Research on Optimal Configuration of Distributed Generation based on Multi-scenario Analysis

Jianguo Jiang; Lili Yang

doi:10.54097/fcis.v4i1.9468

Authors

Jianguo Jiang
Lili Yang

DOI:

https://doi.org/10.54097/fcis.v4i1.9468

Keywords:

Distributed Generation, Uncertainty, Multi-Scenario Analysis, Improved Particle Swarm Optimization Algorithm

Abstract

In order to make the grid-connected planning of distributed generation more reasonable, the uncertainties of intermittent distributed generation output and load forecasting are included in the solution process. Firstly, multi-scenario analysis is introduced to transform the source load uncertainty problem into deterministic problem, and the Latin super-force sampling method is used to generate the initial planning scene. The density peak clustering idea and elbow method are used to improve the K-means clustering algorithm and reduce the scene. Secondly, the optimal allocation model of grid-connected distributed generation is constructed with the minimum annual comprehensive cost as the objective function. Finally, in view of the slow convergence speed and easy to fall into local optimum of particle swarm optimization (PSO), an adaptive inertia weight factor is adopted to improve PSO, and the effectiveness of the proposed model and method is verified by IEEE 33-node standard simulation example.

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References

Georgilakis P S, Hatziargyriou N D. Optimal distributed generation placement in power distribution networks: models, methods, and future research[J]. IEEE Transactions on Power Systems, 2013,28(3): 3420-3428.

Bernards R,Morren J, Slootweg H. Development and Implementation of Statistical Models for Estimating Diversified Adoption of Energy Transition Technologies[J]. IEEE Transactions on Sustainable Energy, 2018, 9(4):1540-1554.

D'Adamo, Christian, Jupe, Samuel, Abbey, Chad. Global survey on planning and operation of active distribution networks - Update of CIGRE C6.11 working group activities [P]. Electricity Distribution - Part 1, 2009. CIRED 2009. 20th International Conference and Exhibition on,2009.

ZHANG Shenxi,CHENG Haozhong,XING Haijun,et al.Review of DG planning considering uncertainties for distribution network[J].Electric Power Automation Equipment, 2016, 36( 8) : 1-9.

Lin Ma, Liu Jianpeng. Multi-objective and multi-type distributed generation planning considering time sequence characteristics and environmental benefits [J]. Power System Protection and Control, 2016, 44 (19): 32-40.

Jiang Huizhe, Zhang Chunyan.Study on location and capacity of distributed units considering renewable energy [J]. Renewable Energy, 2021, 39 (09): 1248-1254.

Chen Jiajun, Jiang Tiezheng, Zhou Yong, et al.Constant capacity and location of distributed generation considering voltage sag [J]. Power Grid Technology, 2014, 38 (08): 2244-2249.

Zhang S, Cheng H, Ke L, et al. Optimal siting and sizing of intermittent distributed generators in distribution system[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2015,10(6).

Mola J H, Barforoshi T, Firouzjaee J A. Distributed Generation Expansion Planning Considering Load Growth Uncertainty: A Novel Multi-Period Stochastic Model[J]. International Journal of Engineering, Transactions B: Applications, 2018(3).

JAMSHID A,TAHER N,RASOUL A A,et al. Scenario-based dynamic economic emission dispatch considering load and wind power uncertainties [J]. Electrical Power and Energy Systems,2013(47):351-367.

Fu Wenjie, Wang Yuxi, Shen Hongtao, Tao Peng, Wang Shaolin, Li Kangping, Ge Xinxin, Wang Fei. Baseline load estimation method for residential users based on Latin hypercube sampling and scenario subtraction [J]. Power Grid Technology, 2022, 46 (06): 2298-2307.

Chen Z P,Yan Z.Scenario Tree Reduction Methods Through Clustering Nodes [J].Computers & Chemical Engineering, 2018, 109: 96-111.

Yao Huangjin, Lei Xia, Fu Xinquan, Hu Yi.Clustering analysis of daily load curve based on improved adaptive density peak algorithm [J]. Power System Protection and Control, 2022, 50 (03): 121-130.

YU Tingfang,LIN Zhongda. Application of float genetic algorithms-partially solved combined with punishing function in power plant units commitment problem[J].Proceedings of the CSEE,2009,29(2):107-112.

BARAN M E,WU F E. Network reconfiguration in distribution systems for loss reduction and load balancing [J]. IEEE Transactions on Power Delivery,1989,4(2):1401-1407.