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Sustainability 2022, 14, 10125 4 of 39 • District heating and cooling network; • Thermal and cooling storage; • Electricity sharing among EC members; • Renewable sources considered; • Considerable number of heating-, cooling-, and power-related technologies. As can be observed in Table 1, the reviewed literature has applied different optimiza- tion methodologies and objective functions to analyse ECs. Nevertheless, the implementa- tion of SE among EC members and its effects on the optimal cost-related solution of the district heating and cooling network (DHCN) and adopted technologies have not been evaluated yet. Table 1. Comparison of the EC-related literature regarding key modelling aspects. Ref. [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] Present work Optimization Methodology MILP • Evolutionary • algorithm • Objective Total costs Investment costs Operational costs District Thermal Electricity Network Type Storage Sharing DHN Heat - DHN - - DHN Heat - - - Yes - Heat Yes - - Yes Renewable Sources - Biomass Biomass Solar Solar, biogas Solar Adopted Technologies HP, BOI, TStor BOI BOI, HP, TStor PVp, ES ICE, PVp, STp, TStor, BOI, ES PVp, ES MILP • Total costs • CO2 emissions DHN Heat - Solar GT, ICE, BOI, TStor, STp GA • Total costs DHCN - - - HP MILP/GAMS • Max. cost savings or • Min. GHG emissions DHN Heat - Biomass GT, ICE, HP, BOI, TStor • Total costs MILP • • Min. CO2 emissions Amount of biomass SLP • Min. energy costs - Heat Yes Solar, wind STp, PVp, WT, ES, ICE, HP NLP • MIP • MILP • MILP • Min. distance and losses among EC users MILP • Max. EC profit - - Yes Solar PVp Min. total costs • Min. GHG emissions NSGA-II • Annual total costs • Annual carbon emissions - Heat - Solar, PVp, STp, HP, ABS, geothermal TStor, ES Total costs Total costs DHCN Heat and Yes Solar cooling GT, ICE, BOI, TStor, STp, PVp, ABS, CC, HP, CS For this reason, it comes to light that the literature has still a research gap regarding: • Optimal SE solution for an EC and its effects on the optimal DHCN solution; • Amount of electricity exchanged between EC and the national electric grid according to utility price variations and, consequently, to the adopted technologies in the solution; • Evaluation of the EC performance in relation to the possibility of moving towards a self-sufficient scenario in terms of electricity production/consumption. The study is developed by performing a single-objective MILP optimization to define the optimal synthesis, design, and operation of a distributed generation (DG) system in an EC, in the northeast of Italy, in a similar way as presented in a previous work of this research group [6]. Such an EC comprises nine tertiary sector buildings connected through a DHCN. The single-objective optimization of the DG energy system considers the total annual cost for owning, operating, and maintaining the whole system as the economic objective function, while the total annual CO2 emissions have been calculated after optimizationPDF Image | Optimal Sharing Electricity and Thermal Energy
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