ERCOT electricity market: Transition from zonal to nodal market operation

Author(s):  
H. Daneshi ◽  
A. K. Srivastava
2021 ◽  
Vol 71 ◽  
pp. 101232
Author(s):  
Sam Wilkinson ◽  
Martin J. Maticka ◽  
Yue Liu ◽  
Michele John

2021 ◽  
Author(s):  
theresia SUMARNO ◽  
Jonathan Wagner ◽  
Martin Weibelzahl ◽  
Raphael J. Heffron ◽  
Marc-Fabian Körner ◽  
...  

2020 ◽  
Vol 11 (5) ◽  
pp. 4044-4055
Author(s):  
Hossein Haghighat ◽  
Hossein Karimianfard ◽  
Bo Zeng

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Zhenyu Zhao ◽  
Shuguang Yuan ◽  
Qingyun Nie ◽  
Weishang Guo

In a spot wholesale electricity market containing strategic bidding interactions among wind power producers and other participants such as fossil generation companies and distribution companies, the randomly fluctuating natures of wind power hinders not only the modeling and simulating of the dynamic bidding process and equilibrium of the electricity market but also the effectiveness about keeping economy and reliability in market clearing (economic dispatching) corresponding to the independent system operator. Because the gradient descent continuous actor-critic algorithm is demonstrated as an effective method in dealing with Markov’s decision-making problems with continuous state and action spaces and the robust economic dispatch model can optimize the permitted real-time wind power deviation intervals based on wind power producers’ bidding power output, in this paper, considering bidding interactions among wind power producers and other participants, we propose a gradient descent continuous actor-critic algorithm-based hour-ahead electricity market modeling approach with the robust economic dispatch model embedded. Simulations are implemented on the IEEE 30-bus test system, which, to some extent, verifies the market operation economy and the robustness against wind power fluctuations by using our proposed modeling approach.


2005 ◽  
Vol 3 (6) ◽  
pp. 47-56 ◽  
Author(s):  
R. Piwko ◽  
D. Osborn ◽  
R. Gramlich ◽  
G. Jordan ◽  
D. Hawkins ◽  
...  

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