A wind power curtailment mitigation strategy via co-location and co-operation of compressed air energy storage with wind power generation

• Quantified analysis reveals the great potential of wind power curtailment mitigation via co-location and co-operation of AA-CAES with wind farms. • Develops a temporal decoupling algorithm to provide an effective method for solving the temporal-coupling MISOCP problem. • The outcomes of case studies using the UK transmission network data provide strong evidence to support the UK clean energy agenda. Wind power curtailment has resulted in notable economic and energy losses due to the rapid increase of wind energy in recent years. This paper presents our recent work on developing a wind power curtailment mitigation strategy via co-location and co-operation of compressed air energy storage (CAES) (in particular, Advanced Adiabatic CAES (AA-CAES)) with wind power generation. The work is built on our previous study on co-relation mapping of CAES storage locations and wind power resources. In this study, a stochastic optimisation (SO) model is formulated which successfully addresses the challenge of the lack of a modelling platform to accommodate and coordinate AA-CAES dynamics, uncertain wind power generation, integration with AC power networks, and dynamic co-operation analysis. The SO problem is described in a mixed-integer second-order cone programming (MISOCP) model. A novel generation ramp constraints temporal decoupling (GRC-TD) method is developed to cope with the temporal-coupling generation ramp constraints in the MISOCP problem. This method is integrated with the benders decomposition (BD) approach as an improved GRC-TD-BD algorithm. Numerical simulation studies using the UK transmission network data are conducted, which verify the feasibility and effectiveness of the proposed strategy. The simulation study shows very encouraging results. The co-location and co-operation of AA-CAES with wind power generation can make wind power more dispatchable and significantly reduce wind curtailment in the UK power system.

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Publication Details

Journal
Electric Power Systems Research
Published
2024-12-06
DOI
https://doi.org/10.1016/j.epsr.2024.111318
Citations
10
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
3.28

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article

A wind power curtailment mitigation strategy via co-location and co-operation of compressed air energy storage with wind power generation

Sunku Prasad Jenne, Yulong Ding, Xuecen Zhang, J. W. Spencer et al.
10 citations
Electric Power Systems Research
Microgrid Control and Optimization
3.28
article

A wind power curtailment mitigation strategy via co-location and co-operation of compressed air energy storage with wind power generation

Sunku Prasad Jenne, Yulong Ding, Xuecen Zhang, J. W. Spencer, Jihong Wang, Wei He
article en
10 citations

Abstract

• Quantified analysis reveals the great potential of wind power curtailment mitigation via co-location and co-operation of AA-CAES with wind farms. • Develops a temporal decoupling algorithm to provide an effective method for solving the temporal-coupling MISOCP problem. • The outcomes of case studies using the UK transmission network data provide strong evidence to support the UK clean energy agenda. Wind power curtailment has resulted in notable economic and energy losses due to the rapid increase of wind energy in recent years. This paper presents our recent work on developing a wind power curtailment mitigation strategy via co-location and co-operation of compressed air energy storage (CAES) (in particular, Advanced Adiabatic CAES (AA-CAES)) with wind power generation. The work is built on our previous study on co-relation mapping of CAES storage locations and wind power resources. In this study, a stochastic optimisation (SO) model is formulated which successfully addresses the challenge of the lack of a modelling platform to accommodate and coordinate AA-CAES dynamics, uncertain wind power generation, integration with AC power networks, and dynamic co-operation analysis. The SO problem is described in a mixed-integer second-order cone programming (MISOCP) model. A novel generation ramp constraints temporal decoupling (GRC-TD) method is developed to cope with the temporal-coupling generation ramp constraints in the MISOCP problem. This method is integrated with the benders decomposition (BD) approach as an improved GRC-TD-BD algorithm. Numerical simulation studies using the UK transmission network data are conducted, which verify the feasibility and effectiveness of the proposed strategy. The simulation study shows very encouraging results. The co-location and co-operation of AA-CAES with wind power generation can make wind power more dispatchable and significantly reduce wind curtailment in the UK power system.

Electric Power Systems ResearchVol. 241
University of Liverpool (GB), King's College London (GB), University of Warwick (GB), University of Birmingham (GB)
Engineering and Physical Sciences Research Council
Affordable and clean energy
Openalex Percentile: Top 7%
Microgrid Control and Optimization
3.28
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