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 renew‐ able 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 deploy‐ ment of batteries can increase the benefits of a renewab...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:dnet:addi________::d5d9990a4d555c210b89b56f8fcbbd63 |
| Acceso en línea: | http://hdl.handle.net/10810/78913 |
| Access Level: | acceso abierto |
| Palabra clave: | Battery energy storage system particle swarm optimization. energy arbitrage |
| Sumario: | Considering the increasing integration of renew‐ able 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 deploy‐ ment of batteries can increase the benefits of a renewable pow‐ er 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 con‐ ducted 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 val‐ ue of the battery. A particle swarm optimization algorithm is de‐ veloped and fitted in order to solve this non-linear multi-objec‐ tive function. With the aim of analyzing the importance of con‐ sidering 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 provid‐ ed to the plant by using batteries just for arbitrage purposes in the day-ahead market. |
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