This paper aims to establish the strategy of the large-scale consumer for maximizing the total benefits under the circumstance of the day-ahead (DA) and real-time (RT) coupling market. Among the various possible bidding behaviors in demand side, three typical bidding modes are concluded and four typical trade scenes are further formulated. Moreover, a unified general function for trades has been constructed and the corresponding solutions have been discussed. Furthermore, an optimal bidding model is proposed for the large-scale consumer and the corresponding assessment indices have been utilized to quantify the effectiveness of the proposed method. A case study based on the data from a provincial power market is carried out, which demonstrates that the reasonable power portfolios between the DA and RT market can decrease the purchase costs of the large-scale consumer.
A novel hybrid system using biomass as fuel for both power and heat generation, which consists of biomass gasification unit, solid oxide fuel cell, homogeneous charge compression ignition engine and waste heat recovery subsystems, is proposed in this work. Based on the thermodynamic modeling, the system is comprehensively evaluated by energy, exergy and thermo-economic analyses. The results show that the proposed hybrid system has an energy conversion efficiency of approximately 68% and the exergy efficiency of 51%, both of which are comparable to other biomass fueled hybrid fuel cell systems reported in literature. The exergy destruction of the gasifier is the largest, whose relative exergy destruction is up to 21.5%. The fuel cell component contributes to 71% of the total power but with small relative exergy destruction. Besides, the specific electric energy cost of the proposed hybrid system is calculated to be 0.054 $/kWh. The payback period and annual return on investment can reach 2.4 year and about 9.83%. These results reveal that the proposed conversion technology of biomass to power is efficient and economical, which could be a promising way for biomass utilization.
A new cross-scale load prediction model on building level based on the k-means clustering method is proposed in this paper. An office building with 26 conditioned thermal zones is the main research object. The data set is composed of 5785h cooling/heating load data by Energyplus simulation and real-world monitoring, besides, a kind of accumulative effect considered data is also included. The proposed model is based on quantifying the intra-cluster relationships. The quantification tool consists of a well-trained LSTM model and a representative load time series input which create by cluster centroid zone in one prediction cell. By combining the prediction cells under different scales, the cross-scale prediction model from the zone to building scale is built. To investigate the association between each explanatory variable and cluster belongings, ANN logistic regression model is applied. Some explanatory physical variables (e.g. the ratio of â€œnon-equilibriumâ€ temperature difference) calculated by â€œnon-equilibriumâ€ thermal insulation method are first proposed and used in logistic regression. Applying the simulation and accumulative effect considered data to the proposed model, the result shows that there is a trade-off between the ratio of the sample size of the cluster and mean cross-scale prediction accuracy, and the optimal prediction period can be obtained. In logistic regression, the result shows the maximum demand, start and end time of HVAC system, the west to the south ratio of temperature difference, and the exterior window area together determine the belonging of the cluster. At last, the proposed model is validated by real-world data and showed itâ€™s effectiveness, and the cumulative effect makes the cross-scale prediction accuracy better.
To overcome the data shortage problem of model training, this study proposes a novel transfer learning strategy for short term cross-building energy prediction using long short term memory (LSTM) and domain adversarial neural network (DANN). The proposed strategy can utilize transferred knowledge learnt from related domains with sufficient historical data. LSTM based feature extractor is used to extract temporal features across source and target domains. DANN attempts to find domain invariant features between the source and target domains via domain adaptation. Then, the domain adaptation based transfer learning model (i.e. LSTM-DANN) trained with data from different buildings can be directly applied to predict the target building energy without having its prediction performance degradation caused by domain shift. Experiments are conducted to evaluate the performance of the proposed transfer learning strategy in different scenarios. Results demonstrate that domain adaptation can well overcome the domain shift between the source and target domains by learning the domain invariant features. Furthermore, the proposed strategy can significantly enhance the building energy prediction performance compared to models trained on the target only data, the source only data, both the target and source data, but without domain adaptation.
Photochemistry and thermochemistry are two ways to store solar energy into chemical energy directly. For photochemical process, the major challenge is that the catalyst cannot absorb the full spectrum of solar energy, and just the energy in short-wavelength spectrum can be stored while the energy in long-wavelength spectrum is wasted. Therefore, photochemistry has not yet been found widespread industrial adoption, in spite of decades of active research, because the relatively low solar photochemical efficiency. For thermochemical process, it often operates at relatively high temperature to achieve reasonable product yield, requiring high ratio concentrators and large mirror fields. To achieve higher solar-to-chemical efficiency on relatively mild condition, photo-thermo synergetic catalytic chemistry is proposed. In this work, we synthesized different kinds of non-metal carbon nitride catalyst for photo-thermo catalytic hydrogen production from water. The hydrogen generation rate is experimentally tested on photo catalytic condition, thermo catalytic condition and photo-thermo catalytic condition. Results show that the photo-thermo catalytic reaction rate is much bigger than the sum of the photo catalytic reaction rate and thermo catalytic reaction rate, which verifies the synergetic effect between the photo catalysis and thermo catalysis with non-metal catalyst. This work would inspire a pathway toward the chemical storage of solar full-spectrum energy.
The supercapacitor thermal management system is of great significance to the safe operation and aging moni-toring of the supercapacitor. This article provides a solu-tion for estimating the internal temperature through the surface temperature, instead of directly measurement. By adopting a suitable electrothermal coupling model of supercapacitor, the internal temperature can be estimat-ed online via an H-infinity filter. Besides, in order to re-duce error caused by model inaccuracy and noise chang-ing, this paper uses the neural network to correct the result of the H-infinite filter. To verify the effectiveness method proposed in this paper, a series of experiments are designed and conducted. The results shows that the H-âˆž-ANN joint filter has less error than H-âˆž filter alone.
Transformer oil is usually heated by electrical heating devices, which has defects of high temperature of heating devices thus low reliability. Therefore, an electromagnetic induction heating device of circulating oil was proposed in this paper. An electromagnetic-thermal coupling model of the heating device was built, and the modeling results were verified by experiments. Using the model, the structure of heating tube of circulating oil is optimized, and temperature-velocity field synergy analysis for each tube was carried out. The results show that the alternating elliptical axis tube has better heat transfer performance and temperature uniformity than the circular or elliptic straight tube. At last, the thermal resistance networks of the electromagnetic induction heating device and the traditional electrical device were built. The thermal resistance analysis results show that the electromagnetic induction heating device has much lower temperature thus higher reliability than the traditional electrical heating device.
The development of the electronic devices with the growing heat generation raises the high requirement for heat dissipation devices. Extensive research has been carried out for enhancing convective heat transfer rate in various heatsinks. The curve-wave channel proposed in the authorsâ€™ previous work exhibits the superior thermal performance with a slight increase in pressure drop compared with a smooth-curve channel. In the present study, the thermal behaviors, the overall performance and the secondary flow characteristics in a curve-wave channel and a conventional wavy channel are numerically investigated and compared.
The results show that the thermal performance of the conventional wavy channel is improved and the maximum temperature on the heated wall can be lowered 1.2-3.6 K after introducing the overall curvature. However, it is also noted that the overall performance factor decreases while Reynolds number grows, in other words, the superiority of the curve-wave channel is more obvious at small Reynolds number. The analysis of the secondary flow characteristics shows that the stronger secondary flow is generated in the curve-wave channel regardless of Reynolds number, which indicates the overall curvature has an important effect on the flow in the wavy channel.
Recent research suggests that the integration of radiative cooling (RC) technology in photovoltaic-thermal (PVT) systems, can improve the overall system efficiency during the day and provide additional cooling at night. Considering the potential benefits of such a combined system, this study measured the improvements that are achieved in a PVT system performance when using an ideally emissive top layer, and compared them to those achieved with regular glass encapsulation. Results showed that enhanced RC in a PVT system reduced the solar cell operating temperature by most 2 Â°C and increased total exergy efficiency by 0.65% during the day, and also provided an additional 4-8 W/m2 cooling power at night. Although improvements were achieved in the system performance, it was found that when considering realistic atmospheric conditions and spectral properties, an enhancement in RC does not substantially improve the PVT system performance.
Replacing traditional fossil energy with renewable biofuel is considered to be an effective way to achieve the emission reduction target. The optimal design and operation of the supply chain is the key step in the large-scale development of biofuel. To fully utilize the extensive oil and gas supply chain infrastructure in China, this paper intends to incorporate liquid biofuel into the existing refined product supply network and explore the benefits of this integrated supply chain. Firstly, a mixed-integer linear programming model for a single-cycle integrated supply chain is developed to obtain the optimal supply scheme, transportation scheme and demand scheme of both liquid biofuel and refined product. The objective is set to minimize the total cost, including depreciated investment cost of bio-refinery, liquid fuel transportation cost, backlog cost on supply side and stock-out cost on demand side. The geographical distribution of biofuel yield, refinery production capacity, oil depot inventory levels and transportation volume requirements are rigorously taken into account. Finally, the existing refined product supply train in China is taken as a case study and two scenarios (with and without biofuel participation) are carried out for comparison from the perspective of economy and environment. The results demonstrate the economic and environmental benefit of the proposed integrated liquid biofuel-refined product supply chain, which can provide significant guidelines for the decision makers.