While the “smart grid” conjures up numerous definitions, there can be little doubt that it signals both a shift of focus to distribution networks, and a revolution in the way they operate.
Over the years, distribution engineers have carefully designed and managed the grid, responding to problems as they develop. By and large, the industry has managed the distribution network in a reactive manner and without significant real-time visibility. As new energy consumers and supply resources appear on the distribution grid, gaining more visibility and control is a necessity for any grid owner.
At the center of the many technologies that are being applied is a complex analytics engine called advanced DMS (distribution management system). The advanced DMS technology, still new to North America, is the tool that unites a host of traditional technologies, such as outage management, SCADA (supervisory control and data acquisition), and operations planning and network analytics. advanced DMS changes the game for both utilities and suppliers, who typically have focused on the transmission system when it comes to information technology. It introduces systems that are less reactive, and driven more by process and real-time state estimation. With only a few systems fully deployed around the world, advanced DMS stands at the cusp of what promises to be a long and exciting expansion of technology solutions for electric distribution networks.
What’s a DMS?
Before digging deeper into the realm of advanced DMS, it’s helpful to establish a common understanding of the basics of distribution management systems. Like many aspects of a smart grid, the term “DMS” has come to be broadly applied: Because of a growing recognition of the need for more automation and control at the medium voltage level, “DMS” has become a popular catch-phrase for almost anything that manages some aspect of a distribution system. That may be fair enough, but for purposes of this discussion, a clear definition of the tool is important. So, let’s begin by outlining the four critical traits of a modern DMS:
• Loadflow Calculations.
• State Estimation.
• Real-Time Systems Integration.
• Fault Calculations.
Also, in thinking about a modern DMS, it’s important to always keep in mind certain differences between transmission and distribution networks.
In fact, many DMS tools on the market today originated as EMS (energy management systems) technology applied to transmission systems. Conceptually the solution techniques are the same (loadflow, state estimation, real-time integration, fault calculations). However, the networks themselves are very different.
Transmission networks are sparse, even pristine. They have very few elements and a high proportion of telemetered data points. Transmission systems don’t change often; once created, managing a transmission model is straightforward. In contrast, a distribution system is a complex, intense, dirty, ever-changing organism. Mathematical techniques that work well for the transmission grid can easily bog down when applied in the distribution context.
Within a modern DMS, loadflow calculations help solve the iterative mathematics of an electric distribution model. Because current and voltage vary in proportion to one another across the network, solutions to accurately compute voltage and current flow at any node are iterative. Some software uses a closed-form voltage drop computation, but those are generally estimated and based on assumptions that can limit accuracy. For DMS to work well in a smart grid context, the loadflow calculations must be accurate.
Power system engineers use the term “state estimation” to mean the ability to monitor certain points in the network for things like voltage and current, and solve those parameters for other, non-telemetered points of interest. This technique is non-trivial, and has been applied to the sparse networks of transmission systems for many years. For distribution networks, the problem becomes much more intensive and state estimation has to work hand-in-glove with the loadflow method chosen for analysis.
For DMS to do its job, it must be able to to accept real-time data from SCADA systems and other sources, and incorporate that information into its network solver. This capability is closely related to the state estimation function described above and adds another level of complexity. That’s because as real-time parameters change, the state estimator must be capable of solving and resolving the network, deciding which, if any, of the monitored parameters justify a re-running of the loadflow calculations.
Among the most important functions for a modern DMS are fault location and service restoration. This capability is an extension of basic short-circuit current and voltage computations. The DMS uses fault calculations to help system operators plan protective schemes, analyze system failures and plan service restoration.
In addition to these core capabilities, there are two more important characteristics for DMS. North American style networks require that network solvers be capable of solving three-phase, unbalanced systems. In European networks, a three-phase balanced solution is adequate, since nearly all distribution systems operate with all three phases in balance. But North American networks often consist of segments of single-phase circuits, so that the simplifying assumptions of the balanced solution do not apply. Most importantly, modern DMSs must have the ability to quickly solve large, complex networks, often with hundreds of thousands of nodes or more. In the complex equation of loadflow + state estimation + real-time integration, solving a large network in near-real-time is a challenge. But high performance is the price of effective control of the distribution grid.
Capturing Dollar Benefits
It might come as a surprise to some, but tightening the control of the operation of medium- and low-voltage systems for greater efficiency can lead to significant savings for the utility and its ratepayers. Operating efficiencies can come through a wide array of techniques, including balancing phasing, managing system voltage and power factor, and optimizing network configuration. To take one example, most utilities today operate the distribution grid in a fairly static configuration. At most, they may be able to reconfigure feeders through switching once a year or so to optimize performance as seasonal loads change. As the grid gets smarter, with more points of switching control and more places where operating parameters are measured, more flexibility and data will lead to more opportunities for improvements.
More knowledge about the performance of the network can also lead to improvements in construction practices that can reduce capital costs. It’s common practice in the industry to build distribution circuits to carry peak loading, plus some safety factor for margins against unanticipated conditions. Utilities can reap benefits from better analysis of operating conditions, building up a history of loads and being able to monitor and manage configuration in near-real-time.
DMS solutions also can help the electric industry capture efficiencies related to generation dispatch.
As the smart grid develops, generation supply is becoming more diversified, to include distributed energy resources such as solar and wind, and fossil-based micro-generation. These new distributed energy resources will change not only carbon footprints, but also the very idea of generation dispatch. High penetrations of small point sources added to the management of a few large, centralized generators complicate the monitoring and control equation. On top of that, utility personnel today are starting to think of load as a resource to be served or curtailed as required, depending on operating, reliability, and economic parameters. Carefully and continuously balancing supply and demand assets in the most effective way can lead to significant savings, and reductions in the carbon footprint of electric energy usage.
But delivering optimal resource balance is not easy, and as the mix of loads and sources changes and expands, the operational problem will only get harder. System operators will need automated tools to monitor, analyze and control the distribution system in real time. By enabling operators to clearly understand the shifting patterns of loads and sources, and helping to execute control that keeps the system within acceptable operating parameters of capacity and voltage, DMS enables the grid to optimize any resource, be it supply or load.
A third category of DMS benefits involves improvements in electric reliability. After all, poor reliability represents more than an inconvenience to consumers—it has a huge cost as well.
In this case, DMS can support better reliability in a number of ways, first by providing critical insights into areas of the system with problems or congestion. Through real-time analysis, operators can get early warnings of poor voltage or overloads, and can support decisions to shed load or relieve congestion through switching. Ability to anticipate problems and give dispatchers tools to calculate solutions is one key to a more resilient network. And when things do go wrong and outages occur, DMS can help to locate and isolate faults, and determine the optimal switching sequence to restore service.
Of course, many distribution utilities today have highly reliable networks, so opportunities for improvement may be limited. In fact, the increased complexity in the network, and exposure to areas of cyber failure or even attack, cause some smart grid implementation teams to worry that continuity of service could deteriorate. For some network operators, a smarter grid may be mostly about sustaining reliability, rather than improving it.
DMS as Systems Integrator
As grid configurations and available technology grow ever more complex, advanced DMS tools can march in lockstep by integrating both data and function, especially in the area of outage management and system restoration.
Outage analysis (call management, prediction) and crew management are typically the purview of an outage management system (OMS) rather than a DMS. For most North American utilities, when the lights go out, the OMS becomes the focal point of an all-out effort to restore service. From SCADA events, phone calls, AMI last gasp messages and ancillary other sources, the OMS analyzes patterns and predicts outage locations, the enables dispatchers to route the right resources to the appropriate locations. An advanced DMS embellishes the restoration workflow by integrating both OMS and DMS functions, adding real-time measurement and analysis accuracy to enhance outage device prediction capabilities. Putting prediction, real-time network analysis, and crew management tools all together in a single streamlined user experience can substantially improve response and reduce overall outage time.
A self-healing network marks one of the key precepts of a smarter grid. To date, most deployed smart switching deployments have been stand-alone systems, designed to change from one configuration to another in predetermined sequence, given a fault within a particular zone or areas of the system. These schemes are a good first step, but fast but inflexible, and able to account for only some of the operating conditions that could arise.
By contrast, an advanced DMS tool could build on what are essentially binary capabilities, to cover many more configurations and potential switching scenarios in an automated way. As more process-controlled switches are deployed in the distribution system, more options for restoration will become available.
Imagining the Future
Distribution management software systems are growing and improving at an unprecedented pace, but there are many more things on the drawing board. Among the most interesting is the concept of distributed control for distributed renewable resources.
Even as the smart grid communications infrastructure grows, covering more and more of the distribution network, high-bandwidth, low-latency communications probably won’t reach all the areas where distributed resources will want to interconnect with the grid, making centralized control and dispatch impossible. One solution option is simply to allow a fairly rudimentary interconnection scheme, quickly isolating the distributed resource for just about any abnormal condition. But another approach, one that would support the full scope of reliability and economic benefits intended, would be to implement a hybrid, hierarchical control scheme. In this technique, the advanced DMS would operate in both distributed and centralized modes, building up a network of micro-controlled sources with overlapping models. The hybrid hierarchical control system, powered by a very advanced DMS, would afford the local capability to keep distributed resources operating smoothly, with minimal grid impact, while still offering the ability to analyze and optimize the system in a holistic way.
As the DMS gets smarter about the things it can monitor and control, it will also have to get better about the way it enables users to interact with the grid. The ever-increasing list of features and functions of the software itself will be coupled with a massive influx of data from new information sources like AMI and line sensors. Growing scope and complexity in the distribution grid, and the rigorous demands of the operator’s job at times, will mandate new and simpler ways to use automation to manage the system. The next generation DMS will expose the data and functionality required to keep a finger on the smarter grid in new and more focused ways, guiding the user to make the right choices and, at times, making those choices itself. Tomorrow’s DMS will reduce operator error and enhance cyber security.
And there’s more—such as highly distributed, highly secure applications for use in a service vehicle or at home, and sentient asset databases and network models that update themselves for changes in the field—but perhaps those are topics for another day. The potential for growth in the advanced distribution management system’s ability to make the smart grid even smarter will reach into the far distance for some time to come.