Abstract
The study of highly efficient methods for performing multiple energy flow (MEF) computation with epistemic and aleatory uncertainties, known as interval-probabilistic energy flow (IPEF) calculation, is a fast-growing area of research. However, existing approaches are easily caught in a quandary among accuracy, efficiency, and scope of applicability. This paper presents a dual-level surrogate structure (DLSS) for dealing with the IPEF calculation of an integrated energy system (IES) in an accurate and efficient manner. The structure accommodates electricity, heat, and gas, attaining a high-level balance among precision, speed, and range of applicability. At the lower level of DLSS, a sparse polynomial chaos expansion (sPCE) model is employed to directly map random input variables to output boundary values, saving massive repetition of deterministic calculation. At the upper level of DLSS, a heterogeneous-learning-based multiple energy flow (HL-MEF) model is proposed to accelerate the indispensable deterministic MEF calculation for online sampling, thereby easing the computational burden of sPCE training. Finally, numerical results on a real-world electricity-heat-gas system demonstrate that the proposed DLSS can solve the IPEF problem at nearly 400 times the speed of the standard double-layer Monte Carlo simulation (DLMCS) with guaranteed accuracy.
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