The Experts below are selected from a list of 25965 Experts worldwide ranked by ideXlab platform
Stefan Kimball - One of the best experts on this subject based on the ideXlab platform.
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reviewing progress in pjm s capacity market structure via the new reliability pricing model
The Electricity Journal, 2007Co-Authors: Adil Caner Sener, Stefan KimballAbstract:The Reliability Pricing Model introduces significant changes to the capacity market structure of PJM. The main feature of the RPM design is a downward-sloping Demand Curve, which replaces the highly volatile vertical Demand Curve. The authors review the latest RPM structure, results of the auctions, and the future course of the implementation process.
Ali E Abbas - One of the best experts on this subject based on the ideXlab platform.
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Demand Curve prediction via bayesian probability assignment over a functional space
Winter Simulation Conference, 2009Co-Authors: Michael G Traverso, Ali E AbbasAbstract:One of the important aspects of energy modeling is the process of Demand Curve prediction. Existing Demand Curve prediction methods generally rely on statistical Curve fittings which assume a certain functional form such as constant price elasticity. There are a number of disadvantages to this approach. For one, this method makes certain assumptions about the functional form of the price-Demand Curve that may not be exhibited in practice. In addition, since Curve fits rely on only a single function, and not a distribution of functions, they do not capture the uncertainty about price-Demand Curves. In this work, Demand Curve prediction is instead treated by assigning a probability measure to the space of all functions that meet the global regularity (non-decreasing conditions). Using this method, a numerical example of Bayesian Demand Curve prediction is presented.
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Winter Simulation Conference - Demand Curve prediction via bayesian probability assignment over a functional space
Proceedings of the 2009 Winter Simulation Conference (WSC), 2009Co-Authors: Michael G Traverso, Ali E AbbasAbstract:One of the important aspects of energy modeling is the process of Demand Curve prediction. Existing Demand Curve prediction methods generally rely on statistical Curve fittings which assume a certain functional form such as constant price elasticity. There are a number of disadvantages to this approach. For one, this method makes certain assumptions about the functional form of the price-Demand Curve that may not be exhibited in practice. In addition, since Curve fits rely on only a single function, and not a distribution of functions, they do not capture the uncertainty about price-Demand Curves. In this work, Demand Curve prediction is instead treated by assigning a probability measure to the space of all functions that meet the global regularity (non-decreasing conditions). Using this method, a numerical example of Bayesian Demand Curve prediction is presented.
Adil Caner Sener - One of the best experts on this subject based on the ideXlab platform.
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reviewing progress in pjm s capacity market structure via the new reliability pricing model
The Electricity Journal, 2007Co-Authors: Adil Caner Sener, Stefan KimballAbstract:The Reliability Pricing Model introduces significant changes to the capacity market structure of PJM. The main feature of the RPM design is a downward-sloping Demand Curve, which replaces the highly volatile vertical Demand Curve. The authors review the latest RPM structure, results of the auctions, and the future course of the implementation process.
Xuewei Zhang - One of the best experts on this subject based on the ideXlab platform.
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effect of loss of load probability distribution on operating reserve Demand Curve performance in energy only electricity market
IEEE Transactions on Power Systems, 2020Co-Authors: Sreelatha Aihloor Subramanyam, Xuewei ZhangAbstract:In energy-only electricity markets, the operational reserve Demand Curve is a scarcity pricing mechanism adopted to address the shortage of reserves and incentivize the generators. It is constructed based on the assumption of the normality of loss of load probability, which is estimated from reserve error data. In this Letter, the historical record of reserve error is collected and analyzed to test the assumption and find the fittest probability distribution. It is found that the normal distribution is generally not as good as other distributions like log-logistic, general gamma, and Weibull. Using a dynamic simulation framework, the installed reserve margins with different distributions are evaluated over a fifty-year period, demonstrating that the choice of distribution could significantly impact the subsequent pricing calculations and therefore the reserve margin in an energy-only electricity market. This work is of potential importance and immediate relevance to system reliability and resource adequacy in power systems.
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dynamic analysis of operating reserve Demand Curve in energy only electricity markets
IEEE Innovative Smart Grid Technologies-Asia, 2019Co-Authors: Sreelatha Aihloor Subramanyam, Xuewei Zhang, Huihui SongAbstract:This paper studies the performance of operating reserve Demand Curve (ORDC) used in energy-only electricity markets in terms of the installed reserve margin (IRM) and the generator’s profitability, both of which are critical to ensuring system reliability and resource adequacy. For this, a dynamic simulation framework is developed to evaluate the impacts of different scenarios on the indices. The analysis confirms that the ORDC can serve as a mechanism to maintain IRM at certain levels and produce the expected response and evolution of IRM under conditions such as cost shocks. In the simulations, it is found that the generator’s gross margin, usually negatively correlated with IRM, can stabilize IRM when the capacity addition decisions are made based on the average of prior years’ gross margins. Finally, by varying the downward slope of the ORDC using different methods to construct it, IRM can be more efficiently steered toward a targeted value. It is demonstrated that the simulation framework and these observations would be helpful to the design of ORDC in energy-only markets.
Harry M. Kaiser - One of the best experts on this subject based on the ideXlab platform.
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Measuring and testing advertising-induced rotation in the Demand Curve
Applied Economics, 2010Co-Authors: Yuqing Zheng, Henry W. Kinnucan, Harry M. KaiserAbstract:Advertising can rotate the Demand Curve if it changes the dispersion of consumers' valuations. We provide an elasticity form measure of the advertising-induced Demand Curve rotation in five Demand models and test for its presence in the US nonalcoholic beverage market. The Almost Ideal Demand System (AIDS) model reveals that doubling advertising spending rotates the Demand Curves clockwise for milk, and coffee and tea with associated slope changes of 7 and 12%. Soft-drink advertising rotates its Demand Curve counterclockwise. Our policy suggestion is that milk and soft-drink firms time advertising to coincide with high-and low-price periods, respectively.
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Measuring and Testing Advertising-Induced Rotation in the Demand Curve
2007Co-Authors: Yuqing Zheng, Henry W. Kinnucan, Harry M. KaiserAbstract:Advertising can rotate the Demand Curve if it changes the dispersion of consumers' valuations. We provide an elasticity form measure of the advertising-induced Demand Curve rotation in five Demand models and test for its presence in the U.S. non-alcoholic beverage market. The AIDS model reveals that doubling advertising spending rotates the Demand Curves clockwise for milk, and coffee and tea with associated slope changes of 7.3% and 11.6%. Soft-drink advertising rotates its Demand Curve counterclockwise. Our policy suggestion is that milk and soft-drink firms might enhance profits by timing advertising to coincide with high- and low-price periods, respectively.
