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Last week, my mentor mentioned that high-voltage power transmission circuits could sometimes be used to provide reactive power support when on potential but off load, particularly for parallel lines.  Anecdotally, based on my limited understanding of the Ferranti Effect, this seems perfectly reasonable: light loading on the line results in elevated line charging capacitance, which is then injected to the system at the point of connection.

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:

Source: Wikipedia

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):

Dual 230kV Circuits, One Line 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:

Mvar flow at receiving end of 230kV circuits

 

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:

Mvar flow at sending end of 230kV circuits

 

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.

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This is the second part of a two-part series (the first part provides an introduction) discussing the role of smart grids in electric power distribution systems. We will explore some past and current installations of smart grids, discussing their motivating factors, planning, implementation and results. Essentially, this article is a discussion where we learn both from our successes and our failures in the power industry, to inform our future decisions.

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.

[1] Wilmer Heck. (2009, April) Smart energy meter will not be compulsory. [Online].   http://www.nrc.nl/international/article2207260.ece/Smart_energy_meter_will_not_be_compulsory
[2] Trilliant, Inc. (2010, March) Trilliant, Inc. – Communications for the Smart Grid. [Online].   http://www.trilliantinc.com/
[3] Cagil Ozansoy, “Turning Down the Heat,” Australia’s Fast-Growing Electricity Sector Ramps Up Its Global Warming Initiatives, vol. 8, no. 1, pp. 29-36, January-February 2010.

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.

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Over the coming months, Canadian utilities will overhaul installations of electricity consumption meters at residential and commercial premises in order to accommodate the upcoming smart grid.  Until very recently, the most common method of energy metering was by means of an analog electromechanical device that functions based on eddy currents.  While this meter has served the utility well, it is only capable of recording the cumulative amount of power consumed and must be manually recorded from time to time.  However, it is not capable of recording how much has been used corresponding to a specific time of the day.

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.

[1] D Y Raghavendra Nagesh, J V Vamshi Krishna, and S S Tulasiram, “A Real-Time Architecture for Smart Energy Management,” in Innovative Smart Grid Technologies, Washington, D.C., January 2010, pp. 1-4.
[2] Brian Seal. (2005, May) Demand Responsive Appliance Interface from the EPRI Demand Responsive Appliance Interface Project. [Online].   http://osgug.ucaiug.org/sgsystems/openhan/HAN%20Use%20Cases/OpenHAN%202.0%20use%20cases/Appliance%20Interface%20Connector%20-%20Contribution%20to%20OpenHAN.doc
[3] Ali Vojdani, “Smart Integration,” Power and Energy Magazine, vol. 6, no. 6, pp. 71-79, November-December 2008.
[4] IEEE Emerging Technologies. (2009, January) A Smart Grid for Intelligent Energy Use. [Online].   http://www.youtube.com/watch?v=YrcqA_cqRD8

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.

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