A Monte-Carlo Simulation Method for Setting the Underfrequency Load Shedding Relays and Selecting the Spinning Reserve Policy in Autonomous Power Systems

2004 ◽  
Vol 19 (4) ◽  
pp. 2044-2052 ◽  
Author(s):  
E.J. Thalassinakis ◽  
E.N. Dialynas
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power Systems ◽  
Simulation Method ◽  
Load Shedding ◽  
Monte Carlo Simulation Method ◽  
Spinning Reserve ◽  
Autonomous Power Systems

scholarly journals Reliability Assessment of Active Distribution System Using Monte Carlo Simulation Method

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shaoyun Ge ◽  
Li Xu ◽  
Hong Liu ◽  
Mingxin Zhao
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Distribution System ◽  
Reliability Assessment ◽  
Simulation Method ◽  
Load Shedding ◽  
Monte Carlo Simulation Method ◽  
Simulation Process ◽  
High Penetration ◽  
Active Distribution System

In this paper we have treated the reliability assessment problem of low and high DG penetration level of active distribution system using the Monte Carlo simulation method. The problem is formulated as a two-case program, the program of low penetration simulation and the program of high penetration simulation. The load shedding strategy and the simulation process were introduced in detail during each FMEA process. Results indicate that the integration of DG can improve the reliability of the system if the system was operated actively.

scholarly journals Uncertainty Cost Functions in Climate-Dependent Controllable Loads in Commercial Environments

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2885
Author(s):  
Daniel Losada ◽  
Ameena Al-Sumaiti ◽  
Sergio Rivera
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Method ◽  
Electricity Sector ◽  
Cost Functions ◽  
Density Estimator ◽  
Monte Carlo Simulation Method ◽  
Commercial Building ◽  
Complementary Method ◽  
The Cost

This article presents the development, simulation and validation of the uncertainty cost functions for a commercial building with climate-dependent controllable loads, located in Florida, USA. For its development, statistical data on the energy consumption of the building in 2016 were used, along with the deployment of kernel density estimator to characterize its probabilistic behavior. For validation of the uncertainty cost functions, the Monte-Carlo simulation method was used to make comparisons between the analytical results and the results obtained by the method. The cost functions found differential errors of less than 1%, compared to the Monte-Carlo simulation method. With this, there is an analytical approach to the uncertainty costs of the building that can be used in the development of optimal energy dispatches, as well as a complementary method for the probabilistic characterization of the stochastic behavior of agents in the electricity sector.

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