<i>N</i> − <i>k</i> Static Security Assessment for Power Transmission System Planning Using Machine Learning
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 s...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/2/292 |
| _version_ | 1797344141922795520 |
|---|---|
| author | David L. Alvarez Mohamed Gaha Jacques Prévost Alain Côté Georges Abdul-Nour Toualith Jean-Marc Meango |
| author_facet | David L. Alvarez Mohamed Gaha Jacques Prévost Alain Côté Georges Abdul-Nour Toualith Jean-Marc Meango |
| author_sort | David L. Alvarez |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>−</mo><mi>k</mi></mrow></semantics></math></inline-formula> contingency scenario as inputs. The output is the reliability metric Expecting Load Shedding Cost (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>L</mi><mi>S</mi><mi>C</mi></mrow></semantics></math></inline-formula>). To train and test the regression model, stochastic data are performed, resulting in a set of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>−</mo><mi>k</mi></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>k</mi><mo>=</mo><mfenced separators="" open="{" close="}"><mn>1</mn><mo>,</mo><mn>2</mn><mo>,</mo><mn>3</mn></mfenced></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>L</mi><mi>S</mi><mi>C</mi></mrow></semantics></math></inline-formula>. Experimental results on the well-known IEEE-39 bus test system and PEGASE-1354 system demonstrate the potential of the proposed methodology in generalizing <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>L</mi><mi>S</mi><mi>C</mi></mrow></semantics></math></inline-formula> during an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>−</mo><mi>k</mi></mrow></semantics></math></inline-formula> contingency. For up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>k</mi><mo>=</mo><mn>3</mn></mrow></semantics></math></inline-formula> the coefficient of determination <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mfenced separators="" open="(" close=")"><msup><mi>R</mi><mn>2</mn></msup></mfenced></semantics></math></inline-formula> 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>L</mi><mi>P</mi></mrow></semantics></math></inline-formula>). This approach and its results have not been addressed in the literature, making this methodology a contribution to the state of the art. |
| first_indexed | 2024-03-08T10:58:03Z |
| format | Article |
| id | doaj.art-4cd0a4bb4d0a4600b4c289a7627db1c5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T10:58:03Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4cd0a4bb4d0a4600b4c289a7627db1c52024-01-26T16:15:25ZengMDPI AGEnergies1996-10732024-01-0117229210.3390/en17020292<i>N</i> − <i>k</i> Static Security Assessment for Power Transmission System Planning Using Machine LearningDavid L. Alvarez0Mohamed Gaha1Jacques Prévost2Alain Côté3Georges Abdul-Nour4Toualith Jean-Marc Meango5Hydro-Québec’s Research Institute—IREQ, Varennes, QC J3X 1P7, CanadaHydro-Québec’s Research Institute—IREQ, Varennes, QC J3X 1P7, CanadaHydro-Québec’s Research Institute—IREQ, Varennes, QC J3X 1P7, CanadaHydro-Québec’s Research Institute—IREQ, Varennes, QC J3X 1P7, CanadaDépartement de Génie Industriel, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC G8Z 4M3, CanadaHydro-Québec’s Research Institute—IREQ, Varennes, QC J3X 1P7, CanadaThis 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>−</mo><mi>k</mi></mrow></semantics></math></inline-formula> contingency scenario as inputs. The output is the reliability metric Expecting Load Shedding Cost (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>L</mi><mi>S</mi><mi>C</mi></mrow></semantics></math></inline-formula>). To train and test the regression model, stochastic data are performed, resulting in a set of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>−</mo><mi>k</mi></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>k</mi><mo>=</mo><mfenced separators="" open="{" close="}"><mn>1</mn><mo>,</mo><mn>2</mn><mo>,</mo><mn>3</mn></mfenced></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>L</mi><mi>S</mi><mi>C</mi></mrow></semantics></math></inline-formula>. Experimental results on the well-known IEEE-39 bus test system and PEGASE-1354 system demonstrate the potential of the proposed methodology in generalizing <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>L</mi><mi>S</mi><mi>C</mi></mrow></semantics></math></inline-formula> during an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>−</mo><mi>k</mi></mrow></semantics></math></inline-formula> contingency. For up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>k</mi><mo>=</mo><mn>3</mn></mrow></semantics></math></inline-formula> the coefficient of determination <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mfenced separators="" open="(" close=")"><msup><mi>R</mi><mn>2</mn></msup></mfenced></semantics></math></inline-formula> 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>L</mi><mi>P</mi></mrow></semantics></math></inline-formula>). This approach and its results have not been addressed in the literature, making this methodology a contribution to the state of the art.https://www.mdpi.com/1996-1073/17/2/292load shedding optimal power flowmachine learningstatic security assessmenttransmission system planning |
| spellingShingle | David L. Alvarez Mohamed Gaha Jacques Prévost Alain Côté Georges Abdul-Nour Toualith Jean-Marc Meango <i>N</i> − <i>k</i> Static Security Assessment for Power Transmission System Planning Using Machine Learning Energies load shedding optimal power flow machine learning static security assessment transmission system planning |
| title | <i>N</i> − <i>k</i> Static Security Assessment for Power Transmission System Planning Using Machine Learning |
| title_full | <i>N</i> − <i>k</i> Static Security Assessment for Power Transmission System Planning Using Machine Learning |
| title_fullStr | <i>N</i> − <i>k</i> Static Security Assessment for Power Transmission System Planning Using Machine Learning |
| title_full_unstemmed | <i>N</i> − <i>k</i> Static Security Assessment for Power Transmission System Planning Using Machine Learning |
| title_short | <i>N</i> − <i>k</i> Static Security Assessment for Power Transmission System Planning Using Machine Learning |
| title_sort | i n i i k i static security assessment for power transmission system planning using machine learning |
| topic | load shedding optimal power flow machine learning static security assessment transmission system planning |
| url | https://www.mdpi.com/1996-1073/17/2/292 |
| work_keys_str_mv | AT davidlalvarez iniikistaticsecurityassessmentforpowertransmissionsystemplanningusingmachinelearning AT mohamedgaha iniikistaticsecurityassessmentforpowertransmissionsystemplanningusingmachinelearning AT jacquesprevost iniikistaticsecurityassessmentforpowertransmissionsystemplanningusingmachinelearning AT alaincote iniikistaticsecurityassessmentforpowertransmissionsystemplanningusingmachinelearning AT georgesabdulnour iniikistaticsecurityassessmentforpowertransmissionsystemplanningusingmachinelearning AT toualithjeanmarcmeango iniikistaticsecurityassessmentforpowertransmissionsystemplanningusingmachinelearning |