有可再生能源和电力存储设施并网的智能电网优化用电策略

doi:10.16381/j.cnki.issn1003-207x.2019.02.015

Abstract

Abstract: With a large number of renewable energy and storage facilities centralized or distributed accessible to the grid, the supply pressure of the power grid is eased, but at the same time, a new threat to the safety of power systems arises.Rational use of new energy and storage facilities to better serve the grid is an urgent problem for the modern grid.In this paper, a study on smart grid with the integration of renewable energy and storage equipments is made. According to the complex situation of renewable energy, they are first diviede two categories:one is private renewable energy and the other is public renewable energy.Private renewable energy can be used for users directly, and the excess part will be put into the grid.However,public renewable energy will be put into the griddirectly.Then, aiming at the above complex situation, combining with the actual demand of the user, an optimized strategy of rational use of renewable energy and storage equipment is given based on the maximization of users' utility and the minimization of users' cost. The properties of the model are also studied. Considering that the model is a convex programming and strong duality is founded, the solution of the model is given by Lagrangian dual algorithm. In the process of solving, because the objective function is non-smooth, the smoothing method is used to smooth the objective function,which transforms the optimal value of the non-smooth function into that of the smoothing function,and then the problem is further solved by the quasi-Newton method.This strategy not only gives priority to the use of renewable energy but also makes the most of it while maximizing users utility and minimizing cost.This strategy also avoids instability of grid caused by renewable energy, and the method of smoothing is applicable not only to this article, but also to the case where the objective functions are not differentiable.Simulation results verify the rationality of the model and the feasibility of the algorithm.

Key words: smart grid, demand side management, renewable energy, storage device, Lagrangian dual method, non-smooth optimization, Quasi-Newton method

CLC Number:

TM743
O224

TAO Li, GAO Yan, ZHU Hong-bo, CAO Lei. Optimal Scheduling for Smart Grids with the Integration of Renewable Resourcesand Storage Devices[J]. Chinese Journal of Management Science, 2019, 27(2): 150-157.

References

[1] He Miao, Murugesan S, Zhang Junshan. A multi-timescale scheduling approach for stochastic reliability in smart grids with wind generation and opportunistic demand[J]. IEEE TransactionsonSmart Grid, 2013, 4(1):521-529.

[2] Nunna H K, Doolla S. Demand response in smart distribution system with multiple microgrids[J]. IEEE Transactions on Smart Grid, 2012, 3(4):1641-1649.

[3] Guerrero J M, Blaabjerg F, Zhelev T, et al. Distributed generation:Toward a new energy paradigm[J]. Industrial Electronics Magazine IEEE, 2010, 4(1):52-64.

[4] Bahramirad S, Reder W, Khodaei A. Reliability-constrained optimal sizing of energy storage system in a micro-grid[J]. IEEE Transactions on Smart Grid, 2012, 3(4):2056-2062.

[5] Xu Zhanbo, Guan Xiaohong, Jia Qingshan, et al. Performance analysis and comparison on energy storage devices for smart building energy management[J]. IEEE Transactions on Smart Grid, 2012, 3(4):2136-2147.

[6] Silva A M L D, Nascimento L C, Rosa M A D, et al. Distributed energy resources impact on distribution system reliability under loadtransfer restrictions[J]. IEEE Transactions on Smart Grid, 2012, 3(4):2048-2055.

[7] Wang K, Ciucu F, Lin C, et al. A Stochastic power network calculus for integrating renewable energy sources into the power grid[J]. IEEE Journal on Selected Areas in Communications, 2012, 30(6):1037-1048.

[8] Momoh J.Smart grid:Fundamentals of design and analysis[M]. New Jersey:Jokn Wiley & Sons, Inc., 2012.

[9] 吴振信, 石佳. 基于STIRPAT和GM(1,1)模型的北京能源碳排放影响因素分析及趋势预测[J].中国管理科学, 2012,20(S2):803-809.

[10] Sioshansi R. Evaluating the impacts of real-time pricing on the cost and value of wind generation[J]. IEEE Transactions on Power Systems, 2010, 25(2):741-748.

[11] Vittal V. The impact of renewable resources on the performance and reliability of the electricity grid[J]. The bridge, 2010, 40(1):5-12.

[12] Amin S M, Wollenberg B F. Toward a smart grid:Power delivery for the 21st century[J]. IEEE Power & Energy Magazine, 2005, 3(5):34-41.

[13] Groot R J W, Morren J, Slootweg J G. Smart integration of distribution automation applications[C]//IEEE Pes International Conference and Exhibition on Innovative Smart Grid Technologies, IEEE, 2012:1-7.

[14] Wang Wenye, Lu Zhuo. Survey cyber security in the smart grid:Survey and challenges[J]. Computer Networks, 2013, 57(5):1344-1371.

[15] Deng Ruilong, Yang Zaiyue, Chow M Y, et al. A survey on demand response in smart grids:mathematical models and approaches[J]. IEEE Transactions on Industrial Informatics, 2015, 11(3):570-582.

[16] Cheung K W, Rios-Zalapa R. Smart dispatch for large grid operations with integrated renewable resources[C]//Innovative Smart Grid Technologies, IEEE, 2011:1-7.

[17] Makarov Y V, Etingov P V, Ma J, et al. Incorporating uncertainty of wind power generation forecast into power system operation, dispatch, and unit commitment procedures[J]. IEEE Transactions on Sustainable Energy, 2011, 2(4):433-442.

[18] 徐智威, 胡泽春, 宋永华,等. 充电站内电动汽车有序充电策略[J]. 电力系统自动化, 2012, 36(11):38-43.

[19] 杨珺, 冯鹏祥, 孙昊,等. 电动汽车物流配送系统的换电站选址与路径优化问题研究[J]. 中国管理科学, 2015, 23(9):87-96.

[20] 常方宇, 黄梅, 张维戈. 分时充电价格下电动汽车有序充电引导策略[J]. 电网技术,2016, 40(9):2609-2615.

[21] Yang Zaiyue, Sun Lihao, Chen Jiming, et al. Profit maximization for plug-in electric taxi with uncertain future electricity prices[J]. IEEE Transactions on Power Systems, 2014, 29(6):3058-3068.

[22] Raziei A, Hallinan K P, Brecha R J. Cost optimization with solar and conventional energy production, energy storage, and real time pricing[C]//Innovative Smart Grid Technologies Conference, IEEE, 2014:1-5.

[23] Cecati C, Citro C, Siano P. Combined operations of renewable energy systems and responsive demand in a smart grid[J]. IEEE Transactions on Sustainable Energy, 2011, 2(4):468-476.

[24] Samadi P, Mohsenian-Rad H, Wong V W S, et al. Utilizing renewable energy resources by adopting DSM techniques and storage facilities[C]//IEEE International Conference on Communications, IEEE, 2014:4221-4226.

[25] Qian Liping, Zhang Yingjun, Huang Jianwei, et al. Demand response management via real-time electricity price control in smart grids[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(7):1268-1280.

[26] 代业明, 高岩, 高红伟, 等. 基于需求响应的智能电网实时电价谈判模型[J]. 中国管理科学, 2017(3):102-110.

[27] 王宜举, 修乃华. 非线性最优化理论与方法.第2版[M]. 北京:科学出版社, 2016.

[28] 高岩. 非光滑优化[M]. 北京:科学出版社, 2008.

[29] 王宏杰,高岩.基于非光滑方程组的智能电网实时定价[J].系统工程学报,2018,33(3):320-327.

Optimal Scheduling for Smart Grids with the Integration of Renewable Resourcesand Storage Devices

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 4

Metrics

Comments

Recommended 10

[1]	LI Jun-xiang, ZHANG Wen-cai, GAO Yan. Research on Real-time Pricing Based on the Classification of the User's Electrical Appliances in Smart Grid [J]. Chinese Journal of Management Science, 2019, 27(4): 210-216.
[2]	CHENG Cheng, WANG Zhen, LIU Hui-hui, ZHAO Guo-hao, LIU Ming-ming, REN Xiao-hang. Study on the Optimization of Investment Incentive Policies for Renewable Energy Projects-from a Perspective of Execution Time [J]. Chinese Journal of Management Science, 2019, 27(3): 157-167.
[3]	LI Li, ZHU Lei, FAN Ying. Competitive Investment Strategy for Renewable Power Generation Under Uncertainty [J]. Chinese Journal of Management Science, 2017, 25(7): 11-17.
[4]	DAI Ye-ming, GAO Yan, GAO Hong-wei, JIN Feng. Real-time Pricing Contract Bargaining Based on Demand Response in Smart Grid [J]. Chinese Journal of Management Science, 2017, 25(3): 130-136.