基于目标级联法的多主体主动配电网自治协同优化

doi:10.11930/j.issn.1004-9649.202311008

摘要/Abstract

摘要：

随着“双碳”目标的战略推进，可再生能源在主动配电网（active distribution network，ADN）的大规模消纳提上日程，但受限于调度控制方式和数据交互模式，现有的集中式控制方法无法有效满足分布式能源消纳需求和配电网运行经济性目标。为此，提出了一种基于目标级联法（analytical target cascading，ATC）的主动配电网区域多主体自治协同优化方法，将柔性负荷、分布式电源和储能视为受控单元主体，根据配电网整体的经济性优化目标和微电网区域的局部自治优化需求，设计“ADN主体-节点主体-受控单元主体”的调度框架；并通过ATC处理主体间的共享交互信息，将不同层级的系统解耦为主系统和子系统，达到兼顾整体和局部目标协同优化的效果。最后，通过搭建D9M2和IEEE 33节点配电系统，验证了该方法的有效性。

关键词: 主动配电网, 多主体协同, 目标级联法, 分布式电源, 区域自治

Abstract:

As part of the strategic push toward achieving carbon neutrality, the imperative to integrate renewable energy into active distribution networks (ADNs) on a large scale has become a priority. However, the traditional centralized control methods are hamstrung by dispatch control strategies and data interchange modes, falling short in accommodating the needs for distributed energy assimilation and meeting the economic operation goals of distribution networks. In response, this work introduces an innovative regional multi-agent autonomous collaborative optimization approach for ADNs, grounded in Analytical Target Cascading (ATC). This approach treats flexible loads, distributed energy resources, and storage systems as controllable unit agents. It aligns with the overall economic optimization objectives of the distribution network and the localized autonomous optimization requirements of microgrid domains, framing a dispatch architecture structured around "ADN entity-node entity-controlled unit entity." Leveraging ATC to handle the inter-agent shared interactive information, the approach effectively separates complex system hierarchies into primary systems and subsystems. This separation facilitates a synergistic optimization that respects both the comprehensive and specific regional goals. The method's robustness and efficiency are substantiated through the development and testing within D9M2 and IEEE 33 node distribution systems, demonstrating its validity in practical scenarios.

Key words: active distribution network, multi-agent collaboration, analytical target cascading, distributed generation, regional autonomy

王家武, 赵佃云, 刘长锋, 陈康, 张玉敏. 基于目标级联法的多主体主动配电网自治协同优化[J]. 中国电力, 2024, 57(7): 214-226.

Jiawu WANG, Dianyun ZHAO, Changfeng LIU, Kang CHEN, Yumin ZHANG. Analytical Target Cascading Based Active Distribution Network Level Multi-agent Autonomous Collaborative Optimization[J]. Electric Power, 2024, 57(7): 214-226.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202311008

https://www.electricpower.com.cn/CN/Y2024/V57/I7/214

图/表 20

图 1 “ADN主体-节点主体-受控单元主体”框架

Fig.1 'ADN agent-node agent-controlled unit agent' framework

图 2 区域自治和协同优化过程

Fig.2 Regional autonomy and collaborative optimization process

图 3 ADN主体与节点主体解耦机制

Fig.3 Decoupling mechanism of ADN agent and node agent

图 4 基于ATC的模型协同优化求解

Fig.4 Model collaborative optimization based on ATC

图 5 D9 M2测试系统

Fig.5 D9 M2 test system

图 6 分时电价

Fig.6 Time-of-use electricity prices

表 1 节点配置情况

Table 1 Node configuration

节点号	可控资源/MW			可再生能源/MW
节点号	负荷	发电	储能	风电	光伏
1	—	—	—	—	—
2	5.30	30	—	—	—
3	8.60	—	—	—	—
4	8.00	1	10	5	4
5	6.81	—	—	—	—
6	8.96	—	—	—	—
7	7.00	1	10	4.5	3.6
8	6.09	—	—	5	—
9	—	—	2	—	5

图 7 ADN主体风光出力不确定性典型场景

Fig.7 Typical scenarios of uncertainty in wind and photovoltaic power for ADN agent

图 8 节点主体1风光出力不确定性典型场景

Fig.8 Typical scenarios of uncertainty in wind and photovoltaic power for node agent 1

图 9 节点主体2风光出力不确定性典型场景

Fig.9 Typical scenarios of uncertainty in wind and photovoltaic power for node agent 2

表 2 不确定性典型场景概率

Table 2 Probability of typical scenarios of uncertainty

场景	场景概率
场景	ADN主体	节点主体1	节点主体2
1	0.20415	0.17470	0.236670
2	0.14380	0.11481	0.128030
3	0.25612	0.11406	0.127530
4	0.14111	0.43036	0.363300
5	0.25482	0.16607	0.144467

图 10 节点主体1调度出力

Fig.10 Node agent 1 scheduling output

图 11 节点主体2调度出力

Fig.11 Node agent 2 scheduling output

图 12 负荷优化情况

Fig.12 Load optimization

图 13 节点电压分布

Fig.13 Node voltage distribution

图 14 联络线交换功率优化情况

Fig.14 Power exchange optimization of Tie-lines

表 3 调度模式对比

Table 3 Comparison of Scheduling Modes

方法	成本/元	可再生能源消纳率/%	求解时间/s
本文所提方法	284305.44	100.00	14.57
双层调度方法	289991.55	97.24	13.47

表 4 算法对比

Table 4 Algorithm comparison

方法	成本/元	迭代次数/次	求解时间/s
TLR	301363.77	19	57.32
ADMM	287148.49	14	37.68
PSO	292834.60	457	69.45
ATC	284305.44	10	14.57

图 15 IEEE 33测试系统

Fig.15 IEEE 33 test system

图 16 IEEE 33算例结果

Fig.16 IEEE 33 example results

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