Dynamic Pricing and Prices Spike Detection for Industrial Park with Coupled Electricity and Thermal Demand

This paper presents a dynamic pricing mechanism in the industrial park with demand response programs. A Lagrangian relaxation based dynamic pricing model for electricity and thermal coupled industrial park is formulated, taking into account energy balance, feeder exchange and other systems operating constraints. Considering two-markets clearing mechanism and two types of demand response programs, a dynamic prices prediction model is proposed by long short-term memory (LSTM) technique. Based on the prediction model, we proposed a real-time prices spike detection model for industrial park, which can detect prices spike hourly by history data and give rolling prices spike warning for next short-term operating horizon. Simulation experiments validate the theoretic results and show the effectiveness of the dynamic prices spike detection model.