Today’s deregulated electric energy markets seek to stimulate competitive energy production and consumption. But a major limitation on the efficiency of any such market, and therefore on the price of electricity, is that the interconnected transmission grid has limited capacity. Electric power flow divides itself in complex ways among the branches of the meshed network, which frequently exhibits bottlenecks (congestion). This gives rise to the concept, now fully implemented in the USA’s “nodal markets”, of penalizing each grid user who contributes to congestion, and rewarding each user who reduces it.
The approach is of special interest because the resulting congestion pricing is rational, analytical, exact and real-time (among other things). Each user who affects congestion differently gets a different price. The approach involves very large scale online modeling of the entire grid, and congestion-constrained mathematical optimization.
However, there is one big downside to this — the congestion prices are volatile. That is, they vary considerably and somewhat unpredictably through the load cycle and over longer times. This exposes market participants to big congestion-cost risks. For many of these participants, the hedging of these risks becomes a necessary and normal part of doing business.
As a result, each nodal energy market has an ancillary market in CRRs (congestion revenue rights), which allow transmission users to hedge (insure) themselves against large swings in congestion prices. CRRs can be acquired by market participants in various ways, including periodic auctions and secondary trading. Again, this requires very-large-scale network modeling and optimization.
This lecture briefly outlines the nodal congestion-pricing approach; its main focus is then on the ancillary market for CRRs (also called by various other names).