隨著物聯網(Internet of Things, IoT)的成熟，其廣泛的應用也將改變我們的生 活。智慧型電表基礎建設(Advanced Metering Infrastructure, AMI)將大幅提升了需量反 應中實時電價的可行性，智慧家電與家庭能源管理系統(Home Energy Management Systems, HEMS)的搭配也使得家庭能源的排程能更加地自動化。其中，如何將新技 術融入人們的生活並使其接納是一項重要的課題。
本研究以使用者為核心，考量舒適度(使用者對排程執行的滿意度)與電費支出 為雙目標做需量反應下家庭能源的最佳化管理，目的為根據使用者對不同家庭活動的 偏好輔助其個人化地找到最適解。本研究別於過去研究以電器做排程，改以用戶活動 作為排程依據，開發一套用戶活動的排程模型，並建立更準確的舒適度計算方式。我 們定義了用戶活動與電器間的關係使其能夠作為排程的依據，並透過問卷的方式求出 不同種類的活動的在提早、延後的舒適度函數與活動縮短時的舒適度修正係數以更準 確地計算舒適度，使得此模型之排程能比過往的研究更加貼近民眾的實際生活模式。 此外，本研究在求出最適化雙目標前緣後以願付價格的方式尋找最適解。透過使用者 對不同用戶活動進行時間挪移的願付價格，我們將舒適度轉換為價格並個人化地產生 最適解。
以此為基礎，本研究建立了一套需量反應下考量使用者活動的家庭能源最佳化排 程系統。此系統保留了使用者自行設定的彈性，可供不同使用者根據習慣輸入活動內 容與電器，使排程結果更加個人化。用戶在使用此系統時，能因系統高度彈性的個人 化以及追求舒適度同時明顯感受電費支出的降低而更願意使用此系統，進而有效推動 實時電價並減少能源的使用。

In recent years, there have been increasing numbers of energy suppliers trending toward imported demand response (DR) policies. Among all DR policies, real-time pricing (RTP) is the most popular policy. However, most people do not have the ability to make decisions as to when to turn on/off their household appliances under RTP policies. Therefore, in this study, a user-centered home energy management system (HEMS) optimization model is proposed that is aimed toward assisting end consumers with scheduling their use of electricity. This model schedules household appliances based on the price of electricity in real-time and measures the degree of comfort/satisfaction of the end consumer. There are three innovations in this model: First, different from most previous research, this study schedules user activities rather than household appliances. Second, this study establishes an accurate method to measure people’s comfort/satisfaction when the execution time and duration of user activities are different from user expectations. Third, this study applies the concept of willingness-to-pay to find an optimal solution among non-dominate solutions. To prove the effectiveness of the proposed model, a system is built with Excel and Visual Basic based on the model and is used to demonstrate the effectiveness of the proposed model using two cases. The results for both cases indicate that this model can significantly reduce costs while maximizing consumer comfort/satisfaction under RTP. Due to highly personalized scheduling, end consumers will be more willing to use this system and will be more likely to accept RTP. Furthermore, the use of energy can be reduced, and the percentage of renewable energy in power generation structures can rise significantly.

[1] Aalami, H., Moghaddam, M. P., & Yousefi, G. (2010). Demand response modeling considering interruptible/curtailable loads and capacity market programs. Applied Energy, 87(1), 243-250.
[2] Aksanli, B., Akyurek, A. S., & Rosing, T. S. (2016). User behavior modeling for estimating residential energy consumption. In Smart City 360° (pp. 348-361). Springer.
[3] Aksanli, B., & Rosing, T. S. (2017). Human behavior aware energy management in residential cyber-physical systems. IEEE Transactions on Emerging Topics in Computing.
[4] Albadi, M. H., & El-Saadany, E. F. (2007). Demand response in electricity markets: An overview. 2007 IEEE power engineering society general meeting,
[5] Alberini, A., & Filippini, M. (2011). Response of residential electricity demand to price: The effect of measurement error. Energy economics, 33(5), 889-895.
[6] Althaher, S., Mancarella, P., & Mutale, J. (2015). Automated demand response from home energy management system under dynamic pricing and power and comfort constraints. IEEE Transactions on Smart Grid, 6(4), 1874-1883.
[7] Arias, L. A., Rivas, E., Santamaria, F., & Hernandez, V. (2018). A review and analysis of trends related to demand response. Energies, 11(7), 1617.
[8] Bhattacharyya, S. C. (2019). Energy economics: concepts, issues, markets and governance. Springer Nature.
[9] Bidgely. (2017). Case Study Demand Response. Bidgely.
[10] Caramanis, M. C., Bohn, R. E., & Schweppe, F. C. (1982). Optimal spot pricing: Practice and theory. IEEE Transactions on Power Apparatus and Systems(9), 3234-3245.
[11] Conejo, A. J., Morales, J. M., & Baringo, L. (2010). Real-time demand response model. IEEE Transactions on Smart Grid, 1(3), 236-242.
[12] Cui, Q., Wang, X., Wang, X., & Zhang, Y. (2016). Residential appliances direct load control in real-time using cooperative game. IEEE Transactions on Power Systems, 31(1), 226-233.
[13] Deng, R., Yang, Z., Chow, M.-Y., & Chen, J. (2015). A survey on demand response in smart grids: Mathematical models and approaches. IEEE transactions on industrial informatics, 11(3), 570-582.
[14] Doostizadeh, M., & Ghasemi, H. (2012). A day-ahead electricity pricing model based on smart metering and demand-side management. Energy, 46(1), 221-230.
[15] Druitt, J., & Früh, W.-G. (2012). Simulation of demand management and grid balancing with electric vehicles. Journal of power sources, 216, 104-116.
[16] Gelazanskas, L., & Gamage, K. A. (2014). Demand side management in smart grid: A review and proposals for future direction. Sustainable Cities and Society, 11, 22-30.
[17] Gellings, C. W. (1985). The concept of demand-side management for electric utilities. Proceedings of the IEEE, 73(10), 1468-1470.
[18] Gellings, C. W., & Samotyj, M. (2013). Smart Grid as advanced technology enabler of demand response. Energy efficiency, 6(4), 685-694.
[19] Haider, H. T., See, O. H., & Elmenreich, W. (2016). A review of residential demand response of smart grid. Renewable and sustainable energy reviews, 59, 166-178.
[20] Hajimiragha, A., Canizares, C. A., Fowler, M. W., & Elkamel, A. (2009). Optimal transition to plug-in hybrid electric vehicles in Ontario, Canada, considering the electricity-grid limitations. IEEE Transactions on Industrial Electronics, 57(2), 690-701.
[21] He, Y., Wang, B., Wang, J., Xiong, W., & Xia, T. (2012). Residential demand response behavior analysis based on Monte Carlo simulation: The case of Yinchuan in China. Energy, 47(1), 230-236.
[22] IEA. (2020a). Demand Response. IEA, Paris. https://www.iea.org/reports/demand-response
[23] IEA. (2020b). Demand response potential in the Sustainable Development Scenario, 2018-2040. IEA, Paris. https://www.iea.org/data-and- statistics/charts/demand-response-potential-in-the-sustainable-development- scenario-2018-2040
[24] IEA (2020c), Global EV Outlook 2020, IEA, Paris. https://www.iea.org/reports/global-ev-outlook-2020
[25] Javaid, N., Ahmed, F., Ullah, I., Abid, S., Abdul, W., Alamri, A., & Almogren, A. S. (2017). Towards cost and comfort based hybrid optimization for residential load scheduling in a smart grid. Energies, 10(10), 1546.
[26] Jordehi, A. R. (2016). Time varying acceleration coefficients particle swarm optimisation (TVACPSO): A new optimisation algorithm for estimating parameters of PV cells and modules. Energy Conversion and Management, 129, 262-274.
[27] Jordehi, A. R. (2019). Optimisation of demand response in electric power systems, a review. Renewable and sustainable energy reviews, 103, 308-319.
[28] Kamyab, F., Amini, M., Sheykhha, S., Hasanpour, M., & Jalali, M. M. (2015). Demand response program in smart grid using supply function bidding mechanism. IEEE Transactions on Smart Grid, 7(3), 1277-1284.
[29] Kempton, W., & Letendre, S. E. (1997). Electric vehicles as a new power source for electric utilities. Transportation Research Part D: Transport and Environment, 2(3), 157-175.
[30] Kempton, W., & Tomić, J. (2005a). Vehicle-to-grid power fundamentals: Calculating capacity and net revenue. Journal of power sources, 144(1), 268-279.
[31] Kempton, W., & Tomić, J. (2005b). Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy. Journal of power sources, 144(1), 280-294.
[32] Kostková, K., Omelina, Ľ., Kyčina, P., & Jamrich, P. (2013). An introduction to load management. Electric Power Systems Research, 95, 184-191.
[33] Kristoffersen, T. K., Capion, K., & Meibom, P. (2011). Optimal charging of electric drive vehicles in a market environment. Applied Energy, 88(5), 1940-1948.
[34] Liu, B., & Wei, Q. (2013). Home energy control algorithm research based on demand response programs and user comfort. Proceedings of 2013 2nd international conference on measurement, information and control,
[35] López, M. A., De La Torre, S., Martín, S., & Aguado, J. A. (2015). Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support. International Journal of Electrical Power & Energy Systems, 64, 689-698.
[36] Lotfi, M., Monteiro, C., Shafie-khah, M., & Catalão, J. P. (2018). Evolution of demand response: A historical analysis of legislation and research trends. 2018 twentieth international middle east power systems conference (MEPCON),
[37] Lund, H., & Kempton, W. (2008). Integration of renewable energy into the transport and electricity sectors through V2G. Energy policy, 36(9), 3578-3587.
[38] Ma, K., Yao, T., Yang, J., & Guan, X. (2016). Residential power scheduling for demand response in smart grid. International Journal of Electrical Power & Energy Systems, 78, 320-325.
[39] Madzharov, D., Delarue, E., & D'haeseleer, W. (2014). Integrating electric vehicles as flexible load in unit commitment modeling. Energy, 65, 285-294.
[40] Mahapatra, C., Moharana, A. K., & Leung, V. (2017). Energy management in smart cities based on internet of things: Peak demand reduction and energy savings. Sensors, 17(12), 2812.
[41] Mathieu, J. L., Price, P. N., Kiliccote, S., & Piette, M. A. (2011). Quantifying changes in building electricity use, with application to demand response. IEEE Transactions on Smart Grid, 2(3), 507-518.
[42] 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.
[43] Mohsenian-Rad, A.-H., Wong, V. W., Jatskevich, J., & Schober, R. (2010). Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid. Innovative smart grid technologies (ISGT), 2010, 1-6.
[44] Monteiro, J., Cardoso, P. J., Serra, R., & Fernandes, L. (2014). Evaluation of the human factor in the scheduling of smart appliances in smart grids. International Conference on Universal Access in Human-Computer Interaction,
[45] Muratori, M., Schuelke-Leech, B.-A., & Rizzoni, G. (2014). Role of residential demand response in modern electricity markets. Renewable and sustainable energy reviews, 33, 546-553.
[46] Ogunjuyigbe, A., Ayodele, T., & Akinola, O. (2017). User satisfaction-induced demand side load management in residential buildings with user budget constraint. Applied Energy, 187, 352-366.
[47] Palensky, P., & Dietrich, D. (2011). Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE transactions on industrial informatics, 7(3), 381-388.
[48] Park, L., Jang, Y., Bae, H., Lee, J., Park, C. Y., & Cho, S. (2017). Automated energy scheduling algorithms for residential demand response systems. Energies, 10(9), 1326.
[49] Qdr, Q. (2006). Benefits of demand response in electricity markets and recommendations for achieving them. US Dept. Energy, Washington, DC, USA, Tech. Rep.
[50] Qu, Z., Qu, N., Liu, Y., Yin, X., Qu, C., Wang, W., & Han, J. (2018). Multi- objective optimization model of electricity behavior considering the combination of household appliance correlation and comfort. Journal of Electrical Engineering & Technology, 13(5), 1821-1830.
[51] Rastegar, M., Fotuhi-Firuzabad, M., & Choi, J. (2014). Investigating the impacts of different price-based demand response programs on home load management. Journal of Electrical Engineering & Technology, 9(3), 1125-1131.
[52] Richardson, D. B. (2013). Electric vehicles and the electric grid: A review of modeling approaches, Impacts, and renewable energy integration. Renewable and sustainable energy reviews, 19, 247-254.
[53] Roh, H.-T., & Lee, J.-W. (2015). Residential demand response scheduling with multiclass appliances in the smart grid. IEEE Transactions on Smart Grid, 7(1), 94-104.
[54] Saebi, J., Taheri, H., Mohammadi, J., & Nayer, S. S. (2010). Demand bidding/buyback modeling and its impact on market clearing price. 2010 IEEE International Energy Conference,
[55] Safdarian, A., Fotuhi-Firuzabad, M., & Lehtonen, M. (2015). Optimal residential load management in smart grids: A decentralized framework. IEEE Transactions on Smart Grid, 7(4), 1836-1845.
[56] Schuller, A., Flath, C. M., & Gottwalt, S. (2015). Quantifying load flexibility of electric vehicles for renewable energy integration. Applied Energy, 151, 335-344.
[57] Shariatzadeh, F., Mandal, P., & Srivastava, A. K. (2015). Demand response for sustainable energy systems: A review, application and implementation strategy. Renewable and sustainable energy reviews, 45, 343-350.
[58] Shirazi, E., & Jadid, S. (2015). Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS. Energy and Buildings, 93, 40-49.
[59] Silva, B. N., Khan, M., & Han, K. (2020). Futuristic Sustainable Energy Management in Smart Environments: A Review of Peak Load Shaving and Demand Response Strategies, Challenges, and Opportunities. Sustainability, 12(14), 5561.
[60] Soares, A., Gomes, Á., & Antunes, C. H. (2014). Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions. Renewable and sustainable energy reviews, 30, 490-503.
[61] Son, Y.-S., Pulkkinen, T., Moon, K.-D., & Kim, C. (2010). Home energy management system based on power line communication. IEEE Transactions on Consumer Electronics, 56(3), 1380-1386.
[62] Sovacool, B. K., & Hirsh, R. F. (2009). Beyond batteries: An examination of the benefits and barriers to plug-in hybrid electric vehicles (PHEVs) and a vehicle-to- grid (V2G) transition. Energy policy, 37(3), 1095-1103.
[63] Steiner, P. O. (1957). Peak loads and efficient pricing. The Quarterly Journal of Economics, 71(4), 585-610.
[64] Torriti, J. (2012). Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy. Energy, 44(1), 576-583.
[65] Tyagi, R., & Black, J. W. (2010). Emergency demand response for distribution system contingencies. IEEE PES T&D 2010,
[66] Veras, J. M., Silva, I. R. S., Pinheiro, P. R., & Rabêlo, R. A. (2018). Towards the handling demand response optimization model for home appliances. Sustainability, 10(3), 616.
[67] Veras, J. M., Silva, I. R. S., Pinheiro, P. R., Rabêlo, R. A., Veloso, A. F. S., Borges, F. A. S., & Rodrigues, J. J. (2018). A multi-objective demand response optimization model for scheduling loads in a home energy management system. Sensors, 18(10), 3207.
[68] Wang, L., Wang, Z., & Yang, R. (2012). Intelligent multiagent control system for energy and comfort management in smart and sustainable buildings. IEEE Transactions on Smart Grid, 3(2), 605-617.
[69] Waseem, M., Lin, Z., Liu, S., Sajjad, I. A., & Aziz, T. (2020). Optimal GWCSO- based home appliances scheduling for demand response considering end-users comfort. Electric Power Systems Research, 187, 106477.
[70] Wolinetz, M., Axsen, J., Peters, J., & Crawford, C. (2018). Simulating the value of electric-vehicle–grid integration using a behaviourally realistic model. Nature Energy, 3(2), 132-139.
[71] Xiong, G., Chen, C., Kishore, S., & Yener, A. (2011). Smart (in-home) power scheduling for demand response on the smart grid. ISGT 2011,
[72] Xiong, Y., Wang, B., Chu, C.-c., & Gadh, R. (2018). Vehicle grid integration for demand response with mixture user model and decentralized optimization. Applied Energy, 231, 481-493.
[73] Yaghmaee, M. H., Leon-Garcia, A., & Moghaddassian, M. (2017). On the performance of distributed and cloud-based demand response in smart grid. IEEE Transactions on Smart Grid, 9(5), 5403-5417.
[74] Yan, X., Ozturk, Y., Hu, Z., & Song, Y. (2018). A review on price-driven residential demand response. Renewable and sustainable energy reviews, 96, 411- 419.
[75] Yao, L., Hashim, F. H., & Sheng, S. (2019). An Optimal Load Scheduling Approach Considering User Preference and Convenience Level for Smart Homes. 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe).
[76] Zhang, Y., Zeng, P., & Zang, C. (2014). Review of home energy management system in smart grid. Power System Protection and Control, 42(18), 144-154.
[77] Zhao, Z., Lee, W. C., Shin, Y., & Song, K.-B. (2013). An optimal power scheduling method for demand response in home energy management system. IEEE Transactions on Smart Grid, 4(3), 1391-1400.
[78] Zhou, B., Li, W., Chan, K. W., Cao, Y., Kuang, Y., Liu, X., & Wang, X. (2016). Smart home energy management systems: Concept, configurations, and scheduling strategies. Renewable and sustainable energy reviews, 61, 30-40.
[79] 工業技術研究院(2019)，工研院攜手日本東京電力 Power Grid、東京瓦斯 開 創新世代智慧節能綠生活。取得日期:2020/11/29，資料來源: https://www.itri.org.tw/ListStyle.aspx?DisplayStyle=01_content&SiteID=1&M mmID=1036276263153520257&MGID=1037156506754140644
[80] 行政院(2016)，推動低壓智慧電表建置。取得日期:2020/11/27，資料來源: https://www.ey.gov.tw/Page/5A8A0CB5B41DA11E/e69c2971-a86e-43bc-b49d- dfffec54f886
[81] 行政院(2019)，智慧電網推動現況及未來規劃。取得日期:2021/6/15，資 料來源: https://www.ey.gov.tw/Page/448DE008087A1971/8d397c1e-ac65-415c-8248- 89bafa0e8fef
[82] 經濟部能源局(2020)，智慧電網總體規劃。取得日期:2020/11/27，資料來源: https://www.moeaboe.gov.tw/ECW/populace/content/Content.aspx?menu_id=99 22
[83] 數位時代(2021)，「在家充電」才是核心，租來的車位也能裝!看特斯拉 在台 4 年的充電成績單。取得日期:2021/05/13，資料來源: https://www.bnext.com.tw/article/61476/tesla-manuals-wall-connector
[84] AGL. (2020). AGL expands Virtual Power Plant with solar battery sales across eastern states. AGL.Retrieved 2020/10/18 form https://www.agl.com.au/about-agl/media-centre/asx-and-media- releases/2020/september/agl-expands-virtual-power-plant-with-solar-battery- sales-across-eastern-states
[85] ComEd. (2021). ABOUT. ComEd. Retrieved 2021/05/01 from https://hourlypricing.comed.com/about/
[86] DDCAR (2021)，2021 年 3 月份與第一季台灣電動車銷售排行榜。取得日 期:2021/05/13，資料來源: https://www.ddcar.com.tw/blogs/articles/detail/19656/2021-年-3-月份與第一季 台灣電動車銷售排行榜
[87] ENERES. (2020). Demonstration experiments are currently under way to realize virtual power plants. ENERES. Retrieved 2020/10/18 from https://www.eneres.co.jp/english/future
[88] Kaluza. (2020). Delivering the UK's first paid DSO flexibility service using domestic devices. Kaluza. Retrieved 2020/10/19 from https://www.kaluza.com/case-studies/wpd-flex/
[89] nextDrive. (2020).Ecogenie. Retrieved 2020/11/29 from https://www.nextdrive.io/ecogenie-home-energy-management/
[90] TPC. (2019). Demand response. Taiwan Power Company Smart Grid Project Management Office. Retrieved 2020/10/19 from https://smartgrid.taipower.com.tw/en/page.aspx?mid=44