Abstract
While there are growing interests in using pumped hydro storage to facilitate the integration of renewable resources, the flexibility of storage is not being fully utilized by existing energy and market management systems. Today, one common approach to operate pumped hydro storage is to determine schedules for a future time horizon based on a look-ahead operational planning stage, with limited adjustment in real-time. However, as renewable penetration levels increase, such approaches do not fully utilize the flexibility of pumped hydro storage. In this paper, a policy function approach is proposed to enhance the utilization of pumped hydro storage in real-time operations. The performance of the approach is evaluated and compared with other benchmark approaches using the IEEE RTS 24-bus system. The result shows that the policy function approach is effective in utilizing the flexibility of pumped hydro storage and has minimal added computational complexity to the existing dispatch process.
Original language | English (US) |
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Article number | 7492185 |
Pages (from-to) | 1089-1102 |
Number of pages | 14 |
Journal | IEEE Transactions on Power Systems |
Volume | 32 |
Issue number | 2 |
DOIs | |
State | Published - Mar 2017 |
Keywords
- Classification
- data mining
- energy storage
- machine learning
- policy functions
- power system economics
- pumped hydro storage (PHS)
- renewable resources
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering