Optimal Placement Of Wind Turbines Research Articles

Multi-objective Mathematical Model for Optimal Wind Turbine Placement in Wind Farm under Uncertainty

The main objective of this research is to introduce three energy risk management models grounded in optimization techniques for the strategic placement of wind turbines, considering wake effects and uncertainties in wind speed and direction. For this purpose, wind speed and direction data are gathered, and Monte Carlo simulation is employed to model the uncertainties. Subsequently, the risk management models undergo optimization using Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Pareto envelope-based selection algorithm II (PESA-II), and Multi-Objective Particle Swarm Optimization (MOPSO) algorithms. Findings reveal that the wind farm's maximum power output reaches approximately 5.8 megawatts across all three algorithms and optimal turbine placements. A risk assessment was conducted using a tenth percentile criterion, revealing a significant production risk within the study area, with production falling below 1.8 megawatts in 90% of cases. Regarding the performance evaluation of the algorithms across all three models, superior performance in terms of solution proximity to the ideal solution is exhibited by PESA-II, while enhanced diversity and solution spread compared to the other algorithms are demonstrated by NSGA-II.

Optimal Design and Operation of Wind Turbines in Radial Distribution Power Grids for Power Loss Minimization

This research proposes a strategy to minimize the active power loss in the standard IEEE 85-node radial distribution power grid by optimizing the placement of wind turbines in the grid. The osprey optimization algorithm (OOA) and walrus optimization algorithm (WOA) are implemented to solve the problem. The two algorithms are validated in three study cases of placing two wind turbines (WTs) in the system for power loss reduction. Mainly, in Case 1, WTs can only produce active power, while in Case 2 and Case 3, WTs can supply both active and reactive power to the grid with different ranges of power factors. In Case 4, the best-applied methods between the two are reapplied to reach the minimum value of the total energy loss within one year. Notably, this case focuses on minimizing the total power loss for each hour in a day under load demand variations and dynamic power supply from WTs. On top of that, this case uses two different sets of actual wind power data acquired from the Global Wind Atlas for the two positions inherited from the previous case. Moreover, the utilization of wind power is also evaluated in the two scenarios: (1) wind power from WTs is fully used for all values of load demand, (2) and wind power from WTs is optimized for each load demand value. The results in the first three cases indicate that the WOA achieves better minimum, mean, and maximum power losses for the two cases than the OOA over fifty trial runs. Moreover, the WOA obtains an excellent loss reduction compared to the Base case without WTs. The loss of the base system is 224.3 kW, but that of Case 1, Case 2, and Case 3 is 115.6, 30.6 kW, and 0.097 kW. The placement of wind turbines in Case 1, Case 2, and Case 3 reached a loss reduction of 48.5%, 84.3%, and 99.96% compared to the Base case. The optimal placement of WTs in the selected distribution power grid has shown huge advantages in reducing active power loss, especially in Case 3. For the last study case, the energy loss in a year is calculated by WSO after reaching hourly power loss, the energy loss in a month, and the season. The results in this case also indicate that the optimization of wind power, as mentioned in Scenario 2, results in a better total energy loss value in a year than in Scenario 1. The total energy loss in Scenario 2 is reduced by approximately 95.98% compared to Scenario 1. So, WOA is an effective algorithm for optimizing the placement and determining the power output of wind turbines in distribution power grids to minimize the total energy loss in years.

Wind farm layout optimization through optimal wind turbine placement using a hybrid particle swarm optimization and genetic algorithm.

The placement and configuration of wind turbines (WTs) are the key factors in determining the performance and energy output of a wind farm (WF). This involves considering various elements such as wind speed, wind direction, and the interspacing between turbines in the design process. To achieve an optimized and consistent wind farm layout optimization (WFLO) for maximum output power, a novel hybrid algorithm hybrid particle swarm optimization and genetic algorithm (HPSOGA), combining particle swarm optimization (PSO) and genetic algorithm (GA), is proposed. HPSOGA can effectively handle problems with multiple local optima, as PSO explores multiple regions and GA refines solutions found by PSO. The framework has two phases, where PSO improves initial parameters in the first phase, and parameters are adjusted in the second phase for improved fitness. The wake effect is analyzed using the Jenson-Wake model, and the objective function considers the total cost of WTs and the power output of the WF. The interspacing of WTs is evaluated by the rule of thumb. HPSOGA outperforms other methods such as GA, BPSO-TVAC, L-SHADE, BRCGA, and EO-PS, producing better results in terms of total output power generation. The simulation results validate the reliability of HPSOGA in WFLO.

A Review of Wind Farm Layout Optimization Techniques for Optimal Placement of Wind Turbines

A Review of Wind Farm Layout Optimization Techniques for Optimal Placement of Wind Turbines

A review of 10 × 10 and 20 × 20 grid-type wind turbine placement problems solving by metaheuristics.

Wind energy is the most important renewable energy source produced by wind turbines. The optimal placement of wind turbines is a challenging binary optimization problem. Maximizing production capacity and minimizing the number of turbines (accordingly minimizing the installation cost) are the main objectives. Metaheuristic algorithms can solve binary optimization problems with optimal or near-optimal solutions. In the literature, the wind turbine placement problem (WTPP) is solved by metaheuristic algorithms from 1994 to 2020. In this work, a literature review of solving 10 × 10 and 20 × 20 grid-type WTPPs with metaheuristic algorithms is conducted. Forty-six different papers were deeply discussed and presented all the experimental results to shed light on future studies for presenting more robust metaheuristics for solving WTPPs. The key results of this review are precaution against false comparisons; presenting the current experimental results; determining the comparison parameters; and demonstrating the benchmark problem clearly. Future directions were clearly spotlighted for practitioners and researchers and new research topics for potential studies are also presented.

Micro-Siting of Wind Turbines in an Optimal Wind Farm Area Using Teaching–Learning-Based Optimization Technique

Nowadays, wind energy is receiving considerable attention due to its availability, low cost, and environment-friendly operation. Wind turbines are rarely placed individually but rather in the form of a wind farm with a group of several wind turbines. The purpose of this research is to perform studies on wind turbine farms in order to find the best distribution for wind turbines that maximizes the produced power, hence minimizing the wind farm area. Wind Farm Area Optimization (WFAO) is performed for optimal placement of wind turbines using elitist teaching–learning-based optimization (ETLBO) techniques. Three different scenarios of wind (first is fixed wind direction and constant speed, second is variable wind direction and constant speed, and third is variable wind direction and variable speed) are considered to find the optimal number of turbines and turbine positioning in a minimized squared land area that maximizes the power production while minimizing the total cost. Other research carried out in the past was to find the optimal placement of the wind turbines in a fixed squared land area of 2 km×2 km. In the present study, WFAO–ETLBO algorithm has been implemented to get the optimal land area for the placement of the same number of turbines used in the past research. For Case 1, there is a significant reduction in land area by approximately 30.75%, 45.25%, and 51.75% for each wind scenario, respectively. For Case 2, the reductions in land area for three different wind scenarios are respectively 30.75%, 7.2%, and 7.2%. For Case 3, there is a reduction of 7.2% in land area for each wind scenario. It has been observed that the results obtained by the WFAO–ETLBO algorithm with a significant reduction in the land area along with optimal placement of wind turbines are better than the results obtained from the wind turbines placement in the fixed land area of 2 km×2 km.

Improved Design of a Large Offshore Wind Farm by Using Biogeography based Optimization

This paper investigates optimal placement of wind turbines (WTs) within a large offshore wind farm (LOFWF). 88 wind farm (WF) configurations are invested to search the optimal layout for the Horns rev 1 offshore WF using twenty years of wind data knowing that wind characteristics are modeled from long term reanalysis data based on MERRA-2. The regular and the irregular placement of the Horns Rev 1 (HR1) offshore WF are investigated with Jensen wake model. Therefore, the objective is to assess the effect of wind turbine spacing (WTS) on the power output loss in a LOFWF and, also to find the best configuration that gives the maximum power with minimum investment cost. The use of the Biogeography based optimization (BBO), as a bio-inspired evolutionary approach, represents the advantage of being effective on strongly non-convex spaces such as a large offshore WF. Due to the iterative process applied to the initial population, and the multiplicity of the population, the BBO process limit the risk of getting stuck in a local optimum, by distributing the individuals in the whole solution space. The results obtained show that the wind data extracted from MERRA-2 can be applied reliably to any existing WF to simulate wind power production. The results also demonstrate that significant power losses occur when the turbines are arranged in a condensed manner and the significant power gains are not obtained from too large configurations, but rather from the best placement in configurations. The proposed approach shows promise in terms of applicability with MERRA-2 and is effectively suitable for arranging turbines and assessing wind resources in an offshore wind farm project (OFWFP). https://dorl.net/dor/20.1001.1.13090127.2021.11.2.27.6

Optimal Placement of Wind Turbines in Wind Farm Layout Using Particle Swarm Optimization

An optimal geographical location of wind turbines can ensure the optimum total energy output of a wind farm. This study introduces a new solution to the optimization of wind farm layout (WFLO) problem based on a three-step strategy and particle swarm optimization as the main method. The proposed strategy is applied to a certain WFLO to generate highly efficient optimal output power. Three case scenarios are considered to formulate the non-wake and wake effects at various levels. The required wind turbine positions within the wind farm are determined by the particle swarm optimization method. The rule of thumb, which determines the wind turbine spacing, is thoroughly considered. The MATLAB simulation results verify the proposed three-step strategy. Moreover, the results are compared with those of existing research works, and it shows that the proposed optimization strategy yields a better solution in terms of total output power generation and efficiency with a minimized objective function. The efficiencies of the three case studies considered herein increase by 0.65%, 1.95%, and 1.74%, respectively. Finally, the simulation results indicate that the proposed method is robust in WFLO design because it further minimizes the objective function.

The Application of Water Cycle Optimization Algorithm for Optimal Placement of Wind Turbines in Wind Farms

Wind farms (WFs) include an enormous number of wind turbines (WTs) in order to achieve high capacity. The interaction among WTs reduces the extracted amount of wind energy because wind speed decreases in the wake region. The optimal placement of WTs within a WF is therefore vital for achieving high performance. This permits as many WTs as possible to be installed inside a narrow region. In this work, the water cycle algorithm (WCA), a recently developed optimizer, was employed to identify the optimal distribution of WTs. Minimization of the total cost per kilowatt was the objective of the optimization process. Two different cases were considered: the first assumed constant wind speed with variable wind direction, while the second applied variable wind speed with variable wind direction. The results obtained through the WCA optimizer were compared with other algorithms, namely, salp swarm algorithm (SSA), satin bowerbird optimization (SBO), grey wolf optimizer (GWO), and differential evolution (DE), as well as other reported works. WCA gave the best solution compared to other reported and programmed algorithms, thus confirming the reliability and validity of WCA in optimally configuring turbines in a wind farm for both the studied cases.

Optimal Micro-Siting of Wind Turbines in an Offshore Wind Farm Using Frandsen–Gaussian Wake Model

This paper proposes a method to obtain the optimal placement of wind turbines (WTs) in an offshore wind farm (WF). The optimization objective is to minimize the levelized average cost per net electric power generated by a WF with a fixed number of WTs while the distance between WTs is not less than the allowed minimal distance in the far wake region. The WT wake losses have been taken into account, with the Frandsen-Gaussian (F-G) wake model and the optimization problem is subsequently solved by the hybrid grey wolf optimization (HGWO) algorithm. Synthesis methods that contain a special WT ranking strategy for multiple WTs are described in detail. Both the F-G model and Jensen's model are applied in the offshore WF optimization simulation platform for comparison. Simulation results demonstrate that the F-G model is more consistent with real wakes and thus the optimization result is more accurate than the commonly used Jensen's model.

RETRACTED ARTICLE: Wind Farm Layout Design Using Cuckoo Search Algorithms

ABSTRACTWind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms requires optimal placement of wind turbines. Due to complex nature of micrositing of wind turbines, the wind farm layout design problem is considered a complex optimization problem. In the recent past, various techniques and algorithms have been developed for optimization of energy output from wind farms. The present study proposes an optimization approach based on the cuckoo search (CS) algorithm, which is relatively a recent technique. A variant of CS is also proposed that incorporates a heuristic-based seed solution for better performance. The proposed CS algorithms are compared with genetic and particle swarm optimization algorithms which have been extensively applied to wind farm layout design. Empirical results indicate that the proposed CS algorithms outperformed the genetic and particle swarm optimization algorithms for the given test scenarios in terms of yearly power output and efficiency.

Determining layout of a wind farm with optimal number of turbines: A decomposition based approach

A novel suite of algorithms, exploiting the concept of Space And Variable decomposition based Evolutionary (SAVE) formulations, has been proposed while addressing the wind farm micro-siting. The efficacies of these algorithms are further enhanced by designing novel repair strategies for the evolutionary operators and adopting intermediate rejuvenation of solution pool with fresh seeding. Optimal placement of wind turbines towards achieving the target of maximization of power (in kW) and minimization of cost avoiding wake effects has been approached through the multi-objective cost-power formulation. Significant improvements in the power production (61–70%) and cost to power ratio (∼14%) have been recorded not only as compared to the most popular benchmark case study reported in the literature, but also as compared to many other improved results reported on the same case study. The decomposition based approaches have been demonstrated to provide better quality Pareto fronts, in terms of uniformity of spread as well as the convergence of the solutions, over their continuous variable solution methodologies. Moreover, the use of repair strategy helped to improve solution quality to the tune of 9% improvement in generated power on another realistic case study as compared to the case when repair is not used. The approach is extremely generic and can be applied to other optimization problems of interest.

Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm

Most wind turbines within wind farms are set up to face a pre-determined wind direction. However, wind directions are intermittent in nature, leading to less electricity production capacity. This paper proposes an algorithm to solve the wind farm layout optimization problem considering multi-angular (MA) wind direction with the aim of maximizing the total power generated on wind farms and minimizing the cost of installation. A twostage genetic algorithm (GA) equipped with complementary sampling and uniform crossover is used to evolve a MA layout that will yield optimal output regardless of the wind direction. In the first stage, the optimal wind turbine layouts for 8 different major wind directions were determined while the second stage allows each of the previously determined layouts to compete and inter-breed so as to evolve an optimal MA wind farm layout. The proposed MA wind farm layout is thereafter compared to other layouts whose turbines have focused site specific wind turbine orientation. The results reveal that the proposed wind farm layout improves wind power production capacity with minimum cost of installation compared to the layouts with site specific wind turbine layouts. This paper will find application at the planning stage of wind farm.

Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D

Abstract Wake effect and wind uncertainty are the key factors resulting in low efficiency in wind energy extraction. Classic micro-siting approaches focus on reducing the wake effect to determine the best number and positions of the turbines. However, very little literature has addressed the issue of risk due to wind uncertainty which causes the expected production to be distantly deviated from what is actually produced. Multi-objective modeling is of particular interest due to its potential of managing risk. This paper proposes several multi-objective risk management (MORM) models which set the foundation on Monte Carlo simulation to conduct cost, benefit, and risk analyses. We develop an enhanced multi-objective evolutionary algorithm with decomposition (MOEA/D) algorithm by taking advantages of wind farm structure. The experiment result with real wind farm data shows the application differences in gauging the risks with various MORM models. The enhanced MOEA/D is compared with two state-of-the-art algorithms and the former produces the best frontier in the objective space in most of the simulations with mean absolute percentage improvement (API) of 46%. We demonstrate what-if analysis with various risk scenarios to assist the decision maker to realize his/her risk tolerance and to reach quality tradeoff decisions.

Risk management of wind farm micro-siting using an enhanced genetic algorithm with simulation optimization

Wind farm micro-siting is the decision problem for determining the optimal placement of wind turbines in consideration of the wake effect. Existing micro-siting models seek to minimize the cost of energy (COE). However, little literature addresses the production risk under wind uncertainty. To this end, we develop several versions of the simulation optimization based risk management (SORM) model which embeds the Monte Carlo simulation component for obtaining a large number of samples from the wind probability density function. Our SORM model is flexible and allowing the decision makers to conduct various forms of what-if analysis trading profit, cost and risk according to their business value. Then we propose an enhanced genetic algorithm (EGA) which is customized to the properties of wind farm dimensions. The experimental results show that the EGA can obtain the SORM decision both effectively and efficiently as compared to other metaheuristic approaches. We demonstrate how the risk under wind uncertainty can be effectively handled with our SORM models. The simulations with what-if analyses are conducted to disclose important characteristics of the risky micro-siting problem.

Wind Farm Layout Design Using Cuckoo Search Algorithms

ABSTRACTWind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms requires optimal placement of wind turbines. Due to complex nature of micrositing of wind turbines, the wind farm layout design problem is considered a complex optimization problem. In the recent past, various techniques and algorithms have been developed for optimization of energy output from wind farms. The present study proposes an optimization approach based on the cuckoo search (CS) algorithm, which is relatively a recent technique. A variant of CS is also proposed that incorporates a heuristic-based seed solution for a better performance. The proposed CS algorithms are compared with genetic and particle swarm optimization (PSO) algorithms, which have been extensively applied to wind farm layout design. Empirical results indicate that the proposed CS algorithms outperformed the genetic and PSO algorithms for the given test scenarios in terms of yearly power output and efficiency.

RETRACTED: Survey of the most influential parameters on the wind farm net present value (NPV) by adaptive neuro-fuzzy approach

The main objective of wind farm modeling is to maximize wind farm efficiency. The optimal wind turbine placement on a wind farm could be modified by taking economic aspects into account. The net present value (NPV) is the most important criteria for project investment estimating. The general approach in deciding the distinctive choice for a task through NPV is to treat the money streams as known with conviction. Even little deviations from the decided beforehand values might effectively negate the choice. To assess the investment risk of wind power project, this paper constructed a process that selected the most influential wind farm parameters on the NPV with adaptive neuro-fuzzy (ANFIS) method. This procedure is typically called variable selection, which corresponds to finding a subset of the full set of recorded variables that exhibits good predictive abilities. Variable seeking utilizing the ANFIS system was performed to figure out how the seven wind farm parameters affect the NPV of the wind farm.

New Analytical Model for Optimal Placement of Wind Turbines in Power Network under Pool and Reserve Markets Conditions

In this paper a new market based analytical model is proposed for optimal placement of Wind Turbines (WTs) in power systems. In addition to wind turbines, thermal units (THUs) and Pumped Storage Hydro Power Plants (PSHPPs) owners participate in power market. Objective function is defined as participants’ social welfare achieved from power pool and ancillary markets in yearly horizon. Wind turbines have been modeled by probability-generation tree scenarios based on statistical information. We concentrate on investment profits of WTs numbers and its generation capacity beside to PSHPPs and THUs power plants in power systems due to increase in high flexible tools for Independent system operator into the planning and operation planning time interval. For effectiveness evaluation of proposed model, simulation studies are applied on 14-Bus IEEE test power system.

A Model for Wind Turbines Placement Within a Distribution Network Acquisition Market

This paper proposes an innovative exhaustive search method for the optimal placement of wind turbines (WTs) in electrical distribution systems taking into account wind speed and load demand uncertainty, and the variability of electrical energy prices within a distribution network operator (DNO) acquisition market environment. The method combines Monte Carlo simulation (MCS) and market-based optimal power flow (OPF) to maximize the net present value (NPV) related to the investment made by WTs' developers over a planning horizon. In particular, the MCS data feed the market-based OPF problem with inter-temporal constraints in order to find the most convenient WTs allocation and priority on the network, based on distribution-locational marginal prices (D-LMPs) in a competitive electricity market. The effectiveness of the proposed method is demonstrated with an 84-bus 11.4-kV radial distribution system.

Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients

This paper proposes a binary particle swarm optimization (BPSO) with time-varying acceleration coefficients (TVAC) for solving optimal placement of wind turbines within a wind farm. The objective is to extract the maximum turbine power output in a minimum investment cost within a wind farm. The BPSO–TVAC algorithm is applied to 100 square cells test site considering uniform wind and non-uniform wind speed with variable direction characteristics. Linear wake model is used to calculate downstream wind speed. Test results indicate that BPSO–TVAC investment cost per installed power of both uniform and non-uniform wind speed with variable wind direction are lower than those obtained from genetic algorithm and evolutive algorithm, BPSO–TVIW (time-varying inertia weight factor), BPSO–RANDIW (random inertia weight factor) and BPSO–RTVIWAC (random time-varying inertia weight and acceleration coefficients), leading to maximum power extracted in a least investment cost manner.