Modeling forecasting errors of fluctuating renewables and electrical loads

Nobis, Moritz; Clever, Jacqueline; Thie, Nicolas; Schnettler, Armin

doi:10.1109/eem.2019.8916515

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Finally, the optimization problem is formulated with cost terms (9), variables (10), the objective function (11), variable limits (12), and constraints (13):…”

Section: A Orpf (Base Model)mentioning

confidence: 99%

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Voltage Control in Transmission Grids Considering Uncertainties of Renewable Energy Sources_preprint.pdf

Knittel¹,

Majumdar²,

Schneider³

et al. 2020

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Power traders and system operators need to balance the uncertain generation of renewable energy sources by adapting the dispatch of conventional power plants. This poses a challenge to voltage control in power system operation. Accordingly, this paper presents a method to determine voltage magnitude probability densities which are integrated into an optimal reactive power flow to consider an uncertain active power generation. First, the probability densities are determined by Monte Carlo simulations including a unit commitment problem to derive the dispatch of conventional power plants. Second, uncertainty restrictions are used to create soft constraints for the optimal reactive power flow, which mitigates the risk of voltage limit violations. By adapting the soft constraints slack costs, the consideration of uncertainties can be prioritized in relation to other objectives, such as the reduction of active power losses, or reactive power costs.<br>

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“…Finally, the optimization problem is formulated with cost terms (9), variables (10), the objective function (11), variable limits (12), and constraints (13):…”

Section: A Orpf (Base Model)mentioning

confidence: 99%

“…During system operation, the current system state t 0 is known while future system states are unknown. A Monte Carlo simulation is used to forecast future system states and to derive robust dispatch decisions [12]. Within these Monte Carlo simulations, all relevant uncertainties (e.g.…”

Section: A Orpf (Base Model)mentioning

confidence: 99%