Modeling solar power plant electricity supply chain toward renewable energy consumption - Mohammad Reza Eslami Rasekh; Farzad Mohammad Sharifi; Somaieh Alavi; Nassibeh Janatyan (e-Prime: Advances in Electrical Engineering, Electronics and Energy)
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.