Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System

Considering the increasing integration of renewable energies into the power grid, batteries are expected to play a key role in the challenge of compensating the stochastic and intermittent nature of these energy sources. Besides, the deployment of batteries can increase the benefits of a renewable p...

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Main Authors: Jon Martinez-Rico, Ekaitz Zulueta, Ismael Ruiz de Argandona, Unai Fernandez-Gamiz, Mikel Armendia
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:Journal of Modern Power Systems and Clean Energy
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9237068/
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spelling doaj-174f3268522d4aa69874d29ee6fdfd2a2021-04-23T16:14:36ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202021-01-019228529410.35833/MPCE.2019.0000219237068Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage SystemJon Martinez-Rico0Ekaitz Zulueta1Ismael Ruiz de Argandona2Unai Fernandez-Gamiz3Mikel Armendia4Automation and Control Unit, Fundación Tekniker, Basque Research and Technology Alliance (BRTA),Eibar,Spain,20600College of Engineering at Vitoria-Gasteiz, University of the Basque Country,Department of Systems Engineering and Control,Vitoria-Gasteiz,Spain,01006Automation and Control Unit, Fundación Tekniker, Basque Research and Technology Alliance (BRTA),Eibar,Spain,20600College of Engineering at Vitoria-Gasteiz, University of the Basque Country,Department of Nuclear and Fluid Mechanics,Vitoria-Gasteiz,Spain,01006Automation and Control Unit, Fundación Tekniker, Basque Research and Technology Alliance (BRTA),Eibar,Spain,20600Considering the increasing integration of renewable energies into the power grid, batteries are expected to play a key role in the challenge of compensating the stochastic and intermittent nature of these energy sources. Besides, the deployment of batteries can increase the benefits of a renewable power plant. One way to increase the profits with batteries studied in this paper is performing energy arbitrage. This strategy is based on storing energy at low electricity price moments and selling it when electricity price is high. In this paper, a hybrid renewable energy system consisting of wind and solar power with batteries is studied, and an optimization process is conducted in order to maximize the benefits regarding the day-ahead production scheduling of the plant. A multi-objective cost function is proposed, which, on the one hand, maximizes the obtained profit, and, on the other hand, reduces the loss of value of the battery. A particle swarm optimization algorithm is developed and fitted in order to solve this non-linear multi-objec-tive function. With the aim of analyzing the importance of considering both the energy efficiency of the battery and its loss of value, two more simplified cost functions are proposed. Results show the importance of including the energy efficiency in the cost function to optimize. Besides, it is proven that the battery lifetime increases substantially by using the multi-objective cost function, whereas the profitability is similar to the one obtained in case the loss of value is not considered. Finally, due to the small difference in price among hours in the analyzed Iberian electricity market, it is observed that low profits can be provided to the plant by using batteries just for arbitrage purposes in the day-ahead market.https://ieeexplore.ieee.org/document/9237068/Battery energy storage systemenergy arbitragehybrid renewable energy systemparticle swarm optimization
collection DOAJ
language English
format Article
sources DOAJ
author Jon Martinez-Rico
Ekaitz Zulueta
Ismael Ruiz de Argandona
Unai Fernandez-Gamiz
Mikel Armendia
spellingShingle Jon Martinez-Rico
Ekaitz Zulueta
Ismael Ruiz de Argandona
Unai Fernandez-Gamiz
Mikel Armendia
Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System
Journal of Modern Power Systems and Clean Energy
Battery energy storage system
energy arbitrage
hybrid renewable energy system
particle swarm optimization
author_facet Jon Martinez-Rico
Ekaitz Zulueta
Ismael Ruiz de Argandona
Unai Fernandez-Gamiz
Mikel Armendia
author_sort Jon Martinez-Rico
title Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System
title_short Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System
title_full Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System
title_fullStr Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System
title_full_unstemmed Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System
title_sort multi-objective optimization of production scheduling using particle swarm optimization algorithm for hybrid renewable power plants with battery energy storage system
publisher IEEE
series Journal of Modern Power Systems and Clean Energy
issn 2196-5420
publishDate 2021-01-01
description Considering the increasing integration of renewable energies into the power grid, batteries are expected to play a key role in the challenge of compensating the stochastic and intermittent nature of these energy sources. Besides, the deployment of batteries can increase the benefits of a renewable power plant. One way to increase the profits with batteries studied in this paper is performing energy arbitrage. This strategy is based on storing energy at low electricity price moments and selling it when electricity price is high. In this paper, a hybrid renewable energy system consisting of wind and solar power with batteries is studied, and an optimization process is conducted in order to maximize the benefits regarding the day-ahead production scheduling of the plant. A multi-objective cost function is proposed, which, on the one hand, maximizes the obtained profit, and, on the other hand, reduces the loss of value of the battery. A particle swarm optimization algorithm is developed and fitted in order to solve this non-linear multi-objec-tive function. With the aim of analyzing the importance of considering both the energy efficiency of the battery and its loss of value, two more simplified cost functions are proposed. Results show the importance of including the energy efficiency in the cost function to optimize. Besides, it is proven that the battery lifetime increases substantially by using the multi-objective cost function, whereas the profitability is similar to the one obtained in case the loss of value is not considered. Finally, due to the small difference in price among hours in the analyzed Iberian electricity market, it is observed that low profits can be provided to the plant by using batteries just for arbitrage purposes in the day-ahead market.
topic Battery energy storage system
energy arbitrage
hybrid renewable energy system
particle swarm optimization
url https://ieeexplore.ieee.org/document/9237068/
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