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Short-term probabilistic load forecasting is essential to power systems management and optimization of power flows across transmission networks. Developing forecasting tools capable of providing accurate predictions must comply with their practical implementation in short-term operations, mainly in terms of fast computing and high efficiency. Many data-driven forecasting solutions are often unnecessarily verbose, thus making their practical value limited. This problem occurs more frequently when multiple loads have to be predicted simultaneously, as in power transmission system analysis and optimization. In this paper, we propose a new cooperative forecasting system that refines probabilistic forecasts of individual loads online. The refining procedure is based on a multivariate quantile regression, which is dynamically applied to the individual forecasts as new observations become available. The proposal is validated on the load data published by ISO New England for eight regions, covering six states of the United States. The quality of probabilistic forecasts is assessed in terms of reliability and sharpness, comparing the results to three benchmarks. The proposed method outperforms the best benchmark by up to 6% w.r.t. the reduction in pinball loss. [ABSTRACT FROM AUTHOR]

The continual increase in the power network topological structure and long distances between load‐generation sources lead to modern powers systems are operated near theirs stability margins. For this issue, reliable and secure operations of electric devices through inter‐area oscillations remain an essential issue in the system dynamic stability. In the case of improper actions of controlling devices, the severe inter‐area interactions may lead to wide‐area instabilities and large blackouts. Using wide‐area measurement system (WAMS), remote signals are transferred through communications links with different inherent time latencies. In the case of large time delays, the main controllers cannot work appropriately which leads to improper performance of damping controllers through severe inter‐area fault event scenarios. For this issue, this paper presents an adaptive wide‐area damping controller (AWADC) for damping inter‐area oscillations in large power systems equipped with WAMS technology consisting of various time latencies. In this case, based on the IEEE C37.118.1 protocol, the generator buses are equipped with phasor measurement unit devices. Also, owing to the different communication links, the measured global signals (GSs) are involved within different time latencies. In this case, at each time window, considering time delay compensator (TDC) technology provided through Simevents discrete event tool, the overall time delay is evaluated and compensated. For identifying the best controlling GSs, considering two observability and controllability concepts, the system dynamic signals are evaluated which the best input decisioning signals and corresponding output controlling signals are provided. The proposed AWADC scheme consists of different compensating blocks which are adjusted through simple and well‐known Jaya optimization algorithm (JOA) through online evaluations. The structure of the proposed AWADC controlling scheme is carried out on a modified 39‐bus New England test system consisting of large synchronous generators whose damping performances are evaluated through severe fault events with the potential of unstable inter‐area oscillations. Simulation results indicate the effectiveness of developed TDC and JOA techniques to provide proper controlling signals through the AWADC controller in the presence of different time latencies. [ABSTRACT FROM AUTHOR]

In this paper, it is proposed to apply the mayfly optimization algorithm (MOA) to perform the coordinated and simultaneous tuning of the parameters of supplementary damping controllers, i.e., power system stabilizer (PSS) and power oscillation damping (POD), that actuate together with the automatic voltage regulators of the synchronous generators and the static synchronous series compensator (SSSC), respectively, for damping low-frequency oscillations in power systems. The performance of the MOA is compared with the performances of the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm for solving this problem. The dynamics of the power system is represented using the current sensitivity model, and, because of that, a current injections model is proposed for the SSSC, which uses proportional-integral (PI) controllers and the residues of the current injections at the buses, obtained from the Newton–Raphson method. Tests were carried out using the New England system and the two-area symmetrical system. Both static and dynamic analyses of the operation of the SSSC were performed. To validate the proposed optimization techniques, two sets of tests were conducted: first, with the purpose of verifying the performance of the most effective algorithm for tuning the parameters of PSSs, PI, and POD controllers, and second, with the purpose of performing studies focused on small-signal stability. The results have validated the current injections model for the SSSC, as well as have indicated the superior performance of the MOA for solving the problem, accrediting it as a powerful tool for small-signal stability studies in power systems. [ABSTRACT FROM AUTHOR]

Summary: An improved whale optimization algorithm (IWOA) for the design of a robust power system stabilizer (PSS) for the multi‐machine power system is developed in this paper. Tuning of PSS parameters using the proposed IWOA is carried out by minimizing a multi‐objective function comprising the damping ratio and damping factor of lightly damped oscillating modes of all the generators. The advantage of considering the objective function is that the lightly damped and undamped oscillating modes of all the generators can shift to a prespecified zone of the s‐plane. The performance of the proposed IWOA is tested on standard benchmark test functions and compared with familiar optimization algorithms. The efficacy of the proposed design technique is tested on three benchmark test systems: three‐generator nine‐bus system, two‐area four‐machine interconnected system, and the New England 10‐generator 39‐bus system working on various operating conditions under several typical disturbances. The potential of the proposed method is demonstrated through eigenvalue analysis. The proposed IWOA‐based PSS is compared with the other stabilizers to show its efficacy. [ABSTRACT FROM AUTHOR]

• To propose sooty-tern optimization (STO) for designing PSS parameters of multi-machine power system. • The performance of STO is evaluated using eigenvalue analysis, simulation results and performance indices. • The results are matched with other powerful optimization techniques. • Robustness is verified on hidden operating cases also under critical disturbances. This paper reports a comparative study among four bio-inspired meta-heuristic techniques i.e. Sooty-Tern Optimization (STO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to tune the robust Power System Stabilizer (PSS) parameters of the multi-machine power system. These approaches are successfully tested on two bench-mark systems: sixteen-machine, sixty-eight-bus New England Extended Power Grid (NEEPG) and three-machine, nine-bus Western System Coordinating Council (WSCC). The efficacy of planned PSS via STO and GWO is validated by extensive non-linear simulations, eigenvalue analysis, and performance indices for numerous operating conditions under decisive perturbations, and outcomes are matched with those of GA and PSO techniques. In addition, the robustness is also tested for these algorithms. The results indicate that the PSS design using STO and GWO improves the small-signal stability and damping performance for mitigating inter-area and local area modes of low-frequency oscillations compared to GA and PSO. [ABSTRACT FROM AUTHOR]

• A systematic planning method to allocate dynamic VAR sources is proposed. • The concept of voltage control area is proposed to partition a bulk power system. • A zone-based voltage supporting index is defined in the site selection process. • A zone-based distributed optimization framework is put forward. In this paper, a practical planning method to allocate dynamic VAR sources (DVSs) using the concept of voltage control areas (VCAs) is proposed to enhance short-term voltage stability of power systems. Firstly, based on the defined transient distance index, a spectral clustering method is employed to partition a system into VCAs. Then, candidate DVS sites in each zone are determined by the proposed voltage supporting index. In each VCA, the capacity of candidate sites is preliminarily estimated by use of the dichotomy method to decide the most effective control bus and the initial capacity guess, based on which further system-wide optimization is implemented using particle swarm optimization (PSO) to obtain the final allocation scheme. Case study on the New England 39-bus and 68-bus test systems along with the comparisons with the existing methods confirm the effectiveness and advantages of the proposed methodology. [ABSTRACT FROM AUTHOR]

The firefly algorithm (FA) and the artificial bee colony (ABC) algorithm are used in this study to perform a coordinated parametrization of the proportional-integral and supplementary damping controllers, i.e., power system stabilizers (PSSs) and the unified power flow controller (UPFC)–power oscillation damping set. The parametrization obtained for the controllers should allow them to damp the low-frequency oscillatory modes in the power system for different loading scenarios. The power system dynamics is represented using a model based on current injections, known as the current sensitivity model, which implies that a formulation by current injections for the UPFC should be formulated. To validate the proposed optimization techniques and the current injection model for the UPFC for small-signal stability, simulations are carried out under two distinct perspectives, namely, static and dynamic analysis, using the New England system. The results demonstrated the effectiveness of the UPFC's current injection model. Moreover, it was possible to verify that the FA performed better than the ABC algorithm to solve the discussed problem, accrediting both the UPFC current injection model and the FA algorithm as new tools for small-signal stability analysis in electrical power systems. [ABSTRACT FROM AUTHOR]

This paper proposes an effective computing framework for Short-Term Load Forecasting (STLF). The proposed technique copes with the stochastic variations of the load demand using a stacked generalization approach. This approach combines three models, namely, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting machine (XGB), and Multi-Layer Perceptron (MLP). The inner mechanism of Stacked XGB-LGBM-MLP model consists of generating a meta-data from XGB and LGBM models to compute the final predictions using MLP network. The performance of the proposed Stacked XGB-LGBM-MLP model is validated using two datasets from different locations: Malaysia and New England. The main contributions of this paper are: 1) A novel stacking ensemble-based algorithm is proposed; 2) An effective STLF technique is introduced; 3) A critical multi-study analysis for hyperparameter optimization with five techniques is comprehensively performed; 4) A performance comparative study using two datasets and reference models is conducted. Several case studies have been carried out to prove the performance superiority of the proposed model compared to both existing benchmark techniques and hybrid models. • A novel Stacked XGB-LGBM-MLP model is proposed to improve the overall regression performance. • A novel STLF technique is explored and developed in this study. • A comparative analysis of five hyperparameter optimization algorithms was comprehensively presented for STLF. • An assessment of the proposed technique is conducted using two real datasets. • A comparative study with the recent benchmark techniques is performed. [ABSTRACT FROM AUTHOR]

• Gauss-Hermite integral based multi-point estimation method is proposed to calculate the probabilistic transient stability criterion derived through the PDSR. • To guarantee the calculation accuracy and speed at the same time, the optimal number for the sampling points is determined based on PDSR hyperplanes and improved particle swarm optimization algorithm (IPSO). • Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. • Calculation accuracy of the proposed method is higher than other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. With the increasing penetration of wind power, probabilistic transient stability assessment becomes critical to deal with the uncertainties and correlations of wind power, thus improving the reliability of power system. This paper proposes an improved multi-point estimation based probabilistic transient stability assessment method. Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. Gauss-Hermite integral based multi-point estimation method is proposed to calculate transient stability criterion derived through practical dynamic security region (PDSR). Optimal number of the sampling points is determined based on PDSR hyperplanes and an improved particle swarm optimization algorithm (IPSO). Test results on the New England 10-generators 39-buses system and a practical provincial power system in China show that the calculation accuracy of the proposed method is higher than that of other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. [ABSTRACT FROM AUTHOR]

In this work we present a novel technique, based on a trust-region optimization algorithm and second-order trajectory sensitivities, to compute the extreme trajectories of power system dynamic simulations given a bounded set that represents parametric uncertainty. We show how this method, while remaining computationally efficient compared with sampling-based techniques, overcomes the limitations of previous sensitivity-based techniques to approximate the bounds of the trajectories when the local approximation loses validity because of the nonlinearity. We present several numerical experiments that showcase the accuracy and scalability of the technique, including a demonstration on the IEEE New England test system. [ABSTRACT FROM AUTHOR]

This manuscript proposes a novel modified whale optimization algorithm-based solution approach for congestion controlling cost problem in power system framework. The integral objective of the proposed methodology is twofold: firstly, to identify the most sensitive bus in the power system framework using the Bus Sensitivity Factor for optimal positioning of the wind power source that will influence the power flow in the overburden line. Secondly, the selection of the most sensitive generators participating in the power rescheduling process is achieved considering the Generator Sensitivity Factor that are computed based on the congested line power flow status. Further, the proposed MWOA is implemented to achieve the minimum congestion cost with congestion alleviation. The notion of MWOA has been developed with the introduction of two correction factors that enhances the coordination between the exploration and exploitation processes which prevents it from premature convergence and aids in its searching capability towards the global optima. The objective function is subjected to the sequential execution of the proposed optimization technique. The potency of the proposed approach is justified considering the system parameters and constraints for the 39-bus New England test system and IEEE-118 bus system. MATLAB 2016(a) is utilized as the simulation platform. The simulation results obtained with the proposed approach shows a substantial curtailment in the congestion cost, improvement in the system losses and voltages, and provide a better convergence profile when compared to other existing optimization techniques. [ABSTRACT FROM AUTHOR]

• Six bioinspired optimization techniques used for the Dynamic TTC calculation. • Contingencies, operational and small-signal stability limits are considered. • Tools for power flow calculation and modal analysis are easily integrated. • Quality of solutions, computational effort and variability are discussed. • The proposed approach is suitable for planning studies. This paper proposes a straightforward approach to determine the Total Transfer Capability (TTC) between two power system areas taking into account security constraints. The main goal is to adjust the generator's control variables (active generated power and terminal voltage) to maximize the TTC considering contingency scenarios and small-signal stability constraints (minimum damping ratio requirement). It is a challenging nonconvex and nonlinear optimization problem that is generally solved in the literature by methods based on approximations in which the computational complexity involved outweighs the accuracy. Here, conventional power flow and modal analysis tools are easily integrated into an optimization approach solved by six bioinspired optimization techniques. Simulations are conducted for the well-known New-England test system, and transient stability studies validate the obtained results. [ABSTRACT FROM AUTHOR]

In order to guarantee the continuous operation of electric power systems (EPS), a proper planning of these networks in steady and transient state must be performed. This paper presents an N − 1 multi-contingency AC optimal power flow (OPF) that embeds, in the same mathematical optimization model, a set of transient stability constraints (TSC) that guarantee rotor angle and angular velocity variation within technical limits, at given N − 1 contingencies. Furthermore, the proposed model considers the operation of volt/var controllers, such as shunt elements and OLTC transformers, to further improve the operation of the EPS, under given levels of demand and generation. Taking advantage of the classical first-order transient stability model of synchronous machines and the implicit trapezoidal integration rule, the proposed model can be formulated as a stand-alone mixed-integer nonlinear programming (MINLP) model. Then, through well-established linearization techniques, the initially proposed MINLP model is transformed into a new mixed-integer linear programming (MILP) model, which can be implemented via algebraic programming languages, such as AMPL, and solved using convex optimization solvers, such as CPLEX. Three systems with dissimilar number of synchronous machines and nodes have been used for tests (i.e., the 9-Bus/3-Generator Western System Coordinating Council (WSCC) system, the 39-Bus/10-Generator New England system and the 68-Bus/16-Generator IEEE system). The efficiency of the proposed linearization techniques and the stability requirements of the solutions have been validated using an exact AC power flow and the transient stability analysis program PSAT-Matlab. Results show the ability of the proposed MILP model to provide stable operating points under N − 1 contingencies, at minimum production cost. [ABSTRACT FROM AUTHOR]

The initiative to manage congestion has gained interest in the current deregulated scenario. The principle commitment of the work in this article is to extend the gravitational search algorithm (GSA) as an efficient metaheuristic optimizing algorithm to diminish the rescheduling cost and efficiently attenuate the overloading of the line with the minimal deviation in the active power generation. The congestion management drive is accomplished by prioritizing the generators based on their sensitivity values. Thereafter, the GSA is introduced to optimally minimize the rescheduling cost along with the minimization of the total amount of active power output and system losses. The potency of the proposed method is tested on the 39-bus New England System and the IEEE 30-bus system and 118-bus system, and the outcomes achieved with the GSA outperform the results reported with other algorithms. [ABSTRACT FROM AUTHOR]