Near-Optimal Day-Ahead Scheduling of Energy Storage System in Grid-Connected Microgrid

Teo, TT; Logenthiran, T; Woo, WL; Abidi, K

doi:10.1109/ISGT-Asia.2018.8467921

Near-Optimal Day-Ahead Scheduling of Energy Storage System in Grid-Connected Microgrid

Lookup NU author(s): Tiong Teck Teo, Dr Thillainathan Logenthiran, Dr Wai Lok Woo, Dr Khalid Abidi

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Abstract

© 2018 IEEE. This paper proposes a near-optimal day-ahead scheduling of energy storage system based on the mismatch between supply and demand, state-of-charge and real-time electricity price when deciding how much to charge and discharge the energy storage system. An artificial neural network, the extreme learning machine is used for the day-ahead forecast of the mismatch between supply and demand and real-time electricity market price. After the day-ahead forecast is obtained, the scheduling problem is formulated into a mixed-integer linear programming and implemented in AMPL and solved using CPLEX. This paper also considers the impact of forecasting errors in the day-ahead scheduling. Empirical evidence shows that the proposed near-optimal day-ahead scheduling of ESS can achieve lower operating cost and life-cycle.