Creating Load Profiles for Enhanced ADMS Operations

Posted to UDC in the Digital Utility Group
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Sreenivas Sudarshan  Seshadri's picture
Senior ADMS Consultant, UDC, UDC

Sreenivas has 9 years’ experience in the GIS industry with 3 years specializing in Power Distribution and ADMS services. Prior to joining UDC, Sreenivas implemented keystone ADMS projects at...

  • Member since 2023
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  • Aug 18, 2023

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Utilities are rapidly adopting ADMS (Advanced Distribution Management Systems) to monitor the growing Distribution grid, model complex electric networks, and integrate renewable energy sources. An ADMS requires additional data pointers along with a Distribution network model to run advanced power system applications, including Power Flow, Volt/VAR Optimization (VVO), and Fault Location, Isolation, and Service Restoration (FLISR). In order for modules to function properly and reliably in the ADMS, utilities should assign load profiles for each customer.

Load profiles are built by monitoring the amount of consumer energy usage over a period to determine average energy consumption during specific times of day/night. This data can help utilities identify and adjust for peak energy demand windows, leading to better energy usage and reduced outages overall. The recommended methods for generating load profiles vary based on a utility’s service area, geography, and number of customers. This article describes three different approaches utilities can apply to generate load profiles for their ADMS model that will enhance operations:

  1. Class Specific Load Profiles
  2. Station-based Load Profiles
  3. Region-based Load Profiles

Load Profiles in the ADMS Data Flow

Like the utility’s GIS data, load profile data originates outside the SCADA (Supervisory Control and Data Acquisition) network and needs to be combined with the GIS data to formulate the ADMS ready and optimized load profiles. Because of this, process automation for generation and maintenance is key. The load profiles can be generated and maintained on a cloud-server or a local server and then transferred over to the ADMS integration server where the required GIS data is also placed.

For enhanced operations, every service transformer in an ADMS requires an associated load profile. The ‘Service Transformer’ asset class in the GIS contains fields that enable the transformers to be related to their load profiles. ADMS’s Extract, Transform, and Load Process (ETL) module joins the GIS data and load profile data to build an ADMS model with the correct profiles. These profiles then drive the advanced power applications in the ADMS model, as shown in the figure below.

Figure 1: Data requirements and flow for advanced power applications in ADMS


Managing Load Changes in ADMS

Distribution Power Flow (DPF) and Load Management (LM) modules can help utilities better manage changes in load. To operate optimally in the ADMS, the modules must have current load profiles.

Distribution Power Flow Benefits

When a network parameter, such as Phase Voltage, Current, or Loss, exceeds user-defined thresholds, DPF automatically records the security violation, highlights the segments of the connectivity model where the issue occurred, and generates an alarm to alert operators to the change. Leveraging this information, the Operations team can then use the switching and load studies from the ADMS modules to determine alternate network configurations that can avert loading issues, improve poor voltage conditions, and reduce line losses.

Load Management Benefits

Operators can use an LM solution to decrease high load demand on the Distribution network. This can significantly reduce the penalties some utilities pay to Transmission service providers for peak usage.

Three Load Profile Creation Approaches

To choose the correct methodology for generating load profiles, whether by class, station, or region, utilities should consider different factors regarding their customer bases and service areas.

1. Class Specific

This profile approach is best suited for rurally located utilities with a customer base in the range of 100,000 to 500,000, such as some Co-Ops.

The Utility can build load profiles for a specific number of load classes, including Residential, Commercial, Industrial, and Street Lighting. These load classes can be further sub-divided based on electric heating, presence of demand meters, and voltage levels. Usually, these load profiles are aggregates (sum or average) of all the customers in a specific class and can be generated from the billing data, meter data, and other available data sources. The Distribution load transformer asset class feature in the GIS contains a load class identifier field that enables the load profiles to be applied to the ADMS during the daily model update process.

Considerations with this approach include:

  • Data validity of the class identifier field in GIS: The class identifier field must be up to date in the GIS. Missing attribute data can result in wrong profile assignments to transformers due to the predefined logic set in the ADMS model ETL Process.
  • Maintenance of the aggregate load profiles: The aggregate load profiles should be updated whenever new substations or feeders are added to the model. Load profile maintenance will require additional IT resources and process automation to perform updates in the ADMS.  
  • Load profiles for Distribution transformers that have mixed customers: In some areas, the Distribution transformers serve both residential and commercial customers. In such cases, separate load classes must be defined based on the proportion of residential and commercial customers.
2. Station-based

Utilities that serve an entire state or denser geographic area may choose to implement station specific load profiles for their ADMS model. In this approach, the load profiles for the stations are derived from data generated by the AMI (Advanced Metering Infrastructure) at each station and can be made more accurate through load classes. If the utility wants a granular load profile, the load profiles for each feeder at the station can be derived from the AMI data.

Considerations with this approach include:

  • Generating load profiles for stations without AMI: The load profile generation for stations without AMI is not straightforward. Station level profiles should be generated by the Load Research team and then scaled to the specific billed energies of accounts associated with respective stations.
  • Generating load profiles for newer stations: Though the newer station may be equipped with AMI, the data generated by AMI will not be sufficient to build load profiles. A generic station level load profile can be used for the newer stations until sufficient AMI data is gathered.
3. Region-based

Region-based load profiles are the aggregated load profiles of stations within a specific region or geographical sub-division of the service area. This approach may be more suitable for utilities that have service areas spread across multiple states. The number of load profiles is more manageable for both the initial model build and the future maintenance efforts. The considerations associated with this approach are similar to the Station-based approach since region-based load profiles are consolidations of station-based load profiles.

Choosing the Right Approach

By assigning and maintaining accurate load profiles, utilities can power advanced applications in the ADMS as well as ensure the ADMS modules for managing load are performing properly. Load profiles can additionally increase savings and reduce energy waste by helping to optimize the ADMS. To choose the correct load profile generation approach, utilities should consider the relative size of their organization and the communities they serve.

Contact UDC or connect with Sreenivas Sudarshan Seshadri to discuss how optimizing and incorporating load profiles can make an impact on your business.

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UDC is a spatially focused company centered on solving business challenges. We provide a full set of GIS services, ADMS readiness and consulting services, and Digital Utility® solutions supporting operations and increasing reliability for utilities.

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Sreenivas Sudarshan  Seshadri's picture
Thank Sreenivas Sudarshan for the Post!
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