Day-Ahead Scheduling Optimization for Microgrid with Battery Life Model
Yang Yanhong1, 2, Pei Wei1, Deng Wei1, Shen Ziqi1, Qi Zhiping1, Zhou Meng3
1. Institute of Electrical Engineering Chinese Academy of Science Beijing 100190 China; 2. University of Chinese Academy of Sciences Beijing 100190 China; 3. China Electric Power Research Institute Beijing 100192 China
Abstract：Battery energy storage is an important element to be considered when the day-ahead dispatch of microgrid is carried out. In order to maximize the abilities of battery energy storage for stabilizing the fluctuations of renewable energy, regulating the difference between peak and valley and reducing the back capacity, the impacts on the battery life need to be considered, such as remaining capacity, charge power discharge power and the number of use times. In this paper, the mathematical model of each battery discharge loss is established. Before the model is introduced into the scheduling optimization objective function appropriate simplification is carried out by the weighting factor. For the characteristics of battery energy storage constraints coupled in continuous time, the Lagrangian relaxation and interior point method are introduced to solve dynamic programming model. The numerical example shows that the proposed method has good optimization results.
[1] Lasseter R H. Integration of distributed energy resources: The CERTS microgrid concept[R]. Consortium for Electric Reliability Technology Solutions, 2002. [2] Lee T Y. Operating schedule of battery energy storage system in a time-of-use rate industrial user with wind turbine generators: a multipass iteration particle swarm optimization approach[J]. IEEE Transactions on Energy Conversion, 2007, 22(3): 774-782. [3] Liu Xiaoping, Ding Ming, Han Jianghong, et al. Dynamic economic dispatch for microgrids including battery energy storage[C]. IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2010: 914-917. [4] Drouilhet S, Johnson B L. A battery life prediction method for hybrid power applications[C]. AIAA Aerospace Sciences Meeting and Exhibit, 1997. [5] Schiffer J, Sauer D U, Bindner H, et al. Model prediction for ranking lead-acid batteries according to expected lifetime in renewable energy systems and autonomous power-supply systems[J]. Journal of Power Sources, 2007, 168(1): 66-78. [6] 陈健, 王成山, 赵波, 等. 考虑储能系统特性的独立微电网系统经济运行优化[J]. 电力系统自动化, 2012, 36(20): 25-31. Chen Jian, Wang Chengshan, Zhao Bo, et al. Economic operation optimization of a stand-alone microgrid system considering characteristics of energy storage system[J]. Automation of Electric Power Systems, 2012, 36(20): 25-31. [7] Zhao B, Zhang X, Chen J, et al. Operation optimi- zation of standalone microgrids considering lifetime characteristics of battery energy storage system[J]. IEEE Transactions on Sustainable Energy, 2013, 4(4): 934-943. [8] 丁明, 张颖媛, 茆美琴. 包含钠硫电池储能的微网系统经济运行优化[J]. 中国电机工程学报, 2011, 31(4): 7-14. Ding Ming, Zhang Yingyuan, Mao Meiqin, et al. Economic operation optimization for microgrids including Na/S battery storage[J]. Proceedings of the CSEE, 2011, 31(4): 7-14. [9] Gu W, Wu Z, Yuan X. Microgrid economic optimal operation of the combined heat and power system with renewable energy[C]. IEEE Power and Energy Society General Meeting, 2010: 1-6. [10] 陈昌松, 段善旭, 蔡涛, 等. 基于改进遗传算法的微网能量管理模型[J]. 电工技术学报, 2013, 28(4): 196-201. Chen Changsong, Duan Shanxu, Cai Tao, et al. Microgrid energy management model based on improved genetic arithmetic[J]. Transactions of China Electrotechnical Society, 2013, 28(4): 196-201. [11] Chacra F A, Bastard P, Fleury G, et al. Impact of energy storage costs on economical performance in a distribution substation[J]. IEEE Transactions on Power Systems, 2005, 20(2): 684-691. [12] Cau T D H, Kaye R J. Multiple distributed energy storage scheduling using constructive evolutionary programming[C]. Proceedings of IEEE Power Engineering Society International Conference on Power Industry Computer Applications, Sydney, Australia, 2001: 402-407. [13] Maly D K, Kwan K S. Optimal battery energy storage system (BESS) charge scheduling with dynamic programming[J]. IEE Proceedings Science, Measurement and Technology, 1995, 142(6): 453-458. [14] Oudalov A, Cherkaoui R, Beguin A. Sizing and optimal operation of battery energy storage system for peak shaving application[C]. IEEE Proceedings of Power Tech, Lausanne, Switzerland, 2007: 621-625. [15] Osterwald C R. Translation of device performance measurements to reference conditions[J]. Solar Cells, 1986, 18(3-4): 269-279. [16] 白龙, 吴息, 丁宇宇, 等. 用于风电场功率预测的逐时风速预报[J]. 气象科技, 2013, 41(4): 777-783. Bai Long, Wu Xi, Ding Yuyu, et al. Hourly wind speed forecasting for wind farm prediction[J]. Meteorological Science and Technology, 2013, 41(4): 777-783. [17] 罗文, 王莉娜. 风场短期风速预测研究[J]. 电工技术学报, 2011, 26(7): 68-74. Luo Wen, Wang Lina. Short-term wind speed forecasting for wind farm[J]. Transactions of China Electrotechnical Society, 2011, 26(7): 68-74. [18] Boncompagni S C, Macchi E. Microturbines and trigeneration: optimization strategies and multiple engine configuration effects[J]. Journal of Engineering for Gas Turbines and Power, 2004, 126(1): 92-101. [19] 杨秀, 陈洁, 朱兰, 等. 基于经济调度的微网储能优化配置[J]. 电力系统保护与控制, 2013, 41(1): 53-60. Yang Xiu, Chen Jie, Zhu Lan, et al. Optimization allocation of energy storage for microgrid based on economic dispatch[J]. Power System Protection and Control, 2013, 41(1): 53-60. [20] Manwell J F, McGowan J G. Lead acid battery storage model for hybrid energy systems[J]. Solar Energy, 1993, 50(5): 399-405. [21] Symons P. Life estimation of lead-acid battery cells for utility energy storage[C]. Proceedings of the Fifth Conference on Batteries for Utility Storage, San Juan, Puerto Rico, 1995. [22] Dohrmann C R, Robinett R D. Dynamic programming method for constrained discrete-time optimal control[J]. Journal of Optimization Theory and Applications, 1999, 101(2): 259-283.
2015, Vol. 30 
