电力系统自动化2017，Vol.41Issue(15)：18-25,8.DOI:10.7500/AEPS20170103003
基于知识迁移Q学习算法的多能源系统联合优化调度
Knowledge Transfer Based Q-learning Algorithm for Optimal Dispatch of Multi-energy System
摘要
Abstract
The recent development of the Energy Internet has urged the conventional inefficient utilization of single energy to change towards the more developed energy usage of optimal dispatch of the multi-energy system.Against the above-mentioned background,an optimal joint dispatch of multi-energy system model framework is firstly proposed based on the energy hub modeling approach.Then a typical multi-energy system model is developed considering carbon emission and energy supply costs with valve point effect.To solve this non-linear problem with non-convex,discontinuously differentiable characteristic,the cascaded algorithm combined with the knowledge transfer based Q-learning algorithm and interior point method is applied on the model.That is,the active power of generators is taken as an action variable of Q-learning in the upper structure and solve the multi-energy system model with the interior point method in the lower structure.Meanwhile,the efficiency is greatly improved by knowledge transfer.Case studies have been carried out on a 33 energy hubs test system to verify the effectiveness of the proposed model and algorithm.关键词
多能源系统/优化调度/能源中心/级联式算法/知识迁移Q学习/内点法Key words
multi-energy system/optimal dispatch/energy hub/cascaded algorithm/knowledge transfer based Q-learning/interior point method引用本文复制引用
瞿凯平,张孝顺,余涛,韩传家..基于知识迁移Q学习算法的多能源系统联合优化调度[J].电力系统自动化,2017,41(15):18-25,8.基金项目
国家重点基础研究发展计划(973 计划)资助项目(2013CB228205) （973 计划）
国家自然科学基金资助项目(51477055).This work is supported by National Basic Research Program of China (973 Program) (No.2013CB228205) and National Natural Science Foundation of China (No.51477055). （51477055）
