Now showing 1 - 9 of 9
  • Publication
    Smart Grid Topology Designs
    (OpenProceedings.org, 2019-06-12) ;
    This paper addresses supports for evolving design demands of electricity low voltage networks in urban areas. Innovations in how electricity is generated and supplied are required to support transformation of energy systems in response to climate change. We describe a MIP model to support grid upgrade decisions in the context of an energy community in an existing urban setting. We evaluate the MIP model on an adaption of an IEEE radial network benchmark instance augmented with geographic data. We present interesting computational results which suggest additional arcs to be added. Our results highlight potential research opportunities for the network optimisation community to facilitate the desired energy systems transformation challenge.
      300
  • Publication
    Validating unit commitment models: A case for benchmark test systems
    Due to increasing penetration of non-traditional power system resources; e.g. renewable generation, electric vehicles, demand response, etc. and computational power there has been an increased interest in research on unit commitment. It therefore may be important to take another look at how unit commitment models and algorithms are validated especially as improvements in solutions and algorithmic performance are desired to combat the added complexity of additional constraints. This paper explores an overview of the current state of unit commitment models and algorithms, and finds improvements for both comparing and validating models with benchmark test systems. Examples are provided discussing the importance for a standard benchmark test system(s) and why it is needed to compare and validate the real world performance of unit commitment models.
      582Scopus© Citations 7
  • Publication
    Reflections on Sustainability Issues in Learning Object Development
    (Universitat Politècnica València, 2019-06-28) ; ;
    Data science is a relatively new requirement in business curricula. Historically many business students have shied away from business statistics. We describe a project to create learning objects to enhance business students confidence and capabilities in performing statistical and analytics business tasks. In this paper we focus on the content development process, rather than the impact of the learning objects on student learning outcomes. We reflect on the steps in the learning object design and implementation project and conclude that the Plan, Act, Observe and Reflect iterative cycle worked well for the project team. We include recommendations on how this framework could be augmented to improve the sustainability of learning objects.
      327
  • Publication
    A Genetic Algorithm for a Green Vehicle Routing Problem
    We propose a Genetic Algorithm (GA) to address a Green Vehicle Routing Problem (G-VRP). Unlike classic formulations of the VRP, this study aims to minimise the CO 2 emissions per route. The G-VRP is of interest to policy makers who wish to reduce greenhouse gas emissions. The GA is tested on a suite of benchmark, and real-world instances which include road speed and gradient data. Our solution ap- proach incorporates elements of local and population search heuristics. Solutions are compared with routes currently used by drivers in a courier company. Reductions in emissions are achieved without incurring additional operational costs.
      1574
  • Publication
    Creating and Characterising Electricity Load Profiles of Residential Buildings
    Intelligent planning, control and forecasting of electricity usage has become a vitally important element of the modern conception of the energy grid. Electricity smart-meters permit the sequential measurement of electricity usage at an aggregate level within a dwelling at regular time intervals. Electricity distributors or suppliers are interested in making general decisions that apply to large groups of customers, making it necessary to determine an appropriate electricity usage behaviour-based clustering of these data to determine appropriate aggregate load profiles. We perform a clustering of time series data associated with 3670 residential smart meters from an Irish customer behaviour trial and attempt to establish the relationship between the characteristics of each cluster based upon responses provided in an accompanying survey. Our analysis provides interesting insights into general electricity usage behaviours of residential consumers and the salient characteristics that affect those behaviours. Our characterisation of the usage profiles at a fine-granularity level and the resultant insights have the potential to improve the decisions made by distribution and supply companies, policy makers and other stakeholders, allowing them, for example, to optimise pricing, electricity usage, network investment strategies and to plan policies to best affect social behavior.
      462Scopus© Citations 5
  • Publication
    Sub-hour Unit Commitment MILP Model with Benchmark Problem Instances
    Power systems are operated to deliver electricity at minimum cost while adhering to operational and technical constraints. The introduction of smart grid technologies and renewable energy sources offers new challenges and opportunities for the efficient and reliable management of the grid. In this paper we focus on a Mixed Integer Programming sub-hour Unit Commitment model. We present analysis of computational results from a large set of problem instances based on the Irish system and show that problem instances with higher variability in net demand (after the integration of renewables) are more challenging to solve.
      367Scopus© Citations 1
  • Publication
    Program Optimisation with Dependency Injection
    (Springer, 2013-04) ;
    For many real-world problems, there exist non-deterministic heuristics which generate valid but possibly sub-optimal solutions. The program optimisation with dependency injection method, introduced here, allows such a heuristic to be placed under evolutionary control, allowing search for the optimum. Essentially, the heuristic is “fooled” into using a genome, supplied by a genetic algorithm, in place of the output of its random number generator. The method is demonstrated with generative heuristics in the domains of 3D design and communications network design. It is also used in novel approaches to genetic programming.
      496Scopus© Citations 3
  • Publication
    Learning to Sparsify Travelling Salesman Problem Instances
    In order to deal with the high development time of exact and approximation algorithms for NP-hard combinatorial optimisation problems and the high running time of exact solvers, deep learning techniques have been used in recent years as an end-to-end approach to find solutions. However, there are issues of representation, generalisation, complex architectures, interpretability of models for mathematical analysis etc. using deep learning techniques. As a compromise, machine learning can be used to improve the run time performance of exact algorithms in a matheuristics framework. In this paper, we use a pruning heuristic leveraging machine learning as a pre-processing step followed by an exact Integer Programming approach. We apply this approach to sparsify instances of the classical travelling salesman problem. Our approach learns which edges in the underlying graph are unlikely to belong to an optimal solution and removes them, thus sparsifying the graph and significantly reducing the number of decision variables. We use carefully selected features derived from linear programming relaxation, cutting planes exploration, minimum-weight spanning tree heuristics and various other local and statistical analysis of the graph. Our learning approach requires very little training data and is amenable to mathematical analysis. We demonstrate that our approach can reliably prune a large fraction of the variables in TSP instances from TSPLIB/MATILDA (>85%) while preserving most of the optimal tour edges. Our approach can successfully prune problem instances even if they lie outside the training distribution, resulting in small optimality gaps between the pruned and original problems in most cases. Using our learning technique, we discover novel heuristics for sparsifying TSP instances, that may be of independent interest for variants of the vehicle routing problem.
      25
  • Publication
    Motivational Assessment Strategies in Business Analytics
    (Association for Information Systems, 2016-12-11) ;
    This teaching case describes an assessment component which was developed as part of a core undergraduate module in data analysis. The data analysis module is taken by all undergraduate business students at the School of Business in University College Dublin. We give a detailed description of the team project assessment component. This component was designed to address two key problems in business analytics education: how to engage business students in quantitative analysis and how to foster decision-making based on data analysis. We present promising results with analysis and some recommendations.
      288