Unit Commitment Problem for Wind Turbines Power Generation with Batching Characteristics Consideration
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摘要: 电力机组组合问题是在给定的计划周期内确定火电、风电和蓄 电池机组的开关机状态及发电量, 以满足系统的负荷需求、旋转备用等约束要求. 为了降低风电在电网中的供电不稳定 性, 引入蓄电池储能系统与风机进行协调调度. 由于大数量风机的介入, 明显增加了问 题处理的难度和复杂性. 本文从一个新的视角 将相近物理位置的风机进行组批, 基于批的视角对问题建立了批模型. 为 了提高批模型的性能, 提出了批模型参数的变换方法. 根据问题的NP-难特征和模 型的复杂结构, 开发了拉格朗日松弛(Lagrangian relaxation, LR)算法进 行求解. 为了加速算法的求解效率, 提出了子 问题近似求解的代理次梯度的拉格朗日松弛算法. 实验结果表明, 提出的批模型明 显优于传统的单机模型. 基于批模型开发的拉格朗日松弛算法与CPLEX优化软 件相比, 能够在较短的时间内获得高质量的解.Abstract: The unit commitment problem is to determine the start-up/shut-down schedule and economical dispatch schedule of thermal generators, wind turbines and batteries to meet system load demand, reserved constraints, minimum up/down time constraints and other constraints within a certain time horizon. In order to reduce the power supply instability when wind power generation is plugged in the grid, coordinated scheduling of battery energy storage system introduced into the gird and wind turbines is performed. As a large number of wind turbines are plugged in the grid, the difficulties and complexities of the problem are increased significantly. In this paper, from a new batching perspective, we group wind turbines based on their physics locations to formulate the problem. In order to improve the performance of the batch model, a transformation method of model parameters is proposed. For tackling the complicated batch model and its NP-hardness, we develop a Lagrangian relaxation (LR) algorithm. In order to accelerate the algorithm, a surrogate subgradient Lagrangian relaxation algorithm is derived, in which subproblems are solved approximately. The experimental results show that the proposed batch model is superior to the ordinal single-unit model. Compared with CPLEX 11.0, the Lagrangian relaxation algorithm based on the batch model can obtain high quality solutions in a relatively short computation time.
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Key words:
- Wind turbines /
- unit commitment /
- batching /
- Lagrangian relaxation (LR)
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[1] European Wind Energy Association, Greenpeace. Wind Force 12. Beijing: China Environmental Science Press, 2004.(欧洲风能协会, 国际绿色和平. 风力12. 北京: 中国环境科学出版社, 2004.) [2] Sadanandan N D, Hilson D W, Needham M E, Morris K W, Sendaula M. Impact assessment of wind generation on the operations of a power system. IEEE Transactions on Power Apparatus and Systems, 1983, 102(9): 2905-2911 [3] Wang J H, Shahidehpour M, Li Z Y. Security-constrained unit commitment with volatile wind power generation. IEEE Transactions on Power Systems, 2008, 23(3): 1319-1327 [4] Chen C L. Optimal wind-thermal generating unit commitment. IEEE Transactions on Energy Conversion, 2008, 23(1): 273-281 [5] Chen C L, Lee T Y, Jan R M. Optimal wind-thermal coordination dispatch in isolated power systems with large integration of wind capacity. Energy Conversion and Management, 2006, 47(18-19): 3456-3472 [6] Denny E, O'Malley M. Wind generation, power system operation, and emissions reduction. IEEE Transactions on Power Systems, 2006, 21(1): 341-347 [7] Tuohy A, Meibom P, Denny E, O'Malley M. Unit commitment for systems with significant wind penetration. IEEE Transactions on Power Systems, 2009, 24(2): 592-601 [8] Miranda V, Hang P S. Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers. IEEE Transactions on Power Systems, 2005, 20(4): 2143-2145 [9] Hetzer J, Yu D C, Bhattarai K. An economic dispatch model incorporating wind power. IEEE Transactions on Energy Conversion, 2008, 23(2): 603-611 [10] Abreu L. Wind Power Generation and Dispatch in Competitive Power Markets [Ph.D. dissertation], Illinois Institute of Technology, USA, 2008. [11] Yuan Y, Zhang X S, Ju P, Li Q, Qian K J, Fu Z X. Determination of economic dispatch of wind farm-battery energy storage system using genetic algorithm. International Transactions on Electrical Energy Systems, 2014, 24(2): 264-280 [12] Thatte A A, Xie L, Viassolo D E, Singh S. Risk measure based robust bidding strategy for arbitrage using a wind farm and energy storage. IEEE Transactions on Smart Grid, 2013, 4(4): 2191-2199 [13] Zhao C Y, Wang J H, Watson J P, Guan Y P. Multi-stage robust unit commitment considering wind and demand response uncertainties. IEEE Transactions on Power Systems, 2013, 28(3): 2708-2717 [14] Jiang R W, Wang J H, Guan Y P. Robust unit commitment with wind power and pumped storage hydro. IEEE Transactions on Power Systems, 2012, 27(2): 800-810 [15] Lee C, Liu C, Mehrotra S J, Shahidehpour M. Modeling transmission line constraints in two-stage robust unit commitment problem. IEEE Transactions on Power Systems, 2014, 29(3): 1221-1231 [16] Jiao Chun-Ting, Guan Xiao-Hong, Wu Jiang, Lei Xue-Jiao, Li Pan. Security-constrained unit commitment with large-scale cross-region wind penetration. In: Proceedings of the 33rd Chinese Control Conference. Nanjing, China: 2014. 28 -30(焦春亭, 管晓宏, 吴江, 雷雪娇, 李盼. 考虑大规模风电跨区消纳的电力系统调度. 第33届中国控制会议. 南京, 中国: 2014. 28-30) [17] Wu Xiong, Wang Xiu-Li, Li Jun, Guo Jing-Li, Zhang Kai, Chen Jie. A joint operation model and solution for hybrid wind energy storage systems. Proceedings of the CSEE, 2013, 33(13): 10-17(吴雄, 王秀丽, 李骏, 郭静丽, 张凯, 陈洁. 风电储能混合系统的联合调度模型及求解. 中国电机工程学报, 2013, 33(13): 10-17) [18] Hu Ze-Chun, Ding Hua-Jie, Kong Tao. A joint daily operational optimization model for wind power and pumped-storage plant. Automation of Electric Power Systems, 2012, 36(2): 36-41, 57(胡泽春, 丁华杰, 孔涛. 风电--抽水蓄能联合日运行优化调度模型. 电力系统自动化, 2012, 36(2): 36-41, 57) [19] Wang Cheng-Xu, Zhang Yuan. Wind Power Generation. Beijing: China Electric Power Press, 2003.(王承煦, 张源. 风力发电. 北京: 中国电力出版社, 2003.) [20] Chedid R, Akiki H, Rahman S. A decision support technique for the design of hybrid solar-wind power systems. IEEE Transactions on Energy Conversion, 1998, 13(1): 76-83 [21] Shapiro J F. Generalized Lagrange multipliers in integer programming. Operations Research, 1971, 19(1): 68-76 [22] Fisher M L. The Lagrangian relaxation method for solving integer programming problems. Management Science, 1981, 27(1): 1-18 [23] Guan X, Luh P B, Yan H, Amalfi J A. An optimization-based method for unit commitment. International Journal of Electrical Power and Energy Systems, 1992, 14(1): 9-17 [24] Kaskavelis C A, Caramanis M C. Efficient Lagrangian relaxation algorithms for industry size job-shop scheduling problems. IIE Transactions, 1998, 30(11): 1085-1097 [25] Bard J F. Short-term scheduling of thermal-electric generators using Lagrangian relaxation. Operations Research, 1988, 36(5): 756-766
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