Optimal battery operation for revenue maximization of wind-storage hybrid power plant

Kaushik Das; Anatole Louis Theodore Philippe Grapperon; Poul E. Sørensen; Anca D. Hansen

doi:10.1016/j.epsr.2020.106631

Optimal battery operation for revenue maximization of wind-storage hybrid power plant

Kaushik Das^*, Anatole Louis Theodore Philippe Grapperon, Poul E. Sørensen, Anca D. Hansen

^*Corresponding author for this work

Technical University of Denmark

Research output: Contribution to journal › Journal article › Research › peer-review

Abstract

In order to participate in energy market, variable renewable energy sources need to reduce the uncertainty of forecast errors. Inclusion of storage can be a viable option not only to minimize the penalties due to forecast uncertainties but also to maximize the revenue generation. This paper presents a decision framework for respecting the market constraints and maximise the revenues of a wind-storage based hybrid power plant. Wind power and price forecast are used in convex optimisation algorithm for making day ahead decisions on battery operation. This day ahead optimisation results feed to an algorithm for operating in the balancing market. Several scenarios and case studies have been simulated to assess the value of storage for revenue maximization of a wind power plant. The results show that proposed algorithms can increase the revenue by more than 10% compared to the operation of wind power plant without battery.

Original language	English
Article number	106631
Journal	Electric Power Systems Research
Volume	189
Number of pages	7
ISSN	0378-7796
DOIs	https://doi.org/10.1016/j.epsr.2020.106631
Publication status	Published - 2020

Keywords

Balancing market
Battery storage
Day-ahead optimisation
Hybrid power plant
Revenue maximization
Spot market
Wind power

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.epsr.2020.106631

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HYBRIDize: Optimized Design and Operation of Hybrid Power Plant
Sørensen, P. E. (Project Manager), Das, K. (Project Coordinator), Cavar, D. (Project Participant), Hansen, A. D. (Project Participant), Long, Q. (Project Participant), Göçmen, T. (Project Participant), Koivisto, M. J. (Project Participant) & Murcia Leon, J. P. (Project Participant)
01/05/2019 → 30/06/2022
Project: Research

Cite this

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title = "Optimal battery operation for revenue maximization of wind-storage hybrid power plant",

abstract = "In order to participate in energy market, variable renewable energy sources need to reduce the uncertainty of forecast errors. Inclusion of storage can be a viable option not only to minimize the penalties due to forecast uncertainties but also to maximize the revenue generation. This paper presents a decision framework for respecting the market constraints and maximise the revenues of a wind-storage based hybrid power plant. Wind power and price forecast are used in convex optimisation algorithm for making day ahead decisions on battery operation. This day ahead optimisation results feed to an algorithm for operating in the balancing market. Several scenarios and case studies have been simulated to assess the value of storage for revenue maximization of a wind power plant. The results show that proposed algorithms can increase the revenue by more than 10% compared to the operation of wind power plant without battery.",

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AU - Sørensen, Poul E.

AU - Hansen, Anca D.

PY - 2020

Y1 - 2020

N2 - In order to participate in energy market, variable renewable energy sources need to reduce the uncertainty of forecast errors. Inclusion of storage can be a viable option not only to minimize the penalties due to forecast uncertainties but also to maximize the revenue generation. This paper presents a decision framework for respecting the market constraints and maximise the revenues of a wind-storage based hybrid power plant. Wind power and price forecast are used in convex optimisation algorithm for making day ahead decisions on battery operation. This day ahead optimisation results feed to an algorithm for operating in the balancing market. Several scenarios and case studies have been simulated to assess the value of storage for revenue maximization of a wind power plant. The results show that proposed algorithms can increase the revenue by more than 10% compared to the operation of wind power plant without battery.

AB - In order to participate in energy market, variable renewable energy sources need to reduce the uncertainty of forecast errors. Inclusion of storage can be a viable option not only to minimize the penalties due to forecast uncertainties but also to maximize the revenue generation. This paper presents a decision framework for respecting the market constraints and maximise the revenues of a wind-storage based hybrid power plant. Wind power and price forecast are used in convex optimisation algorithm for making day ahead decisions on battery operation. This day ahead optimisation results feed to an algorithm for operating in the balancing market. Several scenarios and case studies have been simulated to assess the value of storage for revenue maximization of a wind power plant. The results show that proposed algorithms can increase the revenue by more than 10% compared to the operation of wind power plant without battery.

KW - Balancing market

KW - Battery storage

KW - Day-ahead optimisation

KW - Hybrid power plant

KW - Revenue maximization

KW - Spot market

KW - Wind power

U2 - 10.1016/j.epsr.2020.106631

DO - 10.1016/j.epsr.2020.106631

M3 - Journal article

SN - 0378-7796

VL - 189

JO - Electric Power Systems Research

JF - Electric Power Systems Research

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ER -

Optimal battery operation for revenue maximization of wind-storage hybrid power plant

Abstract

Keywords

UN SDGs

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Projects

HYBRIDize: Optimized Design and Operation of Hybrid Power Plant

Cite this