Journal of advances in information science and technology
Volume 2, Issue 4, April 2024
1.
TD3-based Adaptive Economic Dispatch Optimization Strategy for Multi-energy Microgrid
Jiakai Gong, Nuo Yu, Fen Han, Bin Tang, Haolong Wu, Yuan Ge
Pages: 1 - 8
The multi-energy microgrid (MEMG) improves the
overall economy of the system by coupling scheduling among
multiple energy sources. However, in the case of renewable energy
power generation and load demand fluctuations, traditional
methods are difficult to apply to energy dynamic management
and control under the changing situation of multi-energy microgrid systems, which poses a huge challenge to the multi-energy
coupling optimal operation of MEMG. In this paper, a multienergy allocation model based on deep reinforcement learning
(DRL) is established to optimize the multi-energy coupling
scheduling, which can automatically adapt to changes in the
environment. In order to make the optimal scheduling strategy
effectively reduce the cost, a multi-energy scheduling strategy
based on the twin delayed deep deterministic policy gradient
(TD3) algorithm is proposed. The experimental results display
that our proposed strategy can reduce the cost by 21.45% and
14.71% compared with particle swarm optimization algorithm
in summer and winter.