Unified algorithm for solving stochastic storage problems using statistical learning.
problem Solving stochastic storage problems through regression Monte Carlo methods.
method Developed the dynamic emulation algorithm (DEA) that unifies different approaches.
result Illustrated the DEA template with examples from natural gas storage and microgrid control.
Paper provides a method to price electricity storage contracts using COS technique.
problem Valuation of electricity storage contracts considering physical and operational constraints.
method Uses Fourier-based COS method to price contracts based on stochastic polynomial process.
result The COS method accurately and efficiently prices electricity storage contracts.
SER tool predicts storage capacity risk with probabilistic accuracy.
problem Managing data storage growth to avoid financial losses.
method Stochastic Estimated Risk (SER) using Brownian motion with drift model.
result Probabilistic approach is more accurate for non-linear storage patterns.
Paper introduces a new volatility model for natural gas markets and discusses swing option pricing.
problem Modeling price and storage dynamics in natural gas markets with path-dependent volatility.
method Developed a novel stochastic path-dependent volatility model and used deep learning for swing option pricing.
result Proposed a deep learning method for numerical approximations of swing option pricing.
The paper explains why futures prices often differ from spot prices in grain markets.
problem Non-convergence of futures and spot prices in grains markets.
method Incorporates stochastic spot price and storage cost, solves an optimal double stopping problem.
result Explicit no-arbitrage prices for shipping certificates and futures contracts are derived.
Study on energy storage's impact on electricity prices and profitability.
problem Analyzing the profitability of energy storage in electricity markets.
method Characterized optimal operating strategy for storage systems, determined equilibrium price in a market with storage, renewables, and conventional producers, and characterized price process using stochastic differential equations.
result Increased average revenues and interquantile ranges for storage assets in energy transition scenarios.
The mathematical problem of the static storage optimisation is formulated and solved by means of a variational analysis. The solution obtained in implicit form is shedding light on the most important features of the optimal exercise strategy. We show how the solution depends on different constraint types including carr…
Optimizes energy storage under fluctuating prices and partial information.
problem Optimizing energy storage in a changing economic environment with partial information.
method Applying filtering theory to derive an adapted state process, solving the Hamilton-Jacobi-Bellman equation, and proving existence and uniqueness of a solution.
result Derives an optimal control policy for energy storage, proving its admissibility.
This study optimizes energy storage scheduling under price uncertainty, balancing risk and reward.
problem Optimizing energy storage operation under price uncertainty and risk.
method Two-stage stochastic risk-constrained approach using conditional value-at-risk.
result Increasing risk aversion leads to substantial benefits in terms of risk reduction and expected reward.
The mathematical problem concerning intrinsic storage optimisation is formulated and solved by means of variational analysis. The solution, though obtained in implicit form, still sheds light on many important features of the optimal exercise strategy. It is shown how the solution depends on different constraint types …
Paper proposes a new model to assess risks in energy storage systems considering both exogenous and endogenous uncertainties.
problem Current risk assessment ignores the stochastic nature of energy storage availability.
method Data-driven unified model with exogenous and endogenous uncertainty description for four types of generic energy storage.
result Comparative results show more severe risks for endogenous uncertainty, suggesting new strategies for system operators.
A new ADMM variant reduces storage needs while maintaining fast convergence.
problem High storage requirements in ADMM variants.
method Integrates SVRG with ADMM, reducing storage needs.
result Converges faster and uses less memory than existing methods.
Model predicts BESS interactions and price impacts in energy markets.
problem Understanding BESS interactions and price formation in energy markets.
method Stochastic game-theoretic model with linear-quadratic differential game.
result Equilibrium controls and prices derived for BESSs in both heterogeneous and homogeneous settings.
Develops a deep RL algorithm for ESS control in electricity markets.
problem Controlling ESSs for arbitrage in real-time electricity markets under price uncertainty.
method Formulated as a Markov decision process, developed a deep RL algorithm using a recurrent neural network.
result Effectiveness of the algorithm verified using real-time PJM electricity prices.
New method optimizes pumped hydroelectric storage with state constraints.
problem Optimal management of pumped hydroelectric production with state constraints.
method Transformed constrained problem into an unconstrained one in augmented spaces with state constraints penalized.
result Solved the problem using dynamic programming.
New SPD methods improve online policy estimation in MDPs with reduced storage and complexity.
problem Online estimation of optimal policies in Markov decision processes (MDPs).
method Stochastic Primal-Dual (SPD) methods that update few coordinates of value and policy estimates.
result SPD methods find absolute-ε-optimal policies with high probability using a specified number of iterations/samples. The paper applies deep learning to solve complex control problems.
problem Solving stochastic control problems on finite horizons.
method Deep neural networks algorithms for control learning.
result Performance of algorithms on various control problems demonstrated.
Paper optimizes neural network layers to reduce energy usage without sacrificing accuracy.
problem Energy consumption in deep neural networks during inference.
method Layerwise noise maximization to optimize reliability of memory elements.
result Reduces memory energy consumption by 3.3 times at equal accuracy.
We consider a class of discrete time stochastic control problems motivated by some financial applications. We use a pathwise stochastic control approach to provide a dual formulation of the problem. This enables us to develop a numerical technique for obtaining an estimate of the value function which improves on purely…
Investigates optimal strategies for behavioral control problems with finite variation controls.
problem Behavioral singular stochastic control problems with finite variation controls.
method Abstract framework, applied to storage management and portfolio investment problems, using CPT preferences and Skorokhod representation theorem.
result Existence of optimal strategies for various goal functionals, including CPT preferences.
Game theory models storage investment to balance market competition and profits.
problem Strategic storage investment impacts electricity market prices and revenues.
method Formulated a non-cooperative game between investors to model strategic storage decisions.
result Increasing storage capacity reduces individual profits but increases total investment.
Canonical Correlation Analysis (CCA) is a widely used spectral technique for finding correlation structures in multi-view datasets. In this paper, we tackle the problem of large scale CCA, where classical algorithms, usually requiring computing the product of two huge matrices and huge matrix decomposition, are computa…
We derive a consistent differential representation for the dynamics of a self-financing portfolio for different hedging strategies. In the basis of the derivation there is the so called "retarded action principle", which represents the causality in the evolution of dependent stochastic variables. We demonstrate this pr…
New method assesses energy storage value beyond cost reduction.
problem Improving energy storage value beyond cost reduction.
method Market potential method to evaluate and compare energy storage technologies.
result High-cost hydrogen storage can be more valuable than low-cost hydrogen storage.
Deep learning approximates high-dimensional stochastic control problems.
problem High-dimensional stochastic control problems with the curse of dimensionality.
method Approximates time-dependent controls as neural networks and trains them through model dynamics.
result Achieves satisfactory accuracy in high-dimensional problems.
SARAH uses stochastic recursive gradient for efficient machine learning.
problem Finite-sum minimization problems in machine learning.
method SARAH and SARAH+ algorithms with recursive gradient estimates.
result Linear convergence rate proven under strong convexity assumption.
Variance reduction (VR) methods boost the performance of stochastic gradient descent (SGD) by enabling the use of larger, constant stepsizes and preserving linear convergence rates. However, current variance reduced SGD methods require either high memory usage or an exact gradient computation (using the entire dataset)…
Generative models improve carbon storage site prediction using Bayesian inversion.
problem Predicting suitable geologic sites for long-term carbon dioxide storage.
method Generative adversarial networks and Bayesian inversion to condition models on physical measurements and historic data.
result Improved resolution of carbon dioxide storage capacity forecasts.
Paper proposes a risk-averse approach to energy storage price arbitrage using conformal uncertainty quantification.
problem Inherent volatility and uncertainty of real-time electricity prices create financial risks for storage arbitrage.
method Two-layer prediction model with conformal uncertainty quantification for high coverage of real-time price uncertainty.
result The framework achieves good profit margins with minimal losses, demonstrating effectiveness in real-time market.
Improved machine learning for reservoir optimization problems.
problem Optimizing control in high-dimensional storage problems.
method Modified dynamic programming algorithm with neural networks for Bellman values and conditional cuts.
result Neural networks outperform classical feedforward networks in estimating Bellman values.
Stochastic variational inference (SVI) lets us scale up Bayesian computation to massive data. It uses stochastic optimization to fit a variational distribution, following easy-to-compute noisy natural gradients. As with most traditional stochastic optimization methods, SVI takes precautions to use unbiased stochastic g…
It has been known for a long time that the classical spherical perceptrons can be used as storage memories. Seminal work of Gardner, \cite{Gar88}, started an analytical study of perceptrons storage abilities. Many of the Gardner's predictions obtained through statistical mechanics tools have been rigorously justified. …
Apollo improves nonconvex stochastic optimization efficiency.
problem Nonconvex stochastic optimization challenges.
method Adaptive parameter-wise diagonal quasi-Newton method approximating Hessian.
result Significant improvements in convergence speed and generalization over SGD and Adam.
Framework uses RL and simulation for optimal microgrid energy storage planning.
problem Optimal investment in diverse energy storage technologies for microgrids.
method Combines reinforcement learning with simulation-based optimization for long-term planning.
result Derives better engineering solutions for future microgrid applications.
Global convergence of an online (stochastic) limited memory version of the Broyden-Fletcher- Goldfarb-Shanno (BFGS) quasi-Newton method for solving optimization problems with stochastic objectives that arise in large scale machine learning is established. Lower and upper bounds on the Hessian eigenvalues of the sample …
Study estimates risks of nuclear waste storage projects.
problem Cost and schedule risks in nuclear waste storage projects.
method Reference class of 216 past projects for cost risk, 200 for schedule risk.
result Cost and schedule risks are substantial for nuclear waste storage projects.
AppStreamer reduces mobile game storage by predicting needed files.
problem Expanding storage needs of mobile games and apps.
method Predictive streaming of app files from cloud or edge servers.
result Reduces storage by 87% for Dead Effect 2 and 86% for Fire Emblem Heroes.
Deep learning optimizes gas storage operations.
problem Optimizing underground natural gas storage operations.
method Reinforcement learning techniques applied to high-dimensional forward markets with constraints.
result Performance of deep learning method superior to least-squares Monte-Carlo approach.
ARBO-DART optimizes battery storage dispatch in day-ahead and real-time markets.
problem Optimizing battery storage dispatch in day-ahead and real-time markets.
method Adaptive Refinement Bayesian Optimization (ARBO) for Day-Ahead and Real-Time (ARBO-DART) markets.
result ARBO-DART optimizes battery storage dispatch without requiring analytic gradients or finite-scenario approximations.
Paper restricts non-negative matrix factorization to stochastic matrices for data analysis.
problem Analyzing unstructured data like topic models and face storage retrieval.
method Necessary and sufficient conditions for unique factorization, natural bounds on parameters, consistent estimator.
result Unique factorization conditions and parameter bounds for observed data.
Preconditioned conjugate gradients improve kernel machine scalability.
problem Poor convergence of conjugate gradients on kernel matrices.
method Developed preconditioned conjugate gradients for kernel machines.
result Preconditioned conjugate gradients outperform state-of-the-art approximations.
RL agent learns to save costs by managing household energy storage.
problem Maximizing cost savings in smart grids with household energy storage.
method Data-driven RL agent learns from tariff structures and storage capacity.
result RL agent explains its learning process and strategies.
Adaptive sampling method reduces variance in stochastic optimization.
problem Reducing variance in stochastic optimization with limited gradient computations.
method Adaptive increase in sample size based on inner product test.
result Algorithm converges globally on nonconvex functions and linearly on strongly convex functions.
Study on neural networks' storage capacity and solution space structure.
problem Understanding the storage capacity and solution space structure of neural networks.
method Replica method from statistical physics.
result Storage capacity per parameter remains finite even with infinite width and weights exhibit negative correlations.
New approach for feature evolution in streaming data with limited storage.
problem Rarely-provided labels in feature evolving streams.
method Incorporates manifold regularization and a buffer to adapt to different storage budgets.
result Preserves the performance of feature evolving learning across different storage budgets.
New framework predicts future shipments with less error and saves labor.
problem Optimizing storage assignment in warehouses under uncertainty.
method Introduces a new framework that combines neural networks to predict future shipments and integrates it into a storage assignment system.
result Achieves up to 29% decrease in MAPE compared to CNN-LSTM on unseen future shipments.
Introduces an unobservable intrinsic electricity price to link storage theory with risk premium.
problem Connecting storage theory with risk premium in electricity markets.
method Introduces an unobservable intrinsic electricity price and derives prices for various contracts.
result Finds an overall negative risk premium in empirical analysis.
Two SGD-like algorithms reduce memory usage for stochastic optimization with infinite data.
problem Optimizing with infinite data sets resulting from random noise.
method Proposes SSAG and S-SAGA algorithms for expected risk minimization.
result SSAG has faster convergence rate than SGD with comparable space requirement.