Risk‐constrained two‐stage scheduling of a virtual energy hub integrated with intelligent parking lots in the presence of electrical and thermal energy markets and responsive loads

Abstract

The energy hub applications have been investigated from different perspectives in recent years due to their benefits such as high flexibility and reliability. This paper proposes a two‐stage stochastic programming to enable the participation of a virtual energy hub integrated with parking lots in the electrical and thermal energy markets under the uncertainty of renewable resources, load demands and energy prices. In such modeling, the first stage optimizes the offers of the virtual hub in the day‐ahead markets before realizing uncertain parameters and the second stage minimizes the real‐time imbalanced cost and determines the operation of components considering actual data. In order to evaluate the risk of decisions, the conditional value‐at‐risk metric is utilized and the results are discussed in the risk‐neutral and risk‐averse strategies for a sample day of summer and winter. In the following, a time‐based program is implemented to indicate the influence of responsive loads and a sensitivity analysis is conducted to assess the effectiveness of the flexible units. The simulations approve that although the risk‐averse optimization significantly decreases the profit, the initiative against fluctuations enhances. The outcomes also demonstrate the notable role of flexible units and loads for energy trading in the electrical and thermal markets.