Short term energy management in stand-alone PV-battery-diesel systems Rodolfo Dufo-López, Juan M. Lujano-Rojas, José L. Bernal-Agustín, Jesús S. Artal-Sevil, Ángel A. Bayod-Rújula, Juan A. Tejero-Gómez   2024/07/20

Abstract

In this work, the short term (daily) operation of an off-grid hybrid PV-diesel-battery system is optimized by genetic algorithms. An integer variable (0, 1 or 2) for each hour of the day decides the way the battery works. With the forecast of the hourly irradiation, temperature and load consumption for the next day, and estimating the state of charge of the battery (SOC) at the first hour of the day, we perform the optimization of the integer variables for the 24 hours of next day. To avoid inadmissible computation time, the optimization is performed by using genetic algorithms (GA) obtaining in roughly 1 hour the optimal solution or a solution near the optimal one. The optimization tries to obtain the minimal total cost of the daily operation while supplying the whole load. We compare the results of the optimization with the typical control strategies (load following, cycle charging and set point strategies), obtaining better results with the new optimized strategy. The reduction in the operational cost obtained varies from 2.5% to 62%, compared to the typical control strategies (load following or cycle charging). 

Authors and affiliations

Rodolfo Dufo-López, Juan M. Lujano-Rojas, José L. Bernal-Agustín, Jesús S. Artal-Sevil, Ángel A. Bayod-Rújula, Juan A. Tejero-Gómez

Department of Electrical Engineering. E.I.N.A., Zaragoza University. C/María de Luna, 3, 50018 Zaragoza, Spain

Key Words

PV-diesel-battery systems, off-grid, daily operation, control strategy, optimization, genetic algorithms.

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