Robustness of district heating versus electricity-driven energy system at district level: A multi-objective optimization study

• Published in: Smart energy (Amsterdam) 2022-05, Vol.6, p.100073, Article 100073
• Main Authors: Fitó, Jaume, Vallée, Mathieu, Ruby, Alain, Cuisinier, Etienne
• Format: Article
• Language: English
• Subjects: District heating; Energy storage; Mathematics; Model predictive control; Multi-energy networks; Multi-objective optimization; Optimization and Control; Sensitivity analysis
• ISSN: 2666-9552; 2666-9552
• DOI: 10.1016/j.segy.2022.100073

This article compares the robustness of the optimal choice of technologies for two Smart Energy Systems architectures at district level, illustrated by a case study representative of a newly built district in Grenoble, France. The electricity-driven architecture relies on the national electric grid, decentralized photovoltaic panels and decentralized heat pumps for heat production building by building. The alternative architecture consists of a district heating network with multiple sources and equipment for centralized production of heat. Those are a gas boiler plant, a biomass-driven cogeneration plant, a solar thermal collector field, and a geothermal heat pumping plant (grid-driven or photovoltaics-driven). Electric and heat storages are considered in both architectures. The sizing and operation of both architectures are optimized via linear programming, through a multi-objective approach (total project cost versus carbon dioxide emissions). Both architectures are compared at nominal scenario and at sensitivity scenarios. It is concluded that the electricity-driven architecture is less robust, especially to uncertainties in space heating demands (+150%/−30% impact on costs) and in heat pump performance (+35%/−15% in costs). Meanwhile, the multi-source architecture is less sensitive to space heating demands (+110%/−30%) and has negligible sensitivity to the rest of parameters except photovoltaic panels efficiency (+14%/−7%). [Display omitted] •Decentralized electricity-driven architecture versus centralized district heating.•Interconnected electrical, thermal and gas networks through various units.•Optimal sizing by linear programming simulation and model predictive control.•Sensitivity analysis of both architectures on performance parameters.•Discrepancies observed between sensitivity of both architectures.