The authors hope that this overview and suggestions will stimulate and contribute to further ongoing discussions and interesting research work and industrial applications in some fields of Systems, Man and Cybernetics.

Understanding the associations among microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and various diseases as biomarkers holds significant biological importance. Developing efficient, straightforward prediction models is essential to reduce the high cost of experimental research. However, most existing methods typically predict miRNA-disease associations (MDAs), lncRNA-disease associations (LDAs), and lncRNA miRNA interactions (LMIs) separately, often relying on both similarities and associations as inputs. These approaches complicate their application across diverse biological and medical domains. Moreover, few models are capable of simultaneously predicting all three types of associations in a unified framework. In this work, we propose a novel and simplified model, called Simplified input strategy for Multiple Associations Prediction (SimpleMAP). Unlike previous approaches, SimpleMAP eliminates the need for similarity networks or external biological data and instead uses only known associations as input, reducing feature contamination and ensuring better generalization. SimpleMAP is designed to predict MDAs, LDAs, and LMIs concurrently, by constructing a three-layer heterogeneous biomolecular network that captures the associations among miRNAs, lncRNAs, and diseases. Our method employs a single, end-to-end architecture based on stacked deep matrix factorization (SDMF) to process sparse input data and learn latent features effectively. SimpleMAP is designed to concurrently predict MDAs, LDAs, and LMIs by constructing a three-layer heterogeneous biomolecular network that captures multi-relational associations among miRNAs, lncRNAs, and diseases. To enhance predictive performance, we incorporate multiple feature integration strategies to fuse representations extracted by SDMF. This streamlined design makes SimpleMAP one of the first models to predict multiple bio-entity associations jointly using only minimal input data, offering a highly scalable and biologically meaningful solution. SimpleMAP demonstrates superior performance against strong baselines. Further validation on two additional datasets involving miRNA-circRNA-disease associations confirms the models robustness and adaptability. Finally, biologically validated case studies underscore the realworld applicability of SimpleMAP for biomarker discovery in complex biological systems. Overall, SimpleMAP introduces a new paradigm in bio-entity association predictionłachieving multi-type, high-performance prediction with minimal input complexityłmaking it a valuable tool for computational biology and biomedical research.

Identifying circular RNA (circRNA)-drug sensitivity association (CDsA) is crucial for advancing drug development. As conducting traditional wet experiments for determining CDsA is costly and inefficient, calculation methods have already proven to be a valid approach to cope with this problem. However, there exists limited research addressing the prediction of the CDsA prediction problem, and certain discrepancies persist, particularly concerning false-negative associations. As a consequence, we present a multi-view framework, called MAGSDMF, for identifying latent CDsA. Firstly, MAGSDMF applies ultiple ttention mechanisms and raph learning methods to dynamically extract features and strengthen the features of inside and across multi-similarity networks of circRNA and drug. Secondly, the tack eep atrix Factorization (SDMF) is devised to directly extract features from CDsAs. We consider multi-similarity networks with the original CDsAs as multi-view information. Thirdly, MAGSDMF utilizes a multi-attention channel mechanism to integrate these features for the purpose of reconstructing CDsA. Finally, MAGSDMF performs another DMF based on the reconstruction to identify the latent CDsAs. Simultaneously, contrastive learning (CL) is implemented to enhance the generalization capability of MAGSDMF and oversee the learning process of the underlying links prediction task. In comparative experiments, MAGSDMF achieves superior performance on two datasets with AUC values of 0.9743 and 0.9739 based on 5-fold cross-validation. Moreover, in case studies, the achievements further validate the identification reliability of MAGSDMF.

The precise location of insulators in infrared images is of great significance for insulator condition monitoring and fault diagnosis. Due to the characteristics of insulators themselves and the use of handheld infrared cameras, insulators usually appear in infrared images with different aspect ratios and main axis orientations. Therefore, it is very important and necessary to make full use of the prior knowledge of the insulator itself to accurately locate it. However, most of the existing methods use axial horizontal detection boxes to detect insulators, which cannot take into account the characteristics of the insulator well. When there are large overlapping areas of two horizontal detection boxes, the nonmaximum suppression algorithm may lead to missed detection of the object. To further improve the accuracy of the detection algorithm, this article makes full use of the prior features carried by the insulator itself, and optimizes faster region-based convolutional neural networks (R-CNN) from five aspects: rectangular box representation, feature extraction, candidate box generation, anchor design, and feature alignment. An oriented detection model for infrared images of insulators is constructed. Comparative experiments with a variety of mainstream detection methods were carried out on the constructed infrared dataset. The results show that the proposed method is superior to other models in detection accuracy. When the intersection and union ratio is 0.5, the average precision reaches 95.08%. In addition, it can also effectively predict the shape and angle information of insulators in complex scenes, laying a beneficial foundation for subsequent diagnosis automation tasks.

To achieve net-zero emissions economy, the transition to online entertainment and retail, aging populations, urban population growth, and pressures on public finance have created huge interests for human to run cities differently and smartly. A term titled smart city is created which is considered as an idealistic city, where the quality of life for citizens is greatly improved by utilizing information and communication technology (ICT), new services, and new city infrastructures to efficiently achieve the value, such as sustainable and resilient development. The eco-sustainable method has to be used in several aspects, such as energy, mobility, environment, and social services. Research and development in smart cities is expanding exponentially. SMC is one of the core sponsors of the IEEE Smart Cities.

Currently, multi-view subspace clustering has obtained wide attention in the field of machine learning and pattern recognition. Since different views contain different view-specific information of the same object, how to utilize these views to recover a latent shared representation is important for subsequent clustering. In this paper, we propose a latent shared representation model for multi-view subspace clustering. Our model uses the view-specific generated matrices to recover a latent shared representation from the multi-view data. To consider the correlation information of multi-view data, we adopt an empirical Hilbert-Schmidt independence criterion to constrain these view-specific generated matrices. Based on the inexact augmented Lagrangian method, we also develop an alternating optimization algorithm to solve our model. Experimental results on the real-world multi-view data sets have validated the effectiveness of our model.

Data centers have experienced dramatic growth in recent years in order to meet the ever-increasing demand for computing. As a result, minimizing the electrical cost to operate data centers has become a crucial issue. In this paper, we observe that electricity prices change over time, and that we can take advantage of periods with low prices by scaling up processor speeds to perform more work, while scaling down speeds during high price periods to reduce cost. We apply this observation to several settings. First, we consider an offline setting which assumes future electricity prices are given, and propose an efficient algorithm for optimally scaling a processor's speed in order to minimize the total electrical cost for completing a task by a deadline. We then consider a more realistic stochastic setting in which future prices are not known, but vary according to a Markov model. We present another efficient algorithm for minimizing the expected cost to meet a deadline. We performed a number of experiments using real electricity price traces to test the performance of our algorithms. We show that our stochastic algorithm is light-weight and relies only on easily obtainable price data, but that it achieves excellent performance, with only a 1 percent cost difference on average from the optimal offline algorithm. In addition, the stochastic algorithm significantly reduced costs compared to several candidate algorithms.

Moisture is one of the critical factors to determine the service life of transformers. The moisture inside the transformer oil-immersed insulation could be quantified with feature parameters. This article proposes and develops a genetic algorithm support vector machine (GA-SVM) model to carry out the moisture diagnosis. Present findings reveal that these feature parameters can be obtained by using frequency-domain spectroscopy. Therefore, a novel model for predicting the frequency-domain spectroscopy curves is first reported based on a small number of samples, which could be utilized to obtain the feature parameters database to develop GA-SVM. Then, the moisture diagnosis in the lab and field conditions is presented to verify its feasibility and accuracy. The novelty of this article is in an exploration of the reported model as an intelligent based moisture diagnosis tool for power transformers.

The multimicrogrids (MMGs) system of the island group is geographically dispersed with different ownership. In this article, a control strategy based on distributed model predictive control is proposed to optimize the economic scheduling of MMGs on an island group. The strategy is designed based on the dynamic noncooperative game theory to regulate the trading behavior among microgrids (MGs) belonging to different owners. The mechanism maximizes the economic benefits of the MGs under the premise of ensuring the closed-loop stability of the single MG system. Only a minimum amount of communication information exchange is needed, which avoids the demands of the central controller and can help the MG to protect its privacy of operating information. The proposed strategy can maximize the benefits of power trading and significantly reduce the operating cost of the system while ensuring the balance between supply and demand. Simulation results are presented to prove the fairness and validity of the proposed control strategy.

Power and energy systems are lifeline infrastructures to civilization. Their stable operation and security of supply are essential for the daily life of the people. Typically, they are characterized by a central generation infrastructure using large-scale power plants. The electricity is transported via long-distance transmission lines on high-voltage levels and distributed via distribution grids to customers on medium and low-voltage levels.

With the widespread of consumer electronics, household appliances and electric vehicle (EV), the household energy consumption is gradually increasing. To reduce the burden of distribution grid and meet the growing energy demand, photovoltaic (PV) panels and energy storage could be introduced and deployed at home. Thus, the home energy system is gradually becoming an integrated multiple energy system including the distribution grid, PV panels, battery energy storage, EV and home loads. An efficient energy management is important for the integrated energy system to save cost and comprehensively utilize their distinct characteristics. In this paper, the energy management problem is formulated to minimize the daily electricity purchase cost. The dual attributes of EV, i.e. energy storage and mobility, are both considered in the problem. The numerical result demonstrates that the energy management solution can well meet the demand requirement and significantly reduced the electricity purchase cost. In addition, comparison of results shows that benefits can be acquired from the usage of vehicle to grid and battery energy storage.

Driven by the global need for decarbonization, low carbon power generators are currently being deployed at a rapid rate. Subsequently, the power grid is posed with high operational risks due to intermittent power generation and uncertain energy demand. Along with demand-side management, energy storage plays a critical role in balancing energy supply with demand. The techno-economic analysis is conducted to compare different technological options to meet an energy problem (e.g., grid support and off-grid energy supply). Several techno-economic analyses have been conducted for low carbon energy technologies; however, different approaches were used and it is difficult to compare the present works of literature. This paper describes the on-going work of IEEE P2814, a current standards project developing recommended practices on techno-economic metrics for hybrid energy and storage systems. This standards project defines techno-economic terminologies used in the development, construction, and operation of renewable energy and electrical energy storage systems. Here, a preliminary techno-economic framework is presented and discussed. Some key aspects that need to be considered in the techno-economic analysis include the timescale of analysis and quantifying carbon emission contribution of the technologies. Future work for the standards project will be described.

Datacenters have experienced dramatic growth in recent years, and the cost for powering them has become a significant problem. This paper proposes methods to minimize the energy cost for performing a task on a datacenter server before a deadline. We observe that energy prices fluctuate over time, and schedule the task to execute in periods of relatively low cost, despite not having knowledge of future costs during the execution. This problem is studied in several models, starting with an online setting where electricity prices can change arbitrarily. A$\sqrt{\varphi }$-competitive algorithm is proposed, where$\varphi$is the ratio between the maximum and minimum electricity prices, and this algorithm is also shown to be optimal by proving a matching lower bound. Next, we consider a stochastic setting in which prices vary in a Markovian fashion and propose an optimal algorithm based on dynamic programming. We then study the performance of our algorithms in practice using prices derived from real world data. The results show that the stochastic algorithm is very effective, and achieves cost that is within 3.4 percent of the optimum. Moreover, it performs well compared to several heuristics used in practice.

In recent years, electric vehicles (EVs) have been widely applied to improve environment. The EV could provide environmentally friendly transportation but have the demerit of low battery capacity. Rapid charging by charging stations (CS) is critically needed especially for those drivers in long distance trip. Thus, a routing optimization problem for EVs charging should be addressed. Furthermore, this problem becomes more practical when the EV density is high at peak. In this scenario EVs are only allowed to obtain energy that render them able to arrive at the destination. In this paper, we formulate an emergent EV charging optimization problem in EV high density area which has not been discussed in related work and a novel genetic algorithm based emergent charging scheduling (GECS) scheme is proposed. The genetic algorithm (GA) is presented to simplify the multi-objectives optimization process in this case. Furthermore, incorporation of the Earliest Deadline First (EDF) which indicates the minimum recharging deadline time as the subject and Nearest Job First (NJF) which indicates the minimum recharging path as the subject into genetic optimization process can relieve the charging emergent condition and improve optimized results. The simulation results show that the proposed scheme can provide an optimal solution to minimize the average distance and waiting time for emergent charging in EV high density region.

For microgrids (MGs) optimal operation, one heated topic is the uncertainty management associated with renewable variations and electricity load forecasting errors. On the other hand, the networking of MGs is receiving an increasing attention in recent years. In this paper, an interactive energy management strategy is developed for high renewable-penetrated MGs. The control method includes two steps. In the first step, a local optimization is proposed for each microgrid to minimize the operation cost during the whole scheduling periods. In the second step, a global optimization is conducted for networked microgrids. CVaR based risk averse measure is introduced here to provide a risk-hedging strategy for microgrids energy management. Formulated models are solved by the easily implemented and computationally inexpensive mix integer linear programming (MILP) solver. Case studies demonstrate the feasibility of the proposed method by identifying optimal scheduling results.

This paper focuses on the development of Security-Constrained Unit Commitment (SCUC) with natural gas systems. This paper proposes an Integrated Electricity and Natural Gas System (IEGS) to describe the day-ahead scheduling including natural gas purchase contract and transmission constraints. An Second-order Cone Programming (SOCP) is proposed to handle the nonlinear steady-state natural gas flow And through penalty and linearization method, the original Mixed-Integer Non-linear Programming (MINLP) is converted into Mixed-Integer Second-Order Cone Programming (MISOCP) problem. Case study simulations demonstrate the workable solution of the scheduling method.

In data streaming environments such as a smart grid, it is impossible to restrict each data chunk to have the same number of samples in each class. Hence, in addition to the concept drift, classification problems in streaming data environments are inherently imbalanced. However, streaming imbalanced and concept drifting problems in the power system and smart grid have rarely been studied. Incremental learning aims to learn the correct classification for the future unseen samples from the given streaming data. In this paper, we propose a new incremental ensemble learning method to handle both concept drift and class imbalance issues. The class imbalance issue is tackled by an imbalance-reversed bagging method that improves the true positive rate while maintains a low false positive rate. The adaptation to concept drift is achieved by a dynamic cost-sensitive weighting scheme for component classifiers according to their classification performances and stochastic sensitivities. The proposed method is applied to a case study for the electricity pricing in Australia to predict whether the price of New South Wales will be higher or lower than that of Victorias in a 24-h period. Experimental results show the effectiveness of the proposed algorithm with statistical significance in comparison to the state-of-the-art incremental learning methods.

Current methods for nonintrusive load monitoring (NILM) problems assume that the number of appliances in the target location is known, however, this may not be realistic. In real-world situations, the initial setup of the site can be known but new appliances may be added by users after a period of time, especially in a household or nonrestrictive scenarios. In this sense, current methods without detecting new appliances may not accurately monitor loads of different appliances and scenarios. In this paper, a novel new appliance detection method is proposed for NILM with imbalance classification for appliances switching ON or OFF. The prediction of appliances being switched ON or OFF is an important step in load monitoring and the switching on frequencies for coffee machine and air conditioning in a household are different, making the problem inherently imbalanced. Experimental results show that the proposed method yields outstanding performance against the well-known oversampling method, synthetic minority oversampling technique, on real NILM applications in scenarios with new appliances emerging.

In Day-ahead Power Market (DAM), Load Serving Entities (LSEs) needs to submit their load schedule to market operator beforehand. For reduction of the total cost, the disparity of the price of DAM and the price of RDM (Real Day Market) should be considered by the LSEs. Therefore, the problem is that a more accurate load-forecasting model sometimes provide a price that has an interspace will lead to a lower cost. Facing this issue, this paper initiates a load forecasting model considering the Costing Correlated Factor (CCF) with deep Long Short-term Memory (LSTM). The target of the forecast model contains both accuracy section and power cost section. At the same time, the construct of LSTM can of fset the sacrificed accuracy. Also, this paper uses an Adaptive Moment Estimation algorithm for network training and the type of neuron is Rectified Linear Unit (ReLU). A numerical study based on practical data is presented and the result shows that LSTM with CCF can reduce energy cost with acceptable accuracy level.

The growth in installed solar photovoltaic (PV) capacity and the ever-increasing power demand due to the use of energy-hungry appliances have caused voltage issues. In this paper, a hierarchical dispatch strategy is proposed for coordinating multiple groups of virtual energy storage systems (VESSs), i.e., residential houses with air conditioners, to regulate voltage in low-voltage (LV) grids with high solar PV penetration. Specifically, the two levels of the proposed model are: 1) in the lower level, VESSs within each intelligent residential district are controlled locally by individual aggregator; 2) in the upper level, multiple aggregators are coordinated to achieve voltage regulation through a consensus control strategy. By exchanging information through sparse communication links, each aggregator shares the required active power adjustment among all participating groups, without compromising users' thermal comfort. Simulation result demonstrates that the proposed control scheme can effectively regulate voltage in LV grids with greater robustness and scalability.

In day-ahead market (DAM), load serving entities (LSEs) are required to submit their future load schedule to market operator. Due to the cost computation, we have found the inconformity between load accuracy and cost of power purchase. It means that more accurate load forecasting model may not lead to a lower cost for LSEs. Accuracy pursuing load forecast model may not target a solution with optimal benefit. Facing this issue, this paper initiates a beneficial correlated regularization (BCR) for neural network (NN) load prediction. The training target of NN contains both accuracy section and power cost section. Also, this paper establishes a virtual neuron and a modified Levenberg-Marquardt algorithm for network training. A numerical study with practical data is presented and the result shows that NN with BCR can reduce power cost with acceptable accuracy level.

Water management is an important issue in economics and environment. Recently, amount of water control system has been proposed and developed. For the type of intelligent water control, the related parameters will be the input of the control system. Hence, there is a need of developing a scalable, flexible and reliable sensor network for related parameters monitoring. To install and replace water sensors in building networks, wireless connection will be the first priority. However, improper time synchronization in the network will cause packet loss and long latency which degrades the network performance. In this paper, time-synchronized ZigBee building network (TS-ZBN) is proposed for water management. The node-to-node time synchronization is proposed. The concept is to calculate the clock difference by studying the propagation delay model. The simulation result shows that the mean synchronization error and variance are low.

The aim of this Special Section is to attract and report the latest advances toward the trend of applying advanced informatics techniques resolving complex problems facing power system operation and planning in the new era of smart grids. Special interests are given to the new methods that can handle various tasks of risk hedging and decision making appeared in eleven system operation and planning, though the scope has been slightly expanded to other topical issues in smart grids as well. The accepted eleven high-quality papers represent how the newadvances and solutions toward resolving complex problems facing power system operation and planning can be brought forward by continuously leveraging emerging techniques in the field of data analytics and informatics. It should be highlighted that with the increased penetration of various emerging technologies such as renewables and electric vehicles (EVs), secure and economic system operation and planning deserve continuous research efforts in producing the most up-to-date methods and solutions dealing with issues of diversified natures and complexities in future power grids. Specifically, the covered topics in this Special Section are topical and broad, concerning mainly power system security analysis and electricity market planning and operation under risks and uncertainties, which are briefly summarized.

The network reconfiguration during power system restoration after blackouts usually takes a long and complex procedure. To address the uncertainties in the restoration steps and time involved, a CCP (Chance Constrained Programming) based method for network reconfiguration scheme optimization is proposed in this paper. The proposed method can generate the best restoration sequence to maximize the benefit of the reconfiguration scheme by taking into account the number of restarted generator-nodes and the cost of power outage saved by the load restoration accordingly. The Differential Evolution (DE) is employed to solve for the optimal solution subject to special requirements of the network reconfiguration operation. A numerical example over the New England 39-bus power system is conducted to demonstrate the effectiveness of the proposed method.

An interference-mitigated ZigBee-based advanced metering infrastructure (AMI) solution, namely IMM2ZM, has been developed for high-traffics smart metering (SM). The IMM2ZM incorporates multiradios multichannels network architecture and features an interference mitigation design by using multiobjective optimization. To evaluate the performance of the network due to interference, the channel-swapping time (Tcs) has been investigated. Analysis shows that when the sensitivity (PRχ) is less than -12 dBm, Tcs increases tremendously. Evaluation shows that there are significant improvements in the performance of the application-layer transmission rate (σ) and the average delay (D). The improvement figures are σ > ~300% and D > 70% in a 10-floor building, σ > ~280 % and D > 65% in a 20-floor building, and σ > ~270% and D > 56% in a 30-floor building. Further analysis reveals that IMM2ZM results in typically less than 0.43 s delay for a 30-floor building under interference. This performance fulfills the latency requirement of less than 0.5 s for SMs in the USA (Magazine of Department of Energy Communications, USA, 2010). The IMM2ZM provides a high-traffics interference-mitigated ZigBee AMI solution.

Successful development of smart grid demands strengthened system security and reliability, which requires effective security analysis in conducting system operation and expansion planning. Classical N - 1 criterion has been widely used to examine every creditable contingency through detailed computations in the past. The adequacy of such approach becomes doubtful in many recent blackouts where cascading outages are usually involved. This may be attributed to the increased complexities and nonlinearities involved in operating conditions and network structures in context of smart grid development. To address security threats, particularly from cascading outages, a new and efficient security analysis approach is proposed, which comprises cascading failure simulation module (CFSM) for post-contingency analysis and risk evaluation module (REM) based on a decorrelated neural network ensembles (DNNE) algorithm. This approach overcomes the drawbacks of high computational cost in classical N-k-induced cascading contingency analysis. Case studies on two different IEEE test systems and a practical transmission system-Polish 2383-bus system have been conducted to demonstrate the effectiveness of the proposed approach for risk evaluation of cascading contingency.

The advance in the research of Smart Grid methodologies opens the doors toward the conceptualization of new tools aimed at effectively addressing most challenging issues of modern power distribution systems, including the massive pervasion of renewable power generators, the strictest power quality limits, the complex interactions with the energy markets, the raising levels of security and reliability constraints, and the need for maximizing the exploitation of existing electrical infrastructures

As plug-in hybrid electric vehicles (PHEVs) are expected to be widely used in the near future, a mathematical model is developed based on the traditional security constrained unit commitment (SCUC) formulation to address the power system dispatching problem with PHEVs taken into account. With the premise of power system secure operation, both the economic benefit for PHEV users and the carbon-emission costs are taken into account. Then, the features of PHEVs as mobile energy storage units are exploited to decouple the developed model into two sub-models, involving the unit commitment model and the charging and discharging scheduling model that includes AC power flow constraints. The optimal plug-in capacities for PHEVs and the schemes, including when and where charging and discharging occur, are obtained through a mixed integer programming algorithm and the Newton-Raphson load flow algorithm in addition to the optimal day-ahead unit commitment scheme. Finally, the feasibility and efficiency of the proposed model are verified with a 6-bus test system.

Smart grid development highlights the "self-healing" capability as it enables a power system to efficiently and automatically react to disturbances and guide the system to the best possible state. Effectively partitioning the power network (PN) into suitable areas or zones to accommodate subsequent control actions is useful. In this paper, a novel partitioning approach that combines Laplacian spectrum of a PN and self organizing map (SOM) algorithm is proposed. This approach aims to optimize the partitioning solution so as to minimize the real power imbalance and simultaneously maintain a satisfactory voltage profile. Case study is carried out on New England 39-bus system, which demonstrates the effectiveness of the proposed approach.

In this paper, the state-of-the-art of big data is reviewed. Challenges, opportunities and tools will be discussed. Some emerging technologies will be looked to promote big data applications. The applications of big data in smart grid in some countries will be summarized too.

Low carbon emission is one of the main targets for smart grid planning. To achieve this goal, intermittent energies such as wind and solar are integrated to the power systems increasingly. However, this may create huge challenges to the power system operators for balancing the generation and demand at all times and guaranteeing the system reliability at the same time. With high penetration of renewable energies, power system operators are compelled to curtail the loads when the power system cannot rely on power from renewable energies continuously due to strong dependence on the environment. As an important defense to protect the power network from collapsing and to keep the system integrating, load shedding has been designed and proposed for decades. However, most of the shedding schemes consider the load increasing instead of lack of generation. This paper applies a load shedding scheme with considering both voltage and frequency changes when the generation is inadequate since the power system cannot obtain the expected renewable generation and renewable energies are highly penetrated into the grid.

With more uncertainty and variability existing in smart grid, deterministic load flow, which is used to analyze the operation conditions on a daily routine and planning the power systems for future investment, could not solve the problems with consideration of renewable generation intermittence and load variation. Nowadays, with more attentions are paid to the environment, carbon emission problem is one of the main concerns in smart grid strategies and planning. It is a great opportunity and challenge for planners to take good care of all stakeholders' interests. Multi-objectives need to be considered when making a critical decision. This paper presents result comparisons between probabilistic optimal load flow and probabilistic load flow by considering both carbon emission and minimization of power loss of the entire grid. The framework is applied to the modified IEEE 14-bus system, which is modeled in Power Factory Dig SILENT, with intermittent wind energy source and load variation consideration.

Time-based demand response (DR) enables industrial consumers to transfer their power consumption by following daily price curve. However, general time-based DR is basically a passive tariff. Utilities usually create general pricing tariff to the whole industrial consumers at the same voltage connection level. Under this situation, consumption transformation of all possible industries occurs together. It may reduce the effect of load characteristics improvement. This paper introduces a new pricing framework named active time-based (ATB) DR to overcome this weak point. Under this tariff, consumers are classified in details. Utilities select target consumers, communicate with them actively, and provide a specified price curve for the industries covered by target consumer group. With a practical survey, this paper implements ATB with the best behavioral scheme (BBS) model and industrial consumer attitude model. This paper includes a numerical case study on cement manufacturing for further analysis. Data acquisition, BBS simulation, consumer attitude estimation, and an investigation on electricity pricing are covered by this case study.

This paper presents few key issues that must be taken into account for energy system development and planning. Discussions on energy efficiency, renewables deployment and energy policy are required to improve energy security of any countries. Some figures in China will be used to demonstrate the proposed ideas. Some novel strategies and methods will be discussed. An example on demand response will be included.

With the rapidly development of smart grid, communication network will play more and more fundamental role in many smart grid applications and services. The interaction between power system and communication network will appear almost everywhere in the new services of smart grid, the investigation of mixture system combined power system and communication network reveals the mutual influence of each other, and will give accuracy and quantitative data for the planning of the future smart grid. This paper presents a novel cosimulation platform combined power system and communication network to meet the decision-making need in smart grid environment. The platform connects power system simulator and communication system simulator together via a middleware with interfaces, a synchronization method is proposed for the correct time and sequence of data exchange, a time step adjustment algorithm is proposed as well to balance the requirement of accuracy and efficiency.

The purpose of Demand Response (DR) is to guarantee that the power grid runs in an efficient and stable status by letting the consumers participate in the grid operation. Numerous studies of DR modeling and analysis are implemented for consumers in the residential and commercial sectors, while the studies on the DR in the industrial sector are relatively immature. The ability to participate in DR differs due to the differences in the production processes of different industries. In this paper, we analyze the process and characteristics of cement production, one of the biggest industrial power consumers in China. A simulation model for cement plant based on the start probability of process equipment is also described. Simulation results of a case study show how the plant's loads shift under a Time-of-use (TOU) application.

A Multipurpose Patient Monitoring system embedded with a new ZigBee mobile application profile is proposed and developed. This system enables the mobility of ZigBee devices. The usage, the architecture and the mobility framework are discussed in details. It saves people life by providing panic button and location tracking service. A case study based on the Pamela Youde Nethersole Eastern Hospital is also presented and findings are given. This investigation reveals that the developed mobile application profile offers promising value-added services for many potential ZigBee applications.

This paper reports some design and requirements needed for applying smart meters to micro grid. Few practical applications will be used to demonstrate the benefit obtained with this approach. International standard used for communication will be included in the design. Good practice will be proposed.

In this paper, both bottom-up and top-down models for demand response with agent-base approach and neural networks have been investigated. Simulations have been carried out with practical load data from the UK and Canada. Results show that each approach has its advantages and disadvantages depending on difference application scenarios.

With the deployments of smart grid technologies and communication networks in modern power systems, frequency relays, which are used for the purpose of power system protection, are to achieve more advanced and reliable performance. Load shedding strategies, which prevent power systems from suffering frequency instability based on the real time data frequency relays, are facing a challenge to adequately and accurately take shedding actions, as power system loads are varying all the time. On the generation side, increasingly non-dispatch able and inflexible renewable power generations being integrated to the system complicates generation predictions and results in frequent power imbalance. This paper proposes a load shedding scheme with different magnitudes and load shedding orders for distribution networks, with its effect on power system frequency stability verified in a benchmark IEEE 33-bus distribution system, which is simulated in Dig SILENT Power Factory package.

In this paper, Decision Support System (DSS) from 30 years ago to the near future will be studied to investigate the development trend of DSS. A typical DSS architecture has been described and the requirement of the DSS functionality implementation is illustrated in this article. Because all of the DSSs are projected-oriented, researchers are focused on developing general architecture to adapt as many cases as possible. Researchers begin to investigate the dynamic interaction and multi-criteria for the DSS application. In the near future, with the development of cloud computing, some cloud-based and agent-based DSSs will be adopted to various applications.

The aim of this paper is to construct a bio-inspired hierarchical neural network that could accurately represent visual images and facilitate follow-up processing. Our computational model adopted a ganglion cell (GC) mechanism with a receptive field that dynamically self-adjusts according to the characteristics of an input image. For each GC, a micro neural circuit and a reverse control circuit were developed to self-adaptively resize the receptive field. An array was also designed to imitate the layer of GCs that perform image representation. Results revealed that this GC array could represent images from the external environment with a low processing cost, and this nonclassical receptive field mechanism could substantially improve both segmentation and integration processing. This model enables automatic extraction of blocks from images, which makes multiscale representation feasible. Importantly, once an original pixel-level image was reorganized into a GC array, semantic-level features emerged. Because GCs, like symbols, are discrete and separable, this GC-grained compact representation is open to operations that can manipulate images partially and selectively. Thus, the GC-array model provides a basic infrastructure and allows for high-level image processing.

Nowadays, electricity is mainly produced by large central generation plants. However, generators with lower capacities are increasingly used for distributed generation (DG). Distributed generators are applicable for improving supply reliability with considerations of renewable energy, environmental protection, peak load shaving and deferred investments in network expansion. Comparing to central generation, DG technologies are flexible in size, fuel, operation and expandability. Most of DGs are installed at distribution level. Various kinds of loads as well as active compensation devices may be connected in parallel with DG. Moreover, low capacity generation systems are more susceptible to power quality issues. This paper presents challenges of implementation of DG in practical area electric power systems under the impacts from both utility side and demand side.

An image comprises information, such as color, texture, shape, and intensity, which humans use in parallel for perception. Based on this knowledge, three methods of constructing visual codebook ensembles are proposed in this paper. The first technique introduced diverse individual visual codebooks by randomly choosing interesting points. The second technique was based on a random subtraining image data set with random interesting points. The third method directly utilized different patch information for constructing an ensemble with high diversity. The codebook ensembles were learned to capture and convey image properties from different aspects. Based on these codebook ensembles, different types of image presentations could be obtained. A classification ensemble could be learned based on the different expression data sets from the same training image set. The use of a classification ensemble to categorize new images can lead to improved performance. The detailed experimental analyses on several data sets revealed that the present ensemble approaches were resistant to variations in view, lighting, occlusion, and intraclass variations. In addition, they resulted in state-of-the-art performance in categorization.

In view of the uncertainties in off-line available transfer capability (ATC) evaluation, a method based on blind number was proposed. The study on ATC offers important reference information for secure operation of power system, decision-making of power market participants, system planning and so on. The representation of ATC will be more accurate with full consideration of uncertainties. Blind number is able to comprehensively express various types of uncertainties, and in this paper, the content of blind number is theoretically extended with the provision of probability density information. With the OPF based ATC model, the optimization algorithm based on the expanded blind number theory is detailed and the effectiveness of the approach is justified by the IEEE 30-bus system.

This paper proposes a genetic algorithm (GA) tuned differential evolution (DE) method for solving economic dispatch (ED) problem with non-smooth cost curves. The tuning of the weights in differential evolution is the key issue in designing an efficient differential evolution algorithm. Their values are dependent on nature and characteristic of objective function. As there is no explicit rule or guideline in determining these parameters, they are generally determined after a number of experimentations. In this paper floating point GA is used in tuning these parameters. The developed algorithm is experimented on a medium size of 40 units. The performance of the proposed algorithm is compared with standard improved fast evolutionary programming (IFEP) techniques. The simulation results demonstrate that GA tuned DE method is very efficient in finding higher quality solutions in high order non-convex ED problems.

The detection of insulators contamination is difficult in power systems because many factors can influence the pollution. The contamination condition of insulators is usually estimated by detecting the root mean square (r.m.s) of surface leakage current via online-monitoring system. It ignores the influence of environmental factors, such as temperature, humidity, etc. As these factors are fuzzy-characterized, a new method based on Fuzzy Neural Network (FNN) is proposed to improve traditional insulation contamination detection. The renewed structure of FNN is put forward. The evaluation of contamination severity of insulators is achieved through FNN, which are trained by the field samples. The results prove the validity of the method proposed in the paper and can be used to eliminate the insulator from flashover fault and improve the condition-based maintenance (CBM).

For real-time typhoon eye tracking, artificial ant colony (AAC) methodology has been proved valuable for the efficient & effective identification of snake contour model boundary, which was built to simulate the real unclear typhoon eye whirly shape. While satellite digital photograph technology make it possible to capture real-time meteorological information; by means of constructing solution space and heuristic information, the contour of non-clear typhoon eye can be tracked intelligently. However, the practical conditions and meteorological phenomena are very complicated, only using discrete energy parameters as the heuristic information to lead the intelligent directing of ants are not reliable enough. In this paper, continuous space multi-kernel functions are introduced to optimize the heuristic information. In order to supply more practical factors for the energy converging procedure, corresponding Gaussian parameters calculation method will be given. In comparison, the iteration numbers can be decreased concerning same complexity of the problem to be solved, which proves that proposed optimization could provide the improvement on the practicability and effectiveness of original solutions.