An Overview of Optimal Power Flow with Wind Farm and Relevant Key Technology

Qiu Ming-jin; Chen Jing-hua; Tang Jun-jie

doi:10.12052/gdutxb.170104

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

An Overview of Optimal Power Flow with Wind Farm and Relevant Key Technology

更新时间：2026-05-27

- An Overview of Optimal Power Flow with Wind Farm and Relevant Key Technology
- Vol. 35, Issue 2, Pages: 63-68(2018)
- 作者机构：
  
  广东工业大学　自动化学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.170104
  CLC： TM744
- Online First：09 March 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
邱明晋, 陈璟华, 唐俊杰. 含风电场最优潮流及其关键技术研究综述[J]. 广东工业大学学报, 2018, 35(2): 63-68.

Qiu Ming-jin, Chen Jing-hua, Tang Jun-jie. An Overview of Optimal Power Flow with Wind Farm and Relevant Key Technology[J]. 2018, 35(2): 63-68.
邱明晋, 陈璟华, 唐俊杰. 含风电场最优潮流及其关键技术研究综述[J]. 广东工业大学学报, 2018, 35(2): 63-68. DOI： 10.12052/gdutxb.170104.

Qiu Ming-jin, Chen Jing-hua, Tang Jun-jie. An Overview of Optimal Power Flow with Wind Farm and Relevant Key Technology[J]. 2018, 35(2): 63-68. DOI： 10.12052/gdutxb.170104.

摘要

大型风电场并网发电给电网带来清洁能源的同时也使电力系统安全可靠运行面临诸多挑战

研究含风电场最优潮流对于提高电力系统稳定运行与能源利用率具有重要意义. 含风电场最优潮流是一个新型的高维、非线性的复杂优化问题

且由于风电的随机性、间歇性和不可控性使其复杂程度和计算难度进一步加大. 针对这一问题

本文通过数学模型和求解方法这一主线

阐述了含风电场最优潮流的发展现状以及因风电的随机性对电力系统造成的影响; 总结了对风电场处理的现有研究技术

分析了求解含风电场最优潮流的各种算法的优点与不足; 在此基础上对未来含风电场最优潮流的研究方向进行展望.

Abstract

Large-scale wind farm brings clean energy to the grid

and at the same time also many challenges to the power system’s safe and reliable operation. A research is conducted on the optimal power flow with wind farm for improving the stability and energy efficiency of power system operation is great significance. The optimal power flow with wind farms is a new complex problem of high dimension and nonlinearity

and the complexity and computational complexity of optimal power flow are further increased due to the randomness

intermittency and uncontrollability of wind power. With this problem in view

the development of the optimal power flow with wind farms and the influence of the randomness of wind power on the power system through the main line of mathematical model and solving method are expounded

and the existing research on wind farm treatment summarized

and the advantages and disadvantages of various algorithms for solving the optimal power flow with wind farm analyzed. On this basis

the future research direction of optimal power flow with wind farm is prospected.

关键词

Keywords

references

WANG T S, YUAN S. Effects of wind power generation on power grid with countermeasures[J]. High Voltage Apparatus, 2010, 46(8): 89-92.

WU J, LI J S, ZHOU J, et al . Influence of wind power generation integrating into power grids[J ] . Southern Power Systems Technology, 2010, 4(5): 48-52.

WU L W, ZHANG J H, LIU R X. A wind farm reliability model considering wind turbine faults and its application[J]. Automation of Electric Power Systems, 2012, 36(16): 31-35.

ZHANG W, WEI Z N, SUN G Q. Power flow calculation for power system including offshore wind farm grid integration[C]//International Conference on Sustainable Power Generation and Supply, 2009. SUPERGEN '09. Nanjing, China: IEEE, 2009.

FEIJOO A E, CIDRAS J. Modeling of wind farms in the load flow analysis[J]. IEEE Trans on Power Systems, 2000, 15(1): 110-115.

ZHANG W, WEI Z N, LIU Y J. Chaos optimal algorithm based power flow calculation involving wind farms[J]. Electric Power, 2011, 44(10): 25-28.

GU C H, AI Q. Optimal power flow calculation based on the improved interior method for a system integrated with wind farms[J].Electric Power, 2007, 40(1): 89-93.

WU J L, ZHOU S X, SUN J F, et al . Analysis on maximum power injection of wind farm connected to power system[J ] . Power System Technology, 2004, 28(20): 28-32.

FEIJÓO A E, CIDRÁS J. Modeling of wind farms in the load flow analysis[J]. IEEE Transactions on Power Systems, 2000, 15(1): 110-115.

CHEN Z, SPOONER E. Grid power quality with variable speed wind turbines[J]. IEEE Transactions on Energy Conversion, 2001, 16(2): 148-154

CHEN J F, CHEN H Y, DUAN X Z. Multi-period dynamic optimal power flow in wind power integrated system[J]. Proceedings of the CSEE, 2006, 26(3): 31-35.

FAN R Q, CHEN J F, DUAN X Z, et al . Impact of wind speed correlation on probabilistic load flow[J ] . Automation of Electric Power Systems, 2011, 35(4): 18-22+76.

DENG W, LI X R, XU Z H, et al . Calculation of probabilistic load flow considering wind speed correlation and analysis on influence of wind speed correlation[J ] . Power System Technology, 2012, 36(4): 45-50.

HUANG C M, HUANG Y C, HUANG K Y, et al. Deterministic and probabilistic wind power forecasting using a hybrid method[C]//2017 IEEE International Conference on Industrial Technology (ICIT). Canada, Toronto: IEEE,2017: 400-405.

SUN G Q, LI Y C, XIANG Y P, et al . Dynamic stochastic optimal power flow of wind integrated power system considering temporal and spatial correlation of wind speed[J ] . Proceedings of the CSEE, 2015, 35(17): 4308-4317

LUO J Y, BAO H B, GUO X X. Calculation of probabilistic optimal power flow considering wind speed correlation of wind farm[J]. Guangxi Electric Power, 2014, 37(1): 13-17.

LIAO Y C, GAN D Q, CHEN X Y, et al . Fuzzy optimal power flow analysis considering indeterminacy of distributed generation for urban power grid[J ] . Electric Power Automation Equipment, 2012, 32(9): 35-39.

LIN H M, LIU T Q, LI X Y. Optimal active power flow considering uncertainties of wind power output and load[J]. Power System Technology, 2013, 37(6): 1584-1589.

LOU S H, WANG Z L, WU Y W, et al . Coordinated economic dispatch for power system with significant wind power generation based on chance-constrained programming[J ] . Transactions of China Electrotechnical Society, 2013, 28(10): 337-345.

BAO H B, WEI H. Probabilistic optimal power flow computation in power systems including large-scale wind farms based on unscented transformation[J]. Automation of Electric Power Systems, 2014, 38(12): 46-53.

SHENG S Q, SUN X X. Hybrid chance-constrained economic dispatch model with wind farm[J]. Proceedings of the CSU-EPSA, 2015, 27(8): 82-86+102.

CHEN G G, LI Z H, CHEN J F, et al . SFL algorithm based dynamic optimal power flow in wind power integrated system[J ] . Automation of Electric Power Systems, 2009, 33(4): 25-30.

CHEN J M, QI L Z, SUN M Y, et al . Reactive power optimization for distribution network with multi-scenario wind power generator[J ] . Power System Protection and Control, 2016, 44(9): 129-134.

CHEN H Y, CHEN J F, DUAN X Z. Reactive power optimization in distribution system with wind power generators[J]. Proceedings of the CSEE, 2008, 28(7): 40-45.

HE J, DING X Q, CHEN G Y, et al . Reactive power optimization of distribution network with wind power based on chaotic parallel differential evolution algorithm[J ] . Electric Power Construction, 2014, 35(11): 1-6.

LIU X D, WANG L, YU Y. The research and application of improved simplifying gradient algorithm in the optimal power flow[J]. Shandong Dianli Jishu, 2003(1): 19-22.

HOU F, WU Z Q, WANG L Y. Optimal power solution based on fast decouple interior point algorithm[J]. Proceedings of the EPSA, 2001, 13(6): 8-12.

ZHU X L, ZHANG C Y, ZHAO C P, et al . Power system optimal power flow calculation based on newton's method[J ] . Journal of North China University of Water Resources and Electric Power (Natural Science Edition), 2014, 35(3), 71-74.

NIE Y H, XIAO B, LIU F L.New optimal power flow model and its solution by using nonlinear interior point method[J]. Proceedings of the CSU-EPSA, 2014, 26(11): 53-57.

DEVARAJ D, YEGNANARAYANA B. Genetic-algorithm-based optimal power flow for security enhancement[J]. Generation, Transmission and Distribution, IEE Proceedings, 2005, 152(6): 899-905.

CHEN W N, ZHANG J, LIN Y, et al . Particle swarm optimization with an aging leader and challengers[J ] . IEEE Transactions on Evolutionary Computation, 2013, 17(2): 241-258.

ELA A A E, ABIDO M A, SPEA S R. Optimal power flow using differential evolution algorithm[J]. Electrical Engineering, 2009, 91(2): 69-78.

KHORSANDI A, HOSSEINIAN S H, GHAZANFARI A. Modified artificial bee colony algorithm based on fuzzy multi-objective technique for optimal power flow problem[J]. Electric Power Systems Research, 2013, 95(95): 206-213.

LIU Q J, XU H M, SHI C. Research on power flow optimization based on multi-objective artificial bee colony algorithm[J]. Power System Protection and Control, 2015, 43(8): 1-7.

ZHOU Q, WEI P, LIU C J. Stochastic optimal power flow based on modified cuckoo search algorithm[J]. Electric Power Science and Engineering, 2017, 33(2): 14-20.

ZHANG D Y, WANG P T, YUAN Y B, et al . An improved cuckoo search algorithm for optimal power flow problem[J ] . Water Resources and Power, 2017, 35(1): 200-204.

BHASSKAR M M, BENERJI M, SYDULU M. A novel shrinking search space (SSS) genetic algorithm based approach to security constrained optimal power flow[J]. Journal of Kunming University of Science and Technology(Natural Science Edition), 2011, 36(3): 46-53.

WANG J, CHEN J Y, LAN K. PSO optimal power flow algorithm for a microgrid based on spot power prices[J]. Power System Protection and Control, 2013, 41(16): 34-40.

WANG Y T, WANG L H, LUO S T. The application of novel hybrid particle swarm algorithm for optimal power flow calculation of power systems with considering wind power integration[J]. Renewable Energy Resources, 2013, 31(6): 34-37.

ZHAO S B, ZHANG F S. Solution of optimal power flow based on differential evolution and its modified algorithm[J]. Power System Technology, 2010, 34(8): 123-128.

WANG C J, WANG X H, CHEN G C, et al . Tidal current turbines micrositing based on improved differential evolution algorithm[J ] . Transactions of China Electrotechnical Society, 2016, 31(15): 99-108.

HUANG L, ZHOU J H. Hybrid optimization algorithm for optimal power flow problems[J]. Journal of Electric Power, 2011, 26(2): 111-115+162.

HAN F C, WANG J, YANG C R, et al . The analysis of genetic algorithm and ant algorithm fusion' s electric power system optimal power flow[J ] . Journal of Electric Power, 2005, 20(4): 340-342.

LI J W, ZHAO J Q, ZHANG Y. Optimal power flow on basis of combining improved differential evolution algorithm with biogeography-based optimization algorithm[J]. Power System Technology, 2012, 36(9): 115-119.

MARCELINO C G, ALMEIDA P E M, WANNER E F, et al. Fundamentals of the C-DEEPSO algorithm and its application to the reactive power optimization of wind farms[C]//2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC: IEEE, 2016 : 547-1554.doi: 10.1109/CEC. 2016.7743973.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Multi-objective Reactive Power Optimization in Electric Power System with Wind Farm Based on Fuzzy Entropy Weight Method and CCPSO Algorithm

A Coordinated Framework for Power Demand Response Resources Participating in Gas Market

A Hybrid Algorithm Based on Improved Differential Evolution and Particle Swarm Optimization for Power System Optimal Power Flow Calculation

Study on Adaptive Protection System Based on Multi-agent

An Optimal Operation Strategy for Community Microgrid Alliances Considering Power Exchange under Multi-package Shared Energy Storage Model

Related Author

Chen Jing-hua

Qiu Ming-jin

Guo Jing-tao

Tang Jun-jie

Fu Zheng-xin

Liu Shuang

Duan Yi-qiang

Zhu Wei-dong

Related Institution

Guangdong Electric Power Design Institute Co, Ltd. of China Energy Engineering Group

School of Automation, Guangdong University of Technology

College of Mechanical Engineering, University of South China

AI问答

⁰