Managing power system congestion and residential demand response considering uncertainty

Electric power grids are becoming increasingly stressed due to political and environmental difficulties in upgrading transmission capacity. This challenge receives even more interest with the paradigm change of increasing renewable energy sources and demand response (DR) programs. Among DR technologies, existing DR programs are primarily designed for industrial and commercial customers. However, household energy consumption accounts for 38% of total electricity consumption in the U.S., suggesting a significant missed opportunity. This dissertation presents an in-depth study to investigate managing power system congestion and residential DR program under uncertainty.First, an interval optimization model is presented for available transfer capability (ATC) evaluation under uncertainties. The conventional approaches of ATC assessment include deterministic and probabilistic methods. However, the proposed interval optimization model can effectively reduce the accuracy requirements on the renewable forecasting, and lead to acceptable interval results by mitigating the impacts of wind forecasting and modeling errors. Second, a distributed and scalable residential DR program is proposed for reducing the peak load at the utility level. The proposed control approach has the following features: 1) it has a distributed control scheme with limited data exchange among agents to ensure scalability and data privacy, and 2) it reduces the utility peak load and customers’ electricity bills while considering household temperature dynamics and network flow.Third, the impacts of weather and customers’ behavior uncertainties on residential DR are also studied in this dissertation. A new stochastic programming-alternating direction method of multipliers (SP-ADMM) algorithm is proposed to solve problems related to weather and uncertain customer behavior. The case study suggests that the performance of residential DR programs can be further improved by considering these stochastic parameters.Finally, a deep deterministic policy gradient-based (DDPG-based) HVAC control strategy is presented for residential DR programs. Simulation results demonstrate that the DDPG-based approach can considerably reduce system peak load, and it requires much less input information than the model-based methods. Also, it only takes each agent less than 3 seconds to make HVAC control actions. Therefore, the proposed approach is applicable to online controls or the cases where accurate building models or weather forecast information are not available.