The Intelligent Optimization of Energy Systems and Social Value: The Contribution of Edge Computing Technology to Sustainable Development Goals

Abstract

As global energy demand continues to grow and environmental challenges escalate, traditional energy management models face significant challenges. Smart optimization of energy systems has emerged as a critical approach to drive energy transition and sustainable development. Edge computing, as an emerging technology, decentralizes data processing to the edge nodes of energy systems, enabling real-time data collection, processing, and intelligent decision-making, which enhances the efficiency and accuracy of energy management. This study focuses on the application of edge computing in energy systems, exploring how intelligent optimization strategies can improve energy efficiency, reduce costs, and promote a green low-carbon transformation. The research analyzes key application areas of edge computing in energy systems, such as smart grids, energy resource management, load scheduling, and demand response. By utilizing real-time monitoring and big data analytics, edge computing optimizes energy distribution, balances loads, predicts energy demand, and automatically adjusts system operations, thereby improving system responsiveness. The study also examines edge computing's contribution to sustainable development goals, particularly in advancing a green low-carbon economy, enhancing energy system flexibility, and addressing climate change. Additionally, the socio-economic impact of edge computing is explored, highlighting its potential to drive industrial upgrading, create jobs, and stimulate economic development. This research underscores the importance of edge computing in the energy internet and proposes a strategic framework for the integration of energy systems and sustainable development, offering both theoretical insights and practical guidance for global energy transformation.