LAUSR

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. Note to Practitioners—This paper focuses on the dynamic pricing mechanism and prices spike detection for the customers in the industrial park. We improve the pricing model based on the Lagrangian relaxation method and develop a dynamic prices prediction model to handle the uncertainty in real-time. Furthermore, we develop a prices spike detection mechanism, which can achieve rolling detect whether the electricity and thermal prices may exceeded the threshold in the next short-term operating horizon. This technique can give the customers a prices spike early warning service and let them to reschedule their own strategy to minimize their operation cost with respect to the uncertainties in the energy price. Experimental results show that the proposed prices spike detection mechanism can issue spike warnings correctly in most supply-demand mismatching cases.