A linear programming optimization model for optimal operation strategy design and sizing of the CCHP systems

Combined cooling, heat, and power (CCHP) system offers numerous potential advantages for the supply of energy to residential buildings in the sense of improved energy efficiency and reduced environmental burdens. To realize the potential for being more beneficial, however, such systems must reduce total costs relative to conventional systems. In this study, a linear programming optimization model was presented for optimum planning and sizing of CCHP systems. The purpose of the model is to give the design of the CCHP system by considering electrical chiller and absorption chiller simultaneously in economic viewpoint. A numerical study was conducted in Tehran to evaluate the CCHP system model. The linear programming (LP) model determines the optimal sizes of the CCHP equipment by considering capital cost of the system. It showed that by considering electricity buyback, the optimum size of the electrical chiller decrease and the optimum size of the combined heat and power (CHP) unit and the absorption chiller increase dramatically with respect to without electricity buyback. Also, the LP model determines the optimal operation strategy of the system by neglecting capital cost. The optimally operated CCHP system encompassing electrical and absorption chiller could result in an 18% decrease in operating cost when compared to a CHP system encompassing electrical chiller only. Without electricity buyback, the profitability of CCHP was 23%, while with electricity buyback, profitability became 39%. Furthermore, a sensitivity analysis was conducted to show how the important parameters affect the entire system performance.     

Modeling and optimization of HVAC energy consumption

A data-driven approach for minimization of the energy to air condition a typical office-type facility is presented. Eight data-mining algorithms are applied to model the nonlinear relationship among energy consumption, control settings (supply air temperature and supply air static pressure), and a set of uncontrollable parameters. The multiple-linear perceptron (MLP) ensemble outperforms other models tested in this research, and therefore it is selected to model a chiller, a pump, a fan, and a reheat device. These four models are integrated into an energy optimization model with two decision variables, the setpoint of the supply air temperature and the static pressure in the air handling unit. The model is solved with a particle swarm optimization algorithm. The optimization results have demonstrated the total energy consumed by the heating, ventilation, and air-conditioning system is reduced by over 7%.     

An online adaptive optimal control strategy for complex building chilled water systems involving intermediate heat exchangers

This paper presents an adaptive optimal control strategy for online control of complex chilled water systems involving intermediate heat exchangers to enhance operation and energy performances. This optimal control strategy determines the optimal settings of the heat exchanger outlet water temperature (hot side) and the required operating number of heat exchangers and pumps in order to minimize the total energy consumption of pumps under varying working conditions. Adaptive method is utilized to update the key parameters of the proposed models online. A simulated virtual platform representing a chilled water system in a super high-rise building was established to validate and evaluate the proposed optimal strategy. Test results show that the strategy has enhanced control robustness and reliability, particularly in avoiding deficit flow problem. Significant energy of chilled water pumps is saved when compared with conventional methods.     

综合能源系统运行优化设计与运行策略研究

综合能源系统能整合协调各异质能源,是提高能源利用率和降低运行费用的有效途径。提出了两种运行策略及一种基于穷举法的优化策略方法,建立了包括小型燃气轮机系统、地源热泵、电制冷机、吸收式制冷机、换热器的综合能源系统能量转换模型。在案例研究中,提出了以典型日费用最小为目标的综合能源系统优化方案,对以电定热、以热定电两种运行策略下的场景进行了优化分析,确定设备的最佳容量和运行参数,给出了运行策略对比分析结果。结果表明：以电定热策略的整体经济性优于以热定电策略约10%;与传统供能系统在经济性上的适用性进行对比发现,综合能源系统成本节省比例在5%～30%之间。     

Model-based optimal control of a dedicated outdoor air-chilled ceiling system using liquid desiccant and membrane-based total heat recovery

This study presents a model-based control strategy for a novel dedicated outdoor air-chilled ceiling (DOAS-CC) system with the aim of optimizing the overall system performance. The DOAS-CC system incorporates liquid desiccant dehumidification and membrane-based total heat recovery technologies. Simplified but reliable models of major components in the DOAS-CC system are firstly developed to predict the system performance. A cost function is then constructed to minimize total energy consumption while properly maintaining thermal comfort reflected by indoor air temperature and relative humidity. Genetic algorithm is used to search for optimal set-points of the supply air temperature and humidity ratio of the dedicated outdoor air subsystem as well as the supply water temperature. The performance of this strategy is tested and evaluated with different control settings in a simulated multi-zone space served by the DOAS-CC system under various weather conditions. The results show that optimized control variables produced by the optimal strategy can improve the system energy performance and maintain indoor thermal comfort.     

Optimization of water-cooled chiller system with load-based speed control

This study investigates the energy performance of chiller and cooling tower systems integrated with variable condenser water flow and optimal speed control for tower fans and condenser water pumps. Thermodynamic-behaviour chiller and cooling tower models were developed to assess how different control methods of cooling towers and condenser water pumps influence the trade-off between the chiller power, pump power, fan power and water consumption under various operating conditions. Load-based speed control is introduced for the tower fans and condenser water pumps to achieve optimum system performance. With regard to an example chiller system serving an office building, the optimal control coupled with variable condenser water flow could reduce the annual system electricity use by 5.3% and operating cost by 4.9% relative to the equivalent system using constant speed fans and pumps with a fixed set point for cooling water temperature control.     

Assessing performance and controlling operating conditions of a solar driven absorption chiller using simplified numerical models

In cooling buildings, the use of solar energy can save around 50% of primary energy. Many studies have demonstrated the interest of such systems. However, developing and improving reliability of new components, design, control, and implementation remain a major concern. The performances of solar cooling systems are greatly influenced by climatic conditions. Indeed they affect both the driving energy of the chiller and the heat rejection. It is important to mention that internal loads and control strategy also have an impact on energy performances. Therefore, assessing the energy performance during the design phase is a key point in evaluating the economic interest of an installation. Moreover, once the commissioning of the installation is accomplished, there is a need to follow through and ensure its performance, since a large number of malfunctions can affect the quality of the system. Actual performances can be very different from those calculated in the design phase.With this aim, the present article deals with the development of an absorption chiller model used in an existing solar cooling system. This installation includes a single effect absorption chiller with a nominal chilling capacity of 30 kW (EAW LB30 chiller functioning with water and lithium bromide), and it cools four classrooms of a University building in Reunion Island which is situated under a tropical climate. This pilot plant is very good monitored and can thus be used to develop and validate the absorption chiller model. The present paper first recalls the absorption principle and presents the pilot plant, the metrology, and the control strategy. Secondly, the experimental results are analysed and the steady state chiller model and also the identification method are developed. Thereafter, the simplex method is used to determine the design parameters of the machine. Finally, the simulation results are presented. The good agreement between the prediction and the experimental results allows the use of the model not only to design an installation but also to follow and control its performances.     

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.     

A simplified dynamic model of building structures integrated with shaped-stabilized phase change materials

The use of phase change materials (PCM) to enhance the building energy performance has attracted increasing attention of researchers and practitioners over the last few years. Thermodynamic models of building structures using PCMs are essential for analyzing their impacts on building energy performance at different conditions and using different control strategies. There are few PCM models of detailed physics providing good accuracy in simulating thermodynamic behavior of building structures integrated with PCM layers. However, simplified models with acceptable accuracy and good reliability are preferable in many practical applications concerning computation speed and program size particularly when involving large buildings or models are used for online applications. A simplified physical dynamic model of building structures integrated with SSPCM (shaped-stabilized phase change material) is developed and validated in this study. The simplified physical model represents the wall by 3 resistances and 2 capacitances and the PCM layer by 4 resistances and 2 capacitances respectively while the key issue is the parameter identification of the model. The parameters of the simplified model are identified using genetic algorithm (GA) on the basis of the basic physical properties of the wall and PCM layer. Two GA-based preprocessors are developed to identify the optimal parameters (resistances and capacitances) of the model by frequency-domain regression and time-domain regression respectively. Validation results show that the simplified model can represent light walls and median walls integrated with SSPCM with good accuracy.     

Cooling output optimization of an air handling unit

A data-driven optimization approach for minimization of the cooling output of an air handling unit (AHU) is presented. The models used in this research are built with data mining algorithms. The performance of dynamic models build by four different data mining algorithms is studied. A model extracted by a neural network is selected for identifying the functional mapping between specific outputs and controllable and non-controllable inputs of the AHU. To minimize the cooling output while maintaining the corresponding thermal properties of the supply air within a certain range, a bi-objective optimization model is proposed. The evolutionary strategy algorithm is applied to solve the optimization problem with the optimal control settings obtained at each time stamp. The minimized AHU’s cooling output reduces the chiller’s load, which leads to energy savings.     

Hybrid feedforward and feedback controller design for nuclear steamgenerators over wide range operation using genetic algorithm

In this paper, a simplified model with a lower order is first developed for a nuclear steam generator system and verified against realistic environments. Based on this simplified model, a hybrid multi-input and multi-out (MIMO) control system, consisting of feedforward control (FFC) and feedback control (PEC), is designed for wide range conditions by using the genetic algorithm (GA) technique. The FFC control, obtained by the GA optimization method, injects an a priori command input into the system to achieve an optimal performance for the designed system, while the GA-based FBC control provides the necessary compensation for any disturbances or uncertainties in a real steam generator. The FBC control is an optimal design of a PI-based control system which would be more acceptable for industrial practices and nuclear power plant control system upgrades. The designed hybrid MIMO FFC/FBC control system is first applied to the simplified model and then to a more complicated model with a higher order which is used as a substitute of the real system to test the efficacy of the designed control system. Results from computer simulations show that the designed GA-based hybrid MIMO FFC/FBC control can achieve good responses and robust performances. Hence, it can be considered as a viable alternative to the current control system upgrades     

Online performance evaluation of alternative control strategies for building cooling water systems prior to in situ implementation

This paper presents the online test and evaluation of the performance of five practical control strategies (fixed set-point control method, fixed approach control method, two near optimal strategies and one optimal strategy) for building cooling water systems to identify the best strategy for future field validation. All of these strategies were tested and evaluated in a simulated virtual environment similar to the situation when they are actually implemented in practice. A virtual building system representing the real building and its central chilling system was developed and used to test the operational performance of the system controlled by different strategies. The packages of each control strategy are separately computed by the application program of Matlab, as the control optimizers to identify the necessary control settings for the given condition based on the collected operation data. The data exchanger between the virtual building system and the control optimizer was managed by a software platform through a communication interface. The results showed that the optimal control strategy is more energy efficient and cost effective than the other strategies, and its computational cost is manageable and can satisfy the requirements of practical applications. This strategy is being implemented in a super high-rise building for field validation.     

Neuro-evolutionary approach applied for optimizing the PEMFC performance

A multi-objective optimization strategy, based on stacked neural network–genetic algorithm (SNN–GA) hybrid approach, was applied to study the C/PBI content on a high temperature PEMFC performance. The operating conditions of PEMFC were correlated with power density and electrochemical active surface area for electrodes. The structure of the stack was determined in an optimal form related to the contribution of individual neural networks, after applying an interpolation based procedure. Multi-objective optimization using SNN as model and GA as solving procedure provides optimal working conditions which lead to a high PEMFC performance. Simulation results were in agreement with experimental data, both for model validation and system optimization (the C/PBI content in the range of 17–21%).     

Multi-objective design optimization of a large-scale directdrive permanent magnet generator for wind energy conversion systems

This paper presents a simultaneous multiobjective optimization of a direct-drive permanent magnet synchronous generator and a three-blade horizontal-axis wind turbine for a large scale wind energy conversion system. Analytical models of the generator and the turbine are used along with the cost model for optimization. Three important characteristics of the system i.e., the total cost of the generator and blades, the annual energy output and the total mass of generator and blades are chosen as objective functions for a multi-objective optimization. Genetic algorithm (GA) is then employed to optimize the value of eight design parameters including seven generator parameters and a turbine parameter resulting in a set of Pareto optimal solutions. Four optimal solutions are then selected by applying some practical restrictions on the Pareto front. One of these optimal designs is chosen for finite element verification. A circuit-fed coupled time stepping finite element method is then performed to evaluate the no-load and the full load performance analysis of the system including the generator, a rectifier and a resistive load. The results obtained by the finite element analysis (FEA) verify the accuracy of the analytical model and the proposed method.     

Simplified ejector model for control and optimization

In this paper, a simple yet effective ejector model for a real time control and optimization of an ejector system is proposed. Firstly, a fundamental model for calculation of ejector entrainment ratio at critical working conditions is derived by one-dimensional analysis and the shock circle model. Then, based on thermodynamic principles and the lumped parameter method, the fundamental ejector model is simplified to result in a hybrid ejector model. The model is very simple, which only requires two or three parameters and measurement of two variables to determine the ejector performance. Furthermore, the procedures for on line identification of the model parameters using linear and non-linear least squares methods are also presented. Compared with existing ejector models, the solution of the proposed model is much easier without coupled equations and iterative computations. Finally, the effectiveness of the proposed model is validated by published experimental data. Results show that the model is accurate and robust and gives a better match to the real performances of ejectors over the entire operating range than the existing models. This model is expected to have wide applications in real time control and optimization of ejector systems.     

Investigating the Optimum Operational Strategy of Energy Storage Tank by Using Particle Swarm Algorithm

Optimal design of an energy storage tank system is presented in this study. Total annual cost is considered as the objective. To minimize the total annual cost, 24 design parameters including the operational strategy of the chiller in each hour during a sample day are selected. A Particle Swarm Optimization (PSO) algorithm is used for three different strategies including partial storage (PS), full storage (FS), and variable storage (VS), separately. In addition, this procedure is performed for both electrical and absorption chillers. There was a 25.21% and 13.80% improvement in total annual cost observed in the VS strategy compared with the PS and FS strategies, respectively, in the case of an electrical chiller. Furthermore, 23.47% and 8.01% improvement in total annual cost is observed in the VS strategy compared with the PS and FS strategies, respectively, in the case of an absorption chiller. Moreover, the electrical chiller was found to be more suitable in this study but no sensible difference is observed in the FS strategy. Finally the optimum results of the PSO algorithm is compared with the Genetic Algorithm (GA) and differences are reported.     

Developing a simplified model for evaluating chiller-system configurations

Optimizing system configuration is always an interest of building designers. By statistical analysis of the design data of 50 commercial buildings in Hong Kong and the performance data of 186 chiller models, it is ascertained that the chiller capacity and the fraction of full-load capacity have little influence on the system’s energy performance. Following on this, this paper presents an evaluation of the energy performance of a multiple-chiller system consisting of 2–10 equally sized chillers. Such an analysis was based on performance data from three major manufacturers. It is found that the energy efficiency of multiple-chiller system improves with a higher number of chillers, and the maximum saving is estimated to be 9.5%. Based on the results of the study, a simplified model relating energy use with number of chillers has been established. The model can help designers more quickly determine how the energy efficiency can be weighted against other factors, such as the additional plant room space and the financial implications.     

Energy management and design of centralized air-conditioning systems through the non-revisiting strategy for heuristic optimization methods

It is getting more and more popular to apply heuristic optimization methods, like genetic algorithm (GA) and particle swarm optimization (PSO), to handle various engineering optimization problems. In this paper, optimization problems of typical centralized air-conditioning systems were solved by the non-revisiting (Nr) strategy, which was proposed to be incorporated into the common heuristic methods for improving the optimization effectiveness and reliability. This approach can store the evaluated fitness values in an archive with minimal computer memory, detect the revisits and prevent them from re-evaluating. It is particularly useful for the problems formulated by dynamic simulation or detailed modeling with very demanding computational time for function evaluation. The non-revisiting strategy can facilitate the search of the global optimum by its parameter-less adaptive mutation capability. In the optimization problems of central air-conditioning systems, it was found that the NrGA and NrPSO could search better solutions at a limited number of function evaluations than the conventional GA and PSO did. The ultimate goal is to determine the required parameters for optimal design and energy management. The proposed strategy can be applied to similar types of air-conditioning or engineering optimization problems, and possibly incorporated into other kinds of heuristic optimization methods.     

Thermodynamic multi-objective optimization of a solar-dish Brayton system based on maximum power output,thermal efficiency and ecological performance

Solar-dish Brayton system driven by the hybrid of fossil fuel and solar energy is characterized by continuously stable operation, simplified hybridization, low system costs and high thermal efficiency. In order to enable the system to operate with its highest capabilities, a thermodynamic multi-objective optimization was performed in this study based on maximum power output, thermal efficiency and ecological performance. A thermodynamic model was developed to obtain the dimensionless power output, thermal efficiency and ecological performance, in which the imperfect performance of parabolic dish solar collector, the external irreversibility of Brayton heat engine and the conductive thermal bridging loss were considered. The combination of NSGA-II algorithm and decision makings was used to realize multi-objective optimization, where the temperatures of absorber, cooling water and working fluid, the effectiveness of hot-side heat exchanger, cold-side heat exchanger and regenerator were considered as optimization variables. Using the decision makings of Shannon Entropy, LINMAP and TOPSIS, the final optimal solutions were chosen from the Pareto frontier obtained by NSGA-II. By comparing the deviation index of each final optimal solution from the ideal solution, it is shown that the multi-objective optimization can lead to a more desirable design compared to the single-objective optimizations, and the final optimal solution selected by TOPSIS decision making presents superior performance. Moreover, the fitted curve between the optimal power output, thermal efficiency and ecological performance derived from Pareto frontier is obtained for better insight into the optimal design of the system. The sensitivity analysis shows that the optimal system performance is strongly dependent on the temperatures of absorber, cooling water and working fluid, and the effectiveness of regenerator. The results of this work offer benefits for related theoretic research and basis for solar energy industry.     

Optimal chiller loading by particle swarm algorithm for reducing energy consumption

Generally, huge energy consumption is required in the operation of the multi-chiller system in air-conditioning system. Concerning minimizing energy consumption, both Lagrangian method and genetic algorithm have been applied to optimize the partial loading rate in each chiller. As is demonstrated by previous studies, though the Lagrangian method could minimize energy consumption, it could not effectively execute convergence at low demands. And despite its capability to execute convergence at low demands, the genetic algorithm may not get the minimum energy consumption solution as Lagrange method did of solving the optimal chiller loading problem. As an efficient method, the particle swarm algorithm has been proposed to solving continuous parameters optimization problems. This paper applies particle swarm algorithm to minimize energy consumption of multi-chiller system. The objective function is energy consumption and the optimum parameter is the partial loading ratio of each chiller. To further testify the feasibility of the proposed method, the paper adopts two case studies to compare the results of the developed optimal model with Lagrangian method and genetic algorithm. The result of the two case studies shows that the particle swarm algorithm outperforms the genetic algorithm not only in overcoming the divergence of Lagrangian method occurring at low demands, but also in getting the minimum energy consumption solution of solving the optimal chiller loading problem.