scholarly journals Reliability assessment of an isolated hybrid microgrid using Markov modeling and Monte Carlo simulation

2020 ◽  
Vol 41 (2) ◽  
Author(s):  
Ahmad F. Saleem ◽  
Mohammad AlMuhaini
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Gas Turbines ◽  
Sequential Monte Carlo ◽  
Reliability Assessment ◽  
Ambient Air ◽  
Test System ◽  
Markov Modeling ◽  
Reliability Indices ◽  
Renewable Power

The importance of conducting adequacy assessments of standalone systems with integrated renewable generation sources is growing. In this work, the focus is placed on the reliability assessment of an isolated microgrid operating on renewable energy generated by wind turbines (WTs) and photovoltaic (PV) panels. Batteries for storage were included in the model because of their crucial role in the system’s feasibility. Additional micro-gas turbines (MGTs) served as conventional backup. The sequential Monte Carlo simulation (SMCS) method was used to carry out simulations of the system, which was modeled using Markov matrices. Input data, such as wind speed, solar irradiance, and ambient air temperature, were used to simulate the power outputs of the generators. These historical data were fitted into appropriate distributions to extract corresponding parameters when simulating essential key factors necessary to produce the renewable power generation models. The adequacy model of the MGTs was obtained by employing the two-state reliability model, which was also superimposed with the generation models of WTs, PV panels, and batteries. The IEEE Roy Billinton test system (RBTS) was used for demand modelling. Common reliability indices were computed, and the system availability margins were evaluated.

scholarly journals Reliability Evaluation of Roy Billinton Test System

2021 ◽  
Vol 9 (4) ◽  
pp. 491-498
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Number ◽  
Test System ◽  
Random Number Generators ◽  
Reliability Indices ◽  
Continuous Power ◽  
Simple Systems ◽  
Planning And Operation

Reliability play's a major role at power system planning and operation. Reliability means continuous power supply to end users without outages. So in order to study reliability of any system we consider two methods which are Analytical and Monte-Carlo simulation. Analytical methods are mathematical models which gives numerical calculations for simple systems. Monte Carlo Simulation is a proposed method which is used in case of complex systems. RBTS BUS-2 test system is used as case study with DG’s at different locations and without DG’s to evaluate fundamental reliability indices, customer oriented indices SAIFI, SAIDI, CAIDI .Cost/worth indices such as EENS, ECOST and IEAR are calculated and compared by both Analytical and Monte-Carlo simulation. In Monte-Carlo time sequential technics indices are calculated by using random number generators with UP and DOWN states times of system elements.

Reliability Assessment of Power System Using Importance Sampling Technique Based on Layer Optimization Simulation

2011 ◽  
Vol 88-89 ◽  
pp. 554-558 ◽  
Author(s):  
Bin Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power System ◽  
Importance Sampling ◽  
System Reliability ◽  
Sampling Method ◽  
Sampling Technique ◽  
Test System ◽  
Importance Sampling Method ◽  
Importance Sampling Technique

An improved importance sampling method with layer simulation optimization is presented in this paper. Through the solution sequence of the components’ optimum biased factors according to their importance degree to system reliability, the presented technique can further accelerate the convergence speed of the Monte-Carlo simulation. The idea is that the multivariate distribution’ optimization of components in power system is transferred to many steps’ optimization based on importance sampling method with different optimum biased factors. The practice is that the components are layered according to their importance degree to the system reliability before the Monte-Carlo simulation, the more forward, the more important, and the optimum biased factors of components in the latest layer is searched while the importance sampling is carried out until the demanded accuracy is reached. The validity of the presented is verified using the IEEE-RTS79 test system.

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