The value of storage in electricity distribution: The role of markets

  • Dirk Lauinger
  • Deepjyoti Deka
  • Sungho Shin
    • Categories Energy Tags , , , ,

    Electricity distribution companies deploy battery storage to defer grid upgrades by reducing peak demand. In deregulated jurisdictions, such storage often sits idle because regulatory constraints bar participation in electricity markets. Here, we develop an optimization framework that, to our knowledge, provides the first formal model of market participation constraints within storage investment and operation planning. … Read more

    A Gradient Sampling Algorithm for Noisy Nonsmooth Optimization

  • Lara Zebiane
  • Frank E. Curtis
  • Albert S. Berahas
    • Categories Convex and Nonsmooth Optimization, Nonlinear Optimization, Nonsmooth Optimization Tags , ,

    An algorithm is proposed, analyzed, and tested for minimizing locally Lipschitz objective functions that may be nonconvex and/or nonsmooth. The algorithm, which is built upon the gradient-sampling methodology, is designed specifically for cases when objective function and generalized gradient values might be subject to bounded uncontrollable errors. Similarly to state-of-the-art guarantees for noisy smooth optimization … Read more

    Bilevel Learning

  • Alain Zemkoho
    • Categories Bilevel Optimization, Optimization in Data Science Tags , , ,

    Bilevel learning refers to machine learning problems that can be formulated as bilevel optimization models, where decisions are organized in a hierarchical structure. This paradigm has recently gained considerable attention in machine learning, as gradient-based algorithms built on the implicit function reformulation have enabled the computation of large-scale problems involving possibly millions of variables. Despite … Read more

    KDE Robust Satisficing for Optimal Load Shedding Under Renewable Uncertainty

  • Min Xin Shu
  • Wei Liu
  • Qiao Yi Wang
  • Bo Yu
    • Categories Applications - Science and Engineering, Energy, Robust Optimization Tags , , , ,

    Abstract—Renewable-driven direct-current optimal load shedding (DC-OLS) requires a model that is interpretable to operators, data driven under continuous forecast errors, sensitive to severe security failures, and computationally tractable. This paper develops a budgeted KDE-ϕ-HMCR-RS-OLS framework for that purpose. Robust satisficing (RS) replaces ambiguity-radius tuning with an admissible shedding budget. A one-dimensional KDE reference family with … Read more

    An objective-function-free algorithm for nonconvex stochastic optimization with deterministic equality and inequality constraints

  • Serge Gratton
  • Philippe L. Toint
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Stochastic Programming Tags , , , ,

    An algorithm is proposed for solving optimization problems with stochastic objective and deterministic equality and inequality constraints. This algorithm is objective-function-free in the sense that it only uses the objective’s gradient and never evaluates the function value. It is based on an adaptive selection of function-decreasing and constraint-improving iterations, the first ones using an Adagrad-type … Read more

    Beyond binarity: Semidefinite programming for ternary quadratic problems

  • Frank de Meijer
  • Veronica Piccialli
  • Renata Sotirov
  • Antonio M. Sudoso
    • Categories (Mixed) Integer Nonlinear Programming, Quadratic Programming, Semi-definite Programming Tags , , ,

    We study the ternary quadratic problem (TQP), a quadratic optimization problem with linear constraints where the variables take values in {0,±1}. While semidefinite programming (SDP) techniques are well established for {0,1}- and {±1}-valued quadratic problems, no dedicated integer semidefinite programming framework exists for the ternary case. In this paper, we introduce a ternary SDP formulation … Read more

    Globalized Adversarial Regret Optimization: Robust Decisions with Uncalibrated Predictions

  • Jannis Kurtz
  • Bart Paul Gerard van Parys
    • Categories Robust Optimization, Stochastic Programming Tags , ,

    Optimization problems routinely depend on uncertain parameters that must be predicted before a decision is made. Classical robust and regret formulations are designed to handle erroneous predictions and can provide statistical error bounds in simple settings. However, when predictions lack rigorous error bounds (as is typical of modern machine learning methods) classical robust models often … Read more

    New Proofs of Exact LP Reformulations for Binary Polynomial Optimization with Bounded Treewidth

  • Sourabh Kumar Choudhary
  • Santanu S. Dey
  • Nikolaos Sahinidis
    • Categories Combinatorial Optimization Tags , ,

    In this work, we revisit binary polynomial optimization (BPO) problems with limited treewidth of the associated graph. We provide alternate proofs of the exactness of a reformulated linear program (LP) with O(n.2^d) variables, n being the number of variables and d being the treewidth of the associated graph. The first proof relies on expressing any … Read more

    On lifting strategies for optimal control problems

  • Robert Lampel
  • Sebastian Sager
    • Categories Nonlinear Optimization Tags , , , ,

    The representation of a function in a higher-dimensional space, often referred to as lifting, can be used to reduce complexity. We investigate how lifting affects the convergence properties of Newton-type methods. For the first time, we conduct a systematic comparison of several lifting strategies on a set of 40 optimal control problems. In addition, we … Read more