Open Access
Table 1
Summary of recently reviewed literature sources.
| Reference | Methodology | Major outcomes and analysis | Identified research lacks |
|---|---|---|---|
| [4], 2024 | Hybrid optimization models integrating Renewable Energy Sources (RES) and EV charging systems. Performance analysis using MATLAB and Python. | EV integration improved microgrid energy efficiency. Optimized charging schedules reduced peak demand. | Lacks real-world validation of proposed solutions. Optimization constrained by static load profiles. Limited focus on multi-energy systems beyond RES and EVs. |
| [5], 2024 | Probabilistic modeling of RES and EV load demand. Monte Carlo simulations to manage uncertainties in energy generation and consumption. | EV integration enhances system reliability and models reduce cost variability. Improved system resilience under high renewable penetration scenarios. | Does not explore AI-based dynamic forecasting for load and generation. Minimal consideration of diverse grid scenarios (e.g., urban vs. rural). |
| [6], 2024 | Hybrid energy storage system integrating batteries and supercapacitors. Energy management strategy based on Particle Swarm Optimization (PSO). Case study analysis under dynamic load conditions. | HESS reduced energy losses and Supercapacitors improved system responsiveness to peak loads. PSO-based optimization ensured smoother power transitions, enhancing battery longevity. | Limited exploration of alternative energy storage technologies. Static PSO parameters may limit scalability in larger systems. Minimal exploration of cost factors for HESS deployment. |
| [7], 2024 | Comparative analysis of heuristic and evolutionary algorithms. Application to various microgrid configurations. Focus on load balancing and cost minimization. | Evolutionary algorithms outperformed traditional heuristics in convergence speed and solution quality. Hybrid techniques (e.g., PSO-GA) showed in solving multi-objective problems. | Limited real-time implementation and validation of hybrid techniques. Lack of emphasis on computational efficiency in large-scale applications. |
| [8], 2024 | Use of metaheuristic algorithms (NSGA-II, MOPSO) for on-grid and off-grid systems. Life-cycle cost and environmental impact optimization. Sensitivity analysis for varying energy mix scenarios. | NSGA-II provided better Pareto front solutions compared to MOPSO. Hybrid systems achieved operational costs in off-grid scenarios. Environmental impact reduced with optimized renewable energy proportions. | Requires integration of more advanced hybrid algorithms. Limited focus on energy storage scalability. Sensitivity analysis lacked inclusion of rare extreme weather scenarios affecting renewables. |
| [9], 2023 | Hybrid metaheuristic approach combining Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Focus on optimizing energy reliability and sustainability for islanded systems. | Hybrid GA-ACO outperformed standalone algorithms, achieving higher reliability. Optimized solutions resulted lower energy costs. Improved renewable energy utilization. | Lack of real-world implementation or long-term evaluation of proposed solutions. Minimal consideration of socio-economic factors in islanded systems. Limited evaluation of extreme load scenarios or disaster-resilience testing. |
| [10], 2024 | Model predictive control, adaptive control, AI-based control. | The paper examines advanced control strategies in hybrid microgrid systems with EVs and battery storage, concluding that while promising, these techniques need further real-world validation. | The study highlights the need for field trials to validate advanced control strategies and their real-world robustness, and suggests future research should focus on adaptive control for dynamic system changes. |
| [11], 2024 | Rule-based, optimization-based, learning-based methods. | This review highlights recent advancements in hybrid microgrid control and optimization, noting progress in EV and battery integration but on-going challenges with stability and control complexity. | The paper calls for more research on control layer interactions, operational scenarios, and the interoperability of communication and control standards. |
| [12], 2023 | Mixed-Integer Linear Programming (MILP). | The study proposes an optimization framework for energy management in hybrid microgrids with EVs and battery storage, effectively balancing supply and demand while minimizing costs and emissions. | Research gaps include improving optimization models’ adaptability to various grid configurations and load profiles, and conducting more studies on the economic impacts of large-scale EV integration. |
| [13], 2023 | Centralized, decentralized, distributed control; optimize cost, reliability, efficiency for enhanced performance. | The review discusses energy management strategies for hybrid microgrids with EVs and battery storage, noting progress but ongoing challenges in real-time control, cost reduction, and system reliability. | Research gaps include scalability issues with current strategies for varying loads and renewable energy availability, and a need for robust algorithms to handle uncertainties in EV charging and renewable energy generation. |
| [14], 2022. | Dispersed control approaches enhance effectiveness, dependability, affordability, and reduce intricacy. | The review offers a detailed overview of energy management and control strategies for hybrid microgrids with EVs and battery storage, highlighting advancements and key challenges in improving system efficiency and reliability. | The paper calls for advanced algorithms to manage dynamic interactions between EVs, batteries, and renewables, and improved models to address variability in EV usage and renewable energy generation. |
| [15], 2022. | Control architectures, optimization techniques, and the integration of renewable energy | The review analyzes current control strategies for hybrid microgrids with EVs and energy storage, highlighting their effectiveness but noting a lack of flexibility and robustness in practical use. | Research is needed on adaptive control for uncertain demand/supply and advanced communication technology integration impacts. |
| [16], 2021 | Strategies: rule-based, optimization, predictive; compared by cost, reliability, renewable integration. | Paper reviews energy management strategies for hybrid microgrids with EVs and battery storage, highlighting a focus on cost optimization and power supply reliability. | The paper highlights the need for real-time adaptive control strategies to dynamically address load and generation changes, along with better coordination between EV charging and renewable energy availability. |
Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.
Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.
Le chargement des statistiques peut être long.