A Robust Optimization Model to the Day-Ahead Operation of an Electric Vehicle Aggregator Providing Reliable Reserve

Abstract

This paper presents a robust optimization model to find out the day-ahead energy and reserve to be scheduled by an electric vehicle (EV) aggregator. Energy can be purchased from, and injected to, the distribution network, while upward and downward reserves can be also provided by the EV aggregator. Although it is an economically driven model, the focus of this work relies on the actual availability of the scheduled reserves in a future real-time. To this end, two main features stand out: on one hand, the uncertainty regarding the EV driven pattern is modeled through a robust approach and, on the other hand, a set of non-anticipativity constraints are included to prevent from unavailable future states. The proposed model is posed as a mixed-integer robust linear problem in which binary variables are used to consider the charging, discharging or idle status of the EV aggregator. Results over a 24-h case study show the capability of the proposed model.

Challenges and opportunities of FACTS devices interacting with electric vehicles in distribution networks: A technological review

“…In this research, we propose an enhanced particle swarm OPT method (IPSO) as an optimum planning approach for EVs CS [56]. This study proposes a rigorous OPT model to determine the day-ahead energy and reserve that an EVs aggregator should plan [57]. The grid-connected front-end of an EVs ultra-fast charging (UFC) station with incorporated energy storage is controlled using a virtual synchronous compensator (VSC) in this article [58].…”

Optimization of Control Strategy for Orderly Charging of Electric Vehicles in Mountainous Cities

“…The scenario in this paper is set in the context of a charging contract between an electric vehicle user and an aggregator [14], and the aggregator provides the user with certain infrastructure, such as charging pile installation, communication services, and battery repair and replacement services. Based on these conditions, the charging demand of the EV user is met and the EV of the contracted user is controlled for orderly charging and utility-scale EV operation [15,16].…”

A Robust Optimization Model of Aggregated Resources Considering Serving Ratio for Providing Reserve Power in the Joint Electricity Market

“…One way to consider uncertainty is the robust optimization method, which is generally used to derive optimal results by considering the worst-case scenario for optimization variables with uncertainty. Based on the characteristics of the robust optimization problem, the robust optimization used in the study establishes an optimal operation plan by considering the risk of variables with uncertainty, such as power generation and price in the real-time operation [8][9][10][11][24][25][26][27][28]. In a situation in which an aggregator who uses wind turbine and energy storage together participates in the energy and reserve markets, an optimization model that considers the uncertainties of both wind-power generation and reserve-power provision is introduced [25].…”

“…Meanwhile, the robust optimization model that considers price uncertainty in the real-time operation also analyzes the trade-off characteristics of maximizing aggregators' profit and the real-time operation feasibility of the power system [26]. By considering the driving pattern of electric vehicle owners as an uncertainty, a robust optimization method was proposed to secure reserve power used in the day-ahead market and real-time operation through charging and discharging of electric vehicle chargers [27]. On the other hand, a robust optimization method was proposed, taking into account uncertainties such as power system failures, renewable energy fluctuations, and demand forecast errors.…”

“…There are various methods for solving optimization problems, but mixed-integer linear programming (MILP), which employs linearized objective functions and constraints, is frequently preferred for utilizing commercial software and reducing calculation time. While robust optimization with complex uncertainty constraints may be addressed using nonlinear objective functions and constraints [28], it can also be solved by linearizing the objective function and constraints and employing MILP techniques [25,27]. Additionally, linearizing the objective function and constraints helps find a mathematically guaranteed global optimum.…”