An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system ? a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.European CommissionMinisterio de Economía y CompetitividadComunidad de Madri

An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system ? a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.European CommissionAgencia Estatal de InvestigaciónComunidad de Madri

Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems

The transition to 100% renewable energy in the future is one of the most important ways of achieving "carbon peaking and carbon neutrality" and of reducing the adverse effects of climate change. In this process, the safe, stable and economical operation of renewable energy generation systems, represented by hydro-, wind and solar power, is particularly important, and has naturally become a key concern for researchers and engineers. Therefore, this book focuses on the fundamental and applied research on the modeling, control, monitoring and diagnosis of renewable energy generation systems, especially hydropower energy systems, and aims to provide some theoretical reference for researchers, power generation departments or government agencies

Computer aided design of 3D of renewable energy platform for Togo's smart grid power system infrastructure

The global requirement for sustainable energy provision will become increasingly important over the next fifty years as the environmental effects of fossil fuel use become apparent. Therefore, the issues surrounding integration of renewable energy supplies need to be considered carefully. The focus of this work was the development of an innovative computer aided design of a 3 Dimensional renewable energy platform for Togo’s smart grid power system infrastructure. It demonstrates its validation for industrial, commercial and domestic applications. The Wind, Hydro, and PV system forming our 3 Dimensional renewable energy power generation systems introduces a new path for hybrid systems which extends the system capacities to include, a stable and constant clean energy supply, a reduced harmonic distortion, and an improved power system efficiency. Issues requiring consideration in high percentage renewable energy systems therefore includes the reliability of the supply when intermittent sources of electricity are being used, and the subsequent necessity for storage and back-up generation The adoption of Genetic algorithms in this case was much suited in minimizing the THD as the adoption of the CHB-MLI was ideal for connecting renewable energy sources with an AC grid. Cascaded inverters have also been proposed for use as the main traction drive in electric vehicles, where several batteries or ultra-capacitors are well suited to serve as separate DC sources. The simulation done in various non-linear load conditions showed the proportionality of an integral control based compensating cascaded passive filter thereby balancing the system even in non-linear load conditions. The measured total harmonic distortion of the source currents was found to be 2.36% thereby in compliance with IEEE 519-1992 and IEC 61000-3 standards for harmonics This work has succeeded in developing a more complete tool for analysing the feasibility of integrated renewable energy systems. This will allow informed decisions to be made about the technical feasibility of supply mix and control strategies, plant type, sizing and storage sizing, for any given area and range of supply options. The developed 3D renewable energy platform was examined and evaluated using CAD software analysis and a laboratory base mini test. The initial results showed improvements compared to other hybrid systems and their existing control systems. There was a notable improvement in the dynamic load demand and response, stability of the system with a reduced harmonic distortion. The derivatives of this research therefore proposes an innovative solution and a path for Togo and its intention of switching to renewable energy especially for its smart grid power system infrastructure. It demonstrates its validation for industrial, commercial and domestic applicationsN/

Strategic planning of electricity systems: Integrating renewable energies

Tese de Doutoramento - Programa Doutoral em Engenharia Industrial e Sistemas (PDEIS)The decision making process applied to the energy sector, particularly to the electricity sector, is complex and frequently requires the use of optimization models to deal with problems in the scope of electricity planning. The continuous growth of renewable energy sources (RES) to generate electricity became sustainable over the last years. This growth is justified by the increasing concerns related to the security of supply, the reduction of external energy dependency of most European countries, including Portugal, and the reduction of greenhouse gases emissions. Despite of the RES benefits, their integration is characterized by the difficulties on forecasting and variable electricity output. These difficulties bring considerable challenges to the electricity system management and to its planning. This work is focused on the assessment of the RES impacts on the electricity system and on its integration in the long and short-term electricity planning. The main goals of this work are to analyse in which way the growth of RES can affect the electricity system and its power plants, and also to propose new optimization models for the strategic planning of the electricity system, which are able to recognize and include RES impacts. This will provide the decision maker with tools that will support the design of long-term scenarios for the electricity sector. According to the outlined goals, four different optimization models were developed. All models were tested for a mixed hydro-thermal-wind power system, with characteristics close to the Portuguese one. The first one was proposed for the long-term strategic electricity power planning and a 10 years planning period was considered. Its usefulness was demonstrated by applying it for the analysis of the wind power integration in the electricity system. The second one, with a short-term horizon, aimed to solve the problem of the commissioning of power plants based on the available resources. The implementation of this model showed that modelling the electricity power systems requires a large set of constraints and a large number of data and information, resulting in significant computational effort to obtain a optimal solution. The development of a third model, a simplified approach of the short-term model, became therefore necessary. As previously, both short-term model and its simplified approach were used for the analysis of the impacts of wind power in the electricity system and in the operation of the different power plants. The last model resulted from the combination of the strategic electricity power planning model with the simplified model proposed for the commissioning of the power plants. The goal of this fourth model is to allow the inclusion of RES impacts in the design of scenarios for the electricity system, for a 10 years planning period. The models application demonstrated the need to acknowledge and include the impacts of RES integration, particularly wind power, on the strategic electricity expansion planning. Throughout the work, the complexity of models was evidenced along with the difficulties that non-experienced users may face when applying them. A user-friendly platform enabling researchers and stakeholders to deal with electricity planning problems in a simpler but reliable way was then proposed, resulting in an important contribution for the effective dissemination and usage of these models.A tomada de decisão no sector da energia, e em particular no sector da eletricidade, é uma atividade complexa, sendo frequentemente suportada em modelos de otimização, para apoio à resolução de problemas relativos ao planeamento elétrico. O crescimento da utilização das fontes de energia renováveis para a produção de eletricidade tem sido consistente nos últimos anos, sendo este crescimento justificado pelas preocupações relativas à segurança do abastecimento, passando por estratégias de diversificação de tecnologias e fornecedores, pela necessidade de redução da dependência energética externa de diversos paises Europeus, onde se inclui o caso português, e pelos objetivos de redução dos gases com efeito de estufa. Apesar dos seus benefícios, a integração das energias renováveis está frequentemente associada à dificuldade de previsão da produção de eletricidade e à produção variável, trazendo assim desafios consideráveis à gestão do sistema elétrico e ao seu planeamento. Este trabalho centra-se na avaliação dos impactos das energias renováveis nos sistemas elétricos e na sua inclusão no planeamento elétrico de curto e longo prazo. Tem assim como objetivos principais analisar de que modo o crescimento das energias renováveis poderá afetar o sistema elétrico e as diferentes unidades produtoras, bem como propor novos modelos de otimização para planeamento estratégico para o setor elétrico que permitam reconhecer e incluir estes impactos, dotando assim o decisor de ferramentas que o possam apoiar da definição de cenários estratégicos de longo prazo. De acordo com os objetivos traçados, são apresentados quatro diferentes modelos de otimização. Todos estes modelos foram testados para um sistema elétrico com caracteristicas próximas do caso português, detacando-se as componentes hídrica, térmica e eólica. O primeiro modelo visa o planeamento estratégico a longo prazo resultando na apresentação e caracterização de cenários para o setor elétrico para um período de 10 anos, tendo sido demonstrada a sua aplicação para a análise da integração da energia eólica no sistema. O segundo modelo utiliza um horizonte temporal de curto prazo, tendo como objetivo apoiar a resolução do problema de comissionamento das unidades de geração eléctrica com base nos recursos disponíveis. A sua implementação demonstrou que a modelação dos sistemas de geração de energia eléctrica pressupõe um conjunto de restrições e um elevado número de dados e informações que resultam num esforço computacional significativo. Desta forma, surgiu a necessidade de desenvolver um terceiro modelo que consiste numa versão simplificada deste modelo de curto-prazo. Ambos os modelos de curto prazo, foram também utilizados para a análise dos impactos da energia eólica no funcionamento das diferentes unidades de produção de eletricidade. O último modelo desenvolvido resulta da combinação do modelo estratégico de expansão do sistema elétrico com o modelo aplicado ao problema do comissionamento das unidades de geração eléctrica, tendo como objetivo ter em consideração os impactos das energias renováveis na definição de cenários para o setor elétrico para um período de 10 anos. Da aplicação dos modelos fica demonstrada a importância de reconhecer e incluir no planeamento elétrico estratégico de longo prazo os impactos resultantes da integração de fontes renováveis de energia de produção variável, e em particular da energia eólica, nos sistemas elétricos. Fica ainda evidente, a complexidade dos modelos e a dificuldade de aplicação por utilizadores menos experientes. Resulta por isso como uma importante contribuição, o desenvolvimento de uma aplicação gráfica com interface amigável que deverá permitir a ampla disseminação dos modelos desenvolvidos e sua adaptação a diferentes sistemas elétricos

K-Means and Alternative Clustering Methods in Modern Power Systems

As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies

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

Advances in Optimization and Nonlinear Analysis

The present book focuses on that part of calculus of variations, optimization, nonlinear analysis and related applications which combines tools and methods from partial differential equations with geometrical techniques. More precisely, this work is devoted to nonlinear problems coming from different areas, with particular reference to those introducing new techniques capable of solving a wide range of problems. The book is a valuable guide for researchers, engineers and students in the field of mathematics, operations research, optimal control science, artificial intelligence, management science and economics

Multi Agent Systems

Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems

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