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

T. T. Teo, T. Logenthiran, W. L. Woo, K. Abidi

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationInternational Conference on Innovative Smart Grid Technologies, ISGT Asia 2018
PublisherIEEE
Pages1257-1261
Number of pages5
ISBN (Electronic)9781538642917, 9781538642900
ISBN (Print)9781538642924
DOIs
Publication statusPublished - 20 Sep 2018

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