Multi-Objective Hydro-Thermal Scheduling Problem Using Two Novel Optimization Techniques

2021 ◽  
Vol 12 (3) ◽  
pp. 1-36
Author(s):  
Provas Kumar Roy ◽  
Moumita Pradhan ◽  
Tandra Pal
Keyword(s):  
Power Systems ◽  
Test System ◽  
Optimization Techniques ◽  
Computational Time ◽  
Environmental Aspects ◽  
Grey Wolf Optimization ◽  
Multi Objective ◽  
Efficiency And Effectiveness ◽  
Loading Effects ◽  
Solution Accuracy

This article describes an efficient and reliable strategy for the scheduling of nonlinear multi-objective hydrothermal power systems using the grey wolf optimization (GWO) technique. Moreover, the theory of oppositional-based learning (OBL) is integrated with original GWO for further enhancing its convergence rate and solution accuracy. The constraints related to hydro and thermal plants and environmental aspects are also considered in this paper. To show its efficiency and effectiveness, the proposed GWO and OGWO algorithms are authenticated for the test system consisting of a multi-chain cascade of 4 hydro and 3 thermal units whose valve-point loading effects are also taken into account. Furthermore, statistical outcomes of the conventional heuristic approaches available in the literature are compared with the proposed GWO and OGWO approaches, and these methods give moderately better operational fuel cost and emission in less computational time.

A Multi-Objective Fuzzy-Based Procedure for Reactive Power-Based Preventive Emergency Strategy

2014 ◽  
Vol 13 ◽  
pp. 91-102 ◽  
Author(s):  
Ragab A. El Sehiemy ◽  
Adel A. Abou El Ela ◽  
Abdelallah Shaheen
Keyword(s):  
Power Systems ◽  
Reactive Power ◽  
Test System ◽  
Operating Conditions ◽  
Fuzzy Linear Programming ◽  
Objective Functions ◽  
Transmission Losses ◽  
The West ◽  
Multi Objective ◽  
Control Actions

This paper proposes a multi-objective fuzzy linear programming (MFLP) procedure for maximizing the effects of preventive reactive power control actions to overcome any emergency condition when they occurred. The proposed procedure is very significant and seeks to eliminate violation constraints and give an optimal reactive power reserve for multi-operating conditions. The proposed multi-objective functions are: minimizing the real transmission losses, maximizing the reactive power reserve at certain generator and maximizing the reactive power reserve at all generation systems and/or switchable devices. The proposed MFLP is applied to 5-bus test system and the West Delta region system as a part of the Egyptian Unified network. The numerical results show that the proposed MFLP technique achieves a minimum real power loss with maximal reactive reserve for power systems for different operating conditions.

scholarly journals Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting

2021 ◽  
Vol 13 (24) ◽  
pp. 13609
Author(s):  
Diaa Salman ◽  
Mehmet Kusaf
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Wind Power ◽  
Unit Commitment ◽  
Optimization Technique ◽  
Test System ◽  
Optimization Techniques ◽  
Support Vector ◽  
Wind Power Forecasting ◽  
Power Forecasting

Unit Commitment (UC) is a complicated integrational optimization method used in power systems. There is previous knowledge about the generation that has to be committed among the available ones to satisfy the load demand, reduce the generation cost and run the system smoothly. However, the UC problem has become more monotonous with the integration of renewable energy in the power network. With the growing concern towards utilizing renewable sources for producing power, this task has become important for power engineers today. The uncertainty of forecasting the output power of renewable energy will affect the solution of the UC problem and may cause serious risks to the operation and control of the power system. In power systems, wind power forecasting is an essential issue and has been studied widely so as to attain more precise wind forecasting results. In this study, a recurrent neural network (RNN) and a support vector machine (SVM) are used to forecast the day-ahead performance of the wind power which can be used for planning the day-ahead performance of the generation system by using UC optimization techniques. The RNN method is compared with the SVM approach in forecasting the wind power performance; the results show that the RNN method provides more accurate and secure results than SVM, with an average error of less than 5%. The suggested approaches are tested by applying them to the standard IEEE-30 bus test system. Moreover, a hybrid of a dynamic programming optimization technique and a genetic algorithm (DP-GA) are compared with different optimization techniques for day ahead, and the proposed technique outperformed the other methods by 93,171$ for 24 h. It is also found that the uncertainty of the RNN affects only 0.0725% of the DP-GA-optimized UC performance. This study may help the decision-makers, particularly in small power-generation firms, in planning the day-ahead performance of the electrical networks.

scholarly journals Improvement of the Voltage Profile and Loss Reduction in Distribution Network Using Moth Flame Algorithm: Wolaita Sodo, Ethiopia

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
S. Balakumar ◽  
Akililu Getahun ◽  
Samuel Kefale ◽  
K. Ramash Kumar
Keyword(s):  
Power Systems ◽  
Distribution System ◽  
Voltage Stability ◽  
Distribution Network ◽  
Test System ◽  
Computational Time ◽  
Voltage Profile ◽  
Voltage Deviation ◽  
Current Scenario ◽  
Wolaita Sodo

Voltage stability and line losses are inevitable issues even in modern power systems. There are several techniques that emerged to solve problems in the power system to provide quality and uninterrupted supply to customers. The algorithms used in this paper to determine the appropriate location and size of the Static Var Compensator (SVC) in the Distribution Network (DN) are Moth Flame Optimization (MFO) and Particle Swarm Optimization (PSO). The objective function is defined to minimize voltage deviation and power loss. The burning problem of voltage stability improvement current scenario is because of a rise in electricity demands in all sectors. Paramount duties of power engineers are to keep the system stable and maintain voltage magnitude constant even during peak hours. The results were checked with the aid of MATLAB on Wolaita Sodo radial distribution of 34 bus data networks. The potential use of SVC is key to solve distribution system power quality issues and estimating the advantage of the installation. The results obtained from the test system were compared with PSO results. This comparison was done to know the computational time of proposed techniques. The performance of the MFA based SVC was superior in distribution system and highlighted the importance of device.

scholarly journals Effect of Crossover Probability on Performance of Genetic Algorithm in Scheduling of Parallel Machines for BI- Criteria Objectives

2019 ◽  
Vol 9 (1) ◽  
pp. 2827-2831
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Parallel Machines ◽  
Optimization Techniques ◽  
Computational Time ◽  
Planning And Scheduling ◽  
Multi Objective ◽  
Mutation Operators ◽  
Crossover Probability ◽  
Wide Range

Optimization of multi objective function gain the importance in the scheduling process. Many classical techniques are available to address the multi objective functions but the solutions yield the unsatisfactory results when the problem becomes complex and large. Evolutionary algorithm would be the solution for such problems. Genetic algorithm is adaptive heuristic search algorithms and optimization techniques that mimic the process of natural evolution. Genetic algorithms are a very effective way of obtaining a reasonable solution quickly to a complex problem. The genetic algorithm operators such as selection method, crossover method, crossover probability, mutation operators and stopping criteria have an effect on obtaining the reasonably good solution and the computational time. Partially mapped crossover operators are used to solve the problem of the traveling salesman, planning and scheduling of the machines, etc., which are having a wide range of solutions. This paper presents the effect of crossover probability on the performance of the genetic algorithm for the bi-criteria objective function to obtain the best solution in a reasonable time. The simulation on a designed genetic algorithm was conducted with a crossover probability of 0.4 to 0.95 (with a step of 0.05) and 0.97, found that results were converging for the crossover probability of 0.6 with the computational time of 3.41 seconds.

scholarly journals Economic and Emission Dispatch Problems Using a New Hybrid Algorithm

2021 ◽  
Vol 15 ◽  
pp. 21-28
Author(s):  
Mimoun Younes ◽  
Fouad Khodja ◽  
Riad Lakhdar Kherfene
Keyword(s):  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Optimization Techniques ◽  
Computational Time ◽  
Environmental Legislation ◽  
Multi Objective ◽  
Economic Power Dispatch ◽  
Emission Dispatch

Environmental legislation, with its increasing pressure on the energy sector to control greenhouse gases, is a driving force to reduce CO2 emissions, forced the power system operators to consider the emission problem as a consequential matter beside the economic problems, so the economic power dispatch problem has become a multi-objective optimization problem. This paper sets up an new hybrid algorithm combined in two algorithm, the harmony search algorithm and ant colony optimization (HSA-ACO), to solve the optimization with combined economic emission dispatch. This problem has been formulated as a multi-objective problem by considering both economy and emission simultaneously. The feasibility of the proposed approach was tested on 3-unit and 6-unit systems. The simulation results show that the proposed algorithm gives comparatively better operational fuel cost and emission in less computational time compared to other optimization techniques.

scholarly journals MULTI-OBJECTIVE OPTIMIZATION FOR DESIGN OF SINGLE ROW LAYOUT IN FLEXIBLE MANUFACTURING SYSTEM WITH SCHEDULING CONSTRAINT:AN APPROACH OF NONTRADITIONAL OPTIMIZATION TECHNIQUES

2013 ◽  
pp. 41-51
Author(s):  
K. MALLIKARJUNA ◽  
V. VEERANNA ◽  
K.HEMACHANDRA REDDY
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Great Influence ◽  
Optimization Techniques ◽  
Computational Time ◽  
Single Row ◽  
Multi Objective

Single row layout is one of the most usually used layout patterns in industries, particularly in flexible manufacturing systems. Here actual sequencing of machine and arrangement of parts, no doubt, have a great influence on the throughput of the flexible manufacturing system i.e., (F.M.S). This paper discusses the single row layout design in flexible manufacturing system (F.M.S). This paper furnishes the design, development and testing of simulated annealing technique and genetic algorithm to solve the single row layout problem by considering multi-objective i.e., minimizing the make span of jobs on all machines and minimizing the total transportation cost. The various line layout problems are tested for performance of objective function with respect to computational time and number of iterations involved in GA and SA. A necessary code is generated in C++ and the code is run by the IDE tool in which C++ compiler used as plug in. This tool has Eclipse based features which affords the competency to figure, correct, steer, and sort out the tasks that use C++ as a programming language using Intel core i3-380M processor. The results of the different optimization algorithms (Genetic Algorithm and simulated annealing method) are compared and finally, we observed that GA provide optimum results than SA.

Solving OPF Problems using Biogeography Based and Grey Wolf Optimization Techniques

2017 ◽  
Vol 6 (3) ◽  
pp. 55-77 ◽  
Author(s):  
Kingsuk Majumdar ◽  
Puja Das ◽  
Provas Kumar Roy ◽  
Subrata Banerjee
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
High Efficiency ◽  
Test System ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
System A ◽  
Optimal Power

This paper presents biogeography-based optimization (BBO) and grey wolf Optimization(GWO) for solving the multi-constrained optimal power flow (OPF) problems in the power system. In this paper, the proposed algorithms have been tested in 9-bus system under various conditions along with IEEE 30 bus test system. A comparison of simulation results reveals optimization efficacy of the proposed scheme over evolutionary programming (EP), genetic algorithm (GA), mixed-integer particle swarm optimization (MIPSO) for the global optimization of multi-constraint OPF problems. It is observed that GWO is far better in comparison to other listed optimization techniques and can be used for aforesaid problems with high efficiency.

scholarly journals Multi-Objective Optimal Reactive Power Planning under Load Demand and Wind Power Generation Uncertainties Using ε-Constraint Method

2020 ◽  
Vol 10 (8) ◽  
pp. 2859 ◽  
Author(s):  
Amir Hossein Shojaei ◽  
Ali Asghar Ghadimi ◽  
Mohammad Reza Miveh ◽  
Fazel Mohammadi ◽  
Francisco Jurado
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Wind Power ◽  
Reactive Power ◽  
Test System ◽  
Wind Power Generation ◽  
Multi Objective ◽  
Load Demand ◽  
Reactive Power Planning ◽  
Constraint Method

This paper presents an improved multi-objective probabilistic Reactive Power Planning (RPP) in power systems considering uncertainties of load demand and wind power generation. The proposed method is capable of simultaneously (1) reducing the reactive power investment cost, (2) minimizing the total active power losses, (3) improving the voltage stability, and (4) enhancing the loadability factor. The generators’ voltage magnitude, the transformer’s tap settings, and the output reactive power of VAR sources are taken into account as the control variables. To solve the probabilistic multi-objective RPP problem, the ε-constraint method is used. To test the effectiveness of the proposed approach, the IEEE 30-bus test system is implemented in the GAMS environment under five different conditions. Finally, for a better comprehension of the obtained results, a brief comparison of outcomes is presented.

Sign in / Sign up

Export Citation Format

Share Document