A Rome district transition towards optimal and sustainable heat and power generation

In this work, a specific district of Rome energy metabolism has been studied to reduce the carbon footprint of electric and thermal energy generation. Two scenarios were investigated, one “ex-ante” with the district as it was, fully dependent on the national grid for electricity and methane-fuelled boilers for heating. And an “ex-post” scenario with the buildings retrofitted with more efficient thermal insulation, LED lights, photovoltaic panels and trigeneration systems. The energy metabolism of the district was modelled in TRNSYS in both cases. For the ex-post scenario, the digital twin implemented a control logic for energy flows with several functionalities including prioritizing the power flow, setting rules and boundaries on the operation of the co-generators, preventing batteries from overcharging/discharging. The sizing of PV panels, batteries, and cogeneration units in the ex-post scenario was optimized in terms of both energy and economic performance using a Python library with the Non-dominated Sorting Genetic algorithm (NSGA-II). In the ex-post scenario, the optimal energy system delivers significant reductions in energy costs and CO2 emissions. The energy transition results in a decrease in purchased electricity of 68.2%, CO2 emissions by 56% and generation costs by 48.9%. The Levelized Cost Of Energy (LCOE) of the district is estimated to be 196 €/MWh.