journal article·2024-12-03Open access

Demand Flexibility: A Review of Quantification Methods, Models, and Required Data

Morgan M. Shepherd, Salman Mohagheghi

Applied Sciences· Volume 14· Issue 23

Abstract

As renewable energy is increasingly integrated into our electricity supply, it becomes more challenging to ensure reliability and security due to the intermittent nature of these resources. With the electrification of buildings and technological advancements across various aspects of their operations, the building sector is expected to play a key role in reducing emissions while supporting the needs of the grid. Buildings and the loads they house can provide grid resources via demand flexibility, shifting, and shedding electric load, as necessary. This key resource has received increased attention from researchers, building operators, electric utilities, policymakers, and system operators as a tool to improve power grid reliability and reduce system costs. Before increasing reliance on demand flexibility, however, a better understanding of its availability is needed to inform planning efforts. This paper includes a review of the literature on current methods and data used to model the available flexibility of power delivered to customers. This review also summarizes how demand flexibility is defined and quantified to help inform future studies in this field. The results of this review illustrate the diversity found within this field of research and the innovation that researchers are employing to solve this complex problem.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Smart Grid Energy Management · Energy, Environment, and Transportation Policies · Transportation and Mobility Innovations

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Advanced Energy Systems Program, Colorado School of Mines, Golden, CO 80401, USA

Electrical Engineering Department, Colorado School of Mines, Golden, CO 80401, USA

Funding

Advanced Energy Systems (AES) Graduate Program at the Colorado School of Mines

References (74)

Show source-provided references
1. Flexibility quantification of thermostatically controlled loads for demand response applications10.1016/j.epsr.2021.107592 ↗
2. Study of energy storage systems and environmental challenges of batteries10.1016/j.rser.2019.01.023 ↗
3. Munankarmi, P., Jin, X., Ding, F., and Zhao, C. (2020, January 1–3). Quantification of Load Flexibility in Residential Buildings Using Home Energy Management Systems. Proceedings of the 2020 American Control Conference (ACC), Denver, CO, USA.10.23919/ACC45564.2020.9147459 ↗
4. Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives10.1016/j.apenergy.2023.121217 ↗
5. Demand Flexibility of Residential Buildings: Definitions, Flexible Loads, and Quantification Methods10.1016/j.eng.2022.01.010 ↗
6. The demand response potential in copper production10.1016/j.jclepro.2022.132221 ↗
7. Potential capability estimation for real time electricity demand response of sustainable manufacturing systems using Markov Decision Process10.1016/j.jclepro.2013.08.033 ↗
8. For better or for worse? On the economic and ecologic value of industrial demand side management in constrained electricity grids10.1016/j.enpol.2023.113781 ↗
9. Cost and carbon reductions from industrial demand-side management: Study of potential savings at a cement plant10.1016/j.apenergy.2017.03.083 ↗
10. Achievable load shifting potentials for the European residential sector from 2022–2050—ScienceDirect10.1016/j.rser.2023.113959 ↗
11. The value of flexible load in power systems with high renewable energy penetration10.1016/j.energy.2019.115960 ↗
12. The potential and usefulness of demand response to provide electricity system services10.1016/j.apenergy.2017.07.034 ↗
13. A methodology to assess demand response benefits from a system perspective: A Dutch case study10.1016/j.jup.2016.11.001 ↗
14. Estimating demand response potential under coupled thermal inertia of building and air-conditioning system10.1016/j.enbuild.2018.10.022 ↗
15. Romanchenko, D., Nyholm, E., Odenberger, M., and Johnsson, F. (2019). Flexibility Potential of Space Heating Demand Response in Buildings for District Heating Systems. Energies, 12.10.3390/en12152874 ↗
16. Using smart meter data to estimate demand response potential, with application to solar energy integration10.1016/j.enpol.2014.05.053 ↗
17. An evaluation of the demand response potential of integrated dynamic window and HVAC systems10.1016/j.enbuild.2023.113481 ↗
18. Assessment of the theoretical demand response potential in Europe10.1016/j.energy.2014.02.019 ↗
19. Assessment of Model-Based peak electric consumption prediction for commercial buildings10.1016/j.enbuild.2021.111031 ↗
20. Assessing the electric demand-side management potential of Helsinki’s public service building stock in ancillary markets10.1016/j.scs.2021.103460 ↗
21. Assessment of the regionalised demand response potential in Germany using an open source tool and dataset10.1016/j.adapen.2020.100001 ↗
22. Homaei, S., and Hamdy, M. (2021). Quantification of Energy Flexibility and Survivability of All-Electric Buildings with Cost-Effective Battery Size: Methodology and Indexes. Energies, 14.10.3390/en14102787 ↗
23. Lucas, A., Jansen, L., Andreadou, N., Kotsakis, E., and Masera, M. (2019). Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector. Energies, 12.10.3390/en12142725 ↗
24. Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling10.1016/j.apenergy.2021.118372 ↗
25. Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads10.1016/j.apenergy.2020.115708 ↗
26. A novel tool for the evaluation and assessment of demand response activities in the industrial sector10.1016/j.energy.2016.07.146 ↗
27. Toosty, N.T., Shimoda, S., and Hagishima, A. (2023). Aggregated peak-load analysis and demand response potential of a residential building in Osaka, Japan. Environ. Dev. Sustain., 1–18.10.1002/2475-8876.12394 ↗
28. Flexibility provision of residential energy hubs with demand response applications10.1049/gtd2.12392 ↗
29. A framework to formulate and aggregate performance indicators to quantify building energy flexibility10.1016/j.apenergy.2023.121590 ↗
30. Crawley, J., Manouseli, D., Mallaburn, P., and Elwell, C. (2022). An Empirical Energy Demand Flexibility Metric for Residential Properties. Energies, 15.10.3390/en15145304 ↗
31. Quantifying the operational flexibility of building energy systems with thermal energy storages10.1016/j.apenergy.2016.08.055 ↗
32. Development of a data driven approach to explore the energy flexibility potential of building clusters—ScienceDirect10.1016/j.apenergy.2018.09.187 ↗
33. Quantifying demand flexibility based on structural thermal storage and comfort management of non-residential buildings: A comparison between hot and cold climate zones10.1016/j.apenergy.2017.03.004 ↗
34. A fundamental unified framework to quantify and characterise energy flexibility of residential buildings with multiple electrical and thermal energy systems10.1016/j.apenergy.2020.116096 ↗
35. Quantification of the energy flexibility of residential building clusters: Impact of long-term refurbishment strategies of the italian building stock—ScienceDirect10.1016/j.enbuild.2023.113416 ↗
36. Quantifying the flexibility of lighting systems by optimal control in commercial buildings: Insight from a case study10.1016/j.enbuild.2020.110310 ↗
37. Quantification of electricity flexibility in demand response: Office building case study10.1016/j.energy.2019.116054 ↗
38. The potential of energy flexibility of space heating and cooling in Portugal10.1016/j.jup.2020.101086 ↗
39. Analysis of residential EV energy flexibility potential based on real-world charging reports and smart meter data10.1016/j.enbuild.2021.110923 ↗
40. Quantifying energy flexibility of commuter plug-in electric vehicles within a residence–office coupling virtual microgrid. Part II: Case study setup for scenario and sensitivity analysis10.1016/j.enbuild.2021.111552 ↗
41. IEA EBC Annex 67 Energy Flexible Buildings10.1016/j.enbuild.2017.08.044 ↗
42. (2023, December 10). National Assessment and Action Plan on Demand Response | Federal Energy Regulatory Commission, Available online: https://www.ferc.gov/electric/industry-activity/demand-response/national-assessment-and-action-plan-demand-response.
43. The demand-side resource opportunity for deep grid decarbonization10.1016/j.joule.2022.04.010 ↗
44. From prosumer to flexumer: Case study on the value of flexibility in decarbonizing the multi-energy system of a manufacturing company10.1016/j.apenergy.2023.121430 ↗
45. Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach10.1016/j.apenergy.2018.11.014 ↗
46. Quantifying Changes in Building Electricity Use, with Application to Demand Response10.1109/TSG.2011.2145010 ↗
47. Quantifying flexibility of residential electric vehicle charging loads using non-intrusive load extracting algorithm in demand response10.1016/j.scs.2019.101664 ↗
48. Demand response potential evaluation for residential air conditioning loads10.1049/iet-gtd.2018.5299 ↗
49. Load flexibility potential across residential, commercial and industrial sectors in Brazil10.1016/j.energy.2020.117483 ↗
50. Ding, S., Xu, C., Rao, Y., Song, Z., Yang, W., Chen, Z., and Zhang, Z. (2022). A Time-Varying Potential Evaluation Method for Electric Vehicle Group Demand Response Driven by Small Sample Data. Sustainability, 14.10.3390/su14095281 ↗
51. A peak-load reduction computing tool sensitive to commercial building environmental preferences10.1016/j.apenergy.2015.10.009 ↗
52. Data-driven and physical model-based evaluation method for the achievable demand response potential of residential consumers’ air conditioning loads10.1016/j.apenergy.2021.118017 ↗
53. Estimating cooling demand flexibility in a district energy system using temperature set point changes from selected buildings10.1016/j.apenergy.2023.120816 ↗
54. Predicting the demand flexibility of wet appliances at national level: The case of France10.1016/j.enbuild.2020.109900 ↗
55. von Bonin, M., Dörre, E., Al-Khzouz, H., Braun, M., and Zhou, X. (2022). Impact of Dynamic Electricity Tariff and Home PV System Incentives on Electric Vehicle Charging Behavior: Study on Potential Grid Implications and Economic Effects for Households. Energies, 15.10.3390/en15031079 ↗
56. Quantifying the flexibility of hydrogen production systems to support large-scale renewable energy integration10.1016/j.jpowsour.2018.07.101 ↗
57. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market10.1016/j.enconman.2015.06.059 ↗
58. Quantifying flexibility of commercial and residential loads for demand response using setpoint changes10.1016/j.apenergy.2016.05.090 ↗
59. Estimating the value of jointly optimized electric power generation and end use: A study of ISO-scale load shaping applied to the residential building stock10.1080/19401493.2021.1998222 ↗
60. Technical Potential of Buildings in Germany as Flexible Power-to-Heat Storage for Smart-Grid Operation10.1002/ente.201600655 ↗
61. Strobel, N., Fuhrländer-Völker, D., Weigold, M., and Abele, E. (2020). Quantifying the Demand Response Potential of Inherent Energy Storages in Production Systems. Energies, 13.10.3390/en13164161 ↗
62. Assessments of demand response potential in small commercial buildings across the United States10.1080/23744731.2019.1629245 ↗
63. Residential loads flexibility potential for demand response using energy consumption patterns and user segments10.1016/j.apenergy.2019.113693 ↗
64. Load shift potential of electric vehicles in Europe10.1016/j.jpowsour.2014.01.019 ↗
65. Photovoltaic systems coupled with batteries that are optimally sized for household self-consumption: Assessment of peak shaving potential10.1016/j.apenergy.2018.04.023 ↗
66. Rapid quantification of demand response potential of building HAVC system via data-driven model10.1016/j.apenergy.2022.119796 ↗
67. Dynamic Demand Response: A Solution for Improved Energy Efficiency for Industrial Customers10.1109/MIAS.2014.2345799 ↗
68. Evaluating building decarbonization potential in U.S. cities under emissions based building performance standards and load flexibility requirements—ScienceDirect10.1016/j.jobe.2023.107375 ↗
69. Assessing the potential of residential demand response systems to assist in the integration of local renewable energy generation10.1007/s12053-013-9236-4 ↗
70. Impact of forecast uncertainty and electricity markets on the flexibility provision and economic performance of highly-decarbonized multi-energy systems10.1016/j.apenergy.2023.120825 ↗
71. A review of hybrid renewable energy systems: Solar and wind-powered solutions: Challenges, opportunities, and policy implications10.1016/j.rineng.2023.101621 ↗
72. Benefits and challenges of electrical demand response: A critical review10.1016/j.rser.2014.07.098 ↗
73. Chadwick, R. (2011). Encyclopedia of Applied Ethics, Elsevier Inc.. [2nd ed.].
74. Arsad, F.S., Hod, R., Ahmad, N., Ismail, R., Mohamed, N., Baharom, M., Osman, Y., Radi, M.F.M., and Tangang, F. (2022). The Impact of Heatwaves on Mortality and Morbidity and the Associated Vulnerability Factors: A Systematic Review. Int. J. Environ. Res. Public Health, 19.10.3390/ijerph192316356 ↗