عنوان مقاله
الگوریتم ترکیبیSOA-SQP برای مخابره اقتصادی دینامیکی با درنظرگرفتن اثر نقطه سوپاپ
فهرست مطالب
مقدمه
فرمولاسیون (فرمول نویسی) مسئله
برنامه نویسی درجه دوم متوالی
اجرای الگوریتم ترکیبیSOA-SQP برای مسئلهDED
سیستم های تست و نتایج شبیه سازی
نتیجه گیری
بخشی از مقاله
اجرای الگوریتم ترکیبیSOA-SQP برای مسئلهDED
مسئله مخابره اقتصادی دینامیکی بر مبنای الگوریتم ترکیبی به شکل زیر توصیف شده است:
1. پارامترهای سیستم متشکل از ضرایب هزینه سوخت، ضرایب تلفات خط انتقال، حد بالا و پائین متغیرهای کنترل و تقاضاهای بار پیش بینی شده برای انتروالهایT در افق زمانی زمان بندی شده به عنوان ورودی بکار برده می شوند.
کلمات کلیدی:
Hybrid SOAeSQP algorithm for dynamic economic dispatch with valve-point effects S. Sivasubramani, K.S. Swarup* Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, India article info Article history: Received 9 February 2010 Received in revised form 15 July 2010 Accepted 14 August 2010 Keywords: Seeker optimization algorithm Sequential quadratic programming Dynamic economic dispatch Valve-point effects abstract This paper proposes a hybrid technique combining a new heuristic algorithm named seeker optimization algorithm (SOA) and sequential quadratic programming (SQP) method for solving dynamic economic dispatch problem with valve-point effects. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient (EG) by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. In this paper, SOA is used as a base level search, which can give a good direction to the optimal global region and SQP as a local search to fine tune the solution obtained from SOA. Thus SQP guides SOA to find optimal or near optimal solution in the complex search space. Two test systems i.e., 5 unit with losses and 10 unit without losses, have been taken to validate the efficiency of the proposed hybrid method. Simulation results clearly show that the proposed method outperforms the existing method in terms of solution quality. 2010 Elsevier Ltd. All rights reserved. 1. Introduction Dynamic economic dispatch (DED) is one of the important power system optimization problems which is a non-linear and complicated dynamic optimization problem. DED is a method to dispatch the generating units to the predicted load demands over a certain period of time at minimum operating cost while satisfying equality, inequality and ramp-rate limit constraints. Normally, DED is solved by considering the cost function as monotonically increasing one. However, the cost function is non-convex and nonsmooth due to the effects of valve-point loading. This will make the problem harder in finding the optimum solution. Many mathematical techniques have been addressed to solve the DED problem with valve-point effects [1e4]. However, none of these methods may be able to provide an optimal solution. They usually get struck at local optimum because of non-linear and non-convex characteristics of the generating units. Recently, stochastic optimization techniques such as genetic algorithm (GA) [5], evolutionary programming (EP) [6], simulated annealing (SA) [7], particle swarm optimization (PSO) [8] and differential evolution (DE) [9] have been used to solve both static and dynamic economic dispatch problem with valve-point effects. They are found to be effective in solving the problem without any restriction on the shape of the cost curve due to their ability to find global optimal solution. Though, these stochastic methods do not always guarantee the global solution, they generally provide a reasonable solution which is suboptimal. However, the main drawback of the above methods is premature convergence. To overcome the deficiencies in stochastic methods, many strategies have been used such as adaptive particle swarm optimization (APSO) [10], improved particle swarm optimization (IPSO) [11], modified differential evolution (MDE) [12] and hybrid differential evolution (HDE) [13] to address the DED problem with valve-point effects. More precisely, hybrid algorithm combining stochastic and deterministic methods is found to be effective in solving optimization problems with complex, non-linear and non-convex characteristics. Based on this, hybrid algorithm combining evolutionary programming (EP) and sequential quadratic programming (SQP) [14] and PSO with SQP [15] have been reported to address DED problem with valve-point effects. Seeker optimization algorithm (SOA) proposed by Dai and Chen [16], is a new population based heuristic search algorithm which uses the act of human searching for solving optimization problem. This algorithm has been applied to power system optimization problem such as optimal reactive power dispatch [17,18] which is a mixed integer and highly non-linear problem. The SOA algorithm has been successfully applied and proved to
