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The use of power converters to regulate voltage and power in electrical power systems has been attracting attention due to the increasing number of research and applications on renewable energy. The integration of these switched devices may cause instability and undesired oscillations depending on the operation conditions, such as when they feed constant power loads (CPLs). Therefore, this paper proposes an analysis of the effect of the power regulation in a multi-stage converter system made by buck-buck cascaded converters, in which the first converter operates as a voltage regulator and the second operates as a power regulator. To support that analysis, the region of attraction (ROA) is estimated based on convex optimization with linear matrix inequalities (LMIs) constraints considering the effect of the power variation and the voltage regulator dynamics. These regions are validated through tests in a simulated environment, showing that the trajectories initiated within those regions remain confined in those despite the CPL power variation disturbance. Finally, a case study for the experimental environment is presented. The power variation test is carried out and shows the efficacy of the stability analysis approach ratifying the simulation results. • In this paper, an LMI-based stability analysis for the microgrid operation point is presented considering the CPL effects. • The region of attraction (ROA) of a microgrid with CPL is estimated to ensure the operational stability conditions. • Analysis of the impact of the voltage regulation speed on the stability of the microgrid with a CPL whose dynamics are previously known. [ABSTRACT FROM AUTHOR]

• Challenges that mass amounts of electric vehicles (EVs) impose to power system operations. • Opportunities of intelligently managing the flexibility of smart charging of EVs are discussed. • Hybrid DERMS concept is proposed as a key tool for safe integration and proper management of emerging distribution grids with high amounts of EVs. • A real-life case study of integrating two large-scale EV charging stations into an existing feeder is presented. Global initiatives are actively progressing to integrate large numbers of electric vehicles (EVs) as part of efforts to electrify and decarbonize the transportation sector. This approach plays a crucial role in addressing global greenhouse emissions and pollution problems. However, mass integration of EVs imposes unprecedented challenges to electrical power systems, especially to their distribution grids. These challenges range from planning issues, where the traditional panning procedures are not adequate for integration of EVs and their charging stations, to operational challenges, where a huge number of dispersed EVs can cause peak load increase, voltage violations, congestions, feeder overloads, etc. Hence, despite the significant importance of maximizing the deployment of EVs, their stochastic integration can give rise to critical challenges in electric power systems, thereby impeding their subsequent expansion. Therefore, in order to properly integrate mass amounts of EVs, without causing technical violations in the electrical power system, intelligent software tools are required, to aid distribution system operators and engineers in the transition to a new era of electrified transportation. In this paper, we argue that novel software solutions called Distributed Energy Resource Management Systems (DERMSs) are a key solution for enabling a safe integration of mass amounts of EVs into emerging distribution grids. Specifically, we introduce a novel Hybrid DERMS framework, as an integration of multiple hierarchical levels of DER management solutions, and propose this solution as a suitable instrument for distribution system operators and grid engineers to safely integrate and manage high volumes of EVs within the grid infrastructure. The hypothesis is validated through a real-life use case for integration and management of high amounts of EVs into an existing distribution grid, using an industrial DERMS solution. Specifically, an in-depth analysis of adding two large charging stations, boasting capacities of 5 MW each, has been performed through all stages of integration, from the planning stage, up to using their flexible output to manage technical constraint violations in real time. The influence of these EV charging stations on both the feeder where they are planned to be integrated, as well as on the entire supply substation, have been analyzed in detail. The resultant findings, especially on successfully managing overloads, peak load increases, as well as voltage violations through intelligent use of aggregated EVs through DERMS, have proven to be notably encouraging, further substantiating the paper's hypothesis. [ABSTRACT FROM AUTHOR]

• Compared with the traditional flexible interconnection method, the EDC-DN power supply architecture proposed in this paper does not simply use MTDC to achieve feeder interconnection but first optimizes and adds dynamic reactive power compensation devices based on voltage source converters in the context of a high percentage of DG access to the grid, and then the dynamic reactive power compensation equipment between different feeders is interconnected on the DC side by adding additional lines, thus weakening the voltage fluctuation of the grid when DG is connected while achieving closed-loop flexible interconnection between multiple feeders. This can achieve closed-loop flexible interconnection between multiple feeders while weakening the voltage fluctuation of the grid when DG is connected, provide a flexible active power adjustment path for high proportional DG access to DN, and realize power mutual aid between different feeders and network-level bearing of DG. • For the issue of power supply recovery after EDC-DN fault, a comprehensive analysis was conducted on the impact of different fault locations on the power supply recovery process. The collaborative plan between EDC and DAS was first proposed, clarifying the information interaction content between the distribution network automation central station, feeder sub-station, and EDC sub-station during the power supply recovery process. An EDC-DN two-stage power supply restoration architecture considering EDC continuous regulation, combinatorial optimization of interconnection switch and sectionalizing switch is proposed and designed, which fills the technical gap in the integration of EDC and DAS systems. • This paper demonstrates the real-time dynamics of EDC in the EDC-DN power supply restoration process through simulation. The restoration process includes a fast restoration phase based on EDC continuous regulation of power supply, a non-fault section transfer process based on mechanical switch action, and an economically optimal operation phase using an optimal combination of switches to reconfigure the distribution network topology. The current power supply restoration strategy of SOP focuses on the final restoration state of EDC continuous regulation and switch combination rather than on the whole process of power supply restoration. This paper considers the real-time dynamics of the entire EDC-DN power supply restoration process. It fills the technical gap in the current SOP power supply restoration without considering the intermediate process. The flexible interconnection device (FID) enables multiple distribution feeders in closed-loop mode to support a higher proportion of distributed generation (DG) and large-scale new loads connected to the distribution network (DN). Moreover, the closed-loop operation mode based on FIDs provides an effective technology roadmap for high-quality and highly reliable power users, especially the quick restoration of power supply after the failure of DN. This paper proposes a new supply shape in which the multi-terminal DC links are flexibly embedded in the AC DN (EDC-DN) to realize closed-loop operation, achieving lower investment costs than the existing mode that replaces tie switches with soft open points (SOPs). Meanwhile, a two-stage power supply quick recovery control technology is proposed for the EDC-DN, including the fast power supply recovery stage based on EDC and the optimizing restoration stage based on network reconfiguration. Firstly, the role of EDC in the process of power supply restoration of three different faults on AC feeder is analyzed. Secondly, a detailed power supply recovery process is proposed, which is based on a distribution automation system (DAS) and the real-time control capability of EDC. Thirdly, the power supply restoration second stage is implemented, which is based on network reconfiguration with switch combination and EDC continuous variable optimization. Finally, a 58-node DN with an EDC system was constructed using PSCAD/MTDC simulation software to verify the effectiveness of the two-stage strategy for EDC-DN power restoration. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• In the context of the Deep Convolutional Neural Networks (CNNs), there are developed and evaluated six novel CNN models for short-term prediction of multi-energy vectors of a novel integrated energy system consisting of three different electric, heat and gas networks and a total of 39 interconnected buildings. • The novel prediction CNN models can be classified function of the type and the number of input and output variables, and type of learning (centralized or distributed). • High correlations between the next and the previous 24 hours building power consumptions are envisaged, and higher correlations than the ones calculated with regard to the weather temperature data (°C) or the solar radiance. • The numerical results obtained for the testing datasets and evaluated in terms of Signal-to-Noise Ratio (SNR) and Normalized Root Mean Square Error (NRMSE), show that the single input variable/single output variable CNN models (CNN_1 and CNN_3), which input/output variables are corresponding to each building and each type of energy consumption, predict the best in comparison to the other models. • For the novel integrated energy system comprising the three distinct networks (electric, heat and gas), it is shown that as the input data is more non-linear and scarce, then it becomes more difficult to predict, and this is to be expected also for any other single or integrated energy system. Forecasting power consumptions of integrated electrical, heat and gas network systems is essential in order to operate more efficiently the multi-energy network system. Multi-energy systems are increasingly seen as a key component of future energy systems, and a valuable source of flexibility, which can significantly contribute to a cleaner and more sustainable integrated energy system. Therefore, there is a stringent need for developing novel and performant models for forecasting multi-energy demands of integrated energy systems, which to account for the different types of interacting energy vectors and of the coupling between them. Previous efforts in demand forecasting focused only on electrical power consumptions or, more recently, on the single heat or gas power consumptions. Therefore, in order to address the multi-energy demand forecasting problem, in this paper six novel prediction models based on Convolutional Neural Networks (CNNs) are developed, for either individual or joint prediction of multi-energy power consumptions: the single input/single output variable CNN model with determining the optimum number of epochs (CNN_1), the multiple input/single output variable CNN model (CNN_2), the single input/single output CNN model with training/validation/testing datasets (CNN_3), the joint prediction CNN model (CNN_4), the multiple-building input/output CNN model (CNN_5) and the federated learning CNN model (CNN_6). All six models are applied in a comprehensive manner on a novel integrated electrical, heat and gas network system, which only recently has started to be used for forecasting. The forecast horizon is short-term (i.e. next half an hour) and all the prediction results are evaluated in terms of the Signal to Noise Ratio (SNR) and the Normalized Root Mean Square Error (NRMSE), while the Mean Absolute Percentage Error (MAPE) is used for comparison purposes with other existent results from literature. The numerical results show that the single input/single output variable CNN model with training/validation/testing datasets (CNN_3) is able to equal the performances of the single input/single output variable CNN model with determining the optimum number of epochs (CNN_1), and to outperform the other four prediction models. The prediction accuracy of the multi-energy networks loads is shown to significantly depend on the level of non-linearity and scarcity existent in the input training dataset(s). Furthermore, this extensive multi-model study reveals that the characteristics (i.e. connections between the different networks, correlations between the different energy vectors) of the considered integrated energy system need to be explored when designing the CNNs prediction models. [ABSTRACT FROM AUTHOR]

Highlights • To extract event information by exploiting historical data such as cable asset inventory. • To estimate the failure rate of every individual cables using PHM. • To demonstrate that, although many previous works make different approximations which are designed for non-repairable systems, the results of applying these approaches are misleading for repairable power cables. Abstract In this paper Weibull parametric proportional hazard model (PHM) is used to estimate the failure rate of every individual cable based on its age and a set of explanatory factors. The required information for the proposed method is obtained by exploiting available historical cable inventory and failure data. This data-driven method does not require any additional measurements on the cables, and allows the cables to be ranked for maintenance prioritization and repair actions. Furthermore, the results of reliability analysis of power cables are compared when the cables are considered as repairable or non-repairable components. The paper demonstrates that the methods which estimate the time-to-the-first failure (for non-repairable components) lead to incorrect conclusions about reliability of repairable power cables. The proposed method is used to evaluate the failure rate of each individual Paper Insulated Lead Cover (PILC) underground cables in a distribution grid in the south of Sweden. [ABSTRACT FROM AUTHOR]

This work proposes different prediction models based on multi-block forecast engine for load and price forecast in electricity market. Due to high correlation of load and price signals, the density of this reaction can affect the demand curve and shift it in market. Furthermore, to improve the operation and planning improvement in the power system, an accurate prediction model can play an important role. So, in this paper, a complex prediction approach is presented based on feature selection, and multi-stage forecast engine. The forecast engine is comprised of multi-block neural network (NN) and optimized by an intelligent algorithm to increase the training mechanism and forecasting abilities. Moreover, different models of multi-block forecast engine are presented in this paper to choose the effective model. In other words, different combinations of NN are tested in the same prediction condition to show their abilities. The proposed model is tested over real-world engineering test cases through comparison with other prediction methods. Obtained results demonstrate the validity of the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, it is analyzed how the measurement uncertainty of the overhead power line conductor heights influences the calculation results of electric field strength and magnetic flux density in the vicinity of the line. It is assumed that the conductor heights have been determined by measurements, by using a laser telemeter which has a certain measurement error. The laser telemeter measures the distance to the target object and inclination, and based on these two quantities and the manually entered data concerning the height on which the telemeter is placed it calculates the height of the target object, as well as the horizontal distance to it. Each of these measured quantities (distance and inclination) is measured with a defined error. The error of the height measurement has to be calculated based on the two defined errors. The procedure for calculating these errors is explained in detail in the paper, and the errors referring to typical heights of the overhead power line conductors are determined. If the calculations of electric field strength and magnetic flux density are made for the conductor heights with the established deviation in relation to the real heights, this will reflect on the calculated field results. The influence of this factor on the results of the electric field strength and magnetic flux density calculations is thoroughly analyzed. [ABSTRACT FROM AUTHOR]

Under the thermal storage strategies in present studies, the power generation unit (PGU) in combined cooling, heating, and power (CCHP) system is turned on/off only based on the state of storage tank; or the operation state of PGU is determined by thermal demand or/and electric demand, which has no relationship with the state of storage tank. For these traditional strategies, either energy demands or state of storage tank is considered for the operation of system. This paper proposes a novel thermal storage strategy (TSS2) for CCHP systems, which determines the operational state of the PGU based upon electric demand, thermal demand and the state of thermal storage device. In this paper, the operation principle of TSS2 is described and compared with the traditional strategies. A hospital in Shanghai was used to evaluate and compared the performance of the system operating under different storage strategies. The results show that TSS2 improves the performance of CCHP system more significantly compared with the traditional strategies. TSS2 can make both PGU and capacity of thermal storage tank be fully used by CCHP system to produce more electric and thermal energy to meet demands of building. Compared with system without thermal storage, the minimum and maximum increase rates of annual total cost saving for traditional strategies are 0% and 14.84%, respectively. However, for TSS2, they can reach 3.79% and 20.22%. Similar conclusions could be achieved for CO 2 emission reduction and primary energy saving. The effect of TSS2 on the CCHP system performance is different under different operation strategies. [ABSTRACT FROM AUTHOR]

This paper discusses the losses analysis of low power high-frequency Wireless Power Transfer Systems (WPTSs). Ideal models for efficiency evaluation of WPTS can predict performances that are quite far from the real ones. The model proposed in this paper includes semiconductor devices losses, as well as modulation of duty-cycle and phase for the secondary side rectifier. The global influence of semiconductor devices and control parameters on the overall WPTS performances is numerically determined by solving the herein discussed non linear equations system. Experimental measurements realized on a 2 W@6.78 MHz WPTS demonstrate the validity of this analysis. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose a simple transmission congestion pricing scheme based on tracing principle by considering generator fixed cost, cost for incurring loss and transmission congestion cost. Restructuring has brought about considerable changes by the virtue of which electricity is now a commodity and has converted into deregulated type. Such a competitive market has paved way for innumerable participants. This concept of restructuring has led to overloading of transmission lines. In this paper, power flow tracing has been employed by using suitable optimization algorithm, where the real power generation has been maximized. Congestion in the transmission line has been produced in a new fashion by maximizing the real power demand. The power flow under normal operating condition and congestion is determined and hence the difference in power flow is estimated. Based on the estimated power flow difference, the transmission line congestion cost is computed. Pool model and bilateral model has been considered in simulation study to introduce the concept of deregulation. The proposed method is tested and validated on Modified IEEE 30 bus test system and Indian utility 69 bus test system. [ABSTRACT FROM AUTHOR]

Superconducting fault current limiters (SFCLs) offer an efficient means of limiting fault currents and supporting system reliability. However, they weaken the fault characteristics and reduce the sensitivity of traditional relay protection. The seamless integration of SFCLs with protective relays remains a complex and under-explored area, impeding their widespread industrial adoption. In parallel, current differential protective (CDP) relays are almost the primary protection for all high-voltage electrical equipment and are the cornerstone of global power system security. This paper fills a critical knowledge gap by researching the intricate interaction between resistive superconducting fault current limiters (R-SFCLs) and current differential protective relays. Our investigation commences with a comprehensive mathematical analysis, while researching the influence of R-SFCLs on CDP operation. Subsequently, we conduct a series of comparative experiments using the Matlab Simulink software platform. These tests evaluate the sensitivity, dependability, and security of CDPs in scenarios with and without R-SFCLs. The simulation results not only confirm the accuracy of our analytical framework but also shed light on the multifaceted relationship between R-SFCLs and CDPs. This research contributes to a deeper understanding of how R-SFCLs can be effectively integrated into power systems, offering a roadmap for enhancing grid protection. • Innovative Integration and Mathematical Analysis: This study presents a comprehensive exploration and rigorous mathematical analysis of the challenges in integrating Resistive Superconducting Fault Current Limiters (R-SFCLs) with current differential protective relays (CDPs) in power systems. It revealed the relationship between these components, providing a theoretical foundation for their integration. • Simulink Experiments and Validation of Framework: Extensive comparative experiments conducted using Matlab Simulink offer practical insights into the behavior of R-SFCLs and CDPs under various fault scenarios. The simulation results validate the accuracy of the analytical framework, confirming its effectiveness in addressing key challenges such as fault current limitation and relay sensitivity improvement. • Practical Implications and Contribution to Grid Protection: The study offers practical recommendations for effectively incorporating R-SFCLs into power systems, enhancing grid protection without compromising the reliability and sensitivity of protective relays. It contributes to the field of electrical power and energy systems, providing insights with the potential to influence industrial practices and enhance grid resilience. [ABSTRACT FROM AUTHOR]

The growing penetration of renewable energy resources (RERs) and the ever-increasing implementation of information and communication technologies in smart grids necessitate the utilization of fast and safe operation methods. These schemes should be able to cope with the intermittency of uncertain RERs and fully explore the capabilities of new technologies while considering the vulnerabilities as a result of digitalization. Here, the integrity of the energy and flexibility scheduling and trading heavily depends on the security of the cyber-physical system. Local energy networks (LENs) are introduced to easily incorporate and utilize distributed energy resources, improving electrical power reliability and efficiency. This study develops a cyber-secure energy and flexibility scheduling platform for interconnected local energy networks. Hence, it introduces a novel false data detection and correction method based on the XGBoost machine learning method and investigates the performance of the proposed algorithm in the context of an interconnected local energy network and the corresponding energy and flexibility transactions. In this regard, this paper initially presents a bilevel energy and flexibility scheduling problem for interconnected local energy networks (ILENs) considering cyber-attacks (B L E F S CA ILEN), which tries to satisfy the energy and flexibility requirements of the ILEN guaranteeing a resilient decision against false data injection cyber-attacks. Within the proposed B L E F S CA ILEN a bi-level multi-objective optimization problem is defined where the operator of ILEN optimizes both energy and flexibility trading among LENs in the upper level to maximize its revenue. In the proposed structure, the operator of each LEN is situated at the lower level and tries to minimize the scheduling cost and simultaneously maximize the local flexibility index. Furthermore, in order to evaluate and improve the proposed system's cyber-security, a false data injection attack is constructed using the Gaussian probability distribution function. Herein, the scheduling of ILEN is optimized considering vulnerabilities of electricity consumption data as a consequence of cyber-attack. Finally, the novel XGBoost-assisted deviation bound-based false data detection/correction (XGBFDD) algorithm is suggested to mitigate the above-mentioned cyber-attack and attain near real-world conditions. Simulation results demonstrate that the proposed false data detection/correction strategy exhibits a maximum accuracy of 91.67 precent in mitigating the attack effects. This is because the acquired post-correction results exhibit minor deviations in comparison to the original optimization output that is calculated with the pre-attack consumption data. [ABSTRACT FROM AUTHOR]

• Proposing an new integration method that considers the relationship between simulation time-step and time constants of elementary transfer function blocks (ETFBs), including three integration modes: single-step integration, multiple-step integration, and static approximation. • Giving the implementation of static approximation for ETFBs with small time constants. • Discussing the determination of the threshold values of internal time-step for ETFBs. • Applying the proposed method to an open-source Simulation Toolkit for Electrical Power Systems (STEPS). With the development of renewable energy sources, the scale and complexity of power systems are continuously increasing, resulting in a high requirement for the efficiency of dynamic simulation. Increasing the simulation time-step is a possible measure for the fixed time-step (FTS) method to accelerate the simulation, yet also a potential cause for deteriorating the simulation accuracy. To improve the overall power system dynamic simulation speed without jeopardizing the expected accuracy, this paper proposes a novel Uneven Internal Time-Step Adjustment method based on Trapezoidal integration of elementary transfer function blocks (ETFBs). For ETFBs with different time constants, three different integration modes are defined, including single-step integration, multiple-step integration, and static approximation. Each ETFB can select an appropriate integration mode and adjust its internal time-step, automatically expanding the size of the simulation time-step. Implementation of static approximation for ETFBs with small time constants is given. Discussion on the determination of threshold values of internal time-step is provided, which mainly affect the accuracy of the system. The proposed method is applied to the open-source Simulation Toolkit for Electrical Power Systems (STEPS). Different cases are tested to obtain the maximum simulation time-step that can maintain numerical stability and validate the efficiency and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a methodological framework using a multi model approach to assess power system security. System security is viewed here as a multidimensional systemic property of the entire energy system. The paper shows that the different dimensions of a secure energy system are correlated, and hence their behaviour cannot be explained solely by an understanding of the individual dimensions or by system elements in isolation. The implication of this is that a proper assessment of the security of a power system requires a combination of different techno-economic models. The paper develops a comprehensive multi-model approach for investigating energy security issues within power systems, and applies it to a case study focussing on the Italian power sector. The core research activity involves using an energy systems model of Italy (MONET) to build a dedicated power systems model (PLEXOS_IT) and then undertaking a soft-linking exercise between the two models. The purpose is to use PLEXOS_IT to investigate the system adequacy of the power system results produced by MONET for future possible energy system scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a model for maintenance scheduling (MS) of generators using combined simulated annealing and binary particle swarm optimization (CSABPSO) based stochastic approach. The objective function of this paper is to reduce the loss of load probability (LOLP) for a power system. The hybrid algorithm combines BPSO with simulated annealing behavior. The CSABPSO algorithm takes both of the advantages, good solution quality of SA and fast searching ability of BPSO. As stochastic optimization algorithms are sensitive to their parameters, proper weight update procedure for parameters selection is introduced in this paper to improve solution quality. Weight update operators are introduced in the BPSO in order to obtain the diversified solutions in the search space. In this paper the impacts of transmission constrained MS is considered. Moreover, the CSABPSO based economic dispatch (ED) and load flow have been decomposed as a sub - problem in the maintenance model that results to a more practical transmission constrained maintenance schedule. A case study for IEEE reliability test system (RTS) is used for detailed analysis and further results are presented for roy billinton test system (RBTS) which shows the proposed algorithm can accomplish a significant levelization in the reliability indices over the planning horizon and demonstrates the usefulness of the proposed approach. The simulation results obtained by proposed algorithm indicate transmission line constraint influence on establishment of optimal MS. The CSABPSO could have higher efficiency, better quality than the other compared algorithms. [ABSTRACT FROM AUTHOR]

Microgrids comprising multiple distributed resources are being increasingly considered for integration into electricity networks. Considering the multiplicity of the distributed resources in a single location and distributed nature of such single entities, it is impractical to represent them as detailed models in power system simulations. Hence, it will be attractive for electricity utilities to employ simplified models of microgrids in dynamic studies. This paper proposes a method for representing grid-tied microgrids as simplified generator or load units in such studies. For this purpose, the paper takes an approach in investigating the applicability of modal analysis as a tool for model equivalencing of grid-tied microgrids with inverter and non-inverter interfaced distributed generators. Validity of the reduced order dynamic equivalents is tested under different operating conditions: power export and import modes, with different load types and fault conditions. Scalability of the model equivalencing approach is demonstrated using a case study developed based on the IEEE 13-Bus test feeder. Furthermore, the model equivalencing is applied on microgrids in a multi-microgrid environment to validate the methodology. The dynamic model equivalent of the microgrid can be represented as a current source at the point of common coupling while retaining all imperative dynamic characteristics. The proposed methodology is also applicable to distribution network clusters comprising distributed generators. [ABSTRACT FROM AUTHOR]

This paper describes an approach using an adaptive neuro-fuzzy inference system (ANFIS) for the assessment of online critical clearing time (CCT). The ANFIS can integrate neural networks and fuzzy logic principles, and has a potential to combine the advantages of both in a single framework. In this paper, the ANFIS is applied for the prediction of CCT by varying load levels and fault locations in buses and transmission lines. The IEEE 39-bus system and 9-bus western system coordinating council are tested and implemented in this study. All machines of the IEEE 39-bus system are considered as the classical model without considering any generator’s exciters. While three machines in the 9-bus western system coordinating council are considered as detailed models, forth-order differential equation is described for all machines by considering the excitation system controller. CCT values obtained by the time domain simulation method using step-by-step calculation are used as the benchmark. The power world version 17 is used for transient simulation, and the ANFIS is implemented using MATLAB version 2014B. The results obtained from the ANFIS approach are quite satisfied with high accurate solutions and much lower computation time. Finally, the graphical user interface in MATLAB is applied for the online CCT estimation of two test power systems by using appropriate ANFIS models obtained from simulations. [ABSTRACT FROM AUTHOR]

This paper introduces an approach based on stochastic data envelopment analysis (SDEA) for performance assessment of electricity distribution units. A new approach is applied for assessment of Iranian distribution units from 2001 to 2011 in this paper. There are usually incomplete and stochastic data or lack of data with respect to electricity distribution companies. Due to lack of information about some parameters, theory of probability is imported to the model. Different Iranian distribution units are considered as decision making units (DMUs). Network length, transport capacity and the number of employees are chosen as inputs while number of customers and total electricity sales are chosen as stochastic outputs. Then, the best electricity distributions units are selected with respect to efficiency scores in stochastic environment. Also, SDEA model is performed for each input, separately to identify the most important input indicators by comparing the results of associated efficiencies with SDEA model. The empirical results show that network length is the most important and influential input factor in this particular case study. To the best of our knowledge this is the first paper that examines stochastic outputs for assessment of electricity distribution units by SDEA in Iran. [ABSTRACT FROM AUTHOR]

Distribution network expansion planning (DNEP) is one of the most important tools to deal with the demand growth in a system. DNEP is usually carried out through reinforcement or installation of new components. In this paper, a new and combined methodology is used to consider several practical aspects in DNEP such as uncertainty, distributed generation (DG), load growth, electricity market and multi stage dynamic expansion are included in the planning. So that DNEP is addressed in the presence of distributed generation (DG), considering load and price uncertainties under electricity market environment. The proposed planning aims at minimizing investment and operational costs simultaneously. Since DNEP in coordination with DG planning leads to reduce planning cost; therefore, the coordinated DNEP and DG planning are presented in this paper. The proposed planning is implemented by the particle swarm optimization (PSO) technique. Besides, the uncertainties are modeled as the probability distribution function (PDF) and Monte-Carlo simulation (MCS) is used to insert the uncertainties into the programming. The proposed planning is carried out based on the 9-bus as well as Kianpars–Ahvaz test systems (Kianpars–Ahvaz is a practical network in Ahvaz province, Iran). The simulation results demonstrate the ability and effectiveness of the proposed planning to deal with uncertainties under electricity market environment. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all current techniques rely on personnel experience more than analytical formulation. As a result, the current techniques do not necessarily lead to the same conclusion for the same oil sample. A significant number of DGA results fall outside the proposed codes of the ratio-based interpretation techniques and cannot be diagnosed using these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach that aids in standardizing DGA interpretation and identifies transformer critical ranking based on DGA data. The approach relies on incorporating all traditional DGA interpretation techniques (Roger, Doerenburg, IEC, key gas and Duval triangle methods) into one expert model. In this context, DGA results of 338 oil samples of pre-known fault conditions that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used first to analyze the DGA results to evaluate the consistency and accuracy of each method in identifying various faults. Results of this analysis were then used to develop the proposed fuzzy logic model. The model is validated using another set of DGA data that were collected form previously published papers. [ABSTRACT FROM AUTHOR]

Demand Response (DR) programs have been implemented in many competitive electricity markets to prevent price spikes and power systems unreliability. Mathematical modeling of these programs helps regulators to evaluate the impact of price responsive loads on market conditions. In this paper, several nonlinear economic models of price responsive loads are derived based on price elasticity of demand and customer benefit function. The main objectives of the paper include extracting different mathematical models for Time of Use (TOU) programs, and comparing these models to find out which model shows more conservative and which one shows non-conservative results compared with the initial load curve. This could be used by ISOs or DR programs developers as a guideline to use conservative models to have lower error in load profile characteristics estimation, such as variation in peak load or amount of energy consumptions. In order to evaluate the performance of the proposed nonlinear DR models, numerical studies are conducted on the load curve of different markets. Results obtained by using different models are presented and compared considering different scenarios for price, elasticity and potentiality of DR programs implementation. Characteristics of both linear and nonlinear economic models of price responsive loads have been evaluated. [ABSTRACT FROM AUTHOR]

• Improvement of grid-connected wave power plants and wave energy converters. • A new MMC-based strategy for offshore HVDC grid-side converter. • Fast control of grid-side HVDC-MMC converters. Modular multilevel converter (MMC) is a type of electronic converter currently used as voltage source in real HVDC transmission systems. This paper presents, as an alternative, a control system designed to operate an MMC as a nonlinear vector current source (NLVCS). From the design point of view, it features advantages such as constant switching frequency, robustness against grid parameters changes and that the algorithm is entirely designed from space vectors. From the application point of view, the main advantages are its fast dynamic response combined with its capability to handle large amounts of electric power. Together, these two features allow the MMC to deal with highly variable renewable energies. To show the advantages of the proposed control system, it has been chosen a very demanding application: wave energy. In this type of applications, the random nature of the sea produces large and fast variations of the generated power, that enters the DC link and that the grid side converter has to handle very fast, injecting it into the grid in order to maintain the DC voltage constant. The capability of the NLVCS for MMC to handle the energy generated has been tested in two different wave energy applications. Firstly, on a single wave energy converter (WEC), a point absorber. This case is characterized by smaller and faster power changes. Secondly, on a wave farm made up of 200 WECs connected to the grid through an MMC. This case is characterized by much larger but smoother power changes. Wave farms, WECs and MMC are large and sophisticated facilities or devices that are almost impossible to gather in the same laboratory. Consequently, the features of the new control system for MMC and the advantages that provide in the connection of wave energy power plants and point absorbers to the grid has been simulated using detailed models. The results demonstrated the capability of the MMC operating as NLVCS to deal with the fast and random power changes present in renewable energy sources. [ABSTRACT FROM AUTHOR]

• A fully decentralized P2P power market with AC modelled network constraints is proposed. • Network operational security is guaranteed under proposed P2P market mechanism. • User privacy is protected as nodal voltage variables are the only public variables shared among participants. • Extensive numerical simulations are conducted to validate proposed models and solving algorithm. The rapid growth of Distributed Energy Resources (DERs) and demand response in modern power grids calls for decentralized electricity trading mechanisms to effectively induce and coordinate responses from massive DERs and load entities. This paper proposes a fully decentralized peer-to-peer (P2P) distribution electricity trading mechanism backboned by a modified version of Alternating Direction Method of Multipliers (ADMM). Comparing with the state-of-the-art P2P electricity markets, the proposed electricity trading mechanism comprehensively incorporates power network constraints formulated by an alternating current power flow model. In the proposed method, nodal voltages are designed as the only public variables shared among participants. Privacy of each participant is thus sufficiently preserved without exposing the bid data to the others. Meanwhile, the proposed P2P method can also ensure the global optimality of social welfare in electricity transactions. Comprehensive case studies are carried out using the IEEE 33-node distribution system to demonstrate the feasibility and efficiency of proposed P2P electricity trading mechanism. The results indicate that the proposed model can efficiently integrates network constraints in the market clearing process while maintaining computational performance, ensuring stable convergence regardless of distribution system congestion. The proposed P2P electricity market can achieve global maximization of social welfare, with a relative difference of no more than 5.0% compared to the conventional centralized market. Even in worst-case scenarios, the relative difference remains within 7.32%. Meanwhile, the method accurately accounts for network losses, guaranteeing the feasibility of trading plans within the physical network. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• A novel robust pitch controller to enhance the dynamic performance in WT output power/speed in the high wind speed domain. • The proposed controller is independent of the exact model parameters of WTs, easy to implement in practice. • Improving access to the rotor acceleration signal with minimizing system noise amplification. • Giving a rigorous stability demonstration for the closed-loop system. • Developing a refined HIL platform for wind energy conversion systems to validate the approach's effectiveness. The wind energy conversion system is a complex dynamic system with strong nonlinearity, perturbation, and uncertainty. In this paper, a novel optimal pitch control strategy is proposed to improve the ability to stabilize the captured wind energy, so as to realize robust operation in the high-wind-speed condition for wind turbines (WT) subject to unmodeled system disturbances and uncertainty. This control strategy combines three critical techniques: optimal pitch control law determination, disturbance compensation, and acceleration estimation. Among them, the optimal pitch control law is determined by utilizing the Hamilton-Jacobi-Bellman equation, regarding the minimization of the quadratic performance index. The total system uncertain disturbance is compensated by a designed extended state observer, making the pitch control approach almost independent of precise WT model parameters, markedly reinforcing the control system robustness. To prevent system noise amplification from direct differential means in acquiring rotor acceleration, we employ a fast-converging acceleration estimator to obtain the rotor acceleration feedback signal required by the pitch control law. System stability is proved using the Lyapunov theory. The comparative results on the developed hardware-in-the-loop test platform validate the effectiveness of the proposed solution, demonstrating that it provides superior dynamic performance in WT electrical power and speed with reduced fluctuation, overshoot, and regulation time while maintaining robustness against disturbances. [ABSTRACT FROM AUTHOR]

The renewable energy sources based distributed generation is increasingly penetrating into conventional distribution network. Aside from the technical and economical benefits, distributed generation may incapacitate the sensitivity of backup protection devices , which may face partial blindness in grid-connected, even full blindness in islanded mode of operation and during high impedance faults. This paper proposes a novel stratagem to completely plummet the blindness of backup relay by inducing the artificial fault current. A blindness severity index is developed to induce artificial fault current in a controlled manner according to blindness severity. Two methods are proposed. First, the OCR vulnerable to blindness is provided with an auxiliary current transformer, whose output is tuned in conjunction with blindness severity index and merged with main current transformer's output to modulate its secondary current responsible for sensitivity. Second, the microprocessor based relay is used and the time current characteristics is modified by augmenting a new variable named as artificial fault current factor to scale the fault current. As, the current is increased by the TCC modification and no external equipment is added here, so named as AFC. To assure that the AFC is induced only in fault event and to prevent mal-functioning of OCR during normal operation, a simple logic is designed using AND gates to fend off trip signal to circuit breaker. The proposed protection scheme is evaluated on typical distribution feeder in Tianjin, China and IEEE-34 standard test benchmark and results show that the blindness of backup PDs is dwindled in a proficient manner. • Renewable energy sources help to improve electrical power quality, continuity and economic factors. • Deep analysis of renewable energy sources impact on power system protection. • The backup protection lost coordination with primary protection due to fault current variation by renewable energy sources. • Compensation of fault current in backup protection through artificially increasing fault current it in and restoring coordination with backup protection. [ABSTRACT FROM AUTHOR]

• Classification of CPPS testbeds and its design processes are illustrated. • 72 types of CPPS testbeds for sustainable cybersecurity research focus is covered. • Sustainable cybersecurity assessment of CPPS testbeds are proposed. Cyber-physical power systems (CPPS) are interconnected architectures that interact with the physical power system environment by utilizing communication, control, and computing resources. Enterprise, industries, and critical infrastructure rely on CPPS. As a result of their critical importance, they are prime targets for cyberattacks aimed at disrupting their operations. CPPS are mission-critical, attacks on them can have disastrous consequences. CPPS security can be improved by utilizing testbed capabilities to replicate power system operations, discover threats, develop sustainable cybersecurity solutions, and evaluate grid operation in physical fault-induced or cyberattack scenarios. This Part-II paper reviews the CPPS testbeds in the view of the testbed type, targeted research area, CPPS domain, and communication infrastructure with the fusion of physical and cyber systems. Furthermore, this review gives an outline, framework, and application-based assessment of various industry and institutional testbeds. The design process of CPPS testbeds is reviewed first in this paper(Part-II) , followed by description of the classification of CPPS testbeds for cyberattacks and sustainable cybersecurity analysis in CPPS. A brief overview of CPPS testbeds in various academic institutions, R&D laboratories, and industries are provided and each testbed is evaluated using the eleven proposed assessment criteria. Finally, the current issues and future research directions for CPPS testbeds to develop a secure and sustainable electric power grid are discussed. The Part-I paper reviewed the major research areas in CPPS, the importance of cyberattack testbeds, and long-term cybersecurity analysis in CPPS, as well as the NIST framework for CPS, CPPS domains and research areas. [ABSTRACT FROM AUTHOR]

• A comprehensive review of consolidated works on CPPS is presented. • History of cyberattacks in energy sector and need for CPPS testbeds are provided. • NIST framework for deploying CPS in CPPSs are presented with neat illustrations. The integration of advanced Information and Communication Technologies (ICTs) in the conventional electric power grid is evolving into a Cyber-Physical Power System (CPPS). The seamless integration of control, communication, and computing operations allow CPPS to be fully monitored and controlled. Threats, vulnerabilities, and catastrophic attacks will intrude as CPPS monitoring, protection, and control functions advance. For the CPPS to operate safely, securely, and efficiently, sustainable cybersecurity solutions must be developed, and the power grid's reliability and resilience must be maintained even when exposed to unfavorable network conditions. While more distributed and renewable energy sources are connected to the grid, cybersecurity helps to ensure the supply of electricity is sustainable and of high quality. The validation of such sustainable cybersecurity analysis goes beyond the traditional power grid network analysis, which means that the test should integrate the physical and cyber system behavior and respond to the network attacks. To analyze the cybersecurity and cyberattacks in the practical CPPS, comprehensive and realistic CPPS testbeds are needed. The heterogeneous nature of the CPPS concept urges multidisciplinary testbeds with various functions and capabilities to evaluate the new cyber-attacks, vulnerabilities, and threats in CPPS. Using this CPPS testbed framework, the different types of cyber-attack can be detected, and detection algorithm can be evaluated, and helps to enhance the development of sustainable cybersecurity defenses for realistic CPPS environment with the increasingly dense integration of distributed energy resources (DERs). This Part-I paper review the CPPS testbeds in the view of the physical power system layer, sensing layer, communication layer, control layer, application layer, test platforms, and research goals with the fusion of cyber and physical systems. In addition, this review presented an overview, structure, and application-based evaluation of existing testbeds from the industry and institutions. The various research areas in CPPS are reviewed first to show the research trends on different aspects of CPPS which have gained significant attention in the past decade. The necessity of testbeds for cyberattacks and sustainable cybersecurity analysis in CPPS are then described. Finally, the NIST framework for CPS, CPPS domains, and research areas are presented. Further the Part-II paper will review the classification, overview, and assessment of CPPS testbeds. [ABSTRACT FROM AUTHOR]

• A new combination forecasting model is proposed to improve the ultra-short-term forecast accuracy of traction load. • The design concept of the combined model proved to be reasonable. • This model can get the changing trend of the predicted load in different time scales. The ultra-short-term prediction of traction load is a key aspect of electric power control in electrified railroads. In order to provide more accurate traction load prediction data for the control process, this paper designs a combined prediction method combining Discrete Wavelet Transform(DWT), Temporal Convolutional Network (TCN) and Support Vector Regression based on Particle Swarm Optimization(PSO_SVR) for the characteristics of traction load with strong random fluctuation, large jump amplitude and frequent no-load. This method will use DWT model to decompose the traction load with strong random fluctuation and difficult to predict into sub-series with single fluctuation frequency and easy to predict; then, according to the frequency difference of different series, TCN model is selected to predict medium and low frequency series. The SVR model is selected to predict the high frequency sequences. Finally, the prediction results of each sub-sequence are summed to obtain the final prediction. The combined prediction model designed in this paper is used to predict the traction load of traction power supply stations. The experimental results show that the prediction method proposed in this paper has higher prediction accuracy than the existing prediction methods in the current prediction field. [ABSTRACT FROM AUTHOR]

• State-of-the-art of model predictive control (MPC) of microgrids is reviewed. • The first paper in a two-part series on MPC of ac and dc microgrids. • Advantages of MPC for power quality, inertia emulation, transportation electrification. • Experimental comparison of MPC versus linear control for microgrid primary level. • Emerging trends of MPC for autonomous and networked microgrids. • Demonstration of new adaptive MPC of grid-forming converters. The smart-grid has requirements of flexible automation, efficiency, reliability, resiliency and scalability. These are necessitated by the increasing penetration of power-electronics converters that interface distributed renewable energy systems which energize the fast-evolving electric power network. Microgrids (MGs) have been identified as modular grids with the potential to effectively satisfy these characteristics when enhanced with advanced control capabilities. Model predictive control (MPC) facilitates the multivariable control of power electronic systems while accommodating physical constraints without the necessity for a cascaded structure. These features result in fast control dynamic response and good performance for systems involving non-linearities. This paper is a survey of the recent advances in MPC-based converters in MGs. Schemes for the primary control of MG parameters are presented. We also present opportunities for the MPC converter control of autonomous MGs (power quality and inertia enhancement), and transportation electrification. Finally, we demonstrate MPC's capabilities through hardware-in-the-loop (HiL) results for a proposed adaptive MPC scheme for grid-forming converters. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new framework for the valuation of small signal stability as an ancillary service through Power System Stabilizers (PSSs). PSSs can improve small signal stability and simultaneously reduce the generation costs in a competitive market. This saving could be treated as a source to compensate the service provided by PSSs. This paper proposes an allocation approach to compensate the PSSs, proportional to their effectiveness in overall system small signal behavior. NSGA-II approach is used to solve the resulting multi objective optimization problem. A test system is used to represent the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: Several electric powers were defined during the last century with the intention of being useful in electrical power systems, sometimes without considering other physical properties. For this reason, a few of these powers may seem limited to specific purposes and distant from the general physical theory of power. This paper investigates which of these electrical powers satisfy the most important property of physical power, which is the property derived from the energy conservation law. The paper also shows the usefulness this property confers to the electric powers that possess it and how it is possible to give those powers broader definitions. Particularly, the power definitions for single and three-phase systems can be extended to any multi-terminal network providing those powers satisfy the energy conservation law. On the contrary, this generalization is not possible for those powers that do not meet the conservation law, and, what is more, some general concepts become confusing when applied to these powers. [Copyright &y& Elsevier]

Abstract: The long-term hydrothermal scheduling is one of the most important problems that must be solved in power systems area. This problem aims to obtain an optimal policy, under water resources uncertainty, for the hydro and thermal plants over a multi-annual planning horizon. The purpose of this paper is twofold. Firstly, we present a computational model which is in development for solving the long-term hydrothermal scheduling of the Brazilian hydrothermal power system. Secondly, it is described some modeling issues which may cause problems to achieve an optimal policy and are currently included in the official long-term optimization model of the Brazilian regulatory framework. This paper presents some solutions, for those problems, that were implemented on our model. We evaluate the solutions related to the original modeling and the modified one, in order to show evidences of the problems and that the solutions proposed are a good start point. To accomplish this evaluation, we consider in the model the whole Brazilian power system, with a reduced planning horizon. [Copyright &y& Elsevier]

Abstract: The transient behavior of many large scale systems is heavily influenced by perturbations and in particular, usually, due to changes in operating points. Load Frequency Control (LFC) in power systems is very important in order to supply reliable electric power with good quality. The goal of LFC is to maintain zero steady state errors for frequency deviations in each control area. Several control strategies, such as classical control, optimal control, suboptimal control, adaptive control, variable structure control etc. have been employed in the past to explore an optimum controller for LFC. In practice LFC systems use simple Proportional Integral (PI) controllers, Proportional Integral Derivative (PID) controllers etc. This paper presents a method based on Type-2 Fuzzy System (T2FS) for Load frequency control (LFC) of power systems including Superconducting magnetic energy storage (SMES) units of a two-area interconnected reheat thermal system. This paper proposes a Type-2 (T2) fuzzy approach for load frequency control of two-area interconnected reheat thermal power system with the consideration of Generation Rate Constraint (GRC), Boiler Dynamics (BD) and SMES. The salient advantage of this controller is its high insensitivity to large load changes and plant parameter variations even in the presence of non-linearities. The proposed method is tested on a two-area power system to illustrate its robust performance with various area load changes. The performance of the Type-2 (T2) fuzzy controller is compared with optimal PID (Khamsum’s optimal PID) controller and Fuzzy PI Controller (Type-1 Fuzzy) controller in the presence of GRC, BD and SMES. Simulation results confirm the high robustness of the proposed SMES controller with small power capacity against various disturbances and system uncertainties in comparison with SMES in the previous research. [Copyright &y& Elsevier]

Abstract: This paper focuses on the application of a novel heuristic population-based seeker optimization algorithm (SOA) for the optimization of the different tunable parameters of an autonomous power system model. In the SOA, the act of human searching capability and understanding are exploited for the purpose of optimization. In this algorithm, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple Fuzzy rule. The SOA yields off-line, nominal system parameters. This paper also presents the design and the performance analysis of a Sugeno Fuzzy logic (SFL) controller for the studied model which tracks the deviation of terminal voltage in real-time for any sort of input perturbation. Genetic algorithm is taken for the sake of comparison. Time-domain simulation of the investigated power system model reveals that the proposed SOA–SFL yields on-line, off-nominal controller parameters, resulting in on-line terminal voltage response. [Copyright &y& Elsevier]

Abstract: In this paper a novel supplementary subsynchronous damping controller (SSDC) is proposed for the STATic synchronous COMpensator (STATCOM) which is capable of damping out subsynchronous oscillations in power system with series compensated transmission lines. An auxiliary subsynchronous damping controller (SSDC) for a STATCOM using the generator rotor speed deviation signal as the stabilizing signal has been designed to damp subsynchronous oscillations. Eigenvalue analysis and transient simulations of detailed nonlinear system are considered to investigate the performance of the controller. Robustness of the controller has been analyzed by facing the system with disturbances leading to significant changes in generator operating point. This paper deals with a cascaded multilevel converter model, which is a 48-pulse (three levels) source converter. The IEEE Second Benchmark (SBM) model is considered for the analysis and the complete digital simulation of the STATCOM within the power system is performed in the MATLAB/Simulink environment. [Copyright &y& Elsevier]

Abstract: Power quality (PQ) interest has increasingly evolved over the past decade. The paper surveys the application of signal processing, intelligent techniques and optimization techniques in PQ analysis. This paper carries out a comprehensive review of articles that involves a comprehensive study of signal processing techniques used for PQ analysis. Within this context intelligent techniques such as fuzzy logic, neural network and genetic algorithm as well as their fusion are reviewed. Tabular presentation (i.e. highlighting the important techniques) has also been provided for comprehensive study. Although this review cannot be collectively exhaustive, it may be considered as a valuable guide for researchers who are interested in the domain of PQ and wish to explore the opportunities offered by these techniques for further improvement in the field of PQ. [Copyright &y& Elsevier]

Abstract: This paper presents the equivalent circuit and the parameters of a Tesla transformer. The resonance frequency to generate a high test voltage in the air-cored transformer is determined. An experimental Tesla transformer system is designed by using a few simple tools such as a Tesla coil, function generator and ultra-fast semiconductor switch. By using high frequency DC chopper, a resonance voltage induced between the primary and secondary coils. Result is a power supply that generates test voltages in the level of 200.000–250.000V. The paper presents the input/output relations of the Tesla transformer and properties of semiconductor controlled driver system. [Copyright &y& Elsevier]

Abstract: This paper presents an effective fault location technique for transmission grids based on phasor measurement units (PMUs). High accuracy in fault location is achieved by using both the fault observability with PMUs placement and an accurate distributed parameter line model for the transmission lines. The work performed in this paper is provided with high performance which is simplicity, sensitivity, stability and featuring robustness to the uncertainties in fault inception angle, load current and fault path resistance, and the accuracy and reliability of the calculation for the technique is very high. This paper illustrates the characteristics of the proposed algorithm and evaluates its performance by using transient fault data obtained from PSCAD/EMTDC simulations. [Copyright &y& Elsevier]

Abstract: The increasing need to improve security monitoring of networks by system operators is the main motivation of the work described in this paper. The paper provides an algorithm for the on-line probabilistic transient stability assessment of existing or forecasted operating conditions. The proposed algorithm is an analytical method making use of two types of information: (i) transient stability assessment tool, and (ii) probabilistic factors. The corrected transient energy margin (CTEM), which is a hybrid method, is used to on-line transient stability assessment. The probabilistic factors of the conditional probability theorem were included in our method. Hence, our algorithm calculates the probability of the transient instability through using the CTEM and probabilistic factors in the transient stability in an on-line manner for exciting or forecasted operation condition for short term operation condition (e.g., next 1h period). The implementation of the proposed method to New England 39-bus test system showed the ability and good performance of the method for the probabilistic assessment of the transient stability. Also, the method provides the system operator with a profound insight into the system stability conditions. The results retrieved again with changed operation conditions; therefore, the system operator can sometimes improve the system stability condition through making little changes in the power output of generators. [Copyright &y& Elsevier]

Abstract: A decision support tool helping the transmission system operator (TSO) dispatchers to minimize the costs for the load–frequency control is proposed in this paper. Dispatch of tertiary control reserves is done primarily with an aim to ensure secure operation of the transmission system, while the costs associated with utilization of the control reserves are traditionally of less importance. The mixed-integer linear programming based optimization tool proposed in this paper suggests such activation of tertiary control reserves which minimizes the costs for the control reserves utilization and ensures secure operation of the transmission system at the same time. Through a case study comparing the control based on the decision support tool with historical control performed manually by the TSO dispatchers, it is demonstrated that the costs for the load–frequency control could be reduced. [Copyright &y& Elsevier]

Abstract: This paper aims to select the optimal location and setting parameters of SVC (Static Var Compensator) and TCSC (Thyristor Controlled Series Compensator) controllers using PSO (Particle Swarm Optimization) to mitigate small signal oscillations in a multimachine power system. Though Power System Stabilizers (PSSs) associated with generators are mandatory requirements for damping of oscillations in the power system, its performance still gets affected by changes in network configurations, load variations, etc. Hence installations of FACTS devices have been suggested in this paper to achieve appreciable damping of system oscillations. However the performance of FACTS devices highly depends upon its parameters and suitable location in the power network. In this paper the PSO based technique is used to investigate this problem in order to improve the small signal stability. An attempt has also been made to compare the performance of the TCSC controller with SVC in mitigating the small signal stability problem. To show the validity of the proposed techniques, simulations are carried out in a multimachine system for two common contingencies, e.g., load increase and transmission line outage. The results of small signal stability analysis have been represented employing eigenvalue as well as time domain response. It has been observed that the TCSC controller is more effective than SVC even during higher loading in mitigating the small signal stability problem. [Copyright &y& Elsevier]

Abstract: This paper presents a meshed algorithm for optimal reconfiguration of distribution systems. In the reconfiguration problem, the final objective is to reach a radial network that optimizes some function like the network losses. Many algorithms start from a radial network where a switch closure is complemented by opening of another switch to ensure a radial network. These radial algorithms have an inherent inconvenient, that is, the final solution depends on the initial radial network selected. Other group of algorithms initially represent the distribution network as a meshed network and then open switches until a radial system is obtained. In this paper, to avoid the above aforementioned inconvenient of radial algorithms, a meshed algorithm is used. Furthermore, taking into account that breakpoint nodes are defined as the nodes where the branch power flows converge, the contribution of this paper is to provide a method for solving the problem when multiple loops are considered using an approach based on the breakpoint nodes, since it is complicated to associate each breakpoint node with its corresponding loop. On the other hand, one of the drawbacks of the reconfiguration problem is the need to solve a great number of power flow computations for calculating the losses in each stage of the algorithm. The algorithm proposed has the property that reduces the number of power flows. By this way, the execution time of the algorithm is improved. Besides, it is not necessary to check the network connectivity at each step of the procedure. To prove the effectiveness of the proposed algorithm several test systems have been used, achieving good results. [Copyright &y& Elsevier]

Highlights • Frequency emergency control system classification and control. • Wide area frequency monitoring, protection and control. • Growing a decision tree to classify the emergency frequency contingencies. • Heat ventilation air conditioning system application in frequency regulation. Abstract Without a balanced compensation power for connected load demands, the power system frequency may be subjected to a severe drop during a massive loss of generation units. The emergency frequency control system traditionally depends on under-voltage and under-frequency load shedding techniques of power system feeders in cascading steps to control frequency during an emergency. As a result, the power systems suffer from a series of internal and external disturbances, leading to various degrees of blackouts due to the cascading failure effect, which may occur due to the nonlinear dynamic response of frequency at different locations. Smart grids have become a very important trend nowadays. Traditional electric power grids are developed to be smarter, especially with control and protection issues underpinned by advanced measurement and communication technologies. This paper presents a new controlling action to limit the probabilities of disconnecting loaded feeders from power grids due to maloperation of under-frequency load shedding (UFLS) relays. The suggested technique is based on a decision tree algorithm to take an accurate decision for controlling thermostatically controllable loads (TCL). A comparison among the suggested technique, UFLS, and centralised frequency control of TCL is conducted. The proposed technique satisfies a high degree of power system stability, lower cost, and faster response. Moreover, it prevents the operation of the first stage of UFLS relays. [ABSTRACT FROM AUTHOR]

Highlights • A multi-stage robust optimization-based formulation is proposed to model the TCUC problem with uncertainty. • Non-anticipativity of the ED decisions and multi-stage robustness of the UC decisions are guaranteed. • A decomposition framework with implicit decision rules is proposed and the formulation scale is small. • Subproblems in the decomposition framework have a time-decoupled structure and are easy to solve. • A numerical example is given to clarify the difference between two-stage robustness and multi-stage robustness. Abstract With the integration of large-scale renewable energy sources to power systems, many optimization methods have been applied to solve the stochastic/uncertain transmission-constrained unit commitment problem. Among all methods, two-stage and multi-stage robust optimization-based methods are the most widely adopted ones. In the two-stage methods, non-anticipativity of economic dispatch (ED) decisions are not considered. While in multi-stage methods, explicit decision rules (like affine decision rules) are adopted to guarantee non-anticipativity of ED decisions, the computational burden can be heavy and the optimality of the solution is affected. In this paper, a multi-stage robust TCUC formulation with implicit decision rules is proposed, as well as a decomposition framework to solve it. The solutions are proved to be multi-stage robust and non-anticipativity of ED decisions is guaranteed. Meanwhile, a computationally efficient time-decoupled solution method for the feasibility check subproblems is also proposed, such that the method is suitable for large-scale TCUC problems with uncertain power injections. Numerical tests are conducted on IEEE 118-bus system and Polish 2383-bus system. The proposed method outperforms the multi-stage robust method with explicit decision rules in solution's economic performances, and is more computationally efficient than the all-scenario-feasible method. The effectiveness of time-decoupled solution method is also verified. [ABSTRACT FROM AUTHOR]

Highlights • Reliable PLL method to cope with grid unbalances. • Real time estimation of voltage symmetrical components and higher harmonics content. • Solution complexity can be tuned to the expected higher harmonics order. Abstract In this paper, a novel PLL technique based on Multi-Resonant disturbance Observer (MRO) is investigated. The proposed technique can achieve grid synchronization under distorted voltages and simultaneously quantify grid unbalances, including voltage sags and higher harmonics. Real-time quantification of grid condition is very important in estimating the overall power quality, which in turn has a drastic effect on power regulation. The proposed technique is easily applicable and expandable to any desired number of higher harmonics. In addition, we provide an in-depth analysis of the connection between original grid unbalances in the stationary reference frame and its image under dq transform. Finally, we propose a methodology capable of real-time estimation of positive and negative harmonic sequences based on the observed properties of fundamental and higher harmonics. [ABSTRACT FROM AUTHOR]

Highlights • Two linear models of VS incorporation with LWOPF are developed and solved using SLP. • The difference between the two models is based on the way the VCI is utilized. • In model #1, VCIs are used as a set of VS constraints and as an objective function in model #2. • Models are tested on several IEEE benchmark systems. • Results show the effectiveness of the models in attaining a higher VS at lower cost. Abstract Ensuring the voltage stability of power systems is one of the main goals of operators and researchers. The incorporation of voltage stability with optimal power flow (OPF) has received a great deal of attention in the past few years. The line-wise power flow method is shown to provide a faster solution, up to twice the speed of bus-wise power balance equations, and has a better solution space due to the use of square of voltage magnitudes. Further, the line voltage collapse index (VCI) developed using the set of line-wise power balance equations is shown to be an accurate voltage stability assessment indicator in the literature. In this paper, two novel linear models of voltage stability incorporation with line-wise optimal power flow are proposed based on the set of line-wise power balance equations and its line voltage collapse index. The difference between the two models is based on the way the VCI is utilized. In the first model, VCIs are employed as a set of voltage stability constraints while in the second model VCIs form the objective function. The two models are solved using successive linear programming (SLP) technique. The study test results on several IEEE benchmark systems demonstrate the effectiveness of the proposed two models in attaining a higher voltage stable system at lower cost. [ABSTRACT FROM AUTHOR]

Highlights • Two-level virtual MGC structure is presented to simplify internal power interactions. • The cooperative energy management scheme is proposed based on distributed MPC. • The distributed problem is optimized based on cooperative logarithmic-barrier method. Abstract With the fast development of microgrids (MGs), the microgrid community (MGC) integrating adjacent MGs has raised more and more attentions. In an MGC, each MG shares available power reciprocally to minimize the operation costs and maintain power balance. As the amount of MGs grows, the MGC becomes complicated and difficult to control. Different from centralized strategy, this paper proposes a cooperative energy management scheme based on distributed model predictive control (DMPC) for grid-connected MGC. The normal MGC is virtualized as two-level structure to simplify the internal interactions among subsystems. All the subsystems iterate in parallel adopting the proposed cooperative logarithmic-barrier method, which follows a communication protocol to derive control signals. The real-time hardware-in-the-loop (HIL) study verifies that, the optimization results of proposed DMPC approximate to Pareto solutions of centralized MPC (CMPC), whereas the computing speed is much faster than CMPC. [ABSTRACT FROM AUTHOR]

Highlights • Implementation of hundreds of simultaneous parallel power flow calculations on GPU. • Practices, standards and transparency used facilitates replication and comparison. • Fair comparison suggests managing expectations for tangible speedup levels by GPUs. • Proposal can be seamlessly extended for using evolutionary computation based applications. Abstract The task of online operational planning and control functions in a control center, such as contingency analysis, is computationally demanding and hundreds of simultaneous power flow (PF) calculations should be performed. As practical and cost-effective tools for speedup calculation, this paper presents the implementation of simultaneous parallel PF calculations using GPU. For the practical and fair comparison, the proposed GPU based parallel PF is compared with CPU based PF. The test results using 14, 30, 57, 118, 300, 1354, 2869, and 9241 bus systems for single, 10, 50, 100, 200 simultaneous parallel PF calculations verify the practical speedup of the proposed method. [ABSTRACT FROM AUTHOR]

Highlights • A new scenario-based two-stage wind farms planning model is proposed. • The annual installation capacities of wind turbines are optimally planned. • The Levelized Cost of Energy (LCOE) of wind farms is minimized. • Load and wind uncertainties are modeled via scenario based approach. • A desired loading margin is satisfied in the entire planning horizon. Abstract Recently, penetration of intermittent power sources has been increased in power systems due to an international drive for clean and sustainable energies; but these alternative sources could encounter power systems with some problems, which need planning and prevention. This paper proposes a two-stage scenario-based planning model for large-scale wind farms development, based on a project management approach. Considering a 10-year project of large wind farms development, the annual installation capacities of wind turbines are first optimally planned in order to minimize the Levelized Cost of Energy (LCOE) of wind farms. Second, as wind power penetration is consistently increasing in the grid, some stability concerns will come up such as voltage instability. To remedy this condition, the optimum dispatch of grid's conventional power sources and control variables is determined in such a way that not only in any state of operation (taking into account wind and load uncertainties) but also in post-contingency conditions, the prescribed security margin is ensured at the lowest possible cost. This study has been conducted on an actual power system of Iran's southeast grid, as well as IEEE 118-bus standard test system. Also, modified crow search algorithm (MCSA) is utilized to solve the developed optimization model. The numerical studies substantiate the effectiveness of the proposed method for long-term planning of wind farms. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed coordinated active and reactive power control scheme for wind farms based on the model predictive control (MPC) along with the consensus-based distributed information synchronization and estimation, which can optimally dispatch the active power of wind turbines (WTs) and regulate the voltages within the wind farm. For the active power control, the pitch angle and generator torque of WTs are optimally controlled to alleviate fatigue loads of WTs while tracking the power reference of the wind farm required by system operators. For the reactive power/voltage control, the reactive power outputs of WTs are controlled to mitigate the voltage deviations and simultaneously optimize reactive power sharing. Considering the high R / X ratio of the wind farm collector systems, the impact of active power variations on voltages is taken into account to improve the voltage regulation. The proposed scheme is center-free and only requires a sparse communication network. Each WT only exchanges information with its immediate neighbors and the local optimal control problems are solved in parallel, implying good scalability and flexibility for large-scale wind farms. The predictive model of a WT is derived and then the MPC problem is formulated. A wind farm with ten WTs was used to verify the proposed control scheme. [ABSTRACT FROM AUTHOR]