A Stress-Cognizant Optimal Battery Dispatch Framework for Multimarket Participation

The economic operation of lithium-ion battery energy storage in electricity markets requires optimally balancing the tradeoff between maximizing the revenue from energy arbitrage and minimizing the capacity loss due to usage. This optimal balance can be achieved by incorporating the stress due to the depth of discharge and battery temperatures in the optimal dispatch framework. However, the stress models are nonlinear and the quantification of partial charge–discharge cycles requires the rainflow cycle counting algorithm, which does not have an analytical form. Considering the challenges, a set of physics-inspired sufficient conditions are developed to handle the nonanalytical form of the
rainflow
algorithm and to consider cell-level temperatures. The proposed stress cognizant optimal battery dispatch (SC-OBD) framework is applied to a battery participating in both the day-ahead and real-time balancing market. A model predictive control-based framework is proposed to handle uncertain electricity prices in the real-time market and to guarantee the fulfilment of day-ahead market commitments. The numerical results indicate that the proposed SC-OBD can efficiently utilize the cooling to reduce degradation with/without modifying the market-benchmark dispatch.