The Future of Distribution Management Systems

Jul 28, 2011 No Comments by

The Smart Grid is emerging as a collection of applications and approaches which will enable greater customer participation, an increased penetration of renewable energy and significant efficiency gains for the electrical utility. Utilities are hoping that large efficiency gains can be achieved through the deployment and enhancement of Distribution Management Systems (DMSs), which are widely accepted as a fundamental component of a future Smart Grid [1].

DMSs are currently installed and operational in most utility networks. DMSs originally started as an extension of the supervisory control and data acquisition (SCADA) system [2], and have advanced to cover a variety of applications. The Smart Grid DMS will both increase the pervasiveness of existing applications and incorporate newer more advanced techniques.

The applications covered by existing DMSs are focussed on helping utilities operate their networks more efficiently and help reduce and inform future capital expenditure. To cover these objectives, DMSs cover both online and offline analytical functions.

Existing online functions of a DMS include a topology processor, which is able to assess the state of all network switches and line sections as well as providing accurate information to other DMS functions (see, for example Distribution Management System Open++ Opera v.3.3 or Siemens PowerCC DM Distribution Management). Network protection analysis works alongside the topology processor to determine the state of protection devices within the distribution network . The results of the topology processor and protection analyses feed into a state estimator [3] which typically models the network as single phase (therefore assuming that the network operation is balanced). For accurate power flow, a DMS must perform load modelling and load estimation [4] utilising SCADA input data of busbar voltage, feeder current and other single phase measurements. The accuracy of the resulting state estimate [3] (or load flow for offline studies) is entirely dependent on these load estimates being accurate.

A DMS includes online components to help with fault detection, isolation and service restoration (FDIR) (see for example, SNC-Lavalin’s Fault Detection, Isolation, and Service Restoration). FDIR algorithms automatically restore service to the maximum number of customers using intelligent optimization restoration algorithms. A DMS can provide approximate information on fault location, and pre-empt network issues by highlighting overloaded cables and transformers. FDIR significantly improves the reliability of networks by reducing fault restoration time from several hours to a few minutes [2].

The operational efficiency of the network is enhanced by online DMS functionality such as integrated voltage / VAR control (IVVC). IVVC helps to reduce feeder losses and maintain an optimum voltage profile during peak and normal operating conditions. Optimal network reconfiguration reduces power losses through load balancing of transformers and maintaining an optimal voltage profile. A DMS also typically provides online switch order management functionality which feeds back switching plans to operators for execution (or rejection) and relay coordination which verifies protection settings.

DMSs contain functionality to assist with several offline processes which ultimately help DNOs plan network reinforcements and maintenance in a structured and informed manner. Contingency and short circuit analysis evaluates the performance of the network given certain outage conditions [2]. DMSs can also help plan the optimal placement of voltage re-enforcement devices by performing offline studies and optimizing the network wide voltage profile for maximal loss reduction.

The current body of research around DMSs suggests that future commercial DMSs will include further functionality to reduce the operational expenditure of network operators and advanced techniques to defer long term infrastructure investment and operate the network closer to stability boundaries. It is likely that increased monitoring of customers (through the deployment of an Advanced Metering Infrastructure (AMI)) and DNO assets (through increased monitoring of in-accessible units such as pole top transformers) will enable significant advances in the accuracy of a distribution system state estimator. Load estimation and load modelling algorithms are likely to significantly enhance in accuracy, utilising accurate recordings from meters installed at customer premises and the wider network [2].

Future DMSs are likely to facilitate the anticipated increases in renewable generation with functionality able to automatically control fault levels through busbar splitting and network reconfiguration. Future DMSs will also improve network efficiency through advanced voltage control and voltage reduction strategies (see for example, voltage optimisation), made possible by increased network monitoring and improved accuracy of a DMS’s state estimator.The overall reliability of the network may be enhanced through a DMS’s advanced FDIR techniques which cover optimization for complicated network topologies [2], potentially utilising advanced algorithmic techniques (for example, advanced statistics or artificial intelligence (AI) techniques such as Neural Networks and Support Vector Machines (SVM)). Fault location using short circuit analysis may be available through the DMS using impedance based fault location algorithms. The information architecture of a DMS is likely to incorporate greater use of visualisation incorporating DNO geographical information systems (GIS). It is also likely to integrate securely and more fully into other DNO systems such as outage management systems (OMS) (such as eMeter’s Outage Event Management) and meter data management (MDM) systems (for example, Oracle’s Utilities Meter Data Management or Siemens’ Meter Data Management (MDMS)).

Lastly, future DMSs may be able to perform advanced offline studies. The increase in network monitoring will allow DNOs to assess system wide power quality issues such as voltage sags , unbalance , harmonics and flicker, and estimate their resulting impacts on consumers. The DMS will supply accurate historical data which will allow DNOs to analyse the effects of new loads and generation such as PHEVs and distributed generation.

Acknowledgement:The image of the futuristic control centre is the Moesk Control Center by Arch-group and ABTB in Moscow, Russia.

References

  1. H. Farhangi, "The path of the smart grid", IEEE Power and Energy Magazine, vol. 8, pp. 18-28, 2010. http://dx.doi.org/10.1109/MPE.2009.934876
  2. . Jiyuan Fan, and S. Borlase, "The evolution of distribution", IEEE Power and Energy Magazine, vol. 7, pp. 63-68, 2009. http://dx.doi.org/10.1109/MPE.2008.931392
  3. . Ke Li, "State estimation for power distribution system and measurement impacts", IEEE Transactions on Power Systems, vol. 11, pp. 911-916, 1996. http://dx.doi.org/10.1109/59.496174
  4. E. Handschin, and C. Dornemann, "Bus load modelling and forecasting", IEEE Transactions on Power Systems, vol. 3, pp. 627-633, 1988. http://dx.doi.org/10.1109/59.192915
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About the author

The owner of Thinking Grids is a published author in smart grid topics ranging from smart monitoring and advanced computational techniques for distribution networks, power quality and stability. He's particularly interested in the business benefits of Smart Grid technology, and the overlap between information technology and electrical engineering.
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