Jai Govind Singh

An Adaptive Time-Efficient Technique for Harmonic Estimation of Nonstationary Signals

Model-based parametric techniques offer many advantages over conventional discrete Fourier transform-based methods for harmonic/interharmonic estimation. However, high computational requirements restrict their applications to offline analysis purpose. In this paper, an adaptive technique based on estimation of signal parameters via rotational invariance technique (ESPRIT) is proposed that optimizes the accuracy and computation time for harmonic/interharmonic estimation of stationary as well as nonstationary power supply signals. This method first estimates the order of the model (number of sinusoids present in the distorted power supply signal) and then adjusts the autocorrelation matrix dimension based on reconstruction error. The performance of the proposed method is validated on the time-varying simulated signal, measured synthetic signal, and actual voltage signal of a distribution system supplying electric arc welding load. The comparison of the results with the short-time Fourier transform and the sliding window ESPRIT techniques shows that the proposed approach considerably reduces the computational time of high-resolution ESPRIT method along with better accuracy.

Optimal placement of UPFC for maximizing system loadability and minimize active power losses by NSGA-II

This paper presented application of a new variant of Genetic Algorithm, specialized in multi-objective optimizations problem known as Non-dominated Sorting Genetic Algorithm II (NSGA-II), to obtain the optimal allocation of Unified Power Flow Controller (UPFC) for enhancing the power system loadability as well as minimizing the active power loss in transmission line. An Optimal Power Flow (OPF) problem with mixed integer programming has been formulated for optimizing the above two objectives as well as obtaining the optimal location of the UPFC while maintaining the system security and stability margins, e.g., small signal stability, voltage stability index, and line stability factor. In addition, a fuzzy based mechanism has been employed to extract the best compromise solution from the Pareto front. The effectiveness of the proposed methodology has been investigated on a standard IEEE 30-bus and practical Java-Bali 24-bus of Indonesian systems. Results demonstrate that the static and dynamic performances of the power system can be effectively enhanced by the optimal allocation of the UPFC. Moreover, UPFC installation cost is also calculated and overall performance has been compared with existing method.

Sortino Ratio Based Portfolio Optimization Considering EVs and Renewable Energy in Microgrid Power Market

Portfolio optimization in finance is the optimal allocation of financial assets in different stocks, mutual funds, bonds etc. to maximize the returns with risk tolerance. Sortino ratio is a measure for calculating risk adjusted return of investment portfolios. Here, it is adapted for power portfolio optimization in microgrid where total load demand (including losses) is optimally distributed to different microsources so that profit per unit risk of aggregator is maximized. The diminishment in profit (from energy and reserve markets) with reference to a target profit, for different levels of uncertainties in renewable energy and EVs is consolidated to find an estimate of risk. The profit relating to deterministic forecasted data of renewable energy and pre-dispatch information from the EV parking lots is considered as the risk free target profit. The reserve market is balanced using demand response, grid power purchase, EV discharging and other dispatchable energy sources to compensate possible discrepancy between scheduled and actual dispatch. Stochastic weight tradeoff particle swarm optimization (SWT-PSO) is used to maximize Sortino ratio subjected to constraints of a modified backward-forward sweep (BFS) power flow problem. The results are found to be better in terms of reduced financial risk and increased robustness to uncertainties.

Development of PSO based control algorithms for maximizing wind energy penetration

In this paper, new methodologies have been proposed for attaining the maximum safe instantaneous wind energy penetration. Various types of control algorithms namely, load increase, generation displacement and the combined load increase and generation displacement have been developed to obtain the maximum penetration. Wind Turbine used is DFIG and dynamic model of the system by considering Turbine governor (TG), Automatic voltage regulator (AVR) have been considered. Grid stability at high penetration level is obtained by conducting eigenvalue analysis of the complete power system grid. All the control algorithms are powered by Particle Swarm Optimization Algorithm (PSO) which adjusts the grid parameters for achieving maximum wind penetration. The developed algorithms have been tested with 25-bus, 220kV practical system. The results have shown the maximum safe instantaneous wind energy penetration limit possible by various methodologies proposed.

Microgrid Energy Management Combining Sensitivities, Interval and Probabilistic Uncertainties of Renewable Generation and Loads

This paper proposes an optimal energy management approach combining sensitivities, interval, and probabilistic uncertainties of wind and solar power sources and loads in microgrid. Affine arithmetic (AA) is used to model the interval uncertainties and sensitivities in nodal power injections. However, all the elements in the interval solutions of AA-optimal power flow may not be significant in view of the probabilistic nature of statistical data. So, those elements which are significant with a desired confidence level are boxed using probability boxes obtained by deriving best fitting discrete state probability distribution functions (PDFs) for load and renewable power injections. Thus, the original hard bounded affine intervals are made soft bounded using the derived joint PDFs, forming new less conservative and more feasible intervals of cost and power flow variables. The minimization of the operational cost is taken care of by stochastic weight tradeoff particle swarm optimization. The method is tested in CIGRE LV benchmark microgrid with fuel cell, microturbine, diesel generator, wind, and solar power sources.

An integrated approach for the voltage stability enhancement of large wind integrated power systems

This paper investigates the applicability of an integrated approach for the enhancement of voltage stability margin and there by the wind penetration of large wind integrated power systems. The proposed approach involves in two objectives; the identification of weak buses in the given power systems, enhancement of voltage stability margin of the weak buses and there by the wind penetration by optimal placement and tuning of Flexible AC Transmission System (FACTS) controllers. Multiple of one type of FACTS controller namely SVC is used in the current analysis for placement in suitable weak buses. Weak bus identification is carried out by conducting the tangent vector analysis. Voltage stability enhancement at high wind penetration is assessed by using the dynamic voltage security index, the index for accessing the proximity of voltage collapse under dynamic loading conditions. The optimization of grid control parameters are carried out by using Particle Swarm Optimization (PSO) by incorporating FACTS Controllers. The developed algorithm have been tested on Wind integrated Kerala grid 25-bus practical system.

A Novel Single-Stage Single-Phase Reconfigurable Inverter Topology for a Solar Powered Hybrid AC/DC Home

This paper suggests a reconfigurable single-phase inverter topology for a hybrid ac/dc solar powered home. This inverter possesses a single-phase single-stage topology and the main advantage of this converter is that it can perform dc/dc, dc/ac, and grid tie operation, thus reducing loss, cost, and size of the converter. This hybrid ac/dc home has both ac and dc appliances. This type of home helps to reduce the power loss by avoiding unnecessary double stages of power conversion and improves the harmonic profile by isolating dc loads to dc supply side and rest to ac side. Simulation is done in MATLAB/Simulink and the obtained results are validated through hardware implementation using Arduino Uno controller. Such type of solar powered home equipped with this novel inverter topology could become a basic building block for future energy efficient smart grid and microgrid.

Optimal placement of FACTS controllers for maximising system loadability by PSO

In this paper, a multi objective-based method has been suggested to enhance the power system loadability with optimal placement of flexible AC transmission system (FACTS) controllers using particle swarm optimisation (PSO) technique. The objective function is to maximise the system loadability subjected to maintaining the system security, integrity, and stability margins within limits by obtaining the optimal location, installation costs, and control settings of the FACTS controllers. The various FACTS controllers, i.e., static var compensator (SVC), thyristor controlled series compensator (TCSC), and unified power flow controller (UPFC), have been considered in this study. The effectiveness of the proposed methodology has been investigated on the standard IEEE 14-bus, 30-bus, and practical Java-Bali 24-bus Indonesian system and the results are compared with the method suggested in the literatures. Moreover, the results obtained by PSO have also been compared with other evolutionary approach, viz., genetic algorithm (GA).

Optimal Placement of a Series FACTS Controller in Java-Bali 24-bus Indonesian System for Maximizing System Loadability by Evolutionary Optimization Technique

In this paper, a series FACTS controller namely Thyristor Controller Series Compensator (TCSC) has been suggested to enhance the power system loadability. The location of the controller and the setting of their control parameters are optimized by one type of Evolutionary Optimization Technique to improve the performance of the power network. The objective functions are to maximize the system loadability whereas maintaining system security and stability margins, e.g., small signal stability, voltage stability index, and line stability factor within limits by considering the investment costs of the controller and minimizing active power loss of the system. The series FACTS controller is modeled and incorporated in the Newton Raphson power flow problem. The effectiveness of the proposed methodology has been investigated on a practical Java-Bali 24-bus Indonesian grid system.

Economic operational Scheduling of a smart distribution network considering demand response, electric vehicles and network reconfiguration

There is an increase in complexity of distribution system operational planning due to the integration of electric vehicles (EVs), responsive loads (RLs), and distributed generation (DG) along with the possibility of distribution network reconfiguration (DNR). This paper presents a comprehensive framework to be incorporated as the central core of distribution management system (DMS) in day-ahead operational scheduling of a smart distribution network (SDN). The optimal operational decisions by system operator (DMS) will be taken by deploying the supplemented active elements including DGs, RLs, EVs, and remotely controlled switches (RCS) incorporated for realizing the DNR. Also, changes in price of power market will be under the surveillance of DMS to wisely schedule the network elements for attaining further cost savings. An unregulated charging pattern is adopted by EVs to realize the vehicle to grid (V2G) notion. A modified IEEE 33-bus test system is considered to numerical validation of the proposed method by considering two different cases which is tackled based on a modified particle swam optimization approach. In this way, the initial scheduling of the network has been done without DNR and subsequently the technical and monetary benefits associated with DNR is proved.