• Energy Research

This letter has developed an electrical circuit analogy-based maximum latency calculation (MLC) method of the internet data center (IDC) in power-communication network. Firstly, by analogy with the circuit model, the basic concepts to describe information flow are defined, including information current, information resistance, information conductivity, and information voltage. Based on these concepts, the information processing model considering both channel blocking and user priority is established. By analogy with the electrical circuit, the information flow calculation laws are introduced to calculate the maximum latency of IDCs. Verification results show that the maximum latency of IDCs in power-communication network can be accurately calculated by the proposed MLC method.

The scale of electric vehicles (EVs) in microgrids is growing prominently. However, the stochasticity of EV charging behavior poses formidable obstacles to exploring their dispatch potential. To solve this issue, an optimization strategy for EV-integrated microgrids considering peer-to-peer (P2P) transactions has been proposed in this paper. This research strategy contributes to the sustainable development of microgrids under large-scale EV integration. Firstly, a novel cooperative operation framework considering P2P transactions is established, in which the impact factors of EV charging are regarded to simulate its stochasticity and the energy trading process of the EV-integrated microgrid participating in P2P transactions is defined. Secondly, cost models for the EV-integrated microgrid are established. Thirdly, a three-stage optimization strategy is proposed to simplify the solving process. It transforms the scheduling problem into three solvable subproblems and restructures them with Lagrangian relaxation. Finally, case studies demonstrate that the proposed strategy optimizes EV load distribution, reduces the overall operational cost of the EV-integrated microgrid, and enhances the economic efficiency of each microgrid participating in P2P transactions.

The energy interactions and uncertain factors of integrated energy systems (IES) have brought risks to the reliable energy supply. A large number of states need to be analyzed to obtain a stable reliability value. However, different operating characteristics complicate the optimal energy flow (OEF) model, which brings tremendous computational cost. To address that, a deep-learning-based approach is proposed as an alternative way to solve the OEF problems. This approach constructs the mapping between system state and energy allocation to directly obtain the optimal load curtailment. Thereafter, the deep-learning-based reliability assessment framework for IES is proposed to improve efficiency. Additionally, the Gaussian noise and data-processing strategies are involved to achieve higher accuracy. Compared to the model-based approach, the proposed method increases the reliability assessment efficiency by 6 orders of time. With an accuracy of over 95%, it outperforms other autoencoder and random forest methods. Method accuracy has remained above 90% in various scenarios.

Aggregated electric vehicles (EVs) are emerging as promising resources for the frequency regulation of the electrical grid, but the aggregation dynamics caused by various aggregation delays make unknown threats against the frequency stability. To address this stability uncertainty, a general aggregation-delay model is established for the electrical grid-electric vehicle system, which integrates heterogeneous-delay effect and reveals the sequential impact process of aggregation delays during the frequency regulation. To precisely visualize the aggregation delay influence, the maximum aggregation delay interval is discretized by the Chebyshev nodes, which formulates a delay distorted matrix reducing the infinite operator dimension. With this matrix, the damping ratio and the sensitivity indexes are then extracted constructing a complete workflow to characterize the asymptotic stability for EV aggregation delays. By the proposed approach, the distorted stability space and uniform target eigenvalue are accurately revealed in a typical electrical grid-electric vehicle system. It is also demonstrated that the aggregation delays infect the frequency stability through three unstable modes in a low-frequency oscillation form.

In this paper, a three-terminal ac/dc hybrid microgrid with two dc terminals and one ac terminal is proposed. The proposed system consists of cascaded H-bridge (CHB) converters based ac grid interface and two dual active bridge (DAB) converters based dc subgrid interface that connects two isolated dc buses. In order to reduce the number of power conversion stages and power devices, the DAB converters are directly connected to CHB dc rails according to the system operation requirement. To overcome the imbalanced grid currents and dc rail voltages issues caused by this modified system configuration with only two power conversion stages, an improved method is proposed through the zero-sequence voltage injection in the CHB converters. In addition, to avoid the conflicts between zero-sequence voltage injection and the voltage/current regulation of the system, the impacts of the control parameters to the system stability and dynamic response are investigated. Evaluation results from both three-terminal and five-terminal hybrid ac/dc microgrids show that the generalized effectiveness of the proposed three-phase ac current and dc rail voltage balancing method.

With the high penetration of the power electronic loads in the grid, the stability of static synchronous compensator (STATCOM) devices is greatly challenged. However, the conventional control methods for the modular multilevel converter (MMC) based STATCOM only consider the stability with small signal disturbances. This article proposes a novel dual-layer back-stepping control (BSC) for the MMC-based STATCOM. In the first layer, the BSC aims to regulate the sum of the capacitor energy and the reactive output current. In the second layer, the BSC aims to control the circulating current. Therefore, the proposed method possesses a fast dynamic response and accurate tracking with the Lyapunov stability of the MMC-based STATCOM. Compared with the arm-control-based BSC for MMC-based inverters, the proposed method has a simplified structure and a reduced computation burden. Moreover, the proposed method realizes the independent control between the output current and the circulating current. The simulation and experimental results verify the effectiveness of the proposed method. In addition, its robustness toward different circuit parameters and the operation ability under unbalanced grid fault is also verified.

Pulsed-Power-Loads (PPLs) are becoming more common in medium-voltage naval DC micro-grids. To reduce their impact on the electrical system, energy storage elements can be installed. For optimal performance the interface converters need to have fast dynamics and excellent disturbance rejection capabilities. Moreover, these converters often need to have a voltage transformation capability and galvanic isolation since common energy storage technologies like batteries and super capacitors typically operate at relatively low voltages. In order to address these issues a Dual-Active-Bridge converter with a Moving Discretized Control Set Model Predictive Control (MDCS-MPC) is proposed in this paper. The controller has a fixed switching frequency, which allows straightforward filter design. The operating principles of the MDCS-MPC are introduced in this paper with the development of a cost function that provides stiff voltage regulation. Resonance damping and sampling noise resistance have been achieved by an adding additional term in the cost function. This paper presents an assessment of the performance of the proposed MDCS-MPC and comparisons with other control methods. Experimental validation from a 300V/300V 20kHz 1kW Dual-Active-Bridge converter are also presented to verify the theoretical claims.