Energy storage system is a key device for load-leveling which can shift the load from on-peak time to offpeak time in time-of-use. Customers of the behind-the-meter energy storage system can schedule charging/discharging of energy storage system for electricity cost saving at peak-load. In order to maximize the reduction of electricity cost, smart charging and discharging algorithms based on accurate load forecasting are needed. This paper proposes an energy storage system scheduling algorithm based on water filling optimization followed by short-term load forecasting by using long short-term memory neural network. The proposed method is expected to reduce electricity cost for customers in behind-the-meter by scheduling charging and discharging of an energy storage system. For practical implementation, the satisfaction index of the optimization and the daily electricity cost are compared according to the change of scheduling intervals. Finally, case studies are conducted to confirm the effectiveness of the proposed method.
|Title of host publication||2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2019 Apr 1|
|Event||2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Kyoto, Japan|
Duration: 2019 Feb 27 → 2019 Mar 2
|Name||2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings|
|Conference||2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019|
|Period||19/2/27 → 19/3/2|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning #NRF-2017R1A2A1A05001022 and the framework of international cooperation program managed by NRF of Korea #NRF-2017K1A4A301357. This research was also supported by Korea Electric Power Corporation (KEPCO) #R18XA05.
All Science Journal Classification (ASJC) codes
- Information Systems and Management
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems