Distributed method for Economic Dispatch Problem in power network with multiple uncertainties

K, Kubicek; Wolf, Jindrich

doi:10.1109/etfa52439.2022.9921437

Cited by 2 publications

(3 citation statements)

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“…In this work, several uncertainties will be considered at the same time, such as time-variable traffic delay, representation of drop-off packets (corrupted or lost data), noisy gradient calculation, and line losses and their treatment. Partial results and extensions by the authors can be found in [49][50][51]. The goal is to provide a robust decentralized algorithm that remains stable even in such cases.…”

Section: State Of the Artmentioning

confidence: 99%

“…The proposed algorithm is based on the algorithm presented in [8] utilizing the Lagrangian dual problem for EDP and further improves it. The designed algorithm derives a distributed EDP solution using local λ estimation using only local communication for each generator [8,49].…”

Section: Algorithm Developmentmentioning

confidence: 99%

“…This section will enhance the algorithm presented earlier to address various uncertainties concurrently, including line losses, imprecise gradient calculations, time-varying traffic delay, and representation of drop-off packets [49].…”

Section: Algorithm With Multiple Uncertaintiesmentioning

confidence: 99%

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A Robust Distributed Algorithm for Solving the Economic Dispatch Problem with the Penetration of Renewables and Battery Systems

Kubicek,

Cech,

Strelec

2024

Applied Sciences

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In the field of energy networks, for their effective functioning, it is necessary to distribute the required load between all online generating units in a proper way to cover the demand. The load schedule is obtained by solving the so-called Economic Dispatch Problem (EDP). The EDP can be solved in many ways, resulting in a power distribution plan between online generating units in the network so that the resulting price per unit of energy is minimal. This article focuses on designing a distributed gradient algorithm for solving EDP, supplemented by models of renewable sources, Battery Energy Storage System (BESS), variable fuel prices, and consideration of multiple uncertainties at once. Specifically, these are: time-variable transport delays, noisy gradient calculation, line losses, and drop-off packet representations. The algorithm can thus be denoted as robust, which can work even in unfavorable conditions commonly found in real applications. The capabilities of the presented algorithm will be demonstrated and evaluated on six examples.

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Section: State Of the Artmentioning

confidence: 99%