一种计及光伏发电功率不确定性的实时调度方法

doi:10.3969/j.issn.1000-7229.2019.10.001

电力建设 ›› 2019, Vol. 40 ›› Issue (10): 1-8.doi: 10.3969/j.issn.1000-7229.2019.10.001

• 促进高比例新能源消纳的光伏发电功率与负荷预测·栏目主持王飞教授· • 上一篇下一篇

一种计及光伏发电功率不确定性的实时调度方法

周瑜1, 杨明1，王飞2，王士柏3，朱毅4

1. 电网智能化调度与控制教育部重点实验室（山东大学），济南市 250061；2. 新能源电力系统国家重点实验室（华北电力大学），河北省保定市 071003；3. 国网山东省电力公司电力科学研究院，济南市 250002；4.国网山东省电力公司经济技术研究院，济南市 250021

出版日期:2019-10-01
作者简介:周瑜（1996），男，硕士研究生，主要研究方向为电力系统经济调度；杨明（1980），男，教授，博士生导师，通信作者，主要研究方向为电力系统优化调度、电力系统运行与控制；王飞（1973），男，博士，教授，主要研究方向为智能电网与新能源电力系统，包括光伏发电功率预测、智能电网能量管理与运行控制；王士柏（1987），男，博士，工程师，主要研究方向为电力系统及其自动化；朱毅（1978），男，硕士，工程师，主要研究方向为电力系统及其自动化。
基金资助:
国家电网公司科技项目（52060018000X）

A Real-Time Dispatch Model Considering the Uncertainty in Photovoltaic Power

ZHOU Yu1, YANG Ming1 ,WANG Fei2 ,WANG Shibo3，ZHU Yi4

1.Key Laboratory of Power System Intelligent Dispatch and Control, Ministry of Education （Shandong University）, Jinan 250061, China；2. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources（ North China Electric Power University）, Baoding 071003, Hebei Province, China；3. State Grid Shandong Electric Power Research Institute, Jinan 250002, China；4. Economic & Technology Research Institute， State Grid Shandong Electric Power Company, Jinan 250021, China

Online:2019-10-01
Supported by:
This work is supported by State Grid Corporation of China Research Program （No. 52060018000X）.

摘要/Abstract

摘要： 大规模光伏发电注入电力系统，其功率的不确定性给实时调度带来了挑战，进而对光伏发电功率和负荷预测的精度提出了更高的要求。针对此问题，文章提出一种计及光伏发电功率不确定性的实时调度方法。首先，基于实时调度的有效稳态安全区域法和线性潮流模型，提出一种计及电压幅值的实时调度模型；其次，通过鲁棒优化等方式将原问题转化为确定性的线性规划问题求解；然后，根据所提方法，分析光伏发电功率和负荷预测精度对于电力系统实时调度安全性和经济性的影响；最后，通过改进IEEE 9节点系统和IEEE 118节点系统进行算例分析，说明所提方法的有效性和高效性。

关键词: 光伏发电, 功率预测, 电压幅值, 实时调度, 安全性, 经济性

Abstract: When large-scale photovoltaic power generation is injected into power system, the uncertainty of its power output brings new challenges to real-time dispatch, and further puts forward higher requirements for the accuracy of photovoltaic power prediction. To solve this problem, a real-time scheduling method considering the power uncertainty of photovoltaic power generation is proposed in this paper. Firstly, on the basis of the effective steady-state security region method and linear power flow model of real-time dispatching, a real-time dispatching model considering voltage amplitude is proposed in this paper. Secondly, the original problem is transformed into a deterministic linear programming problem by robust optimization. Finally, according to the proposed method, effect of the accuracy of photovoltaic power and load forecasting on the security and economy of power system real-time dispatching is analyzed. In this paper, the effectiveness and efficiency of the method are illustrated by an example of modified IEEE 9-node system and the IEEE 118-node system.

Key words: photovoltaic generation, power prediction, voltage magnitude, real-time dispatch, security, economy

中图分类号:

TM 73

周瑜, 杨明，王飞，王士柏，朱毅. 一种计及光伏发电功率不确定性的实时调度方法[J]. 电力建设, 2019, 40(10): 1-8.

ZHOU Yu, YANG Ming,WANG Fei,WANG Shibo，ZHU Yi. A Real-Time Dispatch Model Considering the Uncertainty in Photovoltaic Power[J]. Electric Power Construction, 2019, 40(10): 1-8.

参考文献

［1］ ZHOU A P, YANG M, WANG Z Y, et al. A linear solution method of generalized robust chance constrained real-time dispatch［J］. IEEE Transactions on Power Systems, 2018, 33（6）: 7313-7316.
［2］魏路平, 张小白, 卢敏, 等. 日前电力市场环境下的实时调度［J］. 电力系统自动化, 2007, 31（24）: 38-41.
WEI Luping, ZHANG Xiaobai, LU Min, et al. Study and realization of real-time dispatch in day-ahead power market［J］. Automation of Electric Power Systems, 2007, 31（24）: 38-41.
［3］赵唯嘉, 张宁, 康重庆, 等. 光伏发电出力的条件预测误差概率分布估计方法［J］. 电力系统自动化, 2015, 39（16）: 8-15.
ZHAO Weijia, ZHANG Ning, KANG Chongqing, et al. A method of probabilistic distribution estimation of conditional forecast error for photovoltaic power generation［J］. Automation of Electric Power Systems, 2015, 39（16）: 8-15.
［4］DUAN C, FANG W L, JIANG L, et al. Distributionally robust chance-constrained approximate AC-OPF with Wasserstein metric［J］. IEEE Transactions on Power Systems, 2018, 33（5）: 4924-4936.
［5］YANG Z F, ZHONG H W, XIA Q, et al. Solving OPF using linear approximations: Fundamental analysis and numerical demonstration［J］. IET Generation, Transmission & Distribution, 2017, 11（17）: 4115-4125.
［6］MADANI R, SOJOUDI S, LAVAEI J. Convex relaxation for optimal power flow problem: Mesh networks［J］. IEEE Transactions on Power Systems, 2015, 30（1）: 199-211.
［7］SOJOUDI S, LAVAEI J. Physics of power networks makes hard optimization problems easy to solve［C］//2012 IEEE Power and Energy Society General Meeting. New York, USA: IEEE, 2012.
［8］YANG J W, ZHANG N, KANG C Q, et al. A state-independent linear power flow model with accurate estimation of voltage magnitude［J］. IEEE Transactions on Power Systems, 2017, 32（5）: 3607-3617.
［9］AKBARI T, TAVAKOLI BINA M. Linear approximated formulation of AC optimal power flow using binary discretisation［J］. IET Generation, Transmission & Distribution, 2016, 10（5）: 1117-1123.
［10］FATEMI S M, ABEDI S, GHAREHPETIAN G B, et al. Introducing a novel DC power flow method with reactive power considerations［J］. IEEE Transactions on Power Systems, 2015, 30（6）: 3012-3023.
［11］YANG Z F, ZHONG H W, BOSE A, et al. A linearized OPF model with reactive power and voltage magnitude: A pathway to improve the MW-only DC OPF［J］. IEEE Transactions on Power Systems, 2018, 33（2）: 1734-1745.
［12］YANG Z F, ZHONG H W, XIA Q, et al. A novel network model for optimal power flow with reactive power and network losses［J］. Electric Power Systems Research, 2017, 144: 63-71.
［13］BERTSIMAS D, LITVINOV E, SUN X A, et al. Adaptive robust optimization for the security constrained unit commitment problem［J］. IEEE Transactions on Power Systems, 2013, 28（1）: 52-63.
［14］JABR R A, KARAKI S M, KORBANE J A. Robust multi-period OPF with storage and renewables［J］. IEEE Transactions on Power Systems, 2015, 30（5）: 2790-2799.
［15］WANG J H, SHAHIDEHPOUR M, LI Z Y. Security-constrained unit commitment with volatile wind power generation［J］. IEEE Transactions on Power Systems, 2008, 23（3）: 1319-1327.
［16］RYAN S M, WETS R J B, WOODRUFF D L, et al. Toward scalable, parallel progressive hedging for stochastic unit commitment［C］//2013 IEEE Power & Energy Society General Meeting. New York, USA: IEEE, 2013.
［17］LUBIN M, DVORKIN Y, BACKHAUS S. A robust approach to chance constrained optimal power flow with renewable generation［J］. IEEE Transactions on Power Systems, 2016, 31（5）: 3840-3849.
［18］杨明, 程凤璐, 韩学山. 电力系统实时调度的有效静态安全域法［J］. 中国电机工程学报, 2015, 35（6）: 1353-1362.
YANG Ming, CHENG Fenglu, HAN Xueshan. Real-time dispatch based on effective steady-state security regions of power system［J］. Proceedings of the CSEE, 2015, 35（6）: 1353-1362.
［19］余贻鑫, 陈礼义. 电力系统的安全性和稳定性［M］. 北京: 科学出版社, 1988.
［20］ZHU J Z, FAN R Q, XU G Y, et al. Construction of maximal steady-state security regions of power systems using optimization method［J］. Electric Power Systems Research, 1998, 44（2）: 101-105.
［21］丁明, 王伟胜, 王秀丽, 等. 大规模光伏发电对电力系统影响综述［J］. 中国电机工程学报, 2014, 34（1）: 1-14.
DING Ming, WANG Weisheng, WANG Xiuli, et al. A review on the effect of large-scale PV generation on power systems［J］. Proceedings of the CSEE, 2014, 34（1）: 1-14.
［22］SOYSTER A L. Technical note: Convex programming with set-inclusive constraints and applications to inexact linear programming［J］. Operations Research, 1973, 21（5）: 1154-1157.

[1]	郑惠萍, 曾鹏, 刘新元, 程雪婷, 薄利明, 杨尉薇, 张一帆, 崔杨. 基于误差前馈预测的多时空尺度风电集群有功功率分层控制策略[J]. 电力建设, 2020, 41(8): 120-128.
[2]	陈明庆，李建林. 基于局部电压幅值与云边协同的分散式充电桩充电协调方法[J]. 电力建设, 2020, 41(6): 60-68.
[3]	杨朋朋，张修平，王明强，杨明，王飞，王士柏. 计及调峰能力的风光储联合系统优化调度[J]. 电力建设, 2019, 40(9): 124-130.
[4]	张涛，胡泽春，张丹阳. 楼宇综合能源系统容量配置优化[J]. 电力建设, 2019, 40(8): 3-11.
[5]	孙充勃，宋毅，蔡超，原凯，杨雄. 考虑多场景需求的配电网分布式储能多目标优化配置方法[J]. 电力建设, 2019, 40(7): 48-56.
[6]	杨德友，高子昂，李音璇. 基于双变量经验模态分解和最小二乘支持向量机的风电功率区间预测[J]. 电力建设, 2019, 40(5): 118-127.
[7]	林鸿基，闫园，文福拴，胡嘉骅，曲昊源，赵铮. 高比例可再生能源电力系统中计及灵活调节产品的实时调度[J]. 电力建设, 2019, 40(10): 18-27.
[8]	黄校娟，付蓉，吴英俊，倪明，李满礼. 网络攻击下基于贝叶斯图论的配电系统安全分析[J]. 电力建设, 2019, 40(1): 86-95.
[9]	李大勇1，都明亮2，田春光2，韩晓娟3. 基于总体平均经验模态分解的混合储能系统协调优化控制[J]. 电力建设, 2018, 39(4): 83-.
[10]	彭小圣，樊闻翰，王勃，张涛，文劲宇，邓迪元，熊磊，车建峰. 基于改进空间资源匹配法的风电集群功率预测技术[J]. 电力建设, 2017, 38(7): 10-.
[11]	史佳琪，张建华. 基于深度学习的超短期光伏精细化预测模型研究 [J]. 电力建设, 2017, 38(6): 28-.
[12]	徐科，刘洪，田喆，王博，牛纪德. 区域分布式供能系统经济性概率分析#br#[J]. 电力建设, 2017, 38(2): 138-.
[13]	张倩文，王秀丽，杨廷天，李言，王尚宁. 大用户直购电模式下含大规模风光的电力系统优化调度[J]. 电力建设, 2017, 38(10): 24-.
[14]	章竹耀,肖欣,郭晓丽,姜亚海,顾磊,陈伟峰. 基于储能电池的光伏功率波动平抑策略[J]. 电力建设, 2016, 37(8): 90-.
[15]	张婳，张斌，籍天明，韩晓娟. 基于双向互补的储能系统控制策略及经济性分析 [J]. 电力建设, 2016, 37(8): 96-.

一种计及光伏发电功率不确定性的实时调度方法

A Real-Time Dispatch Model Considering the Uncertainty in Photovoltaic Power

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价