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Article

The Impact of Energy Storage on the Efficiency of Photovoltaic Systems and Determining the Carbon Footprint of Households with Different Electricity Sources

by
Patrycja Walichnowska
*,
Weronika Kruszelnicka
*,
Andrzej Tomporowski
and
Adam Mroziński
Faculty of Mechanical Engineering, Bydgoszcz University of Science and Technology, al. Prof. S. Kaliskiego 7, 85-796 Bydgoszcz, Poland
*
Authors to whom correspondence should be addressed.
Sustainability 2025, 17(6), 2765; https://doi.org/10.3390/su17062765
Submission received: 22 January 2025 / Revised: 9 March 2025 / Accepted: 19 March 2025 / Published: 20 March 2025
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Abstract

:
The article designs a home photovoltaic installation equipped with energy storage using PVSyst software 7.4. The aim of the research was to design and select an energy storage for a household that uses an average of 396.7 kWh per month. The designed PV installation system was characterised by a significant share of stored energy—at the level of 32%, which allows the household to reduce energy consumption from the power grid. The results of the analysis showed that the use of energy storage increases leads to a reduction in energy losses and improves the energy self-sufficiency of the facility. The article also compared, using the IPCC 2013 GWP 100a and IMPACT World+ methods, three variants of households with different energy sources. It was shown that a household using the national energy mix generates a significant carbon footprint, higher compared to variants powered by renewable energy. The study showed that obtaining energy from renewable sources reduces the potential negative impact of energy consumption on the environment.

1. Introduction

Contemporary trends in the energy market are characterised by a constantly growing demand for electricity and actions aimed at reducing the negative impact on the environment. To reduce the negative impact of national energy mixes, solutions for energy production based on renewable energy sources are being implemented. Despite their numerous benefits, these new technologies are burdened with certain limitations. The main challenge associated with these technologies is their instability, which requires full integration of energy storage systems with existing sources, enabling greater control over production. For several years, there has been a noticeable increase in interest in efficient and cost-effective home systems for storing energy obtained from renewable sources.
The most frequently chosen energy storage devices in households are lithium-ion batteries, which are characterised by high energy density, efficiency in storing and supplying energy, and long service life. Another feature that affects their great popularity in use is their lightness, which facilitates installation in various conditions. In addition, the ability to work in a wide temperature range, as well as the possibility of integration with intelligent energy management systems, make this type of storage device useful among many individual users who, by adding RES to their installation, want to increase their efficiency and strengthen their energy independence. Lithium-ion batteries are beginning to play an important role in energy transformation by being the foundation of modern energy management systems in sustainable development. In home installations, they are becoming a basic element influencing the optimal use of energy obtained from renewable energy source installations. Thanks to such solutions, households can store surplus energy and use it during periods of greatest demand when production from their own source is insufficient to cover energy needs. Such operation of the installation will not only reduce electricity bills but also reduce dependence on the domestic supplier. In combination with energy management systems, lithium-ion storage can automatically control energy consumption and storage, improving the energy efficiency of the installation. Moreover, with technological progress, lithium-ion batteries will find even wider applications, from intelligent home installations to large energy systems and transport, becoming the foundation of a modern, low-emission economy [1]. Home energy storage is key in modern energy systems, becoming an increasingly popular solution in many households. In combination with photovoltaic installations, they enable effective management of the energy produced, which translates into lower electricity bills. Their use brings a number of benefits, such as ensuring emergency power supply in the event of power outages, no need to modernise low-voltage grids, reducing energy losses in low- and medium-voltage grids, and limiting overloads resulting from the simultaneous operation of numerous distributed energy sources. In addition, energy storage supports the decentralisation of the national energy sector by enabling the production and consumption of energy at the household level and also reduces dependence on nationwide electricity suppliers, allowing the use of one’s own energy resources [2,3,4].
Kumar et. al. [5] analysed the energy requirement of the mechanical engineering college office in Bikaner and designed and installed a stand-alone photovoltaic system. The simulation was performed in PVsyst software to evaluate the performance factor and losses of the system and obtained an average annual efficiency of 72.8%. The results indicate that the system delivers energy close to the requirement, with the difference being due to different types of losses affecting its efficiency. Ghafoor and Munir [6] investigated a stand-alone photovoltaic system for household electrification in Pakistan by analysing the energy requirements and availability of solar energy. They developed a complete system design model, panel power, battery capacity, charge controller and inverter size, and conducted an economic evaluation based on life cycle cost (LCC) analysis. The results showed that the PV system is a technically and economically viable solution, offering a lower cost of electricity compared to traditional power supply. Aberilla et al. [7] conducted an environmental analysis of off-grid renewable energy systems designed for rural communities in the Philippines. The authors analysed 21 system configurations that included PV, wind, hybrid, and storage technologies using a life-cycle assessment (LCA) across 18 environmental impact categories. The results showed that hybrid systems consisting of PV and wind with storage were more sustainable than stand-alone systems, with batteries accounting for the largest share of the LCI. The best option was found to be a household-scale PV system integrated with a community-scale wind microgrid and lithium-ion batteries. Akinsipe et al. [8] conducted a technical and economic analysis of the implementation of an off-grid photovoltaic installation to power a residential building in Nigeria. Using mathematical modelling, the authors designed an installation consisting of 10 photovoltaic modules with a power of 275 Wp and five batteries with a capacity of 100 Ah. The installation selected in this way was able to cover the annual energy demand of approx. 3132 kWh. The technical and economic analysis showed that such a system is cost-effective and can effectively power households in regions with limited access to power grids. Hassan [9] analysed the designed home photovoltaic installation in Iraq in two variants: on-grid and off-grid. The results showed that on-grid systems are more economical compared to off-grid systems. However, both systems are environmentally friendly and meet the requirements of households, with excess energy in on-grid systems supporting the national power grid. Mermoud et al. [10] analysed photovoltaic systems with battery storage, which allows shifting the use of solar energy to more favourable moments. The authors conducted simulations in the PVSyst program for three strategies of using energy storage in the system: peak demand reduction, maximisation of self-consumption and grid support. The research shows that peak shaving systems are most effective in sunny climates, but their profitability depends on favourable price assumptions. For island operation, it was indicated that the appropriate selection of battery capacity allows achieving the desired level of self-consumption and reliability, and the planned introduction of the “Load Shifting” strategy in PVSyst can additionally improve the economics of the systems in dynamic network tariffs. Coşgun et al. [11] analysed the integration of solar energy production and agriculture in Turkey through agriphotovoltaic (AV) systems. They indicated which sites have the potential for the best conditions for this technology. They also showed that combining solar energy with land productivity can meet the growing energy demand and improve agricultural efficiency. Hollmuller et al. [12] conducted an analysis of the financial profitability of a PV installation with energy storage. The authors simulated systems of different sizes using the PVsyst software, considering the influence of such factors as load profile, meteorological conditions, battery cost and tariffs. They created a methodology for optimising PV systems with energy storage based on parametric simulations. Additionally, they presented a simplified method for estimating the optimal size of the system based on the results of a single simulation. Cosgun and Demir [13] conducted a study comparing the energy performance of bifacial and monofacial photovoltaic modules installed above the water at a height of 1 m. They performed simulations for a 1 MW system at the Mamasın Dam in Aksaray Province, analysing the impact of the albedo effect, water savings, and CO2 emission reduction. The results showed that bifacial modules generate 12.4% more energy per year than monofacial ones, mainly due to the albedo effect. Demir [14] presented a photovoltaic energy harvesting system that powers monitoring systems in places without access to traditional energy sources. The energy from the PV panels was captured and used to generate electricity using thermoelectric generators and then converted and stored in a lithium-ion battery. Field tests showed a maximum temperature gradient of 21.08 °C, and the stable output power reached about 350 mW. The obtained results showed that it is possible to reduce the maintenance costs of PV monitoring systems and their further development.
The analysis of the current state of knowledge and technology has shown that there are many studies in the literature on environmental and technical analyses of off-grid photovoltaic installations [15,16,17,18,19] and on-grid installations using energy storage [20,21,22,23]. The literature on the subject also includes studies on the carbon footprint and environmental assessment of households [24,25,26]. Alkahtani et al. [27] simulated a 400 MW solar power plant using PVSyst 8. The results showed that higher-power PV modules (580 WP) were more efficient than lower-power ones (330 WP and 255 WP). The analysis included the effect of different tilt and orientation settings of PV panels to better match geographical conditions. Yadav et al. [28] conducted an analysis of a 4 kW PV installation in Odisha. It was determined that the system generates 17.8 kWh of energy per day and reduces CO2 emissions by 113 tons for every 4 kWp. This reduction in CO2 is equivalent to planting 181 trees. Simulations were conducted in the PVsyst program, analysing the system efficiency, excess energy fed to the grid, and various energy losses. However, the studies conducted so far do not consider the specific conditions prevailing in Poland. Therefore, their results may have limited direct usefulness in the national context. With the above in mind, the article designed a photovoltaic installation with energy storage for the adopted household to determine the amount of energy that can be produced by the installation. As well as to determine how much of this energy can be effectively stored and then used by this house. In addition, the following research question was asked in this article:
  • How much energy obtained from the designed PV installation is used at the current time, fed into the grid, and stored?
  • What is the expected annual energy production from the designed installation?
  • What are the biggest losses in the designed PV system related to?
  • At what level will the average performance coefficient (PR) be in the analysed photovoltaic system, considering the expected operating conditions?
  • Will changing the energy source of a given house to 100% renewable energy sources reduce the potential negative impact on the environment?
  • How will the carbon footprint values change with the change in the electricity source?
The distinguishing feature of this article from the others is the combination of the photovoltaic installation project with environmental analysis. The analysis aims to compare the carbon footprint and potential negative impacts on the environment depending on the energy source powering the adopted household. A limitation of the conducted research is the fact that it focuses on an assumed example household located in Poland. The adoption of a specific research object may make it difficult to generalise the results to other regions, types of buildings or different energy consumption profiles. The main goal of this article is to design a photovoltaic (PV) installation with energy storage for a household and to determine the degree to which the energy demand is covered by the generated energy. In addition, the carbon footprint of energy consumption before the analysed household will be determined in three different cases. The analysed variants differ in the source of energy used to power electrical devices. The analysed energy sources come from the national energy mix, a wind farm and a PV installation. The novelty presented in this article is the combination of research on the optimal design of energy storage installations with an environmental analysis of energy consumption by a given household in the conditions adopted for Poland. In this study, both approaches were combined, considering both energy efficiency and environmental impact.

2. Materials and Methods

2.1. Home Installation Project with Energy Storage

With the development of renewable energy sources, there has been an increased need for energy storage for its efficient use. Modern storage technologies are sought that will enable the storage of excess energy and provide the possibility of using it at times of higher demand. In home installations, energy storage allows the user to use their own energy when energy prices are high. Adding energy storage to a PV installation is still a challenge because it requires considering different energy management strategies that depend on the goals of the system owner. Three strategies are available in the PVSyst program: peak demand levelling, maximising self-consumption and partial island support, which can be simulated in hourly steps throughout the year. In the article, for example, household with an average monthly energy consumption of 396.7 kWh/mth (Table 1), a photovoltaic installation with a capacity of 4.40 kWp was designed in the PVSyst simulation program together with energy storage with a capacity of 900 Ah.
The installation consists of 8 monocrystalline photovoltaic modules with a capacity of 550 Wp and an inverter with a capacity of 4.2 kWp (Table 2). The modules have a total area of 21m2 and a nominal efficiency (STC) of 21.29%. The analysis assumed a self-consumption distribution strategy, which aims to reduce the exchange of power with the grid.
An example installation was designed at the latitude 53.22° N and longitude 18.07° E, where the average solar radiation is about 1060 kWh/m2. Meteorological data, depending on the geographical location, are presented in Table 3. Thanks to this, the photovoltaic installation can produce enough free electricity, contributing to significant savings in electricity bills.
Figure 1 shows visualisation of a photovoltaic installation, which is located on the roof. Thanks to this arrangement, the installation effectively converts solar radiation energy into electrical energy, which can be used for the needs of the building or stored in battery systems.
To better illustrate the design assumptions and operation of the system, a diagram of the designed installation is presented (Figure 2). The presented diagram includes key system elements such as photovoltaic modules, inverters, energy storage and energy management systems. Additionally, it contains information on electrical connections, energy flow and how the installation is integrated with the power grid.

2.2. Carbon Footprint and Environmental Analysis

Energy consumption in a household is largely responsible for the carbon footprint it generates. To calculate emissions resulting from energy use, the total amount of energy used by a given house in each period must be considered. This study compares three variants of energy consumption in a household (Table 4). The individual variants differ in the source of energy used to meet the household’s demand. The adopted functional unit (FU) is the annual energy consumption of the household under study.
The carbon footprint was determined using SimaPro version 9.6, an advanced and widely used program for environmental assessment of selected facilities. This tool offers an extensive database, containing numerous libraries of processes and materials, which allows for detailed modelling of complex supply chains and calculation of emissions and resource requirements [29,30]. SimaPro is regularly updated to include the latest technology and market data, which increases the reliability and precision of the analyses performed. To determine the carbon footprint, the IPCC 2013 GWP 100a method was used, which presents greenhouse gas emissions in kg CO2eq considering their impact on the climate over a 100-year period. This method includes a variety of greenhouse gases, not limited to CO2, which allows for a more comprehensive understanding of the impact of the selected object of study on global warming. The carbon footprint indicator, called global warming potential (GWP), was developed by the Intergovernmental Panel on Climate Change. It concerns a 100-year period and includes various greenhouse gases, such as CO2, CH4, chlorofluorocarbons (CFCs), N2O, hydrofluorocarbons (HFCs) and other halogenated hydrocarbons [31]. In addition, the IMPACT World+ method was used to determine the potential impact of the studied variants on the environment. The method used is used to analyse the research objects on the environment on a global and regional scale. It considers various impact categories, such as climate change, toxicity, eutrophication, or resource depletion. The IMPACT World+ method introduced new indicators that have not been included in other methods so far. Of particular importance are the assessments of the impact of water consumption on human health, long-term acidification and eutrophication of the season the state of ecosystems, which is a key factor contributing to global environmental damage [32,33].

3. Results

3.1. Results of the Analysis of the Designed PV Installation with Energy Storage

The analysis of the designed PV installation with energy storage showed that the annual energy production from the designed PV installation is 4822.3 kWh (Table 5). This value exceeds the total annual demand of the user, which is 4826.3 kWh. The higher value of energy supplied from the installation to the energy needed by the user indicates that the installation can cover his energy needs. The highest production took place in the spring and summer months: May (638.7 kWh), June (620.7 kWh), July (617.1 kWh) and August (590.9 kWh). On the other hand, the lowest production was recorded in the winter months in January (135.5 kWh) and December (89.3 kWh). The annual demand assumed in the analysis in the analysed case is 4826.3 kWh and varies depending on the length of the month (average value 396.7 kWh). Autoconsumption for the analysed installation is 2020 kWh. This means that over 40% of the energy produced is consumed in real time. In moments of excess production, when storage energy is already full, the excess electricity produced is fed into the grid.
As part of the simulation, the performance coefficient (PR) of the photovoltaic system was also determined (Figure 3), which is the ratio of the energy effectively generated to the energy that would be generated if the system operated continuously at its nominal STC efficiency [29]. The analysis of the performance coefficient (PR) showed that the highest PR of the installation was 85.8% in March, and the lowest PR was 79.1% in December. The average annual PR of the tested installation was 81.9%.
The study also provided data on losses (Figure 4), which include losses on the energy storage side. In total, the passage of energy through the storage causes a loss of about 5–6% of the stored energy. The loss analysis showed a small compensation of losses due to the positive energy balance in the battery (+0.2%), which means that some energy is recovered during the charging and discharging cycles. In addition, in the designed installation, about 68% of solar energy was used immediately and 32% passed through the storage.
Figure 5 shows the sun’s trajectory for different days of the year for the assumed location. From the graph, it can be concluded that the greatest energy production occurs in the afternoon. In the evening and morning, there is limited access to sunlight, so the installation is not able to operate effectively.

3.2. Analysis of the Determined Carbon Footprint for the Tested Variants

The IPCC 2013 GWP 100a method was used to compare the carbon footprint of energy used by a sample household. Analysis of three different energy sources showed that the variant based on the national energy mix (Figure 6), in which over 60% of energy is obtained from coal, has the largest potential carbon footprint (4.82 × 103 kg CO2eq/FU). Significantly lower values were obtained for variants using energy from renewable sources, for which the carbon footprint was 135 kg CO2eq/FU—the variant with photovoltaic installation and 0.787 kg CO2eq/FU—the variant with energy obtained from wind. Similarly to photovoltaics, certain environmental costs are generated mainly at the stage of manufacturing the turbines and energy installations themselves, their transport, assembly and subsequent service.
Table 6 presents a comparison of emissions of three gases such as carbon dioxide, methane and nitrogen oxides emitted to air per functional unit (FU) for three energy source variants in the examined household. Variant 1 is characterised by the highest emissions of all analysed substances: carbon dioxide (107 kg/FU), methane (20,900 ng/FU) and nitrogen oxides (9.28 kg/FU). Variant 2 generates significantly lower values, from 0.0463 kg/FU CO2 to 241 ng/FU CH4, and the third variant is characterised by almost negligible emissions, especially in the scope of CO2 (0.00457 kg/FU) and nitrogen oxides (0.00066 kg/FU). The difference in the obtained emission values between the variants illustrates the environmental benefits resulting from the transition to energy sources based on renewable sources.
The article also included an environmental analysis of the studied household variants. Using the Impact World+ Endpoint method, it was shown how the potential impact on the categories of human health and ecosystems changes depending on the source of energy supplying the studied household during the year. The results presented in Table 7 show that the greatest environmental burden among the three studied variants is generated by Variant 1. For this variant, the highest impact values were determined for the impact category freshwater ecotoxicity, long term (30,300 PDF × m2 × year), and the lowest for ionising radiation, ecosystem quality (2.16 × 10−7 PDF × m2 × year). Variant 2 is characterised by a potentially smaller impact on the environment than Variant 1, the highest impact value is achieved for the impact category Climate change, eco-system quality, long term (78.60 PDF × m2 × year), and the lowest for ionising radiation, ecosystem quality (9.58 × 10−10 PDF × m2 × year). Variant 3 is characterised by the least harmfulness among the analysed variants. Its highest impact value is determined for the freshwater ecotoxicity, long-term category (0.532 PDF × m2 × year), and the lowest in the ionising radiation, ecosystem quality category (2.10 × 10−10 PDF × m2 × year).
Variant 1 is mainly based on fossil fuels, which generate a high level of pollution affecting ecotoxicity (e.g., freshwater) and long-term climate change. The conducted studies have shown that Variant 2 is much less harmful to the environment, which results from the fact that PV modules do not emit pollutants during operation. The greatest burdens are associated with the production and disposal of modules, including emissions in the energy supply chain needed to produce them, which translates into a higher result in terms of long-term climate change. Variant 3 generates the lowest values in almost all categories because relatively few emissions and waste are generated in the entire life cycle of a wind turbine (from production through operation to disposal) compared to the other variants. The main burdens in the freshwater ecotoxicity, long-term category, result from the acquisition and processing of raw materials, e.g., steel and concrete, but these values are still much lower than in the case of the domestic mix or the production of photovoltaic panels.

4. Discussion

The use of photovoltaic modules in home installations is one of the most popular solutions among renewable energy installations. The variability of energy production by photovoltaic systems raises doubts about their ability to provide a stable power supply. However, thanks to the integration of energy storage technologies, these systems become a safe and ecological source. A properly selected energy storage device with the appropriate capacity will enable more efficient operation of the photovoltaic system connected to the network while reducing the negative impact on the quality of the network power supply and increasing its level of use [34,35]. The simulation of a 4.2 kWp home photovoltaic installation using an energy storage device showed that the annual yields from the system amount to 4822.3 kWh, of which 68% are consumed by the household in the current time, and 32% are stored using a selected energy storage device with a capacity of 900 Ah. The high level of self-consumption indicates that the planned PV installation power is optimally matched to the needs of a given household. In practice, the more energy is consumed on an ongoing basis, the greater the savings the user gains from their installation. The energy storage device selected for the installation allows for storing 1/3 of the energy produced. This level of storage allows for greater independence from the grid and the use of one’s own energy also during low production hours, e.g., in the evening or at night, when energy production from the PV installation is impossible [36]. The conducted analysis clearly indicates the high effectiveness of the tool, which is the PVSyst program, in the context of designing, modelling and optimising photovoltaic systems. This program allows for precise estimation of the efficiency of PV installations, considering key factors such as climate conditions, location, panel and inverter characteristics and potential losses related to shading, dirt or non-optimal angle of module inclination. The PVSyst program provides effective support in the process of designing photovoltaic systems, enabling optimisation of efficiency, minimisation of operating costs and increase in the profitability of investments in renewable energy sources, including installations using energy storage.
Carbon dioxide emissions generated by households are one of the key factors influencing global emissions, which is becoming increasingly common. The analysis of the carbon footprint of the examined household variants conducted in the article, using the IPCC 2013 GWP 100a method, clearly indicates that a greater share of renewable sources in energy production reduces its value. The lower environmental impact in the case of powering the process with energy from a wind farm compared to a photovoltaic plant results from the differences in the impact related to the acquisition of raw materials and disposal after the end of use of these two energy sources [37]. Available data on energy production from various sources available in the Ecoinvent database are averaged and cover all life cycle phases. In the case of photovoltaic power plants and wind farms, the impact on the environment during use is minimal, but the greatest impact is exerted by processes related to raw material acquisition, production and end-of-life cycle [38,39,40]. The conducted research confirms that most of the emissions for both tested RES technologies are generated at the stage of raw material extraction, production and installation. As writes Hamed et al. [38], the longer the service life of the system, the lower the emissions over the entire period of operation of the installation. Nugent and Sovacool [39] report that a wind system with an expected lifespan of 20 years generates an average of 40.7 g CO2/kWh while extending the period to 30 years reduces this value to 25.3 g CO2/kWh. In the use of wind farms for electricity production, the greatest environmental burden is the non-recyclable turbine blades [37]. In photovoltaic farms, the use of specific materials that significantly affect the environment and restrictions in the recycling processes of all farm elements means that the energy obtained from this source shows higher impact values than the wind energy variant. The obtained results confirm the carbon footprint analyses conducted by other scientists [38,40].
The study was conducted to determine the carbon footprint of a household with an assumed energy consumption, analysing different variants that differed in energy sources. To finally generalise the conclusions and recommendations, the limitations of this analysis were considered. The results were based on data specific to the specific energy consumption of the household, based on specific operating parameters and regional boundaries. In this study, the regional boundary was the European Union, considering the Polish energy mix, which is largely based on coal. In regions where non-renewable energy sources dominate, switching to renewable energy sources (e.g., wind or photovoltaic energy) could result in a greater reduction of the carbon footprint than indicated in this analysis. Data on energy acquisition came from the Ecoinvent database, and the analysed variants included different energy sources, such as PV installations and wind farms.

5. Conclusions

In this article, a 4.4 kWp photovoltaic installation with energy storage for an example household was designed using the PVSyst program, which showed that:
  • For the assumed parameters and operating conditions of the system, the simulated annual energy production was 4822.3 kWh.
  • The average performance coefficient (PR) of the photovoltaic system was 81.9%.
  • The largest amount of electricity produced was observed in the period from April to September, with the highest level at 638.7 kWh in May.
  • Among all the losses specified, the largest losses result from the efficiency of the inverter (−3.9%), followed by losses related to the temperature of the modules (−3.5%).
  • Approx. 2020 kWh of energy produced from the designed photovoltaic installation is consumed in real time (of which approx. 68% is consumed in real-time, and approx. 32% of energy is effectively stored in selected energy storage).
  • Approx. 2802 kWh of energy yields as a surplus not used in the building at a given moment or stored in batteries goes to the grid.
The analysis of the carbon footprint and the potential impact on the environment of energy used by a given household, depending on its source, conducted as part of the research showed that:
  • The smallest carbon footprint was demonstrated for energy from wind farms (Variant 3–0.787 kg CO2eq/FU).
  • Among all three variants studied, the variant using the national energy mix in Poland is characterised by the highest potential emission of carbon dioxide (107 kg/FU), methane (20,900ng/FU) and nitrogen oxides (9.28 kg/FU).
  • In terms of impact on human health, Variant 1 is characterised by higher impact values compared to Variants 2 and 3.
  • In terms of impact on ecosystems, Variant 1 is also characterised by higher impact values than the variants using renewable energy sources.
  • Comparing the variants where energy was obtained from renewable sources, a lower potential environmental impact of Variant 3 is noticeable, which is based on energy obtained from wind farms.
Within this article, the assumed goals were successfully achieved by designing an appropriate photovoltaic installation for the adopted assumptions and conducting an environmental analysis of the studied variants. In addition, the conducted research and the obtained results made it possible to provide comprehensive answers to the research questions, confirming the validity and effectiveness of the proposed solutions.

Author Contributions

Conceptualisation, P.W., A.T., W.K. and A.M; methodology, P.W. and W.K.; software, P.W. and A.M.; validation P.W., A.T., W.K. and A.M.; formal analysis W.K. and A.M.; investigation, P.W. and A.M.; resources, P.W.; data curation, P.W. writing—original draft preparation, P.W and W.K. writing—review and editing, A.T. and A.M. visualisation, P.W.; supervision, A.T. project administration, P.W. and W.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 17 02765 g001
Figure 1. Visualisation of a photovoltaic installation placed on the roof of a building, red line shows horizon (own elaboration).
Figure 1. Visualisation of a photovoltaic installation placed on the roof of a building, red line shows horizon (own elaboration).
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Figure 2. Diagram of the designed PV installation (own elaboration).
Figure 2. Diagram of the designed PV installation (own elaboration).
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Figure 3. Determined performance coefficient for the tested PV installation with energy storage (own elaboration).
Figure 3. Determined performance coefficient for the tested PV installation with energy storage (own elaboration).
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Figure 4. Graph of losses occurring in the designed PV installation with energy storage (own elaboration).
Figure 4. Graph of losses occurring in the designed PV installation with energy storage (own elaboration).
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Figure 5. Solar path for analysed installation from PVSyst (own elaboration).
Figure 5. Solar path for analysed installation from PVSyst (own elaboration).
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Figure 6. Carbon footprint values determined (own elaboration).
Figure 6. Carbon footprint values determined (own elaboration).
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Table 1. Daily consumptions (own elaboration).
Table 1. Daily consumptions (own elaboration).
ApplianceAmountPowerDaily Use [h/day]Daily Energy [Wh]
Lamps660 W4 1440
TV/PC/Mobile2120 W4960
Domestic appliances1200 W4800
Fridge/Deep-freeze12.0 kWh241999
Dish and clothes washers2200028000
Total Daily Energy: 13,223 Wh/day
Total Monthly Energy: 396.7 kWh/mth
Table 2. System configuration (own elaboration).
Table 2. System configuration (own elaboration).
System Configuration
Module orientationSouth
Angle30°
Type of installationRoof installation
Number of modules8
Nominal power of module550 Wp
Amount of inverter1
Power of inverter4.2 kW
Battery storageLi-Ion, 26 V 180 Ah
Table 3. Monthly weather data values (own elaboration).
Table 3. Monthly weather data values (own elaboration).
Horizontal Global Irradiation
[kWh/m2/mth]		Horizontal
Diffuse Irradiation
[kWh/m2/mth]		Extraterrestrial
[kWh/m2/mth]		Ambient Temp.
[°C]		Wind Velocity
[m/s]
January17.810.762.1−1.83.0
February35.521.4101.1−0.62.9
March 78.040.4180.83.02.9
April123.457.7254.18.72.7
May159.184.9325.914.22.6
June165.379.8344.817.22.6
July161.373.2342.620.02.4
August138.964.5289.619.22.3
September94.247.0206.613.72.3
October52.628.5136.98.72.5
November20.713.673.94.32.8
December12.69.150.40.52.9
Year 1059.4530.82368.78.92.7
Table 4. Characteristics of the analysed variants (own elaboration).
Table 4. Characteristics of the analysed variants (own elaboration).
Parameter/Type of VariantVariant 1Variant 2Variant 3
Used energy [kWh]4826
Source of energyPoland’s national energy mixPhotovoltaic
installation		Wind installation
Table 5. Results of the analysis of the designed PV installation (own elaboration).
Table 5. Results of the analysis of the designed PV installation (own elaboration).
Produced Energy from
Installation [kWh]		Energy Need of the User
[kWh]		Energy Injected into the Grid [kWh]
January135.5409.90.0
February226.9370.248.2
March 423.4409.9235.6
April570.2396.7390.8
May638.7409.9451.9
June620.7396.7437.6
July617.1409.9428.2
August590.9409.9405.6
September459.1396.7279.2
October310.4409.9124.9
November140.1396.70.0
December89.3409.90.0
Year 4822.34826.32802
Table 6. Determination of harmful gas emission values for the tested household variants.
Table 6. Determination of harmful gas emission values for the tested household variants.
EmissionUnitVariant 1Variant 2Variant 3
Carbon dioxidekg/FU1070.04630.00457
Methane ng/FU20,90024129.1
Nitrogen oxideskg/FU9.280.07080.00066
Table 7. Results of environmental analysis of variants (own elaboration).
Table 7. Results of environmental analysis of variants (own elaboration).
Impact CategoryUnitVariant 1Variant 2Variant 3
Climate change, human health, short termDALY4.05 × 10−31.12 × 10−46.47 × 10−7
Climate change, human health, long termDALY1.33 × 10−23.58 × 10−42.19 × 10−6
Photochemical oxidant formationDALY4.53 × 10−74.97 × 10−92.39 × 10−10
Ionising radiation, human healthDALY2.89 × 10−61.02 × 10−81.55 × 10−9
Ozone layer depletionDALY1.74 × 10−62.32 × 10−82.21 × 10−10
Human toxicity cancer, short termDALY3.48 × 10−43.70 × 10−72.65 × 10−8
Human toxicity cancer, long termDALY4.38 × 10−44.26 × 10−81.04 × 10−9
Human toxicity non-cancer, short termDALY1.08 × 10−35.08 × 10−63.33 × 10−8
Human toxicity non-cancer, long termDALY1.07 × 10−31.76 × 10−62.45 × 10−8
Particulate matter formationDALY1.80 × 10−38.48 × 10−61.51 × 10−7
Water availability, human healthDALY6.74 × 10−42.55 × 10−61.11 × 10−7
Climate change, ecosystem quality, short termPDF × m2 × year877.0024.200.140
Climate change, ecosystem quality, long termPDF × m2 × year2930.0078.600.480
Marine acidification, short termPDF × m2 × year76.402.060.013
Marine acidification, long termPDF × m2 × year704.0019.000.116
Freshwater ecotoxicity, short termPDF × m2 × year10.500.140.002
Freshwater ecotoxicity, long termPDF × m2 × year30,300.0023.400.532
Freshwater acidificationPDF × m2 × year59.100.2360.003
Terrestrial acidificationPDF × m2 × year372.001.5400.019
Freshwater eutrophicationPDF × m2 × year0.060.0030.0004
Marine eutrophicationPDF × m2 × year2.742.49 × 10−22.02 × 10−4
Ionising radiation, ecosystem qualityPDF × m2 × year2.16 × 10−79.58 × 10−102.10 × 10−10
Land transformation, biodiversityPDF × m2 × year47.504.85 × 10−21.49 × 10−3
Land occupation, biodiversityPDF × m2 × year33.402.70 × 10−21.5 × 10−3
Water availability, freshwater ecosystemPDF × m2 × year0.0461.61 × 10−48.80 × 10−7
Water availability, terrestrial ecosystemPDF × m2 × year0.2026.54 × 10−32.05 × 10−5
Thermally polluted waterPDF × m2 × year0.0431.06 × 10−56.57 × 10−7
DALY—Disability Adjusted Life Years; PDF × m2 × year—Potentially Disappeared Fraction.
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MDPI and ACS Style

Walichnowska, P.; Kruszelnicka, W.; Tomporowski, A.; Mroziński, A. The Impact of Energy Storage on the Efficiency of Photovoltaic Systems and Determining the Carbon Footprint of Households with Different Electricity Sources. Sustainability 2025, 17, 2765. https://doi.org/10.3390/su17062765

AMA Style

Walichnowska P, Kruszelnicka W, Tomporowski A, Mroziński A. The Impact of Energy Storage on the Efficiency of Photovoltaic Systems and Determining the Carbon Footprint of Households with Different Electricity Sources. Sustainability. 2025; 17(6):2765. https://doi.org/10.3390/su17062765

Chicago/Turabian Style

Walichnowska, Patrycja, Weronika Kruszelnicka, Andrzej Tomporowski, and Adam Mroziński. 2025. "The Impact of Energy Storage on the Efficiency of Photovoltaic Systems and Determining the Carbon Footprint of Households with Different Electricity Sources" Sustainability 17, no. 6: 2765. https://doi.org/10.3390/su17062765

APA Style

Walichnowska, P., Kruszelnicka, W., Tomporowski, A., & Mroziński, A. (2025). The Impact of Energy Storage on the Efficiency of Photovoltaic Systems and Determining the Carbon Footprint of Households with Different Electricity Sources. Sustainability, 17(6), 2765. https://doi.org/10.3390/su17062765

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