Modeling solar power plant electricity supply chain toward renewable energy consumption

Mohammad Reza Eslami Rasekh , Farzad Mohammad Sharifi , Somaieh Alavi , Nassibeh Janatyan
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Abstract

This paper aims to introduce a model of the solar plant electricity supply chain, encompassing mixed power plants, transmission lines, and consumers, with a focus on optimization and consideration of uncertainties. Within this article, the supply chain of solar power plants is delineated based on various parameters. The quantities of power plants and solar panels are determined by different priorities, such as investment levels, pollution mitigation, and reduction of gas consumption by conventional power plants, utilizing the particle swarm algorithm for optimal outcomes. The proposed model addresses uncertainties related to electricity demand, solar radiation levels, and consequently, the power production of solar panels, through the application of type 2 fuzzy logic. The optimization of the model is done keeping in mind various constraints including the supply of electricity and the maximum allowed use of solar cells. The innovation of this article is in the design of the supply chain model from the point of view of the uncertainty of electric power production and the amount of consumer demand and the optimal selection of solar panels for solar power plants to minimize the electricity consumption of the gas power plant and the amount of pollution caused by it. Based on the results obtained from the simulation of this article, it has been shown that considering the maximum investment capacity, up to 76 % of the electric energy can be supplied by building five solar power plants at certain distances from the electric substations around the case study. Considering the maximum weight coefficients for CO2 plant emissions and gas consumption, five solar power plants are the optimal number that is achieved by proposed algorithm. The power capacity of five solar power plants is optimized 4.100,4.222,3.920,4.375and 3.991MW, respectively. To evaluation of the proposed model, PSO algorithm is compared to GA and the results show that cost function and convergence time in PSO is less than GA in the various weight coefficients scenarios. This optimal mode leads to the maximum reduction of gas consumption in the gas power plant, and on the other hand, the amount of pollution is minimized. The prediction of this number of power plants with different priorities is presented in this article and different policies can be considered strategically for this model of the supply chain.
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模拟太阳能发电厂电力供应链向可再生能源的消耗
本文旨在引入太阳能发电厂电力供应链模型,包括混合发电厂、输电线路和消费者,重点关注优化和考虑不确定性。在本文中,根据各种参数对太阳能发电厂的供应链进行了描述。发电厂和太阳能电池板的数量由不同的优先级决定,如投资水平、污染缓解和减少传统发电厂的天然气消耗,利用粒子群算法获得最佳结果。该模型通过应用2型模糊逻辑,解决了与电力需求、太阳辐射水平以及太阳能电池板发电相关的不确定性。模型的优化是在考虑各种限制条件的情况下完成的,包括电力供应和太阳能电池的最大允许使用。本文的创新之处在于从电力生产的不确定性和消费者需求量的角度出发,设计了供应链模型,并对太阳能发电厂的太阳能电池板进行了优化选择,使燃气发电厂的电力消耗和由此产生的污染量最小化。根据本文的模拟结果表明,考虑到最大投资能力,在离案例周围变电站一定距离的地方建造5座太阳能发电厂,可提供高达76%的电能。考虑到CO2厂排放和燃气消耗的最大权重系数,5个太阳能厂是该算法实现的最优数量。5座太阳能电站的优化容量分别为4.100、4.222、3.920、4.375、3.991MW。为了对所提出的模型进行评价,将粒子群算法与遗传算法进行了比较,结果表明,在各种权重系数场景下,粒子群算法的代价函数和收敛时间都小于遗传算法。这种最优模式一方面最大限度地减少燃气电厂的用气量,另一方面使污染量最小化。本文提出了对不同优先级的发电厂数量的预测,并且可以针对该供应链模型考虑不同的策略。
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