MILP Optimized Management of Domestic PV-Battery Using Two Days-Ahead Forecasts

Abstract

This paper proposes an Energy Management System (EMS) for domestic PV-battery applications with the aim of reducing the absolute net energy exchange with the utility grid by utilizing the two days-ahead energy forecasts in the optimization process. A Mixed-Integer Linear Programming (MILP) exploits two days-ahead energy demand and PV generation forecasts to schedule the day-ahead battery energy exchange with both the utility grid and the PV generator. The proposed scheme is tested using the real data of the Active Office Building (AOB) located in Swansea University, UK. Performance comparisons with state-of-the-art and the commercial EMS currently running at the AOB reveal that the proposed EMS increases the self-consumption of PV energy and at the same time reduces the total energy cost. The absolute net energy exchange with the grid and the total operating costs are reduced by 121% and 54% compared to the state-of-the-art and 194% and 8% when compared to the commercial EMS over a six-month period. Furthermore, the results show that the proposed method can reduce the energy bill by up to 46% for the same period compared to the state-of-the-art. The paper also investigates the effect of using different objective functions on the performance of the EMS and shows that the proposed EMS operate more efficiently when it is compared with another cost function that directly promotes reducing the absolute net energy exchange.

Keywords

• Battery storage
• energy management system
• energy tariffs
• forecast
• mixed-integer linear programming
• PV