Co-simulation Framework for Power Distribution System Analysis with Humans in the Loop

Recent developments in the area of power generation have led to an increased penetration of storage and distributed energy resources (DER) in power distribution systems. As a result, new and enhanced energy management systems will be necessary as the deployment of DERs, as well as the need to control loads, continues to increase in the coming years. Advanced management systems are especially important to achieve resilient power delivery during emergency situations. During a blackout for example, a section of a distribution feeder could island to operate as a microgrid to ensure critical services such as water, food, and medical care remain online. The development of such management systems will also require the ability to integrate human behavior models with power flow simulators, as technological advances lead to more customer-owned devices with the potential to be used to balance the power flow of a feeder. This thesis describes a co-simulation framework that combines a bottom-up residential load generator, a load aggregator for real-time (RT) residential demand response (DR), a utility-scale battery controller, and the GridLAB-D distribution system simulator. The behavior of a distribution feeder model is analyzed under different scenarios. The model is based on an existing feeder located in Los Alamos, NM, which serves residential customers and a set of critical loads including a hospital, a supermarket, and a water treatment plant. This feeder also hosts a utility-scale solar array and battery storage that are used to operate the feeder as a microgrid. Additionally, a real-time simulation is described in which real-time residential demand response is implemented on a virtual community using the load generator and aggregator. The simulation is part of a project invested in the development of a modern microgrid control system employing a virtual power plant approach and a model predictive controller to optimize the use of resources within a distribution feeder. The capabilities to study power distribution systems with humans in the loop using these platforms is showcased here. Furthermore, their potential as instrumental tools in the development and design of new technology essential to improve grid resilience is also discussed