Modeling and learning-based optimization of the energy dispatch for a combined cooling,heat and power microgrid system with uncertain sources and loads
LI Yi-jin
TANG Hao
Lü Kai
GUO Xiao-rui
XU Dan
Abstract：The dynamic dispatch optimization of the combined cooling,heat and power(CCHP)microgrid system with uncertain renewable sources and demands is focused in this paper. Firstly, the variations of photovoltaic and loads are described as continuous Markov processes considering their random properties. Then, define state vector of the system which consists of decision epoch,multiple load demands level,and outputs level of distributed energy sources(DESs),and the action vector which consists of the actions of micro turbines(MT)and storages.The time-of-use electricity price mode is applied in the system to minimize operating cost including electricity purchasing cost,fuel cost and starting-stopping cost. The dynamic optimal dispatch problem for CCHP microgrid system is described as a discrete Markov decision process (MDP),and a reinforcement learning method is adopted to obtain the optimal or suboptimal policy. Different policies are compared in simulation part and it shows that optimal policy can achieve a better performance to reduce the daily operating cost of the system.At last,simulation experiments including the comparison of different policies are performed to validate the effectiveness of the method.
Keywords：combined coolingheat and power microgrid systemenergy dispatchMarkov processreinforcement learning
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 56-64 )