USE OF INTELLIGENT OPTIMIZATION IN POWER SYSTEMS (original) (raw)
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Application of Meta-Heuristic Optimization Algorithms in Electric Power Systems
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.
Meta-heuristics Applied 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.
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.
Optimization Techniques in Power System: Review
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.
Performance assessment of an optimization strategy proposed for power systems
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.
Optimization Methods Applied to Power Systems
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 [...]
A Decade Survey of Engineering Applications of Genetic Algorithm in Power System Optimization
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.
Review on population-based metaheuristic search techniques for optimal power flow
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.
Optimization techniques to improve energy efficiency in power systems
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.