82 research outputs found

    Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm

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    AbstractPower generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm (POA) in combination with the genetic algorithm (GA). After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching

    Technological Innovations and Advances in Hydropower Engineering

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    It has been more than 140 years since water was used to generate electricity. Especially since the 1970s, with the advancement of science and technology, new technologies, new processes, and new materials have been widely used in hydropower construction. Engineering equipment and technology, as well as cascade development, have become increasingly mature, making possible the construction of many high dams and large reservoirs in the world. However, with the passage of time, hydropower infrastructure such as reservoirs, dams, and power stations built in large numbers in the past are aging. This, coupled with singular use of hydropower, limits the development of hydropower in the future. This book reports the achievements in hydropower construction and the efforts of sustainable hydropower development made by various countries around the globe. These existing innovative studies and applications stimulate new ideas for the renewal of hydropower infrastructure and the further improvement of hydropower development and utilization efficiency

    A comprehensive survey on cultural algorithms

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    Peer reviewedPostprin

    Optimal energy control of a grid connected solar-wind based electric power plant.

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    Doctor of Philosophy in Electrical Engineering. University of KwaZulu-Natal, Howard College 2016.In the present context of urge energy demand, renewable energy is considered as an alternative source of clean energy. In view of the increase in the price of fossil fuel due to its rarity and emissions, more integration of renewable sources is needed for better economic management of the grid. This research work has been done in two parts. The first part deals with the daily energy consumption variations for the low demand season and high demand season on weekdays and weekends. The intention is to correlate the corresponding fuel cost and estimate the operational efficiency of the hybrid system, which comprises the PV, PW, DG, battery system, for a period of 24 hours taken as control horizon. The latest published research literature has shown that a good deal of work has been done using a fixed load and uniform daily operational cost. The economic dispatch strategy, fuel cost, energy flows and energy sales are analysed in this study. The results show that a renewable energy system, which combines the PV/PW/diesel/battery models, achieves more fuel saving during both the high demand and low demand seasons than a model where the diesel generator satisfies the load on its own. The fuel cost during the low demand and high demand seasons for weekdays and weekends shows considerable fluctuations, which should not be neglected if accurate operational costs are to be obtained. The model shows the achievement of a more practical estimate of fuel costs, which reflects the fluctuation of power consumption behaviour for any given model. In the last part of the thesis model predictive control (MPC) is introduced in the management and control of power flow. The highlight in this thesis is the management of the energy flow from the hydro pump, wind, photovoltaic system and turbine when the system is subject to severe disturbances. The results demonstrated in the thesis prove the advantages of the approach and its robustness against uncertainties and external disturbances. When analysed with the open loop control system, MPC is more robust because of its stability of the system when external disturbances occur in the system. This thesis presents a practical solution to energy sale, control, optimization and management

    Electric Power Conversion and Micro-Grids

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    This edited volume is a collection of reviewed and relevant research chapters offering a comprehensive overview of recent achievements in the field of micro-grids and electric power conversion. The book comprises single chapters authored by various researchers and is edited by a group of experts in such research areas. All chapters are complete in themselves but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on electric power conversion, micro-grids, and their up-to-the-minute technological advances and opens new possible research paths for further novel developments

    Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

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    The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

    Applied Metaheuristic Computing

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    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

    Flood Forecasting Using Machine Learning Methods

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    This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

    Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

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    The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

    Applied Methuerstic computing

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    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
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