Power flow control of grid connected hybrid renewable energy system using hybrid controller with pumped storage

This paper presents a grid-connected HRES using a hybrid controller with PHS for optimal power flow control and minimizing the production cost. The novelty of the proposed approach is the joined execution of the SSA and CSA named as SSA-CS are apparently a very new metaheuristic algorithm. Moreover, the proposed method is the cost-effective power production of the microgrids and effective utilization of renewable energy sources without wasting the available energy. Here, the energy sources in particular PV system, WT, MT and battery with PHS are utilized to generate the power of the MG system. In the proposed approach, the required power demand of the energy system is predicted by the ANN technique. After that, the production cost minimization is done in view of the anticipated load demand by utilizing the optimization approaches to be a specific SSA-CS algorithm. The result of the proposed approach is actualized in the MATLAB/Simulink working platform. The performance of the proposed approach is examined by comparing the current methodologies such as SSA and PSO with the proposed SSA-CS approach. The simulation results show that the proposed method generates maximum power and furthermore the proposed framework has less production cost in light of the power demand.     

Modeling and optimization of a typical fuel cell–heat engine hybrid system and its parametric design criteria

A theoretical modeling approach is presented, which describes the behavior of a typical fuel cell–heat engine hybrid system in steady-state operating condition based on an existing solid oxide fuel cell model, to provide useful fundamental design characteristics as well as potential critical problems. The different sources of irreversible losses, such as the electrochemical reaction, electric resistances, finite-rate heat transfer between the fuel cell and the heat engine, and heat-leak from the fuel cell to the environment are specified and investigated. Energy and entropy analyses are used to indicate the multi-irreversible losses and to assess the work potentials of the hybrid system. Expressions for the power output and efficiency of the hybrid system are derived and the performance characteristics of the system are presented and discussed in detail. The effects of the design parameters and operating conditions on the system performance are studied numerically. It is found that there exist certain optimum criteria for some important parameters. The results obtained here may provide a theoretical basis for both the optimal design and operation of real fuel cell–heat engine hybrid systems. This new approach can be easily extended to other fuel cell hybrid systems to develop irreversible models suitable for the investigation and optimization of similar energy conversion settings and electrochemistry systems.     

An innovative optimal power allocation strategy for fuel cell, battery and supercapacitor hybrid electric vehicle

An optimal design of a three-component hybrid fuel cell electric vehicle comprised of fuel cells, battery, and supercapacitors is presented. First, the benefits of using this hybrid combination are analyzed, and then the article describes an active power-flow control strategy from each energy source based on optimal control theory to meet the demand of different vehicle loads while optimizing total energy cost, battery life and other possible objectives at the same time. A cost function that minimizes the square error between the desired variable settings and the current sensed values is developed. A gain sequence developed compels the choice of power drawn from all devices to follow an optimal path, which makes trade-offs among different targets and minimizes the total energy spent. A new method is introduced to make the global optimization into a real-time based control. A model is also presented to simulate the individual energy storage systems and compare this invention to existing control strategies, the simulation results show that the total energy spent is well saved over the long driving cycles, also the fuel cell and batteries are kept operating in a healthy way.     

计及多类型电储能的综合能源系统优化运行对比分析研究

综合能源系统是泛在电力物联网的重要组成部分,多种类型的储能设备在其中发挥了重要作用。目前的多数研究只考虑了单一类型电储能对综合能源系统优化运行的影响,或是通过对多种能量形式的储能进行联合优化调度展开研究。至于多种类型电储能在综合能源系统中的优化运行结果以及定量对比分析工作还开展的较少。鉴于此,文中提出了计及飞轮储能、电化学储能和超导储能等多类型电储能的综合能源系统优化运行模型,以系统日运行成本最低为目标,并采用人工智能算法来保障预测数据的准确性,利用混合整数线性规划方法进行求解,同时设置了3种运行场景,对不同场景下的优化结果进行了定量分析对比。算例分析表明,采用电化学储能可获得最低的系统运行成本以及最为显著的售电交易量;而超导储能具备充放调度灵活性高,充放潜力大的特点。     

Solid-state diffusion limitations on pulse operation of a lithium ion cell for hybrid electric vehicles

A 1D model based on physical and electrochemical processes of a lithium ion cell is used to describe constant current and hybrid pulse power characterization (HPPC) data from a 6 Ah cell designed for hybrid electric vehicle (HEV) application. An approximate solution method for the diffusion of lithium ions within active material particles is formulated using the finite element method and implemented in the previously developed 1D electrochemical model as an explicit difference equation. Reaction current distribution and redistribution processes occurring during discharge and current interrupt, respectively, are driven by gradients in equilibrium potential that arise due to solid diffusion limitations. The model is extrapolated to predict voltage response at discharge rates up to 40 C where end of discharge is caused by negative electrode active material surface concentrations near depletion. Simple expressions are derived from an analytical solution to describe solid-state diffusion limited current for short duration, high-rate pulses.     

Hybrid optimization algorithm for modeling and management of micro grid connected system

In this paper, a hybrid optimization algorithm is proposed for modeling and managing the micro grid (MG) system. The management of distributed energy sources with MG is a multi-objective problem which consists of wind turbine (WT), photovoltaic (PV) array, fuel cell (FC), micro turbine (MT) and diesel generator (DG). Because, perfect economic model of energy source of the MG units are needed to describe the operating cost of the output power generated, the objective of the hybrid model is to minimize the fuel cost of the MG sources such as FC, MT and DG. The problem formulation takes into consideration the optimal configuration of the MG at a minimum fuel cost, operation and maintenance costs as well as emissions reduction. Here, the hybrid algorithm is obtained as artificial bee colony (ABC) algorithm, which is used in two stages. The first stage of the ABC gets the optimal MG configuration at a minimum fuel cost for the required load demand. From the minimized fuel cost functions, the operation and maintenance cost as well as the emission is reduced using the second stage of the ABC. The proposed method is implemented in the Matlab/Simulink platform and its effectiveness is analyzed by comparing with existing techniques. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the problem.     

Implementation of self-adaptive Harris Hawks Optimization-based energy management scheme of fuel cell-based electric power system

This paper presents a hybrid Fuel Cell-based Power System (FCPS) consisting of fuel cell and hybrid Energy Storage Systems (ESSs), including a battery with high energy density and supercapacitor with high power density to overcome the sudden load demand change and improving the reliability of the delivered power. Any hybrid power system needs Energy Management Strategies (EMS) to balance the power between the different energy sources. In this paper, a comparative analysis of three energy management strategies, including the state machine control method, the classical PI control method and equivalent consumption minimization strategy (ECMS) is performed. The paper's main objective is enhancing the DC-bus voltage profile of a hybrid fuel cell/battery/supercapacitor power system equipped with the developed under-mentioned EMS by using a hybrid modified optimization technique that combines Harris Hawks optimization (HHO) and Sine Cosine Algorithm (SCA). The new hybrid HHO-SCA is employed to determine the optimal control parameters of the DC-bus voltage controller, which significantly assists in enhancing the DC-bus voltage profile as well as the performance of the applicable ESS in terms of improving efficiency and SoC. The effectiveness of the suggested control schemes is simulated using MATLAB/SIMULINK software. The simulation results confirmed that the proposed HHO-SCA is superior and efficient in improving the DC-bus voltage.     

A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems

The power management strategy (PMS) plays an important role in the optimum design and efficient utilization of hybrid energy systems. The power available from hybrid systems and the overall lifetime of system components are highly affected by PMS. This paper presents a novel method for the determination of the optimum PMS of hybrid energy systems including various generators and storage units. The PMS optimization is integrated with the sizing procedure of the hybrid system. The method is tested on a system with several widely used generators in off-grid systems, including wind turbines, PV panels, fuel cells, electrolyzers, hydrogen tanks, batteries, and diesel generators. The aim of the optimization problem is to simultaneously minimize the overall cost of the system, unmet load, and fuel emission considering the uncertainties associated with renewable energy sources (RES). These uncertainties are modeled by using various possible scenarios for wind speed and solar irradiation based on Weibull and Beta probability distribution functions (PDF), respectively. The differential evolution algorithm (DEA) accompanied with fuzzy technique is used to handle the mixed-integer nonlinear multi-objective optimization problem. The optimum solution, including design parameters of system components and the monthly PMS parameters adapting climatic changes during a year, are obtained. Considering operating limitations of system devices, the parameters characterize the priority and share of each storage component for serving the deficit energy or storing surplus energy both resulted from the mismatch of power between load and generation. In order to have efficient power exploitation from RES, the optimum monthly tilt angles of PV panels and the optimum tower height for wind turbines are calculated. Numerical results are compared with the results of optimal sizing assuming pre-defined PMS without using the proposed power management optimization method. The comparative results present the efficacy and capability of the proposed method for hybrid energy systems.     

Current status of research on optimum sizing of stand-alone hybrid solar–wind power generation systems

Solar and wind energy systems are omnipresent, freely available, environmental friendly, and they are considered as promising power generating sources due to their availability and topological advantages for local power generations. Hybrid solar–wind energy systems, uses two renewable energy sources, allow improving the system efficiency and power reliability and reduce the energy storage requirements for stand-alone applications. The hybrid solar–wind systems are becoming popular in remote area power generation applications due to advancements in renewable energy technologies and substantial rise in prices of petroleum products. This paper is to review the current state of the simulation, optimization and control technologies for the stand-alone hybrid solar–wind energy systems with battery storage. It is found that continued research and development effort in this area is still needed for improving the systems’ performance, establishing techniques for accurately predicting their output and reliably integrating them with other renewable or conventional power generation sources.     

A novel predictive power flow control strategy for hydrogen city rail train

Hydrogen-based vehicular traction has already reached a mature technological level and can replace the more polluting diesel engines. The adoption of this technology can also alleviate the carbon footprint issue of the rail trains running on non-electrified lines.This study presents a model and a numerical performance analysis of an electric hybrid train in an urban context. The train uses hydrogen as fuel and operates over non-electrified lines with zero local emission.The electric traction motors of the train are fed by a hybrid power unit consisting of several hydrogen fuel cell stacks operating independently in on/off mode and a set of flywheel energy storage devices.Each component of the power train is modeled separately and its operating limits are chosen on the base of technical literature.An innovative predictive logic to manage power flows is defined and proposed with the aim to minimize the fuel consumption. Furthermore, this approach uses a regenerative electrical braking and eliminates dissipative devices, like rheostats, which are commonly utilized onboard electric trains.This predictive approach is based on the optimal management of the power unit components according to the advanced knowledge of the data of the rail vehicle, the characteristics of path, drive cycle and payload for an established route.The fuel cell stacks operate accordingly to the average traction power requirement in each railway line section, whereas the flywheel energy storage system manages the dynamic power.A parametric model of the system and a respective software tool have been developed; this implementation, that incorporates many tunable parameters of the train and rail path, is able to simulate the rail train operating on a specific railway path by implementing the novel control strategy.An existing single track non-electrified line, designed again for urban service, has been selected as a case study to evaluate the performance of the proposed system.The specific fuel consumptions obtained with the novel control strategy and with a single fuel cell system operating at constant power are compared under the same operating conditions.The results highlight that significant fuel savings can be achieved.     

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%).     

Design and techno-economical optimization for stand-alone hybrid power systems with multi-objective evolutionary algorithms

The optimal design of the hybrid energy system can significantly improve the economical and technical performance of power supply. However, the problem is formidable because of the uncertain renewable energy supplies, the uncertain load demand, the nonlinear characteristics of some components, and the conflicting techno-economical objectives. In this work, the optimal design of the hybrid energy system has been formulated as a multi-objective optimization problem. We optimize the techno-economical performance of the hybrid energy system and analyse the trade-offs between the multi-objectives using multi-objective genetic algorithms. The proposed method is tested on the widely researched hybrid PV-wind power system design problem. The optimization seeks the compromise system configurations with reference to three incommensurable techno-economical criteria, and uses an hourly time-step simulation procedure to determine the design criteria with the weather resources and the load demand for one reference year. The well-known efficient multi-objective genetic algorithm, called NGAS-II (the fast elitist non-dominated sorting genetic algorithm), is applied on this problem. A hybrid PV-wind power system has been designed with this method and several methods in the literature. The numerical results demonstrate that the proposed method is superior to the other methods. It can handle the optimal design of the hybrid energy system effectively and facilitate the designer with a range of the design solutions and the trade-off information. For this particular application, the hybrid PV-wind power system using more solar panels achieves better technical performance while the one using more wind power is more economical. Copyright © 2006 John Wiley & Sons, Ltd.     

针对并网型风储微网的飞轮储能阵列系统控制方法CSCD

由于新能源发电存在波动性和间歇性,其大量接入时会给电网运行带来新的困难,而利用储能技术提高新能源的可调度性是当前研究的热点。为缓解新能源并网对电力系统的不良影响,针对并网型风储微网提出了一种基于飞轮储能阵列系统的分层优化控制方法,上层优化中心根据功率缺额和各台飞轮的转速建立相应充/放电优化模型,并求解相应飞轮的功率参考值;下层飞轮控制器采用双模双环控制方法,实现飞轮转速和输出功率的控制,最后通过MATLAB/Simulink仿真验证了所提控制方法的有效性和可行性。     

Toward the optimization of operating conditions for hydrogen polymer electrolyte fuel cells

A study is performed to find the optimal operating conditions of hydrogen polymer electrolyte fuel cells using an efficient optimization approach based on validated multi-resolution fuel cell simulation tool developed in house. Through the design of experiment method, a set of designed simulation runs were carried out using the fuel cell simulation tool. Based on the simulation results, an analytic metamodel was then constructed using the radial basis function approach. A feasible sequential quadratic programming scheme was then employed to optimize the metamodel to achieve the global optimal solutions. To illustrate the optimization approach, four control parameters including cell temperature, cathode stoichiometry, cathode pressure, and cathode relative humidity were considered. The optimization objective is defined as the maximization of the overall efficiency of the fuel cell system under ideal or realistic system assumptions. The study shows that different optimal solutions exist for different system assumptions, as well as different current loading levels, classified into small, medium, and large current densities. The approach adopted in this study is generic and can be readily applied to a larger number of control parameters and further to the fuel cell design optimizations.     

Comparison of commercial supercapacitors and high-power lithium-ion batteries for power-assist applications in hybrid electric vehicles: I. Initial characterization

Commercial supercapacitors, also known as ultracapacitors or electrochemical capacitors, from Saft, Maxwell, Panasonic, CCR, Ness, EPCOS, and Power Systems were tested under constant current and constant power discharges to assess their applicability for power-assist applications in hybrid electric vehicles (HEVs). Commercial lithium-ion batteries from Saft and Shin-Kobe were also tested under similar conditions. Internal resistances were measured by electrochemical impedance spectroscopy (EIS), as well as by the “iR drop” method. Self discharge measurements were also recorded. Compared with earlier generations of supercapacitors, the cells showed improved current and power capability. However, their energy densities are still too low to meet goals set by Partnership for a New Generation of Vehicles (PNGV) for HEV propulsion. Cells that use acetonitrile as the electrolyte solvent yield better performance, although safety issues need to be addressed. New high-power lithium-ion batteries show high energy densities, with high power capabilities.     

Adaptive real-time optimal energy management strategy based on equivalent factors optimization for hybrid fuel cell system

Fuel cell, a new kind of energy supply equipment, has several advantages such as high efficiency, low noise, and no emission. Proton exchange membrane fuel cell (PEMFC) is considered to have the potential to take the place of the conventional engine on unmanned underwater vehicle (UUV). Besides the power sources in the hybrid power system, the energy management system (EMS) is crucial to operating performance. In this paper, an on-line adaptive equivalent hydrogen consumption minimization strategy (ECMS) is proposed to solve the problem of prior knowledge demand and poor adaptability of current energy management algorithms. In this presented method, a battery state of charge (SOC) constituted penalty term is designed to calculate the equivalent factor (EF), and then the equivalent factor obtained by optimization is substituted into the original objective equation to realize the real-time energy regulation. In this paper, a typical UUV load curve is used to verify the control effect under different working conditions, and the performance is compared with three conventional algorithms’. Simulation results show that the hydrogen consumption of proposed algorithm is close to the optimal solution obtained in offline environment, and it is reduced by more than 3.79% compared with the traditional online methods.     

A new perspective in optimum sizing of hybrid renewable energy systems: Consideration of component performance degradation issue

The ever increasing demand for energy and the concerns on the environmental sustainability issue all around the world lead to more interest in alternative sources for energy production. However, as the current costs of the alternative sources such as solar, wind energy conversion systems etc. are relatively higher as compared to the conventional means of energy production, an optimum sizing approach is quite necessary in order to avoid over-sizing of such systems without lowering the reliability of load demand supply in all possible conditions including the variability of meteorological conditions or the changing power demand of load. There are many research papers available in the literature dealing with this optimum sizing issue. Even the mentioned papers significantly contribute to the wider penetration of such sources, none of them consider the power output degradation of alternative energy sources due to aging during their pre-defined operating life time. Besides, there are a few studies utilizing detailed dynamic models of energy sources apart from first-degree linear equations based models that may fall short in presenting the exact dynamics of the related system. Thus, an “observe and focus” algorithm based optimization of a hybrid alternative energy system considering the power output degradation and detailed models of each hybrid system component is performed in this study. Related details presented within the paper can provide a new perspective in optimum sizing of such hybrid systems and may further be considered in future updates of famous sizing software programs commercially or freely available in websites of several laboratories or universities.     

Optimum Pareto design of non-linear predictive control with multi-design variables for PEM fuel cell

The control process of Proton Exchange Membrane Fuel Cells (PEMFCs) is a difficult task due to the non-linearities and uncertainties associated with the electrochemical processes governing it. Designing of a non-linear controller based on model predictive control for PEMFCs is presented in some previous works to regulate the cell voltage or power output based on just one of the input variables like hydrogen pressure or operating temperature respectively, but they use a constant signal for other important control variables due to computational limitations in on-line optimization of multivariable highly non-linear system. In this paper, by use of Approximate Predictive Control (APC) method based on neural network model, the whole control process is designed with three input variables. Operating temperature and hydrogen pressure are assumed as control design variables and current density as measured disturbance to manage the cell voltage. Moreover, multi-objective optimization based on multi-objective uniform-diversity genetic algorithm (MUGA) is used for optimal selection of the parameters of controller. The comparison of the obtained results with those in literature demonstrates the superiority of the results of this work.     

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.