Iterative sizing of solar-assisted mixed district heating network and local electrical grid integrating demand-side management. (1st January 2022)

• Record Type: Journal Article
• Authors: Gronier, Timothé; Fitó, Jaume; Franquet, Erwin; Gibout, Stéphane; Ramousse, Julien
• Is Part Of: Energy. Volume 238:Part A(2022)
• Publication Date: 2022-01-01
• Subjects: Sizing -- Demand-side management (DSM) -- District energy networks (DEN) -- Solar energy -- Mixed-energy grids -- Exergy; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.121517
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Demand-side management and load-shifting strategies can reduce peak loads as well as temporal production/consumption mismatch, two classic issues in district energy networks that integrate solar sources. Nevertheless, the classic current sizing methods for such networks only consider the total demand, and not the possible loads after use of such techniques. The present paper aim is so to ascertain the connection between the possible demand reductions and the capacity design of generation sources. The study proposes an iterative sizing method with demand-side management as the central pillar. It retro-fits production units by assessing the network's overall performance through several criteria, both energetic and economical and with operational considerations. Exergy, which accounts for the quality of energy and is especially useful for multi-energy networks, is also considered. The method is illustrated on a mixed grid coupling a standalone heating network with a local electrical grid. Thousands of residential dwellings, with haphazard demands covered by solar-assisted technologies and a heat-pump are used in a series of ten scenarios with various management strategies, pricing policies and types of end-user contracts. In summary, the iterative method reduced the number of installed solar thermal collectors and photovoltaic panels by 13–38 % and 8–30 %, respectively. Furthermore, the method is stable: results converged after 2 iterations, in all scenarios. We also discuss the influence of low or high demand-side management penetration rate, and the final sizing selection by the decision-maker. Graphical abstract: Image 1 Highlights: Iterative sizing method to account for the load control strategy in the design stage. Method is illustrated through an example case with both electric and thermal loads. 10 scenarios evaluated, different load control strategies and types of contracts. Method proved to reduce equipment size and improve several other indicators. Method proved iteration-stable: all scenarios converged after 2 iterations.