Bidding strategies for battery energy storage in the energy and regulation electricity market

With the increasing penetration of renewable energy in the power system, regulation capacity in the power system is highly demanded. To ensure the flexible operations of the power system, it is necessary to explore the potential flexibility regulation capacity and further promote the accommodation of the renewable energy. Under this context, a joint bidding strategy for battery energy storage in the regulation and energy electricity market is proposed in this paper. Firstly, a deep neural network method is used to predict the power system load, and reasonably divide the bid-accepted probability of flexible ramping products in the electricity market according to the predicted load. Then, an optimization model is proposed to offer the bidding strategies for battery electric storage providing flexible ramping products in the energy and regulation market. Finally, the effectiveness of the proposed model is verified by case studies and sensitivity analysis.