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• W2893650307 abstract "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." @default.
• W2893650307 created "2018-10-05" @default.
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• W2893650307 date "2018-05-01" @default.
• W2893650307 modified "2023-09-29" @default.
• W2893650307 title "Near-Optimal Day-Ahead Scheduling of Energy Storage System in Grid-Connected Microgrid" @default.
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• W2893650307 doi "https://doi.org/10.1109/isgt-asia.2018.8467921" @default.
• W2893650307 hasPublicationYear "2018" @default.
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