2026 Lyu Industrial Load Modeling

China’s transition toward a renewable-dominated power system increases the flexibility needed to balance net-load variations. Industrial users account for over 60% of the country’s electricity consumption and can shift large amounts of demand through automated production scheduling. Using this flexibility in electricity markets remains difficult, however, because industrial process constraints are expensive to solve at scale, equipment parameters are often private, and device-level models are too detailed for aggregator and power-system optimization. This dissertation addresses these barriers from the perspective of a load aggregator. The first contribution reformulates two widely used industrial process models—the State Task Network (STN) and Resource Task Network (RTN)—and proposes the Linearized State Task Network (LSTN) and Continuous Resource Task Network (cRTN). By reducing the number of integer variables while preserving the represented process constraints, the reformulation shortens the solution time of a standard grid-interaction case from 24 hours to 30 minutes and supports coordinated optimization of 2,000 industrial users. To estimate private equipment parameters, this dissertation develops a Production Scheduling Identification (PSI) method for hourly smart meter data, incorporating industrial process mechanisms and cost-minimizing scheduling behavior to narrow the parameter estimation process.