Optimal Commissioning of Power Plant Units by Including an Energy Storage System and Demand-Side Program Using a Hybrid Bacterial Foraging and Honey Bee Optimization Method
Unit commitment (UC) programming is a critical task in power system operations, which faces problems such as uncertainty in generation and loads with the significant rise in the generation of electrical energy through renewable energy sources (RES) such as wind and responsive load programs. The prob...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Bilijipub publisher
2022-10-01
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| Series: | Advances in Engineering and Intelligence Systems |
| Subjects: | |
| Online Access: | https://aeis.bilijipub.com/article_158303_4eeface6c0213d34683ba287f63be065.pdf |
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| Summary: | Unit commitment (UC) programming is a critical task in power system operations, which faces problems such as uncertainty in generation and loads with the significant rise in the generation of electrical energy through renewable energy sources (RES) such as wind and responsive load programs. The problem of UC, or the unit commissioning problem, is a major optimization problem, the exact solution of which can lead to a significant reduction in costs. In this article, smart grids are considered which aim to reduce costs and environmental problems. Thus, this paper solves the UC problem in smart grids by considering the emission of generation units, resulting in a multi-objective function for minimization. With the introduction of smart grids, energy storage systems (ESS) have also been considered in the grid. This paper proposes the optimal charging and discharging of ESS. Another problem modeled in this article is that of demand response (DR) in smart grids. To validate the performance of the proposed model, it is tested on a 4-unit system with ESS and the results show its optimal performance. To solve the problem of UC programming, a hybrid honey bee mating and bacterial foraging algorithm are used to reduce the complexity of the problem and achieve optimal results. |
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| ISSN: | 2821-0263 |