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 language | English |
|---|---|
| Title of host publication | International Conference on Innovative Smart Grid Technologies, ISGT Asia 2018 |
| Publisher | IEEE |
| Pages | 1257-1261 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538642917, 9781538642900 |
| ISBN (Print) | 9781538642924 |
| DOIs | |
| Publication status | Published - 20 Sept 2018 |
Keywords
- Energy management system
- electricity market
- mixed-integer linear programming
- renewable source
- storage system
