scholarly journals Optimal Power Flow Management System for a Power Network with Stochastic Renewable Energy Resources Using Golden Ratio Optimization Method

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3671
Author(s):  
Khaled Nusair ◽  
Feras Alasali
Keyword(s):  
Heuristic Search ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Golden Ratio ◽  
Optimization Method ◽  
Power Network ◽  
Gas Emission ◽  
Optimal Power ◽  
Heuristic Search Methods ◽  
Ratio Optimization

An optimal operation system is a potential solution to increase the energy efficiency of a power network equipped with stochastic Renewable Energy Sources (RES). In this article, an Optimal Power Flow (OPF) problem has been formulated as a single and multi-objective problems for a conventional power generation and renewable sources connected to a power network. The objective functions reflect the minimization of fuel cost, gas emission, power loss, voltage deviation and improving the system stability. Considering the volatile renewable generation behaviour and uncertainty in the power prediction of wind and solar power output as a nonlinear optimization problem, this paper uses a Weibull and lognormal probability distribution functions to estimate the power output of renewable generation. Then, a new Golden Ratio Optimization Method (GROM) algorithm has been developed to solve the OPF problem for a power network incorporating with stochastic RES. The proposed GROM algorithm aims to improve the reliability, environmental and energy performance of the power network system (IEEE 30-bus system). Three different scenarios, using different RES locations, are presented and the results of the proposed GROM algorithm is compared to six heuristic search methods from the literature. The comparisons indicate that the GROM algorithm successfully reduce fuel costs, gas emission and improve the voltage stability and outperforms each of the presented six heuristic search methods.

scholarly journals Three-Phase Unbalanced Optimal Power Flow Using Holomorphic Embedding Load Flow Method

2019 ◽  
Vol 11 (6) ◽  
pp. 1774 ◽  
Author(s):  
Bharath Rao ◽  
Friederich Kupzog ◽  
Martin Kozek
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Low Voltage ◽  
Distribution Networks ◽  
Optimization Method ◽  
Load Flow ◽  
Flow Method ◽  
Optimal Power ◽  
Three Phase ◽  
Holomorphic Embedding

Distribution networks are typically unbalanced due to loads being unevenly distributed over the three phases and untransposed lines. Additionally, unbalance is further increased with high penetration of single-phased distributed generators. Load and optimal power flows, when applied to distribution networks, use models developed for transmission grids with limited modification. The performance of optimal power flow depends on external factors such as ambient temperature and irradiation, since they have strong influence on loads and distributed energy resources such as photo voltaic systems. To help mitigate the issues mentioned above, the authors present a novel class of optimal power flow algorithm which is applied to low-voltage distribution networks. It involves the use of a novel three-phase unbalanced holomorphic embedding load flow method in conjunction with a non-convex optimization method to obtain the optimal set-points based on a suitable objective function. This novel three-phase load flow method is benchmarked against the well-known power factory Newton-Raphson algorithm for various test networks. Mann-Whitney U test is performed for the voltage magnitude data generated by both methods and null hypothesis is accepted. A use case involving a real network in Austria and a method to generate optimal schedules for various controllable buses is provided.

scholarly journals Artificial Bee Colony Algorithm for Solving Optimal Power Flow Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Luong Le Dinh ◽  
Dieu Vo Ngoc ◽  
Pandian Vasant
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Capacity Limits ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Optimal Power ◽  
Line Flow ◽  
Power Flow Problem

This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem.

scholarly journals A search group algorithm for optimal power flow in power systems

2019 ◽  
Vol 20 (K9) ◽  
pp. 15-22
Author(s):  
Truong Hoang Bao Huy ◽  
Vo Ngoc Dieu
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Electric Energy ◽  
Quadratic Function ◽  
Optimization Method ◽  
Economic Operation ◽  
Optimal Power ◽  
The Common ◽  
Common Problems

Economic operation of the electric energy generating system is one of the common problems in power system. This paper presents a new metaheuristic optimization method, the Search Group Algorithm (SGA) for solving optimal power flow (OPF) problem. The proposed method is tested for 11 different cases on the IEEE 30-bus and IEEE-118 bus systems, in which the IEEE 30-bus system is tested with different objective functions including quadratic function, valve point effects and multiple fuels. The obtained results are compared with some well-known optimization algorithms to emphasize the effectiveness of the SGA method for solving different OPF problems with complicated functions.

scholarly journals Solution for Optimal Power Flow Problem Using WDO Algorithm

2021 ◽  
Vol 12 (2) ◽  
pp. 889-895
Author(s):  
Ranjani Senthilkumara, Et. al.
Keyword(s):  
Global Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Optimization Method ◽  
Fuel Cost ◽  
Flow Solution ◽  
Optimal Power ◽  
Power Flow Problem ◽  
Search Domain

Wind driven optimization (WDO) algorithm is a best optimization method based on atmospherically motion, global optimization nature inspired method. The method is based on population iterative analytical global optimization for multifaceted and multi prototype in the search domain for constraints to implement. In this paper, WDO algorithm is accustomed to find optimal power flow solution. To find the efficacy of the technique, it is applied to IEEE 30 bus systems to find fuel cost for generation of power as a main objective. Obtained results were compared with other techniques shows the better solution for optimal power flow problem.

Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

2016 ◽  
Vol 17 (3) ◽  
pp. 327-337 ◽  
Author(s):  
Jia Cao ◽  
Zheng Yan ◽  
Guangyu He
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Pareto Front ◽  
Flow Problem ◽  
Optimization Method ◽  
Human Learning ◽  
Point Of View ◽  
Multi Objective ◽  
Optimal Power ◽  
Power Flow Problem

Abstract This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

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