Electric Power Systems Research最新文献

The optimal power flow methods for AC-DC systems containing VSC-HVDC generally only consider the economy during normal operation, overlooking the distribution of line transmission power in fault conditions. As a result, lines that continue to operate after a fault may experience overloading or operate at full capacity. Thus, a method for optimal power flow calculation is proposed that incorporates N-k security constraints in the preventive-corrective control stage for secure and economic operation of hybrid AC-DC systems. This method ensures that the line transmission power in the system meets the limits in the normal, short-term fault, and long-term fault states. In addition to the optimal power flow in the normal state, the method incorporates the system's imbalance as an indicator to evaluate system resilience. It combines this indicator with the economic, network loss, and performance metrics of the system, forming a two-stage bi-level multi-objective optimization model. Furthermore, to address the curse of dimensionality in anticipating system fault sets, a method for generating the anticipated fault set using non-sequential Monte Carlo simulation is proposed, along with a fault scenario search approach based on robust thinking to identify the most severe faults. Finally, the traditional IEEE 30-bus system was improved, and simulation verification was conducted using examples of an AC/DC system with a three-terminal DC network and a wind-solar-storage hybrid AC/DC system with a three-terminal DC network. The simulation results indicate that the proposed optimal power flow method considering the preventive-corrective control stage with N-k security constraints can effectively enhance system resilience. Furthermore, it improves the economic efficiency while ensuring the secure operation of the system.

In the European context, balancing energy markets are established to optimise transmission system operator balancing actions closer to real-time. These actions aim to match total generation and consumption subject to a suite of security constraints (e.g., reserve requirements). However, there is no clear border between those actions that are taken due to the reserve requirements (non-energy actions) and those that are primarily taken to supply the demand mismatches (energy actions). To recognise the effect of non-energy actions, existing methods require comparing the results of counterfactual optimisation problems in which the non-energy-action-related constraints were deliberately omitted. This paper proposes a one-off solution enabling TSOs to distinguish energy actions from non-energy ones in the balancing market scheduling problem. By decomposition of the dual variables and clustering the constraints as proposed in this paper, there is no need to solve repetitive counterfactual optimisation problems. Case studies show that in addition to the non-energy actions caused by non-energy-based balancing requirements, the proposed method is able to recognise the energy actions that should be taken due to the non-energy root causes. This feature enables TSOs to efficiently retrace the effect of non-energy actions on the energy-based dispatch instructions issued according to the balancing market schedule.

This study examines the impact of smart transformers on the optimal management of microgrids within a combined heat and power framework. Utilizing a Genetic Algorithm for optimization, the research identifies optimal settings for control variables and resource capacities. The integration of smart transformers significantly enhances performance, improving voltage profiles and reducing electrical losses, while minimizing costs and pollution levels.

Key gaps in existing literature include insufficient exploration of smart transformers' advantages and a lack of holistic approaches that integrate technical and economic objectives. This study proposes a comprehensive optimization framework that simultaneously addresses multiple goals, such as reducing power losses and environmental impacts.

The contributions include the innovative application of smart transformers for accurate control of active power flow and the development of a robust optimization strategy. Comparative analyses with other techniques, such as Particle Swarm Optimization and Interior Point Method, demonstrate the superior performance of the Genetic Algorithm approach in achieving optimal microgrid management.

Forecasting wind power generation accurately is crucial for reliable, economical, and efficient integrations in smart grids, promoting applications of cleaner energy sources. Although effective wind power forecasting methods exist, power grids still require resilient schemes enabling accurate predictions under cyber-attacks. This paper introduces civil attack (CA) and fast gradient sign method (FGSM) attacks to wind power forecasting to analyze their impacts with countermeasures. The impacts of CA and FGSM attacks on a deep learning-based forecasting method are evaluated, finding FGSM attacks more severe. Also, an attack identification and corrupted data replacement-based pre-processing robust framework is proposed, outperforming other countermeasures. To detect and classify attacks, random forest (RF) has outperformed extreme gradient boosting (XGBoost), decision tree (DT), support vector machine (SVM), and k-nearest neighbors (KNN). Experimental results on two different zones during CA and FGSM attacks indicate that the decrease in accuracy can be up to 0.4103, 0.3152, and 0.1683 in terms of root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE), respectively. The proposed framework successfully achieves an accuracy of 0.1204, 0.0835, and 0.0145 for the worst case in terms of RMSE, MAE, and MSE, respectively, signifying its importance for academic and industrial applications.

Large computational burden, time delay, and the necessity for precise modeling accuracy are the three main challenges for Finite Control Set-Model Predictive Control (FCS-MPC) in single-phase grid-tied inverters. To solve these issues, a twisted parameter scheme is proposed for the single-phase inverter in this article. Firstly, the law regarding the influence of the model parameter on the current total harmonic distortion (THD) is outlined, emphasizing that a decrease in the inductance parameter leads to a corresponding reduction in current THD. Second, a linear observer is constructed to identify the actual value of inductance and resistance, and an RBF-GA (Radial Basis Function neural network-Genetic Algorithm) scheme is used to obtain the optimal twisted parameter. Subsequently, the efficacy of the proposed methods was verified utilizing MATLAB/Simulink simulations, with further validation conducted through hardware-in-the-loop (HIL) experiments performed on Speedgoat performance real-time target machines. Simulation and experimental results demonstrate that within a specific range, decreasing the inductance parameter can significantly improve the quality of the current. Furthermore, the proposed method outperforms the traditional delay compensation method by reducing computational complexity, minimizing prediction error, and decreasing the number of switching transitions.

With the frequent occurrence of large-scale power outages caused by extreme disasters, coordinated dispatch of power emergency resources (PERs) and repair crews (RCs) has gradually become an essential method to enhance the resilience of distribution networks (DNs). Previous works solve this coordinated dispatch problem in a risk-neutral approach with the assumption that the supply of power repair materials (PRMs) is still fully available after the disaster. However, growing evidence indicates that certain extreme disasters may lead to limited availability of PERs in the DN, rendering risk-neutral decision making impractical. To fill this gap, this paper proposes a resilience-oriented PERs pre-disaster allocation and post-disaster dispatch strategy in a risk-averse manner. In the pre-disaster prevention process, a two-stage distributionally robust optimization (DRO) model based on mean-CVaR is developed, considering the uncertainties of both system damage and PERs demands, along with the decision maker's preference for risk. In the post-disaster restoration process, a collaborative spatio-temporal dispatching model for PERs and RCs with the goal of minimizing outage losses is established. To tackle the computational challenges associated with PRMs allocation based on DRO, robust counterpart transformation and dual theory are utilized. Case studies are conducted using a real world modified 118-bus distribution network. In comparative analysis, five control groups are established to validate the necessity to consider the limited supply of PRMs and the risk-averse attitude of decision makers in enhancing DNs resilience.

Regional integrated energy system (RIES) is an effective way to achieve the goal of energy conservation and emission reduction of the whole energy system, and regional heating network (HN) is an important energy transmission way in the RIES. This paper focuses on the RIES optimal scheduling problem considering HN. Firstly, the RIES structure model with multiple combined cooling heating and power (CCHP) subsystems is established. Secondly, according to the basic principle of energy transmission through pipelines, a general simplified model of HN considering node flow balance and heat flow constraints is established. At the same time, considering the excellent distribution of solutions in the population method and the operability of the robust optimization over time (ROOT) in solving dynamic optimization problems, an improved ROOT based on particle swarm optimization (PSO) is proposed. Finally, an optimal scheduling model with the goal of minimum daily operating cost is established. The simulation results show that the improved ROOT can reduce the RIES operation cost by 4.58 %, and the operating cost of RIES can be reduced by 6.34 % after the HN is configured. Considering the above two measures, the total operating cost of RIES can be reduced by 10.93 %.

Differential protection stands out as the optimal choice for protecting AC microgrids, compared to overcurrent and distance-based schemes, because of its adaptability to different network topologies, ability to manage bi-directional power flow, and better selectivity for system transients and variable fault current. However, high impedance faults, time synchronization error, and high bandwidth communication requirements are significant challenges faced by differential protection schemes. Considering such issues, this paper has proposed a novel differential protection scheme based on loss function (Percentage Bias Error), evaluated by using line's both end superimposed positive and negative sequential currents magnitude, which enhances the sensitivity in identifying internal fault that occurs in either grid-connected or islanded microgrid mode of operation. Its effectiveness is validated on ring and radial distribution networks with high impedance fault (500 Ω) at different fault locations. Additionally, the relaying scheme is stable under different system transients, CT error in noisy environments, and robust for time synchronization error. Moreover, the proposed scheme is compared with the existing techniques to illustrate its high sensitivity, fast operation (within one cycle), and high accuracy. The proposed scheme is simulated in a MATLAB Simulink environment, and results are validated using a laboratory-level hardware setup.

The expansion of solar power plants introduces changes in the consumer profile, as well as complexities related to their distributed settings and production patterns. To address these new challenges, the digitalization of the energy sector has become critical. In this context, solutions such as digital twins and industrial metaverses can help provide digital replicas for planning and testing diverse scenarios. In particular, the energy metaverse can provide stakeholders with an integrated digital platform that allows for experimentation and analysis of complex power systems. Nonetheless, developing such applications within the energy sector requires further research. Hence, this paper proposes a framework for developing specialized metaverse applications for power systems. The framework embraces modularity as a core principle, allowing the expansion of the application according to diverse services and needs. The following presents a case study that illustrates the feasibility of the proposed framework using Unity as a development platform and Open Platform Communications - Unified Architecture (OPC-UA) for data communication. A power plant located at the Fluminense Federal University, Brazil, is considered. The case study demonstrates the practicality of the proposed framework by providing a 3D visualization of the system, real-time data from the Supervisory Control and Data Acquisition (SCADA) and fault detection systems, and weather forecasts.

Medium-term feeder load forecasting plays a pivotal role in efficiently operating and planning electrical distribution systems. It provides valuable insights into future electricity demand trends, enabling utilities to make informed decisions regarding infrastructure upgrades, resource allocation, and energy management strategies. However, few studies have been done on medium-term load forecasting for the distribution network’s operational planning at the medium voltage level. Moreover, conventional load forecasting techniques mainly consider the impact of limited external factors, which is typically challenging to forecast accurately. In this work, multiple influential features have been utilized for accurate medium-term load prediction. Accurate load forecasting is paramount for efficient operation and planning in power systems. This study proposes a novel approach for medium-term feeder load forecasting that enhances peak accuracy using the Prominence-guided Weighted Peaks (PWP) in conjunction with the eXtreme Gradient Boosting (XGBoost) model. To evaluate the performance of the proposed model, we conduct experiments using real-world load data from a distribution feeder. Comparative analysis with traditional forecasting methods demonstrates the superior accuracy of the PWP-XGBoost model, particularly in predicting peak loads. Enhanced peak accuracy is crucial for utilities to effectively manage peak demand, optimize resource allocation, and ensure grid stability.