With the increasing digitalization and informatization of distribution network systems, distribution networks have gradually developed into distribution network cyber physical systems (CPS) which are deeply integrated with traditional power systems and cyber systems. However, at the same time, the network risk problems that the cyber systems face have also increased. Considering the possible cyber attack vulnerabilities in the distribution network CPS, a dynamic Bayesian network approach is proposed in this paper to quantitatively assess the security risk of the distribution network CPS. First, the Bayesian network model is constructed based on the structure of the distribution network and common vulnerability scoring system (CVSS). Second, a combination of the fuzzy analytic hierarchy process (FAHP) and entropy weight method is used to correct the selectivity of the attacker to strike the target when cyber attack vulnerabilities occur, and then after considering the defense resources of the system, the risk probability of the target nodes is obtained. Finally, the node loads and node risk rates are used to quantitatively assess the risk values that are applied to determine the risk level of the distribution network CPS, so that defense strategies can be given in advance to counter the adverse effects of cyber attack vulnerabilities.

The increasing factors of uncertainty faced by the system are due to the deep coupling of the electric power cyber network and the physical network. Consequently, ensuring the efficient, secure, and stable operation of the cyber–physical power system (CPPS) has become a key concern. To achieve this, vulnerability assessment plays a crucial role, as it identifies and protects the vulnerable points of the system. The application of complex network theory to assess the vulnerability of CPPSs has garnered significant attention from scholars. This paper delves into the research connotation of vulnerability assessment for CPPSs, starting with the origin, definition, and classification of vulnerability. Subsequently, the assessment framework of vulnerability based on complex network theory is presented, and the status of current domestic and international research in this field is summarized. Furthermore, the interrelationship between system vulnerability and cascading failures is analyzed from the perspective of complex network theory. In conclusion, the ideas of CPPS coupling modeling in vulnerability assessment are summarized, the concept of situation awareness is introduced, and a prospective approach for dynamic vulnerability assessment is proposed. This approach is based on situation awareness combined with complex network theory. Security protection and optimal operation of CPPSs based on vulnerability assessment are also discussed, along with the assessment of vulnerability within integrated energy cyber–physical systems (IECPSs).

Amidst the evolving communication technology landscape, conventional distribution networks have gradually metamorphosed into cyber–physical power systems (CPPSs). Within this transformative milieu, the cyber infrastructure not only bolsters grid security but also introduces a novel security peril—the false data injection attack (FDIA). Owing to the variable knowledge held by cyber assailants regarding the system’s network structure, current achievements exhibit deficiencies in accommodating the detection of FDIA across diverse attacker profiles. To address the historical data imbalances encountered during practical FDIA detection, we propose a dataset balancing model based on generating adversarial network-gated recurrent units (GAN-GRU) in conjunction with an FDIA detection model based on the Transformer neural network. Harnessing the temporal data extraction capabilities of gated recurrent units, we construct a GRU neural network system as the GAN’s generator and discriminator, aimed at data balance. After preprocessing, the balanced data are fed into the Transformer neural network for training and output classification to discern distinct FDIA attack types. This model enables precise classification amidst varying FDIA scenarios. Validation involves testing the model on load data from the IEEE 118-bus system and affirming its high accuracy and effectiveness in detecting power systems after multiple attacks.

With the development of communication technology, traditional distribution networks have gradually developed into cyber–physical systems (CPSs), from which the cyber system provides more protection for the grid and brings new security threat–cyber disturbances. Current research cannot scientifically measure the impact of cyber disturbances on the system and lacks reliability indices for a comprehensive quantitative assessment of CPS reliability from the perspective of cyber–physical fusion. If the impact of information disturbances on system reliability is not assessed accurately, it will not be possible to provide a scientific and reasonable decision basis for system planning and operation. Therefore, a set of reliability assessment methods and indices for distribution network CPSs considering cyber disturbances is proposed. Firstly, a reliability modeling method combining fault tree and Petri net is proposed to model the reliability of a distribution network CPS, which can improve the efficiency and accuracy of the modeling. Secondly, the system state is divided into two categories: normal operation state and cyberattack state. Then, generalized reliability indices considering cyber disturbances for distribution network CPSs are defined. Finally, through the tests on the modified IEEE RBTS BUS2 distribution network CPS, an analysis of the effects of information component failures, cyberattacks, and access network structures on system reliability is conducted in this paper to verify the efficiency of the proposed method and the rationality of the newly defined reliability indices.

With the development in the field of energy and the growing demand for sustainable energy, gas–electric integrated energy systems are attracting attention as an emerging energy supply method. At the same time, with the deep application of information technology, the cyber–physical interactions of gas–electric integrated energy systems are increasingly enhanced. To this end, first, the reliability assessment indices of a gas–electric integrated energy system, which comprehensively considers the interactions between cyber–physical and different energy sources, are established in this paper to quantitatively assess the reliability level of the system under different fault and failure conditions. Second, to solve the reliability optimization problem, a comprehensive reliability enhancement optimization model is constructed in this paper, which targets the sum of the total penalties of the failure rate and average repair time modification. The impact of the cyber systems on the gas–electric integrated energy systems is transformed into a modification of the failure rate and the average repair time, and the model is solved by an adaptive Gaussian particle swarm optimization algorithm. Finally, the applicability and superiority of the adaptive Gaussian particle swarm optimization algorithm to the reliability optimization of the gas–electricity integrated energy system are verified by conducting simulation tests on the gas–electricity integrated energy system coupled with an 8-node distribution system and the 11-node natural gas system in Belgium. Furthermore, the effects of cyber systems and cyber-attacks on system reliability optimization are also analyzed to verify the effectiveness of the proposed method and the rationality of the newly defined reliability indices.

Future power systems will face more extreme operating condition scenarios, and system emergency dispatch will face more severe challenges. The use of distributed control is a well-designed way to handle this. It enables multi-energy complementation by means of autonomous communication, which greatly improves the flexibility of the grid. First, in the context of global energy conservation and emission reduction, this paper adopts the energy usage method of “renewable energy is the main source of energy, supplemented by thermal power and energy storage” to reduce the system abandoned wind (light) rate while supplementing the energy storage capacity. Second, a consensus algorithm is added to the system while considering the coordination between thermal units and energy storage. An “interface” for autonomous communication between thermal units and energy storage is created using the incremental cost of each agent. To address the recurring issue of power imbalance during emergency dispatch of the system, the consensus algorithm is enhanced so that the communication interval varies with the unit rate. This is based on the climbing characteristics of each thermal power unit. Finally, the effectiveness of the proposed method is verified in an IEEE-30 bus system.