Design and Simulation of Multi Objective Power Flow Optimization in IEEE-30 Bus System using Modified Particle Swarm Optimization

Execution and Reliability of OPF algorithms is an important issue of research for gainful power structure control and orchestrating. Perfect Power Flow is driven for restricting the objective work. This objective limit can be single regarded target work or different objective limits. In the present research, we have executed perfect power stream in order to constrain the fuel cost while satisfying the constraints, for instance, the voltages, power yields of the generator kept inside embraced purpose of repression. Some other objective can be used reliant on utility's preferred position and needs. Many streamlined framework models have been combined in the past by various researchers for OPF issue, for instance, Linear Programming, Non Linear Programming, Quadratic Programming, Newton Based Techniques, Parametric Methods, and Interior Point Methods, etc. A wide variety of bleeding edge optimization methodologies like Evolutionary Programming, Genetic Algorithm, PSO Algorithm, etc are proposed recorded as a hard copy for handling OPF issue. In this proposition, we have executed improved particle swarm optimization algorithm to restrain cost limit while keeping goals inside acceptable most extreme. The adjustments in particle swarm optimization is finished by introducing the idea of quantum computing and optimization of quickening coefficients. The proposed algorithm is associated with IEEE-30 bus structure. Resuts indicated unrivaled execution of proposed algorithm as contrasted and contemporary techniques

Simulated Annealing

The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Optimal demand-supply energy management in smart grids

Everything goes down if you do not have power: the financial sector, refineries and water. The grid underlies the rest of the country’s critical infrastructure. This thesis focuses on four specific problems to balance demand-supply gap with higher reliability, efficiency and economical operation of the modern power grid. The first part investigates the economic dispatch problem with uncertain power sources. The classic economic dispatch problems seek thermal power generation to meet the demand most efficiently. However, this project exploits two different power sources such as wind and solar power generation into the standard optimal power flow framework. The stochastic nature of renewable energy sources (RES) is modeled using Weibull and Lognormal probability density functions. The system-wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. The calculation of best power dispatch is proposed using a cost function. The second part investigates demand-side management (DSM) strategies to minimize energy wastage by changing the time pattern and magnitude of utility load at the consumer side. The main objective of DSM is to flatten the demand curve by encouraging end-users to shift energy consumption to off-peak hours or to consume less power during peak times. It is more appropriate to follow the generation pattern in many cases instead of flattening the demand curve. It becomes more challenging when the future grid accommodates the penetration of distributed energy resources in a greater manner. In both scenarios, there is an ultimate need to control energy consumption. Effective DSM strategies would help to get an accurate balance between both ends, i.e., the supply-side and demand-side, effectively reducing power demand peaks and more efficient operation of the whole system. The gap between power demand and supply can be balanced if power peak loads are minimized. The third part of the thesis then focuses on modeling the consumption behavior of end-users. For this purpose, a novel artificial intelligence and machine learning-based forecasting model is developed to analyze big data in the smart grid. Three modules namely feature selection, feature extraction and classification are proposed to solve big data problems such as feature redundancy and high dimensionality to generate quality data for classifier training and better prediction results. The last part of this thesis investigates the problem of electricity theft to minimize non technical losses and power disruptions in the power grid. Electricity theft with its many facets usually has an enormous cost to utilities compared to non-payment because of energy wastage and power quality problems. With the recognition of the internet of things (IoT) technologies and data-driven approaches, power utilities have enough tools to combat electricity theft and fraud. An integrated framework is proposed that combines three distinct modules such as data preprocessing, data class balancing and final classification to make accurate electrical consumption theft predictions in smart grids. The result of our solution to balance the electricity demand-supply gap can provide helpful information to grid planners seeking to improve the resilience of the power grid to outages and disturbances. All parts of this thesis include extensive experimental results on case studies, including realistic large-scale instances

Proceedings of the 1st WSEAS International Conference on "Environmental and Geological Science and Engineering (EG'08)"

This book contains the proceedings of the 1st WSEAS International Conference on Environmental and Geological Science and Engineering (EG'08) which was held in Malta, September 11-13, 2008. This conference aims to disseminate the latest research and applications in Renewable Energy, Mineral Resources, Natural Hazards and Risks, Environmental Impact Assessment, Urban and Regional Planning Issues, Remote Sensing and GIS, and other relevant topics and applications. The friendliness and openness of the WSEAS conferences, adds to their ability to grow by constantly attracting young researchers. The WSEAS Conferences attract a large number of well-established and leading researchers in various areas of Science and Engineering as you can see from http://www.wseas.org/reports. Your feedback encourages the society to go ahead as you can see in http://www.worldses.org/feedback.htm The contents of this Book are also published in the CD-ROM Proceedings of the Conference. Both will be sent to the WSEAS collaborating indices after the conference: www.worldses.org/indexes In addition, papers of this book are permanently available to all the scientific community via the WSEAS E-Library. Expanded and enhanced versions of papers published in this conference proceedings are also going to be considered for possible publication in one of the WSEAS journals that participate in the major International Scientific Indices (Elsevier, Scopus, EI, ACM, Compendex, INSPEC, CSA .... see: www.worldses.org/indexes) these papers must be of high-quality (break-through work) and a new round of a very strict review will follow. (No additional fee will be required for the publication of the extended version in a journal). WSEAS has also collaboration with several other international publishers and all these excellent papers of this volume could be further improved, could be extended and could be enhanced for possible additional evaluation in one of the editions of these international publishers. Finally, we cordially thank all the people of WSEAS for their efforts to maintain the high scientific level of conferences, proceedings and journals

Energy management engineering : a predictive energy management system incorporating an adaptive neural network for the direct heating of domestic and industrial fluid mediums.

The objective of this research project is to improve the control and provide a more cost-efficient operation in the direct heating of stored domestic or industrial fluid mediums; such to be achieved by means of an intelligent automated energy management system. For the residential customer this system concept applies to the hot water supply as stored in the familiar hot water cylinder; for the industrial or commercial customer the scope is considerably greater with larger quantities and varieties of fluid mediums. Both areas can obtain significant financial savings with improved energy management. Both consumers and power supply and distribution companies will benefit with increased utilisation of cheaper 'off-peak' electricity; reducing costs and spreading the system load demand. The project has focussed on domestic energy management with a definite view to the wider field of industrial applications. Domestic energy control methodology and equipment has not significantly altered for decades. However, computer hardware and software has since then flourished to an unprecedented proportion and has become relatively cheap and versatile; these factors pave the way for the application of computer technology in this area of great potential. The technology allows the implementation of a 'hot water energy management system', which makes a forecast of the hot water demand for the next 24 hours and proceeds to provide this demand in the most efficient manner possible. In the (near) future, the system, known as FEMS for Fluid Energy Management System, is able to take advantage and in fact will promote the use of a retail 'dynamic spot price tariff’. FEMS is a combination of hardware and software developed to replace the existing cylinder thermostat, take care of the necessary data-acquisition and control the cylinder's total energy instead of it's (single point) temperature. This provides, besides heating cost reduction, a greater accuracy, a degree of flexibility, improved feedback, legionella inhibition, and a diagnostic capability. To the domestic consumer the latter three items are of greatest relevance. The crux of the system lies in its predictive ability. Having explored the more conventional alternatives, a suitable solution was found in the utilisation of the Elman recurrent neural networks, which focus on the temporal characteristics of the hot water demand time series and are able to adapt to changing environments, coping with the presence of any non-linearity and noise in the data. Prior to developing FEMS a study was made of the basic fluid behaviour in medium and high pressure domestic hot water cylinders, an area not well-covered to date and of interest to engineers and manufacturers alike. For this step data acquisition equipment and software was purposely created. The control software plus equipment were combined into a fully automated test system with minimal operator input, allowing a large amount of data to be gathered over a period measured in months. A similar system was subsequently used to collect actual hot water demand data from a residential family, and in fact forms the basis for FEMS. Finally an enhanced version of FEMS is discussed and it is shown how the system is able to output multiple prediction and utilise varying tariff rates