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Article TitleAuthor, YearPublication YearSource TitleURL with DOIUse CaseBuilding SectorScopeMethodFlexibility ResourcesControl StrategyStakeholdersData-driven?KPI Has Formula?KPI Needs Baseline?KPI Level of Complexity
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Large-scale demonstration of precise demand response provided by residential heat pumps(Müller & Jansen, 2019)2019APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2019.01.202322 households with HPs222HP coordinationRBCDSO; TSOdirect calculationYesNoMedium
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Management and Activation of Energy Flexibility at Building and Market Level: A Residential Case Study(Taddeo et al., 2020)2020Energieshttp://dx.doi.org/10.3390/en130511881 semi-virtual multi-family residential building113electricity storage; temperature setpoint adjustmentMPCDSO; TSO; homeownerscalculation with modelingYesYesVarious
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Quantifying energy flexibility of commuter plug-in electric vehicles within a residence-office coupling virtual microgrid. Part I: System configuration, quantification framework, and optimization model(Yu et al., 2022)2022ENERGY AND BUILDINGShttps://doi.org/10.1016/j.enbuild.2021.111551Virtual microgrid with residential buildings, an office, distributed generation, and EVs1, 221EVRBCDSO; TSO; homeowners; EV ownerscalculation with modelingYesYesHigh
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Influence of envelope, structural thermal mass and indoor content on the building heating energy flexibility(Johra et al., 2019)2019ENERGY AND BUILDINGShttps://doi.org/10.1016/j.enbuild.2018.11.012Simulation of single-family dwellings in Denmark with variations of envelope properties and building thermal inertia211temperature setpoint adjustmentRBCDSO; TSO; householdscalculation with modelingYesYesLow
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Simulation-based techno-economic feasibility study on sector coupled net-zero/positive energy metro railway system in Hong Kong(Kumar & Cao, 2021)2021ENERGY CONVERSION AND MANAGEMENThttps://doi.org/10.1016/j.enconman.2021.114786Battery-based metro train interacting with several metro station buildings321electricity storageRBCDSO; TSOdirect calculationYesNoLow
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Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data(Ziras et al., 2019)2019APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2019.03.156138 households with HPs and electric heaters222temperature setpoint adjustmentRBCDSO; TSOcalculation with modelingYesNoLow
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Demand response implementation for improved system efficiency in remote communities(Wrinch et al., 2012)2012IEEE Electrical Power and Energy Conferencehttps://doi.org/10.1109/EPEC.2012.647493232 commercial buildings122temperature setpoint adjustmentRBCDSO; TSO; electricity producerdirect calculationNoYesLow
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Responsiveness of residential electricity demand to dynamic tariffs: Experiences from a large field test in the Netherlands(Klaassen et al., 2016)2016APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2016.09.051188 households222shiftable loadsRBCDSO; homeownersdirect calculationYesYesMedium
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Assessing the energy flexibility of building clusters under different forcing factors(Vigna et al., 2021)2021JOURNAL OF BUILDING ENGINEERINGhttps://doi.org/10.1016/j.jobe.2021.1028884 single-family houses connected to a district heating network221temperature setpoint adjustmentRBCDSO; district heating network operator; homeownerscalculation with modelingYesYesLow
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Quantifying flexibility of commercial and residential loads for demand response using setpoint changes(Yin et al., 2016)2016APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2016.05.090Simulation of demand flexibility potential of TCLs in U.S. commercial and multi-dwelling residential buildings with field validation1, 221, 2temperature setpoint adjustment; DWHRBCgrid operator; DSON.A.YesYesLow
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Comparison of Flexibility Factors and Introduction of A Flexibility Classification Using Advanced Heat Pump Control(Hall & Geissler, 2021)2021ENERGIEShttps://doi.org/10.3390/en14248391small multi-family dwelling equiped with a ground-source heat pump211GSHP; DWHRBCbuilding managersN.A.YesNoMedium
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Quantifying changes in building electricity use, with application to demand response(Mathieu et al., 2011)2011IEEE TRANSACTIONS ON SMART GRIDhttps://doi.org/10.1109/TSG.2011.2145010Proposed a load profile characterization method for demand response events1, 312N.A.N.A.building managerYesYesMedium
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Rolling-horizon dispatch of advanced adiabatic compressed air energy storage based energy hub via data-driven stochastic dynamic programming(Bai et al., 2021)2021ENERGY CONVERSION AND MANAGEMENThttps://doi.org/10.1016/j.enconman.2021.114322 This paper investigates the external characteristics of advanced adiabatic compressed air energy storage and exploits its ability to implement an energy hub.N.A.N.A.2energy huboptimal controlN.A.calculation with modelingNoYesLow
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Investigation of design strategies and quantification of energy flexibility in buildings: A case-study in southern Italy(Marotta et al., 2021)2021JOURNAL OF BUILDING ENGINEERINGhttps://doi.org/10.1016/j.jobe.2021.102392Residential building in the Mediterranean climate: model created in TRNSYS and calibrated with real data of energy consumption derived from electricity and gas bills211electricity storage; temperature setpoint adjustmentRBCDSO; homeownersN.A.YesYesHigh
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An integrated flexibility optimizer for economic gains of local energy communities - A case study for a University campus(Tomar et al., 2021)2021SUSTAINABLE ENERGY GRIDS & NETWORKShttps://doi.org/10.1016/j.segan.2021.100518A Dutch university campus network with 14 large heterogeneous buildings122temperature setpoint adjustment; HP; PVoptimal controlgrid operator; campus operatorcalculation with modelingYesYesVarious
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Mapping demand flexibility: A spatio-temporal assessment of flexibility needs, opportunities and response potential(Agbonaye et al., 2021)2021APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2021.117015GIS-defined area of buildings in Nothern Ireland, in this case referred to as 'small area' consisting of ~ 155 households (400 persons)/small area where there are 4537 small areas in Northern Ireland. The aim of the study is to link socio-economic and housing data to electricity grid network and use to quantify energy flexibilty availability, opportunities, equality and equity on a spatio-temporal dimension. .222electricity storage; HP; TES; EVoptimal controlpolicymakers; TSO; DSOdirect calculationYesNoMedium
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Characterization of Aggregated Building Heating, Ventilation, and Air Conditioning Load as a Flexibility Service Using Gray-Box Modeling(Kohlhepp et al., 2021)2021ENERGY TECHNOLOGYhttps://doi.org/10.1002/ente.202100251Characterize aggregated (pooled) TCL flexibility considering stochastic modeling of on/off switches behaviors.221ASHP; TESoptimal controlgrid operator; DSOcalculation with modelingYesYesHigh
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Improving the energy flexibility of single-family homes through adjustments to envelope and heat pump parameters(Feldhofer & Healy, 2021)2021JOURNAL OF BUILDING ENGINEERINGhttps://doi.org/10.1002/ente.202100251The Net-Zero Energy Residential Test Facility (NZERTF) on the campus of the National Institute of Standard and Technology (NIST) in Gaithersburg, MD, USA. Is a single family completely electrified test house modeled in TRANSYS to study effect of DR, changes to building envelope, setpoints and HP system on KPIs.211HP; PCMRBChomeowners; building managers; utility companiescalculation with modelingYesYesLow
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An Evaluation Framework for Sustainable Plus Energy Neighbourhoods: Moving Beyond the Traditional Building Energy Assessment(Salom et al., 2021)2021ENERGIEShttps://doi.org/10.3390/en14144314Evaluation framework for integrated design processes aiming to select solutions for a positive energy district, including flexibility aspects.1, 2, 321, 2solar thermal; PV; electricity storage; TESN.A.facility managers; grid operators; policymakers; building owners; occupantsN.A.YesYesMedium
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Demand response through decentralized optimization in residential areas with wind and photovoltaics(Dengiz et al., 2021)2021ENERGYhttps://doi.org/10.1016/j.energy.2021.119984Coordinated decentralized control of flexible resources for reduced surplus energy from grid, reduced peak load and increased self-consumption for a cluster of residential buildings. Involves creation of schedules for the use of flexible resources as well as design of algorithm for wind power assignment to be added to available EV generation in achieiving the objective function.222building thermal mass; GSHP; ASHP; TES; EVoptimal controlaggregators, power plant operator, grid operatorcalculation with modelingYesYesHigh
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Agent-based stochastic model of thermostat adjustments: A demand response application(Vellei et al., 2021)2021ENERGY AND BUILDINGShttps://doi.org/10.1016/j.enbuild.2021.110846Two residential homes with smart thermostats221temperature setpoint adjustmentRBCbuilding owners; building operators; building occupantsVariousNoYesLow
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An assessment of power flexibility from commercial building cooling systems in the United States *(Huang et al., 2021)2021ENERGYhttps://doi.org/10.1016/j.energy.2020.11957110 DOE Commercial Prototye E+ models in 14 U.S. climate locations121temperature setpoint adjustmentRBCpolicymakers; funding agenciescalculation with modelingYesYesMedium
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The Role of Flexibility in Photovoltaic and Battery Optimal Sizing towards a Decarbonized Residential Sector(Dallapiccola et al., 2021)2021ENERGIEShttps://doi.org/10.3390/en14082326Analyse the impact of different aggregated demand
profiles and how flexibility can improve the penetration of photovoltaic systems towards
more sustainable districts. 		121electricity storage; PVRBCbuilding operatorsdirect calculationNoNoN.A.
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Effects of intermittent heating on an integrated heat and power dispatch system for wind power integration and corresponding operation regulation(Zheng et al., 2021)2021APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2021.116536A mix of offices and residential buildings connected to a district heating system (10 000 m²)1, 221CHP; district heating systemRBCdistrict energy designers and operatorsN.A.NoYesHigh
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Multi-objective two-stage adaptive robust planning method for an integrated energy system considering load uncertainty(Yan et al., 2021)2021ENERGY AND BUILDINGShttps://doi.org/10.1016/j.enbuild.2021.110741an industrial park in China where the floor area of four building types (residential, commercial, industrial, and educational) is 3285.7 × 103 m2, 1284 × 103 m2, 9820.6 × 103 m2, and 642 × 103 m2, respectively.321electricity storage; TESN.A.building owners; grid operatorscalculation with modelingYesNoMedium
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Cost/comfort-oriented clustering-based extended time of use pricing(Azizi et al., 2021)2021SUSTAINABLE CITIES AND SOCIETYhttps://doi.org/10.1016/j.scs.2020.102673a residential building with an electrical thermal system modeled as a controllable load and an electrical vehicle modeled as a delay-tolerant demand211electric space heating; EVN.A.homeowners; consumers; power suppliers; governmentcalculation with modelingYesYesMedium
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A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building through Predictive Control of Passive and Active Storage(Date et al., 2021)2021ENERGIEShttps://doi.org/10.3390/en14051387An MPC-based simulation of an office building used to estimate day-ahead heating requirements and determine zone temperature setpoints for optimized electric thermal storage111TESMPCoccupants; building ownerscalculation with modelingYesYesMedium
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Data Model for Residential and Commercial Buildings. Load Flexibility Assessment in Smart Cities(Oprea et al., 2021)2021SUSTAINABILITYhttps://doi.org/10.3390/su13041736DOE Commercial Prototye E+ models + IECC SF E+ models to generate load profiles to quantify EF for demand response in the form of load shifting and shedding of HVAC & DHW system.121electric space heating; DWH; HVAC load adjustmentN.A.policymakers; TSO; DSOcalculation with modelingYesNoLow
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A fundamental unified framework to quantify and characterise energy flexibility of residential buildings with multiple electrical and thermal energy systems(Bampoulas et al., 2021)2021APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2020.116096Develop framework and introduce indicators to assess system level energy flexibility from passive and active TES, battery and EV in residential buildings during demand response actions, while considering the effect of self generation on available flexibility.211building thermal mass; TES; PV; EV; electricity storageRBCbuilding owners; occupants; building managers; aggregators; grid operatorscalculation with modelingYesYesLow
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A GIS-based methodology to increase energy flexibility in building cluster through deep renovation: A neighborhood in Seville(Camporeale & Mercader-Moyano, 2021)2021ENERGY AND BUILDINGShttps://doi.org/10.1016/j.enbuild.2020.1105731, 221PV; HPN.A.policymakers; grid operatorsN.A.YesYesLow
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District heating energy efficiency of Danish building typologies(Kristensen & Petersen, 2021)2021ENERGY AND BUILDINGShttps://doi.org/10.1016/j.enbuild.2020.110602District heating efficiency of 42 969 b Danish building typologies. The paper suggest that required energy-efficiency improvement will lead to increased heat load variations which need to be addressed in the transition towards 4GDH, for instance by focusing on technologies to leverage demand-side flexibility.1, 222district heating systemN.A.utility companiesdirect calculationYesNoMedium
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Decarbonising heat with optimal PV and storage investments: A detailed sector coupling modelling framework with flexible heat pump operation(Rinaldi et al., 2021)2021APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2020.116110Residential buildings221PV; electricity storage; HP; building thermal massN.A.utility companies; DSON.A.NoNoN.A.
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Evaluating the impact of thermostat control strategies on the energy flexibility of residential buildings for space heating(K. Zhang & Kummert, 2021)2021BUILDING SIMULATIONhttps://doi.org/10.1007/s12273-020-0751-xRBC vs MPC control of HVAC system (thermostat setpoints) for flexibility during DR events for a three-story residential building in Canada (simulation)211temperature setpoint adjustmentMPC & RBChomeownerscalculation with modelingYesYesMedium
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Using collective intelligence to enhance demand flexibility and climate resilience in urban areas(Nik & Moazami, 2021)2021APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2020.116106Representative group of buildings in Stockholm221general DSM; collective intelligenceN.A.DSO; TSON.A.YesYesLow
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Impact of photovoltaic self-consumption curtailment on building-to-grid operations(Rehman et al., 2021)2021INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMShttps://doi.org/10.1016/j.ijepes.2020.106374A commercial five story academic block 1 building with an area of 78, 132ft2 at the main campus of COMSATS University Islamabad112temperature setpoint adjustment; electricity storage; PVMPCDSO; TSOcalculation with modelingYesNoMedium
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Heat consumption scenarios in the rural residential sector: the potential of heat pump-based demand-side management for sustainable heating(Campos et al., 2020)2020ENERGY SUSTAINABILITY AND SOCIETYhttps://doi.org/10.1186/s13705-020-00271-4A region in Hungary of >13k houses;36k inhabitants; typical rural area22N.A.N.A.N.A.
Urban Planners; Building Designers; Energy Planners
Calculation With Modeling
YesYesLow
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Quantifying the flexibility of lighting systems by optimal control in commercial buildings: Insight from a case study(Yu et al., 2020)2020ENERGY AND BUILDINGShttps://doi.org/10.1016/j.enbuild.2020.110310Four-floor office building in Beijing, China111lighitng systemoptimal controlutility companiesdirect calculationYesYesLow
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The potential of energy flexibility of space heating and cooling in Portugal(Majdalani et al., 2020)2020UTILITIES POLICYhttps://doi.org/10.1016/j.jup.2020.101086Single archetype house under different geographical conditions in Portugal211temperature setpoint adjustmentoptimal controlpolicymakers; grid operators; homeownerscalculation with modelingYesNoMedium
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Comparison of metaheuristic optimisation methods for grid-edge technology that leverages heat pumps and thermal energy storage(Schellenberg et al., 2020)2020RENEWABLE & SUSTAINABLE ENERGY REVIEWShttps://doi.org/10.1016/j.rser.2020.109966Operational optimization of heat pump and thermal energy sytem by metaheuristic algorithms in a residential building211HP; TESoptimal controlgrid operators; building ownerscalculation with modelingNoNoMedium
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Influence of electricity prices on energy flexibility of integrated hybrid heat pump and thermal storage systems in a residential building(Fitzpatrick et al., 2020)2020ENERGY AND BUILDINGShttps://doi.org/10.1016/j.enbuild.2020.110142Residential building located in Germany, equipped with a hybrid heat pump, thermal energy storage and boiler.211gas boiler; TESMPCutility companiescalculation with modelingYesYesLow
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Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control(Buffa et al., 2020)2020ENERGIEShttps://doi.org/10.3390/en13174339Small multi-family house with five floors and two apartment per floor located in Rome211district heating system; TESMPChomeowners; building managerscalculation with modelingYesYesLow
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Multi-objective optimisation of an interactive buildings-vehicles energy sharing network with high energy flexibility using the Pareto archive NSGA-II algorithm(Zhou et al., 2020)2020ENERGY CONVERSION AND MANAGEMENThttps://doi.org/10.1016/j.enconman.2020.113017Using multi-objective optimization to decrease carbon emissions, reduce import electricity costs, and increase energy flexibility for a network including EVs, an office building, and a hotel.121EV; elecitrcity storage; PVoptimal controlgrid operators; building managersN.A.YesYesMedium
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Towards standardising market-independent indicators for quantifying energy flexibility in buildings(Kathirgamanathan et al., 2020)2020ENERGY AND BUILDINGShttps://doi.org/10.1016/j.enbuild.2020.110027Standardization of market independent energy flexibility indicators evaluated upon different climate and building types, control schemes, DR strategies and model types to help with the design of contractual framework between end-users and grid operators.1, 211GSHP; electric space heating; gas boiler; temperature setpoint adjustmentRBCbuilding owners, occupants, building managers; aggregators; grid operatorscalculation with modelingYesYesLow
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Analysis of the Building Smart Readiness Indicator Calculation: A Comparative Case-Study with Two Panels of Experts(Vigna et al., 2021)2021ENERGIEShttps://doi.org/10.3390/en13112796Office building, Bolzano Italy113N.A.no mentionedbuilding owners and grid operatorsdirect calculationNoYesLow
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Optimal operation of AC-DC distribution network with multi park integrated energy subnetworks considering flexibility(Geng et al., 2020)2020IET RENEWABLE POWER GENERATIONhttps://doi.org/10.1049/iet-rpg.2019.0862Distribution network flexibility considering renewable energyN.A.N.A.1electricity storage; TES; PV; wind turbineoptimal controlDSOcalculation with modelingYesYesN.A.
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Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration(Finck et al., 2020)2020APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2020.114671Control of a single house's heat pump electricity consumption for 3 one-day test periods to compare RBC to two MPC models.211, 2HP; TESMPCTSO; homeownersVariousYesYesVarious
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Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities(Zhou & Zheng, 2020)2020APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2019.114416Using a ML controller to exploite flexibility provided from lighting modes, working chiller settings, and thermostat setpoints to minimize the peak energy demand of an office building.111lighting system; TES; PV; solar thermal; temperature setpoint adjustmentRBCoccupants; building owners; building operatorscalculation with modelingYesYesLow
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Quantification of energy flexibility of residential net-zero-energy buildings involved with dynamic operations of hybrid energy storages and diversified energy conversion strategies(Zhou & Cao, 2020)2020SUSTAINABLE ENERGY GRIDS & NETWORKShttps://doi.org/10.1016/j.segan.2020.100304Net-zero residential building energy flexibility by energy system control, evaluated with several flexibility indicators211chiller; TESoptimal controlbuilding owners; building operatorsdirect calculationYesYesMedium
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A sensitivity analysis on the heating and cooling energy flexibility of residential buildings(Vivian et al., 2020)2020SUSTAINABLE CITIES AND SOCIETYhttps://doi.org/10.1016/j.scs.2019.101815Assess the effect of building envelope, climate conditions, occupant behaviour on the energy flexibility during cooling and heating seasons.211building thermal mass; temperature setpoint adjustmentRBCTSOcalculation with modelingYesYesLow
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Residential loads flexibility potential for demand response using energy consumption patterns and user segments(Afzalan & Jazizadeh, 2019)2019APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2019.113693Data-driven assessment of the energy flexibility potential of individual residential consumer in DR programs.221EV; shiftable loadsRBChomeownersdirect calculationYesYesLow
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Heat demand peak shaving in urban integrated energy systems by demand side management - A techno-economic and environmental approach(Arnaudo et al., 2019)2019ENERGYhttps://doi.org/10.1016/j.energy.2019.115887The design of this implementation case is directly inspired by the pilot district of Hammarby Sjostad (Sweden)221building thermal mass; GSHPN.A.grid operators; building operatorsN.A.YesNoLow
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Energy flexibility investigation of advanced grid-responsive energy control strategies with the static battery and electric vehicles: A case study of a high-rise office building in Hong Kong(Zhou & Cao, 2019)2019ENERGY CONVERSION AND MANAGEMENThttps://doi.org/10.1016/j.enconman.2019.111888Develop non-linear component based model for quantification of EF from building and vehicle integrated PV.111PV; electricity storage; TES; EVRBCpolicymakers; homeowners; occupantscalculation with modelingYesNoLow
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Energy flexible buildings: A methodology for rating the flexibility performance of buildings with electric heating and cooling systems(Arteconi et al., 2019)2019APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2019.113387Two different extreme climatic zones (B and E) Italian apartments modeled in TRNSYS equipped with an ON-OFF air to water reversible heat pump (AWHP). Messina (zone B 707 degree days), Turin(zone E 2617 degree days)221HP; TESRBCbuilding designerscalculation with modelingYesNoMedium
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Unlocking the Potential of Flexible Energy Resources to Help Balance the Power Grid(Mueller et al., 2019)2019IEEE TRANSACTIONS ON SMART GRIDhttps://doi.org/10.1109/TSG.2018.2878998Aggregation of flexible systems with complementary physical properties to increase the amount of Secondary Frequency Regulation (SFR) capacity.311electricity storage; freezer; steam turbineoptimal controlDSO; TSON.A.YesNoHigh
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Energy Retrofitting Effects on the Energy Flexibility of Dwellings(Mancini & Nastasi, 2019)2019ENERGIEShttps://doi.org/10.3390/en12142788419 dwellings in Italy211building thermal mass; electric space heatingN.A.grid operators; building operatorsN.A.YesNoLow
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Investigating the potential for energy flexibility in an office building with a vertical PV and a PV roof system(Aelenei et al., 2019)2019RENEWABLE ENERGYhttps://doi.org/10.1016/j.renene.2018.07.140Assess the load matching potential of a passive solar office building with battery energy storage.111, 2PV; electricity storageRBCbuilding designers; building ownersdirect calculationYesNoMedium
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Economic model predictive control for demand flexibility of a residential building(Finck et al., 2019)2019ENERGYhttps://doi.org/10.1016/j.energy.2019.03.171A three floors Dutch residential building. 211, 2HP; PV; building thermal massMPChomeowners; building managerscalculation with modelingYesNoMedium
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Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey(Mancini et al., 2019)2019ENERGIEShttps://doi.org/10.3390/en12112055Energy flexibility potential model for residential buildings in Italy212PV; shiftable loads; EV; DWH; electricity storageN.A.homeowners; utility companies.direct calculationNoYesLow
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Performance of heat pump integrated phase change material thermal storage for electric load shifting in building demand side management(Hirmiz et al., 2019)2019ENERGY AND BUILDINGShttps://doi.org/10.1016/j.enbuild.2019.02.026Sizing PCM thermal storage tanks for heat pumps used for demand side management in a residential household with peak periods ranging from 2 to 6 hours.211HP; PCMRBChomeownerscalculation with modelingYesYesMedium
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Optimal scheduling strategy of district integrated heat and power system with wind power and multiple energy stations considering thermal inertia of buildings under different heating regulation modes(D. Wang et al., 2019)2019APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2019.01.199District Heating with integrated power and heat systems221district heating system; building thermal massoptimal controlDSO; TSON.A.NoN.A.N.A.
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A regulation capacity reset strategy for HVAC frequency regulation control(Cai & Braun, 2019a)2019ENERGY AND BUILDINGShttps://doi.org/10.1016/j.enbuild.2018.12.018a regulation capacity reset strategy for a RTU111, 3temperature setpoint adjustment; building thermal massoptimal controlbuilding owners; regulation market; ISO; RTOcalculation with modelingYesYesHigh
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Rapid visualization of the potential residential cost savings from energy storage under time-of-use electric rates(Lanahan et al., 2019)2019JOURNAL OF BUILDING PERFORMANCE SIMULATIONhttps://doi.org/10.1080/19401493.2018.1470203regionalized potential energy cost savings visualization using energy storage with TOU rate plans in California211PCM; electricity storageRBChomeownersN.A.NoN.A.Low
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Energy flexibility of a nearly zero-energy building with weather predictive control on a convective building energy system and evaluated with different metrics(Liu & Heiselberg, 2019)2019APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2018.10.070comparison of different measure to exploit the flexibility with thermal storage111temperature setpoint adjustment; building thermal massRBCgrid operators; building opereators; building ownerscalculation with modelingYesNoMedium
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Building-to-grid flexibility: Modelling and assessment metrics for residential demand response from heat pump aggregations(L. Zhang et al., 2019)2019APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2018.10.0581000 dwellings in the UK equipped with (air source) heat pumps221temperature setpoint adjustment; building thermal massRBCgrid operators; retailler; building ownersN.A.YesYesMedium
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Characterizing the energy flexibility of buildings and districts(Junker et al., 2018)2018APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2018.05.037A dynamic function to characterize demand flexibility with various signals (e.g. price, CO2), and a KPI to quantify the effectiveness of flexibility1, 2, 322N.A.N.A.grid operatorsN.A.YesYesMedium
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Energy flexibility from the consumer: Integrating local electricity and heat supplies in a building(Y. Zhang et al., 2018)2018APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2018.04.041Single office building111HP; electric space heating; electricity storage; TESoptimal controlbuilding ownersN.A.YesYesMedium
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Demand Response and Renewable Energy Management Using Continuous-Time Optimization(Leithon et al., 2018)2018IEEE TRANSACTIONS ON SUSTAINABLE ENERGYhttps://doi.org/10.1109/TSTE.2017.2771359propose a demand response strategy for a nondeferrable load facility with renewable energy harvesting and storage
capabilities.		111PV; electricity storageoptimal controlN.A.N.A.NoN.A.N.A.
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New domain for promoting energy efficiency: Energy Flexible Building Cluster(Vigna et al., 2018)2018SUSTAINABLE CITIES AND SOCIETYhttps://doi.org/10.1016/j.scs.2018.01.038Review of KPIs to quantify energy flexibility at cluster scale and supporting the smart readiness metrics N.A.2N.A.N.A.optimal controlN.A.N.A.YesVariousMedium
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Quantifying demand flexibility of power-to-heat and thermal energy storage in the control of building heating systems(Finck et al., 2018)2018APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2017.11.036A small-scale office building equipped with heat pump,electric heater, and thermal energy storage tanks in De Bilt Netherlands modeled in MATLAB111HP; TESoptimal controlbuilding operatorsN.A.YesVariousMedium
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Building-to-grid predictive power flow control for demand response and demand flexibility programs(Razmara et al., 2017)2017APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2017.06.040MPC is used to control power flow from grid to PV, ESS, & HVAC systems for an office building.111HP; PV; electricity storage; building thermal massMPCgrid operatorsN.A.YesYesLow
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Quantifying the operational flexibility of building energy systems with thermal energy storages(Stinner et al., 2016)2016APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2016.08.055Examine the flexibility of a residential building using a framework of three metrics and indicating how the indicators can scale to multiple buildings 211CHP; HP; TESN.A.grid operatorsN.A.YesYesLow
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Active demand response with electric heating systems: Impact of market penetration(Arteconi et al., 2016)2016APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2016.05.146Evaluating the benefits of different penetration rates of ADR programmes in terms of electricity consumption and operational costs, both from the final user’s and the overall system’s perspective, taking into account both demand and supply through an integrated modelling approach.221HP; electric space heating; building thermal mass; DWH; PVN.A.policymakerscalculation with modelingYesYesLow
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Frequency Regulation From Commercial Building HVAC Demand Response(Beil et al., 2016)2016PROCEEDINGS OF THE IEEEhttps://doi.org/10.1109/JPROC.2016.2520640compare performance results from experiments and simulation providing frequency regulation DR from commercial HVAC systems and components, and present experimental results from a single ~30 000-m 2 office building113variable speed van; temperature setpoint adjustmentRBCgrid operators; DSOdirect calculationYesYesHigh
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Quantification of flexibility in buildings by cost curves - Methodology and application(De Coninck & Helsen, 2016)2016APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2015.10.114Methodology for computing the flexibility of buildings using cost curves.1, 211HP; TESoptimal controlaggregatorsN.A.YesYesLow
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Optimization Under Uncertainty of Thermal Storage-Based Flexible Demand Response With Quantification of Residential Users' Discomfort(Good, Karangelos, et al., 2015)2015IEEE TRANSACTIONS ON SMART GRIDhttps://doi.org/10.1109/TSG.2015.2399974Exploiting energy flexibility across 50 residential units through deviations from indoor setpoint temperatures within an acceptable deadband; various thermal energy systems are compared.221DWH; building thermal massoptimal controlaggregatorscalculation with modelingYesYesLow
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High resolution modelling of multi-energy domestic demand profiles(Good, Zhang, et al., 2015)2015APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2014.10.028A high resolution domestic multi-energy model comprised of physically based space heating, domestic hot water (DHW), cooking and electrical appliance models211PV; shiftable loads; EV; DWH; electricity storageRBChomeowners; aggregatorsN.A.NoYesMedium
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Further exploring the potential of residential demand response programs in electricity distribution(Bartusch & Alvehag, 2014)2014APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2014.03.054Empirical study to estimate the extent of response to a demand-based time-of-use electricity distribution tariff among
Swedish single-family homes		222N.A.N.A.homeowners; aggregatorsN.A.YesYesMedium
79
An optimal day-ahead load scheduling approach based on the flexibility of aggregate demands(Ayón et al., 2017)2017APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2017.04.038Exploiting shiftable appliances (residential) and HVAC settings (commercial) to reduce peak demand for a building cluster of roughly 4000 residential customers and 22 commercial buildings.1, 221temperature setpoint adjustment; shiftable loadsoptimal controlaggregators; building ownerscalculation with modelingYesYesLow
80
Price-responsive model predictive control of floor heating systems for demand response using building thermal mass(Hu et al., 2019)2019APPLIED THERMAL ENGINEERINGhttp://dx.doi.org/10.1016/j.applthermaleng.2019.02.107Residential building equipped with a floor heating system, co-simulation framework (TRNSYS-Matlab) used to generate the model212electric space heating; building thermal massMPCbuilding owners; grid operatorsN.A.YesNoLow
81
Quantification of electricity flexibility in demand response: Office building case study(Chen et al., 2019)2019ENERGYhttps://doi.org/10.1016/j.energy.2019.116054Office building111temperature setpoint adjustment; building thermal mass; shiftable loadsRBCbuilding owners; grid operatorscalculation with modelingYesNoLow
82
An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems(Bampoulas et al., 2022)2022APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2022.118947Residential building212HP; PV; electricity storageRBChomeowners; policymakerscalculation with modelingYesYesLow
83
Energy flexibility assessment of a zero-energy office building with building thermal mass in short-term demand-side management(Lu et al., 2022)2022JOURNAL OF BUILDING ENERGINEERINGhttps://doi.org/10.1016/j.jobe.2022.104214Office building111HP; PV; temperature setpoint adjustment; building thermal massRBChomeowners; grid operatorsN.A.YesYesLow
84
Cooling system energy flexibility of a nearly zero-energy office building using building thermal mass: Potential evaluation and parametric analysis(Lu et al., 2021)2021Energy & Buildingshttps://doi.org/10.1016/j.enbuild.2021.110763Office building111temperature setpoint adjustment; building thermal massRBCbuilding operatorsN.A.YesYesMedium
85
Development of a data driven approach to explore the energy flexibility potential of building clusters(A. Wang et al., 2018)2018APPLIED ENERGYhttps://doi.org/10.1016/j.apenergy.2018.09.187A building cluster, community222HP coordinationRBCgrid operatorscalculation with modelingYesNoLow
86
Development of a dynamic energy flexibility index for buildings and their interaction with smart grids(Athienitis et al., 2020)2020ACEEE conference proceedingshttps://www.researchgate.net/publication/343725542_Development_of_a_dynamic_energy_flexibility_index_for_buildings_and_their_interaction_with_smart_gridsAn institutional & a school building1, 211electric space heating; building thermal mass; lighting system; electricity storageRBCbuilding owners; grid operatorscalculation with modelingYesYesLow
87
A method for energy consumption optimization of air conditioning
systems based on load prediction and energy flexibility		(Li et al., 2022)2022Energyhttps://doi.org/10.1016/j.energy.2022.123111A new method for heating ventilation and air conditioning (HVAC) energy consumption optimization based on load prediction and energy flexibility is proposed. First, the energy consumption prediction of the chillers and air conditioning terminals is made. Then, an optimal chiller loading (OCL) equation is built and is new in the following aspects: the electricity consumption of air conditioning terminals is included and amended by a penalty coefficient to consider thermal comfort. This penalty coefficient is calculated based on energy flexibility.112temperature setpoint adjustment; chiller; building thermal massoptimal controlbuilding owners; grid operatorsN.A.YesNoMedium
88
Laboratory-based assessment of HVAC equipment for power grid frequency regulation: Methods, regulation performance, economics, indoor comfort and energy efficiency(Cai & Braun, 2019b)2019ENERGY AND BUILDINGShttps://doi.org/10.1016/j.enbuild.2018.12.022a methodology and case study results for laboratory-based assessments of power frequency regulation service performance for variable-speed HVAC cooling equipment.113HP; RTU; temperature setpoint adjustmentRBCbuilding owners; grid operatorsdirect calculationYesNoHigh