基于一致性算法的虚拟电厂调度指令动态跟踪策略

doi:10.12204/j.issn.1000-7229.2024.05.008

电力建设 ›› 2024, Vol. 45 ›› Issue (5): 71-79.doi: 10.12204/j.issn.1000-7229.2024.05.008

• 大模型小样本条件下新能源规划设计与优化运行技术·栏目主持葛磊蛟副教授、孙铭阳教授、郑锋副教授、黄文焘副教授· • 上一篇下一篇

基于一致性算法的虚拟电厂调度指令动态跟踪策略

蔡光宗¹, 王伊晓¹, 袁智强¹, 杨莉²(), 黄文焘², 余墨多²

1.上海电力设计院有限公司,上海市 200025
2.上海市智能船舶综合电力系统工程技术研究中心(上海交通大学),上海市 200240

收稿日期:2023-06-13 出版日期:2024-05-01 发布日期:2024-04-29
通讯作者: 杨莉(1999),女,硕士研究生,主要研究方向为综合能源系统优化运行,E-mail:yangli_0726@sjtu.edu.cn。
作者简介:蔡光宗(1972),男,硕士,高级工程师,主要研究方向为电力系统规划;
王伊晓(1990),男,硕士,高级工程师,主要研究方向为能源互联网、综合智慧能源、电力交易与碳交易等;
袁智强(1969),男,教授级高级工程师,研究方向为电力系统规划;
黄文焘(1989),男,博士,副教授,主要研究方向为微电网、电力系统继电保护等;
余墨多(1993),男,博士,助理研究员,研究方向为微电网稳定性及控制等。
基金资助:
上海市教委科研创新重大项目“基于多能流耦合的综合能源电力系统关键技术研究”(2019-01-07-00-02-E00044);中国电力建设股份有限公司科技项目经费

Consensus-Based Dynamic Dispatching Instruction Tracking Strategy for Virtual Power Plant

CAI Guangzong¹, WANG Yixiao¹, YUAN Zhiqiang¹, YANG Li²(), HUANG Wentao², YU Moduo²

1. Shanghai Electric Power Design Institute Co., Ltd., Shanghai 200025, China
2. Shanghai Intelligent Ship Integrated Power System Engineering and Technology Research Center (Shanghai Jiao Tong University), Shanghai 200240, China

Received:2023-06-13 Published:2024-05-01 Online:2024-04-29
Supported by:
Major Project of Scientific Research and Innovation of Shanghai Municipal Education Commission(2019-01-07-00-02-E00044);China Power Construction Co., Ltd.

摘要/Abstract

摘要：

虚拟电厂(virtual power plant, VPP)可以聚合大量的分布式能源资源(distributed energy resources, DER)作为一个整体参与电力系统的调度。为实现VPP内部DER的高效调控,提出了一种虚拟电厂集群自治调度架构。为解决理想通信网络下VPP内部DER动态跟踪调度指令的优化问题,分别从集群层面和DER层面提出了调节成本一致性和调节时间一致性算法,通过本地相邻DER的信息交互,更新自治节点参数,降低了通信压力,且提高了求解效率。在此基础上,通过引入共识增益函数和虚拟共识变量,解决了实际通信网络中的传输延迟和噪声问题。仿真结果验证了所提算法的有效性和可行性。

关键词: 虚拟电厂, 多智能体, 分布式能源, 一致性算法, 调度指令跟踪控制

Abstract:

Virtual power plant (VPP) can aggregate a large number of distributed energy resources (DER) to participate in the dispatching of power system as a whole. In order to realize efficient control of internal DER in VPP, a virtual power plant cluster autonomous scheduling architecture was proposed. In order to solve the optimization problem of internal DER dynamic tracking scheduling instruction in VPP under ideal communication network, the algorithm of adjusting cost consistency and adjusting time consistency is proposed from the cluster level and DER level respectively. Through the information exchange of local neighboring DER, the parameters of autonomous node are updated, which reduces the communication pressure and improves the solving efficiency. On this basis, by introducing consensus gain function and virtual consensus variable, the transmission delay and noise problems in the real communication network are solved. Simulation results verify the effectiveness and feasibility of the proposed algorithm.

Key words: virtual power plant, multi-agent system, distributed energy resources, consensus algorithm, scheduling instruction tracking control

中图分类号:

TM732

蔡光宗, 王伊晓, 袁智强, 杨莉, 黄文焘, 余墨多. 基于一致性算法的虚拟电厂调度指令动态跟踪策略[J]. 电力建设, 2024, 45(5): 71-79.

CAI Guangzong, WANG Yixiao, YUAN Zhiqiang, YANG Li, HUANG Wentao, YU Moduo. Consensus-Based Dynamic Dispatching Instruction Tracking Strategy for Virtual Power Plant[J]. ELECTRIC POWER CONSTRUCTION, 2024, 45(5): 71-79.

图/表 9

图1 虚拟电厂集群自治分层调度架构

Fig.1 Cluster autonomous hierarchical scheduling structure for VPP

图2 一致性功率分配算法流程

Fig.2 Flow chart of power allocation based consensus algorithm

表1 VPP 系统内部 DER 参数

Table 1 Parameters of DERs

图3 调节成本一致性收敛过程 (ΔPC = 1 000 MW)

Fig.3 Convergence of the regulation cost consensus (ΔPC = 1 000 MW)

图4 调节成本一致性收敛过程(ΔPC=10 418 MW)

Fig.4 Convergence of the regulation cost consensus (ΔPC=10 418 MW)

图5 调节时间一致性收敛过程(ΔP1 = -943.16 MW)

Fig.5 Convergence of the regulation time consensus (ΔP1 = -943.16 MW)

图6 调节时间一致性收敛过程(ΔP1 = 5 810 MW)

Fig.6 Convergence of the regulation time consensus (ΔP1 = 5 810 MW)

图7 调度指令跟踪效果

Fig.7 Scheduling instruction tracking effect

表2 不同算法的性能比较

Table 2 Performance comparison of different algorithms

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基于一致性算法的虚拟电厂调度指令动态跟踪策略

Consensus-Based Dynamic Dispatching Instruction Tracking Strategy for Virtual Power Plant

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