隨著用電需求與日俱增以及環保意識抬頭，分散式能源的發展與推廣日漸受到關注。然而目前分散式電源的成本較高，進而減少用戶使用的意願。為促進微電網營運商在分散式能源的投資與應用，本文提出應用在社區微電網的分散式電源共享服務機制。將營運商預期投資的分散式能源透過長期規劃並以共享經濟的營運方式提供給用戶。藉由分散式能源容量分配的想法，營運商可透過收取租金獲得利潤。另一方面，在考量規模經濟下，用戶們可透過較低的成本使用分散式能源並且執行能源需求的管理與調度來降低用電成本。
本文考量在有限的供給下藉由自適應動差估計法調整價格模擬微電網營運商與用戶之間的競價關係。用戶方面則使用線性規劃根據租賃價格同時求出分散式電源最佳租用容量以及一年的排程結果。為了驗證所提出的共享服務之有效性，採用一年的太陽能與用戶負載實際數據做為測試資料。此外，為了顯示該服務的經濟效益，我們也藉由不同大小的分散式電源對所提出的方法進行靈敏度分析。模擬結果顯示所提出之方法在不同條件下均可有效提升電網營運商的利潤，而用戶也可減少用電成本。未來再生能源及電池投資成本降低，電網營運商在考量長期投資下仍可藉由該服務增加獲益，而用戶在供給足夠的情形下也可以租賃較多的太陽能與儲能系統服務以滿足需求。

Owing to the increase in electricity demand and awareness regarding the environment, the development and promotion of distributed energy resources (DERs) have increasingly attracted attention. However, the high cost of DERs reduces the willingness of users wanting to use them. To encourage microgrid operators to invest in and apply the resources, our thesis proposes a sharing mechanism of DERs for a community microgrid. The operators provide the resources to users by long-term planning and a sharing economy. Through the proposal mentioned above, the operators can profit by renting the resources. By contrast, considering the economy of scale, users can save on electricity fees by scheduling and managing of energy demand and the resources at lower cost.
In this thesis, a bidding process considering the finite supply between an operator and users is simulated by adjusting renting prices through adaptive moment estimation (ADAM). In terms of users, linear programming (LP) is utilized to decide the optimal capacity of DERs and annual scheduling results. To verify the effectiveness of our proposed service, annual real data is utilized. A sensitivity analysis is also conducted for different amounts of DERs to demonstrate the economic benefits. The results demonstrate that our proposed method not only increases the operator’s profit but also reduces users’ energy cost. Furthermore, the operator can invest more capacity of photovoltaic (PV) and energy storage system (ESS) to become more profit owing to the economy of scale. Finally, owing to the decrease in investment cost in the future, the operator can still be profitable with long-term planning and investment, and users can rent PV and ESS capacity to meet their energy demand with sufficient supply.

REFERENCES
[1] 蔡禹擎、楊鏡堂與葛復光, “淺談間歇性再生能源之開發對我國電力供應的影響,” Dec. 2016. [Online] Available: http://eip.iner.gov.tw/msn.aspx?datatype=YW5hbHlzaXM=&id=MTAz.
[2] 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.
[3] A. C. Chapman, G. Verbic, and D. J. Hill, “Algorithmic and Strategic Aspects to Integrating Demand-Side Aggregation and Energy Management Methods,” IEEE Transactions on Smart Grid, vol. 7, no. 6, pp. 2748-2760, Nov. 2016.
[4] X. Yang, Y. Zhang, H. He, S. Ren, and G. Weng, “Real-Time Demand Side Management for a Microgrid Considering Uncertainties,” IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 3401-3414, May 2019.
[5] 莊昇勳, “台灣、日本電力市場及憑證概況之比較,” 中華民國環境工程學會電子報，107年第3期。
[6] 經濟部, “能源轉型白皮書,” Apr. 2018 [Online] Available: https://energywhitepaper.tw/.
[7] A. Rinne, “4 big trends for the sharing economy in 2019,” Jan. 2019 [Online] Available: https://www.weforum.org/agenda/2019/01/sharing-economy/.
[8] S. Potter, “The Sharing Economy’s Next Frontier: Energy Sharing,” Jan. 2019. [Online] Available: https://www.renewableenergyworld.com/articles/2019/01/the-sharing-economys-next-frontier-energy-sharing.html.
[9] J. Liu, N. Zhang, C. Kang, D. S. Kirschen, and Q. Xia, “Decision-Making Models for the Participants in Cloud Energy Storage,” IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 5512-5521, Nov. 2018.
[10] D. Zhao, H. Wang, J. Huang, and X. Lin, “Virtual Energy Storage Sharing and Capacity Allocation,” IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1112-1123, March 2020.
[11] E. Oh and S. Son, “Shared Electrical Energy Storage Service Model and Strategy for Apartment-Type Factory Buildings,” IEEE Access, vol. 7, pp. 130340-130351, 2019.
[12] W. Tushar, B. Chai, C. Yuen, S. Huang, D. B. Smith, H. V. Poor, and Z. Yang, “Energy Storage Sharing in Smart Grid: A Modified Auction-Based Approach,” IEEE Transactions on Smart Grid, vol. 7, no. 3, pp. 1462-1475, May 2016.
[13] N. Liu, M. Cheng, X. Yu, J. Zhong, and J. Lei, “Energy-Sharing Provider for PV Prosumer Clusters: A Hybrid Approach Using Stochastic Programming and Stackelberg Game,” IEEE Transactions on Industrial Electronics, vol. 65, no. 8, pp. 6740-6750, Aug. 2018.
[14] A. Fleischhacker, H. Auer, G. Lettner, and A. Botterud, “Sharing Solar PV and Energy Storage in Apartment Buildings: Resource Allocation and Pricing,” IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3963-3973, Jul. 2019.
[15] LAZARD, “LAZARD’s Levelized Cost of Energy Analysis” Nov. 2018.
[16] LAZARD, “LAZARD’s Levelized Cost of Storage Analysis,” Nov. 2019.
[17] D. Kalathil, C. Wu, K. Poolla, and P. Varaiya, “The Sharing Economy for the Electricity Storage,” IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 556-567, Jan. 2019.
[18] I. S. Bayram, M. Abdallah, A. Tajer, and K. A. Qaraqe, “A Stochastic Sizing Approach for Sharing-Based Energy Storage Applications,” IEEE Transactions on Smart Grid, vol. 8, no. 3, pp. 1075-1084, May 2019.
[19] P. Chakraborty , E. Baeyens , K. Poolla, P. P. Khargonekar, and P. Varaiya, “Sharing Storage in a Smart Grid: A Coalitional Game Approach,” IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 4379-4390, Jul. 2019.
[20] T. AlSkaif, M. G. Zapata, and B. Bellalta, “A Reputation-Based Centralized Energy Allocation Mechanism for Microgrids,” IEEE 2015 International Conference on Smart Grid Communications (SmartGridComm).
[21] M. R. Sarker, Y. Dvorkin, and M. A. Ortega-Vazquez, “Optimal Participation of an Electric Vehicle Aggregator in Day-Ahead Energy and Reserve Markets,” IEEE Transactions on Power System, vol. 31, no. 5, pp. 3506-3515, Sep. 2016.
[22] W. Kenton, “Equivalent Annual Cost – EAC Definition,” [Online] Available: https://www.investopedia.com/terms/e/eac.asp.
[23] G. B. Dantzig, “Maximization of a Linear Function of Variables Subject to Linear Inequalities,” Activity Analysis of Production and Allocation, edited by T. C. Koopmans, John Wiley and Sons, New York, pp. 339-347, 1951.
[24] 方述誠, “線性規劃(Linear Programming),” 數學傳播，第十七卷一期，1983年。
[25] Linear Programming, [Online] Available: https://en.wikipedia.org/wiki/Linear_programming.
[26] D. P. Kingma and J. L. Ba, “ADAM: A Method for Stochastic Optimization,” in International Conference on Learning Representations (ICLR), 2015. Available: https://arxiv.org/abs/1412.6980.
[27] S. Ruder, “An overview of gradient descent optimization algorithms,” [Online] Available: https://ruder.io/optimizing-gradient-descent/index.html#fn14.
[28] SCE, “Time-Of-Use (TOU) Rate Plans,” [Online] Available: https://www.sce.com/.
[29] OpenEI, “Commercial and Residential Hourly Load Profiles for all TMY3 Locations in the United States,” [Online] Available: https://openei.org/wiki/Main_Page.
[30] NREL, “Solar Power Data for Integration Studies,” [Online] Available: https://www.nrel.gov/grid/solar-power-data.html.
[31] PV Magazine, “Feed-in tariffs (FITs) in America,” [Online] Available: https://www.pv-magazine.com/features/archive/solar-incentives-and-fits/feed-in-tariffs-in-america/#california.
[32] A. Z. Amin, “Electricity storage and renewables: Costs and markets to 2030,”　Int. Renew. Energy Agency (IRENA), Abu Dhabi, UAE, Tech.　Rep. 9789292600389, Oct. 2017.