Powernet : a cloud-based environment for behind the meter resources coordination

Grid decarbonization and resilience demands are increasing the electrification of consumers and the adoption of distributed energy resources such as energy storage systems, electric vehicles, solar photovoltaic, wind and smart loads e.g., heating and ventilation air conditioning systems, water heaters, and electric motors. As electrification increases the load, the electrical infrastructure needs to support in the transmission and distribution systems that increase in power. This has the potential to overload local grid infrastructure and create reliability issues. Moreover, the poor correlation between renewable generation and load consumption further increases the challenge grid operators face today. However, the complementary nature of various distributed energy resources together with advancements in communication technologies, increase in local and cloud-based computing power, and sensing technologies, allows coordination of such resources to support grid operation. This dissertation focuses on developing a complete environment that is capable of design, test and field deploy solutions to support grid operation while at the same time enabling decarbonization of the grid and other economic sectors such as transportation. Developing this environment is the first step in this process. The Powernet environment consists of two state-of-the-art laboratory facilities designed from the ground-up to support the conceptualization, design and test of solutions and algorithms for coordination. The Powernet Labs, as they are called, were designed to include various off-the-shelf and prototype equipment to enable students and researchers to develop solutions that can be transferred to real-world application. These experimental facilities are integrated and operated via the Powernet Platform. The Powernet platform is an end-to-end, cloud-based system for cost effective, scalable and secure coordination of behind of the meter (BTM) resources within and across multiple consumer sites. It is a cloud-based software system that integrates disparate operational data and coordinate equipment utilization to automate operation and provide insights about the system. It enables different cloud-based coordination algorithms to be tested and evaluated. The platform is hardware agnostic and controller gateways were developed to interface the hardware in the field. The Powernet environment was developed to enable users with different backgrounds to leverage the complete system capabilities. Abstraction of low level software code and development of an application programming interface, simple user interface, and safe connection of electrical equipment are all features of this ecosystem. A major goal of this dissertation is translational research and how to deploy the Powernet platform in real-world applications. We discuss three relevant use-cases, their challenges, and performance. We start by briefly discussing the application in the residential sector and how coordination of battery energy storage system and house load can reduce electricity cost and reliance on grid power. The second use-case, targeting the agriculture sector, focuses on coordination of ventilation loads, battery energy storage system and rooftop solar to reduce electricity cost in a large dairy farm in California. We demonstrated that using distributed sensors and our novel coordination algorithm, the Powernet platform was able to reduce electricity cost by up to 92% and emissions related to grid usage by up to 93%. The third use-case focuses on a fleet of electric buses and the coordination of vehicle charging with solar, constrained by fleet route schedules and electricity rate structure. We show the potential reduction of electricity cost by up to 87% when coordinating solar with charging schedules. Finally, to contrast the benefits of coordination and impacts in local grid infrastructure of uncoordinated loads, we analyze how electric vehicle charging at workplace can be detrimental to infrastructure if not properly managed. We do this by assessing accelerated transformer aging due to increased temperature caused by power overload. The analysis is conducted with charging data from a real-world workplace facility and we use the recommended IEEE model for the type of transformer at that facility. We show that coordinated charging of electric vehicles with a goal of reducing peaks in distribution transformers, directly through peak minimization or indirectly by minimizing cost under rate structures with demand charge component, can minimize the aging effects while allowing 67\% more vehicles to be serviced by the same infrastructure

Keywords: Engineering.
Advisor: Rajagopal Ram
Rivas-Davila Juan
Rivetta Claudio