• Energy Research
• AU close
• ES close
• Applied Energy close

Abstract The present energy system faces at least two challenges. For one thing, the power system’s stability is challenged by the increasing penetration of variable renewable energies, especially wind power, due to its fluctuation and intermittency. For the other, the transport sector is facing enormous difficulty to decarbonize. This paper proposes a new energy system that integrates the hydrogen production and distribution system to the combined cooling, heating and power (CCHP) system with significant wind power to solve these two challenges simultaneously. The new energy system can meet the energy needs of the building. At the same time, the wind power utilization rate reaches 92.6%, and the typical daily hydrogen production capacity in winter, transition season and summer is 500 kg, 500 kg and 266 kg, respectively. The system’s energy efficiency is 72%, and the energy of the system is utilized efficiently. By comparison, the new system can reduce costs and carbon dioxide emissions, save primary energy, and effectively improve energy efficiency.

This paper is focused on the development of a model for achieving optimal control of the cooling system of a polymer electrolyte membrane fuel cell (PEMFC)-based cogeneration system. This model is developed to help facilitate the development and application of control strategies to maximize the energy efficiencies of PEMFCs, so that the costs associated with electric and thermal generation can be reduced. The results of experimental analysis conducted using an actual PEMFC-based combined heat and power system that can produce 600 W of electrical power are presented. Then, the development and validation of a simulation model of the experimental system are discussed. This model is based on a combination of an artificial neural network (ANN) with a non-linear autoregressive exogenous configuration and a 3D lookup table (LUT) that updates the data input into the ANN as a function of the electrical power demand and the flow rate and input temperature of the coolant fluid. Due to the nonlinearity of the data contained in the 3D LUT, an algorithm based on linear interpolation and shape-preserving piecewise cubic Hermite dynamic functions is implemented to interpolate the data in 3D. As a result, the model can predict the outlet temperature of the coolant fluid and hydrogen consumption rate of the PEMFC as functions of the inlet temperature and flow rate of the coolant fluid and the electrical power demand. The proposed model exhibits high accuracy and can be used as a black box for the development of new optimization strategies. University of The Basque Country - UPV/EHU [UFI 11/28]

Abstract In this paper, R32 is investigated as a replacement refrigerant for R410A. The Global Warming Potential (GWP) of R32 is only 675, 32% of that of R410Awhich has a GWP of 2088. Theoretical and experimental investigations are carried out on the performance of the condensation process within a mini-channel tube. Mini-channel heat exchangers technology allows reducing refrigerant charge and lets use flammable refrigerants. Due to the aspect ratio, high heat transfer coefficients are also registered. The experimental data recorded show that, for any given saturation temperature or refrigerant mass velocity, both the heat transfer coefficient and the frictional pressure gradient are always higher for R32. So, a numerical analysis based on the experimental data was developed to determinate which refrigerant performs better. The results of this numerical analysis show that, although at high refrigerant mass velocities R410A performs better, a given heat power can be always achieved with lower mass velocities and thus with a lower compressor power input when using R32. Therefore, it can be concluded that using R32 in a mini-channel condenser reduces the environmental impact and improves the energy efficiency of the system.

Sensorless control of Electric Vehicle (EV) drives is considered to be an effective approach to improve system reliability and to reduce component costs. In this paper, relevant aspects relating to the sensorless operation of EVs are reported. As an initial contribution, a hybrid sensorless control algorithm is presented that is suitable for a variety of synchronous machines. The proposed method is simple to implement and its relatively low computational cost is a desirable feature for automotive microprocessors with limited computational capabilities. An experimental validation of the proposal is performed on a full-scale automotive grade platform housing a 51¿kW Permanent Magnet assisted Synchronous Reluctance Machine (PM-assisted SynRM). Due to the operational requirements of EVs, both the strategy presented in this paper and other hybrid sensorless control strategies rely on High Frequency Injection (HFI) techniques, to determine the rotor position at standstill and at low speeds. The introduction of additional high frequency perturbations increases the power losses, thereby reducing the overall efficiency of the drive. Hence, a second contribution of this work is a simulation platform for the characterization of power losses in both synchronous machines and a Voltage Source Inverters (VSI). Finally, as a third contribution and considering the central concerns of efficiency and autonomy in EV applications, the impact of power losses are analyzed. The operational requirements of High Frequency Injection (HFI) are experimentally obtained and, using state-of-the-art digital simulation, a detailed loss analysis is performed during real automotive driving cycles. Based on the results, practical considerations are presented in the conclusions relating to EV sensorless control. Peer Reviewed

Fossil fuels are an integral part of the US energy portfolio, playing a prominent role for current and future domestic energy security. A sustainable, low-carbon future will require CO2 to be captured from major coal and natural gas power plants. However, fossil fuel electricity generation CO2 emissions are typically highly variable throughout each day with daily generation profiles varying greatly between plants. We demonstrate that understanding this variability is absolutely critical for setting a suitable carbon price as well as identifying if and how much CO2 a power plant will capture. For example, we show that a CO2 emissions price (or tax) of anywhere between $85/tCO2 and $135/tCO2 will be required to incentivize a gas power plant to manage all its capturable CO2; this range is solely due to differences in CO2 emissions profile. Further, we show that the setting a carbon price is very sensitive to system-wide costs including the CO2 value for enhanced oil recovery and, in particular, the costs for CO2 transport and storage. We also find that, even though coal-fired plants are more CO2-intensive and thus incur greater CO2 management costs, coal plants require a significantly lower carbon price ($15/tCO2 lower) in order to encourage CO2 capture. We conclude that integrating fossil fuel power, particularly natural gas, into a large-scale CO2 capture and storage system is a complex problem that will require detailed research and modeling.

Abstract We present a mathematical model to diagnose HVAC systems in buildings based upon the analysis of a small number of ambient state variables. In particular, the equations of the model accurately fit recorded data of temperature, relative humidity and carbon dioxide concentration in different workplaces. For validation, data were obtained under different conditions and with different sensors. In particular, we designed and fabricated a wireless sensor that measures and transmits data to a remote device and we also applied our model to data collected using a commercial sensor. For each case, information was obtained that could be used to understand and predict the evolution of ambient variables that impact thermal comfort and energy consumption in buildings. The tools presented here can thus be of great interest to achieve affordable, smart energy-efficient buildings, while adhering to environmental laws and comfort for work spaces.

Abstract A Microgrid (MG) Energy Management System (EMS) is a vital supervisory control to make decisions regarding the best use of the electric power generation resources and storage devices within this MG. This paper presents an operational architecture for Real Time Operation (RTO) of an islanded MG. This architecture considers two different parts including Central Control Unit (CCU) and MG Testbed. CCU implements an EMS based on Local Energy Market (LEM) to control a MG. In order to reach this objective, this unit executes Day Ahead Scheduling (DAS) and Real Time Scheduling (RTS). Regarding DAS, a Modified Conventional EMS (MCEMS) based on LEM (MCEMS−LEM) algorithm has been proposed to find out hourly power set-points of Distributed Energy Resources (DERs) and customers. LEM is also presented in MCEMS−LEM to obtain the best purchasing price in Day-Ahead Market (DAM), as well as to maximize the utilization of existing DER. With regard to RTS, it must update and feedback the power set-points of DER by considering the results of DAS. The presented architecture is flexible and could be used for different configurations of MGs also in different scenarios. Simulations and experimental evaluations have been carried out using real data to test the performance and accuracy of the MG testbed. This paper aims to operate the MG in islanded mode, ensuring uninterruptable power supply services and reducing the global cost of generated power. Results demonstrate the effectiveness of the proposed algorithm and show a reduction in the generated power cost by almost 8.5%.

[EN] Research on combustion systems for Internal Combustion Engines (ICE) is guided by the necessity of improving engine efficiency while achieving the pollutant regulations. In this framework, this study identifies and describes the effect of the bore-to-stroke ratio (B/S) on the combustion system performance and emissions by means of computational fluid dynamics (CFD). The study is applied to a 4-cylinder 4-stroke High Speed Direct Injection (HSDI) CI engine. It is divided in two parts, the first part is focused on one operating point and presents a detailed description of the main effects of different B/S ratios configurations, and the second part compares the results with different engine operating conditions. For both parts the air management, injection settings and compression ratio were kept constant in order to isolate the impact of the B/S ratio. The results confirmed that the indicated thermal efficiency was increased for lower B/S ratio because of the combustion chamber surface area decrease and faster combustion. Regarding the emissions, NOx and soot presented a strong and opposed dependence on B/S ratio generated mostly due to enhanced air¿fuel mixing for lower B/S ratio. Finally, those trends were proven to be independent from the operating condition, giving the study a more general value. Authors acknowledge that this work was possible thanks to the Ayuda para la Formation de Profesorado Universitario (FPU 13/02817) belonging to the Subprogramas de Formacion y de Movilidad del Ministerio de Educacion, Cultura y Deporte from Spain. The authors would also like to recognize the funding and technical support from PSA Groupe