Optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on metaheuristic algorithms
Introduction. The integration of battery energy storage systems (BESS) with photovoltaic (PV) systems has become crucial for managing renewable energy intermittency and optimizing economic benefits in modern power grids. However, the complexity of battery scheduling optimization involving multiple c...
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National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine
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| author | Desai, J. P. Bhatt, G. V. |
| author_facet | Desai, J. P. Bhatt, G. V. |
| author_institution_txt_mv | [
{
"author": "J. P. Desai",
"institution": "Parul University"
},
{
"author": "G. V. Bhatt",
"institution": "Government Engineering College"
}
] |
| author_sort | Desai, J. P. |
| baseUrl_str | http://eie.khpi.edu.ua/oai |
| collection | OJS |
| datestamp_date | 2026-07-01T21:42:56Z |
| description | Introduction. The integration of battery energy storage systems (BESS) with photovoltaic (PV) systems has become crucial for managing renewable energy intermittency and optimizing economic benefits in modern power grids. However, the complexity of battery scheduling optimization involving multiple conflicting objectives necessitates advanced computational approaches beyond traditional optimization methods. Problem. Current battery scheduling strategies often fail to adequately balance economic optimization with battery degradation costs, leading to suboptimal performance and reduced system profitability. The challenge lies in developing robust optimization algorithms that can handle the non-linear, multimodal nature of the battery scheduling problem while considering realistic operational constraints and long-term economic viability. Goal. To evaluate and compare the performance of three metaheuristic algorithms – particle swarm optimization (PSO), modified PSO with mutation operators, and grey wolf optimizer (GWO) – for optimal battery scheduling in grid-connected PV systems, with emphasis on economic viability and comprehensive degradation cost considerations. Methodology. The study employs mathematical modeling of battery dynamics, economic objective functions incorporating degradation costs, and realistic system constraints. Three metaheuristic algorithms are implemented and tested using real PV generation and load consumption data overextended periods. Performance evaluation includes convergence analysis, economic metrics, and battery utilization patterns with detailed cost structure analysis. Results. Simulation results demonstrate that GWO achieves superior economic performance with net losses of 2.86 million INR compared to 5.96 million INR for standard PSO, representing a 52 % improvement in economic outcomes. All algorithms show satisfactory convergence properties within 50 iterations, with degradation costs representing approximately 21 % of total system costs, highlighting their critical importance in optimization decisions. Scientific novelty. The study provides the first comprehensive comparative analysis of these three metaheuristic algorithms specifically for BESS scheduling with detailed degradation cost modeling, revealing the critical importance of balanced optimization approaches that consider both short-term arbitrage benefits and long-term degradation impacts. Practical value. The research demonstrates that aggressive battery cycling strategies are not economically viable under current market conditions when degradation costs are properly accounted for, providing valuable insights for BESS deployment and operational strategies in renewable energy systems and highlighting the need for additional revenue streams for economic viability. References 23, tables 2, figures 7. |
| doi_str_mv | 10.20998/2074-272X.2026.4.02 |
| first_indexed | 2026-07-02T01:00:22Z |
| format | Article |
| fulltext |
Electrical Engineering & Electromechanics, 2026, no. 4 11
© J.P. Desai, G.V. Bhatt
UDC 621.311 https://doi.org/10.20998/2074-272X.2026.4.02
J.P. Desai, G.V. Bhatt
Optimal battery energy storage system scheduling in grid-connected photovoltaic systems
based on metaheuristic algorithms
Introduction. The integration of battery energy storage systems (BESS) with photovoltaic (PV) systems has become crucial for
managing renewable energy intermittency and optimizing economic benefits in modern power grids. However, the complexity of battery
scheduling optimization involving multiple conflicting objectives necessitates advanced computational approaches beyond traditional
optimization methods. Problem. Current battery scheduling strategies often fail to adequately balance economic optimization with
battery degradation costs, leading to suboptimal performance and reduced system profitability. The challenge lies in developing robust
optimization algorithms that can handle the non-linear, multimodal nature of the battery scheduling problem while considering realistic
operational constraints and long-term economic viability. Goal. To evaluate and compare the performance of three metaheuristic
algorithms – particle swarm optimization (PSO), modified PSO with mutation operators, and grey wolf optimizer (GWO) – for optimal
battery scheduling in grid-connected PV systems, with emphasis on economic viability and comprehensive degradation cost
considerations. Methodology. The study employs mathematical modeling of battery dynamics, economic objective functions
incorporating degradation costs, and realistic system constraints. Three metaheuristic algorithms are implemented and tested using real
PV generation and load consumption data overextended periods. Performance evaluation includes convergence analysis, economic
metrics, and battery utilization patterns with detailed cost structure analysis. Results. Simulation results demonstrate that GWO achieves
superior economic performance with net losses of 2.86 million INR compared to 5.96 million INR for standard PSO, representing a 52 %
improvement in economic outcomes. All algorithms show satisfactory convergence properties within 50 iterations, with degradation
costs representing approximately 21 % of total system costs, highlighting their critical importance in optimization decisions. Scientific
novelty. The study provides the first comprehensive comparative analysis of these three metaheuristic algorithms specifically for BESS
scheduling with detailed degradation cost modeling, revealing the critical importance of balanced optimization approaches that
consider both short-term arbitrage benefits and long-term degradation impacts. Practical value. The research demonstrates that
aggressive battery cycling strategies are not economically viable under current market conditions when degradation costs are properly
accounted for, providing valuable insights for BESS deployment and operational strategies in renewable energy systems and
highlighting the need for additional revenue streams for economic viability. References 23, tables 2, figures 7.
Key words: battery energy storage systems, photovoltaic systems, metaheuristic optimization, grid integration, degradation
costs, renewable energy.
Вступ. Інтеграція систем накопичення енергії на акумуляторних батареях (BESS) із фотоелектричними (PV) системами стала
критично важливою для компенсації нестабільності генерації відновлюваної енергії та оптимізації економічних показників сучасних
електроенергетичних систем. Проте складність задачі оптимізації режимів роботи акумуляторних батарей за наявності кількох
суперечливих цілей потребує застосування сучасних обчислювальних підходів, що перевищують можливості традиційних методів
оптимізації. Проблема. Існуючі стратегії керування режимами роботи акумуляторних батарей часто не забезпечують належного
балансу між економічною оптимізацією та витратами, пов’язаними з деградацією батарей, що призводить до неоптимальної
роботи та зниження рентабельності системи. Основна складність полягає у розробленні надійних алгоритмів оптимізації, здатних
ефективно розв’язувати нелінійну багатомодальну задачу планування режимів роботи BESS з урахуванням реалістичних
експлуатаційних обмежень і довгострокової економічної ефективності. Мета. Оцінити та порівняти ефективність трьох
метаевристичних алгоритмів – оптимізації роєм частинок (PSO), модифікованого PSO з операторами мутації та алгоритму сірого
вовка (GWO) – для оптимального керування режимами роботи акумуляторних батарей у мережевих PV-системах з акцентом на
економічну доцільність і комплексний облік витрат деградації. Методика. У дослідженні використано математичне моделювання
динаміки акумуляторних батарей, економічні цільові функції з урахуванням витрат деградації, а також реалістичні системні
обмеження. Реалізовано та протестовано три метаевристичні алгоритми із застосуванням реальних даних генерації PV-систем
та профілів споживання навантаження протягом тривалих періодів часу. Оцінювання ефективності включає аналіз збіжності,
економічні показники та режими використання батарей із детальним аналізом структури витрат. Результати моделювання
показали, що алгоритм GWO забезпечує найкращі економічні показники із чистими втратами 2,86 млн рупій порівняно з 5,96 млн
рупій для стандартного PSO, що відповідає покращенню економічних результатів на 52 %. Усі алгоритми продемонстрували
задовільні властивості збіжності в межах 50 ітерацій, при цьому витрати деградації становили приблизно 21 % загальних
витрат системи, що підкреслює їх критичну важливість під час прийняття оптимізаційних рішень. Наукова новизна. У роботі
вперше наведено комплексний порівняльний аналіз трьох метаевристичних алгоритмів саме для задачі керування режимами
роботи BESS із детальним моделюванням витрат деградації, що дозволило виявити критичну важливість збалансованих підходів
до оптимізації з урахуванням як короткострокових переваг енергетичного арбітражу, так і довгострокових наслідків деградації
батарей. Практична значимість. Дослідження показало, що стратегії інтенсивного циклування акумуляторних батарей не є
економічно доцільними за поточних ринкових умов за умови коректного врахування витрат деградації, що надає цінні рекомендації
щодо впровадження та експлуатації BESS у системах відновлюваної енергетики та підкреслює необхідність формування
додаткових джерел доходу для забезпечення економічної ефективності. Бібл. 23, табл. 2, рис. 7.
Ключові слова: системи накопичення енергії на акумуляторних батареях, фотоелектричні системи, метаевристична
оптимізація, інтеграція до електричної мережі, витрати на деградацію, відновлювані джерела енергії.
Introduction. The global transition toward
renewable energy sources has accelerated significantly in
recent years, with photovoltaic (PV) systems becoming
one of the most rapidly deployed technologies worldwide.
However, the inherent intermittency and variability of
solar energy generation present substantial challenges for
grid integration and economic optimization. Battery
energy storage systems (BESS) have emerged as a critical
enabling technology to address these challenges by
providing energy arbitrage capabilities, grid stability
services, and enhanced renewable energy utilization.
The optimization of battery scheduling in grid-
connected PV systems represents a complex multi-
objective problem that must balance several conflicting
objectives including economic profit maximization, battery
12 Electrical Engineering & Electromechanics, 2026, no. 4
degradation minimization, and operational constraint
satisfaction. Traditional optimization approaches, such as
linear programming and dynamic programming, often
struggle with the non-linear, multimodal nature of this
problem, particularly when considering realistic battery
models and market dynamics.
Metaheuristic algorithms have gained significant
attention in recent years for solving complex optimization
problems in power systems due to their ability to handle non-
linear objectives, multiple constraints, and large solution
spaces without requiring gradient information. These
population-based algorithms can effectively explore the
solution space and avoid local optima, making them
particularly suitable for battery scheduling optimization
problems. The economic viability of BESS deployment is
critically dependent on the optimization strategy
employed. Aggressive battery cycling can increase energy
arbitrage revenues but may result in accelerated battery
degradation, ultimately reducing the system’s overall
profitability. This trade-off between immediate economic
benefits and long-term degradation costs requires
sophisticated optimization approaches that can properly
model and balance these competing objectives.
Recent market developments, including declining
battery costs and evolving electricity tariff structures, have
created new opportunities for BESS deployment. However,
the success of these systems heavily depends on the
effectiveness of the optimization algorithms used for
operational scheduling. Understanding the comparative
performance of different metaheuristic algorithms for this
application is therefore of significant practical importance.
Problem statement. The optimal scheduling of
BESSs in grid-connected PV applications presents complex
multi-objective challenges where economic benefits
through energy arbitrage must be balanced against battery
degradation costs and operational constraints. Current
literature lacks comprehensive comparative studies of
metaheuristic algorithms for BESS scheduling that
incorporate realistic degradation cost modeling. Most
existing studies either ignore degradation costs or use
oversimplified models, leading to optimization strategies
that appear profitable in simulation but prove economically
unviable in practice. The non-linear relationship between
battery utilization and degradation costs, combined with
discrete operational constraints and continuous power flow
variables, creates a complex mixed-integer optimization
problem that traditional methods cannot effectively handle.
Additionally, the stochastic nature of PV generation and
load demand adds further complexity, though this study
focuses on deterministic optimization using historical data.
Goal. This work aims to evaluate and compare the
performance of three metaheuristic algorithms – particle
swarm optimization (PSO), modified PSO with mutation
operators, and grey wolf optimizer (GWO) – for optimal
battery scheduling in grid-connected PV systems, with
emphasis on economic viability and comprehensive
degradation cost considerations
Review of the literature. Metaheuristic
optimization algorithms have been extensively studied for
power system applications over the past 2 decades.
Kennedy and Eberhart introduced particle swarm
optimization (PSO) in [1], which has since become one of
the most widely applied algorithms for power system
optimization problems. The simplicity and effectiveness
of PSO have made it particularly attractive for battery
scheduling applications. Recent studies by [2, 3] have
demonstrated the effectiveness of PSO for battery energy
management in microgrid applications. However, these
studies primarily focused on technical performance
metrics rather than comprehensive economic analysis
including degradation costs. The GWO [4] has gained
significant attention for power system applications due to
its balanced exploration and exploitation capabilities.
Recent applications by [5, 6] have shown promising
results for renewable energy optimization problems.
Modified PSO variants incorporating mutation
operators have been studied extensively to address the
premature convergence issues of standard PSO. The work
[7] demonstrated that mutation-based PSO modifications
could significantly improve solution quality for energy
management problems. Their study showed that mutation
rates between 5–15 % provided optimal balance between
exploration and exploitation.
Battery degradation modeling has received increased
attention in recent literature. The comprehensive study by [8]
established that degradation costs can represent 20–40 % of
total system costs in aggressive cycling scenarios. This
finding has significant implications for optimization
strategies and highlights the importance of accurate
degradation modeling in BESS scheduling studies.
Economic modeling of BESS operations has evolved
significantly with changing market structures. The works
[9, 10] has shown that simplified economic models without
degradation considerations can overestimate system
profitability by 200–300 %. This overestimation has led to
numerous commercially unsuccessful BESS deployments.
The comprehensive review by [11, 12] compared multiple
algorithms for renewable energy applications, but did not
specifically focus on BESS scheduling problems. Similarly,
the study [13] provided valuable insights into algorithm
performance but lacked the detailed economic analysis
required for practical applications.
Recent advancements clearly indicate that the
integration of realistic degradation modeling with advanced
metaheuristic algorithms is no longer optional but essential
for practical BESS scheduling and energy system
optimization. Traditional approaches that prioritize short-
term technical performance often overlook long-term
economic sustainability, leading to inaccurate profitability
projections and premature system failures. For example,
work [14] demonstrated that incorporating state-of-health
models into scheduling can significantly reduce capacity
fade while improving owner profits, while [15] showed that
hybrid metaheuristic frameworks achieve more cost-
effective charging station operation when degradation is
considered. Similarly, study [16] highlighted that integrating
physics-based degradation models into optimization reduce
business-case error from 170 % to just 13 %, reinforcing the
importance of realistic modeling in financial assessments.
More recently, work [17] introduced the non-dominated
sorting dung beetle optimizer algorithm, which
outperformed GWO in microgrid scheduling by reducing
operating costs by more than 50 %, emphasizing the
growing role of novel bio-inspired optimization approaches.
Electrical Engineering & Electromechanics, 2026, no. 4 13
Collectively, these findings reflect a paradigm shift from
purely performance-driven optimization to economically
grounded, reliability-oriented frameworks that are critical
for the large-scale deployment of battery systems in modern
power markets. Recent studies have further advanced
optimization and control applications in power and energy
systems. These include the design optimization of planar
inductors for power electronics [18], indirect adaptive fuzzy
synergetic control for power systems [19], network
reconfiguration using extended mixed integer quadratic
programming [20], PV power quality improvement through
multilevel inverters [21], and improved GWO approaches
for reactive power dispatch in renewable-integrated grids
[22]. Despite the extensive literature on both metaheuristic
optimization and BESS applications, there remains a
significant gap in comprehensive comparative studies that
combine realistic battery modeling, detailed economic
analysis, and rigorous algorithm evaluation. Most existing
studies focus on either the optimization methodology or the
application domain but rarely provide the integrated analysis
required for practical implementation.
Materials and methods. The mathematical
formulation of the battery scheduling optimization
problem involves several interconnected components
including the battery model, economic objective function,
and operational constraints [23]. The system
configuration consists of a grid-connected PV array with
an integrated BESS operating under time-of-use tariff
structures. The power balance equation governing the
system operation at each time step t is expressed as:
Pgrid(t) = Ppv(t) – Pload(t) + Pbatt(t), (1)
where Pgrid(t) is the power exchange with the grid (positive
values indicate export to grid, negative values indicate
import from grid); Ppv(t) is the PV power generation;
Pload(t) is the local load demand; Pbatt(t) is the battery power
(positive for discharge, negative for charge).
The battery state of charge dynamics is modeled
using a round-trip efficiency approach:
,0if,1
;0if,1
tPbatt
Cbattη
tPbatttSoCtSoC
tPbatt
Cbattη
tPbatttSoCtSoC
(2)
where SoC(t) is the state of charge of the battery at time t;
SoC(t–1) is the state of charge of the battery at the
previous time step (t–1); is the round trip efficiency (set
to 0.95 based on Li-ion battery characteristics); Cbatt is the
total battery capacity in kWh. The optimization problem
is subject to several operational constraints. The state of
charge must remain within safe operating limits:
SoC(t)min SoC(t) SoC(t)max,
where SoC(t)min = 0.1 and SoC(t)max = 0.9 represent 10 % and
90 % of rated capacity respectively, reflecting typical Li-
ion battery operating constraints.
The battery power is constrained by the maximum
charge and discharge rates:
–Pmax Pbatt Pmax,
where Pmax = 20 kW is the maximum charging and
discharging power.
The economic objective function aims to maximize
the net profit over the optimization horizon is:
f = Rexport + Cimport – Cdegradation, (3)
where Rexport is the revenue of export power; Cimport is the
cost of import power; Cdegradation is the cost of degradation.
The revenue from energy export is:
ttPR
T
t grid 1 exportexport 0,max , (4)
where export = 4.15 INR/kWh is the feed-in tariff for
exported energy.
The cost of energy import is:
ttPC
T
t grid 1 importexport 0,max , (5)
where import = 12.45 INR/kWh is the retail electricity
tariff.
The battery degradation cost, which represents the
economic impact of battery cycling, is calculated based
on the total energy throughput:
ttPC
T
t batt 1 degndegradatio , (6)
where deg = 1.66 INR/kWh is the degradation cost per
unit of energy throughput, derived from battery
replacement costs and expected cycle life.
The 3 metaheuristic algorithms implemented in this
study each employ different search strategies and
population management approaches. PSO maintains a
population of particles, each representing a potential
solution vector of battery power commands for all time
steps. The velocity update equation for each particle i and
dimension j is given as:
,,,,11,,,11,
1
,
t
jijibest
t
jijibest
t
ji
t
ji xgrCxprCvwv (7)
where w = 0.9 is the inertia weight (decreased by factor
0.99 each iteration); C1 = C2 = 2 are the acceleration
coefficients; r1, r2 are the random numbers uniformly
distributed between 0 and 1.
The modified PSO incorporates a mutation operator to
enhance exploration capabilities. With probability pmut = 0.1
a randomly selected dimension of each particle is reset to a
random value within the feasible range is expresses as:
mut
t
ji pifxxxx randrand minmaxmin
1
, . (8)
The GWO models the hunting behavior of grey wolves
through a hierarchical social structure. The 3 best solutions are
designated as , , wolves, with the remaining solutions
representing wolves. The position update mechanism
involves calculating distances to the 3 leader wolves:
XXCD
1 ; XXCD
1 ; XXCD
1 .
The position updates are then calculated as:
DAXX
11 ; DAXX
22 ; DAXX
33 .
The final position is determined as:
31 321 XXXtX
. (9)
The flowcharts of PSO, modified PSO and GWO are
shown in Fig. 1–3 respectively with mathematical models.
Experiments. The simulation environment utilizes
real PV generation and load consumption data with hourly
resolution. The dataset spans multiple seasonal variations to
ensure robust algorithm evaluation under diverse operating
conditions. All algorithms are implemented with identical
population sizes (30 individuals) and maximum iteration
counts (50 iterations) to ensure fair comparison.
14 Electrical Engineering & Electromechanics, 2026, no. 4
Fig. 1. Flowchart of PSO
Fig. 2. Flowchart of modified PSO
The results demonstrate that all metaheuristic
algorithms result in negative net profits (Table 1), indicating
that aggressive battery cycling strategies are not
economically viable under the current tariff structure.
However, the GWO shows high performance with net losses
of 2.86 million INR, representing a 52 % improvement over
standard PSO. Table 2 provides detailed analysis of the cost
structure breakdown, revealing the significant impact of
degradation costs on system economics.
Results. The comprehensive evaluation of the
3 metaheuristic algorithms reveals significant differences in
their performance characteristics and economic outcomes.
Figure 4 shows battery state of charge trajectories, while
Fig. 5 presents algorithm convergence comparison. Table 1
presents the detailed economic performance comparison
across all optimization approaches.
Fig. 3. Flowchart of GWO
Table 1
Detailed economic performance comparison
Performance metric Baseline PSO
Modified
PSO
GWO
Export revenue 178.140 2.232.600 2.227.700 1.174.400
Import cost 424.100 6.447.500 6.463.900 3.177.600
Degradation cost 0 1.744.900 1.744.800 861.160
Net profit –245.960 –5.959.800 –5.980.900 –2.864.400
Final SoC, % 50 57.1 25.9 15.8
Energy throughput,
kWh
0 1.051.747 1.051.566 518.747
time step
SoC
Fig. 4. Battery state of charge trajectories
Iteration
Best cost (Negative net profit)
Fig. 5. Algorithm convergence comparison
The analysis reveals that degradation costs represent
approximately 21 % of total system costs for all
optimization algorithms as shown in Table 2, highlighting
their critical importance in economic evaluations.
Battery utilization patterns reveal significant
differences between algorithms (Fig. 4). The PSO variants
exhibit aggressive charging and discharging strategies with
high energy throughput (exceeding 1 million kWh), but
Electrical Engineering & Electromechanics, 2026, no. 4 15
substantial degradation costs. In contrast, GWO
demonstrates more conservative battery utilization with
approximately 50 % lower total energy throughput. The
convergence analysis (Fig. 5) shows that GWO reaches
near-optimal solutions within 20–25 iterations, while PSO
variants require 35–40 iterations for similar convergence.
Table 2
Cost structure analysis
Cost component Baseline, % PSO, % Modified PSO, % GWO, %
Import cost 100 76.8 76.8 78.7
Degradation cost 10 20.8 20.7 21.3
Net export revenue –42 –26.6 –26.5 –29.1
Discussions. Figure 6 shows power to grid patterns
and optimizer convergence behavior, while Fig. 7
demonstrates the battery degradation impact across
different algorithms. The superior performance of the
GWO can be attributed to its unique search mechanism
that balances exploration and exploitation through
hierarchical wolf pack social structure. The 52 %
improvement in economic performance achieved by
GWO translates to approximately 3.1 million INR
difference in net losses compared to standard PSO as
evident from the results shown in Fig. 6.
a
Pgrid, kW
SoC, kWh
b
Fig. 6. Power to grid (a); convergence of optimizer (b)
Fig. 7. Battery degradation impact
The high degradation costs observed in Fig. 7 across
all optimization scenarios (representing 20–21 % of total
system costs) underscore the importance of accurate
degradation modeling in BESS optimization studies. The
negative economic outcomes suggest that current market
conditions, including the tariff differential of approximately
8.3 INR/kWh between import and export prices, are
insufficient to justify aggressive battery cycling when
degradation costs are properly accounted for. From a
practical implementation perspective, the results suggest
that BESS scheduling optimization should focus on
moderate cycling strategies rather than aggressive arbitrage
approaches. The GWO algorithm’s preference for partial
charge/discharge cycles provides a more sustainable
approach to battery utilization that balances short-term
economic benefits with long-term system viability.
Conclusions. This comprehensive comparative study
of PSO, modified PSO and GWO for BESS scheduling
provides numerous important insights. The GWO
demonstrates high performance, achieving 52 % lower
losses compared to standard PSO due to its balanced
battery utilization approach. However, all algorithms result
in negative financial outcomes under current tariff
structures, with degradation costs constituting 21 % of total
system costs, highlighting the critical importance of
accurate cost modeling in BESS optimization.
The findings suggest that sustainable BESS operation
requires moderate cycling strategies and additional revenue
streams beyond energy arbitrage. This work provides the
first comprehensive comparison integrating detailed
degradation cost modeling, revealing the trade-off between
optimization aggressiveness and economic sustainability.
The research highlights limitations of current BESS
deployment strategies and offers guidance for realistic
performance expectations. Future research should focus on
multi-objective optimization frameworks, uncertainty
modeling, and hybrid metaheuristic approaches for
improved real-world applicability.
Conflict of interest. The authors declare that they
have no conflicts of interest.
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Received 07.09.2025
Accepted 30.01.2026
Published 03.07.2026
J.P. Desai1, Associate Professor,
G.V. Bhatt 2, Assistant Professor,
1 Department of Robotics and Automation,
Parul University, India,
e-mail: 11meee04@nirmauni.ac.in (Corresponding Author)
2 Department of Electrical Engineering,
Government Engineering College, Bhuj, India.
How to cite this article:
Desai J.P., Bhatt G.V. Optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on
metaheuristic algorithms. Electrical Engineering & Electromechanics, 2026, no. 4, pp. 11-16. doi: https://doi.org/10.20998/2074-
272X.2026.4.02
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| last_indexed | 2026-07-02T01:00:22Z |
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| publisher | National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine |
| record_format | ojs |
| resource_txt_mv | eiekhpieduua/39/2e9ed6bc9285919dd0dce2c7a2f8f939.pdf |
| spelling | eiekhpieduua-article-3386532026-07-01T21:42:56Z Optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on metaheuristic algorithms Optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on metaheuristic algorithms Desai, J. P. Bhatt, G. V. системи накопичення енергії на акумуляторних батареях фотоелектричні системи метаевристична оптимізація інтеграція до електричної мережі витрати на деградацію відновлювані джерела енергії battery energy storage systems photovoltaic systems metaheuristic optimization grid integration degradation costs renewable energy Introduction. The integration of battery energy storage systems (BESS) with photovoltaic (PV) systems has become crucial for managing renewable energy intermittency and optimizing economic benefits in modern power grids. However, the complexity of battery scheduling optimization involving multiple conflicting objectives necessitates advanced computational approaches beyond traditional optimization methods. Problem. Current battery scheduling strategies often fail to adequately balance economic optimization with battery degradation costs, leading to suboptimal performance and reduced system profitability. The challenge lies in developing robust optimization algorithms that can handle the non-linear, multimodal nature of the battery scheduling problem while considering realistic operational constraints and long-term economic viability. Goal. To evaluate and compare the performance of three metaheuristic algorithms – particle swarm optimization (PSO), modified PSO with mutation operators, and grey wolf optimizer (GWO) – for optimal battery scheduling in grid-connected PV systems, with emphasis on economic viability and comprehensive degradation cost considerations. Methodology. The study employs mathematical modeling of battery dynamics, economic objective functions incorporating degradation costs, and realistic system constraints. Three metaheuristic algorithms are implemented and tested using real PV generation and load consumption data overextended periods. Performance evaluation includes convergence analysis, economic metrics, and battery utilization patterns with detailed cost structure analysis. Results. Simulation results demonstrate that GWO achieves superior economic performance with net losses of 2.86 million INR compared to 5.96 million INR for standard PSO, representing a 52 % improvement in economic outcomes. All algorithms show satisfactory convergence properties within 50 iterations, with degradation costs representing approximately 21 % of total system costs, highlighting their critical importance in optimization decisions. Scientific novelty. The study provides the first comprehensive comparative analysis of these three metaheuristic algorithms specifically for BESS scheduling with detailed degradation cost modeling, revealing the critical importance of balanced optimization approaches that consider both short-term arbitrage benefits and long-term degradation impacts. Practical value. The research demonstrates that aggressive battery cycling strategies are not economically viable under current market conditions when degradation costs are properly accounted for, providing valuable insights for BESS deployment and operational strategies in renewable energy systems and highlighting the need for additional revenue streams for economic viability. References 23, tables 2, figures 7. Вступ. Інтеграція систем накопичення енергії на акумуляторних батареях (BESS) із фотоелектричними (PV) системами стала критично важливою для компенсації нестабільності генерації відновлюваної енергії та оптимізації економічних показників сучасних електроенергетичних систем. Проте складність задачі оптимізації режимів роботи акумуляторних батарей за наявності кількох суперечливих цілей потребує застосування сучасних обчислювальних підходів, що перевищують можливості традиційних методів оптимізації. Проблема. Існуючі стратегії керування режимами роботи акумуляторних батарей часто не забезпечують належного балансу між економічною оптимізацією та витратами, пов’язаними з деградацією батарей, що призводить до неоптимальної роботи та зниження рентабельності системи. Основна складність полягає у розробленні надійних алгоритмів оптимізації, здатних ефективно розв’язувати нелінійну багатомодальну задачу планування режимів роботи BESS з урахуванням реалістичних експлуатаційних обмежень і довгострокової економічної ефективності. Мета. Оцінити та порівняти ефективність трьох метаевристичних алгоритмів – оптимізації роєм частинок (PSO), модифікованого PSO з операторами мутації та алгоритму сірого вовка (GWO) – для оптимального керування режимами роботи акумуляторних батарей у мережевих PV-системах з акцентом на економічну доцільність і комплексний облік витрат деградації. Методика. У дослідженні використано математичне моделювання динаміки акумуляторних батарей, економічні цільові функції з урахуванням витрат деградації, а також реалістичні системні обмеження. Реалізовано та протестовано три метаевристичні алгоритми із застосуванням реальних даних генерації PV-систем та профілів споживання навантаження протягом тривалих періодів часу. Оцінювання ефективності включає аналіз збіжності, економічні показники та режими використання батарей із детальним аналізом структури витрат. Результати моделювання показали, що алгоритм GWO забезпечує найкращі економічні показники із чистими втратами 2,86 млн рупій порівняно з 5,96 млн рупій для стандартного PSO, що відповідає покращенню економічних результатів на 52 %. Усі алгоритми продемонстрували задовільні властивості збіжності в межах 50 ітерацій, при цьому витрати деградації становили приблизно 21 % загальних витрат системи, що підкреслює їх критичну важливість під час прийняття оптимізаційних рішень. Наукова новизна. У роботі вперше наведено комплексний порівняльний аналіз трьох метаевристичних алгоритмів саме для задачі керування режимами роботи BESS із детальним моделюванням витрат деградації, що дозволило виявити критичну важливість збалансованих підходів до оптимізації з урахуванням як короткострокових переваг енергетичного арбітражу, так і довгострокових наслідків деградації батарей. Практична значимість. Дослідження показало, що стратегії інтенсивного циклування акумуляторних батарей не є економічно доцільними за поточних ринкових умов за умови коректного врахування витрат деградації, що надає цінні рекомендації щодо впровадження та експлуатації BESS у системах відновлюваної енергетики та підкреслює необхідність формування додаткових джерел доходу для забезпечення економічної ефективності. Бібл. 23, табл. 2, рис. 7. National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine 2026-07-02 Article Article application/pdf https://eie.khpi.edu.ua/article/view/338653 10.20998/2074-272X.2026.4.02 Electrical Engineering & Electromechanics; No. 4 (2026); 11-16 Электротехника и Электромеханика; № 4 (2026); 11-16 Електротехніка і Електромеханіка; № 4 (2026); 11-16 2309-3404 2074-272X en https://eie.khpi.edu.ua/article/view/338653/351593 Copyright (c) 2025 J. P. Desai, G. V. Bhatt http://creativecommons.org/licenses/by-nc/4.0 |
| spellingShingle | battery energy storage systems photovoltaic systems metaheuristic optimization grid integration degradation costs renewable energy Desai, J. P. Bhatt, G. V. Optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on metaheuristic algorithms |
| title | Optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on metaheuristic algorithms |
| title_alt | Optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on metaheuristic algorithms |
| title_full | Optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on metaheuristic algorithms |
| title_fullStr | Optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on metaheuristic algorithms |
| title_full_unstemmed | Optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on metaheuristic algorithms |
| title_short | Optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on metaheuristic algorithms |
| title_sort | optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on metaheuristic algorithms |
| topic | battery energy storage systems photovoltaic systems metaheuristic optimization grid integration degradation costs renewable energy |
| topic_facet | системи накопичення енергії на акумуляторних батареях фотоелектричні системи метаевристична оптимізація інтеграція до електричної мережі витрати на деградацію відновлювані джерела енергії battery energy storage systems photovoltaic systems metaheuristic optimization grid integration degradation costs renewable energy |
| url | https://eie.khpi.edu.ua/article/view/338653 |
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