Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems

Multi-objective swarm intelligence-based (MOSI-based) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) with conflicting objectives. Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. The reference point is determined by the decision maker to guide the search process to a particular region in the true Pareto front. However, HHMO algorithm produces a poor approximation to the Pareto front because lack of information sharing in its population update strategy, equal division of convergence parameter and randomly generated initial population. A two-step enhanced non-dominated sorting HHMO (2SENDSHHMO) algorithm has been proposed to solve this problem. The algorithm includes (i) a population update strategy which improves the movement of hawks in the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. The population update strategy calculates a new position of hawks based on the flush-and-ambush technique of Harris’s hawks, and selects the best hawks based on the non-dominated sorting approach. The adjustment strategy enables the parameter to adaptively changed based on the state of the search space. The initial population is produced by generating quasi-random numbers using Rsequence followed by adapting the partial opposition-based learning concept to improve the diversity of the worst half in the population of hawks. The performance of the 2S-ENDSHHMO has been evaluated using 12 MOPs and three engineering MOPs. The obtained results were compared with the results of eight state-of-the-art multi-objective optimization algorithms. The 2S-ENDSHHMO algorithm was able to generate non-dominated solutions with greater convergence and diversity in solving most MOPs and showed a great ability in jumping out of local optima. This indicates the capability of the algorithm in exploring the search space. The 2S-ENDSHHMO algorithm can be used to improve the search process of other MOSI-based algorithms and can be applied to solve MOPs in applications such as structural design and signal processing

Estimação de parâmetros para regulador de velocidade de usina hidrelétrica utilizando algoritmos de otimização evolução diferencial e otimização de lobos cinzentos

Orientador: Prof. Dr. Gideon Villar LeandroCoorientador: Prof. Dr. Marlio José do Couto BonfimDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 29/05/2018Inclui referências: p.72-75Área de concentração: Sistemas EletrônicosResumo: O modelo matem'atico 'e, sem d'uvida, o modelo mais vers'atil e barato que um cientista pode usar, uma vez que diferentes varia¸c˜oes nas caracter'?sticas e entradas de um sistema podem ser testadas, analisadas e aprimoradas sem a necessidade de implementa¸c˜ao f'?sica. A teoria da identifica¸c˜ao de sistemas 'e uma t'ecnica desenvolvida com objetivo de obter bons modelos matem'aticos a partir de dados experimentais. A estima¸c˜ao de parˆametros 'e considerada uma etapa fundamental na identifica¸c˜ao de sistemas, 'e um procedimento no qual o objetivo 'e minimizar uma fun¸c˜ao de erro (diferen¸ca entre uma vari'avel temporal do sistema e do modelo). Esta minimiza¸c˜ao est'a diretamente ligada aos valores dos parˆametros encontrados pelo m'etodo utilizado. Neste trabalho s˜ao utilizadas duas metaheur'?sticas, o algoritmo de otimiza¸c˜ao dos Lobos Cinzentos (GWO), e o algoritmo de otimiza¸c˜ao da Evolu¸c˜ao Diferencial (ED) para a estima¸c˜ao de parˆametros de sistemas n˜ao lineares. Estes dois algoritmos s˜ao implementados de forma mono-objetivo e multiobjetivo. Na forma mono-objetivo, os parˆametros s˜ao estimados considerando a entrada e a sa'?da do sistema, necessitando minimizar apenas uma fun¸c˜ao objetivo. J'a na forma multi-objetivo, os parˆametros s˜ao estimados considerando apenas uma entrada e uma sa'?da, por'em h'a necessidade de minimizar v'arias fun¸c˜oes objetivo. Estas metaheur'?sticas s˜ao inicialmente utilizadas em trˆes fun¸c˜oes testes para sua valida¸c˜ao, e obt'em os resultados reportados pela literatura. Depois elas s˜ao utilizadas no regulador de velocidade de uma usina hidrel'etrica, que faz parte do Sistema Interligado Nacional (SIN), sendo inicialmente estimados os parˆametros de cada malha de forma mono-objetivo, depois estima-se todos os parˆametros utilizando a forma multi-objetivo. Os resultados obtidos mostram que ambos algoritmos e ambas as formas obt'em solu¸c˜oes que levam os modelos a terem suas respostas similares aos dados obtidos em campo. Palavras-chave: Estima¸c˜ao de parˆametros, Otimizador de Lobos Cinzentos, Evolu¸c˜ao Diferencial, Usina Hidrel'etrica.Abstract: The mathematical model is undoubtedly the most versatile and inexpensive model that a scientist can use, since different variations on the characteristics and inputs of a system can be tested, analyzed and improved without the need for physical implementation. The theory of system identification is a technique developed to obtain good mathematical models from experimental data. The estimation of parameters is considered a fundamental step in the identification of systems, it is a procedure in which the objective is to minimize an error function (difference between a system and model of time variable). This minimization is directly linked to the values of the parameters found by the method used. In this work, two metaheuristics, the Grey Wolf Optimization Algorithm (GWO), and the Differential Evolution algorithm (DE) are used for the estimation of nonlinear system parameters. These two algorithms are implemented in a mono-objective and multi-objective. In the mono-objective, the parameters are estimated considering the input and output of the system, needing to minimize only one objective function. In multi-objective, the parameters are estimated considering only one input and one output, but there is a need to minimize several objective functions. These metaheuristics are initially used in three test functions for validation, and obtain the results reported in the literature. Then are used in the speed regulator of a hydroelectric plant, which belongs of the National Interconnected System, initially estimated the parameters of each mesh in a mono-objective algorithm, then it is estimated all of the parameters using the multi-objective algorithm. The obtained results show that both algorithms in multi-objective and mono-objective, can get solutions that take the models to have their answers similar to the data obtained in the field. Keywords: Parameter estimation, Grey Wolf Optimizer, Differential Evolution, Hydroelectric Power Plant

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