Enhanced Microgrid Control through Genetic Predictive Control: Integrating Genetic Algorithms with Model Predictive Control for Improved Non-Linearity and Non-Convexity Handling

Muhammed Cavus and Adib Allahham ()
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Muhammed Cavus: School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Adib Allahham: Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK

Energies, 2024, vol. 17, issue 17, 1-20

Abstract: Microgrid (MG) control is crucial for efficient, reliable, and sustainable energy management in distributed energy systems. Genetic Algorithm-based energy management systems (GA-EMS) can optimally control MGs by solving complex, non-linear, and non-convex problems but may struggle with real-time application due to their computational demands. Model Predictive Control (MPC)-based EMS, which predicts future behaviour to ensure optimal performance, usually depends on linear models. This paper introduces a novel Genetic Predictive Control (GPC) method that combines a GA and MPC to enhance resource allocation, balance multiple objectives, and adapt dynamically to changing conditions. Integrating GAs with MPC improves the handling of non-linearities and non-convexity, resulting in more accurate and effective control. Comparative analysis reveals that GPC significantly reduces excess power production, improves resource allocation, and balances cost, emissions, and power efficiency. For example, in the Mutation–Random Selection scenario, GPC reduced excess power to 76.0 W compared to 87.0 W with GA; in the Crossover-Elitism scenario, GPC achieved a lower daily cost of USD 113.94 versus the GA’s USD 127.80 and reduced carbon emissions to 52.83 kg CO2e compared to the GA’s 69.71 kg CO2e. While MPC optimises a weighted sum of objectives, setting appropriate weights can be difficult and may lead to non-convex problems. GAs offer multi-objective optimisation, providing Pareto-optimal solutions. GPC maintains optimal performance by forecasting future load demands and adjusting control actions dynamically. Although GPC can sometimes result in higher costs, such as USD 113.94 compared to USD 131.90 in the Crossover–Random Selection scenario, it achieves a better balance among various metrics, proving cost-effective in the long term. By reducing excess power and emissions, GPC promotes economic savings and sustainability. These findings highlight GPC’s potential as a versatile, efficient, and environmentally beneficial tool for power generation systems.

Keywords: genetic algorithms (GA); genetic predictive control (GPC); energy management system (EMS); microgrid (MG) control; model predictive control (MPC); non-linear systems (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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