Search

Power systems have been transformed into cyber-physical systems that integrate electric grids with advanced information technology and operational technology. To ensure reliable and resilient operations of such systems, it is important to understand the system vulnerability and quantify system resilience. This article provides an overview of existing work on vulnerability assessment and resilience quantification related to cyber-physical power systems, and it identifies research gaps and opportunities to enhance resilience. Specifically, we first review the definition and architecture of cyber-physical power systems, and then, summarize existing approaches to assess vulnerability and resilience. Later, we identify several research gaps that have not been well addressed yet and point out possible future work to fill the gap. Although this article focuses on cyber-physical power systems, the research can also benefit stakeholders of other critical infrastructures.

This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to optimize the restoration. Due to the complexities stemming from the large policy search space, renewable uncertainty, and nonlinearity in a complex grid control problem, directly applying RL algorithms to train a satisfactory policy requires extensive tuning to be successful. To address this challenge, this paper leverages the curriculum learning (CL) technique to design a training curriculum involving a simpler steppingstone problem that guides the RL agent to learn to solve the original hard problem in a progressive and more effective manner. We demonstrate that compared with direct learning, CL facilitates controller training to achieve better performance. To study realistic scenarios where renewable forecasts used for decision-making are in general imperfect, the experiments compare the trained RL controllers against two model predictive controllers (MPCs) using renewable forecasts with different error levels and observe how these controllers can hedge against the uncertainty. Results show that RL controllers are less susceptible to forecast errors than the baseline MPCs and can provide a more reliable restoration process., Comment: IEEE Transactions on Power Systems

Modern distribution systems are becoming more resilient against power outages thanks to the flexible generation capacity provided by the increasing integration of distributed energy resources (DERs). Compared to utility-scale DERs, which can be directly controlled by system operators, small-scale, behind-the-meter DERs are much more difficult to coordinate despite being the vast majority. Two major obstacles to the implementation of DERs for service restoration are the coordination of behind-the-meter DERs and the management of uncertainty. In this paper, a novel distribution system restoration strategy is proposed to overcome these two obstacles. The flexibility of behind-the-meter DERs is quantified and integrated into the restoration planning models within a hierarchical structure. The model predictive control technique is employed to enable adaptive restoration planning solution adjustments to accommodate fault scenarios and forecast errors. Besides, real-time power dispatch models are proposed to maintain power balance using available DER capability. Comprehensive integration of adaptive restoration planning schemes and real-time dispatch results are achieved through a collaborative restoration framework. The effectiveness of the proposed restoration strategy is verified through a modified IEEE 123-bus system and a real-world utility system.

The rapid growth of distributed generator (DG) capacities has introduced additional controllable assets to improve the performance of distribution systems in terms of service restoration. Renewable DGs are of particular interest to utility companies, but the stochastic nature of intermittent renewable DGs could have a negative impact on the electric grid if they are not properly handled. In this study, we investigate distribution system service restoration using DGs as the primary power source, and we develop an effective approach to handle the uncertainty of renewable DGs under extreme conditions. The distribution system service restoration problem can be described as a mixed-integer second-order cone programming model by modifying the radial topology constraints and power flow equations. The uncertainty of renewable DGs will be modeled using a chance-constrained approach. Furthermore, the forecast errors and noises in real-time operation are solved using a novel model-free control algorithm that can automatically track the trajectory of real-time DG output. The proposed service restoration strategy and model-free control algorithm are validated using an IEEE 123-bus test system.

The intermittency introduced by the increasing integration of distributed renewable energy sources is challenging the efficient operation of residential distribution systems. A promising solution to tackle this challenge is the implementation of residential demand response through responsive household appliances such as heat pumps, refrigeration devices, and energy storage units. In this article, a peer-to-peer energy trading platform among residential houses is proposed to coordinate demand response schemes and level off potential generation/consumption disturbances in the hour-ahead intraday context. First, the day-ahead and intraday energy management models for residential houses are established considering the characteristics of responsive household appliances and energy storages. The discomfort and possible economic losses for performing demand responses are quantified with respect to the risk preferences of residential customers. The peer-to-peer energy trading platform is developed and a double-auction mechanism employed to promote the collaborative demand response schemes in the face of disturbances. An optimal bidding strategy of residential houses is also proposed. The feasibility of the proposed models and bidding strategy are verified through case studies. It is also illustrated that the residential demand response schemes and intraday peer-to-peer energy trading are effective in managing the uncertainties of load demand and renewable generation.

Increasingly frequent severe weather events in recent years threaten the security of power systems and result in major power outages throughout the world. The development of reasonable power system restoration (PSR) solutions is therefore urgently needed to speed up the recovery of the power supply while at the same time steering clear of vulnerable and risky equipment. This paper aims to develop a case-sensitive PSR model for power transmission systems that can adjust restoration solutions according to the evaluated availability of outage equipment in specific blackout scenarios and weather conditions. A novel PSR model that integrates the startup of generating units, formulation of the restoration network, renewable energy sources, and availability assessment of devices is proposed. A reformulated model is also proposed to relieve the computational burden of complex PSR problems. The availability of outage equipment is comprehensively assessed based on historical operating records, fault diagnosis results, and weather conditions. The assessed availability results are sensitive to the characteristics of real blackout cases and will support system operators generate case-sensitive PSR solutions while mitigating the vulnerable equipment. The feasibility and effectiveness of the proposed PSR model and its reformulations are verified through case studies.

Miniaturized flexible microsupercapacitors (MSCs) that can be integrated into self-powered sensing systems, detecting networks, and implantable devices have shown great potential to perfect the stand-alone functional units owing to the robust security, continuously improved energy density, inherence high power density, and long service life. This review summarizes the recent progress made in the development of flexible MSCs and their application in integrated wearable electronics. To meet requirements for the scalable fabrication, minimization design, and easy integration of the flexible MSC, the typical assembled technologies consist of ink printing, photolithography, screen printing, laser etching, etc., are provided. Then the guidelines regarding the electrochemical performance improvement of the flexible MSC by materials design, devices construction, and electrolyte optimization are considered. The integrated prototypes of flexible MSC-powered systems, such as self-driven photodetection systems, wearable sweat monitoring units are also discussed. Finally, the future challenges and perspectives of flexible MSC are envisioned.

This letter presents an optimal energy flow (OEF) for integrated energy systems considering the transient process of natural gas flows, whose main dynamic features are efficiently approximated in our setup. To achieve this, new discretization criteria are proposed to divide the gas flow equations into reasonable temporal and spatial segments to accurately model the gas flow dynamics in a set of difference equations. Simulation results validate the effectiveness of the OEF based on the proposed discretization criteria and its advantages over existing methods.

This paper proposes a restorability improvement strategy to accelerate system restoration through the implementation of a wind farm-battery energy storage system (WF-BESS) system. The concept of restorability is introduced and a restorability improvement model (RIM) is proposed and formulated as a mixed integer linear programming problem. To simulate the cylinder temperature drop during outages, the cranking time of a unit is modeled as a stepwise function of its startup time in the RIM. The WF might fail to meet its scheduled generation outputs optimized by the RIM due to the intermittency of wind. To tackle this problem, a linearized control strategy for the BESS (CSBESS) is proposed to minimize the difference between the scheduled wind power outputs and the combined power of the WF-BESS system. An iterative method is employed to efficiently solve the RIM and CSBESS by introducing optimality cuts. An actual power system case is employed to illustrate the effective performance of the proposed approach.

Collaboration of networked microgrids (NMGs) with diverse generation sources is a promising solution to smooth volatile generation output and enhance the utilization efficiency of renewable energies. In addition to centralized and decentralized collaboration mechanisms, transactive energy control (TEC) is an emerging and effective market-based control to enable energy transactions among distributed entities such as NMGs. However, existing studies on TEC suffer from several major weaknesses such as unconstrained/simplified model formulations and slow convergence rates. This paper proposes a novel TEC mechanism to tackle these weaknesses. First, the centralized mechanism, decentralized mechanism, and subgradient-based TEC mechanism to coordinate the operation of NMGs are briefly reviewed and modeled by a scenario-based stochastic optimization method. A new TEC mechanism is then proposed, consisting of a TEC framework, mathematical model, pricing rule, and algorithm. The optimality of the proposed TEC mathematical model and pricing rule is demonstrated. The effectiveness of the proposed TEC mechanism is verified in case- studies where the NMGs operate in grid-connected, islanded, and congested modes. The advantages of the proposed TEC mechanism are also illustrated through comparisons with the centralized mechanism, decentralized mechanism, and subgradient-based TEC mechanism.

Zn-ion hybrid supercapacitors (SCs) are considered as promising energy storage owing to their high energy density compared to traditional SCs. How to realize the miniaturization, patterning, and flexibility of the Zn-ion SCs without affecting the electrochemical performances has special meanings for expanding their applications in wearable integrated electronics. Ti3C2Tx cathode with outstanding conductivity, unique lamellar structure and good mechanical flexibility has been demonstrated tremendous potential in the design of Zn-ion SCs, but achieving long cycling stability and high rate stability is still big challenges. Here, we proposed a facile laser writing approach to fabricate patterned Ti3C2Tx-based Zn-ion micro-supercapacitors (MSCs), followed by the in-situ anneal treatment of the assembled MSCs to improve the long-term stability, which exhibits 80% of the capacitance retention even after 50,000 charge/discharge cycles and superior rate stability. The influence of the cathode thickness on the electrochemical performance of the MSCs is also studied. When the thickness reaches 0.851 µm the maximum areal capacitance of 72.02 mF cm−2 at scan rate of 10 mV s−1, which is 1.77 times higher than that with a thickness of 0.329 µm (35.6 mF cm−2). Moreover, the fabricated Ti3C2Tx based Zn-ion MSCs have excellent flexibility, a digital timer can be driven by the single device even under bending state, a flexible LED displayer of “TiC” logo also can be easily lighted by the MSC arrays under twisting, crimping, and winding conditions, demonstrating the scalable fabrication and application of the fabricated MSCs in portable electronics.

Cyber security significantly relies on the dynamic communities in social networks. The location-based social network (LBSN) is a new type of social system that has sprung up recently that. It turns traditional social networks into heterogeneous networks by incorporating location information, which is used as the medium between the real world and the online social networks, thus bringing new challenges to the community discovery problems. This paper proposes a LBSN homogeneous network model (LSHNM) based on the user social relations and temp-spatial behaviors to calculate the user similarity relations in multi-dimensional features and construct LBSN isomorphism network topology, which can be used to improve cyber security practices. After that non-negative matrix decomposition (NMF) is used to find communities from above isomorphism network topology. The experimental results show that the LSHNM can find more satisfactory community structures. (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Dynamic thermal rating (DTR) is an important smart grid technology that can bring considerable economic benefits. One of the most important benefits of DTR is to postpone new investment. This paper proposes a novel stochastic transmission expansion planning (STEP) model considering the DTR of overhead lines. The objective function of the STEP model includes operational costs and the investment costs of new line construction and DTR systems installation. The model can determine where to build new lines and install DTR systems. The model cannot only realize the benefits that occur when the DTR is higher than the static thermal rating (STR) but also avoid overload risk, i.e., the power flow on a line being larger than the line's real capacity, caused by the DTR being lower than the STR. The model can consider both the voltage magnitude and phase angle of each bus. The model is linearized and therefore can be effectively solved by a Benders decomposition method. Furthermore, a new way of scenario reduction is proposed to obtain a better set of reduced scenarios. The effectiveness of the model is verified on a modified IEEE reliability test system and a modified IEEE 300-bus system.

With the rapid development of Internet technology, online social networks (OSNs) have got fast development and become increasingly popular. Meanwhile, the research works across multiple social networks attract more and more attention from researchers, and community detection is an important one across OSNs for online security problems, such as the user behavior analysis and abnormal community discovery. In this paper, a community detection method is proposed across multiple social networks based on overlapping users. First, the concept of overlapping users is defined, then an algorithm CMN NMF is designed to discover the stub communities from overlapping users based on the social relevance. After that, we extend each stub community in different social networks by adding the users with strong similarity, and in the end different communities are excavated out across networks. Experimental results show the advantage on effectiveness of our method over other methods under real data sets.

In this study, a multi-stage long-term expansion planning model for an active distribution network (ADN) is presented, with the aim of minimising the investment and operation cost in a coordinated manner over an established horizon. The planning model optimises the following alternatives: upgrading the capacities of substations, reinforcing and/or constructing cable circuits, placing voltage regulators (VRs) and/or static VAR generators, and determining the connection points for distributed generators (DGs). The investment decisions are optimised over the entire planning horizon which can be further divided into multiple periods, and the operation strategies, e.g. active management of DG as well as ADN topology reconfiguration, are determined according to the profiles of representative scenarios. To relieve the computational burden, the original model is properly simplified as a mixed-integer quadratic constrained programming problem through linearisation and approximation techniques, and the solution optimality is guaranteed after invoking the off-the-shell solver. A 24-node test system is employed to validate the effectiveness of the proposed model.

The variability and uncertainty of renewable energy resources introduce significant challenges to power system operation. One particular example is the occurrence of ramp capability shortage in real-time dispatch, which can cause power balance violations and price spikes. To meet the increasing need for ramp capability, some independent system operators in the USA have led initiatives to promote the implementation of flexible ramping product (FRP). More potential FRP providers, apart from conventional generators, are being explored, among which battery energy storage (BES) appears to be a feasible option owing to its good controllability and fast responsive characteristics. This study proposes an optimisation model for a BES aggregator to optimally provide FRP in day-ahead energy and reserve markets, aiming to maximise its monetary benefits. The basic concept of FRP is first introduced, including comparisons with traditional ancillary services, pricing mechanisms, and the extensions of market models to integrate FRP. The modes and strategies for BES aggregators to participate in the electricity markets are then addressed. Case studies indicate that an aggregator can gain more profit by optimally allocating its resources among various products than only providing energy and reserves. A sensitivity analysis on several key factors is also conducted.

The applications of battery energy storage systems (BESSs) in power systems have been paid increasing attention in recent years. Apart from all the benefits that BESSs can bring to the power systems, their potential applications in assisting power system restoration after a major blackout have not yet been systematically investigated. The flexible charging and discharging characteristics of BESSs could help maintain the balance between power supply and demand during the system restoration process, thus the recovery speed of the power system concerned after a blackout could be accelerated. Given this background, the applications of BESSs in power system restoration is investigated. First, the potential applications of BESSs during power system restoration process are discussed. A multi-objective optimisation model is next proposed, aiming at minimising the number of circuit breaker operations and outage durations of both the non-black-start generating units and the important loads. Meanwhile, the application of intermittent renewable energy sources for improving power system restoration is also discussed. The operation strategy of BESSs will also be optimised after the system restoration scheme is attained through the multi-objective optimisation. The proposed method is demonstrated by numerical tests on the IEEE 30-bus and 162-bus test systems.

The energy stored in the batteries of electric vehicles (EVs) could be employed for starting generators when a blackout or a local outage occurs. Considering the feature of the battery swapping mode, an available capacity model of the batteries in a centralised charging station is first developed. Then, the authors analyse the start-up characteristics of a generator powered by batteries and propose a bi-level optimisation-based network reconfiguration model to determine the restoration paths with an objective of maximising the overall generation capability. In the upper-level optimisation model, the generator start-up sequence is optimised, whereas the restoration paths are optimised in the lower-level one. Moreover, they consider the uncertainties associated with the available capacity of the batteries. The bi-level optimisation model for the network reconfiguration is developed in the chance-constrained programming framework and solved by the well-established particle swarm optimisation algorithm. Finally, case studies are employed to demonstrate the effectiveness of the presented model. Simulation results show that a centralised EV charging station could act as a power source to effectively restore a power system without black-start (BS) generators or with insufficient cranking power from BS generators, and the presented model could be used to guide actual system restorations.

This paper is concerned with linear and nonlinear free vibrations of an axially stressed clamped---clamped micro-/nanobeam actuated by one-sided electrode configuration. Galerkin procedure and a proper shape function are used to establish a dynamic reduced-order model, where the integral of the electromechanical forcing term is obtained in an explicit and closed form. This model makes the analysis of dynamic behaviors convenient for the micro-/nanobeam. For linear free vibration, the fundamental natural frequency formulas of the system are validated by comparing with published experiment data while the Newton method coupled with the harmonic balance method is developed to obtain analytical approximate period and periodic solution for the nonlinear system. These approximate solutions have explicit expressions and they can readily be used to derive the effects of various physical parameters on dynamic behavior of micro-/nanobeams. Exact solution in an integral form is also presented. For small as well as large amplitudes of nonlinear vibration, the new analytical approximate solutions show excellent agreement with respect to the numerically integrated solutions obtained from the integral-form exact solutions.

Fast restoration of critical loads and non-black-start generators can significantly reduce the economic losses caused by power system blackouts. In a parallel power system restoration scenario, the sectionalization of restoration subsystems plays a very important role in determining the pickup of critical loads before synchronization. Most existing research mainly focuses on the startup of non-black-start generators. The restoration of critical loads, especially the loads with cold load characteristics, has not yet been addressed in optimizing the subsystem divisions. As a result, sectionalized restoration subsystems cannot achieve the best coordination between the pickup of loads and the ramping of generators. In order to generate sectionalizing strategies considering the pickup of critical loads in parallel power system restoration scenarios, an optimization model considering power system constraints, the characteristics of the cold load pickup and the features of generator startup is proposed in this paper. A bi-level programming approach is employed to solve the proposed sectionalizing model. In the upper level the optimal sectionalizing problem for the restoration subsystems is addressed, while in the lower level the objective is to minimize the outage durations of critical loads. The proposed sectionalizing model has been validated by the New-England 39-bus system and the IEEE 118-bus system. Further comparisons with some existing methods are carried out as well.

With the significant decrease in natural gas prices in many parts of the world, the employment of gas turbine (GT) units has increased steadily in recent years. The ever-increasing deployment of GT units is strengthening the interconnections between electric power and natural gas systems, which could provide a higher level of operational flexibility and reliability. As a result, the planning and operation issues in the interconnected electric power and natural gas systems have aroused concern. In these circumstances, the impacts of increasing deployment of GT units in power system operation are studied and evaluated through well-being analysis (WBA). The fast responsive characteristics of GT units are analyzed first, and the definition and adaption of WBA in a power system with increasing deployment of GT units are addressed. Then the equivalent reserve capacity of GT units is estimated, taking demand fluctuations, commitment plans, and operational risks of GT units into account. The WBA of a power system with increasing deployment of GT units is conducted considering the uncertainties of system operation states and renewable energy sources. Finally, the proposed methods are validated through an integrated version of the IEEE 118-bus power system and a 10-bus natural gas system, and the impacts of GT units on power system security under various penetration levels are examined. Simulation results demonstrate that the role of a GT unit as a low-cost electricity producer may conflict with its role as a reserve provider, but through maintaining a proper proportion of idle GT capacities for reserve, the well-being performance of the power system concerned can be significantly improved.

To tackle the challenges introduced by the fast-growing charging demand of electric vehicles (EVs), the power distribution systems (PDSs) and fast charging stations (FCSs) of EVs should be planned and operated in a more coordinated fashion. However, existing planning approaches generally aim to minimize investment costs in PDSs while ignoring the risk of worsening traffic conditions. To overcome this research gap, this article integrates the interests of traffic networks into PDS and FCS joint planning model to mitigate negative impacts on traffic conditions caused by installing FCSs. First, a novel microscopic method that is different from traditional assignment methods is proposed to simulate the influences of FCSs on traffic flows and EV charging loads. Then, a multiobjective joint planning model is developed to minimize both the planning costs and unbalanced traffic flows. A new bilayer Benders decomposition algorithm is designed to solve the proposed joint planning model. Numerical results on two practical systems in China validate the feasibility of our microscopic method by comparing the simulated results with real data. Compared with existing approaches, it is also demonstrated that the proposed joint planning approach helps to balance traffic flow assignments and relieve traffic congestion.

With the ever-increasing penetration level of renewable energy generation in a power system, more uncertainties are introduced and hence risk management in the electricity market associated is becoming a more difficult issue for a market participant in the context of optimising his/her portfolio. Among a lot of risk factors in the competitive electricity market environment, the highly volatile electricity price contributes most to the financial risk of the power portfolio, especially in a short-term risk management scenario such as the spot market and real-time balancing market. Some research work has shown that the fluctuations of electricity prices exhibit multifractal characteristics, but less work has been done on the price volatility risk evaluation based on the multifractal theory. This study hence examines the feasibility of applying the multifractal theory to analyse the electricity price fluctuation, and applies the multifractal theory for evaluating the financial risk caused by electricity price volatility. A modified return interval approach considering the parameters of multifractal characteristics is employed to estimate the value-at-risk (VaR) of the electricity price. The fluctuant electricity price data series in the Pennsylvania-New Jersey-Maryland energy market are employed to demonstrate the effectiveness of the proposed VaR estimation method for short-term electricity price volatility risk evaluation.

We propose ultra compact all-optical XOR, XNOR, NAND and OR gates based on photonic crystal multi-mode interference waveguides for binary-phase-shift-keyed signals. The logic gates have been simulated and analyzed by finite difference time domain method. The extinction ratio between the ON state and the OFF state for XOR, XNOR, NAND and OR gates are more than 28.6 dB, 28.6 dB, 25 dB and 26.6 dB in the whole C-band, respectively. The proposed structure can achieve logical function when the radius of all rods is fabricated with relaxed error tolerance within −10% to 30% from designed parameters. The device possesses ultra compact size with approximately 6.9 μm×6.7 μm. The proposed logic gates may potentially be used as key components in all-optical information networks for processing binary-phase-shift-keyed signals.

An ultra compact photonic crystal (PC) ring-shaped channel drop filter (RSCDF) is proposed to generate single side band (SSB) signal for 60 GHz ROF system. This PC-RSCDF contains a bus waveguide, a resonant cavity, a ring-shaped waveguide (RSW) and a coupled cavity output waveguide. The notch depth and notch bandwidth of CDF can get 98% and 0.4 nm respectively by inducing RSW and shifting the center defect rod of cavity and the rod around the cavity. Then, a method of fine tuning the resonant frequency of PC-RSCDF is proposed by adjusting the radius of insensitive rods which have weak coupling interaction with the light and the sensitivity of structure can be decreased, and the resonant frequency can be shifted to the sideband frequency of double side band (DSB) signal accurately. Finally, a 60 GHz ROF SSB modulation system is demonstrated by using designed PC-RSCDF that can filter lower or upper sideband by changing its structure parameters. The extinction ratio (ER) of lower and upper sideband can get 20.9 dB and 18.6 dB respectively.

To make full use of the flexible charging and discharging capabilities of the growing number of electric vehicles (EVs), a bidding strategy for EV aggregators to participate in a day-ahead electricity energy market is proposed in this work. The proposed bidding strategy is able to reduce the operating cost of the EV aggregators and to handle the uncertainties of day-ahead market prices properly at the same time. Agreements between the EV owners and the aggregators are discussed, and a hierarchical market structure is proposed. While assuming the aggregators as economic rational entities, the bidding strategy is established based on the market prices, extra battery charging/discharging costs and the expected profits. The bidding clearing system will display the current/temporal market clearance results of the day-ahead market before the final clearance, and hence the market participants can revise their bids and mitigate the risks, to some extent, of forecasted market price forecast errors. Numerical results with a modified IEEE 30-bus system have demonstrated the feasibility and effectiveness of the proposed strategy.

As the first stage of power system restoration after a blackout, an optimal black-start scheme is very important for speeding up the whole restoration procedure. Up to now, much research work has been done on generating or selecting an optimal black-start scheme by a single round of decision-making. However, less attention has been paid for improving the final decision-making results through a multiple-round decision-making procedure. In the group decision-making environment, decision-making results evaluated by different black-start experts may differ significantly with each other. Thus, the consistency of black-start decision-making results could be deemed as an important indicator in assessing the black-start group decision-making results. Given this background, an intuitionistic fuzzy distance-based method is presented to analyse the consistency of black-start group decision-making results. Moreover, the weights of black-start indices as well as the weights of decision-making experts are modified in order to optimise the consistency of black-start group decision-making results. Finally, an actual example is served for demonstrating the proposed method.

An enhanced photonic crystal coupled-cavity waveguide (PC-CCW) is proposed to realize compact, ultra-fast modulated and high-performance buffering application. The slow light and buffer performances of PC-CCW are optimized by adjusting the cavity spacing and the hole size around the cavity. In the optimized structure, the group velocity is below 2.713 × 10 − 4 c . The corresponding delay time, buffer capacity and Q factor can reach as high as 12.286 ns, 68.5 bit and 3525, respectively. Then the dynamic modulation of slow light transmission and buffer capacity in PC-CCW are systemically studied. The simulation shows that the buffer capacity and the physical size of each bit are unchanged as the applied voltage increases. While the wavelength shift and delay time decrease almost linearly as the applied voltage increases. And the modulation sensitivities are about 3.726 nm/mV and 0.875 ns/mV, respectively. The Q factor also decreases accordingly.

Determining the optimal of black-start strategies is very important for speeding the restoration speed of a power system after a global blackout. Most existing black-start decision-making methods are based on the assumption that all indexes are independent of each other, and little attention has been paid to the group decision-making method which is more reliable. Given this background, the intuitionistic fuzzy set and further intuitionistic fuzzy Choquet integral operator are presented, and a black-start decision-making method based on this integral operator is presented. Compared to existing methods, the proposed algorithm cannot only deal with the relevance among the indexes, but also overcome some shortcomings of the existing methods. Finally, an example is used to demonstrate the proposed method. © 2012 The Institution of Engineering and Technology.

Restoring a large-scale power system has always been a complicated and important issue. A lot of research work has been done on different aspects of the whole power system restoration procedure. However, more time will be required to complete the power system restoration process in an actual situation if accurate and real-time system data cannot be obtained. With the development of the wide area monitoring system (WAMS), power system operators are capable of accessing to more accurate data in the restoration stage after a major outage. The ultimate goal of the system restoration is to restore as much load as possible while in the shortest period of time after a blackout, and the restorable load can be estimated by employing WAMS. Moreover, discrete restorable loads are employed considering the limited number of circuit-breaker operations and the practical topology of distribution systems. In this work, a restorable load estimation method is proposed employing WAMS data after the network frame has been reenergized, and WAMS is also employed to monitor the system parameters in case the newly recovered system becomes unstable again. The proposed method has been validated with the New England 39-Bus system and an actual power system in Guangzhou, China.

Recently, research on and applications of human fixation detection in video compressed domain have gained increasing attention. However, prediction accuracy and computational complexity still remain a challenge. This paper addresses the problem of compressed domain fixations detection in the videos based on residual discrete cosine transform coefficients norm (RDCN) and Markov random field (MRF). RDCN feature is directly extracted from the compressed video with partial decoding and is normalized. After spatial–temporal filtering, the normalized map [Smoothed RDCN (SRDCN) map] is taken to the MRF model, and the optimal binary label map is obtained. Based on the label map and the center saliency map, saliency enhancement and nonsaliency inhibition are done for the SRDCN map, and the final SRDCN-MRF salient map is obtained. Compared with the similar models, we enhance the available energy functions and introduce an energy function that indicates the positional information of the saliency. The procedure is advantageous for improving prediction accuracy and reducing computational complexity. The validation and comparison are made by several accuracy metrics on two ground truth datasets. Experimental results show that the proposed saliency detection model achieves superior performances over several state-of-the-art compressed-domain and pixel-domain algorithms on evaluation metrics. Computationally, our algorithm reduces 26% more computational complexity with comparison to similar algorithms.

The authors of [1] are to be commended for presenting an interesting approach for managing the charging of electric vehicles (EVs) through distribution locational marginal prices (DLMPs) with the aim of congestion management. A coordinated mechanism is proposed between the distribution system operator (DSO) and EV aggregators, and its feasibility proved based on the Karush-Kuhn-Tucker (KKT) conditions. However, the discussers have the following comments and would appreciate the authors' response.