Model Predictive Control for Photovoltaic Plants with Non-Ideal Energy Storage Using Mixed Integer Linear Programming
Angel L. Cedeño, Reinier López Ahuar, José Rojas, Gonzalo Carvajal, César Silva, Juan C. Agüero
Abstract
This paper proposes a model-based predictive control strategy based on mixed-integer linear programming for a photovoltaic power plant with battery energy storage. The control objective is to maximize the revenues from energy delivered from both photovoltaic panels and batteries to the grid in a deregulated electricity market. For each control interval, the proposed algorithm incorporates information on solar radiation, market prices, and the state of charge of the batteries to determine the intervals of energy injection into the grid to maximize the economic benefits. The proposed strategy considers the rate-based variable efficiency in the battery model and time-varying energies prices, thus providing a more general implementation than previous schemes proposed in the literature for the same purpose. Simulations considering the operational procedures of the Spanish market as a case study show that, by integrating the battery efficiency in the model, the proposed control strategy increments the economic benefits in 21% compared to previous results reported in the literature for the same operational conditions. Additionally, the proposed approach reduces the number of charge and discharge cycles, potentially extending the lifespan of batteries.