International Journal of Electrical Power & Energy Systems最新文献

3D printing forming quality control is a research hotspot and difficulty. The traditional approach is either to optimize the printing material properties, to optimize the printing device structure, or to control the printing process, for achieving the purpose of precision printing. In fact, in the 3D printing control system (3DPCS), control and measurement signals are transmitted through open communication network, inevitably leads to communication and transmission delays, which can adversely affect the 3DPCS dynamics and even cause instability, not to mention guarantee precision printing. For such a time-delay system, this paper presents an open issue, i.e. the stability analysis and stabilization control problem of the 3DPCS, in order to serve the improvement of the printing accuracy. First, the 3DPCS framework and dynamic model with time-varying delay are proposed. Second, the delay-dependent stability and stabilization conditions of the 3DPCS are derived by constructing the augmented Lyapunov–Krasovskii functional (LKF) and by using the relaxed mixed convex combination technologies, after which less conservative conditions are obtained by introducing a free weighting matrix to improve the accuracy. Thus, the corresponding controller gain is further obtained. Finally, the 3DPCS example and a well-known numerical example are carried out. Simulation results show that the upper bound of acceptable time delay of systems are larger, and the controller designed based on the stabilization condition can ensure the stable operation of the 3DPCS. Both aspects demonstrate the advantages of the proposed approach.

Power transformer diagnostic methods based on traditional intelligent learning are affected by the scarcity of transformer fault data, which hinders their further application and prevents them from obtaining high diagnostic accuracy. To solve this problem, a few-shot method based on Gaussian Prototype Network (GPN) is proposed to achieve an effective and accurate diagnosis of power transformers using even a small number of fault samples. The method is an organic combination of embedding network and distance metric. The proposed approach is verified by datasets of dissolved gas and literature, which come from real power transformers and historical data. The results show that the method can achieve up to 96.7% accuracy, which is suitable for the field of power transformer fault diagnosis.

This work presents a technique to estimate on-line the global inertia of an electric power system by exploiting the footprint of the principal frequency system dynamics. This method can estimate the inertia provided as a whole by synchronous machines, as well as by converter-interfaced generators controlled to emulate the behavior of the former through virtual inertia. Probing tones are injected by a grid-forming converter-interfaced generator and its virtual rotor speed is used to extrapolate its footprint. As a result, the method requires neither measuring the active power exchange of each synchronous generator nor extrapolating their rotor speeds. Since the proposed technique is entirely data driven, it does not require any model of the power system generators/prime-movers/controllers and of the interconnecting grid. The method is comprehensively tested on a modified version of the IEEE 39-BUS system and a dynamic version of the IEEE 118-BUS system, both containing grid-forming converter-interfaced generators.

Commercial virtual power plant is one of the most efficient means to solve the problem of multi-type distributed power supply and flexible load scheduling. Aiming at the problems of uncertain renewable energy sources and reliable distribution system, a flexible interactive coordinated control method for commercial virtual power plants based on worst-case conditional value-at-risk (WCVAR) is proposed in this paper. In order to reduce the risk of voltage exceeding limits and increased network losses at distribution network nodes, combined with the requirements of technology virtual power plants for power flow control technology in distribution networks, a commercial virtual power plant net profit model based on the reliability of distribution network operation is established. The worst-case conditional risk value theory is used to quantify the risks caused by the uncertainty of wind, water and photovoltaic outputs. A commercial virtual power plant optimization scheduling model based on the WCVAR is established with the goal of minimizing risk cost, and the model is solved by commercial software Cplex. By simulating and analyzing the actual operating data of the improved IEEE-33 distribution system, the output characteristics of commercial virtual power plants under different risk confidence levels are compared and analyzed. The simulation results show that this method fully explores the scheduling potential between electricity, power grid, load and energy storage in commercial virtual power plants, flexibly adjusts risk preferences according to scheduling needs, and achieves flexible control of benefits and risks in commercial virtual power plants.

The modular multilevel converters (MMC) based high-voltage direct-current (HVDC) grids have a fast rise rate and higher peak value of fault current. The active current limiting control (ACLC) of MMC can effectively reduce the breaking current of DC circuit breakers (DCCB) and reduce the requirement for rapid fault identification in the line protection. However, the current ACLC based on changing the MMC control strategy cannot meet the selectivity requirements. The lack of selectivity may lead to power transmission on non-faulty lines in a multi-terminal DC power grid, thus expanding the range of fault influence. Therefore, this paper proposed a selective and staged ACLC. Firstly, the starting range of ACLC is determined according to the selective requirement and purpose of ACLC in the flexible DC power grid. Secondly, a staged ACLC approach was proposed to balance the speed and selectivity of ACLC. In this approach, the single end and both terminals electrical quantities of the line are fully utilized as the starting criteria for staged ACLC, and corresponding ACLC principles are proposed. Finally, the AC side current and MMC bridge arm current under ACLC were analyzed, and an ACLC scheme was proposed in coordination with DC circuit breakers (DCCB) and fault identification. The simulation results show that the proposed ACLC guarantees the current limiting ability and the MMC does not block during the whole fault period, and has a certain adaptability to the fault type, fault resistance, and noise.

With the current trend, the power generation in the future power grids is expected to consist of a large number of converter-interfaced generations (CIGs) with a small percentage of synchronous generators (SGs) producing power from hydro, solar thermal, or even nuclear resources. These notable changes pose significant challenges to the secure operation of the power grid. One such issue is new cascading failure mechanisms introduced by CIGs, especially due to the dc-link voltage collapse in a dc-current limited grid-forming converter (GFC) following a generation outage. Therefore, in the online dynamic security assessment (DSA), the system operators would be interested in knowing the critical outages that may trigger this cascading mechanism in the system. To address this issue, this paper presents a contingency screening and ranking approach (which can be a part of a DSA) for generator outages in a modern grid that consists of SGs and GFCs. Our specific focus is to identify the critical outages that may lead to a cascading failure in the system due to a dc-voltage collapse in the GFCs. To that end, first, a novel contingency screening and ranking index for an (N-1) contingency scenario (i.e., outage of a single generator) is derived based on the traditional generator power tracing algorithm. Then, it is shown that the proposed ranking index can be extended to any (N-k) contingency scenarios. Finally, the effectiveness of the proposed approach following generator outages (up to (N-2) contingencies) is demonstrated using detailed time-domain simulations in two different modified test configurations of a 16-machine 68-bus New York-New England test system with GFCs using the MATLAB/Simulink platform.

Traditional prices at the distribution level cannot efficiently guide the comprehensive cost recovery. To this end, this paper proposes a novel integrated nodal price by solving a comprehensive investment problem. The proposed integrated nodal price can be inherently decomposed into three price components: i) extended distribution locational marginal price of electricity, ii) distribution locational price of carbon, and iii) distribution locational price of investment. Specifically, the extended distribution locational marginal price enables to recover the power generation cost and on–off cost of generation units via a convexified mixed-integer optimal power flow model; the distribution locational price of carbon enables to recover the carbon emission cost through a carbon footprint tracing method; and the distribution locational price of investment enables to recover the investment cost of distribution network via a reverse power flow tracking method. These three price components work in a complementary manner and are coupled with each other through the shared optimal power flow results. Numerical results demonstrate that the proposed extended distribution locational marginal price can reduce the production cost by 10.41% in contrast to the traditional distribution locational marginal price.

Given the significant challenges posed by the vast and diverse data in energy management, this study introduces a two-stage approach: optimal energy management system (OEMS) and dynamic real-time operation (DRTOP). These stages employ a multi-agent policy-oriented deep reinforcement learning (DRL) approach, aiming to minimize operating and energy exchange costs through interactions in the networked microgrid (NMG) energy market. The primary objectives include minimizing the distribution system operator (DSO) cost and optimizing the exchanged power between DSO and NMG, and the power transmission losses and the secondary include minimizing MG’s operating cost, optimal use of renewable energy resources (RER) and energy storage systems (ESS), minimizing the exchanged power cost with the main grid and, risk analysis. The OEMS&DRTOP model is developed based on the Stackelberg game theory and the DRL structure. The DRL model is developed in two offline learning and online distributed operation phases to minimize the computational burden, time, and DRL operation process. This study’s results show the high efficiency of the presented approach to minimizing the operating cost, the exchanged power based on the price uncertainty, power transmission losses, and, RER and ESSs optimal participation. In addition, regarding computational load, the proposed concept demonstrates a 12.9% reduction compared to the dueling deep Q-network method and a 17% reduction compared to the deep Q-network method. Also regarding computational time, the proposed concept demonstrates a 17.13% reduction compared to the dueling deep Q-network method and a 25.6% reduction compared to the deep Q-network method.

Security-constrained unit commitment (SCUC) is a crucial procedure in power system planning and operation. As renewable resources are integrated, it is suggested to perform sub-hourly SCUC with a 15-minute interval. This change increases the computational burden due to more binary commitment variables. Despite the use of advanced MIP solvers, poor performance continues to be a challenge. Therefore, this paper proposes a feasible-enabled integer-variable warm-start strategy to provide feasible estimated starting values for MIP solvers before optimization. To achieve this objective, a data-driven model based on a deep neural network architecture is designed. This data-driven model takes into consideration the structural characteristics of input data, allowing it to predict the corresponding value of binary commitment variables effectively. Subsequently, an auxiliary optimization model is constructed by combining predicted values with the physical constraints of SCUC, ensuring estimated starting values are within the feasible region and mitigating the adverse effects of incorrect predicted values. Case studies conducted on two large-scale testing systems illustrate the effectiveness of the proposed method.

This paper addresses the issue of enhancing the low voltage ride-through (LVRT) capability of doubly-fed wind farms during asymmetric faults. This research is of critical importance for ensuring the safe and stable operation of the power grid and the sustainable development of wind power technology. Given the stricter LVRT requirements set by the latest national standards in China, existing schemes are insufficient to meet these challenges. This paper first analyzes the control performance of the rotor side converter under asymmetric fault conditions. Based on national standard requirements, the availability of STATCOM, optimized operation targets, and converter voltage and current limitations, an optimized LVRT scheme is proposed. Validation through PSCAD simulations demonstrates that the proposed scheme effectively meets the national LVRT operation requirements. It also shows significant advantages in reducing electromagnetic torque fluctuations, enhancing active power output, protecting converters, and mitigating power fluctuations during faults. The innovation of this research lies in proposing a multi-target coordinated LVRT strategy, providing a new technical approach for improving the LVRT performance of wind farms and the stability of power grid operation.