Energy Management System Research Articles

Restructuring the Coupling Coordination Mechanism of the Economy–Energy–Environment (3E) System Under the Dual Carbon Emissions Control Policy—An Exploration Based on the “Triangular Trinity” Theoretical Framework

Against the backdrop of the profound restructuring in global climate governance, China’s energy management system is undergoing a comprehensive transition from dual energy consumption control to dual carbon emissions control. This policy shift fundamentally alters the underlying logic of energy-focused regulation and inevitably impacts the economy–energy–environment (3E) system. This study innovatively constructs a “Triangular Trinity” theoretical framework integrating internal, intermediate, and external triangular couplings, as well as providing a granular analysis of their transmission relationships and feedback mechanisms. Using Guangdong Province as a case study, this study takes the dual control emissions policy within the external triangle as an entry point to research the restructuring logic of dual carbon emissions control for the coupling coordination mechanisms of the 3E system. The key findings are as follows: (1) Policy efficacy evolution: During 2005–2016, dual energy consumption control significantly improved energy conservation and emissions reduction, elevating Guangdong’s 3E coupling coordination. Post 2017, however, its singular focus on total energy consumption revealed limitations, causing a decline in 3E coordination. Dual carbon emissions control demonstrably enhances 3E systemic synergy. (2) Decoupling dynamics: Dual carbon emissions control accelerates economic–carbon emission decoupling, while slowing economic–energy consumption decoupling. This created an elasticity space of 5.092 million tons of standard coal equivalent (sce) and reduced carbon emissions by 26.43 million tons, enabling high-quality economic development. (3) Mechanism reconstruction: By leveraging external triangular elements (energy-saving technologies and market mechanisms) to act on the energy subsystem, dual carbon emissions control leads to optimal solutions to the “Energy Trilemma”. This drives the systematic restructuring of the sustainability triangle, achieving high-order 3E coupling coordination. The Triangular Trinity framework constructed by us in the paper is an innovative attempt in relation to the theory of energy transition, providing a referenceable methodology for resolving the contradictions of the 3E system. The research results can provide theoretical support and practical reference for the low-carbon energy transition of provinces and cities with similar energy structures.

A Comparative Study of Customized Algorithms for Anomaly Detection in Industry-Specific Power Data

This study compares and analyzes statistical, machine learning, and deep learning outlier-detection methods on real power-usage data from the metal, food, and chemical industries to propose the optimal model for improving energy-consumption efficiency. In the metal industry, a Z-Score-based statistical approach with threshold optimization was used; in the food industry, a hybrid model combining K-Means, Isolation Forest, and Autoencoder was designed; and in the chemical industry, the DBA K-Means algorithm (Dynamic Time Warping Barycenter Averaging) was employed. Experimental results show that the Isolation Forest–Autoencoder hybrid delivers the best overall performance, and that DBA K-Means excels at detecting seasonal outliers, demonstrating the efficacy of these algorithms for smart energy-management systems and carbon-neutral infrastructure

Design of intelligent energy management system for electric vehicles based on multi-objective optimization

This study proposes an intelligent energy management system for electric vehicles. This system uses multi-objective optimization to overcome the limitations of existing electric vehicles, including limited range, battery life degradation, and low energy utilization efficiency. The research aims to comprehensively optimize the vehicle’s power, battery life, and energy utilization efficiency. The method involves creating an energy management strategy based on multi-objective optimization that incorporates the Pontryagin minimum principle and deep Q-Network. This method uses the Pontryagin minimum principle to create an initial optimization framework and adjusts it in real time using a deep Q-network to address the complex, dynamic characteristics of an electric vehicle’s energy management system. The simulation results demonstrated that the proposed system achieved significant improvements. Compared to mainstream energy management systems, it had the lowest fuel cell and power cell degradation rates of 19.21% and 40.28%, respectively. Additionally, the system exhibited an average acceleration time of 5.38 s and an average hill climbing ability of 25.91%. These outcomes demonstrate the effectiveness of the proposed EMS in optimizing power, extending battery life, and improving energy utilization efficiency. This makes it an innovative solution for developing electric vehicle energy management systems.

Real-time monitoring and optimization methods for user-side energy management based on edge computing

This paper presents a comprehensive framework for real-time monitoring and optimization of user-side energy management systems leveraging edge computing technology. The proposed approach addresses key challenges in traditional centralized energy management by bringing computation and data processing closer to end devices. The framework encompasses three main components: an edge computing-based system architecture for data acquisition and processing, real-time monitoring methods for energy consumption and power quality, and optimization techniques for demand response and distributed energy resource coordination. Through case studies and experimental analysis, we demonstrate that the proposed framework achieves significant improvements in energy efficiency, response time, and cost reduction compared to conventional centralized approaches. The results show up to 30% increase in renewable energy utilization and 25% reduction in operating costs across various deployment scenarios. This work provides valuable insights into the application of edge computing for next-generation energy management systems while highlighting remaining challenges and future research directions.

Capabilities of battery and compressed air storage in the economic energy scheduling and flexibility regulation of multi-microgrids including non-renewable/renewable units

Economic scheduling of multi-microgrids containing distributed units and storage devices is expressed in this scheme according to the multi-objective energy management system. Microgrid operator considers the economic, security, flexibility and operation objectives. The present method minimizes the weighted sum of voltage security index, energy loss, and energy cost. Constraints consider the optimal power flow formulation, flexibility and voltage stability limits in microgrids, and mathematical formulation of sources and storages operation. Microgrid includes non-renewable and renewable units, and storage system in network are battery and compressed air storage. Unscented Transformation approach models the uncertainties of the renewables output, price of energy, and demand. Fuzzy decision approach obtains a compromise point between economic, security and operation objectives. Combining grey wolf and red panda optimizers is able to obtain an optimal solution with low value for variance of the final point. Energy management according to various technical and economic indicators in the several renewable multi-bus microgrids considering battery, compressed air storage and non-renewable unit as flexibility sources based on the Unscented Transformation model and hybrid solver are the advantage, goal and innovation of this project. According to simulation results, the energy management of the energy storage and non-renewable sources in the microgrids with renewable sources can be improved the various indicators, such as reducing the energy cost and loss as well as voltage drop about to 30–60%, 46%, 46–50%, and improving voltage security equal to 10.55% compared with power flow studies. Flexibility of 100% is also reached for microgrids thanks to the incorporation of storage equipment and non-renewable power sources.

Day-ahead energy scheduling of grid connected microgrids for demand side management with modified Harris Hawks optimisation

ABSTRACT Microgrids (MG) that can sustain themselves are made possible by the utility’s incorporating renewable energy sources (RES) and communication technology. Because these resources are uncertain of market prices and the time-varying load is unpredictable. It is necessary to have an efficient energy management system (EMS). It’s important to evaluate how combining demand-side management (DSM) with the EMS issues would affect peak reduction and total operational costs. To manage demand-side effects, this article suggests using modified Harris Hawks optimization for day-ahead energy scheduling of microgrids connected to the grid. By investigating the consequences of non-dispatchable energy sources of a utility-induced flexibility load structuring approach, this study seeks to close this gap. To solve the uncertainty issue, the first stage entails creating two potential scenarios for solar, wind power using current meteorological data. The objective function will be connected with the DSM load participation data assignment, operational restrictions, and MG system design covered in the second stage. The simulation results demonstrate the suggested stochastic framework’s competency in reducing costs by 43.81% when 20% of the load participating in DSM is implemented. For scenarios 1 and 2, the predicted total cost of energy generation during 24 hours is 3540.00 euros/3514.04 euros.

Green Energy Management in Electric Vehicles with Regenerative Braking Using Supercapacitors and Batteries

<p>This work presents the design and implementation of an energy management system for electric vehicles utilizing regenerative braking. The hybrid power supply comprises a lithium-ion battery bank and a supercapacitor bank. Bidirectional DC-DC converters are employed to manage power flow between the energy storage elements and the DC motor. Regenerative braking allows kinetic energy recovery during deceleration, which is stored in the supercapacitors due to their high power density. The supercapacitors also handle high current demands during acceleration, reducing battery stress. Passive equalization circuits are used for the batteries and supercapacitors to avoid overvoltage conditions. The control system is implemented in MATLAB/Simulink and hardware-in-the-loop testing is performed with a dSPACE platform. Experimental results demonstrated up to 65% regeneration efficiency, with good agreement between simulated and practical values. The supercapacitors successfully supplied momentary high current loads, avoiding premature current limitation by the battery protection system. The results confirm the feasibility of the proposed architecture for electric vehicle applications requiring high power and energy recovery.</p>

Dynamic appliance scheduling and energy management in smart homes using adaptive reinforcement learning techniques

Smart home energy management is complicated because of varying user preferences, expenses, and consumption. These dynamics are difficult for traditional systems to handle, but new developments in reinforcement learning and optimization may be able to help. The paper introduces a novel Demand Response (DR) method that integrates a Self-Adaptive Puma Optimizer Algorithm (SAPOA) with a Multi-Objective Deep Q-Network (MO-DQN), improving smart home energy consumption, cost, and user preferences management. SAPOA adaptively maximizes numerous objectives, while DQN improves decision-making by assimilating interactions. The proposed method adapts to user preferences by learning from previous energy usage patterns and optimizing the scheduling of critical household appliances, enhancing energy efficiency. Static optimization in traditional home energy management systems (HEMS) makes it difficult to handle changing expenses and dynamic user preferences. Reinforcement learning (RL) methods now in use frequently lack sophisticated optimization integration. The experimental results show that the outperforming multiobjective reinforcement learning puma optimizer algorithm (MORL–POA), SAPOA, and POA methods, the suggested solution dramatically lowers the peak-to-average ratio (PAR) value from 3.4286 to 1.9765 without RES and 1.0339 with RES. By combining SAPOA with DQN, the suggested approach maximizes energy management, optimizes appliance scheduling, and efficiently manages uncertainty, improving performance and flexibility. Metrics like peak average ratio (PAR), energy usage, and electricity cost are used to assess performance, while the Matlab platform is used for implementation.

A fuzzy logic based energy management model for solar PV-wind standalone with battery storage system

Access to reliable electricity is essential for delivering quality healthcare. However, off-grid health facilities in rural regions like Kalangala, Uganda, often face persistent power outages and high operational costs due to dependence on diesel generators. This study proposes a fuzzy logic-based energy management system (FLC-EMS) to optimize power flow in a hybrid renewable energy system (HRES) combining solar photovoltaics (PV), wind turbines (WT), and battery storage. The system was modeled in MATLAB/Simulink, using 27 fuzzy IF–THEN rules and triangular membership functions to manage four switching ports that prioritize renewable energy based on real-time load demand, renewable availability, and battery state-of-charge (SOC). Simulation results showed that the FLC-EMS ensured continuous power supply during peak demand periods (e.g., 9:00 AM and 7:00 PM) by dynamically balancing solar, wind, and battery inputs. The optimized PV-WT-BAT configuration achieved a Levelized Cost of Electricity (LCOE) of $0.281 and a Net Present Cost (NPC) of $269,246 over a 20-year period. Compared to diesel-based systems, it reduced operational costs by 11.87–18.7% and significantly lowered carbon emissions. The proposed FLC-EMS demonstrates strong potential to improve energy reliability and cost-effectiveness for off-grid healthcare facilities. Its adaptability to variable loads and intermittent renewable sources makes it a scalable solution for sustainable rural electrification. Future research will focus on real-world implementation and enhancing predictive control through machine learning.

Evaluating China’s Electric Vehicle Adoption with PESTLE: Stakeholder Perspectives on Sustainability and Adoption Barriers

The electric vehicle (EV) business model integrates advanced battery technology, dynamic power train architectures, and intelligent energy management systems with ecosystem strategies and digital services. It incorporates environmental sustainability through lifecycle analysis and renewable energy integration. China, with 9.49 million EV sales in 2023 (33% market share), faces infrastructure gaps constraining further growth. China is strategically mitigating CO2 emissions while fostering economic expansion, notwithstanding constraints such as suboptimal battery technology advancements, elevated production expenditure, and enduring ecological impacts. This Political, Economic, Social, Technological, Legal, Environmental (PESTLE) assessment, operationalized through a survey of 800 stakeholders and Statistical Package for the Social Sciences IBM SPSS SPSS (Version 28) quantitative analysis (factor loading = 0.73 for Technology; eigenvalue = 4.12), identifies infrastructure gaps as the dominant barrier (72% of stakeholders). Political factors (β = 0.82) emerged as the strongest adoption predictor, outweighing economic subsidies in significance. The adoption of EVs in China presents a significant prospect for reducing CO2 emissions and advancing technology. However, economic barriers, market dynamics, inadequate infrastructure, regulatory uncertainty, and social acceptance issues are addressed in the assessment. The study recommends prioritizing infrastructure investment (e.g., 500 K fast-charging stations by 2027) and policy stability to overcome adoption barriers. This study provides three key advances: (1) quantification of PESTLE factor weights via factor analysis, revealing technological (infrastructure) and political factors as dominant; (2) identification of infrastructure gaps, not subsidies, as the primary adoption barrier; and (3) demonstration of infrastructure’s persistence post-subsidy cuts. These insights redefine EV adoption priorities in China.

Artificial-Intelligence-Based Energy Management Strategies for Hybrid Electric Vehicles: A Comprehensive Review

The worldwide drive towards low-carbon transportation has made Hybrid Electric Vehicles (HEVs) a crucial component of sustainable mobility, particularly in areas with limited charging infrastructure. The core of HEV efficiency lies in the Energy Management Strategy (EMS), which regulates the energy distribution between the internal combustion engine and the electric motor. While rule-based and optimization methods have formed the foundation of EMS, their performance constraints under dynamic conditions have prompted researchers to explore artificial intelligence (AI)-based solutions. This paper systematically reviews four main AI-based EMS approaches—the knowledge-driven, data-driven, reinforcement learning, and hybrid methods—highlighting their theoretical foundations, core technologies, and key applications. The integration of AI has led to notable benefits, such as improved fuel efficiency, enhanced emission control, and greater system adaptability. However, several challenges remain, including generalization to diverse driving conditions, constraints in real-time implementation, and concerns related to data-driven interpretability. The review identifies emerging trends in hybrid methods, which combine AI and conventional optimization approaches to create more adaptive and effective HEV energy management systems. The paper concludes with a discussion of future research directions, focusing on safety, system resilience, and the role of AI in autonomous decision-making.

Microgrids’ Control Strategies and Real-Time Monitoring Systems: A Comprehensive Review

Microgrids (MGs) technologies, with their advanced control techniques and real-time monitoring systems, provide users with attractive benefits including enhanced power quality, stability, sustainability, and environmentally friendly energy. As a result of continuous technological development, Internet of Things (IoT) architectures and technologies are becoming more and more important to the future smart grid’s creation, control, monitoring, and protection of microgrids. Since microgrids are made up of several components that can function in network distribution mode using AC, DC, and hybrid systems, an appropriate control strategy and monitoring system is necessary to ensure that the power from microgrids is delivered to sensitive loads and the main grid effectively. As a result, this article thoroughly assesses MGs’ control systems and groups them based on their degree of protection, energy conversion, integration, advantages, and disadvantages. The functions of IoT and monitoring systems for MGs’ data analytics, energy transactions, and security threats are also demonstrated in this article. This study also identifies several factors, challenges, and concerns about the long-term advancement of MGs’ control technology. This work can serve as a guide for all upcoming energy management and microgrid monitoring systems.

Battery state of charge estimation for electric vehicle based on hybrid barnacles mating optimizer-feed forward neural network

ABSTRACT Accurately determining battery charge levels remains a significant challenge in electric mobility, particularly given the complex electrochemical processes and varying operational conditions that affect battery behavior. This research introduces an innovative approach combining Feed Forward Neural Networks (FFNN) with the Barnacles Mating Optimizer (BMO) technique to enhance battery charge level predictions, addressing the limitations of conventional estimation methods through advanced computational intelligence. The key innovation lies in leveraging BMO’s capabilities to fine-tune the FFNN’s parameters, resulting in improved of State of Charge (SoC) estimation precision. The integrated system was evaluated using operational data collected from a BMW i3 across 70 different journeys. Performance assessment utilized three distinct error metrics: Normalized Mean Square Error (NMSE), Root Mean Square Percentage Error (RMSPE), and Mean Absolute Percentage Error (MAPE). The experimental results revealed superior performance of our BMO-FFNN, achieving error rates of 0.0954 (NMSE), 5.0954% (RMSPE), and 3.7919% (MAPE). These figures demonstrated marked improvement over comparable hybrid approaches incorporating alternative optimization methods such as Salp-Swarm Algorithm (SSA), Moth-Flame Optimization (MFO), and Whale Optimization Algorithm (WOA) when paired with the same FFNN architecture. The demonstrated accuracy of this novel BMO-FFNN suggests promising applications in advancing electric vehicle energy management systems.

Comprehensive Review of Artificial Intelligence for Shaping Renewable Energy Power Systems

Renewable Energy Sources (RESs) are widely penetrating power systems, due to their environmental compatibility and shortage reserve of the fossil fuels. This mandates the application of intelligent, innovative and smart techniques for forecasting, controlling and managing of RESs. However, RESs suffer from uncertainty, weather and operating condition dependence, which considers as a major challenge of the conventional controlling strategy. Artificial Intelligence (AI) enjoys the advantage of adapting the control and operating routines according to the system status, which is attributed to the numerous training scenarios. AI in the areas of RESs could improve their reliability, security and sustainability. Moreover, AI could boost the operation of different energy storage systems, which are considered integral part for different RESs system. This article comprehensively analyzes several literatures regarding AI for RESs. Moreover, comprehensive comparisons between conventional controlling and driving systems of AI in fields of RESs are given in the article. The article moreover addresses the storage system for RESs and the impact of application of AI in improving the energy management of such systems. The article acts as simple and reliable tools for researchers and engineers in the area of AI for RES.

Techno-economic Evaluation of Renewable Energy Microgrids for EV Charging Applications

This research performs an economic and technological assessment of a renewable energy system optimized by Self adaptive grey wolf optimizer (SA-GWO) which enables sustainable EV charging in microgrids. While reducing the cost of operations and hence carbon emissions from EV charging infrastructure, the combination of solar panels and wind turbines with storage units helps lessen the reliance on the primary power grid. Optimal system architecture consists of renewable energy component sizes together with storage facilities and energy management systems to enable sustainable, dependable and affordable EV charging. Operating inside an optimized framework, the SA-GWO controls optimization parameters to enable suitable exploitation-exploration balances that produce optimal solutions for reducing system energy losses while minimizing operational expenses, thus maximizing system reliability. Technical needs for operational effectiveness and logistical dependability as well as financial concerns (including capital expenses, maintenance costs and financial returns) are handled by this optimizing system. In this study, SA-GWO was used to achieve microgrid design goals with improved energy system efficiency, environmental responsibility, economic sustainability and grid independence. In future transportation systems, SA-GWO will be a useful tool for both environmental sustainability and expansion of EV market. This paper combines the ideas of renewable energy-based microgrid, battery-powered automobile charging and SA-GWO with energy storage systems.

Optimal Operating Patterns for the Energy Management of PEMFC-Based Micro-CHP Systems in European Single-Family Houses

Commercial proton exchange membrane fuel cell (PEMFC)-based micro-combined heat and power (micro-CHP) systems are operated by rule-based energy management systems (EMSs). These EMSs are easy to implement but do not perform an explicit economic optimization. On the other hand, an optimal EMS can explicitly incorporate an economic optimization, but its implementation is more complex and may not be viable in practice. In a previous contribution, it was shown that current rule-based EMSs do not fully exploit the economic potential of micro-CHP systems due to their inability to adapt to changing scenarios. This study investigates the economic performance and behavior of an optimal EMS in 46 scenarios within the European framework. This EMS is designed using a model predictive control approach, and it is formulated as a mixed integer linear programming problem. The results reveal that there are only four basic optimal operating patterns, which vary depending on the scenario. This finding enables the design of an EMS that is computationally simpler than the optimal EMS but capable of emulating it and, therefore, is able to adapt effectively to changing scenarios. This new EMS would improve the cost-effectiveness of PEMFC-based micro-CHP systems, reducing their payback period and facilitating their mass market uptake.

Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection

Accurate forecasting of shipboard electricity demand is essential for optimizing Energy Management Systems (EMSs), which are crucial for efficient and profitable operation of shipboard power grids. To address this challenge, this paper introduces a novel hybrid forecasting approach that combines multivariate time series decomposition with Machine Learning (ML) techniques. Specifically, the method utilizes Long Short-Term Memory (LSTM) networks to generate forecasts from multivariate input time series that have been decomposed using a newly formulated Variational Mode Decomposition (VMD), termed Variational Mode Decomposition with Mode Selection (VMDMS). VMDMS enables a selective detection process, identifying modes across channels that synergistically enhance forecasting accuracy. The proposed hybrid forecasting method is validated using a dataset of electric power demand time series collected from a real-world large passenger ship. Experimental results confirm the effectiveness of the approach, extending the applicability of VMD to multivariate forecasting without imposing restrictive assumptions on the data. This work contributes to ongoing efforts in optimizing decomposition methods for predictive modeling in energy management, opening new avenues for improving shipboard power grid efficiency.

Enhancing smart city sustainability with explainable federated learning for vehicular energy control

The rise of electric and autonomous vehicles in smart cities poses challenges in vehicular energy management due to un-optimized consumption, inefficient grid use, and unpredictable traffic patterns. Traditional centralized machine learning models and cloud-based Energy Management Systems (EMSs) struggle with real-time adaptability, high-dimensional data processing, and data privacy risks. These issues lead to high costs, excessive energy waste, and regulatory concerns. Federated Learning (FL) offers a decentralized approach where multiple edge devices collaboratively train models without sharing raw data. This enhances privacy, reduces communication overhead, and is well-suited for smart city applications. However, FL’s black-box nature limits interpretability, reducing trust in AI-driven decisions. Explainable AI (XAI) addresses this by enhancing transparency, interpretability, and regulatory compliance. This research introduces Explainable FL (XFL) for optimizing vehicular energy management in smart cities. The proposed XFL framework integrates distributed learning with explainability techniques for interpretable and accountable decision-making. Using a real-world AEV telemetry dataset of approximately 1,219,567 records with features like speed, energy consumption, and traffic density, it employs a hierarchical FL architecture to ensure secure and decentralized learning. It efficiently analyzes real-time traffic, vehicle energy states, and grid load balancing while preserving privacy. Experimental results show that the proposed Multi-Layer Perceptron (MLP)-based global model achieves superior predictive accuracy, with R² values of 94.73% for energy consumption and 99.83% for traffic density, significantly outperforming previous methods.

A smart energy management system for surface unmanned vehicles for border surveillance missions

This work proposes a methodology to extend the range of marine unmanned surface vehicles (USV) for border surveillance missions. The typical small scale of USVs and their lack of in-board pilots make USVs an important tool for remote applications, such as border surveillance missions and for dangerous areas operations. However, also due to their small scale, their mission range is typically limited. In this paper, it is proposed a combination of a smart energy management system (SEMS) with electric propulsion and photovoltaic panels to find the optimal path and speed capable of extending the mission range. The developed SEMS is capable of planning a mission profile based on the predicted environmental conditions. To achieve this, one presents a new A-star algorithm with probabilistic behaviour to avoid local minimums and find alternative paths that would reduce the energy consumption in later hours of the mission. The developed system was included in a USV prototype and tested under real environmental conditions at the interface between the Tejo River and the Atlantic Sea, in Lisbon, Portugal. Experimental results showed that the inclusion of photovoltaic panels and the SEMS allowed for planning the mission including the time-variable environmental conditions, leading to an extension of up to 50% of the mission range.

Optimal design of a hybrid ship energy management system under various sea conditions using Model Predictive Control.

This paper introduces an optimal design and control approach for a hybrid ship energy management system under various sea conditions by employing model predictive control. Ship reliability and environmental sustainability can be enhanced by reducing emissions and ecological impact. When a ship navigates, it encounters varying sea conditions, and as a result, the ship's generator can experience substantial loading stress due to power fluctuations, particularly in unfavorable conditions. These fluctuations can disrupt the generator or even cause it to fail to supply the necessary power to the ship. A model predictive control (MPC) law has been devised to effectively manage the hybrid energy storage system of batteries and supercapacitors, dynamically responding to power variations induced by ocean waves. This study investigates the performance characteristics of the energy storage system across various battery weight configurations (1,5,10,20,30,50). We explore different weightings of batteries and supercapacitors to analyze their impact on system behavior. The numbers related to the battery weight configurations represent different configurations or setups of the hybrid energy storage system within the ship. The significance of these numbers lies in their impact on the performance of the energy management system and consequently, the overall operation of the vessel. By exploring various battery weight configurations, the study aims to understand how different setups affect the behavior and effectiveness of the hybrid energy storage system. The effectiveness of the proposed methodology is demonstrated through MATLAB simulations under varying sea conditions, including light, moderate, and heavy, successfully mitigating power variations and averting generator failure. Interestingly, the findings reveal that saturation occurs in their respective currents when the weightage difference among these energy storage components surpasses 20.