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Three essays on electricity pricing,...

Zou, Yu.

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Three essays on electricity pricing, congestion, and market efficiency.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Three essays on electricity pricing, congestion, and market efficiency./
作者:	Zou, Yu.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	86 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-09(E), Section: A.
Contained By:	Dissertation Abstracts International78-09A(E).
標題:	Economic theory. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10265477
ISBN:	9781369689563

Three essays on electricity pricing, congestion, and market efficiency.
Zou, Yu.

Three essays on electricity pricing, congestion, and market efficiency. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 86 p.

Source: Dissertation Abstracts International, Volume: 78-09(E), Section: A.

Thesis (Ph.D.)--State University of New York at Albany, 2017.

In the first chapter, I analyze the price variations among zones in the electricity forward market of New York State, and how electricity forward prices react to the economic risks in the market. It applies a VAR model with monthly/holiday dummies and weather data to forecast the three components of electricity prices--energy price, marginal cost of congestion and losses. The GARCH estimates of the conditional variance of the VAR residuals are treated as risks. We find that participants will bid higher price in the forward market when they expect higher risks. Particularly, the risk of congestion has a larger effect on the forward price in Northwestern zones compared to the Southeastern zones of NYS.

ISBN: 9781369689563Subjects--Topical Terms:

1556984
Economic theory.

Three essays on electricity pricing, congestion, and market efficiency.
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245 1 0 $a Three essays on electricity pricing, congestion, and market efficiency.
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300 $a 86 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-09(E), Section: A.
500 $a Adviser: Hany A. Shawky.
502 $a Thesis (Ph.D.)--State University of New York at Albany, 2017.
520 $a In the first chapter, I analyze the price variations among zones in the electricity forward market of New York State, and how electricity forward prices react to the economic risks in the market. It applies a VAR model with monthly/holiday dummies and weather data to forecast the three components of electricity prices--energy price, marginal cost of congestion and losses. The GARCH estimates of the conditional variance of the VAR residuals are treated as risks. We find that participants will bid higher price in the forward market when they expect higher risks. Particularly, the risk of congestion has a larger effect on the forward price in Northwestern zones compared to the Southeastern zones of NYS.
520 $a The second chapter aims to study the spatial correlation in congestion cost based on the data from New York State wholesale electricity spot market. By excluding the same price component of all the 11 zones, energy price, we think the spatial pattern in electricity prices comes from the variation in congestions. The changes in congestions capture the feature of the underlying structure of transmission network. Both traditional panel data model and censored spatial Durbin model are used to study congestions. The evidence suggests that the congestion cost is spatially correlated to the load in the system, and the degree of this spatial dependence increases during peak hours compared to off-peak hours.
520 $a The third chapter studies the efficiency of the TCC market in New York State by examining the one-month TCCs. Two representative TCCs, HUD-NYC and HUD-CAPITL are selected due to their high trading volume. An econometric framework is estimated to simulate the daily temperature, electricity demand, and electricity price using the information before the auction. It finds out that the market clear price of the most heavily traded TCC of HUD-NYC is higher than its average simulated payout. While the opposite result is found for the TCC of HUD-CAPITL. There is no consistent evidence for under-pricing in one-month TCCs. This analysis supports the results in Mount and Ju (2014).
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