促进间歇性可再生能源发电消纳的水电定价与能量管理策略

doi:10.12204/j.issn.1000-7229.2021.03.013

电力建设 ›› 2021, Vol. 42 ›› Issue (3): 107-116.doi: 10.12204/j.issn.1000-7229.2021.03.013

促进间歇性可再生能源发电消纳的水电定价与能量管理策略

王一铮¹, 李忠憓¹, 黄晨宏², 胡亦玺³, 郑真², 马小丽², 颜华敏², 文福拴¹

1.浙江大学电气工程学院,杭州市 310027
2.国网上海青浦供电公司,上海市 201700
3.浙江省能源业联合会,杭州市 310012

收稿日期:2020-07-20 出版日期:2021-03-01 发布日期:2021-03-17
通讯作者: 文福拴
作者简介:王一铮 (1997),男,硕士研究生,主要从事综合能源系统和能源管理等方面的研究工作|李忠憓 (1996),女,硕士研究生,主要从事电力市场、电力经济和电动汽车等方面的研究工作|黄晨宏 (1969),男,学士,高级工程师,主要从事电力管理工作|胡亦玺(1961),男,学士,高级会计师,主要从事电力市场运营管理、电力体制改革以及能源方面政策研究等工作|郑真 (1990),男,硕士,工程师,主要从事电网规划管理方面的研究与实践工作|马小丽 (1972),女,学士,高级工程师,主要从事电网规划管理工作|颜华敏 (1981),男,硕士,高级工程师,主要从事电网规划管理工作
基金资助:
国家自然科学基金项目(U1910216)

Hydropower Pricing and Energy Management Strategies for Promoting Accommodation of Intermittent Renewable Energy Power Generation

WANG Yizheng¹, LI Zhonghui¹, HUANG Chenhong², HU Yixi³, ZHENG Zhen², MA Xiaoli², YAN Huamin², WEN Fushuan¹

1. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
2. State Grid Shanghai Qingpu Electric Power Supply Company,Shanghai 201700,China
3. Zhejiang Provincial Energy Association, Hangzhou 310012, China

Received:2020-07-20 Online:2021-03-01 Published:2021-03-17
Contact: WEN Fushuan
Supported by:
National Natural Science Foundation of China(U1910216)

摘要/Abstract

摘要：

小水电站是电力系统中重要的灵活性资源,可以促进风电、光伏等间歇性可再生能源 (intermittent renewable energy,IRE)的开发与利用。在此背景下,研究了促进IRE发电消纳的水电定价与能量管理策略。首先,根据小水电站的日调节能力,将其分为统一调度水电站(coordinately-scheduled hydropower station, CHS)和自主调度水电站(independently-scheduled hydropower station,IHS)两类,并基于Stackelberg博弈提出了包含多个主体的水电定价与能量管理框架。然后,构建了双层优化决策模型,上层用于制定针对IHS的水电峰谷电价,并确定CHS的发电出力,决策目标是弃水量与弃风弃光量的加权和最小;下层用于IHS确定各自的发电功率,决策目标是各自的发电收益最大。接着,应用Karush-Kuhn-Tucker条件与强对偶理论方法将所得到的非凸非线性优化问题转化为混合整数线性规划问题,并采用Yalmip/Gurobi求解器求解。最后,用IEEE标准算例系统对所提方法的可行性进行了说明,仿真结果表明所提出的水电定价与能量管理策略可以提升对IRE发电的消纳。

关键词: 间歇性可再生能源发电(IRE), 水电, 能量管理, Stackelberg博弈

Abstract:

Small hydropower stations could contribute to the development and utilization of intermittent renewable energy (IRE) power generation such as wind power and solar power, as a significant flexible resource in a power system. Given this background, hydropower pricing and energy management strategies for promoting accommodation of large amount IRE power generation are addressed. First, small hydropower stations are divided into two classes according to their daily regulation capacity, namely coordinately-scheduled hydropower stations (CHS) and independently-scheduled hydropower stations (IHS), and a framework of hydropower pricing and energy management with multiple entries considered is proposed on the basis of a Stackelberg game. Then, a bi-level optimization model is established, where the peak-valley electricity prices for the IHS as well as the power output of the CHS are determined at the upper level, with an objective of minimizing the curtailment of IRE power generation and hydropower. Each IHS determines its own power output at the lower level, with an objective of maximizing its generation profit, and a non-linear bi-level optimization problem is attained. The optimization problem is transformed into a mixed integer liner programming problem by applying the Karush-Kuhn-Tucker (KKT) optimality condition and the strong duality theory, which can be solved by the Yalmip/Gurobi solver. Finally, case studies based on an IEEE sample system are carried out and simulation results demonstrate that the developed strategies could promote the accommodation of IRE generation.

Key words: intermittent renewable energy (IRE), hydropower, energy management, Stackelberg game

中图分类号:

TM73

王一铮, 李忠憓, 黄晨宏, 胡亦玺, 郑真, 马小丽, 颜华敏, 文福拴. 促进间歇性可再生能源发电消纳的水电定价与能量管理策略[J]. 电力建设, 2021, 42(3): 107-116.

WANG Yizheng, LI Zhonghui, HUANG Chenhong, HU Yixi, ZHENG Zhen, MA Xiaoli, YAN Huamin, WEN Fushuan. Hydropower Pricing and Energy Management Strategies for Promoting Accommodation of Intermittent Renewable Energy Power Generation[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(3): 107-116.

图/表 13

表1 小水电站的分类与特点

Table 1 Classification and characteristics of small hydropower stations

图1 基于Stackelberg博弈的水电定价与能量管理框架

Fig.1 Framework of hydropower pricing and energy management based on the Stackelberg game

图2 水电定价与能量管理的博弈模型及其数学模型

Fig.2 Gaming model and mathematic model of the hydropower pricing and energy management

图3 算例系统

Fig.3 A sample power system

图4 3个IHS在2种场景中的水电峰谷电价

Fig.4 Peak-valley prices of hydropower of 3 IHSs in two scenarios

图5 4种场景中的小水电站发电出力与PVS出力

Fig.5 Power outputs of small hydropower stations and PVSs in four scenarios

表2 两种场景中的水电平均电价与水电标杆电价的比值

Table 2 Ratios of average hydropower prices to the benchmark hydropower price in two scenarios

表3 4种场景中的光伏发电消纳情况

Table 3 Accommodation status of photovoltaic power generation in 4 scenarios

图6 所构建模型的敏感性分析

Fig.6 Sensitivity analysis of the presented model

表B1 小水电站参数

Table B1 Parameters of small hydropower stations

图B1 光伏电站最大有功出力

Fig.B1 Maximum active power output of the photovoltaic power station

图B2 负荷功率与外部电网输入/输出功率上限

Fig.B2 Load power and upper limits of power input/output from/to the external power system

表B2 仿真案例参数

Table B2 Parameters of simulation cases

参考文献 18

[1]	International Renewable Energy Agency. Global Renewables Outlook: Energy transformation 2050 [R]. Abu Dhabi:International Renewable Energy Agency, 2020.
[2]	International Energy Agency. Renewable energy policies in a time of transition [R]. Paris:International Energy Agency, 2018.
[3]	国家能源局. 2019年可再生能源并网运行情况[EB/OL]. [2020-03-06]. http://www.nea.gov.cn/2020-03/06/c_138850234.htm.
[4]	陈启超, 李晖, 王帅, 等. 促进可再生能源消纳的多能互补时序运行模拟技术[J]. 电力建设, 2019,40(7):18-25.
	CHEN Qichao, LI Hui, WANG Shuai, et al. Sequential operation simulation technology for renewable energy accommodation considering multi-energy complementarity[J]. Electric Power Construction, 2019,40(7):18-25.
[5]	黄春雷, 丁杰, 田国良, 等. 大规模消纳风电的常规水电运行方式[J]. 电力系统自动化, 2011,35(23):37-40.
	HUANG Chunlei, DING Jie, TIAN Guoliang, et al. Hydropower operation modes of large-scale wind power grid integration[J]. Automation of Electric Power Systems, 2011,35(23):37-40.
[6]	叶林, 屈晓旭, 么艳香, 等. 风光水多能互补发电系统日内时间尺度运行特性分析[J]. 电力系统自动化, 2018,42(4):158-164.
	YE Lin, QU Xiaoxu, YAO Yanxiang, et al. Analysis on intraday operation characteristics of hybrid wind-solar-hydro power generation system[J]. Automation of Electric Power Systems, 2018,42(4):158-164.
[7]	温正楠, 刘继春. 风光水互补发电系统与需求侧数据中心联动的优化调度方法[J]. 电网技术, 2019,43(7):2449-2460.
	WEN Zhengnan, LIU Jichun. A optimal scheduling method for hybrid wind-solar-hydro power generation system with data center in demand side[J]. Power System Technology, 2019,43(7):2449-2460.
[8]	陆秋瑜, 罗澍忻, 胡伟, 等. 集群风储联合系统广域协调控制及利益分配策略[J]. 电力系统自动化, 2019,43(20):183-191.
	LU Qiuyu, LUO Shuxin, HU Wei, et al. Wide-area coordinated control and benefit assignment strategy of clustering wind-energy storage integrated system[J]. Automation of Electric Power Systems, 2019,43(20):183-191.
[9]	薛友, 李杨, 高滢, 等. 计及风电出力随机特性的电-气综合能源系统随机优化[J]. 电力建设, 2018,39(12):2-12.
	XUE You, LI Yang, GAO Ying, et al. Stochastic optimization in integrated electricity-gas energy systems considering stochastic characteristics of wind poweroutputs[J]. Electric Power Construction, 2018,39(12):2-12.
[10]	苗树敏, 罗彬, 刘本希, 等. 考虑经济性与可靠性的大小水电短期协调消纳模型[J]. 电力系统自动化, 2018,42(8):101-109.
	MIAO Shumin, LUO Bin, LIU Benxi, et al. Short-term coordination model for large and small hydropower consumption considering economy and reliability[J]. Automation of Electric Power Systems, 2018,42(8):101-109.
[11]	张高峰, 郑浩, 刘双全, 等. 适应不同优化模型的水电梯级系统短期调度算法[J]. 电网技术, 2020,44(1):230-237.
	ZHANG Gaofeng, ZHENG Hao, LIU Shuangquan, et al. Short-term scheduling algorithm of hydropower cascade system adapted to different optimization models[J]. Power System Technology, 2020,44(1):230-237.
[12]	张利升, 武新宇, 曹瑞, 等. 多受端梯级水电站厂网多目标协调优化调度模型[J]. 电网技术, 2018,42(12):3935-3940.
	ZHANG Lisheng, WU Xinyu, CAO Rui, et al. Multi-objective station-grid coordinated operation model for cascade hydropower systems with multiple power receiving regions[J]. Power System Technology, 2018,42(12):3935-3940.
[13]	李媛, 冯昌森, 文福拴, 等. 含电动汽车和电转气的园区能源互联网能源定价与管理[J]. 电力系统自动化, 2018,42(16):1-10.
	LI Yuan, FENG Changsen, WEN Fushuan, et al. Energy pricing and management for park-level energy internets with electric vehicles and power-to-gas devices[J]. Automation of Electric Power Systems, 2018,42(16):1-10.
[14]	顾洁, 白凯峰, 时亚军. 基于多主体主从博弈优化交互机制的区域综合能源系统优化运行[J]. 电网技术, 2019,43(9):3119-3134.
	GU Jie, BAI Kaifeng, SHI Yajun. Optimized operation of regional integrated energy system based on multi-agent master-slave game optimization interaction mechanism[J]. Power System Technology, 2019,43(9):3119-3134.
[15]	栗然, 党磊, 董哲, 等. 分时电价与风储联合调度协调优化的主从博弈模型[J]. 电网技术, 2015,39(11):3247-3253. doi: 10.13335/j.1000-3673.pst.2015.11.035 URL
	LI Ran, DANG Lei, DONG Zhe, et al. Coordinated optimization of time-of-use price and dispatching model combining wind power and energy storage under guidance of master-slave game[J]. Power System Technology, 2015,39(11):3247-3253. doi: 10.13335/j.1000-3673.pst.2015.11.035 URL
[16]	王一铮, 庞凯元, 文福拴, 等. 促进用户侧能源转型的区域能源定价与管理策略[J]. 电力系统自动化, 2020,44(16):21-29.
	WANG Yizheng, PANG Kaiyuan, WEN Fushuan, et al. Regional energy pricing and management strategies for promoting user-side energy transition[J]. Automation of Electric Power Systems, 2020,44(16):21-29.
[17]	ZHOU H S, LI Z G, ZHENG J H, et al. Robust scheduling of integrated electricity and heating system hedging heating networkuncertainties[J]. IEEE Transactions on Smart Grid, 2020,11(2):1543-1555. doi: 10.1109/TSG.5165411 URL
[18]	YAZDANI-DAMAVANDI M, NEYESTANI N, SHAFIE-KHAH M, et al. Strategic behavior of multi-energy players in electricity markets as aggregators of demand side resources using a Bi-level approach[J]. IEEE Transactions on Power Systems, 2018,33(1):397-411. doi: 10.1109/TPWRS.2017.2688344 URL

促进间歇性可再生能源发电消纳的水电定价与能量管理策略

Hydropower Pricing and Energy Management Strategies for Promoting Accommodation of Intermittent Renewable Energy Power Generation

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