Clustering Urban Areas for Optimizing the Design and the Operation of District Energy Systems

Abstract Solving the MILP model for optimizing the design and operating strategy of district energy systems (DES) is a computationally demanding task due to the large number of subsystems (i.e. resources, conversion technologies, buildings and networks) and corresponding decision variables. In order to reduce the number of decision variables and therefore the computational load of the problem, this paper presents a systematic procedure to represent an urban area with a macroscopic view as a set of “integrated zones”. The integrated zone is an area where consumers, resources and energy conversion technologies are integrated. This is obtained by developing aggregated district integration models based on GIS data and applying k-means clustering techniques. By using the proposed method, the regional DES is partitioned into limited number of integrated zones. The selected zones allow us to achieve accurate representation of the whole district while significantly reducing the number of decision variables for which more detailed optimization methods can be applied.