Optimization of Energy Systems for a Sustainable District in Stockholm Using Genetic Algorithms : The case of Albano

Abstract: Multi-objective optimization tools using genetic algorithms (GAs) are being increasingly used for improving building performances and sustainability. However, few research studies focus on district-scale solutions. In the present project, a multi-objective optimization method using genetic algorithms was applied in order to help decision makers find the optimal energy mix of a district energy system in the preliminary design phase.   A case study consisting of the new campus Albano in Stockholm (comprising lecture buildings and student residences) was used for the analysis. A wide range of energy systems was included as a design variable: wind turbines, solar thermal collectors and photovoltaic cells, ground-source heat pumps, biomass boilers, combined cooling, heating and power, district heating and district cooling. The energy provided by the chosen technologies and the district energy balances are simulated on an annual basis using a steady-state method with an hourly resolution.   Three objectives functions were to be minimized: (1) the life-cycle costs; (2) the greenhouse gas emissions; and (3) the annual non-renewable primary energy consumption of the district. The optimization process was implemented on MOBO, a multi-objective optimization tool based on genetic algorithms.   The findings include understanding the trade-offs among the three objectives and a selection of alternatives of energy supply systems to be further investigated in the detailed design phase.