現今電力市場中，電力系統即時平衡電力的生產和消費是一項主要任務。有鑑於此，電力供應商通常與其關鍵的消費者 (如大型工業或商業用戶) 簽訂合約來管理他們的電力容量需求。而隨著電業自由化的開放，市場中引入用戶群代表的角色，將消費者進行群聚，並藉由他們的用電需求與電力供應商進行價格談判。用戶群代表通過將眾多消費者合理分組並對用電負載進行聚合，有助於確保穩定的電力系統平衡供應，並減輕管理許多消費者的負擔。在此研究中，我們提出了一種新的資料分群方法，根據消費者每日電力負載曲線 (即消費者用電模式) 找出合併後可以平緩電力負載曲線尖峰、用電型態相對互補的消費者進行群聚。除此之外，我們採用小波轉換技術來加速分群運算過程。具體而言，透過少數小波係數推導的近似值可以精確地迫近原始用電負載形狀。而基於真實資料所進行的實驗結果顯示，我們所提出之方法極具實際應用的價值。

In the electricity market, the real-time balance of electricity generation and consumption is a main task. In view of this, power suppliers usually sign contracts capacity with their essential consumers (i.e., large-scale industrial and commercial companies) for managing their capacity demands. With the electricity liberalization, the characters called aggregators, join to the electricity market and group consumers to integrate their demands to negotiate with power suppliers. With a proper grouping of numerous consumers and aggregating their electricity load profile, aggregators help to ensure stable and balance electric supply, and reduce the burden of managing many consumers. In this approach, we propose a novel data clustering algorithm to cluster complementary consumers based on their daily load profile to damp the aggregation load. Furthermore, we incorporate the technique of discrete wavelet transform to speed up the clustering process. Specifically, approximations from only a few wavelet coefficients may precisely capture the shape of original usage patterns. Experimental results based on a real dataset show that our approach is promising in practical applications.

[1] I. Atzeni, L.-G. Ordóñez, G. Scutari, D.-P. Palomar, and J.-R. Fonollosa, “Demand-Side Management via Distributed Energy Generation and Storage Optimization,” IEEE Transactions on Smart Grid, 4(2):866-876, June 2013.
[2] G-E. Asimakopoulou and N-D. Hatziargyriou, “Evaluation of Economic Benefits of DER Aggregation,” IEEE Transactions on Sustainable Energy, 9(2):499-509, APRIL 2018.
[3] A. Bartolozzi, S. Favuzza, M.-G. Ippolito, D.-L. Cascia, E.-R. Sanseverino and G. Zizzo, “A New Platform for Automatic Bottom-Up Electric Load Aggregation,” Energies, 1682:1-24, October 2017.
[4] S. Burger, J.-P. Chaves-Ávila, C. Batlle, and I-J. Pérez-Arriaga, “The Value of Aggregators in Electricity Systems,” MIT Center for Energy and Environmental Policy Research, January 2016.
[5] S. Burgera, J.-P. Chaves-Ávilab, C. Batllea, and I-J. Pérez-Arriagaa, “A review of the value of aggregators in electricity systems,” Renewable and Sustainable Energy Reviews, 77:395-405, April 2017.
[6] C.-Y. Chen, and C.-J. Liao, “A Linear Programming Approach to the Electricity Contract Capacity Problem,” Applied Mathematical Modelling, 35(8):4077-4082, August 2011.
[7] I. Cattinelli, G. Valentini, E. Paulesu, and N.-A. Borghese, “A Novel Approach to the Problem of Non-uniqueness of the Solution in Hierarchical Clustering,” IEEE Transactions on Neural Networks, 24(7):1165-1173, July 2013.
[8] G. Erbach, “Understanding Electricity Markets in the EU,” European Parliamentary Research Service, pages 1-10, 2016.
[9] C. Greer, et al, “NIST Framework and Roadmap for Smart Grid interoperability standards, release 3.0,” Nat. Inst. Standards Technol., Technical Report NIST SP-1108r3, October 2014.
[10] M-A. Heldeweg, M-J. Arentsen, “The Effects of Electricity Market Liberalisation in the European Union,” Bachelor of Science in European Public Administration, pages 1-37, 2015.
[11] M. Kopsakangas-Savolainen and R. Svento, “Restructuring of Electricity Markets,” Modern Energy Markets, pages 5-19, 2012.
[12] J. Kwac, J. Flora, and R. Rajagopal, “Household Energy Consumption Segmentation Using Hourly Data,” IEEE Transactions on Smart Grid, 5(1):420-430, January 2014.
[13] D. Le Comte, and H. Warren, “Modeling the Impact of Summer Temperatures on National Electricity Consumption,” Journal of Applied Meteorology, 20(12):1415-1419, December 1981.
[14] R. Li, J. Li, G. Poulton, and G. James, “Agent-Based Optimisation Systems for Electrical Load Management,” ICT Centre, Commonwealth Scientific and Industrial Research Organisation, 2008.
[15] T. Logenthiran, D. Srinivasan, and T-Z. Shun, “Demand Side Management in Smart Grid Using Heuristic Optimization,” IEEE Transactions on Smart Grid, 3(3):1244-1252, September 2012.
[16] W. Lee, L. Xiang, R. Schober, and V.-W.-S. Wong, “Direct Electricity Trading in Smart Grid: A Coalitional Game Analysis,” IEEE Journal on Selected Areas in Communications, 32(7):1398-1411, July 2014.
[17] W.-T. Li, C. Yeun, N. Hassan, W. Tushar, C.-K. Wen, K. Wood, and K. Hu, X. Liu, “Demand Response Management for Residential Smart Grid: From Theory to Practice,” IEEE Access, 3:2431-2440, November 2015.
[18] S. Maharjan, Q. Zhu, Y. Zhang, S. Gjessing, and T. Başar, “Demand Response Management in the Smart Grid in a Large Population Regime,” IEEE Transactions on Smart Grid , 7(1):189-199, January 2016.
[19] K. Ma, C. Wang, J. Yang, Z. Tian, and X. Guan, “Energy Management Based on Demand-Side Pricing: A Super Modular Game Approach,” IEEE Access, 5: 18219-18228, August 2017.
[20] S.-A. Saleh, P. Pijnenburg, and E. Castillo-Guerra, “Load Aggregation From Generation-Follows-Load to Load-Follows-Generation: Residential Loads,” IEEE Transactions on Industry Application, 53(2):833-842, March 2017.
[21] M. Salani, A. Giusti, G. Di Caro, A.-E. Rizzoli, and L.-M. Gambardella, “Lexicographic Multi-Objective Optimization for the Unit Commitment Problem and Economic Dispatch in a Microgrid,” Proceedings of the 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, December 2011.
[22] P. Samadi, A-H. Mohsenian-Rad, R. Schober, V.-W.-S. Wong, and J. Jatskevich, “Optimal Real-time Pricing Algorithm Based on Utility Maximization for Smart Grid,” Proceedings of the First IEEE International Conference on Smart Grid Communications, pages 415-420, October 2010.
[23] P. Samadi, A-H. Mohsenian-Rad, R. Schober, and V.-W.-S. Wong, “Advanced Demand- side Management for the Future Smart Grid Using Mechanism Design,” IEEE Transactions on Smart Grid, 3(3): 1170-1180, September 2012.
[24] M. Weiss, A. Helfenstein, F. Mattern, and T. Staake, “Leveraging Smart Meter Data to Recognize Home Appliances,” Proceedings of the IEEE International Conference on Pervasive Computing and Communications, March 2012.
[25] 許志義, 洪穎正, “電力需求面管理與用戶群代表法制革新：先進國家案例及其對臺灣之政策意涵,” Journal of Taiwan Energy, 3(2):137-153, June 2016.
[26] 我國需量反應制度可行性研究, http://www.tri.org.tw/research/impdf/576.pdf
[27] Comverge, http://comverge.com.au/
[28] Cpower, https://cpowerenergymanagement.com/
[29] EnerNOC, https://www.enernoc.com/
[30] Existing Business Models for Renewable Energy Aggregators, http://bestres.eu/wp-content/uploads/2016/08/BestRES_Existing-business-models-for-RE-aggregators.pdf
[31] Electricity Aggregations: Starting off on the Right Foot with Consumers, http://www.beuc.eu/publications/beuc-x-2018-010_electricity_aggregators_starting_off_on_the_right_foot_with_consumers.pdf
[32] Frequently Asked Questions: Cincinnati Electric & Natural Gas Aggregation Program, https://www.cincinnati-oh.gov/oes/residential-programs/aggregation-program/frequently-asked-questions-cincinnati-electric-natural-gas-aggregation-program/
[33] Increase Sustainability & Community Fulfillment with Integrated Energy Solutions, https://www.constellation.com/solutions/for-government/governmental-aggregation.html