Critical Peak Pricing Optimization Considering Photovoltaic Utilization and Consumer Heterogeneity

To support the sustainable transition of power systems with high penetration of renewable energy, this study proposes an optimization model for critical peak pricing (CPP) that integrates photovoltaic (PV) utilization and consumer heterogeneity. With the aim of improving renewable energy consumption, reducing carbon emissions, and enhancing the long-term sustainability of distribution networks, electricity consumers are classified according to their diverse behavioral characteristics, and a differentiated CPP mechanism is designed accordingly. Time periods are dynamically segmented using fuzzy membership functions based on net load curves, enabling price signals to better align electricity demand with PV generation profiles. Consumer psychology is further incorporated to develop a user response model that reflects heterogeneous demand-side behavior. A multi-objective CPP optimization framework is established to balance the economic interests of electricity consumers, retailers, and other stakeholders, while simultaneously promoting renewable energy integration and system-level sustainability. The proposed model is solved using a genetic algorithm. Case studies demonstrate that the approach effectively smooths net load curves, encourages electricity consumption during periods of high PV output, improves economic benefits for all participants, and enhances carbon emission reduction performance. Finally, a sensitivity analysis under multiple scenarios is conducted to evaluate the robustness and sustainability implications of the proposed CPP mechanism.