Future district heating systems (DHS) will be supplied by renewable sources, most of which are limited in temperature and flow rate. Therefore, operational optimization of DHS is required to maximize the use of renewable sources and minimize (fossil) peak loads. In this paper, we present a robust and fast model-predictive control approach to use the thermal mass of buildings as a daily storage without violating temperature constraints. The novelty of this paper includes two elements. First, the focus on an operational control strategy that explicitly accounts for temperature-limited renewable sources, like a geothermal source. Secondly, the optimization problem is formulated as a (nearly) convex optimization problem, which is required for adoption of model-predictive control in practice. The examples show that the peak heating demand can be reduced by 50%, if the thermal inertia of the buildings is used and the heating setpoints are adapted. Furthermore, the operational optimization finds the proper balance between benefits of pre-heating using renewable sources with limited capacity and costs of additional heat losses due to pre-heating.

未来的区域供热系统（DHS）将由可再生能源提供，其中大部分温度和流量受到限制。因此，需要对DHS进行操作优化，以最大程度地利用可再生资源并最大程度地减少（化石）峰值负荷。在本文中，我们提出了一种鲁棒且快速的模型预测控制方法，可将建筑物的热质量用作日常存储而不会违反温度限制。本文的新颖性包括两个要素。首先，将重点放在明确说明受温度限制的可再生能源（例如地热能源）的运行控制策略上。其次，将优化问题表述为（几乎）凸优化问题，这是在实践中采用模型预测控制所必需的。示例显示，如果使用建筑物的热惯性并调整供热设定点，则峰值供暖需求可以减少50％。此外，运行优化可以在使用容量有限的可再生资源进行预热的好处与因预热导致的额外热量损失的成本之间找到适当的平衡。