Now showing 1 - 10 of 13
  • Publication
    Quantification and characterization of energy flexibility in the residential building sector
    (International Building Performance Association, 2019-09-04) ; ; ; ; ;
    Demand 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.
      276
  • Publication
    A Study on the Trade-off between Energy Forecasting Accuracy and Computational Complexity in Lumped Parameter Building Energy Models
    The development of urban scale cost-optimal retrofit decision making requires the development of simplified building energy models which provide satisfactory energy prediction accuracy while remaining tractable when implemented at scale. Lumped parameter building energy models are computationally efficient representations of building thermal performance. The current paper introduces a user-led iterative model reduction methodology which identifies potential trade-offs between model complexity (thus computational requirements) and energy estimation accuracy. Model complexity is progressively reduced using an energy performance criterion prior to model trimming. The methodology is applied to a building energy model of a mixed-use building, which is developed in the EnergyPlus Building Energy Model Simulation (BEMS) environment. The energy performance of the building is evaluated using a linear energy minimisation problem. The proposed methodology shows a potential reduction by half of the model complexity is possible, while retaining annual energy estimation errors below 10% for the target building.
      461
  • Publication
    Feature Assessment in Data-Driven Models for Unlocking Building Energy Flexibility
    Data-driven approaches are playing an increased role in building automation. This can, in part, be attributed to building operation and energy management system data becoming more readily accessible. A particular application is models to allow predictive control harnessing building energy flexibility, which is of interest to different stakeholders including; energy utilities, aggregators and end-users. Given the possibility of thousands of data features, feature selection becomes a critical part of the model development process. This paper considers various filter, wrapper and embedded methods applied in conjunction with three predictors in addressing the problem of constructing a suitable data-driven model to facilitate predictive control and provision of energy flexibility in a large commercial building. The feature selection algorithms are generally shown to significantly reduce model evaluation time and, in some cases, increase model accuracy. A random forest model with embedded feature selection was found to be the optimal solution in terms of model accuracy.
      351
  • Publication
    Technical and economic assessment of a hybrid heat pump system as an energy retrofit measure in a residential building
    Air to water electric heat pumps are one technological solution to achieve energy defossilisation goals for heating of residential building stock. Nevertheless, they may not necessarily be the only solution for all residential building stock. A case in point is where extensive fabric refurbishment is impracticable or where electric heat pumps are installed where low ambient temperatures prevail and/or high water delivery temperatures must be utilised. For such instances, hybrid (gas and electric) heat pumps offer an alternative option by facilitating fuel source switching between electricity and gas, when ambient temperatures are low or high water supply temperatures are required. In the current study, the effectiveness of an air-to-water electric heat pump and hybrid heat pump are examined for different building retrofit scenarios for a residential dwelling located in Ireland. This is achieved by means of a sensitivity study of a validated building simulation model, incorporating both heat pump systems, subject to different building retrofit scenarios. Relative to a conventional oil-fired boiler, for a deep building retrofit scenario, the hybrid and electric heat pumps achieve primary energy reduction of 128 kWh/m2/year (72%) and of 123 kWh/m2/year (70%), respectively. Considering the associated carbon footprints, the reductions were found to be 29.7 gCO2e/m2/year (74%) for the hybrid heat pump, and 27.6 gCO2e/m2/year (68%) for the electric heat pump. Finally, the deployment of either an electric heat pump or hybrid heat pump for deep building fabric retrofit achieves approximately half of the heating system capital cost return within 20 years.
      17Scopus© Citations 10
  • Publication
    Towards Standardising Market-Independent Indicators for Quantifying Energy Flexibility in Buildings
    Buildings are increasingly being seen as a potential source of energy flexibility to the smart grid as a form of demand side management. Indicators are required to quantify the energy flexibility available from buildings, enabling a basis for a contractual framework between the relevant stakeholders such as end users, aggregators and grid operators. In the literature, there is a lack of consensus and standardisation in terms of approaches and indicators for quantifying energy flexibility. In the present paper, current approaches are reviewed and the most recent and relevant market independent indicators are compared through analysis of four different case studies comprising varying building types, climates and control schemes to assess their robustness and applicability. Of the indicators compared, certain indicators are found to be more suitable for use by the end user when considering energy and carbon dioxide emission reductions. Other indicators are more useful for the grid operator. The recommended indicators are found to be robust to different demand response contexts, such as type of energy flexibility, control scheme, climate and building types. They capture the provided flexibility quantity, its shifting efficiency and rebound effect. A final cost index is also recommended given specific market conditions to capture the cost of a building providing energy flexibility.
      324Scopus© Citations 37
  • Publication
    Impact of intelligent control algorithms on demand response flexibility and thermal comfort in a smart grid ready residential building
    The present paper investigates the impact of advanced control algorithms on harnessing building energy flexibility in a smart-grid ready full-electric residential building. The impact on thermal comfort is also analysed. The building is located in Ireland and is equipped with a geothermal heat pump and a thermal energy storage system. Two Energy Management systems, based on rule-based and intelligent optimisation algorithm approaches, are developed which use real-time building smart meter and weather data. This data is utilised by various dynamic flexibility metrics within the respective control algorithms. Different time of use tariffs, based on data from the Irish Commission for Energy Regulation and structured on the basis of peak, off-peak and night periods, are also used. Results show that energy cost reductions of up to 21% and 43% can be achieved by the rule-based and intelligent algorithm, respectively, without compromising the thermal comfort within the building. Moreover, total shifting and forcing flexibility potential of up to 34 and 54 kWh, respectively, based on the month of January, can be achieved by the adoption of the intelligent control algorithm.
    Scopus© Citations 21  10
  • Publication
    Data-Driven Predictive Control for Commercial Buildings with Multiple Energy Flexibility Sources
    Data-Driven Predictive Control, representing the building as a cyber-physical system, shows promising potential in harnessing energy flexibility for demand side management, where the efforts in developing a physics-based model can be significant. Here, predictive control using random forests is applied in a case study closed-loop simulation of a large office building with multiple energy flexibility sources, thereby testing the suitability of the technique for such buildings. Further, consideration is given to the feature selection and feature engineering process. The results show that the data-driven predictive control, under a dynamic grid signal, is capable of minimising energy consumption or energy cost.
      256
  • Publication
    Influence of electricity prices on energy flexibility of integrated hybrid heat pump and thermal storage systems in a residential building
    The aim of the present paper is to investigate the influence of electricity tariffs on energy flexibility in buildings and associated energy costs. A residential building located in Stuttgart, Germany, equipped with a hybrid heat pump which is coupled with a thermal energy storage unit and a gas boiler is used as a case study. A model predictive control algorithm is used to minimise the daily operational cost over a full heating season. Several demand response programs based on controlling the heat pump power consumption were tested and analysed by adopting different metrics capable of describing the flexibility potential and cost of demand response programs. Several tariff structures, including: real-time pricing, two-level day-night tariffs and critical-peak pricing with both fixed and variable feed-in price components, were investigated. The results show that the building can provide up to 1370 kWhe of energy flexibility over the heating season with an average specific (marginal) costs of between €0.024–0.035 per kWhe of flexibility provided. The demand response programs lead to higher utilisation of thermal energy storage along with increased boiler consumption, by up to 17.1% and 12.1%, respectively in case of maximum demand response intensity. This in turn leads to a higher overall primary energy consumption of between 1.6% and 9.1% depending on demand response intensity. Typically, real-time pricing is the most favourable tariff structure, capable of offering the greatest energy flexibility with lowest associated electricity costs.
      28Scopus© Citations 59
  • Publication
    On the assessment and control optimisation of demand response programs in residential buildings
    The 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.
      399Scopus© Citations 99
  • Publication
    Data-Driven Predictive Control for Commercial Buildings with Multiple Energy Flexibility Sources
    Data-Driven Predictive Control, representing the building as a cyber-physical system, shows promising potential in harnessing energy flexibility for demand side management, where the efforts in developing a physics-based model can be significant. Here, predictive control using random forests is applied in a case study closed-loop simulation of a large office building with multiple energy flexibility sources, thereby testing the suitability of the technique for such buildings. Further, consideration is given to the feature selection and feature engineering process. The results show that the data-driven predictive control, under a dynamic grid signal, is capable of minimising energy consumption or energy cost.
      200