The aim of the ISORC/OPTIMISER project is to increase and improve the use of solar thermal energy in district heating networks. One of the main tasks of the project is to develop an optimization tool for the sizing and operation of a solar district heating network. This is the first optimization tool using an open-source interface (Julia, JuMP) and solver (Ipopt) to solve nonlinear problems. This paper presents the multi-period optimization problem which is implemented to consider the dynamic variations in a year, represented by four typical days, with an hourly resolution. The optimum is calculated for a total duration of 20 years. First, this paper presents the modeling of the different components of a solar district heating network production plant: district network demand, storage and three sources, i.e., a fossil (gas) and two renewable (solar and biomass) sources. In order to avoid prohibitive computational time, the modeling of sources and storage has to be fairly simple. The multi-period optimization problem was formulated. The chosen objective function is economic: The provided economic model is accurate and use nonlinear equations. Finally the formulated problem is a nonlinear Programming problem. Optimization of the studied case exhibits consistent operating profiles and design. A comparison is made of different types of storage connection at the production site, highlighting the relevance of placing the storage at the solar field outlet. The optimum configuration supplies 49% of demand using solar energy, achieving a renewable rate of 69% in combination with the biomass boiler.

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To continue improving the integration of solar thermal in district heating networks, optimization tools that can study both sizing and operation of heating plants are needed. In this article, the ISORC tool was used to study the sizing and coupled operation of smaller storage and solar fields with other heating sources such as biomass and gas boilers. For this, a k-medoids algorithm was applied to select consecutive characteristic days to size the system based on an optimal operation of consecutive days in the same season. The formulated problem was nonlinear, and the objective function to be minimized was the total cost. Two case studies with different day constructions and distributions were studied with various sensitivity analysis. The formulation and methodology allowed us to study different cases and situations easily and proved the importance of the selection and attribution of typical days. In all cases, the results showed that even with a daily approach, solar thermal covers approximately 20% of the demand, which demonstrates the relevance of considering and developing small daily storage with small solar fields.