Distribution automation: the path to the self-healing grid

The layperson and others have made the smart grid almost synonymous with the smart meter. People have forgotten the need for actual control and about healing. 

Go to the profile of Mani Vadari
Aug 03, 2017
0
0
Upvote 0 Comment

Author(s): Dr Mani Vadari

Abstract

Distribution automation (DA) holds the rightful position as the foundation of smart grid. This article starts by explaining transmission and DA. It then moves to a detailed definition of DA and its components. Understanding these components is important to appreciate what it takes to implement them. The components of DA drive the clear understanding of how DA is implemented in all of its forms and also which components of the smart grid are a part of this portfolio of capabilities. The dos and don'ts of DA section focuses on key considerations that need to be kept in mind as utilities worldwide focus on their own mechanisms for implementing DA in their grid. The last section before conclusion focuses on ongoing challenges and the future of DA.

DA will form the underlying driver for smart grid for the next 10+ years. As the control of the grid becomes more complex, it is DA that provides the eyes, arms and legs to the utility and other operators to continue their path to a reliable and resilient grid.

Introduction

The self-healing grid is the holy grail of the smart grid. So what does this mean?

The grid today gets many attacks, some weather related (storms, hurricanes and others) and some man made (car hits electric pole and the more recent cyber-security attacks as well). Most of these impact the grid in the form of outages which mean loss of power. This loss of power can be localised (e.g. one house or a block of houses), medium sized (e.g. a neighbourhood, or major parts of a city) or wide spread (e.g. regional such as the 2003 blackout in Northeast United States). It is believed that there would be more of these and the grid resiliency (the ability to recover readily from major attacks such as the ones identified above – a new term that is being used a lot) will become more and more of an issue in the future [1].

Innovations in the smart grid arena are also resulting in the grid getting new distributed energy resources (DERs) components which include storage, distributed renewables (solar, wind and others), microgrids and so on. These bring new challenges to the distribution grid operator, of an entirely different nature – (i) supply coming from sources within the distribution grid – moving the grid from a one-way flow to a two-way flow, (ii) supply coming from customer-owned sources instead of utility-owned sources – requiring the need to decide how to take this power and how to compensate them for it and (iii) increased levels of intermittency that comes from these kinds of sources putting additional burden on the operator's ability to manage, and operate the grid in a reliable, efficient ‘and optimal’ manner.

New network configurations such as microgrids are also bringing their own complexities, one in which parts of the grid (some small and some mid-sized) such as commercial and college campuses are looking to (i) save money (ii) go green and are trying to create microgrids that can function in either grid-connected or grid-isolated conditions based on their need. This also requires the grid operator to now collaborate with external entities within their own distribution system to ensure the reliability ‘and resiliency’ of both their own system as well as the interconnection with others.

The three major conditions identified above could easily morph into several more off-shoots that could drive the distribution grid into a variety of directions each more complex than the other. With each change, the management and operation of the distribution grid still needs to deliver a reliable and efficient grid, one that is capable of handling both the attacks identified earlier but also the intermittency and variability from the newer forms of energy. For all this to happen, the distribution grid (and by definition the grid operator) needs access to several key technological components that allow them to (i) see and know about what is happening on their grid, understand what the options they have for response, select the right response and implement the response.

In transmission, these sets of technologies are called transmission automation (TA) and in distribution, they are called distribution automation or DA for short. This article is all about distribution. So, let us start by defining DA – and also how is it different from TA.

Transmission against DA

The electric grid is generally distinguished as transmission and distribution systems based on voltage class. Generally, voltage class of equipment above 132 kV is considered as transmission and equipment below 33 kV is considered as distribution. Depending on location, equipment at 69/70 kV can be considered transmission or distribution.

Control of the transmission system is called TA even though this is not a widely used term. The primary goal of TA is to increase grid reliability through better monitoring and control, and wide-area situational awareness. Typically, electric utilities have extensive control over their transmission-level equipment, through SCADA and other wide-area monitoring systems. In the wake of the smart grid, these networks are increasingly being transformed from analogue-to-digital systems through the wide-spread deployment of intelligent electronic devices (IEDs). IEDs are the foundation of T&D automation and are used to control equipment such as circuit breakers, relays, transformers and capacitor banks. Examples of transmission-level automation technologies include phasor measurement units (PMUs), digital protective relays, digital substations and so on. Of these PMUs are the newest set of technologies that have the potential to change the entire landscape of TA in all forms.

On the other hand, DA is a more widely used term and also the focus of this article.

Defining DA and its connection to the self-healing grid

DA is the process of monitoring and controlling the distribution network via the use of intelligent devices, instruments and advanced components. It is enabled by integrating the devices and components in the field with the analytical tools in the control centre via two-way communications networks.

DA increases control over distribution-level equipment, however, to realise a truly self-healing grid, DA needs to extend to even smaller network entities such as DERs, industrial, commercial, residential loads and so on. As such, DA is considered one of the fundamental dimensions of the larger smart grid, and the fundamental capability of the smart grid is to self-heal.

Per NETL (US DOE's National Energy Technology Laboratory) – One of the characteristics of the smart grid is that ‘it will heal itself’. The smart grid is touted to perform continuous self-assessments to detect, analyse, respond to, and as needed, restore grid components or network sections. Acting as the grid's ‘immune system’, self-healing will help maintain grid reliability, security, affordability, power quality and efficiency’ [2].

In other words, a self-healing grid is designed to sense issues and automatically respond to them. It is designed to avert, confine and reduce harm by protecting both the power infrastructure, human beings working on the infrastructure and provide information to allow the right people/systems to make the right decisions at the right time [3].

So, how does this happen? At a high level – we need to

  • Sense: One needs to be able to see what is going on in the network. This can happen from a variety of sources with varying degrees of resolution and speed. Examples are smart meters (once every 15 min), SCADA (once every 2-4-6 s) and so on.
  • Analyse: Analysis is all about taking in the data and coming to conclusions about what steps are needed. The analysis can be to support any one of (i) reliability, (ii) optimisation and efficiency or (iii) support the new technologies such as storage or other DERs. Analysis can either be centralised in a control centre environment or distributed.
  • Control: The end result of analysis is the need to do something about it. This could be either of a manual nature (the operator performs a remote operation of a switch movement) or automatic.
  • Communicate: For the combination of sensing, analysis and control to happen, adequate communications need to be present so that the sensing can send the data to where it is needed and so that control can be sent to operate the right device in the right manner and send information back about whether the operation successfully happened or not [4].

Core components of DA

Fig 1 shows a way to categorise some of these foundational technologies in a logical manner. There are several components to DA. These components enable advanced sensing and measurement in the field, advanced control and operator decision support that are critical to automation. The main intent of DA focuses on distribution-level equipment with a goal to optimise utility operations and improve reliability of the distribution grid. Some of the technologies include smart fault indicators, smart switches, smart reclosers, smart meters and so on. These technologies are intended to reduce outage frequency and duration, improve power quality, control demand and so on. Let us discuss each of these further at a high level first and then in more detail.

Fig 1: Key components of T&D automation (adapted from source: NETL)

Sensing and measurement

The purpose of advanced sensing and measurement technologies is to acquire data from the field and provide it to system operators and others. This data would source from the distribution grid as well as customer premises and would include – fault location, transformer and line loading, feeder voltages, equipment health, power factor, outage notification, energy consumption and so on. Some of these technologies are presented in Fig 2, along with their corresponding applications [5,6].

Fig 2: Advanced sensing and measurement technologies by application

Advanced control methods

Advanced control methods are algorithms either embedded in the devices and function in a distributed manner or execute in the control centre environment. These methods can be centralised or decentralised, and take actions to correct grid behaviour along the electric value chain all focusing on analysis, diagnosis or prediction-level effort on the grid [7].

Advanced components

Examples of advanced components as shown in Fig 3 are another critical group of technologies that enable automation [8]. Some of them are still research or pilot/prototype stage, but they reach out across a broad set of areas such as power electronics, superconductivity, materials, chemistry and microelectronics. They have the ability to drive grid behaviour and can be applied in either standalone applications or connected together to create complex systems [9].


Fig 3: Advanced components by category

Improved interfaces and decision support

Improved interfaces and decision-support technologies are an essential tool-kit for the system operator to operate the grid. These are technologies that convert power system data from multiple sources in the field into information that the operator in the control centre can understand at a glance, and take action. They help reduce critical operator decision making from hours or minutes to seconds.

Ubiquitous communications

The dynamic self-healing grid cannot exist without an effective wide-spread communications infrastructure. It is the medium for bi-directional transmission of data and control, among the field devices, as well as between the field devices and the control centre. It is the foundation of the self-healing grid.

What makes it self-healing? DA interactions with smart grid

Fig 4 shows the smart grid dimensions and highlights DA as one aspect of the self-healing grid albeit a very key and fundamental aspect. The self-healing grid is also dependent and enabled by a whole slew of other technological components that are collectively called smart grid technologies. Some of these such as distributed generation, energy storage, microgrids and several others are technologies that are implemented by either utilities or their customers. Others, such as advanced operational systems, smart meter, big data and analytics, communications and cyber security are all smart grid technologies that are important to DA and the self-healing grid. In the next set of sections, this article will elaborate on these technologies and inform on how they augment DA in defining and delivering on the grid's self-healing capabilities.

Fig 4: Smart grid dimensions

Smart meters

The smart meter is the utility's primary interface with customer's premise. It measures, collects and stores end-user energy consumption (and other) data, and communicates with utility systems. It enables two-way communication between the customer premises and the utility, thereby acting as a point of contact and control. In the event of an outage, the first step of a self-healing grid is to detect and identify outage locations. The last-gasp capability of smart meters provides outage alerts to the utility, thus pin-pointing outage locations in real time. The automation algorithms can then quickly aggregate this data to the nearest distribution asset and perform restoration tasks that can dramatically cut down outage duration.

Advanced operational systems

A broad range of systems, some simple and some complex, some centralised and some decentralised, are available and are continually evolving. These systems take the data collected by sensors in the field and perform the analysis which results in decisions that can manage and control grid behaviour. The execution of the actual decisions (results) is finally implemented by the control mechanisms that ultimately affect grid conditions.

Centralised systems

Fig 5 shows examples of centralised operational systems. Many of these systems are in use by utilities today in their control centres, and some are still evolving.

Fig 5: Examples of centralised operational systems

Decentralised systems

Fig 6 shows examples of decentralised control methods. The intent of decentralised systems, as their name implies is for them to be installed in the field in a way that they can react autonomously and automatically. Their data inputs may come from the same sensors that feed the SCADA systems in the control centres, but these have the advantage of not requiring operator input or attention.


Fig 6: Examples of decentralised control mechanisms

Advanced decision support systems

It is easy to see how the data gathered from both the sensors and that created as outputs from the various applications and algorithms can be overwhelming. However, the system operator could use as much help in this rapidly changing system in order to stay ahead of developments and be able to continue to keep the system reliable and resilient to attacks by providing a broader view of the grid. Advanced decision-support systems are therefore a key piece of the automation puzzle that provide visualisation, decision support and system operator training to utility control centre personnel. Fig 7 presents examples of some of these technologies [10].

Fig 7: Examples of operator decision support systems

The three parts of advanced operational systems form the underpinning of the implementation arm of DA and provide the basics of the steps towards the self-healing grid.

Big data and analytics

Big data and analytics can be thought of as the layer between the raw data from the field and the advanced operational systems. It is the layer that converts the plethora of data collected from meters, sensors, switches and other devices deployed in the field into actionable intelligence, for use by the utility. It provides the utility with insights into the performance of the power grid, consumer energy use, peak demand and business risks. The results of analytics are fed into broad array of operational and decision-support systems, making the information processing capabilities of the control centre a lot more accurate and timely, thereby allowing DA to become more powerful and accurate. Another aspect of analytics is in predictive analytics that provides future insight into grid conditions thereby enabling operators to enhance, fine tune and test automatic restoration strategies ahead of grid disturbances.

Communications

Communications is the backbone of the self-healing grid. Coordination between sensors and advanced components in the field requires transfer of data among them. Communication is bounded by a combination of bandwidth and latency that is driven by the specific needs of the problem being solved.

  • Devices such as smart meters sending data approximately once every 15 min or so.
  • Automation devices that work through SCADA-based systems tend to end data every 4–6 s or so.
  • PMU-based systems communicated their data ∼3–60 times a second.

To enable automation, many of the existing grid devices are either retrofitted or replaced with ones that come with communication packages. Examples include, radio controlled smart switches, reclosers retrofitted with control boxes that contain communications and relay packages. Devices such as these can then be programmed as part of automatic restoration logic to implement self-healing on the distribution grid [11].

Privacy and cyber security

A self-healing grid should be designed to avert, confine and reduce harm to the grid by protecting both its power infrastructure and data. In other words, the self-healing grid should be resistant to attacks both physical as well as virtual. For the energy system to be truly resistant to attacks, attention to privacy and cyber security is critical. For cyber security, all aspects of the system (right from sending to analysis to control) need to be designed to handle intrusions from any direction. Similarly, when customer data was being stored, adequate privacy considerations needed to be in place [12].

Dos and don'ts of DA

As utilities look to implement DA in their jurisdictions, the article puts forward a set of items for consideration for the implementation.

  • Don't – ignore the business case: Implementing DA is expensive. Between the costs of the technology, implementation/maintenance costs, communications and others, implementation can be quite expensive. All costs should be weighed against the value being provided. For example, progressive utilities assess their 50 worst performing circuits when looking at implementing key DA technologies such as integrated volt-VAR control (IVVC) and fault location isolation and service restoration (FLISR).
  • Do – expect technology to be always changing: Any person who is tracking the dynamic changes in PCs, smartphones and even in the electric power industry also has realised that technology changes rapidly both in terms of capabilities and pricing. This means that any utility implementing them should be planning for these changes and improvements. The planning process should include defining architectures that anticipate technology changes and also to plan the spending process in such a way that the cost for the implementation goes down over time.
  • Do – need for a geographical information systems (GIS): Automation works when it understands the connectivity of the power system it is working with. The static model of this (also called the as-designed or as-built model) is generally held in a GIS or similar system. Automation also works best when it is able to protect the grid under different connectivity configurations.
  • Do – plan for cyber security: DA implementations are now moving away from private utility specific networks using proprietary protocols into industry-standard internet protocol (IP)-based protocols and public networks. All such implementations are vulnerable to cyber-attacks. Cyber-attacks can force the grid to perform and behave unpredictably thereby causing interruption in services to customers. The design needs to take cyber security into consideration from the beginning [13].
  • Do – plan for safety of line and other personnel: Unlike communications and other networks where automatic routing of information can be performed, power networks are designed to transport energy. In addition, most distribution networks are also designed for one-way power flow. When a switch is opened, power flow is stopped, but when a switch is closed, then power flows in the direction of flow. This means that specific safety steps need to be in place to ensure that any utility or other personnel working downstream is not impacted fatally if actions such as switch closings are not planned out.

Ongoing challenges and the future

The industry has just scratched the surface of DA – only two main applications are becoming prevalent and they are (i) volt-VAR control (also called by other names such as IVVC, conservation voltage reduction and so on), and (ii) FLISR [14]. Several other applications are in the pipeline with some in pilot stages and some still evolving [15]. The restrictions on many of these are not from a pure technology perspective – it is not as if the industry is looking for other key technologies such as fault locators. The restrictions stem more from the need for more sensing, analysis and control mechanisms in the field and a more accurate representation of the location and connectivity model of the distribution grid (generally implemented in systems such as the GIS). Some of these new capabilities are:

  • Remote fuse condition monitoring.
  • Feeder load balancing.
  • Load survey monitoring.
  • Equipment condition monitoring.
  • Faulted circuit indication monitoring.
  • Several others.

As the visibility and the controllability of the system evolves and as the accuracy of the system representation evolves, it is expected that many of these and other more sophisticated capabilities will come into play moving the grid towards the self-healing future that all power system engineers are looking for.

Conclusions

Power system engineers and utility personnel have been looking for technologies that help them achieve the self-healing grid for a long time. The capabilities of the technologies that were available until now were a combination of (i) inadequate capability, (ii) too expensive and (iii) even the communications were too expensive to implement. However, much is changing and the DA world is able to take advantage of the technological breakthroughs in other industries as they impact the electric utility industry also. Key aspects of these changes include:

  • Communications: Advances in communications technologies are now allowing utilities to move away from the older generation of wired technologies into wireless. Communications such as mesh, cellular are allowing utilities to implement sensing and control mechanisms more easily and cheaper.
  • Interconnection protocols: Proprietary protocols such as Conitel, CDC, Landis+Gyr are now giving way to newer and more standardised protocols such as DNP3, DNP3/IP and others that allow a diverse set of industry hardware and software components to interact with each other using standard mechanisms.
  • Automation software: Automation implementations are also moving away from legacy and extremely restrictive software and (e.g. Prologic) their implementation environments several of which were based on line editors. These extremely restrictive situations made it difficult to develop complex protection and automation schemes because of the limitations on their development and even more importantly on their testing. The newer environments are better from a programming perspective and also have mechanisms to code and test them. In addition, the newer systems also integrate with industry leading software systems such as DMS thereby allowing them to function from a centralised location.
  • Sensing technologies: Sensing technologies are getting smaller, cheaper, more sophisticated, better in their ability to network, get their power supply locally (through energy harvesting) and sensing many pieces of information at the same time. As a result, single sensor location can sense and deliver data required by multiple applications and systems thereby making it more convenient both for installation as well as for interacting.
  • GIS and mapping: More and more utilities are implementing GIS systems along with the associated processes necessary to keep the GIS as close to being up-to-date with the reality in the field so that the automation systems that perform the analysis will always have the latest and greatest connectivity model.

These changes portend well for the future of DA in the electric system distribution grid. This also means that while the self-healing grid is not in place today, it is imperative to note advances in technology are well positioned to help the electric system attain that in the near to mid-term future [16].

Acknowledgments

The author acknowledges support of Ms Mrudhula Balasubramanyan of Modern Grid Solutions. Material used in this article came from the training content that Modern Grid Solutions delivers to its clients.

References 

  1. U.S. Capitol, W. D.: ‘Remarks by the president in the state of the union address’, 12 February 2013. Retrieved from https://www.whitehouse.gov/the-press-office/2013/02/12/remarks-president-state-union-address.
  2. The NETL Modern Grid Initiative: ‘A vision for the modern grid’, conducted by the National Energy Technology Laboratory for the U.S. Department of Energy Office of Electricity Delivery and Energy Reliability, March 2007.
  3. DeBlasio D.: ‘Toward a self-healing smart grid’, August 2013. Retrieved from fortnightly magazine: http://www.fortnightly.com/fortnightly/2013/08/toward-self-healing-smart-grid?page=0%2C0.
  4. Office of Electric Transmission and Distribution, U.S. DoE: ‘‘Grid 2030’. A national vision for electricity's second 100 years’, July 2003. Retrieved from http://energy.gov/sites/prod/files/oeprod/DocumentsandMedia/Electric_Vision_Document.pdf.
  5. National Energy Technology Laboratory, U.S. DoE.: ‘A systems view of the modern grid, sensing and measurement’, March 2007. Retrieved from http://www.netl.doe.gov/File%20Library/research/energy%20efficiency/smart%20grid/whitepapers/Sensing-and-Measurement_Finsal_v2_0.pdf.
  6. Gilbert E. Gelbien L. Rogers B.: ‘A truly ‘self-healing’ distribution grid requires technology AND operational change’, 2009. Retrieved from http://www.gridwiseac.org/pdfs/forum_papers09/gelbien.pdf.
  7. National Energy Technology Laboratory, U.S. DoE.: ‘A systems view of the modern grid, advanced control methods’, March 2007. Retrieved fromhttps://www.netl.doe.gov/File%20Library/research/energy%20efficiency/smart%20grid/whitepapers/Advanced-Control-Methods_Final_v2_0.pdf.
  8. National Energy Technology Laboratory, U.S. DoE.: ‘A systems view of the modern grid, advanced components’, March 2007. Retrieved from https://www.smartgrid.gov/files/appendix_b3_advanced_components.pdf.
  9. McCarthy C. A.: ‘Utilities seeking intelligence on electric distribution circuits’, January 2011. Retrieved from renew grid: http://www.sandc.com/edocs_pdfs/EDOC_067830.pdf.
  10. NETL: ‘A compendium of smart grid technologies’, July 2009. Retrieved from https://www.smartgrid.gov/document/netl_modern_grid_strategy_powering_our_21st_century_economy_compendium_smart_grid_technolog.
  11. McCarthy C.: ‘Lights on – new technologies for a smart distribution grid’, (n.d.). Retrieved from Electric Energy online: http://www.electricenergyonline.com/show_article.php?mag=62&article=476.
  12. Spoonamore S. Krutz R. L.: ‘Smart grid and cyber challenges, national security risks and concerns of smart grid’, (n.d.). Retrieved from Whitehouse: https://www.whitehouse.gov/files/documents/cyber/Spoonamore-Krutz%20-%20Smart%20Grid%20CyberSecurity%20Risks%20and%20Concerns.pdf.
  13. National Energy Technology Laboratory, U.S. DoE: ‘A systems view of the modern grid, resists attack’, January 2007. Retrieved from Smartgrid: https://www.smartgrid.gov/files/Systems_View_Modern_Grid_Appendix_A3_Resists_Attack_v20_200704.pdf.
  14. Zajkowski S.: ‘Live data – the keys to the energy kingdom, the bigger picture’, (n.d.). Retrieved from Electric Energy online: http://www.electricenergyonline.com/show_article.php?mag=95&article=763.
  15. Uluski R.: ‘Distribution automation and the self-healing network’, April 2014. Retrieved from IEEE: http://smartgrid.ieee.org/newsletters/april-2014/distribution-automation-and-the-self-healing-network.
  16. Zajkowski S. Mays K.: ‘Smart distribution: a self-healing and optimized grid’, 16 October 2013. Retrieved from Electric Light & Power: http://www.elp.com/articles/print/volume-91/issue-5/sections/smart-distribution-a-self-healing-and-optimized-grid.html.
  17. U.S. Department of Energy: ‘Integrated smart grid provides wide range of benefits in Ohio and the Carolinas’, September 2014. Retrieved from https://www.smartgrid.gov/files/C7-Duke-Energy-Case-Study-FINAL-092914.pdf.
  18. AG, Siemens: ‘Self-healing grid for a reliable power supply’, 2014. Retrieved from http://w3.siemens.com/smartgrid/global/SiteCollectionDocuments/References/Reference_Self-healing%20grid_Stedin_e_final.pdf.

Case studies 

Case study 1 – Duke Energy, DA

As part of its DA project, Duke Energy deployed self-healing capabilities on some of its key circuits to improve reliability. These self-healing capabilities include and extend beyond sectionalisation capabilities. Duke installed multiple new devices throughout the system to improve reliability. Inside the fence deployments on utility substations included remote terminal units, circuit breakers, capacitor bank and voltage regulator controls and smart relays. Outside the fence, it added new and upgraded components to power lines on particular circuits, including upgraded network capability, capacitor bank and voltage regulator controls, new electronic reclosers, line sensors and communication retrofits to existing reclosers. Operating Duke's self-healing groups of field devices enables auto-reconfiguration and enhances reliability by rapidly restoring power after a fault is identified. Fig 8 shows how these components work together.

Fig 8: Duke Energy's DA components

Duke's customers are seeing measurable improvements in outage frequency and faster restoration after disruptions from ‘self-healing’ capabilities built into 64 distribution circuits in Ohio. These self-healing capabilities are made possible by 30 groups of field devices that enable fault detection and automate rapid isolation and restoration of the fault. By August of 2014, Duke's 30 self-healing device groups in Ohio had activated 84 times, reducing outage frequency and duration. Fig 9 shows measurable improvements in system average interruption frequency index (SAIFI) – which measures average interruptions per customer [17].

Fig 9: Duke Energy's self-healing teams show year-over-year reductions in SAIFI

Case study 2 – Stedin, DA

Stedin, one of the largest distribution network operators in The Netherlands, has a total of 21,240 secondary substations in service and serves approximately two million customers in the Randstad region, which is one of the largest conurbations in Europe. Stedin has experienced power failures in its distribution grid, in the past. Not only did these outages displease customers, but they entailed expensive compensation payments for Stedin. Stedin's main objective was therefore to significantly reduce the system average interruption duration index (SAIDI) in their medium-voltage grids.

Its distribution grid consists mainly of underground cables, which cannot be quickly repaired in the event of faults, and cannot be fixed with automatic reclosers either. Therefore, it developed a self-healing network solution that would work for its MV network (Fig 10).

Fig 10: Distribution network with local control units in each secondary substation

The solution is based on a regional controller at the substation level, which ensures automatic fault localisation, isolation and restoration. It was implemented by upgrading its substation automation with new distribution grid automation functions. The regional controller serves as an interface to the control centre, which collects data from the distribution grid and hosts the regional, centralised applications of Stedin's self-healing grid. Part of its solution consists of an intelligent local substation. Upgrade kits were either installed to modernise older ring main units (RMUs), or were replaced with new RMUs.

With the innovative self-healing grid solution installed, Stedin is expected to significantly reduce its SAIDI and restore power to most of its customers in less than a minute in the event of a power outage. This is expected to increase customer satisfaction as well as provide considerable cost savings by minimising the heavy contractual penalties that must be paid in case of power outages [18].

 

Go to the profile of Mani Vadari

Mani Vadari

President, Modern Grid Solutions

No comments yet.