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Optimal Intelligence Planning of Wind Power Plants and Power System Storage Devices in Power Station Unit Commitment Based

Yuchen Hao*, Dawei Su, Zhen Lei

Control Center of State Grid of Jiangsu Electric Power Co., Ltd., Nanjing, 210000, China

* Corresponding Author: Yuchen Hao. Email: email

(This article belongs to the Special Issue: Application of Artificial Intelligence for Energy and City Environmental Sustainability)

Energy Engineering 2022, 119(5), 2081-2104. https://doi.org/10.32604/ee.2022.021342

Abstract

Renewable energy sources (RES) such as wind turbines (WT) and solar cells have attracted the attention of power system operators and users alike, thanks to their lack of environmental pollution, independence of fossil fuels, and meager marginal costs. With the introduction of RES, challenges have faced the unit commitment (UC) problem as a traditional power system optimization problem aiming to minimize total costs by optimally determining units’ inputs and outputs, and specifying the optimal generation of each unit. The output power of RES such as WT and solar cells depends on natural factors such as wind speed and solar irradiation that are riddled with uncertainty. As a result, the UC problem in the presence of RES faces uncertainties. The grid consumed load is not always equal to and is randomly different from the predicted values, which also contributes to uncertainty in solving the aforementioned problem. The current study proposes a novel two-stage optimization model with load and wind farm power generation uncertainties for the security-constrained UC to overcome this problem. The new model is adopted to solve the wind-generated power uncertainty, and energy storage systems (ESSs) are included in the problem for further management. The problem is written as an uncertain optimization model which are the stochastic nature with security-constrains which included undispatchable power resources and storage units. To solve the UC programming model, a hybrid honey bee mating and bacterial foraging algorithm is employed to reduce problem complexity and achieve optimal results.

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Cite This Article

APA Style
Hao, Y., Su, D., Lei, Z. (2022). Optimal intelligence planning of wind power plants and power system storage devices in power station unit commitment based. Energy Engineering, 119(5), 2081-2104. https://doi.org/10.32604/ee.2022.021342
Vancouver Style
Hao Y, Su D, Lei Z. Optimal intelligence planning of wind power plants and power system storage devices in power station unit commitment based. Energ Eng. 2022;119(5):2081-2104 https://doi.org/10.32604/ee.2022.021342
IEEE Style
Y. Hao, D. Su, and Z. Lei "Optimal Intelligence Planning of Wind Power Plants and Power System Storage Devices in Power Station Unit Commitment Based," Energ. Eng., vol. 119, no. 5, pp. 2081-2104. 2022. https://doi.org/10.32604/ee.2022.021342



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