考虑可再生能源不确定性的热电联供型微网随机鲁棒经济调度

doi:10.12204/j.issn.1000-7229.2022.01.003

电力建设 ›› 2022, Vol. 43 ›› Issue (1): 19-28.doi: 10.12204/j.issn.1000-7229.2022.01.003

• 综合多元能源及信息技术的能源互联网规划与运行·栏目主持刘洋副教授、韩富佳博士· • 上一篇下一篇

考虑可再生能源不确定性的热电联供型微网随机鲁棒经济调度

欧阳翰¹^,²(), 吕林¹(), 刘俊勇¹, 高红均¹()

1.四川大学电气工程学院,成都市 610065
2.国网四川省电力公司天府新区供电公司,成都市 610213

收稿日期:2021-04-29 出版日期:2022-01-01 发布日期:2021-12-21
通讯作者: 欧阳翰 E-mail:ouyanghan_scu@163.com;lvlin@scu.edu.cn;gaohongjun@scu.edu.cn
作者简介:吕林(1963),男,博士,教授,硕士生导师,主要研究方向为综合能源系统规划运行等,E-mail: lvlin@scu.edu.cn;
刘俊勇(1963),男,博士,教授,博士生导师,主要研究方向为电力系统经济性分析及电力市场等;
高红均(1989),男,博士,副教授,硕士生导师,主要研究方向为主动配电网规划运行、综合能源系统优化等,E-mail: gaohongjun@scu.edu.cn。
基金资助:
国家重点研发计划资助项目(2019YFE0111500);四川省科技计划资助项目(2020YFH0040);四川省科技计划资助项目(2021YFSY0052)

Stochastic Robust Economic Dispatch of Combined Heat and Power Microgrid Considering Renewable Energy Uncertainty

OUYANG Han¹^,²(), LÜ Lin¹(), LIU Junyong¹, GAO Hongjun¹()

1. College of Electrical Engineering, Sichuan University, Chengdu 610065, China
2. State Grid Sichuan Electric Power Company Tianfu Power Supply Company, Chengdu 610213, China

Received:2021-04-29 Online:2022-01-01 Published:2021-12-21
Contact: OUYANG Han E-mail:ouyanghan_scu@163.com;lvlin@scu.edu.cn;gaohongjun@scu.edu.cn
Supported by:
National Key Research and Development Program of China(2019YFE0111500);Sichuan Science and Technology Program(2020YFH0040);Sichuan Science and Technology Program(2021YFSY0052)

摘要/Abstract

摘要：

为应对可再生能源出力不确定性和传统微网供能形式单一造成经济性较低的问题,提出了多能源微网两阶段随机鲁棒优化模型。模型考虑了电网与热网的网架结构,目标函数旨在最小化最恶劣风电出力场景下的两阶段微网成本,其中包括第一阶段启停成本与第二阶段运行成本。由于第一阶段与第二阶段的决策与优化结果相互影响,因此两阶段优化问题难以直接求解,文章采用线性决策随机鲁棒优化框架对模型求解。首先,应用线性决策方式相关理论对第二阶段进行转化;其次,采用锥化模糊集刻画可再生能源出力的不确定性;最后,将第二阶段的sup-min问题推导为锥优化的min问题,进而与第一阶段的min问题合并,得到能够直接求解的单层锥优化问题,并采用求解器求得最优解。仿真结果验证了所提模型和方法的有效性。

关键词: 可再生能源, 区域热电网, 随机鲁棒, 锥化模糊集

Abstract:

In order to deal with the problem of low economy caused by the uncertainty of renewable energy output and the single energy supply form of traditional microgrid, this paper proposes a two-stage stochastic robust optimization model for multi-energy microgrid. The model considers the grid structure of the power grid and the heating network. The objective function is to minimize the two-stage microgrid cost in the worst wind power output scenario, which includes the start and stop costs of the first stage and the operating costs of the second stage. Because the decision-making and optimization results of the first stage and the second stage influence each other, the two-stage optimization problem is difficult to solve directly. This paper uses a stochastic robust optimization framework for linear decision-making to solve the model. Firstly, the related theories of linear decision-making methods are applied to transform the second stage. Secondly, the cone-shaped fuzzy set is used to describe the uncertainty of renewable energy output. Finally, the“sup-min”problem in the second stage is derived as the“min”problem of cone optimization, and then combined with the“min”problem in the first stage to obtain the single-layer cone optimization problem which can be solved directly, and the optimal solution is obtained by using the solver. The simulation results verify the effectiveness of the proposed model and method.

Key words: renewable energy, regional heat and power grid, stochastic robust optimization, conical fuzzy set

中图分类号:

TM73

欧阳翰, 吕林, 刘俊勇, 高红均. 考虑可再生能源不确定性的热电联供型微网随机鲁棒经济调度[J]. 电力建设, 2022, 43(1): 19-28.

OUYANG Han, LÜ Lin, LIU Junyong, GAO Hongjun. Stochastic Robust Economic Dispatch of Combined Heat and Power Microgrid Considering Renewable Energy Uncertainty[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(1): 19-28.

图/表 15

图1 热电联供型微网能量流动示意图

Fig.1 Schematic diagram of energy flow in a combined heat and power microgrid

图2 多能源微网系统构架

Fig.2 System architecture of multi-energy microgrid

图3 风电、光伏预测出力

Fig.3 Forecast of wind power and photovoltaic output

图4 负荷需求量

Fig.4 Load demand

表1 算例基础参数

Table 1 Basic parameters of the example

表2 EES基础参数

Table 2 EES basic parameters

表3 管道水流速度

Table 3 Pipeline flow rate

表4 机组基础参数

Table 4 Basic parameters of units

表5 各时段购电电价

Table 5 Electricity purchase price by period

图5 日前调度方案

Fig.5 Day-ahead schedule

图6 有无热网网络模型CHP和EB出力对比

Fig.6 Comparison of CHP and EB output of model with or without heat network

表6 含/未含热网的运行成本比较

Table 6 Comparison of operating costs with or without the consideration of heat network

图7 不同方案经济性对比

Fig.7 Economic comparison of different schemes

表7 不同方案的经济性

Table 7 Economics of different schemes

表8 不同历史数据对比

Table 8 Comparison of different historical data

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考虑可再生能源不确定性的热电联供型微网随机鲁棒经济调度

Stochastic Robust Economic Dispatch of Combined Heat and Power Microgrid Considering Renewable Energy Uncertainty

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