lv network templates for a low carbon future
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LV Network Templates for a Low Carbon Future. Mark Dale & Gavin Shaddick 26 th October 2012. Outline. Why do we need LV templates? Monitoring & Data: How are we monitoring? The Data Journey Lessons Learnt Statistical Analysis: Overview Clustering m ethodology Classification - PowerPoint PPT PresentationTRANSCRIPT
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LV Network Templates for a Low Carbon Future
Mark Dale & Gavin Shaddick26th October 2012
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OutlineWhy do we need LV templates?
Monitoring & Data: • How are we monitoring?• The Data Journey• Lessons Learnt
Statistical Analysis:• Overview• Clustering methodology• Classification
Steps Forward
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Why do we need LV Network Templates?
New Challenges
• Lack of real data/ understanding of the LV Network
• Assets on the network are expensive
• Growing consumer market more connections
• Consumer Prosumer adopting more green technology e.g. PV, SHP
LV Network Templates
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What does the LV Network Templates project deliver to you?
• Identify the impact of low carbon technologies on the LV Network and enable the creation of LV Templates
• Identify the possibility of using proxy FIT meters to reflect local area generation output
• Share findings with all interested parties including other DNO’s
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Monitoring &
Data
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Sub-station Enclosure
EDMI Customer Monitoring
Ground Mounted
Pole Mounted
Street Furniture: Haldo Pillar
How are we monitoring?
800 Sub-station Monitors3500 Voltage Monitors120 PV Monitors
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The data journey• Primary data (variable)
• Secondary data (fixed)• WAG ARBED• Fixed distribution sub-station data• SSEG generation data
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Lessons Learnt• Majority of householders not receptive to installation of voltage
monitors• do not understand the role of a DNO • saw WPD’s approaches as sales calls
• Multiple routes required for communication to maximise impact and reaction• consider using trusted and known organisations
• Make more use of the initial phase-check visit• educate householders about WPD and the meter to be installed• combine with site survey to speed up the installs
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Statistical Analysis
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Overview
• Develop network templates• Use statistical clustering
techniques• group sub-stations based on
load profiles• Overlay fixed data onto
resulting clusters
• Create set of classification rules
• Using three months of data we have already started to identify clusters which form basis of templates
Example of data received over 24 hour period (Wed. 25th July)
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Statistical methodology: clustering
• Based on (dis)similarities in the data• Data structured according to - Time (within days, 10 min intervals) - Date (days, months, season) - Sub-stations • Real Power Delivered (RPD)• Allocates ‘units’ to groups (clusters)
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Dendrogram• Black vertical lines indicate how sub-station clusters join together• Highest level split is commercial (left) and residential (right)• Further splits are based on magnitude (RPD) and temporal patterns
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Classification
1. Domestic Unrestricted (single rate)2. Domestic Economy 7 (two rate)3. Non-Domestic Unrestricted (single rate)4. Non-Domestic Non-Maximum Demand Economy 7 type (two rate)5. Non-Domestic Max. Demand Customers with Load Factor 0-20%6. Non-Domestic Max. Demand Customers with Load Factor 20-30%7. Non-Domestic Max. Demand Customers with Load Factor 30-40%8. Non-Domestic Max. Demand Customers with Load Factor >40%
• Sub-station clusters contain a mix of customer profile classes
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• Clusters based on overall levels of RPD (kW) and different temporal patterns
Sub-station profiles over time (within day)
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Sub-station profiles over time (weekly)
• Clusters exhibit different patterns of RPD (kW)
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Steps Forward…
• Primary data is being continuously received• Update clustering • Refine classification rules• Create a set of LV templates
• Identify stresses on LV networks due to low carbon technologies
• WPD and DNOs to discuss LV Network Templates and their applicable use
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Questions?