Elizabeth (Lily) Buechler

Elizabeth (Lily) Buechler

PhD Candidate at Stanford University

Recent Research Projects

Model Predictive Control Strategies for Grid-Interactive Water Heaters

Residential electric water heaters have significant load shifting capabilities due to their thermal heat capacity and large energy consumption. Model predictive control (MPC) has been shown to be an effective control strategy to enable water heater load shifting in home energy management systems. In this project, we analyze how modeling tank stratification in an MPC formulation impacts control performance for stratified electric water heaters under time-of-use (TOU) rates, using both simulation and experimental validation in a laboratory testbed.

Impacts of the COVID-19 Pandemic on Electricity Demand

Understanding how the COVID-19 pandemic affected electricity consumption patterns can provide insight into how extreme events may impact electricity systems in the future. To investigate this, we estimated changes in electricity consumption in 58 different countries/regions during the first ten months of the pandemic, and analyzed how changes relate to different factors such as government restrictions, GDP, mobility metrics, health outcomes, and characteristics of individual electricity systems. Results showed that government restrictions and mobility were significantly associated with changes in electricity consumption, confirming that policies affecting individual behavior represent powerful tools to impact consumption. However, certain variables associated with intrinsic properties of electricity systems also appear to be linked to electricity consumption changes.

Residential Load Modeling and Forecasting

Short-term or near real-time load forecasting at the home or appliance level is important for the provision of various services to the grid and the consumers, including demand-side management and home energy management systems. However, modeling and forecasting of demand at the appliance level is challenging due to the intrinsic variability and uncertainty of exogenous environmental variables and human behavior. In this work, we propose a conditional hidden semi-Markov model (CHSMM) for modeling the probabilistic nature of appliance power consumption, which can be used for short-term load forecasting. The state transition probabilities and emission and duration distributions of the CHSMM are conditioned on exogenous variables, which allows factors such as temperature and seasonal effects that impact the physical dynamics of the load to be incorporated into the model. Case studies performed using granular sub-metered power measurements from the Pecan Street database for various types of appliances demonstrate the effectiveness of the proposed load model for short-term prediction.

EV-EcoSim: A grid-aware co-simulation platform for the design and optimization of electric vehicle charging infrastructure

To enable the electrification of transportation systems, it is important to understand how technologies such as grid storage, solar photovoltaic systems, and control strategies can aid the deployment of electric vehicle charging at scale. In this work, we present EV-EcoSim, a co-simulation platform that couples electric vehicle charging, battery systems, solar photovoltaic systems, grid transformers, control strategies, and power distribution systems, to perform cost quantification and analyze the impacts of electric vehicle charging on the grid. This python-based platform can run a receding horizon control scheme for real-time operation and a one-shot control scheme for planning problems, with multi-timescale dynamics for different systems to simulate realistic scenarios. We demonstrate the utility of EV-EcoSim via a case study focused on economic evaluation of battery size to reduce electricity costs while considering impacts of fast charging on the power distribution grid.