Integration Of Renewable Sources Research Articles

A Survey on Anomalies and Faults That May Impact the Reliability of Renewable-Based Power Systems

The decarbonization of the electricity grid is one of the actions that can help reduce fossil fuel emissions, and thus their impact on global warming in the future. This decarbonization will be achieved mainly through the integration and widespread diffusion of renewable power sources. This is also going to be supported by the shift from the paradigm of production–transmission–distribution, where electricity production oversees large-size power plants, to renewable-based distributed/diffused production, where electricity is generated very close or even by the same (group of) user(s) (or prosumers in the latter case). The number of mid-/small-size installations based on renewable energy technologies will therefore increase substantially, and the related renewable generation will be dominant against that from large-size power plants. Unfortunately, this will very likely reduce the reliability of the grid, unless appropriate countermeasures are taken/implemented, hopefully at the same time that the paradigm shift is being achieved. To this aim, it is important to identify the anomalies and main fault causes that might possibly affect some of the central renewable (wind, PV, hydrogen) and ancillary technologies that will be used to establish future renewable-based power systems. Accordingly, this paper presents a literature survey, also extending the focus to related datasets that can be used for deeper investigation. It is highlighted that the gaps mainly refer to a lack of a common taxonomy that prevents the establishment of structured knowledge in the scope of renewable-based power systems, a lack of contributions to anomalies/faults specific to wind turbines, and a lack of datasets related to electrolyzers, fuel cells, DC/x conversion, and monitoring and communication systems. Further, in the case of monitoring and communication systems, the scientific literature is both very dated, therefore not considering possible new aspects that would be currently worthy of investigation, and not oriented toward the particular domain addressed, thus considering peculiar aspects that are left out.

Impact of ABT based control strategies on ALFC and AVR in DFIG-integrated interconnected power systems

To maintain a stable electricity supply in interconnected power grids, effective frequency and voltage regulation are required. Interconnected power systems use Automatic Load Frequency Control (ALFC) and Automatic Voltage Regulator (AVR) for this purpose. The integration of renewable sources, such as Doubly Fed Induction Generators (DFIGs), creates challenges for such control loops. This study investigates the impact of using various Frequency-Linked Pricing (FLP)-based operating strategies under Availability-Based Tariff (ABT) on combined ALFC and AVR in a two-area interconnected power system with DFIGs. FLP-based operating strategies under ABT react to frequency changes through price signals, which can act faster compared to traditional AGC methods that rely on communication between control centers. This quicker response helps maintain frequency and voltage during load disturbances. Most of the earlier research focused mostly on using only a single type of FLP-based operating strategy under ABT within the ALFC loop, neglecting the combined ALFC-AVR interaction. This work addresses this gap by evaluating three FLP-based operating strategies under ABT along with the traditional approach for their impact on frequency and voltage under step load disturbance. Simulation studies conducted in MATLAB 2022a demonstrate the effectiveness of the proposed strategies, providing valuable insights for improving frequency and voltage control in modern power systems. The investigation reveals that the strategy that considers the difference between the actual and reference values of the unscheduled interchange (UI) charge and the marginal cost of generation to produce the control command outperforms the other strategies and restores the frequency and voltage to their nominal values.

A techno-economic framework for optimizing multi-area power dispatch in microgrids with tie-line constraints

Fuel-fired power plants are the primary source of electricity production in Saudi Arabia. The main goal of this investigation is to reduce the overall costs and emissions of producing electricity from thermal units operated by the Shuqaiq Water and Electricity Company. This work also considers practical thermo-electric constraints, such as power losses, ramps-rate constraints, and environmental optimization function, to align with the sustainability goals of Saudi Vision 2030. The optimization of electric energy delivery is a crucial operation within the energy control center. The focus of this work is to develop an advanced computational technique that reduces the reliance on fossil fuels and minimizes the production cost of electricity, while also considering environmental emissions as an optimization function. An examination is conducted through two distinct practical case studies with power demand of 850 MW—one involving renewable sources and the other without—to assess the effectiveness of the hybrid crow search and JAYA optimization algorithm in optimizing performance. This system includes a multi-area setup with limitations on tie-line power flow constraints. Additionally, the study also involved RETScreen modeling to assess the practical financial, emission, and benchmark analysis of a proposed integrated solar plant. This analysis offers a thorough assessment of the facility’s performance and reliability in practical situations. This study serves as a motivating factor for energy operators, stakeholders, governmental organizations, and grid administrators to invest resources in environmentally-friendly initiatives. The simulation results showed that the proposed approach successfully reduces the electricity cost 2.29% and 3.70% in hybrid energy networks while effectively following system constraints. In addition, the analysis revealed that the optimization algorithm effectively decreases toxic emissions from power plants while also supporting the sustainability objectives of United Nation SDG-7 and Saudi Vision 2030 through the successful integration of renewable sources.

Design of real time closed loop control in hybrid energy system using cascaded converters: a dynamic solution for high power applications

ABSTRACT This paper presents a Hybrid Renewable Energy system combining multiple sources of renewable energy, such as PV, Wind, and energy storage devices, to provide a reliable and sustainable power supply. Cascaded converters are used to efficiently transform and manage the energy from these sources. To achieve optimal performance and reliable operation, a real-time closed-loop control algorithm is designed and implemented for the cascaded converter. The control algorithm facilitates the smooth integration of energy from various renewable sources, optimizing the utilization of available resources and ensuring a consistent power supply. The algorithm maximizes the energy conversion efficiency of the cascaded converter, reducing energy losses and minimizing waste. It also optimizes the integration of renewable sources, regulates output parameters, and ensures efficient energy conversion.

Modified Tilt-integral active disturbance rejection controller with inertia emulated direct current link for frequency control of marine microgrid

The increasing integration of intermittent renewable sources (RSs) poses a dynamic frequency stability challenge for modern marine vessel microgrids. To address this issue, this paper proposes a novel control approach, specifically targeting frequency and tie-line power stabilization in a diverse source marine microgrid (MμG) with two intertied areas featuring renewable (wind-wave) sources. The suggested approach introduces a modified tilt-integral active disturbance rejection (TI-ADRC) controller designed to ensure effective damping of power frequency oscillations. As the control scheme depends on the optimal setting of the proposed controller, a recently developed marine predator technique (MPT) has been adopted. The performance of the proposed controller is compared with other recent controllers viz. PID, tilt-integral derivative (TID), two-degree-of-freedom (2DOF)-PID, fuzzy-PI, and ADRC to validate its superiority. To further enhance the system dynamics, a precise modeling of inertia emulated direct current (IEPDC) tie link is incorporated in microgrid system. The impact assessments, considering time delays with pre/post IEPDC link, demonstrate a substantial 57.79% and 81.53% reduction in peak frequency overshoot compared to DC link (conventional model) and AC link, respectively. The analysis of the eigen plot confirms the stability of the control system. Sensitivity assessments of the controller against ± 30% parametric variations and load fluctuations are conducted, affirming its robustness. Finally, the result from OPAL-RT confirm the practicality of the proposed method. It is asserted that the suggested controller is reliable and functions well in n the presence of diverse disruptions and parametric variations.

A Data–Physics-Driven Modeling Approach of Key Equipment for Large-Scale Distribution Network Simulation

Fueled by pressing global climate concerns, the integration of large-scale renewable distributed generation sources, including distributed wind power and photovoltaics, along with electricity substitution loads into the distribution network has been accelerated to diminish carbon emissions. This shift introduces significant challenges and necessitates the advanced operation and control of distribution systems to accommodate these changes effectively. Against this backdrop, there is a growing expectation for an open and scalable central control mode, equipped with compatible interfaces, to offer a visionary development platform for the grid. This platform is anticipated to meet the evolving needs of future distribution system development, ensuring adaptability and forward compatibility. The aforementioned platform requires open, scalable, and interface-compatible models of key distribution network equipment as its foundation. To address the challenges presented, this paper proposes a data–physics-driven modeling approach for automating simulations in distribution systems. This method employs a simplified and standardized system of linear differential equations with undetermined coefficients to capture the common physical characteristics of specific device types. The models designed through this approach are notably open, allowing for real-time data to refine undetermined coefficients and accurately depict the dynamic behavior of equipment over various periods. Their scalability also stands out, rendering them apt for large-scale distribution network simulations. The paper elaborates on models for distributed photovoltaic, wind turbine, energy storage, and electric vehicle, and demonstrates their application within an IEEE-33 node distribution network topology built on Python.

Towards low-carbon power networks: Optimal location and sizing of renewable energy sources and hydrogen storage

This paper proposes a systematic optimization framework to jointly determine the optimal location and sizing decisions of renewables and hydrogen storage in a power network to achieve the transition to low-carbon networks efficiently. We obtain these strategic decisions based on the multi-period alternating current optimal power flow (AC MOPF) problem that jointly analyzes power network, renewable, and hydrogen storage interactions at the operational level by considering the uncertainty of renewable output, seasonality of electricity demand, and electricity prices. We develop a tailored solution approach based on second-order cone programming within a Benders decomposition framework to provide globally optimal solutions. In a test case, we show that the joint integration of renewable sources and hydrogen storage and consideration of the AC MOPF model significantly reduces the operational cost of the power network. In turn, our findings can provide quantitative insights to decision-makers on how to integrate renewable sources and hydrogen storage under different settings of the hydrogen selling price, renewable curtailment cost, emission tax price, and conversion efficiency.

Development of an Energy Planning Model Using Temporal Production Simulation and Enhanced NSGA-III

This paper presents an innovative model of Energy Planning Model which allows navigating the complexities of modern energy systems. Our model utilizes a combination of Temporal Production Simulation and an Enhanced Non-Dominated Sorting Genetic Algorithm III to address the challenge associated with fluctuating energy demands and renewable sources integration. The model represents a significant advancement in energy planning due to its capacity to simulate energy production and consumption dynamics over time. The unique feature of the model is based on Temporal Production Simulation, meaning that the model is capable of accounting for hourly, daily, and seasonal fluctuations in energy supply and demand. Such temporal sensitivity is crucial for optimization in systems with high percentages of intermittent renewable sources, as existing planning solutions largely ignore such fluctuations. Another component of the model is the Enhanced NSGA-III algorithm that is uniquely tailored for the nature of multi-objective energy planning where one must balance their cost, environmental performance, and reliability. We have developed improvements to NSGAIII to enhance its efficiency when navigating the complex decision space associated with energy planning to reach faster convergence and to explore more optimal solutions. Methodologically, we use a combination of in-depth problem definition approach, advanced simulation, and algorithmic adjustments. We have validated our model against existing models and testing it in various scenarios to illustrate its superior ability to reach optimal energy plans based on efficiency, sustainability, and reliability under various conditions. Overall, through its unique incorporation of the Temporal Production Simulation and an improved optimization algorithm, the Energy Planning Model provides novel insights and practical decision support for policymakers and energy planners developed to reach the optimal sustainable solutions required for the high penetration of renewables.

Transmission Expansion Planning Considering Storage, Flexible AC Transmission System, Losses, and Contingencies to Integrate Wind Power

To meet future load projection with the integration of renewable sources, the transmission system must be planned optimally. Thus, this paper introduces a comparative analysis and comprehensive methodology for transmission expansion planning (TEP), incorporating the combined effects of wind power, losses, N-1 contingency, a FACTS, and storage in a flexible environment. Specifically, the optimal placement of the FACTS, known as series capacitive compensation (SCC) devices, is used. The intraday constraints associated with wind power and energy storage are represented by the methodology of typical days jointly with the load scenarios light, heavy, and medium. The TEP problem is formulated as a mixed-integer nonlinear programming (MINLP) problem through a DC model and is solved using a specialized genetic algorithm. This algorithm is also used to determine the optimal placement of SCC devices and storage systems in expansion planning. The proposed methodology is then used to perform a comparison of the effect of the different technologies on the robustness and cost of the final solution of the TEP problem. Three test systems were used to perform the comparative analyses, namely the Garver system, the IEEE-24 system, and a real-world Colombian power system of 93 buses. The results indicate that energy storage and SCC devices lead to a decrease in transmission requirements and overall investment, enabling the effective integration of wind farms.

Experimental demonstration of a dispatchable power-to-power high temperature latent heat storage system

The development of electricity storage solutions is crucial to support the integration of variable renewable electricity sources in electricity systems. This study experimentally characterizes a state-of-the-art full-scale electrical thermal energy storage (ETES) system in outdoor conditions. The system integrates a 600 kWhth high-temperature latent heat storage module and a 13 kWe Stirling engine. The heat storage module uses an 88Al12Si metallic alloy as the phase change material to store electricity converted to thermal energy by a resistive heater in charging mode. The Stirling engine uses hydrogen as the working fluid for re-electrification (discharging). The system uses liquid sodium as the heat transfer fluid between the latent heat storage module and both the charging (electrical heater) and discharging (engine) sub-systems. Over the characterization period, the ETES prototype produced stable electricity at an average rate of 10.5 ± 1 kW for 13 consecutive hours daily with overall and power block first-law efficiencies of 23 % and 25 %, respectively. The effective storage utilization ratio varied between 0.58 and 0.94 depending on the discharge parameters. The response time was <5 s for output power regulation. The results highlight the potential of this latent heat thermal energy storage system to satisfy long-duration electricity storage applications and therefore contribute to the stabilization of electricity grids.

Joint contribution of RTEM and AGC system for frequency stabilisation in renewable energy integrated power system

AbstractIncreasing penetration of variable renewable generations will diminish system inertia thereby degrading the conventional frequency regulation capability. As a result, maintaining frequency stability will be more and more challenging with traditional approaches. Even though renewable sources integration would jeopardise the grid stability, it also presents several opportunities as well. For example, converter‐interfaced generators can bid in real‐time electricity markets (RTEM) and provide short‐time dispatch to minimise load‐generation mismatch. In this paper, an integrated approach that accommodates discrete automatic generation control (AGC) system with a regulation mileage framework and RTEM model to balance generation and consumption is proposed. The RTEM model is assumed to have a five‐minute dispatch trading interval which is to some extent comparable to the discrete AGC system. Furthermore, a fractional order PID (FOPID) controller is equipped in the AGC system whose parameters are tuned using a novel metaheuristic‐based optimisation called Lichtenberg Algorithm (LA). The proposed framework is tested in a three‐area system under several operating conditions to reveal the improvement in the dynamic performance of the system. The objective function is also incorporated with mileage payment that allows a fair compensation rule for all the units.

Enhancing micro grid sustainability: Genetic-driven reptile search algorithm based carbon emission and cost reduction strategy

Grid architecture is continuously changing in response to modern energy rules that ensure the integration of more renewable sources to lower the carbon footprint. The integration of non-dispatchable renewable energy sources (RESs) using low voltage grids is a possible alternative to a centralized method. Due to a recent trend, microgrids (MGs) are reducing their reliance on the primary grid by incorporating localized sources of electricity including energy storage devices, micro turbines, and fuel cells. Due to the expanding number of spread generating sources with diverse features, power dispatching schedule for distribution micro grids is becoming more challenging. This study offers a dispatch scheduling method from the perspective of an operator. The primary objective of this study is to employ the Genetic-driven Reptile search algorithm (G-RSA) for the dual purpose of minimizing carbon emissions and reducing costs within each microgrid. Additionally, fewer sale and purchase are made from the primary grid. Transmission losses are consequently reduced.

Shifting Tides: Affordability of Malaysia's Energy Transition

The Energy Commission of Malaysia (EC) harnesses the Energy Trilemma, focusing on security, affordability, and sustainability, to strategically navigate the nation's energy transition towards increasing the share of Renewable Energy (RE). This transition aims to balance the need for reliable and cost-effective energy while reducing carbon emissions and dependency on thermal sources. Despite the potential of RE to contribute to a sustainable energy pathway, there are significant challenges concerning the affordability of this shift, especially when compared to conventional coal plants. The study aims to examine the affordability aspect of RE through a SWOT analysis, assessing the strengths, weaknesses, opportunities, and threats associated with the transition from coal to renewable sources. Employing a qualitative approach, this research relies on structured interviews with experts from a key utility company to delve into the complexities of Malaysia's energy transition. The findings highlight the cost-effectiveness and innovative potential of RE, particularly solar power, against the backdrop of its intermittent nature and land acquisition costs. Moreover, the SWOT analysis points to opportunities for decentralized energy models and technological advancements that can ensure the affordability of solar energy. The research underscores the need for strategic partnerships and policy frameworks that support scalable and economically viable energy solutions, despite the high initial costs and reliance on tariff mechanisms. In conclusion, while coal plants currently offer more stable and cost-effective energy production, the future of Malaysia's energy affordability hinges on a balanced integration of renewable sources, addressing the highlighted SWOT elements. This integrated approach is vital for a sustainable energy economy that is both environmentally responsible and economically feasible.

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research.

Conjoint Enhancement of VSG Dynamic Output Responses by Disturbance-Oriented Adaptive Parameters

Virtual synchronous generator(VSG) is an encouraging concept to ensure provision of inertia, following large integration of renewable sources in system. However, apart from VSG contribution in improving system's frequency stability, it is also important to ensure long term robust and stable operation of VSG itself. In this regard, active power loop(APL) of VSG constituting variable control parameters is analyzed subsequent to contingency scenarios. It is observed that dynamics of APL outputs (virtual frequency and active power) is critically sensitive and prone to intrinsic oscillations. Further, during disturbance in system, utilization of constant control parameters of APL illustrates contradictory effects in dynamics of its outputs. As a solution to above issues, novel adaptive inertia and adaptive damping coefficient is proposed. The work provides distinct approach in following manner- 1) judicious selection of different operation range of natural undamped frequency( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ω_{n}$</tex-math></inline-formula> ) and damping ratio(ζ) of APL as compared to existing strategies, along with 2) reduced computational complexity and improved accuracy of controller, 3) thereby reduction in over(-under)shoots and intrinsic oscillations in virtual frequency and output active power of APL during transient period. This strengthens classical VSG design and contributes in its robust long term industry/real-time application. The efficacy of proposed strategy is showcased by real-time simulations along with validation at PHIL level.

Comprehensive Analysis of Power System

The contemporary power landscape is marked by escalating complexity driven by surging energy demands, the integration of renewable sources, and the imperative for heightened system performance. In response to these challenges, this paper presents a thorough investigation. Firstly, the load factor Lambda (λ) is expounded upon, emphasizing its significant impact on system stability due to load characteristics. Static load models are employed for voltage stability studies, providing insights into resource allocation and optimization by comparing actual energy consumption to the maximum potential consumption. Secondly, the paper delves into the power balance, a critical aspect that scrutinizes the equilibrium among generation, consumption, and distribution, underscoring its pivotal role in ensuring system stability. Within a 6-node network, the concept of power balance, denoted as "load λ," is illustrated by tracking its variations across iterations, representing the disparity between Power Generator (PG) and Load (PL). To maintain balance during continuous power flow (CPF) analysis, power supply can be finely adjusted using the load factor lambda (λ). Thirdly, the integration of renewable sources introduces active load variation, underscoring the necessity to comprehend load fluctuations over time for ensuring grid reliability. Active load variation is demonstrated based on the number of iterations. Additionally, the paper elucidates the increment factor τ, explaining its impact on the number of correction iterations by selecting the size of the step factor. The graphical representation of the increment factor for iterations 8 and 11 in a network with load nodes provides further clarification. This paper explores the intricate interactions of load factors, power balance, active load changes, and increment factors within power systems. The presented findings contribute to the enhancement of the reliability, efficiency, and sustainability of modern power systems.

Medium-term stochastic hydrothermal scheduling with short-term operational effects for large-scale power and water networks

The high integration of variable renewable sources in electric power systems entails a series of challenges inherent to their intrinsic variability. A critical challenge is to correctly value the water available in reservoirs in hydrothermal systems, considering the flexibility that it provides. In this context, this paper proposes a medium-term multistage stochastic optimization model for the hydrothermal scheduling problem solved with the stochastic dual dynamic programming algorithm. The proposed model includes operational constraints and simplified mathematical expressions of relevant operational effects that allow more informed assessment of the water value by considering, among others, the flexibility necessary for the operation of the system. In addition, the hydrological uncertainty in the model is represented by a vector autoregressive process, which allows capturing spatio-temporal correlations between the different hydro inflows. A calibration method for the simplified mathematical expressions of operational effects is also proposed, which allows a detailed short-term operational model to be correctly linked to the proposed medium-term linear model. Through extensive experiments for the Chilean power system, the results show that the difference between the expected operating costs of the proposed medium-term model, and the costs obtained through a detailed short-term operational model was only 0.1%, in contrast to the 9.3% difference obtained when a simpler base model is employed. This shows the effectiveness of the proposed approach. Further, this difference is also reflected in the estimation of the water value, which is critical in water shortage situations.

Exploration of Alternatives to Reduce the Gap in Access to Electricity in Rural Communities—Las Nubes Village Case (Barranquilla, Colombia)

The global crisis associated with COVID-19 and the Russia-Ukraine conflict has affected progress towards the Sustainable Development Goals (SDGs). Projections for SDG7 (Ensure access to affordable, reliable, sustainable, and modern energy for all) indicate a slowdown in the pace of electrification. Thus, the problem of poverty will persist in many regions as long as access to electricity remains difficult. This work analyzes some solutions to the lack of electricity supply in a rural community using organic waste from its economic activity and the integration of other available renewable sources to make electricity affordable and reliable. A model that minimizes the levelized cost of energy and restricts the proportion of annual energy not supplied to less than 5% of the community’s annual demand optimizes the performance of off-grid and on-grid systems. These systems have in common the production of electricity from biogas produced from swine manure, supplemented with wind and solar generation. Batteries and diesel generators support the operation of off-grid systems. As expected, the grid-connected system presented the best performance; however, the result reaffirms the need for governments to ensure the policy and infrastructure conditions that facilitate the grid connection of vulnerable communities to achieve SDG7.

Towards a Carbon Neutral Future: Integrating Renewable Sources and Energy Storage in Sustainable Energy Solutions

This research examines the way to a carbon-neutral future by looking at the integration of renewable vitality sources and vitality capacity advances in feasible energy arrangements. Through a multidisciplinary approach, enveloping information collection, numerical modelling, and scenario investigations, the study investigates the flow of transitioning vitality frameworks. The optimization of energy capacity capacities is educated by scientific models, uncovering that as renewable infiltration increments (20%, 40%, and 60%), optimal capacity capacities rise correspondingly (300 MWh, 700 MWh, and 1200 MWh). Situation investigations illustrate that higher renewable entrance and appropriately measured energy capacity capacities lead to significant diminishments in CO2 outflows (25%, 45%, and 65%) while keeping up positive financial reasonability. Sensitivity investigations confirm the vigour of the models, showing the versatility to varieties in key parameters such as renewable asset accessibility, energy capacity productivity, and capital costs. A comparative investigation against related work underscores the competitiveness and uniqueness of the proposed approach, emphasizing the noteworthiness of numerical modelling in optimizing energy frameworks. This research contributes profitable insights for policymakers, industry partners, and analysts committed to exploring the complexities of accomplishing a carbon-neutral future. The discoveries displayed here, together with those from different studies crossing worldwide vitality exchange, green hydrogen production, and urban arranging, collectively contribute to the broader discourse on economic energy move.

Neural network-assisted integration of renewable sources in microgrids: A case study

This study examines the incorporation of renewable energy sources into microgrids using neural network-assisted optimization methods. The objective is to tackle the difficulties related to the fluctuation and uncertainty of renewable energy production. An examination of the collected data over various time periods indicates encouraging patterns in the production of renewable energy. The solar energy use shows a steady rise from 120 kWh to 140 kWh, representing a 16.67% increase. Similarly, wind energy usage also demonstrates an upward trend, increasing from 80 kWh to 95 kWh, marking an 18.75% expansion. The biomass energy production has seen a substantial increase from 50 kWh to 65 kWh, representing a significant 30% rise. The examination of microgrid load consumption demonstrates the increasing energy needs in residential, commercial, and industrial sectors. The household load consumption has increased from 150 kWh to 165 kWh, representing a 10% spike. Additionally, the commercial load and industrial load have also seen a surge of 15%. The predictions made by the neural network demonstrate a high level of accuracy, closely matching the actual output of renewable energy. The accuracy rates for solar, wind, and biomass projections are 98.4%, 95.5%, and 97.3% correspondingly. The assessment of improved energy distribution emphasizes the effective usage of renewable sources, guaranteeing grid stability and optimal resource utilization. The results highlight the capacity of neural network-assisted methods to precisely predict renewable energy outputs and efficiently incorporate them into microgrids, hence promoting sustainable and resilient energy solutions. This report provides valuable insights on improving microgrid operations, decreasing reliance on traditional energy sources, and accelerating the shift towards sustainable energy systems.