Optimal probabilistic operation of energy hub with various energy converters and electrical storage based on electricity, heat, natural gas, and biomass by proposing innovative uncertainty modeling methods

Abstract

In recent years, attention to energy hubs (EHs) has increased significantly as active and intelligent elements in multi-energy systems (MESs). This article proposes a stochastic framework for the optimal operation of a new EH structure with various energy converters and electric storage based on electricity, heat, natural gas, and biomass. The proposed framework plays the role of a bidding strategy for a smart element in MESs. For modeling uncertainties in this framework, such as energy market prices, wind speed, and solar radiation, it is necessary to generate random scenarios based on recorded past behaviors or forecasting future trends. The Monte Carlo (MC) and the ARIMA methods have received significant attention in the literature to generate scenarios. Proposing uncertainty modeling methods in this paper, including the MC based on the Kolmogorov-Smirnov test, the ARIMA model based on Akaike Information Criterion, TBATS model, and the LSTM model of deep learning, as another innovation, has been such that efforts are made to make a significant improvement in the generated scenarios. Comparing various proposed uncertainty modeling methods is one of the most contributions. Based on the actual data in Finland, the simulation results demonstrate the effectiveness of the proposed operation strategy and the uncertainty modeling methods. Increasing the accuracy of uncertainty modeling has a significant impact on EH's profit and energy storage behavior and can also reduce the dependence of EHs on incoming energy carriers.