双重不确定环境下的微网优化运行调度风险分析研究

发布时间：2019-03-11 08:35

【摘要】：相对于传统大电网,新能源高渗透率下微网中的不确定因素是限制微网发展的关键。为克服传统优化运行中仅考虑单一不确定性导致的调度结果保守性问题,该文深入研究微网中的不确定因素,从不确定理论出发,将风电出力和光伏出力分别处理为模糊随机变量和模糊变量,构造双重不确定环境下的机会约束模糊随机规划模型。为更为灵活直观地描述该环境下的复杂双重不确定性,利用机会测度衡量模糊随机事件的机会,并提出一种模糊随机模拟技术与遗传算法相结合的混合智能算法对机会约束模糊随机规划模型进行求解。针对不同置信水平下的最优解,进一步将传统优化问题中单一的经济分析扩展为风险分析,分析结果表明,随着置信水平的减小,微网运行成本降低,而对新能源发电的管控也随之降低,导致风险增大。以一个包含风、光、储以及燃料电池和微型燃气轮机的微网系统为实例进行计算分析,验证该模型及求解算法的可行性及有效性。
[Abstract]:Compared with the traditional large power grid, the uncertainty factor in the micro-grid under the new energy and high permeability is the key to limit the development of the micro-grid. In order to overcome the conservative problem of scheduling results caused by single uncertainty in the traditional optimal operation, the uncertainty factors in micro-grid are studied in this paper, and the uncertainty theory is set out. The wind power and photovoltaic output are treated as fuzzy random variables and fuzzy variables respectively, and the chance constrained fuzzy stochastic programming model is constructed under double uncertain environment. In order to describe the complex double uncertainty in this environment more flexibly and intuitively, the opportunity of fuzzy random events is measured by chance measure. A hybrid intelligent algorithm based on the combination of fuzzy stochastic simulation and genetic algorithm is proposed to solve the chance constrained fuzzy stochastic programming model. In view of the optimal solution at different confidence levels, the single economic analysis in traditional optimization problem is further extended to risk analysis. The results show that with the decrease of confidence level, the running cost of micro-grid decreases, and the cost of micro-grid operation decreases with the decrease of confidence level. And the control of new energy generation will also be reduced, resulting in increased risk. Taking a micro-grid system including wind, light, storage, fuel cell and micro gas turbine as examples, the feasibility and effectiveness of the model and the algorithm are verified.
【作者单位】：新能源电力系统国家重点实验室(华北电力大学);
【基金】：国家自然科学基金项目(51577068) 国家863高技术基金项目(2015AA050104)~~
【分类号】：TM73

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