An optimized trading strategy for an energy storage systems aggregator in an ancillary service market

With increasing proportion of intermittent renewable energy sources in the electric power system, the system operator (SO) is facing a challenge of maintaining the demand-supply balance which is more dynamic and uncertain than before. To manage the balance, the SO procures different services from balancing service providers like balancing mechanism units and distributed energy resources aggregators. In this work, we propose a model for an aggregator of energy storage systems (ESS). The distributed small size ESS can be grouped and utilized by an aggregator for trading of multiple services with different specifications and bidding rules in ancillary service markets. ESS, being flexible and having quick response time, can contribute in both directions for all the services and assist in maintaining the real time demand-supply balance of the system. The trading strategy proposed in this work captures the regulations and the bidding characteristics of all the services individually. This proposed solution is a Mixed Integer Linear Programming (MILP) problem which becomes computationally complex as the size of ESS dataset of the aggregator increases. Hence, we also propose an alternate approximation method which is scalable, comparatively easy to solve and takes less computation time while giving comparable optimal schedules. These proposed methods have also been compared with an intuitive baseline method. We have demonstrated the efficacy of the proposed methods with real world data of France balancing market.