4.7 Article

Risk management of a renewable-based compressed air energy storage system using downside risk constraints approach

Journal

RENEWABLE ENERGY
Volume 161, Issue -, Pages 470-481

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2020.07.095

Keywords

Renewable energy; Stochastic programming; Risk management; Downside risk constraints; Demand response programs; Compressed air energy storage

Funding

  1. Open Fund of State Key Laboratory of Power Grid Safety and Energy Conservation (China Electric Power Research Institute) Research on Energy Flow Optimization Theory Oriented to Cooperative and Efficient Integrated Energy Systems [YDB51201901276]

Ask authors/readers for more resources

The financial risks imposed from the uncertain parameters is a considerable issue in the optimization problem of renewable-based energy systems. Due to the various risks in renewable-based energy systems, a practical and simple risk measurement approach can be efficient in the risk-based strategy selection process. In this paper, the downside risk constraints (DRC) approach as a novel risk measurement approach is proposed to manage the imposed risks from the uncertain parameters over the stochastic problems. Therefore, various uncertainties, including solar irradiation, temperature, wind speed, electricity demand, and electricity market price uncertainties, are modeled using the DRC approach along with the stochastic programming. In addition, the compressed air energy storage (CAES) and demand response program (DRP) is implemented to manage the imposed risks. By using the proposed risk measurement method, the system operator can obtain a risk-strategy that is independent of scenarios. According to the obtained results, the expected cost of the stochastic problem is $ 6145.62, which by using the DRC approach, the system operator by paying the 3.4% ($ 6353.5) more cost can guarantee itself against the financial risks. The advantage of the proposed DRC approach is the conversion of the stochastic problem to a deterministic scenario-independent problem. (C) 2020 Elsevier Ltd. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available