基于优化算法的经济电力调度负荷管理

Ravindra M. Malkar, S. Sushanth Kumar, M. Tarambale
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引用次数: 0

摘要

电力经济调度是电网的重要组成部分。它在考虑系统约束的同时,将所需的负荷分配给在线生产单元。环保署的根本目的是降低整体发电成本。例如,双目标排放-经济调度(CE-ED)问题解决了化石燃料发电厂排放对环境的影响。这一双目标挑战既降低了燃料成本,又降低了排放。利用二次代价函数来模拟发电机的输出限制。非线性、非凸目标函数以及等式和不等式要求使EPD问题更具可行性。最近的进化算法已经改进了结果,特别是对于复杂的优化问题。这些方法在非凸和大解域上识别全局最优解。最近的算法利用大量候选解随机搜索解空间。进化算法必须能够通过探索可行区域而不是研究复杂区域来得到答案。因此,在寻找全局最优的同时,优化过程更快,消耗的计算时间更少。传统的方法由于过早收敛而失败。
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Load Management of Economic Power Dispatch using Optimization Algorithm
Economic Power Dispatch is a critical component of the power system network (EPD). It assigns required load to online producing units while accounting for system constraints. The fundamental purpose of EPD is to lower overall power generation costs. For example, the bi-objective Combined Emission- Economic Dispatch (CE-ED) problem addresses the environmental impacts of fossil fuel power plant emissions. The bi-objective challenge reduces both fuel costs and emissions. Generators are simulated using quadratic cost functions to match their output restrictions. The non-linear, non-convex objective functions and equality and inequality requirements make the EPD issue more feasible. Recent evolutionary algorithms have improved results, especially for complex optimization problems. These methods identify global optimum solutions in non-convex and large solution domains. Recent algorithms search the solution space at random utilising numerous candidate solutions. Evolving algorithms must be able to get answers by exploring the feasible zone rather than researching the complex region. So the optimization process is speedier and consumes less calculation time while still finding the global optimum. The traditional technique fails due to premature convergence.
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