Day-ahead bidding strategy of regional integrated energy systems considering multiple uncertainties in electricity markets

The regional integrated energy system (IES) is considered a promising paradigm for economic multi-energy provision while promoting the accommodation of renewable distributed energy resources (RDER). However, various uncertainties faced by the IES pose challenges to fulfilling its functionalities and economic promises. This paper proposes a day-ahead optimal bidding strategy of IES in electricity markets with multiple time scales of electricity settlement and energy dispatch for minimizing its expected operational cost based on the hybrid scenario-based stochastic and chance-constrained programming. It enables the IES to determine the optimal bidding electricity quantities in the day-ahead market in a multi-energy complementary manner by incorporating the potential real-time clearing price scenarios, with the uncertainties introduced by RDER generation and multi-energy loads efficiently addressed using chance constraints. Herein, a gradient boosted regression tree (GBRT) based quantile forecasting method is developed to equivalently reformulate the chance constraints to tractable deterministic constraints without any prior knowledge or probability distribution assumptions. The proposed day-ahead bidding strategy is extensively assessed through simulation experiments in the PJM and Guangdong electricity markets and the numerical results confirm the effectiveness of the proposed solution.