Mitigating Short-Term Variations of Photovoltaic Generation Using Energy Storage with VOLTTRON

Abstract

A smart-building communications system performs smoothing on photovoltaic (PV) power generation using a battery energy storage system (BESS). The system runs using VOLTTRON™, a multi-agent python-based software platform dedicated to power systems. The VOLTTRON™ system designed for this project runs synergistically with the larger University of Washington VOLTTRON™ environment, which is designed to operate UW device communications and databases as well as to perform real-time operations for research. One such research algorithm that operates simultaneously with this PV Smoothing System is an energy cost optimization system which optimizes net demand and associated cost throughout a day using the BESS. The PV Smoothing System features an active low-pass filter with an adaptable time constant, as well as adjustable limitations on the output power and accumulated battery energy of the BESS contribution. The system was analyzed using 26 days of PV generation at 1-second resolution. PV smoothing was studied with unconstrained BESS contribution as well as under a broad range of BESS constraints analogous to variable-sized storage. It was determined that a large inverter output power was more important for PV smoothing than a large battery energy capacity. Two methods of selecting the time constant in real time, static and adaptive, are studied for their impact on system performance. It was found that both systems provide a high level of PV smoothing performance, within 8% of the ideal case where the best time constant is known ahead of time. The system was run in real time using VOLTTRON™ with BESS limitations of 5 kW/6.5 kWh and an adaptive update period of 7 days. The system behaved as expected given the BESS parameters and time constant selection methods, providing smoothing on the PV generation and updating the time constant periodically using the adaptive time constant selection method.