隨著全球暖化議題越顯嚴峻，我國政府積極推動能源轉型政策。大量再生能源併入電網後，傳統機組發電占比減少造成系統慣量下降，若再生能源發電因天氣影響而發電不穩，將造成電網電力即時供需失衡，導致電網電壓與頻率驟升驟降，而嚴重影響電力的正常使用，故動態維持電網穩定度以提供良好電力品質，將是台灣電力公司的首要任務。為此，台灣電力公司創立日前輔助服務交易市場，鼓勵民間企業透過非傳統機組參與不同類型輔助服務商品，以提供每日備轉容量需求。
現今眾多用電大戶皆設有分散式發電設備或儲能系統，可進一步作為輔助服務交易資源，然而再生能源發電與負載用電具有不確定性，將導致儲能系統調度困難，進而影響個體戶執行輔助服務績效。為解決上述情況，本論文提出一電能管理系統作為多資源聚合平台，結合投標策略設計與最佳化排程方法，整合虛擬電廠中的分散式資源參與輔助服務市場。採用歷史市場交易資訊作為投標價格決策模型之類神經網路模型輸入資料，以及再生能源發電、負載用電之日前預測數據作為投標容量決策模型輸入資料，決策出投標容量與價格並參與輔助服務市場競標，隨後依據輔助服務得標結果，使用混合整數線性規劃進行用戶資源最佳化排程，達成用戶整體用電成本最小化之目標。研究結果證明聚合多資源參與輔助服務之電能管理系統，不僅能夠透過即時最佳化調度有效改善因再生能源發電與負載用電不確定性所導致之單一電力用戶執行輔助服務績效未達標情形，更可以增加電力用戶參與輔助服務收入及降低用電成本。

As the issue of global warming aggravated, the Taiwan government has actively implemented energy transition policies. With a large amount of renewable energy connected to the grid, the proportion of traditional power decreases, which reduces inertia in a power system.
The power generation of variable renewable energy is susceptible to the weather. In that case, it causes an imbalance between the immediate supply and demand of grid power, leading to a sudden surge in grid voltage and frequency. Therefore, dynamically maintaining grid stability has become the primary task of Taipower company. Taipower established a day-ahead ancillary service (AS) market to promote more private enterprises participating in each type of AS transaction by nonconventional unit.
However, the perturbation of renewable energy resources (RESs) and user demand lead to trouble with BESS control and poor performance in AS market. Therefore, this research proposes an energy management system (EMS) as a platform for multiple resources. The system built in this research combines energy management method and neural network-based bidding strategy to aggregate multiple distributed resources in a virtual power plant (VPP) for AS market. The bidding capacity and price in the AS market could decide from the input information: RESs forecasting, load demands forecasting, and historical transaction record. Then, combined with the above result, a mixed integer linear programming (MILP) algorithm can provide optimized day-ahead scheduling based on minimized operation costs.
The simulation results demonstrate that the individual user’s poor performance caused by uncertainties of RESs and demands can be improved with real-time control. At the same time, with the energy management system aggregating multiple resources, the users can gain more income in AS market and save more power costs.

[1] 吳國賓、吳進忠、蔡昊廷、陳俊宇與梁佩芳，國際獨立型電力系統調頻備轉技術規範之現況與未來發展，《臺灣能源期刊》第7卷4期，2020年12月。
[2] D. Zhu and Y.-J. A. Zhang, “Optimal coordinated control of multiple battery energy storage systems for primary frequency regulation,” IEEE Transactions on Power Systems, vol. 34, no. 1, pp. 555-565, Jan. 2019.
[3] Y. Shi, B. Xu, D. Wang, and B. Zhang, “Using battery storage for peak shaving and frequency regulation: joint optimization for superlinear gains,” IEEE Transactions on Power Systems, vol. 33, no. 3, pp. 2882-2894, May 2018.
[4] N. B. Arias, J. C. López, S. Hashemi, J. F. Franco, and M. J. Rider, “Multi-objective sizing of battery energy storage systems for stackable grid applications,” IEEE Transactions on Smart Grid, vol. 12, no. 3, pp. 2708-2721, May 2021.
[5] M. Delfanti, D. Falabretti, M. Merlo, and G. Monfredini, “Distributed generation integration in the electric grid: energy storage system for frequency control,” Journal of Applied Mathematics, vol. 2014, Art. no. 198427, Jun. 2014.
[6] H. Firoozi, H. Khajeh, and H. Laaksonen, “Optimized operation of local energy community providing frequency restoration reserve,” IEEE Access, vol. 8, pp. 180558-180575, Jan. 2020.
[7] A. Zurborg, “Unlocking Customer Value: The Virtual Power Plant,” US Department of Energy, pp. 1-5, 2010.
[8] S. Bahrami and A. Sheikhi, “From demand response in smart grid toward integrated demand response in smart energy hub,” IEEE Transactions on Smart Grid, vol. 7, no. 2, pp. 650-658, Mar. 2016.
[9] S. Mohseni, A. C. Brent, S. Kelly, and W. N. Browne, “Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review,” Renewable and Sustainable Energy Reviews, vol. 158, Art. no. 112095, Apr. 2022.
[10] A. G. Zamani, A. Zakariazadeh, and S. Jadid, “Day-ahead resource scheduling of a renewable energy based virtual power plant,” Applied Energy, vol. 169, pp. 324-340, May 2016.
[11] 彭金堂、林國勝，我國電力自由化市場交易機制與配套措施研究，《管理與系統》第16卷2期，2009年4月。
[12] J. Hu, H. Zhou, Y. Li, P. Hou, and G. Yang, “Multi-time scale energy management strategy of aggregator characterized by photovoltaic generation and electric vehicles,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 4, pp. 727-736, Jul. 2020.
[13] D. Yang, S. He, M. Wang, and H. Pandzic, “Bidding strategy for virtual power plant considering the large-scale integrations of electric vehicles,” IEEE Transactions on Industry Applications, vol. 56, no. 5, pp. 5890-5900, Sep. 2020.
[14] T. Zhang, S. X. Chen, H. B. Gooi, and J. M. Maciejowski, “A hierarchical EMS for aggregated BESSs in energy and performance-based regulation markets,” IEEE Transactions on Power System., vol. 32, no. 3, pp. 1751-1760, May 2017.
[15] Y. Jin, Z. Wang, C. Jiang, and Y. Zhang, “Dispatch and bidding strategy of active distribution network in energy and ancillary services market,” Journal of Modern Power Systems and Clean Energy, vol. 3, no. 4, pp. 565-572, Dec. 2015.
[16] L. Li, “Coordination between smart distribution networks and multi-microgrids considering demand side management: A trilevel framework,” Omega, vol. 102, Art. no. 102326, Jul. 2021.
[17] M. Ansari, A. T. Al-Awami, E. Sortomme, and M. A. Abido, “Coordinated bidding of ancillary services for vehicle-to-grid using fuzzy optimization,” IEEE Transactions on Smart Grid, vol. 6, no. 1, pp. 261-270, Jan. 2015.
[18] S. Sadeghi, H. Jahangir, B. Vatandoust, M. A. Golkar, A. Ahmadian, and A. Elkamel, “Optimal bidding strategy of a virtual power plant in day-ahead energy and frequency regulation markets: A deep learning-based approach,” International Journal of Electrical Power & Energy Systems, vol. 127, Art. no. 106646, May 2021.
[19] 中央氣象局氣象資料開放平台，[Online]. Available: https://opendata.cwb.gov.tw/index [Accessed 28 Jun. 2022].
[20] 台電公司電力調度處，電力交易平台參考資料：日前輔助服務市場之交易商品規格，2022年5月。
[21] 國家發展委員會，論電力自由化下虛擬電廠之商業模式：德國Harz示範計畫之經驗及對我國之政策意涵，《台灣經濟論衡》第14卷2期，2016年6月。
[22] 台電公司電力調度處，電力交易平台參考資料：日前輔助服務市場之運作，2022年5月。
[23] 台電公司電力調度處，輔助服務及備用容量交易試行平台第二次公開說明會，2020年11月。
[24] 台電公司電力調度處，電力交易平台「公告事項4-6：需量反應交易表計設置規定及執行容量計算基準認定說明文件」，2022年3月。
[25] Y. Xuan, W. Si, J. Zhu, Z. Sun, J. Zhao, M. Xu, and S. Xu, “Multi-model fusion short-term load forecasting based on random forest feature selection and hybrid neural network,” IEEE Access, vol. 9, pp. 69002-69009, Jan. 2021.
[26] A. Shrestha and A. Mahmood, “Review of deep learning algorithms and architectures,” IEEE Access, vol. 7, pp. 53040-53065, Apr. 2019.
[27] S. Taheri, M. Jooshaki, and M. M.-Aghtaie, “Long-term planning of integrated local energy systems using deep learning algorithms,” International Journal of Electrical Power & Energy Systems, vol. 129, Art. no. 106855, Jul. 2021.
[28] R. Jozefowicz, W. Zaremba, and I. Sutskever, “An empirical exploration of recurrent network architectures,” in Proc. 32nd International Conference on International Conference on Machine Learning, Lille, France, 2015, pp. 2342-2350.
[29] J. Wang, X. Li, J. Li, Q. Sun, and H. Wang, “NGCU: A new RNN model for time-series data prediction,” Big Data Research, vol. 27, Art. no. 100296, Feb. 2022.
[30] X. Li, G. Chen, and Z. Y. Dong, “Co-optimisation model for the long-term design and decision making in community level cloud energy storage system,” IET Renewable Power Generation, vol. 14, no. 17, pp. 3518-3525, Feb. 2020.
[31] SAMSUNG SDI, Energy Storage System Battery Business, [Online]. Available: http://www.samsungsdi.com/upload/ess_brochure/201803_SamsungSDI%20ESS_EN.pdf [Accessed 28 Jun. 2022].
[32] 台達電子工業股份有限公司，PCS100/125，[Online]. Available: https://www.deltaww.com/zh-TW/products/Energy-Storage-Systems/5919 [Accessed 28 Jun. 2022].
[33] Generator Source, [Online]. Available: https://www.generatorsource.com/Diesel-Generators.aspx [Accessed 28 Jun. 2022].
[34] 聚恆科技股份有限公司，太陽能光電板M660系列，[Online]. Available: http://www.hengs.com/pdf/Solar-panel-265W.pdf [Accessed 28 Jun. 2022].
[35] OpenEl, “Commercial and Residential Hourly Load Profiles for all TMY3 Locations in the United States,” Jul. 2013, [Online]. Available: https://openei.org/doe-opendata/dataset/commercial-and-residential-hourly-load-profiles-for-all-tmy3-locations-in-the-united-states [Accessed 28 Jun. 2022].
[36] F. Angizeh, A. Ghofrani, M. A. Jafari, “Dataset on Hourly Load Profiles for a Set of 24 Facilities from Industrial, Commercial, and Residential End-use Sectors,” Aug. 2020, [Online]. Available: https://data.mendeley.com/datasets/rfnp2d3kjp/1 [Accessed 28 Jun. 2022].
[37] 台電公司，電價表，107年6月26日，[Online]. Available: https://www.taipower.com.tw/upload/238/2022060213502566374.pdf [Accessed 28 Jun. 2022].