Abstract
With the progress in renewable energy and smart grid technologies, electricity users are evolving into prosumers, capable of both consuming and generating electricity through distributed photovoltaic (DPV) systems. Concurrently, the liberalization of the electricity retail market has prompted retailers to design customized electricity packages based on users’ needs and preferences, aiming to enhance service quality, efficiency, and user retention. However, previous studies have not fully addressed the multidimensional characteristics and electricity consumption behaviors that influence package selection. This paper initially dissects user characteristics across three key dimensions: electricity demand preferences, price sensitivity, and risk tolerance. Therefore, leveraging utility functions and autonomous choice behavior models, we propose two innovative electricity purchase and sale combination packages: a fluctuating pricing package and a discount-based pricing package. Furthermore, we introduce the Self-Adaptive Weight and Reverse Learning Particle Swarm Optimization (SAW&RL-PSO) algorithm to address the complexities of these choices. Simulation results indicate that the methodologies presented significantly enhance user benefits and retailer revenues while also effectively managing electricity usage fluctuations and the challenges of integrating large-scale DPV systems into the electrical grid.
Published Version
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