Size optimization for hybrid photovoltaic–wind energy system using ant colony optimization for continuous domains based integer programming

In this paper, ant colony optimization for continuous domains (ACO_R) based integer programming is employed for size optimization in a hybrid photovoltaic (PV)–wind energy system. ACO_R is a direct extension of ant colony optimization (ACO). Also, it is the significant ant-based algorithm for continuous optimization. In this setting, the variables are first considered as real then rounded in each step of iteration. The number of solar panels, wind turbines and batteries are selected as decision variables of integer programming problem. The objective function of the PV–wind system design is the total design cost which is the sum of total capital cost and total maintenance cost that should be minimized. The optimization is separately performed for three renewable energy systems including hybrid systems, solar stand alone and wind stand alone. A complete data set, a regular optimization formulation and ACO_R based integer programming are the main features of this paper. The optimization results showed that this method gives the best results just in few seconds. Also, the results are compared with other artificial intelligent (AI) approaches and a conventional optimization method. Moreover, the results are very promising and prove that the authors’ proposed approach outperforms them in terms of reaching an optimal solution and speed.     

Wind Farm Layout Design Using Cuckoo Search Algorithms

ABSTRACT

Wind 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.     

Wind Farm Layout Design Using Cuckoo Search Algorithms

Wind 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.     

An intelligent adaptive system for the optimal variable selections of R&D and quality supply chains

The cost of research & development (R&D) and quality management are always regarded as two major parts of total cost. The variable performance of R&D and quality design is an important index that will reflect the effectiveness of the cost reduction. This research has attempted to simultaneously vary all of the variables to achieve the global optimum for the optimal variable selections of R&D and quality design. Genetic algorithm (GA) can treat all of the variables for the global search. In this study, fuzzy refinement with orthogonal arrays was effective in improving the performance of the GA, and also showed the benefits of a good chromosome structure on the behavior of GA. It is also proposed the postponement design with temporal concept, to select the effective variables for the cost reduction of R&D and quality management design. The experimental results showed that tempo-postponement design will increase the flexibility and quick response for supply chain management. Hence, this approach can act as a useful guideline for researchers working on the optimization of the key variable selections for R&D and quality model design.     

An intelligent adaptive system for the optimal variable selections of R&D and quality supply chains

The cost of research & development (R&D) and quality management are always regarded as two major parts of total cost. The variable performance of R&D and quality design is an important index that will reflect the effectiveness of the cost reduction. This research has attempted to simultaneously vary all of the variables to achieve the global optimum for the optimal variable selections of R&D and quality design. Genetic algorithm (GA) can treat all of the variables for the global search. In this study, fuzzy refinement with orthogonal arrays was effective in improving the performance of the GA, and also showed the benefits of a good chromosome structure on the behavior of GA. It is also proposed the postponement design with temporal concept, to select the effective variables for the cost reduction of R&D and quality management design. The experimental results showed that tempo-postponement design will increase the flexibility and quick response for supply chain management. Hence, this approach can act as a useful guideline for researchers working on the optimization of the key variable selections for R&D and quality model design.     

Multi-disciplinary constrained optimization of wind turbines

We describe procedures for the multi-disciplinary design optimization of wind turbines, where design parameters are optimized by maximizing a merit function, subjected to constraints that translate all relevant design requirements. Evaluation of merit function and constraints is performed by running simulations with a parametric high-fidelity aero-servo-elastic model; a detailed cross-sectional structural model is used for the minimum weight constrained sizing of the rotor blade. To reduce the computational cost, the multi-disciplinary optimization is performed by a multi-stage process that first alternates between an aerodynamic shape optimization step and a structural blade optimization one, and then combines the two to yield the final optimum solution. A complete design loop can be performed using the proposed algorithm using standard desktop computing hardware in one-two days. The design procedures are implemented in a computer program and demonstrated on the optimization of multi-MW horizontal axis wind turbines and on the design of an aero-elastically scaled wind tunnel model.     

Dynamic power management in energy-aware computer networks and data intensive computing systems

Energy awareness is an important aspect of modern network and computing system design and management, especially in the case of internet-scale networks and data intensive large scale distributed computing systems. The main challenge is to design and develop novel technologies, architectures and methods that allow us to reduce energy consumption in such infrastructures, which is also the main reason for reducing the total cost of running a network. Energy-aware network components as well as new control and optimization strategies may save the energy utilized by the whole system through adaptation of network capacity and resources to the actual traffic load and demands, while ensuring end-to-end quality of service. In this paper, we have designed and developed a two-level control framework for reducing power consumption in computer networks. The implementation of this framework provides the local control mechanisms that are implemented at the network device level and network-wide control strategies implemented at the central control level. We also developed network-wide optimization algorithms for calculating the power setting of energy consuming network components and energy-aware routing for the recommended network configuration. The utility and efficiency of our framework have been verified by simulation and by laboratory tests. The test cases were carried out on a number of synthetic as well as on real network topologies, giving encouraging results. Thus, we come up with well justified recommendations for energy-aware computer network design, to conclude the paper.     

Soft computing based multi-objective optimization of Brayton cycle power plant with isothermal heat addition using evolutionary algorithm and decision making

An irreversible regenerative Brayton cycle model considering internal and external irreversibilities is developed in matrix laboratory (MATLAB) simulink environment and thermodynamic optimization based on finite time thermodynamic analysis along with multiple criteria is implemented. Evolutionary algorithms based on second version of non-dominated sorting genetic algorithm (NSGA-II) and multi-objective evolutionary algorithm based on decomposition (MOEA/D) are employed to optimize power output and thermal efficiency simultaneously where isobaric-side heat exchanger effectiveness (ε_H), isothermal-side effectiveness (ε_H1), sink-side effectiveness (ε_L), regenerator-side effectiveness (ε_R), and working medium temperature (T₅) are taken as design variables. The optimal values of aforementioned design variables are investigated. Pareto optimal frontiers between dual objectives are obtained and the final optimal values of power output and thermal efficiency are chosen via LINMAP, fuzzy Bellman–Zadeh, Shannon’s entropy and TOPSIS decision making approaches. The obtained results are compared and the best one is preferred. An improvement in thermal efficiency from 18.29% to 21.10% is reported. In addition to this, variations of different input parameters on the power output and thermal efficiency are conferred and presented graphically. With the goal of error investigation, the maximum and average errors for the obtained results are designed at last.     

基于组合模型的燃气轮机NOx排放影响因素研究

为了减少电厂污染物的排放, 如何准确估计燃气轮机氮氧化物(NOx)排放值并识别其关键影响因素, 对有效采取优化设计是至关重要的. 由于燃气轮机的运作过程存在变工况等情况, 单一模型的准确度与泛化性能难以达到工业应用的要求. 将偏最小二乘法(PLS)和互信息(MI)组合建模保证了NOx特征变量选取的有效性与精确性. 利用PLS确定影响燃气轮机NOx的特征变量数目, 避免了选择变量的主观因素并降低维数. 再用互信息(MI)选择出最优的特征变量, 通过不同的预测模型进行仿真分析, 并把单一和组合特征选择进行对比. 结果表明, 对燃气轮机NOx排放影响因素的研究中, PLS-MI组合模型选取的特征变量更具代表性, 并能够保证预测模型的泛化精度, 降低模型复杂度, 为电厂优化控制提供了理论依据, 具有一定的应用前景.     

基于参考输入优化的变速风电机组最大化风能捕获方法

变速风电机组在额定风速以下应用最大功率点跟踪实现最大化风能捕获. 然而, 大惯量风电机组在面对快 速波动的湍流风速时, 因转速调节慢而难以保持运行于最大功率点. 本文研究进一步发现, 平均转速跟踪误差与整 体的风能捕获效率并非单调关系, 这使得当前以减小转速跟踪误差为目标的控制器设计难以有效提升风电机组的 发电效率. 为此, 本文以提升风能捕获效率(而非减小转速跟踪误差)为目标, 提出一种基于参考输入优化的风电机 组最大化风能捕获方法. 考虑到参考转速对风能捕获效率的复杂影响难以准确建模, 本文借助深度确定性策略梯度 (DDPG)强化学习算法实现参考输入优化. 仿真结果表明该方法能够有效提升湍流风下变速风电机组的风能捕获效 率.     

Using genetic algorithms to improve the thermodynamic efficiency of gas turbines designed by traditional methods

A method for optimizing the thermodynamic efficiency of aeronautical gas turbines designed by classical methods is presented. This method is based in the transformation of the original constrained optimization problem into a new constrained free optimization problem which is solved by a genetic algorithm. Basically, a set of geometric, aerodynamic and acoustic noise constraints must be fulfilled during the optimization process. As a case study, the thermodynamic efficiency of an already optimized by traditional methods real aeronautical low pressure turbine design of 13 rows has been successfully improved, increasing the turbine efficiency by 0.047% and reducing the total number of airfoils by 1.61%. In addition, experimental evidence of a strong correlation between the total number of airfoils and the turbine efficiency has been observed. This result would allow us to use the total number of airfoils as a cheap substitute of the turbine efficiency for a coarse optimization at the first design steps.     

Integrated aero-structural optimization of wind turbines

The present work describes methods for the integrated aero-structural optimization of wind turbines. The goal of the algorithms is to identify the structural and aerodynamic design characteristics that achieve the minimum cost of energy for a given wind turbine configuration. Given the strong couplings that exist between aerodynamic and structural design choices, the methods are formulated so as to address both problems simultaneously in an integrated manner, resulting in tools that may help avoid suboptimal solutions or lengthy design loops.All methods considered herein use the same high fidelity multibody aeroservoelastic simulation environment and operate the design according to standard certification guidelines. The methods, however, differ in the way the optimization is conducted, realizing different tradeoffs amongst computational efficiency, generality, level of automation and overall robustness.The proposed formulations are exercised on the design of a conceptual 10 MW horizontal axis wind turbine, illustrating the main characteristics of the various methods.     

混合控制策略动态优化多负荷CCHP微网系统

为了降低含冷、热、电、气负荷多能互补微网的运行成本,并解决在传统控制方法下微网系统耗能大、响应慢及稳定性差等问题,提出了一种基于最短距离的CCHP系统混合控制策略.首先从系统运行经济性、稳定性和环保角度建立微网综合能源模型,结合传统控制方式建立4个运行场景,采用主副协同动态粒子群算法进行优化.实验结果表明,该控制策略在...     

Reserve-constrained multiarea environmental/economic dispatch based on particle swarm optimization with local search

The objective of economic dispatch (ED) is to minimize the total operational cost while satisfying the operational constraints of power systems. Multiarea economic dispatch (MAED) deals with the optimal power dispatch of multiple areas. In this investigation, multiarea environmental/economic dispatch (MAEED) is proposed to address the environmental issue during the ED. Its target is to dispatch the power among different areas by simultaneously minimizing the operational costs and pollutant emissions. In this paper, the MAEED problem is first formulated and then an improved multiobjective particle swarm optimization (MOPSO) algorithm is developed to derive a set of Pareto-optimal solutions. In the proposed version of MOPSO, local search is used to increase its search efficiency. To ensure the system security, tie-line transfer limits between different areas are incorporated as a set of constraints in the optimization process. Moreover, the reserve-sharing scheme is applied to ensure that each area is able to fulfill its reserve requirement. Numerical studies based on a four-area test power generation system are carried out to demonstrate the validity of the proposed optimization method as well as the results from different problem formulations. Comparative results with respect to other optimization methods are also presented.     

Minimum cost design of a welded orthogonally stiffened cylindrical shell

In this study the optimal design of a cylindrical orthogonally stiffened shell member of an offshore fixed platform truss, loaded by axial compression and external pressure, is investigated. Ring stiffeners of welded box section and stringers of halved rolled I-section are used. The design variables considered in the optimization are the shell thickness as well as the dimensions and numbers of stiffeners. The design constraints relate to the shell, panel ring and panel stringer buckling, as well as manufacturing limitations. The cost function includes the cost of material, forming of plate elements into cylindrical shape, welding and painting. In the optimization a number of relatively new mathematical optimization methods (leap-frog - LFOPC, Dynamic-Q, ETOPC, and particle swarm - PSO) are used, in order to ensure confidence that the finally computed optimum design is accurately determined, and indeed corresponds to a global minimum. The continuous optimization procedures are adapted to allow for discrete values of the design variables to be used in the final manufacturing of the truss member. A comparison of the computed optimum costs of the stiffened and un-stiffened assemblies, shows that significant cost savings can be achieved by orthogonal stiffening, since the latter allows for considerable reduction of the shell thickness, which results in large material and manufacturing cost savings.     

宽通道换热板片结构的多目标头脑风暴优化设计

换热板片作为构成宽通道板式换热器的核心部件,对其换热效果具有直接影响.为有效提升换热器性能,减少能量损耗,提出一种宽通道换热板片结构的多目标头脑风暴优化设计方法.首先,根据换热板片的形状和布局特点,提取梯形凸台尺寸及其分布间隔构成结构参数,采用正交法,基于Fluent数值模拟软件获得25组换热板片结构样本;然后,采用回归构建换热努塞尔数和压力损失的代理模型,以最大换热效果和最小能量损耗作为优化目标,采用基于网格的多目标头脑风暴优化算法,寻优获得最佳换热板片的结构设计方案.统计实验结果表明,换热性能代理模型可以有效降低评价代价,所提出优化设计方法可以更加高效地获得具有最佳换热效果和能量损耗的换热板片结构.     

Modified neo-fuzzy neuron-based approach for economic and environmental optimal power dispatch

At the central energy management center in a power system, the real time controls continuously track the load changes and endeavor to match the total power demand with total generation in such a manner that the operating cost is minimized while all the operating constraints are satisfied. However, due to the strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained economic dispatch formulation is to estimate the optimal generation schedule of generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. Conventional optimization techniques become very time consuming and computationally extensive for such complex optimization tasks. These methods are hence not suitable for on-line use. Neural networks and fuzzy systems can be trained to generate accurate relations among variables in complex non-linear dynamical environment, as both are model-free estimators. The existing synergy between these two fields has been exploited in this paper for solving the economic and environmental dispatch problem on-line. A multi-output modified neo-fuzzy neuron (NFN), capable of real time training is proposed for economic and environmental power generation allocation.This model is found to achieve accurate results and the training is observed to be faster than other popular neural networks. The proposed method has been tested on medium-sized sample power systems with three and six generating units and found to be suitable for on-line combined environmental economic dispatch (CEED).     

基于数据处理与BP神经网络的SCR脱硝效率预测模型

基于数据处理与分析的方法,充分利用电厂DCS系统中存储的大量实际运行数据,以SCR系统相关参数为输入,SCR出口NOx浓度为输出,采用BP神经网络构建SCR脱硝系统预测模型。该模型充分考虑脱硝效率与其他变量的关系。实验结果表明模型预测结果可靠,为下一步脱硝系统优化运行、实现节能减排提供模型基础。     

Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem

Solution of optimal power flow (OPF) problem aims to optimize a selected objective function such as fuel cost, active power loss, total voltage deviation (TVD) etc. via optimal adjustment of the power system control variables while at the same time satisfying various equality and inequality constraints. In the present work, a particle swarm optimization with an aging leader and challengers (ALC-PSO) is applied for the solution of the OPF problem of power systems. The proposed approach is examined and tested on modified IEEE 30-bus and IEEE 118-bus test power system with different objectives that reflect minimization of fuel cost or active power loss or TVD. The simulation results demonstrate the effectiveness of the proposed approach compared with other evolutionary optimization techniques surfaced in recent state-of-the-art literature. Statistical analysis, presented in this paper, indicates the robustness of the proposed ALC-PSO algorithm.     

动态旅行商问题的研究

传统的旅行商问题都是静态的,但在现实中许多问题是动态的。该文提出动态旅行商问题,问题的规模随时间不断变化。实时问题对算法的求解效率要求很高,为此设计了基于模糊规则的在线遗传算法,可以根据求解问题的变化,在线精炼模糊控制规则来控制算法的参数。仿真实验验证了算法的有效性。