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

Teo, T.T.ORCID: 0000-0002-7552-8497, Logenthiran, T., Woo, W.L. and Abidi, K. (2018) Near-Optimal Day-Ahead Scheduling of Energy Storage System in Grid-Connected Microgrid. In: IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) 2018, 22 - 25 May 2018, Singapore

Link to Published Version: https://doi.org/10.1109/ISGT-Asia.2018.8467921
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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.

Item Type: Conference Paper
URI: http://researchrepository.murdoch.edu.au/id/eprint/46671
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