M.P Dave

Bio: M.P Dave is an academic researcher from Shiv Nadar University. The author has contributed to research in topics: Particle swarm optimization & Metaheuristic. The author has an hindex of 3, co-authored 6 publications receiving 61 citations.

Economic Load Dispatch Using Particle Swarm Optimization

Abstract: Economic load dispatch is a non linear optimization problem which is of great importance in power systems. While analytical methods suffer from slow convergence and curse of dimensionality particle swarm optimization can be an efficient alternative to solve large scale non linear optimization problems. This paper presents an overview of basic PSO to provide a comprehensive survey on the problem of economic load dispatch as an optimization problem. The study is carried out for three unit test system and then for six unit generating system for without loss and with loss cases.

A two stage solution methodology for deterministic unit commitment problem

Abstract: This paper presents a two stage solution methodology for deterministic unit commitment problem. In the first stage the ON/OFF status of units is determined by priority list method while in the second stage the economic load dispatch has been solved by Particle swarm optimization technique. The proposed technique is applied in MATLAB environment on two different cases comprising of four and ten generating units respectively. It is observed that for both the cases the overall production cost obtained through the proposed technique is better than the results reported thus far.

Optimized Generation Scheduling of Thermal Generators Integrated to Wind Energy System with Storage

Abstract: The rising energy demand and climate change issues have warranted the inclusion of renewable energy resources with existing conventional fuel based generation system. The intermittent renewable generation require adequate battery support in order to minimize load deficit issues in electrical grid. Hence, an attempt has been made in this paper to formulate a short term deterministic Unit Commitment problem in renewable integrated environment with battery storage. Ten thermal generators are scheduled with a 500 MW wind energy generation system supported by 200 MWh battery with backup of four hours. A three stage solution methodology is evolved involving hybrid Particle Swarm Optimization (PSO) technique to provide techno-economic solution to this complex optimization problem. The charge/ discharge scheduling of battery energy storage integrated to wind generation system is taken up as a co-optimization problem. The generation of battery energy storage integrated wind energy system is so scheduled that it relieves the costlier thermal generating units in the most economic manner.

A fast technique for unit commitment problem by extended priority list

Abstract: This paper introduces a new unit commitment problem, adapting extended priority list (EPL) method. The EPL method consists of two steps, in the first step we get rapidly some initial unit commitment problem schedules by priority list (PL) method. At this step, operational constraints are disregarded. In the second step unit schedule is modified using the problem specific heuristics to fulfill operational constraints. To calculate efficiently, however, note that some heuristics is applied only to solutions can expect improvement. Several numerical examples demonstrate the effectiveness of proposed method.

Solution of an Economic Dispatch Problem Through Particle Swarm Optimization: A Detailed Survey – Part II

Abstract: A number of modern metaheuristic optimization techniques are being exploited to work out a single-objective economic dispatch (ED) problem. The dispatch problems even become more complicated and complex when they consider operational and system constraints, such as network transmission losses, valve-point loading effects originating due to sequential opening of a number of steam admission valves to meet the ever-increasing demand, ramp rate limits, prohibited operating zones, multiple fuel options, spinning reserve, and so on. The heavy constraints make the otherwise convex linear smooth dispatch problem as highly nonconvex nonlinear nonsmooth one. Finding optimal solution for such kind of a constrained nonlinear problem through the deterministic numerical and convex characteristics-based optimization techniques is a difficult task to accomplish. Researchers have frequently employed one of the metaheuristic optimization techniques with powerful computational ability named particle swarm optimization (PSO) to deal with this rather a complicated and toilsome dispatch problem. In Part I of the two-part paper, a comprehensive review or a survey of PSO and its modified versions (involve alterations in the basic structure of PSO) to resolve the constrained ED problem is presented. Part II covers purely the survey of hybrid forms of PSO (hybridization of PSO with other optimization techniques) to tackle the ED problem. The survey is presented in such a way that readers may understand how PSO can be made computationally more efficient.

Special issue on intelligent tools and techniques for signals, machines and automation

Abstract: This special issue is a selected collection of papers submitted to the First International Conference on Signals, Machines and Automation (SIGMA-2018), February 23–25, 2018, New Delhi, India. These papers have been reviewed and accepted for presentation at the conference and for publication in the Journal of Intelligent & Fuzzy Systems (JIFS). In this special issue there are 49 papers covering a wide range of topics in the area of intelligent systems and their application in various domains. The International Conference SIGMA-2018 aims to provide a common platform to the researchers in related fields to explore and discuss various aspects of artificial intelligence applications and advances in soft computing techniques. The Conference will provide excellent opportunities for the presentation of interesting new research results and discussion about them, leading to knowledge transfer and the generation of new ideas. Among the 49 papers in this special issue, there are twelve papers addressing the applications of intelligent tools and techniques in the operation and control of power systems. In [1], the Ant Colony Optimization (ACO) algorithm is used for mitigating contingencies by means of reconfiguration in an electrical distribution network. The results are validated on the IEEE 30-bus system and show that the algorithm is able to better reduce real power losses