Abstract
Distributed energy resources, especially residential behind-the-meter photovoltaics (BTM PV), have been playing increasingly important roles in modern smart grids. Residential netload, which is closely tied with customers' gross load consumption and weather, is usually the only data available for the market operator in a local electricity market (LEM). This paper seeks to design customized prices for an LEM that consists of an agent, BTM PV, energy storage (ES), prosumers, and consumers. The LEM agent who owns a community-scale ES system is responsible for operating the market, determining the internal price, and facilitating the energy sharing within the community. A hierarchical energy trading infrastructure is considered, where the LEM agent acts as the mediator between the external utility grid and customers. A two-stage decision-making framework, including both look-ahead ES scheduling and real-time customized price design, is developed for the agent's profit maximization. Besides, the impacts of netload forecasting and BTM PV disaggregation are also investigated. The customer's consumption behavior is modeled as a utility maximization problem. Compared with the benchmark uniform price design, it is found that the customized pricing scheme could further improve the LEM agent's profit by 4% to 130%, depending on the weather conditions and seasonal load patterns.
Published Version
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