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Found 8 papers, 0 papers with code
A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study
This paper addresses the challenge of efficiently solving the optimal power flow problem in real-time electricity markets.
Weather-Driven Flexibility Reserve Procurement: A NYISO Offshore Wind Power Case Study
To address this, probability- and risk-based reserve sizing models have been proposed, which use probabilistic VRES power forecasts that mostly rely on historical forecast and actual VRES power data for model training.
Markovian Decentralized Ensemble Control for Demand Response
With the advancement in smart grid and smart energy devices, demand response becomes one of the most economic and feasible solutions to ease the load stress of the power grids during peak hours.
Operation-Adversarial Scenario Generation
This paper proposes a modified conditional generative adversarial network (cGAN) model to generate net load scenarios for power systems that are statistically credible, conditioned by given labels (e. g., seasons), and, at the same time, "stressful" to the system operations and dispatch decisions.
Uncertainty-Aware Capacity Allocation in Flow-Based Market Coupling
However, explicitly analysing FBMC for a future power system with a very high share of intermittent renewable generation is often overlooked in the current literature.
Cyber Insurance Against Cyberattacks on Electric Vehicle Charging Stations
In this paper, we propose cyber insurance for EVCSs to hedge the economic loss due to such cyberattacks and develop a data-driven cyber insurance design model for public EVCSs.
Inertia Pricing in Stochastic Electricity Markets
Maintaining the stability of renewable-dominant power systems requires the procurement of virtual inertia services from non-synchronous resources (e. g., batteries, wind turbines) in addition to inertia traditionally provided by synchronous resources (e. g., thermal generators).
Risk-Aware Dimensioning and Procurement of Contingency Reserve
Current contingency reserve criteria ignore the likelihood of individual contingencies and, thus, their impact on system reliability and risk.
