Distributed Dispatch Method for Distribution Network and Microgrid Considering Diverse Regulating Resources

doi:10.16182/j.issn1004731x.joss.25-0528

Abstract

Abstract:

Traditional centralized optimization-based dispatch methods for distribution networks struggle to balance the interests of multiple stakeholders while ensuring economic efficiency and operational reliability of the system. To address this issue, a distributed dispatch method for distribution network and microgrid considering diverse regulating resources was proposed. The operational models of the distribution network and microgrid were established by comprehensively incorporating active management elements and demand response mechanisms. A fuzzy chance-constrained method was employed to model the uncertainty in renewable energy output, thereby constructing a coordinated optimization dispatch model for distribution network and microgrid under renewable generation uncertainty. An improved goal cascade algorithm was proposed to decompose the model into sub-optimization models for the distribution network and microgrids, enabling distributed solution. The simulation results show that proposed model and algorithm can achieve coordinated optimization dispatch of the distribution network and microgrid while preserving the privacy of each stakeholder's information.

Key words: distribution network and microgrid coordination, active management, demand response, fuzzy chance constraint, target cascade method

CLC Number:

TP391.9

Chen Yanbo, Yin Jiahao, Qiang Tuben, Tian Haoxin, Liang Yuxin, Zhang Zhi. Distributed Dispatch Method for Distribution Network and Microgrid Considering Diverse Regulating Resources[J]. Journal of System Simulation, 2025, 37(10): 2469-2484.

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情景	AM	DR
I	×	×
II	√	×
II	×	√
IV	√	√

情景	线路有功损耗/(MW·h)	节点电压偏差p.u.	用电总成本/万元	用能舒适度	综合目标函数
I	0.166	17.716	2.115 6	0.152	0.657
II	0.143	11.184	2.060 4	0.126	0.355
III	0.152	15.407	2.025 2	0.065	0.304
IV	0.105	6.836	1.981 7	0	0.165

季节	电网购电	DPV发电	ESS放电
春	82.5	14.3	3.2
夏	77.8	18.1	4.1
秋	86.7	10.6	2.7
冬	89.6	8.3	2.1

算法	计算时间/s	迭代次数	优化间隙/%	综合目标函数
集中式	45.78	0	0	0.158
传统ADMM	586.32	19	4.38	0.183
自适应ADMM	274.53	12	1.65	0.169
传统ATC	492.96	15	2.68	0.172
改进ATC	132.12	8	0.38	0.165