Proper data modeling is essential for supporting an Advanced Distribution Management System (ADMS) and its related software applications, which require accurate data from multiple sources to function correctly. Critical components of the ADMS for modern grid management include Distribution Power Flow (DPF); Fault Location, Isolation, and Service Restoration (FLISR); Volt/VAR Control (VVC); Volt/VAR Optimization (VVO); Feeder Reconfiguration (FR); and Short Circuit Analysis (SCA).
Electric distribution network models are generally extensive and composed of numerous elements. The ADMS database is typically divided into distinct objects that represent the various components of the distribution network model. Therefore, it is imperative to understand the required data for every network model component and subsequently search for the relevant source data in enterprise systems, such as GIS, System Planning, and Protection and Controls (P&C), and the needed information from field devices to include within the ADMS data modeling.
ADMS – Key Applications and Functionality
The functionality of each key application in an operational ADMS is described below.
Distribution Power Flow
The functionality of the ADMS heavily depends on the DPF application, which serves as its core. In turn, numerous other ADMS applications rely on the Power Flow module. This module is responsible for computing the voltages at each bus within the system and determining the current flows, including the real and reactive load flows. In the analysis of the real-time model, the module can incorporate Supervisory Control and Data Acquisition (SCADA) data to scale the loads accurately to avoid overloading and ensure optimal network and equipment performance. Additionally, if historical load profiles are available, the module will utilize them to enhance the precision of the results.
Fault Location, Isolation, and Service Restoration
FLISR is a highly sought-after distribution automation application among utilities. Its primary function is to operate switch groups on distribution feeders, which helps to enhance the dependability of power delivery by localizing outages. The significance of FLISR lies in its ability to enable a self-healing electric grid, which directly impacts the grid’s reliability and resilience and improves customer service.
Volt/VAR Control
The VVC feature is designed to identify the best control actions for voltage regulating and VAR control devices to achieve one or more specified operational objectives while maintaining compliance with essential operating constraints, including high/low voltage and load limits. VVC regulates voltage levels and reduces energy demand by controlling load tap changers (LTCs) on substation transformers and voltage regulators. It also improves power factor and decreases power losses by managing capacitor banks, operating based on predefined rules.
Volt/VAR Optimization
VVC does not adjust to changes in feeder configuration or varying operational needs, and its voltage and VAR-controlled devices are not well-coordinated, leading to suboptimal performance. To address this, VVO was developed as an advanced function to optimize the coordination of VVC devices and create an optimal switching plan for voltage control devices, targeting multiple operational goals like reducing power losses, minimizing voltage deviation, and lowering peak load. These objectives are constrained by factors such as power balance, current flow limits, bus voltage limits, switching limits, and tap position limits.
VVO relies on SCADA measurements and status feedback to calculate real-time power flow and voltage profiles, determining the optimal switching plan to meet specific objectives. It also adapts to feeder configurations and can accurately model the impact of distributed energy resources (DER).
Feeder Reconfiguration
FR refers to modifying the topological structures of distribution feeders by manipulating the open/closed states of sectionalizing and tie switches. It allows utilities to optimize their assets by running it much more frequently than just for the summer and winter feeder reconfigurations. The application introduces a scheme that leverages FR as a planning and/or real-time control technique to restructure the primary feeder and mitigate power losses.
The complexity of FR lies in the vast number of possible switching actions, making it a challenging task for system operators. Reconfiguring the network serves two primary goals: reducing power losses and alleviating overloads. The optimal switching solution enhances the grid’s overall efficiency by placing it in its best global configuration, extending asset lifespan, and minimizing the risk of overloading. Another aspect of configuration management is restoring service to the maximum number of customers following a fault.
Short Circuit Analysis
SCA enables the identification of potential fault locations along a feeder. By coordinating data from fault indicators and fault currents recorded by reporting relays, baseline impedance and fault current values derived from the analysis can be compared with fault impedance readings from the relays. This comparison provides an effective method for estimating fault locations accurately.
ADMS Data Modeling
The following overview covers seven primary modeling considerations needed to achieve optimal results, the data requirements for each network component, and the impact of data attributes on the ADMS applications for every network device.
1. Line
Overhead/Underground line segments, busbars, jumpers, and reactors are modeled as lines in the distribution network. Line types contain the impedance properties of the lines, which are inputs for DPF calculations. Depending on each platform architecture, standard accessible techniques for generating impedance characteristics are:
- Information on conductor type and construction type — This approach utilizes the geometric specifications of conductors for each phase and neutral and their positioning to generate matrices representing impedance.
- Sequence components — This technique employs resistance and reactance values for positive and zero sequences (optionally charging and conductance) to generate matrices representing impedance.
- Inputting the impedance matrix directly — It is possible to specify the impedance matrix directly by providing resistance and reactance values for self (A, B, C) and mutual (AB, AC, BC) impedance for each conductor configuration.
2. Transformer/Regulator
Power transformers, commonly found in substations, serve as step-down delta-wye transformers that convert high transmission voltage to medium distribution voltage. On the other hand, step transformers are typically wye-wye connected and are located within the distribution system, either as pole-mounted or pad-mounted transformers. In the context of distribution transformers, they are usually represented as loads in modeling.
Primary and secondary normal operating voltages, connection types, apparent power, and transformer impedances are undoubtedly required. The data needs to define whether it is a fixed tap or LTC transformer or a regulator. Regarding LTC, tap and tap range settings, along with target voltage, are essential for the proper functioning of DPF and VVC. Additionally, optional core loss impedances ensure the optimal performance of the DPF system. Including ground connection impedances on both sides in the model is beneficial, as it is crucial in determining the short circuit current for SCA and FLISR.
3. Capacitor
Capacitors aid in adjusting power factor and voltage on the distribution circuit, thereby enhancing the efficiency of electricity distribution. Negative vars can be utilized to model shunt reactors as capacitors.
Electrical connection type, normal operating voltage, and reactive power data are required input for DPF. Nominal reactive power and control limit settings, encompassing current limits, reactive power limits, time limits, voltage limits, and temperature limits, are all crucial to ensure the effective operation of VVC.
4. Load
In the context of modeling the distribution network, distribution transformer banks are typically represented as loads, while devices and equipment on the secondary side are generally not modeled. Furthermore, primary service points are also modeled as loads and can be assigned load classes to improve load allocation (LA) and DPF accuracy.
Nominal active and reactive power, operating voltage, and connection type are required inputs for DPF. Additionally, the load profile can be populated for the model to identify and adapt to peak energy demand windows, thereby enhancing energy utilization and minimizing outages overall. The utilization of load profile serves as a valuable input for LA and DPF. If the load is an induction machine, it is essential to have stator, rotor impedances, and magnetizing reactance. If transient parameters are considered for power flow calculation, synchronous/transient/sub-transient reactance must be provided. The transient parameters hold significant importance in the context of FLISR and SCA.
FLISR aims to restore as many customers as possible, making it necessary to have customer count information at each load modeled from the distribution transformers. Moreover, the rated apparent power holds significant importance in the process of FR.
At the meter level, Advanced Metering Infrastructure (AMI) and Meter Data Management Systems (MDMS) work together to remotely monitor electricity usage, analyze data, and manage services or outages. These systems handle vast amounts of data from millions of smart meters. MDMS receives interval meter data from residential smart meters every 15 minutes, with the possibility of even more frequent updates in the future. This data includes energy consumption, engineering metrics such as current and voltage, and power quality events like outage reports.
5. Generator
The model incorporates distributed generation in the form of generators such as solar and small diesel facilities. Sources responsible for energizing the network are usually represented as large generators.
Nominal real/reactive power, per unit voltage deadband (voltage correction by the generator will only be initiated once the nominal voltage threshold has been surpassed), and generator type (PV/PQ/Fixed) are essential to DPF and VVC. For PV regulation, reactive power limits and percent change per voltage changes must be defined.
6. Switch
The classification of all series switchable devices, such as switches, cutouts, elbows, breakers, and reclosers, falls under the category of switches. Switching data, especially current ratings, is essential to FLISR and FR. Current settings include maximum continuous current and maximum fault current. If the switchable device is designed to operate in either direction, it is essential to furnish forward and reverse trip currents, which are also utilized for SCA.
7. SCADA Field Devices
The SCADA system gathers data from field devices through sensors (either discrete or analog) and control relays, then issues commands accordingly. The engineering SCADA data needed by DPF from the field includes phase states of devices and source-side and load-side measurements such as voltages, real power, reactive power, apparent power, power factors, voltage angles, and currents. A breaker lockout or fault indicator activates FLISR and aims to identify the faulted section. With SCADA-controlled switches, it develops a strategy to isolate the fault and restore service to customers above and below the affected area where feasible.
The SCA application requires fault current measurements from the relay of the tripped device to identify potential fault locations on a feeder. VVC relies on voltage, status, tap position, and load measurements from VVC field devices. VVO is the advanced upgrade of VVC that depends on the DPF module, so all parameters needed for the DPF module are also applicable to the VVO analysis.
Optimizing Grid Management
When supported by well-executed, experience-based data modeling, the ADMS’s essential functions can be fully leveraged to achieve specific distribution grid management goals for electric utility operators. The outlined approach optimizes feeder performance and mitigates power losses as primary operational benefits. Another benefit can include injected energy optimization related to proper load calculations across usage periods (time) measured at kilowatt or gigawatt hours. For most organizations, this value can range from 4-7 percent (up to 14,000 GWh yearly for the larger system operators). Areas of large consumption or EV charging station use will have varying results due to the nature of usage.
Connect with UDC to explore data modeling constructs and solutions that can help optimize your ADMS and electric distribution operations.