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A survey of contextual optimization methods for decision-making under uncertainty
Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …
learning (ML) community in combining prediction algorithms and optimization techniques to …
A survey of contextual optimization methods for decision making under uncertainty
Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …
learning (ML) community in combining prediction algorithms and optimization techniques to …
Prescribed robustness in optimal power flow
For a timely decarbonization of our economy, power systems need to accommodate
increasing numbers of clean but stochastic resources. This requires new operational …
increasing numbers of clean but stochastic resources. This requires new operational …
Learning with adaptive conservativeness for distributionally robust optimization: Incentive design for voltage regulation
Information asymmetry between the Distribution System Operator (DSO) and Distributed
Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage …
Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage …
Revealing decision conservativeness through inverse distributionally robust optimization
This letter introduces Inverse Distributionally Robust Optimization (I-DRO) as a method to
infer the conservativeness level of a decision-maker, represented by the size of a …
infer the conservativeness level of a decision-maker, represented by the size of a …
Scheduling Distributed Energy Resources Under Limited Observability of Distribution Grids
Distributed energy resources (DERs) should be scheduled in a coordinated manner to
postpone or avoid costly capacity upgrades. Nonetheless, the pervasive lack of data at the …
postpone or avoid costly capacity upgrades. Nonetheless, the pervasive lack of data at the …
Solving Optimal Power Flow on a Data-Budget: Feature Selection on Smart Meter Data
How much data is needed to optimally schedule distributed energy resources (DERs)? Does
the distribution system operator (DSO) have to precisely know load demands and solar …
the distribution system operator (DSO) have to precisely know load demands and solar …
A Joint Energy and Differentially-Private Smart Meter Data Market
Given the vital role that smart meter data could play in handling uncertainty in energy
markets, data markets have been proposed as a means to enable increased data access …
markets, data markets have been proposed as a means to enable increased data access …
Analysis of Data Value in Stochastic Optimal Power Flow for Distribution Systems
The rise of advanced data technologies in electric power distribution systems enables
operators to optimize operations but raises concerns about data security and consumer …
operators to optimize operations but raises concerns about data security and consumer …
An Uncertainty-Aware Data-Driven Predictive Controller for Hybrid Power Plants
M Desai, H Sharma, S Mukherjee… - arxiv preprint arxiv …, 2025 - arxiv.org
Given the advancements in data-driven modeling for complex engineering and scientific
applications, this work utilizes a data-driven predictive control method, namely subspace …
applications, this work utilizes a data-driven predictive control method, namely subspace …