考虑新能源多重相关性的柔性配电网分布鲁棒优化策略

doi:10.11930/j.issn.1004-9649.202409078

摘要/Abstract

摘要：

高比例分布式新能源与全控型柔性电力电子元器件的大量接入，给传统配电网带来更多清洁电能与调控选择的同时，其出力的时空分布不均衡性与海量器件调节的复杂性，亦使得配电网的运行面临严峻挑战。对此，提出了一种考虑新能源多重相关性的柔性配电网分布鲁棒优化策略。首先，以配电网有功损耗与电压偏移最小为目标，推导并构建含多种源网荷协同调控措施的柔性配电网最优潮流模型。然后，计及新能源在时间空间与功率维度上的多重相关性，建立了基于数据驱动的柔性配电网两阶段分布鲁棒优化模型，采用1-范数与∞-范数进行源荷不确定性集合描述，并采用二阶锥将其线性化与凸松弛。最后，采用列与约束生成算法对该模型进行求解，并以改进的IEEE 33节点测试系统为例进行仿真分析，验证了该方法的有效性和实用性。

关键词: 柔性配电网, 新能源, 分布鲁棒优化, 数据驱动, 二阶锥规划, 列与约束生成算法

Abstract:

The integration of a high proportion of distributed renewable energy sources (DRES) and a substantial number of fully controllable flexible power electronic devices has brought more clean electrical energy and control options to traditional distribution networks. However, the uneven temporal and spatial distribution of their output power and the complexity of regulating a vast number of devices pose significant challenges to the operation of distribution networks. In response to this, a distributionally robust optimization strategy for flexible distribution networks, considering the multiple correlations of renewable energy, is proposed. Firstly, aiming to minimize active power loss and voltage deviation in the distribution network, an optimal power flow model for flexible distribution networks is derived and constructed, incorporating various coordinated control measures for sources, grids and loads. Secondly, taking into account the multiple correlations of renewable energy in terms of time, space and power dimensions, a two-stage distributionally robust optimization model for flexible distribution networks is established based on data-driven approaches. The 1-norm and ∞-norm are employed to describe the uncertainty sets of sources and loads, and second-order cones are utilized for linearization and convex relaxation. Finally, the column and constraint generation algorithm is adopted to solve the model, and a simulation analysis is conducted using an improved IEEE 33-bus test system as an example, verifying the effectiveness and practicality of the proposed method.

Key words: flexible distribution network, renewable energy, distributionally robust optimization, data-driven, second-order cone programming, column-and-constraint generation algorithm

刘志伟, 马悦, 沙志成, 邵云姝, 牛远方, 董晓明, 王成福. 考虑新能源多重相关性的柔性配电网分布鲁棒优化策略[J]. 中国电力, 2024, 57(12): 97-108.

Zhiwei LIU, Yue MA, Zhicheng SHA, Yunshu SHAO, Yuanfang NIU, Xiaoming DONG, Chengfu WANG. Distributionally Robust Operation for Flexible Distribution Networks Considering Multi-correlation of Renewable Power Generation[J]. Electric Power, 2024, 57(12): 97-108.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202409078

https://www.electricpower.com.cn/CN/Y2024/V57/I12/97

图/表 14

图 1 H-FMSS接入配电网结构

Fig.1 Installation of FMSS with HESS in a distribution network

图 2 改进的IEEE 33节点测试系统

Fig.2 Improved IEEE 33 bus testing system

图 3 源荷预测出力曲线

Fig.3 Predicted output curve of source and load

图 4 改变β/α值时优化结果对比

Fig.4 Comparison of optimization results with changing β/α values

表 1 是否考虑储氢路由优化结果对比

Table 1 Optimization results comparison of hydrogen storage transfer with and without consideration

是否考虑储氢路由	网损值/kW	电压偏移/p.u.
是	1021.034	2.693
否	1054.447	2.841

表 2 改变βFL取值时优化结果对比

Table 2 Optimization results with changing βFL

β^FL	网损值/kW	电压偏移/p.u.
0.05	1042.718	2.765
0.10	1033.609	2.740
0.15	1028.218	2.719
0.20	1028.173	2.705
0.25	1025.400	2.698
0.30	1021.034	2.693

表 3 不同新能源渗透率对其消纳率的影响

Table 3 The influence of different new energy penetration rate on its absorption rate

渗透率/%		10.7		32.3		53.8		75.4		96.9
消纳率/%		100		100		98.6		97.6		86.1

图 5 3种方案下CB的无功功率输出

Fig.5 Reactive power output of CB under three schemes

表 4 3种方案下的优化结果对比

Table 4 Comparison of optimization results under three schemes

方案	网损值/kW	电压偏移/p.u.
1	620.826	4.251
2	618.363	4.238
3	615.853	4.220

表 5 不同置信水平下优化结果对比

Table 5 Optimization results under different confidence levels

α₁	目标函数值/kW
α₁	α_∞=0.5	α_∞=0.8	α_∞=0.95
0.50	869.866	869.868	869.882
0.80	869.866	869.873	869.888
0.95	869.866	869.886	869.891

表 6 综合范数与∞-范数优化结果对比

Table 6 Optimization results of synthetic and ∞-norm

α₁	目标函数值/kW
α₁	综合范数	∞-范数
0.50	869.868	871.157
0.80	869.873	871.157
0.95	869.873	871.157

表 7 综合范数与1-范数结果对比

Table 7 Optimization results of synthetic and 1-norm

α_∞	目标函数值/kW
α_∞	综合范数	1-范数
0.50	869.867	871.117
0.80	869.873	871.117
0.95	869.888	871.117

表 8 不同历史场景数下优化结果对比

Table 8 Optimization results under different historical scenarios

历史场景数/个		500		1000		2000		5000		10000
目标函数值/kW		879.418		874.348		871.330		869.873		860.636

表 9 不同方法下优化结果对比

Table 9 Optimization results under different methods

方法	系统网损/kW	电压偏移/p.u.
分布式鲁棒	615.853	4.220
随机规划	612.717	4.188
鲁棒优化	639.770	4.275

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