State of the Art in the Optimisation of Wind Turbine Performance Using CFD

Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained

Incremental evolution strategy for function optimization

This paper presents a novel evolutionary approach for function optimization Incremental Evolution Strategy (IES). Two strategies are proposed. One is to evolve the input variables incrementally. The whole evolution consists of several phases and one more variable is focused in each phase. The number of phases is equal to the number of variables in maximum. Each phase is composed of two stages: in the single-variable evolution (SVE) stage, evolution is taken on one independent variable in a series of cutting planes; in the multi-variable evolving (MVE) stage, the initial population is formed by integrating the populations obtained by the SVE and the MVE in the last phase. And the evolution is taken on the incremented variable set. The other strategy is a hybrid of particle swarm optimization (PSO) and evolution strategy (ES). PSO is applied to adjust the cutting planes/hyper-planes (in SVEs/MVEs) while (1+1)-ES is applied to searching optima in the cutting planes/hyper-planes. The results of experiments show that the performance of IES is generally better than that of three other evolutionary algorithms, improved normal GA, PSO and SADE_CERAF, in the sense that IES finds solutions closer to the true optima and with more optimal objective values

Lost in optimisation of water distribution systems? A literature review of system design

This is the final version of the article. Available from MDPI via the DOI in this record.Optimisation of water distribution system design is a well-established research field, which has been extremely productive since the end of the 1980s. Its primary focus is to minimise the cost of a proposed pipe network infrastructure. This paper reviews in a systematic manner articles published over the past three decades, which are relevant to the design of new water distribution systems, and the strengthening, expansion and rehabilitation of existing water distribution systems, inclusive of design timing, parameter uncertainty, water quality, and operational considerations. It identifies trends and limits in the field, and provides future research directions. Exclusively, this review paper also contains comprehensive information from over one hundred and twenty publications in a tabular form, including optimisation model formulations, solution methodologies used, and other important details

Intelligent power system operation in an uncertain environment

This dissertation presents some challenging problems in power system operations. The efficacy of a heuristic method, namely, modified discrete particle swarm optimization (MDPSO) algorithm is illustrated and compared with other methods by solving the reliability based generator maintenance scheduling (GMS) optimization problem of a practical hydrothermal power system. The concept of multiple swarms is incorporated into the MDPSO algorithm to form a robust multiple swarms-modified particle swarm optimization (MS-MDPSO) algorithm and applied to solving the GMS problem on two power systems. Heuristic methods are proposed to circumvent the problems of imposed non-smooth assumptions common with the classical approaches in solving the challenging dynamic economic dispatch problem. The multi-objective combined economic and emission dispatch (MO-CEED) optimization problem for a wind-hydrothermal power system is formulated and solved in this dissertation. This MO-CEED problem formulation becomes a challenging problem because of the presence of uncertainty in wind power. A family of distributed optimal Pareto fronts for the MO-CEED problem has been generated for different scenarios of capacity credit of wind power. A real-time (RT) network stability index is formulated for determining a power system\u27s ability to continue to provide service (electric energy) in a RT manner in case of an unforeseen catastrophic contingency. Cascading stages of fuzzy inference system is applied to combine non real-time (NRT) and RT power system assessments. NRT analysis involves eigenvalue and transient energy analysis. RT analysis involves angle, voltage and frequency stability indices. RT Network status index is implemented in real-time on a practical power system --Abstract, page iv

An Algorithmic Framework for Multiobjective Optimization

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization

A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization

In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy with a satisfactory trade-off between exploration and exploitation capabilities was added to the model predictive control. The proposed strategy was evaluated using a representative microgrid that includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage system. The achieved results demonstrate the validity of the proposed approach, outperforming a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost. In addition, the proposed approach also better manages the use of the energy storage system.Ministerio de Economía y Competitividad DPI2016-75294-C2-2-RUnión Europea (Programa Horizonte 2020) 76409

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

Stochastic angular momentum slews and flips and their effect on discs in galaxy formation models

The angular momentum of galactic discs in semi-analytic models of galaxy formation is usually updated in time as material is accreted to the disc by adopting a constant dimensionless spin parameter and little attention is paid to the effects of accretion with misaligned angular momenta. These effects are the subject of this paper, where we adopt a Monte-Carlo simulation for the changes in the direction of the angular momentum of a galaxy disc as it accretes matter based on accurate measurements from dark-matter haloes in the Millennium II simulation. In our semi-analytic model implementation, the flips seen the dark matter haloes are assumed to be the same for the cold baryons; however, we also assume that in the latter the flip also entails a difficulty for the disc to increase its angular momentum which causes the disc to become smaller relative to a no-flip case. This makes star formation to occur faster, specially in low mass galaxies at all redshifts allowing galaxies to reach higher stellar masses faster. We adopt a new condition for the triggering of starbursts during mergers. As these produce the largest flips it is natural to adopt the disc instability criterion to evaluate the triggering of bursts in mergers instead of one based on mass ratios as in the original model. The new implementation reduces the average lifetimes of discs by a factor of 2, while still allowing old ages for the present-day discs of large spiral galaxies. It also provides a faster decline of star formation in massive galaxies and a better fit to the bright end of the luminosity function at z = 0.Comment: 15 pages, 12 figures, MNRAS in pres