A fundamental unified framework to quantify and characterise energy flexibility of residential buildings with multiple electrical and thermal energy systems

Abstract To date, the energy flexibility assessment of multicomponent electrical and thermal systems in residential buildings is hindered by the lack of adequate indicators due to the different interpretations, properties, and requirements that characterise an energy flexible building. This paper addresses this knowledge gap by presenting a fundamental energy flexibility quantification framework applicable to various energy systems commonly found in residential buildings (i.e., heat pumps, renewables, thermal and electrical storage systems). Using this framework, the interactions between these systems are analysed, as well as assessing the net energy cost of providing flexibility arising from demand response actions where onsite electricity production is present. A calibrated white-box model of a residential building developed using EnergyPlus (including inter alia a ground source heat pump, a battery storage system, and an electric vehicle) is utilised. To acquire daily energy flexibility mappings, hourly independent, and consecutive demand response actions are imposed for each energy system, using the proposed indicators. The obtained flexibility maps give insights into both the energy volumes associated with demand response actions and qualitative characteristics of the modulated electricity consumption curves. The flexibility potential of each studied energy system is determined by weather and occupant thermal comfort preferences as well as the use of appliances, lighting, etc. Finally, simulations show that zone and water tank thermostat modulations can be suitably combined to shift rebound occurrences away from peak demand periods. These insights can be used by electricity aggregators to evaluate a portfolio of buildings or optimally harness the flexibility of each energy system to shift peak demand consumption to off-peak periods or periods of excess onsite electricity generation.

[1]  Paul S. Moses,et al.  Impacts of battery charging rates of Plug-in Electric Vehicle on smart grid distribution systems , 2010, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe).

[2]  Omer Tatari,et al.  Getting to net zero energy building: Investigating the role of vehicle to home technology , 2016 .

[3]  Anna Joanna Marszal,et al.  IEA EBC Annex 67 Energy Flexible Buildings , 2017 .

[4]  Geert Deconinck,et al.  Demand response flexibility and flexibility potential of residential smart appliances: Experiences from large pilot test in Belgium , 2015 .

[5]  T. Bräunl,et al.  The technical, economic and commercial viability of the vehicle-to-grid concept , 2012 .

[6]  Donal Finn,et al.  Development of occupancy-integrated archetypes: Use of data mining clustering techniques to embed occupant behaviour profiles in archetypes , 2019, Energy and Buildings.

[7]  S. Vermette,et al.  Development of a Severe Winter Index: buffalo, New y ork , 2008 .

[8]  Shahram Jadid,et al.  Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system , 2015 .

[9]  Lieve Helsen,et al.  Reduction of heat pump induced peak electricity use and required generation capacity through thermal energy storage and demand response , 2017 .

[10]  Adam Hawkes,et al.  Performance assessment of tariff-based air source heat pump load shifting in a UK detached dwelling featuring phase change-enhanced buffering , 2014 .

[11]  Jaume Salom,et al.  Evaluation of energy flexibility of low-energy residential buildings connected to district heating , 2020, Energy and Buildings.

[12]  José Sánchez Ramos,et al.  Contributions of heat pumps to demand response: A case study of a plus-energy dwelling , 2018 .

[13]  Lieve Helsen,et al.  Quantification of flexibility in buildings by cost curves – Methodology and application , 2016 .

[14]  Donal Finn,et al.  A restful API to control a energy plus smart grid-ready residential building: demo abstract , 2014, BuildSys@SenSys.

[15]  Bing Dong,et al.  Market and behavior driven predictive energy management for residential buildings , 2018 .

[16]  Francesco D’Ettorre,et al.  On the assessment and control optimisation of demand response programs in residential buildings , 2020, Renewable and Sustainable Energy Reviews.

[17]  Miadreza Shafie-Khah,et al.  A Stochastic Home Energy Management System Considering Satisfaction Cost and Response Fatigue , 2018, IEEE Transactions on Industrial Informatics.

[18]  Dirk Saelens,et al.  Generic characterization method for energy flexibility: Applied to structural thermal storage in residential buildings , 2017 .

[19]  Linda Barelli,et al.  Residential micro-grid load management through artificial neural networks , 2018 .

[20]  Neil Hewitt,et al.  Heat pumps and energy storage – The challenges of implementation , 2012 .

[21]  P. Pinson,et al.  Wind power in electricity markets and the value of forecasting , 2017 .

[22]  Thomas Nuytten,et al.  Flexibility of a combined heat and power system with thermal energy storage for district heating , 2013 .

[23]  Henrik Madsen,et al.  Characterizing the energy flexibility of buildings and districts , 2018, Applied Energy.

[24]  Dirk Saelens,et al.  Energy flexible buildings: an evaluation of definitions and quantification methodologies applied to thermal storage , 2018 .

[25]  Standard Ashrae Thermal Environmental Conditions for Human Occupancy , 1992 .

[26]  Hussain Kazmi,et al.  Determinants of energy flexibility in residential hot water systems , 2019, Energy and Buildings.

[27]  Sunliang Cao,et al.  Quantification of energy flexibility of residential net-zero-energy buildings involved with dynamic operations of hybrid energy storages and diversified energy conversion strategies , 2020 .

[28]  Donal P. Finn,et al.  A high-temporal resolution residential building occupancy model to generate high-temporal resolution heating load profiles of occupancy-integrated archetypes , 2020 .

[29]  Steve Pye,et al.  Assessing the benefits of demand-side flexibility in residential and transport sectors from an integrated energy systems perspective , 2018, Applied Energy.

[30]  Carsten Rode,et al.  Heating system energy flexibility of low-energy residential buildings , 2018, Energy and Buildings.

[31]  P. André,et al.  Smart grid energy flexible buildings through the use of heat pumps and building thermal mass as energy storage in the Belgian context , 2015 .

[32]  Daniel Coakley,et al.  Calibration of detailed building energy simulation models to measured data using uncertainty analysis , 2014 .

[33]  Rp Rick Kramer,et al.  Quantifying demand flexibility of power-to-heat and thermal energy storage in the control of building heating systems , 2018 .

[34]  Thibault Péan,et al.  Towards standardising market-independent indicators for quantifying energy flexibility in buildings , 2020 .

[35]  D. Finn,et al.  Environmental and economic benefits of building retrofit measures for the residential sector by utilizing sensor data and advanced calibrated models , 2020, Advances in Building Energy Research.

[36]  Mo M. Jamshidi,et al.  A New Intelligent Neuro–Fuzzy Paradigm for Energy-Efficient Homes , 2014, IEEE Systems Journal.

[37]  P Pieter-Jan Hoes,et al.  Analysis of control strategies for thermally activated building systems under demand side management mechanisms , 2014 .

[38]  R. Newman Promotion of the use of energy from renewable sources , 2014 .

[39]  Jakob Stoustrup,et al.  Contribution of domestic heating systems to smart grid control , 2011, IEEE Conference on Decision and Control and European Control Conference.

[40]  Federico Milano,et al.  SimApi, a smartgrid co-simulation software platform for benchmarking building control algorithms , 2019, SoftwareX.

[41]  Per Heiselberg,et al.  Energy flexibility of residential buildings using short term heat storage in the thermal mass , 2016 .

[42]  Andrea Costa,et al.  Model calibration for building energy efficiency simulation , 2014 .

[43]  M. Mourshed Relationship between annual mean temperature and degree-days , 2012 .

[44]  Sebastian Stinner,et al.  Quantifying the operational flexibility of building energy systems with thermal energy storages , 2016 .