This paper proposes an algorithm for the optimal operation of community energy storage systems (ESSs) using a machine learning (ML) model by solving a nonlinear programming (NLP) problem iteratively to obtain synthetic data. The NLP model minimizes the network's total energy losses by setting the community ESS's operation points. The optimization model is solved recursively by Monte Carlo simulations in a distribution system with high PV penetration, considering uncertainty in exogenous parameters. Obtained optimal solutions provide the training dataset for a stochastic gradient boosting trees (SGBT) ML algorithm following an imitation learning approach. The predictions obtained from the ML model have been compared to the optimal ESS operation to assess the model's accuracy. Furthermore, the ML model's sensitivity has been tested considering the sampling size and the number of predictors. Results showed a 98% of accuracy for the SGBT model compared to optimal solutions. This accuracy was obtained even after a reduction of 83% in the number of predictors.
Embedded controllers, sensors, actuators, advanced metering infrastructure, etc. are cornerstone components of cyber-physical energy systems such as microgrids (MGs). Harnessing their monitoring and control functionalities, sophisticated schemes enhancing MG stability can be deployed. However, the deployment of 'smart' assets increases the threat surface. Power systems possess mechanisms capable of detecting abnormal operations. Furthermore, the lack of sophistication in attack strategies can render them detectable since they blindly violate power system semantics. On the other hand, the recent increase of process-aware rootkits that can attain persistence and compromise operations in undetectable ways requires special attention. In this work, we investigate the steps followed by stealthy rootkits at the process level of control systems pre-and post-compromise. We investigate the rootkits' precompromise stage involving the deployment to multiple system locations and aggregation of system-specific information to build a neural network-based virtual data-driven model (VDDM) of the system. Then, during the weaponization phase, we demonstrate how the VDDM measurement predictions are paramount, first to orchestrate crippling attacks from multiple system standpoints, maximizing the impact, and second, impede detection blinding system operator situational awareness.
A photovoltaic (PV)-rich low-voltage (LV) distribution network poses a limit on the export power of PVs due to the voltage magnitude constraints. By defining a customer export limit, switching off the PV inverters can be avoided, and thus reducing power curtailment. Based on this, this paper proposes a mixed-integer nonlinear programming (MINLP) model to define such optimal customer export. The MINLP model aims to minimize the total PV power curtailment while considering the technical operation of the distribution network. First, a nonlinear mathematical formulation is presented. Then, a new set of linearizations approximating the Euclidean norm is introduced to turn the MINLP model into an MILP formulation that can be solved with reasonable computational effort. An extension to consider multiple stochastic scenarios is also presented. The proposed model has been tested in a real LV distribution network using smart meter measurements and irradiance profiles from a case study in the Netherlands. To assess the quality of the solution provided by the proposed MILP model, Monte Carlo simulations are executed in OpenDSS, while an error assessment between the original MINLP and the approximated MILP model has been conducted.
Generalized operational scaling laws have been developed for the peak plasma parameters at the exit of an electrothermal plasma capillary discharge. These parameters are the total ablated mass, plasma kinetic temperature, pressure, bulk velocity, and the radiant heat flux. The obtained scaling laws have particular coefficients depending on the material of the ablating liner inside the capillary source. The generalized scaling laws include the magnitude of the peak discharge current, material property, and the dimensions of the ablating sleeve. The values given by the scaling laws are compared with the ones calculated by the computational ElectroThermal Flow (ETFLOW) code and against the experimental data. The obtained results of the scaling laws have shown low error, especially the scaling laws of the kinetic temperature and the plasma exit velocity.Index Terms-Capillary discharges, electrothermal (ET) plasmas, high heat flux, high-density plasmas, plasma launchers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.