Renewable Energy Storage Systems Research Articles

Negawatt-Driven Sustainable Energy Management Based-on Nanomaterial-Enhanced Batteries

With the dramatic increase of energy demand around the world, it has become a necessity to monitor and optimize energy consumption to conserve its use and decrease carbon emissions. This paper presents a comprehensive analysis of MicroGrids (MGs) that encompass a diverse array of energy sources, including renewable energy, Diesel Generators (DGs), and Battery Storage Systems (BSS). In this study, critical constraints are addressed such as distributed generator limits and grid power exchange. Moreover, the potential integration of nanomaterials within BSS to enhance energy efficiency and achieve Negawatt energy are explored. The main objectives are to evaluate the performance of various energy system configurations, with a specific focus on nanomaterial-enhanced batteries, and identify the optimal strategy to minimize operating costs while maximizing Negawatt energy production. By considering technical, economic, and environmental factors, this research aims to provide valuable insights into sustainable and efficient Microgrid solutions for future energy management.

Innovative hierarchical control of multiple microgrids: Cheetah meets PSO

The expansion of microgrids (MGs) comes from the increasing integration of renewable energy resources (RES) and energy storage systems (ESs) into distribution networks. However, effective integration, coordination, and control of Multiple Microgrids (MMGs) during energy transition from microgrid to grid, and vice versa presents a formidable challenge. This arises from the necessity to ensure smooth operation, coordination, and control of MMGs for stability and efficiency during transitions. The dynamic operation of MMGs due to intermittent renewable energy in microgrids and load variations presents an additional challenge for hierarchical control techniques. This paper provides an Artificial Intelligent (AI)-based control technique and introduces an innovative hybrid optimization technique, denoted as HYCHOPSO, derived from the fusion of Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques, and the quantitative findings of extensive benchmark testing, constitute key aspects of novelty. The strategic choice of this novel optimization technique, coupled with a Proportional-Integral (PI) controller, provides key insights. HYCHOPSO is tested within benchmark functions and reaches its optimal score before 50 iterations as compared with optimization algorithms namely CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO which reaches after approximately 200 iterations. HYCHOPSO consistently exhibits the lowest mean values, indicating its robust convergence capabilities. The proposed control technique results in a significant reduction of the microgrid's Current Total Demand Distortion to 1.1%, meeting acceptable limits for a 600 V-bus According to IEEE519–2014 standard, even under nonlinear loads. Additionally, precise regulation of voltage and frequency is achieved with0.19%−+ deviations and a frequency overshoot of 1.9%. Furthermore, optimization contributes to seamless energy transition from microgrid to grid, achieving synchronization in less than 5 s. This enhances the real application's reliability, flexibility, scalability, and robustness. Furthermore, HYCHOPSO is introduced as an AI-based technique to facilitate control parameter design under dynamic conditions, with substantial simulation cases demonstrating control feasibility

Shaping techniques' influence on the electrochemical properties of BaCe0.6Zr0.3Y0.1O3-δ proton conductor

Hydrogen's significance as a clean and high-energy source spans various industries, driving advancements in fuel cell technology, transportation, and renewable energy storage systems. In particular, solid-state proton conductors like perovskite-type materials exhibit promising attributes for applications such as fuel cells and hydrogen sensors. However, conventional shaping techniques like uniaxial pressing impose limitations on device scalability and geometry. To address these challenges, alternative methods are gaining traction, like cold isostatic pressing or additive manufacturing. Each technique offers distinct advantages in shaping materials, impacting their structural and morphological properties. In this study, pellets of BaCe0.6Zr0.3Y0.1O3-δ (BCZY) solid-state electrolyte were fabricated using four different shaping techniques: uniaxial pressing, cold isostatic pressing, 3D extrusion, and lithography. Characterization via X-ray diffraction, scanning electron microscopy, and electrochemical impedance spectroscopy provided insights into changes in crystalline structure, sintering quality, and electrochemical properties, respectively.

Enhancing thermal management in cylindrical Li-ion battery through PCM integration with variable contact area

Cylindrical Li-ion batteries are widely embraced in various sectors, notably electric vehicles and renewable energy storage systems. An effective thermal management system is vital for their safe and dependable operation, enhancing both the performance and reliability of the system. This study employs a distinctive hybrid cooling strategy consisting of a phase change material (PCM) at the centre of the battery and liquid cooling at the surface of the cylindrical batteries. The contact area between the coolant and the battery’s surfaces varies to make sure same heat transfers from each battery. This approach is instrumental in maintaining thermal uniformity and regulating the maximum temperature. In the numerical analysis, we employed ANSYS Fluent 2022 R1 to create the computational model encompassing the Li-ion battery, PCM, and liquid cooling system. This arrangement utilized eight 18650Li-ion batteries, with each battery housing a 2 mm radius PCM rod at the centre of the batteries. A heat-conducting element (HCE) was introduced to facilitate contact between the battery and the coolant channel. Water was selected as the coolant, and the coolant channel cross-sectional area is 65 × 2 mm2. The relationship governing the variable contact area is determined by fixing the velocity and the first battery contact area. The variable contact geometry maintains thermal uniformity throughout the battery module, resulting in a 74% enhancement in thermal uniformity. Nevertheless, the integration of PCM inside the battery effectively prevents individual batteries from surpassing specified temperature limits. It yields a remarkable 36.64% enhancement in thermal uniformity compared to situations in which PCM is not present, albeit with a 3.75% capacity loss. Furthermore, the study investigates the effects of coolant flow rates and performs an extensive analysis of temperature variation and melting fraction.

Deciphering Advanced Sensors for Life and Safety Monitoring of Lithium Batteries

AbstractThe rapid commercialization of lithium batteries greatly promotes the development of the electric vehicles, renewable energy storage systems and consumer electronics. The service lifetime and safety of lithium batteries are extremely concerned by terminal customers. Sensor technology is powerful in monitoring the physical and chemical signals of lithium batteries, serving for the state of health and safety warning/evaluation of lithium batteries and guide for future development of battery materials. In this review, the primary concern is the generation mechanisms of different physicochemical signals in lithium batteries. Then, the sensing methods and sensing designs according to different physicochemical signals of lithium batteries is summarized. Finally, some perspectives are provided to guide further development of sensing technology for lithium batteries. This review will greatly accelerate the terminal application of advanced sensors, whether in commercial products or scientific research.

Techno-economic assessment of hydrogen-based energy storage systems in determining the optimal configuration of the nuclear-renewable hybrid energy system

Population growth and economic development have significantly increased global energy demand. Hence, it has raised concerns about the increase in the consumption of fossil fuels and climate change. The present work introduced a new approach to using carbon-free energy sources, such as nuclear and renewable, to meet energy demand. The idea of using the Nuclear-Renewable Hybrid Energy System (N-R HES) is suggested as a leading solution that couples a nuclear power plant with renewable energy and hydrogen-based storage systems. For this purpose, using a meta-heuristic method based on Newton's laws, the configuration of the N-R HES is optimized from an economic and reliability point of view. The optimal system is selected from among six cases with different subsystems such as wind turbine, photovoltaic panel, nuclear reactor, electrolysis, fuel cell, and hydrogen storage tank. Furthermore, the performance of hydrogen-based energy storage systems such as high-temperature electrolysis (HTE) and low-temperature electrolysis (LTE) is evaluated from technical and economic aspects. The results of this work showed that using nuclear energy to supply the base load increases the reliability of the system and reduces the loss of power supply probability to zero. More than 70 % of the power is produced by nuclear reactors, which includes more than 80 % of the system costs. The key findings showed that despite HTE's higher efficiency, using LTE as a storage system in N-R HES is more cost-effective. Finally, due to recent developments and the safer design of nuclear reactors, they can play an important role in combination with renewable energies to support carbon-free energy sectors, especially in remote areas, for decades to come.

Integrating an Ensemble Reward System into an Off-Policy Reinforcement Learning Algorithm for the Economic Dispatch of Small Modular Reactor-Based Energy Systems

Nuclear Integrated Energy Systems (NIES) have emerged as a comprehensive solution for navigating the changing energy landscape. They combine nuclear power plants with renewable energy sources, storage systems, and smart grid technologies to optimize energy production, distribution, and consumption across sectors, improving efficiency, reliability, and sustainability while addressing challenges associated with variability. The integration of Small Modular Reactors (SMRs) in NIES offers significant benefits over traditional nuclear facilities, although transferring involves overcoming legal and operational barriers, particularly in economic dispatch. This study proposes a novel off-policy Reinforcement Learning (RL) approach with an ensemble reward system to optimize economic dispatch for nuclear-powered generation companies equipped with an SMR, demonstrating superior accuracy and efficiency when compared to conventional methods and emphasizing RL’s potential to improve NIES profitability and sustainability. Finally, the research attempts to demonstrate the viability of implementing the proposed integrated RL approach in spot energy markets to maximize profits for nuclear-driven generation companies, establishing NIES’ profitability over competitors that rely on fossil fuel-based generation units to meet baseload requirements.

Mixed biomass-derived multi-heteroatom doped carbon-decorated Mn3O4 for a high-performance asymmetric all solid-state supercapacitor

Recently, the development of non-conventional electrochemical energy storage technologies has attracted great attention. Herein, in accordance with green strategies for the producing renewable energy storage systems, eco-friendly electrode was successfully produced from mixed biomass precursors through carbonization and activation processes. Obtained electrode (MB-AC) as a negative and its composite (Mn3O4@MB-AC) as positive electrodes were exhibited excellent electrochemical performance with high specific capacitance of 716.49 Fg−1 at 0.75 Ag−1 and 848.21 Fg−1 at 1.0 Ag−1 respectively, in 3 M KOH electrolyte. Further, for the practical application, flexible asymmetric all-solid-state supercapacitor was made. Which offer high specific capacitance of 266.46 Fg−1 at 2 Ag−1, high energy density of 119.91 Wh kg−1 at a high-power density of 1800.08 W kg−1 within a voltage window of 1.8 V. These prominent electrochemical performances suggest that selected waste biomass materials derived carbon have considerable applications in the field of energy storage technologies.

Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO’s superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness.

Integrating Life Cycle Principles in Home Energy Management Systems: Optimal Load PV–Battery–Electric Vehicle Scheduling

Energy management in the residential sector contributes to energy system dispatching and security with the optimal use of renewable energy systems (RES) and energy storage systems (ESSs) and by utilizing the main grid based on its state. This work focuses on optimal energy flow, ESS parameters, and energy consumption scheduling based on demand response (DR) programs. The primary goals of the work consist of minimizing electricity costs while simultaneously extending the lifetime of ESSs in conjunction with extracting maximum benefits throughout their operational lifespan and reducing CO2 emissions. Effective ESS and photovoltaic (PV) energy usage prices are modeled and an efficient energy flow management algorithm is presented, which considers the life cycle of the ESSs including batteries, electrical vehicles (EVs) and the efficient use of the PV system while reducing the cost of energy consumption. In addition, an optimization technique is employed to obtain the optimal ESS parameters including the size and depth of discharge (DOD), considering the installation cost, levelized cost of storage (LCOS), winter and summer conditions, energy consumption profile, and energy prices. Finally, an optimization technique is applied to obtain the optimal energy consumption scheduling. The proposed system provides all of the possibilities of exchanging energy between EV, battery, PV system, grid, and home. The optimization problem is solved using the particle swarm optimization algorithm (PSO) in MATLAB with an interval time of one minute. The results show the effectiveness of the proposed system, presenting an actual cost reduction of 28.9% and 17.7% in summer and winter, respectively, compared to a base scenario. Similarly, the energy losses were reduced by 26.7% in winter and 22.3% in summer, and the EV battery lifetime was extended from 9.2 to 19.1 years in the winter scenario and from 10.4 to 17.7 years in the summer scenario. The integrated system provided a financial contribution during the operational lifetime of EUR 11,600 and 7900 in winter and summer scenarios, respectively. The CO2 was reduced by 59.7% and 46.2% in summer and winter scenarios, respectively.

An Adaptive Energy Management Approach for Battery-Supercapacitor Hybrid Energy Storage System

Energy storage systems (ESS) are indispensable in daily life and have two types that can offer high energy and high power density. Hybrid energy storage systems (HESS) are obtained by combining two or more energy storage units to benefit both types. Energy management systems (EMS) are essential in ensuring HESS's reliability, high performance, and efficiency. One of the most critical parameters for EMS is the battery state of health (SoH). Continuous monitoring of the SoH provides essential information regarding the system's status, detects unusual performance degradations and enables planned maintenance, prevents system failures, helps keep efficiency at a consistently high level, and helps ensure energy security by reducing downtime. The SoH parameter depends on parameters such as Depth of Discharge (DoD), charge and discharge rate (C-Rate), and temperature. Optimal values of these parameters directly affect the lifetime and operating performance of the battery. The proposed Adaptive Energy Management System (AEMS) uses the SoH parameter of the battery as the control input. It provides optimal control by dynamically updating the C-Rate and DoD parameters. In addition, the supercapacitor integrated into the system with filter-based power separation prevents deep discharge of the batteries. Under the proposed AEMS control, HESS has been observed to generate 6.31% more energy than a system relying solely on batteries. This beneficial relationship between supercapacitors and batteries efficiently managed by AEMS opens new possibilities for advanced energy management in applications ranging from electric vehicles to renewable energy storage systems.

Feasibility Study of Three-Phase Modular Converter for Dual-Purpose Application in DC and AC Microgrids

The modern concept of a universal converter is intended as a power converter suitable for application in both dc or ac grids using the same external connectors. This novel family was recently proposed to allow an easier integration of renewable energy sources and energy storage systems, interfacing with dc/ac grids and/or loads with a minimum redundancy of power switches and passive elements. This kind of solution and applications are expected to be a reality in the nearest decade, as ac and dc low voltage distribution networks will coexist. Nevertheless, the initial solutions proposed as universal converter were focused on ac single-phase power conversion systems. In this sense, this article proposes and describes a new member of the universal converter family suitable for dc to dc and dc to ac power conversion (both in three-phase three-wire and in three-phase four-wire). The proposed power topology is derived as a modular extension from the single buck-boost bidirectional cell. Its main operation modes (buck and boost) are discussed, and a pulse-width-modulation technique is developed to generate the corresponding switching patterns. The proposed solution is successfully validated in open-loop mode both in simulation and experimentally with a laboratory prototype. The measured efficiency of the power converter was above 97% in the dc to ac mode and around 99% in the dc to dc mode.

COMPREHENSIVE CONDITION ANALYSIS AND PERSPECTIVES OF ENERGY DEVELOPMENT IN UKRAINE ACCORDING TO THE SMART GRID CONCEPT

The article examines development trends of the energy complex of Ukraine, possibility and prospects of creating a modern energy complex through the use of an innovative technical base for energy management in accordance with the Smart Grid concept. The general properties of Smart Grid technology and its main advantages are given by authors. It is shown that within the framework of the Smart Grid concept, the intelligent electric power system is considered as a single network of information and control systems. The process of electric power industry modernization in the direction of the creation of "smart" power supply networks in a number of European countries, which aims to ensure stable development, economic growth, growth of living standards and protection of the natural environment, is considered. The paper describes modernization methods of the electric power complex of Ukraine based on foreign experience. It is shown that from the point of view of energy security and sustainable development, Smart Grid is able to ensure the operation of the power grid even in case of damage or destruction of one segment, which is a key advantage during post-war reconstruction. They will also make it possible to effectively integrate renewable generation and energy storage systems into the grid, as well as provide auxiliary services for forecasting the power system operation. The paper analyzes the problematic issues of implementing the Smart Grid concept in Ukraine. They include the lack of technological solutions and methods that provide an effective algorithm for determining the state of power networks cyber security; large length of power distribution networks and insufficiently developed infrastructure. A comparative description of the existing energy systems functional properties and energy systems based on the Smart Grid concept is presented in general. An analysis of possible ways of electric power industry development was carried out, which showed the presence of serious limitations of electric power industry development within the framework of the former concept, based mainly on the improvement of certain types of equipment and technologies. It is shown that Ukraine is at the initial familiarization and formation stage of the first organizational initiatives for Smart Grid, namely the implementation of the government-approved Concept of the "smart grids" introduction in Ukraine for the period up to 2035. The purpose and expected results of this Concept implementation are considered.

Application of activated carbon in renewable energy conversion and storage systems: a review

Application of activated carbon in renewable energy conversion and storage systems: a review

Graphitic carbon nitride (g-C3N4) decorated on CuCo2O4 nanosphere composites for enhanced electrochemical performance for energy storage applications

Researchers sought to develop high-performance electrode materials to meet the rising need for renewable energy storage systems in order to build smart technology gadgets with portable characteristics and with global energy security. ACuCo2O4/g-C3N4 nanocomposite was synthesised by employing a facile hydrothermal method. The XRD, FTIR, TEM, XPS, and FESEM results conformed the formation of CuCo2O4/g-C3N4 to a spinel cubic structure in space group Fd3m.For a CuCo2O4/g-C3N4 sample, the specific surface area determined using the Brunauer-Emmett-Teller (BET) isotherms is 51 m2g−1, which is much higher than the surface area for pure CuCo2O4 (32 m2g−1). The g-C3N4 (n-type) wrapped CuCo2O4 (p-type) nanoparticles exhibits incredible specific capacitance of 1948 Fg−1 at 1 Ag−1 and Power density of 1556 W/ kg at 6 Ag−1). Moreover, the long life cycle stability shows 88% capacitance returns after 8000 charge discharge cycles.

Effect of Heteroatom Doping on Electrochemical Properties of Olivine LiFePO4 Cathodes for High-Performance Lithium-Ion Batteries.

Lithium iron phosphate (LiFePO4, LFP), an olivine-type cathode material, represents a highly suitable cathode option for lithium-ion batteries that is widely applied in electric vehicles and renewable energy storage systems. This work employed the ball milling technique to synthesize LiFePO4/carbon (LFP/C) composites and investigated the effects of various doping elements, including F, Mn, Nb, and Mg, on the electrochemical behavior of LFP/C composite cathodes. Our comprehensive work indicates that optimized F doping could improve the discharge capacity of the LFP/C composites at high rates, achieving 113.7 mAh g-1 at 10 C. Rational Nb doping boosted the cycling stability and improved the capacity retention rate (above 96.1% after 100 cycles at 0.2 C). The designed Mn doping escalated the discharge capacity of the LFP/C composite under a low temperature of -15 °C (101.2 mAh g-1 at 0.2 C). By optimizing the doping elements and levels, the role of doping as a modification method on the diverse properties of LFP/C cathode materials was effectively explored.

A Linked Swing Contract Market Design With High Renewable Penetration and Battery Firming

As renewable generation and energy storage systems become increasingly cost-effective, modern power systems are increasingly reliant on variable energy resources (VERs) to meet demand. However, the variability and uncertainty of VERs' generation require additional flexibility to mitigate their impacts. Unfortunately, the current power market designs rigidly define different power and reserve products, making it difficult to fully recognize and compensate for this flexibility. Furthermore, since the marginal operating costs of the system quickly approach zero, current marginal cost-based market mechanisms struggle to provide price signals that allow independent power producers to make informed decisions about constructing, operating, and retiring plants. To address these issues, this paper proposes a new linked electricity market design based on swing contracts for operating power systems with high penetration of VERs and energy storage resources. The feasibility of the proposed approach is demonstrated through study results on the modified IEEE 118-bus test system.

Probing layer interface behavior in Lithium-ion batteries via concurrent ultrasonic and modal measurements

Lithium-ion batteries are pivotal in various technological applications, from powering electric vehicles to supporting renewable energy storage systems. Understanding and monitoring the intricate chemo-mechanics within lithium-ion cells is imperative for ensuring their reliability and performance over time. Previous research has shown that both ultrasonic and vibrational measurements provide a measure of a cell’s state of charge (SOC) and state of health (SOH) and provide indications of existing or previous thermal or electrical abuse. Recent work suggests that ultrasonic and modal analysis may provide complementary insights into the evolution of layer interfaces during early-life aging due to charge-discharge cycling [J. Acoust. Soc. Am., 154, A284 (2023)]. This work presents the results of concurrent ultrasonic and modal measurements over multiple charge-discharge cycles. Namely, we monitor concurrent changes in the ultrasonic time-of-flight and signal amplitude and the resonance frequency of a clamped-clamped 10 Ah Nickel–Manganese–Cobalt pouch cell as a function of electrical cycling. The evolution of these metrics paired with analytical and numerical models of the cell will be used to understand changes in the material properties at layer interfaces and potential structural alterations within the battery which may be important indicators of SOC and early-life aging mechanisms.

A rule-based energy management system for hybrid renewable energy sources with battery bank optimized by genetic algorithm optimization

A Nanogrid (NG) model is described as a power distribution system that integrates Hybrid Renewable Energy Sources (HRESs) and Energy Storage Systems (ESSs) into the primary grid. However, this process is affected by several factors, like load variability, market pricing, and the intermittent nature of Wind Turbines (WTs) and Photovoltaic (PV) systems. Hence, other researchers in the past have used a few optimization-based processes to improve the development of Energy Management Systems (EMSs) and ESSs, which further enhanced the operational performance of NGs. It was seen that EMS acts as the distributed energy source in the NG setup and assists in power generation, usage, dissemination, and differential pricing. Hence this study employed the MATLAB Simulink software for modelling the grid-connected NG that included HRES; such as wind and PV; in addition to 3 Battery Storage Devices (BSDs) to design an effective EMS for the NG system and decrease its overall costs. For this purpose, a Rule-Based EMS (RB-EMS) that employs State Flow (SF) to guarantee a safe and reliable operating power flow to the NG has been developed. In addition to that, a Genetic Algorithm (GA)-based optimization system and Simulated Annealing optimization Algorithm (SAA) were proposed to determine an economical solution for decreasing the cost of the NG system depending on its operational constraints. Lastly, comparison about the cost between RB-EMS, GA and SAA has been presented. According to the simulation results, the proposed GA displayed an economical performance since it could achieve a 40% cost saving whereas the SAA system showed a 19.3% cost saving compared to the RB-EMS. It can be concluded from the findings that the GA-based optimization technique was very cost-effective displays many important features, like rapid convergence, simple design, and very few controlling factors.

Performance Enhancement of Droop Controlled DC Microgrid Using Solar Fed Hybrid BIFRED Converter with RBFNN

Electric power system’s structure is shifting from a generator to a converter-based, posing the risk of reduced system inertia. This transition is due to the increased penetration of power electronics-based renewable energy sources (RESs) and Energy Storage Systems (ESS). The microgrid concept supports their accommodation into electric networks; however, it requires appropriate control to solve the voltage stability issues created by reduced inertia. The proposed DC microgrid aims to maintain voltage stability in DC-link comprising wind, solar, and ESS, irrespective of changes in operating conditions. An improved Z source- Boost Integrated Fly Back Rectifier/Energy Storage DC–DC (BIFRED) converter is proposed to increase the output voltage of photovoltaic (PV) system to achieve better DC-link voltage stability. Radial basis function neural network (RBFNN) approach is used to harvest the maximum power possible from the PV panel and to improve the conversion efficiency. The proposed DC microgrid also includes Wind Energy Conversion System (WECS) powered by a Doubly Fed Induction Generator (DFIG). It integrates droop control with virtual inertia and damping control and effectively handles the voltage stability issues brought out by RESs and loads. Artificial neural network (ANN) optimized droop-controlled bidirectional converter (BDC) is implemented to integrate ESS and resolve the imbalance created by ESS. The work is validated by MATLAB simulation model and a laboratory prototype.