electricity prices and that there is an upper bound for the power that can be drawn from the grid.…”

Multi Objective Optimization in Charge Management of Micro Grid Based Multistory Carpark

“…In lines 7-13, the algorithm tries to cover the energy demand by RES, while the rest of it is stored in the system's battery. In cases when the production is insufficient, the demand is simply decreased by the amount of available RES energy (lines [15][16]. The second part of the algorithm simulates covering the rest of demand by energy from the system's batteries (lines 18-26).…”