Economic Nonlinear Model Predictive Control of Prosumer District Heating Networks: The Extended Version

TL;DR

This paper presents an economic nonlinear MPC algorithm for district heating networks with prosumers, producers, and storage, optimizing operations for cost reduction and system flexibility in a real-time setting.

Contribution

It introduces a graph-based optimization model for large-scale nonlinear systems in district heating networks, enabling real-time economic MPC with improved performance.

Findings

Achieved up to 9% cost reduction compared to rule-based controllers

Effectively maintained system constraints during optimization

Demonstrated real-time applicability in numerical experiments

Abstract

In this paper, we propose an economic nonlinear model predictive control (MPC) algorithm for district heating networks (DHNs). The proposed method features prosumers, multiple producers, and storage systems, which are essential components of 4th generation DHNs. These networks are characterized by their ability to optimize their operations, aiming to reduce supply temperatures, accommodate distributed heat sources, and leverage the flexibility provided by thermal inertia and storage, all crucial for achieving a fossil-fuel-free energy supply. Developing a smart energy management system to accomplish these goals requires detailed models of highly complex nonlinear systems and computational algorithms able to handle large-scale optimization problems. To address this, we introduce a graph-based optimization-oriented model that efficiently integrates distributed producers, prosumers, storage buffers, and bidirectional pipe flows, such that it can be implemented in a real-time MPC setting. Furthermore, we conduct several numerical experiments to evaluate the performance of the proposed algorithms in closed-loop. Our findings demonstrate that the MPC methods achieved up to 9% cost improvement over traditional rule-based controllers while better maintaining system constraints.