Abstract
Renewable energy systems have gained significant attention in recent years due to their potential to mitigate climate change and reduce reliance on fossil fuels. However, ensuring the efficient utilization of resources and optimizing the performance of these systems remain critical challenges. This chapter focuses on applying data envelopment analysis (DEA) as a valuable tool for assessing and optimizing the efficiency of renewable energy systems in various scientific, information technology, and engineering contexts. The chapter overviews DEA principles, methodologies, and models and explores its specific application in renewable energy systems. The integration of DEA with other analytical tools, such as life cycle assessment (LCA) and optimization techniques, is also examined. The chapter highlights data availability and quality challenges and identifies future research directions in DEA for renewable energy systems.
TopBackground
The burning of fossil fuels for energy generation is a major contributor to greenhouse gas emissions, leading to global warming and its associated consequences, including extreme weather events, rising sea levels, and loss of biodiversity. Traditional fossil fuels such as coal, oil, and natural gas are finite resources. As they become scarcer, their prices rise, and their extraction becomes environmentally damaging and economically unsustainable. Renewable energy systems, such as solar, wind, hydro, and geothermal power, offer a sustainable and clean alternative. These sources are virtually inexhaustible and produce little to no greenhouse gas emissions during energy generation.
The efficient operation of renewable energy systems (see Figure 1) is critical for their effectiveness and widespread adoption. Maximizing efficiency ensures that these systems produce the most energy from available resources, reducing waste and improving their economic viability. Efficiency assessment helps in directing resources, investments, and incentives toward the most efficient technologies and practices within the renewable energy sector. This allocation ensures that investments yield the best returns in terms of energy production. Assessing and optimizing the efficiency of renewable energy systems aligns with international sustainability goals, including the United Nations' Sustainable Development Goals. It promotes responsible resource use, environmental stewardship, and the mitigation of climate change. Efficient renewable energy systems are not only environmentally friendly but also economically competitive with traditional fossil fuels. This competitiveness is crucial for the long-term affordability of clean energy. Renewable energy systems contribute to energy security by diversifying energy sources, reducing dependence on fossil fuel imports, and enhancing a nation's resilience to energy supply disruptions.
DEA plays a crucial role in evaluating and optimizing renewable energy systems by providing a comprehensive framework for assessing their efficiency and identifying areas for improvement. DEA allows for the quantification of efficiency in renewable energy systems. It assesses how effectively these systems convert inputs, such as sunlight, wind, or water, into useful energy output. Efficiency scores provide a clear picture of how well a system is performing relative to its peers. By comparing the performance of various renewable energy systems, DEA helps identify best practices and benchmarks. It pinpoints which technologies or operational strategies are the most efficient, enabling others to learn from and replicate their success. DEA assists in the allocation of limited resources, such as funding, manpower, and land, to maximize energy output. Decision-makers can use DEA results to prioritize investments in the most efficient technologies or geographic locations. It evaluates the operational efficiency of renewable energy facilities, including power plants and solar arrays. DEA can identify bottlenecks or areas where improvements in processes or maintenance are needed to enhance efficiency. DEA provides insights into how to optimize renewable energy systems. By analyzing the relationships between inputs and outputs, it offers guidance on the ideal mix of resources, equipment, and operational procedures to achieve maximum efficiency.
Key Terms in this Chapter
Machine Learning (ML): ML is a subset of artificial intelligence (AI) that focuses on developing algorithms and statistical models enabling computer systems to improve their performance on a specific task through learning from data and experiences. ML algorithms enable computers to identify patterns, make predictions, and improve their decision-making without being explicitly programmed for each task. It finds applications in various fields, including data analysis, pattern recognition, natural language processing, and autonomous systems.
Photovoltaic (PV): PV refers to the technology that converts sunlight into electricity using semiconductor materials, typically silicon solar cells. When exposed to sunlight, these cells generate a direct electrical current, making PV systems a clean and renewable source of energy commonly used in solar panels for electricity generation.
Data Envelopment Analysis (DEA): DEA is a quantitative method used for evaluating the relative efficiency and performance of decision-making units, such as organizations, processes, or systems. It assesses how well these units convert multiple inputs into outputs and identifies areas for improvement by comparing them to efficient peers or benchmarks. DEA is particularly useful for optimizing resource allocation and enhancing efficiency in various sectors, including finance, healthcare, and renewable energy.
Life Cycle Assessment (LCA): LCA is a systematic and comprehensive method for evaluating the environmental impacts of a product, process, or service throughout its entire life cycle, from raw material extraction to disposal. LCA quantifies the environmental aspects, including resource use, emissions, and energy consumption, to support more sustainable decision-making and reduce the environmental footprint of products and systems.
Decision-Making Units (DMUs): DMUs are entities or organizations that are subject to evaluation and comparison in Data Envelopment Analysis (DEA). These units can represent various entities like companies, institutions, or departments, and they are assessed based on their input and output parameters to determine their relative efficiency and performance. DEA helps identify DMUs that are operating efficiently and those that may need improvements in resource utilization or productivity.