One-day forward premiums and the impact of virtual bidding on the New York wholesale electricity market using hourly data

2007 ◽  
Vol 27 (11) ◽  
pp. 1107-1125 ◽  
Author(s):  
Lester Hadsell ◽  
Hany A. Shawky
Keyword(s):  
New York ◽  
Electricity Market ◽  
Hourly Data ◽  
Wholesale Electricity Market ◽  
The Impact

Detecting the impact of fundamentals and regulatory reforms on the Greek wholesale electricity market using a SARMAX/GARCH model

Energy ◽  
2018 ◽  
Vol 142 ◽  
pp. 1083-1103 ◽  
Author(s):  
George P. Papaioannou ◽  
Christos Dikaiakos ◽  
Athanasios S. Dagoumas ◽  
Anargyros Dramountanis ◽  
Panagiotis G. Papaioannou
Keyword(s):  
Electricity Market ◽  
Garch Model ◽  
Wholesale Electricity Market ◽  
The Impact ◽  
Regulatory Reforms

scholarly journals A Practical Approach to Developing Climate Change Scenarios for Water Quality Models

2019 ◽  
Vol 20 (6) ◽  
pp. 1197-1211 ◽  
Author(s):  
Rakesh K. Gelda ◽  
Rajith Mukundan ◽  
Emmet M. Owens ◽  
John T. Abatzoglou
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
New York ◽  
Wind Speed ◽  
Solar Radiation ◽  
Climate Change Scenarios ◽  
Quality Models ◽  
Hourly Data ◽  
Water Quality Models ◽  
The Impact

Abstract Climate model output is often downscaled to grids of moderately high spatial resolution (~4–6-km grid cells). Such projections have been used in numerous hydrological impact assessment studies at watershed scales. However, relatively few studies have been conducted to assess the impact of climate change on the hydrodynamics and water quality in lakes and reservoirs. A potential barrier to such assessments is the need for meteorological variables at subdaily time scales that are downscaled to in situ observations to which lake and reservoir water quality models have been calibrated and validated. In this study, we describe a generalizable procedure that utilizes gridded downscaled data; applies a secondary bias-correction procedure using equidistance quantile mapping to map projections to station-based observations; and implements temporal disaggregation models to generate point-scale hourly air and dewpoint temperature, wind speed, and solar radiation for use in water quality models. The proposed approach is demonstrated for six locations within New York State: four within watersheds of the New York City water supply system and two at nearby National Weather Service stations. Disaggregation models developed using observations reproduced hourly data well at all locations, with Nash–Sutcliffe efficiency greater than 0.9 for air temperature and dewpoint, 0.4–0.6 for wind speed, and 0.7–0.9 for solar radiation.

scholarly journals The Impact of Regulatory Reforms on Demand Weighted Average Prices

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1112
Author(s):  
Sherzod N. Tashpulatov
Keyword(s):  
Electricity Market ◽  
Weighted Average ◽  
England And Wales ◽  
On Demand ◽  
Volatility Model ◽  
History Of ◽  
Wholesale Electricity Market ◽  
Model Residuals ◽  
The Impact ◽  
Regulatory Reforms

Average prices are popularly used in the literature on price modeling. Calculating daily or weekly prices as an average over hourly or half-hourly trading periods assumes the same weight ignoring demand or traded volumes during those periods. Analyzing demand weighted average prices is important if producers may affect prices by decreasing them during low-demand periods and increasing them during high-demand periods within a day. The prediction of this price manipulation might have motivated the regulatory authority to introduce price caps not only on annual average prices but also on annual demand weighted average prices in the England and Wales wholesale electricity market. The dynamics of demand weighted average prices of electricity has been analyzed little in the literature. We show that skew generalized error distribution (SGED) is the appropriate assumption for model residuals. The estimated volatility model is used for evaluating the impact of regulatory reforms on demand weighted average prices during the complete history of the England and Wales wholesale electricity market.

scholarly journals A Cluster-Based Baseline Load Calculation Approach for Individual Industrial and Commercial Customer

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 64 ◽  
Author(s):  
Tianli Song ◽  
Yang Li ◽  
Xiao-Ping Zhang ◽  
Jianing Li ◽  
Cong Wu ◽  
...  
Keyword(s):  
Demand Response ◽  
Electricity Market ◽  
Average Model ◽  
Load Calculation ◽  
Different Types ◽  
Usage Patterns ◽  
Wholesale Electricity Market ◽  
Multiple Power ◽  
Baseline Calculation ◽  
The Impact

Demand response (DR) in the wholesale electricity market provides an economical and efficient way for customers to participate in the trade during the DR event period. There are various methods to measure the performance of a DR program, among which customer baseline load (CBL) is the most important method in this regard. It provides a prediction of counterfactual consumption levels that customer load would have been without a DR program. Actually, it is an expected load profile. Since the calculation of CBL should be fair and simple, the typical methods that are based on the average model and regression model are the two widely used methods. In this paper, a cluster-based approach is proposed considering the multiple power usage patterns of an individual customer throughout the year. It divides loads of a customer into different types of power usage patterns and it implicitly incorporates the impact of weather and holiday into the CBL calculation. As a result, different baseline calculation approaches could be applied to each customer according to the type of his power usage patterns. Finally, several case studies are conducted on the actual utility meter data, through which the effectiveness of the proposed CBL calculation approach is verified.

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