Robust model predictive control for an uncertain smart thermal grid

Abstract

The focus of this paper is on modeling and control of Smart Thermal Grids (STGs) in which the uncertainties in the demand and/or supply are included. We solve the corresponding robust model predictive control (MPC) optimization problem using mixed-integer-linear programming techniques to provide a day-ahead prediction for the heat production in the grid. In an example, we compare the robust MPC approach with the robust optimal control approach, in which the day-ahead production plan is obtained by optimizing the objective function for entire day at once. There, we show that the robust MPC approach successfully keeps the supply-demand balance in the STG while satisfying the constraints of the production units in the presence of uncertainties in the heat demand. Moreover, we see that despite the longer computation time, the performance of the robust MPC controller is considerably better than the one of the robust optimal controller.

Physics-Informed Neural Network Modeling and Predictive Control of District Heating Systems

“…In fact, DHS physical models typically include many state variables governed by nonlinear thermo-hydraulic equations, e.g., describing fluid and heat transport in water pipelines, resulting in a modeling complexity hardly tractable by standard optimization-based controllers. To overcome this issue, predictive controllers exploiting simplified models have been proposed in the literature, such as [9], [10], and [11], where the thermal dynamics of the DHS network are not modeled. Nevertheless, the accurate modeling of the network thermal dynamics is crucial for the optimal operation of DHS plants for several reasons: 1) network temperatures must respect operational constraints due to technical limits of thermal generators and to the proper heat supply to thermal loads (e.g., the water temperature supplied to each load must exceed a minimum lower bound [4], [12]) and 2) network thermal dynamics, if modeled, can be optimized to minimize heat losses and to increase the overall DHS efficiency.…”

Optimal control of a fourth generation district heating network using an integrated non-linear model predictive controller

“…However, the buildings connected to the DH network were not modelled and therefore the flexibility provided by their thermal inertia was not considered. Farahani et al [8] also developed an MILP-based MPC to minimise the total heat production fed into a DH network with TES tanks. The MPC controlled the energy flows to minimise the total heat production costs of the CHP and boiler in the network.…”

Probabilistic Energy Management for Building Climate Comfort in Smart Thermal Grids with Seasonal Storage Systems

“…A deterministic view on STGs was studied by a few researchers [24], [25], [26]. STGs with uncertain thermal energy demands have been considered in [27], where a MPC strategy was employed with a heuristic Monte Carlo sampling approach to make the solution robust. A dynamical model of thermal energy imbalance in STGs with a probabilistic view on uncertain thermal energy demands was established in [28], where a stochastic MPC with a theoretical guarantee on the feasibility of the obtained solution was developed.…”