Online Generators Research Articles

Condition-based switching, loading, and age-based maintenance policies for standby systems

Standby techniques are widely incorporated in structural design to enhance the inherent reliability of systems. To further leverage the system performance during operation, decision-makers can adopt operational policies to manage system degradation. Specifically, at the system level, unit switching that dynamically determines the online unit contributes to avoiding unexpected shutdowns. At the unit level, adjusting load levels to manage the trade-off between condition degradation and revenue accumulation is crucial for maximizing profit. Additionally, adopting age-based maintenance as a tactical decision, which effectively facilitates the integration of maintenance resources, can be implemented to restore a degraded system. For instance, maintenance is typically scheduled at fixed moments for multi-generator power systems located in remote areas. In between maintenance moments, the proactive switching of generators can ensure uninterrupted output, and the adjustment of load levels for online generators helps to maximize output. Motivated by such engineering practices, this paper investigates condition-based switching, loading, and age-based maintenance policies for standby systems to maximize the expected profit rate in the long-run horizon. The problem is formulated as a Markov decision process. The structural properties of the control-limit switching and monotone loading policies are analyzed for easy policy implementation and efficient problem solutions. For comparative purposes, several heuristic policies are proposed and evaluated. Finally, numerical examples are presented to validate theoretical results and illustrate the superiority of the proposed risk control policy.

On the robustness of machine-learnt proxies for security constrained optimal power flow solvers

In this paper, we focus on the robustness of machine learning based proxies used to speed up, alone or jointly with state-of-the-art mathematical optimization methods, optimal power flow and security-constrained optimal power flow calculations. On data sets for the Nordic32 alternative current security-constrained optimal power flow benchmark, we evaluate the robustness of proxies with respect to load distribution, power factors, on-line generators and network topology, and generator costs. We show that simplified random load sampling procedures that are used in most published academic studies, are insufficient to yield robust machine learnt proxies, and consequently limit their usefulness in the real world. Based on these results, we formulate recommendations for future research.

Efficient Generators of the Generalized Fractional Gaussian Noise and Cauchy Processes

In the last years of the past century, complex correlation structures were empirically observed, both in aggregated and individual traffic traces, including long-range dependence, large-timescale self-similarity and multi-fractality. The use of stochastic processes consistent with these properties has opened new research fields in network performance analysis and in simulation studies, where the efficient synthetic generation of samples is one of the main topics. Nowadays, networks have to support data services for traffic sources that are poorly understood or still insufficiently observed, for which simple, reproducible, and good traffic models are yet to be identified, and it is reasonable to expect that previous generators could be useful. For this reason, as a continuation of our previous work, in this paper, we describe efficient and online generators of the correlation structures of the generalized fractional noise process (gfGn) and the generalized Cauchy (gC) process, proposed recently. Moreover, we explain how we can use the Whittle estimator in order to choose the parameters of each process that give rise to a better adjustment of the empirical traces.

Dali-dali at dekonstruksyon: Poetika at politika sa panahon ng pinagmamadaling pagtula

Abstract This paper studies the digital modes of producing and distributing Philippine poetry during the presidencies of Gloria Macapagal Arroyo, Benigno Aquino III, and Rodrigo Duterte. The corpus used for this study are various poems created and dispensed through online generators, single message service (SMS) or text messaging, and social media, Twitter in particular. Democratic access to these new technologies has given literary producers newer platforms to exercise creativity and to interrogate the times. Poetry production via digital platforms is both an exploration of the genre and of political praxis. This paper studies select digital poems traversing what Leonardo Dianzon expounded as the cultural traditions of panunudyo (parody) and panunuya (irony or sarcasm). Further, this paper explores how poets in this digital age have variably contributed to the literary continuum and protest tradition of the Propaganda Movement’s Marcelo H. Del Pilar.

ЦИФРОВОЕ РАЗВИТИЕ СИБИРСКОГО ФЕДЕРАЛЬНОГО ОКРУГА: КЛАСТЕРИЗАЦИЯ РЕГИОНОВ В ОБЛАКЕ ТЕГОВ4

Free access to the flow of regional news and online generators of tag clouds opens up new opportunities for human geography with regard to processing big data and detecting geographical patterns. The aim of the study was to identify the current digital development priorities of ten Siberian regions by clustering them in a tag cloud from the stream of official regional news. There were identified twelve tags (labels, keywords) that reflect the priorities of digital development. Based on the flow of news (texts) from the regional ministries of digital development for the first five months of 2021, clouds of the most common tags were created. Five bands of tag frequency were set. A measure of the distance between the regions in the tag cloud and an algorithm for grouping regions into clusters were proposed. It has been found that there are no regions in Siberia with the same priorities for digital development; the existing differences allow us to group all the regions into two clusters. On this basis, the initial hypothesis about the uniformity of digital development priorities in all the regions has been rejected. Ten features of the digital development of Siberia are listed. The directions of further research on this issue are presented.

Addressing Abrupt PV Disturbances, and Mitigating Net Load Profile’s Ramp and Peak Demands, Using Distributed Storage Devices

At high penetration level of photovoltaic (PV) generators, their abrupt disturbances (caused by moving clouds) cause voltage and frequency perturbations and increase system losses. Meanwhile, the daily irradiation profile increases the slope in the net-load profile, for example, California duck curve, which imposes the challenge of quickly bringing on-line conventional generators in the early evening hours. Accordingly, this paper presents an approach to achieve two objectives: (1) address abrupt disturbances caused by PV generators, and (2) shape the net load profile. The approach is based on employing battery energy storage (BES) systems coupled with PV generators and equipped with proper controls. The proposed BES addresses these two issues by realizing flexible power ramp-up and ramp-down rates by the combined PV and BES. This paper presents the principles, modeling and control design aspects of the proposed system. A hybrid dc/ac study system is simulated and the effectiveness of the proposed BES in reducing the impacts of disturbances on both the dc and ac subsystems is verified. It is then shown that the proposed PV-BES modifies the daily load profile to mitigate the required challenge for quickly bringing on-line synchronous generators.

An WAMS-Based Online Generators Coherency Identification Approach for Controlled Islanding

In this paper, an online model-free approach based on the largest Lyapunov exponents (LLE) and angular velocity deviation is provided for coherent groups identification (CGI). Firstly, by using a model-free LLE calculation method, the generator coherency identification (GCI) criterions are proposed by establishing the mathematical relationship between the LLE and angular velocity difference. Secondly, an online CGI scheme for power systems is designed. Compared with the existing measurement-based methods, the breakthrough work of this paper is that the proposed GCI criterions combines the stability theory of nonlinear dynamic trajectory based on LLE with the physical mechanism of the out-of-step between generators, such that it can improve both the speed and the accuracy of the GCI. Besides, the proposed approach needs not to find the optimal observation window size and shorten the required observation window size greatly. Moreover, it can be applied to online CGI with small computation requirement. Extensive test results on New England 39-Bus System and practical East China (EC) power grid, as well as the comparisons with existing CGI methods verify high accuracy and efficiency of the proposed approach.

Characteristic Analysis of Subsynchronous Resonance in Practical Wind Farms Connected to Series-Compensated Transmissions

The emerging subsynchronous resonance (SSR) caused by the interaction of wind turbine generators (WTGs) with series compensation has aroused great concerns. For this particular issue, this paper is aimed to fill the gap between theoretical studies and actual observations. By analyzing the field data of 58 SSR events captured in a practical wind power system and examining the observed dynamics with previous theoretical results, the mechanism and characteristics of SSR are revealed in a more explicit and substantial way. The necessary conditions and dominant influential factors are identified and the underlying reasons are discovered. Theoretically derived as well as practically measured impedance models have demonstrated that the converter control of doubly fed induction generator (DFIG) produces negative resistance at the slip frequency and thus causes unstable SSR; while permanent magnet synchronous generators and self-excited induction generators are just passively engaged in those SSR incidents. The distribution of the oscillation frequency has also been examined with field measurements. It is discovered that WTGs at different locations participate into the same SSR mode and their frequencies are not fixed but keep changing with the time, the variation of grid topology, and the number of online generators.

Optimisation model for online generators when a new generator is about to get started during power system restoration process

Rapid restoration of power systems is vitally important following an outage; however, existing optimal objectives and models to start up all the generators may cause problems where by some generators are ramping while others are waiting shown in the calculation results. To address this problem considering the generator regulation characteristics, a variable-constrained maximum-value minimisation model is proposed in this study to describe the practical problem. By introducing the time variable t, the variable-constrained optimisation problem is converted to a constrained optimal power flow problem, which can be solved using common optimisation approaches. Applying the proposed model and method, the optimisation method is discussed considering the characteristics of generators during power system black start. Numerical results show that the algorithm is effective and can significantly reduce the restoration time. The algorithm described here is applied to the Guangdong power grid self-healing decision-making system.

Honey Bees Searching Optimization for Non Convex Dynamic Power Dispatch Problem in Thermal Units

Dynamic Power Dispatch (DPD) intends to schedule the online generators output with the forecasted load demand over a certain period of time, in order to operate an electric power system most economically within its operating limits. The behavior and the activity of honey bees during food foraging is implemented as a Honey Bees Searching Optimization (HBSO) technique in solving the DPD problem. The HBSO algorithm provides a balance between exploration and exploitation of a search space. Further, the HBSO is independent of control parameters when compared to other techniques. The DPD formulation includes ramp rate constraint, rectified sinusoidal effect, uniformity and disparity constraint, which normally present in the realistic power system. In order to express the efficacy of the HBSO algorithm a test case of 6 units DPD problem is considered and lower fuel cost is obtained when compared with other algorithm.

Swarm Intelligence Approaches to Optimal Power Flow Problem With Distributed Generator Failures in Power Networks

Distributed generation becomes more and more important in modern power systems. However, the increasing use of distributed generators causes the concerns on the increasing system risk due to their likely failure or uncontrollable power outputs based on such renewable energy sources as wind and the sun. This work for the first time formulates an optimal power flow problem by considering controllable and uncontrollable distributed generators in power networks. The problem for the cases of single and multiple generator failures is addressed as an example. The methods are presented to find its power output solution of controllable online generators via particle swarm optimization and group search optimizer for coping with the difficult scenarios in a power network. The proposed methods are tested on an IEEE 14-bus system, and several population initialization strategies are investigated and compared for the algorithms. The simulation results confirm their effectiveness for optimal power management and effective control of a power network.

An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch

Economic dispatch is carried out at the energy control center to find out the optimal output of thermal generating units such that power balance criterion is met, unit operating limits are satisfied and the fuel cost is minimized. With growing environmental awareness and strict government regulations throughout the world, it has become essential to optimize not only the total fuel cost but also the harmful emissions, both, under static as well as dynamic conditions. The static environment economic dispatch finds the optimal output of generating units for a fixed load demand at a given time, while the dynamic environmental economic dispatch schedules the output of online generators with changing power demands over a certain time period (normally one day) so as to minimize these two conflicting objectives, simultaneously. In this paper, the price penalty factor approach is employed for simultaneous minimization of cost and emission. The generator ramp rate constraints, non-convex and discontinuous nature of cost function and the large number of generators in practical power plants, make this problem very difficult to solve. Here, a fuzzy ranking approach is employed to identify the solution which offers the best compromise between cost and emission objectives.This paper proposes an improved bacterial foraging algorithm (IBFA) in which a parameter automation strategy and crossover operation is used in micro BFA to improve computational efficiency. The performance of IBFA is compared with classical BFA and with previously published literature on four standard test systems and is found to be better.

Considering transmission loss for an economic dispatch problem without valve-point loading using an EP-EPSO algorithm

Economic dispatch (ED) is one of the most important optimization problems in a power system. The objective of ED is sharing the power demand among the online generators while keeping the minimum cost of generation as a constraint. The aim of this paper is to operate an electric power system as economically as possible within its security limits. This paper proposes the following 2 new particle swarm optimization (PSO) algorithms to solve a nonconvex economic dispatch problem: an efficient PSO is termed as efficient particle swarm optimization (EPSO), and a hybrid of evolutionary programming (EP) and EPSO is termed as EP-EPSO. Since ED was introduced, several methods have been used to solve these problems. However, none of these methods can provide an optimal solution because they become trapped at some local optima. Stochastic optimization techniques such as EPSO and EP have the advantage of a good convergent property. A significant speed-up can be obtained by the hybrid of these algorithms. The proposed techniques are tested on standard test systems available in the literature. The performance of the proposed EP-EPSO is compared with a) biogeography-based optimization, b) adaptive particle swarm optimization, c) the genetic algorithm, d) a 2-phase neural network, e) PSO with time-varying acceleration coefficients, f) NEW-PSO, and g) differential evolution with biogeography-based optimization. It is observed that the EP-EPSO has a higher convergence rate, advanced quality, and better optimal cost when compared to the other techniques. The considered ED problems have been solved, including transmission losses without valve-point loading effects.

An Improved Differential Evolution and Its Industrial Application

In this paper, an improved Differential Evolution (DE) that incorporates double wavelet-based operations is proposed to solve the Economic Load Dispatch (ELD) problem. The double wavelet mutations are applied in order to enhance DE in exploring the solution space more effectively for better solution quality and stability. The first stage of wavelet operation is embedded in the DE mutation operation, in which the scaling factor is governed by a wavelet function. In the second stage, a wavelet-based mutation operation is embedded in the DE crossover operation. The trial population vectors are modified by the wavelet function. A suite of benchmark test functions is employed to evaluate the performance of the proposed DE in different problems. The result shows empirically that the proposed method out-performs signifycantly the conventional methods in terms of convergence speed, solution quality and solution stability. Then the proposed method is applied to the Economic Load Dispatch with Valve-Point Loading (ELD-VPL) problem, which is a process to share the power demand among the online generators in a power system for minimum fuel cost. Two different conditions of the ELD problem have been tested in this paper. It is observed that the proposed method gives satisfactory optimal costs when compared with the other techniques in the literature.

An Improved Differential Evolution Algorithm for Dynamic Economic Dispatch of Power Systems

Differential Evolution (DE), a vector population based stochastic optimization method has been introduced to the public in 1995. During the last 25 years research on and with DE has reached an impressive state, yet there are still many open questions, In this paper ,An improved differential evolution (IDE) algorithm was presented for power system Dynamic economic dispatch(IDED), Dynamic economic dispatch (DED), an extension of the economic dispatch problem, is a method of scheduling the online generators with a predicted load demand over acertain period of time taking into account the various constraints imposed on the system operation. The results indicate that IDE algorithm outperforms GA ,PSO and DE algorithms in solving DED problems.

A novel differential evolution application to short-term electrical power generation scheduling

This paper proposes a new way of applying a differential evolution algorithm to short-term electrical power generation scheduling. Traditionally, the problem is divided into two subproblems. An evolutionary algorithm, which works with binary decision variables, is applied to the first subproblem to find a low cost scheduling of power generators, satisfying some operational constraints. Then, the lambda-iteration method, is used to calculate the power generated by the online generators. In this study, the problem is treated as a whole for the first time in literature and an application of a real-valued differential evolution algorithm is proposed. This approach eliminates the use of an iterative local search technique such as lambda-iteration in all solution evaluations. Through comparisons with results from literature, it is shown that the proposed method achieves a similar solution quality to existing methods, without needing the time consuming lambda-iteration step. Finally, the new approach is applied to real-world data from the Turkish interconnected power network.

A new honey bee mating optimization algorithm for non-smooth economic dispatch

Abstract The non-storage characteristics of electricity and the increasing fuel costs worldwide call for the need to operate the systems more economically. Economic dispatch (ED) is one of the most important optimization problems in power systems. ED has the objective of dividing the power demand among the online generators economically while satisfying various constraints. The importance of economic dispatch is to get maximum usable power using minimum resources. To solve the static ED problem, honey bee mating algorithm (HBMO) can be used. The basic disadvantage of the original HBMO algorithm is the fact that it may miss the optimum and provide a near optimum solution in a limited runtime period. In order to avoid this shortcoming, we propose a new method that improves the mating process of HBMO and also, combines the improved HBMO with a Chaotic Local Search (CLS) called Chaotic Improved Honey Bee Mating Optimization (CIHBMO). The proposed algorithm is used to solve ED problems taking into account the nonlinear generator characteristics such as prohibited operation zones, multi-fuel and valve-point loading effects. The CIHBMO algorithm is tested on three test systems and compared with other methods in the literature. Results have shown that the proposed method is efficient and fast for ED problems with non-smooth and non-continuous fuel cost functions. Moreover, the optimal power dispatch obtained by the algorithm is superior to previous reported results.

Particle swarm optimization with crazy particles for nonconvex economic dispatch

The paper presents an effective evolutionary method for economic power dispatch. The idea is to allocate power demand to the on-line power generators in such a manner that the cost of operation is minimized. Conventional methods assume quadratic or piecewise quadratic cost curves of power generators but modern generating units have non-linearities which make this assumption inaccurate. Evolutionary optimization methods such as genetic algorithms (GA) and particle swarm optimization (PSO) are free from convexity assumptions and succeed in achieving near global solutions due to their excellent parallel search capability. But these methods usually tend to converge prematurely to a local minimum solution, particularly when the search space is irregular. To tackle this problem “crazy particles” are introduced and their velocities are randomized to maintain momentum in the search and avoid saturation. The performance of the PSO with crazy particles has been tested on two model test systems, compared with GA and classical PSO and found to be superior.

A Probabilistic Reserve With Zero-Sum Settlement Scheme

A probabilistic reserve scheme with iterated linear programming is proposed to maintain a uniform system reliability level in each dispatch period. Besides, a new zero-sum settlement scheme is introduced. It maintains a net zero sum of payment (loss) and compensation (gain) among the online generators by considering the expected energy not served (EENS) or reserve responsibility share of each unit. Solution results of the IEEE 26-unit system have revealed that the combined probabilistic reserve and zero-sum settlement scheme gives the Gencos stimuli to enhance their performance.

Recent developments in electrolytic devices for ion chromatography

Recent developments in new electrolytic devices that utilize the electrolysis of water and charge-selective electromigration of ions through ion-exchange media have significantly changed the routine operation of ion chromatographic methods. Examples of these new electrolytic devices include on-line eluent generators that produce high-purity electrolyte eluents using deionized water as the carrier stream, continuously regenerated trap columns that remove ionic contaminants in the eluents, and continuously regenerated suppressors that reduce eluent background conductance prior to conductivity detection. The combined use of these electrolytic devices has made it possible to perform various ion chromatographic separations using only deionized water as the mobile phase. This paper reviews the operation principles of these electrolytic devices and their applications in the ion chromatographic determination of anionic and cationic analytes.