基于多阶段随机规划的中国未来电源结构研究

doi:10.12204/j.issn.1000-7229.2022.04.011

电力建设 ›› 2022, Vol. 43 ›› Issue (4): 100-107.doi: 10.12204/j.issn.1000-7229.2022.04.011

基于多阶段随机规划的中国未来电源结构研究

王帅¹(), 王志成²(), 王智冬¹(), 王秀丽²(), 张艳¹(), 张静怡¹(), 辛超山³()

1.国网经济技术研究院有限公司, 北京市 102209
2.西安交通大学电气工程学院, 西安市 710049
3.国网新疆电力有限公司经济技术研究院,乌鲁木齐市 830000

收稿日期:2021-07-22 出版日期:2022-04-01 发布日期:2022-03-24
通讯作者: 王帅 E-mail:wangshuai@chinasperi.sgcc.com.cn;xjtuwzc@stu.xjtu edu.cn;wangzhidong@chinasperi.sgcc.com.cn;xiuliw@mail.xjtu.edu.cn;zhangyan@chinasperi.sgcc.com.cn;zhangjingyi@chinasperi.sgcc.com.cn;13999864263@126.com
作者简介:王志成(1996),男,硕士研究生,主要研究方向为电力系统规划、运行和能源经济学,E-mail: xjtuwzc@stu.xjtu edu.cn;
王智冬(1981),男,博士,教授级高级工程师,主要研究方向为电力系统规划及运行,E-mail: wangzhidong@chinasperi.sgcc.com.cn;
王秀丽(1961),女,教授,博士生导师,主要研究方向为电力系统规划、电力市场等,E-mail: xiuliw@mail.xjtu.edu.cn;
张艳(1983),女,博士,高级工程师,主要研究方向为电网规划、新能源发电等,E-mail: zhangyan@chinasperi.sgcc.com.cn;
张静怡(1991),女,博士,工程师,主要研究方向为电网规划、新能源发电等,E-mail: zhangjingyi@chinasperi.sgcc.com.cn;
辛超山(1989),男,硕士,高级工程师,研究方向为电网规划及建设、电网节能优化等,E-mail: 13999864263@126.com。
基金资助:
国家电网有限公司总部科技项目“高比例新能源电力系统网架形态与演化技术研究”(SGXJJY00GHJS2000091)

Future Power Structure of China Based on Multi-stage Stochastic Planning

WANG Shuai¹(), WANG Zhicheng²(), WANG Zhidong¹(), WANG Xiuli²(), ZHANG Yan¹(), ZHANG Jingyi¹(), XIN Chaoshan³()

1. State Grid Economic and Technological Research Institute Co.,Ltd., Beijing 102209, China
2. School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
3. State Grid Xinjiang Electric Power Company Economic and Technological Research Institute, Urumqi 830000,China

Received:2021-07-22 Online:2022-04-01 Published:2022-03-24
Contact: WANG Shuai E-mail:wangshuai@chinasperi.sgcc.com.cn;xjtuwzc@stu.xjtu edu.cn;wangzhidong@chinasperi.sgcc.com.cn;xiuliw@mail.xjtu.edu.cn;zhangyan@chinasperi.sgcc.com.cn;zhangjingyi@chinasperi.sgcc.com.cn;13999864263@126.com
Supported by:
Science and Technology Project of State Grid Corporation of China(SGXJJY00GHJS2000091)

摘要/Abstract

摘要：

作为“碳达峰,碳中和”目标实现的重要部分,电力系统的低碳化转型始终是中国能源行业的关键问题。为了更好地实现能源资源的优化配置和低碳目标的顺利实现,建立了基于多阶段随机规划和多区域电源结构规划的中国未来电源结构发展规划模型。模型以规划期内总成本的期望值最小为目标,计及电力电量平衡、调峰平衡、区域互联等多方面约束条件,并引入了序贯决策所需的非预期约束条件使得规划结果集在多个不确定场景下分别达到最优,更好地模拟未来发展决策过程。考虑中国未来负荷和用电量增长的不确定性,文章对2020—2060年中国电源装机结构和发电结构进行规划,预测和分析了中国未来电源的发展和演进情况。

关键词: 电源结构, 多阶段随机规划, 不确定性, 序贯决策

Abstract:

As an important part of achieving the goal of “Carbon Neutrality”, the low-carbon transformation of the power system has always been a key issue for China’s energy industry. In order to better realize the optimal allocation of energy resources and the smooth realization of low carbon targets, this paper established a planning model of China’s future power structure applying multi-stage stochastic programming and multi-region power structure planning methodology. The model is objected to minimize the total expected cost during the planning period, and subjected to constraints including load balancing, inter-regional transmission and peak regulation. The proposed model also introduces the non-anticipated constraint which is necessary for the sequential decision making of multi-stage planning, thus achieving the optimal schemes in sequential and better simulating the decision making process. Considering the uncertainty of the future load and electricity consumption growth, this paper optimizes the power capacity structure and power generation structure in China from 2020 to 2060, and forecasts the future power sector development in China.

Key words: power sector structure, multi-stage stochastic planning, uncertainty, sequential decision

中图分类号:

TM73

王帅, 王志成, 王智冬, 王秀丽, 张艳, 张静怡, 辛超山. 基于多阶段随机规划的中国未来电源结构研究[J]. 电力建设, 2022, 43(4): 100-107.

WANG Shuai, WANG Zhicheng, WANG Zhidong, WANG Xiuli, ZHANG Yan, ZHANG Jingyi, XIN Chaoshan. Future Power Structure of China Based on Multi-stage Stochastic Planning[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(4): 100-107.

图/表 10

图1 区域划分及跨区通道示意图

Fig.1 Region division and inter-regional transmission pathway

图2 不同场景下全国负荷及用电量

Fig.2 Load and electricity consumption of China under different scenarios

图3 最小负荷场景下2020—2060年全国电源装机容量结构

Fig.3 Power capacity structure of 2020—2060 under the scenario of minimum load

图4 最大负荷场景下2020—2060年全国电源装机容量结构

Fig.4 Power capacity structure of 2020—2060 under the scenario of maximum load

图5 最小负荷场景下2020—2060年全国电源发电量结构

Fig.5 Power generation structure of 2020—2060 under the scenario of minimum load

图6 最大负荷场景下2020—2060年全国电源发电量结构

Fig.6 Power generation structure of 2020—2060 under the scenario of maximum load

图7 最小负荷场景下2020—2060年全国跨区电力流总量

Fig.7 Total inter-regional transmission capacity of 2020—2060 under the scenario of minimum load

图8 最大负荷场景下2020—2060年全国跨区电力流总量

Fig.8 otal inter-regional transmission capacity of 2020—2060 under the scenario of maximum load

图9 累计总成本轨迹

Fig.9 Cumulative total cost path

图10 发电碳排放轨迹(以2020年为基准)

Fig.10 Carbon emission pathway of power generation from 2020 to 2060(2020 as baseline)

参考文献 24

[1]	CAI W J, WANG C, WANG K, et al. Scenario analysis on CO₂ emissions reduction potential in China’s electricity sector[J]. Energy Policy, 2007, 35(12):6445-6456. doi: 10.1016/j.enpol.2007.08.026 URL
[2]	ZHU L, FAN Y. Optimization of China’s generating portfolio and policy implications based on portfolio theory[J]. Energy, 2010, 35(3):1391-1402. doi: 10.1016/j.energy.2009.11.024 URL
[3]	CHEN Q X, KANG C Q, XIA Q, et al. Preliminary exploration on low-carbon technology roadmap of China’s power sector[J]. Energy, 2011, 36(3):1500-1512. doi: 10.1016/j.energy.2011.01.015 URL
[4]	ZHANG D J, LIU P, MA L W, et al. A multi-period modelling and optimization approach to the planning of China’s power sector with consideration of carbon dioxide mitigation[J]. Computers & Chemical Engineering, 2012, 37:227-247. doi: 10.1016/j.compchemeng.2011.09.001 URL
[5]	ALMANSOORI A, BETANCOURT-TORCAT A. Design optimization model for the integration of renewable and nuclear energy in the United Arab Emirates’ power system[J]. Applied Energy, 2015, 148:234-251. doi: 10.1016/j.apenergy.2015.03.068 URL
[6]	杨悦, 陈仕军, 杨博宇, 等. 基于环境和经济双重约束的电源结构优化研究[J]. 四川电力技术, 2021, 44(2):24-27.
	YANG Yue, CHEN Shijun, YANG Boyu, et al. Research on power supply structure optimization based on environment and economy constraints[J]. Sichuan Electric Power Technology, 2021, 44(2):24-27.
[7]	孙菊. 考虑碳排放和调峰能力约束的电源结构演化路径研究[D]. 济南: 山东大学, 2021.
	SUN Ju. Research on evolution path of power supply structure considering constraints of carbon emission and peak regulation ability[D]. Jinan: Shandong University, 2021.
[8]	XIE B C, FAN Y, QU Q Q. Does generation form influence environmental efficiency performance? An analysis of China’s power system[J]. Applied Energy, 2012, 96:261-271. doi: 10.1016/j.apenergy.2011.11.011 URL
[9]	CHENG R, XU Z F, LIU P, et al. A multi-region optimization planning model for China’s power sector[J]. Applied Energy, 2015, 137:413-426. doi: 10.1016/j.apenergy.2014.10.023 URL
[10]	GUO Z, CHENG R, XU Z F, et al. A multi-region load dispatch model for the long-term optimum planning of China’s electricity sector[J]. Applied Energy, 2017, 185:556-572. doi: 10.1016/j.apenergy.2016.10.132 URL
[11]	GUO Z, MA L W, LIU P, et al. A multi-regional modelling and optimization approach to China’s power generation and transmission planning[J]. Energy, 2016, 116:1348-1359. doi: 10.1016/j.energy.2016.06.035 URL
[12]	YI B W, XU J H, FAN Y. Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: A multi-region bottom-up optimization model[J]. Applied Energy, 2016, 184:641-658. doi: 10.1016/j.apenergy.2016.11.021 URL
[13]	WANG H, NAKATA T. Analysis of the market penetration of clean coal technologies and its impacts in China’s electricity sector[J]. Energy Policy, 2009, 37(1):338-351. doi: 10.1016/j.enpol.2008.09.045 URL
[14]	KOLTSAKLIS N E, DAGOUMAS A S, KOPANOS G M, et al. A spatial multi-period long-term energy planning model: A case study of the Greek power system[J]. Applied Energy, 2014, 115:456-482. doi: 10.1016/j.apenergy.2013.10.042 URL
[15]	QADRDAN M, WU J Z, JENKINS N, et al. Operating strategies for a GB integrated gas and electricity network considering the uncertainty in wind power forecasts[J]. IEEE Transactions on Sustainable Energy, 2014, 5(1):128-138. doi: 10.1109/TSTE.2013.2274818 URL
[16]	ZHANG X P, CHE L, SHAHIDEHPOUR M, et al. Electricity-natural gas operation planning with hourly demand response for deployment of flexible ramp[J]. IEEE Transactions on Sustainable Energy, 2016, 7(3):996-1004.
[17]	DING T, HU Y, BIE Z H. Multi-stage stochastic programming with nonanticipativity constraints for expansion of combined power and natural gas systems[J]. IEEE Transactions on Power Systems, 2018, 33(1):317-328. doi: 10.1109/TPWRS.2017.2701881 URL
[18]	姚力, 王秀丽, 肖汉, 等. 基于多场景随机规划的电力系统生产模拟方法[J]. 电力建设, 2016, 37(12):74-81.
	YAO Li, WANG Xiuli, XIAO Han, et al. Production simulation method of power system based on multi-scenario stochastic programming[J]. Electric Power Construction, 2016, 37(12):74-81.
[19]	宋云超, 王丹, 何伟, 等. 基于场景构建技术的含多种清洁能源微能源网多目标随机规划研究[J]. 电力系统保护与控制, 2021, 49(3):20-31.
	SONG Yunchao, WANG Dan, HE Wei, et al. Research on multi-objective stochastic planning of a micro energy grid with multiple clean energy sources based on scenario construction technology[J]. Power System Protection and Control, 2021, 49(3):20-31.
[20]	王志成, 王秀丽, 王帅. 考虑实时市场平衡费用的含风电日前市场电能-备用联合出清模型[J]. 中国电力, 2020, 53(9):19-27.
	WANG Zhicheng, WANG Xiuli, WANG Shuai. Day-ahead market energy-reserve joint clearing model considering real-time market balancing cost under participation of wind power generation[J]. Electric Power, 2020, 53(9):19-27.
[21]	韩照洋, 周琳, 刘硕, 等. 考虑不确定性的综合能源系统日前市场申报策略[J]. 电力建设, 2021, 42(4):121-131.
	HAN Zhaoyang, ZHOU Lin, LIU Shuo, et al. Day-ahead market offering strategy in integrated energy system considering uncertainty[J]. Electric Power Construction, 2021, 42(4):121-131.
[22]	GROWE-KUSKA N, HEITSCH H, ROMISCH W. Scenario reduction and scenario tree construction for power management problems[C]// 2003 IEEE Bologna Power Tech Conference Proceedings. Bologna, Italy. IEEE.
[23]	丁涛, 李澄, 胡源, 等. 考虑非预期条件的电力系统多阶段随机规划建模理论与方法[J]. 电网技术, 2017, 41(11):3566-3573.
	DING Tao, LI Cheng, HU Yuan, et al. Multi-stage stochastic programming for power system planning considering nonanticipative constraints[J]. Power System Technology, 2017, 41(11):3566-3573.
[24]	张晓辉. 含风电的电力系统动态经济调度研究[D]. 秦皇岛: 燕山大学, 2015.
	ZHANG Xiaohui. Research on dynamic economic dispatch of power system with wind power[D]. Qinhuangdao: Yanshan University, 2015.

基于多阶段随机规划的中国未来电源结构研究

Future Power Structure of China Based on Multi-stage Stochastic Planning

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