N − k Static Security Assessment for Power Transmission System Planning Using Machine Learning

David L. Alvarez (), Mohamed Gaha, Jacques Prévost, Alain Côté, Georges Abdul-Nour and Toualith Jean-Marc Meango
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David L. Alvarez: Hydro-Québec’s Research Institute—IREQ, Varennes, QC J3X 1P7, Canada
Mohamed Gaha: Hydro-Québec’s Research Institute—IREQ, Varennes, QC J3X 1P7, Canada
Jacques Prévost: Hydro-Québec’s Research Institute—IREQ, Varennes, QC J3X 1P7, Canada
Alain Côté: Hydro-Québec’s Research Institute—IREQ, Varennes, QC J3X 1P7, Canada
Georges Abdul-Nour: Département de Génie Industriel, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC G8Z 4M3, Canada
Toualith Jean-Marc Meango: Hydro-Québec’s Research Institute—IREQ, Varennes, QC J3X 1P7, Canada

Energies, 2024, vol. 17, issue 2, 1-17

Abstract: This paper presents a methodology for static security assessment of transmission network planning using machine learning (ML). The objective is to accelerate the probabilistic risk assessment of the Hydro-Quebec (HQ) TransÉnergie transmission grid. The model takes the expected power supply and the status of the elements in a N − k contingency scenario as inputs. The output is the reliability metric Expecting Load Shedding Cost ( E L S C ). To train and test the regression model, stochastic data are performed, resulting in a set of N − k and k = 1 , 2 , 3 contingency scenarios used as inputs. Subsequently, the output is computed for each scenario by performing load shedding using an optimal power flow algorithm, with the objective function of minimizing E L S C . Experimental results on the well-known IEEE-39 bus test system and PEGASE-1354 system demonstrate the potential of the proposed methodology in generalizing E L S C during an N − k contingency. For up to k = 3 the coefficient of determination R 2 obtained was close to 98% for both case studies, achieving a speed-up of over four orders of magnitude with the use of a Multilayer Perceptron ( M L P ). This approach and its results have not been addressed in the literature, making this methodology a contribution to the state of the art.

Keywords: load shedding optimal power flow; machine learning; static security assessment; transmission system planning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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