Application of Shuffled Frog-Leaping Algorithm on Clustering

Scientific Reports, 2023

Increased innovation on finding new ways to generate energy from different sources to meet the growing demand of consumers has led to various challenges in controlling the power network when it faces different disruptions. To address these challenges, a new approach has been proposed in this research paper, which combines a controller with a soft computing technique called Particle Swarm Optimization (PSO). The study considers a power system with four units, where three different energy sources are utilized and distributed across two areas. Each area has two power sources, with one area having a combination of thermal and gas power plants, and the other area consisting of a nuclear power plant and a gas power plant. Transmitting power from the nuclear power plant is particularly complex due to its high sensitivity to disturbances. Therefore, an intelligent and efficient controller is needed to ensure robust control in this type of power network that includes nuclear power. The paper also conducts a thorough analysis of the harmful emissions associated with electricity generation from the different power plants considered. The goal is to reduce the carbon footprint associated with power generation. The proposed work and analysis in the paper are implemented using the MATLAB/ SIMULINK environment. Conventional power emits enormous harmful emission and also fossil fuels are limited on the earth, so nonconventional power plant is required to address this environmental issue. Nuclear power plants have the potential to meet power needs but are highly sensitive to disturbances and sudden loading. This paper focuses on a power system with a nuclear power plant as the primary unit and emphasizes the need for an efficient controller to handle sudden loading or disturbances in the power network. The authors of 1 have provided mathematical models in the s-domain for different sources of energy, including thermal, hydro, wind, and nuclear power systems. These models serve as a foundation for understanding and analyzing the behavior of these energy sources. In 2 , the authors specifically address thermal energy systems and present the automatic voltage regulating loop and load frequency loop. These control loops help maintain stable voltage and frequency levels in thermal power systems. The authors in 3 discuss the tuning of controller parameters, such as TID, PID, and FOPID, using soft computing techniques. This approach helps optimize the performance of the controllers and improve the overall stability and response of the system. 4 focuses on multi-area power systems and highlights the successful control of power system frequency using hybrid controllers. Hybrid controllers combine different control strategies to achieve improved performance in regulating the frequency of the power system. Authors in 5, 6 discuss the design and optimization of fitness or cost functions. These functions play a crucial role in determining the optimal control strategies and parameters for power system operation. The authors employ a metaheuristic technique called Particle Swarm Optimization (PSO) to solve the optimization problems. In 7 , the authors present the implementation of a fractional-order controller for an inverted pendulum case. They also employ PSO for parameter tuning of the controller, which helps enhance the stability and control performance of the system. 8 investigates the effect of Super Magnetic Energy Storage (SMES) in a multi-area power system with multiple sources of power. The authors provide a detailed analysis of the system's frequency response, examining the impact of SMES on system stability and response to disturbances. Load frequency control in an interconnected power system with enhanced

International Journal of Hybrid Intelligent Systems, 2019

Automatic Generation Control (AGC) is an important tool to ensure the stability and reliability of power systems. For stable operation of power systems, the frequency of the system should be reserved within the nominal value. Towards this, the estimation of states is of supreme implication. In this paper, a comparison is made on the estimation of the states using Kalman estimator method and optimal control approach to the Automatic Generation Control (AGC) of an isolated power system. The performance of optimized Linear Quadratic Regulator (LQR) in pole placement is compared with Kalman estimator. Optimization algorithms such as Genetic Algorithm and Particle Swarm Optimization are used to optimize positive definite matrices Q and R, weighting matrices of a LQR controller. Kalman estimator estimates the states of the system by measuring only one output signal which in this paper is mentioned as the change in frequency for the system considered. The comparison is made on the basis of the mean of the variances of the output, using the mentioned approaches. Study is conducted under different noise levels for independent Monte Carlo simulations. Modeling of an isolated power system is done using Simulink/MATLAB.

2010

The primary objective of this dissertation is to develop a black box optimization tool. The algorithm should be able to solve complex nonlinear, multimodal, discontinuous and mixed-integer power system optimization problems without any model reduction. Although there are many computational intelligence (CI) based algorithms which can handle these problems, they require intense human intervention in the form of parameter tuning, selection of a suitable algorithm for a given problem etc. The idea here is to develop an algorithm that works relatively well on a variety of problems with minimum human effort. An adaptive particle swarm optimization algorithm (PSO) is presented in this thesis. The algorithm has special features like adaptive swarm size, parameter free update strategies, progressive neighbourhood topologies, self learning parameter free penalty approach etc. The most significant optimization task in the power system operation is the scheduling of various generation resource...

2011

Power and Energy Society General Meeting, 2010 …, 2010

Grid operation under market competition forces systems closer to their instability boundaries, and operating decisions must be based on accurate online system identifications. This paper presents a new framework for online power system dynamic stability enhancement with a new rescheduling market construction. The approach is to solve the online transient and oscillatory stability constrained economic power system operation by a mixture of a modified particle swarm optimization (PSO) and artificial neural network (ANN). The problem is formulated as nonlinear constrained optimization problem and PSO has been used as optimization tool to guarantee searching the optimal economic solution within the available hyperspace reducing the time consumed in the computations by using ANN to assess power system dynamic stability. The rescheduling process based on the generation companies (GENCOs)/consumer's bids is used as a remedial action to maintain system operation away from the limits of system stability. The goal of the approach is to minimize the opportunity cost payments for GENCOs/consumers backed down in generation/load and the additional cost for GENCOs/consumers increased their generation/load in order to enhance system dynamic stability. The critical clearing time (CCT) at the critical contingency is considered as an index for transient stability. System minimum damping of oscillation (MDO) is considered as indicator for oscillatory stability. The proposed framework is examined on a 66-bus test system.

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...

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.

2020

This paper presents the objective of generation expansion planning is to aim at serving the demand at a specified level of reliability, at the lowest possible cost. The optimization techniques such as the Dynamic programming, Particle Swarm Optimization, are applied to solve GEP problem. The original GEP problem is modified using the proposed methods Virtual Mapping Procedure (VMP) and Penalty Factor Approach (PFA), to improve the efficiency of the techniques. Further, Intelligent Initial Population Generation (IIPG), is introduced in the solution techniques to reduce the computational time. The VMP, PFA, and IIPG are used in solving all the two test systems. The GEP problem considered synthetic test systems for 6-year and 14-year, planning horizon having five types of candidate units. The results obtained by these proposed techniques are compared and validated against conventional Dynamic Programming and the effectiveness of each proposed methods has also been illustrated in detail.

2020 16th International Conference on Network and Service Management (CNSM), 2020

In the present paper the authors have introduced the definitions, unique attributes and resources of Intelligent Organisations. The authors have justified the abilities to flexibly adjust the supply of electricity produced in micro-installations of Renewable Energy Sources (RES) to the dynamically changing demand on the side of final customers. Additionally, the nature of "learning" of VPP has been indicated, in particular the system learning through utilisation of learning qualities of Artificial Intelligence (AI) generators. The objective function determining system learning has been adopted with regard to balancing the electricity supply and demand, considering non-linearity of RES micro-installations generation and non-linearity of the demand for this type of energy. Having defined the objective function the authors have included the logical and functional presentation of the experimental model of the System for Intelligent Power Plant Management (SIPPM) developed base...

Because of the rapid consumption of conventional energy sources, renewable energy sources have become one of the important sources for electrical power generation. This paper considers wind-turbine generator, photo voltaic and Pico hydro generator as a small autonomous hybrid power system which acts as a distributed generation.

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.

Advances in Engineering and Intelligence Systems, 2022

In recent years, changing the structure and decentralizing management of energy systems have been started as a serious movement in many countries and past experience in this field has demonstrated that relying on market mechanism to ensure the adequacy of energy production and supply in the long run lacks efficiency. Hence, the issue of planning for the optimal development of the energy supply system is one of the key issues in the restructured condition. Planning for developing systems in new conditions is much more complicated than the classical systems. In the present study, the development planning of electric power production system with the collaboration of thermal, wind, hydro and solar power plants is reviewed. A multi-level model for electric charge, wind and hydro power plants is introduced. By specifying the optimal capacity of wind and water units and reducing the contribution of thermal units as well as environmental pollution, the total cost of developing the production system is decreased. The presented method is carried out for IEEE 24 busbar network and the combined particle swarm optimization and gravitation search algorithm optimization process, which includes two method for optimization process.

We introduce in this paper an algorithm called chaotic swarm intelligence algorithm which solve Economic dispatch problem of the thermal power plant at some extent this algorithm is more better than other techniques and the convergence of Chaotic PSO is faster as compared to standard PSO. We discuss the generator unit constraint, objective function, power balance constraint and prohibited operating and discuss the advantages of the technique apply on these constraint. .

2013

From the power systems perspective, the world has experienced dramatic changes over the past few years. Global climate warnings associated with economic progress and other factors have led the power systems to undergo changes, mainly affecting the way they are designed, planned, operated and managed. A significant amount of activity is underway on the technological, policy and economic fronts, which necessarily affects the entire electric power sector. Investments in research and development (R&D) have been the solution to mitigate the technological and policy risks of these changes and have attracted market capital to the power system industry. One of the first areas affected by these changes is the power systems analysis area, which requires the ability of a set of simulation tools to represent the changes associated with the technological front, such as wind and solar power energy penetrations, and/or analysis of policy impacts (e.g. maintenance schedule of large equipment in the open market energy). This thesis has explored new ways for power system analyses through the use of agentbased technology. First, the research work discusses the development of a Multi-Agent Systems (MAS) technology-based platform with potential applications in management and simulation processes in power systems. In order to explore some of the features of MAS, a new methodology is proposed to assess power systems reliability based on the Monte Carlo simulation (MCS), exploiting the benefits of the distributed artificial intelligence area and, mainly, the use of the distributed capacity in two ways: building autonomous behaviors for the applications and mitigating computational effort. Through the use of this technology, it was possible to divide the MCS algorithm into distinct tasks and submit them to the agent processing. Two different approaches to solve reliability problems in generating capacity based on chronological MCS illustrate the potential of MAS in power systems reliability assessment. Second, the maintenance schedule generating units are discussed. Maintenance decisions in electricity markets are one of the most important strategic conflicts among power players. For instance, a generation company is a selfinterested entity that is responsible for its own risk-based level. On the other hand, for large equipment supplying many customers, it is the Transmission System Operator"s task to schedule the most suitable period for maintenance, thus preserving system reliability. This relationship is sometimes conflicting and can be seen as an obstacle to the generation companies maximizes profits, as well as to the transmission system operator, since it is Index INDEX Contents CHAPTER 1 ..