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High mountains and cold climate in the north-west of Iran are critical factors for the design of optimized District Heating (DH) systems and energy-efficient buildings. It is essential to consider the Life Cycle Cost (LCC) that includes all costs, such as initial investment and operating costs, for designing an optimum DH system. Moreover, considering climate change for accurately predicting the required heating load is also necessary. In this research, a general optimization is carried out for the first time with the aim of a new design concept of a DH system according to a LCC, while considering all-involved parameters. This optimized design is based on various parameters such as ceiling and wall insulation thicknesses, depth of buried water and heating supply pipes, pipe insulation thickness, and boiler outlet temperature. In order to consider the future weather projection, the mentioned parameters are compared with and without climate change effects in a thirty-year period. The location selection was based on the potential of the region for such a system together with the harsh condition of the area to transport the common fossil fuel to the residential buildings. The obtained results show that insulation of walls is more thermally efficient than a roof with the same area in the selected case. In this case, polyurethane is the best material, which can cause a reduction of 59% in the heating load and, consequently, 2332 tons of CO2 emission annually. The most and the least investment payback periods are associated with the polyurethane and the glass wool insulation materials with the amounts of seven and one years. For the general optimization of the DH system, the Particle Swarm Optimization (PSO) method with a constriction coefficient was chosen. The results showed that the optimal thickness of the polyurethane layer for the thermal insulation of the building exterior walls is about 14 cm and the optimal outlet temperature of the boiler is about 95 °C. It can be also concluded that the optimal depth for the buried pipes is between 1.5 to 3 m underground. In addition, for the pipe with elastomeric insulation layer, the thickness of 2 cm is the optimal choice.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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The new generation of lithium-ion batteries (LIBs) possesses considerable energy density that arise the safety concern much more than before. One of the main issues associated with LIB safety is the heat generation and thermal runaway in LIBs. The importance of characterizing the heat generation in LIBs is reflected in numerous studies. The heat generation in LIBs can be related to energy efficiency as well. In this work, the heat generation in LIB is predicted using two different approaches (physics-based and machine learning-based approaches). A validated multiphysics-based and neural network-based models for commercial LIBs with lithium iron phosphate/graphite (LFP/G), lithium manganese oxide/graphite (LMO/G), and lithium cobalt oxide/graphite (LCO/G) electrodes are used to predict the heat generation toward shaping the LIB energy efficiency contours, illustrating the effect of the nominal capacity as a key parameter in the manufacturing process of the LIBs. The developed contours can provide the energy systems designers a comprehensive view over the accurate efficiency of LIBs when they need to incorporate LIBs into their devices. In addition, the effect of temperature on charge/discharge energy efficiency of LFP/graphite LIBs is obtained, and the performance of three typical LIBs in the market at a very low temperature is compared, which have a wide range of applications from consumer applications such as electric vehicles (EVs) to industrial applications such as uninterruptible power sources (UPSes).

Summary In this study, a vapor‐compression cooling system utilizing different phase change materials (PCMs) has been studied whereby the electricity consumption peak load is shifted. More specifically, the dynamic performance of cooling systems with and without using PCM is evaluated for the hottest day of the year. The proposed system uses the cooling energy to freeze, or “discharge” the PCM during nighttime when the cooling load is minimally needed and uses the stored thermal energy during the peak load hours by melting, or “charging” the PCM. This leads to better performance during peak load hours when higher cooling loads are needed. Different PCMs including oleic acid, SP224A, CL, CaCl2·6H2O have been investigated. The system performance has been investigated by considering several parameters such as coefficient of performance, compressor power consumption, accessible cooling load, and melting fraction of PCM. The effect of different volumes of PCM on the average cooling cost has been evaluated, as well. The results indicated that with using the PCM, the accessible cooling load raises and the electricity consumption falls during hours of the peak load. Peak load reduction in the on‐peak period is different for various PCMs. In comparison with the conventional cooling system, the peak load alters from 315 W to 170, 164, 93, and 74 W for CL, oleic acid, CaCl2·6H2O, and SP224A, respectively. The evaluation of different PCM illustrated that for the volume of 150 L, the peak load shaving for SP224A is the most value which is 76.3%. Moreover, while the volume of different PCMs increases, the average price of the cooling energy decreases, and the Levelized cost of cooling energy rises.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

No abstract