Various criteria in optimization of a geothermal air conditioning system with a horizontal ground heat exchanger

Thermodynamic and thermoeconomic optimization of a horizontal geothermal air conditioning system has been performed. A model based on energy and exergy analysis is presented here. An economic model of the system is developed according to the Total Revenue Requirement (TRR) method. The objective functions based on the thermodynamic and thermoeconomic analysis are developed. An artificial intelligence technique known as evolutionary algorithm has been utilized for optimization. This approach has been applied to minimize either the total levelized cost of the system product or the exergy destruction of the system. Three levels of optimization including thermodynamic single objective optimization, thermoeconomic single objective optimization and multi‐objective optimization (with simultaneous optimization of thermodynamic and thermoeconomic objectives) are performed. In multi‐objective optimization, both thermodynamics and thermoeconomic objectives are considered, simultaneously. In the case of multi‐objective optimization, an example of decision‐making process for selection of the final solution from available optimal points on Pareto front is presented here. The results obtained using the various optimization approaches are compared and discussed. Copyright © 2009 John Wiley & Sons, Ltd.     

Particle swarm optimization applied to the co-design of a fuel cell air circuit

This paper presents an optimization approach applied to a whole fuel cell (FC) air supply system including its geometry and its control. The aim is to optimize its power consumption along with its mass. Particle swarm optimization (PSO) algorithm is used to define the design parameters of both permanent magnet synchronous motor (PMSM) and a fuzzy logic controller (FLC). The results are compared with those obtained by a sequential optimization process and advantages of co-design optimization approach are clearly shown. Indeed, a significant reduction of the objective function (made up on both motor mass and energy consumption) on a considered operating cycle can be obtained.     

Fuzzy Predictive Supervisory Control Based on Genetic Algorithms for Gas Turbines of Combined Cycle Power Plants

This work presents a novel design and development of a fuzzy predictive supervisory controller, based on genetic algorithms (GA), for gas turbines of combined cycle units. The control design is based on an objective function that represents the economic and regulatory performance of a gas turbine by using a dynamic optimal set-point for the regulatory level. A fuzzy model is considered in order to characterize the nonlinear behavior of the gas turbine, which is used in two supervisory control systems. The first fuzzy supervisory control design includes a fuzzy model, where its parameters are held constant for the successive predictions. For the second fuzzy supervisory control design, its parameters are updated in each prediction and its nonlinear optimization problem is solved using GAs. The proposed fuzzy supervisory controllers are compared against a supervisory controller based on linear models and a regulatory controller with constant optimal set-points. Results indicate that the fuzzy GA predictive supervisory controller captures adequately the nonlinearities of the process, which, in turn, provides a promising approach to improve the performance of the combined cycle unit.     

水电项目投资的多目标模糊决策方法

对水电项目投资方案多目标决策问题进行了研究，根据多目标模糊优化的基本原理，结合目标相对优属度概念，给出了多目标模糊决策中通过回归确定权重的方法，同时提出了基于权重回归组合的水电项目投资多目标决策模糊化方法，通过实验证了该模型的合理性和有效性。     

自适应模糊控制在热力系统容错控制中的应用

在热力系统中采用容错控制技术,可以提高控制系统的可靠性,同时降低运行成本。研究采用自适应模糊控制实现非失效的被动容错控制。提出一种新的自适应模糊控制器;采用基于动态神经元网络的辨识模型学习被控对象的动态特性,设计了评价模糊控制器性能的FITAE指标;采用遗传算法优化模糊控制器参数,同时优化比例因子和控制规则表。为了提高模糊控制器的自适应调整速度,采用二阶段优化策略。仿真研究表明:自适应模糊控制器的热力系统被动容错控制方法是可行的。图3参7     

DESIGN AND TUNING OF A FUZZY CONTROLLER FOR INDOOR AIR QUALITY AND THERMAL COMFORT MANAGEMENT

This paper presents the development and testing of a multi objective fuzzy controller. It is dedicated to the control of thermal comfort and indoor air quality while minimising energy consumption and reducing instabilities are other constraints for this controller. After a brief introduction on the difficult task of indoor climate control, the test cell and its corresponding model is described in the second section. The model has been developed within TRNSYS. Section 3 describes the architecture of the fuzzy controller applied to these control objectives in the test cell. A hierarchical architecture has finally been selected since it is a very flexible architecture and it results in reducing the total number of fuzzy rules. The rule optimisation technique, based on genetic algorithms is also presented in the third section. Simulation results are finally presented. They prove that, after optimisation, the fuzzy controller greatly outperforms a typical ON-OFF controller on all control criteria. Energy consumption is in particular reduced by more than 10%. The fourth section of this chapter presents field experiments among 20 subjects of both ON-OFF control and fuzzy control. If major differences are not observed on the thermal comfort and indoor air quality point of view, these experiments however illustrate the importance of on-line tuning of specific fuzzy rules.     

多功能太阳能空调制热兼制热水模式负荷平衡控制策略的研究

为了解决多功能太阳能空调制热兼制热水工况时空调和热水的能量输入与各自的负荷平衡问题,提出了通过调节热水换热器水体积流量以改变空调传热量和热水传热量的控制策略.以空调负荷作为控制目标,建立了神经网络辨识和模糊控制模型,并进行了仿真研究.结果表明,神经网络预测和模糊控制相结合的方法对热水换热器水体积流量的智能调节能有效地解决负荷平衡问题.     

Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach

Optimal reactive power dispatch (ORPD) problem is an important problem in the operation of power systems. It is a nonlinear and mixed integer programming problem, which determines optimal values for control parameters of reactive power producers to optimize specific objective functions while satisfying several technical constraints. In this paper, stochastic multi-objective ORPD (SMO-ORPD) problem is studied in a wind integrated power system considering the loads and wind power generation uncertainties. The proposed multi objective optimization problem is solved using ε-constraint method, and fuzzy satisfying approach is employed to select the best compromise solution. Two different objective functions are considered as follow: 1) minimization of the active power losses and 2) minimization of the voltage stability index (named L-index). In this paper VAR compensation devices are modeled as discrete variables. Moreover, to evaluate the performance of the proposed method for solution of multi-objective problem, the obtained results for deterministic case (DMO-ORPD), are compared with the available methods in literature. The proposed method is examined on the IEEE-57 bus system. The proposed models are implemented in GAMS environment. The numerical results substantiate the capability of the proposed SMO-ORPD problem to deal with uncertainties and to determine the best settings of control variables.     

Thermal comfort control on multi-room fan coil unit system using LEE-based fuzzy logic

Saving consumable energy and maintaining the thermal comfort level are two main topics in the heating, ventilating and air conditioning (HVAC) control field. The reliability of the controller is important as well. This paper proposes a least enthalpy estimator (LEE) that combines the definition of thermal comfort level and the theory of enthalpy into a load predicting way to provide timely suitable settings for a fan coil unit (FCU) fuzzy controller used in HVAC. According to the settings, including temperature and relative humidity, the fuzzy controller can make decisions and adjust the output of the FCU system. From actual experiments, the LEE-based FCU fuzzy controller can achieve the requirements of the FCU control system such as thermal comfort, energy efficiency and reliability.     

基于粒子群优化的锅炉汽包水位模糊PID控制

结合粒子群优化算法、模糊控制和PID参数自整定方法,设计一种新型自适应模糊PID控制器。采用粒子群优化算法离线优化PID控制器参数,再利用模糊控制器在线对PID参数进行整定。Simulink-~件模型仿真对比表明：控制方法具有调节精度高、     

利用剩余风电的感应储能供暖控制系统研究

为了解决我国北方地区风电的弃风问题,改进剩余风电供暖系统的直接消纳储能技术,改善电蓄热供暖控制系统中存在换能装置电热转化效率低下的问题,经过方案对比提出采用电热转换效率高、安全可靠性好的电磁感应加热装置作为供暖系统的换能装置。为优化储能水箱加热供暖系统的控制策略,针对系统给定的控制需求,采取前级附加给定温度模糊控制器串联后级感应加热模糊PID控制器的复合控制策略,使闭环加热系统的给定温度随室外温度的变化而调整;同时为了节约不可再生能源,降低损耗,采用与后级模糊PID控制器的输出构成闭环比值随动控制系统的加热循环流量协调控制策略。利用Matlab/Simulink软件进行仿真实验,结果表明：模糊PID控制特性明显优于传统PID控制,系统的超调量显著减少,动态调节速度明显加快;实现模糊控制和模糊PID控制复合与协调智能控制策略的网络化,提高了系统控制的实时性和可靠性,使系统控制性能大为提升。     

Multi-objective optimization of HVAC system with an evolutionary computation algorithm

A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables — supply air temperature and supply air duct static pressure set points — are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system.     

电能质量变权综合评估及多功能并网逆变目标优化

光伏发电波动性和负荷随机性将引发电能质量评估指标的变化,多功能并网逆变器通常采用固化权重的综合评估指标及单目标优化控制方法,可能造成电能质量治理的劣化及剩余容量的利用率过低。该文利用混合变权原理改进的G1法,提出一种新的电能质量综合指标评估方法,该方法根据源、荷的动态变化,实时调整各电能指标的权重,实现对综合电能质量的客观评估。在此基础上,将电能质量综合指标最优和逆变器投入补偿容量最小作为目标函数,并对单项电能指标进行约束,利用带精英策略的非支配排序遗传算法（NSGA-Ⅱ）实现多目标优化。最后在Matlab中验证所提策略的可行性和有效性。     

基于模糊层次分析法的水库移民安置区优选

鉴于水库移民安置区的优化选择关系到移民搬迁后的生产生活水平、发展潜力以及安置区的社会稳定,涉及社会、经济、环境、自然资源等诸多领域,是一个多层次、多目标、多属性的复杂系统工程,分析了水库移民安置区选择的影响因素,建立了优选指标体系,构建了模糊层次分析法(FAHP)综合评价模型,并以百色水利枢纽云南库区移民安置为例进行分析。结果表明,所采用的模型和方法合理、实用、可信度较高,为水库工程移民安置区的优选提供了借鉴。     

A novel methodology for scrutiny of autonomous hybrid renewable energy system

The optimal design of a hybrid system with different configurations including renewable generation is presented in this paper. A novel multi‐objective function consisting of 6 different objectives of hybrid system is reported using GA, PSO, and TLBO to decide the optimal configurations of parameters. The technical (loss of power supply probability, renewable factor), economical (cost of energy, penalty and fuel consumption), and social (job creation, human development index, and particular matter) features are investigated as objectives simultaneously for optimal design of hybrid system. The different objective indices namely cost of energy, loss of power supply probability, particular matter, human development index, job creation, and renewable factor indices are considered. The newly invented particular matter factor for design consideration of hybrid system directly shows the human health impacts, while pollutant emission is measured in the hybrid system design. The optimum values of objective indices are decided on the basis of the minimum value of multi‐objective function. The distinct cases from I to VI of hybrid system are examined for optimal configuration including different combinations of PV, wind, biomass, diesel generator, and battery bank. The resulting analysis of each case reveals that the performance of TLBO is better than PSO, and PSO is better than GA in all respect through new multi‐objective function and found case I is more efficient solution.     

基于模糊层次分析法的太阳能-空气源热泵复合供暖系统多目标优化

为减小太阳能空气源热泵供暖系统的综合成本和碳排放量,提高热泵运行效率及太阳能使用率,提出一种基于模糊层次分析法的多目标优化设计方法。采用TRNSYS,搭建复合供暖系统仿真模型,通过参数化分析选出7个系统参数作为优化变量,提出复合供暖系统综合评价指标,运用模糊层次分析法确定每个因素的权重并定义综合目标函数并通过正交试验法得到最终优化方案。通过仿真试验对比复合供暖系统优化前后的运行性能,验证了该优化方法的有效性。     

含DG随机出力的配电网多目标无功优化

针对分布式电源(DG)出力具有间歇性和不确定性的问题,建立了基于两点估计法(2PEM)含DG随机出力的配电网概率潮流计算模型,并将基于Pareto最优前沿解集的多目标进化算法(MOEA)与两点估计概率计算模型相结合,建立了以网损、节点电压偏移量及优化成本为目标函数的多目标无功优化模型。将该优化模型应用于IEEE33标准节点测试系统中的仿真结果表明,该方法具有较好的适应性,能为决策者提供多样性选择,增加了决策的灵活性。     

基于需求响应的居民可控能效负荷优化

为解决居民生活用电需求不断增加,居民生活用电方式不合理造成能源浪费越来越严重的问题,从居民用户的可控能效负荷入手,对典型可控能效负荷空调、热水器和照明负荷进行分析,建立负荷能耗数学模型,根据其运行特性,结合居民用户用电习惯和分时电价,制定居民可控能效负荷优化策略;建立以居民用户用电成本和用电满意度为目标的优化模型。为提高和声搜索算法的求解速度与计算精度,对其参数进行动态调整,并与差分进化算法进行融合,应用于可控能效负荷的优化求解。算例结果表明了改进算法具有较好的收敛性和较高的准确性,验证了居民可控能效负荷优化策略的可行性,实现了从需求响应的角度对可控能效负荷进行优化管理的思想。     

基于NSGWO的火电厂负荷优化分配研究

针对传统多 目标灰狼优化算法易出现局部最优和稳定性差的问题,提出了一种改进的多 目标灰狼算法.引入基于顺序查找策略(ENS)的非支配排序方法提高算法的速率,采用基于参考点的选取策略以均衡算法优化结果分布,并通过模拟二进制交叉进化机制改善算法跳出局部最优解的能力.结合多 目标算法的评价指标,开展基准函数仿真试验,验证了改...     

A dynamic compact thermal model for data center analysis and control using the zonal method and artificial neural networks

Full-scale data center thermal modeling and optimization using computational fluid dynamics (CFD) is generally an extremely time-consuming process. This paper presents the development of a velocity propagation method (VPM) based dynamic compact zonal model to efficiently describe the airflow and temperature patterns in a data center with a contained cold aisle. Results from the zonal model are compared to those from full CFD simulations of the same configuration. A primary objective of developing the compact model is real-time predictive capability for control and optimization of operating conditions for energy utilization. A scheme is proposed that integrates zonal model results for temperature and air flow rates with a proportional–integral–derivative (PID) controller to predict and control rack inlet temperature more precisely. The approach also uses an Artificial Neural Network (ANN) in combination with a Genetic Algorithm (GA) optimization procedure. The results show that the combined approach, built on the VPM based zonal model, can yield an effective real-time design and control tool for energy efficient thermal management in data centers.