Multi-Objective Optimal Design of Distributed Energy Systems for Resilience and Sustainability

摘要

This research article highlights the critical role of Distributed Energy Systems (DES) in the global energy transition towards carbon neutrality. While much existing work focuses on optimizing DES for economic and environmental benefits under normal conditions, it often overlooks the importance of resilience to extreme events, such as natural disasters. This paper addresses that gap by proposing a multi-objective optimization design framework that integrates resilience as a key objective alongside economic efficiency and environmental performance. The framework centers on three main goals: System Resilience: Resilience is quantified by the Expected Energy Not Supplied (EENS) during an extreme event scenario, specifically a prolonged grid outage caused by a typhoon. This measures how well the system can maintain energy supply during disruptions. Economic Efficiency: The economic performance is evaluated through the Total Annualized Cost (TAC), which reflects the financial feasibility of the system. Environmental Performance: Environmental impact is assessed by the total annual CO2 emissions associated with the system. The optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a method commonly used in multi-objective optimization problems. The case study focuses on a coastal industrial park in southeastern China, which is highly susceptible to typhoons. By using reproducible load and renewable energy resource profiles, the study demonstrates the trade-offs between these three objectives. Key findings include: Resilience and Investment Costs: Enhancing resilience requires larger capacities of energy storage and renewable generation, leading to higher initial investment costs. Cost-Optimal Configurations: A system designed purely for cost optimization might be vulnerable during extreme events due to insufficient reliability in energy supply. Pareto-Optimal Frontier: The analysis provides a set of optimal system configurations that offer a balance between resilience, cost, and environmental performance, allowing decision-makers to make choices based on their specific risk preferences. The study makes a significant contribution by elevating resilience from a mere constraint or post-assessment metric to a primary optimization objective, placing it on equal footing with economic and environmental goals. This approach offers a scientific and quantitative tool for designing DES that are not only economically viable and environmentally sustainable but also resilient, which is crucial for ensuring energy security in the face of climate change. This framework is valuable for decision-makers in planning and designing future energy systems that need to be robust, sustainable, and resilient, especially in regions vulnerable to extreme weather events like typhoons.