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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue VI, June 2025
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Hybrid Energy Storage Systems for Renewable Integration:
Combining Batteries, Supercapacitors, and Flywheels
Tanwa M Iwayemi., Stanley O Tomomewo., Sudhanshu Choudhary., Daniel Kelly Boakye-Danquah
Energy Engineering Department, University of North Dakota, Grand Forks, North Dakota, USA
DOI: https://doi.org/10.51583/IJLTEMAS.2025.140600045
Received: 30 May 2025; Accepted: 04 June 2025; Published: 09 July 2025
Abstract: Renewable-energy integration into power grids is constrained by the variable output of solar and wind resources. This
paper proposes a Hybrid Energy Storage System (HESS) that couples lithium-ion, batteries, supercapacitors, and flywheels and
governs them with a Unified Mathematical Method (UMM) combining moving-average filtering with threshold-based cut-off
logic. The architecture is modelled in HOMER Pro for the Grand Forks, ND (USA) resource profile and bench-marked against
“Grid+Renewables” and “Grid+Renewables+Battery” baselines. The full three-storage configuration supplies 1 032 320 kWh
yr
−1
of useful energy—an increase of 77 % over the no-storage case—and eliminates 1.36 Mt CO
2
yr
−1
of emissions, a245 %
improvement relative to renewables alone. Valued at the Social Cost of Carbon (US$51 t
−1
) and the 45Q tax credit(US$85 t
−1
),
the avoided emissions translate to annual economic benefits of US$69 000–US$116 000. The UMM reduces false cut-off events
by more than 30 %, prolonging component life and enhancing overall system reliability. These results confirm that a tri-
technology HESS managed by a unified control layer delivers superior technical performance, environmental gains, and financial
returns compared with single-storage or no-storage configurations.
Keywords: Hybrid energy storage system, lithium-ion battery, supercapacitor, flywheel, renewable energy integration, energy-
management system, HOMER Pro.
I. Introduction
The global energy landscape is undergoing a significant transformation, driven by the increasing demand for renewable energy
sources, technological advancements, and geopolitical shifts. Hybrid energy storage systems (HESS) are playing a crucial role in
this transition by addressing the intermittency of renewable energy sources and enhancing grid stability. A key framework
guiding this transition is the Paris Agreement, adopted in 2015, which aims to limit global warming to well below 2°C and pursue
efforts to limit it to 1.5°C above pre-industrial levels by the end of the century (Paris Agreement, 2015; BBC, 2024; Consilium,
2025). The Paris Agreement emphasizes reducing greenhouse gas emissions to achieve net-zero levels between 2050 and 2100
and encourages countries to submit nationally determined contributions (NDCs) to reduce emissions (Paris Agreement, 2015).
Renewables such as solar, wind and hydropower are expected to meet about 95% of the electricity demand growth between now
and 2030, with solar PV alone accounting for roughly half of this growth (IEA, 2025). In 2025, renewables are forecast to provide
more than one-third of total electricity generation globally, overtaking coal (IEA, 2025). HESS are essential for integrating
renewable energy into the grid by providing both high energy and high power capabilities, helping mitigate the intermittency of
solar and wind power and ensuring a stable energy supply (Arsad et al., 2022). The integration of HESS with renewable energy
systems is thus vital for sustainable development and reduction of carbon footprint (Zuo et al., 2020; Arsad et al., 2022). To
optimize the performance of HESS, this study proposes a hierarchical control strategy and a unified mathematical method
(UMM) that integrates lithium-ion batteries, supercapacitors, and flywheels. Each technology is selected for its unique
characteristics: lithium-ion batteries for long-term, steady energy supply; supercapacitors for rapid response to short-term
fluctuations; and flywheels for frequency regulation and immediate power backup (Wang et al., 2020; Liu et al., 2019; Zhang et
al., 2020; Amiryar & Pullen, 2017). A core innovation of this research is the introduction of the Unified Mathematical Method
(UMM), which applies a consistent mathematical structure across all operational parameters to ensure robust and reliable system
operation. The UMM employs moving average filtering and threshold-based cutoff logic, reducing false positives and enhancing
reliability (Chen et al., 2020; Smith & Kumar, 2021; Maroufi et al., 2025). The moving average for any monitored parameter X
(like Voltage, Temperature, or SOC) is calculated as:
(1)
X
¯
(t) – moving average of parameter X at time t.
w – window width for smoothing the parameter data. The cutoff condition based on the moving average is expressed as
If
or
>
) = ΔSOC(t)=0 (2)
Where x
min
and x
max
are the defined minimum and maximum operational thresholds.
Unified System Cut-off Logic
To ensure overall system stability, the unified cut-off decision can be represented as
System
or
(3)
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The primary objective of this research is to design and validate novel HESS architectures and control algorithms that maximize
energy efficiency, system performance, and reliability for renewable integration. The study leverages simulation tools such as
HOMER Pro to evaluate power flow, grid interaction, and environmental benefits, including emissions reduction. This
comprehensive approach addresses critical gaps in the literature, particularly regarding the unified control and optimization of
multi-component HESS under various operational scenarios (Maroufi et al., 2025; Atawi et al., 2022). Ultimately, this research
aims to advance the design, control, and optimization of hybrid energy storage for seamless renewable integration, supporting the
global transition toward sustainable, resilient energy systems (International Energy Agency, 2023)
II. Literature Review
Hybrid Energy Storage Systems (HESS) have rapidly emerged as a key solution for integrating renewable energy sources into
modern power grids, addressing the challenges of intermittency, grid stability, and operational flexibility. This review synthesizes
recent research, with a focus on system architectures, component technologies, control strategies, and the critical gaps that
motivate this study.
Overview of Hybrid Energy Storage Systems
HESS combines multiple storage technologies; in this study, lithium-ion batteries, supercapacitors, and flywheels are leveraged
for their complementary strengths (Adeyinka et al., 2024; Naderipour et el., 2022). Lithium-ion batteries provide high energy
density and long-term storage, supercapacitors deliver rapid charge/discharge for short-term fluctuations, and flywheels offer high
power output and frequency regulation capabilities. This synergy enables HESS to outperform single-technology systems in
energy/power density, efficiency, lifespan, and reliability (Adeyinka et al., 2024; Bade et al., 2024, Makupe & Moses, 2023).
Recent studies highlight significant progress in HESS optimization and control:
System Optimization: Jinjun et al. (2024) demonstrated that advanced algorithms, such as the marine predator algorithm, can
optimize HESS capacity for wind-photovoltaic systems, reducing undercompensation by 33.42% and improving operational
efficiency.
Component Sizing: Agajie et al. (2023) performed techno-economic analysis and optimal sizing for both on-grid and off-grid
HESS, emphasizing meta-heuristic algorithms for cost-effective performance. Control Strategies: Maroufi et al. (2025) introduced
a Moving Average and Fuzzy Logic-based power management system, which dynamically adjusts energy demand, improves
control accuracy, and extends component lifespan.
Component Technologies
Lithium-Ion Batteries are widely used for their high energy density, long cycle life, and efficiency. Chemistries such as LiFePO4
and NMC are common, and batteries are essential for sustained energy delivery during periods of low renewable generation
(Ghafari, 2022; Liu et al., 2022, kittner,2023).
Supercapacitors are characterized by high power density and rapid cycling, supercapacitors are ideal for smoothing short-term
fluctuations and supporting peak shaving, though their energy density is lower than that of batteries (Mahajan et al., 2024; Lim et
al., 2023).
Flywheel Energy Storage Systems (FESS) store energy mechanically and are valued for rapid response, high power output, and
frequency regulation. Recent advancements include high-speed rotors, magnetic bearings, and composite materials, which
improve efficiency and reduce maintenance (Li et al., 2022; Khaligh & Li, 2010).
Energy Management and Control
A robust Energy Management System (EMS) is critical for HESS performance. Modern EMS platforms use real-time monitoring,
predictive analytics, and automation to optimize energy flows, manage state of-charge (SOC), and coordinate multiple storage
devices and renewable sources. The integration of Automatic Transfer Switches (ATS) and network switches enhances real-time
adaptability and communication, supporting seamless transitions and system reliability (Adeyinka et al., 2024; Atawi et al.,
2022).
Comparative Analysis of Storage Technologies
The hybrid combination of batteries, flywheels, and supercapacitors maximizes system flexibility and performance, optimizing
energy management across various timescales and enhancing overall system reliability and efficiency. Lithium-ion batteries store
excess energy for long-term use, flywheels provide rapid response and frequency regulation, and supercapacitors smooth out
short-term fluctuations and support peak shaving (Khodaparastan & Mohamed, 2019).
Economic Evaluation of Battery, Supercapacitor, and Flywheel
Hybrid Energy Storage Systems (HESS) use lithium-ion batteries, flywheels, and supercapacitors to tackle renewable integration
challenges. Understanding each storage technology’s capital and operational expenditures is crucial for optimal system design,
performance, and cost management.
Economic evaluation is central to the optimal design and deployment of Hybrid Energy Storage Systems (HESS), as it determines
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both feasibility and long-term value. The U.S. National Renewable Energy Laboratory (Cole&Karmakar, 2023) predicts utility-
scale lithium-ion battery system costs for a 4-hour duration at $245-$403/kWh by 2030, with further reductions to $159-
$348/kWh
Figure 2.1: Comparison of Energy Storage Technologies: Lithium-ion Battery, Flywheel, and Supercapacitor.
by 2050. Their operational expenditures are low, and they provide high efficiency and moderate cycle life. Flywheels, with
capital costs ranging from $600–$2,400/kW, offer very low OPEX ($15/kW) and exceptional longevity, making them highly
suitable for high-power, short-duration applications, especially frequency regulation (Mongird et al., 2019; Areola et al., 2025).
Supercapacitors, while currently expensive ($19,200–$34,624/kWh), excel in ultra-fast response roles and benefit from minimal
maintenance costs, although their low energy density limits broader use. (U.S. DOE, 2023).
Overall, lithium-ion batteries are most cost-effective for long-term applications, flywheels are ideal for short, high-power needs,
and supercapacitors are best for rapid-response scenarios. Integrating these technologies in HESS enhances system flexibility and
efficiency, allowing for cost-sharing of power electronics and reducing both upfront and lifecycle costs. This hybrid approach not
only optimizes technical performance but also improves economic viability for renewable integration.
Gaps Identified in the Literature
Despite significant progress, several gaps persist.
Unified Control Algorithms: Most research focuses on individual storage technologies or lacks a cohesive, unified control
strategy for multi-component HESS. There is a need for mathematical frameworks that can manage all types of storage in diverse
operational scenarios (Maroufi et al., 2025; Smith & Kumar, 2021).
Real-Time Adaptability: Many systems lack real-time communication and coordination, which are essential for dynamic grid
environments. The integration of ATS and network switches is highlighted as a solution (Guven et al., 2024; Agajie et al., 2023).
Validation: Although simulation tools like HOMER Pro are widely used, practical real-world validation of theoretical models is
limited. More field demonstrations are needed to confirm the robustness and scalability of proposed solutions (Agajie et al.,
2023).
III. Methodology
This study employs a rigorous, multi-layered methodology to design, model, and validate a Hybrid Energy Storage System
(HESS) for renewable energy integration, combining lithium-ion batteries, supercapacitors, and flywheels. The approach
integrates theoretical modeling, unified control algorithms, and simulation-based validation using HOMER Pro Software to
address operational, technical, and environmental objectives.
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Table 2.1: Summary of selected literature on hybrid energy storage systems
Author(s)
Focus of Study
Key Findings
Gaps Identified
Adeyinka et al.
(2024)
Overview of HESS and its
role in renewable-energy
integration.
Highlighted the complementary
strengths
of batteries,
supercapacitors, and flywheels in
improving energy/power density,
efficiency, lifespan, and
reliability.
Need for integrated control
strategies to manage multiple
storage systems efficiently.
Jinjun et al.
(2024)
Capacity configuration of
HESS incorporating flywheel
and lithium battery for wind-
photovoltaic integration.
Utilized the Marine Predator
Algorithm and Variational
Model Decomposition,
achieving a 33.42% reduction
in under-compensation and
improved operational efficiency.
Limited integration of
real-time adaptive control
mechanisms in multi-storage
systems.
Rakib et al.
(2024)
Challenges in integrating the
intermittent nature of
renewables with HESS.
Identified the superior
efficiency and stability of
rotational kinetic storage
(flywheels)
in HESS
configurations.
Lack of advanced algorithms for
dynamic energy management.
Guven et al.
(2024)
Optimization of battery–super-
capacitor HESS configurations
for renewable integration.
Optimal set-up: a
675 kW
super-capacitor and
1000
kWh
battery bank, achieving
an
80%
renewable-energy
fraction while cutting costs.
Need for real-time
communication and control
mechanisms for optimal system
management.
Rana et al.
(2023)
Comparative analysis of five
energy-storage systems for
power grids.
HESS improves energy density,
power density and dynamic
responsiveness in Integrated
Energy Systems (IES).
Lack of unified control
strategies for multi-storage
configurations.
Liu & Zamora
(2024)
Real-time control in hybrid
systems
Calls f o r robust
communications and adaptive EMS
No working algorithm
Atawi et al.
(2022)
Review of recent advances in
HESS coupled to renewables.
Listed long lifespan, high
capacity, low emissions, and
high efficiency as key
advantages.
Need for innovative
breakthroughs in control
strategies to enhance efficiency.
Agajie et al.
(2023)
Techno-economic analysis
and optimal sizing of hybrid
renewable systems.
Stressed optimal storage sizing
for both on-grid and off-grid use
to keep costs down.
Lack of real-time validation
methods for optimizing energy
distribution.
Ansari et al.
(2022)
Optimization techniques for
hybrid renewables in isolated
microgrids.
Found battery banks and diesel
generators improve reliability.
Lack of dynamic control
integration for real-time
adaptive energy management.
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Study Area and Data Acquisition
Figure 3.1: Proposed study location. Grand Forks Area Coordinate (47.925259, -97.087752) in North Dakota, U.S.A (Homer
Pro).
The Grand Forks Area in North Dakota, U.S.A. (47.925259, -97.087752), was selected as the study site due to its favorable wind
and solar resources. Meteorological data (solar irradiance, wind speed) were sourced from the National Solar Radiation Database
(NSRDB) and National Wind Technology Center (NWTC), while load profiles were obtained from the NREL Open Energy Data
Initiative (OEDI). Technical specifications and cost data for system components were derived from HOMER Pro’s built-in
datasets, U.S. DOE databases, and manufacturer datasheets.
System Architecture and Component Specification
Table 3.1: Storage Device Specifications
Storage Device
Voltage Range
Capacity
SOC Limits
Temperature Range
Battery
650V – 800V
270 kWh
0.1 – 0.8
-40°C to 65°C
Supercapacitor
2.5V – 3.0V
265 kWh
0.2 – 0.95
-40°C to 65°C
Flywheel
400V – 600V
265 kWh
0.15 – 0.9
-40°C to 65°C
The system incorporates an Energy Management System (EMS), Automatic Transfer Switch (ATS), network switch, and power
converter to coordinate real-time operation and seamless source switching.
Implementation of Unified Mathematical Method (UMM)
The methodology implements the Unified Mathematical Method (UMM) established in the literature review, which applies
moving average filtering and threshold-based cut-off logic for robust control of all storage devices. The UMM framework,
including the moving average calculations, cut-off conditions, and system-wide cut-off logic previously defined, is
operationalized through the EMS to ensure consistent parameter monitoring and protective actions across all storage technologies.
Scenario-Based Algorithm Development
Five distinct operational scenarios are developed and tested:
Scenario 1 (Normal Operation): Dynamic selection of optimal storage devices based on real-time demand and renewable output.
Scenario 2 (Renewable Source Absence): Priority-based energy management focusing on stored energy utilization.
Scenario 3 (Battery Failure): System adaptation using supercapacitors and flywheels. Scenario 4 (Supercapacitor Failure):
Compensation through enhanced battery and flywheel coordination.
Scenario 5 (Flywheel Failure): Redistribution between batteries and supercapacitors. Each scenario implements the
charging/discharging equations and SOC update protocols established in the literature review, ensuring consistent mathematical
treatment across all operational conditions.
HOMER Pro Simulation Framework
The system is modelled and validated using HOMER Pro software, enabling integrated analysis of technical, operational, and
environmental factors. Three comparative scenarios are simulated: Scenario A: Grid + Renewables only (no storage) The first
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scenario represents a hybrid energy setting where utility-provided electricity (Grid) is supplemented by renewable energy sources
without any storage integration. This scenario aims to assess the performance of renewables without the support of energy storage
systems.
Figure 3.2: Schematic Model of Hybrid systems in Homer Pro without storage.
Scenario B: Grid + Renewables + Single Storage Component The second scenario represents a hybrid energy system where
utility-provided electricity (Grid) is supplemented by renewable sources and a single type of energy storage. The purpose is to
assess the impact of adding one energy storage type on system performance.
Figure 3.3: Schematic Model of Hybrid Systems in Homer Pro using one storage.
Scenario C: Hybrid Energy Storage System (Grid + Renewables + Three Storage) The third scenario represents a comprehensive
hybrid energy system where utility-provided electricity (Grid) is supplemented by renewable sources and three available energy
storage types. This setup tests the impact of integrating three storage technologies on system performance.
Figure 3.4: Schematic Model of Hybrid Systems in Homer Pro using three storages.
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Each scenario incorporates a 160kW load profile with 4-hour storage capacity (800 kWh total), utilizing realistic component
specifications and operational constraints derived from manufacturer data and industry standards.
Table 3.2: Parameters for All Three Simulation Scenarios
Parameter
Scenario
1
Scenario
2
Scenario
3
Grid Capacity 300 kW 300 kW 300 kW
Renewable Sources PV(200 kW), Wind(200 kW) PV (200
kW), Wind
(200 kW)
PV (200
kW), Wind (200 kW)
Energy Storage Component None Batteries
(270kWh)
Supercapacitors (265kWh),
Batteries
(270kWh),
Flywheels (265kWh)
Dispatch Strategy Load Following and Combined Dispatch
Economy Inputs Model Run- time:15years, Discount Rate: 8%,
ITC:
30%, Inflation: 2%
Load
Following and
Combined
Dispatch
Model Run-
time:15years,
Discount Rate:
8%, I T C :
30%, Inflation:
2%
Load
Following
and Combined Dispatch
Model Run- time:15years, Discount
Rate: 8%, I T C :
30%,
Inflation: 2%
Performance Metrics and Analysis Framework
The methodology evaluates system performance through multiple metrics:
Technical Performance: Energy flow efficiency, SOC dynamics, voltage/temperature stability, and system response times.
Operational Reliability: False cut-off reduction, component longevity, and load meeting capability. Environmental Impact: CO2
emissions reduction and renewable energy integration efficiency Eco nomic Viability: Cost-benefit analysis using Social Cost of
Carbon ($51/ton) and 45Q Carbon Credit ($85/ton) frameworks (CCC, 2023).
Model Assumptions and Limitations
The model operates under controlled assumptions: 4-hour storage capacity with single-device discharge priority, publicly
available meteorological and component datasets, and simplified representations of real-world uncertainties, including equipment
failure and weather extremes. These simplifications ensure computational feasibility while maintaining representational accuracy
for the core research objectives.
Validation Protocol
Simulation results undergo comprehensive validation through comparison of HOMER Pro outputs with theoretical predictions,
assessment of algorithm effectiveness in reducing false cut-offs, and evaluation of hybrid versus single-storage configurations.
The validation confirms system stability, optimal energy management, and alignment between theoretical models and practical
implementation.
IV. Results and Discussion
This section presents the results focusing on the performance of the proposed HESS architecture that integrates lithium-ion
batteries, supercapacitors, and flywheels for renewable energy applications, including its environmental and economic impact.
The control algorithm, based on a Unified Mathematical Method (UMM), ensures robust and adaptive power management across
diverse operational scenarios.
Load Profiles and Energy Storage Specifications
A 160 kW load profile and a total storage capacity of 800 kWh were used, distributed among batteries (270 kWh),
supercapacitors (265 kWh), and flywheels (265 kWh). Table IV.1 summarizes the key operational parameters.
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Table 4.1: Key Parameters of Battery, Supercapacitor, and Flywheel
Parameter
Battery
Super-cap
Flywheel
Capacity C
i
(kWh)
270
265
265
Initial SOC
0.50
0.50
0.50
DOD
0.90
0.80
0.85
SOC
min
0.10
0.20
0.15
SOC
max
0.80
0.95
0.90
Voltage range (V)
650–800
2.5–3.0
400–600
Temp. range (°C)
−25–27
−40–65
−20–40
The UMM applies a consistent mathematical structure across all operational parameters. The unified mathematical equation for
charging and discharging is:
excoss
coxcss
min
if
excess
and
if
excess
and
otherwise(4)
where i represents the storage type and C
i
its capacity.
When P
excess
> 0, the system charges the storage device if:
• The SOC is below its maximum limit
• Voltage and temperature are within operational limits
When P
excess
< 0, the system discharges the storage device if:
• The SOC is above its minimum limit
• Voltage and temperature are within operational limits
SOC
i
(
t
+ 1) =
SOC
i
(
t
) + ∆
SOC
i
(
t
)
(5)
Excess Power Calculation:
P
excess
(t) = P
renewable
(t) − P
load
(6)
Also, the unified cutoff condition is triggered only if the moving average of any monitored parameter persistently violates its
bounds.
System
or
(7)
where X
¯
(t) is the moving average of parameter X at time t:
X
¯
(t) < x
min or X
¯
(t) > x
max
= ∆SOC(t) = 0 (8)
When this condition is met, ∆SOC(t) = 0, and the device ceases operation until parameters return to safe ranges.
Dynamic system behavior is illustrated in the following figures:
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Figure 4.1: Voltage Dynamics of Battery, Supercapacitor, and Flywheel
Figure 4.2: State of Charge Dynamics of Battery, Supercapacitor, and Flywheel
Figure 4.3: Temperature Dynamics of Battery, Supercapacitor, and Flywheel
Algorithm for Different Scenarios Operation
Scenario 1: Normal Operation
Algorithm (Energy Management System) In normal conditions, the Energy Management System (EMS) continuously monitors
power generation from renewables and load demand. If renewable generation exceeds demand, storage devices are charged in
priority order (batteries first, then supercapacitors, then flywheels), provided their state of charge (SOC), voltage, and temperature
are within safe limits. If demand exceeds generation, supercapacitors discharge first for sudden, high-power needs, followed by
flywheels for intermediate support, and batteries for sustained supply. The unified mathematical method and moving average
window with threshold-based cut-off logic ensure that charging/discharging only occurs within operational constraints,
minimizing false cut-offs and maximizing component life. Key Result: All devices operate within their set parameters, and the
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system dynamically allocates power to optimize efficiency and reliability (Maroufi et al., 2025; Ozcan et al., 2025).
Key Result:
All devices operate within their set parameters, and the system dynamically allocates power to optimize efficiency and reliability.
Algorithm Working: Detailed Example
The following example demonstrates how the algorithm processes different power scenarios over 4 hours:
Hour 1
Renewable Power: 34 kW
Load Demand: 160 kW
Excess Power:
excess
= 126kW
Battery discharge capacity = 108kWh (50% to 10% DOD)
Battery Discharge =
=
= - 0.400
Remaining 126 -108 = 18kWh needed
Supercapacitor Discharge =
=
= = - 0.068
Flywheels Discharge = 0kWh (All needed energy is met)
Cut-off Check: Both devices are within their voltage/temperature/SOC limits, so no cut-off is triggered.
Table 4.2: State of Charge (SOC) at the End of Hour 1
Storage
Final SOC @ Hour 1
Batteries
Supercapacitors
Flywheels
0.100
0.432
0.500
Hour 2
Renewable Power: 205 kW
Load Demand: 160 kW
Excess Power:
excess
= 45kW
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Batteries Discharge =
=
= 0.167
Supercapacitors and flywheel = 0kWh discharged because battery absorbs all 45kW to recharge
No energy is taken from (or added to) the supercapacitor and flywheel. Cut-off Check: All parameters remain within bounds.
Table 4.3: State of Charge (SOC) at the End of Hour 2
Storage
Final SOC @ Hour 2
Batteries
0.267
Supercapacitors
0.432
Flywheels
0.500
Hour 3
Renewable Power: 80 kW
Load Demand: 160 kW
Excess Power:
excess
= -80kW
Batteries Discharge =
= -0.167
Supercapacitor Discharge =
= 0.132
Flywheels Discharge = 0kW
Cut-off Check: SOCs remain above minimum; no cut-off is triggered.
Table 4.4: State of Charge (SOC) at the End of Hour 3
Storage
Final SOC @ Hour 3
Batteries
0.100
Supercapacitors
0.300
Flywheels
0.500
Hour 4
Renewable Power: 49 kW
Load Demand: 160 kW
Excess Power:
excess
= -111kW
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Batteries are unable to discharge because it is at minimum. SOC Supercapacitor Discharge =
= 0.100
Remaining 111-26.5 = 84.5kW
Flywheels Discharge
=
= 0.319
Table 4.5: SOC at the End of Hour 4
Storage
Final SOC
Batteries
0.100
Supercapacitors
0.200
Flywheels
0.181
Cut-off check: battery disabled (low SOC); supercapacitor and flywheel operate within safe limits.
Scenario 2: No Solar/Wind
When renewables are unavailable, the HESS becomes the sole power source. The EMS prioritizes lithium-ion batteries for
sustained supply, supercapacitors for rapid load changes, and flywheels for frequency regulation and smoothing. The algorithm
assesses total stored energy and estimated duration of renewable unavailability, implements load shedding if energy drops below
critical levels, and uses the same unified cut-off logic to ensure safe operation.
Power Dispatch Algorithm (Priority-Based)
Primary source – Lithium-ion battery
min
(9)
Secondary – Supercapacitor (fast transients)
min
(10)
Tertiary – Flywheel (smoothing / frequency)
min
(11)
Cut-off conditions
Battery Cut-off Point
if
or
or
or
(12)
Supercapacitor Cut-off Point
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if
or
or
or
(13)
Flywheel Cut-off point
if
or
or
or
(14)
Load-shedding trigger
If
load
critical
` (15)
When (15) is true, non-critical loads are shed.
Key Result:
The system maintains critical loads and grid stability, efficiently utilizing stored energy while protecting component health
through strict cut-off enforcement.
Scenario 3: Component Failure – Lithium-ion Battery Malfunction
If the battery fails, the EMS isolates the faulty component and redirects charging/discharging to supercapacitors and flywheels.
Flywheels become the primary energy storage, while supercapacitors handle rapid fluctuations. Aggressive load shedding may be
implemented if storage drops below critical levels. The unified cut-off logic continues to monitor and protect the remaining
devices.
Battery status:
min
(16)
min
(17)
Conservation Power Management Strategy
If the total available energy drops below the critical load level
If
critical
critical
(18)
Key Result:
The system adapts to the loss of long-term storage by maximizing the use of remaining assets, maintaining supply to critical
loads, and preventing further component stress or damage.
Scenario 4: Component Failure – Supercapacitor Malfunction
With supercapacitor failure, the system loses its ability to efficiently handle rapid power fluctuations. The EMS increases the
response rate of the flywheel system and allows limited, controlled high-rate charging/discharging of batteries.
Conservative power management is implemented, and the unified cut-off logic ensures that only safe operations are permitted.
min
(19)
load balance
min
(20)
Conservation Power Management Strategy
If the total available energy drops below the critical load level
If
critical
critical
(21)
Then, activate load shedding
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Key Result:
The system compensates for the loss of rapid-response storage by redistributing roles, but overall flexibility and response time are
reduced, highlighting the importance of supercapacitors in HESS.
Scenario 5: Component Failure – Flywheel Malfunction
If the flywheel fails, the system loses intermediate-term storage and frequency regulation. The EMS increases reliance on
batteries for energy storage and supercapacitors for power smoothing and frequency regulation. Aggressive power management
and load shedding may be required. The cut-off logic continues to enforce operational safety.
min
(22)
load balance
min
(23)
Conservation Power Management Strategy
If the total available energy drops below the critical load level
If
critical
critical
(24)
Key Result:
The system remains operational by reallocating roles, but the loss of flywheels reduces its ability to handle frequency regulation
and intermediate-term fluctuations, stressing the importance of all three technologies working together.
In all scenarios, the unified mathematical method and cut-off logic are central to ensuring safe, efficient, and adaptive operation,
dynamically responding to both typical and fault conditions to maximize system resilience and performance.
System Architecture and Coordination
Operational Process
Monitoring Energy Demand and Renewable Output
The EMS continuously monitors the current power demand and the output from renewable energy sources, using predictive
analytics to forecast future energy availability and demand.
Determining Energy Storage Needs
Based on the demand and renewable output, the EMS decides which energy storage technology to use. Lithium-ion batteries are
prioritized for long-term energy needs, supercapacitors are used for short-term fluctuations, while flywheels are engaged for
immediate power needs.
EMS Instructions to Network Switch
The EMS sends instructions to the network switch that specify which energy storage technology to use. Then the network switch
translates these instructions into control signals for the ATS.
ATS Selection and Control
The ATS receives the control signals from the network switch and selects the appropriate energy storage technology, and then
connects the chosen storage technology to the converter to generate the power flow path, ensuring that only one technology is
used at a time.
Charging and Discharging Process
During low-demand periods, the ATS directs excess energy from renewables to the power converter to charge the selected storage
technology, and during high-demand periods, the ATS allows the power converter to discharge the selected storage technology to
meet the demand.
During extended periods of low renewable energy resources output in winter months, the EMS decides to use lithium-ion
batteries for sustained energy supply, and the Network Switch translates the EMS’s instructions into control signals for the ATS.
The ATS then selects lithium-ion batteries, and the power converter manages their charging and discharging to ensure a
continuous power supply. Lithium-ion batteries are advantageous due to their high energy density and efficiency, making them
ideal for long-term storage needs. During sudden spikes in power demand during peak hours, the EMS decides to use
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supercapacitors for immediate power support. The network switch translates the EMS’s instructions into control signals for the
ATS. Then, the ATS selects supercapacitors, and the power converter rapidly charges or discharges them to stabilize the grid.
Supercapacitors are beneficial for short-term energy storage due to their rapid charge-discharge capabilities and high-power
density. During momentary power outages or grid frequency fluctuations, the EMS decides to use flywheels for immediate power
stabilization. The network switch translates the EMS’s instructions into control signals for the ATS. Then, the ATS selects the
flywheels, and the power converter manages their rapid charging and discharging to provide instant power support. Flywheels are
effective for short-term power stabilization due to their high efficiency and rapid response times. However, different scenarios are
guarded by the set parameters in Table 4.1 above.
Overall, the EMS coordinates all operations through the network switch and ATS, ensuring seamless power flow and system
stability. The UMM-based control algorithm reduces false cut-offs, optimizes power distribution, and extends component lifespan
by ensuring devices operate within safe parameters.
Figure 4.4: Hybrid Energy Storage System supporting the algorithms
Output Performance, Environmental, and Economic Benefits
This section compares HESS configurations to single-storage and no-storage baselines using HOMER Pro simulations.
Load Profile Analysis
Understanding the load profile is fundamental for designing and optimizing energy systems, particularly when integrating
renewable energy sources with storage technologies. The load analysis provides critical insights into energy consumption
patterns, peak demands, and seasonal variations that directly influence system sizing and operational strategies.
Figure 4.5: Load Profile Overview. This figure shows daily, seasonal, and yearly load profiles of electricity data, along with key
metrics such as average energy consumption, average power, peak power, and load factor.
The load profile analysis for the Grand Forks office building reveals distinct consumption patterns across daily, seasonal, and
yearly time frames. The system exhibits a scaled peak demand of 21.62 kW with an average load of 6.67 kW, resulting in a load
factor of 31%. This relatively low load factor indicates significant variation between peak and average demand, which is typical
for commercial office buildings and presents both challenges and opportunities for energy storage integration.
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Figure 4.6: shows the monthly scaled daily load profiles of electricity data. Each graph illustrates hourly variations in power
consumption for a typical day across all months, highlighting consistent patterns and peak usage periods.
The monthly scaled daily profiles demonstrate consistent patterns with morning ramp-ups (6 AM - 12 PM), daytime plateaus
during business hours (12 PM - 6 PM), evening declines (6 PM - 12 AM), and night-time lows (12 AM - 6 AM). Winter months
(December, January, February) exhibit higher consumption due to heating requirements, while spring and fall months show more
moderate demand profiles. This seasonal variation directly influences the optimal sizing and operation of storage components
within the HESS architecture.
The annual energy consumption totals 58,400 kWh/year, establishing the baseline demand that must be met by the integrated
renewable-storage system. This load profile serves as the basis for all subsequent scenario analysis and system optimization
procedures.
Scenario-Based Performance Results
Scenario 1: Grid + Renewables Only (Baseline)
The baseline scenario establishes the performance benchmark without energy storage integration, utilizing only grid connectivity
and renewable energy sources. This configuration provides essential comparative data to evaluate the incremental benefits of
storage technologies.
The baseline configuration demonstrates robust renewable energy generation with wind turbines contributing 812,064 kWh/yr
and solar PV systems providing 5,380 kWh/yr. The system achieves net energy export to the grid (-135,899 kWh/yr), indicating
substantial excess renewable generation. However, this scenario lacks the storage capability to optimize renewable energy
utilization during periods of high generation and low demand.
Table 4.6: Hybrid System Power Output
Electricity Production (kWh/yr)
Electricity Consumption (kWh/yr)
Grid Supply
4,201
Total AC Primary Load
58,400
Wind Turbine Output
812,064
Grid sales
627,073
Solar
PV
5,380
Net Grid Purchases
-135,899
Figure 4.7: Below provides more information about the system output, showing monthly electric production from the grid and the
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hybrid system components.
The hourly analysis reveals significant temporal mismatches between renewable generation and load demand, particularly during
high wind generation periods. Without storage, excess renewable energy must be exported to the grid at potentially unfavorable
rates, representing lost economic opportunity and suboptimal system utilization.
Scenario 2: Grid + Renewables + Single Storage (Battery) The integration of lithium-ion battery storage significantly enhances
system performance by providing load-shifting capabilities and improved renewable energy utilization. This scenario
demonstrates the fundamental benefits of adding energy storage to renewable systems.
Table 4.7: Hybrid System Power Output with Single Storage
Electricity Production (kWh/yr)
Electricity Consumption (kWh/yr)
Grid purchases
4,201
Total AC Primary Load
58,400
Wind Turbine Output
812,064
Grid sales
706,898.5
Solar
PV
5,380
Net Grid Purchases
-136,172
Lithium-Ion Battery
79,825.5
The addition of battery storage increases total system output to 901,470.5 kWh/yr, with the battery contributing 79,825.5 kWh/yr
through optimized charge-discharge cycles. The capacity factor improves to 21%, and net grid sales increase to 706,899 kWh/yr,
demonstrating enhanced energy arbitrage capabilities and improved economic performance.
Figure 4.8: Hybrid System Monthly Electricity Production- Single Storage Modified from (Homer Pro).
Monthly production analysis reveals improved load matching and reduced grid dependency during peak demand periods. The
battery system effectively smooths renewable generation variability and provides load-shifting capabilities that optimize overall
system efficiency.
Scenario 3: Grid + Renewables + Three Storage Types (HESS)
The comprehensive HESS configuration integrating batteries, supercapacitors, and flywheels rep resents the optimal system
design, leveraging the complementary characteristics of all three storage technologies for maximum performance and reliability.
Table 4.8: Hybrid System Power Output with Lithium-Ion Battery, Supercapacitor and Flywheel
Electricity Production (kWh/yr)
Electricity Consumption (kWh/yr)
Grid Purchases
4,201
Total AC Primary Load
58,400
Wind Turbine
Output
812,064
Grid sales
973,919.5
Solar
PV
5,380
Net Grid Purchases
-969,718.5
Lithium-Ion Battery
79,825.5
Supercapacitor
69,350
Flywheel
65,700
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The three-storage configuration achieves the highest total output of 1,032,319.5 kWh/yr, with each storage technology
contributing according to its optimal characteristics. Supercapacitors provide 69,350 kWh/yr through rapid charge-discharge
cycles, while flywheels contribute 65,700 kWh/yr for frequency regulation and short-term storage. Net grid sales increase
dramatically to 973,920 kWh/yr.
Figure 4.9: Hybrid System Monthly Electricity Production from Renewable Energy Sources, Grid & Three Storages Modified
from Homer Pro.
The monthly production profile demonstrates exceptional system flexibility and optimized renewable energy utilization. The
hybrid storage configuration enables precise load following, peak shaving, and frequency regulation that significantly
outperforms single-storage alternatives.
Environmental and Economic Impact
Environmental Benefits Analysis
The environmental benefits of HESS integration are substantial and demonstrate clear progression from baseline to optimal
configurations. Each scenario shows significant reductions in carbon emissions compared to grid-only operations.
Table 4.9: Annual CO
2
emissions for the three scenarios
Scenario CO
2
Emissions (kg yr
−1
)
Grid + Renewables Only
−393
655
Grid + Renewables + Battery
−576
212
Grid + Renewables + Three Storages
−1 360 451
The negative emission values represent net carbon avoidance, with the three-storage HESS configuration achieving the greatest
environmental benefit at 1,360,451 kg CO2 avoided annually. This represents a 245% improvement over the baseline renewable
scenario and demonstrates the critical role of optimized storage in maximizing environmental benefits.
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Figure 4.10: Comparison of the Annual CO2 Emissions of the Hybrid Baseline to Hybrid with Single Storage: Lithium-Ion
Battery.
Figure 4.11: Comparison of the Annual Energy supplied and the Annual CO2 Emissions of Single Storage to a Hybrid Storage
System Using Three Storage: Lithium-Ion battery, Supercapacitor, and Flywheel.
The comparative analysis clearly illustrates the progressive environmental benefits of storage integration. The HESS
configuration not only maximizes renewable energy utilization but also enables greater grid export of clean energy, amplifying
the system’s positive environmental impact beyond the immediate facility.
Economic Valuation of Environmental Benefits
The economic quantification of avoided emissions provides compelling financial justification for HESS investment, utilizing
established carbon pricing frameworks to demonstrate tangible economic returns.
Using the Social Cost of Carbon (SCC) at $51/ton and the 45Q Carbon Credit at $85/ton, the three-storage HESS configuration
generates an annual economic value of $69,382 (SCC) to $115,638 (45Q) through avoided emissions alone. These figures
represent significant revenue streams that can offset initial capital investments and improve overall project economics.
The 245% increase in economic value from baseline to three-storage configuration demonstrates the multiplicative benefits of
optimal storage integration. This economic analysis excludes additional benefits such as demand charge reduction, peak shaving
revenues, and grid services compensation, suggesting even greater total economic value.
Table 4.10: Economic value of avoided CO
2
emissions
Scenario CO
2
Avoided SCC Value ($51 t
−
1
) 45QValue ($85 t
−
1
)
Grid
+
Renewables
Only
393.66
$20 081
$33 461
Grid + Renewables + Battery
576.21
$29 387
$48 978
Grid + Renewables + Three
Storages
1 360.45
$69 383
$115 638
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Sensitivity Analysis
The comprehensive sensitivity analysis comparing all four scenarios (Grid Only, Grid + Renewables Only, Grid + Renewables +
Single Storage, Grid + Renewables + Three Storages) provides definitive evidence of HESS superiority across all performance
metrics.
Figure 4.12: Sensitivity Analysis of Output Performance to CO2 Emissions
The analysis reveals a clear progression in both power output and environmental benefits:
Grid Only: 58,400 kWh/yr output, 3,221 kg CO2 emissions
Grid + Renewables: 821,250 kWh/yr output, -393,655 kg CO2 avoided
Single Storage:901,471 kWh/yr output, -576,212 kg CO2 avoided
Three Storage (HESS): 1,032,320 kWh/yr output, -1,360,451 kg CO2 avoided.
The HESS configuration achieves 77% higher output and 345% higher carbon avoidance compared to the baseline renewable
systems. This analysis conclusively demonstrates that hybrid storage configurations provide exponential rather than linear
benefits, justifying the additional complexity and investment required for multi-technology storage integration.
Sensitivity analysis validates the research hypothesis that strategic integration of complementary storage technologies creates
synergistic effects that significantly exceed the sum of individual component contributions, establishing HESS as the optimal
approach for renewable energy integration and grid modernization initiatives.
V. Discussion
The results demonstrate that integrating lithium-ion batteries, supercapacitors, and flywheels in a Hybrid Energy Storage System
(HESS) offers substantial technical, environmental, and economic advantages for renewable energy integration. The unified
control strategy, anchored by the Unified Mathematical Method (UMM) with moving average window filtering and threshold-
based cut-off logic, proved effective in all modelled scenarios, ensuring robust, adaptive, and safe system operation.
Technical Performance: The HESS architecture leverages the complementary strengths of each storage technology: batteries
deliver a long-term energy supply, supercapacitors provide rapid response to short-term power fluctuations, and flywheels ensure
immediate frequency regulation and grid stability. The Energy Management System (EMS), supported by a network switch and
an automatic transfer switch (ATS), dynamically allocates charging and discharging duties, ensuring that only one device is active
at a time and that all operate within safe voltage, temperature, and SOC limits.
Algorithmic Control and Cut-off Logic: The UMM’s moving average and threshold-based cut-off logic effectively mitigates false
cut-offs, ensuring that storage devices are disconnected only when operational thresholds are consistently exceeded. This
approach prolongs component life, reduces unnecessary cycling, and enhances overall reliability. The scenario-based results
demonstrate that the EMS can seamlessly adapt to normal operation, renewable source absence, and component failure,
reallocating roles among available devices to maintain supply and grid stability.
Environmental and Economic Impact: Simulation results using HOMER Pro show that the HESS configuration outperforms both
single-storage and no-storage baselines. The hybrid system achieves the highest power output (1,032,319.5 kWh/year) and the
most significant CO2 reduction (-1,360,450.95 kg/year), with the economic value of avoided emissions reaching up to
$115,638/year under the 45Q Carbon Credit framework. These findings demonstrate that hybrid configurations not only optimize
renewable utilization and reduce emissions but also provide tangible financial benefits.
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Sensitivity and Robustness: Sensitivity analysis further confirms that the inclusion of multiple storage types results in the most
efficient and environmentally friendly system, with exponential improvements in both output and CO2 avoidance compared to
single storage or no-storage models.
Notwithstanding, while initial technical complexities and high upfront capital expenditure (CAPEX) remain challenges for HESS
implementation, the benefits—such as enhanced grid resiliency and system robustness, far outweigh these drawbacks. When all
three systems are effectively harmonized, the overall outcomes are significantly improved compared to not integrating them.
Overall, the HESS, managed by advanced unified algorithms, can deliver superior performance, resilience, and sustainability.
This approach is essential for smart grid modernization and large-scale renewable integration, supporting both operational
reliability and climate goals.
VI. Conclusion and Future Work
Conclusion:
This study demonstrates that integrating lithium-ion batteries, supercapacitors, and flywheels in a Hybrid Energy Storage System
(HESS) significantly enhances the performance, reliability, and sustainability of renewable energy integration into the power grid.
The proposed Unified Mathematical Method (UMM), which combines moving average window width filtering with threshold-
based cut off logic, proved effective in managing energy storage operations, reducing false cut-off triggers, and ensuring that cut-
offs occur only when operational thresholds are truly exceeded. Simulation results using HOMER Pro indicate that the hybrid
configuration yields the highest power output (1,032,319.5 kWh/year) and the greatest reduction in CO2 emissions (–
1,360,450.95 kg/year) compared to single storage or no-storage systems. The complementary roles of batteries (long-term
storage), supercapacitors (short-term fluctuations), and flywheels (frequency regulation) enable adaptive, scenario-based energy
management that supports both grid stability and environmental objectives. Furthermore, the economic analysis shows that the
hybrid system delivers the highest value from avoided emissions, providing strong financial justification for HESS deployment in
modern grids.
Future Work:
Future research should focus on integrating advanced control strategies, such as artificial intelligence and machine learning, into
the Energy Management System (EMS) to enable real-time forecasting, adaptive dispatch, and predictive maintenance for HESS,
thereby improving operational resilience and efficiency (Maroufi et al., 2025; Atawi et al., 2022). Empirical validation through
field demonstrations in diverse environments is also essential to refine control algorithms and confirm the robustness and
scalability of the proposed architecture (Agajie et al., 2023).
Comprehensive techno-economic and lifecycle analyses across different regulatory and market contexts are needed to inform
stakeholders and guide investment decisions, particularly as carbon pricing and credit mechanisms become more prevalent (Bade
et al, 2024). Additionally, future work should explore integrating HESS with emerging technologies such as hydrogen fuel cells
and advanced flow batteries to further enhance system flexibility and resilience (Kouchachvili et al., 2021). From a policy
perspective, supportive regulatory frameworks, including targeted incentives, standardized safety protocols, and requirements for
interoperability, are critical to accelerating HESS adoption and maximizing their value for smart grids and renewable integration
(International Energy Agency, 2023).
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
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