It is an effective way to regard the electric vehicles as the demand response for reducing the negative impact of large‐scale introduction on the power system. Aiming at the microgrid with demand response, the adaptive uncertainty sets‐based two‐stage robust optimisation method is established in this study. The coordination of micro‐gas turbine, energy storage, and demand response etc. are considered in the economic dispatch model. To effectively consider the uncertain variable contained in the microgrid, the concept of adaptive uncertainty sets is proposed in this study. The uncertainty sets are achieved by the long short‐term memory network and modified fuzzy information granulation. To handle the adaptive uncertainty sets‐based robust optimisation model, the column and constraint generation algorithm and strong duality theory are introduced to decompose the model into a master problem and a subproblem with mixed‐integer linear structure. To verify the performance of the proposed adaptive uncertainty sets‐based two‐stage robust optimisation method, measured data from a plateau city of China are introduced in the simulation test. The simulation results demonstrate the effectiveness of the model and solution strategy.
To better balance the reliability and conservativeness of uncertainty sets of robust optimization, the concept of adaptive uncertainty sets is proposed in this paper. There are two processes contained in the proposed adaptive uncertainty sets, which are point prediction and uncertainty sets determination. In the process of point prediction, the Long Short-term Memory Network (LSTM) is used to predict the renewable energy output. In the process of uncertainty sets determination, firstly, the prediction data is granulated based on the Modified Fuzzy Information Granulation (MFIG). Then the adjustable parameters are introduced to modify the upper and lower limit parameters of the information granules. Based on the above, the modeling of adaptive uncertainty sets can be achieved. To verify the performance of the proposed adaptive uncertainty sets, three groups of wind power output data of California are introduced to the contrast experiments. The simulation results demonstrate that, under 90% confidence level, the adaptive uncertainty sets method has a higher prediction interval coverage probability and a smaller prediction interval average width compared to the box uncertainty sets and the ellipsoidal uncertainty sets, which illustrates the good performance of the adaptive uncertainty sets in reliability and conservativeness.
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