Distributed optimal resource control strategy under correlated uncertainties in isolated microgrid

The output uncertainties caused by the excessive dependence of renewable energy sources (RES) on meteorological factors affect the optimal operation of isolated microgrids. In addition, the superposition of demand fluctuations due to their power usage patterns and different scales severely affects the optimal control of distributed energy resources (DER) in microgrids. To solve the above problems, we design a distributed architecture driven by flexible loads in dual domains to solve the demand imbalance problem in microgrids, i.e., time and power flexible domains. Specifically, we first measure the probability of PV and load prediction error, that is, the conditional probability density function (PDF) by the Copula function. Based on these PDFs, we propose a copula-based correlated discrete convolutional (CopCDC) algorithm to calculate the uncertainty range of netload. Finally, a distributed optimal resources control strategy under correlated uncertainties algorithm(DOC-CU) is proposed, for different DERs to achieve game strategy exchange and individual optimum, guaranteeing the overall optimal consistency of the microgrid. The results show that the DOC-CU algorithm proposed has an 18.4 % reduction in user expenditures, a 15.4 % increase in microgrid benefits, a 13.5 % reduction in microgrid costs, a 65.5 % reduction in penalty expenditures, and finally a 24.5 % increase in user satisfaction.