考虑源-网-荷-储侧设备的微网协调运行控制策略研究

竺建国; 李琪; 翁秉宇; 杨建立; 叶木生; 谢涌

竺建国,李琪,翁秉宇,杨建立,叶木生,谢涌.考虑源-网-荷-储侧设备的微网协调运行控制策略研究[J].电测与仪表,2026,63(3):114-125.
Zhu Jianguo,Li Qi,Weng Bingyu,Yang Jian,Ye Musheng,Xie Yong.Research on coordinated operation control strategy of microgrid considering the source-grid-load-storage side equipment[J].Electrical Measurement & Instrumentation,2026,63(3):114-125.

考虑源-网-荷-储侧设备的微网协调运行控制策略研究

Research on coordinated operation control strategy of microgrid considering the source-grid-load-storage side equipment

DOI：10.19753/j.issn1001-1390.2026.03.012

中文关键词: 源-网-荷-储协调控制新型电力系统

英文关键词:source-grid-load-storage, coordinated control, novel power system

基金项目:国家电网科技项目资助：模型与数据驱动的电网调度控制系统图形自动构建关键技术研究与应用（5211NB23000U）

作者中文名	作者英文名	单位
竺建国	Zhu Jianguo	宁波送变电建设有限公司甬城配电网建设分公司
李琪	Li Qi	国网浙江省电力有限公司宁波供电公司
翁秉宇	Weng Bingyu	国网浙江省电力有限公司宁波供电公司
杨建立	Yang Jian	国网浙江省电力有限公司宁波供电公司
叶木生	Ye Musheng	国网浙江省电力有限公司宁波供电公司
谢涌	Xie Yong	宁波送变电建设有限公司甬城配电网建设分公司

摘要点击次数: 468

中文摘要:

源网荷储协调运行控制是一种可实现能源资源最大化利用的运行模式和技术,可通过多能流交互形式经济、高效和安全地提高电力系统功率动态平衡能力。该文研究了由可再生能源发电-微网-临界可控负荷-储能组成的系统的分布式控制策略。文中建立了源网荷储系统模型,证明最优控制问题可以改写为分布式混合整数线性规划(mixed integer linear programming, MILP)。将两阶段随机规划方法应用于分布式MILP,并表明该问题也可以转化为分布式MILP,考虑的问题既是大规模的(即具有大量优化变量)又包括混合整数(即某些变量被限制为整数)。提出一种新的算法方法来解决这种分布式MILP,该方法经过适当修改以处理随机场景,并给出了约束违反的理论上界,将所提出的方法应用于具有大量设备的模拟场景进行测试,仿真结果验证了所提出方法的有效性。

英文摘要:

Source-grid-load-storage coordinated operation control is an operation mode and technology that can maximize the utilization of energy resources, and can economically, efficiently and securely improve the power dynamic balance capability of power systems through the form of multi-energy flow interaction. This paper investigates the distributed control strategy of the system composed of renewable energy generation-microgrid-critical controllable load-energy storage. The source-grid-load-storage system model is established. It is shown that the optimal control problem can be rewritten as distributed mixed integer linear programming (MILP). A two-stage stochastic programming approach is applied to distributed MILP and it is shown that the problem can also be transformed into distributed MILP, considering problems that are both large-scale (i. e., with a large number of optimization variables) and mixed-integer (i. e., where some of the variables are constrained to be integers). A new algorithmic approach is proposed to solve such distributed MILPs, which is appropriately modified to handle stochastic scenarios and gives theoretical upper bounds on constraint violations. The proposed method is tested by applying it to a simulated scenario with a large number of devices, and the simulation results validate the effectiveness of the proposed method.

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