需求側管理隨著用電量的成長，重要性越受到重視。需量反應作為需求面管理的一種積極手段，能為供電商提供更好的電網安全性，是供電商維護供需平衡的重要方法。
如何在需量反應期間減少用電量，又不會對用戶產生過多負擔，是需求側管理的重要議題。現今多數電器具備物聯網的能力，其中冷暖空調系統十分具備參與需量反應的潛力，其功耗大且又具有可控性可以進行調整，良好的操控之下對使用者影響小，因此我們特別針對冷暖空調在需量反應時的控制進行研究。

在本論文中提出 ComMA 控制演算法，以及一個 Dependent-Independent 的框架。
框架中，Dependent Phase 根據每個空間的環境狀態和空調設備優化控制策略。Independent Phase 同時考量控制策略對各個空間造成的舒適度變化，在用電量符合需量反應的需求下，選擇出兼顧各個空間舒適度以及全體公平性最大化的控制策略。

在實驗中，針對最直接影響室內舒適度的冷暖空調設備進行調控，且空調設備具備不同溫度設定和模式的控制空間。我們的實驗使用真實的環境和冷暖空調設備進行試驗，並依據不同的需量反應限制，驗證不同用電限制的調控效果。結果顯示我們的方法可以在符合需量反應需求的同時，提供使用者更好的舒適度和公平性。

Demand Response (DR), as a positive means of demand-side management (DSM), can provide power suppliers with better grid security and is an important method for power suppliers to maintain the balance of supply and demand.
Nowadays, most electrical appliances have Internet of Things capability. Among them, heating, ventilation, and air conditioning (HVAC) systems have the potential to participate in the DR.
They have significant power consumption, are controllable, and can be adjusted.
This has little impact on a user under good control. Therefore, we are especially researching the control of HVACs during DR.

In this paper, we propose a Comfort-Conscious Multi-Appliances Control Algorithm for Online Demand Control with a Dependent-Independent framework. According to the HVAC in each space, the dependent phase provides a variety of control strategies that are suitable for the environmental conditions and can optimize the degree of comfort.
The independent phase also considers the impact of the control strategy on each space and chooses a control strategy that considers the comfort of each space and maximizes the fairness of the overall system.

In the experiment, we adjust the HVAC equipment that most directly affects indoor comfort. HVAC equipment has control spaces with different temperature settings and modes.
We use a real environment and an actual HVAC to conduct experiments and verify the control effect of different power consumption limits according to different DR limits. The results show that our method can provide users with better comfort and fairness while meeting demand response requirements.

[1] I. Gupta, G. Anandini, and M. Gupta, “An hour wise device scheduling approach for demand side management in smart grid using particle swarm optimization,” in 2016 National Power Systems Conference (NPSC). IEEE, dec 2016.
[2] I.E.A., “The future of cooling,” IEA, Paris, 2018. [Online]. Available: https://www.iea.org/reports/the-future-of-cooling
[3] TaiPower, “台電歷年尖峰負載及備用容量率,” Jun. 2021, updated every year. [Online]. Available: https://data.gov.tw/dataset/8307
[4] T. O. Goverment, “近十年台電系統裝置容量及結構,” 2020. [Online]. Available: https://www.taipower.com.tw/tc/chart/data/%E5%8F%B0%E9%9B%BB%E7%B3%BB%E7%B5%B1%E6%AD%B7%E5%B9%B4%E8%A3%9D%E7%BD%AE%E5%AE%B9%E9%87%8F%E5%88%86%E5%9B%9B%E9%A1%9E(71-109).csv
[5] O. Alliance, “Openadr 2.0 demand response program implementation guide,” 2016. [Online]. Available: https://www.openadr.org/assets/openadr drprogramguide 1 1.pdf
[6] B. Zhou, W. Li, K. W. Chan, Y. Cao, Y. Kuang, X. Liu, and X.Wang, “Smart home energy management systems: Concept, configurations, and scheduling strategies,” Renewable and Sustainable Energy Reviews, vol. 61, pp. 30–40, aug 2016.
[7] C. W. Gellings and J. H. Chamberlin, “Demand-side management: concepts and methods,” 1987.
[8] A. L. da Fonseca, K. M. Chvatal, and R. A. Fernandes, “Thermal comfort maintenance in demand response programs: A critical review,” Renewable and Sustainable Energy Reviews, vol. 141, p. 110847, may 2021.
[9] TaiPower, “需量反應負載管理措施,” Oct. 2020. [Online]. Available: https://www.taipower.com.tw/upload/135/2021022611150421848.pdf
[10] TaiSEIA, “Taiseia website.” [Online]. Available: http://www.taiseia.org.tw/
[11] ECHONET, “Echonet website.” [Online]. Available: https://echonet.jp
[12] C. Vivekananthan and Y. Mishra, “Stochastic ranking method for thermostatically controllable appliances to provide regulation services,” IEEE Transactions on Power Systems, vol. 30, no. 4, pp. 1987–1996, jul 2015.
[13] C. Perfumo, J. H. Braslavsky, and J. K. Ward, “Model-based estimation of energy savings in load control events for thermostatically controlled loads,” IEEE Transactions on Smart Grid, vol. 5, no. 3, pp. 1410–1420, may 2014.
[14] M. F. Haniff, H. Selamat, R. Yusof, S. Buyamin, and F. S. Ismail, “Review of HVAC scheduling techniques for buildings towards energy-efficient and cost-effective operations,” Renewable and Sustainable Energy Reviews, vol. 27, pp. 94–103, nov 2013.
[15] O. Erdinc, A. Tascikaraoglu, N. G. Paterakis, Y. Eren, and J. P. S. Catalao, “End-user comfort oriented day-ahead planning for responsive residential HVAC demand aggregation considering weather forecasts,” IEEE Transactions on Smart Grid, vol. 8, no. 1, pp. 362–372, jan 2017.
[16] S. Katipamula and N. Lu, “Evaluation of residential hvac control strategies for demand response programs (symposium papers - ch06-7 demand response strategies for building systems),” ASHRAE Transactions, vol. 112, pp. 535–546, 02 2006.
[17] N. Lu, “An evaluation of the HVAC load potential for providing load balancing service,” IEEE Transactions on Smart Grid, vol. 3, no. 3, pp. 1263–1270, sep 2012.
[18] H. Hui, Y. Ding, and M. Zheng, “Equivalent modeling of inverter air conditioners for providing frequency regulation service,” IEEE Transactions on Industrial Electronics, vol. 66, no. 2, pp. 1413–1423, feb 2019.
[19] M. Jagirdar, D. Pandelidis, A. Pacak, W. Worek, and S. Cetin, “Performance evaluation of an air conditioning system based on quasi isothermal dehumidifcation,” Energy Conversion and Management, vol. 217, p. 113009, aug 2020.
[20] N. Lu, “An evaluation of the HVAC load potential for providing load balancing service,” IEEE Transactions on Smart Grid, vol. 3, no. 3, pp. 1263–1270, sep 2012.
[21] J. Hwang, “Assessment of air condition load management by load survey in taipower,” IEEE Transactions on Power Systems, vol. 16, no. 4, pp. 910–915, 2001.
[22] K. Tomiyama, J. Daniel, and S. Ihara, “Modeling air conditioner load for power system studies,” IEEE Transactions on Power Systems, vol. 13, no. 2, pp. 414–421, may 1998.
[23] A. Afram and F. Janabi-Sharifi, “Theory and applications of HVAC control systems – a review of model predictive control (MPC),” Building and Environment, vol. 72, pp. 343–355, feb 2014.
[24] C. Zhang, S. R. Kuppannagari, R. Kannan, and V. K. Prasanna, “Building HVAC scheduling using reinforcement learning via neural network based model approximation,” in Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. ACM, nov 2019.
[25] M. Beaudin and H. Zareipour, “Home energy management systems: A review of modelling and complexity,” Renewable and Sustainable Energy Reviews, vol. 45, pp. 318–335, may 2015.
[26] F. Oldewurtel, A. Parisio, C. N. Jones, M. Morari, D. Gyalistras, M. Gwerder, V. Stauch, B. Lehmann, and K. Wirth, “Energy efficient building climate control using stochastic model predictive control and weather predictions,” in Proceedings of the 2010 American Control Conference. IEEE, jun 2010.
[27] V. L. Erickson, M. A. Carreira-Perpinan, and A. E. Cerpa, “Observe: Occupancy-based system for efficient reduction of hvac energy,” in Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks, 2011, pp. 258–269.
[28] V. L. Erickson and A. E. Cerpa, “Occupancy based demand response HVAC control strategy,” in Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys ’10. ACM Press, 2010.
[29] S. Goyal, P. Barooah, and T. Middelkoop, “Experimental study of occupancy-based control of HVAC zones,” Applied Energy, vol. 140, pp. 75–84, feb 2015.
[30] S. Atthajariyakul and T. Leephakpreeda, “Real-time determination of optimal indoorair condition for thermal comfort, air quality and efficient energy usage,” Energy and Buildings, vol. 36, no. 7, pp. 720–733, jul 2004.
[31] N. Chakraborty, A. Mondal, and S. Mondal, “Multiobjective optimal scheduling framework for HVAC devices in energy-efficient buildings,” IEEE Systems Journal, vol. 13, no. 4, pp. 4398–4409, dec 2019.
[32] M. P. Fanti, A. M. Mangini, M. Roccotelli, and W. Ukovich, “A district energy management based on thermal comfort satisfaction and real-time power balancing,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 4, pp. 1271–1284, oct 2015.
[33] S. Moon and J.-W. Lee, “Multi-residential demand response scheduling with multi-class appliances in smart grid,” IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 2518–2528, jul 2018.
[34] J. Sun, Y. Zheng, J. Hao, Z. Meng, and Y. Liu, “Continuous multiagent control using collective behavior entropy for large-scale home energy management,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 01, pp. 922–929, apr 2020.
[35] S. Bhattacharya, V. Chandan, V. Arya, and K. Kar, “Demand response for thermal fairness in district heating networks,” IEEE Transactions on Sustainable Energy, vol. 10, no. 2, pp. 865–875, apr 2019.
[36] N. Li, L. Chen, and S. H. Low, “Optimal demand response based on utility maximization in power networks,” in 2011 IEEE Power and Energy Society General Meeting, 2011, pp. 1–8.
[37] X. Li and A. Malkawi, “Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions,” Energy, vol. 112, pp. 1194–1206, oct 2016.
[38] X. Li, J. Wen, and A. Malkawi, “An operation optimization and decision framework for a building cluster with distributed energy systems,” Applied Energy, vol. 178, pp. 98–109, sep 2016.
[39] E. C. Thom, “The discomfort index,” Weatherwise, vol. 12, no. 2, pp. 57–61, apr 1959.
[40] H.-C. Jo, S. Kim, and S.-K. Joo, “Smart heating and air conditioning scheduling method incorporating customer convenience for home energy management system,” IEEE Transactions on Consumer Electronics, vol. 59, no. 2, pp. 316–322, may 2013.
[41] Y. F. Du, L. Jiang, C. Duan, Y. Z. Li, and J. S. Smith, “Energy consumption scheduling of HVAC considering weather forecast error through the distributionally robust approach,” IEEE Transactions on Industrial Informatics, vol. 14, no. 3, pp. 846–857, mar 2018. 86