Neural Network Based Feasible Region Approximation Model for Optimal Operation of Integrated Electricity and Heating System

This paper proposes a neural network based feasible region approximation model of a district heating system (DHS), and it is intended to be used for optimal operation of integrated electricity and heating system (IEHS) considering privacy protection. In this model, a neural network is trained to approximate the feasible region of the DHS operation and then is reformulated as a set of mixed-integer linear constraints. Based on the received approximation models of DHSs and detailed electricity system model, the electricity operator conducts centralized optimization, and then sends specific heating generation plans back to corresponding heating operators. Furthermore, subsequent optimization is formulated for each DHS to obtain detailed operation strategy based on received heating generation plan. In this scheme, optimization of the IEHS could be achieved and privacy protection requirement is satisfied since the feasible region approximation model does not contain detailed system parameters. Case studies conducted on a small-scale system demonstrate accuracy of the proposed strategy and a large-scale system verify its application possibility.