A Demand Charge is a fee that utility companies charge customers based on their highest rate of electricity usage during a specific period, typically a month. It is separate from the energy charge, which is based on the total electricity consumed. Demand charges are designed to reflect the costs utilities incur to ensure there is enough capacity to meet peak demand. These charges incentivize customers to reduce their peak energy usage and shift it to off-peak times, helping to balance grid load and avoid infrastructure strain.

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Demand Charge and Response with Energy Storage

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Commercial and industry (C& I) customers incur two types of electricity charges on their bills: one for the amount of energy usage and another one for the maximum demand during certain billing periods. The second charge type is known as Demand Charge (DC), which could account for over half of a customers’ electricity bill. Those C& I customers often sign up for Demand Response (DR) programs to contribute to peak demand reduction as well as to receive incentives and rewards from participating in the programs. The critical factor of achieving both DR and DC reduction is to recognize the nature of these two types of problems and create an effective strategy that can handle them at the same time by which the benefits from DR incentives and DC reduction are maximized. This paper discusses the possible DR scenarios with DC reduction framework for C& I customers who use a Behind-the-Meter (BTM) energy storage and proposes a consistent real-time procedure of deciding battery’s charging and discharging set points to solve the problem of maximizing the rewards by conducting DRs as well as the savings by reducing DC costs.

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Battery Optimal Approach to Demand Charge Reduction in Behind-The-Meter Energy Management Systems

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Large monthly demand charge of commercial and industrial entities is a major problem for their economical business. Utilizing a battery by behind-the-meter Energy Management Systems (EMS) has been seen as a solution to demand charge reduction. In state-of-the-art approaches, the EMS maintains sufficient energy for the unexpected large demands and uses the battery to meet them. However, large amount of energy stored in the battery may increase the average battery State-of-Charge (SoC) and cause degradation in battery capacity. Therefore, the current approaches of demand charge reduction significantly shortens the battery lifetime which is not economical. In this paper, we propose a novel battery optimal approach to reduce the monthly demand charges. In our approach, load profile of the previous month is used by daily optimizations to shave daily power demands while considering the battery lifetime model. Evaluated daily demand thresholds and load profile are statistically analyzed to cluster different types of day. Hence, it helps the EMS to find the typical daily load profile and appropriate monthly demand threshold for the entity. The performance of our approach has been analyzed and compared to the state-of-the-arts by experimenting on multiple real-life load profiles and battery configurations. The results show significant reduction of 16% in annual average battery SoC that increases the battery lifetime from 4.1 to 5.6 years while achieving up to 13.4% demand charge reduction.

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