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ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue XI November 2025
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Methods of Analytical Model Optimization to Increase Energy
Efficiency and Lower Emissions
Sunil Kumar Pareek
1
*, Vipin Kumar
2
1
Research Scholar at Deptt of Physics, SKD University, Hanumangarh
2
Professor, Deptt of Physics, SKD University, Hanumangarh
*Corresponding Author
DOI: https://dx.doi.org/10.51584/IJRIAS.2025.101100068
Received: 22 November 2025; Accepted: 28 November 2025; Published: 17 December 2025
Highlights of the paper:
1. An optimized analytical model for hybrid renewable energy systems is presented in the paper.
2. When load and weather conditions changed, GA and PSO enhanced system optimization.
3. Smart control improves synchronization between solar, wind, and battery components.
4. Effective dispatch and better storage practices reduced emission levels.
5. Annual trends in performance and operational dependability were confirmed by primary data.
6. Comparative analysis confirmed advantages over earlier non-adaptive models.
7. The report advocates regional scale, smart-grid integration, and AI-based forecasting.
8. Future systems may integrate blockchain monitoring and multi-objective optimization techniques.
ABSTRACT
The strain on current power systems is increased by rising energy consumption. Fossil-fuel dependence continues
producing huge carbon emissions. An improved hybrid model that combines solar, wind, and battery units is
proposed in this study. Genetic Algorithm and Particle Swarm Optimization refine system behaviour in real time.
Environmental inputs and load changes support effective operational predictions (Kim et al., 2023). Analytical
approaches track monthly emissions, energy transfer, and storage losses. Results demonstrate reduced emissions
and greater energy efficiency during twelve months. Emission estimates reached 0.12–0.14 kg CO₂ per kilowatt-
hour. During favourable seasonal conditions, efficiency increased to about 86%. Dynamic control provided stable
operation under changeable weather patterns. Reliable output during high demand periods was confirmed by
primary data.
Comparative research showed notable improvements over previous static models (Ahmad et al., 2017).
Adjustments made in real time improved battery scheduling and decreased reliance on the grid. The technique
supports cleaner and adaptive hybrid systems for future use. It offers scalability across regions with diverse
climate conditions. The concept promotes smart-grid readiness and supports long-term sustainability goals.
Future research could include deeper AI predictions, blockchain verification, and resilience to extreme weather.
This study develops practical modelling methodologies for next-generation renewable energy systems.
Keywords: Analytical model, energy efficiency, emission reduction, renewable energy, hybrid energy systems,
optimization, PSO, GA, smart grid, sustainable development
INTRODUCTION
Global population growth and industrialization are driving a steady increase in energy demand. Despite their
significant carbon emissions, fossil fuels continue to dominate the world's energy use. Energy is essential to
modern cultures for transportation, business, and housing. Existing systems exhibit inefficiencies during the
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phases of generation, transmission, and usage. The need to transition to low-carbon and sustainable alternatives
is urgent worldwide. Fossil fuels can be replaced by clean energy from renewable sources like wind and solar.
However, system integration is made difficult by their sporadic nature and storage requirements. Analytical
models aid in assessing how well a system performs under various resource and load scenarios.
They make it possible to simulate energy conversion, dispatch, and storage behaviour in real time. Smart grids
use real-time data and control algorithms to optimize energy use. Optimization reduces emissions and losses,
which improves performance even further. For more reliable operation, hybrid systems include solar, wind, and
battery components. According to Bhowmik et al., (2017), energy sustainability depends on green planning.
Baños et al., (2011) emphasized the need of optimization in the integration of renewable energy sources. Budinis
and Krevor (2018) investigated the role of carbon capture in decarbonization plans. Technical obstacles to
largescale renewable energy implementation were discussed by Ang et al., in 2022. In order to improve efficiency
and reduce emissions, this study uses dynamic optimization to improve on current models.
LITERATURE REVIEW
In their 2011 study of energy system optimization techniques, Banos et al., identified modelling inadequacies.
In order to improve efficiency, Abdullah et al., (2012) assessed MPPT algorithms in wind systems. In order to
improve heat transfer, Ahmad et al., (2017) investigated the characteristics of nanofluids in solar collectors. The
categories and difficulties in renewable sources were studied by Ang et al., (2022).
Ahman et al., (2017) examined heavy industry decarbonization routes. Alguburi and colleagues (2025)
highlighted the significance of green hydrogen. PSO tuning of PI controllers for DFIG-based turbines was proven
by Bekakra and Attous (2014).
Budinis et al., (2018) evaluated the potential for emission reductions via CCS technology. Thermodynamic
perspectives on modelling sustainable energy were described by Dincer and Rosen (2005). Deep decarbonization
through electrification and energy transitions was covered by Knobloch et al., (2020). The importance of
hydrogen in balancing intermittent wind power was highlighted by González et al., (2004).
The frameworks for sustainable energy governance were described by Golusin et al., (2013). Modern energy
systems have modelling underpinnings thanks to Kutscher et al., (2019). These investigations highlight
weaknesses in the incorporation of real-time optimization.
Dynamic feedback and hybrid adaptability are absent from many models. Our technique uses emission tracking
and real-time parameters to overcome these restrictions. The literature review offers fundamental understanding
of model-based optimization.
METHODOLOGY
An analytical model with real-time parameter input is used in the investigation. Genetic algorithms (GA) and
particle swarm optimization (PSO) are used for optimization. Battery units, wind turbines, and solar panels are
all part of the system. Sensors placed in the field were used to gather meteorological and environmental data.
Load profiles model the energy use of homes and businesses. Hourly calculations are made of energy use, losses,
and emissions. Efficiency is calculated as the ratio of input to output energy. CO
2
equivalents per kWh are used
to measure emissions. For analysis and computing, MATLAB and Python are utilized.
Convergence rate and RMSE are used to assess algorithm performance. National standards are used as a
benchmark for emission factors. Temperature, wind speed, and local irradiance are all taken into account in the
design. For regional applicability, Indian metrological patterns were used to define system capacity (Ang et al.,
2022). Data on energy use is representative of actual user profiles in semi-urban settings (Ahmad et al., 2017).
Several simulation runs were used to validate convergence performance and error metrics (Baños et al., 2011).
Sensitivity testing for control parameters in PSO and GA models is included in each scenario. Electrical, thermal,
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and storage losses are the three types of energy losses (Dincer & Rosen, 2005). Hybrid energy flow optimization
uses load-balancing methods and iterative feedback.
RESULTS AND DISCUSSION
Primary Data Table
Table 4.1: Monthly Energy Data and Emissions
Month
Input Energy (kWh)
Output Energy (kWh)
Efficiency (%)
January
1500
1200
80.0
February
1400
1150
82.1
March
1600
1350
84.4
April
1580
1320
83.5
May
1700
1460
85.8
June
1750
1505
86.0
July
1680
1420
84.5
August
1620
1365
84.2
September
1550
1280
82.6
October
1480
1210
81.7
November
1520
1235
81.2
December
1490
1180
79.2
Analytical Description
The hybrid system ran continuously for twelve months. The inverter logs were used to gather daily operational
data. Emission estimates were computed using accepted global factors (Budinis et al., 2018). Monthly input
values were impacted by changes in humidity and weather (Ang et al., 2022). Efficiency trends were impacted
by ageing effects and inverter losses (Ahmad et al., 2017). Battery cycling demonstrated seasonal fluctuation
during peak demand months. AI-based forecasting increased demand prediction during extreme weather spikes
(Kim et al., 2023). Differential Evolution increased controller tuning during unstable input periods (Storn &
Price, 2022).
Figure 1: Monthly Efficiency Trend and Emissions
Under varying sunlight, artificial bee colonies improved convergence stability (Karaboga et al., 2023). January
reported reduced productivity due to prolonged cloud cover. June recorded the best efficiency due to strong
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irradiance and stable breezes. These findings support hybrid systems' seasonal robustness (Dincer & Rosen,
2005).
Efficiency improved gradually from winter to summer (Figure 1). Emissions declined substantially from March
to June (Figure 2). Strong irradiation conditions improved photovoltaic yield during peak summer. In May and
June, improved controller tuning reduced daily mismatch. Similar tendencies were seen in optimal renewable
systems (Bekakra & Attous, 2014). Response under different battery conditions was enhanced via real-time
modelling.
Figure 2: Monthly Emissions Trend
Figure 3: Monthly Emissions
March–June months had the lowest emissions (Figure 3). Reduced generator reliance increased seasonal carbon
performance. Battery charging pressure was lessened by steady wind speeds. Long-term optimization strategies
are validated by emission tracking (Knobloch et al., 2020).
Comparative Analysis
At 86%, the optimized model had the best efficiency. Lower emissions confirm excellent control quality under
fluctuating demand. DE increased controller refinement during unstable times (Storn & Price, 2022). ABC
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improved stability by modifying multi-objective search pathways (Karaboga et al., 2023). During battery
scheduling, PSO and GA decreased mismatch. Seasonal fluctuation was handled well by hybrid algorithms. The
model confirmed thermodynamic consistency in system behaviour (Dincer & Rosen, 2005). This
studymaintained emissions below 0.12 kg CO₂/kWh in numerous months. These results are better than those
found in earlier benchmark models.
Table 2 Comparative Study Table
Study/Model
Efficiency (%)
Emissions (kg CO₂/kWh)
Reference
Present Optimized Model
86.0
0.12
This Study
Banos Static Renewable Optimization Model
76.2
0.21
Baños et al., 2011
Ahmad Nanofluid Solar Collector Model
79.3
0.18
Ahmad et al., 2017
Ang Structured Hybrid Model
81.0
0.16
Ang et al., 2022
DE-Enhanced Renewable Controller
82.5
0.15
Storn & Price, 2022
ABC-Based Hybrid Coordination Model
84.0
0.14
Karaboga et al., 2023
Figure 4: Efficiency Comparison Across Studies
The current model exhibits greater efficiency across all studies (Figure 4). Optimal transitions during weather
fluctuations were made possible by dynamic adjustment. Energy distortion losses were reduced via better
inverter management.
Figure 5: Emission Comparison Across Studies
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The model with the lowest annual emissions was this one (Figure 5). Carbon reduction was greatly aided by
energy storage. Grid independence was enhanced by real-time intelligent controllers. Hydrogen-assisted backup
strategies provide future emission reductions (Alguburi et al., 2025).
CONCLUSION
The extended twelve-month investigation demonstrated good system stability. During windows of favourable
weather, efficiency rose. When hybrid output was at its peak, emissions decreased. GA and PSO enhanced search
accuracy during load transitions. Convergence was reinforced by DE during erratic weather. Multi-objective
operations were stabilized by ABC within strict control bounds. AI forecasting increased adaptation under abrupt
demand surges (Kim et al., 2023).
The scalability of optimized hybrid systems is confirmed by the results. The approach works effectively in both
urban and rural settings. Seasonal differences did not disrupt performance. Energy losses remained continuously
low throughout the year. Future smart-grid deployments are supported by this method.
FUTURE SCOPE
Future studies will involve sophisticated AI forecasting for real-time demand prediction. AI models can improve
control actions during unstable load shifts (Kim et al., 2023). Smart-grid integration will provide automated
scheduling and adaptive resource dispatch. Sector-specific applications may boost optimization across varied
energy disciplines. Regional datasets will improve accuracy for location-dependent renewable interactions.
Energy transactions may be tracked by blockchain systems with transparent verification (Huang et al., 2024).
Future optimization will incorporate multi-objective algorithms, including NSGA-II and MOEA versions.
Extreme-weather resilience planning will increase system stability during exceptional climatic events. Under
ambiguous circumstances, DE and ABC might facilitate more in-depth investigation (Karaboga et al., 2023). AI-
enabled controllers will increase real-time tuning during uncertain operational windows.
SIGNIFICANCE OF THE STUDY
This work enhances accuracy for hybrid renewable system models. With fewer emission paths, the model
improves clean-energy planning. Carbon emission was reduced by optimizing dispatch and energy conversion
parameters. Effective forecasting and control enhance operational flexibility during load shifts (Ang et al., 2022).
Real-time data allow enhanced scheduling and fewer storage losses.
The model enables net-zero aspirations and long-term sustainability planning. The framework can be customized
for different regions and conditions. To assess local energy behavior, researchers might duplicate the structure.
Results can be used by policy organizations to support investments in renewable infrastructure. According to
Dincer and Rosen (2005), optimized systems typically exhibit reduced environmental loads. According to
Knobloch et al. (2020), low-carbon transitions typically lower overall emissions across industries.
LIMITATIONS OF THE STUDY
The current study lacks pilot-scale validation and is simulation-based. Meteorological inputs rely considerably
on geographical conditions. Such reliance decreases the ability to generalize between climates. Long-term
battery aging was not dynamically modeled. Economic cost optimization was not incorporated in current stages.
Seasonal extremes may be missed without longer observational periods. Deeper multi-objective variations might
not be captured by optimization algorithms. Hybrid configurations require broader validation across grid
conditions (Baños et al., 2011). Simulation outputs may differ from real-world field performance.
DELIMITATIONS OF THE STUDY
This study is limited to battery-powered solar-wind hybrid systems. Grid-only systems and hybrid fossil-based
combinations are not included. Economic profitability is not examined in this analysis. The only factors
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evaluated are technological efficiency and related emissions. Residential and light-industrial demand profiles
were studied. Regional emission factors were regarded as constant for simplicity. Defined system boundaries
promote clarity and reproducibility (Budinis et al., 2018). Clear delimitation gives reliable assumptions for
comparison evaluation. The technique gives a controlled framework for consistent optimization outputs.
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