Active Power Optimization Considering both Generation Cost and Network Loss

2014 ◽  
Vol 556-562 ◽  
pp. 1643-1646
Author(s):  
Xue Fei Chang ◽  
Xiang Yu Lv ◽  
De Xin Li
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Weighting Factor ◽  
Active Power ◽  
Model Combining ◽  
Proposed Model ◽  
Generation Cost ◽  
Objective Model

In order to improve the calculation efficiency, active power and reactive power are usually optimized separately in optimal power flow considering the decoupling characteristic. However, this would decrease the economy performance of power system. This paper proposed a weighting factor to formulate a multi-objective model, combining the generation cost and system network loss together. The optimization problem is performed using genetic algorithms and quadratic programming respectively. Finally, the feasibility and efficiency of the proposed model are verified with the IEEE 14 Bus test system.

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.     

scholarly journals Optimal Economic–Environmental Operation of BESS in AC Distribution Systems: A Convex Multi-Objective Formulation

Computation ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 137
Author(s):  
Walter Gil-González ◽  
Oscar Danilo Montoya ◽  
Luis Fernando Grisales-Noreña ◽  
Andrés Escobar-Mejía
Keyword(s):  
Co2 Emissions ◽  
Power Factor ◽  
Power Flow ◽  
Distribution Systems ◽  
Optimal Power Flow ◽  
Pareto Front ◽  
Reactive Power ◽  
Global Optimum ◽  
Multi Objective ◽  
Objective Model

This paper deals with the multi-objective operation of battery energy storage systems (BESS) in AC distribution systems using a convex reformulation. The objective functions are CO2 emissions, and the costs of the daily energy losses are considered. The conventional non-linear nonconvex branch multi-period optimal power flow model is reformulated with a second-order cone programming (SOCP) model, which ensures finding the global optimum for each point present in the Pareto front. The weighting factors methodology is used to convert the multi-objective model into a convex single-objective model, which allows for finding the optimal Pareto front using an iterative search. Two operational scenarios regarding BESS are considered: (i) a unity power factor operation and (ii) a variable power factor operation. The numerical results demonstrate that including the reactive power capabilities in BESS reduces 200kg of CO2 emissions and USD 80 per day of operation. All of the numerical validations were developed in MATLAB 2020b with the CVX tool and the SEDUMI and SDPT3 solvers.

scholarly journals Comparative Analysis of a Performance of Metaheuristic Algorithms in Solving Optimal Power Flow Problems with UPFC Device in the Transmission System

2021 ◽  
Vol 9 (5) ◽  
pp. 316-326
Author(s):  
K. Padma ◽  
Yeshitela Shiferaw Maru
Keyword(s):  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Transmission System ◽  
Voltage Profile ◽  
Proper Position ◽  
Optimal Position ◽  
Optimal Power

Incremental industrialization and urbanization is the cause of enhanced energy use as it increases the building of new lines and more inductive loads. As a result, the transmission system losses increased, and the magnitudes of voltage profile values deviated from the stated value, resulting in increased cost of active power generation. To mitigate these issues, adequate reactive power compensation in the transmission line and bus systems should be done. Reactive power is regulated by the proper position of the Flexible AC Transmission System (FACTS). Unified Power Flow Controller (UPFC) is a voltage converter system that increases the voltage profile and reduces loss. In this paper, the optimal power flow solution is considered using a FACTS device based on Multi Population Modified Jaya (MPMJ) optimization algorithm. Using the Analytical Hierarchy Process (AHP) system, the optimal position of the UPFC device is determined by considering the most useful objective function provided by priorities and weighting factors. Therefore, on the standard IEEE-57 bus test system, the proposed MPMJ optimization algorithm is implemented with UPFC for optimal fuel cost values of generation, real power loss, voltage deviation and sum of squared voltage stability index. The result obtained by the proposed algorithm is contrasted with the recent literature algorithm

A Probabilistic Optimal Power Flow in Wind-Thermal Coordination Considering Intermittency of the Wind

2021 ◽  
Vol 10 (1) ◽  
pp. 82-110
Author(s):  
Sriparna Banerjee ◽  
Dhiman Banerjee ◽  
Provas Kumar Roy ◽  
Pradip Kumar Saha ◽  
Goutam Kumar Panda
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Optimization Techniques ◽  
Wind Energy Conversion ◽  
Optimal Power ◽  
Generation Cost ◽  
Bus Voltage ◽  
Doubly Fed ◽  
Probabilistic Nature

This article specifically aims to prove the superiority of the proposed moth swarm algorithm (MSA) in view of wind-thermal coordination. In the present article, a probabilistic optimal power flow (POPF) problem is formulated to reflect the probabilistic nature of wind. Modelling of doubly fed induction generator (DFIG) is included in the proposed POPF to represent the wind energy conversion system (WECS). To reduce DFIG imposed deviation of bus voltage ancillary reactive power support is considered. Moreover, three different optimization techniques, namely, MSA, biogeography-based optimization (BBO), and particle swarm optimization (PSO) are independently applied for the minimization of active power generation cost for wind-thermal coordination, considering different instances in case of IEEE 30-bus and IEEE 118-bus system. From the simulation results, it is confirmed and validated that the proposed MSA performs considerably better than BBO and PSO.

A Solution to Multi Objective Optimal Power Flow Using Hybrid Cultural-Based Differential Evolution

2013 ◽  
Vol 457-458 ◽  
pp. 1236-1240
Author(s):  
Isaree Srikun ◽  
Lakkana Ruekkasaem ◽  
Pasura Aungkulanon
Keyword(s):  
Differential Evolution ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Test System ◽  
System Operation ◽  
Power System Operation ◽  
Multi Objective ◽  
Optimal Power ◽  
Generation Cost ◽  
And Control

This paper presents a hybrid Cultural-based Differential Evolution for solving a multi-objective Optimal Power Flow (OPF) in support of power system operation and control . The multi-objective OPF was formulated for tackling with total generation cost and environmental impacts simultaneously. The proposed method was applied to the standard IEEE 30-bus test system. The results show that solving the multi-objective OPF problem by the Cultural-based Differential Evolution is more effective than other swarm intelligence methods in the literature.

scholarly journals The Optimal Power Flow Solution by Optimal Location of STATCOM Device using AHP Method

2020 ◽  
Vol 9 (4) ◽  
pp. 2561-2569
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Control Variable ◽  
Optimal Solution ◽  
Stability Index ◽  
Test System ◽  
Optimal Location ◽  
Electrical System ◽  
Ahp Method

Optimum power flow is a useful tool for planning and operating the electrical system and maintains the economy and safety of the modern electrical system. Teaching, Learningbased Algorithm is one of the new Metaheuristic algorithms which can influence both teachers and students by expediting the interaction among them in sharing the necessary knowledge. The proposed TLBO is designed here to solve the problem of an optimum power flow with STATCOM FACTS device. The optimal location of STATCOM FACTS device on the weak bus is obtained by Analytical Hierarchy Process (AHP) method. The main objective of this study is to reduce fuel cost of generation, reduce active and reactive power loss, improve voltage deviation and enhance voltage stability index within the given control variable constraints. The proposed TLBO algorithm with STATCOM device is evaluated on the standard IEEE-57 bus system. From the simulation result, it shows that the Teaching Learning-based algorithm gives the optimal solution as compared to the recent algorithm mentioned in the literature with some IEEE-57 bus test system.

Optimal Location of TCSC, TCPS, and SVC Devices for Solving OPF and ORPD Problem Using Different Evolutionary Optimization Techniques

2018 ◽  
pp. 244-273
Keyword(s):  
Power Systems ◽  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Phase Shifter ◽  
Reactive Power ◽  
Test System ◽  
Optimal Location ◽  
Thyristor Control ◽  
Evolutionary Optimization Techniques

The secure operation of power system has become a topmost issue in today's largely complicated interconnected power systems. This chapter presents the implementation of grey wolf optimization (GWO), teaching-learning-based optimization (TLBO), biogeography-based optimization (BBO), krill herd algorithm (KHA), chemical reaction optimization (CRO), and hybrid CRO (HCRO) approaches to find the optimal location of various FACTS devices such as thyristor control series compensator (TCSC), thyristor control phase shifter (TCPS), and static VAR compensator (SVC) to solve optimal power flow (OPF) and optimal reactive power dispatch (ORPD) in power system. In this chapter, a standard IEEE 30-bus test system with multiple TCSC and TCPS and SVC devices are used for different single and multi-objective functions to validate the performance of the proposed methods. The simulation results validate the ability of the HCRO to produce better optimal solutions compared to GWO, TLBO, BBO, KHA, and CRO algorithms.

scholarly journals An Optimization-Based Strategy for Solving Optimal Power Flow Problems in a Power System Integrated with Stochastic Solar and Wind Power Energy

2021 ◽  
Vol 11 (15) ◽  
pp. 6883
Author(s):  
Muhammad Riaz ◽  
Aamir Hanif ◽  
Shaik Javeed Hussain ◽  
Muhammad Irfan Memon ◽  
Muhammad Umair Ali ◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Optimal Solution ◽  
Test System ◽  
Distribution Functions ◽  
Control Variables ◽  
Real Power ◽  
Solution Approach ◽  
Optimal Power ◽  
Generation Cost

In an effort to reduce greenhouse gas emissions, experts are looking to substitute fossil fuel energy with renewable energy for environmentally sustainable and emission free societies. This paper presents the hybridization of particle swarm optimization (PSO) with grey wolf optimization (GWO), namely a hybrid PSO-GWO algorithm for the solution of optimal power flow (OPF) problems integrated with stochastic solar photovoltaics (SPV) and wind turbines (WT) to enhance global search capabilities towards an optimal solution. A solution approach is used in which SPV and WT output powers are estimated using lognormal and Weibull probability distribution functions respectively, after simulation of 8000 Monte Carlo scenarios. The control variables include the forecast real power generation of SPV and WT, real power of thermal generators except slack-bus, and voltages of all voltage generation buses. The total generation cost of the system is considered the main objective function to be optimized, including the penalty and reserve cost for underestimation and overestimation of SPV and WT, respectively. The proposed solution approach for OPF problems is verified on the modified IEEE 30 bus test system. The performance and robustness of the proposed hybrid PSO-GWO algorithm in solving the OPF problem is assessed by comparing the results with five other metaheuristic optimization algorithms for the same test system, under the same control variables and system constraints. Simulation results confirm that the hybrid PSO-GWO algorithm performs well compared to other algorithms and shows that it can be an efficient choice for the solution of OPF problems.

scholarly journals Marine predators algorithm for solving single-objective optimal power flow

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256050
Author(s):  
Mohammad Zohrul Islam ◽  
Mohammad Lutfi Othman ◽  
Noor Izzri Abdul Wahab ◽  
Veerapandiyan Veerasamy ◽  
Saifur Rahman Opu ◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Foraging Strategy ◽  
Test System ◽  
Optimization Techniques ◽  
Encounter Rate ◽  
Marine Predator ◽  
Optimal Power ◽  
Single Objective

This study presents a nature-inspired, and metaheuristic-based Marine predator algorithm (MPA) for solving the optimal power flow (OPF) problem. The significant insight of MPA is the widespread foraging strategy called the Levy walk and Brownian movements in ocean predators, including the optimal encounter rate policy in biological interaction among predators and prey which make the method to solve the real-world engineering problems of OPF. The OPF problem has been extensively used in power system operation, planning, and management over a long time. In this work, the MPA is analyzed to solve the single-objective OPF problem considering the fuel cost, real and reactive power loss, voltage deviation, and voltage stability enhancement index as objective functions. The proposed method is tested on IEEE 30-bus test system and the obtained results by the proposed method are compared with recent literature studies. The acquired results demonstrate that the proposed method is quite competitive among the nature-inspired optimization techniques reported in the literature.

scholarly journals The gravitational search algorithm for incorporating TCSC devices into the system for optimum power flow

2021 ◽  
Vol 11 (6) ◽  
pp. 4678
Author(s):  
Dunya Sh. Wais ◽  
Wafaa S. Majeed
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Test System ◽  
Operating Conditions ◽  
Base Case ◽  
Voltage Profile ◽  
Gravitational Search

<span lang="EN-US">This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust high-quality solution for the problem and improves system performance.</span>

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