Minimum Energy Cost Research Articles

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.

Optimizing tilt angle of PV modules for different locations using isotropic and anisotropic models to maximize power output

The optimal integration of Photovoltaic (PV) systems into an electric grid is dependent upon the total output power of the PV system. To optimize the output power of a PV system, the modules must be positioned at an optimal tilt angle (OTA) to maximize the absorption of solar radiations. This research focused on a mathematical model to optimize incident solar radiation. The proposed model is used to determine the OTA and evaluate its impact on the optimum configuration and power output capacity using MATLAB for six cities located in different temperature zones across Pakistan. The isotropic and anisotropic models have been used to calculate the total solar radiations (HT) on a sloped surface. During the summer season, all four selected models present similar findings in terms of the monthly average daily solar radiations on the tilted surface. During the winter season, the anisotropic models performed better than the isotropic models. The anisotropic model achieved a 14.82% energy increase in January and a 0.16% increase in June compared to the isotropic model. We present monthly and annual OTA calculated from the anisotropic model. The OTA has been determined for the HT across slope values ranging from 0° to 90° with a 1° resolution. The monthly OTA using anisotropic model for the Faisalabad, Lahore, Multan, RYK, Islamabad and Karachi ranges from 7° to 54°, 7° to 53°, 6° to 52°, 5° to 52°, 10° to 58° and 1° to 50° and the annual OTA for cities has been calculated to be 30.5°, 30.25°, 29.33°, 28.66°, 33.34° and 25.5° respectively. Utilizing the OTA calculated from the selected model, a PV array with a rated power of 52.200 kW has been used to analyze the system performance. The annual average output power at the monthly OTA results in gains of 8.83%, 9.40%, 9.78%, 9.77%, 9.82%, and 9.91% compared to the annual OTA. This research study is particularly beneficial for researchers and the industry in deploying PV systems across different climatic zones of Pakistan, intending to maximize output power while minimizing energy costs.

A non-dominated sorting based multi-objective neural network algorithm of ethylene glycol hydrogenation reactor in energy reduction

Artificial intelligence has revolutionized various industries, including chemical process optimization. Artificial intelligence (AI) can be applied to various ethylene glycol (EG) production aspects to improve efficiency, quality, and overall process optimization. Process optimization of dimethyl oxalate (DMO) hydrogenation to ethylene glycol (EG) is carried out to minimize/maximize conversion, energy, productivity, bare module cost (CBM), and side product in the presence of pressure and temperature variables. A non-dominated sorting-based multi-objective neural network algorithm (MONNA) is applied to tackle problem optimization for EG production. The results show that the highest productivity, minimum energy cost, side product, and highest conversion are 172 Million RM/year, 0.00600 Million RM/year, 0.0320 kmol/hr, and 99.6%, respectively. The intermediate points in the Pareto Front PF for various three-objective situations provide lower energy costs than the bi-objective function. While bi-objective optimization might seem more straightforward, the intermediate points on the Pareto Front in three-objective optimization can provide lower energy costs due to more effective balancing of trade-offs, richer exploration of the solution space, capturing complex interdependencies, and offering more robust and flexible solutions. Decision makers can use the resulting pareto to decide on the most acceptable alternative according to their preferences. The decision variable plots show that the pressure highly affected the optimal solution with the opposite action. This work’s analysis is predicted to provide insight into optimization literature for energy savings and sustainability and promote commercial growth for the industry. Identifying optimal trade-offs between various objectives, such as energy cost and productivity, can help reduce overall production costs, making the production process more competitive.

Techno-Economic analysis of A Stand-Alone hybrid renewable energy system (Solar/Fuel Cell/Battery) and grid extension for two residential Districts

This study investigates the technical and economic aspects of grid extension and stand-alone hybrid renewable energy systems (HRES) for Loughborough-England and Afyonkarahisar-Türkiye. For the HRES, the primary energy source is solar panels. Fuel cells and batteries are available as auxiliary sources. In addition, electrolysers and batteries are considered to store excess energy for a sustainable system. All calculations of the HRES are performed by HOMER Pro® software. The results are compared considering the cost of energy and net present cost. The cost of energy is 95.30 and 61.39 $/MWh, respectively. The net present cost of HRES is $298,644 and $1,478,892, respectively. The grid extension is also evaluated for both locations. The minimum cost of energy is calculated at 94.31 and 90.65 $/MWh, respectively. The minimum net present cost of the grid extension is $275,750 and $1,943,175, respectively. The break-even distance is 3.59 km for Loughborough-England. However, it is not available for Afyonkarahisar-Türkiye.

Evaluating Energy Efficiency and Optimal Positioning of Industrial Robots in Sustainable Manufacturing

Optimizing the energy efficiency of robotic workstations is a key aspect of industrial automation. This study focuses on the analysis of the relationship between the position of the robot base and its energy consumption and time aspects. A number of 6-axis robots, including the ABB IRB 120 robot, were investigated in this research by combining measurements and simulations using the energy consumption measurement module in the ABB RobotStudio 2024.1.1 environment. The objective of this study was to develop an energy consumption model that can identify the optimal robot positions to minimize energy costs and time losses. The results suggest that the strategic positioning of the robot has a significant impact on its performance and efficiency. These results demonstrate that the ideal working distance of the robots is approximately 50% of its maximum range, and displacements along the X and Z axes affect the energy and time consumption. These findings suggest the existence of a trade-off between time and energy efficiency, providing a basis for further research into the optimization of robotic systems. Thus, this work offers new perspectives for the design of efficient robotic workstations for cross-sensory applications.

PLANNING OF TRANSPORTATION OF RHEOLOGICAL COMPLEX OILS FROM VARIOUS FIELDS THROUGH AN EXTENSIVE PIPELINE SYSTEM, TAKING INTO ACCOUNT ENSURING THE SPECIFIED QUALITY OF THE DELIVERED OIL

The article discusses the problem of operating an oil pipeline system in the presence of a significant number of suppliers of rheologically complex oils with different sulfur content and a large number of oil consumers with different requirements for oil quality in terms of sulfur and other parameters. The problem of determining optimal cargo flows in such a system is discussed. A method is proposed for determining optimal cargo flows in a branched oil pipeline system, providing a global optimum in terms of minimizing energy costs for pumping. The article provides an algorithm for implementing the method using a conditional example.

Techno economics and energy dynamics of a solar powered smart charging infrastructure for electric vehicles with advanced IoT based monitoring and RFID based security

The widespread adoption of electrical vehicles (EVs) has led to an increased demand for charging stations which required an adequate charging infrastructure. However, this challenge brings substantial financial and ecological implications for the power grid and other major stakeholders such as EV users, system operators and policymakers. To resolve this issue, this work proposes a smart EV charging infrastructure that provides 1) effective load management of EVs, 2) real-time monitoring of charging slots at a charging station, 3) smart security system, and 4) integration of renewable energy resources (RERs) into charging stations. This is achieved by setting out how to incorporate photovoltaic (PV) system into charging station infrastructure while suggesting effective EV load management with optimal energy utilization and minimal energy costs. Also, the annual power production of PV systems and power sharing between different power resources has been investigated to enable effective decisions about EV scheduling. The proposed infrastructure also offers an internet-of-things (IoT)-based monitoring of fast/slow charging units with real-time slot tracking to the users and charging station operators. The security aspect of the charging station is covered by a sensor-driven radio frequency identification (RFID) system. Experimental results that the proposed system generates 1,055,326 kWh/year, reducing grid reliance by 50% (to 335,775 kWh) and lowering CO2 emissions from 425,326 kg to 212,210 kg. Moreover, energy costs drop from $0.30 to $0.13 per kWh and 270,567 kWh/year energy is sold back to the grid. The proposed system yields a net present revenue of $1,020,000 which demonstrates significant economic and environmental benefits.

Creation and research of numerical models of cyclones with inclined and horizontal inlets

The article considers the challenges of using cyclone devices for cleaning gas emissions from solid fuel combustion in power engineering and industry. With the increasing energy consumption and generation, including the use of coal, the requirements for the efficiency of cleaning emissions from small solid particles of classes PM10 and PM2.5 are also rising. One of the key tasks is to maintain high cleaning efficiency while minimizing energy costs, which is also important for reducing the environmental impact of generation and industrial production. The article considers the design features of currently used cyclones, as well as existing approaches to their classification. The main focus is on modern areas of research related to numerical modeling based on CFD (Computational fluid dynamics) aimed at optimizing the design of separators. It is shown that most studies are carried out for cyclone operating conditions in production cycles, rather than in emission cleaning systems that operate at low suspended matter loads. Using the example of creating numerical models of cyclones with inclined and horizontal inlet pipes (CN-11, SK-CN-34), the article discusses certain features of geometry creation in the SpaceClaim Direct Modeler (SCDM) environment, which ensure the correctness of grid generation and calculations in the future. The results of numerical modeling carried out using the ANSYS Fluent software are presented. The turbulence model (RANS, Reynolds Averaged Navier – Stokes), methods for closing the Navier – Stokes equations (k-ε model with near-wall functions), and methods for calculating the dispersed part of the flow (Euler – Lagrange, DPM) are selected based on the conditions relevant to the systems for cleaning emissions from coal generation. The results indicate that, with the same pressure drop, the efficiency of settling suspended matter with a concentration of 100 mg/m3 or less in an apparatus with an inlet angle of 11° to the horizontal can be higher than that of a cyclone with a horizontal inlet.

Mathematical Modeling and Statistical Analysis of Novel Swarm Computing Based Charging Management Framework for Electric Vehicles

This paper presents a novel swarm computing-based charging management framework for electric vehicles (EVs), designed to optimize charging operations and enhance the grid's capacity to handle EV load without compromising system reliability. We introduce a mathematical model that incorporates swarm intelligence algorithms, particularly Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), to efficiently manage the charging schedules of EVs across multiple charging stations. The framework aims to minimize energy costs, balance load during peak hours, and reduce charging time by distributing the charging load in a more organized manner.A statistical analysis is conducted to validate the effectiveness of the proposed model. Using real-time data and simulation, the study evaluates various scenarios to analyze the impact of the swarm-based approach on overall grid stability and EV charging efficiency. The results indicate significant improvements in grid management, with a reduction in peak load demands and enhanced utilization of renewable energy sources. The analysis also demonstrates the scalability and adaptability of the model to different urban settings and EV penetration rates.The paper concludes with a discussion on the potential integration of this model into existing grid systems and its implications for future smart grid and EV infrastructure developments. The proposed framework not only contributes to more sustainable energy management practices but also opens new avenues for research in the domain of intelligent transportation systems and smart grid integration.

Application of supervised and unsupervised learning for enhancing energy efficiency and thermal comfort in air conditioning scheduling under uncertain and dynamic environments

Air conditioning (AC) plays a major role in building energy management because it generally requires a large amount of energy to maintain indoor thermal comfort. The main objective of this study is to develop a novel method for scheduling AC operations to minimize energy costs and ensure the thermal comfort of occupants under uncertainty. The key challenge is the uncertainty and variability in time-series data and their serial dependence in determining AC operation. To address this challenge, we propose an optimization-informed learning approach that integrates unsupervised and supervised learning techniques with a stochastic optimization model. This method derives energy-efficient and thermal comfort-aware AC operation schedules through a comprehensive interpretation of uncertainties and variabilities in time-series data. Numerical experimental results demonstrate that the proposed approach can reduce energy costs by up to 15.6% and decrease thermal comfort violations by up to 63.6% compared to the Deep Q-learning method, while also reducing energy costs by 1.8% and decreasing thermal comfort violations by 37.5% compared to the forecast data-driven AC scheduling method.

A trustworthy reinforcement learning framework for autonomous control of a large-scale complex heating system: Simulation and field implementation

Traditional control approaches heavily rely on hard-coded expert knowledge, complicating the development of optimal control solutions as system complexity increases. Deep Reinforcement Learning (DRL) offers a self-learning control solution, proving advantageous in scenarios where crafting expert-based solutions becomes intricate. This study investigates the potential of DRL for supervisory control in a unique and complex heating system within a large-scale university building. The DRL framework aims to minimize energy costs while ensuring occupant comfort. However, the trial-and-error learning approach of DRL raises concerns about the trustworthiness of executed actions, hindering practical implementation. To address this, the study incorporates action masking, enabling the integration of hard constraints into DRL to enhance user trust. Maskable Proximal Policy Optimization (MPPO) is evaluated alongside standard Proximal Policy Optimization (PPO) and Soft Actor–Critic (SAC). Simulation results reveal that MPPO achieves comparable energy savings (8% relative to the baseline control) with fewer comfort violations than other methods. Therefore, it is selected among the candidate algorithms and experimentally implemented in the university building over one week. Experimental findings demonstrate that MPPO reduces energy costs while maintaining occupant comfort, resulting in a 36% saving compared to a historical day with similar weather conditions. These results underscore the proactive decision-making capability of DRL, establishing its viability for autonomous control in complex energy systems.

Distributed solution of the day-ahead pump and valve scheduling problem for dynamically adaptive water distribution networks with storage

This paper investigates the computation of daily schedules of pumps and boundary valves for the minimization of energy costs in water distribution networks (WDN) with dynamically adaptive configurations. The considered problem combines integer (“on”/“off”) pump control variables, non-convex energy conservation constraints and time-coupling mass conservation constraints. For operational WDNs, the resulting non-convex mixed-integer non-linear program (MINLP) is too large to be solved using available methods. We propose a tailored heuristic solution method based on the Alternating Direction Method of Multipliers which distributes and coordinates the solution of smaller problems corresponding to individual time steps of the original MINLP. The proposed method is applied to a large-scale WDN from the UK. The daily schedule of pumps and boundary valves obtained for the dynamically adaptive network configuration, computed in 12 min, is shown to be at most 6% suboptimal and nearly 5% cheaper than the globally optimal schedule corresponding to the traditional (sectorized) network configuration. The proposed algorithm outperforms alternative off-the-shelf and tailored approaches, providing a scalable method to compute good solutions to the complex day-ahead pump and valve scheduling problem in operational dynamically adaptive WDNs.

Green Order Sorting Problem in Cold Storage Solved by Genetic Algorithm

This study investigates the efficiency of cold storage warehouses and contributes to sustainable supply chain management by integrating eco-friendly practices into storage operations. In facilities for milk and its derivatives, unregulated order handling significantly increases energy consumption due to frequent door openings in the cooler. To address this challenge, we developed a novel mathematical model aimed at optimizing order sequences and minimizing energy costs, addressing a previously unexplored gap in the literature. A genetic algorithm (GA) was employed to solve this model, with careful consideration of carbon emissions generated during the algorithm’s execution. We utilized the Yates notation, an experimental design technique, to systematically optimize the GA’s parameters, ensuring robust and statistically valid results. This methodology enabled a thorough analysis of the factors influencing energy consumption. The findings enhance energy efficiency in cold storage warehouses, leading to reduced carbon dioxide emissions and fostering sustainable practices within supply chain management. Ultimately, this study successfully integrates green practices into cold storage operations, supporting broader sustainability objectives.

Understanding Power Gating Mechanism Based on Workload Classification of Modern Heterogeneous Many-Core Mobile Platform in the Dark Silicon Era

The rapid progress in mobile computing necessitates energy efficient solutions to support substantially diverse and complex workloads. Heterogeneous many core platforms are progressively being adopted in contemporary embedded implementations for high performance at low power cost estimations. These implementations experience diverse workloads that offer drastic opportunities to improve energy efficiency. In this paper, we propose a novel per core power gating (PCPG) approach based on workload classifications (WLC) for drastic energy cost minimization in the dark silicon era. Core of our paradigm is to use an integrated sleep mode management based on workloads classification indicated by the performance counters. A number of real applications benchmark (PARSEC) are adopted as a practical example of diverse workloads, including memory- and CPU-intensive ones. In this paper, these applications are exercised on Samsung Exynos 5422 heterogeneous many core system showing up to 37% to 110% energy efficient when compared with our most recent published work, and ondemand governor, respectively. Furthermore, we illustrate low-complexity and low-cost runtime per core power gating algorithm that consistently maximize IPS/Watt at all state space.

Biomimetic Cooling: Functionalizing Biodegradable Chitosan Films with Saharan Silver Ant Microstructures.

The increasing occurrence of hot summer days causes stress to both humans and animals, particularly in urban areas where temperatures can remain high, even at night. Living nature offers potential solutions that require minimal energy and material costs. For instance, the Saharan silver ant (Cataglyphis bombycina) can endure the desert heat by means of passive radiative cooling induced by its triangular hairs. The objective of this study is to transfer the passive radiative cooling properties of the micro- and nanostructured chitin hairs of the silver ant body to technically usable, biodegradable and bio-based materials. The potential large-scale transfer of radiative cooling properties, for example, onto building exteriors such as house facades, could decrease the need for conventional cooling and, therefore, lower the energy demand. Chitosan, a chemically altered form of chitin, has a range of medical uses but can also be processed into a paper-like film. The procedure consists of dissolving chitosan in diluted acetic acid and uniformly distributing it on a flat surface. A functional structure can then be imprinted onto this film while it is drying. This study reports the successful transfer of the microstructure-based structural colors of a compact disc (CD) onto the film. Similarly, a polyvinyl siloxane imprint of the silver ant body shall make it possible to transfer cooling functionality to technically relevant surfaces. FTIR spectroscopy measurements of the reflectance of flat and structured chitosan films allow for a qualitative assessment of the infrared emissivity. A minor decrease in reflectance in a relevant wavelength range gives an indication that it is feasible to increase the emissivity and, therefore, decrease the surface temperature purely through surface-induced functionalities.

Optimizing polyphenol extraction and UPLC-MS/MS analysis from red clover (Trifolium pratense L.) species using response surface methodology

Polyphenols were extracted from red clover (Trifolium pratense L.) plants following conventional extraction. The combined factors of ethanol concentration (60–80%), extraction time (15–45 min) and extraction temperature (40–80°C) were evaluated. Central composite design software was used to conduct Response Surface Methodology (RSM) modelling and optimize the polyphenol yield from the red clover. Total phenolic content, antioxidant activity and ultra-performance liquid chromatography mass-spectrometry (UPLC-MS/MS) analysis were used to quantify polyphenols. The RSM model identified ethanolic composition having greatest influence (p < 0.05) on the polyphenol yield with extraction yields increasing with percentage ethanol. The optimal extraction conditions were identified as 80% ethanol for 45 minutes at 40°C. When compared with an exhaustive polyphenol extraction method (70% ethanol for 21 h at room temperature), the optimal extraction resulted in a 25.6% increase in TPC yield. UPLC-MS/MS was performed to quantify the individual polyphenols present within the sample, in which biochanin A and formononetin were present in abundance. The outcomes of this modelling and optimization study demonstrate that polyphenols from red clover can be extracted efficiently with minimal environmental and energy cost and thus had the capability to serve as a source of valuable nutraceutical and pharmaceutical products.

Optimization of distributed energy resources planning and battery energy storage management via large-scale multi-objective evolutionary algorithm

This paper investigates the synergistic integration of renewable energy sources and battery energy storage systems to enhance the sustainability, reliability, and flexibility of modern power systems. Addressing a critical gap in distribution networks, particularly regarding the variability of renewable energy, the study aims to minimize energy costs, emission rates, and reliability indices by optimizing the placement and sizing of wind and solar photovoltaic generators alongside battery energy storage systems. An improved large-scale multi-objective evolutionary algorithm with a bi-directional sampling strategy is employed.Two scenarios are considered. In the first scenario, six study cases are analyzed to determine the optimal number, location, and size of distributed generators at peak load demand. The proposed algorithm outperforms existing state-of-the-art methods for small-scale distributed resource allocation. In the second scenario, a multi-period load demand across various seasons is evaluated, introducing new opportunities for battery energy storage systems. The problem is modeled with intertemporal constraints, creating a large-scale optimization challenge. Results demonstrate significant improvements: cost savings of up to 51.67 %, power reductions of up to 76.78 %, and a 99.9 % improvement in voltage deviation. Emission rates decreased by 99.92 % in case 1, 95.19 % in case 2, and 97.38 % in case 3, compared to the base case. The proposed algorithm shows superior convergence and performance in solving both small- and large-scale optimization problems, outperforming recent multi-objective evolutionary algorithms.This study provides a robust framework for optimizing renewable energy integration and battery energy storage, offering a scalable solution to modern power system challenges.

Real-time optimal hierarchy control of HVAC system with maximized ancillary service support in smart buildings: An experimental approach

This article proposes a real-time optimum control algorithm for Heating, Ventilation, and Air Conditioning (HVAC) units of a commercial building aimed to maximize the ancillary power available for frequency regulation services. First, the prediction of energy consumption of air conditioning zones using long short-term memory (LSTM) is achieved, which helps forecast the aggregated regulation capacity bid of the building in the energy markets. Furthermore, to minimize energy costs and thermal discomfort of building dwellers, an optimization algorithm is designed considering the day-ahead electricity tariffs. Moreover, using installed sensors in a lab-scaled hardware prototype, the proposed algorithm is also shown to be capable of interacting with a Building Energy Management System (BEMS) and investigating the electrical and thermal properties of the HVAC unit. The BEMS controller uses an ethernet protocol to interface with the sensors installed at various operational points throughout the HVAC system. The optimal operation of the HVAC system has been executed with the real-time thermal feedback system, which counterbalances the uncertain thermal load or renewable power availability in the building confronted in real-time operation. The simulation results show the performance of the Artificial Neural Network (ANN) based compressor model in deep learning integrated with the other components of the HVAC unit and thermal model of the air-conditioning zone. Finally, the experimental results display the real-time implementation of multiple thermal and electrical conditions within the proposed control scheme of the HVAC unit. This shows the potential of ancillary services capacity through commercial buildings can be maximized with flexible loads and thereby help in power grid support.

Impacts of Plug-in Electric Vehicles and Renewable Energy Source on Unit Commitment Problem using Cat Mouse Based Optimization A lgorithm

Objectives: The main objective of this paper is to solve the Cost Based Unit Commitment (CBIC) problem considering Renewable Energy Sources (RES) and Plug-in Electric vehicles (PEVs). The proposed problem minimizes the total operating of the interconnected system. Methods: A novel and recently developed successful optimization tool of Cat and Mouse Based Optimizer (CMBO) is proposed for optimal solution of Cost Based UC problem. The proposed CMBO approach is having two groups such as Cat group and Mice group for searching the global optimal solution with less computational time. It contains two phases like Cat’s movement towards Mice and Mice escape to a safe place is effectively updating the new position of Cat and Mice to easily reaching the best solutions. The control parameter of CMBO is number of agents is 50, number of variables is 10 and maximum number of iteration is 500. MATLAB 14.0 platform is utilized for the projected problem. Findings: The method tested on standard IEEE-39 bus system (10 generators and 24 hour) with an equivalent PEV and Wind farm. The CMBO approach minimize the total operating cost with various test cases. The outcomes such as UC schedule, Real power output of thermal, wind and PEV units, fuel cost, startup cost and total operating cost of the interconnected system are numerically and graphically reported. The total operating cost is 4.11 % minimized when compared with base case approach and 0.73 % reduced than the other existing method viz. Parallel UCs-RP method. Novelty: The CMBO is recently developed powerful optimizer and parameter free algorithm, so easily reaches the global optimal solutions. The obtained simulated results are compared with other mathematical and intelligent computational approaches for proving the efficiency and performance of the proposed CNBO technique. Keywords: Unit Commitment, Plug­in Electric Vehicles, Renewable Energy Sources, Cost minimization, Cat and Mouse Based Optimizer

Techno-economic analysis of solar, wind and biomass hybrid renewable energy systems in Bhorha village, India

The present study investigates the potential use of Hybrid Renewable Energy Systems (Solar photovoltaic, wind, biomass, and diesel), both with and without the inclusion of battery/supercapacitor storage in the Bhorha village, Bihar, India. A comprehensive assessment of different possible system configurations is conducted using hybrid optimization model for electric renewable (HOMER) software to determine the most economically viable and efficient system for the designated place. The current analysis is focused on six distinct cases in the village community, in view of sufficing the daily energy requirement of 615.625 kWh and a peak demand of 86.47 kW, pertaining to major factors viz. system efficiency, financial viability, and ecological consequences. The primary aim of the research is to elucidate the comparative analysis of energy generation for different proposed designs of hybrid renewable energy systems. Detailed techno-commercial assessments are also carried out to examine the energy production, consumption, and financial impacts of each HRES configuration. The research outcome of this study obtained from HOMER software reveals that the optimized hybrid system comprises 86.7 kW solar photovoltaic, 30 kW wind turbine, 5 kW biogas generator, a 50 kW diesel generator, 280 kWh battery bank with nominal capacity, and 38.8 kW converter to sustain the for energy needs of the nominated place. This system has a minimum cost of energy of 0.309 $/kWh with a net present cost of $854894 along with operating cost 51847 $/year and net carbon dioxide emission of 56728 kg/yr. The research offers useful insights for designers, scholars, and policymakers on the existing design constraints and policies of biomass-based hybrid systems for a safe, sustainable and independent green future for the generations to come.