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Lookup NU author(s): Tiong Teck Teo,
Dr Thillainathan Logenthiran,
Dr Wai Lok Woo,
Dr Khalid Abidi
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© 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.
Author(s): Teo TT, Logenthiran T, Woo WL, Abidi K
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: International Conference on Innovative Smart Grid Technologies, ISGT Asia 2018
Year of Conference: 2018
Online publication date: 20/09/2018
Acceptance date: 22/05/2018
Library holdings: Search Newcastle University Library for this item