Probabilistic Optimal Energy Flow of District Multienergy Systems: An MPLP-Based Online Dictionary-Learning Approach

The integration of multienergy systems (MES) provides an important opportunity to increase the technical, economic, and environmental performance of the overall system compared with separately operated energy systems. The operation of district MES encounters numerous uncertainties such as fluctuations in energy prices as well as loads in different energy forms. In this paper, we formulate a probabilistic optimal energy flow (P-OEF) model of a district MES into a linear program (LP) problem based on the linear energy hub model. We propose an improved multiparametric LP (MPLP) based online dictionary-learning approach to efficiently calculate the P-OEF problem. We then derive a generalized critical region theory of the MPLP problem to jointly consider the change of parameters in terms of both the objective function and the constraints of P-OEF in MES. Finally, we explore the physical significance of the MPLP's critical region in the P-OEF problem. We perform a case study to verify the validity and effectiveness of the proposed approach compared to the traditional Monte Carlo approach for the P-OEF of the MES.