scholarly journals A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2787 ◽  
Author(s):  
Ali Thaeer Hammid ◽  
Omar I. Awad ◽  
Mohd Herwan Sulaiman ◽  
Saraswathy Shamini Gunasekaran ◽  
Salama A. Mostafa ◽  
...  
Keyword(s):  
Power Systems ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Maximum Energy ◽  
Optimization Techniques ◽  
Parameters Optimization ◽  
Ecological Effect ◽  
Scheduling Problems ◽  
Generation Scheduling ◽  
Important Position

The optimal generation scheduling (OGS) of hydropower units holds an important position in electric power systems, which is significantly investigated as a research issue. Hydropower has a slight social and ecological effect when compared with other types of sustainable power source. The target of long-, mid-, and short-term hydro scheduling (LMSTHS) problems is to optimize the power generation schedule of the accessible hydropower units, which generate maximum energy by utilizing the available potential during a specific period. Numerous traditional optimization procedures are first presented for making a solution to the LMSTHS problem. Lately, various optimization approaches, which have been assigned as a procedure based on experiences, have been executed to get the optimal solution of the generation scheduling of hydro systems. This article offers a complete survey of the implementation of various methods to get the OGS of hydro systems by examining the executed methods from various perspectives. Optimal solutions obtained by a collection of meta-heuristic optimization methods for various experience cases are established, and the presented methods are compared according to the case study, limitation of parameters, optimization techniques, and consideration of the main goal. Previous studies are mostly focused on hydro scheduling that is based on a reservoir of hydropower plants. Future study aspects are also considered, which are presented as the key issue surrounding the LMSTHS problem.

Velocity Effects on Robotic Manipulator Dynamic Performance

2006 ◽  
Vol 128 (6) ◽  
pp. 1236-1245 ◽  
Author(s):  
Alan P. Bowling ◽  
ChangHwan Kim
Keyword(s):  
Quadratic Forms ◽  
Degrees Of Freedom ◽  
Optimization Problem ◽  
Dynamic Performance ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
End Effector ◽  
Coriolis Forces ◽  
Torque Curve

This article explores the effect that velocities have on a nonredundant robotic manipulator’s ability to accelerate its end-effector, as well as to apply forces/moments to the environment at the end-effector. This work considers velocity forces, including Coriolis forces, and the reduction of actuator torque with rotor velocity described by the speed-torque curve, at a particular configuration of a manipulator. The focus here is on nonredundant manipulators with as many actuators as degrees-of-freedom. Analysis of the velocity forces is accomplished using optimization techniques, where the optimization problem consists of an objective function and constraints which are all purely quadratic forms, yielding a nonconvex problem. Dialytic elimination is used to find the globally optimal solution to this problem. The proposed method does not use iterative numerical optimization methods. The PUMA 560 manipulator is used as an example to illustrate this methodology. The methodology provides an analytical analysis of the velocity forces which insures that the globally optimal solution to the associated optimization problem is found.

scholarly journals A review of optimal operation of microgrids

2020 ◽  
Vol 10 (3) ◽  
pp. 2842
Author(s):  
N. Karthik ◽  
A. K. Parvathy ◽  
R. Arul
Keyword(s):  
Power Systems ◽  
Optimization Methods ◽  
Optimal Operation ◽  
Small Scale ◽  
Objective Functions ◽  
Electrical Grid ◽  
Generation Scheduling ◽  
Previous Decade ◽  
Conventional Optimization ◽  
Planning And Operation

The term microgrid refers to small-scale power grid that can operate autonomously or in concurrence with the area’s main electrical grid. The intermittent characteristic of DGs which defies the power quality and voltage manifests the requirement for new planning and operation approaches for microgrids. Consequently, conventional optimization methods in new power systems have been critically biased all through the previous decade. One of the main technological and inexpensive tools in this regard is the optimal generation scheduling of microgrid. As a primary optimization tool in the planning and operation fields, optimal operation has an undeniable part in the power system. This paper reviews and evaluates the optimal operation approaches mostly related to microgrids. In this work, the foremost optimal generation scheduling approaches are compared in terms of their objective functions, techniques and constraints. To conclude, a few fundamental challenges occurring from the latest optimal generation scheduling techniques in microgrids are addressed.

Evolutionary Algorithms for Economic Load Dispatch Having Multiple Types of Cost Functions

2016 ◽  
pp. 201-226
Author(s):  
Provas Kumar Roy ◽  
Moumita Pradhan ◽  
Tandra Pal
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Methods ◽  
Population Based ◽  
Optimization Techniques ◽  
Economic Load Dispatch ◽  
Thermal Systems ◽  
Generation Scheduling ◽  
Valve Point Effect ◽  
Real Coded Genetic Algorithm ◽  
Ramp Rate Limits

This chapter presents various novel evolutionary algorithms, namely Real Coded Genetic Algorithm (RGA), two variants of Biogeography-Based Optimization (BBO), and three variants of Particle Swarm Optimization (PSO) in order to find the optimal power generation scheduling to simultaneously optimize fuel cost and power loss for solving constrained economic load dispatch problems of all thermal systems, considering multiple fuel operation and valve point effect. The effectiveness of the proposed algorithms is demonstrated in five different ELD problems, considering different constraints such as transmission losses, ramp rate limits, multi-fuel options and valve point loading. Comparative studies are carried out to examine the effectiveness and superiority of the proposed approaches. A comparison of simulation results reveals optimization usefulness of the proposed BBO scheme over other well established population based optimization techniques. It is also found that the convergence characteristics of the BBO algorithm are better than other optimization methods.

Evaluation of global optimization methods for conceptual rainfall-runoff model calibration

1997 ◽  
Vol 36 (5) ◽  
pp. 53-60 ◽  
Author(s):  
V. A. Cooper ◽  
V. T. V. Nguyen ◽  
J. A. Nicell
Keyword(s):  
Global Optimization ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Global Optimum ◽  
Hydrologic Model ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Global Optimization Methods ◽  
Optimization Search

The calibration of conceptual rainfall runoff (CRR) models is an optimization problem whose objective is to determine the values of the model parameters which provide the best fit between observed and estimated flows. This study investigated the performance of three probabilistic optimization techniques for calibrating the Tank model, a hydrologic model typical of CRR models. These methods were the Shuffled Complex Evolution (SCE), genetic algorithms (GA) and simulated annealing (SA) methods. It was found that performances depended on the choice of the objective function considered and also an the position of the start of the optimization search relative to the global optimum. Of the three global optimization methods (GOM) in the study, the SCE method provided better estimates of the optimal solution than the GA and SA methods. Regarding the efficiency of the GOMs, as expressed by the number of iterations for convergence, the ranking in order of decreasing performance was the SCE, the GA and the SA methods.

scholarly journals Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

2016 ◽  
Vol 3 (2) ◽  
pp. 149-158 ◽  
Author(s):  
Imam Ahmad Ashari ◽  
Much Aziz Muslim ◽  
Alamsyah Alamsyah
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Ant Colony Optimization Algorithm ◽  
Course Scheduling ◽  
The University

Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.

scholarly journals State of Art Optimization Techniques for Machining Parameters Optimization during Milling

2019 ◽  
Vol 8 (6) ◽  
pp. 5104-5114 ◽  
Keyword(s):  
Process Parameters ◽  
Prediction Models ◽  
Unit Cost ◽  
Optimization Methods ◽  
Vital Role ◽  
Optimization Techniques ◽  
Parameters Optimization ◽  
Machining Parameters ◽  
Modeling And Optimization ◽  
Milling Operations

Optimization of machining parameters becomes more important; when high capital cost NC machines are employed for high precision and efficient machining. Minimizations of unit cost and time along with minimum tool and workpiece deflection, improved surface finish & tool life under certain boundary conditions are key objectives of the optimization problem. Optimization methods for milling include in-process parameters relationship with machining objectives and determination of optimal cutting conditions. Development of costeffective mathematical models is still a challenging task. However, there has been a considerable improvement in the techniques of modeling and optimization during the last two decades. In this paper, several modeling and optimization techniques reported for the milling operations have been reviewed and are for milling, classified for different criteria. Issues related to performance of several evolutionary algorithms, machining parameters, objectives and constraints have also been identified. From the survey of optimization techniques during milling operations it has been found that search techniques perform better than experimental approaches for optimization of process parameters. However, the experimental techniques play a vital role in prediction models for different machining objectives

scholarly journals Development and Implementation of a Novel Optimization Algorithm for Reliable and Economic Grid-Independent Hybrid Power System

2020 ◽  
Vol 10 (18) ◽  
pp. 6604
Author(s):  
Mohammed Kharrich ◽  
Omar Hazem Mohammed ◽  
Salah Kamel ◽  
Ali Selim ◽  
Hamdy M. Sultan ◽  
...  
Keyword(s):  
Power Systems ◽  
High Efficiency ◽  
Renewable Energy Sources ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Hybrid Power ◽  
Hybrid Microgrid ◽  
The World

Recently, fast uptake of renewable energy sources (RES) in the world has introduced new difficulties and challenges; one of the most important challenges is providing economic energy with high efficiency and good quality. To reach this goal, many traditional and smart algorithms have been proposed and demonstrated their feasibility in obtaining the optimal solution. Therefore, this paper introduces an improved version of Bonobo Optimizer (BO) based on a quasi-oppositional method to solve the problem of designing a hybrid microgrid system including RES (photovoltaic (PV) panels, wind turbines (WT), and batteries) with diesel generators. A comparison between traditional BO, the Quasi-Oppositional BO (QOBO), and other optimization techniques called Harris Hawks Optimization (HHO), Artificial Electric Field Algorithm (AEFA) and Invasive Weed Optimization (IWO) is carried out to check the efficiency of the proposed QOBO. The QOBO is applied to a stand-alone hybrid microgrid system located in Aswan, Egypt. The results show the effectiveness of the QOBO algorithm to solve the optimal economic design problem for hybrid microgrid power systems.

scholarly journals Adaptive Load Frequency Control in Power Systems Using Optimization Techniques

2021 ◽  
Author(s):  
Tarek Hassan Mohamed ◽  
Hussein Abubakr ◽  
Mahmoud M. Hussein ◽  
Gaber S. Salman
Keyword(s):  
Power Systems ◽  
Frequency Control ◽  
Dynamic Performance ◽  
Digital Simulation ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Load Frequency Control ◽  
Control Technique ◽  
Modeling And Control ◽  
Load Frequency

At present, simple and classical tuned controllers are widely used in the power system load frequency control (LFC) application. Existing LFC system parameters are usually tuned based on experiences, classical methods, and trial and error approaches, and they are incapable of providing good dynamic performance over a wide range of operating conditions and various load scenarios. Therefore, the novel modeling and control approaches are strongly required, to obtain a new trade-off between efficiency and robustness. Thus, the proposed techniques in this chapter are referred to be an adaptive control technique based on new optimization methods such as Jaya, Practical Swarm Optimization Algorithm, etc., which are used to make an on-line tuning of the LFC parameters in order to face the previous challenges in LFC. The system under study is a small microgrid with a renewable energy source and variable demand load. Digital simulation results are discussed.

scholarly journals Optimum Distribution System Expansion Planning Incorporating DG Based on N-1 Criterion for Sustainable System

2021 ◽  
Vol 13 (12) ◽  
pp. 6708
Author(s):  
Hamza Mubarak ◽  
Nurulafiqah Nadzirah Mansor ◽  
Hazlie Mokhlis ◽  
Mahazani Mohamad ◽  
Hasmaini Mohamad ◽  
...  
Keyword(s):  
Power Systems ◽  
Distribution System ◽  
Industrial Revolution ◽  
Optimal Solution ◽  
Search Space ◽  
Planning Problem ◽  
System Expansion ◽  
Optimum Distribution ◽  
Optimal Sizing ◽  
Expansion Planning

Demand for continuous and reliable power supply has significantly increased, especially in this Industrial Revolution 4.0 era. In this regard, adequate planning of electrical power systems considering persistent load growth, increased integration of distributed generators (DGs), optimal system operation during N-1 contingencies, and compliance to the existing system constraints are paramount. However, these issues need to be parallelly addressed for optimum distribution system planning. Consequently, the planning optimization problem would become more complex due to the various technical and operational constraints as well as the enormous search space. To address these considerations, this paper proposes a strategy to obtain one optimal solution for the distribution system expansion planning by considering N-1 system contingencies for all branches and DG optimal sizing and placement as well as fluctuations in the load profiles. In this work, a hybrid firefly algorithm and particle swarm optimization (FA-PSO) was proposed to determine the optimal solution for the expansion planning problem. The validity of the proposed method was tested on IEEE 33- and 69-bus systems. The results show that incorporating DGs with optimal sizing and location minimizes the investment and power loss cost for the 33-bus system by 42.18% and 14.63%, respectively, and for the 69-system by 31.53% and 12%, respectively. In addition, comparative studies were done with a different model from the literature to verify the robustness of the proposed method.

Optimization methods for power scheduling problems in smart home: Survey

2019 ◽  
Vol 115 ◽  
pp. 109362 ◽  
Author(s):  
Sharif Naser Makhadmeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Syibrah Naim ◽  
Ammar Kamal Abasi ◽  
...  
Keyword(s):  
Smart Home ◽  
Optimization Methods ◽  
Scheduling Problems ◽  
Power Scheduling
Sign in / Sign up

Export Citation Format

Share Document