A novel integrated stochastic programming-information gap decision theory (IGDT) approach for optimization of integrated energy systems (IESs) with multiple uncertainties. (15th May 2022) - Sun, Qie; Fu, Yu; Lin, Haiyang; Wennersten, Ronald
Highlights: A novel integrated stochastic programming-IGDT approach for optimizing IES capacity and operation is proposed. The novel approach considers robustness and opportunistic aspects of multiple uncertain parameters of multiple time scale. Two sides of operational strategies with different risk preferences towards uncertainty can be generated. This approach ensures the accuracy of the results and provides flexible options for system developers and operators. Abstract: Uncertainties can affect the operation and planning of integrated energy systems. It is thus critical to understand how uncertainties can be handled in IES optimization. To address this issue, this study proposed a novel integrated stochastic programming-information gap decision theory (IGDT) approach, which can handle multiple uncertainties in the optimization of IES operation and planning. By applying the integrated approach to an office building, two sides of operation schedules and equipment capacities were generated, representing the risk-averse and risk-seeking strategies, respectively. The EES capacity under the risk-averse strategy is smaller than that under the risk-seeking strategy, while the TES capacity is larger under the risk-averse strategy. The consumption of fossil fuels is critical for dealing with uncertainties. Adjusting the operation of the gas turbine, energy storage units, and grid interaction are useful approaches for handling uncertainties and resisting associated risks. Through the comparison between the integrated approach and a two-stage stochastic approach, it is found that the proposed integrated method ensures the accuracy of the results and, at the same time, provides flexible options for system developers and operators regarding various uncertainties and associated risks.