Now showing 1 - 10 of 10
- PublicationHigh Resolution Space - Time Data: Methodology for Residential Building Simulation ModellingA bottom-up approach is developed for the specification of operational data with a high spacetime resolution, to be used as inputs in multi-zone residential building models. These archetype models will be used to analyse demand modulation of total domestic electricity consumption, thus requiring a detailed knowledge of domestic loads. The approach is based on national Time-Use Survey (TUS) resident activity data. To illustrate the approach, the EnergyPlus simulation platform is used to model a multi-zone case study building. Occupancy profiles, lighting load and disaggregated electrical appliance load profiles, as well as their associated heat gains, are generated and spatially mapped within the building. A good match is seen between synthesised and measured profiles. A greater sharing of electrical appliances, as the household size increases, is also seen. Fifteen-minute resolution of the model outputs is found to be sensible in the context of the current project, due to aggregation.
- PublicationQuantification and characterization of energy flexibility in the residential building sectorDemand response can enable residential consumers to take advantage of control signals and/or financial incentives to adjust the use of their resources at strategic times. These resources usually refer to energy consumption, locally distributed electricity generation, and energy storage. The building structural mass has an inherent potential either to modify consumption or to be used as a storage medium. In this paper, the energy flexibility potential of a residential building thermal mass for the winter design day is investigated. Various active demand response strategies are assessed using two flexibility indicators: the storage efficiency and storage capacity. Using simulation, it is shown that the available capacity and efficiency associated with active demand response actions depend on thermostat setpoint modulation, demand response event duration, heating system rated power and current consumption.
- PublicationA Genetic Algorithm for a Green Vehicle Routing ProblemWe 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.
- PublicationLessons learned from the use of the SlideWiki OpenCourseWare platform in different learning contextsThe SlideWiki platform is fostering open education by supporting the collaborative authoring, sharing, reusing and remixing of open educational content online. This paper presents the lessons learned from piloting the SlideWiki platform in different learning contexts and scenarios. In particular, we describe the learning scenarios considered in each pilot and how these scenarios have been implemented with the use of SlideWiki. We then describe the methodology followed in each pilot, in terms of the authoring, teaching and learning activities performed by educators and learners using SlideWiki. Finally, the lessons learned from each pilot are discussed, focusing on the challenges faced in each pilot, how these challenges have been addressed, as well as the best practices that have emerged from the pilots regarding the collaborative authoring and sharing of open educational content.
- PublicationA Generic Energy Flexibility Evaluation Framework to Characterise the Demand Response Potential of Residential BuildingsDemand response can enable residential consumers to take advantage of control signals and/or financial incentives to adjust the use of their resources at strategic times. These resources usually refer to energy consumption, locally distributed electricity generation, and energy storage. The building structural mass has an inherent potential either to modify consumption or to be used as a storage medium. In this paper, the energy flexibility potential of a residential building thermal mass for the winter design day is investigated. Various active demand response strategies are assessed using two flexibility indicators: the storage efficiency and storage capacity. Using simulation, it is shown that the available capacity and efficiency associated with active demand response actions depend on thermostat setpoint modulation, demand response event duration, heating system rated power and current consumption.
- PublicationDefinition of a useful minimal-set of accurately-specified input data for Building Energy Performance SimulationDeveloping BEPS models which predict energy usage to a high degree of accuracy can be extremely time consuming. As a result, assumptions are often made regarding the input data required. Making these assumptions without introducing a significant amount of uncertainty to the model can be difficult, and requires experience. Even so, rules of thumb from one geographic region are not automatically transferrable to other regions. This paper develops a methodology which can be used to determine useful guidelines for defining the most influential input data for an accurate BEPS model. Differential sensitivity analysis is carried out on parametric data gathered from five archetype dwelling models. The sensitivity analysis results are used in order to form a guideline minimum set of accurately defined input data. Although the guidelines formed apply specifically to Irish residential dwellings, the methodology and processes used in defining the guidelines is highly repeatable. The guideline minimum data set was applied to practical examples in order to be validated. Existing buildings were modelled, and only the parameters within the minimum data set are accurately defined. All building models predict annual energy usage to within 10% of actual measured data, with seasonal energy profiles well-matching.
732Scopus© Citations 35
- PublicationOn the assessment and control optimisation of demand response programs in residential buildingsThe ability to control and optimise energy consumption at end-user level is of increasing interest as a means to achieve a balance between supply and demand, particularly when large penetration of distributed renewable energy sources is being considered. Demand Response programs consist of a series of externally-driven control strategies aimed at adapting consumer end-use load to specific grid requirements. In a demand response scenario, a network of connected systems can be exploited to activate balancing strategies, to provide demand flexibility during periods of high stress for the grid. However, the widespread deployment of demand response programs in the building sector still faces significant challenges. Smart technology deployment, the lack of common standardised assessment procedures and metrics, the absence of established regulatory frameworks are among the main obstacles limiting the development of portfolios of competitive flexibility assets. The residential sector is even more affected by these challenges due to a marginal economic case, the issue of long term harmonisation of hardware and software infrastructure and the influence of the end-user behaviour and preferences on energy consumption. The present paper provides a review on the current developments of the Demand Response programs, with specific reference to the residential building sector. Methodologies and procedures for assessing building energy flexibility and Demand Response programs are described with a special focus on numerical models and available control algorithms. Moreover, markets schemes and social aspects - such as technology acceptance and awareness - and their influence on smart control technologies and algorithms are discussed. Current research gaps and challenges are identified and analysed to provide guidance for future research activities.
180Scopus© Citations 58
- PublicationSelf-Learning Control Algorithms for Energy Systems Integration in the Residential Building SectorThis paper provides a research plan focusing on the application of self-learning techniques for energy systems integration in the residential building sector. Demand response is becoming increasingly important in the evolution of the power grid since demand no longer necessarily determines system supply but is now more closely constrained by generation profiles. Demand response can offer energy flexibility services across wholesale and balancing markets. Different applications have focused on the Internet of Things in demand response to assist customers, aggregators and utility companies to manage the energy consumption and energy usage through the adjustment of consumer behaviour. Even though there is extensive work in the literature regarding the potential of the commercial and the residential building sectors to provide flexibility, to date there is no standardised framework to evaluate this flexibility in a customer-Tailored way. At the same time, demand response events may affect occupant comfort expectations hindering the utilisation of flexibility that building energy systems can provide. In this research, the integration of machine learning algorithms into building control systems is investigated, in order to unify the monitoring and control of the separate systems under a holistic approach. This will allow the operation of the systems to be optimised with respect to reducing their energy consumption and their environmental footprint in tandem with the maximisation of flexibility, while maintaining occupant comfort.
158Scopus© Citations 4
- PublicationSimApi, a smartgrid co-simulation software platform for benchmarking building control algorithmsThis paper describes an open source smart grid software infrastructure for co-simulation between cloud-based energy management systems and a building energy model. The core component of the infrastructure is an API, which provides a protocol abstraction as a decoupling mechanism between the control algorithms and the building, thereby facilitating the development and testing of intelligent controllers. The open-source infrastructure can be utilised for the development and benchmarking of smart grid demand response algorithms aimed at reducing the energy consumption and the carbon footprint of buildings, while facilitating the integration of renewable energies into the power system.
349Scopus© Citations 20
- PublicationThe effect of time-of-use tariffs on the demand response flexibility of an all-electric smart-grid-ready dwellingThe paper is concerned with the development and evaluation of control algorithms for the implementation of demand response strategies in a smart-grid enabled all-electric residential building. The dwelling is equipped with a 12 kW heat pump, a 0.8 m3 water storage tank, a 6 kW photovoltaic (PV) array, solar thermal collectors for domestic hot water heating and an electric vehicle. The building, located in Ireland, is fully instrumented. An EnergyPlus building simulation model of the dwelling was developed and calibrated using monitored data from the building. The developed model is used to assess the effectiveness of demand response strategies using different time-of-use electricity tariffs in conjunction with zone thermal control. A reduction in generation cost (−22.5%), electricity end-use expenditure (−4.9%) and carbon emission (−7.6%), were estimated when DR measures were implemented and compared with a baseline system. Furthermore, when the zone control features were enabled, the efficiency of the control improved significantly giving, an overall annual economic saving of 16.5% for the residential energy cost. The analysis also identified an annual reduction of consumer electricity consumption of up to 15.9%, lower carbon emissions of 27% and facilitated greater utilisation of electricity generated by grid-scale renewable resources, resulting in a reduction of generation costs for the utility of up to 45.3%.
151Scopus© Citations 60