Optimal Scheduling for Behind-the-Meter Batteries under Different Tariff Structures

The increasing deployment of photovoltaic systems and behind-the-meter batteries into power distribution systems has increased interest in optimal system operating conditions. Electricity tariff, as an indirect factor, plays a pivotal role in controlling the customers' behavior, especially in the presence of batteries. The residential sector, as one of the largest consumers, requires accurate analysis of the impacts of tariffs on its load profile for short-term and long-term planning. In this paper, a household equipped with a photovoltaic array and battery is modeled and the effects of flat-rate, stepped rate, time-of-use, and demand charge pricing structures on the battery charge/discharge model are analyzed. Furthermore, the effects of COVID-influenced consumption patterns and the increase in feed-in tariff for photovoltaic energy on battery scheduling are investigated. The battery scheduling problem is formulated as a non-linear optimization function, to minimize electricity costs for customers, and is solved using a Genetic algorithm.