&nbsp;Local Accommodation Model of Distributed Wind/Photovoltaic Power Based on TOU Power Price Mechanism

LI Peng1; HUA Haorui1; XU Shaojun2; CHANG Qiankun2

doi:10.3969/j.issn.1000－7229.2016.12.014

PDF(790 KB)

Electric Power Construction ›› 2016, Vol. 37 ›› Issue (12) : 104. DOI: 10.3969/j.issn.1000－7229.2016.12.014

Local Accommodation Model of Distributed Wind/Photovoltaic Power Based on TOU Power Price Mechanism

LI Peng, HUA Haorui, XU Shaojun, CHANG Qiankun

Author information +

History +

Abstract

This paper proposes a local accommodation model of distributed wind/photovoltaic power accessed to active distribution network based on time-of-use (TOU) power price mechanism, which is able to increase the accommodation rate of distributed wind/photovoltaic power by giving active distribution network the right to set the TOU power price. Firstly, this paper analyzes the price-based demand response of load under TOU power price mechanism based on load classification. Then, this paper constructs a local accommodation model of distributed wind/photovoltaic power accessed to active distribution network based on TOU power price mechanism, whose objective function is the maximum wind/photovoltaic power accommodation rate while considering several necessary constraints. And this paper adopts simulated annealing algorithm to solve the proposed model. Lastly, the example results show that the proposed method can increase the accommodation rate of wind/photovoltaic power accessed to active distribution network effectively.

Key words

active distribution network(ADN) / distributed wind/photovoltaic / wind/photovoltaic accommodation / demand response / simulated annealing algorithm

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

LI Peng1, HUA Haorui1, XU Shaojun2, CHANG Qiankun2. Local Accommodation Model of Distributed Wind/Photovoltaic Power Based on TOU Power Price Mechanism[J]. Electric Power Construction. 2016, 37(12): 104 https://doi.org/10.3969/j.issn.1000－7229.2016.12.014

References

［1］刘皓明，陆丹，杨波，等. 可平抑高渗透分布式风光发电功率波动的储能电站调度策略［J］. 高电压技术，2015,41(10): 3214-3223.

LIU Haoming, LU Dan, YANG Bo，et al. Dispatch strategy of energy storage station to smooth power fluctuations of high penetration photovoltaic generation［J］. High Voltage Engineering, 2015,41(10): 3214-3223.

［2］白建华，辛颂旭，贾德香，等. 中国风电开发消纳及输送相关重大问题研究［J］. 电网与清洁能源，2010,26(1)：14-17.

BAI Jianhua, XING Songxu, JIA Dexiang，et al. Study of major questions of wind power digestion and transmission in China［J］. Power System and Clean Energy，2010,26(1)：14-17.

［3］赵波，韦立坤，徐志成, 等. 计及储能系统的馈线风光消纳能力随机场景分析［J］. 电力系统自动化，2015，39(9)：33-40.

ZHAO Bo，WEI Likun，XU Zhicheng，et al. Photovoltaic accommodation capacity determination of actual feeder based on stochastic scenarios analysis with storage system considered［J］. Automation of Electric Power Systems，2015,39(9)：33-40.

［4］栾伟杰，蒋献伟，张节潭，等.考虑主动管理的分布式光伏发电消纳能力研究［J］.电力建设，2016，37(1)：137-143.

LUAN Weijie，JIANG Xianwei，ZHANG Jietan，et al. Consumptive ability analysis for distributed photovoltaic generation considering active management［J］. Electric Power Construction,2016，37 (1)：137-143.

［5］郭飞，王智冬，王帅，等. 我国风电消纳现状及输送方式［J］. 电力建设，2014，35( 2) : 18-22.

GUO Fei，WANG Zhidong，WANG Shuai，et al. Consumption situation and transmission modes of wind power in China ［J］.Electric Power Construction，2014，35(2)：18-22.

［6］欧阳聪，刘明波. 考虑网络传输约束的并网风光发电消纳容量计算［J］. 电力系统保护与控制，2016，44(5):17-23.

OUYANG Cong, LIU Mingbo. Computing of accommodation capacity of grid-integrated photovoltaic generation considering network’s transmission constraints［J］. Power System Protection and Control,2016, 44(5):17-23.

［7］汤奕，鲁针针，伏祥运. 居民主动负荷促进分布式电源消纳的需求响应策略［J］. 电力系统自动化，2015，39(24)：49-55.

TANG Yi, LU Zhenzhen, FU Xiangyun. Demand response strategies for promotion accommodation of distrubuted power generation with residential active loads［J］. Automation of Electric Power Systems, 2015,39(24):49-55.

［8］姚天亮，郑海涛，杨德洲. 甘肃河西500万 kW风光就地消纳及调峰分析［J］. 中国电力，2014，47(3)：14-18.

YAO Tianliang, ZHENG Haitao，YANG Dezhou. Analysis on local accommodation and peaking issues of 5000 MW PV in Hexi area of Gansu province［J］. Electric Power,2014,47(3)：14-18.

［9］宁光涛，谢海鹏，别朝红. 海南电网分布式风光消纳能力评估［J］. 南方电网技术，2015，9(5)：59-65.

NING Guangtao, XIE Haipeng, BIE Zhaohong. Evaluation of distributed photovoltaic integration capacity of Hainan power grid［J］. Southern Power System Technology，2015，9(5)：59-65.

［10］余潇潇，张璞，刘兆燕. 北京电网风电发展与消纳能力［J］.电力建设，2015，36(8) : 49-54.

YU Xiaoxiao，ZHANG Pu，LIU Zhaoyan. Development and maximum accommodating capacity of wind power in Beijing power grid［J］. Electric Power Construction, 2015，36(8): 49-54.

［11］曾丹，姚建国，杨胜春，等.应对风电消纳中基于安全约束的价格型需求响应优化调度建模［J］.中国电机工程学报，2014，34(31)：5571-5578.

ZENG Dan，YAO Jianguo，YANG Shengchun，et al .Optimization dispatch modeling for price-based demand response considering security constraints to accommodate the wind power［J］.Proceedings of the CSEE，2014，34(31)：5571-5578.

［12］阮文骏, 王蓓蓓, 李扬. 峰谷分时电价下的用户响应行为［J］. 电网技术, 2012, 36(7): 86-93.

RUAN Wenjun, WANG Beibei, LI Yang. Customer response behavior in time-of-use price［J］. Power System Technology, 2012, 36(7): 86-93.

［13］王成山，郑海峰，谢莹华，等.计及分布式发电的配电系统随机潮流计算［J］.电力系统自动化，2005，29(24)：39-44.

WANG Chengshan，ZHENG Haifeng，XIE Yinghua，et al.Probabilistic power flow containing distributed generation in distribution system［J］.Automation of Electric Power Systems，2005，29(24)：39-44.

［14］ KARAKI S H，CHEDID R B，RAMADAN R.Probabilistic performance assessment of autonomous solar-wind energy conversion systems ［J］.IEEE Transactions on Energy Conversion，1999，14(3)：766-772.

［15］丁伟，袁家海，胡兆光. 基于用户价格响应和满意度的峰谷分时电价决策模型［J］. 电力系统自动化，2005，29(20)：10-14.

DING Wei，YUAN Jiahai，HU Zhaoguang. Time-of-use price decision model considering users reaction and satisfaction index［J］. Automation of Electric Power Systems，2005，29(20)：10-14.

［16］戴雯霞，吴捷.无功功率优化的改进退火选择遗传算法［J］.电网技术，2001，25(11)：33-37.

DAI Wenxia，WU Jie.A modified genetic algorithm with annealing selection for reactive power optimization［J］.Power System Technology, 2001，25(11)：33-37.

［17］刘科研, 盛万兴, 李运华. 基于改进遗传模拟退化算法的无功优化［J］. 电网技术, 2007, 31(3): 13-19.

LIU Keyan, SHENG Wanxing, LI Yunhua. Research on reactive power optimization based on improved genetic simulated annealing algorithm ［J］. Power System Technology, 2007, 31(3): 13-19.

［18］尤毅，刘东，钟清，等.主动配电网优化调度策略研究［J］.电力系统自动化，2014，38(9)：177-183.

YOU Yi，LIU Dong，ZHONG Qing, et al.Research on optimal schedule strategy for active distribution network［J］.Automation of Electric Power Systems,2014，38(9)：177-183.

［19］李鹏，徐伟娜，周泽远，等. 基于改进万有引力搜索算法的微网优化运行［J］. 中国电机工程学报，2014，34（19）：3073-3079.

LI Peng，XU Weina，ZHOU Zeyuan，et al. Optimal operation of microgrid based on improved gravitational search algorithm ［J］. Proceedings of the CSEE，2014，34（19）：3073-3079.