Conference paper
Near-Optimal Day-Ahead Scheduling of Energy Storage System in Grid-Connected Microgrid
2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)
IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) 2018 (Singapore, 22/05/2018–25/05/2018)
2018
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.
Details
- Title
- Near-Optimal Day-Ahead Scheduling of Energy Storage System in Grid-Connected Microgrid
- Authors/Creators
- T.T. Teo (Author/Creator) - Newcastle University SingaporeT. Logenthiran (Author/Creator) - Newcastle University SingaporeW.L. Woo (Author/Creator) - Newcastle University SingaporeK. Abidi (Author/Creator) - Newcastle University Singapore
- Publication Details
- 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)
- Conference
- IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) 2018 (Singapore, 22/05/2018–25/05/2018)
- Identifiers
- 991005544563007891
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Conference paper
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