Leveraging Game Theory to Design Incentive-Compatible Time-Varying electricity pricing with Demand-Side management

Abstract

This study investigates a novel approach to improve energy efficiency through a Demand Response (DR) program with a Game Theory (GT)-based Time-of-Use (ToU) pricing model. While traditional DR programs encourage consumption shifts towards off-peak periods, they utilize a flat pricing structure. This means all users pay the same regardless of their individual load contribution during peak times, where prices fluctuate based on demand exceeding generation capacity. The proposed GT-based ToU model addresses this by establishing dynamic on-peak and shoulder-peak hour rates tailored to each user’s consumption profile. This personalized pricing incentivizes a more targeted shift away from peak hours, potentially leading to further efficiency gains. The model’s effectiveness is evaluated against the existing ToU system and the current day-ahead real-time pricing scheme. Additionally, the study acknowledges the potential for increased demand during off-peak hours due to load shifting. To address this, the influence of two optimization algorithms, Genetic Algorithm (GA) and Archimedes Optimization Algorithm (AOA), on user electricity bills and peak-to-average ratio following load scheduling is examined. The research concludes by demonstrating the superiority of the GT-based ToU model and highlighting AOA’s superior performance compared to GA in optimizing these factors.

Keywords

• Demand response program
• Pricing scheme
• Archimedes optimization algorithm
• Game theory
• Meta-heuristic optimization