Cost-benefit analysis for multiple agents considering an electric vehicle charging/discharging strategy and grid integration

An increasing number of electric vehicles (EVs) will be charged/discharged in EV charging stations (EVCSs) in distribution systems. Grid-2-vehicle (G2V) and vehicle-2-grid (V2G) operation of EVs are economically and technically rewarding only when an optimal G2V/V2G strategy is properly developed. In this study, a scheduling scheme is developed which mainly focus on guaranteeing the rewards of all agents (e.g EVs, EVCSs, and electricity suppliers (ESs)) participating in V2G and G2V operation. Based on the proposed strategy, EVs independently plan their charging/discharging depending on the shortest driving route and cost/benefit offered by EVCSs. Furthermore, each EVCS finds the best ES to purchase electricity from the wholesale market. The benefits of all agents in EV’s V2G and G2V operation are taken into account by formulating three optimization problems. Each problem belongs to each agent. To implement the proposed strategy, a cloud scheduling system is operated to collect required information from all agents, solve the optimization problems, and ultimately send the results to relevant agents. Optimal hourly electricity prices are determined for the three agents. For simulation purposes, nine EVCSs and three ESs are facilitated for charging/discharging of EVs to visualize and validate the modeling results. The results show that by implementing the proposed strategy, the cost of EVs decreases by 18%, and the revenues of EVCSs and ESs are raised by 21% and 23%, respectively, compared to the case in which EVs do not use the proposed strategy in EV’s V2G and G2V operation

Cost-benefit analysis for multiple agents considering an electric vehicle charging/discharging strategy and grid integration

An increasing number of electric vehicles (EVs) will be charged/discharged in EV charging stations (EVCSs) in distribution systems. Grid-2-vehicle (G2V) and vehicle-2-grid (V2G) operation of EVs are economically and technically rewarding only when an optimal G2V/V2G strategy is properly developed. In this study, a scheduling scheme is developed which mainly focus on guaranteeing the rewards of all agents (e.g EVs, EVCSs, and electricity suppliers (ESs)) participating in V2G and G2V operation. Based on the proposed strategy, EVs independently plan their charging/discharging depending on the shortest driving route and cost/benefit offered by EVCSs. Furthermore, each EVCS finds the best ES to purchase electricity from the wholesale market. The benefits of all agents in EV's V2G and G2V operation are taken into account by formulating three optimization problems. Each problem belongs to each agent. To implement the proposed strategy, a cloud scheduling system is operated to collect required information from all agents, solve the optimization problems, and ultimately send the results to relevant agents. Optimal hourly electricity prices are determined for the three agents. For simulation purposes, nine EVCSs and three ESs are facilitated for charging/discharging of EVs to visualize and validate the modeling results. The results show that by implementing the proposed strategy, the cost of EVs decreases by 18%, and the revenues of EVCSs and ESs are raised by 21% and 23%, respectively, compared to the case in which EVs do not use the proposed strategy in EV's V2G and G2V operation

Coordinated Autonomous Vehicle Parking for Vehicle-to-Grid Services: Formulation and Distributed Algorithm

Autonomous vehicles (AVs) will revolutionarize ground transport and take a substantial role in the future transportation system. Most AVs are likely to be electric vehicles (EVs) and they can participate in the vehicle-to-grid (V2G) system to support various V2G services. Although it is generally infeasible for EVs to dictate their routes, we can design AV travel plans to fulfill certain system-wide objectives. In this paper, we focus on the AVs looking for parking and study how they can be led to appropriate parking facilities to support V2G services. We formulate the Coordinated Parking Problem (CPP), which can be solved by a standard integer linear program solver but requires long computational time. To make it more practical, we develop a distributed algorithm to address CPP based on dual decomposition. We carry out a series of simulations to evaluate the proposed solution methods. Our results show that the distributed algorithm can produce nearly optimal solutions with substantially less computational time. A coarser time scale can improve computational time but degrade the solution quality resulting in possible infeasible solution. Even with communication loss, the distributed algorithm can still perform well and converge with only little degradation in speed.postprin

Dual Market Facility Network Design under Bounded Rationality

A number of markets, geographically separated, with different demand characteristics for different products that share a common component, are analyzed. This common component can either be manufactured locally in each of the markets or transported between the markets to fulfill the demand. However, final assemblies are localized to the respective markets. The decision making challenge is whether to manufacture the common component centrally or locally. To formulate the underlying setting, a newsvendor modeling based approach is considered. The developed model is solved using Frank-Wolfe linearization technique along with Benders’ decomposition method. Further, the propensity of decision makers in each market to make suboptimal decisions leading to bounded rationality is considered. The results obtained for both the cases are compared

Conic optimisation for electric vehicle station smart charging with battery voltage constraints

This paper proposes a new convex optimisation strategy for coordinating electric vehicle charging, which accounts for battery voltage rise, and the associated limits on maximum charging power. Optimisation strategies for coordinating electric vehicle charging commonly neglect the increase in battery voltage which occurs as the battery is charged. However, battery voltage rise is an important consideration, since it imposes limits on the maximum charging power. This is particularly relevant for DC fast charging, where the maximum charging power may be severely limited, even at moderate state of charge levels. First, a reduced order battery circuit model is developed, which retains the nonlinear relationship between state of charge and maximum charging power. Using this model, limits on the battery output voltage and battery charging power are formulated as second-order cone constraints. These constraints are integrated with a linearised power flow model for three-phase unbalanced distribution networks. This provides a new multiperiod optimisation strategy for electric vehicle smart charging. The resulting optimisation is a second-order cone program, and thus can be solved in polynomial time by standard solvers. A receding horizon implementation allows the charging schedule to be updated online, without requiring prior information about when vehicles will arrive