Iterative sizing of solar-assisted mixed district heating network and local electrical grid integrating demand-side management

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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The study introduces an iterative sizing method focusing on demand-side management to optimize the overall performance of district energy networks integrating solar sources. Various criteria, including energetic, economical, and operational aspects, are considered, with exergy playing a key role in evaluating the quality of energy in multi-energy networks. The method effectively reduces the number of installed solar thermal collectors and photovoltaic panels while ensuring stable results after just two iterations in all scenarios.