Cities, covering only 3% of the Earth's land, account for 60-80% of global primary energy consumption and roughly 75% of the world's carbon emissions . Besides, 55% of the world's population live in urban areas, a figure expected to reach 68% by 2050 . Hence, urban areas offer a great potential for reducing the global energy consumption and emissions as well as improving the quality of life for a large portion of the human population.
Building on a previous work by Best et al. , this research tries to simultaneously design and optimize the supply and demand of heating, cooling, electricity, and water/wastewater at a district or community scale, i.e. 100-1000 neighboring buildings, on an hourly time-scale. The natively modeled supply technologies, building prototypes (demand profiles), loss models, and climate characteristics are modular and adaptable to desired inputs and conditions. A genetic algorithm explores thousands of possible solutions given the inputs and constraints of the optimization and tries to find the optimal solutions in terms of energy efficiency, carbon emissions, life-cycle cost, and graywater treatment efficiency. The desired alternatives can be selected based on subjective considerations of the infrastructure designers and urban planners. Figure 1 shows the modeling approach for each infrastructure-neighborhood configuration.
Figure 1 - Modeling approach for a neighborhood-infrastructure configuration
The results of this study can provide a range of building settings and supply technologies that can satisfy the constraints and objectives for developing or renovating an urban neighborhood. This can help the designers inspect and select the most sustainable and resilient of these settings during early-stage design. It can also aid urban planners with enacting regulations leading to more sustainable cities.
 Best, R. E., Flager, F., & Lepech, M. D. (2015). Modeling and optimization of building mix and energy supply technology for urban districts. Applied energy, 159, 161-177.