In this paper, the chaotic bat algorithm (CBA) is applied to solve the optimal reactive power dispatch (ORPD) problem taking into account small-scale, medium-scale and large-scale power systems. ORPD plays a key role in the power system operation and control. The ORPD problem is formulated as a mixed integer nonlinear programming problem, comprising both continuous and discrete control variables. The most outstanding benefit of the bat algorithm (BA) is its good convergence for optimal solutions. The BA, however, together with other metaheuristics, often gets stuck into local optima and in order to cope with this shortcoming, the use of the CBA is proposed in this paper. The CBA results from introducing the chaotic sequences into the standard BA to enhance its global search ability. The CBA is utilized to find the optimal settings of generator bus voltages, tap setting transformers and shunt reactive power sources. Three objective functions such as minimization of active power loss, total voltage deviations and voltage stability index are considered in this study. The effectiveness of the CBA technique is demonstrated for standard IEEE 14-bus, IEEE 39 New England bus, IEEE 57-bus, IEEE 118-bus and IEEE 300-bus test systems. The results yielded by the CBA are compared with other algorithms available in the literature. Simulation results reveal the effectiveness and robustness of the CBA for solving the ORPD problem.

The False data injection attack (FDIA) against the Cyber-Physical Power System (CPPS) is a kind of data integrity attack. With more and more cyber vulnerabilities detected out, different types of FDIAs are emerging as severe threats to the stable operation of CPPS gradually. In this paper, the invasion pathway of the FDIA against CPPS is explored in detail, and a novel FDIA detection model based on ensemble learning is further provided. First, a pseudo-sample database is built to assist the training and evaluation of this model, and it’s more important to update the model in the future. Furthermore, the optimal feature set is extracted to characterize the behavior of the FDIA, which improves the precision of the FDIA detection model. Finally, a focal-loss-lightgbm (FLGB) ensemble classifier is constructed to detect the FDIA behavior automatically and accurately. We illustrated the performance of this model by a fusion of measurement data and power system audit logs. This model utilizes the offline training way, the conclusion shows the high precision and stability of this model, which ensures the stable operation of the smart grid and improves the FDIA resistance ability of the CPPS.

In the analysis of coordinated network attacks on electric power cyber-physical system (CPS), it is difficult to restore the complete attack path, and the intent of the attack cannot be identified automatically. A method is therefore proposed for the extracting patterns of coordinated network attacks on electric power CPS based on temporal-topological correlation. First, the attack events are aggregated according to the alarm log of the cyber space, and a temporal-causal Bayesian network-based cyber attack recognition algorithm is proposed to parse out the cyber attack sequences of the same attacker. Then, according to the characteristic curves of different attack measurement data in physical space, a combination of physical attack event criteria algorithm is designed to distinguish the types of physical attack events. Finally, physical attack events and cyber attack sequences are matched via temporal-topological correlation, frequent patterns of attack sequences are extracted, and hidden multi-step attack patterns are found from scattered grid measurement data and information from alarm logs. The effectiveness and efficiency of the proposed method are verified by the testbed at Mississippi State University.

Existing power cyber-physical system (CPS) risk prediction results are inaccurate as they fail to reflect the actual physical characteristics of the components and the specific operational status. A new method based on dependent Markov chain for power CPS risk area prediction is proposed in this paper. The load and constraints of the non-uniform power CPS coupling network are first characterized, and can be utilized as a node state judgment standard. Considering the component node isomerism and interdependence between the coupled networks, a power CPS risk regional prediction model based on dependent Markov chain is then constructed. A cross-adaptive gray wolf optimization algorithm improved by adaptive position adjustment strategy and cross-optimal solution strategy is subsequently developed to optimize the prediction model. Simulation results using the IEEE 39-BA 110 test system verify the effectiveness and superiority of the proposed method.

The problem of load fluctuation in the distribution network and increasing power grid cost input caused by the unpredictable behavior of electric vehicle (EV) users in response to electricity price is investigated in this paper. An optimization model method for the charging and discharging price of electric vehicles is proposed, considering the vehicle owner response and power grid cost. The rule of EV user travel is first analyzed, and the travel and battery state constraints are defined. Under the constraints of user charging and discharging behavior and battery characteristics, a user transfer rate and unit energy cost function is designed to construct a multi-objective model of charging and discharging price that minimizes electricity expenditure and avoids an increase in power grid investment. Finally, an improved multi-target fish swarm algorithm is presented to solve the model optimization problem. The example analysis shows that the proposed method can reduce the peak-valley load difference of the system and cost input of the power grid, as well as provide users with regulation ability to access the power grid at different time periods.

The essence of the microgrid cyber-physical system (CPS) lies in the cyclical conversion of information flow and energy flow. Most of the existing coupling models are modeled with static networks and interface structures, in which the closed-loop data flow characteristic is not fully considered. It is difficult for these models to accurately describe spatiotemporal deduction processes, such as microgrid CPS attack identification, risk propagation, safety assessment, defense control, and cascading failure. To address this problem, a modeling method for the coupling relations of microgrid CPS driven by hybrid spatiotemporal events is proposed in the present work. First, according to the topological correlation and coupling logic of the microgrid CPS, the cyclical conversion mechanism of information flow and energy flow is analyzed, and a microgrid CPS architecture with multi-agents as the core is constructed. Next, the spatiotemporal evolution characteristic of the CPS is described by hybrid automata, and the task coordination mechanism of the multi-agent CPS terminal is designed. On this basis, a discrete-continuous correlation and terminal structure characteristic representation method of the CPS based on heterogeneous multi-groups are then proposed. Finally, four spatiotemporal events, namely state perception, network communication, intelligent decision-making, and action control, are defined. Considering the constraints of the temporal conversion of information flow and energy flow, a microgrid CPS coupling model is established, the effectiveness of which is verified by simulating false data injection attack (FDIA) scenarios.