A game-theoretic framework for cluster-based dynamic electricity pricing in high-penetration renewable networks

Dynamic pricing is increasingly important for managing the variability of small- to medium-scale photovoltaic (PV) generation and for activating demand-side flexibility in modern distribution networks. Despite this, most energy retailers continue to rely on uniform or static time-of-use tariffs that inadequately reflect temporal demand variation and consumer heterogeneity, leading to limited prosumer engagement and persistent revenue imbalances. To address this gap, this paper develops a scalable framework for designing customized, interval-level electricity tariffs that jointly balance retailer profitability and consumer welfare in renewable-rich environments. The core contribution of this work is a cluster-based, hierarchical leader–follower game-theoretic model in which the retailer optimizes interval-based demand charges, feed-in incentives, and wholesale procurement decisions, while heterogeneous prosumers strategically schedule behind-the-meter PV generation and shift flexible demand in response to announced prices. Consumer diversity is explicitly captured through an unsupervised learning approach that dynamically clusters users based on consumption patterns and flexibility characteristics, enabling equitable and proportionate tariff allocation without excessive model complexity. The results demonstrate that the proposed dynamic tariffs reduce prosumer energy costs by up to 30% while increasing retailer profit margins by approximately 50% compared with conventional flat-rate and peak-pricing benchmarks. These gains are achieved through improved PV self-consumption, enhanced load shaping, and cost-reflective pricing that aligns consumer behavior with system-level objectives. Overall, the proposed methodology provides a practical and implementable pathway for dynamic tariff design that enhances economic efficiency, equity, and financial robustness in distribution networks with high PV penetration, supporting the broader transition toward flexible and consumer-centric electricity markets. • Game-theoretic tariff design aligns retailer profit with prosumer flexibility. • Adaptive cluster-based pricing offers fairness and transparency in cost recovery. • Tariff robustness enhanced by probabilistic PV uncertainty modeling. • Closed-loop revenue matching balances retailer revenue with consumer affordability. • Demonstrated superiority over flat-rate and time-of-use tariff schemes.