An Efficient Decomposition Method for the Integrated Dispatch of Generation and Load


In response to the computational challenges produced by the integrated dispatch of generation and cargo (IDGL), this paper proposes a novel and efficient decomposition methodology. The IDGL is formulated using the mixed-integer quadratic constrained programming (MIQCP) methodology. To efficiently solve this advanced optimization downside, the nodal equivalent load shifting bidding curve (NELSBC) is proposed to represent the aggregated response characteristics of shoppers at a node. The IDGL is subsequently decomposed into a two-level optimization problem. At the higher level, grid operators optimize load shifting schedules based on the NELSBC of every node. Transmission losses are explicitly incorporated into the model to coordinate them with generating costs and cargo shifting prices. At the underside level, client load adjustments are optimized at individual nodes given the nodal load shifting requirement imposed by the grid operators. The key advantage of the proposed method is that the load shifting among totally different nodes can be coordinated via NELSBCs while not iterations. The proposed decomposition method significantly improves the efficiency of the IDGL. Parallel computing techniques are utilized to accelerate the computations. Using numerical studies of IEEE thirty-bus, 118-bus, and practically sized 300-bus systems, this study demonstrates that correct and efficient IDGL scheduling results, which take into account the nonlinear impact of transmission losses, will be achieved.

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