I wrote this piece with my friend Denis Chartrand as a companion document for my CEA presentation back in February 2018 (See https://benoit.marcoux.ca/blog/cea-tigers-den-workshop/) but I now realize that I never published it. So, here it is!
Canada Electricity Industry 2030 20180221
On February 21, 2018, I presented at the annual T&D Corporate Sponsors meeting of the Canadian Electricity Association. This year, the formula what similar to the “dragons” TV program, with presenters facing “tigers” from utilities. They asked me to go first, so I didn’t know what to expect, but it went well. Or, at least, the tigers didn’t eat me alive.
The theme was a continuation of my 2017 presentation, this time focusing on what changes utilities need to effect at a time of low-cost renewable energy.
I’ve attached the presentation, which was again largely hand-drawn: CEA 20180221 BMarcoux.
A utility client told me that they were trying out covered conductors on a feeder in a forested area. This was the first time that this large utility tried covered conductors. The objective is to reduce the impact of tree contacts and falling branches that blow fuses and therefore result in permanent outages for customers. In this context, the great length of feeders and the high system voltage (25 kV) make coordinating reclosers and fuses difficult.
Covered conductors have a thin insulation covering – not rated for the full phase voltage, but sufficient to reduce the risks of flashovers and fire when a tree branch falls between phases, when a tree makes momentary contact with a conductor, or when an animal jumps to it. Covered conductors also allow utilities to use tighter spacing between conductors.
While covered conductors help with tree contacts, they also have a number of operational disadvantages:
These issues got me thinking on how I could provide a better alternative. Replacing fuses with single-phase reclosers appears to be an interesting (if unlikely) alternative to covered conductors. Cutout-mounted single-phase reclosers can easily be installed in existing cutouts to protect lateral circuits. Those circuits are then protected against tree contacts without the disadvantage of covered conductors. Coordination with upstream mainline reclosers is eased by making the single-phase recloser faster than the mainline recloser. Cost is clearly lower than re-conductoring.
Full disclosure: I am employed by S&C, and S&C makes a cutout-mounted recloser.
Close-loop voltage control in distribution networks traditionally relied on Potential Transformers (PT) on feeders communicating with a control algorithm sending setting signals to voltage regulators and capacitor banks. More recently, Faraday devices have been used instead of PTs, being less expensive to purchase and to install.
What about smart meters with voltage measurement capability? Some smart meters measure voltage at the service point, which accounts for voltage drop in secondary feeders and transformers. There are also far more meters than PTs or Faraday sensors, providing greater coverage. But there is a problem: smart meter networks have long internal latency – it may take minutes for voltage signals to get back to a control center. This renders smart meters unusable in a traditional real-time control loop.
However, analytics could make use of delayed smart meter data, combined it with other data such as weather and historical data, to provide pseudo real-time feedback.
This could prove particularly effective with high level of Distributed Generation (DG) penetration that is affected by weather, such as solar and wind. Where a traditional voltage control system relying on real-time feedback could be overwhelmed or mislead by the variability of renewable generation, a control system relying on deep analytics of smart meter and weather data could be more effective in maintaining distribution grid stability.
One of the most critical situations with Distributed Generators (DG – embedded generators in Europe) is that a interrupter on a distribution feeder may trip to isolate a circuit section and the DGs might continue supplying the load on that section, creating an “island”. When load closely match generation in the island, it may be sustained for some time, posing safety hazards – this is known to have caused death.
Distributed generators have various passive or active anti-islanding mechanisms that open a breaker at the point of connection when an islanding condition is detected. However, islanding detection techniques used in small DGs (such as residential photovoltaic generators) are far from perfect – without expensive circuitry, they may not always immediately detect an island when generation and load are closely matched. Therefore, some utilities require that load on any feeder section (i.e., between interrupters) be always greater than generation, ensuring that an island cannot sustain itself. This means that the total distributed generation capacity on a feeder section must be significantly less than the minimum aggregated load on that section. The problem is compounded by the fact the engineers assessing DG connection requests usually do not know actual load and generation per line section – estimations need to be made.
In the end, allowable distributed generation on a line section can be a pretty small number – in Ontario, Hydro One requires that total generation must not exceed 7% of the annual line section peak load – meaning that few customers are allowed to have generators.
Applying analytics on smart meter data can better assess how much distributed generation can safely be connected to a line section. For instance, minimum load may never be correlated with maximum generation – e.g., in hot climates, minimum load occurs at night, when there is no solar generation. Analytics can look into past load and generation records to determine how much generation can be connected without getting into potential islanding condition. Safe generation levels may be many times more than the previous conservative worst-case-that-never-happens engineering guidelines allowed.
Distributed generators (DG – embedded generators in Europe) can cause voltage excursions outside the allowable range and can exacerbate phase imbalance, increasing losses (especially on North American networks). Utilities set engineering rules to try to mitigate those effects, for example by limiting how much generation can be connected per feeder section.
Unfortunately, meter-to-transformer-to-phase (MTP) mapping (MPT in Europe) is notoriously inaccurate, meaning that engineers do not know the distribution of single-phase DGs on a feeder – with DGs often clustered on single-phase laterals, DG dispersal across phases may be far from even. Similarly, distribution transformer tap positions are generally unknown, but often set high because under-voltages was the traditional problem – with DGs, over-voltage can become the issue. This forces engineers to take an overly cautious approach when assessing DG connections or face the risk of network problem later.
In the past, validating MTP mapping and distribution tap settings required extensive fieldwork to track each triplex to a transformer, to track the transformer to a phase, and to visually check tap setting with a bucket truck. Now, analytic applications can correlate voltage levels over time to identify to what transformers and phase each meter belongs, and identify transformers where tap setting is too high or too low. The analytical engine can also correlate service point street address and longitude/latitude coordinates with those of the transformer. The correlations are statistical, but, with enough historical data, the accuracy is equal to or better than a visual survey, at a much-reduced cost.
With reliable phase and tap information, engineers can now assess DG connections requests with greater confidence that voltage stability of the grid will be maintained.