revenue prediction for integrated renewable energy and energy storage system using machine learning techniques""

1 answer:

4 0

The selection of battery sizing that results in the best financial performances must be supported by revenue estimation for combined renewable energy and energy storage systems.

Mixed-integer linear programming is a common strategy for maximizing the profits of a hybrid hydro and energy storage system (MILP). While MILP models are frequently quite computationally expensive, they can offer precise production cost estimates. We suggest a novel method for forecasting the optimal revenues of an integrated energy generating and storage system in order to offer hydropower plant owners or operators with quick yet accurate first-step information when they consider integrating energy storage systems.

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