A Heuristic Approach to Operations and Control of BTM-DERS for Bill Management and Grid Services

Behind-The-Meter (BTM) distributed energy resources (DERs) have emerged as a critical and transformative force within the energy sector. These decentralized energy assets, which include solar photovoltaic (PV) systems, battery energy storage systems (BESS), and thermostatically controlled loads (TCLs), are increasingly essential for empowering customers by granting them greater control over their energy production and consumption, thereby reducing reliance on centralized power sources. Additionally, they have the potential to play a pivotal role in enhancing grid resilience by providing grid services. This study investigates the management of customer electricity bills and grid services through the integration of various BTM-DERs, particularly solar photovoltaic (PV) systems, BESS, air conditioning systems with smart thermostats, and grid-interactive water heaters, under variable electricity rate. A heuristic optimization approach that leverages the coordinated operation and control of these DERs to minimize customer electricity costs and improve grid services is proposed. Recognizing the potential barrier of high upfront costs for residential customers, this research explores Energy-Storage-as-a-Service (ESaaS) as an alternative to individual BTM-BESSS ownership. Similar to community energy storage (CES), ESaaS provides virtual energy storage through a centralized system located in front of the meter, enabling customers to lease storage capacity. By effectively harnessing photovoltaic power generation, optimizing battery charging and discharging cycles, and strategically scheduling thermostat settings for controllable loads, this approach aims to achieve a balance between maximizing cost savings for customers and enhancing overall grid efficiency and reliability. This work presents practical mathematical models for rooftop photovoltaic (PV) systems, battery storage, air conditioners, and electric water heaters. Day-ahead forecasting techniques were then employed to predict customer generation and consumption profiles, which are crucial for planning DER control strategies. Local weather forecasts from the National Oceanic and Atmospheric Administration (NOAA) and customer smart-meter data were utilized as inputs for these predictions. The study addresses two key factors by formulating optimization functions subject to various constraints: Customer Bill Management (CBM) which aims to minimize the customer's electricity bill under time-of-use electricity pricing which coincides with system Peak Load Management (PLM) as a grid service. To validate the effectiveness of the proposed approach, a case study is presented based on a local residential customer's smart-meter data and appliance power consumption. Customer’s annual electricity bill is evaluated under both flat rate and time-of-use pricing after leasing an ESaaS block are presented to support the efficacy of the proposed approach.