Achieving low carbon local energy communities in hot climates by exploiting networks synergies in multi energy systems. (15th December 2019)

• Record Type: Journal Article
• Authors: Comodi, Gabriele; Bartolini, Andrea; Carducci, Francesco; Nagaranjan, Balamurugan; Romagnoli, Alessandro
• Is Part Of: Applied energy. Volume 256(2019)
• Publication Date: 2019-12-15
• Subjects: Polygeneration -- District planning -- District heating and cooling -- Distributed generation -- Thermal microgrid -- Network of multi energy systems; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.113901
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: Local energy community modeled as a network of multi energy systems. Primary Energy Saving as a metric for designing low carbon energy communities. Investigation of low carbon scenarios with 10% and 20% of primary energy savings. Thermal storage & district cooling allow to shave the cooling peak power installed. Trigeneration and district cooling are key technologies to achieve low carbon goals. Abstract: Design and planning of low carbon cities and districts must consider the synergies between all the energy networks available. Energy systems optimal design thus assumes a critical importance in determining both costs and environmental impact of operating such districts. This is particularly true following the concept of Local Energy Community, with a single entity representing both the demand and the manager of the energy generation assets. This paper proposes an innovative model for the optimal design of an energy community aiming at lowering its carbon footprint. The community is modeled as a network of spatially dislocated energy hubs, each with its own demand of electricity, heating and cooling energy. The model aims at defining the optimal mix of energy systems, thermal and electric energy storages and energy network infrastructures needed to satisfy the district's users energy demands. The model is validated using energy demand data from the Nanyang Technological University campus in Singapore by analyzing three scenarios. In the first one, the optimization goal is purely economic and it aims at minimizing the overall cost of operating the district. The second and third scenarios focus on reducing the carbon footprint of the district by imposing an additional constraint, which limits the overall primary energy consumption. In all the scenarios the algorithm chooses to partially or totally connect the five sites with a district cooling network and take advantage of cold thermal storage, proving their potential in hot climates. In the first scenario, the advantages of the district cooling solution are mainly related to the savings in the capital cost of electric chillers that partially offset the cost of the district cooling network; indeed, district cooling network allows the sites to share cooling power thus achieving a reduction in chillers total installed size of 33%. In the second scenario, in order to meet the target of 10% reduction of the overall primary energy, the optimal solution also requires the installation of a photovoltaic system. In the third scenario, imposing a 20% reduction of the overall primary energy, also a natural gas fed trigeneration plant comes into play.