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Evolutionary game-theoretic modeling of electricity market dynamics for elastic load optimization
1Department of Electrical and Electronics Engineering, V.R.S. College of Engineering and Technology, Arasur, Villupuram District, Tamil Nadu, 607107, India. vimalprakash.r@gmail.com.
Scientific Reports
|June 13, 2026
Summary
A new Evolutionary Adaptive Genetic Algorithm (EAGA) improves energy management in smart grids by enabling consumers and providers to adapt consumption strategies. This leads to significant cost and peak load reductions, boosting renewable energy use and grid stability.
Area of Science:
- * Energy Systems Engineering
- * Computational Economics
- * Artificial Intelligence
Background:
- * Modern electricity markets face challenges from distributed energy resources, dynamic pricing, and elastic loads.
- * Existing approaches struggle with behavioral adaptivity and scalability in dynamic market conditions.
- * Decentralized energy markets require advanced solutions for managing heterogeneous actors and real-time decisions.
Purpose of the Study:
- * To introduce a novel Evolutionary Adaptive Genetic Algorithm (EAGA) framework for modeling strategic interactions in real-time energy consumption.
- * To enable heterogeneous agents (consumers, prosumers, aggregators, utilities) to evolve optimal load-shifting strategies.
- * To create a behaviorally realistic and adaptive demand response (DR) environment considering grid constraints and peer influence.
Main Methods:
- * Synergistic integration of Replicator Dynamics, Evolutionary Algorithms, and multi-agent simulation.
- * Development of a novel Evolutionary Adaptive Genetic Algorithm (EAGA) framework.
- * Modeling of agent strategy evolution based on payoff feedback, grid constraints, and real-time price signals.
Main Results:
- * EAGA demonstrated faster convergence and superior optimization compared to WOA, PSO, BAT, and DE, achieving a fitness score of 1.40.
- * Achieved significant operational benefits: 27.0% energy cost reduction, 23.1% peak load reduction, 57.5% increase in renewable energy utilization.
- * Enhanced grid stability with reduced voltage/frequency deviations and a 54.7% reduction in curtailment rate.
Conclusions:
- * The EAGA model offers a scalable and adaptive solution for enhancing smart grid resilience and economic performance.
- * Provides a behaviorally realistic framework for demand response in dynamic electricity markets.
- * Direct relevance for automated demand-side management, tariff design, and energy policy in prosumer-dominated markets.
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