Data-Driven Robust Chance Constrained Problems: A Mixture Model Approach
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Optimization Theory and Applications"
DOI: 10.1007/s10957-018-1376-4
Abstract: This paper discusses the mixture distribution-based data-driven robust chance constrained problem. We construct a data-driven mixture distribution-based uncertainty set from the perspective of simultaneously estimating higher-order moments. Then, we derive a reformulation of the data-driven… read more here.
Keywords: constrained problems; chance constrained; data driven; driven robust ... See more keywords
A Wasserstein distributionally robust chance constrained programming approach for emergency medical system planning problem
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Systems Science"
DOI: 10.1080/00207721.2022.2040641
Abstract: This paper proposes a distributionally robust chance constrained programming model for an emergency medical system location problem with uncertain demands. By minimising the total expected cost, the location of emergency medical stations, the allocation of… read more here.
Keywords: constrained programming; distributionally robust; chance constrained; robust chance ... See more keywords
Scheduling of Energy Hub Resources Using Robust Chance-Constrained Optimization
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3228388
Abstract: This paper develops a robust chance-constrained model for handling the uncertainties of generation and consumption in multi-carrier energy hubs. The proposed model incorporates corresponding loading factors for each type of electrical, heating, and cooling loads.… read more here.
Keywords: energy hub; chance constrained; robust chance; model ... See more keywords
A Linear Programming Approximation of Distributionally Robust Chance-Constrained Dispatch With Wasserstein Distance
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Power Systems"
DOI: 10.1109/tpwrs.2020.2978934
Abstract: This paper proposes a data-driven distributionally robust chance constrained real-time dispatch (DRCC-RTD) considering renewable generation forecasting errors. The proposed DRCC-RTD model minimizes the expected quadratic cost function and guarantees that the two-sided chance constraints are… read more here.
Keywords: wasserstein distance; linear programming; chance; distributionally robust ... See more keywords