Game theory-based demand-side management for efficient energy collaboration in smart networks: a neighborhood-scale optimization framework

This paper presents a novel demand-side management (DSM) framework that leverages game theory for neighborhood-scale energy applications, marking a significant advancement in the integration of electrical and natural gas systems through smart network technologies. One of the main contributions of this research is the development of a unique tariff model for real-time retail pricing, which allows consumers to make more informed decisions about their energy use. Furthermore, the framework includes an optimization model that coordinates building loads and renewable energy resources, focusing on minimizing energy costs while maintaining comfort levels. This model also promotes energy sharing among neighbors by tapping into the flexibility offered by distributed energy resources (DER) and renewable sources. A distinctive feature of the proposed approach is its ability to adjust user profit margins based on deviations from expected consumption patterns, providing a new way to incentivize energy efficiency. To address this problem, a new modified version of the particle swarm optimization (PSO) algorithm is proposed, which enhances both local and global search capabilities through adaptive parameter control and mutation strategies. The effectiveness of this framework is validated through extensive case studies using real building consumption data from Sydney, Australia. The results demonstrate a reduction in community peak-to-average ratios (1.85% in winter and 1.69% in summer) and significant cost savings (9.48% in summer and 9.72% in winter) compared to traditional non-cooperative strategies. Overall, this research contributes valuable insights into community-level energy management, providing a solid foundation for future developments in smart network technologies.