Abstract
To promote the realization of the “dual carbon target”, the implementation of rooftop distributed photovoltaic (RDPV) projects in the whole county will usher in large-scale development in China. However, comprehensive investigations into the economic benefits of RDPV projects in the whole county are still rare. This paper thus aims to evaluate the economic benefits of RDPV projects when considering multiple factors. The net present value (NPV), internal rate of return (IRR) and payback period (PBP) were used to analyze the financial feasibility of RDPV projects. The particle swarm optimization (PSO) algorithm was adopted to optimize the economic benefits of RDPV projects by calculating the different installation ratios of different building types, including industrial and commercial buildings, government buildings, public buildings, and residential buildings. A case study verified the feasibility of the optimization model of the economic benefits of RDPV projects in the whole county in China. The results revealed that the self-use ratio of power generation greatly impacted the economic benefits of RDPV projects and a single RDPV project became economically feasible when the self-use ratio of power generation was greater than 87.753%. When considering the limitations of the self-use ratio of power generation, installed capacity, and grid connection mode of different building types, compared to the single RDPV project, the economic benefits of RDPV projects on a county scale were not financially feasible based on the optimization model, which had an NPV of about −10.35 million yuan. Industrial and commercial buildings had the highest proportion of RDPV systems installed among the four building types, while residential buildings had the lowest proportion. The current results demonstrate the economic benefits of RDPV projects in the whole county, which will promote the development of RDPV projects and reduce carbon emissions in China. Therefore, this study is expected to provide investors of RDPV with recommendations for both decision-making and making scientific plans for the government.
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