小时级月度发购电计划的优化模型及其混合智能求解策略

发布时间：2018-12-21 19:28

【摘要】：结合网内外购电模式的差异,建立了计及网外购电市场的小时级月度发购电计划模型。该模型是一个多时段大规模混合整数规划模型。负荷的时段融合策略可有效减小模型中小时级时段数的规模,但会带来负荷融合误差,从而影响模型的目标甚至安全约束,为此提出了基于负荷融合误差的校正优化策略。针对模型的大规模多时段混合整数规划特点,提出了结合免疫遗传算法与拉格朗日分解协调策略的混合智能算法。一方面,利用免疫遗传算法为拉格朗日松弛法提供满足启停约束和旋转备用约束的机组组合发购电方案。另一方面,利用拉格朗日分解协调算法高效求解运行机组的最优发电方案,快速提升免疫遗传算法优秀个体的质量。两者交替迭代,互相促进,从而实现上述多时段混合整数规划模型的高效求解。最后通过IEEE 57节点系统的仿真分析验证了所提模型和算法的有效性。
[Abstract]:Combined with the difference of the mode of electricity purchase inside and outside the network, the hourly monthly power generation and purchase planning model is established taking into account the power purchase market. The model is a large scale mixed integer programming model with multiple time periods. The time fusion strategy of load can effectively reduce the scale of the number of hours in the model, but it will bring about the error of load fusion, which will affect the target of the model and even the security constraints. Therefore, a correction and optimization strategy based on the load fusion error is proposed. According to the characteristics of large-scale multi-time mixed integer programming, a hybrid intelligent algorithm combining immune genetic algorithm and Lagrange decomposition coordination strategy is proposed. On the one hand, the immune genetic algorithm (IGA) is used to provide a power generation and purchase scheme for the Lagrangian relaxation method. On the other hand, the Lagrangian decomposition coordination algorithm is used to solve the optimal generation scheme of running units efficiently, which can improve the quality of the excellent individuals of immune genetic algorithm (IGA). They are iterated alternately and promote each other so that the efficient solution of the multi-time mixed integer programming model can be realized. Finally, the effectiveness of the proposed model and algorithm is verified by simulation analysis of IEEE 57 bus system.
【作者单位】：输配电装备及系统安全与新技术国家重点实验室(重庆大学);重庆电网电力交易中心;重庆北碚供电公司;
【基金】：国家自然科学基金项目(51177178)~~
【分类号】：TM73

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