International Journal of Electrical Power & Energy Systems最新文献

Conventional model-driven methods are hard to handle large-scale power flow with multivariate uncertainty, variable topology, and massive real-time repetitive calculations. With the ability to deal with non-Euclidean graph-structured power system data, graph deep learning shows great potential in modern power flow calculation. However, general graph deep learning based power flow calculation has limited adaptability because of its sole mapping of node information and black-box attributes. In this paper, an edge graph attention network based power flow calculation (EGAT-PFC) model is proposed with improved adaptability for power flow analysis of complex system scenarios. First, the dual-model structure of the node model and edge model is constructed to realize a complete power flow mapping covering all information in power systems. Second, an improved learnable attention coefficient mechanism fusing node and edge features is proposed to ensure global information can be completely considered. Third, mechanisms of extended first-order neighborhood, dynamic normalization, and regularization-based loss function are designed to improve training performance. Finally, visualized interpretability is developed to show valuable information of vulnerable nodes and lines of power system operation. The numerical simulation verifies that EGAT-PFC has high accuracy, fast mapping, as well as excellent adaptability to variable topologies.

In this paper, a new structure, the so-called ${\text{FBSCUC}}_{\text{FDI}}^{\text{P-P}\text{/}\text{EV}}$ is proposed as flexibility-based security constrained unit commitment (SCUC) in the presence of false data injection (FDI) attack into the communication infrastructure of electric vehicle parking lot (EVPL). Herein, the uncertain EVPLs are integrated into the SCUC problem with the aim of reducing the operation cost. It is notable that growing integration of unspecified EVPLs can introduce novel challenges to the power system, significantly impacting its flexibility. In this study, electric vehicles are leveraged as a means to enhance system flexibility. Meanwhile, the FDI attacks in EVPLs can distort the system’s flexibility and lead to inaccurate assessments of the power system’s ability to adapt to changing conditions. In order to model the FDI attack, a bi-level optimization problem based on mixed integer linear programming is formulated. At the upper level, the impact of EVPLs on the flexibility indices of the SCUC is evaluated, and the false data injected into the EVPL is calculated at the lower level. Since both levels of the proposed ${\text{FBSCUC}}_{\text{FDI}}^{\text{P-P}\text{/}\text{EV}}$ include discrete variables, a reformulation and decomposition technique is utilized to achieve the optimal solution. Instead, an extreme gradient boosting (XGBoost)-based machine learning method is considered to detection and correction of FDI attack. The proposed approach is tested on the IEEE 24-bus system. The simulation results initially indicate the improvement of the flexibility of the power system in proposed structure. Further, injecting false data into all available EVPLs causes to increase the system operation cost. Besides, false data leads to distorted charging and discharging scheduling of EVPLs; likewise, scheduling and commitment of power generation units also changes. Subsequently, the application of the XGBoost algorithm effectively mitigates the impact of FDI attacks, achieving a maximum accuracy of 85.41%.

This paper presents an accurate and efficient initialization strategy for modular multilevel converters (MMCs) based on the shooting method, a numerical technique aimed at deriving the periodic steady-state operating condition of any circuit. This technique is compatible with MMC models of different levels of detail and whose control scheme may include modulation strategies and capacitor voltage balancing algorithms. Electromagnetic transient simulations of the NORDIC32 power system modified by adding a high-voltage direct current link with 128-level MMCs prove that the proposed initialization strategy allows starting simulations close to steady-state, thereby significantly limiting initialization transients and their corresponding extra CPU time.

An increase in the share of weather-dependent generation at low voltage levels necessitates incorporating the low-voltage network in optimizing a distribution network. Optimization in a multi-voltage network requires significant computation time and effort due to many nodes operating at different voltage levels. This research proposes a decomposition and strategic optimization method to reduce the computation requirements for such large multi-voltage distribution networks. The proposed algorithm reduces the space complexity and the computation time required for solving the optimization routines of these multi-voltage distribution networks. A virtual transformer model incorporates tap-changer as a continuous variable in the semidefinite programming power flow optimization model. The zero-duality gap condition for multiple virtual transformers is proven empirically. Compared to a centralized optimization using the same power flow model, the proposed framework reduced the computation time by 96%.

The carbon neutrality policy aiming for net zero carbon emissions has led to a significant increase in the use of renewable energy sources (RES) globally. However, due to their uncertain nature, RES can cause imbalances in power demand. Recently, Power-to-X (P2X) station technology has gained attention as a solution to the uncertainties of RES and as a means to enhance the capacity and efficiency of RES operations. P2X stations can be utilized when power demand imbalances occur due to the uncertain output of RES or when the power system cannot accommodate the power supply from RES due to various stability issues. Specifically, when supply disruptions occur in the power system due to RES uncertainties, P2X stations contribute to preventing RES curtailment by supplying power to electric vehicle (EV) fuel sources, producing heat using electric heat pumps (EHP), or producing hydrogen using electrolyzers (ELZ), thus improving the uncertain financial benefits for independent power producers (IPP). This paper proposes a mixed-integer linear programming (MILP) based chance-constrained two-stage stochastic optimization (CCTS) approach to address imbalances in power demand from RES and to enhance the profitability of IPP by finding the optimal planning and operational solutions for P2X stations. The proposed method provides hierarchical level results, demonstrating that economic benefits can increase by up to 60.2% with the application of P2X stations and that curtailed energy from RES can be reduced by up to 76.5%. The proposed methodology is also validated for its superior performance by being compared with both the non-linear stochastic chance constraint method and the stochastic method.

User clustering is crucial for tapping the flexibility of the load side and realizing dynamic management of power loads in new power system. K-means method is widely used in clustering analysis due to its simplicity, high efficiency, and scalability, but it needs to specify the number of clusters in advance, and is sensitive to the initial clustering centers. The current initialization method does not take into account the neighborhood distribution of the data points, and the direct use of data that has undergone dimensionality reduction processing leads to inaccurate selection of the initial clustering centers. To address the above problems, a new K-means improvement method that takes into account the initialization problem and the adaptive determination of the number of clusters: K-means clustering method based on nearest-neighbor density matrix is proposed in this paper. The method improves the efficiency of nearest neighbor search by building a K-D tree, and enhances the performance of unsupervised classification by utilizing the adaptive selection strategy of the number of clusters and the initial clustering centers selection algorithm. The proposed method is applied to real datasets, and its effectiveness is assessed by calculating three clustering evaluation metrics of the clustering results in comparison with several existing initialization and clustering methods. The experimental results show that the method proposed in this paper has higher stability and better clustering performance than existing clustering methods.

In recent years, non-characteristic harmonic resonance was observed in several high-voltage direct current (HVDC) systems in China. Intuitively, additional filters can be installed to mitigate the resonance. However, such a solution does not prevent the harmonic penetration from nearby substations. As a result, the harmonic issue in HVDC systems cannot be well addressed. In view of the above, this paper presents a system-level harmonic mitigation method. The basic idea is to address the harmonic pollution by mitigating harmonic sources and harmonic resonance simultaneously. To achieve this goal, candidates of filters to suppress the resonance at the HVDC station are investigated first. Then, an optimal filter design problem is established to determine the location, type and capacity of multiple filters that can achieve the system-level harmonic mitigation with the minimum cost. Particularly, an improved particle swarm algorithm with adaptive mutation operators is tailor-designed to solve the proposed optimization problem. Finally, the proposed method is applied to address the harmonic problem faced by a real-life HVDC system in eastern China. The result demonstrates that compared with the solutions solely mitigating harmonic sources or resonance, the method presented in this paper is more cost-effectiveness.

Leveraging the dispatchability of 5G base station energy storage (BSES) not only enables the mobile network operator (MNO) to gain additional revenue, but also facilitates the integration of renewable energy sources in distribution network (DN). However, since BSES and DN are owned by different stakeholders, integrating BSES into DN operations poses significant challenges. In this regard, this paper proposes a DN optimal dispatch model that incorporates the adaptive aggregation of 5G base stations (BSs) through a cooperative game framework. Firstly, the dispatchability of BSESs is analyzed and modelled. Considering it is difficult to dispatch every single unit optimally, an adaptive aggregation model of 5G BSs is established, where the electrical coupling degree and the communication service similarity are taken as comprehensive metrics. On this basis, an optimal dispatch model of DN based on cooperative game is constructed, where the total operational costs of 5G BSs and DN are considered as the characteristic function. The number of 5G BS clusters and the aggregating results are adjusted adaptively during optimization. The optimal aggregation of 5G BSs is achieved using the Affinity Propagation (AP) clustering algorithm. Furthermore, to solve the optimal dispatch model of the DN with enhanced computational efficiency, the particle swarm optimization algorithm integrated with second-order cone programming (PSO-SOCP) is employed. After dispatching, the benefit allocation between MNO and distribution system operator (DSO) is conducted using the Shapley value method and the Equal Profit Method to obtain an entire range of allocation results. Finally, simulations are carried out with results proving the effectiveness of the proposed method.

Proliferation of distributed energy resources (DERs) and proactive consumers has mooted over the pre-existing pricing mechanisms for active distribution networks (ADNs). This entails the need for a pricing mechanism in conjunction with cost-effective ADN operations. Locational marginal price (LMP) is a well-established pricing mechanism of the day-ahead wholesale market in most countries and provides economic signals and incentives to market participants. However, the alternating current optimal power flow (ACOPF) model (being non-deterministic polynomial-time hard) has inherent complexities and convergence issues. Besides, the approximations involved in its implementation for transmission networks may not be applicable to ADN due to their technical and structural differences. Hence, a distribution LMP (DLMP) model is indispensable for the evolving ADN. This paper proposes a network-dependent sensitivity-based branch-flow quadratic OPF model for evaluating active and reactive power DLMPs of ADNs. The DLMPs are calculated using the sensitivities and dual variables of the OPF model, which consist of incremental costs for energy, loss, congestion, and voltage components. These signals would offer an equitable price for each DER, accounting for their contribution to network conditions. The efficacy of the proposed model has been elucidated on the 33, 69, 118, and 141-node ADNs, and the results are compared with five state-of-the-art models.

The efficient utilization of biomass energy and the optimal operation of integrated renewable energy sources in virtual power plants have become critical issues that need addressing to achieve carbon neutrality targets in rural areas. This paper introduces a model for rural biomass energy generation based on the characteristics of rural biogas availability, fermentation efficiency, and utilization status. A model for a rural virtual power plant is proposed, taking into account the flexible response capacity of renewable energy sources consumption and load demand response. Additionally, the correlated uncertainties of photovoltaic and wind turbine outputs are addressed by leveraging the flexible biogas power generation in the rural virtual power plant. The scenario generation method is proposed based on the Copula function to describe the correlated uncertainties. Aiming to minimize the system operating cost while ensuring the safe operation of the system, a two-stage distributionally robust optimization operation model for the rural virtual power plant is proposed. The dual vertices fixing based algorithm is developed to solve the two-stage distributionally robust optimal operation problem. Simulation results demonstrate that the proposed model and method effectively reduce over-robustness by addressing correlated uncertainties and achieve the safe, economical, and green operation of the rural energy system under uncertain conditions.