Timing Planning for Investment of Wind Farm Clusters According to  Copula-UCVaR Risk Measurement

doi:10.12204/j.issn.1000-7229.2020.08.010

Abstract

Abstract: Considering both economic value and adequacy risk of peak load regulation for the coordination of wind farm clusters and conventional units, this paper proposes an invrestment timing decision method for wind farms clusters. Firstly, we establish an economic value assessment model with option theory to measure the economic value of wind farm clusters investment. Secondly, in order to study the influence of wind speed correlation on peaking adequacy risk, and consider risk preference of investors, by using the Copula function to describe the correlation of wind speed, a UCVaR index based on modified value function in prospect theory is proposed. Then we construct a bi-level programming model that integrates investment economy and peaking adequacy risk. The Monte Carlo simulation and genetic algorithm are used to solve the model. Finally, the example gives the optimal timing scheme in six scenarios. The calculation results show that the correlation of wind speed, the investors risk preference, and the selection of risk index have considerable influence on the timing planning for investment of wind farm clusters.

Key words: wind farm clusters, investment timing, peak load regulation, risk measurement , Copula

CLC Number:

TM 71

GAO Jianwei, LANG Yutong, GAO Fangjie, GUO Guiyu , LIANG Pengcheng . Timing Planning for Investment of Wind Farm Clusters According to Copula-UCVaR Risk Measurement[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(8): 78-86.

References ［22］

［1］	曾鸣, 张平, 王良, 等. 不确定条件下基于蒙特卡洛模拟的发电投资评估模型［J］. 电力系统自动化, 2015, 39（5）: 54-60.
	ZENG Ming, ZHANG Ping, WANG Liang, et al. Generation investment evaluation model under uncertainty based on Monte Carlo simulation［J］. Automation of Electric Power Systems, 2015, 39（5）: 54-60.
［2］	田廓, 邱柳青, 曾鸣. 含有柔性输电装置的灵活性输电投资决策模型［J］. 电网技术, 2012, 36（1）: 230-236.
	TIAN Kuo, QIU Liuqing, ZENG Ming. Flexibility investment decision-making model for transmission systems with flexible AC transmission equipments［J］. Power System Technology, 2012, 36（1）: 230-236.
［3］	寻斌斌, 文福拴, 黎小林, 等. 计及排污权交易和多种不确定性的发电投资决策［J］. 电力系统自动化, 2014, 38（1）: 51-56.
	XUN Binbin, WEN Fushuan, LI Xiaolin, et al. Generation investment decision-making in emission trading environment with multiple uncertainties［J］. Automation of Electric Power Systems, 2014, 38（1）: 51-56.
［4］	许志恒, 张勇军, 陈泽兴, 等. 考虑运行策略和投资主体利益的电转气容量双层优化配置［J］. 电力系统自动化, 2018, 42（13）: 76-84.
	XU Zhiheng, ZHANG Yongjun, CHEN Zexing, et al. Bi-level optimal capacity configuration for power to gas facilities considering operation strategy and investment subject benefit［J］. Automation of Electric Power Systems, 2018, 42（13）: 76-84.
［5］	张粒子, 凡鹏飞, 麻秀范, 等. 考虑调峰适应性风险的风电场群时序规划方法［J］. 中国电机工程学报, 2012, 32（7）: 14-22, 186.
	ZHANG Lizi, FAN Pengfei, MA Xiufan, et al. Timing planning method for investment of clustering wind farms considering risk of peak load regulation［J］. Proceedings of the CSEE, 2012, 32（7）: 14-22, 186.
［6］	曹银利, 刘继春, 张放, 等. 考虑电价-出力关联不确定性的风电场投资分析［J］. 电力建设, 2014, 35（4）: 122-126.
	CAO Yinli, LIU Jichun, ZHANG Fang, et al. Investment analysis of wind farm considering electricity price-output associated uncertainty［J］. Electric Power Construction, 2014, 35（4）: 122-126.
［7］	王秀丽, 张凯, 曾平良, 等. 基于多场景的风电场接入与输电网协调规划［J］. 电力建设, 2015, 36（10）: 46-52.
	WANG Xiuli, ZHANG Kai, ZENG Pingliang, et al. Coordinated planning of wind farm integration and transmission network based on multi-scenario［J］. Electric Power Construction, 2015, 36（10）: 46-52.
［8］	杨欢, 邹斌. 含相关性随机变量的概率最优潮流问题的蒙特卡罗模拟方法［J］. 电力系统保护与控制, 2012, 40（19）: 110-115.
	YANG Huan, ZOU Bin. A Monte Carlo simulation method for probabilistic optimal power flow with correlated stochastic variables［J］. Power System Protection and Control, 2012, 40（19）: 110-115.
［9］	YUE Z Y, LIU Y L, YU Y X, et al. Probabilistic transient stability assessment of power system considering wind power uncertainties and correlations［J］. International Journal of Electrical Power & Energy Systems, 2020, 117: 105649.
［10］	沈小军, 周冲成, 吕洪. 基于运行数据的风电机组间风速相关性统计分析［J］. 电工技术学报, 2017, 32（16）: 265-274.
	SHEN Xiaojun, ZHOU Chongcheng, L Hong. Statistical analysis of wind speed correlation between wind turbines based on operational data［J］. Transactions of China Electrotechnical Society, 2017, 32（16）: 265-274.
［11］	李伟, 尤海侠, 杨强华. 基于实物期权理论的风电项目投资评价方法研究［J］. 中外能源, 2012, 17（10）: 23-27.
	LI Wei, YOU Haixia, YANG Qianghua. A study on methods for evaluating wind power investment based on real option theory［J］. Sino-Global Energy, 2012, 17（10）: 23-27.
［12］	刘敏, 吴复立. 基于实物期权理论的风电投资决策［J］. 电力系统自动化, 2009, 33（21）: 19-23.
	LIU Min, WU Fuli. Wind power investment decision-making strategy based on real options theory［J］. Automation of Electric Power Systems, 2009, 33（21）: 19-23.
［13］	张新华, 叶泽. 投资政策不确定条件下的寡头发电容量投资分析［J］. 中国管理科学, 2014, 22（9）: 26-32.
	ZHANG Xinhua, YE Ze. Study on oligopoly power producers capacity investment under policy uncertainty［J］. Chinese Journal of Management Science, 2014, 22（9）: 26-32.
［14］	李盼娟. 基于实物期权理论的风电投资决策研究［D］. 北京: 华北电力大学, 2015.
	LI Panjuan. Research on wind power investment decision-making strategy based on real options theory［D］. Beijing: North China Electric Power University, 2015.
［15］	迟春洁, 麻易帆. 基于改进型学习曲线理论的风电产业学习率估计［J］. 经济与管理研究, 2018, 39（5）: 69-77.
	CHI Chunjie, MA Yifan. Estimating the learning rate of wind power industry based on the modified learning curve theory［J］. Research on Economics and Management, 2018, 39（5）: 69-77.
［16］	ZHONG Y H, BAO Q F, LI S H. FX options pricing in logarithmic mean-reversion jump-diffusion model with stochastic volatility［J］. Applied Mathematics and Computation, 2015, 251: 1-13.
［17］	向红吉, 戴朝华, 明杰, 等. 考虑低谷时刻负调峰能力及风电预测区间的多目标机组组合优化研究［J］. 电网技术, 2017, 41（6）: 1912-1918.
	XIANG Hongji, DAI Zhaohua, MING Jie, et al. Research on multi-objective optimization of unit commitment considering negative peak load regulation ability in valley load period and wind power prediction interval［J］. Power System Technology, 2017, 41（6）: 1912-1918.
［18］	杨宏, 刘建新, 苑津莎. 风电系统中常规机组负调峰能力研究［J］. 中国电机工程学报, 2010, 30（16）: 26-31.
	YANG Hong, LIU Jianxin, YUAN Jinsha. Research of peak load regulation of conventional generators in wind power grid［J］. Proceedings of the CSEE, 2010, 30（16）: 26-31.
［19］	梁浚杰, 兰飞, 农植贵, 等. 考虑风电非线性相关性的配电网概率潮流研究［J］. 电力建设, 2017, 38（7）: 34-43.
	LIANG Junjie, LAN Fei, NONG Zhigui, et al. Probability load flow of distribution network with considering wind power nonlinear correlation［J］. Electric Power Construction, 2017, 38（7）: 34-43.
［20］	高建伟, 郭奉佳. 基于改进前景理论的直觉模糊随机多准则决策方法［J］. 控制与决策, 2019, 34（2）: 317-324.
	GAO Jianwei, GUO Fengjia. Intuitionistic fuzzy stochastic multi-criteria decision-making method based on modified prospect theory［J］. Control and Decision, 2019, 34（2）: 317-324.
［21］	华亮亮, 黄伟, 葛良军, 等. 计及需求响应的主动配电网双层优化调度模型［J］. 电力建设, 2018, 39（9）: 112-119.
	HUA Liangliang, HUANG Wei, GE Liangjun, et al. Bi-level optimal dispatch model of active distribution network with demand response［J］. Electric Power Construction, 2018, 39（9）: 112-119.
［22］	马健, 孙秀霞. 基于效用曲线改进的前景理论价值函数［J］. 信息与控制, 2011, 40（4）: 501-506.
	MA Jian, SUN Xiuxia. Modified value function in prospect theory based on utility curve［J］. Information and Control, 2011, 40（4）: 501-506.

[1]	SUN Rong，L Zhenhua，LIAO Xingxing，CHEN Bing，WANG Rui，ZHU Chenchen，WEI Zhinong. Probabilistic Power Flow Analysis Based on Generalized Cumulant Methods and Maximum Entropy Methods [J]. Electric Power Construction, 2020, 41(7): 33-41.
[2]	ZHANG Jiaan， WANG Kunyue， CHEN Jian， GUO Lingxu， HUANG Xiaoxiao,FAN Ruiqing， LI Zhijun. Research on Prediction of Distributed Photovoltaic Output Considering Spatial Relevance [J]. Electric Power Construction, 2020, 41(3): 47-53.
[3]	JIANG Hao， LIN Shunjiang， LU Yi， HE Sen. Load Probability Modeling and Scenario Generation for Electric Vehicle Charging Station Considering Time Correlation [J]. Electric Power Construction, 2020, 41(2): 47-57.
[4]	JIANG Xuechen, YUAN Yue, WU Han, XU Yundai, HUANG Ruanming, WANG Yuefeng. Multistage Linearization Probabilistic Power Flow Calculation Based on Piecewise Copula and Gaussian Mixture Model [J]. Electric Power Construction, 2018, 39(9): 120-128.
[5]	JIANG Xuechen，ZHU Junpeng，YUAN Yue，WANG Yuefeng，HUANG Ruanming. Simulation Method Based on Improved Scenario Division Considering Temporal Correlation for PV Output Time Series [J]. Electric Power Construction, 2018, 39(10): 63-70.
[6]	LIANG Junjie, LAN Fei, NONG Zhigui, LI Jinghua . Probability Load Flow of Distribution Network with Considering Wind Power Nonlinear Correlation [J]. Electric Power Construction, 2017, 38(7): 34-.
[7]	ZHONG Sheng, HUANG Juanjuan, LI Taijun, Tian Xin, XIA Yi, FAN Xuefeng, SONG Wenqin. ±800 kV UHVDC Planning Operation Curve from Jiuquan to Hunan [J]. ELECTRIC POWER CONSTRUCTION, 2015, 36(9): 96-102.
[8]	ZHOU Shucan，GENG Guangchao，JIANGQuanyuan，WEI Luping，ZHAN Zhenbin. Coordinated Voltage Control Strategy for Wind Farm Clusters Under Extreme Wind Conditions [J]. ELECTRIC POWER CONSTRUCTION, 2015, 36(3): 7-14.
[9]	LU Siyu, WANG Tong, JIN Xiaoming, ZHOU Baorong . Impacts of West-East Power Transmission Characteristics on Renewable Energy Acceptability in China Southern Power Grid [J]. ELECTRIC POWER CONSTRUCTION, 2015, 36(10): 53-59.

Timing Planning for Investment of Wind Farm Clusters According to Copula-UCVaR Risk Measurement

PDF

Knowledge

Abstract

Cite this article

share this article

References ［22］

Related Articles 9

Recommended Articles

Metrics

Comments