Optimal Control of Distributed Energy Resources in Baseline-Based Demand Response Programs
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2024
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Increasingly, customer-sited distributed energy resources (including distributed generation, energy storage systems, controllable appliances, and electric vehicles) are being used as assets to support the operation of electric power systems through grid service programs, in which customers are compensated for their devices' participation or performance in responding to power grid needs. In this dissertation, we study how to best control customer energy resources (with a focus on battery energy storage systems) in order to reduce customer costs while participating in utility tariffs and grid services programs. We focus on a commonly implemented, but not widely studied, class of grid service programs known as baseline-based demand response, where customers are compensated based on how much their electric load during grid service events differs from a ``baseline load'' based on their prior electric load. Optimizing control under baseline-based demand response presents additional challenges (compared to demand response based on time-varying pricing without baselines) due to uncertainty in current effective electricity prices and significant time delay between control decisions and learning the cost impact of these decisions.
This work aims to develop methods and insights to improve the utilization of customer resources in grid services programs, in order to ultimately reduce system cost, improve reliability, and support integration of renewable generation. Control methods studied and developed in this work can be used to manage customer resources in real time or to support studies of grid service program designs. This dissertation addresses our topic from several angles: (1) analytically, with mathematical analysis of the underlying optimization problems, (2) numerically, by developing and evaluating performance of control algorithms based on dynamic programming, stochastic programming with model predictive control, imitation learning, and reinforcement learning, and (3) practically, by demonstrating and testing control algorithms on a hardware-in-the-loop test bed.
This work led to several notable findings and results. We found that optimal distributed energy resource control to save customers money under baseline-based demand response leads to baseline modification under a variety of customer parameters and demand response program structures. For an example simulated Hawaii customer, we found that the optimal battery control strategy led to 66% of the delivered demand response coming from baseline modification. The level of baseline modification does vary with program design, and we found that increasing the number of days in the baseline load calculation can reduce the level of baseline load modification. Our best-performing control algorithm, based on a neural network trained with reinforcement learning, achieved over 4% annual electricity cost savings compared to benchmark controllers in simulations with realistic forecast errors. We showed that controllers like this run thousands of times faster than controllers based on stochastic programming and can be deployed on small distributed smart home hub computers, making them feasible options for widespread use. Finally, we developed an Energy Internet-of-Things test bed, which can be used for future studies of smart home and smart grid technologies and control strategies, in order to understand their potential impacts on customers and the power system.
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Electrical engineering
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112 pages
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