An elitist artificial bee colony algorithm for combined economic emission dispatch incorporating wind power

2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011) Pub Date : 2011-11-10 DOI:10.1109/ICCCT.2011.6075213

H. T. Jadhav, J. Patel, U. Sharma, R. Roy

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引用次数: 7

Abstract

Due to increasing concern over global climate change, renewable energy sources, particularly wind turbine based generation systems are gaining more attention to meet the targets of emissions reduction. Combined economic load dispatch (ELD) and economic emission dispatch (EED) involves the simultaneous optimization of fuel cost and emission (CEED). This bi-objective CEED problem is converted into a single objective function by introducing a price penalty factor (PPF) approach. In this paper, Swarm Intelligence (SI) methods such as particle swarm optimization (PSO), artificial bee colony (ABC) and an elitist's artificial bee colony (EABC) are applied to solve CEED problem. The results are compared by considering ten and forty unit systems having non-linear cost function and valve-point effects. In addition suitable amount of wind power penetration is considered in both cases. It is demonstrated that the results obtained by applying elitist's artificial bee colony (EABC) algorithm are better than other two methods.

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风电联合经济排放调度的精英人工蜂群算法

由于对全球气候变化的日益关注，可再生能源，特别是风力发电系统越来越受到关注，以满足减排目标。经济负荷调度与经济排放调度的结合涉及燃油成本与排放的同步优化。通过引入价格惩罚因子(PPF)方法，将双目标CEED问题转化为单目标函数。本文将粒子群优化(PSO)、人工蜂群(ABC)和精英人工蜂群(EABC)等群体智能(SI)方法应用于CEED问题的求解。通过考虑具有非线性成本函数和阀点效应的10和40单元系统，对结果进行了比较。此外，在这两种情况下都考虑了适当的风力穿透量。结果表明，采用精英人工蜂群(EABC)算法得到的结果优于其他两种方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011)

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