Regarding the Energy-Saving Electricity Consumption Model for Middle-Income Households

Abstract

Against the backdrop of global carbon peaking and carbon neutrality initiatives, green residential energy consumption has become a core component of grid demand response and household expenditure management. Time-of-use (TOU) and tiered electricity pricing policies are widely deployed in Chinese cities to incentivize peak load shifting and restrain excessive residential power consumption. Nevertheless, conventional household load scheduling models only pursue minimum electricity bills without considering residents' living comfort, leading to unrealistic load shifting schemes that cannot be implemented in daily life. To address this research gap, this study establishes an integrated household electricity cost calculation model adapted to Yangzhou's dual TOU-tiered tariff mechanism, with quantitative user comfort requirements embedded into multi-objective optimization. This model innovatively allows segmented transfer of peak-period appliance loads: part of peak consumption can be shifted to flat hours, and the remainder to valley hours, rather than complete one-way load transfer. A quantitative comfort indicator is proposed to measure the deviation between adjusted appliance operating windows and household habitual usage periods, eliminating the over-shifting defect of unconstrained optimization frameworks. Two mainstream intelligent metaheuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are adopted to solve the bi-objective optimization problem of minimizing electricity expenditure while maximizing user comfort. Numerical experiments based on real Yangzhou tariff data and typical household appliance parameters verify the performance of both algorithms. The results demonstrate that PSO achieves faster convergence with identical optimal daily electricity cost of 13.0 CNY, while maintaining a lower comfort deviation index of 1.7 hours within the acceptable comfort threshold of 2 hours. Compared with original unoptimized consumption schedules, the proposed model reduces daily electricity bills by 28.6%, cuts peak load proportion from 45% to 28%, and downgrades annual power consumption from the third high-cost tier to the second tier, generating substantial long-term economic savings for middle-income households. This research provides practical, life-friendly load scheduling guidelines for residents and offers quantitative policy evaluation references for local power utilities to promote demand response and energy conservation.