GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
Author:
Funder
Ministry of Education and Science of the Russian Federation
Russian Science Foundation
Publisher
Elsevier BV
Subject
Software
Reference13 articles.
1. Distributionally-robust chance constrained and interval optimization for integrated electricity and natural gas systems optimal power flow with wind uncertainties;Fang;Appl. Energy,2019
2. Probabilistic optimal power flow considering dependences of wind speed among wind farms by pair-copula method;Cao;Int. J. Electr. Power Energy Syst.,2017
3. Importance sampling approach to chance-constrained DC optimal power flow;Lukashevich,2021
4. Chance constraints for improving the security of AC optimal power flow;Lubin;IEEE Trans. Power Syst.,2019
5. Chance-constrained optimal power flow with non-parametric probability distributions of dynamic line ratings;Viafora;Int. J. Electr. Power Energy Syst.,2020
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1. Leveraging Gaussian Processes in Remote Sensing;Energies;2024-08-07
2. Data-driven stochastic AC-OPF using Gaussian process regression;International Journal of Electrical Power & Energy Systems;2023-10
3. Large-Scale Grid Optimization: the Workhorse of Future Grid Computations;Current Sustainable/Renewable Energy Reports;2023-07-10
4. Fast Data-Driven Chance Constrained AC-OPF Using Hybrid Sparse Gaussian Processes;2023 IEEE Belgrade PowerTech;2023-06-25
5. Importance Sampling Approach to Chance-Constrained DC Optimal Power Flow;IEEE Transactions on Control of Network Systems;2023
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