A coordinated bidding model for wind plant and compressed air energy storage systems in the energy and ancillary service markets using a distributionally robust optimization approach
Title
A coordinated bidding model for wind plant and compressed air energy storage systems in the energy and ancillary service markets using a distributionally robust optimization approach
Subject
Wind power
Optimization
Uncertainty
Regulation
Compressed air energy storage
Uncertainty analysis
Real-time systems
Digital storage
Investments
Electric power generation
Electric energy storage
Stochastic systems
Stochastic models
Pressure vessels
Power markets
Real time systems
Compressed air
Random processes
Stochastic processes
Wind power generation
Spinning
compressed air energy storage
energy market
Deregulation
distributionally robust optimization
linear decision rule
Wind power operation
Description
Clean energy resources, like wind, have a stochastic nature, which involves uncertainties in the power system. Introducing energy storage systems (ESS) to the network can compensate for the uncertainty in wind plant output and allow the plant to participate in ancillary service markets. Advance in compressed air energy storage system (CAES) technologies and their fast response make them suitable for ancillary services. This paper investigates the participation of a combined energy system composed of wind plants and compressed air energy storage system (CAES) in the energy market from a private owner's viewpoint, including trading in energy markets and bidding for frequency regulation and reserve capacity in ancillary service markets. Since this problem contains various uncertainties associated with market prices, wind generation levels, and regulation signals, distributionally robust optimization (DRO) is used to model the uncertainties and enhance the simultaneous participation of a combined wind-CAES system in dayahead energy and ancillary service markets. This method combines the advantages of stochastic and robust optimization. In contrast to robust optimization (RO), the method consolidates specific statistical data to reduce conservative results. Simulation results demonstrate the proposed model's effectiveness in handling uncertainties and provide a framework for investors in this area. In addition, case study analyses are applied to assess the model's performance and validate the coordination of a wind plant and compressed air energy storage system in participating in a deregulated electricity market. Finally, DRO and RO are compared in modeling the uncertainties of the optimization problem. The optimal outputs demonstrate the effectiveness of DRO in terms of achieving higher realized profits with less conservative results. 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
9
Publisher
IEEE Access
Date
2021
Contributor
Aldaadi, Mohsen
Al-Ismail, Fahad
Al-Awami, Ali T.
Muqbel, Ammar
Format
148599-148610
Type
journalArticle
Identifier
21693536
10.1109/ACCESS.2021.3123792
Collection
Citation
“A coordinated bidding model for wind plant and compressed air energy storage systems in the energy and ancillary service markets using a distributionally robust optimization approach,” Lamar University Midstream Center Research, accessed May 15, 2024, https://lumc.omeka.net/items/show/25856.