Probabilistic operational planning using dynamic programming with time-domain simulations

Bødal, Espen Flo; Jakobsen, Sigurd Hofsmo; Gjerde, Oddbjørn

doi:10.1016/j.epsr.2022.108379

Cited by 3 publications

(8 citation statements)

References 10 publications

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“…Alternative methods to [4] for probabilistic operational planning have been presented in the literature [5]- [9]. In [5] a DC power flow was used to include power system response in a probabilistic operational planning model.…”

Section: B Related Workmentioning

confidence: 99%

“…Moreover, the time domain characteristic of protection systems cannot be included. In the proposed approach, a GCN is trained to predict the result of a detailed time-domain simulation that calculates the cost of operating a power system given a set of preventive and corrective actions while considering the time-domain characteristics of protection systems [4].…”

Section: B Related Workmentioning

confidence: 99%

“…The main objective of the method presented in this paper is to identify the preventive operational strategy with the lowest expected socio-economic cost according to the framework presented in [4]. The socio-economic costs are given by:…”

Section: A Probabilistic Operational Planningmentioning

confidence: 99%

“…A dynamic programming model was developed in [4] to quantify the socio-economic cost and detect the most favorable socio-economic operational strategy. Dynamic time-domain simulations were used to capture situations that normally go undetected by traditional static methods.…”

Section: Introduction a Motivationmentioning

confidence: 99%

“…As a response to these challenges, this paper proposes a twostep supervised learning model based on graph convolutional networks (GCNs) to rapidly predict the expected costs of simulated operational strategies, exemplified using data from [4].…”

Section: Introduction a Motivationmentioning

confidence: 99%

See 4 more Smart Citations

Graph Convolutional Networks for probabilistic power system operational planning

Sheikh-Mohamed,

Jakobsen,

Bødal

et al. 2023

2023 IEEE Belgrade PowerTech

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Probabilistic operational planning of power systems usually requires computationally intensive and time consuming simulations. The method presented in this paper provides a time efficient alternative to predict the socio-economic cost of system operational strategies using graph convolutional networks. It is intended for fast screening of operational strategies for the purpose of operational planning. It can also be used as a proxy for operational planning that can be used in long term development studies. The performance of the model is demonstrated on a network inspired by the Nordic power system.

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Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: A Probabilistic Operational Planningmentioning

confidence: 99%

Section: Introduction a Motivationmentioning

confidence: 99%

Section: Introduction a Motivationmentioning

confidence: 99%

See 3 more Smart Citations

Graph Convolutional Networks for probabilistic power system operational planning

Sheikh-Mohamed,

Jakobsen,

Bødal

et al. 2023

2023 IEEE Belgrade PowerTech

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Dynamic programming for master production schedule of white wood oil (oil Cajuput)

Tasijawa,

Marasabessy,

Marasabessy

et al. 2024

The 8th International Conference on Technology and Vocational Teachers 2022

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Solving Security-Constrained Optimal Power Flow Using Analytical Benders' Cuts

Vistnes,

Vadlamudi,

Jakobsen

et al. 2023

2023 International Conference on Smart Energy Systems and Technologies (SEST)

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A fast and scalable iterative methodology for solving the security-constrained optimal power flow (SCOPF) problem is proposed using problem decomposition and the inverse matrix modification lemma. The SCOPF formulation tackles system security operational planning by using short-and long-term postcontingency limits, probability of branch outages, and preventive and corrective actions, a probabilistic corrective-SCOPF problem formulation. Using two post-contingency states and contingency probabilities, the SCOPF could provide good system security at a lower cost than only preventive actions as the typical 'N-1'-formulation does. Additional security is ensured using a post-contingency load-shedding limit constraint based on system operator policy. The proposed methodology is applied to a range of test systems containing up to 10,000 buses with a computational time of up to 3375 s for all 12,706 branch contingencies. Calculating contingency power flows takes 1.3% of the total solution time using the proposed methodology exploiting the inverse matrix modification lemma.

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Probabilistic operational planning using dynamic programming with time-domain simulations

Cited by 3 publications

References 10 publications

Graph Convolutional Networks for probabilistic power system operational planning

Graph Convolutional Networks for probabilistic power system operational planning

Dynamic programming for master production schedule of white wood oil (oil Cajuput)

Solving Security-Constrained Optimal Power Flow Using Analytical Benders' Cuts

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