BESS can provide many crucial grid services including voltage and frequency support, however, capital costs can hinder widespread installation. Thus this work develops a data-driven MILP approach for BESS dispatch to enhance consumer cost recovery through energy arbitrage and mitigate the barrier of high capital costs. The proposed algorithm uses location-relevant weather data, to forecast PV generation and load consumption, as well as market data to create a BESS dispatch schedule. The proposed BESS algorithm is validated on a Raspberry Pi 4b microcontroller using real-time HIL simulation for 72 h on a real-world-based university campus microgrid consisting of a 9MWh/3MW BESS and 1.4MW DC/1.1MW AC PV solar canopy. Economic analyses indicate the proposed algorithms ability to decrease system energy costs when using LMP pricing, translating in a 7.27% decrease in LCOE compared to a 3.12% to 3.20% decrease when using an RB scheduling method. Similarly, LCOE is reduced by 5.32% when using TOU energy pricing. Additionally, a sensitively analysis of PV and load forecasting error with error biases ranging from −10% to 10% indicate a resulting increase in LCOE of 0% to 0.674%. These key findings indicate increased economic viability when implementing the proposed dispatch approach.
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