Scenario Generation Based on Ant Colony Optimization for Modelling Stochastic Variables in Power Systems

Daniel Fernández Valderrama (), Juan Ignacio Guerrero Alonso, Carlos León de Mora and Michela Robba
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Daniel Fernández Valderrama: Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genoa, 16145 Genoa, Italy
Juan Ignacio Guerrero Alonso: Department of Electronic Technology, Escuela Politécnica Superior, University of Sevilla, 41011 Sevilla, Spain
Carlos León de Mora: Department of Electronic Technology, Escuela Politécnica Superior, University of Sevilla, 41011 Sevilla, Spain
Michela Robba: Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genoa, 16145 Genoa, Italy

Energies, 2024, vol. 17, issue 21, 1-14

Abstract: Uncertainty is an important subject in optimization problems due to the unpredictable nature of real variables in the power system area, which can condition the solution’s accuracy. The effective modelling of stochastic variables can contribute to the reduction in losses in the system under evaluation and facilitate the implementation of an effective response in advance. To model uncertainty variables, the most extended technique is the scenario generation (SG) method. This method evaluates possible combinations of complete curves. Classical scenario generation methods are founded on probability distributions or robust stochastic optimization. This paper proposes a novel approach for constructing scenarios using the Ant Colony Optimization (ACO) algorithm, referred to as ACO-SG. This methodology does not require a previous statistical study of uncertainty data to generate new scenarios. A historical dataset and the desired number of scenarios are the inputs inserted into the algorithm. In the case study, the algorithm used historical data from the Savona Campus Smart Polygeneration Microgrid of the University of Genoa. The approach was applied to generate scenarios of photovoltaic generation and building consumption. The low values of the Euclidean distance were used in order to check the validity of the scenarios. Moreover, the error deviation of the scenarios generated with the goal of daily power were 1.77% and 0.144% for the cases of PV generation and building consumption, respectively. The different results for both cases are explained by the characteristics of the specific cases. Despite these different results, both were significantly low, which indicates the capability of the algorithm to generate any kind of feature within curves and its adaptability to any case of SG.

Keywords: ant colony optimization; microgrids; stochastic processes; energy management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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