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.
Highlights
Introduction of an Electric Vehicle AggregatorDue to environmental concerns, an increasing interest in the deployment of distributed energy resources (DERs) and electric vehicles (EVs) is expected [1]
Two main features stand out: the robust formulation used to handle the uncertainty sources in the model, and the set of non-anticipativity constraints able to avoid the unavailability of the committed reserves whether they are required in the real-time stage
The actual state of charge will depend on the amount of reserve that will be deployed in the real-time, the optimal solution achieved for these bounding variables set the limits within the aggregator is able to deploy any reserve committed in the day-ahead stage
Summary
An increasing interest in the deployment of distributed energy resources (DERs) and electric vehicles (EVs) is expected [1]. These new participants in electric systems will be a high number of small users modifying the traditional load curves and, in many cases, able to provide energy to the power system, for instance, through photovoltaic generation or vehicle-to-grid (V2G) capability. Energy communities and, in the scope of this paper, aggregators [3], have emerged as concepts or participants to respond to this new reality
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