Optimal Number Of Wind Turbines Research Articles

Optimal placement of wind turbine in distribution grid to minimize energy loss considering power generation probability

This research proposes an algorithm to determine the optimal position of wind turbines in a distribution grid for minimizing its annual energy loss. In this paper, the probability distribution of power generation at each wind turbine node is considered and the nonsimultaneous occurrence of the power generation at wind turbine nodes is also interested. Moreover, we also consider the wind energy exploitability at each node in the grid. This algorithm is coded in MATLAB environment and it is verified via a sample IEEE 33 bus distribution grid with a sample data of power generation probability. The verifying results indicated that the optimal number of wind turbines at each node to obtain a minimal annual energy loss. Results also indicated that the node voltage is varied because of the change of power generation; however, the voltage at all nodes is in normal operation range and its median value is between about 1.01 pu to 1.05 pu. The power factor of wind turbines has an impact on the optimal number of wind turbines at nodes and the efficiency of wind turbine installation.

Optimization of a solar-wind- grid powered desalination system in Saudi Arabia

The utilization of renewable energy to run desalination plants has enormously expanded in the last two decades. In this study, a grid-connected hybrid solar-wind system is proposed to power a small-scale Reverse Osmosis (RO) desalination unit. In a case study, the system's performance has been analyzed under the weather conditions of the Eastern Province, Saudi Arabia. A numerical model has been developed based on a mixed-integer linear programming (MILP) approach to design and size the proposed system. The developed model is solved on an hourly basis to capture hourly variations of weather conditions with the aim to obtain an efficient design to operate the RO plant and supply freshwater to a small community living in a remote area at minimum cost. The developed model allows finding the optimal number of wind turbines, the number of photovoltaic (PV) modules, and the energy purchased from the national grid. Since the desalination energy consumption depends on the feed water conditions, two energy consumption rates are considered, namely, 2 and 4 kWh/m3. The results show that brackish water can be purified for the two different energy requirements at a cost varying between 1.72 and 1.84 $/m3, respectively.

Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

The optimal layout of wind turbines is an important factor in the wind farm design process, and various attempts have been made to derive optimal deployment results. For this purpose, many approaches to optimize the layout of turbines using various optimization algorithms have been developed and applied across various studies. Among these methods, the most widely used optimization approach is the genetic algorithm, but the genetic algorithm handles many independent variables and requires a large amount of computation time. A simulated annealing algorithm is also a representative optimization algorithm, and the simulation process is similar to the wind turbine layout process. However, despite its usefulness, it has not been widely applied to the wind farm layout optimization problem. In this study, a wind farm layout optimization method was developed based on simulated annealing, and the performance of the algorithm was evaluated by comparing it to those of previous studies under three wind scenarios; likewise, the applicability was examined. A regular layout and optimal number of wind turbines, never before observed in previous studies, were obtained and they demonstrated the best fitness values for all the three considered scenarios. The results indicate that the simulated annealing (SA) algorithm can be successfully applied to the wind farm layout optimization problem.

Reliability analysis of multi-state system with three-state components and its application to wind energy

In most real life situations, the system’s components contribute differently in different performance levels. Such a situation can be modeled by systems with multi-state components having more than one working status, e.g. perfect functioning, and partial working. In this paper, a multi-state system that consists of two types of three-state components is defined and studied. An explicit formula for the probability that the performance of the system is at least a given level is obtained for the most general case when the components are statistically dependent. The model is applied to evaluate the wind power system that consists of two wind plants in different regions. An optimization problem is formulated to find the optimal number of wind turbines that must be installed in the wind plants by minimizing the total cost under specific power production.

Optimal sizing of a stand-alone hybrid photovoltaic/wind system using new grey wolf optimizer considering reliability

In this paper, a methodology of sizing optimization of a stand-alone hybrid PV/wind/battery power generation system (HPWGS) is proposed. The main objective of this optimization is to minimize the total annual cost (TAC) of the hybrid system considering power balanced constraint and determination of the optimal number of wind turbines, PV panels, and batteries. For this purpose, a new meta-heuristic nature-inspired algorithm, called Grey Wolf Optimizer is utilized. The obtained results show that the proposed methodology finds optimal sizing of the HPWGS easily with fast convergence and lower cost. Comparing the results of this new method with the well-known published works in the literature, the superior capabilities of this proposed method are demonstrated. Moreover, the results show that the reliability characteristics are weakened with decreasing the component availability, and the supply reliability of the HPWGS is improved by increasing the inverter efficiency while the TAC is decreased. Furthermore, the HPWGS can cover the load demand variations with high reliability.

Optimal size and cost analysis of stand-alone hybrid wind/photovoltaic power-generation systems

Design of sustainable energy systems for the supply of electricity need correct selection and sizing to reduce investment costs. In this article, a new sizing methodology is developed for stand-alone hybrid wind/photovoltaic (PV) power systems, using multi-objective optimisation algorithms. Multi-objective particle swarm optimisation algorithm and non-dominated sorting genetic algorithm-II are selected related to their match with the nature of renewable energy sizing problem. A match evaluation method is developed based on renewable energy supply/demand match evaluation criteria, to size the proposed system in the lowest cost. As an example of application of this technique, six different wind turbines (WTs) and also six different PV modules have been considered. The sizing methodology determines a multi-objective design, obtaining the best solutions that the applied algorithm has found simultaneously considering three objectives: inequality coefficient, correlation coefficient, and annualised cost of system. The optimal number of WTs, PV modules, and batteries ensuring that the system total cost is minimised while guaranteeing a highly reliable source of load power is obtained. A management strategy has been designed to achieve higher electricity match rate. Based on the proposed technique, the algorithm developed for different cases, using the climatic condition data of the city Zabol, located in south-east of Iran. Additionally, a study of operating hours of diesel generator in optimal configuration is carried out.

An efficient sizing method with suitable energy management strategy for hybrid renewable energy systems

This paper propose a new method for designing a stand-alone hybrid wind-photovoltaic-diesel-battery system that minimizes the inequality coefficient and annualized cost of system and maximizes the correlation coefficient using multi-objective particle swarm optimization algorithm. The proposed method uses data from solar radiation, temperature, and wind speed that are collected from the city of Zabol, located in south-east of Iran. The results are presented as an optimal Pareto front set and the optimal number of devices, as well as objective functions, that is, inequality coefficient, annualized cost of system, and correlation coefficient. Additionally, a study of the operating hours of diesel generator in optimal configuration is carried out. Simulation results show the match rate between demand, supply, and energy storage. The optimal number of wind turbines, photovoltaic modules, and batteries ensuring that the system total cost is minimized, while guaranteeing a highly reliable source of load power is obtained. The proposed sizing method can be applied to any other locations with different weather data, load demands, and different characteristics. Copyright © 2013 John Wiley & Sons, Ltd.

Estimation of wind turbines optimal number and produced power in a wind farm using an artificial neural network model

One of the most significant issues in the design of a new wind farm is the estimation of optimal number of wind turbines that has to be installed in it. The goal of every wind farm designer is the production of the maximum possible power, minimizing the installation cost. The cost can be significantly reduced using the minimum required number of wind turbines for specific power production, occupying at the same time the least possible acreage of land. In this work an artificial neural network (ANN) model is developed which has the ability to estimate the optimal number of wind turbines and the total produced power in a wind farm. The ANN model’s results are compared with those of earlier studies that have followed other approaches, proving that the ANN model is well working and has an acceptable accuracy. The proposed model can be useful in the studies of wind farm designers as a supportive tool for the estimation of the optimal number of wind turbines in a wind farm.

Application of Powell's optimisation method for the optimal number of wind turbines in a wind farm

The goal of every wind farm designer is the production of the maximum possible power and minimising the installation cost. The cost can be significantly reduced using the minimum required number of wind turbines for specific power production, occupying at the same time the least possible acreage of land. In this work, Powell's optimisation method has been used for the estimation of the optimal number of wind turbines and the total produced power in a wind farm. The method's results are compared with those of earlier studies that have followed other approaches, presenting its effectiveness. The proposed method can be useful in the studies of wind farm designers as a supportive tool for the estimation of the optimal number of wind turbines in a wind farm.