Uncertainty Modelled Power Flow Analysis for DG Sourced Power Systems

2013 ◽  
Vol 768 ◽  
pp. 298-300
Author(s):  
P. Sivakumar ◽  
D. Poornima
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Energy Requirement ◽  
Probabilistic Approach ◽  
Flow Analysis ◽  
Energy Sources ◽  
Modern World

For growing of electrical demand in the modern world energy requirement is tremendously increased day to day power market. Nowadays the non-conventional energy sources are utilized to meet out the current power demand through PV, wind and other non-conventional resources etc. In this concern the energy drawn from the other non-conventional energy sources is highly variable due to the nature of uncertainties. Hence the optimal load dispatch of the power is highly difficult, one of the attempts is to eradicate this difficulty by adding developed uncertainty model of PV and wind sourced power generation in power system network. Uncertainties of PV irradiation and wind speed models are developed by using generic probabilistic approach. By using this hybrid system, instantaneous power flow of a DG system is obtained through Monte carlo simulation (MCS) in the MATLAB/SIMULINK packages. Enhancement of optimal power flow (opf) and system reliability due to addition of uncertainty variables in DG sourced power systems.Index TermsLoad flow analysis, Monte Carlo simulation (MCS), integration of Photovoltaic generator and wind (PVG and WEG).

scholarly journals Hybrid Imperialist Competitive and Grey Wolf Algorithm to Solve Multiobjective Optimal Power Flow with Wind and Solar Units

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2891 ◽  
Author(s):  
Jalel Ben Hmida ◽  
Mohammad Javad Morshed ◽  
Jim Lee ◽  
Terrence Chambers
Keyword(s):  
Power Systems ◽  
Electric Power ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Analysis ◽  
Electric Power System ◽  
Energy Integration ◽  
Grey Wolf ◽  
Optimal Power ◽  
Grey Wolf Algorithm

The optimal power flow (OPF) module optimizes the generation, transmission, and distribution of electric power without disrupting network power flow, operating limits, or constraints. Similarly to any power flow analysis technique, OPF also allows the determination of system’s state of operation, that is, the injected power, current, and voltage throughout the electric power system. In this context, there is a large range of OPF problems and different approaches to solve them. Moreover, the nature of OPF is evolving due to renewable energy integration and recent flexibility in power grids. This paper presents an original hybrid imperialist competitive and grey wolf algorithm (HIC-GWA) to solve twelve different study cases of simple and multiobjective OPF problems for modern power systems, including wind and photovoltaic power generators. The performance capabilities and potential of the proposed metaheuristic are presented, illustrating the applicability of the approach, and analyzed on two test systems: the IEEE 30 bus and IEEE 118 bus power systems. Sensitivity analysis has been performed on this approach to prove the robustness of the method. Obtained results are analyzed and compared with recently published OPF solutions. The proposed metaheuristic is more efficient and provides much better optimal solutions.

A Probabilistic Optimal Power Flow Calculation Method with Latin Hypercube Sampling

2014 ◽  
Vol 918 ◽  
pp. 183-190
Author(s):  
Jin Quan Zhao ◽  
Chen Lu Zhang ◽  
Wei Hua Luo ◽  
Jun Zhao
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Sampling ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Latin Hypercube ◽  
Optimal Power ◽  
Accurate Performance

Among the solving methods of probabilistic optimal power flow (P-OPF), Monte Carlo Simulation (MCS) combined with random sampling (RS) is widely used due to its high accuracy. In order to further improve that, this paper proposes a way of using Monte Carlo Simulation with Latin hypercube sampling (LHS) to calculate the consumption of generating cost under many random variables. Numerical results of IEEE 14-bus and IEEE 118-bus systems show that the Latin hypercube sampling method provides more accurate performance in dealing with POPF under the condition of a smaller sample size, comparing with random sampling method. Thus the Latin hypercube sampling method can replace the MCS with random sampling as the benchmark method of other algorithms.

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