scholarly journals On the O(1/n) Convergence Rate of the Auxiliary Problem Principle for Separable Convex Programming and Its Application to the Power Systems Multi-Area Economic Dispatch Problem

2016 ◽  
Vol 21 (3) ◽  
pp. 35 ◽  
Author(s):  
Yaming Ren ◽  
Zhongxian Chen
Keyword(s):  
Convergence Rate ◽  
Power Systems ◽  
Convex Programming ◽  
Auxiliary Problem ◽  
Economic Dispatch ◽  
Auxiliary Problem Principle ◽  
Separable Convex Programming ◽  
Economic Dispatch Problem

scholarly journals Managing Distributed Flexibility under Uncertainty by Combining Deep Learning with Duality

2021 ◽  
Author(s):  
Georgios Tsaousoglou ◽  
Katerina Mitropoulou ◽  
Konstantinos Steriotis ◽  
Nikolaos Paterakis ◽  
Pierre Pinson ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Renewable Energy Sources ◽  
Economic Dispatch ◽  
Multiple Sources ◽  
Arrival Times ◽  
Energy Needs ◽  
Markov Decision ◽  
Dual Variables ◽  
Economic Dispatch Problem

<div>In modern power systems, small distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration of Renewable Energy Sources (RES). In this paper we consider an economic dispatch problem for a community of DERs, where energy management decisions are made online and under uncertainty. We model multiple sources of uncertainty such as RES, wholesale electricity prices as well as the arrival times and energy needs of a set of Electric Vehicles. The economic dispatch problem is formulated as a multi-agent Markov Decision Process. The difficulties lie in the curse of dimensionality and in guaranteeing the satisfaction of constraints under uncertainty.</div><div>A novel method, that combines duality theory and deep learning, is proposed to tackle these challenges. In particular, a Neural Network (NN) is trained to return the optimal dual variables of the economic dispatch problem. By training the NN on the dual problem instead of the primal, the number of output neurons is dramatically reduced, which enhances the performance and reliability of the NN. Finally, by treating the resulting dual variables as prices, each distributed agent can self-schedule, which guarantees the satisfaction of its constraints. As a result, our simulations show that the proposed scheme performs reliably and efficiently.</div>

scholarly journals Managing Distributed Flexibility under Uncertainty by Combining Deep Learning with Duality

2021 ◽  
Author(s):  
Georgios Tsaousoglou ◽  
Katerina Mitropoulou ◽  
Konstantinos Steriotis ◽  
Nikolaos Paterakis ◽  
Pierre Pinson ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Renewable Energy Sources ◽  
Economic Dispatch ◽  
Multiple Sources ◽  
Arrival Times ◽  
Energy Needs ◽  
Markov Decision ◽  
Dual Variables ◽  
Economic Dispatch Problem

<div>In modern power systems, small distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration of Renewable Energy Sources (RES). In this paper we consider an economic dispatch problem for a community of DERs, where energy management decisions are made online and under uncertainty. We model multiple sources of uncertainty such as RES, wholesale electricity prices as well as the arrival times and energy needs of a set of Electric Vehicles. The economic dispatch problem is formulated as a multi-agent Markov Decision Process. The difficulties lie in the curse of dimensionality and in guaranteeing the satisfaction of constraints under uncertainty.</div><div>A novel method, that combines duality theory and deep learning, is proposed to tackle these challenges. In particular, a Neural Network (NN) is trained to return the optimal dual variables of the economic dispatch problem. By training the NN on the dual problem instead of the primal, the number of output neurons is dramatically reduced, which enhances the performance and reliability of the NN. Finally, by treating the resulting dual variables as prices, each distributed agent can self-schedule, which guarantees the satisfaction of its constraints. As a result, our simulations show that the proposed scheme performs reliably and efficiently.</div>

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