Now showing 1 - 3 of 3
  • Publication
    Implementation of demand response strategies in a multi-purpose commercial building using a whole-building simulation model approach
    This paper exploits a whole-building energy simulation approach to develop and evaluate demand response strategies for commercial buildings. The research is motivated by the increasing penetration of renewable energy sources such as wind and solar, which owing to their stochastic nature, means that enhanced integration of demand response measures in buildings is becoming more challenging and complex. Using EnergyPlus, a simulation model of a multi-purpose commercial building was developed and calibrated. Demand response strategies are evaluated for a number of building zones, which utilise different heating, cooling and ventilation equipment. The results show that for events of varying demand response durations, different strategies should be selected for each zone based on their thermal and usage profiles. Overall, a maximum reduction of 14.7% in electrical power demand was recorded when targeting a centralised chiller load, with smaller reductions for other decentralised building loads.
      728Scopus© Citations 41
  • Publication
    The effect of time-of-use tariffs on the demand response flexibility of an all-electric smart-grid-ready dwelling
    The 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%.
    Scopus© Citations 81  180
  • Publication
    Utilising time of use surveys to predict domestic hot water consumption and heat demand profiles of residential building stocks
    (SCIENCEDOMAIN International, 2016-06) ; ; ;
    Aims: The prediction of water consumption patterns is a challenge, especially when water metering is not available at scale. The use of time-of-use survey (TUS) data offers an alternative to metering in order to track the general patterns of water consumption across large and representative groups of end-users. The paper focuses on the prediction of analytical domestic hot water (DHW) demand profiles for detailed building archetype models, using an occupant focused approach based on TUS data. The paper illustrates and discusses the resulting capability of dwelling archetypes to capture variations in heat demand and energy usage for water heating on a national scale and at high time resolution. Methodology: Five dwelling types are considered over different construction periods, representative of the majority of the Irish residential stock, which is used here as a case study. They are modelled at room level using EnergyPlus and converted into archetype models. A bottom-up approach is utilised to develop the required operational data at high space and time resolution. That methodology applies Markov Chain Monte Carlo techniques to TUS activity data to develop activity-specific profiles for occupancy and domestic equipment electricity use. It is extended to DHW demand profiles by combining the probability distributions for particular TUS activities with average daily DHW consumptions, depending on the household size, day type and season. Results: The archetype models capture variations in DHW consumption, heat demand and energy usage for DHW heating, on a national scale and a fifteen-minute basis. Moreover, they are found to be 90% accurate with the Irish standard dwelling energy assessment procedure in estimating the annual energy requirements for DHW heating. Conclusion: This study demonstrates the potential for utilising time of use surveys to predict domestic water demand profiles on a national scale and at high time resolution.
      1010