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

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 c...

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Autores principales:	Mohsen Aldaadi, Fahad Al-Ismail, Ali T. Al-Awami, Ammar Muqbel
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Wind power operation compressed air energy storage energy market distributionally robust optimization linear decision rule Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/2ae89c360b4b418cb7b508b1871f8d62
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spelling	oai:doaj.org-article:2ae89c360b4b418cb7b508b1871f8d622021-11-18T00:04:32ZA Coordinated Bidding Model for Wind Plant and Compressed Air Energy Storage Systems in the Energy and Ancillary Service Markets Using a Distributionally Robust Optimization Approach2169-353610.1109/ACCESS.2021.3123792https://doaj.org/article/2ae89c360b4b418cb7b508b1871f8d622021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591572/https://doaj.org/toc/2169-3536Clean 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 day-ahead 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.Mohsen AldaadiFahad Al-IsmailAli T. Al-AwamiAmmar MuqbelIEEEarticleWind power operationcompressed air energy storageenergy marketdistributionally robust optimizationlinear decision ruleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148599-148610 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Wind power operation compressed air energy storage energy market distributionally robust optimization linear decision rule Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Wind power operation compressed air energy storage energy market distributionally robust optimization linear decision rule Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mohsen Aldaadi Fahad Al-Ismail Ali T. Al-Awami Ammar Muqbel 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
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 day-ahead 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.
format	article
author	Mohsen Aldaadi Fahad Al-Ismail Ali T. Al-Awami Ammar Muqbel
author_facet	Mohsen Aldaadi Fahad Al-Ismail Ali T. Al-Awami Ammar Muqbel
author_sort	Mohsen Aldaadi
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
title_short	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_full	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_fullStr	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_full_unstemmed	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_sort	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
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/2ae89c360b4b418cb7b508b1871f8d62
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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

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