Abstract
With the integration of Renewable Energy Resources (RERs), the Day-Ahead (DA) scheduling for the optimal operation of the integrated Isolated Microgrids (IMGs) may not be economically optimal in real time due to the prediction errors of multiple uncertainty sources. To compensate for prediction error, this paper proposes a Robust Model Predictive Control (RMPC) based on an interval prediction approach to optimize the real-time operation of the IMGs, which diminishes the influence from prediction error. The rolling optimization model in RMPC is formulated into the robust model to schedule operation with the consideration of the price of robustness. In addition, an Online Learning (OL) method for interval prediction is utilized in RMPC to predict the future information of the uncertainties of RERs and load, thereby limiting the uncertainty. A case study demonstrates the effectiveness of the proposed with the better matching between demand and supply compared with the traditional Model Predictive Control (MPC) method and Hard Charging (HC) method.
Highlights
As smart parts of the future power grid, the microgrid is an active distribution system integrated with Distributed Generators (DGs), Energy Storage Systems (ESS), controllable loads, and other electric components [1]
There are usually larger prediction errors in RERs compared to the rest of the uncertainties causing a large impact on energy management [2]. erefore, this paper focuses on the energy management in Islanded Microgrids (IMGs) to reduce the operation cost with the consideration of the uncertainties in power generation of RERs and the demand level of the user loads
E main contributions of this paper can be summarized as follows: (i) With the consideration of uncertainties of RERs and load demand, this paper proposes an Robust Model Predictive Control (RMPC) method for the energy management of IMGs based on interval prediction to reduce operational costs and improve the utilization of RERs. e price of robustness is implemented to present the preference of the planner about the risk of power shortage
Summary
As smart parts of the future power grid, the microgrid is an active distribution system integrated with Distributed Generators (DGs), Energy Storage Systems (ESS), controllable loads, and other electric components [1]. (i) With the consideration of uncertainties of RERs and load demand, this paper proposes an RMPC method for the energy management of IMGs based on interval prediction to reduce operational costs and improve the utilization of RERs. e price of robustness is implemented to present the preference of the planner about the risk of power shortage. (ii) Multiple uncertainty sources are taken into account for the real-time (RT) scheduling of the IMGs, including the load demand and the outputs of RERs. e OL application combined with RMPC is used to provide time-efficient and accurate interval predictions one step ahead for the RMPC to reduce future forecast error.
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