Demand Fluctuations Research Articles

Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning

Volt–VAR control (VVC) is essential in maintaining voltage stability and operational efficiency in distribution networks, particularly with the increasing integration of distributed energy resources. Traditional methods often struggle to manage real-time fluctuations in demand and generation. First, various resources such as static VAR compensators, photovoltaic systems, and demand response strategies are incorporated into the VVC scheme to enhance voltage regulation. Then, the VVC scheme is formulated as a constrained Markov decision process. Next, a safe deep reinforcement learning (SDRL) algorithm is proposed, incorporating a novel Lagrange multiplier update mechanism to ensure that the control policies adhere to safety constraints during the learning process. Finally, extensive simulations with the IEEE-33 test feeder demonstrate that the proposed SDRL-based VVC approach effectively improves voltage regulation and reduces power losses.

Adaptive Energy Management Strategy for Sustainable xEV Charging Stations in Hybrid Microgrid Architecture

Integrating electric vehicles (EVs) into power grids presents critical energy management challenges, especially in microgrid systems powered by renewable energy sources. This study introduces a novel energy management strategy for EV charging stations utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. The system dynamically optimizes the coordination of renewable energy sources solar photovoltaic (PV) panels and wind turbines energy storage, and EV chargers. By leveraging real-time data and predictive algorithms, the ANFIS controller adapts to fluctuations in energy supply and demand, ensuring optimal performance. The innovation of this work lies in combining fuzzy logic with neural network-based learning to enhance decision-making under uncertain and variable renewable energy conditions. The proposed approach employs a robust design methodology, integrating neural network training with fuzzy logic system development, to create an adaptive and intelligent control system. Simulation results using MATLAB/Simulink demonstrate a 92% increase in energy efficiency and an 89% enhancement in load-handling capacity compared to conventional methods. The system effectively manages renewable energy variability, battery state-of-charge, and load demand, maintaining stable electrical characteristics even under dynamic wind and solar conditions. This work underscores the importance of advanced AI-driven control strategies in enabling sustainable EV charging infrastructure within microgrid environments.

THE COMPARISON OF LONG SHORT-TERM MEMORY AND BIDIRECTIONAL LONG SHORT-TERM MEMORY FOR FORECASTING COAL PRICE

Coal remains vital for global energy despite recent demand fluctuations due to the COVID-19 pandemic and geopolitical tensions. The International Energy Agency (IEA) projected a decline in global coal demand starting in early 2024, driven by increasing renewable energy adoption. As one of the top coal exporters, Indonesia must adjust to these changes. This study aims to forecast future coal prices using historical data from Indonesia's Ministry of Energy and Mineral Resources (KESDM), applying and comparing Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models. While BiLSTM has shown advantages in other contexts and studies, its effectiveness for coal price forecasting remains underexplored. To ensure robust predictions, we employ walk-forward validation, which divides the data into six segments and evaluates 90 hyperparameter combinations across all segments. The BiLSTM model consistently outperforms the LSTM model, achieving lower average RMSE and MAPE values. Specifically, BiLSTM records an average MAPE of 7.847 and RMSE of 10.485, compared to LSTM's 10.442 and 11.993, respectively. The Diebold-Mariano (DM) test using squared error and absolute error loss functions further corroborates these findings, with most segments showing significant improvements in favor of BiLSTM, indicated by negative DM-test statistics and p-values below 0.01 or 0.10. This superior performance continues into the testing data, where BiLSTM maintains lower error metrics and a significant result of the DM test, underscoring its reliability for forecasting. In the final stage, the forecasts from both models indicate a nearly linear downward trend in coal prices over the next 18 months, aligning with the International Energy Agency's 2023 projection of a structural decline in coal demand driven by the sustained growth of clean energy technologies.

The impact of contract types on carbon-capped food supply chains: options versus wholesale

Given the rising awareness of environmental issues and the growing demand for eco-friendly food products, stakeholders within the food supply chain are investing more in green technologies to reduce carbon emissions. However, the efficacy of carbon caps in achieving emission reductions is challenged by unpredictable consumer demand, complicating both emission control and supply chain efficiency. This study investigates a scenario involving a food supply chain with an environmentally responsible supplier and a retailer facing demand fluctuations. We explore the incentives for suppliers to adopt option contracts and evaluates their impact on carbon emissions through a Stackelberg game model with strict carbon emission caps. Our analysis reveals that while higher green investment generally enhances supplier profitability, it may not always benefit retailers. Although option contracts can improve supply chain efficiency, they might also lead to increased carbon emissions and food waste. Notably, when green investments are decision-based, option contracts tend to encourage greater investments than wholesale contracts, helping to mitigate the financial burdens of high costs. This trend persists even in competitive markets. Our research suggests that option contracts can effectively balance the dual objectives of maintaining supply chain performance and complying with carbon emission limits.

Ensuring Reliable Network Communication and Data Processing in Internet of Things Systems with Prediction-Based Resource Allocation

The distributed nature of IoT systems and new trends focusing on fog computing enforce the need for reliable communication that ensures the required quality of service for various scenarios. Due to the direct interaction with the real world, failure to deliver the required QoS level can introduce system failures and lead to further negative consequences for users. This paper introduces a prediction-based resource allocation method for Multi-Access Edge Computing-capable networks, aimed at assurance of the required QoS and optimization of resource utilization for various types of IoT use cases featuring adaptability to changes in users’ requests. The method considers the current resource load and predicted changes in resource utilization based on historical request data, which are then utilized to adjust the resource allocation optimization criteria for upcoming requests. The proposed method was developed for scenarios utilizing edge computing, e.g., autonomous vehicle data exchange, which can be susceptible to periodic resource demand fluctuations related to typical rush hours, predictable with the proposed approach. The results indicate that the proposed approach can increase the reliability of processes conducted in IoT systems.

Modulatory responses of physiological and biochemical status are related to drought tolerance levels in peanut cultivars.

Peanut (Arachis hypogaea L.) is the fourth most cultivated oilseed in the world, but its cultivation is subject to fluctuations in water demand. Current studies of tolerance between cultivars and physiological mechanisms involved in plant recovery after drought are insufficient for selection of tolerant cultivars. We evaluated tolerance of different peanut cultivars to water deficit and subsequent rehydration, based on physiological and biochemical status. Gas exchange, photosynthetic pigments, Fv/Fm, MDA, H2O2 and antioxidant enzyme activity were analysed. Drought stress and rehydration triggered distinct changes in pigments, Fv/Fm, gas exchange, and H2O2 across genotypes, with increased MDA in all cultivars under stress. Based on multivariate analysis, 'IAC Sempre Verde' was identified as most drought sensitive, while 'IAC OL3', 'IAC 503', and 'IAC OL6' exhibited variations in physiological responses and antioxidant activity correlated to their respective tolerance levels. Notably, 'IAC OL3' had higher WUE and enhanced enzymatic defence and was classified as the most drought tolerant in this context. The above findings suggest that antioxidant metabolism is a important factor for plant recovery post-rehydration. Our study provides insights into antioxidant and physiological responses of peanut cultivars, which can support breeding programs for selection of drought-tolerant genotypes. Future field studies should be conducted for a better understanding of tolerance of these cultivars, particularly through correlation of these data with crop yield impact.

A hybrid hw-rfr forecasting model: case of moroccan pharmaceutical sector

Sales forecasting is an essential element of effective supply chain management, particularly in the pharmaceutical sector where continuous availability of drugs is crucial. This article examines sales forecasts for fluoxetine, an antidepressant available on the Moroccan market under six trade names and 14 different forms. The main objective of this study is to compare the effectiveness of four forecasting models, namely Prophet Facebook, ARIMA, GRU and Holt-Winters through their accuracy, and to propose a hybrid model that will contribute to improving the accuracy of demand forecasts. Each model was applied individually to predict future sales, and evaluated using MAPE, MAE and RMSE metrics. Next, a hybrid model, integrating Holt-Winters and Random Forest Regressor methods, was developed to leverage the robustness of traditional models while improving predictive performance through machine learning techniques. The results of the study show that traditional models, such as ARIMA and Holt-Winters, offer a solid basis for sales forecasting. However, the hybrid HW-RFR (Holt-Winters Random Forest Regressor) model stands out for a significant improvement in forecast accuracy, demonstrating great robustness to fluctuations in fluoxetine demand. This article highlights the potential of hybrid models for forecasting pharmaceutical sales. The improved forecast accuracy achieved with the HW-RFR model provides stakeholders with more reliable information, enabling them to make informed decisions to optimize pharmaceutical supply chain management

Predictive Energy Demand and Optimization in Metro Systems Using AI and IoT Technologies

Introduction: With the rapid urbanization of modern cities, metro systems have become indispensable for efficient mobility. However, the increasing demand for public transportation has led to rising energy consumption, posing significant challenges for operational sustainability. Current energy management strategies in metro networks rely on static models and centralized systems, which often fail to adapt to real-time fluctuations in energy demand, leading to inefficiencies and wasted resources. Methods: This paper proposes an innovative approach to optimizing energy demand in metro systems by integrating Artificial Intelligence (AI) and the Internet of Things (IoT). By leveraging real-time data collected from IoT sensors deployed throughout the metro network, we apply machine learning algorithms such as Random Forests and Neural Networks to dynamically predict energy demand. These predictions enable metro operators to adjust energy consumption in real-time, thus improving overall system efficiency and reducing operational waste. Our approach was validated using data from the Parisian metro system through extensive simulations. Results: The results of simulations demonstrate significant improvements in energy efficiency. Optimized energy demand management led to a reduction in wasted energy during metro operations, particularly through the utilization of regenerative braking systems. Conclusions: Our findings suggest that integrating AI and IoT technologies into metro systems significantly improves energy efficiency by enabling dynamic energy demand prediction and real-time adjustment of energy consumption. The proposed system is scalable and adaptable, making it suitable for application in metro networks globally, thereby enhancing energy efficiency and supporting sustainable transport initiatives.

Renewable DG allocation in a pre-scheduled grid power supply for different types of loads with and without distribution network reconfiguration

ABSTRACT This paper suggests a technique for dispatchable and non-dispatchable renewable distributed generation (DG) unit deployment under a constant quantity of power supply from the grid. It also accounts for seasonal fluctuations in load demand, solar and wind power generation. To analyse the scenario, the typical load flow approach is changed by inserting PQVδ and zero bus into the system. Three renewable DGs are used based on solar, wind and biomass to meet the load demand. Along with meeting the load demand, DG is operating to enhance the voltage profile and reduce the power loss. The research is conducted on a 33 and 69 bus distribution networks (DNRs) for constant power, constant current, constant impedance, composite and exponential or mixed load types. To reduce the loss even further, the networks are reconfigured in the presence of three different types of DGs. The suggested solution reduces loss by up to 54 % and allows for energy savings of up to 2.5546 × 10 7 kWh and 2.0719 × 10 7 kWh in 33 bus and 69 bus DNRs, respectively. Economic effectiveness is also analysed to indicate the profit earned from the DG installation. The uncertainty of solar and wind energy is also explored in this paper to see how the network responds to such a scenario.

AI-Driven Process Design for Closed-Loop Manufacturing

This study investigates the potential of artificial intelligence (AI) in enhancing closed-loop manufacturing (CLM) systems, which emphasize sustainability through recycling, reuse, and optimized resource utilization. CLM faces significant challenges in minimizing waste, improving operational efficiency, and optimizing resource usage. Although AI holds promise in addressing these challenges, its application in CLM systems, especially in dynamic decision-making and resource optimization, requires further exploration. This research aims to explore how AI technologies can improve material flows, reduce waste, and enhance decision-making to achieve both sustainability and operational efficiency in manufacturing systems. To understand the impact of AI on CLM, the study conducted a comprehensive review of case studies and literature across various industries. It focused on AI techniques, such as machine learning (ML) and reinforcement learning (RL), and their roles in optimizing resource usage, refining recycling processes, and improving production schedules. Statistical methods, including regression and sensitivity analysis, were employed to evaluate the effectiveness of AI-driven models in enhancing closed-loop manufacturing performance. The review revealed promising results, highlighting AI's ability to optimize resource consumption and waste management strategies across diverse manufacturing environments. The study’s findings demonstrated significant improvements in resource efficiency, waste reduction, and cost savings across multiple industries. AI-driven models optimized material consumption, resulting in reductions of up to 12% in raw material use. Additionally, AI-enhanced recycling and waste management strategies led to a 25% increase in material recovery, while operational costs were reduced by up to 15%. AI models also displayed a strong ability to adapt to fluctuations in demand and production conditions, ensuring sustained operational efficiency even amid dynamic market changes. These outcomes highlight AI’s transformative potential in achieving sustainable manufacturing practices and advancing circular economy goals.

Analysis on Inventory Management System in Swiggy Instamart Private Limited Bangalore North

This study investigates Swiggy Instamart Private Limited's inventory management system. Swiggy Instamart Private Limited is an online grocery delivery service that uses the Swiggy platform. Effective inventory management is essential for satisfying customer needs, reducing operating expenses, and upholding good service standards as e-commerce in the grocery industry grows. To optimize stock levels, forecast demand, and automate replenishment procedures, Swiggy Instamart uses a centralized, tech- driven inventory management system that incorporates cutting-edge technologies like artificial intelligence (AI), machine learning (ML), and data analytics. Real-time data tracking, warehouse management, vendor interactions, and the difficulties of managing inventory and demand fluctuation are all included in the study. Swiggy Instamart aims to decrease waste, guarantee product availability, and enhance delivery efficiency by putting these principles into practice. ` Keywords: Inventory Management, Swiggy Instamart, Supply Chain Management, Private Limited, Inventory Control, Stock Management.

MODELLING CHANGES IN THE PRODUCTION COMPONENT OF FLEXIBILITY OF MANUFACTURING SMALL AND MEDIUM-SIZED MACHINE-BUILDING ENTERPRISES UNDER CONDITIONS OF SHARP FLUCTUATIONS IN MARKET SUPPLY AND DEMAND

The paper proposes an enlarged model in the form of an integral functional of enterprise flexibility, which describes the stability of small and medium-sized machine-building enterprises in the conditions of stress factor - fluctuations in supply and demand. This model allows the reduction of the reaction time of enterprise management to negative external factors and to predict the need for management decisions on resource allocation and innovation to minimize the time to restore the stability of machine-building enterprise functioning in new production realities under changes in the market. Based on the analysis of the model, its decomposition has been carried out, and four main factors related directly to the production process that affect the optimization of the proposed function have been identified. Special attention was paid to the detailed analysis of each factor: productivity, product quality, efficient inventory and resource management, and a new important factor in modern conditions, such as responsibility to standards and regulatory documents. This analysis allowed us to propose mathematical models of the influence of each of the factors on the overall flexibility functionality. The proposed mathematical models were analyzed using the benefits-based planning method. This made it possible to analyze how a small and medium-sized engineering enterprise would react to a stress situation associated with changes in market demand, to optimize the overall integral function of small and medium-sized machine-building enterprises flexibility over time, and to propose variants of anti-stress solutions to offset the impact of stress factors on the sustainability of the enterprise over time.

THEORETICAL ASPECTS OF THE FORMATION OF COMPETITIVE ADVANTAGES OF THE ENTERPRISE

The article provides a comprehensive analysis of the theoretical aspects of forming competitive advantages in a company, emphasizing various concepts and approaches. It highlights that competitive advantages play a crucial role in ensuring the company's stability in the market amid global competition and rapid changes in the external environment. Key approaches to defining competitive advantages are examined, including the resource-based theory, which focuses on the effective use of material and intangible assets to create a unique market position. The competitive strategy theory, which emphasizes the development and implementation of differentiation and cost leadership strategies that allow companies to achieve sustainable competitive positions, is also discussed. Special attention is given to the dynamic capabilities theory, explaining how companies can adapt to ever-changing external conditions by enhancing their internal capabilities, innovations, and technologies. In particular, the significance of innovation and technological development as key factors in creating competitive advantages is explored, since new technologies and products enable companies to not only maintain but also strengthen their market positions. The article also highlights the role of human capital, as effective personnel management, skills development, and motivation for innovation are essential elements of a successful competitive strategy. Additionally, the importance of a company's resilience to external factors, such as economic crises, regulatory changes, or demand fluctuations, is justified as a necessary condition for long-term retention of competitive positions in the market. This resilience ensures that a company can adapt and respond to changes swiftly, thereby protecting its market share and reputation. The research concludes that to achieve long-lasting competitive advantages, companies must actively employ innovations, develop dynamic capabilities, and effectively manage their resources, emphasizing the importance of agile leadership and strategic foresight in anticipating market trends and potential disruptions.

Enhancing scalable reconfigurable manufacturing systems through robust optimisation: energy efficiency and cost minimisation under uncertainty

Reconfigurable manufacturing systems are dynamic systems designed with scalable and flexible production capabilities to address changing market demands. This paper presents a novel multi-objective integer programming model aimed at optimising the configuration and capacity scalability of reconfigurable machine tools in uncertain environments. The model focuses on minimising three key objectives: total energy consumption, unused capacity, and total cost. It incorporates critical manufacturing constraints such as peak power thresholds and limited tool availability. To effectively manage uncertainty, particularly in demand fluctuations, a scenario-based robust optimisation approach is applied, striking a balance between solution robustness and model adaptability. A comprehensive case study demonstrates the model's effectiveness, comparing deterministic and uncertain solutions. Additionally, sensitivity analyses are performed on parameters such as peak power thresholds, risk coefficients, and infeasibility weights, highlighting their impact on system performance. The results provide insights into the efficient design and operation of scalable reconfigurable manufacturing systems under uncertainty, with recommendations for future research directions.

Assessing generalizability in deep reinforcement learning based assembly: a comprehensive review

AbstractThe increasing complexity of production environments and fluctuations in short-term demand requires adaptive and robust processes. To cope with the inherent challenges, deep reinforcement learning algorithms were widely deployed in assembly processes in recent years, due to their generalization capabilities, which ensure enhanced usability and flexibility for diverse assembly applications. Despite a growing number of scientific papers investigating deep learning based assembly and associated generalization capabilities, a comprehensive review and assessment of potential generalization capabilities has yet to be conducted. This paper aims to provide researchers and practitioners with an evaluation of key influences which contribute to a successful generalization of deep reinforcement learning within assembly processes, thereby facilitating further implementations. Our findings reveal that current research primarily focuses on examining generalization in insertion and sequence planning assembly tasks. Furthermore, we identified many context-specific approaches to enhance generalization, as well as remaining research challenges and gaps. The results comprise four overarching factors, containing several specific approaches that increase generalizability in assembly processes. However, future research must focus on verifying the context independence of these factors.

Enhancing Power Quality in Standalone Microgrids Powered by Wind and Battery Systems Using HO Algorithm Based Super Twisting Sliding Mode Controllers

This paper addresses the challenge of enhancing power quality in a standalone microgrid powered by wind and battery systems. Fluctuations in wind power generation and unpredictable electricity demand significantly impact power quality. To mitigate these issues, a control strategy utilizing Super Twisting Sliding Mode (STSM) controllers tuned by the Hippopotamus Optimization Algorithm (HOA) is proposed. The HOA algorithm efficiently determines optimal STSM controller parameters, leading to improved system performance and stability. A comparative study was conducted against PI, Fuzzy Logic controllers, and other metaheuristic optimization algorithms (PSO, GWO, WOA). Simulation results, obtained using MATLAB/Simulink, demonstrate the superior performance of the proposed methodology. Specifically, during a simulated abrupt load change, the system exhibited rapid recovery with frequency reaching equilibrium, significantly faster than PI and Fuzzy Logic controllers. Moreover, the DC link voltage remained stable with fluctuations of only 2%, while the three-phase RMS voltages at the Point of Load Bus (PLB) maintained balanced and stable values. These results confirm the enhanced power quality and robust operation achieved with the proposed HOA-tuned STSM control strategy, outperforming other tested methods. The methodology effectively manages both the energy management system and improves power quality in standalone wind and battery-powered microgrids.

Emergency call utilization over a 10-years period: an observational study in Region Zealand, Denmark, 2013–2022

BackgroundImproving prehospital emergency care requires a comprehensive understanding of the efficiency of emergency medical services and demand fluctuations. The medical emergency call is the primary contact between citizens and the emergency medical dispatch center, serving as the gateway to accessing emergency assistance. This study aimed to characterize the emergency call population and analyze the development of emergency call utilization in Region Zealand in Denmark during a 10-years period.MethodsThis was an observational register-based study of administrative data from the emergency medical dispatch center in Region Zealand. Data was collected from 1 January 2013 to 31 December 2022. All unique emergency calls from residents to the emergency number “1-1-2” were included. Descriptive analyses were used to characterize the study population. Poisson regression models were used to calculate ratio estimates for the association between years and hospital catchment areas, using the incidence rate of emergency calls as outcome measure.ResultsA total of 641,457 emergency calls were included. A significant increase in the total number of emergency calls was found, with an increase from 58,454 annual calls to 80,819 calls over the study period. The incidence rate per 1000 residents per year increased from 71.1 to 95.2, a 35% increase. The southern part of the region had significantly more emergency calls per 1000 residents per year during the study period compared to the eastern part of the region (IRR 1.70). Demographically, males comprised 52.3% of cases, and patients aged 65 and older represented 48.2% of calls. Emergency calls were “Emergency level A” in 45.5% and “Emergency level B” in 39.1%. In 22.3% of cases, the emergency call was categorized as “Unclear problem.” The most frequent categories were “chest pain” (12.7%), “impaired consciousness” (9.6%), “breathing difficulties” (8.8%), “accidents” (7.9%), and “minor injuries” (7.6%).ConclusionsThe study revealed a significant increase in emergency calls, both in absolute numbers and per 1000 residents per year, indicating growing demand for emergency care, along with a surge in activity at the region's dispatch center. Regional disparities underscores the potential necessity for tailored developmental approaches over time.

A Study of Mathematical Problems as Control Agents in the Industrial Sector

PD Utama Jaya Plasindo, a company specializing in plastic pellet processing, faces several production challenges, such as fluctuating demand, raw material shortages, machinery downtime, and labor management. To address these issues and improve production planning, the company uses MATLAB's linprog tool, which is designed for Linear Programming (LP) and aims to maximize profits. The company's profit is calculated using the formula: Profit = 37A + 46B + 38C + 46D. This calculation takes into account seven constraints related to raw materials, machinery usage, labor hours, and product demand. With this method, PD Utama Jaya Plasindo earns a daily profit of Rp. 837,600 from its plastic buckle products, totaling Rp. 16,752,000 over 20 working days in a month. These profit figures, based on clear objectives and constraints, help ensure that production goals are met and facilitate the monitoring and optimization of production capacity. By leveraging MATLAB's linprog, the company can better manage demand fluctuations and optimize the use of raw materials, machinery, and labor.

A study on inventory control system for a supply chain using Markov decision processes

This paper examines the application of Markov Decision Processes (MDPs) for controlling supply chain inventories. MDPs effectively simulate decision-making problems related to uncertainty, facilitating the determination of optimal inventory policies. The MDP framework addresses various inventory management challenges, including demand fluctuations, lead times, and holding costs. The study investigates the modeling of inventory management as a Markov decision process, detailing the states, actions, and transitions within the MDP model, along with their respective advantages and disadvantages. The research employs policy and value iteration techniques to evaluate and optimize inventory management policies. The paper assesses the proposed MDP-based inventory control system through simulations that utilize supply chain data, aiming to identify optimal policies using the MDP model. A comparative analysis of the MDP approach against conventional inventory management methods is conducted to demonstrate its efficacy in reducing costs and enhancing service levels. Additionally, the paper proposes the incorporation of multiple commodities, multi-echelon supply chains, and perishability considerations into the MDP model. The findings indicate that MDPs facilitate improved optimization of inventory policies, cost reductions, and enhanced customer service, thereby emphasizing the significance of computational complexity and the necessity for accurate data. This research provides a comprehensive investigation into the role of MDPs within inventory control systems, contributing valuable insights to the field of supply chain management. Ultimately, this study lays the groundwork for advancements in MDP-based inventory control methodologies.

ENERGY MANAGEMENT IN HYBRID PV-WIND-BATTERY STORAGE-BASED MICROGRID USING MONTE CARLO OPTIMIZATION TECHNIQUE

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using three types of Monte Carlo simulation techniques. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing the Monte Carlo technique to extract maximum energy profit using the hybrid microgrid. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.