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This article proposes a novel energy control strategy for distributed energy storage system (DESS) to solve the problems of slow state of charge (SOC) equalization and slow current sharing. In this strategy, a key part of the presented strategy is the integration of a new parameter virtual current defined from SOC and output current. With the help of virtual current, the characteristic of the arcsinh function has a large slope near the zero point, and three acceleration factors, to realize the fast equalization of SOC and the output current quickly distributed by capacity. In addition, current sharing and voltage restoration can be achieved by designing only one controller, thereby simplifying the control structure. In communication layer, only two variables need to be exchanged between neighboring converters, thus, reducing the communication burden. Then, the stability of the system is analyzed by small signal modeling. Finally, the correctness and effectiveness of the proposed control strategy are verified through the experiments.

With the increasing number of electric vehicles (EVs), their uncoordinated charging poses a great challenge to the safe operation of the power grid. In addition, traditional individual-EV scheduling models may be difficult to solve due to the increasing number of constraints. Therefore, this paper proposes a cluster-based EV scheduling model. Firstly, electric vehicle clusters (EVCs) are formed based on the charging and discharging preferences of EV users and the expected time for EVs to leave. Secondly, the EVC energy and power boundary aggregation method based on the Minkowski addition algorithm is proposed. Finally, for the sake of reducing user charging cost and distribution network energy loss, and smoothing the daily load curve, an EVC scheduling model for EV participation in grid auxiliary services is proposed. The optimization model includes the reactive-power compensation of EV charging piles. The simulation results show that the proposed EVC scheduling model can greatly reduce the solution time compared to traditional individual-EV scheduling model. The model has high potential to be applied to large-scale EV scheduling. The reactive-power compensation provided by EV charging piles improves the voltage quality of the grid and enables more EVs to be connected to the grid.

The operational cost of a microgrid is significantly influenced by the response of storage systems and the complexities of the power market’s tariff structures. This paper addresses the challenges arising from the coexistence of new market entries and traditional tariffs, which contribute to a complex market environment. To tackle this issue, the paper establishes a microgrid market environment encompassing four types of tariffs. By modeling the response of electric storage and cold storage in a microgrid, the study formulates a non-linear mixed-integer optimization problem. Numerical studies are then conducted to verify the model and analyze market performance. The results reveal a trade-off in behavior among different market entries when optimizing the total cost of microgrid operation. These findings shed light on the complexities and trade-offs involved in microgrid operational cost optimization within a diverse market environment, offering valuable insights for market participants.

In the electricity market environment, the regional integrated energy system (RIES) can reduce the total operation cost by participating in electricity market transactions. However, the RIES will face the risk of load and electricity price uncertainties, which may make its operation cost higher than expected. This paper proposes a method to optimize the operation cost of the RIES in the electricity market environment considering uncertainty. Firstly, based on the operation cost structure of the RIES in the electricity market environment, the energy flow relationship of the RIES is analyzed, and the operation cost model of the RIES is built. Then, the electricity purchase costs of the RIES in the medium- and long-term electricity markets, the spot electricity market, and the retail electricity market are analyzed. Finally, considering the risk of load and electricity price uncertainties, the operation cost optimization model of the RIES is established based on conditional value-at-risk. Then it is solved to obtain the operation cost optimization strategy of the RIES. Verification results show that the proposed operation cost optimization method can reduce the operation cost of high electricity price scenario by optimizing the energy purchase and distribution strategy, constrain the risk of load and electricity price uncertainties, and help balance the risks and benefits.

Abstract The reform of power spot market in China provides a new profit mode, determining energy trading strategy based on the power spot prices for distributed energy storages. However, individually accessing every distributed energy storage to the dispatch centre results in a high cost and low efficiency, which needs to be improved by connecting through the aggregator. To this end, this paper proposes a regulation mode and strategy for distributed energy storages participating in energy trading through aggregation. First, the optimal centres of distributed energy storages are searched as the aggregation centres according to the electrical distance distributed by the energy storage, and the model of each distributed energy storage aggregation group is established. On this basis, the conditional value at risk (CVaR) method is introduced to quantify the income risk brought by the fluctuation of power spot prices, following with the optimization model for day‐ahead stage and real‐time stage. Finally, case studies under multiple scenarios of power spot market verify that the regulation mode and strategy can effectively guarantee the economic profits of distributed energy storages by setting aggregation groups and reasonable risk preference coefficients.

Photovoltaic (PV) power generation is booming in rural areas, not only to meet the energy needs of local farmers but also to provide additional power to urban areas. Existing methods for estimating the spatial distribution of PV power generation potential either have low accuracy and rely on manual experience or are too costly to be applied in rural areas. In this paper, we discuss three aspects, namely, geographic potential, physical potential, and technical potential, and propose a large-scale and efficient PV potential estimation system applicable to rural rooftops in China. Combined with high-definition map images, we proposed an improved SegNeXt deep learning network to extract roof images. Using the national standard Design Code for Photovoltaic Power Plants (GB50797-2012) and the Bass model, computational results were derived. The average pixel accuracy of the improved SegNeXt was about 96%, which well solved the original problems of insufficient finely extracted edges, poor adhesion, and poor generalization ability and can cope with different types of buildings. Leizhou City has a geographic potential of 1500 kWh/m2, a physical potential of 25,186,181.7 m2, and a technological potential of 442.4 MW. For this paper, we innovatively used the Bass Demand Diffusion Model to estimate the installed capacity over the next 35 years and combined the Commodity Diffusion Model with the installed capacity, which achieved a good result and conformed to the dual-carbon “3060” plan for the future of China.

To fully reveal the interplay of the cyber system and physical system in the microgrid, this paper proposes a generic hierarchical modeling framework for cyber-physical integration modeling of microgrid, including two layers: physical device layer and controller layer. Each layer includes two parts: the continuous part (characterizing the physical system in the microgrid) and the discrete part (characterizing the cyber system in the microgrid). In this paper, firstly, detailed mathematical expressions describing the dynamic characteristics of each layer (including continuous characteristics and discrete characteristics) and describing the interplay of the continuous part and discrete part in each layer are given, which contributes to a better understanding of the interplay of the information flow and energy flow in the microgrid. Secondly, the cyber-physical integration modeling of each unit in the proposed framework is established using hybrid automaton, which can clearly present the operation state of each unit of the microgrid and its state transfer process, which is beneficial to design the optimal operation state trajectory of the system. The proposed modeling framework is generic and can be extended to any dynamic system with cyber-physical integration. Finally, a typical microgrid system is taken as an example to verify the feasibility of the proposed modeling approach.

With the flexible integration of local renewable energy with the smart distribution network system, the problems of high operating costs and power shortage can be effectively solved. However, taking the industrial park microgrid with high penetration photovoltaic as an example, due to the uncertainties and fluctuations arising from the meteorological conditions and the load demands, the safe and reliable operation of the microgrid system has been threatened significantly. Operators often need to pay additional unnecessary costs to maintain stable operations of the microgrid. Therefore, in this study, a dispatch strategy based on robust model predictive control considering low-carbon cost is designed to reduce the adverse effects of uncertainties. First, a low-carbon energy management scheme is formulated based on short-term source and load forecast information in which a two-stage robust optimization solution method is used to generate the optimal dispatch scheme under the worst scenario. Then, an intraday real-time strategy with a closed-loop feedback mechanism is formed based on the model predictive control. Finally, the feasibility of the proposed strategy is simulated and analyzed based on the measured data of the photovoltaic microgrid in the industrial park. The results show that compared with the general intraday scheduling strategy and the day-ahead robust strategy, the proposed strategy can effectively get low-carbon scheduling plans considering the uncertainty of source and load while efficiently balancing the robustness and economy of the grid-connected industrial park photovoltaic microgrid system operation.

Distribution utilities can flexibly control distribution networks by allocating the automatic and remotely controlled sectionalizing switches (SSs), which work with tie lines (TLs) to speed up fault management and are alternative devices for distribution operation mode adjustment. Hence, the SSs and the TLs play an important role in distribution networks. In order to improve the reliability of power supply and achieve economic distribution network operation, this paper proposes a bi-level SSs and TLs planning model, which considers distribution operation mode adjustment to optimize the allocation of SSs and TLs. The upper level of the proposed model aims at minimizing the sum of the total investment cost, the customer interruption loss cost, and the line loss cost. The upper level identifies the number and location of the SSs and the TLs. With the planning scheme obtained from the upper level, the lower level of the model adjusts the distribution operation mode to minimize the distribution line loss. In addition, binary particle swarm optimization (BPSO) is used because it has stable convergence and effectively explores the search space. A second-order cone programming (SOCP) is employed to reduce the complexity of the model and improve the solving process by linearizing the reconstruction calculation of the distribution network. Finally, simulation studies were conducted on bus 2 and bus 4 of the RBTS standard test system to assess the feasibility of the proposed model. The stability and effectiveness of the model are verified through various comparisons.

Microgrids have become increasingly popular in recent years due to technological improvements, growing recognition of their benefits, and diminishing costs. By clustering distributed energy resources, microgrids can effectively integrate renewable energy resources in distribution networks and satisfy end-user demands, thus playing a critical role in transforming the existing power grid to a future smart grid. There are many existing research and review works on microgrids. However, the thermal energy modelling in optimal microgrid management is seldom discussed in the current literature. To address this research gap, this paper presents a detailed review on the thermal energy modelling application on the optimal energy management for microgrids. This review firstly presents microgrid characteristics. Afterwards, the existing thermal energy modeling utilized in microgrids will be discussed, including the application of a combined cooling, heating and power (CCHP) and thermal comfort model to form virtual energy storage systems. Current trial programs of thermal energy modelling for microgrid energy management are analyzed and some challenges and future research directions are discussed at the end. This paper serves as a comprehensive review to the most up-to-date thermal energy modelling applications on microgrid energy management.

To achieve the national carbon-peak and carbon-neutral strategic development goals, it is necessary to build power systems dominated by renewable and sustainable energy. The future power system with a high proportion of renewable and sustainable energy is required to have large-scale, low-cost, flexible, and adjustable resources. To this end, this article aggregates user-side distributed energy storage and electric vehicles into a virtual power plant, considering the uncertainty of wind power fluctuations and the uncertainty of electric vehicle charging and discharging to establish a day-ahead and intra-day peak regulation model for combined peak regulation of virtual and thermal power plants. The bounding algorithm seeks the optimal strategy for the two-stage model of joint peak regulation and obtains the day-ahead and intra-day two-stage optimal peak regulation strategy. The simulation example shows that the virtual power plant and its day-ahead and intra-day optimal peak regulation strategy can reduce the peak regulation cost of the power system, as compared with the deep peak regulation of thermal power plants with a special supporting energy storage power station. This work provides a global perspective for virtual power plants to participate in the formulation of power system peak regulation rules.

Internet data centers are growing rapidly in recent years and they operate with intensive energy activity. Combined cooling, heating and power (CCHP) brings new opportunities for reducing the electricity cost in internet data centers. The main objective of this study is to optimize the energy resources scheduling in the data center coupled energy nets considering the involvement of CCHP and different demand response techniques. In this paper, internet data center coupled energy nets are proposed, where power grid, solar photovoltaic, CCHP, and battery energy storage systems are the primary energy sources. The adjunct residential buildings and commercial buildings near the internet data centers are also included in the proposed energy nets, where different types of load and demand response characteristics are utilized. A two-stage optimized energy management model considering the coordinated operation of CCHP and demand response technologies is established for internet data center coupled energy nets. In the day-ahead stage, the control objective is to minimize system cost while satisfying various constraints. Consider the electricity tariff chance between day-ahead market and real-time market, real-time control is implemented to minimize the imbalance cost between two electricity markets. Case studies are conducted on a practical internet data center coupled energy nets in Foshan City, China. It is observed that the proposed control framework can optimally schedule the energy resources in the energy network to meet system demand and improve the energy efficiency. The economic evaluation demonstrates that the proposed control scheme reduces system daily cost by 22.01%.

Most traditional artificial intelligence-based fault location methods are very dependent on fault signal selection and feature extraction, which is often based on prior knowledge. Further, these methods are usually very sensitive to line parameters and selected fault characteristics, so the generalization performance is poor and cannot be applied to different lines. In order to solve the above problems, this paper proposes a two-terminal fault location fusion model, which combines a convolutional neural network (CNN), an attention module (AM), and multi-head long short-term memory (multi-head-LSTM). First, the CNN is used to accomplish the self-extraction of fault data features. Second, the CBAM (convolutional block attention module) model is embedded into the convolutional neural network to selectively learn fault features autonomously. Furthermore, the LSTM is combined to learn the deep timing characteristics. Finally, a MLP output layer is used to determine the optimal weights to construct a fusion model based on the results of the two-terminal relative fault location model and then output the final location result. Simulation studies show that this method has a high location accuracy, does not require the design of complex feature extraction algorithms, and exhibits good generalization performance for lines with different parameters, which is of great importance for the development of AI-based methods of fault location.

Considering that most of the photovoltaic (PV) data are behind-the-meter (BTM), there is a great challenge to implement effective demand response projects and make a precise customer baseline (CBL) prediction. To solve the problem, this paper proposes a data-driven PV output power estimation approach using only net load data, temperature data, and solar irradiation data. We first obtain the relationship between delta actual load and delta temperature by calculating the delta net load from matching the net load of irradiation for an approximate day with the least squares method. Then we match and make a difference of the net load with similar electricity consumption behavior to establish the relationship between delta PV output power and delta irradiation. Finally, we get the PV output power and implement PV-load decoupling by modifying the relationship between delta PV and delta irradiation. The case studies verify the effectiveness of the approach and it provides an important reference to perform PV-load decoupling and CBL prediction in a residential distribution network with BTM PV systems.

Smart cities employ technology and data to increase efficiencies, economic development, sustainability, and life quality for citizens in urban areas. Inevitably, clean technologies promote smart cities development including for energy, transportation and health. The smart city concept is ambitious and is being refined with standards. Standards are used to help with regulating how smart cities function and contributing to define a smart city. Smart cities must be officially recognized by national and international authorities and organizations in order to promote societal advancement. There are many research and review articles on smart cities. However, technical standards are seldom discussed in the current literature. This review firstly presents the study of smart city definitions and domain. The well-known smart city standards will be presented to better recognize the smart city concept. Well-defined standards allow meaningful comparisons among smart cities implementation. How smart city initiatives make a city smarter and improve the quality of life will be discussed for various countries. This review highlights that technical standards are important for smart cities implementation. This paper serves as a guide to the most recent developments of smart cities standards.

In the case of islanding from the distribution network, problems such as source-load power loss under multi-zone island isolation will challenge the autonomous operation of multi-microgrids with three-phase/single-phase hybrid structure. In order to make full use of the resources in the multi-microgrids to realize the reliable and efficient operation of the system as a whole, this paper proposes a multi-time scale regional autonomous control strategy for multi-microgrids with three-phase/single-phase hybrid structure. The dynamic update islanding combination is controlled by the hourly dynamic island combination and the simultaneous search switching to achieve the overall resource allocation optimization. The source-storage-load power and state redistribution are dynamically realized through minute-level centralized coordination and optimization, so that the constraints of the three-phase unbalance are met and the source-load shedding are minimized. The power fluctuations of the sources and loads within each sub-microgrid are suppressed by using the second-level off-grid real-time power control. Moreover, the tie-line power can be maintained as the original given value and the three-phase unbalance degree index can be achieved. The multi-microgrids model with three-phase/single-phase hybrid structure based on DIgSILENT platform is used to verify the correctness and effectiveness of the proposed strategy.

With the decreasing reserves of fossil energy and the increasing capacity of renewable energy generation, the scale of microgrid based on distributed generations is expanding. However, more operation data and transaction information of microgrids will also bring several problems: the sufficient capacity of the server in the central management needed, the crises of trust among members, the transparency of transaction information and the confidentiality of data storage. In this paper, blockchain technology is used to deal with these problems as distributed data storage technology. A double-layer framework of energy transactions based on blockchain in multi-microgrids is proposed to provide decentralized trading, information transparency and mutual trust system of each node in the trading market. The central node within the microgrid collects the demand information of the trading market in lower layer and sends them to the trading market of multi-microgrids in higher layer to seek the energy transaction. The continuous double auction mechanism is used in the trading market to guarantee free and fair transactions among nodes. The proposed transaction framework effectively reduces the transaction volume with the main grid which improves energy utilization. Comprehensive simulation results are presented to prove the feasibility of the proposed transaction framework.

In this article, a modified decentralized finite control set model predictive control (FCS-MPC) scheme for the distributed energy resources (DERs) is proposed to improve the power management quality of the prosumers integrated microgrids under the condition of harmonic and unbalance loads. The proposed control strategy for the microgrids mainly consists of the power droop controller, the model predictive controller delay compensation, feedback correction and the unbalance compensation mechanism. The feedback correction method is used to correct the delay compensation, which effectively reduces the average switching frequency (ASF) and voltage total harmonic distortion (THD). By sharing the negative sequence reactive power of the microgrid, power distribution between the prosumers' DERs is improved. The DERs in the prosumers can be integrated without any communication wire. The transient response and robustness to parameter changes are far superior to hierarchical cascaded control. Moreover, the proposed control strategy can better suppress harmonic and reduce and share fundamental negative sequence reactive power under microgrid unbalance and nonlinear load conditions. Finally, the effectiveness of the proposed FCS-MPC control strategy is validated by time-domain simulation results and real-time tests with RT-Lab under the condition of unbalanced and nonlinear loads in the microgrids.

The penetration of multi-carrier energy systems in distribution system gains more and more concerns. In this paper, a bi-level transactive energy trading framework is proposed to improve the energy scheduling and operation efficiency for multi-carrier energy systems which are modeled as energy hubs (EHs). In the upper level, each EH in the distribution system not only makes energy scheduling decisions considering supplies and demands of local energy, but also trades energy with each other to further maximize their social welfare. The associated trading payment among EHs is made in a fair manner by applying Nash bargaining theory. We solve the bargaining problem by decomposing it into two subproblems: operation cost minimization problem and payment bargaining problem. Then, based on the trading decision, the nodal equivalent loads of EHs are sent to the distribution system operator (DSO) without publishing trading details. By applying the second-order cone programming (SOCP), DSO reconfigures the network to reduce the transmission loss of the system in the lower level. The network reconfiguration and the trading behavior of EHs interact and iterate until the convergence. Numerical studies on modified IEEE 33-bus distribution system demonstrate the effectiveness of the proposed framework.

The park integrated energy system (PIES) plays an important role in realizing sustainable energy development and carbon neutral. Furthermore, its optimization dispatch can improve the energy utilization efficiency and reduce energy systems operation cost. However, the randomness and volatility of renewable energy and the instability of load all bring challenges to its optimal operation. An optimal dispatch framework of PIES is proposed, which constructs the operation models under three different time scales, including day-ahead, intra-day and real-time. Demand response is also divided into three levels considering its response characteristics and cost composition under different time scales. The example analysis shows that the multi-time scale optimization dispatch model can not only meet the supply and demand balance of PIES, diminish the fluctuation of renewable energy and flatten load curves, but also reduce the operation cost and improve the reliability of energy systems.

A hybrid short-term wind power prediction model based on data decomposition and combined deep neural network is proposed with the inclusion of the characteristics of fluctuation and randomness of nonlinear signals, such as wind speed and wind power. Firstly, the variational mode decomposition (VMD) is used to decompose the wind speed and wind power sequences in the input data to reduce the noise in the original signal. Secondly, the decomposed wind speed and wind power sub-sequences are reconstructed into new data sets with other related features as the input of the combined deep neural network, and the input data are further studied for the implied features by convolutional neural network (CNN), which should be passed into the long and short-term memory neural network (LSTM) as input for prediction. At the same time, the improved particle swarm optimization algorithm (IPSO) is adopted to optimize the parameters of each prediction model. By superimposing each predicted sub-sequence, the predicting wind power could be obtained. Simulations based on a short-term power prediction in different months with huge weather differences is carried out for a wind farm in Guangdong, China. The simulated results validate that the proposed model has a high prediction accuracy and generalization ability.

High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting method based on variational mode decomposition (VMD), a fast correlation-based filter (FCBF) and bidirectional long short-term memory (BiLSTM) network is developed to minimize PV power forecasting error. In this model, VMD is used to extract the trend feature of PV power, then FCBF is adopted to select the optimal input-set to reduce the forecasting error caused by the redundant feature. Finally, the input-set is put into the BiLSTM network for training and testing. The performance of this model is tested by a case study using the public data-set provided by a PV station in Australia. Comparisons with common short-term PV power forecasting models are also presented. The results show that under the processing of trend feature extraction and feature selection, the proposed methodology provides a more stable and accurate forecasting effect than other forecasting models.

Microgrids consider adjustable loads in demand-side management (DSM), which respond to dynamic market prices. A reliable DSM strategy relies on load forecasting techniques in day-ahead (DA) scheduling. This paper applies an error-driven prediction modulation to evaluate these differences. In addition, this paper creates two new DSM methods with an evaluation environment to utilize this modulation. The first method adds this modulation directly to traditional microgrid DSM with electrical storage. The second method creates two virtual sub-storages for behavior adjustment in both DA and real-time (RT) markets. The results of numerical studies indicate that the new DSM methods can reduce microgrid operation costs.

Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research community. However, most of the existing multi-view subspace clustering methods are still limited in two aspects. (1) The subspace representation yielded by the self-expression reconstruction model ignores the local structure information of the data. (2) The construction of subspace representation and clustering are used as two individual procedures, which ignores their interactions. To address these problems, we propose a novel multi-view subspace clustering method fusing local and global information for one-step multi-view clustering. Our contribution lies in three aspects. First, we merge the graph learning into the self-expression model to explore the local structure information for constructing the specific subspace representations of different views. Second, we consider the multi-view information fusion by integrating these specific subspace representations into one common subspace representation. Third, we combine the subspace representation learning, multi-view information fusion, and clustering into a joint optimization model to realize the one-step clustering. We also develop an effective optimization algorithm to solve the proposed method. Comprehensive experimental results on nine popular multi-view data sets confirm the effectiveness and superiority of the proposed method by comparing it with many state-of-the-art multi-view clustering methods.

Simulation and performance evaluation of concurrent Java program execution have been difficult due to the lack of proper model and tools. Previous modeling and simulation approaches cannot simultaneously achieve three tasks: (1) support the direct simulation of unmodified multi-thread Java programs; (2) guarantee deterministic simulation results; and (3) offer low-overhead and scalable simulation. This paper first presents a novel simulation model based on the Java memory model. The model axiomatically defines action ordering, relationships, and constraints to ensure the well-formedness and determinism of a simulated execution. Then, based on the model, we implement the DecompositionJ simulation framework (deterministic, concurrent multi-processsing simulation for Java programs) which enables the direct execution simulation of target program by using compiler-based source-to-source transformation and a purposely designed runtime library. The framework is compatible with any JVM that complies with the Java specifications, and does not require manual modifications on the target program code. The performance of the framework has been evaluated with the Grande Java concurrency benchmark suite, results have shown a geometric mean of 98.9% overhead over all cases, which significantly outperforms full-system simulation techniques.

The use of distributed computation and control is pervasive for a wide range of smart grid research topics. However, recent developments of smart grid co-simulation platforms have not been able to provide effective support for the modeling and simulation of distributed software systems. In particular, cosimulation literatures only focused on the integration of electrical and communication network simulators, and the responsibility for modeling distributed software is often delegated to one of these two simulators. Since these domain specific simulators are not designed for this purpose, such delegation incurs many limitations that prevent convenient, effective, and accurate modeling of software behaviors. To mitigate the problem, this paper presents, to our knowledge, the first co-simulation integration of direct-execution simulators to provide dedicated support for distributed smart grid software. We first present the development of the novel DecompositionJ framework (DEterministic, COncurrent Multi-PrOcessing SImulaTION for Java programs), which is a compiler-based code analyzer and transformer to automatically convert multi-thread Java programs into direct-execution simulators, eliminating the need for manual code or model development. Next, we apply DecompositionJ framework to generate simulators for a popular multi-agent platform JADE. The JADE simulators are then integrated with electromagnetic transient simulator (PSCAD) and packet-level network simulator (OPNET) using standardized co-simulation runtime infrastructure. At last, we conduct a case study on agent-based smart grid restoration using this novel co-simulation platform. Through the analysis of simulation results, it is shown that the proposed direct-execution simulation framework is able to facilitate the understanding, evaluation, and debugging of distributed smart grid software.

Fault-cause identification plays a significant role in transmission line maintenance and fault disposal. With the increasing types of monitoring data, i.e., micrometeorology and geographic information, multiview learning can be used to realize the information fusion for better fault-cause identification. To reduce the redundant information of different types of monitoring data, in this paper, a hierarchical multiview feature selection (HMVFS) method is proposed to address the challenge of combining waveform and contextual fault features. To enhance the discriminant ability of the model, an ε-dragging technique is introduced to enlarge the boundary between different classes. To effectively select the useful feature subset, two regularization terms, namely l2,1-norm and Frobenius norm penalty, are adopted to conduct the hierarchical feature selection for multiview data. Subsequently, an iterative optimization algorithm is developed to solve our proposed method, and its convergence is theoretically proven. Waveform and contextual features are extracted from yield data and used to evaluate the proposed HMVFS. The experimental results demonstrate the effectiveness of the combined used of fault features and reveal the superior performance and application potential of HMVFS.

Research on enhancing power system resilience against extreme events is attracting significant attention and becoming a top global agenda. In this paper, a preventive augmented power dispatch model is proposed to provide a resilient operation. In the proposed model, a new N-1-1 security criterion is proposed to select disruptive N-1-1 contingency cases that might trigger cascading blackouts, and an iterative contingency assessment process based on the line outage distribution factor is proposed to deal with security constraints. In terms of optimization objectives, two objectives related to power flow on the transmission line are considered to reduce the possibility of overload outages. Controllable series compensation devices are also considered in the model to improve the power flow distribution. Case studies conducted on the modified IEEE 30-bus, 118-bus and Polish 2382-bus systems show that the power flow solution of the proposed power dispatch model can avoid some branches from undertaking excessively heavy loads, especially lines forecasted to be affected by extreme events. The results of blackout simulations through a hidden failure cascading outage simulation model show that the average power losses of the proposed model are reduced by around 40% in some cases as compared to the classical economic dispatch model.

The construction and operation of wind turbines have become an important part of the development of smart cities. However, the fault of the main drive chain often causes the outage of wind turbines, which has a serious impact on the normal operation of wind turbines in smart cities. In order to overcome the shortcomings of the commonly used main drive chain fault diagnosis method that only uses a single data source, a fault feature extraction and fault diagnosis approach based on data source fusion is proposed. By fusing two data sources, the supervisory control and data acquisition (SCADA) real-time monitoring system data and the main drive chain vibration monitoring data, the fault features of the main drive chain are jointly extracted, and an intelligent fault diagnosis model for the main drive chain in wind turbine based on data fusion is established. The diagnosis results of actual cases certify that the fault diagnosis model based on the fusion of two data sources is able to locate faults of the main drive chain in the wind turbine accurately and provide solid technical support for the high-efficient operation and maintenance of wind turbines.

This work presents a new coordinated operation (CO) framework for electricity and natural gas networks, considering network congestions and demand response. Credit rank (CR) indicator of coupling units is introduced, and gas consumption constraints information of natural gas fired units (NGFUs) is given. Natural gas network operator (GNO) will deliver this information to an electricity network operator (ENO). A major advantage of this operation framework is that no frequent information interaction between GNO and ENO is needed. The entire framework contains two participants and three optimization problems, namely, GNO optimization sub-problem-A, GNO optimization sub-problem-B, and ENO optimization sub-problem. Decision sequence changed from traditional ENO-GNO-ENO to GNO-ENO-GNO in this novel framework. Second-order cone (SOC) relaxation is applied to ENO optimization sub-problem. The original problem is reformulated as a mixed-integer second-order cone programming (MISOCP) problem. For GNO optimization sub-problem, an improved sequential cone programming (SCP) method is applied based on SOC relaxation and the original sub-problem is converted to MISOCP problem. A benchmark 6-node natural gas system and 6-bus electricity system is used to illustrate the effectiveness of the proposed framework. Considering pipeline congestion, CO, with demand response, can reduce the total cost of an electricity network by 1.19%, as compared to −0.48% using traditional decentralized operation with demand response.

Prevention and mitigation of low probability, high impact events is becoming a priority for power system operators, as natural disasters are hitting critical infrastructures with increased frequency all over the world. Protecting power networks against these events means improving their resilience in planning, operation and restoration phases. This paper introduces a framework based on time-varying interval Markov Chains to assess system’s resilience to catastrophic events. After recognizing the difficulties in accurately defining transition probabilities, due to the presence of data uncertainty, this paper proposes a novel approach based on interval mathematics, which allows representing the elements of the transition matrices by intervals, and computing reliable enclosures of the transient state probabilities. The proposed framework is validated on a case study, which is based on the resilience analysis of a power system in the presence of multiple contemporary faults. The results show how the proposed framework can successfully enclose all the possible outcomes obtained through Monte Carlo simulation. The main advantages are the low computational burden and high scalability achieved.

A probabilistic prediction interval (PI) model based on variational mode decomposition (VMD) and a kernel extreme learning machine using the firefly algorithm (FA-KELM) is presented to tackle the problem of photovoltaic (PV) power for intra-day-ahead prediction. Firstly, considering the non-stationary and nonlinear characteristics of a PV power output sequence, the decomposition of the original PV power output series is carried out using VMD. Secondly, to further improve the prediction accuracy, KELM is established for each decomposed component and the firefly algorithm is introduced to optimize the penalty factor and kernel parameter. Finally, the point predicted value is obtained through the summation of predicted results of each component and then using the nonlinear kernel density estimation to fit it. The cubic spline interpolation algorithm is applied to obtain the shortest confidence interval. Results from practical cases show that this probabilistic prediction interval could achieve higher accuracy as compared with other prediction models.

Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this letter, a new CDC to achieve speedy detection is investigated. This investigation incorporates the analytic hierarchy process (AHP)-based multiple criteria decision analysis (MCDA) to develop feature vectors using a Support Vector Machine. The MCDA facilitates the efficient assignment of appropriate weightings to potential patients, thus scaling down the number of features. Since the new CDC will only adopt the most meaningful features for discrimination between healthy persons versus cardiovascular disease patients, a speedy detection of cardiovascular diseases has been successfully implemented.