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首页> 外文会议>2011 IEEE Electrical Power and Energy Conference >Multi-objective short-term hydro-thermal scheduling using bacterial foraging algorithm
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Multi-objective short-term hydro-thermal scheduling using bacterial foraging algorithm

机译：基于细菌觅食算法的多目标短期水热调度

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In this paper, the optimization problem of the short-term hydro-thermal scheduling (STHTS) is addressed considering the environmental aspects. To solve this multi-objective problem, an improved bacterial foraging algorithm (IBFA) is implemented. In addition to minimizing the cost function, the minimization of gaseous emission is also considered. Operating cost of thermal generating units, NOx SO2 and CO2 emission are minimized over the scheduling period considering various thermal and hydro constraints. The environmentally constrained STHTS problem, as it is the case of the classic one, is a dynamic large-scale nonlinear optimization problem which requires solving unit commitment and economic power load dispatch problems. The bacterial foraging algorithm (BFA) is a recently developed evolutionary optimization technique based on the foraging behavior of the E. coli bacteria. The BFA has been successfully employed to solve various optimization problems; however, for large-scale problems, it shows poor convergence properties. In fact, the basic BFA cannot be applied to solve complex problems such as the STHTS problem. To tackle this problem considering its high-dimensional search space, significant improvements are introduced to the basic BFA. The algorithm is validated using a well known hydro-thermal generation system. Results are obtained and the trade-off set of solutions is successfully captured.
机译：在本文中，考虑到环境因素，解决了短期水热调度（STHTS）的优化问题。为了解决这个多目标问题，实现了一种改进的细菌觅食算法（IBFA）。除了最小化成本函数外，还考虑了气体排放的最小化。考虑到各种热力和水力约束因素，在调度期内，将热力发电机组的运营成本NO x SO 2 和CO 2 的排放降至最低。与经典方法一样，环境约束的STHTS问题是动态的大规模非线性优化问题，需要解决机组承诺和经济用电负荷分配问题。细菌觅食算法（BFA）是最近开发的一种基于大肠杆菌细菌觅食行为的进化优化技术。 BFA已成功用于解决各种优化问题。但是，对于大规模问题，它显示出较差的收敛性。实际上，基本的BFA无法应用于解决诸如STHTS问题之类的复杂问题。考虑到其高维搜索空间来解决该问题，基本BFA进行了重大改进。使用众所周知的水热发电系统对该算法进行了验证。获得结果，并成功捕获了一组折衷的解决方案。

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