Recently, with the introduction of DER (Distributed Energy Resources) such as wind, solar, energy storage systems, and electric vehicles, distribution systems have also started to produce and trade electricity. In order to cope with the changing system environment, countries around the world recognize that new functions for distribution system operation are needed, and application research based on AMI (Advanced Metering Infrastructure) is underway. However, it is still in the research stage and there is no specific functional design. In this paper, in line with the spread of AMI in Korea, four business use cases that can be implemented using AMI are defined. These are related to the distribution system and DER control, and the information exchange sequence was designed and represented as a Unified Modeling Language diagram. DSO (Distribution System Operator) can establish a foundation for improving distribution system operation and secure LV (Low Voltage) distribution system visibility through the implementation of this business use case.
In modern power system, traditional transient stability assessment(TSA) methods undergo great challenges as the time domain and space structure complexity continue to increase. Taking into account the massive features generated by the power system, in order to avoid the dimensionality disaster problem in artificial intelligence methods and machine learning models, this paper proposes a novel feature selection method. Based on interaction gain, this method measures both the effectiveness and combination effects of certain feature subset, thereby simplifying the original input without information loss. Case study on IEEE 39-bus system TSA verifies the validation in accuracy, false alarms, calculation efficiency and feature size.
Long-term optimized operation has been looking to unit commitment to replace the traditional power flow for more accurate modeling of system operation. Alongside the intuitive advantage of representing not only the production of the generation units of consecutive time periods but also their commitment status, comes the heavier computational burden for the multi-unit long-term model. In fact, representing the commitment status of each unit with a binary variable in the corresponding constraints alone complicates the optimization, let alone other accessory variables, such as those for switch actions. Moreover, unit commitment is usually conducted over a narrow window of time, e.g., one day or one week with an hourly resolution; ordinary unit commitment models that run over an annual load profile will be hindered from even converging to a solution by heavy computation. As such, clustering techniques are proposed to be equipped with the unit commitment in two dimensions, one to select representative periods for the load profile of a long horizon, the other to group homogeneous or similar units. As the former is conducted in the time dimension, it can be named the temporal clustering, and the latter hence named the spatial clustering. As a result, this new method with both is therefore named the tempo-spatially clustered unit commitment. The case study on a 39-node 17-unit system proves the efficacy and efficiency of the proposed unit commitment approach.
Chemical energy conversion has a great influence on the cold gas efficiency of coal gasification technology. In this paper, a three-step gasification technology with CO2 recycling is introduced and two external combustion schemes (CO-fueled chemical looping combustion and unconverted coke combustion) are compared. Results showed that the CO-fueled chemical looping combustion scheme has a higher cold gas efficiency of 90.1%, while cold gas efficiency of the unconverted coke combustion scheme is 88.4%. Before the water gas shift subprocess, the chemical energy conversion efficiency in the unconverted coke combustion scheme is 93.2%, which is 1.6 percentage points higher than that in the CO-fueled chemical looping combustion scheme. However, more chemical energy is consumed for CO2 regeneration in the unconverted coke combustion scheme, which results in chemical energy conversion efficiency decreases from 93.2% to 88.4%. Therefore, better energy matching between reactions can effectively improve the cold gas efficiency of the coal gasification technology. Besides, chemical energy consumption for CO2 regeneration should be reduced for gasification technology adopting CO2 as a gasifying agent.
The lithium-ion battery system has strong coupling and nonlinear characteristics, bringing great challenges to its online failure prediction and life estimation. Several studies have shown the potential of deep learning methods on remaining useful life (RUL) prediction of lithium-ion batteries, these methods mostly use historical cycling data from beginning to the prediction point. However, long-term cycling data can be costly and difficult to obtain in practical applications. This article presents a data-driven algorithm using a combination of deep convolutional neural network (DCNN) and long short-term memory (LSTM) to predict the RUL of lithium batteries based on the data of the past 10 continuous cycles. Here the DCNN processes time-series data including capacity, temperature, and capacity difference at the same voltage during discharging, the LSTM is used to process the scalar data of each cycle including internal resistance, discharge time, and discharge capacity. The proposed network uses an open dataset with 124 batteries for training and validation. The generalization ability of the model for batteries under different charging/discharging strategies is also validated. The proposed DCNN-LSTM network demonstrates well performance on capturing the life changes of batteries with different life lengths and working conditions using limited historical cycling data with an average root mean square error of 5%.
In order to find out the relationship of the heat transfer and the dissociation of permafrost gas hydrate in porous media, this study has conducted a hydrate dissociation experiment below freezing point in a cuboid pressure vessel. The dependences of gas production and hydrate dissociation on the following heat fluxes are also analyzed through numerical simulation: the heat conducted across the boundary QB, the heat injected from the well Qinj, the sensible heat change of the deposit QS, the heat absorbed by hydrate dissociation QH, the latent heat of ice melting QI, and the unutilized heat QL. The results show that the dissociation process of frozen gas hydrate can be divided into three steps: ice melting, hydrate dissociation, and ice regeneration. The existence of solid ice shows strong inhibition effect on hydrate exploitation. QH mainly comes from Qinj, and the heat transferred across the boundary is the main component of QL. It implies that the heat injection is the dominating driving force for hydrate dissociation below freezing point.
With the promotion and application of multi-energy integration and distributed generation technologies, integrated community energy system (ICES) has developed rapidly. However, some ICESs have weak links such as poor economy, low energy efficiency, which restrict the effective operation of ICES. To solve this problem, an optimal retrofit method for ICES is proposed in this paper, which includes capacity expansion of the existing equipment and investment of new types of equipment. The proposed method takes the minimum total cost as the objective and sets equipment capacity and operation constraints during the process of retrofit. Whatâ€™s more, the economic index, primary energy efficiency and PV energy consumption rate are adopted to evaluate the effects of the retrofit. Finally, the effectiveness of this method is verified by the case study.
With the rapid development of the low-rank coal chemistry industry, the production of semi-coke has ever been increasing. However, the semi-coke is difficult to burnout and the NOx generation during semi-coke combustion is high. Therefore, the co-combustion of semi-coke and bituminous coal in utility boiler is considered as a promising approach to realize the efficient utilization of the semi-coke. Here, the co-combustion features of bituminous coal and semi-coke blends were investigated in a 660 MW utility boiler. The results show that the NOx generation decreases firstly and then increases with the rises of the load and the fraction of semi-coke. The effects of load and blending ratio of semi-coke are both obvious on burnout behavior than those on NOx generation. With the rise of oxygen concentration, the NOx generation rises and the unburned carbon in fly ash decreases. The Na-bearing mineral are better-preserved with the higher oxygen concentration. The experimental results may provide guidance for the realization of the utilization of the semi-coke in the utility boiler.
The two-phase loop thermosyphon (TPLT) is a high-efficiency heat transfer device which has been applied in many fields in recent years. The conventional flow models of a TPLT do not consider the particularity of the flow characteristics and cannot properly describe the refrigerant distribution under different working conditions. This paper tries to propose a new idea of the flow model which takes the variation of flow characteristics with the change of working conditions into consideration. A numerical simulation was conducted on a TPLT based on the proposed flow model. The change in heat transfer rate and the variation of refrigerant column height (driving force) in downcomer with increasing refrigerant charge were calculated to analyze the effect of filling ratio on the performance. The study in this paper could provide a reference for the design in practical application.
The power consumption of pumps and fans is quite high in the transport systems, especially in data centers. Nearly 60% of the computer room air conditioning (CRAC) systemâ€™s energy consumption is consumed by pumps and fans in winter. In this paper, a synergy optimization method is proposed to save energy in heat transfer process. The heat transfer constraint equations of the heat transfer process are established, and taking the highest energy efficiency as the objective function, the synergistic relationship between the power consumption and temperature difference is derived analytically by using variation principle. A synergy operation factor is defined to guide the practical operation optimization of heat transfer process. The closer the synergy operation factor is to 1, the higher the system energy efficiency is. An experiment of a separated heat pipe system is carried out to verify the accuracy of the synergy optimization analysis.