Distribution Network Security Research Articles

Static voltage bilayer optimization for distribution networks based on Sobol’ method

Abstract In active distribution networks, the uncertainty of a large number of distributed power sources makes the voltage stability problem of distribution networks more and more prominent. The existing optimization methods tend to deal with the economy and voltage stability of distribution networks as independent objectives, with less consideration of their connection, making it difficult to realize the efficient use of flexible resources. The paper introduces upper and lower connection factors and proposes a two-layer optimization model for distribution network voltage using the Sobol’ method. First, taking into account the uncertainty of distributed power sources and loads in active distribution networks, a probabilistic tidal current calculation model for active distribution networks is constructed, based on which the impact of load fluctuations on the L index is quantitatively analyzed by the global sensitivity of the Sobol’ method. Secondly, the upper and lower connection factors are calculated based on the sensitivity analysis results. The upper economic objective and the lower stability objective are connected through the connection factors to establish a two-layer optimization model to solve the flexible resources in the system optimally. Finally, simulations on the modified IEEE33 node system validate that the proposed optimization model enhances distribution network security while considering economic factors.

Research on multi-time scale Volt/VAR optimization in active distribution networks based on NSDBO and MPC approach

To explore the potential of all-round and multiangle voltage and reactive power control to reduce losses in a new power system with a high proportion of distributed resources connected to a distribution network, this study proposes a Volt/VAR optimization method for distribution networks using a nondominated sorting dung beetle optimizer (NSDBO) and model predictive control (MPC). According to the characteristics of the various adjustable resources in the network, they are divided into day-ahead and intraday optimizations. The optimization process in the day-ahead stage uses the NSDBO to create a discrete equipment scheduling plan. The optimization process in the intraday stage combines the discrete equipment scheduling plan with the MPC to create the scheduling plan for photovoltaics, energy storage, and vehicle charging stations. Two-stage optimization scheduling is achieved with the lowest discrete equipment regulation cost and minimization of network loss and node voltage deviation penalty cost as the optimization goal. The feasibility of this method for coordinating various resources in the network, processing discrete and continuous variables, and coping with the volatility and uncertainty of high-proportion distributed resources is verified through case analysis. The effectiveness of this method in improving the security and economy of distribution networks is demonstrated. The superiority of the solution speed and quality is also confirmed.

Multi-demand-side resource regulation capacity assessment for distribution network safety and security

Taking into account the multiple demand differences and the existence of distribution network faults, the study conducts a multiple demand-side resource regulation capability assessment for distribution network safety and security as a way to achieve distribution network resource regulation optimization. The study considers multiple demands and adopts the correlation depth wandering algorithm to assess node faults in the distribution network, and proposes a quantitative assessment strategy for multiple demand-side resource regulation capability, and constructs an index system for assessing the resource regulation capability of the distribution network, and carries out the assessment of the resource regulation capability of the multiple demand-side resources. The results show that the regulation efficiency of wind power, thermal power, solar power and hydro power is above 20%, i.e., the combined regulation efficiency of the four energy sources is able to reach above 80%. The results show that the multi-demand-side resource regulation capability for distribution network security can be significantly improved, and the findings of the study have an important practical value for the electric power industry in terms of resource regulation and security, as well as providing a strong support for the sustainable development of the industry.

Photovoltaic Power Generation Related Physical Quantity Mining Method Based On Historical Time Series Data Analysis

Abstract The impact of distributed photovoltaic grid-connected on distribution network security, power quality and system stability cannot be ignored. In order to better cope with the uncertainty and output of new energy output and understand the characteristics of distributed new energy power output, it is necessary to predict photovoltaic power. The historical time series data of photovoltaic power is large in dimension and quantity. If the data output is not carried out, a large amount of redundant data will affect the accuracy of photovoltaic prediction. Therefore, this paper proposes a feature selection method based on MIC most mutual information coefficient, which filters out the most relevant data of photovoltaic power generation from the original feature variables, and then performs the feature dimension reduction method of linear discriminant analysis (LDA) to map the high-dimensional data to a lower dimension space. Finally, using the prediction simulation example, using the long and short time neural network (LSTM) prediction comparison, the feature dimension reduction method of MIC feature selection and LDA effectively improves the accuracy of photovoltaic power generation prediction.

An Anomaly Detection Ensemble for Protection Systems in Distribution Networks

Due to the complex topology, multi-line branches, and dense spatial distribution characteristics of a distribution network, potential disturbances and failures cannot be eliminated in real scenes, which means that higher levels of both reliability and stability are required in its corresponding protection system. For this reason, the timely monitoring and pinpoint identification of an underlying abnormal operation status in those protection systems must be ensured. To this end, a data-driven-based real-time anomaly detection ensemble is proposed in this paper. First, the kernel principal components investigation (KPCI) process is deployed to compress the dimensionality of input data, which can reduce the computational complexity within such high-dimensional data environments. Next, the isolated forest (IF) model is applied to excavate potential outliers according to the numeric range of the normal operating states of different features. Thus, a better detection performance in biased or sparse distributions can be achieved by reacting swiftly to those outliers. Finally, the operation data of the power distribution network protection system in a certain area is used as a simulation case. It is evident that compared with the single model IF detection method, combining the IF with the data dimension reduction model can effectively reduce data complexity. Due to the addition of kernel functions, KPCI can adapt to high-dimensional data environments better than standard PCI, and it also has certain advantages in calculation efficiency. This validates the theory that the proposed model has a high level of anomaly detection in practical applications, can assist in the automatic identification of and response to power distribution network security risks, effectively dig out potential system operational disturbances and state abnormalities, and achieve real-time anomaly monitoring and early warning.

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.

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

Tampered energy meter information conveyed to concerned authority by wireless communication

Utility companies face a serious problem with energy metre manipulation, which can result in lost income, inaccurate invoicing, and safety issues. Maintaining fair billing practices and the integrity of energy distribution systems depend on the prompt detection of tampering incidents. Nevertheless, current detection techniques frequently fall short in terms of effectiveness and fail to promptly notify authorities. In order to overcome this difficulty, our research suggests a novel method for identifying energy metre tampering and sending pertinent data via wireless communication technologies. The principal objective is to create a dependable system that can precisely identify instances of tampering in real-time and send alerts to relevant authorities with ease. Our solution is carefully designed to fill in the research gaps by drawing on a thorough assessment of the literature on wireless communication protocols and tampering detection techniques. Our solution delivers accurate and quick detection of tampering situations by utilising complex wireless communication protocols, modern sensor technology, and sophisticated data processing algorithms. The system architecture combines a wireless communication module with tampering detection sensors to quickly transmit notifications to the relevant authorities. Extensive tests are carried out to verify the functionality of the system, assessing critical parameters including alert reliability, response speed, and detection accuracy. Our experimental results highlight the effectiveness and reliability of the suggested approach in real-time alerting authorities and identifying tampered energy metres. This study makes a substantial contribution to the field of energy metre tampering detection and emphasises the critical role that wireless communication technologies play in preserving the integrity and security of energy distribution networks. In conclusion, our study presents a novel method for identifying energy metre tampering and wirelessly communicating vital information to authorities. Our research establishes a new benchmark in energy metre tampering detection by providing improved accuracy, efficiency, and timeliness, which promotes increased resilience and security in energy distribution networks.

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.