Model Predictive Control-Based Optimal Operations of District Heating System With Thermal Energy Storage and Flexible Loads

Abstract

Operating heating power plant (DHPP) with fluctuating load is a complex problem. Thermal energy storage (TES), flexible loads, and operating constraints compound this complexity further. This investigation focuses on the design of a model predictive controller (MPC) that reduces the operating and maintenance cost in a DHPP, considering TES and flexible loads. The MPC accomplishes this task by scheduling boilers, TES units, and flexible loads. To handle the fluctuating demand, the MPC uses forecasts and combines it with a constrained optimization problem. The objective function reflects the cost, whereas the generator limits, TES dynamics, thermal loads, including supply temperature, power plant layout, and reliability, are the constraints. The resulting optimization problem is modeled as a mixed-integer linear program with both continuous and logic variables. Here the logic variables model the operating modes of the boiler and storage units. The use of receding horizon approach enhances the robustness to the forecast errors. The constraints modeling plant layout, supply temperature, and grid reliability lead to a more realistic solution. The MPC is illustrated using simulation on historical data and experiments on a DHPP at Ylivieska, Finland. Our results demonstrate the cost benefits of the proposed approach.