FUZZY-BASED REAL-CODED GENETIC ALGORITHM FOR OPTIMIZING NON-CONVEX ENVIRONMENTAL ECONOMIC LOSS DISPATCH

A non-convex Environmental Economic Loss Dispatch (NCEELD) is a constrained multi-objective optimization problem that has been solved for assigning generation cost to all the generators of the power network with equality and inequality constraints. The objectives considered for simultaneous optimization are emission, economic load and network loss dispatch. The valve-point loading, prohibiting operating zones and ramp rate limit issues have also been taken into consideration in the generator fuel cost. The tri-objective problem is transformed into a single objective function via the price penalty factor. The NCEELD problem is simultaneously optimized using a fuzzy-based real-coded genetic algorithm (GA). The proposed technique determines the best solution from a Pareto optimal solution set based on the highest rank. The efficacy of the projected method has been demonstrated on the IEEE 30-bus network with three and six generating units. The attained results are compared to existing results and found superior in terms of finding the best-compromise solution over other existing methods such as GA, particle swarm optimization, flower pollination algorithm, biogeography-based optimization and differential evolution. The statistical analysis has also been carried out for convex multi-objective problem

Filter Feeding Allogenic Engineering Optimization Algorithm for Economic Dispatch

The main objective of the economic dispatch problem in a power system is to minimize the total thermal fuel cost of the committed generators while satisfying the various system equality and inequality operational constraints. This research developed a new optimization algorithm, named the filter feeding allogenic engineering algorithm, for use in solving the economic dispatch problem. This meta-heuristic algorithm has been inspired by the filter feeding and motile behaviour of allogenic engineers. The newly developed algorithm was formulated using the Matlab software environment, and its performance was tested using the IEEE 39-Bus, 10-Generator system. A comparative analysis was also conducted with the Ant lion optimization heuristic algorithm, and the obtained results indicate that the filter feeding allogenic engineering algorithm yields superior performance

SOLVING ECONOMIC LOAD DISPATCH WITH RELIABILITY INDICATORS

Due to the great importance of reliable indicators in electrical operating systems in all its different parts, it has been considered the most important factors in the design and maintenance of the electrical system, especially during its operation. The main reason for attention to reliability indicators relates to interruptions in the power system that are provided to consumers. The introduction of reliable indicators to solving an economic load dispatch (ELD) issue increases the possibility of providing customers with a required load with the highest degree of reliability. The ELD issue has been solved with reliability indicators. This means that the ELD problem with reliability is combined into one problem called combined the economic load dispatch with reliability (CELDR). Solving the above problem lowers the fuel cost while increasing the reliability of the generators while preparing the required load. The exchange market algorithm (EMA), in this work, has been implemented in a system of 26 generating units to solve the CELDR issue. Considering system reliability, inequality, and equality constraints. The results obtained show the direct effect of using reliability indicators in solving the above problem, where the best results were obtained using the EMA algorithm to solve the mentioned problem, compared to other algorithms

Green Low-Carbon Technology for Metalliferous Minerals

Metalliferous minerals play a central role in the global economy. They will continue to provide the raw materials we need for industrial processes. Significant challenges will likely emerge if the climate-driven green and low-carbon development transition of metalliferous mineral exploitation is not managed responsibly and sustainably. Green low-carbon technology is vital to promote the development of metalliferous mineral resources shifting from extensive and destructive mining to clean and energy-saving mining in future decades. Global mining scientists and engineers have conducted a lot of research in related fields, such as green mining, ecological mining, energy-saving mining, and mining solid waste recycling, and have achieved a great deal of innovative progress and achievements. This Special Issue intends to collect the latest developments in the green low-carbon mining field, written by well-known researchers who have contributed to the innovation of new technologies, process optimization methods, or energy-saving techniques in metalliferous minerals development

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

Aplicación de los sistemas de almacenamiento de energía en las redes eléctricas para el abastecimiento de la demanda usando flujos óptimos de potencia AC

La investigación ha desarrollado un modelo matemático de optimización que determina la potencia horaria a ser despachada por los generadores, tomando en cuenta como tecnologías tales como: el hidroeléctrico, eólico, fotovoltaico y térmicos; y, asignando también la participación horaria de los sistemas de almacenamiento de la energía eléctrica en el abastecimiento de la demanda, modelando un sistema de prueba para lo cual se usa los flujos de carga o de potencia AC. El modelo matemático es aplicado a un sistema modelos IEEE 24 nodos, obteniendo como resultados la minimización de los costos en el abastecimiento de la demanda, el despacho de potencia activa y reactiva de cada generador durante el período de 24 horas, los flujos de potencia por los enlaces y el aporte del sistema de almacenamiento en el cubrimiento de la carga horaria, enfocándose el análisis principalmente para la demanda mínima, media y máxima. De la modelación se resalta la modelación de los sistemas de almacenamiento basados en baterías, los cuales, al interactuar en el sistema eléctrico en función del uso de energías intermitentes, restricciones de la red en base a los flujos de potencia AC y los precios del mercado, la demanda obtiene importantes beneficios los cuales son analizados desde un punto de vista técnico y económico.The research has developed a mathematical optimization model that determines the hourly power to be dispatched by generators, taking into account technologies such as: hydroelectric, wind, photovoltaic and thermal; and, also assigning the hourly participation of electrical energy storage systems in the supply of demand, modeling a test system for which load or AC power flows are used. The mathematical model is applied to an IEEE 24 node model system, obtaining as results the minimization of costs in the supply of demand, the dispatch of active and reactive power of each generator during the 24-hour period, the power flows through the links and the contribution of the storage system in the coverage of the hourly load, focusing the analysis mainly on the minimum, average and maximum demand. The modeling highlights the modeling of battery-based storage systems, which, by interacting in the electrical system depending on the use of intermittent energies, network restrictions based on AC power flows and market prices, demand obtains important benefits which are analyzed from a technical and economic point of view

Forecasting Automobile Gasoline Demand in Australia Using Machine Learning-based Regression

We use a variant of machine learning (ML) to forecast Australia’s automobile gasoline demand within an autoregressive and structural model. By comparing the outputs of the various model specifications, we find that training set selection plays an important role in forecasting accuracy. More specifically, however, the performance of training sets starting within identified systematic patterns is relatively worse, and the impact on forecast errors is substantial. Instead of treating these patterns as noise, we explain these systematic variations in machine learning performance, and explore the intuition behind the ‘black-box’ with the support of economic theory. An important finding is that these time points coincide with structural changes in Australia’s economy. By examining the out-of-sample forecasts, the model’s external validity can be demonstrated under normal situations; however, its forecasting performance is somewhat unsatisfactory under event-driven uncertainty, which calls on future research to develop alternative models to depict the characteristics of rare and extreme events in an ex-ante manner

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the `Rush to Heuristics'

In the power and energy systems area, a progressive increase of literature contributions that contain 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 that are 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 that concern 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 that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems

Infrastructure Design, Signalling and Security in Railway

Railway transportation has become one of the main technological advances of our society. Since the first railway used to carry coal from a mine in Shropshire (England, 1600), a lot of efforts have been made to improve this transportation concept. One of its milestones was the invention and development of the steam locomotive, but commercial rail travels became practical two hundred years later. From these first attempts, railway infrastructures, signalling and security have evolved and become more complex than those performed in its earlier stages. This book will provide readers a comprehensive technical guide, covering these topics and presenting a brief overview of selected railway systems in the world. The objective of the book is to serve as a valuable reference for students, educators, scientists, faculty members, researchers, and engineers

Safety Considerations in Optimal Automotive Vehicle Design.

While automobiles provide society with an unprecedented amount of mobility, motor vehicle crashes are a leading cause of injury and death worldwide. Designing safer vehicles is a priority of governments and automakers alike; however, other requirements such as increased fuel economy and performance have driven designs in conflicting directions. Because society benefits from reductions in traffic injuries and fuel consumption, governments impose standards and incentives for safer and more fuel efficient vehicles. One form of incentive is a consumer-information test, such as a New Car Assessment Program (NCAP), using standardized crash tests in various impact directions to help customers compare the crashworthiness of different automobiles. Automakers strive to perform well on these tests by optimizing vehicle designs to the specified scenarios. Another type of standard uses injury thresholds to ensure a minimum level of protection, such as the U.S. Federal Motor Vehicle Safety Standards and the U.S. Army ground vehicle blast protection criteria. This dissertation uses these standards to examine the impact of safety optimization formulations and tradeoffs on vehicle design and competing objectives. Physics-based modeling is used to simulate crash or blast events, and computational designs of experiments are conducted with the resulting data fit to response surfaces. Single- and multi-objective optimization formulations are developed to demonstrate relationships between occupant protection and vehicle weight for civilian vehicle crashes and military vehicle blast events. Using these formulations, the civilian case study is extended to understand the impact of the frontal NCAP test speed on injuries in frontal on-road crashes, as well as the effect safety considerations have on manufacturer profit-maximizing decisions and consumer behavior in a competitive market. The military case study is also expanded to demonstrate how high vehicle weight and fuel consumption increase the need for convoys, posing additional injury risks to personnel and thereby making fuel economy a safety objective in a casualty-minimization formulation. The results of these studies demonstrate the need for designers and engineers to consider safety in new, more holistic ways, and this dissertation establishes a new type of design thinking that can contribute to decreased vehicle-related injuries while also accounting for other objectives.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91402/1/shoffens_1.pd