Differential Evolution With a New Encoding Mechanism for Optimizing Wind Farm Layout

This paper presents a differential evolution algorithm with a new encoding mechanism for efficiently solving the optimal layout of the wind farm, with the aim of maximizing the power output. In the modeling of the wind farm, the wake effects among different wind turbines are considered and the Weibull distribution is employed to estimate the wind speed distribution. In the process of evolution, a new encoding mechanism for the locations of wind turbines is designed based on the characteristics of the wind farm layout. This encoding mechanism is the first attempt to treat the location of each wind turbine as an individual. As a result, the whole population represents a layout. Compared with the traditional encoding, the advantages of this encoding mechanism are twofold: 1) the dimension of the search space is reduced to two, and 2) a crucial parameter (i.e., the population size) is eliminated. In addition, differential evolution serves as the search engine and the caching technique is adopted to enhance the computational efficiency. The comparative analysis between the proposed method and seven other state-of-the-art methods is conducted based on two wind scenarios. The experimental results indicate that the proposed method is able to obtain the best overall performance, in terms of the power output and execution time

Effective Realization of Multi-Objective Elitist Teaching–Learning Based Optimization Technique for the Micro-Siting of Wind Turbines

In this paper, the meta-heuristic multi-objective elitist teaching–learning based optimization technique is implemented for wind farm layout discrete optimization problem. The optimization of wind farm layout addresses the optimum siting among the wind turbines within the wind farm to accomplish economical, profitable, and technical features. The presented methodology is implemented with multi-objective optimization problem through different targets such as minimizing cost, power output maximization, and the saving of the number of turbines. These targets are investigated with some case studies of multi-objective optimization problems in three scenarios of wind (Scenario-I: fixed wind direction and constant speed, Scenario-II: variable wind direction and constant speed, and Scenario-III: variable wind direction and variable speed) for the optimal micro-siting of wind turbines in a given land area that maximizes the power production while minimizing the total cost. To check the effectiveness of the algorithm, firstly, the results obtained for the three different scenarios have been compared with past studies available in the literature. Secondly, the numbers of turbines have also been optimized by using teaching–learning based optimization. It has been observed that the proposed algorithm shows the optimal layouts along with the optimal number of turbines with minimum fitness evaluation. Finally, the concept of elitism has been introduced in the teaching–learning based optimization algorithm. It is proposed that if elitist-teaching–learning based optimization with elite size of 15% is used, computational expense can be significantly reduced. It can be concluded that that the results obtained by the proposed algorithm are more accurate and advantageous than others

Environmental Impacts of Renewable Energy (solar and wind) on Water, Food and Energy Nexus

Rapid increasing of renewable energies and the knowledge about their environmental effects are very limited. As a result, the renewable energies (e.g. solar or wind energies) will play a vital role in the future because it is well accepted by environmental friendly industries. This dissertation presents the modeling, data analysis and field experiment, developed for investigation of the interactions among microclimatological factors, land characteristics and solar/wind renewable energy production systems. The research covers multi scales from high resolution farm scales (six acres’ area), mid-size large wind farms and global scales. The main idea of this research is to study the environmental impacts of renewable energies which affect the water resources and therefore the water, food and energy nexus. This research studies how renewable energy can change the water use efficiency, biomass production, energy efficiency and ultimately relates it to sustainable development. Selecting the best location, crop and climate for renewable energy is an important key component in obtaining a sustainable development. The first part of the dissertation includes an experimental observation study on the effects of solar panel on the adjacent microclimate and vegetation. The field study setup included installations of local weather stations and soil moisture neutron probes to monitor the microclimatological and moisture variations. The monitoring was performed both between solar arrays and outside the area (control area). The data showed that (1) the soil moisture under panels are significantly higher than the control area, (2) dry biomass of grass is higher under panels and (3) the area under panels were significantly more water efficient. The investigations on the grass species under agri-voltaic panels reveals a significant increase in late season biomass (90% more biomass) and areas under PV panels were significantly more water efficient. This is accomplished by harvesting solar excess and converting it to electricity. Secondly, an algorithm developed using the first law of thermodynamics and solar panel efficiency solved for the energy balance equation. Solar panel efficiency found as a function of microclimatological factors include radiation, temperature, relative humidity and wind speed. The validated algorithm was then applied to the global scales. The computations of efficiencies shows the most efficient geographical locations for solar panel installations based on micro-environmental factors, but also a more generalized methodology to relate potential efficiency to land cover. The third part of the thesis assess the crop yield and water use efficiency of major crops grown in Oregon, considering three shade levels 90%, 75% and 50%. AquaCrop model was used to evaluate the potential water use efficiency in Oregon. Our results show there is no difference in yield when shade is applied but the amount of water needed for irrigation is reduced. The biomass results showed no gain or loss occurs in different shade levels but there is a difference in the amount of irrigated water. The forth part of the dissertation relates to the interactions of the wind turbines with farm lands. A numerical framework was developed to process the wind farm LANDSAT snap shots before and after the wind turbine installations. The numerical scheme was developed using Mapping Evapotranspiration at high Resolution with Internal Calibration (METRIC) and can calculate and analyze evapotranspiration on the agricultural field and analyze the resulted pixel-based data. From the data analyses on Fowler wind farm (located in Indiana, US) approximately 10% more evapotranspiration was seen in agricultural fields that are co-located near wind turbines (i.e. footprints) compared to places that have no wind turbine

Mixed Integer Programming Models and Algorithms for Wind Farm Layout

The aim of the thesis is the optimization of wind farm layout: given a specific wind farm site and wind data for the site, an optimal location of turbines is determined such that the power production is maximized and wake effects and other constraints are taken into account. Several Mixed Integer Linear Programming (MILP) models and ad-hoc heuristics have been proposed, and a new approach for very large-scale instances has been developed. Tests on real data show the effectiveness of our methodope

Optimization of Wind Farm Layout taking Load Constraints into Account

Master's thesis Renewable Energy ENE500 - University of Agder 2017Optimization of a wind farm layout is of utmost importance due its economical aspect. The primary aim of optimizing layout is to increase the overall energy production. The higher energy production creates more revenue from wind farm during its operational life time. Wind turbines situated within wind farms are subjected to wake losses due to numbers of factors one of such factor is wind disturbance from the wind turbines installed in front. Therefore, the wind turbines will produce less output as compared to front wind turbines facing winds in free stream. Thus, to have an economically feasible performance, it is necessary to optimize wind farm layout in terms of both maximum energy and load constraints for life time of wind turbines. The turbines in the large wind farm causing increased turbulence that increases the fatigue damage levels, and the increased loads must be analysed. The thesis is devoted to the optimization of wind farm layout to maximize the energy production, and verifying the significance of wake loss effects with respect to optimal placement of wind turbines within wind farm. Thesis is divided into two followings parts: In the first part, in the WFDs approach, the WindSim software for CFD simulations is used to calculate flow fields at various heights over the planned layout to set number of turbines as per IEC 61400-1 standard. Then, the resulting layout from WindSim is fed into the Wind Assessment Tool (WAT) to check if the chosen position of turbines verifies the IEC compliance criteria for effective turbulence. Next, the Park layout is used as in Park Optimizer tool to verify the project constraints, such as exclusion of areas where it is not possible to set up turbines, layout is optimized by calculating the energy production, etc. The Park optimization is based on the following factors: i) minimum distance between turbines, ii) to check the effective turbulence if it’s not violating IEC criteria, and iii) minimizing wake deficits. In the benchmarking of software tools, Wind Farm Designs (WFDs) optimization approach is used to maximize the annual energy production (AEP) by optimizing the turbine positions and comparing it with OpenWind (OW) software tool. OpenWind tool is used significantly for the layout optimization. The difference between both WFDs and Openwind optimization results compared based on gross and net annual energy production, and array efficiency from the park layout. Based on the results, it was found that the WFDs estimated lower net energy and array ii | P a g e efficiency as compared to OpenWind optimizer for the entire wind farm layout, differs same for both -1 %. However, the gross energy is estimated almost similar by both the tools, but WFDs optimizer estimated slightly lower. In the second part of thesis, an analytical approach is used to check the sensitivity of wake losses at distances that are IEC compliant for simple cases between two turbines. Jensen wake model is used for the wake loss analysis due its high degree of accuracy. Frandsen model is used to satisfy effective turbulence criteria. The energy production of downwind turbines decreases from 2 to 20% due to the lower wind speeds as they are located behind upwind turbines, resulting in decreasing the wind farm overall energy production. Higher wake loss also increases the effective turbulence that leads to reduction in overall energy production within wind farm

再生可能エネルギーシステムにおけるエンジニアリング最適化のためのメタヒューリスティックアルゴリズムの設計と適用

富山大学・富理工博甲第217号・楊海川・2023/3/23富山大学202

A novel MOGNDO algorithm for security-constrained optimal power flow problems

The current research investigates a new and unique Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) algorithm for solving large-scale Optimal Power Flow (OPF) problems of complex power systems, including renewable energy sources and Flexible AC Transmission Systems (FACTS). A recently reported single-objective generalized normal distribution optimization algorithm is transformed into the MOGNDO algorithm using the nondominated sorting and crowding distancing mechanisms. The OPF problem gets even more challenging when sources of renewable energy are integrated into the grid system, which are unreliable and fluctuating. FACTS devices are also being used more frequently in contemporary power networks to assist in reducing network demand and congestion. In this study, a stochastic wind power source was used with different FACTS devices, including a static VAR compensator, a thyristor- driven series compensator, and a thyristor-driven phase shifter, together with an IEEE-30 bus system. Positions and ratings of the FACTS devices can be intended to reduce the system's overall fuel cost. Weibull probability density curves were used to highlight the stochastic character of the wind energy source. The best compromise solutions were obtained using a fuzzy decision-making approach. The results obtained on a modified IEEE-30 bus system were compared with other well-known optimization algorithms, and the obtained results proved that MOGNDO has improved convergence, diversity, and spread behavior across PFs.Web of Science1122art. no. 382

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

Modeling, Simulation and Optimization of Wind Farms and Hybrid Systems

The reduction of greenhouse gas emissions is a major governmental goal worldwide. The main target, hopefully by 2050, is to move away from fossil fuels in the electricity sector and then switch to clean power to fuel transportation, buildings and industry. This book discusses important issues in the expanding field of wind farm modeling and simulation as well as the optimization of hybrid and micro-grid systems. Section I deals with modeling and simulation of wind farms for efficient, reliable and cost-effective optimal solutions. Section II tackles the optimization of hybrid wind/PV and renewable energy-based smart micro-grid systems