Energy Analytics for AMI Distribution Transformer Metering Data

Introduction/Background

Electric utilities/Distribution Network Operators (DNOs)/Distribution Companies (DISCOS) are always finding alternative ways to better serve their customers and optimally operate their electricity distribution business while finding alternative planning and network expansion plans to satisfy changes in electricity consumption patterns. The Distribution Transformer (DT) is seen as the last mile in the electricity distribution value chain and consumption patterns in the last mile are critical for the optimal operation of the network by DNOs. In Nigeria for example, the DT energy consumption is particularly important for the DISCOS in energy accounting and commercial viability of the DISCOS.

This explains why DISCOS in Nigeria are keen in investing into Advance Metering Infrastructure (AMI) to better account for energy flows in their networks. With the AMI, DTs and customers can be metered, and data collected in a secured cloud platform using 4G LTE, which opens a near-real time opportunity for energy analytics that would inform decision making and enhance energy accounting/billing for the DISCOs (especially being fairer to un-metered customers).

However, in Nigeria, analytic tools have not been utilized to process and efficiently analyze the large amount of data sets collected by AMI, which in the opinion of this Author, is an opportunity to create insights and make better informed decisions by the DISCOS.

In this article, a sample anonymized dataset collected from the AMI platform of one of the DISCOS in Nigeria is analyzed in a Business Intelligence (BI) tool (Power BI in this case) to show that combining BI analytics and AMI metering data would serve as a better decision platform for DISCOS to account for energy flows in their networks.

The Problem

The anonymized AMI metering sample data is shown in Figure 1 below:

Figure 1: Sample anonymized AMI data

Looking closely at Figure 1 shows that the energy data from the AMI is cumulative and to evaluate the energy consumed at each time stamp, the difference from the previous timestamp must be evaluated, this can easily be done for small data sets in excel or by hand calculations, but when there is a large dataset with meters and feeders required to be looped to compute the energy consumed in each hour, this can be tedious, giving rise to computational errors.

The Solution

With Power BI, the data can be imported, cleaned in power query, relationships built (for time intelligence) and the hourly energy consumed by each DT meter in each feeder computed using the DAX programming built into Power BI. Figures 2 and the code below show respectively the relationship setup and the DAX calculated column used in evaluating the hourly DT meter energy from the cumulative energy readings (shown in the last column of Figure 1) in the Power BI Model.

Figure 2: Model Relationship

Hourly Energy (kWh) = 
var timex = Energy[Capture Time]
var MaxLastTime = CALCULATE(MAX(Energy[Capture Time]),FILTER(ALLEXCEPT(Energy,Energy[Feeders(Voltage Levels)],Energy[Meter Name]),Energy[Capture Time]<timex))
var PreviousEnergy = CALCULATE(SUM(Energy[Total Import Active Energy(kWh)]),FILTER(ALLEXCEPT(Energy,Energy[Feeders(Voltage Levels)],Energy[Meter Name]),Energy[Capture Time]=MaxLastTime))
Return

Results

The report developed using the anonymized AMI data in Figure 1 is presented in the shared Power BI model below. Page 1 (Load-Profile) of the model shows the loading profile of each DT meter. This can be sliced by Feeder and the period the sample data was captured. DISCOS can use the loading profile to evaluate how each DT is performing against its capacity in (kVA). Page 2 (Total Feeder Energy) of the model computes the total energy consumed/imported by each feeder (along with its DTs) within the reporting period. DT meters in each feeder consuming greater than 500 kWh hourly energy are shown in Page 3 (Greater-than 500 kWh Hourly).

Conclusion

Improving the usability of AMI metering data with BI analytics would further enhance the capacity of Nigerian DISCOS to optimally compute energy flows within their networks. This can also be used in conjunction with Power Flow tools like NEPLAN to compute technical network losses and develop viable future network expansion plans.