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      Bi-Level Two-Stage Robust Optimal Scheduling for AC/DC Hybrid Multi-Microgrids

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          Solving two-stage robust optimization problems using a column-and-constraint generation method

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            Is Open Access

            Robust Energy Management for Microgrids With High-Penetration Renewables

            Due to its reduced communication overhead and robustness to failures, distributed energy management is of paramount importance in smart grids, especially in microgrids, which feature distributed generation (DG) and distributed storage (DS). Distributed economic dispatch for a microgrid with high renewable energy penetration and demand-side management operating in grid-connected mode is considered in this paper. To address the intrinsically stochastic availability of renewable energy sources (RES), a novel power scheduling approach is introduced. The approach involves the actual renewable energy as well as the energy traded with the main grid, so that the supply-demand balance is maintained. The optimal scheduling strategy minimizes the microgrid net cost, which includes DG and DS costs, utility of dispatchable loads, and worst-case transaction cost stemming from the uncertainty in RES. Leveraging the dual decomposition, the optimization problem formulated is solved in a distributed fashion by the local controllers of DG, DS, and dispatchable loads. Numerical results are reported to corroborate the effectiveness of the novel approach.
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              Energy Management of Microgrid in Grid-Connected and Stand-Alone Modes

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                Author and article information

                Journal
                IEEE Transactions on Smart Grid
                IEEE Trans. Smart Grid
                Institute of Electrical and Electronics Engineers (IEEE)
                1949-3053
                1949-3061
                September 2018
                September 2018
                : 9
                : 5
                : 5455-5466
                Article
                10.1109/TSG.2018.2806973
                4cc65038-6f3f-41d0-a8dc-3ae6394b09a7
                © 2018
                History

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