Solving Economic Load Dispatch Problem with Mountaineering Team-Based Optimization Technique

Y. Reddy, M. L. Ramanaiah
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Abstract

The Economic load dispatch (ELD) optimally allocating available generating units based on required load demand by satisfying the constraints. Based on the input output characteristics of boiler-turbine-generator set the operating cost is a convex cost function. Under practical condition including the valve point effect (VPE), ramp rate limits (RRL), prohibited operating zones (POZs) and multiple fuels makes the cost function as non-convex and ELD is a complex problem. In order to handle complex ELD problem, a novel human intelligence-based metaheuristic optimization technique called the mountaineering team-based optimization (MTBO) algorithm is proposed in this paper. The MTBO algorithm is based on the social behaviour of people as they climb mountains. In order to solve optimization problems, the proposed MTBO algorithm captures the phases of the regular and guided movement of climbers based on the leader's experience, barriers to reaching the peak and becoming stuck in local optimality, and the coordination and social cooperation of the group to protect members from natural hazards. To evaluate the efficiency of the suggested algorithm, three different cases are considered to solve the ELD problem. The MTBO method outperforms the competition in terms of robustness, ease of implementation, effective optimization performance for optimal global solutions.
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基于登山队优化技术解决经济负荷调度问题
经济负荷调度(Economic load dispatch, ELD)是在满足约束条件的基础上,对发电机组进行优化配置。基于锅炉-汽轮发电机组的输入-输出特性,运行成本是一个凸成本函数。在实际工况下,包括阀点效应(VPE)、匝道限速(RRL)、禁止作业区(POZs)和多种燃料使得成本函数是非凸的,ELD是一个复杂的问题。为了解决复杂的地形优化问题,本文提出了一种基于人类智能的元启发式优化技术——登山队优化算法。MTBO算法是基于人们爬山时的社会行为。为了解决优化问题,提出的MTBO算法根据领队的经验,捕捉登山者的规则和引导运动阶段,到达顶峰的障碍和陷入局部最优状态,以及群体的协调和社会合作,以保护成员免受自然灾害。为了评估该算法的效率,我们考虑了三种不同的情况来解决ELD问题。MTBO方法在鲁棒性、易实现性、对全局最优解的有效优化性能等方面优于同类方法。
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