Generation of typical meteorological years using genetic algorithm for different energy systems

Computer simulation plays an important role in investigating the thermal/energy performance of buildings and energy systems. In order to reduce the computational time and provide a consistent form of weather data, simulation run with multi-year weather files is generally avoided. In contrast, representative weather data is widely adopted. For developing typical meteorological year (TMY) weather files, Sandia method is one of the commonly adopted approaches. During the generation of TMY, different weighting factors are assigned to some key climatic indices. Currently, the values of weighting factors mainly depend on the researchers' judgement. As these weighting factors can express the relative importance of impact of a particular climatic index on the thermal/energy performance of an energy system, computer simulation using different TMYs may lead to different conclusions. Therefore, it is inappropriate to apply one single TMY for all energy systems. In this study, a novel TMY weather file generator has been developed to link up an optimization algorithm and an energy simulation program. Through four application examples (one air-conditioned building and three renewable energy systems), this weather file generator demonstrated its capability to search optimal/near optimal combinations of weighting factors for generating appropriate TMY for computer simulations of different energy systems.     

A new method to develop typical weather years in different climates for building energy use studies

Principal component analysis of 30-year long-term meteorological variables was conducted. Typical principal component years (TPCYs) were determined for Harbin, Beijing, Shanghai, Kunming and Hong Kong representing the five major architectural climates across China: severe cold, cold, hot summer and cold winter, mild, and hot summer and warm winter. In each climate zone, the TPCY was compared with the 30 individual years and the widely used typical meteorological year (TMY). The monthly principal component and the predicted total building energy consumption based on the TPCY and TMY were very close to the 30-year long-term mean estimation. TPCY for the 21st century in each of the five cities was also identified using predictions from general climate models. The TPCY approach is a good alternative to the TMY method. Firstly, predicted building energy use from TPCY is closer to the long-term estimation than that from the TMY in different climates. Secondly, because only monthly data are considered, the development of TPCY is much simpler and less time-consuming. This would have important applications in the regular updating of typical weather years for building energy studies and in the assessment of the impact of climate change on energy use in the built environment.     

Comments on “Generation of typical meteorological year for different climates of China” [Energy, 35 (2010) 1946–1953]

This is the comment to the article “Generation of typical meteorological year for different climates of China” [Energy, 35 (2010) 1946–1953].     

Effect of saturation‐temperature on the performance of a vapour‐compression refrigeration‐cycle working on different refrigerants using exergy method

In this study, the behaviour of a vapour‐compression refrigeration cycle, for different refrigerants such as NH₃, R‐12, R‐22 and HFC‐134a was investigated using the exergy method. The cooling load of the plant and the saturation‐temperature of the cold chamber were held constant, whereas the saturation‐temperatures of the evaporator and the condenser were varied from 303 to 313 K and 258 to 248 K, respectively. The irreversibility rates (or exergy destruction rates) of sub‐regions for the whole cycle, using energy and exergy analysis, were determined for each refrigerant. The effects of changes in the saturation‐temperature in the condenser and evaporator on the irreversibility rate of the cycle were obtained for each refrigerant. The relations between the total irreversibility rate of the plant and the irreversibility rate of the condenser and the evaporator were determined for different values of saturation temperatures of the condenser and the evaporator. The COP of the cycle and the rational efficiency were determined for each of the refrigerants and compared with each other. Among the refrigerants used, R‐12 was found to be the most economical refrigerant as compared with the others. Copyright © 2005 John Wiley & Sons, Ltd.     

Analysis of photovoltaic mini‐grid systems for remote locations: A techno‐economic approach

This paper presents a techno‐economic analysis of photovoltaic mini‐grid systems (PMs), using a group of remote houses in 3 locations in Nigeria, as case studies. It uses a worst‐case users' load demand approach for the design and analysis of the proposed energy system, according to international technical standards. It presents detailed capacity, yield and losses, battery state of charge (SoC), reliability, users' load demand increase (Ldi), and life cycle economic analyses by using the Hybrid Optimisation for Electric Renewables (HOMER) simulation tool. The effect of 25% Ldi is also considered in the paper. The study can be used to develop a practical energy model to address the poor energy situation in those locations when they are implemented. Results indicate that PMs of 68, 76, and 61 kW can meet the users' demand of ~63 500 kWh/year with an availability of 99.2% for the locations, respectively. By including a 30‐kVA diesel generator to the PMs' model, an availability of 100% was obtained, demonstrating that the issue of loss of energy supply for several days in the year due to users' Ldi and the cloudy days is being addressed. The results further show that although the hybrid energy systems have relatively higher initial capital, total life cycle and replacement costs, and the cost of energy, they achieve a higher reliability compared with the proposed PMs. The research can be useful for planning solar PV infrastructure for remote locations around the world.     

Energy and thermal performance of heat pipe/cooling coil systems in hot‐humid climates

The objective of this study is to evaluate the magnitude of reduction in cooling and reheat energy when a heat pipe system is incorporated with the cooling coil of an air‐conditioning system. The heat pipe/cooling coil (HP/CC) system performance is determined by several parameters that are related to both the air‐conditioner cooling coil and the heat pipe physical characteristics as well as the condition of the air entering and leaving the system. In order to appreciate the impact of these parameters and their relative influence on energy consumption and the required indoor air conditions, a simple mathematical model incorporating the parameters of HP/CC is formulated. The model describes the overall system performance at varying entering and leaving air conditions. The model is then applied to a case study as an example of an application to investigate these relationships for a better understanding of the system behaviour and the influencing design parameters. It is evident that due to the coupling nature of the heat pipe and the cooling coil actions, a unique system performance will be obtained for each combination of heat pipe effectiveness and cooling coil by‐pass factor. A proper selection of both the heat pipe and the cooling coil characteristics is found to be necessary for a satisfactory performance under the given operating conditions. Copyright © 2000 John Wiley & Sons, Ltd.     

Least‐squares based coulomb counting method and its application for state‐of‐charge (SOC) estimation in electric vehicles

Coulomb counting method is a convenient and straightforward approach for estimating the state‐of‐charge (SOC) of lithium‐ion batteries. Without interrupting the power supply, the remaining capacities of them in an electric vehicle (EV) can be calculated by integrating the current leaving and entering the batteries. The main drawbacks of this method are the cumulative errors and the time‐varying coulombic efficiency, which always lead to inaccurate estimations. To deal with this problem, a least‐squares based coulomb counting method is proposed. With the proposed method, the coulombic losses can be compensated by charging/discharging coulombic efficiency η and the measurement drift can be amended with a morbid efficiency matrix. The experimental results demonstrated that the proposed method is effective and convenient. Copyright © 2016 John Wiley & Sons, Ltd.     

Diffused introduction of Organic Rankine Cycle for biomass‐based power generation in an industrial district: a systems analysis

Specific Organic Rankine Cycle (ORC) units dedicated to biomass‐based power production have recently been developed through the introduction of novel organic working media and technology innovation. For small systems, ORC technology appears as an efficient alternative to conventional generation if also process waste heat can be exploited, as resulted in the last few years from the successful operation of several demonstration plants in Austria and Switzerland. The present study aims to investigate the impact of the introduction of ORC units in an industrial context from a system perspective, with particular reference to industrial districts, which are characterized by the concentration in small areas of a large number of medium‐ and small‐sized firms. The paper focuses on the opportunity of combining ORCs, traditional Rankine cycles and multi‐source district heating to meet energy requirements in an industrial district in North Eastern Italy. To this end, a mixed‐integer linear programming model oriented to economical optimization of the system is developed and sensitivity analysis is carried out in order to determine the conditions for the expansion of biomass‐based power generation in the analyzed industrial district and to evaluate potential for CO₂ emission reduction. Copyright © 2004 John Wiley & Sons, Ltd.     

A new modeling and solution method for optimal energy flow in electricity‐gas integrated energy system

With the increasing interdependency of electricity and gas, it is necessary to simultaneously investigate electric power system and natural gas system from the perspective of an electricity‐gas integrated energy system (EGIES). As an extension and integration of both optimal power flow (OPF) and optimal gas flow (OGF), optimal energy flow (OEF) is regarded as the cornerstone of the EGIES and lays an essential foundation for further research on the EGIES's operation and analysis considering stochastic conditions and contingency states. The objective of this paper is to develop a generalized mathematical model and a universally applicable simulation tool for the OEF problem. First, natural gas system is modeled in a way similar to electric power system according to electricity‐gas analogy analysis, where gas admittance, gas nodal admittance matrix, and the nodal equation of gas flow conservation are derived. Then, a generalized accurate OEF model is formulated by simultaneously integrating the OPF model and the OGF model as well as their coupling constraints in a unified modeling framework. Furthermore, an available hybrid optimization approach consisting of whale optimization algorithm, MATPOWER, hydraulic calculation iterative program, and nonstationary penalty function method is put forward to solve the OEF problem. The accuracy, feasibility, and applicability of the proposed modeling and solution method is finally demonstrated by analyzing Belgian 20‐node gas system combined with IEEE 30‐bus test system.     

Solution of radiative transfer in a one‐dimensional anisotropic scattering media with different boundary conditions using the DRESOR method

The DRESOR method was applied to analyze the radiative transfer process in anisotropic scattering media with different boundary conditions in this paper. The method was validated by the integral formulation of the radiative transfer equation at first. Some variation regulations about the emissivity were obtained by extensive numerical simulations. When the optical thickness of the media became very large, the emissivity converged to a constant value. The converged emissivity in the forward scattering medium was the largest and that for the backward scattering medium was the smallest. Also the converged emissivity was associated with the scattering albedo of the media. The greater the scattering albedo was, the smaller the converged emissivity was. When the scattering albedo decreased to zero the converged emissivity reached the blackbody emissivity at the same temperature. Furthermore, different boundary conditions were considered. The results showed that if the temperature of the medium and the boundary was equal, the intensity at boundary was the same as that for the blackbody emission at the same temperature, whether the boundary reflectivity was 1.0 or not. When the temperature of the boundary was lower than that of the medium, the boundary emissivity can reach 1.0 only if ρ=1.0. Finally, the radiation flux was studied with different phase functions and different boundary conditions. © 2008 Wiley Periodicals, Inc. Heat Trans Asian Res, 37(3): 138–152, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/htj.20198     

Three‐dimensional modeling method for heat exchange of rotary air preheater in coal‐fired power plant

A numerical modeling method based on a 3‐D heat transfer model and duct models for a dual‐sectional rotary air preheater have been developed in this paper. Owing to different boundary conditions for the heat transfer model obtained by modeling and calculation of the ducts, this method is capable of calculating the 3‐D metal and fluid temperature fields at different radial locations in the rotary air preheater along the rotor height as well as the rotating period, and furthermore, it can calculate temperature and flow field inside the flow passage as well. A case study with a dual‐sectional rotary air preheater of a typical 300 MW_e unit used as the research object is presented in this paper. The calculation results accord well with those obtained by verified numerical methods in published literature. The difference between the calculated and the measured outlet fluid temperature of the rotary air preheater is smaller than 3 °C. The numerical modeling method presented in this paper is proved to have high precision and is beneficial for the secure and economic operation of a rotary air preheater as well as the whole unit. © 2010 Wiley Periodicals, Inc. Heat Trans Asian Res; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/htj.20325     

Investigation of catalyst layer defects in catalyst‐coated membrane for PEMFC application: Non‐destructive method

The commercialization of polymer electrolyte membrane fuel cells has been hindered by durability problems caused by defects in the manufacturing process. We demonstrate for the first time a non‐destructive, non‐contact method that uses optical microscopy and image analysis to identify defects that may lead to failure in catalyst‐coated membranes (CCMs) of polymer electrolyte membrane fuel cells. This method is applied to 2 commercial CCMs produced by the decal transfer technique. Defects in the catalyst layer (CL) at the beginning‐of‐life (BOL) are characterized in terms of their initial size and shape, and their propagation is tracked as the CCMs are aged in a non‐reactive environment. The defected area in one of the commercial CCMs increases from approximately 2.4% of the total CL area at BOL to 10.5% by end‐of‐life (EOL). BOL defects in the CL are found to propagate faster in the CCMs stored for 2 years under atmospheric conditions compared with freshly manufactured CCMs with narrow CL defects. Image analysis of another commercial CCM shows the presence of pores with diameters between 5 and 25 μm that comprise 52% of the total pore area in the CL. Other defects such as scratches and missing/empty catalyst areas are identified and characterized, providing a framework for quality control applications. Finally, the effect of defects on fuel cell performance is characterized by measurement of the open‐circuit voltage (OCV). These experiments show that CCMs with a large number of cracks in the CL exhibit a voltage loss of 2.55 mV/hr, whereas CCMs with thin/missing/empty CL defects show a loss of 1.12 mV/hr.     

Improved thermodynamic and aerodynamic design method and off‐design performance analysis of a radial inflow turbine for ORC system

Radial inflow turbine is one of the crucial components of organic Rankine cycle (ORC) system, which has great impact on the performance of system. R245fa was selected as the working fluid to recycle the waste heat source with a temperature of 350 to 400 K. The genetic algorithm (GA) was employed in thermodynamic design to optimize the 10 key design parameters, which are needed in aerodynamic design of the ORC turbine. Isotropic efficiency was the fitness function of 10 key variables in GA. The three‐dimensional geometry model was built based on the thermodynamic and aerodynamic design and then was imported into the commercial software ANSYS‐CFX to conduct viscous numerical simulation. Based on the three‐dimensional simulation, the off‐design performance in different mass flow rate, static inlet temperature coupled with different rotational speed was investigated respectively. The results show that at design condition, the maximum efficiency deviation is only 2.5% with the rotational speed variations among the range of 10%, so the radial inflow turbine designed in this research possesses great off‐design performance.     

Prediction of the extreme wind speed in the mixed climate region by using Monte Carlo simulation and measure‐correlate‐predict method

The extreme wind speed at an offshore location was predicted using Monte Carlo simulation (MCS) and measure‐correlate‐predict (MCP) method. The Gumbel distribution could successfully express the annual maximum wind speed of extratropical cyclone. On the other hand, the estimated extreme wind speed of tropical cyclones by analytical probability distribution shows larger uncertainty. In the mixed climate like Japan, the extreme wind speed estimated from the combined probability distribution obtained by MCP and MCS methods agrees well with the observed data as compared with the combined probability distribution obtained by the MCP method only. The uncertainty of extreme wind speed due to limited observation period of wind speed and pressure was also evaluated by the Gumbel theory and Monte Carlo simulation. As a result, it was found that the uncertainty of 50 year recurrence wind speed obtained by MCS method is considerably smaller than that obtained by MCP method in the mixed climate. Copyright © 2014 John Wiley & Sons, Ltd.     

Determination of the enthalpy of PCM as a function of temperature using a heat‐flux DSC—A study of different measurement procedures and their accuracy

Thermal energy storage by latent heat allows storing high amounts of energy working in narrow margins of temperature. The use of phase change material (PCM) for the latent heat storage has been studied in different applications and it has been commercialized in containers to transport blood, products sensible to temperature, to decrease their energy demand. The use of PCM in cooling and refrigeration has been attracting a lot of interest lately, but for all applications, the properties of these materials need to be known with sufficient accuracy. Regarding heat storage, it is necessary to know the enthalpy as a function of temperature. The most widely used calorimeter is the heat‐flux differential scanning calorimetry (hf‐DSC). The objective of this study is to investigate different methods for hf‐DSC analysis, namely the dynamic method and the step method, and to test their accuracy in the determination of enthalpy–temperature relationship of PCM. For the dynamic method, a strong influence of heating/cooling rate was observed. For the step method, the resulting enthalpy–temperature relationship is independent of heating/cooling rate. Commercial PCM RT27 was chosen as sample material to avoid subcooling and kinetic effects in the test measurements. The approach introduced in this study can be used to carry out similar investigations for other classes of PCM and/or other DSC instruments. Copyright © 2008 John Wiley & Sons, Ltd.     

An artificial neural network‐based condition monitoring method for wind turbines,with application to the monitoring of the gearbox

Major failures in wind turbines are expensive to repair and cause loss of revenue due to long downtime. Condition‐based maintenance, which provides a possibility to reduce maintenance cost, has been made possible because of the successful application of various condition monitoring systems in wind turbines. New methods to improve the condition monitoring system are continuously being developed. Monitoring based on data stored in the supervisory control and data acquisition (SCADA) system in wind turbines has received attention recently. Artificial neural networks (ANNs) have proved to be a powerful tool for SCADA‐based condition monitoring applications. This paper first gives an overview of the most important publications that discuss the application of ANN for condition monitoring in wind turbines. The knowledge from these publications is utilized and developed further with a focus on two areas: the data preprocessing and the data post‐processing. Methods for filtering of data are presented, which ensure that the ANN models are trained on the data representing the true normal operating conditions of the wind turbine. A method to overcome the errors from the ANN models due to discontinuity in SCADA data is presented. Furthermore, a method utilizing the Mahalanobis distance is presented, which improves the anomaly detection by considering the correlation between ANN model errors and the operating condition. Finally, the proposed method is applied to case studies with failures in wind turbine gearboxes. The results of the application illustrate the advantages and limitations of the proposed method. Copyright © 2017 John Wiley & Sons, Ltd.     

A methodology for detection and classification of power quality disturbances using a real‐time operating system in the context of home energy management systems

Demand‐side management comprises a portfolio of actions on the consumers' side to ensure reliable power indices from the electrical system. The home energy management system (HEMS) is used to manage the consumption and production of energy in smart homes. However, the technology of HEMS architecture can be used for the detection and classification of power quality disturbances. This paper presents low‐voltage metering hardware that uses an ARM Cortex M4 and real‐time operating system to detect and classify power quality disturbances. In the context of HEMS, the proposed metering infrastructure can be used as a smart meter, which provides the service of power quality monitoring. For this type of application, there is a need to ensure that the development of this device has an acceptable cost, which is one of the reasons for the choice of an ARM microprocessor. However, managing a wide range of operations (data acquisition, data preprocessing, disturbance detection and classification, energy consumption, and data exchange) is a complex task and, consequently, requires the optimization of the embedded software. To overcome this difficulty, the use of a real‐time operating system provided by Texas Instruments (called TI‐RTOS) is proposed with the objective of managing operations at the hardware level. Thus, a methodology with low computational cost has been defined and embedded. The proposed approach uses a preprocessing stage to extract some features that are used as inputs to detect and classify disturbances. In this way, it was possible to evaluate and demonstrate the performance of the embedded algorithm when applied to synthetic and real power quality signals. Consequently, it is noted that the results are significant in the analysis of power quality in a smart grid scenario, as the smart meter offers low cost and high accuracy in both detecting (an accuracy rate above 90%) and classifying (an average accuracy rate above 94%) disturbances.     

Break‐even analysis for the storage of PV in power distribution grids

The integration of renewable energy systems poses major challenges on distribution grid operators. Because of the strong growth rates of the installation of photovoltaic (PV) and wind generators, huge needs for reinforcements in grids are expected. Next to conventional reinforcements (with additional and/or bigger dimensioned cables and transformers) also the introduction of decentralized storage systems seems to be promising. In this paper, an economical approach is presented enabling the calculation of break‐even points for storage systems as a substitute to conventional grid reinforcements. The dynamic profitability calculation considers main influencing cost drivers for both alternatives, including operational and capital expenditures. Furthermore, the calculation of benefits of decentralized storage systems for upstream grid levels is also integrated. To enable these calculations, a storage model is derived oriented on battery characteristics to determine main requirements of a storage system to be able to integrate renewable energy systems. These elaborations are reflected on a real‐world distribution grid faced with reinforcement needs due to the integration of PV. For this, measured data for the PV generator are integrated as well. The analysis reveal break‐even points for the storage asset ranging between 100 and 500 € per kWh of installed capacity, depending on the lifetime of the storage asset and the costs for the substitute. Furthermore, main influencing parameters are evaluated using a sensitivity analysis. It is shown that the profitability can be increased significantly if not all peaks of PV generation need to be stored. Furthermore, the analysis of the operation for 1 year indicates that a combined operation of the storage asset (not only oriented on grid objectives such as peak shaving, but considering also the objectives of further stakeholders such as energy traders) seems to be reasonable for increasing the profitability and incentivizing a larger market penetration of storage assets. Copyright © 2013 John Wiley & Sons, Ltd.     

A novel real‐time energy management strategy for plug‐in hybrid electric vehicles based on equivalence factor dynamic optimization method

The universal adaptive equivalent consumption minimization strategy (A‐ECMS) has the potential of being implemented in real‐time for plug‐in hybrid electric vehicles (PHEVs). However, the imprecise prediction of a long‐term future driving cycle and biggish computation burdens remain the barriers for further real vehicle application. Thus, it is of great significance to develop a real‐time optimal energy management strategy for PHEVs by weakening the influence of future driving cycle to the control accuracy and improving its computation efficiency. In this paper, a novel real‐time energy management strategy for PHEVs based on equivalence factor (EF) dynamic optimization method is proposed. Firstly, a novel proportional plus integral adaption law for calculating the dynamic optimal EF is established for A‐ECMS using only instantaneous information of current vehicle speed and battery state of charge. Second, three key coefficients are obtained and converted into a three‐dimensional look up tables, so as to determine the dynamic optimal EF. Finally, the method of fast searching the optimal engine torque is proposed, which significantly enhances the computational efficiency. Compared with A‐ECMS, the computational time of A‐ECMS2 is decreased near 94.8% and the deviation of fuel consumption is controlled within 4.4%. Both the numerical results and hardware‐in‐loop results prove that the proposed novel energy management strategy A‐ECMS2 has better real‐time performance and less computing burden than the general A‐ECMS.     

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Lithium‐ion battery state‐of‐health estimation is one of the vital issues for electric vehicle safety. In this work, a joint model‐based and data‐driven estimator is developed to achieve accurate and reliable state‐of‐health estimation. In the estimator, an increase in ohmic resistance extracted from the Thevenin model is defined as the health indicator to quantify the capacity degradation. Then, a linear state‐space representation is constructed based on the data‐driven linear regression. Furthermore, the Kalman filter is introduced to trace capacity degradation based on the novel state space representation. A series of battery aging datasets with different dynamic loading profiles and temperatures are obtained to demonstrate the accuracy and robustness of the proposed method. Results show that the maximum error of the Kalman filter is 2.12% at different temperatures, which proves the effectiveness of the proposed method.