SLIME MOULD ALGORITHM FOR PRACTICAL OPTIMAL POWER FLOW SOLUTIONS INCORPORATING STOCHASTIC WIND POWER AND STATIC VAR COMPENSATOR DEVICE

Purpose. This paper proposes the application procedure of a new metaheuristic technique in a practical electrical power system to solve optimal power flow problems, this technique namely the slime mould algorithm (SMA) which is inspired by the swarming behavior and morphology of slime mould in nature. This study aims to test and verify the effectiveness of the proposed algorithm to get good solutions for optimal power flow problems by incorporating stochastic wind power generation and static VAR compensators devices. In this context, different cases are considered in order to minimize the total generation cost, reduction of active power losses as well as improving voltage profile. Methodology. The objective function of our problem is considered to be the minimum the total costs of conventional power generation and stochastic wind power generation with satisfying the power system constraints. The stochastic wind power function considers the penalty cost due to the underestimation and the reserve cost due to the overestimation of available wind power. In this work, the function of Weibull probability density is used to model and characterize the distributions of wind speed. Practical value. The proposed algorithm was examined on the IEEE-30 bus system and a large Algerian electrical test system with 114 buses. In the cases with the objective is to minimize the conventional power generation, the achieved results in both of the testing power systems showed that the slime mould algorithm performs better than other existing optimization techniques. Additionally, the achieved results with incorporating the wind power and static VAR compensator devices illustrate the effectiveness and performances of the proposed algorithm compared to the ant lion optimizer algorithm in terms of convergence to the global optimal solution.Мета. У статті пропонується процедура застосування нового метаеврістіческого методу в реальній електроенергетичній системі для розв’язання задач оптимального потоку енергії, а саме алгоритму слизової цвілі, який заснований на поведінці рою і морфології слизової цвілі в природі. Дане дослідження спрямоване на тестування і перевірку ефективності запропонованого алгоритму для отримання хороших рішень для проблем оптимального потоку потужності шляхом включення пристроїв стохастичною вітрової генерації і статичних компенсаторів VAR. У зв'язку з цим, розглядаються різні випадки, щоб мінімізувати загальну вартість генерації, знизити втрати активної потужності і поліпшити профіль напруги. Методологія. В якості цільової функції завдання розглядається мінімальна сукупна вартість традиційної генерації електроенергії і стохастичної вітрової генерації при задоволенні обмежень енергосистеми. Стохастична функція енергії вітру враховує величини штрафів через недооцінку і резервні витрати через завищену оцінку доступної вітрової енергії. У даній роботі функція щільності ймовірності Вейбулла використовується для моделювання і характеристики розподілів швидкості вітру. Практична цінність. Запропонований алгоритм був перевірений на системі шин IEEE-30 і великий алжирської тестовій енергосистемі зі 114 шинами. У випадках, коли мета полягає в тому, щоб звести до мінімуму традиційне вироблення електроенергії, досягнуті результати в обох тестових енергосистемах показали, що алгоритм слизової цвілі функціонує краще, ніж інші існуючі методи оптимізації. Крім того, досягнуті результати з використанням вітрової енергії і статичного компенсатора VAR ілюструють ефективність і продуктивність запропонованого алгоритму в порівнянні з алгоритмом оптимізатора мурашиних левів з точки зору збіжності до глобального оптимального рішення

Techno-economic Study by Teaching Learning-based Optimization Algorithm for Optimal Placement of DG Units in Distribution Systems

A significant improvement in system performance can be achieved by placing Distributed Generator (DG) units of the optimal size in optimum network of radial distribution locations. In order to maximize the economic and technological benefits, it is necessary to reduce yearly economic losses. These losses include expenditures associated with installation and operation of the buses as well as power loss and voltage difference between buses. In view of these multi-objective frameworks, the current problem is assessed and the best compromise solution also referred as the Pareto-optimal solution is provided. In the framework of the multi-objective optimization problem, specific equality as well as inequality constraints is investigated. It is shown in this study that a Multi-Objective Teaching-Learning Based Optimization (MOTLBO) algorithm has been proposed to solve the multi-objective problem. For the purpose of evaluating its performance, the proposed method is being deployed on IEEE-33 and IEEE-69 System of radial bus distribution. A comparison with other recent multi-objective algorithms such as OCDE, KHA and LSFSA is also included in this study. It has been revealed that the algorithm proposed can offer superior outcomes concerning power loss, annual economic loss mitigation and voltage profile enhancement

Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs). This algorithm is being used significantly to minimize the system power loss, voltage deviation at load buses and improve the voltage profile. When solving the high-dimensional datasets optimization problem using the GWO algorithm, it simply falls into an optimum local region. To enhance and strengthen the GWO algorithm searchability, CS algorithm is integrated to update the best three candidate solutions. This hybrid CS-GWO algorithm has a more substantial search capability to simultaneously find optimal candidate solutions for problem. Furthermore, to validate the effectiveness and performances of the proposed hybrid CS-GWO algorithm is being tested and evaluated for standard IEEE 33-bus and 69-bus RDSs by considering different scenarios

Particle Swarm Optimization with CUDA

In recent years, particles’ optimization algorithm has highly been used as an effective method in solving complex and difficult optimization problems. Since particles algorithm is based on recurring population and it can be very inefficient in terms of the time required for implementation and speed to solve optimization problems with large-scale including ones which need a very large population to search in problem solution space. The main reason for this issue is that this algorithm optimization process requires a large number of function evaluations which are usually run serially. This article aims to implement particles’ optimization algorithm in parallel on graphics processing unit and to improve running efficiency and speed. The implementation results on the graphics processor show that the performance of this algorithm has greatly increased as to its implementation in parallel and with change in kernel implementation. In fact, in this study, implementation and velocity evaluation of particles algorithm implementation in parallel and based on CUDA framework has been investigated and compared. Then, there have been efforts to improve acceleration in this method in part and a new method will be proposed in CUDA framework to improve acceleration in particles algorithm and graphic processor setting

Optimasi Injeksi Distributed Generation Menggunakan Algoritma Cat Swarm Optimization dan Krill Herd Algorithm

Abstrak – Pemanfaatan energi listrik sebagai upaya menciptakan green energy memerlukan suatu tindakan yang mendukung produksi energi yang efisien dengan menggunakan satu atau lebih energi alternatif yang dapat digabungkan menjadi sumber energi terdistribusi. Permasalahan pokok permintaan sumber energi yang semakin meningkat adalah keandalan sistem dan kualitas daya. Kualitas daya berkaitan dengan pengurangan nilai rugi-rugi daya listrik, berkurangnya nilai jatuh tegangan serta kontinuitas penyaluran energi listrik yang baik. Penambahan distributed generation (DG) pada sistem eksisting dinilai mampu mengurangi permasalahan kualitas daya dan meningkatkan keandalan sistem. Penelitian ini membahas optimasi penempatan DG dengan menggunakan metode Cat Swarm Optimization dan Krill Herd Algorithm. Optimasi dilakukan dengan menginjeksi DG pada sistem eksisting. Dari proses optimasi dengan CSO diperoleh penurunan rugi daya sebesar 0,2 % sedangkan dengan KHA diperoleh penurunan 29,5 %. Perbaikan profil tegangan dengan metode KHA dinilai lebih baik dari CSO dengan nilai diatas 0,99 p.u sedangkan CSO 0,96 p.u. Penelitian ini mengindikasikan bahwa metode KHA lebih baik daripada metode CSO. Kata Kunci — Distributed Generation, Cat Swarm Optimization, Krill Herd Algorithm

OPTIMAL ALLOCATION OF MULTIPLE DG IN RDS USING PSO AND ITS IMPACT ON SYSTEM RELIABILITY

This article presents the distributed generator (DG) integration in a radial distribution system (RDS). The DG penetration changes the single power source to multiple power sources and bidirectional load flow which enhances the system reliability and reduces system power losses. The particle swarm optimization and gravitational search algorithm are implemented for the optimal siting and sizing of one and three DG units in the RDS to examine its impact on system reliability and loss reduction. The types of DGs considered are Type I (injects real power) and Type IV (injects reactive and real power). The constant power is the chosen load model. The reliability indices taken for the analysis of system reliability are Average Energy Not Supplied, Total Energy Not Supplied and Average System Interruption Duration Index. The efficacy of the proposed method is validated on 33-bus in the presence of single and multiple DGs. The significant decrease in system power losses with the upgraded bus voltage profile, system reliability and remarkable annual loss saving is analyzed for Type IV DG over Type I DG. The results determined are compared to other meta-heuristic approaches as well as analytical techniques to demonstrate the superiority of the proposed methodology. The results are also statistically verified

A Hybrid Approach Based on SOCP and the Discrete Version of the SCA for Optimal Placement and Sizing DGs in AC Distribution Networks

This paper deals with the problem of the optimal placement and sizing of distributed generators (DGs) in alternating current (AC) distribution networks by proposing a hybrid master–slave optimization procedure. In the master stage, the discrete version of the sine–cosine algorithm (SCA) determines the optimal location of the DGs, i.e., the nodes where these must be located, by using an integer codification. In the slave stage, the problem of the optimal sizing of the DGs is solved through the implementation of the second-order cone programming (SOCP) equivalent model to obtain solutions for the resulting optimal power flow problem. As the main advantage, the proposed approach allows converting the original mixed-integer nonlinear programming formulation into a mixed-integer SOCP equivalent. That is, each combination of nodes provided by the master level SCA algorithm to locate distributed generators brings an optimal solution in terms of its sizing; since SOCP is a convex optimization model that ensures the global optimum finding. Numerical validations of the proposed hybrid SCA-SOCP to optimal placement and sizing of DGs in AC distribution networks show its capacity to find global optimal solutions. Some classical distribution networks (33 and 69 nodes) were tested, and some comparisons were made using reported results from literature. In addition, simulation cases with unity and variable power factor are made, including the possibility of locating photovoltaic sources considering daily load and generation curves. All the simulations were carried out in the MATLAB software using the CVX optimization tool

An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach

This paper addresses the classical problem of optimal location and sizing of distributed generators (DGs) in radial distribution networks by presenting a mixed-integer nonlinear programming (MINLP) model. To solve such model, we employ the General Algebraic Modeling System (GAMS) in conjunction with the BONMIN solver, presenting its characteristics in a tutorial style. To operate all the DGs, we assume they are dispatched with a unity power factor. Test systems with 33 and 69 buses are employed to validate the proposed solution methodology by comparing its results with multiple approaches previously reported in the specialized literature. A 27-node test system is also used for locating photovoltaic (PV) sources considering the power capacity of the Caribbean region in Colombia during a typical sunny day. Numerical results confirm the efficiency and accuracy of the MINLP model and its solution is validated through the GAMS package. © 2019 Ain Shams UniversityUniversidad Nacional de Colombia, UN: 38945, 58838 P17211 Universidad Tecnológica de Pereira, UTP: C2019P011, C2018P020 Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), COLCIENCIAS: 727-2015This work was funded in part by the Administrative Department of Science, Technology, and Innovation of Colombia (COLCIENCIAS) through its National Scholarship Program, under Grant 727-2015 ; in part by Instituto Tecnológico Metropolitano de Medellín, under Project P17211; in part by Universidad Tecnológica de Bolívar, under Projects C2018P020 and C2019P011; and in part by Universidad Nacional de Colombia, under Proyect ”Estrategia de transformación del sector energético Colombiano en el horizonte de 2030 - Energética 2030” - ”Generación distribuida de energía eléctrica en Colombia a partir de energía solar y eólica” (Code: 58838, Hermes: 38945). Oscar D. Montoya received his BEE, M.Sc. and Ph.D degrees in Electrical Engineering from Universidad Tecnológica de Pereira, Colombia, in 2012 and 2014 respectively. His research interests include mathematical optimization, planning and control of power systems, renewable energies, energy storage, protective devices and smartgrids. Walter Gil-González received his BEE and M.Sc. degrees in Electrical Engineering from Universidad Tecnológica de Pereira, Colombia, in 2011 and 2013 respectively. He is currently studying a Ph.D in Electrical Engineering at Universidad Tecnológica de Pereira, Colombia. His research interests include mathematical optimization, planning and control of power systems, renewable energies, energy storage, protective devices and smartgrids. Luis F. Grisales received his BEE and M.Sc. degrees in Electrical Engineering from Universidad Tecnológica de Pereira, Colombia, in 2013 and 2015 respectively. He is currently studying a Ph.D in Engineering at Universidad Nacional de Colombia. Actually, is professor in the Instituto TecnolÓgico Metropolitano de Medellín, attached to the Department of Electromechanics and mechatronics, member of the research group MATyER. His research interests include mathematical modelling, optimization techniques, planning and control of power systems, renewable energies, energy storage, power electronic and smartgrids