Optimal load dispatch of energy hub under source-load uncertainty
The rapid development of the energy internet has led to the transformation of the traditional energy system into an integrated energy system with the high coupling of multiple energy sources,which is conducive to promoting the consumption of renewable energy,improving energy efficiency and alleviating environmental pollution.As an important modeling tool and analysis framework for the planning and operation of integrated energy systems,energy hubs have been widely studied and paid attention to since their introduction.In this paper,we propose an energy hub structure consisting of wind turbine,combined heat and power unit,power-to-gas unit and concentrating solar power plant,and propose a two-stage optimal load dispatch model based on distributionally robust optimization for the energy hub under source-load uncertainty.The model takes into account the uncertainties of wind power,solar thermal,electrical load and thermal load,and establishes an ambiguity set of uncertainties based on Wasserstein distance.The model is divided into two scheduling stages:day-ahead scheduling based on day-ahead forecast data in the first stage,and real-time scheduling of flexible equipment in the second stage to suppress the impact of forecast errors in order to obtain the minimum real-time scheduling cost under the worst uncertainty distribution.The model uses an affine strategy to adjust the equipment output and is transformed into a mixed-integer linear programming problem,which is solved by strong dual theory.The simulation results show that the multi-energy coupled devices can enhance the system flexibility and promote the renewable energy consumption,while the proposed distributionally robust optimization algorithm can balance the system economy and robustness.
integrated energy systemenergy hubconcentrated solar power plantoptimal load dispatchdistributionally robust optimization
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