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• Distinct DG Output Modes Validation: The simulation validates distinct DG output modes, impacting prediction. • Mode-Based State Estimation: The paper proposes an innovative mode-based state estimation using IPSO-DBSCAN clustering, overcoming k-means limitations. • Enhanced Data Recovery using DBSCAN: Enhanced data recovery employs DBSCAN with improved secondary clustering, guided by similarity and homogeneity, outperforming traditional methods. • BiGRU-PF State Estimation: BiGRU-PF enhances state estimation accuracy and robustness in distribution networks by detecting DG output modes, addressing k-means limitations, and improving data recovery. This paper proposes a new forecasting-aided state estimation (FASE) method for distribution systems that mitigates issues with uncertain distributed generation (DG) and lost measurements. We utilize an Improved Particle Swarm Optimization (IPSO)-optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for examination of historical DG output data. Based on identified DG output modes, it facilitates precise state prediction and data reconstruction,. The proposed method employs a Bidirectional Gated Recurrent Unit (BiGRU) neural network for state prediction and a particle filter (PF) for final state filtering. The method verification is provided through Python simulations of the Distribution Transformer Unit (DTU)7k distribution network system, demonstrating improved accuracy and robustness against sudden load change and bad data in measurements. [ABSTRACT FROM AUTHOR]

This paper investigates the energy management of an Energy Internet (EI) that integrates multiple energy networks. Based on the structure of EI and the fluctuation of load, an optimal energy management model considering part of the load as time-varying factor is proposed. Under the influence of load, the actual energy management problem (EMP) is formulated as a time-varying optimization problem subject to a set of global and local constraints. Its optimal solution varies continuously with time, which leads to difficulties in obtaining optimal solutions by conventional algorithms. Consequently, to obtain better tracking performance, this paper proposes a distributed inertial projection neurodynamics algorithm (DIPNA) based on the Nesterov accelerated gradient descent method. This algorithm tracks the optimal solution of the EMP with a fast convergence rate O 1 / t 2 , providing a referenceable active power for each energy unit in real time. Finally, the performance evaluation results demonstrated the effectiveness and fast convergence of the proposed algorithm. • Collaborative management of multiple energy sources throughout the energy internet system is achieved. • The model takes into account the fluctuation of load • Proposes a distributed reach dynamics algorithm with fast convergence characteristics. [ABSTRACT FROM AUTHOR]

• Matrix aggregation algorithm considering the direction of power flow. • A new comprehensive index with line outage distribution factor & line load rate. • A new quantification index of the power flow transfer impact ratio is proposed. • Accuracy of the obtained key lines of key transmission sections increases by 19.6 % With the increasing complexity of high-proportion new energy connected to power systems, the volatility and uncertainty of the power system increase, resulting in the changing operation status of the system. The original general sections may become key transmission sections. According to the power flow characteristics of power systems, this paper proposes an improved matrix aggregation algorithm to determine the initial transmission sections. On this basis, this paper defines an improved comprehensive index based on line outage distribution factor and line load rate. According to the principle of emphasis on detection in the actual power system operation, weight coefficients are considered for the two parameters to determine the key transmission sections. Finally, the correlation between multiple transmission sections is analyzed by quantifying the impact of the trip off line on the power flow transfer of other lines, and the strong correlation section is obtained. Case studies of the IEEE 39 bus system show the validity and effectiveness of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

Highlights • We design an attribute identity-based signcryption algorithm. • We improve the remote anonymous attestation protocol based on the designed algorithm. • A privacy protection scheme that can verify the meter's trustworthiness is proposed. • The specific implementation process of the scheme is given. Abstract In order to monitor and predict the status of the power grid effectively in real time, smart meters need to send the power information of users to the control center frequently. However, power consumption data may contain users' privacy, e.g., living habits and hobbies. Further, as the smart meter terminal's working environment is complex and diverse, and smart meters suffer from endless physical attacks and cyber attacks, so the trustworthiness of smart meters is under threat. Aiming at addressing the problems above, a privacy protection scheme for smart meters that can verify the terminal's trustworthiness based on trusted computing is proposed in this paper. We design an attribute identity-based signcryption algorithm, and improve the remote anonymous attestation protocol based on the algorithm, which on the one hand realizes privacy protection of smart meters with high efficiency, and on the other hand completes real time verification of the smart meters' trustworthiness. The specific implementation process of the scheme is given in this paper. The anonymity, privacy protection of the platform configuration, and unforgeability of the scheme are proved in theory. Corresponding experiments show that this scheme has higher computational efficiency than others. [ABSTRACT FROM AUTHOR]

Highlights • The analytic depreciation models for two generic classes of grid assets are derived. • The inter-temporal response and dynamical depreciation of grid assets are captured. • Recasts the grid assets' cost evaluation in the statistical setting of PEV charging. • The findings of this paper can be developed software planning tools for utilities. Abstract Plug-in Electric Vehicles (PEV) exert an increasingly disruptive influence on power delivery systems with penetration surge in the past decade. Therefore, accurately assessing their impact plays a crucial role in managing grid assets and maintaining power grids' reliability. However, PEV loads are stochastic and impulsive , which means they are of high power density and vary in a fast and discrete manner. These load characteristics make conventional assessment methods unsuitable. This paper proposes an algorithm, which captures the inter-temporal response of grid assets and allows fast assessment through an integrated interface. To realize these advantageous features, we establish analytical models for two generic classes of grid assets (continuous and discrete operating assets) and recast their cost functions in the statistical settings of PEV charging. Distinct from simulation-based methods, the proposed method is analytical, and thus greatly reduce the computation resources and data required for accurate assessment. The effectiveness of the proposed algorithm has been demonstrated on a set of power distribution networks in Columbus metropolitan area, in comparison with the conventional assessment methods. [ABSTRACT FROM AUTHOR]

This paper proposes a combined model of Multi-Objective Dynamic Economic and Emission Dispatch (MODEED) with Demand Side Management (DSM) to investigate the benefits of DSM on generation side. This model considers a day ahead based load shifting DSM approach. In order to analyse the effect of DSM on the generation side, the objectives of dynamic economic and emission dispatch problem were minimized individually and simultaneously with and without DSM. A test system with six thermal generating units was considered for the validation of the proposed method. In this paper, authors used Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to minimize the objectives of MODEED problem simultaneously. The simulation results of the MOPSO algorithm have also been compared with the non-dominated sorting genetic algorithm (NSGA-II). It is clear from the results that the proposed combined model is able to give benefits to both utilities and generating companies. [ABSTRACT FROM AUTHOR]

This paper describes a new algorithm for longitudinal differential protection of transmission lines. Classic stabilization is an unreliable method for avoiding unnecessary relay tripping in case of current transformer saturation during faults outside the protected zone. There are various methods for detecting current transformer saturation. The paper’s thesis is that rather than the stabilization current, the direction of the currents introduced into the relay should be monitored. The proposed algorithm has been compared to the algorithms used by renowned relay producers. Different types of faults have been simulated, both within and outside of the protected zone, and the paper demonstrates how the relay trips when the algorithm in question is used. The results have proven that current transformer saturation does not affect protection operation, and that a prompt relay response is obtained for faults occurring within the protected zone. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology for the dynamic reconfiguration of the distribution network (DRDN) which is based on the Lagrange relaxation approach. The aim of DRDN is to determine the optimal topologies (configurations) of the distribution network over the specified time interval. The objective is to minimize the active power losses, subject to the following constraints: branch power flow capacities, allowed ranges of bus voltages, radial network configuration, and limited number of switching (open/close) operations for all switching devices. The paper first introduces the “path-switch-to-switch” approach for the modelling of distribution networks, which is used to formulate DRDN as the mixed integer linear programming (MILP) problem. Then, the specified MILP problem is solved using the Lagrange relaxation approach in two-step procedure. In the first step, the associated Lagrange dual problem is solved, which is created by relaxing the switching operation constraints. The Lagrange dual problem is decoupled and much easier to solve than the original problem. In the second step, the solution of the Lagrange dual problem is used to perform the heuristic search, providing the suboptimal, though feasible solution of the original problem. Finally, the presented DRDN model is extended to multi-objective formulation, which also includes the impact of the network reliability and the switching costs to the DRDN process. The robustness and scalability of the developed algorithm (for application in large-scale distribution networks) are demonstrated with two test examples: 15-bus test benchmark and 1021-bus real-world test system. [ABSTRACT FROM AUTHOR]

Accurate estimation of grid voltage parameters such as amplitude, phase angle and frequency is crucial for advance control and protection in power systems. There are different estimation techniques in the specialized bibliography, so the selection for their implementation involves analyzing a trade-off of operating characteristics such as accuracy, response time or complexity. This paper proposes a three-phase estimation algorithm with high fidelity performance during abnormal grid conditions. The proposed algorithm combines a new Two-Sample Sequence Extractor (TSSE) and a Recursive Least Mean Square (RLMS) frequency estimator in a mutual feedback mode to improve its operation. To estimate the amplitude and phase of the positive and negative sequence components, the TSSE performs a linear approximation between two voltage samples. Furthermore, a slide window approach RLMS is used to estimate the system's frequency. Simulation and experimental results are presented to analyze and validate the performance of the TSSE-RLMS. In addition, the proposed algorithm is compared with other four kinds of estimation methods under different grid conditions. • A 3 Φ voltage parameter estimator with high fidelity performance is presented. • The algorithm combines a Two-Sample Sequence Extractor and a RLMS frequency estimator. • The proposed algorithm is compared with other four kinds of estimation methods. • The TSSE- RLMS presents a fast and accurate estimation of the sequence voltages. [ABSTRACT FROM AUTHOR]

In hybrid ac/multi-terminal medium-voltage dc (AC/MT-MVDC) systems with multiple voltage source converters (VSCs) and dc/dc converters, it is essential to attain accurate initial power flow (PF) and post-event power distribution solutions efficiently. This paper proposes: (i) a novel sequential PF algorithm for hybrid AC/MT-MVDC systems by a Fibonacci search-based Newton–Raphson (FSNR) approach with uniform MVDC bus type definition, while considering power losses and varied control schemes of different converters; (ii) a zero error steady-state post-event power distribution calculation method under droop control by the introduction of dynamic I/V droop coefficients based on the FS algorithm. The FSNR approach simplifies the MVDC PF derivation by only requiring the definition of the dc current bus type, effectively eliminating the need to solve multiple sub-Jacobian matrices. Furthermore, the post-event power distribution analysis offers precise power redistribution calculation approach following disturbances by considering both open- and closed-loop operation in an MT-MVDC distribution system. The computational efficiency and validity of the proposed PF algorithm, along with the accuracy of presented post-event power distribution calculation method are verified through Python and RTDS real-time simulators in an extended MT-MVDC distribution network incorporated with the IEEE 14/33/69 bus transmission/distribution systems. • PF derivation of hybrid AC/MT-MVDC networks by a novel sequential PF algorithm. • Uniform MVDC bus type definition by an FSNR approach. • Post-event power distribution analysis method for MT-MVDC distribution networks. • Zero error power redistribution calculation by an FS method-based dynamic I/V droop coefficients. [ABSTRACT FROM AUTHOR]

With the large-scale application of microgrid technology in distribution networks, emerging energy transactions among multiple microgrids as well as between microgrids and the grid increase the operational burden to the power system. To coordinate these energy transactions, a coalitional game theoretic energy transaction algorithm for networked microgrids is developed in this paper to improve energy exchange efficiency. First, a distance-oriented method for maximizing coalition utilities is proposed to guide the order of transactions in a coalition, and the corresponding coalition utility would be fairly allocated by the Shapley value. Second, a modified coalition formation algorithm consisting of perturbation, merge and split procedures is designed to obtain a D c -stable otherwise a better D h p -stable partition on the network, in which no microgrid has an incentive to leave this partition, and the corresponding theoretical proof is given in this paper. Finally, relative to the non-cooperation mode and the Merge&Split procedure, simulation results show the advantages of the proposed algorithm in reducing costs and facilitating energy transactions in the network. • Solved the cooperative energy transaction problem for networked microgrids in the network. • A distance-oriented method is proposed to calculate the total utility for every valid microgrid coalition. • A perturbation procedure is introduced to obtain a D c -stable otherwise a better D h p -stable partition. • A theoretical proof for stability and optimality of the obtained coalition partition. [ABSTRACT FROM AUTHOR]

This paper focuses on the emergency rush-repair task scheduling of distribution networks in power grid emergencies. As there are many characteristics in distribution network emergency rush-repair such as large number of rush repair points, diverse but with limited repair resources and urgent repair time, this paper takes constraint conditions, such as a variety of execution modes and repair resources into consideration during the model building process of the emergency rush repair scheduling, and we study emergency repair strategies, such as using the emergency generator vehicle, nearby repair strategy and ordinary repair strategy. In order to obtain the optimal solution and satisfy the constraints of the distribution network repair model, this paper improves the traditional serial schedule generation mechanism to a distribution network repair serial schedule generation mechanism by using improved SA algorithm and hierarchical optimization method to search the optimal solution. Finally, we use the model to an example and analyze it. It is validated that the two schedule generation mechanisms that use nearby repair strategy and ordinary repair strategy are both effective. Both the advantages and disadvantages of two mechanisms, and the feasibility and rationality of the model are analyzed in this paper. [ABSTRACT FROM AUTHOR]

• The AC-SCUC problem is decomposed and solved in parallel from the spatial–temporal perspective. • A start–end decision-making mode is proposed to reduce the state space of UC subproblems. • A closed-form solution technique is proposed to improve the DP algorithm. • The accuracy and efficiency are demonstrated via numerical simulations. The security-constrained unit commitment with AC power flow constraints (AC-SCUC) is a typical NP-hard problem, which is difficult to be solved by traditional methods in a reasonable time. In this paper, it is decomposed into a number of tractable subproblems in both spatial and temporal dimensions, and then solved by an improved dynamic programming (IDP) algorithm. Different from traditional dynamic programming (DP) algorithms, which need to enumerate each unit status at each period, we just need to determine the start and end periods of each consecutive status. Such that the state space can be reduced greatly. Moreover, compared with traditional DP algorithms, which acquire the optimal cost of each decision by iteration, IDP directly obtains them through a closed-form solution technique without iteration, and thus has higher computational efficiency. Case studies on various test systems demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• Generic implicit Z-bus based AC and DC power flow solvers in real vector space. • An efficient sequential algorithmic structure and corresponding data exchange mechanism. • Impacts of tolerance settings, initial value selections and load levels on the convergence and accuracy of the proposed algorithm. • Comparable accuracy, efficiency and scalability to existing algorithms. • Advantage of converging to high-voltage solutions compared to existing algorithms. AC/DC power flow algorithms have been proposed and developed as essential analysis tools for the Voltage Source Converter (VSC) based AC/DC hybrid grid. However, prevailing AC/DC power flow algorithms, e.g., the Newton-Raphson based algorithm, suffer from limitations in terms of convergence, multiple solutions, and high computational complexity. To address these issues, this paper presents a novel sequential AC/DC power flow algorithm based on the implicit Z-bus (IZB) method. We firstly provide a general AC/DC power flow model in real vector space, which takes into account the interface data, internal connection and various control modes of VSC stations. Then, simple and compact IZB iterative mappings in real vector space are derived for the AC solver and DC solver. A sequential algorithmic architecture and data exchange mechanisms are designed considering losses calculation, capacity limits and control strategy switching. The impacts of tolerance settings, initial value selections and load levels on the convergence and accuracy of the algorithm are theoretically analysed. Notably, rigorous convergence analyses substantiate the superior attributes of the proposed algorithm over existing models, guaranteeing convergence to high-voltage solutions (feasible and stable solutions). Numerical tests are performed to compare the proposed algorithm with existing works in terms of accuracy, efficiency, scalability and reliability. [ABSTRACT FROM AUTHOR]

• The universal technique is applied in any protective zone surrounded by a particular PMUs. • The technique detects faults independent of fault kind or location matching threshold level of unity. • This algorithm detects faults regardless the fault resistance. • The algorithm has accurate simultaneous faults detection. The transmission system is one of the three major parts besides the generation and distribution systems. Transmission system protection requires a vital especially development it involves in modern wide-area systems. This paper suggests a generalized robust fault detection and location algorithm established on the optimally allocated phasor measurement units (PMUs). The optimum allocation of PMUs ensures the entire observability of all transmission lines, that are included within the zones of protection. The generalized fault detection and location algorithm is based on the roots deduced from the basic transmission line model under normal operation. The algorithm enables to detection of faults closed to buses equipped with PMUs and high impedance single line to ground faults. The algorithm is characterized by generality and independent variable system dynamics. The proposed algorithm can detect simultaneous faults of any type with a high degree of accuracy. To validate the developed algorithm, it is utilized in the IEEE 14-bus system. The simulation study is conducted using Matlab/Simulink® software package. Different fault types are modeled under various fault resistances. The modeled fault cases prove the security of the developed algorithm against external faults with a high degree of dependability in the case of internal faults. [ABSTRACT FROM AUTHOR]

An enormous challenge for the state estimation (SE) process in large-scale distribution networks lies in how to deal with multiple uncertainties and the increasing computational tasks simultaneously. This paper aims to address the issue and propose a multi-area architecture for perceiving system status with uncertain inputs. The first step involves the introduction of a novel Louvain-flow-based partition structure, which effectively divides large-scale unbalanced distribution networks into several sub-areas. Subsequently, an affine state estimation (ASE) model, employing the Neumann series method, is formulated for each sub-area, taking into account uncertainties in system measurements and line parameters. Finally, the interior-point filter line-search algorithm combined with consensus theory is utilized to solve all local ASE models, and the interaction of boundary state between adjacent sub-areas is focused and completed, to output the global state results of the distribution network precisely. Case studies are presented to verify that the proposed distributed ASE model yields obvious advantages in tracking system uncertainties by contrast with the existing SE model. [ABSTRACT FROM AUTHOR]

• Digital substation. • Centralized protection and control (CPC). • Protection functions implemented in a generic hardware platform. • Multicriteria Faulted Phase Selection algorithm. • Improved protection algorithm operation under renewables and conventional scenarios. The integration of devices based on renewable energies and power electronics makes it necessary to rethink some of the methods used for protection and control in distribution and transmission grids. The difference between short circuit current contributions from renewable and conventional generators may affect protection system operation. Fault phase selection is one of the elements to be analyzed under renewable generation scenarios due to its importance in protection functions such as distance protection. The digitization of substations presents new opportunities in control and protection systems, such as functionality increase, reliability improvement, cost reduction of the installation and capacity for remote maintenance. This paper proposes a multi-criteria fault phase selection algorithm developed to be implemented in digital substations. The proposed faulted phase algorithm combines the results provided by four independent protection algorithms and has been designed to be applied in transmission power systems. Depending on the electrical variables measured in each event, quality criteria are defined to consider the strengths and weaknesses of each independent algorithm. The proposed algorithm has been implemented and tested on the centralized protection and control platform called EPICS, obtaining very satisfactory results. [ABSTRACT FROM AUTHOR]

• A novel machine learning approach is proposed for detecting anomalies in AGC loop. • The proposed approach combines K-means, SMOTE, and multiple SVDD models. • For training, the proposed approach requires only one class data. • Feature Engineering concept is used to devise a new feature. • A financially oriented market operation attack is presented that earn illegal profits. This paper focuses on detecting cyber-attacks targeting the Automatic Generation Control (AGC) loop and market operation. To achieve this, a new data-driven learning algorithm is proposed that ensembles various learning tools such as K-Means clustering, Synthetic Minority Oversampling Technique oversampling, and Support Vector Data Description models as the base learners. Next, this paper devises a new feature variable, EF , which generates a relatively higher value for attack scenarios than normal grid states, thus aiding the classifier in predictions with low false alarms. The proposed approach can detect a wide range of cyber-attacks, including unseen attack cases. This paper further modelled and validated the detection of profit-oriented intelligent market operation attacks, absent in most of the previous works. Such attacks project themselves within the proximity of nominal grid states, making them difficult to predict. The algorithm's performance is finally compared with other learning models, and it is found that the proposed technique has superior prediction with True Positive Rate, True Negative Rate, and Geometric Accuracy as 98.13%, 99.85%, and 98.98%, respectively. [ABSTRACT FROM AUTHOR]

• Flicker measurement is mainly divided into two parts: flicker signal detection and flicker calculation. • This paper introduces the common detection methods of flicker signal, such as square detection method, effective value detection method, rectification detection method and synchronous detection method. • The common detection methods are theoretically analyzed, the main advantages and disadvantages are pointed out, and a new fundamental RMS detection method is proposed. • The calculation method of flicker is studied, and the traditional design method of complex filter is improved. The accuracy and rapidity of the algorithm are verified by simulation. This paper mainly introduces the detection method and calculation method of load flicker signal. Firstly, the commonly used detection methods are theoretically analyzed, their advantages and disadvantages are pointed out, and a fundamental effective value detection method is proposed. Then the flicker calculation method is studied. The traditional flicker calculation method is complex and needs the design of some filters. Based on the traditional algorithm, the flicker calculation method is improved, and the accuracy and rapidity of the algorithm are verified by simulation. [ABSTRACT FROM AUTHOR]

This paper presents a new approach using swarm intelligence algorithm called Fireworks Algorithm applied to determine Unit Commitment and generation cost (UC) by considering prohibited operating zones. Inspired by the swarm behaviour of fireworks, an algorithm based on the explosion (search) process and the mechanisms of keeping the diversity of sparks has been developed to minimize the total generation cost over a given scheduled time period and to give the most cost-effective combination of generating units to meet forecasted load and reserve requirements, while adhering to generator and transmission constraints. The primary focus is to achieve better optimization while incorporating a large and often complicated set of constraints like generation limits, meeting the load demand, spinning reserves, minimum up/down time and including more realistic constraints, such as considering the restricted/prohibited operating zones of a generator. The generating units have certain ranges where operation is restricted based upon physical limitations of machine components or instability, e.g., due to steam valve or vibration in shaft bearings. Therefore, prohibited operating zones as a prominent constraint must be considered. In this paper the incorporating of complicated constraints of an optimization problem into the objective function is not considered by neglecting the penalty term. Numerical simulations have been carried out on 10 – unit 24 – hour system. [ABSTRACT FROM AUTHOR]

Energy Storage Systems (ESSs) will have an important role in the optimal operation of Active Distribution Networks (ADNs). Within this context, this paper focuses on the problem of ESSs optimal siting and sizing. Following similar approaches already proposed by the Authors, this paper proposes a multi-objective procedure that accounts for various ancillary services that can be provided by ESSs to ADNs. The proposed procedure takes into account the voltage support and network losses minimization along with minimization of the cost of energy from external grid and congestion management. For the case of large-scale problems, accounting for networks with large number of nodes and scenarios, the selection of the solution methodology is a non-trivial problem. In this respect, the paper proposes and discusses the use of the Alternative Direction Method of Multipliers in order to define an efficient algorithm capable to treat large-scale networks and, also, address the issue of the optimality of the solution. A real large-scale network with real profiles of load and distributed photovoltaic generation is used as the case study to analyze the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm to solve optimal power flow (OPF) in power system which has a unified power flow controller (UPFC). This UPFC can improve power transfer capability and transient stability and can reduce the transmission loss and fuel cost of generation. In this paper a new and simplified model of UPFC based on circuit elements for enhancing power flow is presented. Several power flow models of UPFC which are discussed in the literature require complex program codes for computing power injections. Further modification of Jacobian matrix and load flow program structure is a major problem. To reduce these complexities an alternative model of UPFC is proposed and in this model the power injected by the series converter is designed as negative impedance. In order to overcome the nonlinearity of OPF problem it is essential to use a heuristic algorithm. As a result in this paper a firefly algorithm (FFA) to solve the OPF problem is presented. Significantly, firefly algorithm is very efficient in dealing with multimodal global optimization problems. The proposed model is developed with two bus system and the firefly algorithm is tested with standard IEEE 30 bus network using Matlab. The firefly algorithm approach can obtain better solutions than other optimization algorithms. [ABSTRACT FROM AUTHOR]

Power transformers are protected by different relays that operate independently. Malfunction of each relay has a major role in reducing the reliability of the protection system. In order to mitigate the main drawbacks of the power transformer relays, an overall protection scheme is presented in this paper. This scheme proposes a novel multi criterion algorithm using decision-making based on fuzzy logic. In this paper the outputs of restricted earth fault relay and a directional check unit, are combined with the output of the differential protection relay. Therefore, problems that are pertaining to independent operation of each relay have been mitigated and the relays cover protection blind spots of each other. The improved power transformer protection (IPTP) scheme enhances the sensitivity and reliability of the power transformer protection. Extensive simulations are used to measure the effectiveness and merit of the proposed IPTP relay. The above efforts result in a multi criteria approach for protection of power transformers. [ABSTRACT FROM AUTHOR]

The impact of nonlinear loads produces harmonic pollution in electrical power system. It is considered as a serious concern now a day. Whereas, many algorithms have been proposed for harmonic estimation to improve the power quality performance but till date the accurate estimation of power quality parameters remains a challenge. In this paper a non-linear adaptive algorithm, called Bilinear Recursive Least Square (BRLS), has been applied for the first time for estimating the amplitudes, phases and frequency in case of time varying power signals containing harmonics, sub harmonics, inter harmonics in presence of White Gaussian Noise. The technique is applied and tested for both stationary as well as dynamic signals containing harmonics. Practical validation of the proposed algorithm is also made along with the real time data obtained from a Variable Frequency Drive (VFD) panel used for controlling the speed and torque of the induction motor used at a large paper industry. Comparison of the results achieved with the proposed BRLS algorithm with two recently reported non-linear adaptive algorithms, Volterra Least Mean Square (VLMS), and Volterra Recursive Least Square (VRLS), reveals that the proposed BRLS algorithm is the best in terms of estimation accuracy and computational time. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for separation of bulk power system into several stable subsystems following a severe disturbance. An interconnected power system may become unstable due to wide area contingency when it is operated close to the stability boundaries as a result of increased demand, power industry restructuring and competition in the deregulated electricity markets. Meanwhile, large-scale power system controlled splitting is the last resort to prevent catastrophic cascading outages and wide area blackout. The proposed method of this paper reduces the huge initial search space of the islanding strategy to only interface boundary network by clustering the coherent generators and simplifying the network graph. Then, Backward Elimination Method (BEM) based on Primary Maximum Expansion Areas (PMEAs) has been proposed to generate all proper islanding scenarios in the simplified graph. The Newton–Raphson power flow method and Q–V modal analysis have been used to evaluate the steady state stability of the islands in each generated scenario. Binary Imperialistic Competitive Algorithm (BICA) has then been applied to minimize total load-generation mismatch considering no-isolated bus, voltage permitted range and static voltage stability constraints. Comprehensive discussions have been provided using the simulations on NPCC 68-bus test system. The results demonstrate the speed, effectiveness and capability of the proposed strategy to generate fast feasible splitting solutions considering static and dynamic stability. [ABSTRACT FROM AUTHOR]

• Robust Complex-Valued Levenberg-Marquardt algorithm. • Bi-quadratic convergence property. • Rectangular coordinates and quadratic mismatch functions. • Easier computer implementation. This paper deals with a robust complex-valued Levenberg-Marquardt algorithm specially developed for solving ill-conditioned power flow problems. Moreover, it can also be a useful tool for voltage instability and voltage collapse studies. Because power flow models are nonlinear, the Wirtinger calculus is applied to develop iterative algorithms based on Taylor series expansions of nonlinear functions of complex variables and their complex conjugates. Our proposal in complex plane is straightforward derived in rectangular coordinates. Consequently, its performance is compared to the well-known optimized multiplier based load flow method. Aiming this purpose, we show that few changes in the codes are required to transform the complex-valued Newton-Raphson power flow algorithm into the complex-valued Levenberg-Marquardt power flow one. Furthermore, we show that the latter lends itself well to modeling new smart grid technologies while exhibiting a bi-quadratic convergence rate and superior performance as compared to the former procedure. The performance of our proposal is demonstrated and analyzed on well-conditioned IEEE-14, −30, −57 and −118 bus systems and the Brazilian Southern-equivalent system termed SIN-1916 bus. Furthermore, its performance is also demonstrated on the ill-conditioned IEEE-11, −43 bus systems besides the SIN-1916 under stressed operating conditions or higher R / X ratios of transmission lines. [ABSTRACT FROM AUTHOR]

• A novel algorithm is proposed to detect ramp events in databases with different resolutions. • The proposed algorithm is able to deal with bad data points which previous methods cannot handle. • The detecting efficiency and accuracy is greatly improved by the proposed algorithm. • The proposed algorithm can be employed to provide accurate information about system flexibility for ancillary services. The flexibility of the power system gets more attention of power system operators due to the high penetration of renewable energy sources. This leads to the need of the ramp detection. Based on the detection results of ramp events (REs) in historical and forecast databases, the assessment of flexibility requirements in the power system can be significantly improved. Thus the system operator is able to procure minimum ancillary services and design flexible ramping products. Furthermore, the accuracy of the ramp forecasting can be increased. In the mean time, the rapid development of measuring facilities in the power system, such as the WAMS and the SCADA system, requires a robust detecting method to detect ramp events in the databases with different data properties and resolutions of the power system. This paper proposes a Parameter and Resolution Adaptive Algorithm (PRAA) to identify REs fast and accurately. A fast search method that can process raw databases with tens of millions of data points in seconds is proposed to merge adjacent REs with the same ramp direction. The bad data processing method is directly integrated into the proposed algorithm. A post-processing method is developed to increase the accuracy and robustness of the proposed algorithm. The raw wind power data sets from Bonneville Power Authority (BPA) and a real-world power system in China are utilized to validate the performance of the proposed PRAA. [ABSTRACT FROM AUTHOR]

• Propose an error-and-hold algorithm for the SOGI-FLL that can quickly detect faults and give a response that consists in holding the previous to the fault estimated frequency value during these perturbations. • Develop the algorithm using the absolute value of the SOGI's error, its average, and the average of the FLL's estimated frequency to operate, to reduce induced errors in the SOGI-FLL's quadrature outputs, improves the FLL's transient response, hold the estimated frequency, and restores the phase to its previous value before the fault. • Using the LTP model of the SOGI-FLL, to demonstrate how voltage sags and swells affect the amplitude voltage and frequency via an asymmetrical-gain mutual interaction between the SOGI-FLL estimates and a sinusoidal gain that relies on the grid voltage phase. • Demonstrate the practicality of the proposed algorithm, using MATLAB/Simulink software simulations and experimental results, illustrating how it can enhance the SOGI-FLL's response to these disturbances. The Second Order Generalized Integrator–Frequency Locked Loop (SOGI-FLL) is a widely used and popular adaptive filter for estimating grid voltage parameters with minimal computational burden. However, it is vulnerable to voltage sag and swell faults, especially voltage sags that can significantly distort the estimated frequency. In this paper, we propose an error-and-hold algorithm for the SOGI-FLL that can quickly detect faults and hold the estimated frequency during these perturbations. The algorithm uses the absolute value of the SOGI's error, its average, and the average of the FLL's estimated frequency to operate. It reduces induced errors in the SOGI-FLL's quadrature outputs, improves the FLL's transient response, holds the estimated frequency, and restores the phase to its previous value before the fault. The proposed algorithm is a straightforward and low computational burden algorithm that can be executed on a low-cost processor. We validate the effectiveness of the proposed error-and-hold algorithm through simulations and experimental results. [ABSTRACT FROM AUTHOR]

In this paper, a projection-based distributed algorithm is proposed to solve the economic dispatch problem of power system with communication delays under switching topologies. The algorithm is suitable for economic dispatch problem with strongly convex cost function, and ensures that the output power of each generator is always kept within its output limit. In addition, the topology is only required to be jointly connected. Firstly, the relationship between the equilibrium point and the optimal solution of the algorithm is analyzed, and then a common Lyapunov function is introduced for a set of switching topologies. According to the Lyapunov stability theory and Cauchy convergence criterion, LMIs-based sufficient conditions for the convergence of the algorithm are obtained. Finally, the effectiveness of the algorithm under different cases are verified by MATLAB simulation results. • The economic dispatch under switching topology with communication delay is studied. • A projection-based distributed algorithm with is proposed to solve the problem. • The communication topology is only required to be jointly connected. • The delay-dependent LMI conditions for the convergence of the algorithm are obtained. • The effects of parameters on the delay bound and convergence rate are analyzed. [ABSTRACT FROM AUTHOR]

• This paper proposes a robust modified perturb and observe (MPO) MPPT algorithm. • The proposed algorithm divides the P-V curve into four areas based on open-circuit voltage. • An efficient open-circuit voltage estimation is used to reduce required sensors number. • The proposed algorithm concentrates the search area of P-V curve to 15% of its region. • The proposed algorithm boosts the PV system tracking efficiency to 99.7%. Considering the vast improvement of photovoltaics (PVs) efficiency, this paper proposes a robust modified perturb and observe (MPO) maximum power point tracking (MPPT) algorithm. The control strategy of the proposed MPO-MPPT algorithm is based on dividing the solar module/cell P-V curve into four operating areas depending on the open-circuit voltage estimation method. The two areas, which are located far from the maximum power point (MPP), utilize large voltage fixed step-size that improves the tracking speed of the PV system. Otherwise, a small step-size P&O MPPT algorithm is used to minimize the steady-state oscillations for the other two areas due to the close locations of these areas to the MPP. As well, the proposed tracking algorithm eliminates the necessity of wide small step-size iterations by concentrating small step-size search area to 15% of the whole P-V operation region. To prove the effectiveness of the proposed MPO algorithm compared with the conventional MPPT techniques; sinusoidal, ramp, and one-day (10 hr.) irradiance profiles are applied to the solar PV system. The proposed MPO-MPPT algorithm based solar PV system has been built using MATLAB/SIMULINK software. Obviously, the system results confirm the theoretical analysis of the proposed algorithm, which boosts the PV system tracking efficiency to 99.7%. [ABSTRACT FROM AUTHOR]

• A two-stage optimization framework is presented for the self-healing operational scheduling. • The capacity withholding process of non-utility distributed energy resources is considered. • The proposed framework reduced the maximum values of the capacity-withholding index by 71%. • The algorithm reduced the expected values of aggregated operational and interruption costs by 88%. This paper presents a multi-stage day-ahead and real-time optimization algorithm for scheduling of system's energy resources in the normal and external shock operational conditions. The main contribution of this paper is that the model considers the non-utility electricity generation facilities capacity withholding opportunities in the optimal scheduling of system resources. The real-time simulation of external shock impacts is another contribution of this paper that the algorithm simulates the sectionalizing of the system into multi-microgrids to increase the resiliency of the system. The optimization process is categorized into two stages that compromise normal and contingent operational conditions. Further, the normal operational scheduling problem is decomposed into three steps. At the first step, the optimal day-ahead scheduling of system resources and the switching of normally opened switches are determined. At the second step, the optimal real-time market scheduling is performed and the switching of normally closed switches is optimized. At the third step, different extreme shock scenarios are simulated in the real-time horizon and the effectiveness of sectionalizing the system into multi-micro grids are assessed. Finally, at the contingent operational conditions, the optimal topology of the system and scheduling of energy resources are determined. The proposed method was successfully assessed for the 33-bus and 123-bus test systems. The algorithm were reduced the expected cost of the worst-case contingencies for the 33-bus and 123-bus systems by about 97.89% and 88.11%, respectively. Further, the average and maximum values of the 123-bus system capacity-withholding index for real-time conditions reduced by about 67.40% and 71.05%, respectively. [ABSTRACT FROM AUTHOR]

• A Lyapunov based control algorithm is proposed for harmonic compensation and charging with SAPF. • A Lyapunov based PI with AW control is presented to regulate and charge SAPF DC-link. • The DC-link voltage oscillations are minimized without increasing DC-link capacitor size. • Reducing and keeping THD of source currents well below recommended IEEE-519 limits. • Generating reference current and voltage signals based on Lyapunov estimator. With the increasing proliferation of high-tech loads and electrical vehicle (EV) charging stations, shunt active power filters (SAPFs) are contemplated to play a growingly substantial role in power distribution and delivery. In this regard, this paper proposes a Lyapunov based harmonic compensation and charging with the operation of three-phase SAPF as interface EV applications and the electric grid. The proposed control algorithm provides effective harmonics mitigation capability under various non-linear loads (NLLs) groups and grid disturbances. A noteworthy contribution of this paper is that a Lyapunov based proportional integral with anti-windup (PI with AW) control is proposed to regulate oscillations of the DC-link voltage without increasing DC-link capacitor size. The proposed control algorithm offers comprehensive solutions with several improvements, novelties and features as: (i) keeping the total harmonic distortion (THD) of source currents below recommended IEEE-519 limits, (ii) extraction of reference current and voltage signals based on Lyapunov estimator (LE) with effective and good harmonic rejection capability, (iii) achieving good dynamic performance with utilizing a Lyapunov based PI with AW control for the DC-link capacitor voltage and (iv) eliminating the necessity of any high-pass or low-pass filters with utilizing LE based orthogonal signal generation (OSG). Besides, comparative analyses with previous studies are handled to examine improvements provided by the proposed control algorithm. The feasibility and effectiveness of the proposed control algorithm are confirmed by PSCAD/EMTDC environment and DSP based PIL quasi-real time results. [ABSTRACT FROM AUTHOR]

• High dimensional complex distribution system increases the amount of calculation and computational complexity of in the process of economic dispatch. • A large number of microgrids connected to the distribution network system increases the complexity of the system. • The computational dimension and complexity can be reduced by layering or decomposition system. • The efficiency of network loss calculation can be improved by utilizing the network loss sensitivity to calculate network loss. With the development of the active distribution system with multi-microgrids, increasingly numbers of distributed energy resources are integrated into the distribution network through multi-microgrids. As a result, the economic dispatch problem presents high dimensional complexity. Hence, this paper proposes a decomposition and coordination calculation method to reduce the computational dimension and improve the calculation accuracy. In this paper, the active distribution system with multi-microgrids is divided into two parts: one is the outer power grid and the other is the inner power grid, and the optimal value is obtained by applying the differential evolution algorithm. To improve the efficiency of the traditional network loss calculation, the network loss sensitivity is adopted to calculate the network loss. In order to verify the effectiveness of the proposed method, the test model is constructed based on the IEEE 33 node and 69 node distribution systems. The simulation results show that the proposed method can obtain the global optimal solution, and its economy is better than the overall centralized calculation method. This method is conducive to the subsequent application in the actual power grid system. [ABSTRACT FROM AUTHOR]

• We'd like to emphasize that the algorithm presented in the paper is an enhancement of the Iwamoto's approach [2] once the correction applied to the state variables happens at each iteration instead of just once. In this latter case, probably it was motivated by the processor accuracy informed in [2] which was in that time 5.0 × 10−4 (IBM 370/135). • On the other hand, in the enhanced power flow solution the Jacobian matrix is factorized at each iteration through a very efficient QR algorithm which is a square root- and division-free Givens rotations [12]. • Additionally, we have extended the proposed algorithm to deal with ill-conditioned networks. Thus, the robustness of the Levenberg-Marquardt algorithms as presented in [8] is preserved, but its computational efficiency is substantially increased once the number of iterations to achieve the solution is reduced in average to half. In this paper an enhanced power flow algorithm in complex plane is proposed. The power flow models in complex plane is naturally developed in Cartesian coordinates, thus most of the constraints equations can be written as quadratic functions. Consequently, the Taylor series expansion stops in the third term and the exact nonlinearity of the quadratic complex power flow equations is retained while the remaining functions are dealt through the Newton-Raphson method. Minor changes in the codes are required to transform the Newton-Raphson method into the enhanced power flow approach in complex plane. The new algorithm exihibits either a superior behavior in well- or ill-conditioned networks. The features and advantages of the proposed algorithm are illustrated through a small example and case studies carried out either on the well- or ill-conditioned fashion of the IEEE-14, −30, −57 and −118 bus and the Brazilian Southern-equivalent of 1916-buses, termed as SIN-1916. [ABSTRACT FROM AUTHOR]

• A novel FCS-MPC algorithm to improve Fault ride-through of a stand-alone inverter. • High-speed of fault detection and high controllability of fault currents. • Recovering to normal control mode without overvoltage at the moment of fault clearance. • Computation time optimization of the proposed FCS-MPC algorithm. With high penetration of power inverters in microgrids, the Fault Ride-Through (FRT) strategy is of great importance for the inverter operation. However, the Finite Control Set Model Predictive Control is considered as a powerful control for power electronic devices and inverters. This control technique has not been applied for handling the FRT of stand-alone inverters. Therefore, this paper proposes a new control algorithm based on Finite Control Set Model Predictive Control to control the stand-alone inverter in normal modes and to ride-through the fault without any deterioration. The proposed algorithm considers the following aspects: high-speed fault detection, limiting fault current, power quality during the fault, and soft recovery to normal mode at the fault clearance. These aspects require additional calculations in the control algorithm, resulting in higher computation time and delay. In this paper, the delay is compensated using the two-step prediction horizon principle. Moreover, the algorithm is optimized by removing repeated computations and by separating the fault mode algorithm from the basic algorithm. Simulation and experimental studies are carried out to validate the performance of the proposed FCS-MPC algorithm under symmetrical and asymmetrical faults. The results reveal the high effectiveness of the proposed algorithm to improve the FRT capability of the inverter. [ABSTRACT FROM AUTHOR]

• Low frequency voltages are linear distribution along AC transmission lines. • A voltage incremental quantities based distance relay is proposed in this paper. • Proposed distance relay has fast operation speed under metallic fault conditions. Conventional distance protection elements are predominantly based on voltage and current phasors. As a result, those elements require the half-cycle/full-cycle time window to estimate the accurate voltage and current phasors. Using information in the transients of voltages and currents is a possible way to improve the distance protection operation speed. To break free of phasor limitations, this paper presents a novel incremental quantities distance protection algorithm based on instantaneous values of voltage and current. Firstly, the instantaneous voltage distribution along the AC transmission line is analysed. Then, the principle of the incremental quantities distance protection is presented. The influence of fault angle on the presented algorithm is also analysed and a solution is proposed to deal with the relative small fault angle conditions. Finally, an extensive simulation results based on PSCAD/EMTDC simulation verify the performance of the presented incremental quantities distance protection. [ABSTRACT FROM AUTHOR]

• Novel solution for two phase faults protection in the power distribution system. • Protection reach enhancement for two phase and double phase-to-earth faults. • Negative sequence current used as criterion in instantaneous overcurrent protection. • Zero sequence current additionally used for double phase-to-earth faults protection. • Suggested solution is easy to implement in existing protection relays. Adequate power system protection plays crucial role in ensuring reliability of electricity supply, thus it is a key challenge for distribution system operators to increase the effectiveness of protection schemes operation and protection equipment itself. The main goal of the paper is presenting a multi-criteria protection algorithm that may boost effectiveness of power lines' short-circuit overcurrent relays in medium voltage networks in case of various two phase faults. The idea of enhancing protection performance is based on fault current symmetrical components' analysis and specifically on using negative and zero sequence currents as criterial values for overcurrent line protection used against such faults. The paper describes in detail the proposed solution and analyses its performance based on medium voltage network simulations carried out using DIgSILENT PowerFactory software. Promising simulation results can be observed for definite time overcurrent protection relays used in reactance-earthed networks, as introduction of the proposed method proved to increase the zone of effective operation twice comparing to the traditional phase current based solutions. Moreover, in the proposed solution, coordination of protection settings for small earth-fault current values, which are identified by other protection relays, is not necessary. The proposed solution may complement the traditional algorithms for short-circuit protection (I≫) used in modern protection relays monitoring the level of negative and zero sequence current, and therefore may significantly improve the effectiveness of detecting various phase-to-phase faults in the considered MV line protection zone. [ABSTRACT FROM AUTHOR]

• The electrical equipment was divided into time-shifting and non-time-shifting power. • The starting time of time-shifting load was selected as the variable in PSO algorithm. • The battery capacity was selected to meet the demand of the power peak at night. With the continuous expansion of the development scale, photovoltaic integrated greenhouses are becoming an important type of smart grid prosumer. The demand side management (DSM) of energy is becoming increasingly important in terms of satisfying the energy consumption requirements of production and increasing the economic benefits of the same piece of land. By analyzing greenhouse production activities and the operation of electrical equipment, a particle swarm optimization (PSO) scheme is proposed in this paper, which selects the starting time of time-shifting powers as optimization factors and maximizes the income of power generation as the objective function. In addition, this paper analyzes the problem of energy consumption scheduling in greenhouse production. The results show that this scheme helps to optimally schedule the greenhouse energy consumption and achieves maximum profit by ensuring the reasonable operation of the system. The optimization scheme is beneficial to guide the production activities in greenhouses and the construction of photovoltaic integrated greenhouse systems. [ABSTRACT FROM AUTHOR]

• A hierarchical framework to accurately detect, classify, and locate the FDEs. • All FDEs (Generator Trip, Line Outage, Load Disconnection, and fault) are evaluated. • A limited number of PMUs are applied to collect few samples of the ROCOF time series. • High-precision RNN and LSTM models are employed online to classify and locate FDEs. Frequency Disturbance Events (FDEs) occur due to various events such as Generator Trip (GT), Line Outage (LO), and Load Disconnection (LD), which affect the stability of power systems. Depending on disturbance severity, it has small to severe effects on an integrated system's performance. Accurate and fast detection and classification of the event and its location are crucial in monitoring resource adequacy, preventing economic losses and blackouts. This paper, focusing on an online cutting-edge hierarchical methodology, first detects the event, then identifies its classification. Eventually, the exact location of FDE is found based on Phasor Measurement Units (PMUs) data. Compared to other counterparts, which focused only on classifying or locating events separately, in this paper, both aims are achieved using the suggested novel hierarchical framework. This research employs Deep Learning (DL) advances to develop a Recurrent Neural Network (RNN) model and a Long Short-Term Memory (LSTM) model to distinguish and locate FDEs with significant accuracy. In the research, only a few time-series of Rate-Of-Change-Of-Frequency (ROCOF) received from a limited number of PMUs is used as the DL algorithm's inputs. This hierarchical methodology is tested in New England 39-bus, IEEE 14 bus systems, and modified IEEE 118-bus system. The evaluation results demonstrate the potential application of the proposed models to detect and classify FDEs compared to conventional algorithms and the frequency-based DL model. The proposed models have achieved significant classification accuracy. Simultaneously, the models locate the events with competitive performance in terms of precision, recall, accuracy, and F1-score. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• An integrated framework for dynamic capacity withholding is addressed. • Two indices are proposed for mid-term and day-ahead scheduling. • The proposed framework uses a four-level optimization process. • A full competition algorithm is also carried out. This paper addresses an integrated framework for the dynamic capacity withholding assessment of an independent system operator that determines the mid-term maintenance scheduling of generation companies and day-ahead scheduling of wholesale market participants. The main contribution of this research is that two dynamic capacity-withholding indices are proposed for mid-term and day-ahead scheduling of generation companies that estimate the dynamic capacity withholding opportunities of generation units in an ex-ante manner. The proposed framework is another contribution of this research that uses a four-stage optimization process that the system operator can detect and prevent the formation of withholding groups. The optimal maintenance scheduling from the generation companies viewpoint is assessed in the first-stage problem that considers different mid-term withholding opportunities. The optimal mid-term maintenance scheduling is carried out in the second-stage problem that recognizes and rejects the dynamic capacity withholding of generation companies. The optimal scheduling of day-ahead generation companies considering their dynamic capacity withholding is the third contribution of this paper that optimizes the scheduling of generation units for day-ahead horizon considering responsive loads. The proposed method is applied to 30-bus, 57-bus and 118-bus IEEE test systems. A full competition algorithm is also carried out to evaluate the competition states of generation companies. The proposed algorithm detected that the dynamic capacity withholding might lead to increase of nodal price by about 279.22%, 764.43%, and 851.2% for 30-bus, 57-bus, and 118-bus IEEE test systems with respect to the non-capacity withholding conditions, respectively. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an adaptive-network-based fuzzy inference system (ANFIS)-fuzzy data envelopment analysis (FDEA) algorithm for improvement of long-term natural gas (NG) consumption forecasting and analysis. Two types of ANFIS (Types 1 and 2) have been proposed to forecast annual NG demand. For each type, several ANFIS models have been constructed and tested in order to find the best ANFIS for NG consumption. Two parameters have been considered in construction and examination of plausible ANFIS models (Type 1). Six different membership functions and several linguistic variables are considered in building ANFIS. Also different value of cluster radius has been used to construct ANFIS (Type 2) models. The proposed models consist of two input variables, namely, Gross Domestic Product (GDP) and Population. All trained ANFIS are then compared with respect to mean absolute percentage error (MAPE), Root mean square normalized error (RMSE) and correlation coefficient (R) using data envelopment analysis (DEA). To meet the best performance of the intelligent based approaches, data are pre-processed (scaled) and finally our outputs are post-processed (returned to its original scale). FDEA is used to examine the behavior of gas consumption. To show the applicability and superiority of the ANFIS–FDEA algorithm, actual NG consumption in six Southern America countries from 1980 to 2007 is considered. NG consumption is then forecasted up to 2015. The ANFIS–FDEA algorithm is capable of dealing both complexity and uncertainty as well several other unique features discussed in this paper. [Copyright &y& Elsevier]

Abstract: This paper presents an innovative Tribe-Modified Differential Evolution (Tribe-MDE) for solving multi-objective Environmental/Economic Dispatch (EED) problems. By using this method the multi-objective problem will be changed into a mini-max problem on this first stage and then will be solved using the Tirbe-MDE algorithm. The operator, a person who is competent with respect to the problem, can initiate all the necessary actions to determine an optimal solution. Because of the fact that all of Pareto obtained solutions are of the same level of preference an operator will be able to affect his/her opinions with respect to different conflicting objectives. Using this methodology the operator can achieve the optimal solution. The DE algorithm is an advanced stochastic method that can prepare the necessary preliminaries for an operator to solve the EED problem. DE method has several advantages including its few control variables, local searching capability, fast results, easy using process and simple structure and using the control variables logically has a significant effect on the results. The subject of this paper is studying the modified DE algorithm which is founded on self-adaptive control. In this method a diversity-preserving method is used to solve the premature convergence problem and for proving the effectiveness of this method in solving EED problems the presented algorithms is applied on different systems. [Copyright &y& Elsevier]

Abstract: This paper proposes a new algorithm to determine the best connection of multiple distributed generations (DGs) on distribution networks according to type of customers. The considered DG technology is combined heat and power (CHP) in various sizes which can be extended to other technologies. Different types of demand, such as industrial, residential and commercial are defined by the load model that is voltage dependent and the load patterns from real life hourly demand data. This method which is based on multi-year multi-period power flow is useful for Distribution Network Operators (DNOs) when they are faced with voltage dependent load models in their networks. However it can be used in constant load models with multi-period or single demand. The proposed optimization is from the DNO’s perspective and aims to maximize their present value of profit against the supplying costs of the network. It is shown that by definition of an appreciated economic based objective function, the customer type can significantly affect the optimal DG options in distribution network planning. Finally, this method is tested using the 69-bus radial system. Furthermore, a comparative study for different customer demand types is shown for various cases and results are discussed in this paper. [Copyright &y& Elsevier]

Abstract: To attract more investments for developing smart transmission networks and increasing their flexibility and efficiency, recently policies have been suggested which provide finical incentives in transmission network investment. One of these policies is price biding for incremental transmission capacity and transmission elements in power markets. According to Federal Electricity Regulatory Committee, flowgate bidding is defined as allowing a line’s flow to exceed its rated capacity for a short period of time for a set penalty, i.e., price. This paper concentrates on the development of a comprehensive model for flowgate bidding and Dispatchable Transmission Services (DTSs) in stochastic joint energy and reserve market. In this paper a stochastic joint energy and reserve market with DTS are proposed to minimize the cost of supplying load and reliability expenses. The effectiveness of the proposed model in an electricity market is demonstrated by the use of two stage stochastic mix-integer non-linear programming (TSSMINLP). DTS and flowgate biddings are used during contingencies and steady state to determine optimal required energy and reserve values. As the scale of problem is large, the benders decomposition algorithm is used to solve the stochastic joint energy and reserve market problem. To investigate the efficiency of the proposed strategy, IEEE 6 and 24 bus case tests are studied. According to the obtained results, this strategy decreases energy and reserve marginal prices, as well as reliability cost. Furthermore, the suggested plan is an incentive to the owners of transmission companies. [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: 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: This paper presents a new method for real and reactive power tracing in a deregulated power system by introducing the hybrid artificial bee colony (ABC) algorithm and least squares support vector machine (LS-SVM), namely as ABC-SVM. The idea is to use ABC algorithm to obtain the optimal values of regularization parameter, γ and Kernel RBF parameter, σ 2, which are embedded in LS-SVM toolbox and adopt a supervised learning approach to train the LS-SVM model. The technique that uses Superposition method is utilized as a teacher. Based on power flow solution and power tracing procedure by Superposition method, the description of input–output for training and testing data are created. The generators’ contributions to real and reactive loads in the test system are expected can be traced accurately by proposed ABC-SVM model. In this paper, IEEE-14 bus system is used to illustrate the effectiveness of the proposed ABC-SVM model compared to that of Superposition method. The comparison with the cross-validation (CV) technique and other hybrid technique to obtain the hyper-parameters also has been presented in this paper. [Copyright &y& Elsevier]

Highlights • Phase error based Widely Linear LMK Algorithm is proposed for Frequency estimation. • It uses best features like widely linear modelling, phase error and robust cost function. • It achieves enhanced frequency estimation performance for several critical cases. • Retains the inherent simplicity of the gradient descent based algorithms. Abstract In this paper, an augmented (widely linear) complex Least Mean Kurtosis (WL-LMK) algorithm that uses the negated kurtosis of the phase angle error instead of the conventional magnitude error is proposed for robust frequency estimation of three-phase power systems. The motivation of the proposed method is based on two underlying facts. First, it is primarily the phase rather than the amplitude that conveys useful information in frequency estimation. Second, the negated kurtosis based cost function is computationally efficient and most celebrated in applications where the noise contamination degrades the performance of the classical adaptive filtering algorithms. To further achieve robust frequency estimation for both balanced and unbalanced conditions, the proposed method makes use of the full second-order information within the complex-valued system voltage through the Clarke's αβ transformation and the concept of augmented complex statistics and widely linear modelling. The estimation performance of the proposed method is evaluated for several critical cases that often arise in power system. Simulation results of both synthetic cases and experimental studies show that the proposed method achieves more accurate frequency estimates than its developed widely linear counterparts in the literature. [ABSTRACT FROM AUTHOR]

Highlights • We propose a distributed primal-dual algorithm to solve constrained optimization problems over directed network. • The proposed algorithm adopts stochastic gradient descent method to update values of nodes for dealing with noise. • Theoretical analysis indicates that the algorithm can exactly seek the optimal solution to EDP with probability one. • The algorithm allows uncoordinated step-sizes for local optimization of each node. • To demonstrate the robustness and scalability of the proposed algorithm, we extend the algorithm to the network with communication delays. Abstract Economic dispatch problem (EDP) is one of the fundamental optimization problems in power systems, which involves a coupling linear constraint and several individual box constraints. In this paper, we propose a distributed stochastic gradient descent algorithm based on consensus theory to solve the EDP under directed network, where the convex cost function for each generator only needs to satisfy the condition that the function is strictly convex with Lipschitz continuous gradient. The proposed algorithm utilizes stochastic gradient descent to update values of generators for dealing with the noise which is incurred during the gradient estimation, and the step-sizes are heterogeneous. Under strictly convex assumption on objective functions, the algorithm can seek the exact optimal solution with probability one at the rate of O ln K K , where K is the number of iteration. Furthermore, the algorithm is also suitable and effective to the network with communication delays if the communication delays are bounded. Simulation results illustrate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]