考虑风光不确定性的电气互联虚拟电厂近零碳调度优化模型

doi:10.12204/j.issn.1000-7229.2020.09.002

电力建设 ›› 2020, Vol. 41 ›› Issue (9): 10-19.doi: 10.12204/j.issn.1000-7229.2020.09.002

• 虚拟电厂 ·栏目主持胡泽春副教授、刘敦楠副教授、王宣元高级工程师· • 上一篇下一篇

考虑风光不确定性的电气互联虚拟电厂近零碳调度优化模型

李红霞¹, 樊伟², 李楠¹, 谭彩霞², 许德操³, 鞠立伟²

1.国网青海省电力公司清洁能源发展研究院,西宁市 810008
2.华北电力大学经济管理学院,北京市 102206
3.国网青海省电力公司,西宁市 810008

收稿日期:2020-04-17 出版日期:2020-09-01 发布日期:2020-09-03
通讯作者: 鞠立伟
作者简介:李红霞(1974),女,硕士,高级工程师,主要研究方向为电力规划设计、能源电力经济、能源发展研究;|樊伟(1995),男,硕士研究生,主要研究方向为技术经济及管理;|李楠(1990),男,博士,工程师,主要研究方向为能源电力规划;|谭彩霞(1998),女,硕士研究生,主要研究方向为电力市场、综合能源系统;|许德操(1981),男,硕士,高级工程师,主要研究方向为电网规划。
基金资助:
2018教育部哲学社会科学研究重点项目(18JZD032);国家自然科学基金项目(71573084)

Near-Zero Carbon Dispatching Optimization Model of Virtual Power Plant Connected with Power-to-Gas Considering Uncertainties of Wind and Photovoltaic Power

LI Hongxia¹, FAN Wei², LI Nan¹, TAN Caixia², XU Decao³, JU Liwei²

1. Green Energy Development Research Institute, State Grid Qinghai Electric Power Company, Xining 810008,China
2. School of Economics and Management, North China Electric Power University, Beijing 102206, China
3. State Grid Qinghai Electric Power Company,Xining 810008,China

Received:2020-04-17 Online:2020-09-01 Published:2020-09-03
Contact: JU Liwei
Supported by:
National Natural Science Foundation of China(71573084)

摘要/Abstract

摘要：

风电、光伏出力的不确定性给电力系统的稳定运行带来威胁,含电转气(power-to-gas,P2G)的电气耦合系统能够解决这一难题。文章将电转气与虚拟电厂(virtual power plant,VPP)进行集成,提出电气互联虚拟电厂的基本概念、通用建模及调度优化模型。针对风光不确定性,引入条件风险价值(conditional value at risk,CVaR)方法和鲁棒随机优化方法,分别用于刻画目标函数和约束条件中的不确定性变量,并引入最大碳排放限额(maximum total emission allowances,MTEA)指标,考虑虚拟电厂近零碳运营方案可行性。为求解多目标优化模型,构建了基于目标函数投入产出表和权重偏差校核的模型求解算法,并选取9节点能源集线器系统开展实例分析,结果表明电气互联虚拟电厂能实现分布式能源互补利用,形成电-气-电循环利用模式,所提模型能够同时兼顾分布式能源的高额经济收益和不确定性风险。

关键词: 虚拟电厂(VPP), 电转气(P2G), 低碳, 条件风险价值(CVaR), 鲁棒随机优化, 不确定性

Abstract:

The uncertainty of wind and photovoltaic power output poses a threat to the stable operation of power system, and the power-gas coupling system including power to gas (P2G) can solve this problem. The paper integrates power to gas into virtual power plant (VPP), presents the basic concepts, general modeling, and dispatching optimization model of virtual power plant connected with power-to-gas. In terms of the uncertainty of wind and photovoltaic power, the conditional value-at-risk (CVaR) method and robust stochastic optimization method are introduced to characterize the uncertain variables in the objective function and constraint conditions, respectively. Then, the maximum total emission allowances (MTEA) is applied to achieve the near-zero carbon dispatching scheme of virtual power plant. To solve the multi-objective model, a model solving algorithm based on the input-output table of objective function and the check of weight deviation is constructed, and a 9-node energy hub system is selected for example analysis. The results show that: the virtual power plant connected with power-to-gas can realize the complementary utilization of distributed energy and form the electricity-gas-electricity recycling mode. The proposed model can take into account the high economic benefits and uncertainty risks of distributed energy.This work is supported by National Natural Science Foundation of China (No.71573084).

Key words: virtual power plant(VPP), power-to-gas(P2G), low carbon, conditional value at risk(CVaR), robust stochastic optimization, uncertainty

中图分类号:

TM73

李红霞, 樊伟, 李楠, 谭彩霞, 许德操, 鞠立伟. 考虑风光不确定性的电气互联虚拟电厂近零碳调度优化模型[J]. 电力建设, 2020, 41(9): 10-19.

LI Hongxia, FAN Wei, LI Nan, TAN Caixia, XU Decao, JU Liwei. Near-Zero Carbon Dispatching Optimization Model of Virtual Power Plant Connected with Power-to-Gas Considering Uncertainties of Wind and Photovoltaic Power[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(9): 10-19.

图/表 12

图1 电气互联虚拟电厂结构

Fig.1 Basic structure of GVPP

图2 P2G技术原理

Fig.2 P2G technical schematic diagram

表1 投入收益表

Table 1 The input income table

目标函数	y₁	y₂	…	y_I
y₁	y₁₁	y₁₂	…
y₂	y₂₁	…	…
?	?	?	?
y_I	$y I 1$	$y I 2$	…	y_II
$y I + 1$	$y I + 1,1$	$y I + 1,2$	…	$y I + 1, I$

表1 投入收益表

Table 1 The input income table

目标函数	y₁	y₂	…	y_I
y₁	y₁₁	y₁₂	…
y₂	y₂₁	…	…
?	?	?	?
y_I	$y I 1$	$y I 2$	…	y_II
$y I + 1$	$y I + 1,1$	$y I + 1,2$	…	$y I + 1, I$

图3 9节点能源集线器系统

Fig.3 9-node energy hub system

图4 典型日的负荷、WPP和PV的预测值

Fig.4 Forecast values of load,WPP and PV of the typical day

图5 权重系数和目标函数的变化趋势

Fig.5 The trend of weight coefficient and objective function

表2 不同情景下GVPP的调度优化结果

Table 2 Dispatching results of GVPP operation in different cases

图6 情景4下的PGST的CH4能量流

Fig.6 CH4 flow of PGST in Case 4

图7 不同鲁棒系数和置信度下收益和CVaR值

Fig.7 Value of revenue and CVaR in different confidence degree and robust coefficients

图8 不同GST容量下的WPP和PV出力分布

Fig.8 Output distribution of WPP,PV in different GST's capacity

图9 不同δMETA下GVPP运营优化结果

Fig.9 Results of GVPP operation in different δMETA

表3 不同方法获取GVPP调度结果对比分析

Table 3 Comparative analysis of dispatching results of GVPP in different methods

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考虑风光不确定性的电气互联虚拟电厂近零碳调度优化模型

Near-Zero Carbon Dispatching Optimization Model of Virtual Power Plant Connected with Power-to-Gas Considering Uncertainties of Wind and Photovoltaic Power

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