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Applying consumption time analysis to measure fundamental energy needs: A method to quantify households’ energy services

Abstract

This research utilizes “time use” analysis, rather than the “power” side of energy consumption, to measure households’ fundamental energy needs (FENs) that is helpful for energy poverty alleviation. Households’ FENs contain energy for cooking, cooling, heating, and lighting/entertainment services, which vary in terms of the family size, their lifestyle, weather parameters, and so on. This research monitors and records time usage of FENs activities for a low-income couple family lived in a triplex kind of house in Japan. After fitting statistical distribution for time usage data, simulation model is used to calculate robust results for household energy consumption. The results indicate that the average daily FENs of this family is around 63 Mega Joule. The results also emphasize that for energy poverty reduction, the investment cost should be prioritized for cooking with the highest share of energy service, followed by heating, cooling, and lighting/entertainment service.

Graphical abstract

Highlights

The results of this study showed that investment on cooking and heating services reduced energy poverty up to 75%. While, the cooling and lighting/entertainment services share was around 25%.

Discussion

  • Many studies have analyzed the impact of renewable energies in energy poverty reduction in pre-developing countries. However, the cost of supplying 100% of energy demand through renewable energies to reduce energy poverty, is higher than hybrid power system option. Applying diesel generator along with renewable energies is a viable option with lower cost, while the existence of diesel generator is mostly ignored due to its trivial CO2 emissions compared with significant amount of CO2 emissions in developed countries.

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Notes

  1. India accounted for 25% of this figure, followed by Sub-Saharan African countries (53%).

  2. It comprised India (29%), China (17%), Sub-Saharan African countries (30%), and the rest of world countries (24%).

  3. Traditional biomass does not belong to clean fuel.

  4. Typical focus of energy poverty in developed countries is on affordability.

  5. Wooden table frame covered by a heavy blanket with a heat source underneath the table.

  6. Leftover meals.

  7. ”D” stands for duration between two times.

  8. The paper utilizes the “Oracle Crystal Ball” program to find the best fitted statistical distribution for each data set. This program is embedded into the Microsoft Excel through the “add-in” tool.

  9. Its square root represents the standard deviation.

  10. Recommended range of maximum water velocity is 1 m/s for copper pipe with diameter between 15 to 50 mm.

  11. Total gas consumption for cooking, boiling water, hot water for dishwashing, hand-washing clothes, and taking bath was 10.59 m3, from April 12 to May 15, 2018. While, the amount of gas usage based on time consumption analysis was 10.80 m3.

  12. “R.STYLE” with model “KGE31NSGR”.

  13. Peak hours vary from one country to another and summer vs winter season. However, in most practical terms, peak hours varies from 6p.m. to 10 p.m., while off-peak times change since 10 p.m. to 7 a.m.

  14. Efficiency of renewable energies such as solar and wind highly depends on location, technology specifications, and raw materials (in case of biomass). However, the efficiency of current wind turbines mostly ranges among 20% to 40%, biomass varies among 10%-20%, or most solar panels efficiency range among 16% to 22% (average 19.7%).[4446]

References

  1. P. Action, Poor people’s energy outlook 2019 enabling energy access: from village to nation (Practical Action, Rugby, 2019)

    Google Scholar 

  2. IEA, Energy access outlook 2017 from poverty to prosperity (International Energy Agency, Paris, 2017)

    Google Scholar 

  3. S. Groh, S. Pachauri, R. Narasimha, What are we measuring? An empirical analysis of household electricity access metrics in rural Bangladesh. Energy Sustain. Dev. 30, 21–31 (2016)

    Article  Google Scholar 

  4. T. W. B. Group, Technical and economic assessment of grid, mini-grid and off-grade electrification technologies (World Bank, Washington, DC, 2006)

    Google Scholar 

  5. S. Baurzhan, G.P. Jenkins, Off-grid solar PV: is it an affordable or appropriate solution for rural electrification in Sub-Saharan African countries? Renew. Sustain. Energy Rev. 60, 1405–1418 (2016)

    Article  Google Scholar 

  6. UNDP, World energy assessment: energy and the challenge of sustainability (United Nations Development Programme, New York, 2000)

    Google Scholar 

  7. E. Hartvigsson, M. Stadler, G. Cardoso, Rural electrification and capacity expansion with an integrated modeling approach. Renew. Energy 115, 509–520 (2018)

    Article  Google Scholar 

  8. M.H. Jahangir, A. Shahsavari, M.A. Vaziri Rad, Feasibility study of a zero emission PV/wind turbine/wave energy converter hybrid system for stand-alone power supply: a case study. J. Clea. Prod. 262, 121250 (2020)

    Article  Google Scholar 

  9. L. Ji, X. Liang, Y. Xie, G. Huang, B. Wang, Optimal design and sensitivity analysis of the stand-alone hybrid energy system with PV and biomass-CHP for remote villages. Energy 225, 120323 (2021)

    Article  Google Scholar 

  10. J. Siddiqui, E. Hittinger, Forecasting price parity for stand-alone hybrid solar microgrids: an international comparison. Energy Syst. 9, 953–979 (2018)

    Article  Google Scholar 

  11. C.C. Lauren, The stanford natural gas initiative framework for understanding the role for natural gas in reducing energy poverty (Stanford University, Stanford, 2017)

    Google Scholar 

  12. H. Phoumin, F. Kimura, Cambodia’s energy poverty and its effects on social wellbeing: empirical evidence and policy implications. Energy Policy 132, 283–289 (2019)

    Article  Google Scholar 

  13. S. Okushima, Measuring energy poverty in Japan, 2004–2013. Energy Policy 98, 557–564 (2016)

    Article  Google Scholar 

  14. S. Bouzarovski, S. Petrova, A global perspective on domestic energy deprivation: overcoming the energy poverty–fuel poverty binary. Energy Res. Soc. Sci. 10, 31–40 (2015)

    Article  Google Scholar 

  15. U. a. W. H. Organization, The energy access situation in developing countries: a review focusing on the least developed countries and Sub-Saharan Africa (United Nations Development Programme, New York, 2009)

    Google Scholar 

  16. L. Papada, D. Kaliampakos, A stochastic model for energy poverty analysis. Energy Policy 116, 153–164 (2018)

    Article  Google Scholar 

  17. H. Shakouri, R. Nadimi, S.F. Ghaderi, A hybrid TSK-FR model to study short-term variations of the electricity demand versus the temperature changes. Expert Syst. Appl. 36, 1765–1772 (2009)

    Article  Google Scholar 

  18. P. Nussbaumer, M. Bazilian, V. Modi, Measuring energy poverty: focusing on what matters. Renew. Sustain. Energy Rev. 16, 231–243 (2012)

    Article  Google Scholar 

  19. E. S. M. A. P. (ESMAP), Beyond connections energy access redefined (World Bank, Washington DC, 2015), p. 20433

    Google Scholar 

  20. R. Nadimi, K. Tokimatsu, Fundamental energy needs quantification across poor households through simulation model, in 10th international conference on applied energy (ICAE2018), Hong Kong, China (under publication). (2018)

  21. L.C. Culver, Energy poverty: what you measure matters (Stanford University, California, 2017)

    Google Scholar 

  22. M.T. Sambodo, R. Novandra, The state of energy poverty in Indonesia and its impact on welfare. Energy Policy 132, 113–121 (2019)

    Article  Google Scholar 

  23. L. Turpin Jr., A note on understanding cycle time. Int. J. Prod. Econ. 205, 113–117 (2018)

    Article  Google Scholar 

  24. S. Purcell, Maximum likelihood estimation (2007). http://statgen.iop.kcl.ac.uk/bgim/mle/sslike_1.html. Accessed 3 June 2018

  25. G. Marsaglia, Evaluating the Anderson-Darling distribution. J. Stat. Softw. 9(2), 730–737 (2004)

    Article  Google Scholar 

  26. ASHRAE Handbook Fundamentals, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., Atlanta (2009)

  27. CIBSE, Degree-days: theory and application (2006). https://www.cibse.org/Knowledge/knowledge-items/detail?id=a0q20000008I73TAAS. Accessed 1 June 2018

  28. S. Guillén-Lambea, B. Rodríguez-Soria, J.M. Marín, Comfort settings and energy demand for residential nZEB in warm climates. Appl. Energy 202, 471–486 (2017)

    Article  Google Scholar 

  29. R. Nadimi, K. Tokimatsu, Modeling of quality of life in terms of energy and electricity consumption. Appl. Energy 212, 1282–1294 (2017)

    Article  Google Scholar 

  30. G. S. O. R. Center, AccuWeather, (Global Sales Office Rockefeller Center New York). https://www.accuweather.com/en/jp/machida-shi. Accessed 8 July 2018

  31. BizzEE, BizzEE degree days weather data for energy professionals, Degree Days.net. https://www.degreedays.net/. Accessed 8 July 2018

  32. Timeanddate, @timeanddate.com. https://www.timeanddate.com/sun/japan/tokyo. Accessed 8 July 2018

  33. Risk analysis and simulation add-in for Microsoft® excel (Palisade Corporation, New York, 2015)

  34. R. J. Aristizabal, Estimating the parameters of the three-parameter lognormal distribution (2012). FIU Electronic Theses and Dissertations. 575. https://digitalcommons.fiu.edu/etd/575. Accessed 15 May 2019

  35. CORONA, Kerosene fan heater FH-2506, CORONA Japan. https://www.corona.co.jp/en/products/fh2506.html. Accessed 2 June 2018

  36. e-Stat, Survey on time use and leisure activities, (Japanese Government Statistics, 2016). http://www.stat.go.jp/english/data/shakai/index.html. Accessed 8 July 2018

  37. H. Hendrickx, People's showering habits revealed in survey, UK sustainable shower study_BBC, UK, (2011)

  38. CIBSE, Hydronic system design. www.cibse.org/getmedia/76229a96-c37e-4d2f-b4f9.../Guide-B1-appendix.pdf.aspx. Accessed 12 June 2018

  39. C. Robinson, D. Yan, S. Bouzarovski, Y. Zhang, Energy poverty and thermal comfort in northern urban China: a household-scale typology of infrastructural inequalities. Energy Build. 177, 363–374 (2018)

    Article  Google Scholar 

  40. H. Ahlborg, L. Hammar, Drivers and barriers to rural electrification in Tanzania and Mozambique e Grid-extension, off-grid, and renewable energy technologies. Renew. Energy 61, 117–124 (2014)

    Article  Google Scholar 

  41. C.L. Azimoh, P. Klintenberg, F. Wallin, B. Karlsson, C. Mbohwa, Electricity for development: mini-grid solution for rural electrification in South Africa. Energy Convers. Manage. 110, 268–277 (2016)

    Article  Google Scholar 

  42. K. K. Prasad, B. Frederiks, W. Floor, CookStove.net, (BTG biomass technology group B.V. & The World Bank Group). http://www.cookstove.net/needs/power.html. Accessed 8 June 2018

  43. Power Calculation, © Power-Calculation.com. http://www.power-calculation.com/hot-water-heater-cost-energy-consumption.php. Accessed 8 June 2018

  44. Renewable energy fact sheet: wind turbines, (United States Environmental Protection Agency, 2013). https://pdf4pro.com/amp/view/renewable-energy-fact-sheet-wind-turbines-739288.html

  45. IEA-ETSAP and IRENA, Biomass for heat and power technology, International Renewable Energy Agency (IRENA), 2015. http://www.irena.org/Publications. Accessed 10 Apr 2022

  46. D. Feldman, K. Wu, R. Margolis, Solar industry update (National Renewable Energy Laboratory (NREL), Golden, 2021)

    Google Scholar 

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Correspondence to Reza Nadimi.

Appendices

Appendix A

Energy usage for cooking

The amount of burning fuel mass, Mf, in the cooking stove43 is calculated through the following formula:

$$M_{{\text{f}}} = \frac{{P \times t}}{{{\text{HHV}}}} \quad \mathop{{\text{unit}}}\limits^{ \leftrightarrow } \quad {\text{Kg}} = \frac{{{\text{kW}} \times {\text{Sec}}.}}{{{\text{kJ}}/{\text{kg}}}}$$
(16)

where t and P represent the usage time and output power of cooking stove, respectively.

Energy usage for hot water

The following equation calculates the amount of energy, E, required to raise up temperature as much as ΔT for water with V volume:44

$$E = S \times V \times \Delta T \times R \times 2.77778 \times 10^{{ - 7}} \quad \mathop{{\text{unit}}}\limits^{ \leftrightarrow } \quad {\text{KWh}} = \frac{{{\text{Joule}}}}{{{\text{gram}} \,^\circ{\text{C}}}} \times {\text{gram}} \times \,^\circ{\text{C}}$$
(17)

where S and R represent the water specific heat capacity (equals with 4.186) and the heater performance ratio (default 0.9), respectively. The desired hot water temperature is assumed 42 °C, while the ambient temperature indicates the amount of ΔT. The constant value (2.77778 × 10–7) in Eq. (17) implies the conversion unit from Joule to kWh. The water volume used for a particular activity is calculated through flow rate Eq. (13) and consumption time of the activity.

Appendix B

See Table 7.

Table 7 Statistical distributions and their parameters for each activity.

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Nadimi, R., Tokimatsu, K. Applying consumption time analysis to measure fundamental energy needs: A method to quantify households’ energy services. MRS Energy & Sustainability (2022). https://doi.org/10.1557/s43581-022-00031-1

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Keywords

  • energy generation
  • statistics/statistical methods
  • renewable
  • simulation
  • sustainability