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Modular Multilevel Converter Virtual Synchronous Generator (MMC-VSG) technology is gaining widespread attention for its ability to enhance the inertia and frequency stability of the power grid integrated with converter-interfaced renewable energy sources. However, the excitation voltage regulation in the MMC-VSG can generate equivalent negative damping torque and cause low-frequency oscillation problems similar to those in synchronous machines. This article aims to improve the system's damping torque and minimize low-frequency oscillations by introducing a Virtual Power System Stabilizer (VPSS) into the power control loop. Building on the study of dynamic interactions between various control links of the MMC, this research establishes a reduced-order model (ROM) and a Phillips–Heffron state equation for the MMC-VSG single machine infinite bus system, using a hybrid modeling approach and a zero-pole truncation method. It also analyzes the mechanism of low-frequency oscillations in the MMC-VSG system through the damping torque method. The analysis reveals that the negative damping torque produced during the excitation voltage regulation process causes changes in the virtual power angle, which in turn increases the risk of low-frequency oscillation in the MMC-VSG. To address this issue, the article proposes an optimized control method for the MMC-VSG dual power loop architecture (power-angle/excitation) VPSS. This strategy compensates for the inadequate damping torque of a single loop VPSS and effectively suppresses low-frequency oscillations in the system. [ABSTRACT FROM AUTHOR]

Maintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional‐order proportional‐integral‐derivative‐acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro‐fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi‐area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS‐FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie‐line power exchange. Simulations on a New England IEEE 10‐generator 39‐bus power system demonstrate the approach's effectiveness under various disturbances, including random load‐generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)‐FOPIDA, WOA‐PIDA, and WOA‐ANFIS‐PIDA, and recent control approaches highlight the superior performance of the WOA‐ANFIS‐FOPIDA method in enhancing power system stability. [ABSTRACT FROM AUTHOR]

In this study, we investigate an unrelated parallel batch processing machines scheduling problem (UPBPMSP). A set of jobs with non-identical sizes and arbitrary ready times are scheduled on unrelated parallel batch processing machines with different capacities to minimize the makespan (i.e., the completion time of the last batch). Existing studies have either decomposed the scheduling problem into two sub-problems and employed population-based heuristic interaction for searching the best solutions or used neighborhood search algorithms to search the current solution's neighborhood to find a better solution. However, the performances of these methods are not quite satisfactory due to the complicated interactions or oversimplified neighborhood search strategies, especially for large-scale UPBPMSPs. In this study, we propose a novel tabu-based adaptive large neighborhood search (TALNS) algorithm to obtain high-quality solutions for the UPBPMSP. To avoid complex interactions, we propose a new solution structure, and the proposed TALNS is applied to obtain the optimal solution structure of the UPBPMSP. Extensive experiments are conducted to evaluate the performance of the proposed algorithm on a total of 360 instances from the literature and 30 new instances. Numerical results demonstrate that the proposed TALNS outperforms the neighborhood search methods, which outperforms the population-based methods. With the proposed TALNS, 55 out of 360 instances' best-known solutions are updated from this study. [ABSTRACT FROM AUTHOR]

This paper delves into the multi-port berth allocation problem (MBAP), enriching the traditional berth allocation problem (BAP) with vessel speed optimization (VSO). In the MBAP, it is assumed that there is cooperation between the port and the shipping companies, and the operation of the vessels and the ports is planned to maximize the overall benefits. Exploring this potential collaboration between ports and shipping entities has the potential to mitigate, or even resolve, the challenges plaguing maritime transportation, e.g., port congestion and suboptimal vessel schedules, to ultimately enhance the efficiency of maritime trade. In this paper, a new mixed-integer linear programming (MILP) model for the MBAP is formulated, which attempts to minimize the total cost incurred during operations, with various constraints such as vessel sailing, the vessel space–time relationship in ports, and the planning period. Meanwhile, an innovative variable neighborhood search (VNS) algorithm is presented, in which the initial solution generation method and neighborhood structures are proposed according to the MBAP characteristics. Furthermore, two sets of MBAP instances are generated to test the proposed MILP and VNS, of which the first set is based on real-world port data and the second on existing studies. The numerical experiments verify that the VNS can efficiently and reliably solve instances of all scales, with each neighborhood structure contributing uniquely to the iterative process. In addition, by analyzing the impact of varying oil prices on the MBAP, the study offers valuable management insights. Finally, a case study based on real data from a port group in the Yangtze River Basin is presented to further demonstrate the necessity of considering vessel service time window and planning period in the MBAP as well as the important role of the VSO in scheduling. [ABSTRACT FROM AUTHOR]

Addressed in this study is a vehicle routing problem with two‐dimensional loading constraints and time windows (2L‐CVRPTW), aiming to minimize the transportation cost while satisfying the two‐dimensional loading and routing constraints with time windows. To solve this problem, for the first time a mixed‐integer linear programming model is formulated with considering practical last‐in‐first‐out loading constraints, and a branch‐and‐price‐based (BP‐based) heuristic is proposed based on a set partitioning formulation. In the heuristic, a modified labeling algorithm is proposed for the complex pricing problem, which is a relaxation of the elementary shortest path problem with resource constraints and two‐dimensional loading constraints. Therein, an effective Tabu‐maximum open space packing heuristic is proposed to verify the feasibility of the two‐dimensional packing problem of each route generated by the labeling algorithm. In addition, effective accelerating and branching strategies are introduced to improve the solving efficiency of the heuristic. To evaluate the effectiveness and the advantages of the proposed heuristic, extensive computational experiments are performed based on the generated instances. The computational results show that the proposed BP‐based heuristic can effectively solve the 2L‐CVRPTW, in which the optimal solutions can be achieved much faster than CPLEX in small‐scale problems. Relationships between the transportation cost and the characteristics of the instances are analyzed. The stability of the algorithm and the effectiveness of the accelerating strategies are verified and discussed. [ABSTRACT FROM AUTHOR]

A comprehensive review of uncertainties in power systems, covering modeling, impact, and mitigation, is essential to understand and manage the challenges faced by the electric grid. Uncertainties in power systems can arise from various sources and can have significant implications for grid reliability, stability, and economic efficiency. Australia, susceptible to extreme weather such as wildfires and heavy rainfall, faces vulnerabilities in its power network assets. The decentralized distribution of population centers poses economic challenges in supplying power to remote areas, which is a crucial consideration for the emerging technologies emphasized in this paper. In addition, the evolution of modern power grids, facilitated by deploying the advanced metering infrastructure (AMI), has also brought new challenges to the system due to the risk of cyber-attacks via communication links. However, the existing literature lacks a comprehensive review and analysis of uncertainties in modern power systems, encompassing uncertainties related to weather events, cyber-attacks, and asset management, as well as the advantages and limitations of various mitigation approaches. To fill this void, this review covers a broad spectrum of uncertainties considering their impacts on the power system and explores conventional robust control as well as modern probabilistic and data-driven approaches for modeling and correlating the uncertainty events to the state of the grid for optimal decision making. This article also investigates the development of robust and scenario-based operations, control technologies for microgrids (MGs) and energy storage systems (ESSs), and demand-side frequency control ancillary service (D-FCAS) and reserve provision for frequency regulation to ensure a design of uncertainty-tolerance power system. This review delves into the trade-offs linked with the implementation of mitigation strategies, such as reliability, computational speed, and economic efficiency. It also explores how these strategies may influence the planning and operation of future power grids. [ABSTRACT FROM AUTHOR]

In harsh operating environments such as mines and gas stations, intrinsically safe power converters are required for electronics devices. Reliable communication is the key ensuring good performance of monitoring and coordinated control for intrinsically safe converters. This paper proposes a power and signal dual modulation (PSDM)‐based communication method for intrinsically safe parallel‐connected Buck converters. The bidirectional communication between two parallel Buck converters is realized by combining the frequency shift keying (FSK) and phase shift keying (PSK) methods. In addition, switching frequencies are employed to identify the signals received from the power converters interfacing different sources. According to the intrinsic safety requirement, inductors and capacitors are redesigned based on the modulated voltage ripples during communication. Simulations and prototype experiments are performed to verify the feasibility of the proposed bidirectional communication for intrinsically safe converters. The amplitude of voltage ripples modulated for carrying data is restricted to less than 1.5% of the bus voltage. The transmission speed can be achieved from 0.5 to 2kbps depending on the adopted switching frequencies. The demodulation delay is less than 5% duration of one bit of transmitted data. [ABSTRACT FROM AUTHOR]

Energy is the foundation for human survival and socio-economic development, and electricity is a key form of energy. Electricity prices are a key factor affecting the interests of various stakeholders in the electricity market, playing a significant role in the sustainable development of energy and the environment. As the number of distributed energy resources (DERs) increases, today's power systems no longer rely on a vertical market model and fixed electricity pricing scheme but instead depend on power dispatch and dynamic pricing to match supply and demand. This can help prevent significant fluctuations in supply–load imbalance and maintain system stability. Modern power grids have evolved by integrating information, communication, and intelligent control technologies with traditional power systems, giving rise to the concept of smart electric grids. Choosing an appropriate pricing scheme to manage large-scale DERs and controllable loads in today's power grid become very important. However, the existing literature lacks a comprehensive review of electricity pricing in power systems and its transformative impact on shaping the energy landscape. To fill this void, this paper provides a survey on the developments, methods, and frameworks related to electricity pricing and energy trading. The review mainly considers the development of pricing in a centralized power grid, peer-to-peer (P2P) and microgrid-to-microgrid (M2M) energy trading and sharing, and various pricing methods. The review will cover the pricing schemes in modern power systems, particularly with respect to renewable energy sources (RESs) and batteries, as well as controllable load applications, and the impact of pricing schemes based on demand-side ancillary services (DSAS) for grid frequency support. Lastly, this review article describes the current frameworks and limitations of electricity pricing in the current energy market, as well as future research directions. This review should offer a great overview and deep insights into today's electricity market and how pricing methods will drive and facilitate the future establishment of smart energy systems. [ABSTRACT FROM AUTHOR]

Summary: The modeling of high‐order converter systems and design of their closed‐loop controllers are difficult due to their high nonlinearity, hence high‐order systems, large number of zero‐poles, and complex frequency‐domain characteristics. To ease the design methodologies, this paper studies high‐order power converters and proposes a modeling and control method for such converter systems with order reduction. As an example, the 7th‐order non‐electrolytic capacitor‐I‐Z‐Source converter (NEC‐I‐ZSC) is employed to implement the proposed design method. Operational analysis and small signal modeling of the NEC‐I‐ZSC are conducted, and a reduced‐order controller is then devised. Experimental verification is conducted, which prove the feasibility and effectiveness of the proposed design method. [ABSTRACT FROM AUTHOR]

As the climate crisis worsens, power grids are gradually transforming into a more sustainable state through renewable energy sources (RESs), energy storage systems (ESSs), and smart loads. Virtual power plants (VPP) are an emerging concept that can flexibly integrate distributed energy resources (DERs), managing manage the power output of each DER unit, as well as the power consumption of loads, to balance electricity supply and demand in real time. VPPs can participate in energy markets, enable self-scheduling of RESs, facilitate energy trading and sharing, and provide demand-side frequency control ancillary services (D-FCAS) to enhance the stability of the system frequency. As a result, studies considering VPPs have become the focus of recent energy research, with the purpose of reducing the uncertainty resulting from RESs distributed in the power grid and improving technology related to energy management system (EMS). However, comprehensive reviews of VPPs considering their formation, control techniques, and D-FCAS are still lacking in the literature. Therefore, this paper aims to provide a thorough overview of state-of-the-art VPP technologies for building sustainable power grids in the future. The review mainly considers the development of VPPs, the information transmission and control methods among DERs and loads in VPPs, as well as the relevant technologies for providing D-FCAS from VPPs. This review paper describes the significant economic, social, and environmental benefits of VPPs, as well as the technological advancements, challenges, and possible future research directions in VPP research. [ABSTRACT FROM AUTHOR]

This paper studies a scheduling problem with non-identical job sizes, arbitrary job ready times, and incompatible family constraints for unrelated parallel batch processing machines, where the batches are limited to the jobs from the same family. The scheduling objective is to minimize the maximum completion time (makespan). The problem is important and has wide applications in the semiconductor manufacturing industries. This study proposes a mixed integer programming (MIP) model, which can be efficiently and optimally solved by commercial solvers for small-scale instances. Since the problem is known to be NP-hard, a hybrid large neighborhood search (HLNS) combined with tabu strategy and local search is proposed to solve large-scale problems, and a lower bound is proposed to evaluate the effectiveness of the proposed algorithm. The proposed algorithm is evaluated on numerous compatible benchmark instances and newly generated incompatible instances. The results of computational experiments indicate that the HLNS outperforms the commercial solver and the lower bound for incompatible problems, while for compatible problems, the HLNS outperforms the existing algorithm. Meanwhile, the comparison results indicate the effectiveness of the tabu and local search strategies. [ABSTRACT FROM AUTHOR]

This paper investigates a novel flexible job-shop scheduling problem, where the machines have batch-processing capacity, but incompatible jobs cannot be processed in a batch (FJSPBI) simultaneously. This problem has wide applications in discrete manufacturing, especially in chemical and steel casting industries. For the first time, in this study, a 3-indexed mixed-integer linear programming (MILP) model is proposed, which can be efficiently and optimally solved by commercial solvers for small-scale problems. In addition, an improved large neighborhood search (LNS) algorithmic framework with an optimal insertion and tabu-based components (LNSIT) is proposed, which can achieve high-quality solutions for a large-scale FJSPBI in a reasonable time. A perturbation strategy and an optimal insertion strategy are then additionally embedded to improve the exploitation and exploration ability of the algorithm. The proposed model and algorithm are tested on numerous existing benchmark instances without the incompatibility characteristics, and on newly generated instances of the FJSPBI. The experimental results indicate the effectiveness of the proposed MILP model and the algorithm, including the proposed strategies, and the optimal insertion strategy can significantly reduce the computational burden of the LNS algorithm. The comparison results further verify that the proposed LNSIT can directly solve the specific flexible job-shop batch scheduling problem without incompatibility, with better results than existing methods, especially for large-scale instances. Additionally, the impacts of a wide range of characteristics, including batch capacity, incompatibility rate, instance scale, and machine processing rate, on the performance of the LNSIT and the scheduling results are analyzed and presented. [ABSTRACT FROM AUTHOR]

In practical logistic distributions, uncertainties may exist in each distribution process, and sometimes suppliers have to take undesirable measures to deal with the subsequent schedule variances. In light of the uncertainty of customers in logistics distribution and the widely applied two-dimensional loading patterns in transportation, we propose and formulate a two-dimensional loading-constrained vehicle routing problem with stochastic customers (2L-VRPSC), where each customer has a known probability of presence and customers' demands are a set of non-stackable items. A stochastic modeling platform of 2L-VRPSC is established based on a Monte Carlo simulation and scenario analysis to minimize the expected total transportation cost. To achieve this, an enhanced adaptive tabu search (EATS) algorithm incorporating the multi-order bottom-fill-skyline (MOBFS) packing heuristic is proposed, where the EATS algorithm searches for the optimal routing combination and the MOBFS checks the feasibility of each route and guides the EATS to search for feasible solutions. The widely used two-dimensional loading-constrained vehicle routing problem (2L-VRP) benchmarks under different loading configurations considering items' sequential and rotation constraints are applied for experiments, which demonstrates the comparable efficiency of the proposed EATS-MOBFS for solving 2L-VRP. Furthermore, the results and analysis of experiments based on the new 2L-VRPSC instances verify the versatility of the proposed solving approach, which is capable of providing more practical solutions to some real-life scenarios with customers' uncertain information. [ABSTRACT FROM AUTHOR]

In recent years, increasing fuel prices, depressed market conditions and air pollution issues have brought huge challenges to the tramp shipping industry. This work investigates the tramp ship routing and scheduling problem considering ship speeds and payloads, aiming at minimizing ship fuel consumption. A mixed integer non-linear programming model with a discretized speed variable and a set partitioning model for this problem is established, and a branch-and-price algorithm is proposed to solve the problem. Through the column generation approach, the problem at each branch-and-bound node is decomposed into a linear programming master problem and a pricing problem of the elementary shortest path with resource constraints. A labeling algorithm is adopted for solving the pricing problem. Multiple groups of instances are generated to test the effectiveness of the proposed model and algorithm and analyze the impacts of ship speed, payload, and speed discretization on the solution. Computational experiments are conducted, which verifies the proposed scheduling routing method for tramp ships and confirms that adopting the proposed model can effectively reduce fuel consumption of tramp ships which can not only deepen the theory of tramp routing and scheduling, but also provide theoretical guidance to tramp ship company. The branch-and-price algorithm can effectively solve large-scale tramp ship routing and scheduling problems. Reasonable number of speed discretization points can bring a desirable trade-off between solution accuracy and algorithm runtime. [ABSTRACT FROM AUTHOR]

Although numerous high‐voltage‐gain converters have been proposed, the input current ripple problem associated with high voltage gains is yet to be fully developed. In this paper, a systematic approach is proposed for novel power converter topologies design, to address this problem. With the proposed design guideline, a series of DC‐DC converters with zero input current ripple (ZICR) and high voltage gain are developed. Then, detailed analyses are presented using a typical converter to reveal the working principle and detailed parameters design. Lastly, simulation and experimental verification is conducted, which verifies the effectiveness of this design. [ABSTRACT FROM AUTHOR]

Summary: The slow balancing speed of switched‐capacitor (SC)‐based equalizers makes this structure difficult to apply in series‐connected battery strings. In order to reduce the number of energy conversion processes and achieve leapfrog transmission of energy, a hybrid‐structured voltage equalizer (HSVE) is developed in this work for battery strings to achieve high‐speed any‐unit‐to‐any‐unit (AU2AU) equalization, in which each unit can also achieve internal balance in any arbitrary imbalance status. Compared to the conventional equalizers using complex monitoring and control strategy, the proposed equalizer does not need any cell monitoring circuits—all MOSFETs are triggered by a pair of complementary pulse signals with a fixed switching frequency and constant duty ratio, and electricity can automatically and directly exchange among all battery cells. Hardware experiments on super‐capacitors and Li‐ion batteries are conducted in this study, which prove the feasibility of the proposed voltage equalizer. The proposed battery management strategy will have wide applications in modern battery‐related industries such as electric vehicles (EVs). [ABSTRACT FROM AUTHOR]

The continuous berth allocation and quay crane assignment problem considers the size of berths and ships, the number of quay cranes, the dynamic ships and non-crossing constraints of quay cranes. In this work, a mixed-integer linear programming model of this problem is established, aiming at minimizing the total stay time and delay penalty of ships. To solve the model, the continuous berth is separated into discrete segments via a proposed discretization strategy. Thereafter, a large neighborhood search algorithm composed of the random removal operator and relaxed sorting-based insertion operator and a backtracking comparison-based constraint repair strategy are proposed. The effectiveness of the model and algorithm presented is verified via real-life instances with different characteristics, and the performances of different combinations of removal operators and insertion operators in the large neighborhood search algorithmic framework are analyzed. Numerical results show that the large neighborhood search algorithm can optimally solve the small-scale instances in a reasonable time. Meanwhile, the results of large-scale instances show that the large neighborhood search algorithm incorporating the discretization strategy is more efficient than other genetic algorithms based on continuous optimization. With the proposed approach, high-quality berth and quay crane allocation results can be obtained efficiently. [ABSTRACT FROM AUTHOR]

Proposed in this letter is a novel generalized method to replace all short-life and large-size electrolytic capacitors (ECs) with non-ECs (NECs) in step-up converters. NEC has a longer lifespan and smaller size, leading to extended converter life and reduced converter size. The proposed approach can systematically replace ECs with NECs without affecting the desired converters’ output features. After theory elaboration, five representative converters in two schemes are built with ten $150\,\mathrm{W}$ / $100\,\mathrm{V}$ high power density prototypes for experimental verification. Experiments show that these prototypes can reach maximum power density of $2.91\,\mathrm{W/cm^3}$ , significantly higher than converters with ECs, and maximum efficiency of 95.64%. [ABSTRACT FROM AUTHOR]

Stochastic, nonlinear and time-varying factors bring great challenges to accurate modeling and design of real-time (RT) energy management systems (EMSs) for modern energy systems with intermittent renewable energy sources. In this paper, we propose a novel model-free RT-EMS based on the predictive adaptive dynamic programming (ADPredictive) algorithm for energy scheduling problems in a home EMS environment. The proposed RT-ADPreditive EMS can minimize the total cost of electricity, reduce battery life loss, and maximize the utilization of renewable energy generation. In the proposed RT-ADPredictive EMS, the gated recurrent unit (GRU) neural network (NN) is employed to perform online prediction of renewable energy generation and load consumption of the household based on RT data. The RT-ADPreditive algorithm can approximate the performance index function and the optimal control law based on the RT data and predicted data for the next time step. Convergence of the proposed method is mathematically proven, and a hardware-in-the-loop (HIL) experimental platform comprising dSPACE and RT-lab is built to verify the effectiveness of the proposed RT-ADPreditive home EMS. [ABSTRACT FROM AUTHOR]

Summary: A novel boost converter is proposed in this study, which is based on a newly designed impedance network, instead of using the traditional Z‐network structure and large electrolytic capacitors (ECs) to suppress current ripples. Besides, the proposed topology has ripple‐free output current, which is designed to improve the output current quality of the traditional boost converter and to enhance the voltage conversion ratio compared to the traditional Z‐network boost converter. The newly proposed impedance network consists of two inductor and capacitor (LC) pairs—one LC pair function as the intermediate energy transferring branch and the other pair as the output branch to obtain non‐pulsating output current. The operating principle and performance of the proposed converter are analyzed in detail for demonstrating its advanced features. Furthermore, the feasibility of the proposed converter topology is validated by its simulations and experimental studies. Compared to ECs, non‐electrolytic capacitors (NECs) have the benefit of low cost, small size, and long operating life, which brings high economical advantages to the proposed converter with enhanced voltage conversion performances. [ABSTRACT FROM AUTHOR]

Summary: In this study, a novel high‐step‐up converter is proposed with a new hourglass‐shaped impedance network (HIN). Due to the short lifetime of electrolytic capacitors (ECs), the proposed converter uses nonelectrolytic capacitors (NECs), which however can realize all ECs' features. Besides, the proposed converter has low capacitor voltage stresses, ensuring it could be electrolytic‐capacitors‐free. With this feature, the proposed high step‐up converter has smaller size, lighter weight, smaller parasitic resistance, and longer operation life than the conventional ones with ECs. In this paper, detailed operational principle and performance of the proposed converter are presented, to demonstrate its advanced features. Simulation and experimental studies are also carried out, which validate the feasibility and effectiveness of the proposed converter. [ABSTRACT FROM AUTHOR]

This study proposes a novel non-inverting buck–boost converter topology with extra-wide operational zones under both continuous current mode (CCM) and discontinuous current mode (DCM), by introducing a four-mode modulation (4MM) strategy. The proposed 4MM method is able to eliminate the operation dead zone, thus extending the operation zones of the buck–boost converter to contain boost mode, extended boost mode, extended buck mode, and buck mode. Rigorous analyses and design principles are presented in detail to demonstrate the four-mode function through CCM and DCM operational investigations and illustrations. Theoretical analyses and parameter design are verified by software simulation and hardware experimentation in this study, which validates the proposed 4MM method and the functionality of the proposed 4MM-based buck–boost converter. They clearly indicate the superiority of the proposed 4MM-based four-switch non-inverting buck–boost converter over its conventional counterparts, posing wide applicability in industrial practices. [ABSTRACT FROM AUTHOR]

The modern electric power grid is evolving rapidly into such a state that distributed controllers and two-way energy and information flow are replacing the traditional paradigm of electricity distribution and energy management. Therefore, a power grid coupled with a communication network is playing a pivotal role in establishing modern electric power systems. Previous cascading failure analysis in power systems focused more on the physical network, while falling short of investigations on the coupling effect of interdependency of the integrated electricity and communication networks, i.e., cyber-physical power systems. To address such a pressing issue, this study introduces a novel stochastic cascading failure model, considering the interdependency between the cyber network and power network. A multiagent system and a novel protection relay model are incorporated into the proposed model. To apply the proposed analytical method, a test power system, the IEEE 68-bs power system, is used to study the impacts of a range of interdependencies and cyber network topological structures on the cascading failure. Simulation results show the necessity and effects of consideration of cyber communication network when investigating power system cascading failures. The study also provides useful information on robustness and vulnerability of a particular power grid, given different communication topologies and interdependencies. [ABSTRACT FROM AUTHOR]