On-Line Optimal Charging Coordination of Plug-In Electric Vehicles in Smart Grid Environment

This PhD research proposes a new objective function for optimal on-line PEV coordination. A new enhanced on-line coordinated charging using coordinated aggregated particle swarm particle optimization (OLCC-CAPSO) has been used to solve the PEV coordination objective objection and associated constraints. The objective function provides a chance for all PEVs to start charging as quickly as possible, while customer satisfaction function is being optimized subject to network criteria including voltage profiles, generator and distribution transformer ratings

Optimal energy management of a microgrid system

Mestrado de dupla diplomação com École Superieure en Sciences AppliquéesA smart management strategy for the energy ows circulating in microgrids is necessary to economically manage local production and consumption while maintaining the balance between supply and demand. Finding the optimum set-points of the various generators and the best scheduling of the microgrid generators can lead to moderate and judicious use of the powers available in the microgrid. This thesis aims to apply an energy management system based on optimization algorithms to ensure the optimal control of microgrids by taking as main purpose the minimization of the energy costs and reduction of the gas emissions rate responsible for greenhouse gases. Two approaches have been proposed to nd the optimal operating setpoints. The rst one is based on a uni-objective optimization approach in which several energy management systems are implemented for three case studies. This rst approach treats the optimization problem in a uni-objective way where the two functions price and gas emission are treated separately through optimization algorithms. In this approach the used methods are simplex method, particle swarm optimization, genetic algorithm and a hybrid method (LPPSO). The second situation is based on a multiobjective optimization approach that deals with the optimization of the two functions: cost and gas emission simultaneously, the optimization algorithm used for this purpose is Pareto-search. The resulting Pareto optimal points represent di erent scheduling scenarios of the microgrid system.Uma estrat egia de gest~ao inteligente dos uxos de energia que circulam numa microrrede e necess aria para gerir economicamente a produ c~ao e o consumo local, mantendo o equil brio entre a oferta e a procura. Encontrar a melhor programa c~ao dos geradores de microrrede pode levar a uma utiliza c~ao moderada e criteriosa das pot^encias dispon veis na microrrede. Esta tese visa desenvolver um sistema de gest~ao de energia baseado em algoritmos de otimiza c~ao para assegurar o controlo otimo das microrredes, tendo como objetivo principal a minimiza c~ao dos custos energ eticos e a redu c~ao da taxa de emiss~ao de gases respons aveis pelo com efeito de estufa. Foram propostas duas estrat egias para encontrar o escalonamento otimo para funcionamento. A primeira baseia-se numa abordagem de otimiza c~ao uni-objetivo no qual v arios sistemas de gest~ao de energia s~ao implementados para tr^es casos de estudo. Neste caso o problema de otimiza c~ao e baseado na fun c~ao pre co e na fun c~ao emiss~ao de gases. Os m etodos de otimiza c~ao utilizados foram: algoritmo simplex, algoritmos gen eticos, particle swarm optimization e m etodo h brido (LP-PSO). A segunda situa c~ao baseia-se numa abordagem de otimiza c~ao multi-objetivo que trata a otimiza c~ao das duas fun c~oes: custo e emiss~ao de gases em simult^aneo. O algoritmo de otimiza c~ao utilizado para este m foi a Procura de Pareto. Os pontos otimos de Pareto resultantes representam diferentes cen arios de programa c~ao do sistema de microrrede

A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles

Conventional unit commitment is a mixed integer optimization problem and has long been a key issue for power system operators. The complexity of this problem has increased in recent years given the emergence of new participants such as large penetration of plug-in electric vehicles. In this paper, a new model is established for simultaneously considering the day-ahead hourly based power system scheduling and a significant number of plug-in electric vehicles charging and discharging behaviours. For solving the problem, a novel hybrid mixed coding meta-heuristic algorithm is proposed, where V-shape symmetric transfer functions based binary particle swarm optimization are employed. The impact of transfer functions utilised in binary optimization on solving unit commitment and plug-in electric vehicle integration are investigated in a 10 unit power system with 50,000 plug-in electric vehicles. In addition, two unidirectional modes including grid to vehicle and vehicle to grid, as well as a bi-directional mode combining plug-in electric vehicle charging and discharging are comparatively examined. The numerical results show that the novel symmetric transfer function based optimization algorithm demonstrates competitive performance in reducing the fossil fuel cost and increasing the scheduling flexibility of plug-in electric vehicles in three intelligent scheduling modes

An optimised cuckoo-based discrete symbiotic organisms search strategy for tasks scheduling in cloud computing environment

Currently, the cloud computing paradigm is experiencing rapid growth as there is a shift from other distributed computing methods and traditional IT infrastructure towards it. Consequently, optimised task scheduling techniques have become crucial in managing the expanding cloud computing environment. In cloud computing, numerous tasks need to be scheduled on a limited number of diverse virtual machines to minimise the imbalance between the local and global search space; and optimise system utilisation. Task scheduling is a challenging problem known as NP-complete, which means that there is no exact solution, and we can only achieve near-optimal results, particularly when using large-scale tasks in the context of cloud computing. This paper proposes an optimised strategy, Cuckoo-based Discrete Symbiotic Organisms Search (C-DSOS) that incorporated with Levy-Flight for optimal task scheduling in the cloud computing environment to minimise degree of imbalance. The strategy is based on the Standard Symbiotic Organism Search (SOS), which is a nature-inspired metaheuristic optimisation algorithm designed for numerical optimisation problems. SOS simulates the symbiotic relationships observed in ecosystems, such as mutualism, commensalism, and parasitism. To evaluate the proposed technique, the CloudSim toolkit simulator was used to conduct experiments. The results demonstrated that C-DSOS outperforms the Simulated Annealing Symbiotic Organism Search (SASOS) algorithm, which is a benchmarked algorithm commonly used in task scheduling problems. C-DSOS exhibits a favourable convergence rate, especially when using larger search spaces, making it suitable for task scheduling problems in the cloud. For the analysis, a t-test was employed, reveals that C-DSOS is statistically significant compared to the benchmarked SASOS algorithm, particularly for scenarios involving a large search space.Comment: 21 pages, 5 figures, 2 algorithms, 6 table

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners