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.
15 Aug 2017
TL;DR: An overview of basic PSO to provide a comprehensive survey on the problem of economic load dispatch as an optimization problem is provided in this article, where the study is carried out for three unit test system and then for six unit generating system for without loss and with loss cases.
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.
01 Jan 2016
TL;DR: In this paper, a two-stage solution methodology for deterministic unit commitment problem is presented, 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.
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.
TL;DR: An attempt has been made to formulate a short term deterministic Unit Commitment problem in renewable integrated environment with battery storage involving hybrid Particle Swarm Optimization (PSO) technique to provide techno-economic solution to this complex optimization problem.
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.
••13 Jul 2003
TL;DR: This paper introduces a new unit commitment problem, adapting extended priority list (EPL) method, and proposes a method to modify unit schedule using problem specific heuristics to fulfill operational constraints.
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.
TL;DR: In this paper, a teaching learning based optimization (TLBO) technique was proposed to solve economic load dispatch (ELD) of the thermal unit without considering transmission losses, which can take care of ELD considering nonlinearity such as valve point loading.
Abstract: This paper presents a novel teaching learning based optimization (TLBO) technique to solve economic load dispatch (ELD) of the thermal unit without considering transmission losses. The proposed methodology can take care of ELD considering nonlinearity such as valve point loading. The objective of economic load dispatch is to determine the optimal power generation of the units to meet the load demand, such that the overall cost of generation is minimized, while satisfying different operational constraints. TLBO is a recently developed evolutionary algorithm based on two basic concepts of education namely teaching phase and learning phase. At first, learners improve their knowledge through the teaching methodology of teacher and finally learners increase their knowledge by interactions among themselves. The effectiveness of the proposed algorithm has been verified on three different test systems with equality and inequality constraints. Compared with the other existing techniques demonstrates the superiority of the proposed algorithm.
TL;DR: All the hybrid forms (purely) of PSO applied to a constrained economic dispatch problem are detailed and how PSO overcomes its premature convergence problem while hybridizing with other optimization techniques is well-highlighted.
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.
TL;DR: The Ant Colony Optimization (ACO) algorithm is used for mitigating contingencies by means of reconfiguration in an electrical distribution network and the results are validated on the IEEE 30-bus system and show that the algorithm is able to better reduce real power losses.
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 , 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
TL;DR: New parallel hybrid Genetic Algorithm-Particle Swarm Optimization algorithm (P-GA-PSO) is developed to solve both sizing and energy management problems for micro-grids and demonstrated that P-GA -PSO outperforms ordinary optimization algorithms in terms of convergence time and solution quality.
Abstract: The major objectives of this work are: 1) to develop new efficient optimization algorithm to solve NP-hard problems, 2) to show the potential of integrating renewable energy technologies for Laayoune region- Morocco taken as case study. In this work, new parallel hybrid Genetic Algorithm-Particle Swarm Optimization algorithm (P-GA-PSO) is developed to solve both sizing and energy management problems for micro-grids. The studied micro-grid is composed of four different renewable energy technologies and energy storage system. The objectives of both optimization problems are to satisfy typical load demand, to minimize energy production cost, to maximize renewable energy integration, to avoid energy losses and overload. P-GA-PSO performances are evaluated and compared with the ordinary optimization methods based on: 1) a set of benchmark functions, 2) various scenarios used to solve the proposed micro-grid optimization problems. Obtained results demonstrated that P-GA-PSO outperforms ordinary optimization algorithms in terms of convergence time and solution quality. Moreover, the proposed micro-grid is very promising in terms of load demand satisfaction, cost and pollutant emissions reduction. Indeed, the obtained energy cost does not exceed 0.17 US$/kWh, which is close to fossil fuel energy cost, and fossil fuel replacement rate exceeds 50% during all periods.