K. Shanti Swarup

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

Bidding strategy for pumped-storage plant in pool-based electricity market

Abstract: This paper develops optimal bidding strategies for a pumped-storage plant in a pool-based electricity market. In the competitive regime, when compared to simple hydro electric generator, profit of the pumped-storage plant is maximized by operating it as a generator when market clearing price is high and as a pump when the price is low. Based on forecasted hourly market clearing price, a multistage looping algorithm to maximize the profit of a pumped-storage plant is developed, considering both the spinning and non-spinning reserve bids and meeting the technical operating constraints of the plant. The proposed model is adaptive for the nonlinear three-dimensional relationship between the power produced, the energy stored, and the head of the associated reservoir. Different operating cycles for a realistic pumped-storage plant are considered and simulation results are reported and compared.

Hybrid DE–SQP algorithm for non-convex short term hydrothermal scheduling problem

Abstract: This paper proposes a hybrid method combining differential evolution (DE) and sequential quadratic programming (SQP) for solving short term hydrothermal scheduling problem with non-convex fuel cost function. In this paper, differential evolution (DE) is used as a global optimizer and sequential quadratic programming (SQP) method as a local optimizer to fine tune the solution. The proposed method has been tested on a multichain cascaded reservoir with an equivalent thermal test system and the simulation results are compared with existing methods reported in literatures. From the results, it clearly shows that the proposed method is giving better quality solutions than existing methods.

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.

Evolutionary Tristate PSO for Strategic Bidding of Pumped-Storage Hydroelectric Plant

Abstract: This paper develops bidding strategy for operating multiunit pumped-storage power plant in a day-ahead electricity market. Based on forecasted hourly market clearing price, the objective is to self-schedule and maximize the expected profit of the pumped-storage plant, considering both spinning and nonspinning reserve bids and meeting the technical operating constraints. Evolutionary tristate particle swarm optimization (ETPSO) based approach is proposed to solve the problem, combining basic particle swarm optimization (PSO) with tristate coding technique and genetics-based mutation operation. The discrete characteristic of a pumped-storage plant is modeled using tristate coding technique and mutation operation is used for faster convergence. The proposed model is adaptive for nonlinear 3-D relationship between the power produced, the energy stored, and the head of the associated reservoir. The proposed approach is applied for a practical utility consisting of four units. Simulation results for different operating cycles of the storage plant indicate the attractive properties of ETPSO approach with highly optimal solution and robust convergence behavior.

Artificial neural network using pattern recognition for security assessment and analysis

Abstract: Artificial neural networks using pattern recognition methodology for security assessment of electric power systems is presented. Conventional numerical methods are either too complex or time consuming. An alternative method using neural networks to address the security assessment problem and its effectiveness against conventional methods is discussed. Neural networks using pattern recognition techniques is a promising methodology for different types of security assessment. Feature selection and extraction are used for selecting best features having highest discriminating capabilities. An important feature of the approach is that it can be generalized for steady state, transient and dynamic security assessment, which is a desirable feature for on-line security analysis. The proposed approach has been tested on the WSCC 9-bus 3-generator system. Steady state, transient and dynamic security assessment classification and contingency ranking results are provided to highlight the overall classification accuracy and suitability of the approach.

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.

Practical operation strategies for pumped hydroelectric energy storage (PHES) utilising electricity price arbitrage

Abstract: In this paper, three practical operation strategies (24Optimal, 24Prognostic, and 24Hsitrocial) are compared to the optimum profit feasible for a PHES facility with a 360 MW pump, 300 MW turbine, and a 2 GWh storage utilising price arbitrage on 13 electricity spot markets. The results indicate that almost all (∼97%) of the profits can be obtained by a PHES facility when it is optimised using the 24Optimal strategy developed, which optimises the energy storage based on the day-ahead electricity prices. However, to maximise profits with the 24Optimal strategy, the day-ahead electricity prices must be the actual prices which the PHES facility is charged or the PHES operator must have very accurate price predictions. Otherwise, the predicted profit could be significantly reduced and even become a loss. Finally, using the 24Optimal strategy, the PHES profit can surpass the annual investment repayments required. However, over the 5-year period investigated (2005–2009) the annual profit from the PHES facility varied by more than 50% on five out of six electricity markets considered. Considering the 40-year lifetime of PHES, even with low investment costs, a low interest rate, and a suitable electricity market, PHES is a risky investment without a more predictable profit.

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.

Experimental validation of a real time energy management system for microgrids in islanded mode using a local day-ahead electricity market and MINLP

Abstract: Energy management systems (EMS) are vital supervisory control tools used to optimally operate and schedule Microgrids (MG). In this paper, an EMS algorithm based on mixed-integer nonlinear programming (MINLP) is presented for MG in islanding mode considering different scenarios. A local energy market (LEM) is also proposed with in this EMS to obtain the cheapest price, maximizing the utilization of distributed energy resources. The proposed energy management is based on LEM and allows scheduling the MG generation with minimum information shared sent by generation units. Load demand management is carried out by demand response concept to improve reliability and efficiency as well as to reduce the total cost of energy (COE). Simulations are performed with real data to test the performance and accuracy of the proposed algorithm. The proposed algorithm is experimentally tested to evaluate processing speed as well as to validate the results obtained from the simulation setup on a real MG Testbed. The results of the EMS–MINLP based on LEM are compared with a conventional EMS based on LEM. Simulation and experimental results show the effectiveness of the proposed algorithm which provides a reduction of 15% in COE, in comparison with conventional EMS.