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The rapid penetration of uncertain and unpredictable renewable energy resources into the power grid has made generation planning and real-time power balancing a challenge, thus rendering demand-side advanced control a necessity. In this paper, we introduce a distributed optimization framework for load flexibility control in the grid. The proposed scheme is based on Newton iteration to provide fast convergence and is scalable to a large number of loads at the aggregation level. The developed optimization framework is based on interior-point methods and concepts of model-predictive control. It uses load–aggregator communications to solve a large-scale optimization problem in a computationally distributed manner without geometric structural assumptions. We demonstrate the performance of the algorithm in a large-scale simulation comprising 10 000 grid-connected heterogeneous assets.

Optimal Flexibility Control of Large-Scale Distributed Heterogeneous Loads in the Power Grid