Abstract

Accurate energy consumption prediction is a prerequisite for effectively dispatching distributed power sources. For a building, due to the frequent fluctuations derived from many dynamic factors,the precise energy consumption prediction is still facing challenges. Existing methods usually only use common recurrent neural networks to predict building energy consumption, consider common recurrent neural networks model does not have the ability to extract spatial features and they have a long-term memory problem,so they have limitations to deal with long term task. To overcome these challenges, in this paper, we propose a hybrid model to predict the cooling consumption of a building.
Our hybrid model has the merits of convolutional neural network and gated recurrent unit in capturing spatial-temporal features. Experiment results show that our hybrid model has the best performance, compared with other methods. The result will benefits managers to make reasonable scheduling of power and equipments.

Abstract

In this paper, the temperature mathematical model and compressor model are established to study the effect of different charge/discharge rates on air conditioning energy consumption. The results show that with the increase of the charge/discharge rate, the air conditioning starts earlier and runs longer, and the energy consumption of the air conditioning system also increases. This method considers different charge/discharge rates of batteries and combines with the energy consumption analysis of air conditioning systems, which is of great value for improving the safety and efficient utilization of energy storage systems.

Abstract

For the Seoul Metropolitan Government to meet the goal of 2050 carbon neutrality, there is a crucial need to understand future building energy consumption for more informed policy-making. This study aims to predict residential electricity uses in 6 communities of Seoul Metropolitan City under different future development scenarios. A total of 25 prediction models corresponding to 25 districts in Seoul were constructed using seasonal ARIMA with exogenous variables. These models consider CDD, HDD, total population, elderly ratio, and GRDP from 2010 to 2019 as predictive variables. Electricity consumption from residential buildings in each district at the end of the year 2050 was then estimated from the models under four development scenarios. The four scenarios were defined based on two SSP-RCP climate change scenarios and two KOSIS socioeconomic scenarios. The forecasting results were further aggregated at the community level in Seoul. The aggregated results indicated that even under the same sets of scenario assumptions, the trend of future residential energy change varies across different communities. Therefore, different measures should be taken when implementing community-level plans to reduce building energy.

Abstract

Due to the growing share of intermittent renewable energy sources (RES), the requirement for flexibility in the energy system is increasing to balance the generation and demand of electricity. It has been well recognized that Combined heat and power plants (CHPs) can contribute towards improved flexibility in the energy system. Thermal energy storage (TES), using hot water as working fluid, is a commonly integrated in CHPs, which allows for decoupling of heat and electricity generation. It has been verified that proper control of the operation of TES can improve the flexibility provided by CHP. The development of advanced control system relies on accurate dynamic modeling of TES. In this work, a one-dimension (1D) dynamic model for large scale TES is developed in Dymola, based on mass and energy balances. It is validated against the operational data from a real CHP plant. Results show that the model can capture the dynamic variation in the operation of the TES energy content with maximum deviations of 6.5% from the maximum value.

Abstract

The dual-carbon target has made solar photovoltaic panels widely used. But the normal operation of photovoltaic panels under extreme weather conditions is still an important issue to be solved. For large-area photovoltaic arrays, the effect of photovoltaic panels under extreme wind weather, such as typhoon, is becoming more obvious. To solve the above dilemma, this paper established the numerical simulation model of photovoltaic panels under turbulence field, and studied the displacement of the solar panels when the wind speed is over 25m/s. The results have shown that when the flow rate is 25m/s, the maximum relative pressure of the solar panel is about 70Pa, which appears on the upper right and left of the solar panel. The maximum displacement caused by the wind also appears on the same point of the solar panel, which even reached 1mm in solid surface. After this the economic analysis has also been done for PV operation and maintenance. The machine replacement rate data dropped from 5.6% to 1.7% after added the reinforced facilities. The LCOE for PV also reduced. These results can provide an important reference for the planning of solar panels and the effective operation under extreme weather conditions like typhoon in the future.

Abstract

Considering the current goal of energy-intensive industries such as data centers is mainly to reduce greenhouse gas emissions, a great effort is made to achieve sustainable operations by using renewable energy generation to meet power consumption. This paper proposes a hybrid power system based on a
combination of a natural gas turbine, photovoltaic, wind energy, and battery storage, using a data center in Tianjin of China as a model. Technical and economic analyses are performed for each system configuration by comparing the renewable penetration and the Levelized Cost of Electricity. Taking into account renewable penetration and cost, the various types are ranked from best to worst as PV-wind-battery; PV-wind; PV only; and wind only. The results show that the larger the PV-rated power, the higher the renewable penetration. When considering using wind energy, 1,000 kW is the best solution. When battery storage is used, 3,000 kWh is optimal.

Abstract

Microchannel heat sink is one of the most promising cooling solutions for electronics with high heat flux. In order to further enhance the heat transfer performance of microchannel heat sink, a novel microchannel with rectangular grooves on the wall is designed, and the heat transfer and flow characteristics of Al2O3/water nanofluid in microchannels are numerically studied. The mixture model is used involving the slip velocity between nanoparticles and base fluid. By comparing with the conventional smooth channel, it’s found that grooves are helpful to destroy and redevelop the thermal boundary layer. The disturbance by grooves also leads to much higher pressure drop through the channel than smooth one. This work will be helpful for the design of high-performance microchannel heat sink.

Abstract

This study concerns a hybrid CPV (Concentrator Photovoltaic) system coupled with an Organic Rankine Cycle (ORC), which mainly contains CPV modules and cooling subsystem. The cooling fluid driven by pump is evaporated in tubes under the CPV modules. The superheated vapor of working fluid is generated for additional electric power generation with an ORC. Through the study of the impact of different parameters on the system, the efficiency of CPV-ORC combined system is compared with that of single CPV system. When the temperature of CPV cells increases, the combined system can effectively cooling the CPV and increase the additional energy generation.

Abstract

Methanol is an ideal medium for hydrogen storage and transportation, and is expected to play a crucial role for the low carbon energy system in the foreseeable future. However, hydrogen derivation from methanol (via steam reforming) is faced by critical barriers including high reaction temperature (e.g., 250-300°C) and low methanol conversion (65% at < 200°C), and hydrogen purification process is usually indispensable for deriving high-purity H2. We propose a new method of H2 absorption-enhanced methanol steam reforming to tackle such challenges. The effectiveness of the method is further verified by a prototype reactor sequentially filled with bulk catalyst (CuO/ZnO/Al2O3) and bulk hydrogen absorbent (LaNi4.3Al0.7 alloy), tested at 200°C and 1 bar conditions. As H2 is absorbed by the alloy, both the reforming reaction and water-gas shift reaction are shifted forward, effectively enhancing the conversion of methanol. High-purity H2 is derived by regenerating the alloy under inert gas purge at 200°C, 700 mL min-1. During the 10 min reaction step, the H2 can be nearly completely separated. Furthermore, high purity hydrogen (~85% molar concentration) can be obtained in the regeneration step. Simulations considering the catalytic reaction kinetics further demonstrate the intensification effect of the absorption-enhanced method with different number of cycles and H2 separation ratios. Major advantages of the new method, including low reaction temperature, high-purity H2, non-precious material and membrane-less design, indicate great potentials for commercial applications. The remarkably reduced temperature also opens up wide possibilities of integrating with solar thermal energy and industrial waste heat for sustainable H2 production with significantly reduced CO2 footprint at the same time.

Abstract

With the increasing frequency of extreme weather events, promoting crop production’s resilience to combat climate disaster is urgent for global food security, however, the driving factors of crop production’s resilience are not yet clear to figure out the effective measures to improve it. At the same time, the benefits of agricultural mechanization, especially on resilience are not fully adopted, which may offer solutions for climate change adaptation. Here, we propose a crop production’s climate resilience driving factors assessment framework based on modified Pressure-State-Response concept and two-way fixed effect model. Taking China as the study area, we figure out the spatio-temporal evolution of crop production’s climate resilience and analyze the effect of rapidly developed agricultural mechanization on it. Our primary results show that food production’s climate resilience in China has been promoted since 2005, although drought and flooding events are gradually becoming more frequent. Complementarity among Chinese provinces enhances overall national food production’s climate resilience, to which Jilin Province and Xinjiang Province contributed the most. Due to timely policy adjustments, the autumn harvest has played an increasingly important role in enhancing resilience. Besides, agricultural mechanization played a significant role in guaranteeing food productivity to tackle climate impact. By analyzing the effect of agricultural mechanization on food production’s climate resilience in China, this study can provide insights for strengthening agriculture sector’s resilience and thus avoiding disruptions in food supply chains.