International Journal of Electrical Power & Energy Systems最新文献

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.

Identifying safety risks in distribution networks is of great significance for ensuring the safety of personnel and the stable operation of the distribution system. Existing research on safety risk identification in distribution network operations mainly focuses on personnel irregular dress detection and dynamic unsafe behavior identification. However, the actual operation scenario of the distribution network involves a complex process of multi-element interaction and integration of personnel, tools, and equipment machinery, where the risk of violations is often hidden within the intricate web of interactions. For this reason, this paper focuses on the problem of violation identification of human-object interaction relations in distribution network operation scenarios and proposes a violation risk identification method based on multiple interaction relations. The method firstly extracts the features of the distribution network operation image by convolutional neural network resnet101, then introduces the coding-decoding structure to re-encode the feature vectors to get the feature vectors with different interactions, and at the same time, utilizes the conditional filtering module to improve the convergence speed of the structure, and utilizes the Residual Information Exchange Module and the multi-layer mlp structure to discriminate the interaction pairs of multiple relationships, and finally takes the ladder climbing operation scenario as an example for the experimental validation. The experimental results showed that the proposed method can realize the accurate identification of human-object interaction relationships and violation risk, and has strong practical application value.

This paper addresses two complex areas of research: the detection of turn-to-turn faults (TTF) in power transformers and the impact of inverter-based resources on the TTF protection scheme operation. Detecting turn-to-turn faults in power transformers by protection algorithms poses a challenge due to the minimal fault currents observed at transformer terminals. Yet, the demand for dependable TTF protection is very high because of the high fault currents inside the shorted turns and the resulting damage consequences. On the other hand, for such sensitive protection, adverse conditions such as transformer inrush currents or CT errors may lead to protection maloperation. Moreover, the fault current characteristic of the inverter-based source infeed is very different compared to the synchronous machine infeed, particularly concerning the negative-sequence current used in the TTF protection schemes, which calls for thorough research analysis. A simulation model of the converter transformer capable of simulating TTFs, and the Modular Multilevel Converter (MMC) for a High Voltage Direct Current (HVDC) transmission link, has been developed. The test results of turn-to-turn fault protection schemes in inverter-based generation-dominated power systems compared to the synchronous generator infeed are presented. The negative-sequence current protection quantities are analysed in more detail for TTFs with small and large number of shorted turns, i.e. without and with reactive negative-sequence current injection by the MMC control. Finally, the paper assesses the dependability of the transformer differential protection and sensitive TTF protection schemes in detecting faults with different numbers of shorted turns and fault resistance for TTFs occurring in the star and delta winding of the converter transformer.

The promising features of HVDC technology have led to the possibility of numerous renewable resources integration and enormous DC grids interconnection. In spite of the obstacles, these interconnections encounter such as the necessity to block DC faults, achieving isolation between different schemes, the ability to maintain power flow throughout different power flow profiles, and the interfacing with various infrastructures, the DC-Hub arises to overcome these interconnection obstacles being the excellent approach to enhance the DC grid capabilities. This paper proposes a new monolithic modular thyristor-based multilevel converter, which serves as the fundamental building block of the DC-Hub, offering advantages such as lower switch count, bidirectional power flow, and DC fault blocking capability. Moreover, a control algorithm, for zero reactive power circulation in the DC-Hub, is introduced. The proposed algorithm successfully mitigates the circulation of reactive power throughout the entire range of power flow. A comprehensive mathematical analysis, optimum design of converter parameters, and the proposed control technique, which suppress the circulating reactive power at full range of power flow, are illustrated. Finally, simulation modelling and hardware test rig are established to validate the claims of the DC-Hub at different normal and faulty scenarios.

Blackout events caused by cascading failures have induced enormous losses in power systems. Good defense strategies are helpful for stopping cascading failures spread, which not only reduces system losses, but also improves the system’s ability to cope with the cascading failures. However, the current studies mostly focus on single-stage defense strategy and lack coordination of preventive, emergency and restoration dispatch (CPERD) against cascading failures. This paper proposes a CPERD methodology to counteract cascading failures for resilience enhancement. We first analyze the propagation mechanism and the prediction method for the propagation paths of cascading failures. Second, we explore the necessity and feasibility of whole-process coordinated dispatch under cascading failures and design a CPERD model framework. Based on this framework, we further analyze the impact of whole-process coordinated dispatch on the propagation path of cascading failures. Third, preventive dispatch, emergency dispatch and restoration dispatch sub-models are separately modeled to minimize the dispatch cost. Since the model has the characteristics of multi-stage and multi-objective, a double-layer optimization framework and an improved particle swarm optimization algorithm are introduced to solve the CPERD problem. Finally, the IEEE 39-bus and IEEE 118-bus test systems are taken as examples for simulation analysis. The results show that whole-process coordinated dispatch can effectively reduce the final load loss and overall dispatch cost, and enhance power system resilience against cascading failures.