Showing papers by "K. Shanti Swarup published in 2016"

Mathematical morphology-based islanding detection for distributed generation

Abstract: This study proposes a novel method based on mathematical morphology (MM) for islanding detection in micro-grid integrated with distributed generation (DG). The method uses basic MM operators like dilate erode difference filter (DEDF) to operate on three-phase voltage and current signals on target DG location. These filter output values are fed to a new operator defined as the average of the difference between maximum and minimum value of DEDF at each structuring element called K V and K I . Accordingly, a new operator called the MM ratio index (MM RI) of K I to K V is derived. The MM RI computed is used to track the islanding condition from non-islanding condition. Additionally, to further aid the islanding detection, a mathematical morphological operator close open difference filter on negative sequence voltage is computed, which is passed through a difference operator D. The proposed method is analysed extensively to efficiently detect islanding in electrical power distribution network integrated with DG.

Demand Response based Automatic Generation Control in smart-grid deregulated market

Abstract: Demand response is a dynamic mechanism which helps to manage customer participation in response to power supply conditions under smart grid environment thereby contributing in system operation studies like Automatic Generation Control. This paper, which consists of two parts, presents Automatic Generation Control scheme under a deregulated environment using demand response. The first part proposes a transfer function model for power gain that can be achieved from price based demand response of thermostatically controlled loads. The second part proposes an Automatic Generation Control scheme under a deregulated system involving competing generation companies. In this paper Automatic Generation Control has been modeled as a collaborative stochastic game using Reinforcement based Learning. The advantage of this model is that the generation companies produce optimal amount of power that is required to minimize their cost of power production, while preserving the spirit of deregulation. Reinforcement Learning based Automatic Generation Control scheme incorporating demand response transfer function was simulated. The simulation results show that this approach can minimize the frequency deviation during load frequency control. The main contribution of the paper is the proposal of an AGC scheme that is suitable under smart grid (Demand Response) and deregulated environment (Competition among Generation companies).

Price based Demand Response strategy considering load priorities

Abstract: Demand Response (DR) refers to response of the load according to the supply conditions such as tariff, frequency etc. This feature is relevant in a system with high penetration of intermittent renewable energy resources. Current strategies do not consider the user inconvenience or losses caused by DR. In this paper, a new strategy for DR considering the user inconvenience/losses caused by load re-scheduling is presented. Demand response in smart grid involves changing the electricity usage profile of the customer according to energy pricing for economic advantage. But it causes loss of comfort, energy loss and productivity loss for the user. An approach is proposed to address this issue using Predicted Mean Vote (PMV) based thermal sensation model for estimating thermal comfort with Heating, Ventilating and Air Conditioning (HVAC) Loads. Mathematical formulation to account energy losses and productivity losses during DR is also proposed A multi-objective algorithm based on evolutionary computation for load scheduling was developed and tested for multiple scenarios.

Optimal Electric Vehicle charging schedule for demand side management

Abstract: Demand side management (DSM) aims at reduction of the peak demand thereby flattening the load profile by allowing customers to actively participate in the overall operation of the smart grid. This leads to significant reduction in total generation cost and emission levels. While introduction of Electric Vehicles (EV) in the grid can be looked upon as an additional load thus arising the need for increased generation, the price elasticity of EV load can serve as an opportunity to implement DSM. A V2G strategy is proposed to enable the reshaping of load profile by means of peak shaving and valley filling. By harnessing the energy storage of EVs batteries, an objective function is proposed for V2G control. Substantial savings and reduction in emissions along with reduction in peak demand, on implementation of this strategy, is evident from the simulation results. Optimal penetration level of EVs, based on the environmental consideration is also discussed.

High Impedance Fault detection using DWT for transmission and distribution networks

Abstract: The objective of intelligent electronic devices in grids running fault detection algorithm is to evaluate the special features in patterns of the voltages and currents samples taken at strategic points for transmission and distribution networks and detect if a fault has occurred. Fault detection is necessary to improve power grid reliability and efficiency for safe power supply transmission and distribution. High Impedance Faults (HIF) are an important type of power system faults that are difficult to be detected by conventional over-current protective relays because of their very low fault currents. High impedance faults (HIFs) creates lot of transients in the power system and can result in equipment damage and electrical shocks. Therefore special methods have to be developed and utilized in order to detect them quickly. DWT is an effective method for HIF detection as it is capable of extracting transient information in both frequency and time domains simultaneously. High frequency transient energies is shown to be useful for accurate fault detection and classification.

Evaluation of community virtual power plant under various pricing schemes

Abstract: Technological advancement on the electricity grid has focused on maximizing its use. This has led to the introduction of energy storage. Energy storage could be used to provide both peak and off-peak services to the grid. Recent work on the use of small units of energy storage like battery has proposed the vehicle to grid system. It is propose in this work to have energy storage device embedded inside the house of the energy consumer. In such a system, consumers with battery energy storage can be aggregated in to a community virtual power plant. In this paper, an optimized energy resource allocation algorithm is presented for a virtual power plant using genetic algorithm. The results show that it is critical to have a pricing scheme that help achieve goals for grid, virtual power plant, and consumers.

Sizing of solar DC microgrid for sustainable off-grid communities: Economics, policies and societal implications

Abstract: Nearly 30% of the world's population does not have access to electricity In India, more than 300 million people do not have access to grid power In view of providing reliable electricity to these people, renewable energy solutions with storage are becoming popular In areas were the cost of bringing the conventional grid is high, off-grid solutions are being implemented While designing solar PV systems for rural communities, sizing of the solar PV module and battery storage becomes crucial and plays an important role in determining the overall cost of the project, Levelized Cost Of Energy (LCOE), reliability of supply and battery life Also, using a DC microgrid system reduces the overall losses and energy consumption to meet the same needs In this paper, sizing of solar PV module and battery storage for a village in India has been done using HOMER software The size of the system majorly depends on the solar radiation available in that location, energy usage pattern and the criticality of the load to be met The analysis done shows that the LCOE obtained for the PV-battery system is almost on par with the grid electricity prices The current situation calls for revision of the policies, subsidies and incentives in two years from now

Investigation of an optimized energy resource allocation algorithm for a community based virtual power plant

Abstract: Recently, significant advances in renewable energy generation have made it possible to consider consumers as prosumers. However, with increase in embedded generation, storage of electrical energy in batteries, flywheels and supercapacitors has become important so as to better utilize the existing grid by helping smooth the peaks and troughs of renewable electricity generation, and also of demand. This has led to the possibility of controlling the times when stored energy from these storage units is fed back to the grid. In this paper we look at how energy resource sharing is achieved if these storage units are part of a virtual power plant. In a virtual power plant, these storage units become energy resources that need to be optimally scheduled over time so as to benefit both prosumer and the grid supplier. In this paper, a smart energy resources allocation algorithm is presented for a virtual power plants using genetic algorithms. It is also proposed that the cause of battery depreciation be accounted for in the allocation of discharge rates. The algorithm was tested under various pricing scenarios, depreciation cost, as well as constraint. The results are presented and discussed. Conclusions were drawn, and suggestion for further work was made.

Optimal generation and reactive power scheduling of plug-in electric vehicles in V2G environment

Abstract: With the expected surge of Electric Vehicles (EV) in the market in the near future, a threat to the grid is anticipated Introducing EVs in the grid serves as an additional load thus arising the need for increased generation which increases the total fuel cost An optimization algorithm has been proposed to minimize the total fuel cost by performing Economic Load Dispatch (ELD) of the committed generators and by optimizing the reactive power flow (Q ev ) from EV to grid Implementation of this algorithm leads to an improvement in the voltage profile and decrease in the losses in the system The proposed algorithm gives optimal values of reactive power that must flow from EV to grid and it also gives optimal real power generation of the committed generators System studies have been performed at different penetration levels and the results have been compared At higher penetration levels the improvement in the grid parameters has been found to be more significant

Short term energy forecasting techniques for virtual power plants

Abstract: The advent of smart meter technology has enabled periodic monitoring of consumer energy consumption. Hence, short term energy forecasting is gaining more importance than conventional load forecasting. An Accurate forecasting of energy consumption is indispensable for the proper functioning of a virtual power plant (VPP). This paper focuses on short term energy forecasting in a VPP. The factors that influence energy forecasting in a VPP are identified and an artificial neural network based energy forecasting model is built. The model is tested on Sydney/ New South Wales (NSW) electricity grid. It considers the historical weather data and holidays in Sydney/ NSW and forecasts the energy consumption pattern with sufficient accuracy.

A multiobjective approach for optimal allocation of charging station to electric vehicles

Abstract: A surge of Electric Vehicles (EV) is anticipated in the market in the near future, which will pose a challenge to avail charging facilities to the EV users at affordable rates. This paper presents a Locational Marginal Pricing(LMP) based multiobjective approach for optimal charging station allocation to different types of EVs in the market. With the main objectives of minimizing the charging cost and maximizing the amount of charging, the proposed approach also maximizes the profit of EV aggregator. The multiobjective optimization problem has been solved using two different methods, namely NSGA II and MOPSO and the results are compared. The efficacy of the proposed optimization approach was demonstrated by performing analysis on the 132kV Nagpur Ringmain system. The case study results show that a significant reduction in the charging cost is possible with the two optimization methods as opposed to random allocation. A comparison between the two methods suggest that NSGA II gives better results as compared to MOPSO.

Smart distribution network restoration using multi agent system

Abstract: Distributed restoration strategy for a power system is evaluated in the present paper. This evaluates the benefit from a traditional centralized scheme in terms of speed and reliability giving economical solution to the problem. Multi agent system approach is exploited in this work to get solution faster while reducing the computational burden. Restoration problem is a multi-objective, multi-constraint problem which changes from node to node depending on their characteristics (load bus or generator bus) which can be easily incorporated in a multi agent system with individual agent requirements. This will increase the speed of restoration providing its the capability to self restore utilizing maximum capacity in given condition.

Impact of DFIG based wind energy conversion system on fault studies and power swings

Abstract: The integration of renewable energy sources into power systems changes the transient and fault current characteristics of the conventional grid. The variation in the system response during grid disturbance like sudden load changes or fault would cause the failure of traditional protection and control. Since the operating condition of DFIG depends on the characteristics of the grid and its control, the short circuit current in the system becomes more complex. This paper analyzes the short circuit characteristics of DFIG and develops an analytical expression for the three phase fault current. Depending on the severity of the fault in terms of voltage drop, the fault current responses are developed with and without crow bar resistance. The power swings that originates in the system when the fault is cleared are also analyzed in the paper. The transient simulation studies are performed in PSCAD/EMTDC by integrating DFIG to WSCC system. Case studies are performed to analyze the impact of location of fault to the power swing and fault current contribution.

Modeling and control of DFIG with battery management system for grid power leveling

Abstract: The huge penetration of wind energy sources in the micro grid makes the grid operations more intrinsic. The intermittent nature of wind fluctuates the electrical power output of the wind generator. The battery storage mechanism are usually employed to mitigate these power fluctuations to an extend. This paper presents the modeling and control of grid connected wind driven Doubly Fed Induction Generator (DFIG) with Battery Energy Storage System (BESS) to mitigate the power fluctuations which arises due to the wind speed variations. Super capacitor is employed to improve the transient response and thus life of the battery. The battery State of Charge (SoC) is considered while charging and discharging of BESS. A 0.9 MW DFIG is simulated in PSCAD/EMTDC and verified the control schemes.