Building stock simulation to support the development of a district multi-energy grid

The urbanization process is constantly increasing worldwide. Today over 50 % of the population resides in urban areas and this value is expected to grow up to 68 % by 2050. In this scenario, the development of district scale energy grids and management systems has become crucial to optimize energy use and to balance energy flows within the cities, encouraging the use of renewable sources and self-consumption. This study focusses on a district under development in the city of Milan, involving an urban area of about 920 000 m2, which, once completed, will count for about 4 500 apartments, a school and a few other commercial uses. The existing energy systems consist of an electric grid, including a small photovoltaic field, a district heating system and a local district cooling system exploiting groundwater via heat pumps. They serve, at present, seven residential tower buildings (400 apartments). The overarching aim of the research is to evolve the existing grid into a smart energy grid able to guarantee an intelligent management of the district, empowering eventually people to apply for demand-response schemes, electric mobility and other innovative services. In order to perform such an improvement and extension of the exiting grid, it is necessary to evaluate and simulate the profiles and dynamics of the final energy uses for the residential buildings, that will represent the major load on site. Since monitoring data are not yet available for the district, the evaluation of the energy performance of the existing buildings has been developed through dynamic energy simulations via the definition of profile loads of the most frequent apartment typologies, that allow, moreover, to simulate further developments in the districts. Besides, a monitoring plan for the existing systems has been developed and implemented. Monitoring data will be used at first for validating the developed load profiles; then, they will be analysed to develop optimisation algorithms for the management of the upgraded energy grid. In this paper, the case study is presented and the results of the analysis, via energy simulation, on the existing building stock are reported.