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Found 12 papers, 0 papers with code
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization
We provide improved sample complexity guarantees for both proposed algorithms.
Identifying Operation Equilibrium in Integrated Electricity, Natural Gas, and Carbon-Emission Markets
Carbon emission markets can play a significant role in this transition by putting a price on carbon and giving electricity producers an incentive to reduce their emissions.
Deep Decarbonization of Multi-Energy Systems: A Carbon-Oriented Framework with Cross Disciplinary Technologies
The retirement of unabated coal power plants, the plummeting cost of renewable energy technologies, along with more aggressive public policies and regulatory reforms, are occurring at an unprecedented speed to decarbonize the power and energy systems towards the 2030 and 2050 climate goals.
Clustering Enabled Few-Shot Load Forecasting
While the advanced machine learning algorithms are effective in load forecasting, they often suffer from low data utilization, and hence their superior performance relies on massive datasets.
Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective
In this paper, we cast one-shot non-intrusive load monitoring (NILM) in the compressive sensing framework, and bridge the gap between theoretical accuracy of NILM inference and differential privacy's parameters.
Effective End-to-End Learning Framework for Economic Dispatch
Conventional wisdom to improve the effectiveness of economic dispatch is to design the load forecasting method as accurately as possible.
Robust Data-driven Profile-based Pricing Schemes
To enable an efficient electricity market, a good pricing scheme is of vital importance.
A Data-driven Storage Control Framework for Dynamic Pricing
Dynamic pricing is both an opportunity and a challenge to the demand side.
Vulnerability Analysis for Data Driven Pricing Schemes
In this paper, from an adversarial machine learning point of view, we examine the vulnerability of data-driven electricity market design.
Optimal Storage Control for Dynamic Pricing
Renewable energy brings huge uncertainties to the power system, which challenges the traditional power system operation with limited flexible resources.
Systems and Control Systems and Control Optimization and Control
Conductor Galloping Prediction on Imbalanced Datasets: SVM with Smart Sampling
Conductor galloping is the high-amplitude, low-frequency oscillation of overhead power lines due to wind.
Efficient and Robust Equilibrium Strategies of Utilities in Day-ahead Market with Load Uncertainty
We consider the scenario where $N$ utilities strategically bid for electricity in the day-ahead market and balance the mismatch between the committed supply and actual demand in the real-time market, with uncertainty in demand and local renewable generation in consideration.
