Cognitive fuzzy logic-integrated energy management for self-sustaining hybrid renewable microgrids
March 22, 2025
Abstract
The Sustainable Energy Resource integrated with Energy Storage System is deployed inside a microgrid, using a power management method to effectively regulate energy consumption during peak demand. Demand-based energy management measures, such as distributing load and stalling appliance usage amid peak hours are executed. An Integrated Energy Management System (EMS) was proposed employing fuzzy logic as a solution to manage the energy needs of loads in this work. The system effectively used a combination of the hybrid utility grid, photovoltaic (PV), wind, and battery to optimise the utilisation of renewable energy resources for load supply. The implementation of an effective management system is intended to control the energy production of a novel hybrid electrical power system by changes in load. The EMS concomitantly considers power demand, renewable power, and State of Charge (SoC) through the incorporation of fuzzy logic control. Cost analysis employing three optimisation techniques like Firefly, PSO and Genetic Algorithm for the equivalent load profile and sources also conducted. Fuzzy EMS enhances energy management by 41.40% LCOE in comparison to the Firefly Algorithm. It decreases expenses by 24.09% more effectively than the PSO Algorithm. In comparison to the Genetic Algorithm, the Fuzzy EMS demonstrates a 45.02% reduction in LCOE, hence establishing its capacity to provide a more economical energy solution. This technique considers security constraints and makes an intelligent choice of energy sources based on grid electricity costs. Maximising system resilience and making the most effective feasible utilisation of green energy sources are the primary goals.
Introduction
As global energy demand rises, economic development and environmentally sustainable energy sources are essential for ensuring the enduring viability of all load types, including industrial and residential loads. The difficulty of meeting the energy demand with presently attainable resources often results in several issues amid the utility power grid and its customers. These issues include an increase in electricity costs, a modified time tariff, and the adoption of a maximum demand tariff. Energy management is often used to enhance energy efficiency to satisfy the growing energy demand while minimising costs and also essential for maximizing energy utilization, which in turn improves the power system’s dependability and efficiency. In light of this, the research suggests upgrading the Energy Management System (EMS) and control approach for the independent power system, deploying energy storage systems and sustainable energy resources1.Intermittent renewable sources and fluctuating demand cause a generation-consumption disparity2.To alleviate this inconsistency, it is essential to maintain a storage reserve to neutralize generating deficits, it also facilitates the optimal use of renewable power sources by preventing load shedding during periods of surplus production3.
Microgrids are fundamental for the incorporation of distributed energy storage systems and renewable distributed power sources. To achieve the goal of optimizing the operation of the microgrid, it is necessary to minimize two optimization criteria, namely operating expenses and detrimental polluting substances. Demand side management methods handle the essential uncertainty of generating power from green energy sources such as wind and solar4. The continuous nature of energy generation and consumption tends to be formidable due to the variable allocation of consumer demand for energy over time. The grid’s resiliency is contingent upon a harmonious balance between consumption and production3. The principal source of unpredictability for a utility grid generally lies in electrical power demand and generation systems5. The recommended method intends to curtail the aggregate operating expenditure of the microgrid by implementing a scheduling approach that addresses the variability of renewable energy resource power output, loads, and electric price rating. The proposed system aims to achieve its goals by using demand-side management techniques to reduce uncertainties in the load profile. A Renewable based power system and an energy storage battery will be integrated into the microgrid, which will also interact with external customers6. This research intends to strengthen the functionality of Fuzzy Logic Controllers (FLC) in the context of microgrid control approaches, to achieve efficient implementation2.The attainment of this purpose will be accomplished by the use of linguistic guidelines that are easily understandable7. Figure 1 represents the schematic diagram of proposed architecture of the microgrid. Table 1 present review of energy management approaches in research.
Schematic diagram of proposed architecture.
The notable aspects of this work are as follows
-
The primary systems analysed in literature reviews, rely on solitary optimization techniques. This work employs a hybrid optimization strategy that integrates FLC EMS with advanced algorithms such as Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), and Firefly Algorithm (FFA).
-
Additionally, the proposed FLC EMS employs adaptive decision-making to resolve real-time discrepancies between anticipated and actual energy generation, and enhancing system performance, balanced operation among renewable energy sources, battery storage, and load requirements.
-
This Hybrid methodology ensures the optimization of economic variables like Net Present Cost (NPC) and Levelized Cost of Energy (LCOE) facilitating a comprehensive evaluation of the system’s economic feasibility and sustainability.
This holistic approach provides a more effective solution compared to the analysed optimization models, which typically focus on singular objectives without considering long-term economic viability or system sustainability. This work primarily includes the following sections: Introductory section, advanced FLC EMS methodology, Optimisation Algorithms and Economic analysis of each configuration, final outcomes and discussion, and an inference.
Energy management system
The primary energy source is customarily the conventional grid system, although supplemental green renewable power resources are used to enhance the utility balance and reduce energy costs. The Rising price of electricity during peak times, prospect of power failure, and the inherent penalty for surpassing maximum demand have increased the use of hybrid energy systems in EMS. The use of an EMS is imperative in order to optimize energy utilization, save costs, and enhance overall efficiency in power distribution and consumption17. Most current hybrid EMS have issues related to the unregulated incorporation of renewable Distributed Generation sources into hybrid systems for cost reduction. Furthermore, this enduring behavior jeopardizes the reliability and dependability of the grid system18. In light of these issues, the use of energy storage System (ESS) has emerged as an essential mechanism for mitigating the fluctuations in sustainable energy production in microgrids. In grid-connected microgrids, the use of energy storage’s swift charging and discharging capabilities plays an integral role in ensuring the stability of power production derived from renewable sources19. Since ESS is an expensive and energy-limited resource, the control approach is made so that both the grid operator and the customer may profit from the integration of ESS20.To address the aforementioned concern, the research presented an EMS that integrates conventional grid power with solar photovoltaic (PV) and battery sources. Wind turbines and PV systems, are used to efficiently meet the demand due to their cost-effectiveness and adaptable operation, surpassing that of other power sources. A smart EMS strives to minimize the cost of the energy21. In microgrid environment, load management is keyed on minimizing energy expenses while ensuring an adequate level of comfort and maintaining acceptable power supply quality22. If the microgrid utilizes renewable energy sources, to make a sound choice, it is essential to assess the possible fluctuations in wind and solar. The mathematical modelling of a given microgrid system is carried out by employing the system governing equations that determine the control parameters of each input and output23. The visual representation depicting the proposed system being examined is exhibited in Fig. 2. The microgrid system under consideration comprises a PV system, a wind power generator, a rechargeable energy storage system, a converter, a utility grid, and other loads.
Block diagram of proposed EMS system.
System modelling
Wind turbine modelling
Wind Energy Productivity is maximized by placing the wind turbine on a tall tower and physically connecting its rotor to an electrical generator24.Wind turbines harness the kinetic energy of wind to generate mechanical power. Modelling wind turbines serves as essential to assessing the dynamic behavior of wind energy conversion systems to variations in wind speed. The wind turbine model contributes to designing the MPPT algorithm.
The mathematical modelling of the wind resource is depicted by
Solar photovoltaic modelling
When simulating a photovoltaic system, it is connected to a boost converter to facilitate the system’s design and guarantee that it produces the desired output voltage6. Equations are used to determine the current value of the PV using various formulas given below23, In MATLAB Simulink, the implementation of a PV source is represented by the following equation,
Battery modelling
In order to ascertain the charge state of a battery, it is imperative to measure its energy storage capacity. Battery safety and stability are the primary decisive factors for the efficiency of the ESS. Therefore, it is important to continually limit the battery’s capacity by maintaining the battery at the appropriate temperature to extend its lifespan. For optimal work performance and to prevent ageing, the safe operating limit of charge condition is specified in the equation. Discharging batteries to a great extent accelerates aging while overcharging may lead to degradation of the battery. Additionally, effective battery management is vital to prevent degradation and optimize charge-discharge cycles.
Battery power remaining at time t
Net present cost
The computation of NPC requires deducting the present value of all expenses related to system installation and operation from the net annual profit25.
The NPC is given by12
The Capital Revenue Factor (CRF) is evaluated by26
Levelized cost of energy
LCOE refers to the typical average price per kilowatt-hour of available electrical energy within a given system25.
The levelized cost of energy is calculated by12.
The energy expenses are determined27 by
Fuzzy logic control mechanism
The focus of the research is to design a fuzzy logic system with the intent of attaining energy optimization in the grid-connected configuration. Moreover, it bypasses the limitations in the operation of an ESS and attains the optimal economic performance of a microgrid28.The FLC, compared with typical controllers, employs an approach that converts controlling specifications into qualitative rules that are defined by linguistic descriptions29.
Fuzzy Control techniques based on Mamdani Type 1 are used to achieve this EMS design. A fuzzy system with three input and outputs Gaussian membership functions using the centroid approach is used is shown in Table 2. The EMS has optimized set of 21 new fuzzy rules. Consequently, the primary focus is placed on the available renewable source, followed by an assessment of its sufficiency in meeting the load’s energy demands and excess energy is stored in the Battery. The Fuzzy rule set defined for operating microgrid comprising of a sustainable renewable energy system are determined by renewable power availability, SoC, load demand, battery condition, renewable energy status, and grid condition. When wind and solar energy production is high and the SoC is moderate or low, the battery remains inactive, the renewable energy source operates, and the grid stays idle. Similarly, with moderate renewable energy production and variable SoC levels, the battery is idle, the renewable source functions, and the grid is inert. In cases of inadequate renewable energy combined with high or moderate SoC, the battery engages when both renewable and grid sources are dormant. Figure 3 depicts the deployment of the proposed smart EMS in the Simulink environment and at Fig. 4 showing proposed EMS Flowchart.
Simulation model of proposed EMS system.
When renewable energy production is inadequate and the SoC is low, the grid engages, deactivating both the battery and renewable sources. EMS prioritizes renewable energy and battery over connecting to the grid. This system includes a solar PV subsystem, a wind subsystem, battery integration, and grid integration. By detecting the peak and off-peak hours, the EMS can select the appropriate power sources to run the loads, so ensuring the user comfort and excess energy is stored in the battery. Fuzzy control system evaluates the power needs of loads that rely on power from either the grid or solar PV and battery, considering several aspects such as the current battery status, time, and cost. This article examines the hybrid energy system (HRES), which utilises batteries and PV and wind as input resources.
Proposed EMS flowchart.
The use of batteries to store energy is prevalent in such systems when the renewable system’s output power is either unavailable or inadequate to meet the requirements. To provide a continuous power supply for the load in all circumstances, the battery undergoes charging or discharging by the directives of the management system.
Optimisation algorithms
The microgrid, in conjunction with Fuzzy Logic EMS, is optimised by implementing metaheuristic algorithms30 like the Firefly Algorithm, Particle Swarm Optimisation, and Genetic Algorithm which are advanced problem-solving frameworks intended to identify near-optimal solutions for intricate optimisation challenges, typically within a feasible timeframe. The integration of hybrid optimization techniques like PSO, GA, and Firefly Optimization is challenging because it increases computational complexity.
The objective function of the system under consideration is described as follows:
where the weights ​ allow prioritizing different objectives: ω1 is Reduce energy costs, ω2 is Ensure load demand is met, ω3 is Extend battery lifespan, ω4 is Minimize power losses.
C(x) denotes Minimize the cost of power drawn from the grid or other energy sources, Pgrid denotes Power from Grid and Cgrid denotes grid cost price.
where P(x) is Power lost due to resistive and conversion inefficiencies, Idenotes current,Rdenotes Resistance and ηc is efficiency loss.
where B(x) denotes Battery Degradation which penalizes aggressive charging and discharging cycles, Pcharge/discharge denotes Battery Flow and CBattery denotes Battery Capacity.
where L(x) denotes Load demand which penalizes the unmet load, Pdemand denotes Load Demand, Prenewable denotes Renewable Power, denotes Battery Power, PBattery and Pgrid denotes Grid Power.Table 3 denotes parameters used for optimisation Algorithms.
Firefly algorithm
Considering the complementarity and volatility of wind and solar energy, Firefly algorithm, are gaining popularity for optimising hybrid renewable energy systems due to their speed, accuracy, and efficiency31. The firefly Algorithms uses the update rule for each firefly32,33.
Particle swarm optimization
When comparing, the PSO algorithm has its own primary advantages such as computational efficiency, robustness to control parameters, conceptual simplicity, and ease of implementation34. Particle Swarm Optimisation Algorithm improves the position and velocity of each particle as follows35:
Genetic algorithm
Genetic Algorithm optimally regulate microgrids by addressing challenging, non-linear, and non-convex problems and it also surpass in identifying global optimal solutions36. Genetic Algorithm updates the crossover using the below Eq. 37
Results and discussion
To initiate the study, the research area was selected on the geographical location of Sanasi Hostel Block in SRM Institute of Science and Technology Subsequently, data about wind velocity and solar radiation was obtained. Upon completion, a load profile was developed. The variability and intermittency of green energy sources, such as solar and wind, pose significant difficulties in forecasting and balancing energy generation with consumption. Subsequently, the optimal result was determined by conducting a simulation of the input data utilizing HOMER before further action. Solar and wind profile of the site is depicted in Fig. 5.
Solar and wind profile for one year.
Finally, FLC was utilized as the fundamental basis for the creation of an Energy Management Strategy. In Fig. 6 shown result of convergence curve of Firefly, PSO, GA Algorithm.
Convergence curve of firefly, PSO, GA algorithm.
A comprehensive economic analysis is conducted in HOMER with different input parameters, including load following and cycle charging strategies. The microgrid will incorporate a hybrid arrangement of solar, wind energy, and energy storage devices, all of which will be interconnected with the electrical grid power system. The preferred research site is the Sanasi Hostel Block located in SRM Institute of Science and Technology. The precise geographical location of Chengalpattu, Tamil Nadu, India is at coordinates 12°49.4’N and 80°2.6’E.
Load profile
The forecasting of electricity load demand is conducted meticulously taking into account the requirements and duration of appliance usage for the loads. The electricity demand is particularly high during the early morning and evening hours. Conversely, during daytime hours, the load is anticipated to be relatively lower. Load Analysis is done and the calculated mean load is 303.01 kW, Maximum Load is 780.22 kW, Minimum Load is 61.47 kW as shown in Table 4.
HOMER with numerous input parameters, using load following and cycle charging technique is used to conduct microgrid optimisation, a realistic electricity consumption profile of the study site was accounted for analysis. Load variations for the considered timeframe is depicted in Fig. 7. Table 5 illustrates the computed LCOE and NPC costs derived from optimisation techniques and fuzzy EMS, demonstrating that the unique energy management strategy incorporating Fuzzy Logic incurs reduced costs in comparison to existing optimisation methods. Fuzzy EMS improves LCOE relative to alternative optimization techniques. Fuzzy EMS enhances energy management by 41.40% LCOE in comparison to the Firefly Algorithm. It decreases expenses by 24.09% more effectively than the PSO Algorithm. In comparison to the Genetic Algorithm, the Fuzzy EMS demonstrates a 45.02% reduction in LCOE, hence establishing its capacity to provide a more economical energy solution.
Hourly load variations.
The findings indicate that the Fuzzy EMS enhances energy systems, rendering it a cost-effective and efficient solution for energy cost management.
Optimum configuration
There are six best configurations for the hybrid microgrid that was developed for the chosen region using Homer for analysing. The configurations are based on the NPC and LCOE costs as follows: solar with grid, grid with wind, solar photovoltaic with wind and grid, solar photovoltaic with grid and battery, wind with grid and battery, and solar photovoltaic with wind and battery linked to grid. The research observed the daily energy combination of hybrid sources for a week to assess EMS control functioning for varying energy needs. Table 6 contains a tabulation of the optimal options. It also presents the various expenditures associated with each design, taking into account the optimal result, including LCOE, NPC, and operating costs.
For NPC, the combinations of Solar, Grid, Wind, and Battery (Type 6) and Solar, Grid, and Battery (Type 4) demonstrate the lowest values at $288755.04 but Grid, Wind, and Battery (Type 5) has the greatest value at $2237851.56. LCOE expenses fluctuate, with “Solar, Grid, Wind, and Battery� and “Solar, Grid, and Battery� exhibiting the lowest cost at $0.0087 whereas “Grid, Wind, and Battery� demonstrates the most at $0.067.
Calculated LCOE AND NPC for six cases.
The integration of Solar, Grid, and Battery (Type 4) exhibits superior economic performance across all measures. Figure 8 compares NPC and LCOE among various energy source combinations.
Type 1- solar PV with grid configuration
According to the NPC and LCOE calculated, the Solar and Grid configuration has an NPC of $ 1,371,618 an LCOE of $ 0.041 and an operating cost ($) of $ 96,252. This design considers.
Solar PV system interconnected with Grid. Figure 9 illustrates solar with grid design with solar power generated 24,390 kW power purchased from the grid is 2,798 kW.
Hour-wise energy demand distribution for solar PV with grid configuration.
Type 2- wind with grid configuration
Wind with grid arrangement has a $ 156,409 operational cost ($), an LCOE of $ 0.067, and an NPC of $ 2,237,852. Figure 10 depicts a wind with grid configuration system consisting of 3663 kilowatts of produced from wind energy, 3309 kilowatts of purchased power from the grid.
Hour-Wise Energy Demand Distribution for Wind with Grid Configuration.
Type 3- solar PV with grid and battery configuration
Based on NPC and LCOE, the Solar with Battery and Grid configuration has an NPC of $ 288,755 an LCOE of $ 0.0087 and an operating cost ($) of $ 5826. The Solar PV-battery-utility grid system offers the most economical energy, maintenance, and operating costs. This Solar Battery with Grid configuration design guarantees the lowest possible operating costs among all the different combinations. Figure 11 shows solar PV with grid and battery design with 22,439 kW of solar power.
Hour-wise energy demand distribution for solar PV With grid and battery configuration.
Type 4 wind with grid and battery configuration
The wind energy configuration with grid and battery energy storage has an NPC of $ 1852,845 an LCOE $ 0.056 and an operating cost ($) of $ 86,636. In the wind energy configuration with grid and battery, wind power produced 9,013 kW grid power bought 184 kW are shown in Fig. 12.
Hour-wise energy demand distribution for wind with grid and battery configuration.
Type 5- solar PV wind with grid configuration
The Solar Photo Voltaic system incorporated with Wind and grid system is shown in Fig. 13, which has an NPC of $ 1359,555 an LCOE of $ 0.041 and an operating cost ($) of $ 92,507. It uses solar power to produce 23,414 kilowatts, wind power generated is 674.35 kW and electricity obtained from the grid to amount to 2,754 kilowatts. This arrangement employs renewable source of energy that are integrated with the electrical power grid, resulting in a sustainable system.
Hour-wise energy demand distribution for solar PV, wind with grid configuration.
Type 6- solar PV wind with grid and battery configuration
A solar, grid, wind, and battery energy storage system with an NPC of $ 288,755 an LCOE of $ 0.0087, and an operating cost ($) of $ 6,001 is the final design that was taken into consideration for optimization. The photovoltaic solar power system with an electrical grid and battery is considered the most cost-effective and budget-friendly design, as established by NPC and LCOE.
Hour-wise energy demand distribution for solar PV, wind with grid and battery configuration.
The current system consists of a solar module that is combined with a Battery Storage System, and it also includes a converter that is specifically developed for integration with the grid. As a result, the net present value of the system is $24.1 million, and the energy cost is $0.724 per kWh. Figure 14 illustrates the solar PV Wind integrated with grid and battery energy storage system, which generates 22,195 kilowatts of power through the use of solar energy, 9 kilowatts of electricity through the wind.
Sensitivity analysis
The sensitivity analysis shown in Table 7 indicates that Fuzzy EMS consistently achieves the lowest LCOE (0.000237 $/kWh) and the highest NPC ($1,288,755.00) at multiple variations, indicating its superior cost-effectiveness. Solar and wind power variations have minimal impact on LCOE, but wind tends to be more stable. Battery capacity changes do not significantly influence cost trends, but fluctuations in grid tariffs and load demand cause substantial LCOE variations, emphasizing the importance of demand-side energy management. At high solar and wind power variations, Fuzzy EMS dominates, ensuring minimal LCOE despite its higher investment cost. Load demand fluctuations have the most significant impact on LCOE, suggesting that energy efficiency measures and optimized load management could further enhance cost savings.
The sensitivity of the proposed FLC EMS to computational parameters substantially affects its accuracy, convergence, and computational efficiency. Time step size sensitivity influences LCOE by 5–10%. Population size (PSO, GA) and mutation rate (GA) affect convergence, resulting in fluctuations in NPC and LCOE. Additionally, discount rate sensitivity, with variations of ± 20%, causes NPC changes of 10–20% and LCOE fluctuations of 15–30%. The proposed EMS effectively handles the uncertainties related to renewable energy generation, load demand fluctuations, and system dynamics. Sensitivity analysis validates the robustness of the proposed approach, showcasing the system’s capacity to sustain stability and efficiency in the face of uncertain operating conditions. Instead of relying on fixed thresholds, FLC processes imprecise inputs and makes adaptive decisions based on predefined rules. This improves system resilience, ensures energy efficiency, and maintains grid stability in dynamic conditions. Table 8 compares the proposed work with the existing Literatures.
Conclusion
The Implementation of sustainable energies encompasses the potential to meet the increasing energy requirements while mitigating adverse environmental impacts. The FLC EMS plays a decisive role in making strategic conclusions to achieve the most efficient energy combination, ensuring the lowest energy expenses while sustaining the maximum system efficiency. The inquiry results regarding the FLC EMS highlight the substantial influence of EMS in regulating energy and it also achieves minimum cost comparing with FFA, PSO and GA. Deploying an EMS decreases energy expenses and improves grid dependability. This study examines the Operating Cost, LCOE, NPC, providing insights for feasible, sustainable and economic configurations for microgrid environment. The conclusions drawn confirmed that the proposed microgrid EMS design has the potential to be optimised in terms of economic viability, and cost reduction.
Inference
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Fuzzy EMS decreases expenses by 41.40% LCOE in comparison to the FFA, 24.09% more effectively than the PSO and 45.02% comparing GA, Thus Fuzzy EMS demonstrates a reduction in LCOE, hence establishing its capacity to provide a more economical energy solution.
-
Based on NPC and LCOE, the Solar PV with Grid and Battery system is the most cost-effective design with the reduction of NPC 87.09% and LCOE 87.01% comparing with other configurations.
The designed microgrid EMS System adheres to load demand, optimises renewable electrical supply dependability, and enhances reliability. It efficiently manages energy variations to enhance stability and also provides a prompt response to fluctuations in demand to maintain stability. EMS also optimises the integration of green energy and redundant systems to augment efficiency and promote environmental awareness.
Data availability
The data used to support the findings of this study are available from the corresponding author upon request.
Abbreviations
ω
1
:-
Reduce energy costs
ω
2
:-
Ensure load demand is met
ω
3
:-
Extend battery lifespan
ω
4
:-
Minimize power losses
n
:-
Number of project years
A
s
:-
Wind turbine swept area
B(x)
:-
Battery degradation
C
grid
:-
Grid cost price
C
ta
:-
Total annualized cost
C
rf
:-
Capital revenue factor
C
o(t)
:-
Battery charge remaining
C
nom
:-
Nominal battery charge
C
i
:-
Battery discharge power
E
s
:-
Total electrical load supplied
E
t
:-
Electricity tariff
Ir
t
:-
Solar Irradiance (W/m2)
I
t
:-
Battery current
η
s
:-
Solar converter efficiency
η
bd
:-
Battery charge remaining
P
g
:-
Utility grid power
Pw
t
:-
Wind output power
P
c
:-
Performance coefficient
�
:-
Air density
P(x)
:-
Power loss
P
charge/discharge
:-
Battery power flow
P
deman
:-
Load demand
P
renewable
:-
Renewable power
P
grid
:-
Grid power
Ps
nom
:-
Solar nominal power
SoC
t-1
:-
Initial battery charge
SoC
t
:-
Battery power remaining at time t
Ws
t
:-
Wind speed (m/s)
W
min
:-
Wind cut-in speed
W
nom
:-
Nominal wind speed
- BSS:
-
Battery storage system
- CRF:
-
Capital revenue factor
- DE:
-
Differential evolution
- EMS:
-
Energy management system
- ESS:
-
Energy storage system
- FFA:
-
Firefly algorithm
- FLC:
-
Fuzzy logic controllers
- FEMS:
-
Fuzzy energy management system
- GA:
-
Genetic algorithm
- HRES:
-
Hybrid renewable energy system
- HIL:
-
Hardware in loop
- LCOE:
-
Levelized cost of energy
- MPPT:
-
Maximum power point tracking
- MODE:
-
Multi-objective differential evolution
- MPC:
-
Model predictive control
- NSGA-II:
-
Nondominated sorting genetic algorithm
- NPC:
-
Net present cost
- PV:
-
Photo-voltaic
- PSO:
-
Particle swarm optimisation
- PEV:
-
Plug-in electric vehicle
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E.Q.J.M and V.S wrote the main manuscript text, N.R prepared the figures V.B reviewed entire manuscript and prepared the table and technical methodology sections. All the authors are contributed equally for the manuscript preparation, review and submission.
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M, E.Q.J., Subramanian, V., Bereznychenko, V. et al. Cognitive fuzzy logic-integrated energy management for self-sustaining hybrid renewable microgrids.
Sci Rep 15, 9915 (2025). https://doi.org/10.1038/s41598-025-94077-z
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Received: 25 December 2024
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Accepted: 11 March 2025
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Published: 22 March 2025
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DOI: https://doi.org/10.1038/s41598-025-94077-z
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