Energy Management and Control of Battery Storage Systems in Buildings: Experimental Validation and Profitability Assessment

Battery energy storage systems (BESSs) can be integrated into buildings to reduce the electricity cost under variable electricity pricing schemes. However, battery degradation and the nonlinear efficiency of combined battery-inverter systems pose significant challenges to the optimal management of BESSs. This work develops an optimization scheme for the energy management of BESSs in buildings considering battery degradation and an approximate inverter-battery power loss model. The proposed scheme is formulated as a linear program that can be solved fast and reliably over long-term time horizons, enabling its usage for both operating and planning strategies. Using the optimization scheme, a model predictive control (MPC) approach is proposed to address modeling inaccuracies arising from the utilization of the approximate power loss model. Moreover, a financial analysis is performed using the optimization model to assess the long-term BESS profitability by calculating the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">net present value</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">internal rate of return</i> based on real data from a residential building. An experimental setup that integrates the MPC approach is developed to enable the monitoring and control of a real BESS in energy management applications. Experimental results validate the effectiveness of the proposed MPC approach to effectively manage, control, and operate a real BESS under modeling inaccuracies.