Review of Metaheuristic Optimization Algorithms for Power Systems Problems

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field

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

AC optimal power flow with thermal–wind–solar–tidal systems using the symbiotic organisms search algorithm

Optimal power flow problem is one of the most important non-linear problems for power system planning and the operation of existing modern power networks. Recently, the incremental usage of renewable energy sources in power systems has revealed the significance of power system planning. Thus, the aim is to model the AC optimal power flow problem using thermal–wind–solar–tidal energy systems. In this study, uncertainties of wind, solar, and tidal energy systems were simulated using Weibull, Lognormal, and Gumbel probability distribution functions. Furthermore, the study presents solutions to the AC optimal power flow problem by including test cases of stochastic wind, solar, and tidal energy systems involving minimisation of cost function, active power loss, voltage deviation, enhancement of voltage stability, and contingency conditions. The solutions were tested via IEEE 30-bus and IEEE 118-bus test systems incorporating renewable energy sources, using different locations according to the selected thermal generating units. The symbiotic organisms search algorithm, which is one of the recently introduced optimisation algorithms, was used to solve the proposed power system planning problem, and simulation results of this algorithm were compared to the results of other algorithms such as the imperialist competitive, harmony search, backtracking search optimisation, and gravitational search algorithms

A Comprehensive Review of Congestion Management in Power System

In recent decades, restructuring has cut across all probable domains, involving the power supply industry. The restructuring has brought about considerable changes whereby electricity is now a commodity and has become a deregulated one. These competitive markets have paved the way for countless entrants. This has caused overload and congestion on transmission lines. In addition, the open access transmission network has created a more intensified congestion issue. Therefore, congestion management on power systems is relevant and central significance to the power industry. This manuscript review few congestion management techniques, consists of Reprogramming Generation (GR), Load Shedding, Optimal Distributed Generation (DG) Location, Nodal Pricing, Free Methods, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Logic System Method, as well as Additional Renewable Energy Sources. In this manuscript a review work is performed to unite the entire publications on congestion management

A Comprehensive Review of Congestion Management in Power System

In recent decades, restructuring has cut across all probable domains, involving the power supply industry. The restructuring has brought about considerable changes whereby electricity is now a commodity and has become a deregulated one. These competitive markets have paved the way for countless entrants. This has caused overload and congestion on transmission lines. In addition, the open access transmission network has created a more intensified congestion issue. Therefore, congestion management on power systems is relevant and central significance to the power industry. This manuscript review few congestion management techniques, consists of Reprogramming Generation (GR), Load Shedding, Optimal Distributed Generation (DG) Location, Nodal Pricing, Free Methods, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Logic System Method, as well as Additional Renewable Energy Sources. In this manuscript a review work is performed to unite the entire publications on congestion management

Lightning search algorithm: a comprehensive survey

The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding the minimum or maximum costs to solve a specific problem. Meta-heuristics have grown the focus of researches in the optimization domain, because of the foundation of decision-making and assessment in addressing various optimization problems. A review of LSA variants is displayed in this paper, such as the basic, binary, modification, hybridization, improved, and others. Moreover, the classes of the LSA’s applications include the benchmark functions, machine learning applications, network applications, engineering applications, and others. Finally, the results of the LSA is compared with other optimization algorithms published in the literature. Presenting a survey and reviewing the LSA applications is the chief aim of this survey paper

Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms