博碩士論文 109426033 詳細資訊



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姓名 劉正淙(Cheng-Tsung, Liu)  查詢紙本館藏   畢業系所 工業管理研究所
論文名稱 考慮需量反應與不適成本之居家用電排程
(Residential Power Scheduling with Consideration of Demand Response and Discomfort Costs)
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摘要(中) 電力的使用是日常生活中不可或缺的，然而2020年台灣自發電即產生超過全年57%的溫室氣體排放，脆弱的能源安全更使家庭用戶面臨能源轉型的重大考驗，作為再生能源之一的住宅型太陽能系統規劃於本研究作為解決方案，且其建設之獲益性亦經實驗獲得驗證。需量反應透過誘因的提供促使電力之使用能更加切合電網及環境的合宜管理，結合需量反應的參與，用電排程預期能減少家庭用戶的電費支出。本研究奠基於時間電價模式的參與，將居家電器設備分類為不可調控型（non-schedulable）、時間可控型（time-schedulable）及用電量可控型（power-schedulable）三種來進行排程。用戶透過需量反應的參與進而改變特定電器設備的使用時間或用電量，而據此產生的習慣改變將利用不適成本的計算作為衡量，其明確的用電指引由混合整數規劃的評估而產生。

單位不適成本的設定對於規畫結果的影響程度取決不同電器種類之特性，時間可控型在規劃上與其設備之額定容量更為直接相關，且由於多數居家用電設備額定容量皆小於一瓩，相比於用電量可控型的規劃，時間可控型對於數值較小的單位不適成本設定會形成較明顯的結果變異。不適程度和實體開銷兩者間之取捨透過適切的不適成本作為權衡，排程結果相比於原先用電情況節省26.3%的支出且對於電網的依賴減少17.6%，最終更能進一步達成全年超過一公噸之溫室氣體排放減量。雖排程結果使電力需求傾向集中規劃於離峰時段並進而推升電網之用電峰均比（peak-to-average ratio），惟家庭用戶用電減量的成果及對於環境永續發展的貢獻仍使整體電力市場得以獲得緩解，與此同時，在兼顧生活舒適性之下亦能同時享受經濟及環境層面所帶來的助益。
摘要(英) Power generation has substantially contributed more than 57% of the yearly greenhouse gases (GHGs) emissions of Taiwan in 2020. On top of that, the vulnerable energy security also leads households to the topic of energy transition. Residential photovoltaic systems as one of renewable energy is considered in this study for these issues and has been verified to be beneficial. Power scheduling is expected to lower the payment on electricity bill in line with demand response (DR) programs. DR programs provide benefits as motivation to fulfill a better power usage regarding management on grid and environment. The scheduling problem under time-of-use scheme in this study classifies household appliances into three types: non-schedulable, time-schedulable, and power-schedulable. Households would adjust their daily power consumption either on usage time or power loads on the certain appliance. While changing from initial habit, the discomfort costs would be considered regarding the extent of variation. Through mixed-integer linear programming, a clear arrangement is provided.

The variation characteristics regarding unit discomfort costs varies on different types of appliances. Scheduling on time-schedulable loads is much relevant to the rated power. As general household appliances are less than 1 kW on rated power, time-schedulable appliances are much sensitive to relative smaller unit discomfort costs than power-schedulable ones. With trade-off between discomfort level and payment, the scenario of medium discomfort costs contributes to 26.3% costs saving while mitigating more than 1 ton of GHGs emissions annually as cutting 17.6% power demand from grid. Though a high peak-to-average ratio is displayed as with even intensive usage on off-peak period, taking the overall power market into consideration, the effort of power scheduling would still lead society live comfortably on both economic and environmental perspectives.
關鍵字(中) ★ 居家用電排程
★ 時間電價
★ 時間可控型
★ 用電量可控型
★ 不適成本
★ 太陽能		 關鍵字(英) ★ residential power scheduling
★ time-of-use
★ time-schedulable
★ power-schedulable
★ discomfort costs
★ photovoltaic systems
論文目次 摘要 i
Abstract ii
Table of Contents iii
List of Figures iv
List of Tables v
Acronyms vi
Chapter 1 Research Problem 1
1.1 Global Warming and Energy Security 1
1.2 Research Motivation 6
1.3 Problem Statement 11
Chapter 2 Literature Review 13
2.1 Sustainability and Residential Photovoltaic Systems 13
2.2 Demand Response 15
2.3 Power Scheduling 18
Chapter 3 Research Methodology 24
3.1 Problem Analysis 24
3.2 Research Method 26
3.3 Optimization Model 27
Chapter 4 Results and Discussion 35
4.1 Data Collection 35
4.2 Case Studies and Simulation Results 39
4.3 Sensitivity Analysis 49
Chapter 5 Conclusions and Future Works 55
5.1 Conclusions 55
5.2 Future Works 56
References 58
Appendix 64

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Environmental Protection Agency (2022). National Greenhouse Gas Inventory Report 2022. R.O.C. (Taiwan), Executive Yuan, Environmental Protection Agency. https://unfccc.saveoursky.org.tw/nir/2022nir/uploads/00_abstract_en.pdf
European Commission, Directorate-General for Energy, Badouard, T., Moreira de Oliveira, D., Yearwood, J., Torres, P., & Altman, M. (2020). Cost of energy (LCOE) : energy costs, taxes and the impact of government interventions on investments : final report. Publications Office. https://doi.org/doi/10.2833/779528
European Commission, Joint Research Centre, Olivier, J., Guizzardi, D., Schaaf, E., Solazzo, E., Crippa, M., Vignati, E., Banja, M., Muntean, M., Grassi, G., Monforti-Ferrario, F., & Rossi, S. (2021). GHG emissions of all world : 2021 report. Publications Office. https://doi.org/doi/10.2760/173513
European Environment Agency (2021, October 25). Greenhouse gas emission intensity of electricity generation by country. Retrieved April 27, 2022, from https://www.eea.europa.eu/data-and-maps/daviz/co2-emission-intensity-9
Fang, X., Misra, S., Xue, G. L., & Yang, D. J. (2012). Smart Grid - The New and Improved Power Grid: A Survey. Ieee Communications Surveys and Tutorials, 14(4), 944-980. https://doi.org/10.1109/Surv.2011.101911.00087
Floudas, C. A., & Lin, X. X. (2005). Mixed integer linear programming in process scheduling: Modeling, algorithms, and applications. Annals of Operations Research, 139(1), 131-162. https://doi.org/10.1007/s10479-005-3446-x
Ghazvini, M. A. F., Soares, J., Horta, N., Neves, R., Castro, R., & Vale, Z. (2015). A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers. Applied Energy, 151, 102-118. https://doi.org/10.1016/j.apenergy.2015.04.067
Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252. https://doi.org/https://doi.org/10.1016/j.jclepro.2019.119869
Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24, 38-50. https://doi.org/10.1016/j.esr.2019.01.006
Guo, M., & Shah, N. (2015). Bringing Non-energy Systems into a Bioenergy Value Chain Optimization Framework. In Computer Aided Chemical Engineering (Vol. 37, pp. 2351-2356). Elsevier.
https://doi.org/https://doi.org/10.1016/B978-0-444-63576-1.50086-8
Gurobi (2020). Tutorial: Mixed-Integer Linear Programming. Retrieved October 11, 2022, from https://www.gurobi.com/resource/tutorial-mixed-integer-linear-programming/
Hong, H., & Chen, N. W. (2017). New evidence on breaking trend functions in real GDPs from Great China Economic Area. Applied Economics Letters, 24(10), 663-667. https://doi.org/10.1080/13504851.2016.1218423
Iftikhar, H., Asif, S., Maroof, R., Ambreen, K., Khan, H. N., & Javaid, N. (2017). Biogeography Based Optimization for Home Energy Management in Smart Grid. 177-190. https://doi.org/10.1007/978-3-319-65521-5_16
International Trade Centre (2022, March 2). List of markets for the selected product Product: TOTAL All products. Retrieved March 27, 2022, from https://www.trademap.org/Index.aspx
IPCC (2022). Climate Change 2022: Impacts, Adaptation, and Vulnerability. Intergovernmental Panel on Climate Change. https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_FinalDraft_FullReport.pdf
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https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2021/Jun/IRENA_Power_Generation_Costs_2020.pdf
Izmitligil, H., & Ozkan, H. A. (2018). A home energy management system. Transactions of the Institute of Measurement and Control, 40(8), 2498-2508. https://doi.org/10.1177/0142331217741537
Javadi, M. S., Gough, M., Lotfi, M., Nezhad, A. E., Santos, S. F., & Catalao, J. P. S. (2020). Optimal self-scheduling of home energy management system in the presence of photovoltaic power generation and batteries. Energy, 210. https://doi.org/https://doi.org/10.1016/j.energy.2020.118568
Jordehi, A. R. (2019). Optimisation of demand response in electric power systems, a review. Renewable & Sustainable Energy Reviews, 103, 308-319. https://doi.org/10.1016/j.rser.2018.12.054
Jordehi, A. R. (2020). Enhanced leader particle swarm optimisation (ELPSO): a new algorithm for optimal scheduling of home appliances in demand response programs. Artificial Intelligence Review, 53(3), 2043-2073.
https://doi.org/10.1007/s10462-019-09726-3
Khan, A. R., Mahmood, A., Safdar, A., Khan, Z. A., & Khan, N. A. (2016). Load forecasting, dynamic pricing and DSM in smart grid: A review. Renewable & Sustainable Energy Reviews, 54, 1311-1322. https://doi.org/10.1016/j.rser.2015.10.117
Khan, S. u. R., Khan, A., Mushtaq, N., Faraz, S. H., Khan, O. A., Sarwar, M. A., & Javaid, N. (2017). Genetic Algorithm and Earthworm Optimization Algorithm for Energy Management in Smart Grid. 447-459.
https://doi.org/https://doi.org/10.1007/978-3-319-69835-9_42
Lee, J. Y., Chen, C. L., & Chen, H. C. (2014). A mathematical technique for hybrid power system design with energy loss considerations. Energy Conversion and Management, 82, 301-307. https://doi.org/10.1016/j.enconman.2014.03.029
LSE (2020). Carbon pricing options for Taiwan. The London School of Economics and Political Science.
https://www.lse.ac.uk/granthaminstitute/wp-content/uploads/2020/12/Carbon-pricing-options-for-Taiwan.pdf
Ma, K., Yao, T., Yang, J., & Guan, X. P. (2016). Residential power scheduling for demand response in smart grid. International Journal of Electrical Power & Energy Systems, 78, 320-325. https://doi.org/10.1016/j.ijepes.2015.11.099
Makhadmeh, S. N., Khader, A. T., Al-Betar, M. A., & Naim, S. (2018). Multi-objective power scheduling problem in smart homes using grey wolf optimiser. Journal of Ambient Intelligence and Humanized Computing, 10(9), 3643-3667. https://doi.org/10.1007/s12652-018-1085-8
Makhadmeh, S. N., Khader, A. T., Al-Betar, M. A., Naim, S., Abasi, A. K., & Alyasseri, Z. A. A. (2019). Optimization methods for power scheduling problems in smart home: Survey. Renewable & Sustainable Energy Reviews, 115. https://doi.org/https://doi.org/10.1016/j.rser.2019.109362
Mariano, J. R. L., Liao, M. Y., & Ay, H. (2021). Performance Evaluation of Solar PV Power Plants in Taiwan Using Data Envelopment Analysis. Energies, 14(15). https://doi.org/https://doi.org/10.3390/en14154498
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Mohsenian-Rad, A. H., & Leon-Garcia, A. (2010). Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments. Ieee Transactions on Smart Grid, 1(2), 120-133. https://doi.org/https://doi.org/10.1109/TSG.2010.2055903
Muller, R. (2012). Energy for future presidents: the science behind the headlines (1st ed.). W. W. Norton.
Nan, S. B., Zhou, M., & Li, G. Y. (2018). Optimal residential community demand response scheduling in smart grid. Applied Energy, 210, 1280-1289. https://doi.org/10.1016/j.apenergy.2017.06.066
Ou, W. S., Ho, M. C., Chen, J. L., Chen, J. F., & Lo, S. C. (2008). The Study on the Typical Radiation for Solar Architecture Design of Taiwan. Journal of Architecture, 64, 103-118. https://doi.org/10.6377/JA.200806.0006
Pechmann, A., Scholer, I., & Ernst, S. (2016). Possibilities for CO2-neutral manufacturing with attractive energy costs. Journal of Cleaner Production, 138, 287-297. https://doi.org/10.1016/j.jclepro.2016.04.053
Qayyum, F. A., Naeem, M., Khwaja, A. S., Anpalagan, A., Guam, L., & Venkatesh, B. (2015). Appliance Scheduling Optimization in Smart Home Networks. Ieee Access, 3, 2176-2190. https://doi.org/10.1109/Access.2015.2496117
Rafkaoui, M. A., Khallaayoun, A., & Lghoul, M. R. (2016). Optimal Scheduling of Smart Homes Energy Consumption in Conjunction with Solar Energy Resources. Al Akhawayn University Ifrane: Ifrane, Morocco.
Rahim, S., Javaid, N., Ahmad, A., Khan, S. A., Khan, Z. A., Alrajeh, N., & Qasim, U. (2016). Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy and Buildings, 129, 452-470. https://doi.org/10.1016/j.enbuild.2016.08.008
Ranjbari, M., Esfandabadi, Z. S., Zanetti, M. C., Scagnelli, S. D., Siebers, P. O., Aghbashlo, M., Peng, W. X., Quatraro, F., & Tabatabaei, M. (2021). Three pillars of sustainability in the wake of COVID-19: A systematic review and future research agenda for sustainable development. Journal of Cleaner Production, 297. https://doi.org/https://doi.org/10.1016/j.jclepro.2021.126660
Rezaee Jordehi, A. (2020). Enhanced leader particle swarm optimisation (ELPSO): a new algorithm for optimal scheduling of home appliances in demand response programs. Artificial Intelligence Review, 53(3), 2043-2073.
https://doi.org/10.1007/s10462-019-09726-3
Shariatzadeh, F., Mandal, P., & Srivastava, A. K. (2015). Demand response for sustainable energy systems: A review, application and implementation strategy. Renewable & Sustainable Energy Reviews, 45, 343-350. https://doi.org/10.1016/j.rser.2015.01.062
Sovacool, B. K., & Brown, M. A. (2010). Competing Dimensions of Energy Security: An International Perspective. Annual Review of Environment and Resources, 35, 77-108. https://doi.org/10.1146/annurev-environ-042509-143035
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Doostizadeh, M., & Ghasemi, H. (2012). A day-ahead electricity pricing model based on smart metering and demand-side management. Energy, 46(1), 221-230. https://doi.org/10.1016/j.energy.2012.08.029
Environmental Protection Agency (2022). National Greenhouse Gas Inventory Report 2022. R.O.C. (Taiwan), Executive Yuan, Environmental Protection Agency. https://unfccc.saveoursky.org.tw/nir/2022nir/uploads/00_abstract_en.pdf
European Commission, Directorate-General for Energy, Badouard, T., Moreira de Oliveira, D., Yearwood, J., Torres, P., & Altman, M. (2020). Cost of energy (LCOE) : energy costs, taxes and the impact of government interventions on investments : final report. Publications Office. https://doi.org/doi/10.2833/779528
European Commission, Joint Research Centre, Olivier, J., Guizzardi, D., Schaaf, E., Solazzo, E., Crippa, M., Vignati, E., Banja, M., Muntean, M., Grassi, G., Monforti-Ferrario, F., & Rossi, S. (2021). GHG emissions of all world : 2021 report. Publications Office. https://doi.org/doi/10.2760/173513
European Environment Agency (2021, October 25). Greenhouse gas emission intensity of electricity generation by country. Retrieved April 27, 2022, from https://www.eea.europa.eu/data-and-maps/daviz/co2-emission-intensity-9
Fang, X., Misra, S., Xue, G. L., & Yang, D. J. (2012). Smart Grid - The New and Improved Power Grid: A Survey. Ieee Communications Surveys and Tutorials, 14(4), 944-980. https://doi.org/10.1109/Surv.2011.101911.00087
Floudas, C. A., & Lin, X. X. (2005). Mixed integer linear programming in process scheduling: Modeling, algorithms, and applications. Annals of Operations Research, 139(1), 131-162. https://doi.org/10.1007/s10479-005-3446-x
Ghazvini, M. A. F., Soares, J., Horta, N., Neves, R., Castro, R., & Vale, Z. (2015). A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers. Applied Energy, 151, 102-118. https://doi.org/10.1016/j.apenergy.2015.04.067
Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252. https://doi.org/https://doi.org/10.1016/j.jclepro.2019.119869
Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24, 38-50. https://doi.org/10.1016/j.esr.2019.01.006
Guo, M., & Shah, N. (2015). Bringing Non-energy Systems into a Bioenergy Value Chain Optimization Framework. In Computer Aided Chemical Engineering (Vol. 37, pp. 2351-2356). Elsevier.
https://doi.org/https://doi.org/10.1016/B978-0-444-63576-1.50086-8
Gurobi (2020). Tutorial: Mixed-Integer Linear Programming. Retrieved October 11, 2022, from https://www.gurobi.com/resource/tutorial-mixed-integer-linear-programming/
Hong, H., & Chen, N. W. (2017). New evidence on breaking trend functions in real GDPs from Great China Economic Area. Applied Economics Letters, 24(10), 663-667. https://doi.org/10.1080/13504851.2016.1218423
Iftikhar, H., Asif, S., Maroof, R., Ambreen, K., Khan, H. N., & Javaid, N. (2017). Biogeography Based Optimization for Home Energy Management in Smart Grid. 177-190. https://doi.org/10.1007/978-3-319-65521-5_16
International Trade Centre (2022, March 2). List of markets for the selected product Product: TOTAL All products. Retrieved March 27, 2022, from https://www.trademap.org/Index.aspx
IPCC (2022). Climate Change 2022: Impacts, Adaptation, and Vulnerability. Intergovernmental Panel on Climate Change. https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_FinalDraft_FullReport.pdf
IRENA (2021). Renewable Power Generation Costs in 2020. International Renewable Energy Agency.
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Izmitligil, H., & Ozkan, H. A. (2018). A home energy management system. Transactions of the Institute of Measurement and Control, 40(8), 2498-2508. https://doi.org/10.1177/0142331217741537
Javadi, M. S., Gough, M., Lotfi, M., Nezhad, A. E., Santos, S. F., & Catalao, J. P. S. (2020). Optimal self-scheduling of home energy management system in the presence of photovoltaic power generation and batteries. Energy, 210. https://doi.org/https://doi.org/10.1016/j.energy.2020.118568
Jordehi, A. R. (2019). Optimisation of demand response in electric power systems, a review. Renewable & Sustainable Energy Reviews, 103, 308-319. https://doi.org/10.1016/j.rser.2018.12.054
 
Jordehi, A. R. (2020). Enhanced leader particle swarm optimisation (ELPSO): a new algorithm for optimal scheduling of home appliances in demand response programs. Artificial Intelligence Review, 53(3), 2043-2073.
https://doi.org/10.1007/s10462-019-09726-3
Khan, A. R., Mahmood, A., Safdar, A., Khan, Z. A., & Khan, N. A. (2016). Load forecasting, dynamic pricing and DSM in smart grid: A review. Renewable & Sustainable Energy Reviews, 54, 1311-1322. https://doi.org/10.1016/j.rser.2015.10.117
Khan, S. u. R., Khan, A., Mushtaq, N., Faraz, S. H., Khan, O. A., Sarwar, M. A., & Javaid, N. (2017). Genetic Algorithm and Earthworm Optimization Algorithm for Energy Management in Smart Grid. 447-459.
https://doi.org/https://doi.org/10.1007/978-3-319-69835-9_42
Lee, J. Y., Chen, C. L., & Chen, H. C. (2014). A mathematical technique for hybrid power system design with energy loss considerations. Energy Conversion and Management, 82, 301-307. https://doi.org/10.1016/j.enconman.2014.03.029
LSE (2020). Carbon pricing options for Taiwan. The London School of Economics and Political Science.
https://www.lse.ac.uk/granthaminstitute/wp-content/uploads/2020/12/Carbon-pricing-options-for-Taiwan.pdf
Ma, K., Yao, T., Yang, J., & Guan, X. P. (2016). Residential power scheduling for demand response in smart grid. International Journal of Electrical Power & Energy Systems, 78, 320-325. https://doi.org/10.1016/j.ijepes.2015.11.099
Makhadmeh, S. N., Khader, A. T., Al-Betar, M. A., & Naim, S. (2018). Multi-objective power scheduling problem in smart homes using grey wolf optimiser. Journal of Ambient Intelligence and Humanized Computing, 10(9), 3643-3667. https://doi.org/10.1007/s12652-018-1085-8
Makhadmeh, S. N., Khader, A. T., Al-Betar, M. A., Naim, S., Abasi, A. K., & Alyasseri, Z. A. A. (2019). Optimization methods for power scheduling problems in smart home: Survey. Renewable & Sustainable Energy Reviews, 115. https://doi.org/https://doi.org/10.1016/j.rser.2019.109362
Mariano, J. R. L., Liao, M. Y., & Ay, H. (2021). Performance Evaluation of Solar PV Power Plants in Taiwan Using Data Envelopment Analysis. Energies, 14(15). https://doi.org/https://doi.org/10.3390/en14154498
Ministry of the Interior (2018, August 13). Basic living standard. Retrieved December 5, 2022, from https://www.cpami.gov.tw/%E6%9C%80%E6%96%B0%E6%B6%88%E6%81%AF/%E6%B3%95%E8%A6%8F%E5%85%AC%E5%91%8A/29-%E4%BD%8F%E5%AE%85%E7%AF%87/15748-%E5%9F%BA%E6%9C%AC%E5%B1%85%E4%BD%8F%E6%B0%B4%E6%BA%96.html
 
Mohsenian-Rad, A. H., & Leon-Garcia, A. (2010). Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments. Ieee Transactions on Smart Grid, 1(2), 120-133. https://doi.org/https://doi.org/10.1109/TSG.2010.2055903
Muller, R. (2012). Energy for future presidents: the science behind the headlines (1st ed.). W. W. Norton.
Nan, S. B., Zhou, M., & Li, G. Y. (2018). Optimal residential community demand response scheduling in smart grid. Applied Energy, 210, 1280-1289. https://doi.org/10.1016/j.apenergy.2017.06.066
Ou, W. S., Ho, M. C., Chen, J. L., Chen, J. F., & Lo, S. C. (2008). The Study on the Typical Radiation for Solar Architecture Design of Taiwan. Journal of Architecture, 64, 103-118. https://doi.org/10.6377/JA.200806.0006
Pechmann, A., Scholer, I., & Ernst, S. (2016). Possibilities for CO2-neutral manufacturing with attractive energy costs. Journal of Cleaner Production, 138, 287-297. https://doi.org/10.1016/j.jclepro.2016.04.053
Qayyum, F. A., Naeem, M., Khwaja, A. S., Anpalagan, A., Guam, L., & Venkatesh, B. (2015). Appliance Scheduling Optimization in Smart Home Networks. Ieee Access, 3, 2176-2190. https://doi.org/10.1109/Access.2015.2496117
Rafkaoui, M. A., Khallaayoun, A., & Lghoul, M. R. (2016). Optimal Scheduling of Smart Homes Energy Consumption in Conjunction with Solar Energy Resources. Al Akhawayn University Ifrane: Ifrane, Morocco.
Rahim, S., Javaid, N., Ahmad, A., Khan, S. A., Khan, Z. A., Alrajeh, N., & Qasim, U. (2016). Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy and Buildings, 129, 452-470. https://doi.org/10.1016/j.enbuild.2016.08.008
Ranjbari, M., Esfandabadi, Z. S., Zanetti, M. C., Scagnelli, S. D., Siebers, P. O., Aghbashlo, M., Peng, W. X., Quatraro, F., & Tabatabaei, M. (2021). Three pillars of sustainability in the wake of COVID-19: A systematic review and future research agenda for sustainable development. Journal of Cleaner Production, 297. https://doi.org/https://doi.org/10.1016/j.jclepro.2021.126660
Rezaee Jordehi, A. (2020). Enhanced leader particle swarm optimisation (ELPSO): a new algorithm for optimal scheduling of home appliances in demand response programs. Artificial Intelligence Review, 53(3), 2043-2073.
https://doi.org/10.1007/s10462-019-09726-3
Shariatzadeh, F., Mandal, P., & Srivastava, A. K. (2015). Demand response for sustainable energy systems: A review, application and implementation strategy. Renewable & Sustainable Energy Reviews, 45, 343-350. https://doi.org/10.1016/j.rser.2015.01.062
Sovacool, B. K., & Brown, M. A. (2010). Competing Dimensions of Energy Security: An International Perspective. Annual Review of Environment and Resources, 35, 77-108. https://doi.org/10.1146/annurev-environ-042509-143035
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指導教授 王啟泰(Chi-Tai, Wang) 審核日期 2023-1-19
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