scholarly journals Optimal power flow for distribution networks with distributed generation

2015 ◽  
Vol 12 (2) ◽  
pp. 145-170 ◽  
Author(s):  
Jordan Radosavljevic ◽  
Miroljub Jevtic ◽  
Dardan Klimenta ◽  
Nebojsa Arsic
Keyword(s):  
Distributed Generation ◽  
Wind Turbines ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Distribution Networks ◽  
Point Estimate ◽  
Photovoltaic Systems ◽  
Multi Objective ◽  
Point Estimate Method ◽  
Optimal Power

This paper presents a genetic algorithm (GA) based approach for the solution of the optimal power flow (OPF) in distribution networks with distributed generation (DG) units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs) of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators.

Probabilistic Power Flow Analysis Based on Point-Estimate Method for High Penetration of Photovoltaic Generation in Electrical Distribution Systems

2020 ◽  
Author(s):  
Cordero B. Luis ◽  
Franco B. John
Keyword(s):  
Power Flow ◽  
Edgeworth Expansion ◽  
Distribution Networks ◽  
Point Estimate ◽  
Photovoltaic Systems ◽  
Distributed Energy ◽  
Photovoltaic Generation ◽  
Point Estimate Method ◽  
High Penetration ◽  
Estimate Method

Environmental awareness and energy policies led to decarbonization targets, fostering the adoption of distributed energy resource in the distribution network. Particularly, photovoltaic systems have been gaining momentum due to cost-competitive option and financial benefits. However, traditional distribution networks were not designed for intermittency in power generation. This poses technical issues such as reverse power flow, overvoltage, and thermal overloading. Furthermore, the growth in intermittency and variability of distributed energy resources increases the uncertainty, hence, it brings challenges for the operation, planning, and investment decisions. In this context, probabilistic methods to cater for these uncertainties are essential to address this issue. This paper presents a probabilistic power flow method based on point estimate method combined Edgeworth expansion for high penetration of photovoltaic generation in distribution networks. Normal distribution and Beta distribution are considered for load and solar irradiation modelling, respectively. The method is assessed for different cases using the IEEE 33-bus distribution test system with photovoltaic systems installation. The point estimate method combined Edgeworth expansion provided satisfactory results with lower computational effort and high fitting accuracy of statistical information compared to Monte Carlo simulation.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

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