为实现综合能源系统中能源的高效利用,同时考虑到不同能源系统属于不同能源供应商,通常只进行部分信息交互,提出一种基于近似牛顿方向(approximate Newton directions,AND)的电热气综合能源系统最优能流解耦算法。将整体的优化问题根据...为实现综合能源系统中能源的高效利用,同时考虑到不同能源系统属于不同能源供应商,通常只进行部分信息交互,提出一种基于近似牛顿方向(approximate Newton directions,AND)的电热气综合能源系统最优能流解耦算法。将整体的优化问题根据能源主体解耦,分解为电力系统、热力系统、天然气系统优化子问题,通过部分信息的传递,对子问题交替迭代,进行分布式求解,最终达到整体优化的效果。该算法信息传输量少,结构简单,计算速度快,求解过程中,子问题不需要求最优解,只需要迭代1次,提高了求解效率。针对具体算例,将AND算法与集中式内点法(centralized interior point method,CIPM)的计算结果进行对比分析,验证了所提算法的可行性和有效性。展开更多
In this paper, Bayesian computational method is used to estimate inverse Rayleigh Scale parameter with fuzzy data. Based on imprecision data, the Bayes estimates cannot be obtained in explicit form. Therefore, we prov...In this paper, Bayesian computational method is used to estimate inverse Rayleigh Scale parameter with fuzzy data. Based on imprecision data, the Bayes estimates cannot be obtained in explicit form. Therefore, we provide Tierney and Kadane’s approximation to compute the Bayes estimates of the scale parameter under Square error and Precautionary loss function using Non-informative Jefferys Prior. Also, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the scale parameter in terms of mean squared error values.展开更多
文摘为实现综合能源系统中能源的高效利用,同时考虑到不同能源系统属于不同能源供应商,通常只进行部分信息交互,提出一种基于近似牛顿方向(approximate Newton directions,AND)的电热气综合能源系统最优能流解耦算法。将整体的优化问题根据能源主体解耦,分解为电力系统、热力系统、天然气系统优化子问题,通过部分信息的传递,对子问题交替迭代,进行分布式求解,最终达到整体优化的效果。该算法信息传输量少,结构简单,计算速度快,求解过程中,子问题不需要求最优解,只需要迭代1次,提高了求解效率。针对具体算例,将AND算法与集中式内点法(centralized interior point method,CIPM)的计算结果进行对比分析,验证了所提算法的可行性和有效性。
文摘In this paper, Bayesian computational method is used to estimate inverse Rayleigh Scale parameter with fuzzy data. Based on imprecision data, the Bayes estimates cannot be obtained in explicit form. Therefore, we provide Tierney and Kadane’s approximation to compute the Bayes estimates of the scale parameter under Square error and Precautionary loss function using Non-informative Jefferys Prior. Also, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the scale parameter in terms of mean squared error values.
