Factors affecting ERCOT – Congestion Revenue Rights Analysis
Earlier this week I wrote about a recent webinar that was held in our offices on the ERCOT market. While it gave a short overview of the market and some of the changes that have occurred, I decided to follow-up with a second article detailing our team’s analysis on Congestion Revenue Rights.
Congestion Revenue Rights (CRR)
Since nodal LMPs are based on the cost of generation and the amount of congestion between nodes, ERCOT set up a mechanism for managing the risk associated with congestion. CRRs have been implemented to help market players hedge against increased prices caused by high congestion in transmission lines.
CRRs are directional contracts between a source and sink settlement node. These contracts are auctioned on a monthly and annual basis through ERCOT and are sometimes used by traders as investment vehicles.
For the presentation, our team looked at an individual CRR contract between the Hays Energy node (source) and a Braunig plant (sink).
The following graph shows October 19,2012 hourly prices at the source and sink, along with an effective price that factored in the cost of a CRR.
Figure 1: A single day (October 19, 2012) of CRR contract prices with an effective price calculation. (Graph created in ZEMA)
We found that the mechanism worked as intended. When there is congestion in the transmission line, the sink price rises well above the source price. The contract holder still pays the high sink price but receives a rent payment for holding a CRR contract. When the rent payment is factored into the total cost, we found that CRRs did decrease volatility during hours of high congestion (hours 13-20 in the example).
Our team looked at this contract to see its profitability over the course of several months by comparing cumulative rent payments versus charges:
Figure 2: Cumulative rent payments (received) and rent charges incurred due to holding this CRR. (Graph created in ZEMA)
What we found was that in this particular case, the contract charges always ended up higher than payments received. In October, the charges and payments received were fairly close, but again ended up in the negative by the end of the month.
With that in mind, our analysts decided to figure out just how much a contract holder should have paid in order to break even.
Figure 3: Calculation of the break-even price for four months worth of this CRR contract. (Graph created in ZEMA)
Using ZEMA, we substituted the actual CRR contract price used in our calculations with a figure that would yield a break-even cumulative figure at the end of the month. Again, the month of October was fairly close to breaking even, but ended up slightly in the negative. To analyze contracts for different source and sink nodes, one would simply substitute the associated price and MW quantity into our formulas and run the analysis through the software.
Once our team was familiar with the ERCOT market structure and CRRs, we decided to look at some other factors that played a role. In Part III of this series, we’ll take a look at electricity consumption, generation trends and future outlooks for ERCOT.
To read the final installment of our series, visit Factors affecting the ERCOT market Part III – Electricity consumption, generation and reserve margin challenges.https://blog.ze.com/our-industry-views/factors-affecting-the-ercot-market-part-ii-congestion-revenue-rights-crr-analysis/Industry Viewsanalysis,Congestion Revenue Rights,CRR,data collection,data management,ERCOT,market data,Nodal,Texas,ZonalEarlier this week I wrote about a recent webinar that was held in our offices on the ERCOT market. While it gave a short overview of the market and some of the changes that have occurred, I decided to follow-up with a second article detailing our team’s analysis on Congestion...Maninder ManhasManinder Manhasmaninder.firstname.lastname@example.orgContributorBlogs by data management Experts & Analysts | ZE