Controlling big, diverse, nonlinear load aggregations for grid services by adjusting device setpoints

Many electrical loads seek to maintain a measurement, such as a temperature, pressure, flow rate, fluid level or charge state, near a setpoint. In some cases, setpoints can be adjusted slightly without noticeably affecting quality of service. Small setpoint adjustments have an indirect effect on power use that, when aggregated over a large number of loads, can be significant. This paper develops a framework to provide services to the power grid by adjusting device setpoints. The framework has several practical advantages: it scales to very large load aggregations; accommodates a wide variety of loads, including those with nonlinear behavior; and requires little sensing or communication and no private information. The framework involves (1) learning a model to predict aggregate power under baseline operation, (2) exciting the system to identify a model relating setpoint perturbations to aggregate power perturbations, and (3) embedding baseline predictions and the perturbation model in load-shifting optimization. Simulations of a 50,000-load, 115-MW aggregation in the Texas storms of February, 2021, suggest that this framework can reduce peak demand, arbitrage dynamic energy prices or carbon intensities, and provide utility demand response or wholesale ancillary services.