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2021, Journal of research in Engineering and Applied Sciences
https://doi.org/10.46565/jreas.2021.v06i02.007…
5 pages
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Optimization methodshave been appliedin power systems for the last hundred years. They have been applied across a broad regionspanning from power system design to power system planning and economic power dispatch to protection. This workpresents an overview of the metaheuristicoptimization techniques and their applications to power systems.
2001
This paper describes a number of real applications of meta-heuristics (in this case, Simulated Annealing) and Genetic Algorithms to Power System problems. The research work was developed in the framework of European projects and industrial contracts and addresses areas as: planning and operation of electrical distribution systems, wind park layout, unit commitment of isolated systems with renewable energy sources and voltage collapse in interconnected systems. The combinatorial nature comes naturally in Power Systems, since most of the decision variables are binary or integer due to technical reasons. On the other hand, a common characteristic to these problems is the presence of technical constraints, which poses difficulties to the application of meta-heuristics, leading to the need of penalty factors in the evaluation functions. The extended abstract also includes feature selection for security analysis using Artificial Neural Networks, a related topic, although not really an application of meta-heuristics. The abstract is organized as follows. Regarding each topic, the corresponding problem is briefly described, followed by the presentation of the approach and, in some cases, a summary of the results. Global conclusions and references complete the extended abstract.
Optimization of solutions on expansion of electric power systems (EPS) and their control plays a crucial part in ensuring efficiency of the power industry, reliability of electric power supply to consumers and power quality. Until recently, this goal was accomplished by applying classical and modern methods of linear and nonlinear programming. In some complicated cases, however, these methods turn out to be rather inefficient. Meta-heuristic optimization algorithms often make it possible to successfully cope with arising difficulties. State estimation (SE) is used to calculate current operating conditions of EPS using the SCADA measurements of state variables (voltages, currents etc.
arXiv (Cornell University), 2020
In the power and energy systems area, a progressive increase of literature contributions containing applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods based on weak comparisons. This 'rush to heuristics' does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter, but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems, and aims at providing a comprehensive view of the main issues concerning the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls found in literature contributions are identified, and specific guidelines are provided on how to prepare sound contributions on the application of metaheuristic algorithms to specific problems.
Several heuristic tools have evolved in the last decade that facilitate solving optimization problems that were previously difficult or impossible to solve. These tools include evolutionary computation, simulated annealing, tabu search, particle swarm, etc. Reports of applications of each of these tools have been widely published. Recently, these new heuristic tools have been combined among themselves and with knowledge elements, as well as with more traditional approaches such as statistical analysis, to solve extremely challenging problems. Developing solutions with these tools offers two major advantages: 1) development time is much shorter than when using more traditional approaches, and, 2) the systems are very robust, being relatively insensitive to noisy and/or missing data.
International Journal of Engineering Applied Sciences and Technology, 2019
Power systems are very large and complex, it can be influenced by many unexpected events this makes Power system optimization problems difficult to solve, hence methods for solving these problems ought to be, an active research topic. This review presents an overview of important mathematical optimization methods those are Unconstrained optimization approaches Nonlinear programming (NLP), Linear programming (LP), Quadratic programming (QP), Generalized reduced gradient method, Newton method, Network flow programming (NFP), Mixed-integer programming (MIP), Interior point (IP) methods and Artificial intelligence (AI) techniques such as Artificial Neural Network (ANN), fuzzy logic,Genetic Algorithm (GA), Particle Swarm Optimization (PSO),Tabu Search (TS) algorithm, etc. and Hybrid artificial intelligent techniques are discussed. And also applications of optimization techniques have been discussed. Finally classification, application area, observation, conclusion, and recommendation for future research work will be forwarded.
Energy Informatics
Due to the importance of the energy resource management (ERM) in the energy community, several mathematical formulations have been successfully proposed to solve the problem. However, due to the very dynamic evolution of power systems and the transformation of electrical grids, mainly due to the development of smart grid technologies, traditional formulations, which were designed for an entirely different scenario, sometimes cannot deal with the problem efficiently. It is in those situations, where traditional approaches fail, that modern metaheuristic optimizers have demonstrated been a potent tool to face such challenges. In this paper, we present "Meta-ERM", a MATLAB© platform designed to assess the performance of modern metaheuristics when solving the ERM problem.
Ain Shams Engineering Journal, 2015
Reactive power planning (RPP) is generally defined as an optimal allocation of additional reactive power sources that should be installed in the network for a predefined horizon of planning at minimum cost while satisfying equality and inequality constraints. The optimal placements of new VAR sources can be selected according to certain indices related to the objectives to be studied. In this paper, various solution methods for solving the RPP problem are extensively reviewed which are generally categorized into analytical approaches, arithmetic programming approaches, and meta-heuristic optimization techniques. The research focuses on the disparate applications of meta-heuristic algorithms for solving the RPP problem. They are subcategorized into evolution based, and swarm intelligence. Also, a study is performed via the multi-objective formulations of reactive power planning and operations to clarify their merits and demerits.
2015
Development of solutions to optimisation problems (including electrical power systems) has remained an active research area in the recent decades, with interests in the field growing rapidly because of the high importance of electricity, even as the world‟s energy demand is on a daily increase. Problems in this sector are characterised by increased complexity and dynamism; and artificial intelligent tools have proved the only means of realising optimal and robust solutions. This paper contributes an up-to-date survey of the successful artificially intelligent approaches used to solve electrical power system problems, including their respective application areas. It groups the approaches against the corresponding problems they solve; and makes a critical and comparative analysis of the approaches in terms of their merits and demerits. This is of great importance to researchers and experimentalists in this field. These emerging techniques have provided good platforms for development o...
Electric power from several sources in an electrical power system is to be accurately planned for economical and reliable operation. Power loss minimization, generation, and fuel cost minimization, voltage stability and carbon emission reduction are the prominent advantages of OFP. Thus, recently, the optimal solution of OPF become a valuable part of power system planning and optimization. This paper presents a mini-review on methods applied for the OPF solution. The applied methods include conventional and metaheuristic methodologies for solving the OPF problem. Moreover, the most recently applied metaheuristic methods for solving the OPF problem are covered and presented, including considered OFP type, validation test system, and numerous optimization objectives.
Advances in Computational Intelligence and Robotics
Due to liberalization in the power market stake of the Distributed Generation (DG) in the power industries has increased radically. Integration of DG will result is the change in the operating conditions of the existing power system network. Due to this DG has drawn attention of utility providers, policy makers and, to effectively use the DG, several researchers also. Inclusion of DG in the existing power system may enhance its power transfer capacity, voltage profile, reliability and it can also reduce the overall system losses if installed in proper capacity and at proper place. Benefits of DG can be efficiently extracted only if an appropriate capacity of DG is introduced in the existing power system at appropriate place. This chapter proposes an analytical and heuristic approach suggesting the optimum size and location of type-1 and type-2 DG. The proposed method is implemented on the IEEE-13 bus radial distribution network (RDN) and result shows the validity of the proposed met...
TELKOMNIKA Telecommunication Computing Electronics and Control, 2020
In the present article, the selection process of the topology of an artificial neural network (ANN) as well as its configuration are exposed. The ANN was adapted to work with the Newton Raphson (NR) method for the calculation of power flow and voltage optimization in the PQ nodes of a 10-node power system represented by the IEEE 1250 standard system. The purpose is to assess and compare its results with the ones obtained by implementing ant colony and genetic algorithms in the optimization of the same system. As a result, it is stated that the voltages in all system nodes surpass 0,99 p.u., thus representing a 20% increase in the optimal scenario, where the algorithm took 30 seconds, of which 9 seconds were used in the training and validation processes of the ANN.
Energies
Continuous advances in computer hardware and software are enabling researchers to address optimization solutions using computational resources, as can be seen in the large number of optimization approaches that have been applied to the energy field [...]
2014 5th International Conference on Intelligent Systems, Modelling and Simulation, 2014
The utilization of Genetic Algorithms (GA) in tackling engineering problems has been a major issue arousing the curiosity of researchers and practitioners in the area of systems and engineering research, operations research and management sciences in the past decades. The limitations on the use of conventional methods and stochastic search paved the way to wide applications of GA optimization techniques in tackling problems related to engineering and sciences. In view of this, this paper presents a state-of-the-art survey of applications of GA technique in engineering with focus on system power optimization using GA in the last decade. Hence, the scope of this paper is centred between the years 2003-2013.
Indonesian Journal of Electrical Engineering and Computer Science, 2019
Optimal power flow (OPF) is a non-linear solution which is significantly important in order to analyze the power system operation. The use of optimization algorithm is essential in order to solve OPF problems. The emergence of machine learning presents further techniques which capable to solve the non-linear problem. The performance and the key aspects which enhances the effectiveness of these optimization techniques are compared within several metaheuristic search techniques. This includes the operation of particle swarm optimization (PSO) algorithm, firefly algorithm (FA), artificial bee colony (ABC) algorithm, ant colony optimization (ACO) algorithm and differential evolution (DE) algorithm. This paper reviews on the key elements that need to be considered when selecting metaheuristic techniques to solve OPF problem in power system operation.
Renewable & Sustainable Energy Reviews, 2011
With the 2009/28/EC Directive, the European Union has to guarantee three objectives by 2020: 20% reduction in greenhouse gases emissions, 20% share of renewable energy and 20% improvement of energy efficiency. New technologies and policies applied to power systems can positively influence the overall energy efficiency. The dimensions and complexity of the power system discourage the use of exact optimization techniques and heuristic methods are an effective option to find a rapid, robust and good solution. This paper presents a review of articles with applications of heuristic methods to the transmission and distribution system with the aim of improving energy efficiency.
Lecture Notes in Computer Science, 2010
The implementation of intelligent power grids, in form of smart grids, introduces new challenges to the optimal dispatch of power. Thus, optimization problems need to be solved that become more and more complex in terms of multiple objectives and an increasing number of control parameters. In this paper, a simulation based optimization approach is introduced that uses metaheuristic algorithms for minimizing several objective functions according to operational constraints of the electric power system. The main idea is the application of simulation for computing the fitness-values subject to the solution generated by a metaheuristic optimization algorithm. Concerning the satisfaction of constraints, the central concept is the use of a penalty function as a measure of violation of constraints, which is added to the cost function and thus minimized simultaneously. The corresponding optimization problem is specified with respect to the emerging requirements of future smart electric grids.
2014 Power Systems Computation Conference, 2014
This work proposes a computational methodology for coordinated tuning of the power system controllers based on Genetic Algorithm which acts maximizing simultaneously two objective functions, representing each of them the damping of electromechanical oscillations and the improvement of the automatic voltage regulator responses, considering at the same time several critical operating conditions. The coordinated tuning procedure was posted as a multi-objective optimization problem, through the weighted sum technique. The controllers considered into the adjusting scheme correspond to the automatic voltage regulators, power system stabilizers and static var compensators to enhance the electromechanical and voltage response for a several critical operating conditions considered. The Genetic Algorithm was adapted for parallel computing in order to achieve both dynamic responses encompassing several critical operating conditions to reduce high computational efforts. Simulation results of the parallel implementation were more significant than the sequential version, and the proposed approach becomes an interesting alternative tool for operation planning and stability studies.
Energies
The aim of this manuscript is to introduce solutions to optimize economic dispatch of loads and combined emissions (CEED) in thermal generators. We use metaheuristics, such as particle swarm optimization (PSO), ant lion optimization (ALO), dragonfly algorithm (DA), and differential evolution (DE), which are normally used for comparative simulations, and evaluation of CEED optimization, generated in MATLAB. For this study, we used a hybrid model composed of six (06) thermal units and thirteen (13) photovoltaic solar plants (PSP), considering emissions of contaminants into the air and the reduction in the total cost of combustibles. The implementation of a new method that identifies and turns off the least efficient thermal generators allows metaheuristic techniques to determine the value of the optimal power of the other generators, thereby reducing the level of pollutants in the atmosphere. The results are presented in comparative charts of the methods, where the power, emissions, a...
2014 Power Systems Computation Conference, 2014
Power system planning and operation offers multitudinous opportunities for optimization methods. In practice, these problems are generally large-scale, non-linear, subject to uncertainties, and combine both continuous and discrete variables. In the recent years, a number of complementary theoretical advances in addressing such problems have been obtained in the field of applied mathematics. The paper introduces a selection of these advances in the fields of non-convex optimization, in mixedinteger programming, and in optimization under uncertainty. The practical relevance of these developments for power systems planning and operation are discussed, and the opportunities for combining them, together with high-performance computing and big data infrastructures, as well as novel machine learning and randomized algorithms, are highlighted.
International Journal of Power Electronics and Drive System (IJPEDS) , 2020
The economic dispatch problem of power plays a very important role in the exploitation of electro-energy systems to judiciously distribute power generated by all plants. The unit commitment problem (UCP) consists mainly in finding the minimum cost schedule for a set of generators by switching on or off each one over a given time horizon to meet the demand and satisfy different operational constraints, This research article integrates the crow search algorithm (CSA) as a local optimizer of Eagle strategy (ES) to solve unit commitment problem in smart grid system and economic dispatch of two electricity networks: a testing system 7 units and the Moroccan network.. The results obtained by ES-CSA are compared with various results obtained in the literature. Simulation results show that using ES-CSA can lead to finding stable and adequate power generated that can fulfill the need of both the civil and industrial areas.
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