Practical Optimisation of District Energy Systems: Representation of Technology Characteristics, Demand Uncertainty, and System Robustness

District energy systems are an alternative to conventional national-scale networks to meet demand in urban areas. Decentralising electricity production reduces distribution losses, while centralising thermal energy production capitalises on economies of scale. Furthermore, geographic proximity facilitates the use of waste heat from electricity production to meet thermal demand. However, it is difficult to assess which configuration of technologies will best suit a cluster of users. Mathematical optimisation techniques have been extensively researched as a method to resolve this, as they can simplify the design of investment portfolios and operation schedules for a given set of geolocated demands. However, they are not yet practically applicable. This thesis uses the open-source, mixed integer linear programming framework Calliope to present three methodological enhancements which address model simplification, parameter uncertainty, and conflicting decision-maker objectives. These enhancements enable the practical design of district energy systems through data-centric workflows which can readily represent real system complexities in a tractable optimisation model. This thesis first examines the impact of parameter simplification in a linear model. Piecewise linearisation is applied to nonlinear part-load energy consumption curves and a pre-processing step is developed to optimise breakpoint positioning along a piecewise curve. Second, a three-step method is proposed to handle demand uncertainty in linear models, using historical demand data. These steps are scenario generation, scenario reduction, and scenario optimisation. Using out-of-sample tests, robustness of optimal investments to unmet demand is quantified. Furthermore, system resilience to unexpected events is explored by introducing interruptions to the national electricity grid availability for a district in Bangalore, India. Scenario optimisation is extended to account for these interruptions as well as to mitigate unfavourably high levels of CO$_2$ emissions in system design. Finally, this thesis identifies eight possible decision makers, who each hold a different objective in the design of a district energy system. Optimal technology configuration and out-of-sample test results are compared across all decision-maker objectives. These methodological enhancements demonstrate the capability of optimisation models to be reflective of reality whilst being transparent concerning the impact of simplifications, uncertainty, and conflicting objectives. Calliope is extended in this thesis to be practically applicable for district energy systems. Moreover, its extensibility facilitates the continuation of development, including possible future work into data validation and spatial dimension simplification.This research was supported by the Engineering and Physical Sciences Research Council (grant EP\L016095\1). This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (http://www.csd3.cam.ac.uk/), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP\P020259\1), and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk)

Labelled Graph Strategic Rewriting for Social Networks

International audienceWe propose an algebraic and logical approach to the study of social networks, where network components and processes are directly defined by labelled port graph strategic rewriting. Data structures attached to graph elements (nodes, ports and edges) model local and global knowledge in the network, rewrite rules express elementary and local transformations , and strategies control the global evolution of the network. We show how this approach can be used to generate random networks, simulate existing propagation and dissemination mechanisms, and design new, improved algorithms

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

The role of airports in national civil aviation policies

The concept of a hub airport has evolved widening its scope as a national civil aviation policy-making tool, due to the ability to deliver wider socio-economic benefits to a country. However, not all airports can be converted into hubs. This research proposes a methodological approach to structural analysis of the airport industry, that could be applied to determine the competitive position of an airport in a given aviation network and devise airport strategies and national policy measures to improve the current position of the airport. This study presents a twelve-group taxonomy of airports, which analyses the changing geography of the airport industry in the East (Asia and The Middle East). Multivariate data have been used in a two-step Agglomerative Hierarchical Clustering exercise which represents three airport strategies: namely, degree-of-airport-activity (size and intensity of operations), network strategies (international and domestic hub), and the market segmentation strategies (service and destination orientation). Principal Component Analysis has been utilised as a data reduction tool. The study confirms the general hypothesis that a sound macro environment and liberalised approach to economic regulation in the air transport industry are important for successful hub operations. In addition, it sheds light on the fact that while the factors of geographical advantage, economic development, urbanisation, tourism and business attractiveness, physical and intellectual infrastructure, and political and administrative frameworks, are all basic prerequisites (qualifiers) for successful hubbing in the region, those factors would not necessarily guarantee a hub status unless the governments are also committed to develop the sector and take timely decisions (differentiators) to allow airports to benefit from the first mover advantage. Application of the proposed taxonomy was tested on a case study of the major international airport of Sri Lanka, to provide policy inputs to develop the airport that is currently identified as being overshadowed by the mega hubs in the region

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios