计及需求响应的分布式光伏集群承载能力评估

doi:10.12204/j.issn.1000-7229.2023.02.012

电力建设 ›› 2023, Vol. 44 ›› Issue (2): 122-130.doi: 10.12204/j.issn.1000-7229.2023.02.012

计及需求响应的分布式光伏集群承载能力评估

于昊正¹, 赵寒杰², 李科¹, 朱星旭², 皇甫霄文¹, 樊江川¹, 李翠萍², 李军徽²()

1.国网河南省电力公司经济技术研究院,郑州市 450000
2.现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学), 吉林省吉林市 132012

收稿日期:2022-04-12 出版日期:2023-02-01 发布日期:2023-01-30
通讯作者: 李军徽(1976),男,博士,教授,主要研究方向为新能源发电联网运行关键技术和储能技术应用等,E-mail:lijunhui@neepu.edu.cn。
作者简介:于昊正(1992),男,硕士,工程师,主要研究方向为配电网规划、新型电力系统建设等方面;
赵寒杰(1997),男,硕士研究生,主要研究方向为配电网分布式光伏消纳及储能优化调度;
李科(1987),男,硕士,高级工程师,主要研究方向为电网规划;
朱星旭(1989),男,博士,讲师,主要研究方向为电力系统运行与控制;
皇甫霄文(1995),男,硕士,工程师,主要研究方向为配电网规划、分布式电源;
樊江川(1986),男,硕士,高级工程师,主要研究方向为电网规划;
李翠萍(1982),女,博士,副教授,主要研究方向为新能源发电联网运行关键技术和储能技术应用等。
基金资助:
国网河南经济技术研究院2021年成本项目“高比例分布式电源与中压配电网协同发展措施研究”(SGHAYJOOGHJS2100075);吉林省自然科学基金联合基金项目资助(YDZJ202101ZYTS152)

Carrying Capacity Evaluation of Distribution Network for Distributed Photovoltaic Cluster Considering User-Side Demand Response

YU Haozheng¹, ZHAO Hanjie², LI Ke¹, ZHU Xingxu², HUANGFU Xiaowen¹, FAN Jiangchuan¹, LI Cuiping², LI Junhui²()

1. State Grid Henan Economic Research Institute, Zhengzhou 450000 China
2. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education(Northeast Electric Power University),Jilin 132012, Jilin Province, China

Received:2022-04-12 Online:2023-02-01 Published:2023-01-30
Supported by:
project ‘Coordinated Development of Distribution Network Acceptance and Planning under High Proportion of Distributed Generation’(SGHAYJOOGHJS2100075);Jilin Provincial Natural Science Foundation Joint Fund Project(YDZJ202101ZYTS152)

摘要/Abstract

摘要：

针对传统配电网分布式光伏承载能力评估方法中未考虑光伏集群的问题,构建了一种以光伏集群为评估单元的承载能力双层评估模型。模型外层通过价格弹性系数描述负荷需求变化,以售电商收益最大为目标优化分时电价,采用粒子群优化(particle swarm optimization,PSO)算法进行求解。内层以不往主网反送电作为指标,考虑电压偏差和设备热稳定约束,提出灵敏度排序方法求取光伏承载能力。在我国某县域实际配电网中求取了配电网光伏集群的承载能力,验证了所提模型和算法的有效性。

关键词: 配电网, 分布式光伏, 用户侧需求响应, 承载力, 集群

Abstract:

Aiming at the problem that photovoltaic clusters are not considered in the traditional evaluation method of carrying capacity of distribution network for distributed photovoltaic power, a two-layer model of carrying capacity with photovoltaic clusters as the evaluation unit is constructed. In the outer layer of the model, PSO algorithm is used to describe the change of load demand through price elasticity coefficient, and the time-of-use price is optimized with the goal of maximizing the income of E-seller. In the inner layer, taking no reverse power transmission to the main network as the index, considering the voltage deviation and equipment thermal stability constraints, a sensitivity ranking method is proposed to calculate the carrying capacity for photovoltaic power. In the actual distribution network of a county in China, the impact of a single photovoltaic cluster on the carrying capacity of the distribution network is calculated, and the carrying capacity of the distribution network for the photovoltaic cluster is calculated to verify the effectiveness of the proposed model and algorithm.

Key words: distribution network, distributed photovoltaic power, demand response, carring capacity, cluster

中图分类号:

TM721

于昊正, 赵寒杰, 李科, 朱星旭, 皇甫霄文, 樊江川, 李翠萍, 李军徽. 计及需求响应的分布式光伏集群承载能力评估[J]. 电力建设, 2023, 44(2): 122-130.

YU Haozheng, ZHAO Hanjie, LI Ke, ZHU Xingxu, HUANGFU Xiaowen, FAN Jiangchuan, LI Cuiping, LI Junhui. Carrying Capacity Evaluation of Distribution Network for Distributed Photovoltaic Cluster Considering User-Side Demand Response[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(2): 122-130.

图/表 20

图1 分布式光伏高比例接入的电网结构示意图

Fig.1 Structure schematic of power grid with high proportion of distributed photovoltaic power access

图2 分布式光伏承载能力影响因素

Fig.2 Influencing factors of carrying capacity for distributed photovoltaic power

图3 分布式光伏承载能力双层模型

Fig.3 Two-layer model of carrying capacity for distributed photovoltaic power

图4 分布式光伏承载能力双层模型流程

Fig.4 Flow chart of two-layer model of carrying capacity for distributed photovoltaic power

图5 基于PSO算法的分时电价优化流程

Fig.5 Flow chart of TOU price optimization based on PSO algorithm

图6 极限承载能力计算流程

Fig.6 Flow chart of ultimate carrying capacity

图7 算例电网拓扑

Fig.7 Topology of example power grid

表1 分时电价参数

Table 1 Time-of-use price parameter

表2 价格弹性矩阵参数

Table 2 Parameter table of price elasticity matrix

图8 分时电价优化前后负荷

Fig.8 Load before and after TOU price optimization

表3 收益对比表

Table 3 Income comparison statement

图9 灵敏度排序调节过程图

Fig.9 Process for sensitivity sorting adjustment

图10 分时电价优化前后各集群光伏承载力

Fig.10 Carrying capacity of each PV cluster before and after TOU optimization

图11 不同电价补贴下承载力评估结果

Fig.11 Load carrying evaluation results under different electricity price subsidies

图12 各场景电压幅值

Fig.12 Voltage amplitudes in different scenarios

图13 各场景线路负载率

Fig.13 Reverse load rate of each scenario

图14 光伏集群对配电网承载力灵敏度

Fig.14 Sensitivity of photovoltaic cluster to distribution network carrying capacity

表4 关键集群灵敏度

Table 4 Sensitivity table key clusters

图15 关键集群与非关键集群功率摄动

Fig.15 Power perturbation of critical and non-critical clusters

图16 集群间承载能力灵敏度

Fig.16 Carrying capacity sensitivity of Inter cluster

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计及需求响应的分布式光伏集群承载能力评估

Carrying Capacity Evaluation of Distribution Network for Distributed Photovoltaic Cluster Considering User-Side Demand Response

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