ArticleScientific reports2026
Evolutionary game-theoretic modeling of electricity market dynamics for elastic load optimization.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Modern electricity markets are increasingly shaped by the proliferation of distributed energy resources, dynamic pricing schemes, and elastic load behavior. Existing approaches lack behavioural adaptivity and scalability under dynamic market conditions. This study introduces a novel Evolutionary Adaptive Genetic Algorithm (EAGA) framework designed to model strategic interactions among heterogeneous actors-residential consumers, prosumers, aggregators, and utility providers-engaged in real-time energy consumption decisions. The framework synergizes Replicator Dynamics, Evolutionary Algorithms, and multi-agent simulation to enable agents to evolve optimal load-shifting or consumption strategies based on payoff feedback, grid constraints, and peer influence. The model incorporates real-time electricity price signals, consumption elasticity, and local grid constraints, creating a behaviorally realistic and adaptive DR environment. Compared to WOA, PSO, BAT, and DE, EAGA demonstrates faster convergence and superior optimization performance, achieving a final fitness score of 1.40 at 500 iterations, where the fitness function represents a composite objective of energy cost, peak load, and grid stability, formulated as a minimization problem (lower values indicate better performance), outperforming WOA (1.42), BAT (1.58), PSO (1.60), and DE (1.60). Key operational benefits include 27.0% energy cost reduction, 23.1% peak load reduction, 57.5% increase in renewable energy utilization, and a 25.6% improvement in load shifting accuracy. Moreover, the curtailment rate is reduced by 54.7%, and grid stability is enhanced through lower voltage and frequency deviations. The proposed EAGA model presents a scalable and adaptive solution for enhancing smart grid resilience and economic performance. It holds direct relevance for automated demand-side management systems, tariff mechanism design, and regulatory energy policy development in decentralized and prosumer-dominated energy markets.
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