Output Evaluation Method of Distributed Photovoltaic Cluster Considering Renewable Energy Accommodation and Power Loss of Network

doi:10.12204/j.issn.1000-7229.2022.10.013

Abstract

Abstract:

In order to solve the problems of the increasing power loss of network, power reverse transmission, voltage out-of-limit and so on, caused by the continuous increase of distributed photovoltaic penetration in distribution network, according to the cluster division of distributed photovoltaic generation, an output evaluation method of distributed photovoltaic cluster considering renewable energy accommodation and power loss of network is proposed. Firstly, the modular index based on electrical distance and active-power balance index are used as comprehensive division indices, and the distributed photovoltaic generation in distribution network is divided into clusters by using genetic algorithm. Then, on the basis of the cluster division results, considering the regulation effect of energy storage and the constraints of network security and stability, the output evaluation method of distributed photovoltaic cluster is proposed with the lowest economic cost of distribution system including the power-loss cost of network and distributed photovoltaic generation. Finally, the feasibility and effectiveness of the evaluation method proposed in this paper are proved by simulation analysis on an actual 41-node system in Yangzhou. The proposed method can provide output evaluation data of distributed photovoltaic cluster according to the demand of dispatching center.

Key words: distributed photovoltaic cluster, renewable energy accommodation, output evaluation

CLC Number:

TM715

LI Yan, LÜ Nanjun, LIU Xuetao, HU Junjie, XU Yanhui. Output Evaluation Method of Distributed Photovoltaic Cluster Considering Renewable Energy Accommodation and Power Loss of Network[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(10): 136-146.

Figures/Tables 16

Fig.1 Coding mode of network adjacency matrix

Fig.2 Flow chart of cluster partition based on genetic algorithm

Fig.3 Topology of 41-node distribution network system

Table 1 Constraint of line capacity of distribution network

Table 2 Parameter of distributed photovoltaic generation

Fig.4 Load active-power demand and forecast photovoltaic output curve

Fig.5 Change curve of comprehensive performance index

Fig.6 Results of cluster division

Fig.7 Result of distributed photovoltaic output

Table 3 Economic cost 元

Table 4 Permeability and rejection rate of distributed photovoltaic clusters at 13: 00

Fig.8 Power exchanged between clusters

Table 5 Comparison of optimization results

Fig.9 Power loss of distribution system

Fig.10 Curve of node voltages in distribution network

Table A1 Optimization results under different network loss rate requirements

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