Two-layer multiple scenario optimization framework for integrated energy system based on optimal energy contribution ratio strategy

Rational design and advanced energy management considering multiple uncertainties are imperative for the superior integrated energy system (IES). This work proposed a novel two-layer stochastic multiple scenario optimization framework for the collaborative optimization of capacity and operation of IES. To improve the accuracy of probability density estimation, the improved kernel density estimation (KDE) was employed to obtain the probability density distributions of wind speed, sunlight and multi-demands. Then the scenario sets were generated by Latin hypercube sampling (LHS) simulation and self-organization map (SOM) clustering. To decouple the output, efficiency and part load factor of devices during operation, the following optimal contribution rate (FOCR) strategy was proposed, which could actively adjust the output ratio of energy conversion devices to realize the flexible energy supply. The developed optimization methodology was used for a case study in an office building. The results indicate that the improved KDE for probability distribution estimation of uncertainties achieves the accuracy percentage enhancement, for the average value, of 37.1% and 49.6% for root mean-square error (RMSE), respectively, compared with the conventional KDE and parametric model. Considering energy saving, economy and environmental indicators, the performance of the FOCR strategy is superior to those of the three traditional strategies.