簡易檢索 / 詳目顯示

研究生: Truong Xuan Dan
Truong Xuan Dan
論文名稱: 基於權衡熱舒適度-家庭負擔能力空調消費之電價模型:以德里為例
Electricity pricing model based on thermal comfort-household affordability trade-off in AC consumption: the case of Delhi
指導教授: 喻奉天
Vincent F. Yu
口試委員: Shih-Wei Lin
Shih-Wei Lin
周碩彥
Shuo-Yan Chou
學位類別: 碩士
Master
系所名稱: 管理學院 - 工業管理系
Department of Industrial Management
論文出版年: 2021
畢業學年度: 109
語文別: 英文
論文頁數: 72
中文關鍵詞: thermal comfortelectricity pricing schemeresidential consumptiondissatisfaction indexKano modelincome level
外文關鍵詞: thermal comfort, electricity pricing scheme, residential consumption, dissatisfaction index, Kano model, income level
相關次數: 點閱:247下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • This study focuses on helping the government in constructing a pricing scheme which benefits all income classes in achieving a trade-off level in their comfortability and affordability in energy consumption. To illustrate the approach, we deeply research on the thermal factor, which is accounted for major part of the electric usage but is often ignored when the government design the policy. To ensure the thoughtfulness of the policy not only from the user’s side but also for the government’s side, four factors, namely, thermal comfort, electricity affordability, their trade-off level, and the government’s benefit ratio are also investigated. Their impacts on the pricing scheme are illustrated and analyzed in five scenarios based on the Delhi’s data. The solution coming from the simulated annealing algorithm suggests that with proper subsidy scheme based on usage amount, both government and income classes obtain their satisfactions where users have a higher trade-off level in their comfortability and affordability and government receives a higher benefit.


    This study focuses on helping the government in constructing a pricing scheme which benefits all income classes in achieving a trade-off level in their comfortability and affordability in energy consumption. To illustrate the approach, we deeply research on the thermal factor, which is accounted for major part of the electric usage but is often ignored when the government design the policy. To ensure the thoughtfulness of the policy not only from the user’s side but also for the government’s side, four factors, namely, thermal comfort, electricity affordability, their trade-off level, and the government’s benefit ratio are also investigated. Their impacts on the pricing scheme are illustrated and analyzed in five scenarios based on the Delhi’s data. The solution coming from the simulated annealing algorithm suggests that with proper subsidy scheme based on usage amount, both government and income classes obtain their satisfactions where users have a higher trade-off level in their comfortability and affordability and government receives a higher benefit.

    TABLE OF CONTENTS ABSTRACT 1 ACKNOWLEDGMENT 2 TABLE OF CONTENTS 3 LIST OF FIGURES 6 LIST OF TABLES 8 CHAPTER 1 9 INTRODUCTION 9 1.1 Background 9 1.2 Research purpose 12 1.3 Research Limitations 13 1.4 Structure of Thesis 13 CHAPTER 2 15 LITERATURE REVIEW 15 2.1 Thermal comfort 15 2.2 Pricing response and customer affordability 16 2.3 Consumer satisfaction. 17 2.4 Simulated Annealing 18 CHAPTER 3 19 MODEL DEVELOPMENT 19 3.1 Problem description 19 3.2 Assumptions 20 3.3 Mathematical Model 21 CHAPTER 4 25 SOLUTION METHODOLOGY 25 4.1 Basic model 25 4.1.1 Objective function 25 4.1.2 Equality constraint 32 4.1.3 Inequality constraint 34 4.2 Extend model. 34 4.3 Scenario generation. 35 4.4 Simulated Annealing. 39 CHAPTER 5 41 COMPUTATIONAL STUDY 41 5.1 Numerical example 41 5.2 Experimental result 45 5.2.1 Scenario 1 45 5.2.2 Scenario 2 and 3 46 5.2.3 Scenario 4 48 5.2.4 Scenario 5 50 5.2.5 SA 53 5.2.5 Comparison analysis 58 5.2.5 Sensitivity analysis 59 CHAPTER 6 64 CONCLUSIONS AND FUTURE RESEARCH 64 6.1 Conclusions 64 6.2 Future Research 65 REFERENCES 66

    [1] Albatayneh, A., Alterman, D., Page, A., & Moghtaderi, B. (2018). The Impact of the Thermal Comfort Models on the Prediction of Building Energy Consumption. Sustainability, 10(10). doi:10.3390/su10103609
    [2] Albatayneh, A., Alterman, D., Page, A., & Moghtaderi, B. (2019). The significance of the adaptive thermal comfort limits on the air-conditioning loads in a temperate climate. Sustainability, 11(2). doi:10.3390/su11020328
    [3] A. Panagopoulos, M. l. K., M. Pritoni, G. Fierro, D. Lengyel. (2018). Dealing with Expected Thermal Discomfort.
    [4] Yang, L., Yan, H., & Lam, J. C. (2014). Thermal comfort and building energy consumption implications – A review. Applied Energy, 115, 164-173. doi: 10.1016/j.apenergy.2013.10.062
    [5] Henley, A., & Peirson, J. (1998). Residential energy demand and the interaction ofprice and temperature: British experimental evidence. 2(2), 157-171. doi: https://doi.org/10.1016/S0140-9883(97)00025-X.
    [6] Reiss, P. C., & White, M. W. (2001). Household electricity demand, Revisited. Working Paper 8687. doi:10.3386/w8687
    [7] Jamil, F., & Ahmad, E. (2011). Income and price elasticities of electricity demand: Aggregate and sector-wise analyses. Energy Policy, 39(9), 5519-5527. doi: 10.1016/j.enpol.2011.05.010.
    [8] Conway, L., & Prentice, D. (2020). How much do households respond to electricity prices? Evidence from Australia and abroad. Economic Papers: A journal of applied economics and policy, 39(3), 290-311. doi:10.1111/1759-3441.12284.
    [9] Karimu, A., & Mensah, J. T. (2015). Climate change and electricity consumption in Sub-Saharan Africa: assessing the dynamic responses to climate variability. OPEC Energy Review 39, Volume 3. doi:10.1111/opec.12054.
    [10] Brandemuehl, M. J. AREN 3050 Environmental Systems for Buildings I.
    [11] Yasuda, K., & Ootaki, A. (2001). A study on qualification of Kano's Quality model. The Asian Journal on Quality, 2, 58-68. doi: https://doi.org/10.1108/15982688200100016
    [12] Supasa, T., Hsiau, S.-S., Lin, S.-M., Wongsapai, W., & Wu, J.-C. (2017). Household Energy Consumption Behavior for Different Demographic Regions in Thailand from 2000 to 2010. Sustainability, 9(12). doi:10.3390/su9122328
    [13] (CPR)., P. E. G. a. C. f. P. R. (2020). Trends In India’s Residential Electricity Consumption. “Plugging in: Electricity consumption in Indian Homes. Retrieved from https://www.prayaspune.org/peg/trends-in-india-s-residential-electricity-consumption#:~:text=Electricity%20consumption%20in%20Indian%20homes%20has%20tripled%20since%202000.,more%20than%2080%25%20in%202017
    [14] Tongia, R. (Apr-2017). [Delhi’s inefficient electricity subsidies].
    [15] Powerknotsra.com. (2011). COPs, EERs, and SEERs. How efficient is your air conditioning system. Retrieved from https://powerknotsra.com/2011/03/01/cops-eers-and-seers/
    [16] American Society of Heating, R. a. A.-C. E. (2019). ASHRAE handbook fundamentals (Vol. 4): https://www.ashrae.org/.
    [17] ASHRAE. (2012). ASHRAE Global Thermal Comfort Database II. Retrieved 30-Sep-20 http://www.comfortdatabase.com/
    [18] Development, C. E. a. Cooling Load Calculations and Principles R1. Retrieved from https://www.cedengineering.com/userfiles/Cooling%20Load%20Calculations%20and%20Principles%20R1.pdf
    [19] Commission, D. E. R. Delhi electricity regulatory commission. http://www.derc.gov.in Retrieved from http://www.derc.gov.in
    [20] Foster, V., & Witte, S. (2020). Falling Short: A Global Survey of Electricity Tariff Design.
    [21] Rajan Rawal, C. U., & Yash Shukla, C. U. (2014). RESIDENTIAL BUILDINGS IN INDIA: ENERGY USE PROJECTIONS AND SAVINGS POTENTIALS. Retrieved from Source, Residential Buildings in India: Energy Use Projections and Savings Potentials, GBPN, 2014: https://www.gbpn.org/sites/default/files/08.%20INDIA%20Baseline_TR_low.pdf
    [22] Statista. (2015). India - share of Delhi's annual household income 2015. Available from Statista Retrieved 1-Oct-2020, from Statista https://www.statista.com/statistics/658909/share-of-annual-income-in-delhi-india/
    [23] Statista. (2020). India - Delhi per capita income 2012-2019. Available from Statista Retrieved 1-Oct-2020, from Statista https://www.statista.com/statistics/1117814/india-per-capita-income-delhi/
    [24] Sudeshna Ghosh Banerjee, D. B., & Bipul Singh, K. M., and Hussain Samad (2015). Power for all: electricity access challenge in India. Washington, DC: The World Bank. (2015 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW, Washington DC 20433). doi:10.1596/978-1-4648-0341-3
    [25] Jain, A. (2012). Domestic LT Tariff Slabs and Rates for all states in India in 2012. Retrieved 01-Oct-20, from Bijli Bachao https://www.bijlibachao.com/historical-tariff/domestic-lt-tariff-slabs-and-rates-for-all-states-in-india-in-2012.html
    [26] Jain, A. (2020). Online Electricity Bill Calculator – For all states in India. https://www.bijlibachao.com/electricity-bill/online-electricity-bill-calculator-for-all-states-in-india.html
    [27] Sisodia, M. (2018). Delhi Budget Analysis 2018-19. PRS Legislative Research Retrieved from https://prsindia.org/budgets/states/delhi-budget-analysis-2018-19
    [28] Lin, F. H., Tsai, S. B., Lee, Y. C., Hsiao, C. F., Zhou, J., Wang, J., & Shang, Z. (2017). Empirical research on Kano's model and customer satisfaction. PLoS One, 12(9), e0183888. doi: 10.1371/journal.pone.0183888
    [29] Lin, S., & Niu, D. (2009). Empirical study on electric power customer satisfaction based on Kano model. Paper presented at the 2009 lITA International Conference on Services Science, Management and Engineering.
    [30] Tontini, G. (2007). Integrating the Kano model and QFD for designing new products. Total Quality Management & Business Excellence, 18(6), 599-612. doi:10.1080/14783360701349351.
    [31] Kirkpatrick, S., Gelatt, C. D., Jr., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671
    [32] Yoshifumi Aoki, H. I., Chuzo, N., Junji M. (2018). Smart grid real-time pricing optimization control with Simulated Annealing algorithm for office building air-conditioning facilities Paper presented at the 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France. https://ieeexplore.ieee.org/document/8352367
    [33] Yang, Q., Bian, X., Stark, R., Fresemann, C., & Song, F. (2019). Configuration Equilibrium Model of Product Variant Design Driven by Customer Requirements. Symmetry, 11(4). doi:10.3390/sym11040508
    [34] Matlab. (2020). Find minimum of constrained nonlinear multivariable function - MATLAB fmincon. Retrieved from https://www.mathworks.com/help/optim/ug/fmincon.html.
    [35] Busetti, F. (n.d.). Simulated annealing overview. Heuristics and artificial intelligence in finance and investment. http://www.aiinfinance.com/.

    無法下載圖示 全文公開日期 2024/08/02 (校內網路)
    全文公開日期 本全文未授權公開 (校外網路)
    全文公開日期 本全文未授權公開 (國家圖書館:臺灣博碩士論文系統)
    QR CODE