A distributed and parallel scheduling method for demand response in energy communities with distributed generation and storage
Résumé
This paper presents a distributed optimization methods for energy communities having distributed renewable generation and storage units. We explain how the resulting optimization problem can be cast as a bi-level optimization problem where the followers solve mixed-integer linear programs. Given the difficulty of these problems for the dimensions at hand, we develop a heuristic iterative algorithm where incentives are sent to the followers. Specifically, to maximize the collective-self consumption rate, we exploit the notion of allocation key traditionally used a posteriori for economic gain sharing to build an a priori incentive to Demand response. The proposed distributed management method returns a solution within 5% cost of the optimal centralized realistic benchmark's solution of an instance built over a month with accurate historical data from our demonstrator located in the south of France.
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