计及碳捕集和电转气的农村虚拟电厂多目标随机调度优化模型

doi:10.12204/j.issn.1000-7229.2022.07.003

电力建设 ›› 2022, Vol. 43 ›› Issue (7): 24-36.doi: 10.12204/j.issn.1000-7229.2022.07.003

• 面向新型电力系统的灵活性资源发展关键技术及应用·栏目主持鞠立伟副教授、谭忠富教授· • 上一篇下一篇

计及碳捕集和电转气的农村虚拟电厂多目标随机调度优化模型

李鹏¹(), 余晓鹏²(), 张艺涵¹(), 周青青³(), 田春筝¹(), 乔慧婷⁴()

1.国网河南省电力公司经济技术研究院,郑州市 450000
2.国网河南省电力公司, 郑州市 450000
3.华北电力大学经济与管理学院,北京市 100026
4.南方电网能源发展研究院有限责任公司技术经济中心,广州市 510530

收稿日期:2022-03-28 出版日期:2022-07-01 发布日期:2022-06-30
作者简介:李鹏(1985),男,博士,高级经济师,主要研究方向为农村能源转型、县域综合能源系统、能源互联网应用技术,E-mail: hdlp0830@163.com;
余晓鹏(1974),男,硕士,教授级高级工程师,主要研究方向为电网规划,E-mail: xmli97@126.com;
张艺涵(1993),女,硕士,工程师,主要研究方向为综合能源系统、协调优化关键技术,E-mail: zhangyihan0310@163.com;
周青青(1995),女,硕士,工程师,主要研究方向为能源经济,E-mail: 120212206367@ncepu.edu.cn
田春筝(1985),男,硕士,教授级高级工程师,主要研究方向农村能源转型、县域综合能源系统,E-mail: tianchunzheng@qq.com;
乔慧婷(1990),女,硕士,高级经济师,主要研究方向为能源经济,E-mail: qiaohuiting@163.com。

Multi-objective Stochastic Scheduling Optimization Model for Rural Virtual Power Plant Considering Carbon Capture and Power-to-Gas

LI Peng¹(), YU Xiaopeng²(), ZHANG Yihan¹(), ZHOU Qingqing³(), TIAN Chunzheng¹(), QIAO Huiting⁴()

1. State Grid Henan Electric Power Company Economic Research Institute, Zhengzhou 450000, China
2. State Grid Henan Electric Power Company, Zhengzhou 450000, China
3. School of Economic and Management,North China Electric Power University,Beijing 102206,China
4. Technical and Economic Center,China Southern Power Grid Energy Development Research Institute Co., Ltd., Guangzhou 510530, China

Received:2022-03-28 Online:2022-07-01 Published:2022-06-30

摘要/Abstract

摘要：

针对农村地区存在大量生物质秸秆、垃圾、屋顶光伏、分散式风电等分布式能源,设计了集成燃气碳捕集设备(gas-power plant carbon capture,GPPCC)、电转气设备(power-to-gas,P2G)及垃圾发电(waste incineration power,WI)的农村虚拟电厂(GPPCC-P2G-WI-based virtual power plant, GPW-VPP)。然后,引入信息间隙理论(information gap decision theory,IGDT)和模糊满意度方法构造GPW-VPP近零碳运营优化模型。其中,选择最大化运营收益和最小化碳排放量作为优化目标,并利用模糊满意度理论转化为综合满意度最优化目标。再然后,利用IGDT描述风电、光伏发电和用户负荷3个不确定性变量影响,用于构造GPW-VPP随机调度优化模型。最后,选择中国兰考能源革命试点为对象开展实例分析验证所提模型的有效性,结果表明所提运营优化模型能在兼顾不同主体利益诉求的同时推进农村分布式能源的最优聚合利用,有利于实现整体能源结构清洁低碳转型。

关键词: 虚拟电厂(VPP), 电转气(P2G), 碳捕集, 优化运行, 不确定性

Abstract:

Aiming at utilizing a large number of distributed energy sources in rural areas such as straw and garbage biomass, rooftop photovoltaic power, and decentralized wind power, this paper designs a novel structure of a virtual power plant connected with gas-power plant carbon capture (GPPCC), power-to-gas (P2G), and waste incineration power (WI), namely, a GPW-VPP. Then, the information gap decision theory (IGDT) and fuzzy satisfaction method are applied to construct a nearly zero-carbon optimal operation model. In this model, maximizing revenue and minimizing carbon emissions are selected as the initial goals of the GPW-VPP operation, which are converted into one maximum satisfaction goal. Three uncertainty variables, namely, wind power, photovoltaic power, and user load, are described using the IGDT. Finally, the Lankao Rural Energy Revolution Pilot program in China is selected as the case study to verify the effectiveness of the proposed model. The results show that the proposed operation optimization model and benefit distribution strategy can take into account the interests of different subjects, and at the same time, promote the optimal aggregation and utilization of rural distributed energy, which is conducive to the realization of a clean and low-carbon transformation of the overall energy structure.

Key words: virtual power plant, power to gas, carbon capture, optimized operation, uncertainty

中图分类号:

TM73

李鹏, 余晓鹏, 张艺涵, 周青青, 田春筝, 乔慧婷. 计及碳捕集和电转气的农村虚拟电厂多目标随机调度优化模型[J]. 电力建设, 2022, 43(7): 24-36.

LI Peng, YU Xiaopeng, ZHANG Yihan, ZHOU Qingqing, TIAN Chunzheng, QIAO Huiting. Multi-objective Stochastic Scheduling Optimization Model for Rural Virtual Power Plant Considering Carbon Capture and Power-to-Gas[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(7): 24-36.

图/表 13

图1 GPW-VPP系统结构

Fig.1 GPW-VPP system structure

图2 垃圾焚烧电厂烟气处理系统结构

Fig.2 Structure of the flue gas treatment system of a waste incineration power plant

表1 不同类型负荷参与需求响应方式

Table 1 Different types of loads participating in demand-response methods

图3 GPPCC碳捕集运行流程

Fig.3 Operation process of GPPCC carbon capture

表2 WPP、PV、WI和BPG设备参数

Table 2 Equipment parameters for WPP, PV, WI, and BPG

图4 典型负荷日风光预测出力及用户负荷需求

Fig.4 WPP, PV forecast output and load demand of different users in a typical load day

图5 不确定度α与预测客观系数σrisk的关系

Fig.5 Relationship between uncertainty degree α and predicted objective coefficients σrisk

表3 不同优化目标调度结果对比分析

Table 3 Comparative analysis of results under different optimization objectives

图6 综合优化目标下的GPW-VPP运行优化结果

Fig.6 Operation optimization results of GPW under comprehensive optimization objectives

图7 不同用户需求响应出力

Fig.7 DR output for different user load

图8 综合运行模式下GPW烟气流量和CO2流量

Fig.8 Flue gas flow and CO2 flow of GPW under the integrated operation mode

表4 不同目标函数下各单元出力结果

Table 4 Output results of each unit under different objective functions

图9 最恶劣场景下GPW-VPP优化运营结果

Fig.9 Optimized operation results of GPW-VPP under the worst scenario

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计及碳捕集和电转气的农村虚拟电厂多目标随机调度优化模型

Multi-objective Stochastic Scheduling Optimization Model for Rural Virtual Power Plant Considering Carbon Capture and Power-to-Gas

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