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Recent studies have shown that multi-step optimization based on ModelPredictive Control (MPC) can effectively coordinate the increasing number ofdistributed renewable energy and storage resources in the power system.However, the computation complexity of MPC is usually high which limits its usein practical implementation. To improve the efficiency of MPC, in this paper,we apply a distributed optimization method to MPC. The approach consists of apartitioning technique based on spectral clustering that determines the bestsystem partition and an improved Optimality Condition Decomposition method thatsolves the optimization problem in a distributed manner. Results of simulationsconducted on the IEEE 14-bus and 118-bus systems show that the distributed MPCproblem can be solved significantly faster by using a good partition of thesystem and this partition is applicable to multiple time steps without frequentchanges.

Impact of Power System Partitioning on the Efficiency of Distributed Multi-Step Optimization