Optimal economic dispatch for carbon capture power plants using chaos-enhanced cuckoo search optimization algorithm

Accelerated global demand for low carbon operation of power systems have stimulated interest in Low Carbon Technologies (LCTs). The increased deployment of LCTs within power systems is fundamental to the emission abatement of power system. Carbon Capture Power Plant (CCPP) technology has a good potential for future low carbon emission. Existing Economic Dispatch (ED) formulations do not consider the flexibly-operated CCPPs. Flexible operation of Carbon Capture and Storage (CCS) units transforms conventional power plants in such a way that emission output and power output could be separately controlled. The resulting CCPPs have to be optimized in order to take advantage of the incentives available in both power and carbon markets. This thesis proposes an improved mathematical modelling for flexible operation of CCPPs. The developed work possesses simple and practical variables to appropriately model the flexible operation control of the CCPPs. Using this proposed model a new emission-oriented ED formulation is developed. With this new formulation, the thesis also proposes the concept of decoupling the emission and economic outputs and then quantifies its significance for power system operations. In addition to that, a new Metaheuristic Optimization Technique (MOT) named as Chaos-Enhanced Cuckoo Search Optimization Algorithm (CECSOA) has been developed to improve global optimum result for ED problem. The algorithm has been tested using standard test systems with varying degrees of complexity. The results proved that the CECSOA is superior to the existing techniques in terms of ability to obtain global optimal points and the stability of the solutions obtained. Simulation results also showed the possibility of $1.09 million of annual operational cost savings based on a practical power system located in the Greek island of Crete by applying this methodology in comparison with conventional techniques such as Genetic Algorithm. Further results showed that for a carbon price of 20$/tCO2 and a 60% of system capacity utilization, total emission of a power system is reduced by 10.90% as compared to a “business-as-usual” scenario. In terms of optimal ED for CCPPs, results showed that for carbon prices as low as (~ 8 – 10 $/tCO2), it is economically viable to operate a post-combustion CCS unit

Computational Intelligence Application in Electrical Engineering

The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

Advanced Modeling and Research in Hybrid Microgrid Control and Optimization

This book presents the latest solutions in fuel cell (FC) and renewable energy implementation in mobile and stationary applications. The implementation of advanced energy management and optimization strategies are detailed for fuel cell and renewable microgrids, and for the multi-FC stack architecture of FC/electric vehicles to enhance the reliability of these systems and to reduce the costs related to energy production and maintenance. Cyber-security methods based on blockchain technology to increase the resilience of FC renewable hybrid microgrids are also presented. Therefore, this book is for all readers interested in these challenging directions of research

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

Microgrids

Microgrids are a growing segment of the energy industry, representing a paradigm shift from centralized structures toward more localized, autonomous, dynamic, and bi-directional energy networks, especially in cities and communities. The ability to isolate from the larger grid makes microgrids resilient, while their capability of forming scalable energy clusters permits the delivery of services that make the grid more sustainable and competitive. Through an optimal design and management process, microgrids could also provide efficient, low-cost, clean energy and help to improve the operation and stability of regional energy systems. This book covers these promising and dynamic areas of research and development and gathers contributions on different aspects of microgrids in an aim to impart higher degrees of sustainability and resilience to energy systems