<｜begin▁of▁sentence｜>### Instruction:
As a network analyst working for a power grid management company, you have access to a detailed visual representation of the power grid system you are currently studying. The system's network data is stored in a GML file named 'power.gml.' 

You have been tasked with analyzing this network using community detection techniques to identify clusters of closely connected components within the grid. To accomplish this, you plan to employ the 'spinglass' community detection algorithm available in the cdlib library.

After applying the spinglass algorithm to the power grid network, your goal is to generate a JSON representation of the detected community structure. This representation will provide valuable insights into the cohesive subgroups present in the power grid system, potentially aiding in its optimization and management.

Kindly proceed with executing the community detection analysis using the cdlib library and represent the outcomes in a comprehensive JSON format for further review by the team.
### Response:
import networkx as nx
from cdlib import algorithms

G = nx.read_gml('graph_files/power.gml')
communities = algorithms.spinglass(G, spins=5)

# Exporting to JSON
communities_json = communities.to_json()

print(communities_json)
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