Optimal management and data-based predictive control of district heating systems: The Novate Milanese experimental case-study

Abstract

This paper addresses the modelling and optimal control of district heating systems connected to the electrical grid, with the goal of maximizing their operational efficiency and enabling the participation to electricity markets. Being these systems governed by nonlinear large-scale dynamical models, a novel procedure is proposed, which enables to obtain suitable models for optimization, and consisting in a combination of physical and identified piece-wise linear models. A two-phase optimization and control scheme is then designed, including an offline scheduling problem for participating to the day-ahead energy market, and an online Model Predictive Control system, minimizing the energy consumption of thermal generators while properly satisfying the users thermal demand. The proposed methodology is developed considering a real district heating plant, owned by the energy company A2A S.p.A. and supplying the city of Novate Milanese (Italy), and different experiments on the plant have been carried out. The experimental results and achieved performances are promising, showing a significant reduction of the operational costs and overall gas consumption.