What is it about?
The optimal integrated energy systems (IES) operation is considered a non-trivial task due to the renewable generation uncertainty and the optimization of multiple contradictory objectives (e.g. economic, environmental and risk costs). This paper aims to provide a multi-level optimization model for the real-time optimal IES operation. This work quantifies the uncertainty by the Conditional Value at Risk (CVaR) theory in the optimization model. The uncertainty is further reduced by improving the operation strategy through a model predictive control (MPC)-based method. Also, the multi-objective optimization model is adopted to minimize the economic cost, carbon dioxide emissions (CDE) and primary energy consumption (PEC) for optimal energy scheduling in the intra-day stage. Based on the result of the intra-day stage, the feedback correction model is applied to adjust the schedule to balance the difference between the forecasting and actual values.
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Why is it important?
The main technical contributions made in this work are summarized as follows. (1) A multi-level optimization model is proposed for the real-time optimal operation of the integrated energy systems (IES), considering economic, environmental and risk aspects, which can obtain trade-off solutions for the optimal operation. (2) The uncertainty of the IES is reduced by considering the risk of renewable energy systems (RES) utilization in the optimization model and correcting the prediction error in the real-time optimization model. (3) Through ablation and comparative experiments, the method of this work has performed well by reducing the cost, CDE and PEC with robustness. Advantages of the RES, ESS and CCS of the IES are also given out in experiments.
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This page is a summary of: Multi-level model predictive control based multi-objective optimal energy management of integrated energy systems considering uncertainty, Renewable Energy, August 2023, Elsevier, DOI: 10.1016/j.renene.2023.05.082.
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