消纳大规模间歇性可再生能源对智能电网脆弱性的影响研究

With the enlargement of the electric power capacity from the renewable energy and its large-scale interminttent consumption and acceptance, the negative impacts on the vulnerability of the Smart Grid become increasingly severe. The proposed project aims at optimizing the constitution of energy consumption and putting forward intellectual strategies to guarantee the safe and stable operation of the Smart Grid in China. The outlined contents are listed as follows: (1) On basis of the on the Copula function and the Random Process theory, a dynamically stochastic process model is to be established in multi-dimensional degrees by analyzing the interminttent characteristics of the electric power generated from renewable energy and their impact factors. (2) By combining the factors affecting the intermittence of renewable energy and the vulnerability of the Smart Grid, vulnerability indexes set of Smart Grid will be constructed under the framework of the Rough Set Theory and the Particle Swarm Optimization. (3) A multi-level multi-factor system evaluating the vulnerability of Smart Grid is to be presented based on the Credibility theory and the Bayesian network. (4) By integrating the Six-Sigma management theory, the Prospect theory and the Credibility theory, a K-sigma vulnerability evaluation model is to be proposed. (5) Eventually, with the ulilization of the Reliability distribution and Distortion function, values of the indexes of the vulnerability will be determined by combining the multi-dimensional dynamically stochastic process model and the vulnerability evaluation model. Furthermore, equations of the indexes of the vulnerability are going to be constrcuted and solved, which will provide intectual countermeasures in guiding the stable operation of Smart Grid.

随着间歇性可再生能源发电规模的不断增大，其消纳对智能电网的安全影响明显增强，致使智能电网的脆弱性问题日益突出。本项目的研究意在为改善我国能源消费结构与保障智能电网安全运行提供理论支撑。研究内容有：依据可再生能源发电间歇性的影响因素，利用Copula函数和随机过程理论，构建大规模间歇性可再生能源发电的多维度随机过程动态模型。其次，结合可再生能源间歇性影响因素和智能电网脆弱性影响因素，利用粗糙集理论和粒子群优化算法，建立消纳大规模间歇性可再生能源对智能电网脆弱性影响的指标集；利用可信性理论和贝叶斯网络，构建对智能电网脆弱性的多层多因素影响体系；利用六西格玛理论、前景理论和可信性理论，构建基于前景理论的K-sigma脆弱性评价模型。最后，结合多维度随机过程动态模型和脆弱性评价模型，利用可信性分布和扭曲函数，确定脆弱性指数，建立脆弱性指数方程，以实现对智能电网脆弱性级别的动态预测。

随着环境的日益恶化与传统化石能源弊端的凸显，世界各国都面临着大力发展清洁、高效能源的重要任务。然而，由于可再生能源发电具有较强的间歇性与随机性，对电网安全运行的影响极大，如何大规模消纳可再生能源已经成为我国推进能源革命的重要难题。在上述背景下，研究消纳大规模间歇性可再生能源对智能电网脆弱性的影响，找出导致电网脆弱性的关键因素，进而提升智能电网运行的可靠性具有重要价值。.本项目首先构建了风力发电出力影响因素及作用机理的概念模型和基于ISM模型的光伏发电出力影响因素作用机理模型，在此基础上利用时间序列与混合Copula函数分别构建单、多风电机组的风速时序模型与基于功率曲线的风电机组出力模型，然后，结合Beta分布模型与Copula函数，建立了基于Copula-Beta的光伏发电出力过程动态模型，解决了大规模可再生能源发电间歇性的度量问题。.其次分别从电力系统纵向链条及智能电网横向相关面两个角度出发，建立了两套电网脆弱性评价指标体系，并提出了基于粗糙集-粒子群优化算法和基于ANP-熵权法的两种权重计算模型。在此基础上，将可信性理论、六西格玛理论与前景理论融入综合评价，分别构建基于可信性理论的电网脆弱性评价模型、基于K-sigma的单因素多指标多属性智能电网脆弱性评价模型、以及基于前景-TOPSIS的电网脆弱性评价模型，解决了由消纳大规模间歇性可再生能源导致智能电网脆弱性的评价问题。.最后，依据可信性矩母函数方法构建了智能电网脆弱性指数，并结合多维度随机过程动态模型，运用风险理论与模糊综合评价相结合的方法建立由消纳大规模间歇性可再生能源导致的智能电网脆弱性指数方程，解决了智能电网脆弱性级别的动态评估。.在项目执行过程中，累计培养研究生20名，相关研究成果已发表学术论文23篇。项目的研究成果丰富和完善了智能电网安全运行的理论体系，为提升和加强智能电网安全风险的抵御能力，促进和加快可再生能源的发展与优化配置等方面起到重要的基础性支撑作用。