1. 1 Geographic Integration, Transmission Constraints, and Electricity Restructuring Comments by Erin Mansur Yale University, SOM and FES

2. 2 Are electricity markets integrated? Interesting question and an interesting paper. The authors examine price differences between PJM (West) and New York (West), and between PJM and ECAR (Cinergy). Data are day-ahead daily 16-hour block contract prices. Examine a model (like a switching regression model) of whether there is: –(1) autarky, –(2) trade with low transaction costs, or –(3) trade with capacity constraints.

3. 3 Main findings Transaction costs are a few dollars per MWh. –Forming PJM (in 1998) reduced transaction costs with ECAR. –Transactions costs out of PJM increased after introducing market based bidding (in 1999). The benefits (and costs) of trade are not symmetric. –For example, firms in PJM have difficulty exporting to ECAR while Cinergy can export to PJM. Suggest creation of ISOs beneficial for trade (especially in reducing likelihood of quantity constraints).

4. 4 Should new lines be built? The authors note that some of the congestion is from physical constraints on transmission lines while some is because of “institutional” barriers. These explanations have very different policy implications: build new capacity or create a RTO/ISO. Currently, the authors conclude that the asymmetry between PJM and ECAR implies institutional barrier.

5. 5 Parsing types of transactions However, there are other reasons for the asymmetry like production costs. If some time periods are more likely to be subject to institutional barriers, then the authors could test whether the shadow value of capacity differs by period. The authors could also compare estimates of congestion with actual flows in order to get a better sense of what type of congestion is occurring (e.g., if the line has a zero net flow but the model suggests congestion…).

6. 6 Time periods For NY, separate the time periods even more by adding a variable for when the NY market started in November 1999. Can separate out stories of market compatibility with market based bidding in PJM. Similarly, for PJM there were other market changes like the introduction of the day ahead market in June 2000.

7. 7 Modeling questions How important is the functional form of the error term distribution for identification? Would a distribution with thicker tails change the results (e.g., less congestion)? For modeling the transaction costs, the authors assume a normal distribution and then that the data are truncated. Would a log normal distribution be effective in making the point? Can the model distinguish between capacity constraints and high transaction costs? Could actual tie flows help identify what is occurring?

8. 8 Modeling of errors Serial correlation seems important for the autarky model. I suggest making those results the main ones. Furthermore, serial correlation is not modeled for the other possibilities. It seems likely that transaction costs and transmission capacity rents are also likely to be autocorrelated. Would this be feasible in your framework?

9. 9 Data More information on contracts –How thick is the day ahead contract market in each region? –How do contract prices compare with actual market prices where available (i.e., PJM and NY from 1999 onward)? –For each of the 16 hours in a contract, were the actual hourly prices always relatively greater in one market?