GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow (CC-OPF) is an open-source Python code developed for solving economic dispatch (ED) problem in modern power grids. In recent years, integrating a significant amount of renewables into a power grid causes high fluctuations and thus...
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| creator | Mitrovic, Mile Kundacina, Ognjen Lukashevich, Aleksandr Vorobev, Petr Terzija, Vladimir Maximov, Yury Deka, Deepjyoti |
| description | The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow
(CC-OPF) is an open-source Python code developed for solving economic dispatch
(ED) problem in modern power grids. In recent years, integrating a significant
amount of renewables into a power grid causes high fluctuations and thus brings
a lot of uncertainty to power grid operations. This fact makes the conventional
model-based CC-OPF problem non-convex and computationally complex to solve. The
developed tool presents a novel data-driven approach based on the GP regression
model for solving the CC-OPF problem with a trade-off between complexity and
accuracy. The proposed approach and developed software can help system
operators to effectively perform ED optimization in the presence of large
uncertainties in the power grid. |
| doi_str_mv | 10.48550/arxiv.2302.08454 |
| format | Article |
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(CC-OPF) is an open-source Python code developed for solving economic dispatch
(ED) problem in modern power grids. In recent years, integrating a significant
amount of renewables into a power grid causes high fluctuations and thus brings
a lot of uncertainty to power grid operations. This fact makes the conventional
model-based CC-OPF problem non-convex and computationally complex to solve. The
developed tool presents a novel data-driven approach based on the GP regression
model for solving the CC-OPF problem with a trade-off between complexity and
accuracy. The proposed approach and developed software can help system
operators to effectively perform ED optimization in the presence of large
uncertainties in the power grid.</description><identifier>DOI: 10.48550/arxiv.2302.08454</identifier><language>eng</language><subject>Computer Science - Learning ; Statistics - Machine Learning</subject><creationdate>2023-02</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2302.08454$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2302.08454$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Mitrovic, Mile</creatorcontrib><creatorcontrib>Kundacina, Ognjen</creatorcontrib><creatorcontrib>Lukashevich, Aleksandr</creatorcontrib><creatorcontrib>Vorobev, Petr</creatorcontrib><creatorcontrib>Terzija, Vladimir</creatorcontrib><creatorcontrib>Maximov, Yury</creatorcontrib><creatorcontrib>Deka, Deepjyoti</creatorcontrib><title>GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow</title><description>The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow
(CC-OPF) is an open-source Python code developed for solving economic dispatch
(ED) problem in modern power grids. In recent years, integrating a significant
amount of renewables into a power grid causes high fluctuations and thus brings
a lot of uncertainty to power grid operations. This fact makes the conventional
model-based CC-OPF problem non-convex and computationally complex to solve. The
developed tool presents a novel data-driven approach based on the GP regression
model for solving the CC-OPF problem with a trade-off between complexity and
accuracy. The proposed approach and developed software can help system
operators to effectively perform ED optimization in the presence of large
uncertainties in the power grid.</description><subject>Computer Science - Learning</subject><subject>Statistics - Machine Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj71OwzAURr0woMIDMOEXSHD8kzhsyCIBqVIydEXRtXMjLIW4sgMFnp62dPqWo0_nEHJXsFxqpdgDxG__lXPBeM60VPKavLU9NSbr-uaRtvCZkoeF9jE4TIlaSDjSsF_9h_-F1YeFriHMdAqRmndYHGYmLGmN4Jcj2J1AmGkfDhhpM4fDDbmaYE54e9kN2TXPO_OSbbv21TxtMygrmVVWlhpRg7CIjFs3WixVOZbgJuaKWvNqVKNyrtbW1hy41UrbYqodt6IQKDbk_v_23Dfs41Ej_gynzuHcKf4AOnBOdQ</recordid><startdate>20230216</startdate><enddate>20230216</enddate><creator>Mitrovic, Mile</creator><creator>Kundacina, Ognjen</creator><creator>Lukashevich, Aleksandr</creator><creator>Vorobev, Petr</creator><creator>Terzija, Vladimir</creator><creator>Maximov, Yury</creator><creator>Deka, Deepjyoti</creator><scope>AKY</scope><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20230216</creationdate><title>GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow</title><author>Mitrovic, Mile ; Kundacina, Ognjen ; Lukashevich, Aleksandr ; Vorobev, Petr ; Terzija, Vladimir ; Maximov, Yury ; Deka, Deepjyoti</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a674-7b468ee8a3bee02bcdbe656d6acf0c19827d5d5cc98bb92a2b858b1f9c2b313e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Learning</topic><topic>Statistics - Machine Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Mitrovic, Mile</creatorcontrib><creatorcontrib>Kundacina, Ognjen</creatorcontrib><creatorcontrib>Lukashevich, Aleksandr</creatorcontrib><creatorcontrib>Vorobev, Petr</creatorcontrib><creatorcontrib>Terzija, Vladimir</creatorcontrib><creatorcontrib>Maximov, Yury</creatorcontrib><creatorcontrib>Deka, Deepjyoti</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mitrovic, Mile</au><au>Kundacina, Ognjen</au><au>Lukashevich, Aleksandr</au><au>Vorobev, Petr</au><au>Terzija, Vladimir</au><au>Maximov, Yury</au><au>Deka, Deepjyoti</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow</atitle><date>2023-02-16</date><risdate>2023</risdate><abstract>The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow
(CC-OPF) is an open-source Python code developed for solving economic dispatch
(ED) problem in modern power grids. In recent years, integrating a significant
amount of renewables into a power grid causes high fluctuations and thus brings
a lot of uncertainty to power grid operations. This fact makes the conventional
model-based CC-OPF problem non-convex and computationally complex to solve. The
developed tool presents a novel data-driven approach based on the GP regression
model for solving the CC-OPF problem with a trade-off between complexity and
accuracy. The proposed approach and developed software can help system
operators to effectively perform ED optimization in the presence of large
uncertainties in the power grid.</abstract><doi>10.48550/arxiv.2302.08454</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Learning Statistics - Machine Learning |
| title | GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow |
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