Bi-level non-convex joint optimization model of energy storage in energy and primary frequency regulation markets

Large-scale energy storage as a new type of flexible market player can arbitrage in the energy market and provide primary frequency regulation (PFR) service to make profits. The operation of the battery energy storage system and the market clearing process interact with each other. Therefore, this paper proposes a bi-level optimization joint model of energy storage in energy and primary frequency regulation markets, where the upper-level maximizes the storage profit considering the battery degradation and the lower level simulates the joint market clearing process. Different from the existing research, dynamic frequency nonlinear constraints and the non convex constraints of binary decision variables are considered in the lower level problem. The reformulation-linearization technique (RLT) is used to transform them into linear constraints. The relaxation and primary dual reformulation of the original, and penalization of the associated duality gap are used to transform the non convex model into a single-level mixed integer linear programming problem (MILP) which can be solved directly. The results reflect the influence of energy storage on system frequency and market clearing price. The interactive decision-making relationship between energy storage operation and clearing price is balanced.

Automated summary

This paper proposes a new bi-level optimization joint model, considering dynamic frequency nonlinear constraints and non-convex constraints, transforming them into a single-level mixed integer linear programming problem using reformulation-linearization technique. The study focuses on the impact of energy storage on system frequency and market clearing prices, as well as the balanced decision-making relationship between energy storage operation and clearing prices.