A novel approach to solve transient stability constrained optimal power flow problems

Nguyen-Duc, Huy; Tran-Hoai, Linh; Vo, Dieu Ngoc

doi:10.3906/elk-1704-209

Cited by 6 publications

(6 citation statements)

References 30 publications

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“…In [115], the authors propose a preventive control scheme by rescheduling generating units. First, they assess the transient stability of the system [34], [38], [115] Neural networks [116] Tree-based methods [117]- [121] Linear models [122] with a hybrid method based on a SVM model and time-domain simulation. Then, if the system is unstable, they compute from the SVM model the sensitivity of each generator to a transient stability assessment index (derived from the SVM model) to rank the generators and select the ones that are more effective for improving the stability.…”

Section: Learning a Modelmentioning

confidence: 99%

“…An extension of this work to solve an AC-SCOPF instead of a DC-SCOPF is proposed in [121], while still incorporating N-1 security and small-signal stability with decision tree-learnt rules. In [116], the authors are solving an OPF considering transient stability constraints. An artificial neural network approximating the CCT of a fault is embedded in the OPF formulation.…”

Section: Learning a Modelmentioning

confidence: 99%

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Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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Section: Learning a Modelmentioning

confidence: 99%

Section: Learning a Modelmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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“…Our model's second stage optimizes control variables V ref and P ref to maximize stability in each scenario. For every ψ ∈ Ψ, we add equations (1)' -(15)' discretized only for t ∈ {τ ∈ P| τ ≥ t f } as our second stage constraints (22).…”

Section: Stochastic Modelmentioning

confidence: 99%

Stochastic Optimization of Power System Dynamics for Grid Resilience

Arguello¹,

Stewart²,

Hoffman³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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When faced with uncertainty regarding potential failure contingencies, prioritizing system resilience through optimal control of exciter reference voltage and mechanical torque can be arduous due to the scope of potential failure contingencies. Optimal control schemes can be generated through a two-stage stochastic optimization model by anticipating a set of contingencies with associated probabilities of occurrence, followed by the optimal recourse action once the contingency has been realized. The first stage, common across all contingency scenarios, co-optimally positions the grid for the set of possible contingencies. The second stage dynamically assesses the impact of each contingency and allows for emergency control response. By unifying the optimal control scheme prior and post the failure contingency, a singular policy can be constructed to maximize system resilience.

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“…NG designates the total number of generators connected to the power system. g and h represent the set of equality and inequality constraints of the problem (see Equations (3)(4)(5)(6)(7)(8)(9)). Superscript k = 0 refers to the pre-contingency state of the network, while k = 1 refers to post-contingency state of the network.…”

Section: Contingency Constrained Optimal Power Flow Problemmentioning

confidence: 99%

“…The next group of equality and inequality constraints refers to the post-contingency at short-term time frame, which mainly focuses in keeping system stability by setting or changing system variables during a critical clearing time of the contingency. Works such as that in [2] by Tang and Sun, or that in [3] by Nguyen-Duc, Tran-Hoai, and Vol Ngoc approach the transient stability constrained optimal power flow problem in this short-term time frame.…”

Section: Introductionmentioning

confidence: 99%

Quadratically Constrained Quadratic Programming Formulation of Contingency Constrained Optimal Power Flow with Photovoltaic Generation

2020

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Contingency Constrained Optimal Power Flow (CCOPF) differs from traditional Optimal Power Flow (OPF) because its generation dispatch is planned to work with state variables between constraint limits, considering a specific contingency. When it is not desired to have changes in the power dispatch after the contingency occurs, the CCOPF is studied with a preventive perspective, whereas when the contingency occurs and the power dispatch needs to change to operate the system between limits in the post-contingency state, the problem is studied with a corrective perspective. As current power system software tools mainly focus on the traditional OPF problem, having the means to solve CCOPF will benefit power systems planning and operation. This paper presents a Quadratically Constrained Quadratic Programming (QCQP) formulation built within the matpower environment as a solution strategy to the preventive CCOPF. Moreover, an extended OPF model that forces the network to meet all constraints under contingency is proposed as a strategy to find the power dispatch solution for the corrective CCOPF. Validation is made on the IEEE 14-bus test system including photovoltaic generation in one simulation case. It was found that in the QCQP formulation, the power dispatch calculated barely differs in both pre- and post-contingency scenarios while in the OPF extended power network, node voltage values in both pre- and post-contingency scenarios are equal in spite of having different power dispatch for each scenario. This suggests that both the QCQP and the extended OPF formulations proposed, could be implemented in power system software tools in order to solve CCOPF problems from a preventive or corrective perspective.

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A novel approach to solve transient stability constrained optimal power flow problems

Cited by 6 publications

References 30 publications

Recent Developments in Machine Learning for Energy Systems Reliability Management

Recent Developments in Machine Learning for Energy Systems Reliability Management

Stochastic Optimization of Power System Dynamics for Grid Resilience

Quadratically Constrained Quadratic Programming Formulation of Contingency Constrained Optimal Power Flow with Photovoltaic Generation

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