A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets

Abstract

This paper proposes a stochastic bi-level decision-making model for an electric vehicle (EV) aggregator in a competitive environment. In this approach, the EV aggregator decides to participate in day-ahead (DA) and balancing markets, and provides energy price offers to the EV owners in order to maximize its expected profit. Moreover, from the EV owners' viewpoint, energy procurement cost of their EVs should be minimized in an uncertain environment. In this study, the sources of uncertainty-including the EVs demand, DA and balancing prices and selling prices offered by rival aggregators-are modeled via stochastic programming. Therefore, a two-level problem is formulated here, in which the aggregator makes decisions in the upper level and the EV clients purchase energy to charge their EVs in the lower level. Then the obtained nonlinear bi-level framework is transformed into a single-level model using Karush-Kuhn-Tucker (KKT) optimality conditions. Strong duality is also applied to the problem to linearize the bilinear products. To deal with the unwilling effects of uncertain resources, a risk measurement is also applied in the proposed formulation. The performance of the proposed framework is assessed in a realistic case study and the results show that the proposed model would be effective for an EV aggregator decision-making problem in a competitive environment.

A Decentralized Model for Coordinated Operation of Distribution Network and EV Aggregators

“…This high integration of EVs in the future DNs can bring new issues for economic and secure operation of DNs [2]- [3]. A lot of studies have discussed the potential challenges and opportunities of EVs' integration into the DNs [4]- [5]. In [6] a probabilistic method for the optimal charging of EVs in DN is introduced.…”

A stochastic bi‐level decision‐making framework for a load‐serving entity in day‐ahead and balancing markets

“…However, discharge process of EVs and DR participants has not been studied in decision‐making process. Similarly, in Rashidizadeh‐Kermani et al, the authors have presented a scholastic scheduling model for EV aggregators in a competitive market, considering both charging and discharging process of EVs. A cooperation model between a generating company and several marketers is presented in Guzmán Acuña et al, which considers the optimal decision for the generating company and the group of marketers in terms of maximization of their profits on the basis of bi‐level optimization.…”

“…A cooperation model between a generating company and several marketers is presented in Guzmán Acuña et al, which considers the optimal decision for the generating company and the group of marketers in terms of maximization of their profits on the basis of bi‐level optimization. Nevertheless, the works in Rashidizadeh‐Kermani et al and Guzmán Acuña et al did not address the effect of DR programs.…”

Charging Load Allocation Strategy of EV Charging Station Considering Charging Mode

“…Richardson considers charging needs of the EV owner, and modeled the charging process of the agent dispatching EVs during the contract period under the constraints of the distribution network and the conventional electricity [4]. Rashidizadeh-Kermani proposes a two-layer stochastic model of EV aggregator decision making process for aggregator profit and vehicle purchase cost [5]. Wang comprehensively considers the impact of peak and valley electricity prices and user's satisfaction, and established a coordinated 2 of 12 charging model that can be optimized over time [6].…”