Distribution Network Security Research Articles

Power prediction of regional distributed photovoltaic clusters with incomplete information based on improved weighted fusion and transfer learning

AbstractThe rapid expansion of low‐voltage distributed photovoltaic (PV) systems with decentralized layouts poses significant data collection challenges. The scarcity of data amplifies prediction complexities, affecting the operational security and stability of distribution networks. To enhance predictive accuracy for distributed regional PV power generation, including unmonitored low‐voltage systems, this paper proposes a novel prediction approach that combines weight optimization and transfer learning. Firstly, to more accurately extrapolate from neighbouring monitored PV to estimate unmonitored PV outputs, a novel SSA‐MLP‐XGBoost model is proposed for monitored PV power plants, in which the hyperparameters of multilayer perceptron (MLP) and (extreme gradient boosting) XGBoost models are fine‐tuned using the sparrow search algorithm (SSA), and the optimized models are synergistically integrated to heighten prediction precision for monitored plants. Secondly, based on the predicted power curves of monitored PV power plants, a weight optimization model is presented to optimize the combined weights between unmonitored and monitored power stations. Furthermore, the power generation model from monitored facilities is transplanted to unmonitored plants for power generation estimation. Ultimately, the authors combine the weight optimization and transfer learning model to get more accurate and robust model. Validation across three regional distributed photovoltaic clusters demonstrates a noteworthy improvement in prediction accuracy—20%, 7.5%, and 25% for the respective clusters compared to the existing methods.

AUGMECON2-based multi-objective optimization of virtual power plant considering economical and security operation of the distribution networks

With the rapid development of massive multivariate distributed energy resources on the demand side, the distribution network is transforming from traditional passive to active management. By constructing the virtual power plant, distribution network operators will handle the management and transactions of those distributed resources as their participation in the electricity market becomes normal. Moreover, distributed resources will affect the operating economy and security of the distribution network, such as network losses and peak loads. Thus, distribution network operators must consider both the security of distribution networks as well as the economics of distributed resources. To address this issue, a virtual power plant with the distribution network operator as the stakeholder has been built. A multi-objective optimization model based on the improved augmented ε-constraint method (AUGMECON2) method is then proposed. The key objective is to maximize operating benefits while also accounting for network losses and peak-to-valley load disparity of the slack bus. To address the uncertainty of renewable energy, a dynamic reserve capacity calculation approach is developed, taking into account the economics as well as transaction reliability. The Pareto frontier of the multi-objective optimal problem is obtained by AUGMECON2, and a compromised solution considering fairness is proposed. Finally, the proposed method is verified with case studies based on the modified IEEE33-node distribution network system.

Application of depth feature recognition technology in foreign object recognition in distribution network monitoring video

Foreign objects identification in the distribution network is an important link in the security of electric power, and is of great significance to the normal transportation of electric power. At present, a lot of equipment in the distribution network is in the open air environment, facing a large number of foreign interference. These foreign objects not only bring potential safety hazards to the distribution network, but also easily lead to short circuit, causing power supply difficulties within the region. Therefore, the research first constructs an optimized triplet feature learning model. On this basis, the HOG-SVM depth feature recognition model is proposed. In HOG-SVM, AM is introduced to improve recognition accuracy. In addition, the research enhances the night vision ability of the model by standardizing the features in the image region block. The results show that the AP of the model is stable at more than 90.54%, the average FPR is 2.21%, and the average FNR is 3.17%. The performance of HOG-SVM is significantly better than that of traditional SVM. It verifies the contribution of this research in the field of foreign object recognition and application value in ensuring the security of distribution network.

Optimal configuration of distributed energy storage considering intending island recovery in faulty distribution networks

With the rapid development of distributed generation, represented by photovoltaic power, the access of a large number of distributed generation poses threats to the security and reliable operation of islanded distribution networks. However, relying on the distributed energy storage system can stabilize the island power supply, which can effectively improve the reliability of the island distribution network. To this end, under the premise of knowing photovoltaic output and load forecast curve, this paper proposes a distributed energy storage optimization configuration method in the active islanding operation mode of multi-source distribution network, which satisfies the “N-1″ safety criterion. First, this paper establishes an optimization configuration model for distributed energy storage with multiple objectives, including minimizing the load shedding in the non-fault loss of power zone, the initial investment cost of distributed energy storage, the node voltage deviation and the system frequency offset. Then, aiming at the islanded power flow calculation process mentioned above, an improved trust region algorithm is proposed, and integrating this method with the harmony search algorithm and ”N-1″ safety criterion solves the model. Finally, the IEEE 33-node distribution system is used to test the performance of the proposed algorithm. The results show that the proposed improved trust region algorithm has the advantages of fast convergence speed and short time-consumption compared with the traditional trust region algorithm. And the proposed optimization configuration scheme for distributed energy storage can satisfy the “N-1″ safety criterion of the distribution network under different fault scenarios.

A security-constrained robust optimization for energy management of active distribution networks with presence of energy storage and demand flexibility

A discernible rise in the frequency and intensity of unpredictable events and their significant social and economic effects have made power system planners pay special attention to the security and reliability of power networks. For this purpose, this article tries to provide an efficient model for strengthening the security of active distribution networks based on multiple microgrids by optimally using energy storage resources and consumption management plans. In the proposed plan, a hierarchical two-stage approach has been developed in which the first stage models the incident and their impact on the distribution network. Then, in the next stage, preventive and corrective measures are implemented to increase system readiness and reduce damages caused by severe accidents. At this stage, various tools such as energy storage, distributed and renewable production sources have been used in addition to responsive loads. In the corrective phase, the independent microgrid partitioning method is used to restore the distribution network to increase the speed of load pickup process. In order to consider the uncertainty and the risk caused by events on the performance of the proposed plan, the problem is done by robust optimization to obtain more realistic results. Finally, in order to confirm the effectiveness of the proposed method in improving the security of distribution systems, a standard network of 33 buses was used with different operating conditions. The results confirm that the proposed security-constrained robust optimization plan not only reduces system operating costs, but also improves consumer security in the face of severe accidents. Although the increase in network security is associated with a slight increase in system costs, the benefit of reducing outages and optimal use of network capacity has led to the effectiveness of the proposed plan.

Fault recovery strategy for urban distribution networks using soft open points

AbstractIn recent years, the frequent occurrence of natural disasters has seriously threatened the security and stability of distribution networks. Amidst these natural disasters, researchers have increasingly focused on ensuring fault recovery in distribution networks. Soft open points (SOPs) are new types of power electronic devices that can effectively recover faults in distribution networks. When a fault occurs, SOPs can quickly block and isolate fault short‐circuit current, provide power access to non‐fault areas, and optimise the power flow of distribution networks. Based on the critical role of distributed generation (DG) power supply in fault recovery, this study proposes a method that leverages the synergistic capabilities of SOPs and DG to recover faults in distribution networks. By employing a binary particle swarm optimisation algorithm, this method effectively improves load recovery, reduces the number of switching operations, and minimises network loss. The proposed method was simulated and verified using the IEEE 33‐node and 99‐node systems.

A Survey on Cyber-Physical Security of Active Distribution Networks in Smart Grids

A Survey on Cyber-Physical Security of Active Distribution Networks in Smart Grids

On the Security of Quantum Key Distribution Networks

The main purpose of a quantum key distribution network is to provide secret keys to any users or applications requiring a high level of security, ideally such as to offer the best protection against any computational attack, even of a quantum nature. The keys shared through a point-to-point link between a source and a detector using a quantum key distribution protocol can be proven information-theoretically secure based on the quantum information theory. However, evaluating the security of a quantum key distribution network, especially if it is based on relay nodes, goes far beyond the quantum security of its single quantum links, involving aspects of conventional security for devices and their communication channels. In this contribution, we perform a rigorous threat analysis based on the most recent recommendations and practical network deployment security issues. We show that, at least in the current state of our understanding of quantum cryptography, quantum key distribution networks can only offer computational security and that their security in practical implementations in the shorter term requires resorting to post-quantum cryptography.

Network-constrained flexible ramping product provision of prosumer aggregator: a data-driven stochastic bi-level optimization

Prosumers are expected to provide the flexible ramping product (FRP) in the power system. However, voltage violations and line congestion may arise in the distribution network, when FRP delivered by prosumers. Hence, this paper proposes a data-driven stochastic bi-level optimization model to coordinate the prosumer aggregator to decide FRP-offering while ensuring distribution network security under FRP delivery. In the proposed bi-level model, the upper-level is a min-max problem, representing the minimum expected cost under the worst-case scenario probability distribution for the prosumer aggregator. The lower-level is the operation cost minimization within the distribution network security for distribution network operator. The proposed model is converted into a single-level model using the Karush-Kuhn-Tucker condition and strong duality theory, and applied to the modified IEEE 33-bus network with three prosumers. The results demonstrate the effectiveness of the proposed model.

Inverter reliability-constrained Volt/Var optimization control of distribution network with high-level PV-storage generation

The involvement of renewable energy inverters in regulating the reactive voltage of the distribution network is an efficient approach to enhance the operational security and reliability of high-penetration renewable energy distribution networks. Nonetheless, the reactive power assistance offered by renewable energy inverters leads to an elevation in the maximum junction temperature of photovoltaic inverters and intensifies temperature fluctuations, which will subsequently impact the reliable and secure operation of both the inverters and the distribution network. In order to address this issue, this paper introduces a control strategy for optimizing reactive power and voltage in photovoltaic-storage (PV-storage) distribution networks with significant penetration, taking into account the constraint of IGBT junction temperature. Firstly, the IGBT junction temperature is calculated by the CatBoost algorithm, which improves the efficiency of IGBT junction temperature calculation and avoids the dependence of the traditional junction temperature algorithm on IGBT thermal model parameters. Then, a multi-objective reactive power optimization model of an active distribution network considering IGBT junction temperature constraint is established. The maximum output power of PV-storage power supply under IGBT junction temperature constraint is solved by dichotomy, so the transformation from IGBT junction temperature constraint to output apparent power second-order cone constraint is realized. Finally, the efficiency of the suggested approach is validated using the IEEE 33-bus typical distribution system. At the same time, the setting principle of IGBT junction temperature limit of PV-storage power supply considering the network loss of distribution network and the reliability of PV-storage power supply is proposed.

Modeling and characterization of the impact of capacity limitation services on distribution networks

Various congestion management strategies have been proposed to tackle the operational challenges arising from the widespread integration of distributed energy resources (DERs) in distribution networks. Among these strategies, capacity limitation services have recently gained significant attention. However, comprehensive analyses are essential for system operators to fully understand the impact and effectiveness of these services in distribution networks. In this study, we develop a model to represent the aggregator’s response to capacity limitation services and assess their impact on network states. The proposed model, from the aggregator’s perspective, consists of non-flexible baseload and controllable DERs, optimizing the aggregator’s benefits based on current market conditions without accessing network states. Consequently, system operators must ensure distribution network security while facilitating flexible resources. Given the unavailability of customer privacy and the complexity of multi-period consumption behaviors, system operators concentrate on capturing aggregator behaviors during potentially risky congestion periods. Our analysis specifically aims to quantify the effects of service area coverage, power capacity limitation values, and service durations. We establish an evaluation framework to coordinate various modules by assessing these services under different scenarios. Simulation results demonstrate that the proposed model can effectively represent power consumption behavior using partially accessible information. Furthermore, we design an associated procedure to offer distribution system operators valuable insights into system behavior, empowering them to request appropriate services in anticipation of potential operational issues.

Moving target defense of FDIAs for battery energy storage systems in smart distribution networks

Accurate state of charge (SoC) estimation of battery energy storage systems is essential for ensuring the security, stability, and economy of smart distribution networks. However, SoC estimation is vulnerable to false data injection attacks (FDIAs). To address this challenge, this paper proposes an improved approach to moving target defense (MTD) that takes into account the grid-connected nature of the energy storage system. Specifically, we introduce perturbations to the reactance on the AC side of the battery energy storage systems and the inductance of the distribution network to enhance the traditional MTD. Furthermore, we design a cost-minimizing solution to mitigate the potential impact of MTD on the system. Our defense strategy considers the change in active power loss and the equipment operation cost as the system defense cost. Experimental results demonstrate that our proposed method outperforms conventional MTD in IEEE13 and IEEE33 bus systems, with close-time usage, in terms of defense against FDIAs.

Reactive Power compensation Optimization of Distribution Network with Distributed Power Supply

In recent years, distributed generation has been fully developed, and its permeability is rising, which also brings great challenges to the reactive power regulation of the power distribution network. By analyzing the output characteristics of active power and reactive power of distributed power supply, in this article, a multi-objective optimization mathematical model of reactive power compensation is established, which takes the economy and security of the power distribution network into consideration. The results indicate that the reactive power compensation optimization model and the solution algorithm in this paper have good effectiveness and superiority. This thesis will provide the theoretical foundation and technology support for the design and realization of the DG reactive power compensation scheme.

Network-Constrained Transactive Control for Multi-Microgrids-Based Distribution Networks With Soft Open Points

Different from most transactive control studies only focusing on economic aspect, this paper develops a novel network-constrained transactive control (NTC) framework that can address both economic and secure issues for a multi-microgrids-based distribution network considering uncertainties. In particular, we innovatively integrate a transactive energy market with the novel power-electronics device (i.e., soft open point) based AC power flow regulation technique to improve economic benefits for each individual microgrid and meanwhile ensure the voltage security of the entire distribution network. In this framework, a dynamic two-timescale NTC model consisting of slow-timescale pre-scheduling and real-time corrective scheduling stages is formulated to work against multiple system uncertainties. Moreover, the original bilevel game problems are transformed into a single-level mixed-integer second-order cone programming problem through KKT conditions, duality, linearization and relaxation techniques to avoid iterations of transitional methods, so as to improve the solving efficiency. Finally, numerical simulations on modified 33-bus and 123-bus test systems with multi-microgrids verify the effectiveness of the proposed framework.

Voltage Optimization Control and Risk Assessment Technology for High-proportion Distributed Photovoltaic Access to Distribution Network

The high proportion of distributed photovoltaic connected to the distribution network will lead to an increase in terminal voltage and network loss, which will not only generate many adverse effects on the safe and reliable operation of the distribution network but also easily lead to power supply disconnection and power supply interruption accidents caused by voltage out of limit. At present, the voltage control of the distribution network does not fully consider the regulation characteristics of the photovoltaic inverter itself and ignores the impact of different control methods on the distribution network security risk management and control. Therefore, this paper establishes a voltage closed-loop control model for high-proportion distributed photovoltaic access to the distribution network and proposes a real-time active voltage optimization control strategy with the goal of minimizing the deviation of the actual operation state of the distribution network from the scheduling plan. Finally, a typical industrial park is taken as a case for calculation and analysis. The results show that using real-time data to formulate a real-time voltage optimization control strategy can not only ensure the economic operation of each voltage regulating unit in the distribution network but also reduce operational risks. The control method proposed in this paper provides a new idea for improving the voltage control level of distributed photovoltaic access distribution network and fully guarantees the operation reliability of the entire distribution network.

Distribution Network Security Situation Awareness Method Based on the Distribution Network Topology Layered Model

Distribution network security situation awareness refers to the process of perception, understanding, and state projection of systems, elements, and environmental factors within a certain space-time volume. Security situation awareness is an important part of the security assessment of the distribution network. In response to the rapid and accurate distribution network security situation awareness requirements, this paper proposes a distribution network security situation awareness method based the distribution network topology layered model. First, a hierarchical model of the distribution network topology under the premise of optimizing the location of the synchronous phasor measuring device is constructed. This model can quickly capture the system security situation elements. Then, a support vector data description algorithm fused with information entropy is used to realize the identification and understanding of abnormal information in the security situation elements of the distribution network. The long- and short-term memory network is then used to predict the operation trend of the distribution network under normal operation and fault disturbance. Finally, a simulation is established, and the IEEE-33 distribution network model is used to verify the effectiveness of the method proposed in this paper. The results show that the method of this paper improves the speed and accuracy of obtaining the security situation elements of the distribution network, shortens the identification time of the security situation elements, and realizes the security situation awareness of the nodes of the distribution network.

Two-stage optimization strategy of multi-objective Volt/Var coordination in electric distribution network considering renewable uncertainties

Renewable energy is connected to the distribution network in the form of distributed generation, and its output is random and fluctuating. The high penetration of renewable energy will weaken the security and economy of the electric distribution network. In view of the uncertainty of renewable power output, Monte Carlo simulation method and simultaneous backward reduction method are used to scenario generation and reduction of wind power and photovoltaic, so as to represent their randomness. On this basis, a two-stage optimization strategy for Volt/Var coordination in distribution network is proposed. In the first stage, a network reconfiguration model with the goal of minimizing the power loss of the system is established, and the particle swarm optimization algorithm is used to solve it. In the second stage, a Volt/Var optimization model with the goal of minimizing the node voltage offset and the system power loss is established, and the multi-objective model is solved by combining the network topology obtained in the first stage with the reactive power adjustment device and voltage regulating equipment such as switching capacitor, static var compensator and on-line tap changer. The distribution network’s Volt/Var is optimized using the non-dominated sorted genetic algorithm-II. The optimization results of the first stage are regarded as the conditions of the second stage. Lastly, the IEEE 33-node extension system is used to validate the effectiveness of the suggested strategy.

Research on Load State Sensing and Early Warning Method of Distribution Network under High Penetration Distributed Generation Access

Aiming at the problems of power flow fluctuation and voltage exceeding standard caused by high permeability distributed power supply access, this paper proposes a load state perception early warning method for distribution networks. Firstly, the random behavior characteristics and voltage early warning mechanisms of power supply and load in distribution networks are analyzed, the dynamic model of distribution networks based on complex network theory is established, and the risk index of voltage exceeding limits under the conditions of high permeability distributed power supply access is put forward. Secondly, the random power flow of distribution networks based on the Monte Carlo method is studied by sampling and analyzing the dynamic model of distribution networks. Then, the risk calculation and safety assessment of voltage exceeding limits are carried out on the currently extracted model, and the risk control strategy of distribution network operation is put forward. Finally, an improved IEEE30-node distribution network topology is proposed. Through simulation analysis, it is proven that the load situation awareness early warning method of distribution networks can effectively predict, improve the security of distribution networks, and provide timely early warning information for maintenance personnel.

Peer-to-Peer Energy Trading Under Network Constraints Based on Generalized Fast Dual Ascent

The wide deployment of renewable energy resources, combined with a more proactive demand-side management, is inducing a new paradigm in both power system operation and electricity market trading, which especially boosts the emergence of the peer-to-peer (P2P) market. A more flexible local market mechanism is highly desirable in response to fast changes in renewable power generation at the distribution network level. Moreover, large-scale implementation of P2P energy trading inevitably affects the secure and economic operation of the distribution network. This paper presents a new P2P electricity trading framework with distribution network security constraints considered using the generalized fast dual ascent method. First, an event-driven local P2P market framework is presented to facilitate short-term or immediate local energy transactions. Then, the sensitivity analysis of nodal voltage and network loss with respect to nodal power injections is used to evaluate the impacts of P2P transactions on the distribution network, which ensures the secure operation of the distribution system. Thereby, the external operational constraints are internalized, and the cost of P2P energy trading can be appropriately allocated in an endogenous way. Moreover, a generalized fast dual ascent method is employed to implement distributed market-clearing efficiently. Finally, numerical results indicate that the proposed model could guarantee secure operation of the distribution system with P2P energy trading, and the solution method enjoys good convergence performance.

Distributed optimization for network-constrained peer-to-peer energy trading among multiple microgrids under uncertainty

This paper addresses network-constrained peer-to-peer (P2P) energy trading problems for multiple microgrids (MGs) under uncertainty. A bi-level distributed optimization framework is proposed to bridge the gap between physical power flows supervised by distribution system operators and logical P2P transactions among multiple MGs under uncertainty. At the upper level, a conditional optimal power flow model is formulated to minimize power losses and guarantee the operating security of local distribution networks. At the lower level, a stochastic programming-based P2P trading model for multiple MGs is formulated to pursue the flexibility of energy transactions among different entities. To realize the consistency of decision-making processes between the two levels and among different MGs, a nested bi-level distributed algorithm including a parallel analytical target cascading algorithm and an alternating direction multiplier method is designed to solve the proposed model in a distributed manner. Furthermore, an adaptive updating method for penalty parameters is adopted to decrease the sensitivity to the initialization. Finally, numerical tests are implemented in a modified IEEE 33-node distribution network with four MGs to testify to the validity of the proposed energy trading framework. The results confirm that the obtained P2P trading schemes can protect against uncertainties and satisfy network constraints, especially since the proposed parallel distributed algorithm has better computing performances compared to the traditional sequential distributed algorithm.