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Sustainability 2022, 14, 10125 2 of 39 are apparent due to the irregular pattern of such sources [4]. The intended meaning for an energy community (EC), in this paper, is described by Bauwens et al. [5], i.e., the community as a place where users are able to share energy amongst each other (which is the case, for instance, of a District Heating Network—(DHN) and a district heating and cooling network (DHCN)) with the aim to pursue not only economic objectives, but also environmental ones. Moreover, to reach such objectives, many studies have employed the Mixed Integer Linear Programming (MILP) methodology. This is the case, for instance, of Casisi et al. [6] which applied the MILP method to a community comprising nine tertiary sector buildings. Over the past 50 years, the scientific community’s interest in using the MILP method- ology to optimize energy systems has increased exponentially. To have an idea of this, according to Google Scholar, the number of studies in the 1990s related to the optimization of energy systems through the MILP methodology was almost 45% higher compared to the 1980s. In 2010, the number was 10-times higher compared to the previous decade and, nowadays, the number is about 22 times compared to that of 2010. The energy systems analysed in those studies included several types, such as combined cooling heat and power (CHP) systems comprised by boiler and steam turbine [7], the integration of absorption chillers to a CHP plant [8], the incorporation of a condensation turbine and heat pump to a turbine/boiler CHP plant [9], and an optimization of by-product gas distribution in the iron and steel industry [10]. The versatility of MILP optimizations allows one to apply them not only to energy communities, but also to different types of energy systems such as seaports [11,12], wine cellars, ships [13], and so on. The research developed by Pivetta et al. [14], for instance, used MILP optimization to optimize the energy system of a wine cellar in the Northeast Italy so that it produce with the most efficient system configuration from the point of view of profit maximization and share of RE utilization. In another study performed by Pivetta et al. [15], they developed an MILP model to optimize the design and operation of a small-size ferry. The optimization was focused on minimizing the fuel cells’ and batteries’ degradation, as well as minimizing the capital expenses and the operation cost. As mentioned before, the range of applicability of MILP models also includes the optimization of energy communities. In most cases, the multi-objective functions target the economic optimization of purchasing, maintaining, and operating the whole system as well as the total annual emissions. For example, the authors in reference [6] performed their optimization for the total annual cost and emissions of a DHCN serving a nine- building energy community. They compared this solution with what they called the “conventional solution”, i.e., all thermal, cooling, and electricity demands met, respectively, by a boiler, an electric compression chiller, and electricity bought from the grid. As for the cost optimization, the comparison resulted in a 32% reduction in the DHCN solution with respect to the conventional one, whereas the emissions optimization led to a 41% reduction for the same comparison. The role of DHNs in achieving better economic results for energy systems as well as reducing environmental emissions has been widely considered and studied by the scientific community [16–18]. More specifically, DHNs have also been considered when the transition to a 100% RE scenario is considered. For instance, the work developed by Lund et al. [19], analysed a set of scenarios in which the Danish energy system is converted to a 100% RE scenario by 2060. In such scenario, amongst 10 heating technology types, the DHN one was demonstrated to have the second lowest annual cost (the first was the individual electric heating), and it resulted in the lowest annual fuel consumption option. Still, according to the authors, the best solution for the transition would be achieved through continuing the growth of the DHNs combined with individual heat pumps for areas not covered by DHNs themselves. In the same direction, another largely discussed concept (in the past decade) is the “4th generation district heating”. According to Lund et al. [20], the so-called first (1880s to 1930s), second (1930s to 1970s), and third (from the 1980s) generations of district heating were mostly fed by fossil fuels and based, respectively, on steam, pressurized hot waterPDF Image | Optimal Sharing Electricity and Thermal Energy
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