ANALYSIS OF THE EFFICIENCY OF POLYGENERATION IN A PRIVATE HOUSEHOLD MICROGRID
Modern challenges in the energy sector, particularly rising energy costs and declining reliability of electricity supply, are promoting the adoption of polygeneration microgrids. These systems integrate various energy sources, including photovoltaic modules, battery energy storage system, and backup...
Gespeichert in:
| Datum: | 2025 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
General Energy Institute of the National Academy of Sciences of Ukraine
2025
|
| Schlagworte: | |
| Online Zugang: | https://systemre.org/index.php/journal/article/view/893 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Назва журналу: | System Research in Energy |
| Завантажити файл: |  |
Institution
System Research in Energy| _version_ | 1871104403312738304 |
|---|---|
| author | Holovko, Oleksandr Kovtun, Svitlana Myhailov, Vasyl |
| author_facet | Holovko, Oleksandr Kovtun, Svitlana Myhailov, Vasyl |
| author_institution_txt_mv | [
{
"author": "Oleksandr Holovko",
"institution": null
},
{
"author": "Svitlana Kovtun",
"institution": null
},
{
"author": "Vasyl Myhailov",
"institution": null
}
] |
| author_sort | Holovko, Oleksandr |
| baseUrl_str | https://systemre.org/index.php/journal/oai |
| collection | OJS |
| datestamp_date | 2026-07-18T12:57:49Z |
| description | Modern challenges in the energy sector, particularly rising energy costs and declining reliability of electricity supply, are promoting the adoption of polygeneration microgrids. These systems integrate various energy sources, including photovoltaic modules, battery energy storage system, and backup diesel generators, providing energy autonomy and reducing dependence on utility grid. For Ukraine, which faces regular power outages due to damage to energy infrastructure, studying the efficiency of such microgrids is particularly relevant. The aim of this study is to analyse the efficiency of a private household microgrid equipped with 5 kW photovoltaic modules and a 10 kWh battery energy storage system. The focus is placed on analyzing self-consumption and self-sufficiency ratios. The analysis was conducted using daily, monthly, and annual data, taking into account seasonal variations in generation and consumption. The calculations showed a self-consumption ratio of 0.9997, indicating that the system is configured to effectively utilize locally generated energy. The annual self-sufficiency ratio reached 0.6262, covering 62.6 % of annual consumption. Seasonal data analysis demonstrated that self-sufficiency peaks during summer months due to high solar activity, while dependence on the utility grid increases in winter months. To improve self-sufficiency in winter, integrating alternative renewable energy sources to offset seasonal variations in solar activity is recommended. The results highlight the importance of implementing photovoltaic generation forecasting systems, demand-side management, and optimizing battery energy storage system operations to enhance microgrid efficiency. This study demonstrates the prospects of developing polygeneration systems in private households, particularly in the face of modern energy challenges. |
| doi_str_mv | 10.15407/srenergy2025.02.004 |
| first_indexed | 2026-03-24T02:03:30Z |
| format | Article |
| fulltext |
Системні дослідження в енергетиці. 2025. 2(82) 4
ТЕХНОЛОГІЇ ЕНЕРГЕТИКИ,
ЕНЕРГЕТИЧНІ СИСТЕМИ І КОМПЛЕКСИ
________________________________________________________________________________
ISSN 2786-7102 (Online), ISSN 2786-7633 (Print)
https://doi.org/10.15407/srenergy2025.02.004
UDC 621.311
Oleksandr Holovko*, https://orcid.org/0009-0003-9591-0807
Svitlana Kovtun, Dr. Sci. (Engin.), Senior Researcher, https://orcid.org/0000-0002-6596-3460
Vasyl Myhailov, Dr. Sci. (Engin.), Professor, https://orcid.org/0009-0006-9596-4225
General Energy Institute of NAS of Ukraine, 172, Antonovycha St., Kyiv, 03150, Ukraine
*Corresponding author: oleksandr.holovko.work@gmail.com
__________________________________________________________________________________________
ANALYSIS OF THE EFFICIENCY OF POLYGENERATION IN A PRIVATE
HOUSEHOLD MICROGRID
Abstract. Modern challenges in the energy sector, particularly rising energy costs and declining reliability of
electricity supply, are promoting the adoption of polygeneration microgrids. These systems integrate various
energy sources, including photovoltaic modules, battery energy storage system, and backup diesel generators,
providing energy autonomy and reducing dependence on utility grid. For Ukraine, which faces regular power
outages due to damage to energy infrastructure, studying the efficiency of such microgrids is particularly
relevant. The aim of this study is to analyse the efficiency of a private household microgrid equipped with 5 kW
photovoltaic modules and a 10 kWh battery energy storage system. The focus is placed on analyzing self-
consumption and self-sufficiency ratios. The analysis was conducted using daily, monthly, and annual data,
taking into account seasonal variations in generation and consumption. The calculations showed a self-
consumption ratio of 0.9997, indicating that the system is configured to effectively utilize locally generated
energy. The annual self-sufficiency ratio reached 0.6262, covering 62.6 % of annual consumption. Seasonal
data analysis demonstrated that self-sufficiency peaks during summer months due to high solar activity, while
dependence on the utility grid increases in winter months. To improve self-sufficiency in winter, integrating
alternative renewable energy sources to offset seasonal variations in solar activity is recommended. The results
highlight the importance of implementing photovoltaic generation forecasting systems, demand-side
management, and optimizing battery energy storage system operations to enhance microgrid efficiency. This
study demonstrates the prospects of developing polygeneration systems in private households, particularly in
the face of modern energy challenges.
Keywords: polygeneration, microgrid, self-sufficiency, self-consumption, battery energy storage system,
renewable energy sources, demand-side management.
1. Introduction
The modern challenges in Ukraine’s energy sector, particularly the declining reliability of electricity supply
due to damage to energy infrastructure [1, 2] and rising electricity costs, are driving the development of
polygeneration systems in private households. These systems integrate various generation sources, such as
photovoltaic (PV) modules and diesel generators. They also incorporate energy storage systems, for example
battery energy storage systems (BESS), to ensure energy autonomy and optimal resource utilization.
Microgrids, as a key component of polygeneration systems, are designed to align energy generation with
consumption, improve energy efficiency, and reduce dependence on the utility grid. Their ability to operate in
islanded mode during power outages significantly enhances the resilience of microgrids [2] compared to utility
grid, mitigating the effects of reliability declines [3, 4]. The integration of local generation and BESS introduces
Системні дослідження в енергетиці. 2025. 2(82) 5
new challenges, such as system size optimization [4], BESS management [3], and demand regulation. Demand-
side management in microgrids involves approaches like shifting consumption to periods of maximum local
generation and reducing load amplitude without changing the total consumption volume [5, 6].
Figure 1 illustrates a simplified diagram of a household microgrid that integrates PV modules and a BESS.
The BESS consists of a battery storage unit with a battery management system. A diesel generator serves as a
backup power source, providing additional reliability. The inverter enables bidirectional exchange between the
DC and AC buses, covering local load and exporting surplus electricity to the utility grid.
Fig. 1. Schematic diagram of the microgrid for the studied household
Papers [7−9] emphasize the importance of indicators such as the Self-Sufficiency Ratio (SSR) and the Self-
Consumption Ratio (SCR) for evaluating the efficiency of microgrid operations. However, the optimal distribution
of energy among various types of generation and BESS under different consumption conditions remains a
challenging and complex issue [10].
Microgrid management should ensure reliable and stable operation [2], leveraging the most efficient and
economically viable use of renewable energy sources (RES) and BESS. Management strategies for microgrids
should incorporate models for integrating RES and BESS, with the primary objective of storing surplus electricity
generated by RES in the BESS and utilizing it during periods of local generation deficits [11, 12].
However, relying solely on this strategy is suboptimal, as it does not account for microgrid consumption
patterns or the potential for exporting electricity to the grid. When connected to the utility grid, the microgrid
should minimize electricity costs by storing energy during low-tariff periods and discharging it during high-tariff
periods, taking into account differentiated tariff plans [13, 14].
In Ukraine, a two-tariff and three-tariff differentiated electricity pricing system is currently in place [15]. In
paper [13], it was noted that by applying an advanced genetic algorithm, actively utilizing BESS throughout the
day, and implementing demand-side management, a 14 % reduction in electricity costs was achieved under
differentiated tariffs in Sri Lanka.
The primary hypothesis of this study is that optimizing the use of distributed energy resources enhances the
efficiency of the polygeneration system and stabilizes the energy balance during peak loads. This hypothesis is
examined through an analysis of local generation and consumption data collected from November 2023 to
November 2024 in a private household.
The objective of this work is to evaluate the efficiency of a polygeneration system in a private household
by analyzing self-sufficiency and self-consumption ratios. The study aims to identify patterns in system operation
and provide recommendations for improving its efficiency.
Системні дослідження в енергетиці. 2025. 2(82) 6
2. Methods and Materials
The analysis of the efficiency of a polygeneration microgrid is based on evaluating its ability to meet local
consumption needs through self-generation and energy storage. This includes covering consumption with local
generation capacity, the ability to store surplus energy for use during local generation deficits, and evaluation the
interaction between the microgrid and the utility grid.
The Self-Consumption Ratio (SCR) reflects the efficiency of using locally generated energy. It indicates the
share of energy produced within the microgrid that is directly consumed locally, without being exported to the
utility grid [7, 8]. This indicator is critical for evaluation the microgrid’s ability to reduce grid dependence and
maximize local energy utilization
𝑆𝐶𝑅 = 1 −
𝑊𝐸𝑋𝑃
𝑊𝑃𝑉
, (1)
where 𝑊𝐸𝑋𝑃 is the energy exported to the utility grid, kWh; and 𝑊𝑃𝑉 is the energy produced by PV modules, kWh.
An SCR ranges from 0 to 1. At 0, all locally generated electricity is exported to the utility grid, while at 1,
the entire volume of local generation is utilized within the microgrid. A high self-consumption ratio reflects low
dependence on the utility grid, ensuring high autonomy and economic efficiency of the system.
The Self-Sufficiency Ratio (SSR) characterizes the microgrid’s ability to meet its own consumption needs
through local generation. This indicator determines the share of total consumption covered by energy generated
within the microgrid, without involving imports from the grid [8, 9]. A high SSR is an indicator of the energy
autonomy of the microgrid
𝑆𝑆𝑅 = 1 −
𝑊𝐼𝑀𝑃
𝑊𝐿
, (2)
where 𝑊𝐼𝑀𝑃 is the energy imported from the utility grid, kWh; 𝑊𝐿 is the total electricity consumption, kWh.
The SSR values range from 0 to 1. A value of 0 corresponds to complete dependence on the utility grid and
a mismatch between generation and consumption. A value of 1 reflects an ideal scenario where generation fully
aligns with consumption, indicating high microgrid autonomy.
The Efficiency Coefficient quantifies the energy consumed by microgrid equipment for transformation and
storage purposes and is calculated using the formula:
𝐾𝐸 =
(𝑊𝐸𝑋𝑃+𝑊𝐿)
(𝑊𝐼𝑀𝑃+𝑊𝑃𝑉)
. (3)
The calculation of the SSR for each month requires determining the volume of electricity imported from the
utility grid, which must account for the efficiency coefficient of the microgrid:
𝑊𝐼𝑀𝑃 = 𝑊𝐿 − 𝑊𝑃𝑉 × 𝐾𝐸, (4)
The data on generation, consumption, and electricity imports for the period from November 2023 to
November 2024 are presented in Table 1.
Системні дослідження в енергетиці. 2025. 2(82) 7
Table 1. Summary data of the polygeneration microgrid
Export to Grid, kWh Import from Grid, kWh Total Consumption, kWh Solar Generation, kWh
1.1 1546 4136 3261
The monthly data on PV modules generation and household consumption for the period from November
2023 to November 2024 are presented in Table 2.
Table 2. Energy balance indicators of the polygeneration microgrid
Month Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
Generation, kWh 111 60 70 117 225 253 342 437 570 467 346 208 55
Consumption, kWh 241 219 226 171 216 226 301 416 665 498 480 283 194
To analyze the dynamics of generation, consumption, and energy storage throughout the day, five daily
profiles were selected. The first date, September 23, 2024, was chosen as the day with the highest peak solar
generation during the observation period from September 23 to November 19, 2024. The second date, October
4, 2024, features similar ambient temperature parameters, enabling a comparative analysis of the influence of
wind and solar radiation. The subsequent dates − November 9, 10, and 16, 2024 − were selected as examples of
suboptimal BESS utilization strategies. The data are presented in Table 3.
Table 3. Daily energy performance indicators of the microgrid for selected dates
Date
Export to grid,
kWh
Import from grid,
kWh
Total
consumption,
kWh
Solar generation,
kWh
Peak
consumption,
kWh
Peak generation,
kWh
23.09.2024 0 0 11.3 13.7 0.82 3.38
04.10.2024 0 0 7.6 8.8 0.46 2.362
09.11.2024 0 11.3 11.1 1.2 0.601 0.422
10.11.2024 0.1 8 10 2 0.601 0.422
16.11.2024 0 8.8 9.3 2 0.64 0.37
The weather condition data for the selected dates in the area where the microgrid is located were obtained
from the nearest meteorological station, “Dolyna − IKYIVO14”, on the Weather Underground website
(https://www.wunderground.com/) and are presented in Table 4.
Table 4. Weather conditions in the microgrid area
Date
Average temperature,
°C
Max wind speed,
km/h
Average wind speed,
km/h
Max solar radiation,
W/m²
23.09.2024 16.9 16.1 4.1 638
04.10.2024 16.9 30.6 11.3 534
09.11.2024 4.7 12.9 3.8 65
10.11.2024 3 11.3 4.8 109
16.11.2024 2.3 16.1 7.8 190
Системні дослідження в енергетиці. 2025. 2(82) 8
During periods of surplus local generation, excess energy can either be stored in the BESS for use during
evening and nighttime hours or exported to the grid. The use of BESS reduces grid dependence, enhances
household self-sufficiency, and minimizes energy losses. Additionally, BESS supports electricity supply in the
event of grid outages [2]. Figures 2 and 3 present data on PV modules generation, local consumption, and the state
of charge (SOC) of the battery for two typical days of surplus solar generation: September 23 and October 4, 2024.
Fig. 2. Daily consumption and solar generation data for September 23, 2024
Fig. 3. Daily consumption and solar generation data for October 4, 2024
The following data reflect suboptimal use of the BESS in the context of differentiated tariffs and low PV
modules generation. Figure 4 shows a consistently high SOC of the battery throughout the day on November 9,
2024, highlights the inefficient utilization of the BESS.
Системні дослідження в енергетиці. 2025. 2(82) 9
Fig. 4. Daily consumption and solar generation data for November 9, 2024
One way to solve this issue is by optimizing the BESS settings. Specifically, it is necessary to adapt charge
and discharge management algorithms to ensure active use of the BESS during peak load periods or times of higher
tariffs. It is also worth considering consumption and generation forecasts to avoid excess energy storage.
The data for November 10, 2024 (Figure 5) indicate suboptimal energy storage timing, with the BESS
charging from the utility grid during the morning peak. This led to the export of surplus solar generation to the
utility grid during the day (Table 3).
Fig. 5. Daily consumption and solar generation data for November 10, 2024
In these conditions, exporting energy is suboptimal behavior since the surplus of renewable energy
generation during the day is minimal. Additionally, using the BESS as an energy source after 11:00 PM, when
electricity prices are at their lowest [15], is also inefficient.
The data for November 16, 2024, illustrate another type of suboptimal microgrid behavior, where minimal
solar generation surplus is followed by BESS charging from the utility grid during periods of higher electricity
prices compared to nighttime.
Системні дослідження в енергетиці. 2025. 2(82) 10
Fig. 6. Daily consumption and solar generation data for November 16, 2024
3. Results
The annual analysis of the microgrid provides an evaluation of its key performance indicators. The annual
SCR is 0.9997, indicating nearly complete utilization of local generation. This means that the microgrid minimizes
energy losses through exports and effectively utilizes all locally generated resources.
The SSR is 0.6262, showing that 62.6 % of the microgrid’s energy consumption was covered by its own
generation. This result highlights the significant contribution of local generation sources, though part of the
consumption still relies on imports from the grid.
The annual efficiency coefficient of the microgrid is 0.86, which highlights the high performance of the
energy transformation and storage systems. These metrics reflect the seamless operation of all system components,
ensuring stable and efficient energy supply for the household throughout the year.
Figure 7 illustrates data on PV modules generation, local consumption, electricity imports, and the
calculated SSR for each month from November 2023 to November 2024:
Fig. 7. Self-Sufficiency Ratio
The calculation of the SCR for each month was not performed because the share of electricity exported to
the grid is less than 0.05 % of the generated electricity, and variations in the ratio fall within the margin of error.
Системні дослідження в енергетиці. 2025. 2(82) 11
4. Conclusions
This study analyzed the key performance indicators of a polygeneration microgrid, including the calculation
of self-consumption and self-sufficiency ratios based on annual data for 2023–2024, as well as daily performance
data for the period from September to November 2024.
The minimal volume of electricity exported to the grid over the year indicates that the BESS effectively
compensates for daily fluctuations in solar generation. By accumulating surplus local generation and utilizing it to
optimize the timing of generation and consumption within the microgrid, a balanced energy profile is achieved.
An analysis of the monthly SSR and SCR confirms the positive impact of the BESS, which enables energy
storage and subsequent discharge. The SSR varies from 0.236 in the winter months to 0.997 in the spring and
summer seasons, reflecting the uneven distribution of solar radiation. The high SSR indicates that increasing PV
modules capacity is inefficient for improving the SSR in winter. Increasing PV modules capacity in summer would
negatively affect the SCR due to the BESS’s limited ability to store excess energy, as well as the need for exporting
energy or reducing energy generation in islanded mode. To enhance SSR during the winter season, integrating
wind turbines to compensate for seasonal fluctuations in PV module generation is recommended.
A critical step in improving efficiency involves using predictive models for renewable energy generation,
accounting for differentiated electricity tariffs and battery degradation [16] to actively utilize the BESS during the
autumn and winter seasons. It is important to note that the BESS in microgrids plays a critical role as an
uninterruptible power source, and the system must consider the required autonomy time in islanded mode.
To further enhance system efficiency, it is advisable to implement demand-side management strategies to
optimize the daily consumption structure, particularly by shifting consumption to periods with a positive energy
balance. Such measures will reduce losses caused by the mismatch between generation and consumption and will
also improve the overall SCR of electricity.
References
1. Piddubnyi, I., & Horiunov, D. (2024). Assessment of direct damages and indirect losses in the energy sector of Ukraine
due to the full-scale Russian invasion. Kyiv School of Economics. Retrieved February 15, 2025, from https://kse.ua/wp-
content/uploads/2024/06/KSE_Vpliv-vii--ni-na-energetiku_UA-1.pdf [in Ukrainian].
2. Kostenko, G., & Zaporozhets, A. (2023). Enhancing of the Power System Resilience Through the Application of Micro
Power Systems (microgrid) with Renewable Distributed Generation. System Research in Energy, 3(74), 25–38.
https://doi.org/10.15407/srenergy2023.03.025
3. Lechl, M., de Meer, H., & Fürmann, T. (2025). A stochastic flexibility calculus for uncertainty-aware energy flexibility
management. Applied Energy, 379, 124907. https://doi.org/10.1016/j.apenergy.2024.124907
4. Ullah, S. M. S., Yankson, S., Ebrahimi, S., Ferdowsi, F., & Chambers, T. (2024). Smart investment framework for
energy resilience: A case study of a campus microgrid research facility. Next Energy, 4, 100131.
https://doi.org/10.1016/j.nxener.2024.100131
5. Thiaux, Y., Dang, T. T., Schmerber, L., Multon, B., Ben Ahmed, H., Bacha, S., & Tran, Q. T. (2019). Demand-side
management strategy in stand-alone hybrid photovoltaic systems with real-time simulation of stochastic electricity
consumption behavior. Applied Energy, 253, 113530. https://doi.org/10.1016/j.apenergy.2019.113530
6. Albea, C., Bordons, C., & Ridao, M. A. (2021). Robust hybrid control for demand-side management in islanded
microgrids. IEEE Transactions on Smart Grid, 12(6), 4865−4875. https://doi.org/10.1109/TSG.2021.3101875
7. Griego, D., Schopfer, S., Henze, G., Fleisch, E., & Tiefenbeck, V. (2019). Aggregation effects for microgrid
communities at varying sizes and prosumer-consumer ratios. Energy Procedia, 159, 346−351.
https://doi.org/10.1016/j.egypro.2019.01.004
8. Luo, Y., Yuan, H., Hu, Z., Yang, D., & Zhang, H. (2024). Optimal scheduling of micro-energy grid based on Pareto
frontier under uncertainty and pollutant emissions. IEEE Transactions on Smart Grid, 15(1), 368−380.
https://doi.org/10.1109/TSG.2023.3273816
9. Gergely, L. Z., Barancsuk, L., & Horváth, M. (2025). Beyond net zero energy buildings: Load profile analysis and
community aggregation for improved load matching. Applied Energy, 379, 124934.
https://doi.org/10.1016/j.apenergy.2024.124934
10. Hirsch, A., Parag, Y., & Guerrero, J. (2018). Microgrids: A review of technologies, key drivers, and outstanding issues.
Renewable and Sustainable Energy Reviews, 90, 402−411. https://doi.org/10.1016/j.rser.2018.03.040
https://kse.ua/wp-content/uploads/2024/06/KSE_Vpliv-vii--ni-na-energetiku_UA-1.pdf
https://kse.ua/wp-content/uploads/2024/06/KSE_Vpliv-vii--ni-na-energetiku_UA-1.pdf
Системні дослідження в енергетиці. 2025. 2(82) 12
11. Hotra, O., Kulyk, M., Babak, V., Kovtun, S., Zgurovets, O., Mroczka, J., & Kisała, P. (2024). Organisation of the
Structure and Functioning of Self-Sufficient Distributed Power Generation. Energies, 17(1), 27.
https://doi.org/10.3390/en17010027
12. Buratynskyi, I., & Nechaieva, T. (2022). The Least-Cost Optimization of PV-Station DC/AC Equipment Using Battery
Energy Storage System. Latvian Journal of Physics and Technical Sciences, 59(1), 53−62. https://doi.org/10.2478/lpts-
2022-0006
13. Witharama, W. M. N., Bandara, K. M. D. P., Azeez, M. I., Bandara, K., Logeeshan, V., & Wanigasekara, C. (2024).
Advanced Genetic Algorithm for Optimal Microgrid Scheduling Considering Solar and Load Forecasting, Battery
Degradation, and Demand Response Dynamics. IEEE Access, 12, 83269−83284.
https://doi.org/10.1109/ACCESS.2024.3412914
14. Babu, V. V., Roselyn, P. J., Sundaravadivel, P. (2023). Multi-objective genetic algorithm based energy management
system considering optimal utilization of grid and degradation of battery storage in microgrid. Energy Reports, 9,
5992−6005. https://doi.org/10.1016/j.egyr.2023.05.067
15. Cabinet of Ministers of Ukraine. (2019). Resolution No. 483 of June 5, 2019 (with amendments). Retrieved February
15, 2025, from https://zakon.rada.gov.ua/laws/show/483-2019-%D0%BF [in Ukrainian].
16. Kostenko, G., & Zaporozhets, A. (2024). Integral degradation index of second-life ev batteries application in energy
storage systems: accounting for calendar and cyclic aging. System Research in Energy, 2а(78), 31–33 [in Ukrainian].
https://doi.org/10.15407/srenergy2024.02a
АНАЛІЗ ЕФЕКТИВНОСТІ ПОЛІГЕНЕРАЦІЇ В МІКРОМЕРЕЖІ НА
ПРИКЛАДІ ПРИВАТНОГО ДОМОГОСПОДАРСТВА
Олександр Головко*, https://orcid.org/0009-0003-9591-0807
Світлана Ковтун, д-р техн. наук, ст. досл., https://orcid.org/0000-0002-6596-3460
Василь Михайлов, д-р техн. наук, професор, https://orcid.org/0009-0006-9596-4225
Інститут загальної енергетики НАН України, вул. Антоновича, 172, Київ, 03150, Україна
*Автор-кореспондент: oleksandr.holovko.work@gmail.com
Анотація. Сучасні виклики в енергетичному секторі, зокрема зростання вартості енергії та зниження
надійності електропостачання, стимулюють впровадження полігенераційних мікромереж. Такі
системи інтегрують різні джерела енергії, зокрема фотоелектричні модулі, системи зберігання енергії
та резервні дизель-генератори, що забезпечує енергетичну автономність і зменшує залежність від
централізованих мереж. Для України, яка стикається з регулярними перебоями електропостачання
через пошкодження енергетичної інфраструктури, дослідження ефективності таких мікромереж є
особливо актуальним. Метою роботи є оцінка ефективності мікромережі приватного
домогосподарства, обладнаного фотоелектричними модулями потужністю 5 кВт і акумуляторною
системою зберігання енергії ємністю 10 кВт·год. Основна увага приділена аналізу коефіцієнтів
самоспоживання та самозабезпечення. Аналіз виконано на основі щоденних, місячних і річних даних з
урахуванням сезонних змін генерації та споживання. Розрахунки показали, що коефіцієнт
самоспоживання становить 0.9997. Система налаштована таким чином, щоб ефективно
використовувати локально згенеровану енергію. Річний коефіцієнт самозабезпечення досягнув
значення 0,6262, покриваючи 62,6 % річного споживання. Аналіз сезонних даних продемонстрував, що
в літні місяці самозабезпечення досягає максимальних значень завдяки високому рівню сонячної
активності, тоді як у зимовий період залежність від централізованої мережі зростає. Отримані
результати підкреслюють важливість впровадження систем прогнозування генерації
фотоелектричних модулів, керування попитом та оптимізації роботи системи зберігання енергії для
підвищення ефективності мікромереж. Це дослідження демонструє перспективність розвитку
полігенераційних систем у приватних домогосподарствах, особливо в умовах сучасних енергетичних
викликів.
Ключові слова: полігенерація, мікромережа, самозабезпечення, самоспоживання, система зберігання
енергії, відновлювані джерела енергії, керування попитом.
Надійшла до редколегії: 08.03.2025
https://zakon.rada.gov.ua/laws/show/483-2019-%D0%BF
|
| id | systemreorg-article-893 |
| institution | System Research in Energy |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-19T01:23:32Z |
| publishDate | 2025 |
| publisher | General Energy Institute of the National Academy of Sciences of Ukraine |
| record_format | ojs |
| resource_txt_mv | systemreorg/d1/6fa5ea8887291110d8c3b0a0ac590ad1.pdf |
| spelling | systemreorg-article-8932026-07-18T12:57:49Z ANALYSIS OF THE EFFICIENCY OF POLYGENERATION IN A PRIVATE HOUSEHOLD MICROGRID Аналіз ефективності полігенерації в мікромережі на прикладі приватного домогосподарства Holovko, Oleksandr Kovtun, Svitlana Myhailov, Vasyl polygeneration, microgrid, self-sufficiency, self-consumption, battery energy storage system, renewable energy sources, demand-side management. полігенерація, мікромережа, самозабезпечення, самоспоживання, система зберігання енергії, відновлювані джерела енергії, керування попитом. Modern challenges in the energy sector, particularly rising energy costs and declining reliability of electricity supply, are promoting the adoption of polygeneration microgrids. These systems integrate various energy sources, including photovoltaic modules, battery energy storage system, and backup diesel generators, providing energy autonomy and reducing dependence on utility grid. For Ukraine, which faces regular power outages due to damage to energy infrastructure, studying the efficiency of such microgrids is particularly relevant. The aim of this study is to analyse the efficiency of a private household microgrid equipped with 5 kW photovoltaic modules and a 10 kWh battery energy storage system. The focus is placed on analyzing self-consumption and self-sufficiency ratios. The analysis was conducted using daily, monthly, and annual data, taking into account seasonal variations in generation and consumption. The calculations showed a self-consumption ratio of 0.9997, indicating that the system is configured to effectively utilize locally generated energy. The annual self-sufficiency ratio reached 0.6262, covering 62.6 % of annual consumption. Seasonal data analysis demonstrated that self-sufficiency peaks during summer months due to high solar activity, while dependence on the utility grid increases in winter months. To improve self-sufficiency in winter, integrating alternative renewable energy sources to offset seasonal variations in solar activity is recommended. The results highlight the importance of implementing photovoltaic generation forecasting systems, demand-side management, and optimizing battery energy storage system operations to enhance microgrid efficiency. This study demonstrates the prospects of developing polygeneration systems in private households, particularly in the face of modern energy challenges. Сучасні виклики в енергетичному секторі, зокрема зростання вартості енергії та зниження надійності електропостачання, стимулюють впровадження полігенераційних мікромереж. Такі системи інтегрують різні джерела енергії, зокрема фотоелектричні модулі, системи зберігання енергії та резервні дизель-генератори, що забезпечує енергетичну автономність і зменшує залежність від централізованих мереж. Для України, яка стикається з регулярними перебоями електропостачання через пошкодження енергетичної інфраструктури, дослідження ефективності таких мікромереж є особливо актуальним. Метою роботи є оцінка ефективності мікромережі приватного домогосподарства, обладнаного фотоелектричними модулями потужністю 5 кВт і акумуляторною системою зберігання енергії ємністю 10 кВт·год. Основна увага приділена аналізу коефіцієнтів самоспоживання та самозабезпечення. Аналіз виконано на основі щоденних, місячних і річних даних з урахуванням сезонних змін генерації та споживання. Розрахунки показали, що коефіцієнт самоспоживання становить 0.9997. Система налаштована таким чином, щоб ефективно використовувати локально згенеровану енергію. Річний коефіцієнт самозабезпечення досягнув значення 0,6262, покриваючи 62,6 % річного споживання. Аналіз сезонних даних продемонстрував, що в літні місяці самозабезпечення досягає максимальних значень завдяки високому рівню сонячної активності, тоді як у зимовий період залежність від централізованої мережі зростає. Отримані результати підкреслюють важливість впровадження систем прогнозування генерації фотоелектричних модулів, керування попитом та оптимізації роботи системи зберігання енергії для підвищення ефективності мікромереж. Це дослідження демонструє перспективність розвитку полігенераційних систем у приватних домогосподарствах, особливо в умовах сучасних енергетичних викликів. General Energy Institute of the National Academy of Sciences of Ukraine 2025-05-07 Article Article application/pdf https://systemre.org/index.php/journal/article/view/893 10.15407/srenergy2025.02.004 System Research in Energy; No. 2 (82) (2025): System Research in Energy; 4-12 Системні дослідження в енергетиці; № 2 (82) (2025): Системні дослідження в енергетиці; 4-12 2786-7102 2786-7633 en https://systemre.org/index.php/journal/article/view/893/798 Copyright (c) 2025 Oleksandr Holovko, Svitlana Kovtun, Vasyl Myhailov https://creativecommons.org/publicdomain/zero/1.0 |
| spellingShingle | polygeneration microgrid self-sufficiency self-consumption battery energy storage system renewable energy sources demand-side management. Holovko, Oleksandr Kovtun, Svitlana Myhailov, Vasyl ANALYSIS OF THE EFFICIENCY OF POLYGENERATION IN A PRIVATE HOUSEHOLD MICROGRID |
| title | ANALYSIS OF THE EFFICIENCY OF POLYGENERATION IN A PRIVATE HOUSEHOLD MICROGRID |
| title_alt | Аналіз ефективності полігенерації в мікромережі на прикладі приватного домогосподарства |
| title_full | ANALYSIS OF THE EFFICIENCY OF POLYGENERATION IN A PRIVATE HOUSEHOLD MICROGRID |
| title_fullStr | ANALYSIS OF THE EFFICIENCY OF POLYGENERATION IN A PRIVATE HOUSEHOLD MICROGRID |
| title_full_unstemmed | ANALYSIS OF THE EFFICIENCY OF POLYGENERATION IN A PRIVATE HOUSEHOLD MICROGRID |
| title_short | ANALYSIS OF THE EFFICIENCY OF POLYGENERATION IN A PRIVATE HOUSEHOLD MICROGRID |
| title_sort | analysis of the efficiency of polygeneration in a private household microgrid |
| topic | polygeneration microgrid self-sufficiency self-consumption battery energy storage system renewable energy sources demand-side management. |
| topic_facet | polygeneration microgrid self-sufficiency self-consumption battery energy storage system renewable energy sources demand-side management. полігенерація мікромережа самозабезпечення самоспоживання система зберігання енергії відновлювані джерела енергії керування попитом. |
| url | https://systemre.org/index.php/journal/article/view/893 |
| work_keys_str_mv | AT holovkooleksandr analysisoftheefficiencyofpolygenerationinaprivatehouseholdmicrogrid AT kovtunsvitlana analysisoftheefficiencyofpolygenerationinaprivatehouseholdmicrogrid AT myhailovvasyl analysisoftheefficiencyofpolygenerationinaprivatehouseholdmicrogrid AT holovkooleksandr analízefektivnostípolígeneracíívmíkromerežínaprikladíprivatnogodomogospodarstva AT kovtunsvitlana analízefektivnostípolígeneracíívmíkromerežínaprikladíprivatnogodomogospodarstva AT myhailovvasyl analízefektivnostípolígeneracíívmíkromerežínaprikladíprivatnogodomogospodarstva |