A Structure-Reconfigurable Soft-Switching DC-DC Converter for Wide-Range Applications

In this paper, a structure-reconfigurable resonant DC-DC (direct current – direct current) converter is presented. By controlling the state of the auxiliary switch, the converter could change the resonant structure to acquire a high efficiency and wide voltage gain range simultaneously. The characteristics of the LLC (inductor-inductor-capacitor) resonant converter are firstly analyzed. Based on this, through introducing additional resonant elements and adopting the topology morphing method, the proposed converter can be formed. Moreover, a novel parameter selection method is discussed to satisfy both working states. Then, a detailed loss analysis calculation is conducted to determine the optimal switching point. In addition, an extra resonant zero point is generated by the topology itself, and the inherent over-current protection is guaranteed. Finally, a 500 W prototype is built to demonstrate the theoretical rationality. The output voltage is constant at 400 V even if the input voltage varies from 160 to 400 V. A peak efficiency of 97.2% is achieved

A Knowledge-Graph-Driven Method for Intelligent Decision Making on Power Communication Equipment Faults

The grid terminal deploys numerous types of communication equipment for the digital construction of the smart grid. Once communication equipment failure occurs, it might jeopardize the safety of the power grid. The massive amount of communication equipment leads to a dramatic increase in fault research and judgment data, making it difficult to locate fault information in equipment maintenance. Therefore, this paper designs a knowledge-graph-driven method for intelligent decision making on power communication equipment faults. The method consists of two parts: power knowledge extraction and user intent multi-feature learning recommendation. The power knowledge extraction model utilizes a multi-layer bidirectional encoder to capture the global features of the sentence and then characterizes the deep local semantics of the sentence through a convolutional pooling layer, which achieves the joint extraction and visual display of the fault entity relations. The user intent multi-feature learning recommendation model uses a graph convolutional neural network to aggregate the higher-order neighborhood information of faulty entities and then the cross-compression matrix to solve the feature interaction degree of the user and graph, which achieves accurate prediction of fault retrieval. The experimental results show that the method is optimal in knowledge extraction compared to classical models such as BERT-CRF, in which the F1 value reaches 81.7%, which can effectively extract fault knowledge. User intent multi-feature learning recommendation works best, with an F1 value of 87%. Compared with the classical models such as CKAN and KGCN, it is improved by 5%~11%, which can effectively solve the problem of insufficient mining of user retrieval intent. This method realizes accurate retrieval and personalized recommendation of fault information of electric power communication equipment

A Multi-Agent Optimal Bidding Strategy in Multi-Operator VPPs Based on SGHSA

As an individual plant participating in the power market, the virtual power plant (VPP) is regarded as the ultimate configuration of the energy Internet, and effective dispatching is a challenge. This paper proposes a multi-agent optimal bidding strategy based on a self-adaptive global optimal harmony search algorithm (SGHSA) to solve the problem of multi-operator participation in virtual power station scheduling. The method takes multiple agents to simulate the bidding process in the VPPs and distributes the profits for the operators based on the market mechanism to optimize the distributed energy resources (DERs). Case studies are provided and show that the proposed method realizes the optimal distribution of power generation and demand level, which improves the comprehensive advantage of the VPP in electricity market transactions

Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

After suffering from a grid blackout, distributed energy resources (DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points (SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed, and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy

The Green Photovoltaic Industry Installed Capacity Forecast in China: Based on Grey Relation Analysis, Improved Signal Decomposition Method, and Artificial Bee Colony Algorithm

Despite photovoltaics being a new type of green energy technology, the output of the photovoltaic industry has been declining year by year since 2018. Thus, China’s photovoltaic industry must adapt to transformations from original extensive growth to the pursuit of high-quality energy. In order to accurately predict the installed capacity of photovoltaics in China, based on an extensive literature review and expert consultation, this paper is the first to construct a set of influencing factors that affect the photovoltaic industry, and we selected the main influencing factors as the predictive model’s input through the grey correlation analysis method. Then, we provide a novel grid investment forecast named the CEEMD-ABC-LSSVM predictive model (a least squares support vector machine algorithm based on complete total empirical mode decomposition and an artificial bee colony algorithm). This algorithm is based on the traditional LSSVM algorithm. The ABC algorithm is used to optimize the parameters, while CEEMD decomposes the original time series to obtain multiple components. While maintaining the data information, the data are expanded and the training is fully carried out. Next, in the empirical analysis, by comparing the prediction results of LSSVM, ABC-LSSVM, and the EMD-ABC-LSSVM algorithm, we demonstrate that the CEEMD-ABC-LSSVM model has strong generalization capabilities and achieves good Chinese PV growth based on the predicted effects of the installed capacity. Finally, the CEEMD-ABC-LSSVM model was used to predict the installed PV capacity in China from 2019 to 2035. We find that China’s installed PV capacity will surpass 4000 GW around 2035. As this installed capacity will increase year by year, China’s PV industry development will maintain steady overall growth

Numerical Investigation of Thermal Performance with Adaptive Terminal Devices for Cold Aisle Containment in Data Centers

The energy consumption of data center cooling systems accounts for a large proportion of total energy consumption. The optimization of airflow organization is one of the most important methods to improve the energy efficiency of cooling systems. The adjustment scale of many current air flow organization methods, however, is too large and does not support the data center’s refined operation. In this paper, a new type of air supply terminal device is proposed, and it could adaptively adjust according to the power of servers in the rack for cold air redistribution. In addition, the corresponding regulation strategy is proposed. A CFD model is established according to field investigation of a real data center in Shanghai to investigate the adjustment range and the energy saving potential of the device. The simulation results indicate that the device can suppress the local hot spots caused by excessive server power to some extent and greatly improve the uniformity of servers exhaust temperature. The case study shows that the device can save energy consumption by 20.1% and 4.2% in mitigating local hot spots compared with reducing supply air temperature and increasing supply air flowrate