Bellman–Genetic Hybrid Algorithm Optimization in Rural Area Microgrids

Fatima Zahra Zahraoui (), Mehdi Et-taoussi, Houssam Eddine Chakir, Hamid Ouadi and Brahim Elbhiri
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Fatima Zahra Zahraoui: Equipe de recherche en Electrotechnique, Robotique et Automatique, ENSAM, Mohammed V University in Rabat, Rabat 10000, Morocco
Mehdi Et-taoussi: Advanced School of Biomedical Engineering, (UM6SS), Casablanca 20000, Morocco
Houssam Eddine Chakir: EEIS-Lab, ENSET Mohammedia, Hassan II University, Casablanca 20000, Morocco
Hamid Ouadi: Equipe de recherche en Electrotechnique, Robotique et Automatique, ENSAM, Mohammed V University in Rabat, Rabat 10000, Morocco
Brahim Elbhiri: SmartiLAB EMSI-Rabat, Honoris United Universities, Rabat 10000, Morocco

Energies, 2023, vol. 16, issue 19, 1-26

Abstract: Incorporating renewable Distributed Energy Resources (DER) into the main grid is crucial for achieving a sustainable transition from fossil fuels. However, this generation system is complicated by the fluctuating behavior of renewable resources and the variable load demand, making it less reliable without a suitable energy storage system (ESS). This study proposes an Optimal Power Flow Management (OPFM) strategy for a grid-connected hybrid Micro Grid (MG) comprising a wind turbine (WT), a photovoltaic (PV) field, a storage battery, and a Micro Gas turbine (MGT). This proposed strategy includes (i) minimizing the MG’s daily energy cost, (ii) decreasing CO 2 emissions by considering the variable load, weather forecast, and main grid fees to optimize the battery charging/discharging strategy, and (iii) optimizing the decision-making process for power purchase/sell from/to the main grid. The suggested OPFM approach is implemented using a Genetic Algorithm and compared with the Bellman Algorithm and a restricted management system via several simulations under the Matlab environment. Furthermore, the hybridization of the Bellman Algorithm and the Genetic Algorithm is proposed to enhance the OPFMC strategy’s efficiency by leveraging both algorithms’ strengths. The simulation results demonstrate the effectiveness of the proposed strategy in lowering energy costs and CO 2 emissions and enhancing reliability. Additionally, the comparison of the hybridized GA algorithm reveals a cost 16% higher than the Bellman Algorithm; however, the use of the hybridized GA algorithm leads to a reduction in GHG emissions by 31.4%. These findings underscore the trade-off between cost and environmental impact in the context of algorithmic optimization for microgrid energy management.

Keywords: optimal power flow management (OPFM); hybrid micro-grid; renewable energy; Bellman Algorithm; Genetic Algorithm (GA); energy management system (EMS); distributed energy sources (DES) (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: 2023
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