The integration of large-scale-distributed new energy resources has led to heightened source load uncertainty.As energy prosumers

microgrids urgently require enhanced real-time regulation capabilities over controllable resources amid uncertain environments

rendering real-time and rapid decision-making a critical issue.This paper proposes a tailored twin delayed deep deterministic policy gradient(TD3) reinforcement learning algorithm that explicitly accounts for source load uncertainty.First

following an expert experience-based methodology

Gaussian process regression was implemented using the radial basis function covariance with historical source and load data.The parameters were adaptively adjusted by maximum likelihood estimation to generate the expected curves of demand and wind solar power generation

along with their 95% confidence regions

which were treated as representative uncertainty scenarios.Second

the traditional scheduling model was transformed into a deep reinforcement learning (DRL) environment through a Markov process.To minimize the total operational cost of the microgrid

the tailored TD3 algorithm was applied to formulate rapid intraday scheduling decisions.Finally

simulations were conducted using real historical data from an actual region in Zhejiang province

China

to verify the efficacy of the proposed method.The results demonstrate the potential of the algorithm for achieving economic scheduling for microgrids.