[Objective] To address the strong uncertainty in renewable energy output and load demand

complex operational constraints

and continuous decision variables in the cooperative scheduling of multi-microgrid systems

this paper proposes an economic optimization method based on an improved twin delayed deep deterministic policy gradient (TD3) algorithm. [Methods] Firstly

a three-microgrid cluster model was constructed

incorporating wind power

photovoltaic generation

micro-turbines

energy storage systems

EV charging loads

and main grid interactions. Its economic optimal dispatch process was modeled as a Markov decision process. The state space encompassed load demand

renewable generation

time-of-use electricity prices

and battery state of charge

while the action space comprised energy storage charge/discharge power

micro-turbine output

power exchange between microgrids

and interaction power with the main grid. Next

a reward function integrating normalized operating costs

environmental costs

and constraint penalty terms was designed to guide the agent in learning economical and feasible scheduling strategies. Finally

building on the original TD3 algorithm

a dual experience pool (DEP) mechanism and an adaptive exponentially decaying Gaussian exploration noise (AED-GEN) mechanism were introduced. The DEP mechanism classified experience samples into feasible solution samples and boundary exploration samples

improving sample utilization efficiency and enhancing the learning capability for constraint boundaries. Meanwhile

the AED-GEN mechanism balanced exploration during early training and convergence stability in later stages. [Results] Case studies were conducted on a three-microgrid cluster. The results demonstrated that the improved TD3 algorithm outperformed the deep deterministic policy gradient (DDPG)

original TD3

and SAC algorithms in terms of convergence stability

operational economy

and scheduling security. The standard deviation of reward fluctuation in the last 100 training episodes for the improved TD3 was 1.51

representing reductions of 35.74%

45.09%

and 54.24% compared to SAC

original TD3

and DDPG

respectively. Its total daily operating cost was 4

186.94 yuan

which was 1.38%

6.31%

and 8.71% lower than that of SAC

original TD3

and DDPG

respectively. Meanwhile

the action limit violation rate dropped rapidly during training and gradually approached zero in the later stages. Further comparisons of the improved mechanisms verified that both the adaptive exploration noise mechanism and the dual experience pool mechanism enhanced algorithm performance. [Conclusion] By satisfying the physical operational constraints

the proposed improved TD3 method leverages the mutual support capability among multiple entities within the microgrid cluster

thereby enhancing the economic efficiency and security of the system’s collaborative scheduling.