K. Shanti Swarup

Bio: K. Shanti Swarup is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Smart grid & Relay. The author has an hindex of 13, co-authored 77 publications receiving 677 citations.

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.

Coordinated maintenance scheduling of Gencos and Transcos in restructured power systems

Abstract: This paper proposes a methodology for the development of automatic scheduling techniques, for preventive maintenance of generating units and lines, in a competitive electric energy environment, with the inclusion of transmission constraints and forced outage rates, over a specified operational period. For generator maintenance the objective of the ISO is to maintain adequate level of reliability throughout the operational period (for which Bender's decomposition technique is used) and the objective of the GENCO is to maximize profit or to minimize loss in profit (for which transmission constrained price based unit commitment, TCPBUC, based on Lagrangian relaxation method is used). For line maintenance minimum cost model bender's technique with adequate level of reliability is used. A coordinating technique using penalty factors is incorporated to converge the conflicting objectives. The transmission constraints are modeled using DC sensitivity factors. Case study with a 6 bus, 3 generator, 11 line system is presented and discussed.

Offering strategy for a photovoltaic power plant in electricity market

Abstract: With the large-scale manufacturing and production, the per-unit cost of energy from photovoltaic modules have decreased, and the incentives given by the governments are slowly withdrawn. Consequently, the photovoltaic power plants are able to, and asked to, participate in the electricity markets to trade its power competing with conventional generators. This work contributes to the offering strategies for photovoltaic power plants in the day-ahead electricity market with balancing market. The uncertain variables considered are day-ahead electricity prices and actual solar power production for which scenarios are created from historical data. The photovoltaic power plant is modeled as a price taker and the offering problem is solved as a linear programming problem with stochastic optimization.

Particle Swarm Optimization based corrective strategy to alleviate overloads in power system

Abstract: This paper presents a new Particle Swarm Optimization based corrective strategy to alleviate overloads of transmission lines. A Direct Acyclic Graph (DAG) technique for selection of participating generators and buses with respect to a contingency is presented. Particle Swarm Optimization (PSO) technique has been employed for generator rescheduling and/or load shedding problem locally, to restore the system from abnormal to normal operating state. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 57 and modified IEEE 118 bus systems. The result shows that the proposed approach is computationally fast, reliable and efficient, in restoring the system to normal state after a contingency with minimal control actions.

Online Short-term Solar Power Forecasting

Abstract: This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

Particle Swarm Optimization: Technique, System and Challenges

Abstract: Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling. A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO. On the other hand, basic PSO is more appropriate to process static, simple optimization problem. Modification PSO is developed for solving the basic PSO problem. The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications that have implemented using PSO. The application can show which one the modified or variant PSO that haven’t been made and which one the modified or variant PSO that will be developed.

Synchrophasor Measurement Technology in Power Systems: Panorama and State-of-the-Art

Abstract: Phasor measurement units (PMUs) are rapidly being deployed in electric power networks across the globe. Wide-area measurement system (WAMS), which builds upon PMUs and fast communication links, is consequently emerging as an advanced monitoring and control infrastructure. Rapid adaptation of such devices and technologies has led the researchers to investigate multitude of challenges and pursue opportunities in synchrophasor measurement technology, PMU structural design, PMU placement, miscellaneous applications of PMU from local perspectives, and various WAMS functionalities from the system perspective. Relevant research articles appeared in the IEEE and IET publications from 1983 through 2014 are rigorously surveyed in this paper to represent a panorama of research progress lines. This bibliography will aid academic researchers and practicing engineers in adopting appropriate topics and will stimulate utilities toward development and implementation of software packages.