Abstract
At the heart of Covid-19 responses, the transition from fossil sources to green energy is an urgent issue for nations to address the crisis and secure sustainable economies. As a country in a seismically active zone that relies heavily on imported fossil fuels, Taiwan is vigorously taking the next step in renewable energy development, which is pivotal to securing its position in global supply chains. Solar energy is today the most suitable renewable energy source for Taiwan. However, land prices and policies, and challenges of scale still hinder its development. In this context, identifying optimal sites for solar photovoltaic (PV) construction is a crucial task for major energy stakeholders. In this paper, a two-stage approach, combining the data envelopment analysis (DEA) models and the analytic hierarchy process (AHP), has been done for the first time to identify the most suitable locations among 20 potential cities and counties of Taiwan for constructing solar PV farms. DEA models were applied to filter out the areas with the most potential by measuring their efficiency indices with temperature, wind speed, humidity, precipitation, and air pressure, as inputs, and sunshine hours and insolation, as outputs. The locations with perfect efficiency scores were then ranked with the AHP method. Five selected evaluation criteria (site characteristics, technical, economic, social, and environmental) and sub-criteria of each were utilized to prioritize the locations with solar energy potential. AHP was used to determine the relative weights of the criteria and sub-criteria and the final weights of the areas. For criteria weighting results, “support mechanisms,” “electric power transmission cost,” and “electricity consumption demand” with weights of 0.332, 0.122, and 0.086, respectively, were found as the most significant sub-criteria. The final ranking suggests Tainan, Changhua, and Kaohsiung as the top three most suitable cities for constructing solar PV energy systems.
Highlights
Wind has a cooling effect that enhances the efficiency of PV modules, where 1 degree Celsius decrease in the temperature of the solar module can result in a 0.5% increase in efficiency [74]
This study aims to provide an effective guideline to facilitate the analysis of large areas to filter out a few high-efficiency sites that can be studied in more detail
By combining the two multi-criteria decision-making (MCDM) models data envelopment analysis (DEA) and analytic hierarchy process (AHP), large areas can be analyzed according to different criteria
Summary
A. GLOBAL RENEWABLE ENERGY SITUATION Catastrophic dependence on fossil fuels of the world for energy demand has so far created 60% of total global greenhouse gas emissions, the major cause of warming effects [1]. The associate editor coordinating the review of this manuscript and approving it for publication was Alba Amato. Towards this end, many countries are aiming for 100% renewable electricity by 2045 or 2050, along with Europe, which announced the European Green Deal in 2019, intending to reduce net greenhouse gas emissions to zero by 2050 [3]. The benefits of the transition to renewable energy systems are thereby indisputable for many countries to advance economic development, enhance energy access, and mitigate climate change. Electricity is the cornerstone, yet hundreds of millions worldwide remain unreachable [4]
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