Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018
No.6 June 2018
No.6 June 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

A Mixed Integer Model for Large-Scale New Energy Medium-Term Operation Problem

Volume 13, Number 8, December 2017, pp. 1381-1388
DOI: 10.23940/ijpe.17.08.p19.13811388

Tieqiang Wanga, Fang Liub, Xin Caoa, Chenjun Suna, Zhice Yangb and Jue Wangb

aState Grid Hebei Electric Power Company, Shijiazhuang, China.
bComputer Network Information Center, Chinese Academy of Science, Beijing, China

(Submitted on October 20, 2017; Revised on November 22, 2017; Accepted on November 30, 2017)


Abstract:

In China, new energy is developing rapidly. In recent years, new energy power generation has been installed with explosive growth. However, the coordination problem between new energy penetration capability and the operation mode of the system has not been solved. Especially in the ‘Three North’ areas, new energy is severely limited. As a result, the large-scale new energy medium-term operation optimization algorithm and its parallelization are very urgent. This paper established a mixed integer model for the large-scale new energy medium-term operation problem, and proposed a new method to simplify the 0-1 constraints. Since the most commonly used software has some limitations on solving our mixed integer programming (MIP) problem, we developed a parallel algorithm library (CMIP) V2.0 of our own intellectual-property rights and exploited the parallelism of the algorithm for better performance. Preliminary numerical experiments show that CMIP V2.0 can solve the new energy medium-term operation optimization problem, at least as well as the commercial software CPLEX and the open source software SCIP.

 

References: 23

      1. T. Achterberg and R. Wunderling. 2013. “Mixed Integer Programming: Analyzing 12 Years of Progress”. Springer Berlin Heidelberg, Berlin, Heidelberg, 449-481.
      2. T. Achterberg. “Constraint Integer Programming”. Ph.D. Dissertation. 2007.
      3. T. Achterberg. “SCIP: Solving Constraint Integer Programs”. Mathematical Programming Computation, vol. 1, no.1, pp. 1-41, 2009.
      4. J. H. Bai and S. X. Xing. “Study of Major Question of Wind Power Digestion and Transmission in China”. Power System and Clean Energy, vol. 26, no. 1, pp. 14-17, 2010.
      5. CPLEX. Technical Report. IBM ILOG. http://www.ilog.com/products/cplex.
      6. R. E. Gomory. “Outline of an Algorithm for Integer Solutions to Linear Programs”. Bull. Amer. Math. Soc, vol. 64, no. 5, pp. 275-279, 1958.
      7. Z. H. Gu, G.L. Nemhauser, and M.W.P Savelsbergh. “Lifted Cover Inequalities for 0-1 Integer Programs: Computation”. INFORMS, pp. 427-437, 1998.
      8. Z. H. Gu and G.L. Nemhauser. “Sequence Independent Lifting in Mixed Integer Programming”. Journal of Combinatorial Optimization, vol. 4, no.1, pp.109-129, 2000.
      9. GUROBI. Optimizer. Technical Report. http://www.gurobi.com/.
      10. X. Q. Han, S. G. Sun, and Q. R. Qi. “Evaluation of Wind Power Penetration Limit From Peak Regulation”. Electric Power, vol. 43, no.6, pp. 16-19, 2010.
      11. L. A. Hemaspaandra and Ryan Williams. “SIGACT News Complexity Theory Column 76: An Atypical Survey of Typical-case Heuristic Algorithms”. SIGACT News, vol. 43, no. 4, pp. 70-89, 2012.
      12. Y. Z. Lei. “Studies on Wind Farm Integration into Power System”. Automation of Electric Power Systems, vol. 27, no. 8, pp. 84-89, 2003.
      13. L. Lovȁsz. “On the Ratio of Optimal Integral and Fractional Covers. Discrete Mathematics”, vol. 13, no. 4, pp. 383 -390, 1975.
      14. S. J. Maher, T. Fischer, T. Gally, G. Gamrath, A. Gleixner, R. L. Gottwald, G. Hendel, T. Koch, M. E. Lȕbbecke, M. Miltenberger, B. Mȕller, M.E. Pfetsch, C. Puchert, D. Rehfeldt, S. Schenker, R. Schwarz, F.Serrano, Y. Shinano, D. Weninger, J. T. Witt, and J. Witzig. “The SCIP Optimization Suite 4.0”. Technical Report 17-12. ZIB, Takustr.7, 14195 Berlin.
      15. H. Mittelmann. “Mixed Integer Linear Programming Benchmark (Serial Codes)”. Technical Report. http://plato.asu.edu/ftp/milpf.html. 2017.
      16. T. S. Motzkin and I. J. Schoenberg. 1954. “The Relaxation Method for Linear Inequalities”. Canadian Journal of Mathematics, vol. 6, pp. 393-404, 1954.
      17. M. W. Padberg. “A Note on Zero-One Programming. Operations Research”, vol. 23, no.4, pp. 833-837. 1975.
      18. M. W. Padberg. “(1,k)-Configurations and Facets for Packing Problems”. Mathematical Programming, vol. 18, no. 1, pp. 94-99, 1980.
      19. Z. N Shi, Q. Jin, and J. R. Li. “Research on New Guideline of Distributed PV Connected to Grid”. China Electric Power (Technology Edition), vol. 6, pp. 69-72, 2010.
      20. J. Wang, C. Liu, and Y.H. Huang. “Auto Tuning for New Energy Dispatch Problem: A Case Study”. Future Generation Computer Systems, vol. 54, pp. 501-506, 2015.
      21. R. Weismantel.. “On the 0/1 knapsack polytope. Mathematical Programming”, vol. 77, no. 3, 49-68, 1997.
      22. J. X. Yao and S.Q. Zhang. “Analysis on Capacity of Wind Power Integration into Grid based on Peak Load Regulation”. Advances of Power system and Hydroelectric Engineering, vol. 26, no. 7, pp. 25-28, 2010.
      23. L. A. Wolsey. “Faces for a Linear Inequality in 0C1 Variables. Mathematical Programming”, vol. 8, pp. 165-178, 1975.

           

          Click here to download the paper.

          Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

           

          CURRENT ISSUE

          Prev Next

          Temporal Multiscale Consumption Strategies of Intermittent Energy based on Parallel Computing

          Huifen Chen, Yiming Zhang, Feng Yao, Zhice Yang, Fang Liu, Yi Liu, Zhiheng Li, and Jinggang Wang

          Read more

          Decision Tree Incremental Learning Algorithm Oriented Intelligence Data

          Hongbin Wang, Ci Chu, Xiaodong Xie, Nianbin Wang, and Jing Sun

          Read more

          Spark-based Ensemble Learning for Imbalanced Data Classification

          Jiaman Ding, Sichen Wang, Lianyin Jia, Jinguo You, and Ying Jiang

          Read more

          Classification Decision based on a Hybrid Method of Weighted kNN and Hyper-Sphere SVM

          Peng Chen, Guoyou Shi, Shuang Liu, Yuanqiang Zhang, and Denis Špelič

          Read more

          An Improved Algorithm based on Time Domain Network Evolution

          Guanghui Yan, Qingqing Ma, Yafei Wang, Yu Wu, and Dan Jin

          Read more

          Auto-Tuning for Solving Multi-Conditional MAD Model

          Feng Yao, Yi Liu, Huifen Chen, Chen Li, Zhonghua Lu, Jinggang Wang, Zhiheng Li, and Ningming Nie

          Read more

          Smart Mine Construction based on Knowledge Engineering and Internet of Things

          Xiaosan Ge, Shuai Su, Haiyang Yu, Gang Chen, and Xiaoping Lu

          Read more

          A Mining Model of Network Log Data based on Hadoop

          Yun Wu, Xin Ma, Guangqian Kong, Bin Wang, and Xinwei Niu

          Read more
          This site uses encryption for transmitting your passwords. ratmilwebsolutions.com