Pricing Optimization in Shared Community Energy Storage Systems: A Bilevel Approach

This thesis explores the design of a pricing mechanism for a shared energy storage system within a residential community including diverse households, each with distinct energy consumption patterns and optional rooftop solar generation. The core objective is to develop a fair and efficient pricing strategy that enhances overall community welfare while accommodating two operational models: a profit-oriented storage operator and a non-profit alternative. To achieve this, the system is modeled using a bilevel optimization framework. At the upper level, the storage manager sets energy prices, and at the lower level, households respond by optimizing their energy usage based on economic incentives. The model incorporates key constraints such as load flexibility, appliance scheduling, and storage limitations. Real-world data from Waterloo, Ontario, are used to simulate realistic summer and winter scenarios. Simulations account for real-time electricity pricing, photovoltaic generation, and thermal load modeling. Different levels of household flexibility are also explored, including scenarios with restricted appliance usage windows and simplified time-of-use price tariffs. The results reveal that while profit-driven operation can yield variable revenue for the storage operator across seasons, it also provides tangible cost savings for households. Notably, the nonprofit model further increases the households’ economic benefit, demonstrating up to a 10–20% increase in cost savings compared to baseline grid use. Additionally, the study shows how pricing structures influence household behavior, shifting consumption patterns in ways that reduce peak demand and enhance grid stability.