Economic Savings from Advanced Transmission Technology Deployment: Initial Assessment
Rob Gramlich, Grid Strategies LLC
Transmission congestion can increase electricity bills paid by American households and businesses. It results from limits on transmission flows from one location and “re-dispatch” of electricity generation such that higher cost generators must be turned on to supply electricity demand.
Congestion costs are likely to rise in the US. They had been on the decline after a significant build-out of transmission capacity from 2009-2015. But there is much less transmission expansion planned in coming years than in the past. Interconnection queues and generation deployment estimates suggest continuing development of generation resources in constrained areas. The retirement of some generation in certain areas may create some space on the grid. But the combination of reduced transmission investment and growing remote generation development will likely cause congestion to grow.
Determination of congestion costs and the savings possible from advanced transmission technologies requires detailed region-by-region study. The purpose of this short analysis is to provide an initial rough estimate.
The analysis below indicates that a combination of advanced power flow control, topology optimization, and dynamic line ratings would save American households and businesses around $20 billion over 10 years.
Congestion Costs in the US
Congestion costs are known in two-thirds of the country, and not well known in the other third. Where it is known is where there is a Regional Transmission Organization (RTO) or Independent System Operator (ISO) because they transparently price and post congestion. Other efforts to calculate annual transmission congestion cost such as the Congressionally mandated National Electric Transmission Congestion Study[1] by the Department of Energy faced the same challenge in calculating national transmission congestion cost.
If one assumes that congestion outside of RTOs is similar in magnitude to congestion inside, then one can extrapolate from the known half of the country to the full country. That method is employed here.
Congestion costs in the transparent markets in the country for the most recent year in which all the data are available (2016) are shown in Table 1 below. They total $3.91 billion.
Table 1: 2016 Annual Transmission Congestion Cost, Transparent Markets
| Region | 2016 congestion cost ($ million) |
| PJM | 1,024 |
| CAISO | 142 |
| NYISO | 529 |
| ISO-NE | 39 |
| MISO | 1,400 |
| ERCOT | 497 |
| SPP | 280 |
| Total | 3,911 |
Sources shown below.
To extrapolate from the transparent regions to the full country we need to know what portion of the country is covered by transparent markets. Sixty six percent of the country is covered by RTOs, estimated by comparing the percent of peak loads, using the data below in the Market Size Comparison section. Dividing $3.911 billion by .66 yields $5,952 billion. Thus we estimate annual US transmission congestion cost to be approximately $6 billion per year.
Congestion Cost Reduction from Transmission Technologies
There are a few indicators of how much of the $6 billion could be reduced through advanced transmission technology. The market monitor for MISO estimated that “as much as $155 million in production costs savings could be achieved by fully adopting temperature-adjusted, short-term emergency ratings throughout MISO.”[2] MISO had $1.4 billion in congestion as shown above, so these savings are a little over 10 percent of total congestion.
An estimate of the capacity increase Dynamic Line Ratings make possible on the Oncor system found capacity could be increased by 30-70 percent.[3] The New York Power Authority pilot project showed capacities increased 30-44 percent above static ratings.[4] Clearly these pilots indicate significant economic savings though the amount would depend on how much out-of-merit dispatch occurs at the times when capacity increased, and that was not quantified.
One study of power flow control savings found 30-50 percent reduction in congestion.[5] Another study in the UK found 2 million pounds in savings though it doesn’t say what percentage of the system congestion cost that represents.[6]
A combination of the technologies that increase grid utilization would increase the total savings.
With savings of 10 percent from one technology and over 30 percent from another, and adding these technologies together along with others, it seems safe to estimate that savings from the suite of technologies could be on the order of 1/3 of congestion costs. With annual congestion costs around $6 billion/year, that would be $2 billion/year in savings. So over a 10 year period savings could be $20 billion.
Studies of this potential would be needed to provide a more robust estimate. The studies should be done for each region, and take into account the shape of the dispatch curve and grid configuration.
Market size comparison for use in extrapolating from transparent markets to the US
To compare the size of the transparent markets to the non-transparent markets, in order to extrapolate, these data were used:
| Region | 2015 peak load (GW) |
| PJM | 143.496 |
| CAISO | 45 |
| NYISO | 33.226 |
| ISO-NE | 24.437 |
| MISO | 125.9 |
| ERCOT | 69.62 |
| SPP | 45.279 |
| US | 741 |
| % covered | 66% |
Sources shown below
The seven ISO/RTO regions’ peak load is 66% of the total US peak load. One could also make this comparison based on annual energy.
2016 Congestion cost sources:
Peak Load Sources
Other Sources
[1] https://www.energy.gov/oe/services/electricity-policy-coordination-and-implementation/transmission-planning/national-2
[2]https://www.misoenergy.org/Library/Repository/Report/IMM/2016%20State%20of%20the%20Market%20Report.pdf
[3] https://www.smartgrid.gov/files/SGDP_Transmission_DLR_Topical_Report_04-25-14_FINAL.pdf p. vi.
[4] https://www.smartgrid.gov/files/SGDP_Transmission_DLR_Topical_Report_04-25-14_FINAL.pdf p. vi.
[5] http://newgridinc.com/wp-content/uploads/2016/06/PRuiz-FERCTechConf-28Jun2016.pdf slide 15.
[6] http://www.smarternetworks.org/Files/NIA_&_NIC_170731152223.pdf p. 14.
