Energy utilities in our modern grid era are coming to rely on a vast array of high value new and emerging technologies, but to be truly modern also calls for a streamlined approach that delivers maximum value. This article discusses an approach to supporting and leveraging, organization-wide, the wealth of digital information being collected about distribution systems as part of the implementation of Smart Grids. We begin by laying out the solution to collect underutilized high-value system data and unify it for streamlined modern grid management and analysis. We then explore ways to apply the solution across your organization.
Monitoring Distribution System Data Sources
The following illustration shows many of the different IEDs – intelligent electrical devices or small computers that are monitoring distribution system behavior. They start at the head of the distribution system inside of distribution substations.
The IEDs inside of distribution substation come in two distinct flavors: substation automation devices that coordinate control between devices on its circuits and with other substations, and those that monitor the operations and health and wealth of devices inside of substations such as power transformers, switchgear and circuit breakers (SA), and the phasor measurement units (PMUs) that estimate the magnitude and phase angle for voltages and current and measure frequency in the power grid at rates of 120 measurements per second. PMUs are getting deployed as part of the protective relays inside of distribution substations.
Utility Smart Grid initiatives addressing distribution automation (DA) are adding tens of thousands of IEDs as part of distribution devices with digital controllers, sensors, communications and processors for reclosers, switches, voltage regulators, capacitors as well as deploying newer intelligent FCIs, DER Interconnections, STATCOMs, D-VARs, TCSCs and PQ meters to support system monitoring and control, power quality management and generation of digital footprints of devices. Last, but not least, utilities have added millions of digital meters as part of their AMI deployments collecting power system information at each meter every 15 minutes.
Making Use of All Available Distribution Data
The SA IEDs and PMU IEDs inside of substations present a large amount of information that typically is only gathered from the substation set of IEDs when an event or missed coordination has occurred. The proposed solution supports continuous data collection of these IEDs using a secondary communication path to substations besides what traditional IED traffic is being collected by SCADA systems. This includes the 10MB+ of oscillograph data (waveforms) being generated for coordination events that were missed.
Figure 1: Uses of Substation IEDs
I’ve been fortunate enough to work on a few Smart Grid initiatives to design and develop solutions that support operating a Modern Distribution Smart Grid. These solutions have provided, in addition to SA and DA IEDs, advanced distribution management applications such as: distribution state estimator, volt/var optimization, unbalanced power-flow, fault location isolation and service restoration and switch order management system which also generate a wealth of digital information at the circuit, circuit subsection and device levels.
Proposed Spatial Analytic Engine Solution
The following will layout a strawman solution to leverage the wealth of operational digital data being generated to support system planning, interruption reliability analysis, asset management and power quality management. I should note that the high-level architecture discussed below supports these engineering disciplines as well as provides the current digital twin view of a Smart Grid for both internal utility and external stakeholders.
Smart Grid Data Repository
As utilities invest in Smart Grid Data Repository technology as depicted in the following diagram, the strawman architecture above gets simplified and the integrations with SCADA Historian, OMS, ADMS, DA/SA and AMI could be replaced with a Smart Grid Data Repository for leveraging the historical operational data.
Figure 2 – Conceptual Smart Grid Data Repository
There is an opportunity beyond daily operating and managing a Smart Grid that is introduced by having more and more intelligent electrical devices (IEDs) being added both inside and outside of substations that continuously monitor the health and wealth information as well as collecting fault information and load information. This Spatial Analytic Engine solution supports being able to bring all that sensor information together with other systems such as engineering reliability forecasting systems as well as capacity planning forecasting systems. The architecture is based on using the enterprise class power of GIS systems to integrate with these operational systems and EAM systems and bring that data together to do planning analysis on.
Why GIS?
The enterprise GIS is a great tool to provide access to the masses from a data marshaling point of view both from the web and from remote mobile user standpoint. The enterprise GIS brings with it the ability to view information at device level, aggregated data along circuits into subsections, aggregated at the circuit level and aggregated at the substation. This solution brings an integration paradigm to support creating main memory-based cubes with commonly used dimensions to quickly change views from looking at loads on feeders, to outage frequencies, to outage causes, to outage durations, to customer counts and customer durations. It allows the interruption reliability engineer to see what impact a specific circuit or even a subset of specific circuit has had on the company’s outage indices.
The suite of systems that can be looked at and integrated and have their data both along circuits and within Subs aggregated together with are:
- SCADA historians
- Outage Management Systems (OMS)
- Advanced Distribution Management Systems (ADMS)
- AMI/MDMS Systems
- Distributed Energy Management Systems (DERMS)
- Demand Response Management Systems (DRMS)
- Smart Grid Data Repository
- Phasor Gateway Historians (PGH)
- Enterprise Asset Management (EAM)
- Mobile Work Management (MWM)
- Distribution Planning Systems (DPS)
- Customer Information System (CIS)
Applying the Solution
Now that we’ve explored the solution to collect and serve up the multitude of operational digital data being generated to support system planning, interruption reliability analysis, asset management and power quality management, let’s look at how to apply the Single Spatial Analytic Engine solution across your organization to receive maximum value from your Smart Grid investments.
Interruption Reliability Analysis
Integrating these operational systems, enterprise systems with weather systems data together in one spot allows reliability engineers to look at historic conditions and see how their power system behaved. It allows them to compare actual behavior to their forecasted system behavior based on their recommendations from a system hardening and distribution automation point of view. This allows utilities to think in terms of what impacts to their performance outage indices given specific capital improvement plans will make or will have on their distribution grid based on the detailed frequency occurrences root cause type of analysis that’s available let them be proactive in doing more maintenance or targeted maintenance. Some of the newest prediction software based on IEDs generating digital footprints or signatures of devices provides analysis that would allow utilities to move to a just in time maintenance or replacement asset management paradigm. The analysis supports just in time maintenance or replacement of switches, fuses, reclosers and transformers before the device fails or sees a through fault.
Distribution System Engineering
This same type of approach or paradigm is useful for supporting things like DER connection applications studies. Now you can have a customer self-service system that is looking at peak connected load in system as it was configured during the peak loading condition configuration and allow customers to request connections studies to let them know if they’re going to be allowed to connect or let them know the amount of improvement system improvement they’re going to have to make or be billed for in order to add their distributed energy resources to the distribution grid.
Operational Awareness
Another high value application of this paradigm or approach is to provide a digital twin view of the current state of the power system to the masses based on all the digital data being continuously collected. Using the modern-day power of the GIS and distributing the information in executive dashboards, management dashboards, operation center dashboards, public facing dashboards and for mobile users all from the same system.
This approach bring additional value for operations by potentially using the usage data from AMI, the current as-operating behavior from the ADMS, the planned switching orders from the ADMS, the forecasting information from the DERM systems based on weather forecasts and any higher end distribution planning simulation application or advanced ADMS application to provide a view that shows both the current conditions of the grid and then thematically highlights areas that may have issues within a given time frame (next 2 hours, next 2 days).
From an operational crew perspective, this approach support generating heat maps of all the crew locations so operations know where their crews are with respect to an event but also know how fast each crew can make their current site safe to be available to support the new event.
Summary
The Single Spatial Analytic Engine approach, as depicted in a little more detail below, leverages the utility investments in Substation IEDs, PMUs, field IEDs, meters, operational software: ADMS, DERMS, AMI/MDMS, DRMS, PGH and enterprise GIS. The GIS brings to the table two types of analysis that we see missing from our clients that are just using traditional big-data technologies like data lakes and enterprise BI tools: circuit and subsection of circuit analysis and spatial analysis that are inherent with this spatially enabled BI approach. This approach does need to extend the current capabilities of enterprise GIS with additional functionality highlighted in blue in the diagram: main memory cube technology and integrations.
Figure 3 – Spatially Enabled BI Platform
Connect with Tom and UDC to learn more about building a Single Spatial Analytic Engine for streamlined modern grid management and getting maximum value return on technology investments for your utility.
Source: Energycentral
