Renewable energy adoption in industrial settings is key to sustainable and self-sufficient energy solutions. Advances in solar (photovoltaic and thermal), wind, and micro-cogeneration systems enable on-site power generation (Solar Power Europe, 2023). These technologies support energy-prosumer environments, allowing buildings to generate power, contribute surplus electricity to the grid, and align with economic and regulatory frameworks. Countries like Latvia use net metering systems to balance on-site and grid electricity (Lebedeva et al., 2021). High-efficiency buildings are increasingly net energy producers, benefiting Europe’s energy efficiency and sustainability goals. Despite this progress, research gaps exist in optimizing these technologies for smaller industries, which differ operationally and financially from residential or large-scale setups (Zemite et al., 2024).

Photovoltaic (PV) power is rapidly expanding globally, led by China. In 2023, China added 216.3 GW of new PV capacity, a 148% increase from 2022, with distributed PV set to exceed 500 GW by 2030 (Zhang et al., 2023; National Development and Reform Commission, 2022). Globally, PV installations reached 444 GW in 2023, with significant growth driven by China. The European Green Deal (EC, 2024c) pushes PV adoption in Europe, but widespread use introduces challenges like voltage regulation in distributed systems (Gao et al., 2024). Buildings in the EU account for 40% of energy consumption and 35% of greenhouse gas emissions (European Environmental Agency, 2024). The European Green Deal (EC, 2024a) and Renovation Wave (EC, 2024b) aim to enhance building efficiency and cut emissions by 55% by 2030. Decarbonizing buildings, including in Poland, is essential across sectors to reduce energy costs and increase sustainability (EC, 2024d). Despite the growing adoption of these technologies, research focusing on small-scale industries remains limited, highlighting the need for further study.

Fossil energy consumption has risen due to population and industrial growth (Nejat et al., 2015). Integrating energy storage with renewables like wind and solar is key for reducing fossil fuel reliance and improving energy equity (Wu et al., 2023; Sterner and Stadler, 2019). Storage technology addresses renewables' intermittency and reduces costs (Kittner et al., 2017), supporting energy security, job creation (Ram et al., 2020), and system value (Lott et al., 2014). Global energy demand keeps increasing, driven by key sectors. Fossil fuels made up 85% of 2015s energy consumption (Padamurthy et al., 2024), In 2021, they accounted for around 80% of the world’s primary energy supply, underscoring the continued reliance on oil, coal, and natural gas (IEA, 2022b). At the same time, global energy demand keeps growing; the International Energy Agency projects a rise of roughly 24% between 2021 and 2040 under its Stated Policies Scenario, driven by population growth, urbanization, and expanding industrial activity (IEA, 2022b). This growth highlights the need for alternative energy sources and better storage solutions as fossil fuels pose environmental risks like rising CO2 emissions (Padamurthy et al., 2024). Despite efforts, non-hydro renewables provided just 3.3% of global energy in 2015, projected to rise to 10% by 2035 (Padamurthy et al., 2024). Renewable energy sources—especially wind and solar—have shown notable growth, supported by decreasing costs and supportive policies. According to the International Renewable Energy Agency, the share of renewables in global electricity generation reached approximately 30% in 2021 when including hydropower, and is projected to rise further over the coming decades (IRENA, 2022). Advances in energy storage technologies—spanning lithium-ion batteries, pumped hydro, and emerging solutions such as hydrogen storage—are crucial for reducing the intermittency of renewables and ensuring a stable energy supply (IEA, 2022a; IRENA, 2022). Energy storage is now crucial for integrating renewables into modern industrial processes (Qays et al., 2020). Industries depend on storage systems to capture excess energy and ensure stable operations during low generation periods (Frate et al., 2021).

Energy storage boosts independence, efficiency, and lowers emissions by reducing reliance on fossil fuels (Gschwind et al., 2015). It stabilizes renewable energy’s variability, ensuring smooth industrial operations and grid reliability (Aghmadi and Mohammed, 2024). Advanced storage supports energy management strategies like demand response, reducing costs and optimizing resources (Ali et al., 2023). Continued policy support, robust investments, and international collaboration are essential to accelerate the transition toward a more sustainable and equitable energy future (IPCC, 2023).

Common storage technologies include flywheels, pumped storage, and electrochemical systems (Dincer and Ezzat, 2018). Lithium-ion and flow batteries are widely used in industries requiring frequent cycling or large-scale storage (Chaudhary et al., 2024). Thermal and compressed air systems also offer efficient solutions for specific industrial needs (Dincer and Erdemir, 2023; Olabi et al., 2021). Among these, electrochemical energy storage is the most widely adopted due to its quick response time and flexible configuration. The topology of a PV energy storage access system is illustrated in Figure 1.

Figure 1 illustrates a typical topology of a photovoltaic (PV) based energy storage system. The energy generated by PV modules is transferred to an inverter, which converts direct current (DC) into alternating current (AC) suitable for powering devices and feeding excess energy into the electrical grid. Surplus energy is stored in the energy storage system, allowing it to be used later during periods of higher demand. The energy storage system may include an additional inverter, enabling flexible energy management depending on the user’s load requirements and grid conditions.

The choice of energy storage method typically depends on the specific application and various factors. These include the type of storage medium or material, the technique used, storage capacity, density, efficiency, and duration. Additional considerations are the rates of energy charge and discharge, the durability of the storage device, as well as initial and ongoing costs, and the environmental impact (Padamurthy et al., 2024).

Solar energy has emerged as a critical driver in the shift towards renewable energy worldwide, playing a major role in reducing energy consumption across various sectors (Vijayan et al., 2023; Huo et al., 2018). The building industry, responsible for a significant portion of global energy use and CO2 emissions (UNEP, 2021; IEA, 2020), is increasingly integrating solar technology to enhance energy efficiency and sustainability (Li et al., 2021; Muñiz et al., 2024). Solar panels, which generate clean electricity directly from sunlight, are transforming buildings by lowering grid dependency, cutting utility costs, and promoting energy independence through net metering (Skandalos et al., 2023; Calanter, 2015; Dai et al., 2022). Despite challenges like high initial costs and installation complexities, advancements in technology and economies of scale are making solar solutions more viable (Al-Radhi et al., 2023). Research highlights the environmental advantages of organic photovoltaic cells and silicon solar panels, emphasizing their lower environmental impact and contribution to reducing carbon emissions (Tsang et al., 2016; Oteng et al., 2023). As the global push for a green economy gains momentum, the construction sector’s embrace of solar energy is pivotal in addressing climate change and achieving long-term sustainability.

In this research, the simulation of renewable energy sources in a construction manufacturing company involves modeling and analyzing various scenarios related to the adoption of new energy technologies and infrastructure. The company operates two production lines dedicated to manufacturing chemical products for construction, which are utilized in the execution of building projects. Additionally, the company maintains two storage warehouses to support its operations, alongside an administrative office that coordinates project management and business activities. This tool enables the simulation of the energy system’s behaviour in response to changes such as the introduction of new energy sources, fluctuations in consumption, or shifts in market conditions. The process includes creating a model that incorporates factors like energy consumption, production, greenhouse gas emissions, weather conditions, energy prices, and regulations. Multiple simulations are then conducted to assess the impacts of these changes, including associated costs and benefits. Essentially, simulation serves as an analytical tool that provides companies with a deeper understanding of the effects of integrating renewable energy and supports more informed, data-driven decision-making. When simulating renewable energy implementation in a manufacturing company, focus on the key steps outlined in Figure 2.

The process of simulating the implementation of renewable energy sources begins with detailed modeling of the existing energy infrastructure, considering all production processes and energy consumption patterns (Kunatsa et al., 2024). Goals are then set, focusing on objectives like reducing greenhouse gas emissions, lowering energy costs, or enhancing energy independence. The potential impact on production processes, operational costs, energy balance, and efficiency is evaluated, followed by identifying risks such as supply instability or infrastructure adaptation needs. An optimization analysis is conducted to determine the most cost-effective, efficient, and sustainable solutions. The results are verified through simulations using historical data and forecasts related to energy consumption, prices, regulations, and technology. These findings are presented to stakeholders to gain support and engagement. Finally, the conclusions are incorporated into the company’s strategy to maximize the effectiveness and profitability of the renewable energy implementation. The calculations presented in this study were based on established methodologies and simulation tools widely employed in the evaluation of photovoltaic systems. Specifically, the simulation was conducted using PV*SOL, a leading industry-standard software that incorporates key factors such as the geographical location of the PV system, load profiles, and annual energy consumption. Additionally, the software considers detailed specifications of the PV modules, including manufacturer, model, orientation, and quantity, as well as the inverter’s manufacturer.

The company under investigation is recognized as one of the leading manufacturers of construction materials in Poland, specializing in the development of innovative solutions for the construction sector. Its operations encompass the production of a diverse range of products, including mortars, adhesives, plasters, and other materials utilized at various stages of construction and building renovation. The company has established a strong reputation for the high quality of its products, which are distinguished by both their durability and exceptional technical properties, effectively addressing the increasingly stringent demands of the construction market. Furthermore, the enterprise is engaged in the production and distribution of construction materials for both domestic and international markets, offering a comprehensive portfolio of products that cater to different phases of construction and building modernization.

The electricity consumption over the past 3 years for the analyzed Polish manufacturing company in the construction industry has been presented in Table 1.

In the surveyed company, there has been a noticeable trend of increasing electricity consumption by a few percentage points each year. This rise leads to higher maintenance costs across the organization, creating a need for efficient energy management and the exploration of alternative solutions, such as the adoption of renewable energy sources, to balance expenses. To better understand and address these growing costs, the company conducted a simulation on implementing renewable energy. This simulation allows for the prediction of the impact of rising electricity consumption and helps identify the most effective strategies for managing this issue.

Table 2 provides a cost estimate for the investment in a photovoltaic panel system for a manufacturing company within the construction industry. The table details the net cost of the photovoltaic panel installation along with the 23% value-added tax (VAT), ensuring a clear understanding of the total investment costs. By taking both of these values into account, the company can precisely estimate the overall cost of implementing the photovoltaic system and make informed financial decisions.

Figure 3 presents a comprehensive technical diagram of the photovoltaic installation, which also includes an energy storage system. This diagram provides detailed information on the electrical connections and all essential components, facilitating a thorough evaluation and implementation of the project. The inclusion of energy storage in the system enhances the overall efficiency and reliability of the installation. The detailed documentation ensures that the photovoltaic system can be effectively deployed in line with the planned specifications.

Table 3 presents basic data regarding the photovoltaic installation, including information such as the PV generator’s power, its surface area, the number of photovoltaic panels, and the number of inverters. These key parameters allow for the evaluation of the system’s performance, sizing of the photovoltaic system, and forecasting of investment profits.

The analysis of data related to the proposed photovoltaic installation provides a comprehensive understanding of its potential impact on the company’s operations. This analysis enables the identification of several key indicators crucial for evaluating the efficiency and profitability of the system. First, by examining the energy produced by the installation, it is possible to estimate the amount of electricity generated over a specific period, which is essential for assessing potential savings and its impact on the company’s energy balance. Additionally, analyzing the direct consumption of self-generated energy, the amount of energy returned to the grid, and the energy stored in the battery system allows for a thorough evaluation of the company’s energy self-sufficiency and potential revenue from selling excess electricity back to the grid. The role of the energy storage system is also critical, as it enables the company to optimize energy usage by storing excess power for later use, reducing reliance on external electricity sources, and enhancing overall energy management. The share of self-consumption and the contribution of solar energy in meeting the company’s demand are significant factors for understanding the role of renewable energy in its total energy requirements, which is important both economically and environmentally. Furthermore, the annual yield and efficiency coefficient serve as measures of the photovoltaic system’s effectiveness and profitability, allowing for a comparison of actual performance with projections. Considerations such as reduced efficiency due to shading and the amount of CO₂ emissions that can be avoided highlight the system’s operation under various environmental conditions and its environmental benefits through reduced greenhouse gas emissions.

Simulation of the photovoltaic installation’s operation, based on the previously mentioned data, allows for a comprehensive understanding of its functioning and efficiency. By utilizing the information provided in Table 3, various scenarios of the installation’s operation under different weather and operational conditions can be modeled, enabling assessment of its production potential, impact on the company’s energy balance, and estimation of savings and benefits associated with the implementation of renewable energy sources. The simulation has been presented in Table 4.

The parameters used in the simulation, were carefully selected based on widely accepted industry standards and relevant literature. These parameters ensure that the model accurately represents the operational conditions of photovoltaic (PV) systems in a manufacturing environment, particularly in Poland. The PV generator power was determined based on the company’s energy requirements and available rooftop space for PV module installation. The chosen capacity balances the energy demands of the facility with the physical and financial constraints, ensuring sufficient electricity generation without overinvestment in unused capacity. The annual yield per kilowatt peak reflects the specific energy generation potential of the system, considering local climatic conditions and irradiance levels in Poland. This parameter was validated using PV*SOL simulation software, ensuring its accuracy for the geographic location. The performance ratio (PR) indicates the efficiency of the PV system, accounting for losses due to temperature, shading, and inverter performance (Gao et al., 2024; Skandalos et al., 2023). The value of 85.2% aligns with industry standards and empirical data for similar installations in Central Europe, making it a realistic and reliable choice for the simulation. A shading loss of 1.5% annually was included to account for potential partial obstructions from environmental or structural factors (Oteng et al., 2023; Zhang et al., 2023). This conservative assumption ensures that the analysis reflects real-world conditions, such as temporary shading from nearby objects or seasonal changes. This parameter represents the total annual energy output of the PV installation, providing a direct measure of the system’s contribution to the company’s electricity needs. It forms the basis for assessing cost savings, energy independence, and environmental benefits. The self-consumption rate highlights the extent to which the company can use the generated energy directly, reducing dependency on grid electricity. This parameter is critical for evaluating operational efficiency and financial savings from reduced energy purchases. Energy stored in the battery supports load balancing and ensures energy availability during periods of low generation. This parameter was included to assess the benefits of integrating energy storage and its role in maximizing self-consumption. The amount of energy returned to the grid provides insights into potential revenue from feed-in tariffs and the system’s contribution to the broader energy network. It also reflects the system’s production surplus during peak generation. The self-consumption percentage demonstrates how effectively the company utilizes the generated energy on-site. This parameter is crucial for understanding the alignment of production and consumption profiles and for optimizing energy usage. Avoided CO₂ emissions quantify the environmental impact of transitioning to renewable energy. This metric underscores the sustainability benefits of the PV system, aligning with corporate social responsibility goals and regulatory requirements.

The data from Table 4 demonstrate that the photovoltaic system with a capacity of 22.9 kWp is an efficient solution, generating 21,890 kWh of electricity annually, of which 6,730 kWh is directly consumed by the company and 5,163 kWh is stored in batteries, collectively covering 54.1% of the company’s energy demand. A surplus of 9,997 kWh is fed back into the grid, potentially generating additional financial benefits. This system also avoids the emission of 9,970 kg of CO₂ annually, significantly contributing to the company’s sustainability goals. With a performance ratio of 85.2% and shading losses estimated at 1.5% per year, the installation is well-adapted to local conditions, ensuring stable energy cost savings and substantial environmental benefits.

The next step in the simulation involves presenting a potential scenario of electricity consumption for the past year, assuming the company had a photovoltaic installation in place. The analysis considers the total electricity consumption of 21,189 kWh, where standby consumption (inverter) accounted for 133 kWh. The photovoltaic installation covered 6,730 kWh, and an additional 4,640 kWh was supplied by the battery storage system. The remaining energy demand, totaling 9,952 kWh, was met by the electric grid. Solar energy contributed 53,3% to the total energy demand, underscoring the significant role of the photovoltaic installation in fulfilling the company’s energy needs. This entire simulation step is detailed in Table 5.

The energy flow diagram presented in Figure 4 visualizes the key processes of energy distribution within the manufacturing company, considering the photovoltaic installation and the energy storage system. The photovoltaic installation is capable of producing 6,730 kWh of electricity, which is used to meet various energy needs within the company. Additionally, 4,640 kWh of energy is supplied from the battery storage system, further contributing to the company’s energy requirements. However, due to the variability of solar energy production, the company must also draw a portion of its energy from the electric grid to cover demand during periods of insufficient self-generation. This energy flow diagram helps to understand the role of the photovoltaic installation and the energy storage system in balancing the company’s energy demand and its reliance on the electric grid.

Forecasting energy consumption and demand is essential for manufacturing enterprises, as it enables effective energy resource management and strategic planning. The annual energy consumption forecast, showed in a chart divided into 12 months, provides a detailed analysis of consumption trends and patterns throughout the year. The electricity consumption forecast was made by analyzing historical data from the enterprise, with particular emphasis on year-on-year consumption growth trends. The forecast methodology relied on statistical techniques, which allowed for the identification of patterns and dependencies in the historical consumption data. These trends were then extrapolated to predict future consumption. This figure includes eight key indicators: energy produced by the photovoltaic installation, energy consumption by the company’s equipment, standby consumption, battery energy to cover consumption, battery charge (PV installation), energy returned to the grid, battery charge (grid), consumption from grid. The chart highlights the summer period as having the highest utilization of the photovoltaic installation due to favourable weather conditions typical in Poland. During this time, increased sunlight enhances the efficiency of electricity production by the photovoltaic panels, leading to a greater proportion of the company’s energy demand being met through self-generation. By forecasting energy consumption and demand, companies can better prepare for variations in energy needs, optimize investments in energy infrastructure, and manage costs and energy efficiency throughout the year. This forecasting is presented in Figure 5.

The next step following the forecasting of energy consumption is analysing the utilization of solar energy by the company. Figure 6 provides a detailed monthly breakdown of this energy usage throughout the year. The chart highlights four key parameters: energy produced by the PV system, own consumption of energy directly, energy returned to the grid and battery charging. This detailed presentation allows for an in-depth assessment of how much of the generated solar energy is consumed by the company itself versus how much is transferred to the grid. This stage of analysis is essential for evaluating the efficiency and profitability of the photovoltaic installation, identifying areas for potential optimization, and making informed decisions about energy management.

The next important step is analyzing the profitability parameters of the photovoltaic installation. Table 6 presents key economic indicators that assess the investment’s financial viability. The return on total expenditure stands at 10.31%, serving as a crucial indicator of long-term investment profitability. The cumulative cash flow of 67,195.95 EUR reflects a highly positive evaluation of the project’s financial performance. The payback period of 9.4 years is a key metric, indicating the time required for the investment to generate sufficient returns to cover its initial cost. Additionally, the cost of electricity production at 0.08 EUR/kWh is a significant factor, allowing for comparison with other energy sources and assessing the competitiveness of the photovoltaic system in the energy market. These profitability parameters are essential for evaluating the solar energy investment and making informed strategic decisions for the company’s growth. The calculated Levelized Cost of Electricity (LCOE) for the photovoltaic system is 0,08 EUR/kWh, assuming a 5% discount rate and annual operational costs at 1% of CAPEX. This value represents the total cost of electricity production over the system’s 25-year lifespan, accounting for capital expenditures, operational expenses, and the time value of money.

Figure 7 presents a simulation of the development of energy costs over a period of 26 years, both before and after the installation of the photovoltaic system. It clearly indicates that after the implementation of the photovoltaic installation, the manufacturing company can significantly reduce its expenses on electricity during the analyzed period. This is a crucial reference point illustrating the benefits of employing renewable energy sources and can be a key factor in the company’s investment decision-making process. Long-term analysis of energy costs before and after the installation of the photovoltaic system allows for a comprehensive understanding of the eco-nomic benefits associated with green technologies and their impact on the financial stability of the enterprise.

Figure 8 illustrates the cumulative cash flows over the review period. The data used in the simulation allows for an assessment of the project’s profitability and determination of its long-term impact on the company’s finances. Additionally, price degradation and escalation rates are applied monthly throughout the period under consideration. According to the data used in the simulation, these adjustments begin in the first year.

In summary, the financial analysis of the investment in the photovoltaic system demonstrates promising prospects for long-term profitability and financial savings for the Polish manufacturing company in the construction industry. At the end of the simulation the authors present a sensitivity analysis.

The sensitivity analysis of the investment in the photovoltaic system shows the key factors influencing the profitability and efficiency of the project. The results indicate that the most significant variables are the initial costs, electricity prices, energy production efficiency, storage capacity and regulatory changes. With a 20% increase in investment costs, the payback period is extended to about 11 years, which reduces the financial attractiveness of the project. On the other hand, a 20% reduction in costs, e.g., due to subsidies, shortens this period to 7.5 years, which significantly improves the return on investment (ROI). A 10% increase in electricity prices increases the savings from self-generation, shortening the payback period, while their decrease has the opposite effect. The efficiency of energy production plays a key role - its 10% reduction leads to greater dependence on the power grid and increases operating costs, while a 10% increase in efficiency improves energy self-sufficiency and project profitability. Changes in energy storage capacity by ±10% affect the energy independence of the household, which has a direct impact on the burden related to purchasing energy during peak periods. Changes in regulatory policy, such as the introduction of higher feed-in tariffs, significantly improve the attractiveness of investments by increasing revenues from energy fed into the grid. On the other hand, reducing subsidies or tax breaks significantly increases the effective cost of the installation, extending the payback period and reducing the investment potential. Figure 9 presents an analysis of the investment in a photovoltaic system, taking into account initial costs, energy efficiency, the impact of energy price changes and potential regulatory changes. The initial net price of the system is €18,497.65 (gross: €22,752.11) and the payback period is estimated at about 9.4 years at an energy generation cost of €0.08/kWh. The system generates 950.47 kWh/kWp per year, of which 6,730 kWh is consumed for self-consumption and 9,997 kWh is fed into the grid, reducing CO₂ emissions by 9,970 kg per year. Sensitivity scenarios consider various factors such as increases or decreases in installation costs (affecting the payback period from 7.5 to 11 years), changes in electricity prices (increasing or decreasing savings) and production efficiency (from 855.42 to 1,045.52 kWh/kWp per year). Also changes in energy storage capacity and regulations such as feed-in tariffs can significantly affect the project profitability. These results underline the need to monitor these parameters closely and optimize system configurations. Discusion and practical implications.

The analysis of electricity consumption and the proposed photovoltaic (PV) system for the surveyed Polish manufacturing company in the construction industry reveals several practical implications.

1. The continuous increase in electricity consumption over the past 3 years highlights the growing operational costs associated with energy use. Implementing a PV system offers a sustainable solution for mitigating these costs by generating on-site renewable energy, reducing dependency on grid electricity, and stabilizing energy expenses.