近年來，隨著分散式電力資源與資通訊技術發展，促進需求端電能管理系統之應用日漸普及。本文提出了一套應用賽局理論之分散式最佳化充放電排程策略供社區型電能管理系統(Community Energy Management System, CEMS)調度其轄區內之儲能系統。文中除在現有時間電價費率基礎上最小化用戶用電成本，亦提出促進儲能業者參與社區型用戶群代表之商轉模式，並以抑低系統尖峰負載為目標。鑒於一般CEMS僅扮演統籌管理特定區域之代理人，而未賦予直接操控轄區內各別儲能系統之能力，故不適用於傳統集中式控制架構，本文結合賽局理論設計之分散式架構，可基於各別儲能業者之最佳排程策略達成群體之最佳效益，並透過奈許平衡條件證明可驗證其理論之可行性。
為驗證所提方法，本文採用沙崙綠能科學城開發規劃資料為模擬案例，並搭配不同時間電價方案與負載型態，以分析不同情境下之系統負載因數、儲能業者與用戶群代表之收益。此外，本文探討不同的參與者數量、儲能裝置容量及操作成本對於運轉結果之影響。模擬結果顯示所提方法除可有效提升園區之負載因數外，若配合未來儲能建置成本下降之趨勢，將可形成具有足夠誘因之商業模式，不僅可望吸引業者投資建置儲能並參與用戶群代表，亦得以舒緩電力公司之供電壓力。

Various developments in distributed energy resources (DERs) and communication technology have boosted the popularity of demand-side energy management systems (EMS). This thesis proposes a game-theoretically optimized charging and discharging scheduling strategy for decentralized architectures. The proposed strategy can be applied in a community energy management system (CEMS) to dispatch that community’s energy storage systems (ESSs). This thesis not only considers electricity cost minimization for consumers according to the present time-of-use (TOU) tariff but also proposes a business model, which sets system peak load shedding as a common objective that incentivizes ESS owners to join a community-sized aggregator. The CEMS only acts as an agent rather than an authority with control over individual ESSs in the community most of the time. Therefore, the CEMS may not be suitable to adopt a traditional centralized control architecture. In this thesis, the game-theoretical strategy of the proposed decentralized architecture can globally optimize the scheduling strategies of the individual ESS owners. The feasibility of the theory can be demonstrated by proving the formulation of the Nash equilibrium.
To demonstrate the effectiveness of the proposed method, this thesis uses the planning data for the Shalun Green Energy Science City development in a simulation case that analyzes the load factor of the system and the profit for the ESS owners and aggregator under scenarios with various pricing signals and load types. In addition, this thesis also analyzes the effect of the number, capacity, and operating cost of ESSs on the simulation results. The simulation results indicate that the proposed method can effectively raise the load factor of the community, and can form the basis of a business model that offers sufficient incentive to invest in ESS installation with the declining ESS cost in the future, which can encourage the participation of ESS owners in aggregation. Power supply pressure can also be mitigated for the electricity company.

[1] 經濟部能源局, 新能源政策, online available: http://www.moeaboe.gov.tw/ECW/populace/content/SubMenu.aspx?menu_id=48. [Accessed 14 May 2018].
[2] M. A. Akbari et al., “New Metrics for Evaluating Technical Benefits and Risks of DGs Increasing Penetration,” IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2890-2902, Nov. 2017.
[3] F. Marra, G. Yang, C. Træholt, J. Østergaard, and E. Larsen, “A Decentralized Storage Strategy for Residential Feeders With Photovoltaics,” IEEE Transactions on Smart Grid, vol. 5, no. 2, pp. 974-981, March 2014.
[4] G. Mokhtari, G. Nourbakhsh, F. Zare, and A. Ghosh, “Overvoltage Prevention in LV Smart Grid Using Customer Resources Coordination,” Energy and Buildings, vol. 61, pp. 387–395, Jun. 2013.
[5] J. Kim, E. Muljadi, J. W. Park, and Y. C. Kang, “Adaptive Hierarchical Voltage Control of a DFIG-Based Wind Power Plant for a Grid Fault,” IEEE Transactions on Smart Grid, vol. 7, no. 6, pp. 2980-2990, Nov. 2016.
[6] Y. Han, P. M. Young, A. Jain, and D. Zimmerle, “Robust Control for Microgrid Frequency Deviation Reduction With Attached Storage System,” IEEE Transactions on Smart Grid, vol. 6, no. 2, pp. 557-565, March 2015.
[7] S. Chen, T. Zhang, H. B. Gooi, R. D. Masiello, and W. Katzenstein, “Penetration Rate and Effectiveness Studies of Aggregated BESS for Frequency Regulation,” IEEE Transactions on Smart Grid, vol. 7, no. 1, pp. 167-177, Jan. 2016.
[8] Y. Wang, V. Silva, and M. Lopez-Botet-Zulueta, “Impact of High Penetration of Variable Renewable Generation on Frequency Dynamics in The Continental Europe Interconnected System,” IET Renewable Power Generation, vol. 10, no. 1, pp. 10-16, Jan. 2016.
[9] Q. Li and M. Zhou, “The Future-Oriented Grid-Smart Grid,” Journal of Computers, vol. 6, no. 1, pp. 98-105, 2011.
[10] P. Agrawal, “Overview of DOE Microgrid Activities”, Proc. Symp. Microgrid, 2006.
[11] M. Masera, E. F. Bompard, F. Profumo, and N. Hadjsaid, “Smart (Electricity) Grids for Smart Cities: Assessing Roles and Societal Impacts,” Proceedings of the IEEE, vol. 106, no. 4, pp. 613-625, April 2018.
[12] A. H. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich, R. Schober, and A. Leon-Garcia, “Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid,” IEEE Transactions on Smart Grid, vol. 1, no. 3, pp. 320-331, Dec. 2010.
[13] K. Worthmann, C. M. Kellett, P. Braun, L. Grüne, and S. R. Weller, “Distributed and Decentralized Control of Residential Energy Systems Incorporating Battery Storage,” IEEE Transactions on Smart Grid, vol. 6, no. 4, pp. 1914-1923, July 2015.
[14] D. Papadaskalopoulos, D. Pudjianto, and G. Strbac, “Decentralized Coordination of Microgrids With Flexible Demand and Energy Storage,” IEEE Transactions on Sustainable Energy, vol. 5, no. 4, pp. 1406-1414, Oct. 2014.
[15] B. Celik, R. Roche, D. Bouquain, and A. Miraoui, “Decentralized Neighborhood Energy Management with Coordinated Smart Home Energy Sharing,” IEEE Transactions on Smart Grid, accepted 2017, to appear. DOI: 10.1109/TSG.2017.2710358.
[16] C. P. Mediwaththe, E. R. Stephens, D. B. Smith, and A. Mahanti, “A Dynamic Game for Electricity Load Management in Neighborhood Area Networks,” IEEE Transactions on Smart Grid, vol. 7, no. 3, pp. 1329-1336, May 2016.
[17] H. M. Soliman and A. Leon-Garcia, “Game-Theoretic Demand-Side Management With Storage Devices for the Future Smart Grid,” IEEE Transactions on Smart Grid, vol. 5, no. 3, pp. 1475-1485, May 2014.
[18] 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, vol. 4, no. 2, pp. 866-876, June 2013.
[19] Y. Wang, X. Ai, Z. Tan, L. Yan, and S. Liu, “Interactive Dispatch Modes and Bidding Strategy of Multiple Virtual Power Plants Based on Demand Response and Game Theory,” IEEE Transactions on Smart Grid, vol. 7, no. 1, pp. 510-519, Jan. 2016.
[20] C. Wu, H. Mohsenian-Rad, and J. Huang, “Vehicle-to-Aggregator Interaction Game,” IEEE Transactions on Smart Grid, vol. 3, no. 1, pp. 434-442, March 2012.
[21] C. O. Adika and L. Wang, “Non-Cooperative Decentralized Charging of Homogeneous Households' Batteries in a Smart Grid,” IEEE Transactions on Smart Grid, vol. 5, no. 4, pp. 1855-1863, July 2014.
[22] M. H. K. Tushar, C. Assi, M. Maier, and M. F. Uddin, “Smart Microgrids: Optimal Joint Scheduling for Electric Vehicles and Home Appliances,” IEEE Transactions on Smart Grid, vol. 5, no. 1, pp. 239-250, Jan. 2014.
[23] B. Chai, J. Chen, Z. Yang, and Y. Zhang, “Demand Response Management With Multiple Utility Companies: A Two-Level Game Approach,” IEEE Transactions on Smart Grid, vol. 5, no. 2, pp. 722-731, March 2014.
[24] 經濟部能源局, 陽光屋頂百萬座, [Online] Available: http://mrpv.org.tw/index.php/. [Accessed 17 May 2018].
[25] H. Singh, “Introduction to Game Theory and its Application in Electric Power Markets,” IEEE Computer Applications in Power, vol. 12, no. 4, pp. 18-20, 22, Oct 1999.
[26] R. B. Myerson, Game Theory : Analysis of Conflict, Cambridge, MA: Harvard University Press, pp. vii-xi, 1997.
[27] G. Romp, Game Theory: Introduction and Applications, Oxford, UK: Oxford University Press, pp. 8-10 and 29-38, 1997.
[28] A. Schuller, B. Dietz, C. M. Flath, and C. Weinhardt, “Charging Strategies for Battery Electric Vehicles: Economic Benchmark and V2G Potential,” IEEE Transactions on Power Systems, vol. 29, no. 5, pp. 2014-2022, Sept. 2014.
[29] R. Storn and K. Price, “Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” J. Global Optim., vol. 11, pp. 341-359, 1997.
[30] S. Das, A. Abraham, U. K. Chakraborty, and A. Konar, “Differential Evolution Using a Neighborhood-Based Mutation Operator,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 3, pp. 526-553, June 2009.
[31] J. Vesterstrøm and R. Thomson, “A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems,” Proc. 6th Congr. Evol. Comput. (CEC-2004), vol. 2. Piscataway, NJ: IEEE Press, Jun. 2004, pp. 1980– 1987.
[32] S. Das and P. N. Suganthan, “Differential Evolution: A Survey of the State-of-the-Art,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 4-31, Feb. 2011.
[33] Pecan Street Inc., “Residential Load Data from Austin, Texas,” online available: https://dataport.cloud. [Accessed 7 Jul. 2018].
[34] A. Ellis, “Grid Operations and High Penetration PV,” Utility/Lab Workshop on PV Technology and Systems, Nov. 2010.
[35] S. Beer et al., “An Economic Analysis of Used Electric Vehicle Batteries Integrated Into Commercial Building Microgrids,” IEEE Transactions on Smart Grid, vol. 3, no. 1, pp. 517-525, March 2012.
[36] 台灣電力公司, 台灣電力公司電價表, March 2018. [Online]. Available: https://www.taipower.com.tw/upload/238/2018032911372895293.pdf. [Accessed 12 Jun. 2018].
[37] 陳立誠, 未來十年電價趨勢, March 2016. [Online]. Available: https://www.ettoday.net/news/20160304/657325.htm. [Accessed 12 Jun. 2018].
[38] N. Zart, “Batteries Keep On Getting Cheaper,” Dec. 2017. [Online]. Available: https://cleantechnica.com/2017/12/11/batteries-keep-getting-cheaper/. [Accessed 31 May 2018].
[39] P. Maloney, “Not so fast: Battery prices will continue to decrease, but at a slower pace, GTM says,” Mar. 2018. [Online]. Available: https://www.utilitydive.com/news/not-so-fast-battery-prices-will-continue-to-decrease-but-at-a-slower-pace/518776/. [Accessed 31 May 2018].