Abstract: Energy hub model is a powerful concept allowing the interactions of many energy conversion and storage systems to be optimized. Solving the optimal configuration and operating strategy of an energy hub combining multiple energy sources for a whole year can become computationally demanding. Indeed the effort to solve a mixed-integer linear programming (MILP) problem grows dramatically with the number of integer variables. This paper presents a rolling horizon approach applied to the optimisation of the operating strategy of an energy hub. The focus is on the computational time saving realized by applying a rolling horizon methodology to solve problems over many time-periods. The choice of rolling horizon parameters is addressed, and the approach is applied to a model consisting of a multiple energy hubs. This work highlights the potential to reduce the computational burden for the simulation of detailed optimal operating strategies without using typical-periods representations. Results demonstrate the possibility to improve by 15 to 100 times the computational time required to solve energy optimisation problems without affecting the quality of the results.

Abstract: This paper presents an optimisation model and cost-benefit analysis framework for the quantification of the economic, climate change, and air quality impacts of the installation of a distributed energy resource system in the area surrounding Paddington train station in London, England. A mixed integer linear programming model, called the Distributed Energy Network Optimisation (DENO) model, is employed to design the optimal energy system for the district. DENO is then integrated into a cost-benefit analysis framework that determines the resulting monetised climate change and air quality impacts of the optimal energy systems for different technology scenarios in order to determine their overall economic and environmental impacts.

Abstract: In the paper we present an urban design support tool centered on pedestrian accessibility and walkability of places. Differently from standard decision support systems developed for the purpose of evaluating given pre-defined urban projects and designs, we address the inverse problem to have the software system itself generate hypotheses of projects and designs, given some (user-provided) objectives and constraints. Taking as a starting point a model for evaluating walkability , we construct a variant of a multi-objective genetic algorithm (specifically NSGA-II) to produce the frontier of non-dominated design alternatives to satisfy certain predefined constraints. By way of example, we briefly present an application of the system to a real urban area.