Ferranti Effect

In order to understand the results, we have to understand the cause behind the Ferranti Effect.

All transmission lines (even the kind discussed in Radiation and Propagation courses) behave the same way once the line length approaches a tenth of the signal wavelength.  Due to the relatively low frequency of utility power (60Hz in North America and 50Hz in many other parts of the world), the wavelength is pretty long, so these effects only begin to appear in significantly long (greater than 300km) transmission lines.

The classical model is:

Lines of a moderate length (greater than 300km) can be modelled simply as a series resistance, series inductance and shunt capacitance – in Power Systems, we often call this model a “Pi section” (this moniker makes more sense if you separate the capacitance at the sending and receiving ends of the line, dividing them by two).  Longer lines (those exceeding 500km) are then an extension of an already-solved problem: they can simply be modelled using multiple moderate-length segments as appropriate.

Keen readers will notice that this is, quite simply, a two-port network model: we can consider each Pi section a black box, with sending-end voltage/current and receiving-end voltage/current.  Many of us rely on an approximation of how wires behave: in most applications, they have infinitesimal impedance, and so the impedance may be neglected in calculations.  However, when we approach power transmission, the voltages and currents are much higher than experienced elsewhere, which can have quite a profound impact on system operation.

I hope that this brief discussion provided a reasonable introduction or review.  If not, the Kathmandu University also has a very good handout on the subject.

Surge Impedance Loading

Based on the telegrapher’s equations and the above model, we can determine the characteristic impedance (also called surge impedance) of the transmission line as:

In all transmission lines, for power or signals alike, optimal power transfer occurs when the load impedance matches the characteristic impedance.  In Power Systems, we like to relate these quantities to units Power (Real, Reactive and Apparent) because these quantities can always be directly compared regardless of phase angles, power factors, harmonic distortion levels or voltage levels.

The Surge Impedance Loading converts the characteristic impedance (ohms) into a power (Watts) value:

If the amount of power being transmitted equals the SIL, the line mutual coupling (the inductance and capacitance in the model) cancels each other out, thus resulting in the line operating at unity power factor.  When the amount of power transferred is below the SIL, the power factor is leading (capacitive), and when the amount of power transferred is above the SIL, the power factor is lagging (inductive).

An intuitive model

Intuitively, I understand this behaviour by thinking about the cause of these impedances, though I am not a physicist, so this intuition is best understood as a useful analogy, not as fact.  I imagine lines have some slight twist when installed, giving rise to the series inductance.  Likewise, lines are conductors of different potential separated by a dielectric (air), which results in some capacitive coupling between lines.

Recall that power loss due to the resistance of a power line can be calculated using Joule’s law:

Similarly, the reactive power absorbed by (or injected from, if Q is negative) a power line into the system can be calculated using (where X is defined as negative for capacitors and positive for inductors):

The inductance is fixed, but the amount of reactive power absorbed by the series inductance is proportional to the current flowing across the line.  On a lightly loaded line, or where the receiving end is an open circuit, the current is very small, so the inductive nature of the line is minimized and the capacitive behaviour dominates.  Thus, the line is below the SIL and operates with a leading (capacitive) power factor.

Power transmission lines as capacitors

Finally, to get to the real point of this article. Given the above background, it follows that lightly loaded or open-ended lines will inject reactive power. With lightly loaded parallel redundant lines, it is therefore possible to open one line and use it to provide reactive power (var) support for the system.

For my simulation, I used two parallel 230kV lines, each 600km long, with three ideally-transposed phases on each right-of-way (in delta configuration with four bundled sub-conductors). These lines were supplied by an infinite bus (voltage source at 22kVrms line-to-line) with a 22/230kV Wye-Delta transformer. At the receiving end, a 300MW+5Mvar load was installed.

Here are the two circuits in steady state (note that BRK2 is open):

Note that TLine1 has a depressed receiving end voltage due to the current flowing a cross that line (the line inductance cancels out the Ferranti effect), but TLine2 has an elevated receiving end voltage due to the Ferranti Effect. Also note that the reactive power flow at the sending end for TLine2 is negative, indicating that reactive power is flowing “backwards” to the sending end.

Let’s take a closer look at what’s happening at the receiving end:

The breakers were configured to begin open and close in at 250ms to energize the circuit. Afterward, BRK1 remained closed and BRK2 was reopened at 500ms. We can see that reactive power initially flows across both lines, but when the receiving end circuit breaker is opened, reactive power ceases to flow. Note that the x-axis shows elapsed time of the simulation (in seconds).

The sending end, by contrast, is much more interesting:

When both breakers are opened (until 250ms), there is a significant line charging capacitance drawing reactive power from the system. After the breakers close, the reactive power demand drops significantly (though it is still slightly capacitive due to both lines being lightly loaded). Once TLine2 is opened at the receiving end at 500ms, something interesting happens: the reactive power injected by that line into the system returns to its line-end-open state, while TLine1 increases its reactive power consumption in unison.

In conclusion, it is entirely possible to use a transmission line as a shunt capacitor.

Unfortunately, in equal parts due to its age and design philosophy, the PAUSE system powering CPAN makes it difficult for distributions to be maintained by a group, rather than an individual. The inspiration for this post comes from a discussion I had recently with Florian Ragwitz, who contributes to several key Perl projects, including Catalyst, Moose, DBIx::Class and many more.

Permissions

First, a bit about how permissions on CPAN work.

In order to make a package installable using the CPAN Shell, there must be some mechanism to disambiguate a module name. Consider this simple example:

1. I upload Acme::Package to CPAN.
2. Some time passes, and unbeknownst to me, another author uploads a different package, but which is called Acme::Package to CPAN as well.

In the absence of any permission checking, if I then instructed users to install Acme::Package using the CPAN Shell, they would inadvertently install the wrong distribution! This has some rather serious implications: the other Acme::Package is probably quite different from mine, and a malicious author could have taken my software and added a backdoor vulnerability.

CPAN solves this issue by tracking each module namespace separately using the PAUSE Indexer, which assigns upload permissions to users through two mechanisms:

1. The module namespace registration list.
2. First-come status (the first uploader of a given package namespace “owns” that namespace).

Going back to the example given, the second uploader of Acme::Package would not have permission to use the namespace. The package will be accepted into the archive, but will not be indexed, meaning that users installing Acme::Package will still get my distribution.

If users want to install the other author’s package (which is marked as an UNAUTHORIZED upload in big red letters on CPAN Search), they would need to explicitly specify AUTHOR/Acme-Package-1.00.tar.gz.

For packages maintained by several people, it is also possible to assign co-maintainer status to others, so that they may also upload a package and have it correctly indexed. This way, two or more people can work on the same package together, and upload it under their own accounts (without causing the upload to be marked unauthorized). Thus, PAUSE credentials do not need to be shared.

This provides a nice solution to the malicious upload problem, but also has implications for team-maintained packages. In particular, consider the case where there are two authors working on Acme::Library.

1. Alice uploads the first version to CPAN, containing modules: Acme::Library and Acme::Library::Main.
2. The PAUSE Indexer grants Alice first-come permissions to both Acme::Library and Acme::Library::Main.
3. Alice grants Bob co-maintainer status on both Acme::Library and Acme::Library::Main.
4. Bob creates a new Acme::Library::Other module and adds it to the  package.
5. The PAUSE Indexer grants Bob first-come permissions to Acme::Library::Other.
6. Subsequent uploads by Alice will cause the upload of Acme::Library::Other to be marked UNAUTHORIZED.

Solutions

Clever Perl authors have attempted to solve this problem in many different ways over the years, but none of them have been widely successful because they all rely on some degree of human interaction.

Shared PAUSE Accounts

Some notable projects have attempted to solve the issue by creating a shared PAUSE user to hold the requisite first-come or module list upload permissions, which may then be granted to all other team members through the existing co-maintainer facility.

Alternatively, since it is easier for smaller projects, many modules simply assign first-come permissions to a single person, who is then in charge of providing co-maintainer permissions to others who would like to work on it.

Both of these approaches have the same limitation: any people uploading new modules must remember to assign first-come permissions to the group or user in question. In our case, Bob should have assigned first-come permissions to Acme::Library::Other to Alice, who then must pass co-maintainer permissions back to Bob. Unfortunately, this almost never happens, and Alice must chase down Bob (who happens to be on vacation in Antarctica) or, alternatively, the already over-worked PAUSE administrators.

Single Uploader

Some projects deal with this issue by sharing a version control system and having all the uploads go through a single person, in our case, Alice. This fixes the permission problem, since first-come permissions are always granted to Alice, but it results in a single point of failure. If there are some serious security issues requiring an immediate release, Alice must be available (and, as luck would have it, she is vacationing in Antarctica at the time).

Enter x_authority

One proposed solution, which is used in projects including Moose and Catalyst, is to use a special field in the CPAN Metadata file (META.yml or META.json) that defines someone as the “authority” for first-come namespaces in a distribution.

This is how it would work for Alice‘s Acme::Library distribution:

1. Alice uploads a package to CPAN, containing modules: Acme::Library and Acme::Library::Main.
2. Alice specifies, in META.yml:

   x_authority: cpan:ALICE

   This refers to Alice‘s PAUSE login, and is the person to whom permissions for new modules uploaded in this distribution are assigned.

3. Alice grants Bob co-maintainer status on both Acme::Library and Acme::Library::Main.
4. Bob creates a new Acme::Library::Other module and adds it to the package
5. The PAUSE indexer, seeing the x_authority defined in META.yml, grants Alice (not Bob!) first-come permissions to Acme::Library::Other. At this time, Bob also automatically gets co-maintainer permissions to Acme::Library::Other.
6. Subsequent uploads by Alice will be indexed properly.

Problems

There are still some outstanding issues that need to be resolved, but the x_authority proposal represents a giant leap forward for team-maintained software.

The name: any keys not part of the CPAN Metadata Specification must be prefixed with “x_” – eventually, once it is used by more people and accepted into the specification, this name will become, simply, “authority.”

Other comaintainers: if Charlie joined the project prior to Bob‘s upload of Acme::Library::Other, then Alice still needs to grant co-maintainer permissions to Charlie. Unfortunately, the PAUSE Indexer cannot automatically grant permissions to him, since it has no notion of a “distribution,” only module namespaces.

Malicious uploaders: in the worst case, if Eve joins the project and maliciously (or unintentionally!) changes the x_authority, she will automatically get first-come permissions on the namespace of any modules she adds. However, this is the same behaviour that we had in the absence of x_authority.

Conclusions

Ultimately, the benefits of this feature (making group maintenance easier) drastically outweigh the cost (only a few small changes need to be made to the PAUSE Indexer). They are unlikely to cause any problems in practice, and the worst-case behaviour is the same as if we did not have x_authority at all.

It isn’t perfect, but it is a solution that requires minimal effort and minimal changes to PAUSE. Eventually, the goal is to create a more sophisticated system that will handle the issues outlined above, as well as more complex ones, such as renaming distributions or moving modules between distributions.

Thanks to Florian Ragwitz for spending some time discussing x_authority at length with me. He and Leon Timmermans proofread this article prior to publication.

Netherlands

Smart meters are the some of the earliest intelligent devices installed in distribution networks and critical to enabling the smart grid of the future.  One of the biggest issues that every smart grid initiative encounters when attempting to incorporate the technology into their system is the public perception that smart meters violate the right to privacy.  Consequently, if the utility does not handle the situation tactfully, the reduction in the rate of consumer participation can diminish the practical gain from smart grid installations.

As mentioned previously, smart meters are capable of communicating wirelessly with the utility, receiving consumer usage data with the potential to control OpenHAN-compliant appliances remotely.  In the face of intelligent adversaries with increasingly powerful computing systems, it is important to provide a significant degree of security and future proofing.

In 2005, the Netherlands electricity distribution company Oxxio began widespread introduction of a smart meter for both gas and electricity.  When the European parliament issued a directive to member states to begin installation of smart metering equipment, the public was neither educated nor reassured about the new technology.  Economy minister Maria van der Hoeven decided to push for compulsory installation of smart meters and punishing refusal to install them with a fine of up to €17,000 or six months in prison.  Amidst privacy concerns, consumer protection organizations fought rigorously against the law and won; smart meters can now only be installed on a voluntary basis as requested by consumers [1].

We must learn from this stark lesson and avoid a similar outcome in future installations by ensuring adequate education for the public in order to assuage their fears and uncertainty, ultimately to ensure vital consumer participation.

Ontario

While the amount and timing of data provided by smart meters from the field does not pose serious privacy risks from internal misuse, there many security concerns surrounding external adversaries.  In particular, there is the potential for malicious users to modify their usage data in order to influence consumer billing, either by reducing their own consumption or as a financial attack against someone else.  Since the utilities would be making design decisions based on the recorded trends, outside manipulation of the data could cause catastrophic effects to equipment if not upgraded when needed due to underrepresentation of actual power consumption.

In Ontario, the current smart grid deployment initiative involves the government, Hydro One’s distribution business as well as other local utilities.  It demonstrates the need for very close cooperation between the utilities and their regulatory bodies, especially since much of their current success can be attributed to their work communicating with users.  Learning from errors in past smart grid implementations, the Ontario government established several websites acting as a central point of origin describing smart meters, their function and their overall objectives.

For support for the technical aspects of the deployment, Hydro One has partnered with Trilliant Technologies, which is a company that “provides intelligent network solutions and software to utilities for advanced metering, and Smart Grid management” [2].  Trilliant’s expertise and extensive smart metering technology portfolio reduces Hydro One’s risk and guarantees a higher degree of flexibility than with other vendors.  The smart meters operate in the unlicensed 2.4GHz radio frequency commonly used for ZigBee, Wireless LAN (IEEE 802.11) and Bluetooth, with Trilliant providing both the metering and the related communication infrastructure.  Trilliant also designed the 1.3 million smart meters currently being deployed by Hydro One’s distribution arm.

Thus far, current efforts to ensure network security and likewise to assure and encourage consumer participation in Ontario have been a success, and there are many other similar efforts taking place in other countries at this time.  Because smart meters involve using an extremely complex device to do measurement for billing purposes, it must be completely free of defects, especially in light of Canadian requirements like the Weights & Measures Act.

Australia

As climate change raises the average global temperature, Australia’s climate is one of the hardest hit: becoming hotter and drier than ever before.  Australia continues to consume a considerable amount of electricity; in fact, 261.8 TWh of electricity was produced in Australia during 2006, and that figure is projected to reach 413 TWh by 2030 [3].

With electricity demand continuing to rise, the utility may soon need to consider construction of new generation, transmission and distribution infrastructure.  However, maintenance of an aging system is itself extremely costly, and simultaneously investing in new infrastructure is simply not feasible.  As a result, Australia decided to implement dynamic rating of equipment in both their transmission and distribution systems, allowing them to better utilize existing infrastructure.  For an example comparing static equipment ratings with those dynamically generated by Australia’s control system, see Dynamic Equipment Rating.

–

I originally wrote this article for a report submitted to ECE4439: Conventional, Renewable and Nuclear Energy, taught by Professor Amirnaser Yazdani at the University of Western Ontario.

A smart meter is a two-way digital device that accurately records and wirelessly communicates with the utility company at scheduled intervals (usually hourly), providing information about the amount of power consumed in a given time period [1].  If this metering technology is implemented across an entire city, utilities would be able to observe usage trends and introduce time-of-use pricing in order to reduce demand during periods of peak energy consumption.  By increasing the cost of electricity during times of day where demand is at its highest, consumers are encouraged to delay non-critical tasks until there is a reduction in loading on the system.  In this way, the loading on the overall power system would remain more consistent throughout the day, increasing utilization of existing capacity and potentially reducing voltage fluctuations in the distribution system.  Less variation in power flow will yield better stability of our system and more efficient use of our assets.  Ultimately, it will raise overall consumer awareness of the need to conserve energy.

In addition, a more futuristic goal of smart metering in residential areas is to incorporate the concept of smart appliances.  Using the HAN protocol, the smart meter will be able to control compatible devices and coordinate with local consumer loads to reduce strain on the distribution system.  Smart devices would be able to collaborate with other neighbourhood meters in order to decide when to allow or postpone the operation of non-critical in-home appliances.  In essence, the main goal of smart appliances is to further extend the function of the smart meter, allowing better organization and load management than ever before [2].

The installation of smart meters in homes and businesses in Ontario may already be evident.  The Ontario government, in collaboration with Hydro One and other local distribution companies has already begun the long-term transition to a smarter grid system by mandating the installation of a smart meter in every home in Ontario by the end of 2010 [3].  While the meters are not yet transmitting telemetry, the installation of the smart metering infrastructure will pave the way to a world of future possibilities.

Another significant way that smart grids will benefit residential consumers is providing a means to incorporate growing distributed generation systems.  For example, home customers will be able to integrate solar panels or wind turbines on their roof and sell electricity back to the grid at a predetermined rate set by the government; in Canada, this is known as Feed-in-Tariff rate for alternative and renewable energy sources.  Although consumers are already permitted to connect distributed generation systems, there continues to be very limited deployment of these generation sources in residential areas, particularly since it poses significant problems to the voltage system including the introduction of harmonics and voltage fluctuations.

Another potential issue with integration of distributed generation is that most renewable energy sources depend on natural phenomena and are therefore incapable of consistently and predictably generating power throughout the day.  The utility needs to design compensation for the resulting voltage fluctuations in order to prevent the system parameters from exceeding the safe operating region.  By measuring and recording information about distributed generation installations, the utility will be able to install appropriate compensation systems to protect the system as a whole.

Over the next several decades, demand for electricity is projected to rise by at least 30% [4].  It is becoming less and less practical to construct new large-scale generation plants, so in order to meet this demand, we must turn to renewable energy, making it is imperative that we ensure the system is capable of accepting a significant volume of energy from distributed generation.  The solution of widespread renewable energy in homes will satisfy our increasing thirst for electricity while simultaneously offering a significant advancement in our goal to reduce our overall carbon footprint.

In the next installment, we will discuss some real-world implementations of smart meters in distribution systems, exploring key issues that must be considered when deploying these technologies.

–

I originally wrote this article for a report submitted to ECE4439: Conventional, Renewable and Nuclear Energy, taught by Professor Amirnaser Yazdani at the University of Western Ontario.

Real time system monitoring is a relatively new tool available to power system operators, allowing them to analyze all aspects of a large power system continuously.  Phasor measurement units are the leading technology behind the newfound ability to provide instant analysis for problems in a geographically enormous power system.  Using a synchronous clock based off GPS timing signals, phasor measurement units (PMU) can very accurately measure current and voltage phasors with almost no time difference between meters [1].  This allows for real time information regarding power angles and power flow, system status, and possible problems.  With phasor measurement refreshing frequencies as high as 60Hz, the synchronized clock time of all PMUs is an essential requirement for providing accurate information to data centers, where a delay of several microseconds could lead to corrupt results.  Because of the timing constraints required by PMUs to perform properly, PMUs now abide by the IEEE standard C37.118, which defines standardized measurement methods, timing tolerances and communication channels.

Wide Area Monitoring Systems (WAMS)

The widespread implementation of PMUs has led to wide area monitoring systems (WAMS) allowing for situation reports for large parts of the transmission system.  PMUs transmit this system information continuously to data centers where computers can record and monitor the state of the entire power system, performing actions to maximize power flow while maintaining system stability [2].

Fault detection and proper relay functioning is one of the most important tasks in transmission systems, accounting for approximately 70% of all major disturbances  [3].  By using PMUs and having snapshots of the entire system updated up to 60 times per second, the detection of faults is very quick.  By having all of this data available at a centralized location, coordination of relays during faults can be optimized for the situation, resulting in the best fault clearing schemes.

2003 North-eastern Blackout

Wide area monitoring systems have been integrated into many transmission systems globally, allowing transmission operators to have continuous real time information about the state of the transmission system.  WAMS technology plays an important role in generation and protection, allowing generating facilities to observe system conditions continuously and maximize their output for different loading scenarios.  The eastern North American WAMS recorded all major transmission system information during the 2003 blackout, and provided critical data for the reconstruction of the sequence of events leading up to the blackout [4].  However, the lack of intelligent control schemes left the system incapable to react quickly enough to maintain stability, resulting in the loss of power to millions of people.

The 2003 North American blackout showed the world and the grid operators that the conventional power system ideas put in place over 100 years ago are not sufficient for the complex and continually growing power system of modern times.  The blackout left over 50 million people without power, and was caused by the incorrect tripping of transmission lines and generation facilities [5].  Investigations conducted revealed that the cause of the cascading blackout was due to distance relays operating within Zone 2 and Zone 3, with preset calculations.

The problem originated when the Midwest Independent System Operator (MISO) had a problem with its state estimator [6], and the information gathered from the eastern WAMS due to offset in PMU sampling times [4].  The state estimator is responsible for indicating potential problems with system parameters and operations, without this tool operators were unaware of the initial problems leading up to the blackout.  After initial transmission trips to which operators were not aware, overloading of remaining lines caused them to reach thermal limits, ground faulting through trees.  After losing several large transmission lines, distance relays started operating incorrectly in zone 3, seeing low impedance due to high load current and low voltage from tripped generation capacity.  Had the WAMS been operating correctly, the initial problematic events that lead to the eventual blackout would have been identified, and problems could have been corrected before the situation elevated to such severe levels.

Open Phasor Data Concentrator (OpenPDC)

Phasor Data Concentrators (PDC) are devices distributed throughout the transmission system designed to collect data from the many phasor measurement units.  Due to the high volume of data collected, each node typically collects data from only five or six individual PMUs and forwards the data to concentrator devices.

In October 2009, the Tennessee Valley Authority (TVA) released data collection software for industry use called SuperPDC (Super Phasor Data Concentrator) [7], which is responsible for aggregating measurements from multiple PDCs and archiving measurements for subsequent event analysis.  It is now available under an open source license under the name openPDC.

This software allows the TVA to collect data from its 120 online PMUs (see Phasor Measurement Unit Map) that together measure almost two thousand parameters several times per second.  In all, the TVA archives 150 million measurements per hour (requiring 36 GB of storage space per day) [8].

Tennessee Valley Authority

In conjunction with graduate students from Washington State University (WSU), the Tennessee Valley Authority collected data from its PMUs to observe local area oscillations within the system during a major switching event.  During a planned switching of 500kV transmission lines at the Cumberland Fossil Plant (CUF), the system experienced a dangerous undamped local-mode power oscillation at 1.2Hz, which continued until operators detected the problem and reversed the switching three minutes later.  At its peak, the oscillations escalated up to a 700MW variation in transmitted power (see Cumberland Fossil Plant Oscillation Event).

Without the phasor measurement units in place, detection of this nearly catastrophic event would not have been possible and the system could have suffered a total collapse.  It remains unknown whether the power system stabilization (PSS) equipment was not yet installed or otherwise out of service during the fault [9].  Fortunately, but both local- and inter-area oscillations can be detected using this method and the software is available for immediate use by any utility [10].

Electric Reliability Council of Texas

The amount of power transfer over transmission lines is limited by the thermal limit of the line, putting constraints on profits and maximum generation capacity.  Lines ratings are typically set to conservative constant values for the sake of safety and reliability, but newer technologies are enabling utilities to vary equipment ratings based on environmental factors including humidity and ambient temperature.

When the Electric Reliability Council of Texas (ERCOT) implemented dynamic rating of their transmission lines, they were able to maximize utilization of existing infrastructure, which had direct financial benefit for bulk generation facilities exporting power.  A control system used data including current atmospheric conditions, forecasted temperatures and system loading to determine the maximum power transfer limits, which usually exceeds the constant ratings given by the manufacturer [11].

–

One of my partners wrote the majority of this article for a report submitted to ECE4439: Conventional, Renewable and Nuclear Energy, taught by Professor Amirnaser Yazdani at the University of Western Ontario. It is included here for completeness with the rest of the articles. I edited the article and wrote the sections entitled: Open Phasor Data Concentrator (OpenPDC) and Tennessee Valley Authority.

The ZigBee protocol enables communication using multiple network topologies, including star, tree and mesh [1].  It is particularly challenging to ensure reliability of the communication channel for smart meter designs, especially with the use of wireless-based backhaul channels and ZigBee is particularly suited to this application with its mesh network topology.  In case a meter is out of range of a central tower or otherwise obstructed by buildings or other objects, ZigBee-based meters enable communication between meters and relaying of information back to the data collection point [2].  Furthermore, since ZigBee devices utilize the unlicensed 2.4GHz spectrum, they have a very low cost of deployment and allow seamless integration and networking of many ZigBee devices.  Although they have many benefits, ZigBee devices are designed primarily for short-range communication and low device power requirements [3], requiring a separate wireless protocol for long-range transmission.  ZigBee is a key technology enabling the OpenHAN networking standard discussed later in this paper.

OpenADR (Automated Demand Response)

Because electricity is charged at a constant price in the current power system, regardless of time of use, consumers therefore have little incentive to put forth effort to change their usage patterns.  Introducing smart grids will allow for dynamic billing based on market pricing at the time, and thus give more incentive to customers to plan their energy usage [4].  With the proposed automated demand response, individual smart meters will have the capability of monitoring system wide conditions, determining when the system is stressed and appropriately allocate power to different appliances.  Automated demand response will aim for reducing high loading during peak times, in an attempt to remove excess stress from the power system [5].

Open Automated Demand Response is a standard currently under development [6], which aims to ensure interoperability between various smart meter infrastructure devices.  It will provide a way for users program appliances to operate according to current electricity prices, for example to do laundry when power is cheapest.

OpenHAN (Home Area Network)

Open Home Area Network is a proposed standard to interface with the smart meter in residences with appliances in the home.  OpenHAN can allow for utility control of the appliance, customer coordination and timing of appliance activation, and operational states of appliances based on set-points such as price.  Upon completion and implementation of this standard, residents will be capable of having appliances automatically run during times when electricity is cheapest, and utilities will be able to cease operation of appliances during peak loading times.  OpenHAN is the fundamental idea behind automated demand response, where there exists a link between the smart meter of the customer and the customer’s appliance [7].

Worldwide Interoperability for Microwave Access (WiMAX)

WiMAX is an industrial wireless interoperability standard related to the existing technology known as the Global System for Mobile Communications (GSM) [8].  It is typically used for land-based wireless Internet service providers, particularly those serving rural communities; however, it is finding applications within power systems as a backhaul for smart meter telemetry data [9].

Broadband over Power Lines

Several different startup companies have explored the use of Broadband over Power Lines (BPL) as an Internet service delivery or as a backhaul for telemetry from smart meters [10].  While it is no longer a serious contender for delivering Internet access to remote communities, the technology still has its niche applications, particularly within the realm of power systems.  Some smart meter vendors continue to sell smart metering equipment that transmits telemetry over power lines [11] rather than using its own radio frequency, which requires the purchase of costly spectrum.

Furthermore, using BPL couplers traditionally used for sending and receiving data across power lines can be used to listen for types of noise characteristic of certain types of equipment failures; for example, a cracked insulator beginning to fail will induce a specific signature pattern that can be detected using BPL couplers [12].

–

One of my partners wrote the majority of this article for a report submitted to ECE4439: Conventional, Renewable and Nuclear Energy, taught by Professor Amirnaser Yazdani at the University of Western Ontario. It is included here for completeness with the rest of the articles. I edited the article and wrote the sections entitled: Worldwide Interoperability for Microwave Access (WiMAX) and Broadband over Power Lines.

Distributed Generation

Distributed generation has changed the way that the power system operates, allowing many small generation facilities to contribute power in order to meet current electricity demand collectively.  Consequently, utilities anticipate that distributed generation systems will introduce new problems since it violates the previous assumption of unidirectional power flow.  Distributed generation introduces the phenomenon of bidirectional power flow, resulting in adverse effects on conventional protection and voltage regulation equipment in the existing power system.

Indeed, many American states have adopted renewable portfolio standards, which require a pre-determined amount of electricity to come from renewable sources by as early as 2013 (for details, see Appendix A: Renewable Portfolio Standards).

Plug-in Electric Vehicles

With dwindling supplies of fossil fuels and increasing prices for crude oil and petroleum products, electric vehicles are steadily gaining momentum.  Although electric vehicles are not yet mainstream, they are expected to have a significant impact on the method and amount of power distribution in the near future as drivers begin switching from gasoline-fuelled vehicles to their electric equivalents en masse.  The increasing popularity of plug-in electric and hybrid-vehicles introduces issues to the power system since it effectively doubles or triples power consumption in already strained residential areas.

There are several problems faced due to the way in which the current power system is configured.  The problem lies not with the method by which the electric car is charged, but rather the number of electric cars being charged, as well as the total amount of energy required to charge each car on a daily basis (see Appendix B: PHEV Demand Increase Example).  This large increase in electrical demand will require additional generation facilities to meet the demand, and require new equipment to deal with the increased demand of consumers.  This paradigm shift will severely affect distribution utilities since the current generation of residential transformers is not rated for such high peak demands.

By implementing smart grids, local distribution utilities will be able to mitigate the problem by staggering the charging sequence of each electric vehicle.  Furthermore, utilities can explore the use of hybrid vehicles as a distributed storage technology or as a power factor controller.  Indeed, the smart grid has the potential to reduce loading on residential substations and small distribution transformers, which eliminate the necessity for expensive high-capacity equipment.

Appendix A: Renewable Portfolio Standards

Source: The Smart Grid: An Introduction – For Utilities.  Published by the Office of Electricity Delivery and Energy Reliability, United States Department of Energy.  Page 19.  Retrieved on March 20, 2010 from http://www.smartgrid.gov

Appendix B: PHEV Demand Increase Example

Gasoline car energy

Energy density of gasoline = 32MJ/L*50L/tank = 1600MJ/tank

Gasoline energy per month = 1600MJ/tank * 4tank/month = 6400MJ/month

Note that 50L/week = 200L/month would result in a monthly cost of: 200L/month @ $1.00/L = 200$/month

Electric car energy

1kWh = 3.6MJ

6400MJ/3.6MJ = 1778kWh/month

1778kWh/month @ $0.058/kWh = $103/month

Not only is the total electrical energy usage of the family almost tripled for every month, but charging peaks at night-time would exceed the peaks during the daytime and also prevent transformers from cooling down at night (in case they are being run above rated conditions during the daytime).

–

A partner and I originally wrote this article for a report submitted to ECE4439: Conventional, Renewable and Nuclear Energy, taught by Professor Amirnaser Yazdani at the University of Western Ontario.

History

The term smart grid has been in use since at least 2005, when the article “Toward a Smart Grid,” written by S. Massoud Amin and Bruce F. Wollenberg, appeared in the September-October issue of Power and Energy Magazine.  For decades, engineers have envisioned an intelligent power grid system with many of the capabilities mentioned in formal definitions of today’s smart grids.  Indeed, while the development of modern microprocessor technologies has only recently begun making it economical for utilities to deploy smart measurement devices at a large scale, its humble beginnings can be traced as far back as the late 1970s, when Theodore Paraskevakos patented the first remote meter reading and load management system [1].

Relevance

For the next several decades, our global energy strategy will inevitably involve upgrading to a more intelligent grid system.  Three fundamental motivators are driving this change: current bulk generation facilities are reaching their limit; utilities must maximize operational efficiency today in order to postpone the costly addition of new transmission and distribution infrastructure; and they must do all of this without compromising reliability of the power system.  In fact, many governments, including the Essential Services Commission (ESC) of Victoria, Australia [2] are adopting legislation to make crucial components of a smarter grid system mandatory.  In Canada, Hydro One’s distribution system has millions of smart meters already installed [3] in preparation for time-of-use rates slated to become mandatory by 2011 [4].

Capacity

Over the next several decades, consumer advocacy groups and environmental concerns from the public will prevent the construction of centralized generation plants as a measure to meet quickly growing demand for electric power.  Moreover, global electricity demand will require the addition of 1000 MW of generation capacity as well as all related infrastructure every week for the foreseeable future [5].  Traditional bulk generation plants are now prohibitively expensive to construct due to cap-and-trade legislation, which places severe financial penalties on processes that continue to emit carbon dioxide and other harmful greenhouse gases.  In conjunction with the higher economic cost, there are also social pressures and widespread concerns about long-term sustainability.

Reliability

With the exception of hydroelectric and geothermal power, renewable energy sources such as wind and solar present unique challenges since they are unpredictable by nature and may vary significantly in their power output due to unpredictably- and rapidly-changing external factors.  Subsequently, we must retrofit the existing power grid to ensure that it can maintain system stability despite these fluctuations in power output.  Furthermore, utilities must have the ability to monitor key indicators of system reliability on a continual basis, particularly as we approach the grid’s maximum theoretical capacity.

Efficiency

A smarter grid can also improve operational efficiencies by intelligently routing different sources of energy.  Because we currently send electricity from distant power generation facilities to serve customers across hundreds of kilometres of transmission lines, approximately eight percent of the total generated electric power is lost as waste heat [6].  Moreover, we can make better use of the existing power generation infrastructure by reducing peak demand; in fact, the International Energy Agency found that a 5% demand response capability can reduce wholesale electricity prices by up to 50% [7].

–

I originally wrote this article for a report submitted to ECE4439: Conventional, Renewable and Nuclear Energy, taught by Professor Amirnaser Yazdani
at the University of Western Ontario.

The essence of smart grid technology is the provision of sensors and computational intelligence to power systems, enabling monitoring and control well beyond our current capabilities.  A vital component of our smart grid future is the wherewithal to detect a precarious situation and avert crisis, either by performing preventative maintenance or by reducing the time needed to locate failing equipment.  Moreover, remotely monitoring the infrastructure provides the possibility of improvements to the operational efficiency of the power system, perhaps through better routing of electric power or by dynamically determining equipment ratings based on external conditions such as ambient temperature or weather.

In the face of changing requirements due to environmental concerns as well as external threats, it is becoming extraordinarily difficult for the utility to continue to maintain the status quo.  As the adoption of plug-in [hybrid] electric vehicles intensifies, the utility must be prepared for a corresponding increase in power consumption.  The transition to a more intelligent grid is an inevitable consequence of our ever-increasing appetite for electricity as well as our continued commitment to encouraging environmental sustainability.

The deregulation of the electric power system also presents new and unique challenges, since an unprecedented number of participants need to coordinate grid operations using more information than ever before.  If we are to maintain the level of reliability that customers have come to expect from the power system, we must be able to predict problems effectively, rather than simply react to them as an eventuality.

As the grid expands to serve growing customer demands as well as a changing society, we must proceed cautiously to ensure the system preserves its reputation of reliability.  It is incumbent upon us to carefully analyze past events and implement appropriate protection and control schemes using modern technologies.  It is clear that the power system of tomorrow will depend upon the design and preparation we conduct today.

–

I originally wrote this article for a report submitted to ECE4439: Conventional, Renewable and Nuclear Energy, taught by Professor Amirnaser Yazdani
at the University of Western Ontario.

My student peers were also very intelligent and great to learn from. I enjoyed meeting them virtually and discussing our various projects on the IRC channel as the summer progressed and the Summer of Code kicked into full swing. The Debian project in particular also helps arrange travel grants for students to attend the Debian Conference (this year, DebConf10 is being held in New York City!). DebConf provides a great venue to learn from other developers (both in official talks but also unofficial hacking sessions). As the social aspect is particularly important to Debian, DebConf helps people meet those with whom they are working with the most, thereby creating lifelong friendships and making open source fun.

I have had several interviews for internships, and the bit of my work experience most asked about is my time doing the Google Summer of Code. I really enjoyed seeing a project go from the proposal stage, setting a reasonable timeline with my mentor, exploring the state of the art, and most importantly, developing the software. I think this is the sort of indispensible industry-type experience we often lack in our undergrad education. We might have an honours thesis or presentation, but much of the work in the Google Summer of Code actually gets used “in the field.”

Developing software for people rather than for marks is significant in a number of ways, but most importantly it means there are real stakeholders that must be considered at all stages. Proposing brilliant new ideas is important, however, without highlighting the benefits they can have for various users, the reality is that it simply will not gain traction. Learning how to write proposals effectively is an important skill and working with my prospective mentor (at the time – he later mentored my project once it was accepted) to develop mine was tremendously useful for my future endeavours.

The way I see the Google Summer of Code is, in many ways, similar to an academic grant (and the stipend is about the same as well). It provides a modest salary (this year it’s US$5000) but more importantly, personal contact with a mentor. Mentors are typically veterans in software development or the Debian project and act in the same role as supervisors for post-graduate work: they help monitor your progress and propose new ideas to keep you on track.

The Debian Project is looking for more students and proposals. We have a list of ideas as well as application instructions available on our Wiki. As I will be going on internship starting May, I have offered to be a mentor this year. I look forward to seeing your submissions (some really interesting ones have already begun to filter in as the deadline approaches).

It contains the amount of power being generated as well as the date/time of the last update. I refreshed a few times and realized that updates occur every five minutes. Curious, I thought I’d whip up a quick module to scrape this information from the web site and produce some nice graphs with RRDTool. I used the open source RRDTool::OO module to do this, which is freely available on the CPAN.

Recognizing that web scraping is not the most reliable means of getting data from a web site, I contacted OPG via e-mail and requested an API for this data. In the latest iteration of WWW::OPG (version 1.004 already on CPAN), a smaller machine-readable text file provides the same data in an easier-to-parse format. Thanks to someone I know only as “Rose” from OPG for providing this file, which is much easier to parse and less likely to change.

As OPG supplies roughly 70% of Ontario’s electric power demand, the consumption statistics provide a relatively good reflection on our behaviour patterns over time. During the course of this, I learned how to work with Round Robin Databases (and wrote an article about it) and was able to observe some interesting trends even in the first week of operation:

The graph begins Saturday, December 26th, 2009 (Boxing Day) and continues through the week approaching the new year 2010. These particular trends are interesting because, while two observable peaks occur each day, the overall power consumption (including 95th percentile consumption) seems much lower than usual.

By comparison, consider this graph of a week ended 14 January 2010 (there were some rather long-lasting outages in the data collection which I’m trying to track down, but it still gives a sense of the general trends):

In this case, the 95th percentile consumption is much higher at about 14GW rather than 10GW. Note that the 95th percentile gives a rather good approximation of an infrastructure’s utilization rate, since it works by indicating peak power after removing the highest 5% of data points. This means that 95% of the time, power consumption was at or below the given line.

Percentile is more important than averages because it indicates the minimum infrastructure to satisfy demand most of the time (95% of the time) so it gives us a simple way to determine whether more infrastructure is needed.

In the specific case of electric power utilities, and because electricity is so important for both industrial and commercial use, legal requirements stipulate that the demand must always be supplied, barring exceptional circumstances such as failures of distribution transformers. In this case, maximum power consumption is a more useful measure for infrastructure planning.

In the short term, each data point is significant: we want an accurate picture of every event that has occurred in the last 24 hours, which might include small transient spikes in disk usage or network bandwidth (which could indicate an attack). However, in the long term, only general trends are necessary.

For example, if we sample a signal at 5-minute intervals, then a 24-hour period will have 288 data points (24hrs*60mins/hr divided by 5 minutes per sample). Considering each data point is probably¹ only 4 (float), 8 (double), 16 (quad) bytes, it’s not problematic to store roughly three hundred data points. However, if we continue to store each sample, a year would require about 105120 (365*288) data points; multiplied over many different signals, this can become quite significant.

To save space, we can compact the older data using a Consolidation Function (CF), which performs some computation on many data points to combine it into a single point over a longer period. Imagine that we take an average of those 288 samples at the conclusion of every 24 hour period; in that case, we would only need 365 data points to store data for an entire year, albeit at an irrecoverable loss of precision. Though we have lost precision (we no longer know what happened at exactly 5:05pm on the first Tuesday three months ago), the data is still tremendously useful for demonstrating general trends over time.

Though perhaps not the easiest to learn, RRDtool seems to have the majority of market share (without having done any research, I’d estimate somewhere between 90% and 98%, to account for those who create their own solutions in-house), and for good reason: it gets the job done quickly, provides appealing and highly customizable charts and is free and open source software (licensed under the GNU General Public License).

In a recent project, I learned to use RRDTool::OO to maintain a database and produce some interesting graphs. Since I was sampling my signal once every five minutes, I decided to replicate the archiving parameters used by MRTG, notably:

• 600 samples store 2 days and 2 hours of data (at full resolution)
• 700 samples store 14 days and 12 hours of data (where six samples become a 30-minute average)
• 775 samples store 64 days and 12 hours of data (2-hour average)
• 797 samples store 797 days of data (24-hour average)

F0r those interested, the following code snippet (which may be rather easily adapted for languages other than Perl) constructs the appropriate database:

archive => {
 rows    => 600,
 cpoints => 1,
 cfunc   => 'AVERAGE',
},
archive => {
 rows    => 700,
 cpoints => 6,
 cfunc   => 'AVERAGE',
},
archive => {
 rows    => 775,
 cpoints => 24,
 cfunc   => 'AVERAGE',
},
archive => {
 rows    => 797,
 cpoints => 288,
 cfunc   => 'AVERAGE',
},

There are also plenty of other examples of this technique in action, mainly related to computing. However, there are also some interesting applications such as monitoring voltage (for an uninterruptible power supply) or indoor/outdoor temperature (using an IP-enabled thermostat).

Footnotes

1. This may, of course, vary depending on the particular architecture

–

Introduction

Using PowerWorld‘s Simulator software, we connected a large four-reactor nuclear generation plant (750MW per reactor, constant output) via two parallel transmission lines with a large city (modeled as an infinite bus). At the midline between the nuclear reactors and the large city, a transformer is installed at the midline bus to supply a different voltage to a nearby town. This town is near the proposed connection point of the Wind Turbine Generation (WTG) project; this project has a peak output of 85MW.

From the objectives:

In this lab, our objective is to study the potential impact of integration of distributed generation (in particular, a wind farm) using the PowerWorld Simulator software.  We simulate a very large capacity plant (a nuclear plant consisting of four reactors producing 750 MW each).  Because these reactors are large, they will provide some voltage regulation by supplying or consuming MVARs.

The nuclear plant serves a large city via two parallel transmission circuits, as well as the smaller city at the mid-line of one of these lines.  To simplify this lab, we model the large city as an infinite bus, which consumes any excess power generated by the plant.

In this manner, we can explore various phenomena resulting from distributed generation systems like wind farms, including the effects on power transfer and power system stability.  This lab provides insight on two very important issues in power systems, notably, the addition of distributed generation and the challenges involved with electrifying remote communities.

For my full report, see: Power Systems 4464 Lab 2 (PDF). Note that the small town bus has a constant 20Mvar reactive power demand, with a 40Mvar (nominal) shunt capacitor installed initially. Some modifications are made to the compensation scheme as part of the study and report.

Starting from first principles, let’s look at the equation for instantaneous power in electrical systems:

Of course, there are similar definitions for mechanical power (which involve torque and speed rather than voltage and current as above).

In the best case, the voltage and current waveforms will be identical, which means that they are both sinusoidal with crests and troughs occurring together. For passive devices like light bulbs (which are purely resistive loads), this is exactly what happens. However, some devices (such as capacitors and inductors) store energy for a short period of time, causing the waveform of the current to be phase shifted or displaced, relative to the voltage wave.

If we use the “average” values of voltage and current, we can determine what is known as apparent power. Even though I call them average, what we really use are the “root-mean-square” values — the reasons we use this measure are beyond the scope of this article, but suffice it to say that we can’t use a normal average for sine since it would simply be zero. For you statisticians out there, RMS is related to standard deviation (it’s a special case where your mean is zero).

Though its units are identical to those of Power (Watts), we use a different unit convention for this value, the Apparent Power (Volt-Amps, or VA).

Power factor is simply the ratio of real power compared to apparent power:

For linear devices which do not store energy, real power and apparent power are the same, so the power factor is 1 (sometimes people call this “unity power factor”).

If, however, an energy storage device like an inductor or capacitor stores energy and simply return it back to the source, then the power factor will be reduced (since power is being transmitted over the line, stored temporarily and then sent back). Only real power contributes to work actually done — whether it be heating your room or turning a motor.

As mentioned earlier, inductors and capacitors cause the voltage and current to be shifted relative to each other. This results in what is called the Displacement Power Factor (the angle, φ, refers to the angular displacement between voltage and current):

However, as we are moving forward in semiconductor techologies, we are increasingly encountering more and more nonlinear devices which introduce something known as Harmonic Distortion. Basically, it makes the current waveform “noisy” compared to the voltage reference; usually this is because a device switches on and off (goes from periods of drawing some current to zero current) instead of merely being proportional to applied voltage.

Total harmonic distortion (THD) is a measure of how “noisy” the current waveform is; for example, if you draw lots of power in short-duration bursts, the current wave won’t look like a sinusoidal waveform at all. This is also known as a shape factor since it will be 1 (unity) for perfectly sinusoidal current, and smaller otherwise.

THD is measured as a percentage, so its value is somewhere between 0 and 1.

The overall power factor takes both distortion (current waveform shape) and displacement (current waveform phase difference, relative to the voltage) into account:

So now we have identified an equation useful for determining the overall power factor of your equipment, especially for things like computers, cooking, heating, washing/drying of clothes, etc. But what does power factor have to do with efficiency?

To answer that question, let’s rearrange our power factor vs (real and apparent) power equation to look at what happens to current. We’ll be looking at RMS current, which is important because it is one of the primary factors that determines maximum loading of a given power line.

Since the amount of power we’ll need and the RMS voltage (line voltage) are effectively fixed, we can see that power factor and RMS current are inversely related. That is, with a lower power factor, we will require higher RMS current to deliver the same amount of power to our load; our RMS current is lowest when the power factor is 1 (unity).

Since power dissipated across a transmission line is:

There are finite limits on the amount of current that can be transmitted (greater power dissipated results in more heating of the wires, which can cause them to expand significantly or to melt), a unity power factor means a more effectively utilized infrastructure.

If everything we connect to the power system has a low power factor, it will result in an inefficient use of existing infrastructure (since we are transmitting more current than necessary, which also increases total line losses). It also means we will need a greater investment in infrastructure sooner, which is a challenging issue facing electric power utilities responsible for distribution of power.

Here’s a blurb about it from their homepage:

We also have a bunch of features you won’t find in every wiki, like an attachment system that automatically makes a web gallery of your photos, live AJAX previews as you are editing your text, and a proper full text search engine built straight into the software.

Unfortunately, such a rich feature set comes at a price — this shiny piece of software has a rather large dependency chain. As a result, building the module (after building its prerequisites) from CPAN is both slow and prone to failure, since each module must be individually retrieved, extracted, built, tested and then installed.

To make matters worse, any failure anywhere in the chain (perhaps a new version of a module breaks things) will cause a complete failure to build the module — either Catalyst or MojoMojo — which has some serious implications for production applications.

In Debian, we mitigate this risk by having separate unstable and testing distributions, so if a newer version breaks things in unstable, we will catch it and have a chance to fix it before the package makes it into testing. By packaging these modules for Debian, we get the advantages of a faster installation process (since we’re installing pre-built binaries) combined with better Quality Assurance.

One of the big issues blocking both of these has been missing copyright information for a lot of modules. I’ve worked a lot with Matt S. Trout, one of the primary people behind coordinating the efforts of the Catalyst project, and gathered the necessary information for an upgrade and upload into Debian.

Recently, libcatalyst-modules-perl (version 35) and libcatalyst-modules-extra-perl (version 4) were uploaded to Debian, containing many necessary updates and fixes to improve the Catalyst experience on Debian. The next big push is to get MojoMojo’s dependencies packaged (currently only String::Diff is blocking it, due to missing copyright information).

A bounty of $150 is being offered by one of the MojoMojo developers to the first person who can re-implement the String::Diff functionality in a free/open source way.

TCSC designs operate in the same way as Fixed Series Compensation, but provide variable control of the reactance absorbed by the capacitor device. The basic structure of a TCSC can be seen below:

A thyristor-controlled series compensator is composed of a series capacitance which has a parallel branch including a thyristor-controlled reactor.

TCSC operates in different modes depending on when the thyristors for the inductive branch are triggered.  The modes of operation are as listed:

• Blocking mode: Thyristor valve is always  off, opening inductive branch, and effectively causing the TCSC to operate as FSC
• Bypass mode:  Thyristor valve is always on, causing TCSC to operate as capacitor and inductor in parallel, reducing current through TCSC
• Capacitive boost mode: Forward voltage thyristor valve is triggered slightly before capacitor voltage crosses zero to allow current to flow through inductive branch, adding to capacitive current. This effectively increases the observed capacitance of the TCSC without requiring a larger capacitor within the TCSC.

Because of TCSC allowing different operating modes depending on system requirements, TCSC is desired for several reasons. In addition to all of the benefits of FSC, TCSC allows for increased compensation simply by using a different mode of operation, as well as limitation of line current in the event of a fault. A benefit of using TCSC is the damping of sub synchronous resonance
caused by torsional oscillations and inter-area oscillations.  The ability to dampen these oscillations is due to the control system controlling the compensator. This results in the ability to transfer more power, and the possibility of connecting the power systems of several areas over
long distances.

–

This article was taken from the introduction of a report which was written by a partner and I, submitted to ECE3333: Power Systems I, taught by Professor Rajiv Varma at the University of Western Ontario.

Fixed Series Compensation

Fixed series compensation (FSC) of a line is desirable for power transmission due to the effects of line reactance modification.  By adding series capacitance, the reactive impedance of the line decreases, thus lowering the voltage drop across the transmission line.  This effect can be seen through the simplified power flow equation (see the post about Power Transfer) obtained by neglecting line resistance and line charging capacitance.

Line reactance is counteracted by a series capacitance, resulting in overall lower line impedance and a lower voltage drop across the line.

By adding the series capacitance, it can be seen that the receiving line end voltage will be closer to the sending line end voltage.  This decrease in voltage drop across the line allows more power to be transferred over the line for any given sending line end voltage.

The advantage to using FSC compared to thyristor controlled series compensation is price.  Usually FSC allows for a majority of compensation for a lower cost when compared to thyristor controlled series compensation. The following phasor diagram demonstrates the effect of series compensation:

–

This article was taken from the introduction of a report which was written by a partner and I, submitted to ECE3333: Power Systems I, taught by Professor Rajiv Varma at the University of Western Ontario.

Using PowerWorld‘s Simulator software, we repeated one of our basic labs from ECE3333 as an introduction to computerized modelling of power systems. We connected a single synchronous machine to an infinite bus across a 600km, 1000MW-SIL power line.

It is simplest for me to just lift the objectives from my lab report:

In this lab, our objective is to simulate a simple single machine infinite-bus configuration using the PowerWorld Simulator software.  We design a local generator system (a synchronous generator) having a nominal generation capacity of 500MW and with no predefined peak generation (that is, the generator is modelled as having infinite generation capability).

In this manner, we can explore various phenomena like power transfer, power system stability and the effect of shunt compensation on the midline.  We model a 600km span of transmission line with a shunt compensation device installed at the midline (300km from both ends) and determine the stability limit with and without this compensation device enabled.

Please see the following images, which show the simulation being run in PowerWorld:

500 MegaWatt generation, no compensation

500 MegaWatt generation with Synchronous Condenser Compensation

For my full report, see: Power Systems 4464 Lab 1 (PDF). Note that the synchronous condenser installed at the midline is a Switched Shunt Compensation unit. I thought the standard inductor/capacitor schematic symbol looked a little boring, so I overlaid a synchronous condenser on top of it.

Thyristors can be conceptualized (and indeed, are drawn on schematic diagrams) as diodes with a switch (the gate voltage or photoelectric stimulus). In FACTS correction systems, whereby a thyristor should act as essentially a fast-acting switch, power needs to be transferred in both directions. Thyristors are also used in high-power rectifier circuits as well, particularly for High-Voltage Direct Current (HVDC) transmission.

How much faster is thyristor-based switching compared to mechanically-switched circuit breakers? Because thyristors are semiconductor devices, they can switch on the order of milliseconds. Conventional circuit breakers, on the other hand, take much longer to switch. They can switch in one or two cycles (of the 50-60Hz mains frequency), though for power system protection purposes, this is considered a rather slow switching speed.

The graph below illustrates the difference:

Additionally, mechanically-switched capacitors do not have sufficient switching speed to support extremely rapid switching nor can they be realistically switched more than a few times per day.

We can see that advanced communications and control systems play an important role in flexible transmission and distribution systems.

–

This article was taken from the introduction of a report which was written by a partner and I, submitted to ECE3333: Power Systems I, taught by Professor Rajiv Varma at the University of Western Ontario.