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Multi-Objective Optimization of Hybrid Renewable Energy Systems Using Deep Reinforcement Learning and Genetic Algorithms.

Najaad OubeBlika
Volume 5

Abstract

Hybrid Renewable Energy Systems (HRESs) have emerged as an effective solution for improving energy reliability, reducing fossil-fuel dependence, and supporting the transition toward low-carbon power systems. By integrating multiple energy resources such as photovoltaic panels, wind turbines, battery energy storage systems, diesel backup generators, hydrogen storage units, and grid-interconnection components, HRESs can provide flexible and resilient electricity supply for microgrids, remote communities, industrial facilities, and smart distribution networks. However, the design and operation of hybrid renewable systems involve complex multi-objective optimization challenges, including minimizing total lifecycle cost, reducing carbon emissions, maximizing renewable energy penetration, improving power supply reliability, extending battery lifetime, and maintaining grid stability under uncertain weather and load conditions. To address these challenges, this paper proposes a hybrid optimization framework that combines Deep Reinforcement Learning (DRL) and Genetic Algorithms (GAs) for multi-objective planning and real-time energy management of HRESs.

The proposed framework formulates HRES optimization as a coupled design-operation problem. At the planning level, a Genetic Algorithm is employed to search for optimal system configurations, including photovoltaic array size, wind turbine capacity, battery storage capacity, inverter rating, backup generator capacity, and hydrogen system sizing where applicable. The GA-based optimizer evaluates candidate system designs using multi-objective fitness functions that consider Net Present Cost, Levelized Cost of Energy, Loss of Power Supply Probability, renewable fraction, carbon dioxide emissions, battery degradation cost, and energy curtailment. Pareto-front analysis is incorporated to identify trade-off solutions that allow system planners to balance economic, environmental, and reliability objectives according to application-specific priorities.

At the operational level, a Deep Reinforcement Learning agent is developed to learn adaptive energy management policies under dynamic and uncertain operating conditions. The DRL agent observes system states such as solar irradiance, wind speed, load demand, battery state of charge, electricity price, grid availability, weather forecasts, and generator status. Based on these states, the agent selects control actions including battery charging and discharging, generator dispatch, grid import or export, renewable energy curtailment, and load-shifting decisions. Advanced DRL algorithms such as Deep Q-Network, Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Soft Actor–Critic are investigated to support both discrete and continuous control actions. The reward function is designed to minimize operational cost, emissions, unmet load, battery stress, and switching frequency while maximizing renewable utilization and supply reliability.

A comprehensive simulation and evaluation environment is developed using historical renewable resource profiles, load demand data, electricity tariffs, component degradation models, and technical constraints of HRES components. Data preprocessing includes weather–load alignment, missing value imputation, normalization, scenario generation, and uncertainty modeling for solar irradiance, wind speed, and demand variability. The proposed DRL–GA framework is evaluated under grid-connected, islanded, and off-grid microgrid scenarios. Performance is assessed using economic, technical, environmental, and reliability metrics, including total net present cost, Levelized Cost of Energy, renewable energy fraction, carbon emissions, Loss of Power Supply Probability, battery cycling degradation, energy curtailment ratio, peak load reduction, and computational efficiency.

Experimental evaluation is expected to demonstrate that the proposed hybrid DRL–GA approach outperforms conventional rule-based energy management, standalone Genetic Algorithm optimization, particle swarm optimization, mixed-integer linear programming-based scheduling, and non-adaptive heuristic control strategies. The GA component identifies cost-effective and reliable system configurations, while the DRL component continuously adapts operational decisions to real-time variations in renewable generation, load demand, electricity prices, and storage availability. This integration enables the system to achieve better trade-offs between cost reduction, emission mitigation, renewable utilization, and reliability enhancement compared with static optimization approaches.

The proposed framework provides a scalable, intelligent, and adaptive solution for the multi-objective optimization of hybrid renewable energy systems. By combining evolutionary search with sequential decision-making intelligence, the system supports both long-term capacity planning and real-time operational control. Its ability to generate Pareto-optimal design alternatives and learn dynamic control policies makes it suitable for smart grids, microgrids, remote electrification, industrial energy systems, and sustainable community power planning. The framework can contribute to improved renewable energy integration, reduced operational uncertainty, enhanced storage utilization, and more resilient low-carbon energy infrastructure

Keywords

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