Comparing the Financial and Environmental Impact of Battery Energy Storage Systems and Diesel Generators on Microgrids

1. Introduction

Since the onset of the Industrial Revolution in the late nineteenth century, the demand for reliable and continuous energy has grown exponentially. Over the years, energy sources have undergone transformations influenced by technological advancements, economic needs, and environmental concerns. A significant milestone was Rudolf Diesel’s invention of the diesel engine in 1892, which, compared to steam engines, was more compact and reliable and approximately 25% efficient. Modern diesel engines can achieve efficiencies of up to 48% [1].

In the first half of the 20th century, there was an increasing demand for decentralized energy, especially in remote regions. The Diesel Generator emerged as a solution, which proved vital in areas where grid electrification was impractical. Between 1950 and 1980, DG were instrumental in sectors such as mining, construction, agriculture, and even as emergency power sources in hospitals and military installations. However, increasing awareness of the environmental impacts of fossil fuel use and the volatility of oil prices led to a reevaluation of the dependency of these generators [2,3].

In the 1990s, heightened awareness of the environmental ramifications of fossil fuel use, coupled with the volatility of oil prices, spurred a reconsideration of the reliance on diesel generators. CO₂ emissions, along with other pollutants, have led many to seek cleaner alternatives [4]. One primary limitation of DG is its high acquisition and operational expenses. The investments required for the installation of DGs, including equipment purchases, ancillary infrastructure, and installation expenses, can be substantial and pose a financial challenge for many businesses. In addition, ongoing maintenance, part replacement, and diesel fuel costs can be significant, increasing long-term operational expenses. However, DGs are still widely used in Brazil, often serving as emergency, backup, grid support and in precarious electrical infrastructure applications in sectors such as industry, commerce, and telecommunications [5].

Another limitation of DGs is their dependence on fossil fuels, such as diesel. Diesel prices are volatile and subject to fluctuations in the global oil market, leading to unpredictable operational expenses for DG. Their operational principle involves burning diesel at high temperatures, resulting in the emission of atmospheric pollutants, including concentrations of carbon dioxide (CO₂), nitrogen oxide (NO_x), and particulate matter. This contributes to air pollution and has negative impacts on human health and the environment [1,6].

Beyond financial and environmental aspects, DGs have technical limitations. Typically, these systems have slower response times, frequently exceeding 10 s, leading to delays in power availability during electrical supply interruptions. Additionally, the scalability of DGs is limited, making it challenging to adapt to varying energy demands over time.

As the twentieth century progressed, alternative energy sources such as solar and wind emerged. However, the challenge remained in energy storage. The solution came with battery energy storage systems (BESSs), which revolutionized the sector by offering efficient storage and rapid discharge performance. Now, these systems, along with renewable energy sources, are at the forefront of future energy solutions, emerging as viable alternatives to DG, especially in regions previously heavily dependent on diesel generators [7,8,9].

Studies, such as those by Costa et al. [10], Araujo et al. [11], and Nascimento et al. [12], have identified battery energy storage systems as more efficient and sustainable alternatives. From an economic perspective, BESS can significantly reduce long-term operational expenses. From an environmental perspective, they contribute to reductions in emissions and pollutants.

Beyond economic and environmental benefits, battery storage systems offer greater flexibility and adaptability, which can be adjusted to each company’s specific energy demand. In the open energy market, this adaptability is vital.

Economically, the use of BESS can result in significant operational cost reductions. The acquisition and installation costs of a BESS can be offset over time by reduced costs for diesel fuel and DG maintenance. Moreover, BESS enables more efficient energy management, optimizing consumption during lower-cost periods, such as during reduced tariff hours [13,14,15].

Environmentally, replacing DG with BESS contributes to reducing greenhouse gas emissions and other atmospheric pollutants. By eliminating the burning of fossil fuels, such as diesel, BESS mitigates the negative environmental impacts associated with DG. This is especially pertinent in the pursuit of cleaner, more sustainable energy sources, in line with international commitments to carbon emission reductions and a transition toward a greener energy matrix.

Studies address several thematic areas related to the use of BESS, such as isolated grid supply, grid-connected applications, distribution system management, and transmission line support.

Regarding the application in distributed generation, the study by Sharma et al. [16] focuses on optimizing the operation of the distribution grid, minimizing energy losses and the cost of grid demand, using BESSs, demand response (DR), and stochastic wind power generation. The approach uses a multi-objective problem solved using a genetic algorithm in combination with the TOPSIS technique. This method allows for a broader and more flexible approach to optimization, considering multiple objectives simultaneously. The article by Oshiro et al. [17] proposes a methodology for the operation of distribution systems using BESS, inverters, and distribution generation with the objective of maintaining voltage within an acceptable range, smoothing the power flow, and reducing distribution losses. The methodology is validated via simulations in MATLAB. This study highlights the need for improvements in power systems to accommodate the increase in distribution generations, especially in contexts such as Japan, where there is an increase in the adoption of renewable energy to achieve low-carbon targets. Priyadharsen et al. [18] present an energy management (EM) scheme in an IEEE-33 hybrid microgrid using BESS and solar, wind, and thermal distribution generations. The methodology, validated in MATLAB, aims to optimize the operational cost of the microgrid and is scalable for large-scale integrated energy systems. The study also emphasizes the importance of proper sizing of BESS and reviews various optimization techniques and price elasticity models for EM.

Marqusee et al.’s [19] work highlights the role of BESS with photovoltaic (PV) and diesel generators in hybrid microgrids. BESS is identified as a key part of making microgrids more resilient and cost-effective, reducing the dependence on emergency diesel generators. The REopt optimization platform is suggested as a useful tool for exploring the various cost and revenue savings opportunities provided by BESS. In turn, Hidalgo-Leon et al. [20] analyze the impact of reducing the diesel subsidy on an off-grid hybrid power system (OHPS), consisting of a diesel generator, a BESS, and a solar power system (SPS). The Bellavista community, Ecuador, is used as a case study, where it was observed that BESS becomes a viable economic alternative as the price of diesel increases. Thirugnanam et al. [21] propose a battery energy management system (BEMS) for microgrids that use photovoltaic systems and diesel generators as the main sources of electricity. The recommended BEMS manages multiple battery types and simultaneously reduces the DG operating hours, extending the battery life and smoothing any fluctuations in photovoltaic power. Anglani et al. [22] present the Optimized Energy Management System (OEMS) to integrate diesel generators, BESS, and photovoltaic panels at a remote temporary military base (FOB). However, the study lacks a detailed analysis of the impact of the size and characteristics of the BESS on its efficiency and useful life. Finally, Azuara-Grande et al. [23] highlight the need to consider battery degradation when optimizing fuel consumption in an isolated hybrid system (solar, diesel, and battery). The study suggests a more comprehensive comparative analysis between the hybrid system with BESS and the conventional solar/diesel system, as well as it also evaluates the CO₂ emissions avoided with the use of BESS.

Given the above, the importance of BESS is evident, especially as a vital tool to reduce dependence on diesel generators, thus promoting greater sustainability. In this context, this paper aims to provide an analysis based on the financial and environmental metrics between both technologies. The findings are based on real data from an installation in Brazil equipped with a 250 kW/560 kWh BESS and a 75 kVA DG to accommodate loads that incur high costs during peak demand periods on the grid. The article is structured as follows: Section 2 discusses the research methodology; Section 3 describes the case study; Section 4 delves into the results and discussions; and Section 5 concludes the study.

Contributions

This paper contributes significantly to the existing literature by offering an innovative comparative analysis between BESS and diesel generators based on real data from an operating microgrid. The novelty of the study lies in the simulation of the use of DG instead of BESS to serve the same load at high-energy prices. This reveals a tangible and concrete understanding of the financial and environmental consequences of this choice.

It will be noted throughout the article that these results clearly demonstrate the advantages of BESS in relation to DG and the conventional electrical grid. In environmental terms, the BESS does not emit CO₂, while the DG emits tons of CO₂ per year. In financial terms, the total cost of BESS over 20 years is assessed as lower than that of DG and the power grid.

The article thus substantiates the significance of BESS in terms of environmental sustainability and financial viability. Furthermore, it enhances the academic and practical context by furnishing precise data and analyses that highlight BESS as an energy-saving and financially advantageous option for the operation of microgrids during periods of elevated energy charges.

2. Materials and Methods

The methodology used clearly defines the materials and methods of the study, such as the economic evaluation of both technologies and the quantification of their respective CO₂ emissions and other pollutants.

The paper bases its findings on a specific case study: the operational performance of a facility in Brazil equipped with a 250 kW/560 kWh BESS and a 75 kVA DG. This offers practical insight into the implications of our analysis, providing a tangible perspective on the findings. Therefore, data collection is a critical part of the approach, collecting real operational data and supplementing it with information from previous research, manufacturer publications, and government reports.

With the acquired data, financial analysis proceeds using tools like the required average discharge price (RADP) and the required average operational profit (RAOP). These metrics help to understand the economic viability of BESS compared to the DG. Additionally, the levelized cost of electricity (LCOE) and the levelized cost of Energy Storage (LCOS) are used to better understand the long-term associated costs [24,25].

Regarding environmental evaluation, the analyses focus primarily on greenhouse gas emissions. CO₂ emissions and other pollutants from both technologies are quantified, allowing for a direct comparison of their environmental impacts.

After these separate analyzes, the data is put into a direct comparison between BESS and DG, weighing the environmental benefits and associated costs. In this context, the challenges and limitations of transitioning from DG to BESS are also briefly explored, including considerations of infrastructure and regulation.

2.1. Financial Analysis Methods

With the rapid growth and advancement of energy technologies, the concept of LCOE emerged. LCOE has become a standard metric for comparing the cost of producing energy from different sources. It accounts for installation, operation, maintenance, and decommissioning costs, allowing for a fair comparison between different energy generation technologies. This metric reveals that, in many situations, solar and wind energy are cost competitive with other traditional sources. The formulation of the LCOE is based on the premise of equivalence between the present value of cumulative revenues and the present value of accumulated costs over the operating life of a power plant. Mathematically, this relationship is expressed as:

$\sum P M W h \times M W h \times {(1 + r)}^{- t} = \sum (C a p i t a l_{t} + e p l a c e m e n t_{b} + O & M_{t} + F u e l_{t} + C a r b o n_{t} + D_{t}) \times {(1 + r)}^{- t}$

(1)

where PMWh is the constant remuneration to the electricity provider over the plant’s lifespan; MWh denotes the amount of electricity produced annually, and r is the real discount rate, representing the cost of capital. The terms Capital_t, replacement_b, O&M_t, Fuel_t, Carbon_t, and D_t signify the capital, battery replacement, operation and maintenance, fuel, carbon, and decommissioning costs in a given year t, respectively.

Similarly, as with renewable energies, BESS emerged with the need for a metric to compare its associated costs, LCOS. LCOS refers to the cost per unit of energy (such as kWh) that is stored and then discharged from an energy storage system.

The LCOS emerged as a logical extension of the LCOE, accounting for all costs associated with a storage system throughout its life cycle, including initial capital costs, operating and maintenance expenses, and storage efficiency. Mathematically, the LCOS mirrors the LCOE and can be expressed as [25,26,27,28]:

$L C O S = \frac{\sum (C a p i t a l_{t} + r e p l a c e m e n t_{b} + O & M_{t}) \times {(1 + r)}^{- t}}{\sum E n e r g y_{o u t} \times {(1 + r)}^{- t}}$

(2)

Here, Capital_t, replacement_b, and O&M_t represent capital, replacement, and operating and maintenance costs in year t, respectively. Meanwhile,

{Energy}_{o u t}

denotes the total energy released from the storage system during its useful life, adjusted for system efficiency, and r is the real discount rate.

The LCOS calculation does not weigh the energy’s value or its release timing. Since the energy’s value can fluctuate daily or annually, storage technologies that dispatch energy during demand peaks might have higher economic value despite a higher LCOS. In this light, three specific metrics have been developed to compute energy storage costs, chiefly differentiating based on charging costs: RADP (required average discharge price), RAPS (required average price spread), and RAOP (required average operating profit).

Required Average Discharge Price (RADP): This metric is the average discharge price needed for an energy storage solution to break even. Essentially, it is the price at which the stored energy should be sold to cover all associated costs; mathematically [24]:

$R A D P = \frac{T o t a l C o s t_{B E S S} + {replacement}_{b} + O & M t}{E n e r g y_{o u t}}$

(3)
Required Average Price Spread (RAPS): considers the price difference between energy’s purchase (charging) and sale (discharging). A significant price gap is typically necessary between the energy bought during low-demand times and the energy sold during peak demand. This metric represents the average price difference for the storage solution to reach break-even; expressed as [24]:

$R A P S = R A D P - P_{c h a r g e}$

(4)

where
$P_{c h a r g e}$
is the average charging price, found from the distribution between the total charging cost and the total energy charged.
Required Average Operating Profit (RAOP): This metric determines the average operating profit required for an energy storage system to be deemed viable. It is the difference between the revenue from the discharging of energy and the costs of charging and maintaining the storage solution. In particular, unlike RAPS which focuses on price differences, RAOP includes other operational costs. Defined as [24]:

$R A O P = \frac{R A D P \times E n e r g y_{o u t}}{P_{c h a r g e} \times E n e r g y_{c h a r g e} + O p e r a t i o n a l C o s t s}$

(5)

2.2. Environmental Analysis Method

Within the methodology, environmental analysis plays a central role. The aim of this section is to discern and quantify the disparities in the environmental impact between the DG and the BESS. The foundation for environmental analysis is a quantitative model with the formulas delineated for each component involved. The first parameter addressed is greenhouse gas emissions (GHG). These are calculated on the basis of the amount of fuel consumed (measured in liters) and the standard emission rates for the specific type of fuel, often diesel. This calculation offers an estimate of how many tons of CO₂ are emitted per hour or per year of operation. Although battery storage systems (BESS) do not produce GHG emissions during their operation, it is imperative to account for the emissions generated during the production of the electricity that powers these batteries. Therefore, the corresponding GHG calculation for these units will be presented next [29].

$E_{DG} = Q_{comb} \times E F_{C O_{2}}$

(6)

where

E_{DG}

is the total CO₂ emission from DG (in tons),

Q_{comb}

is the amount of fuel consumed (in liters), and

E F_{C O_{2}}

is the specific emission factor for the fuel (in tons of CO₂ per liter). While the BESS is represented as follows:

$E_{BESS} = E_{prod} \times E F_{grid}$

(7)

where

E_{BESS}

is the total CO₂ emission associated with the charging of the batteries (in tons),

E_{prod}

is the energy produced to charge batteries (in kWh), and

E F_{grid}

is the electric grid’s emission factor (in tons of CO₂ per kWh).

In addition to CO₂, DG emits a range of other atmospheric pollutants, such as nitrogen oxides (NO_x), sulfur oxides (SO_x), and fine particles. These emissions are calculated similarly to CO₂, based on the amount of fuel consumed and the standard emission rates. For the BESS, once again, only the emissions associated with the electricity production used to charge the batteries are considered. Thus, the mathematical representation for both is given below [29].

$P_{DG} = Q_{comb} \times E F_{pol}$

(8)

where

P_{DG}

are the total pollutant emissions from the DG (in tons) and

E F_{pol}

is the specific emission factor for pollutants (in tons of pollutants per liter). For the BESS, it is given that:

$P_{BESS} = E_{prod} \times E F_{pol – grid}$

(9)

where

P_{BESS}

are the total pollutant emissions associated with battery charging (in tons), and

E F_{pol – grid}

is the electric grid’s pollutant emission factor of the electric grid (in tons of pollutants per kWh).

After collecting and calculating the emissions for both technologies, a direct comparison can be made. This comparison allows for quantifying the environmental advantages of BESS over DG in terms of reductions in GHG and other pollutants. To compare the emissions, the following are used [29]:

$Δ E = E_{DG} - E_{BESS}$

(10)

$Δ P = P_{DG} - P_{BESS}$

(11)

where

Δ E

is the difference in CO₂ emissions between DG and BESS, and

Δ P

is the difference in pollutant emissions between DG and BESS.

3. Case Study

The microgrid in the case study is part of the Living Lab Project, located in Belo Jardim, Pernambuco, Brazil. The Edson Mororó Moura Technology Institute solution portfolio places a particular emphasis on the development and testing of battery energy storage systems and microgrids. Additionally, it contributes to energy efficiency within the institution by utilizing BESS (Battery Energy Storage System) during peak hours, backup, power factor correction, and the establishment of an islanded microgrid. The microgrid is connected to the grid and isolated. In this context, the database is made up of operational data from the system connected to the grid, focusing on the operation of supply times when demand or consumption is exceeded.

The complete system has been in operation since 2020, comprising an energy storage system with 560 ventilated lead acid batteries, totaling a capacity of 560 kWh, and a PCS (power conversion system) with a nominal power of 250 kVA. The energy storage system plays a fundamental role not only in backup but also in the formation of the microgrid during grid failures. Thus, it operates by maintaining a photovoltaic arrangement of approximately 311.6 kWp for complete load supply, and in the event of severe tests or emergencies, the microgrid operates with a 75 kVA diesel generator. This entire system is integrated with an intelligence system that monitors and dispatches all components according to load requirements or energy quality. The microgrid diagram is shown in Figure 1.

Based on the operation of this system, two-day operation data was collected and used as a reference for the simulation of the case study. The data are specific to the time of the highest load demand and high-energy costs, between 5:30 p.m. and 8:30 p.m. The reduced database is because it is a private microgrid for product development and the use of its data is restricted.

Figure 2 separately displays the instantaneous power of the components during their operation. Observing Figure 2a, the behavior of the load is observed with a maximum peak of 177.94 kW. The BESS, Figure 2b, mirrors the load behavior, trying to meet as much demand as possible. This is observed by the abrupt decline in active power from the electric grid (Figure 2c) with the integration of BESS into the grid.

From approximately 260 minutes of operation data, referring to the second day of operation, the DG set contributes 20 kW to meet the demand, and during its operation, the BESS reduces its power to the minimum value of 100 kW. This demonstrates how much the BESS alleviates facilities that generally make use of the DG set daily for cost reduction or backup promotion.

In the comparison between DG and BESS, the context of the application is paramount and depends on various factors. With regard to energy efficiency, the set of diesel generators is composed of two essential elements: the engine and the alternator. Thus, the efficiency of the diesel generator sets is depicted as the combined efficiency of these two subcomponents. Typically, the joint efficiency of the diesel generators fluctuates between 30–55%, whereas the standalone efficiency of the diesel engine and the alternator varies between 35–60% and 85–95%, respectively, with notable energy losses in the form of heat. In contrast, BESS generally exhibits very high charge and discharge efficiencies, frequently exceeding 80%, with the ability to store and release electricity with minimal losses. Regarding carbon emissions, diesel generators emit substantial amounts of CO₂, NO_x, and other pollutants, while a BESS itself does not release pollutants directly. However, the source of electricity used to charge the batteries may have correlated emissions if the electricity is derived from fossil-fuel-powered plants.

The purpose of the case study is to compare the DG set and the BESS using data from the two days of operation. The simulation will include the DG operating with the same active power as the BESS, calculating the diesel consumption, the annual fuel cost, and the operation and maintenance. The other assumptions considered in the operational and financial simulation of these systems for the calculation of LCOS and LCOE are given in Table 1.

Regarding DG, it has been observed that it has a capacity of 300 kVA and operates at a diesel price of USD 1.24 per liter [30]. It is estimated that the fuel price will experience an annual growth rate of 7.19%. This system has a maximum active power of 255 kW, supporting the demand of the load in question, and consumes diesel at 35.5 L/h.

In contrast, the BESS exhibits an active power of 250 kW and a substantial energy capacity of 560 kWh. Despite its significantly higher capital cost of USD 370,459.5, it compensates for the annual increase in energy prices and inflation rates of 5% and 6.05%, respectively. It is important to note that of the capital cost, 30% goes toward replacing batteries around the tenth year of their useful life.

At first, the analysis of these data without appropriate technical and financial treatment could lead to erroneous considerations. Therefore, this technical–financial analysis allows stakeholders to carefully evaluate the options of the DG and BESS, considering both technical capacities and financial impacts.

Furthermore, the simulation considers the environmental analysis related to CO₂ emissions and carbon credit remuneration. The analysis is carried out by estimating the CO₂ emissions of the DG compared to the reduction induced by a BESS, which does not have adverse effects from its operation. For the development of the simulation, the assumptions presented in Table 2 were considered.

4. Discussion of Results

The comprehensive analysis of the energy systems analyzed, the diesel generator, the battery energy storage system, and the electrical grid revealed decisive insights into their performance, allowing for a detailed evaluation of their relative efficacy. The evaluation focused on various parameters, encompassing both operational capacities and associated costs. The parameters analyzed included RADP, RAOP, and RAPS. It is essential to note that systems are assessed for load service during times when the electrical grid has the highest kWh cost.

The initial observation of operational costs (OPEX) and total costs highlights a clear advantage of BESS over DG regarding OPEX, which costs approximately USD 340,790.67 less. Although the total cost of BESS and DG is quite similar, BESS stands out for its marginally lower cost, although the electrical grid presents a significantly higher expense. Regarding energy, both the DG and BESS and the electrical grid show equivalent values of generated, discharged, and consumed energy, respectively. The LCOE (levelized cost of energy) and LCOS present a sharper comparison. BESS has a marginally lower LCOS (USD 0.38) than the LCOE of DG (USD 0.39), indicating a higher cost efficiency in energy generation. The LCOE of the electrical grid (USD 0.67) is significantly higher, making it the least cost-efficient option for energy generation.

RADP analysis reinforces the calculated values. This parameter refers to the average discharge price for a system to reach equilibrium. In this particular case, it is lower for BESS (USD 0.38/kWh) in comparison to DG (USD 0.39/kWh) and the electric grid, which is significantly lower at a cost of USD 0.67/kWh. This suggests that BESS can sell energy at a lower average price and still reach the break-even point, offering considerable cost advantages. These results are seen in Figure 3.

RAOP is another critical metric that shows the average operational profit generated to cover all operational and capital costs throughout its lifecycle. In this perspective, the simulated DG has the lowest value of USD 0.10/kWh, closely followed by BESS with USD 0.11/kWh, with both exceeding the electrical grid (USD 0.10/kWh). This indicates that although DG requires a lower average operating price, BESS is not far behind, providing a competitive operational performance.

A similar analysis is performed for RAPS, with results indicating a higher average price of USD 0.22/kWh for the DG when compared to the BESS with a price of USD 0.18/kWh, but lower than the grid (USD 0.38/kWh). Specifically, BESS requires a smaller spread to achieve the break-even point, which refers to the point at which the total (fixed and variable) operating expenses of an energy system, whether DG or BESS, reach their maximum. This means that, at the breakeven point, the system is neither generating profit nor incurring a loss. Therefore, the evaluated indicator shows that BESS needs a smaller difference between the purchase and sale prices of energy to cover its operational and capital costs, while the electric grid presents the largest value difference.

In financial terms, the BESS stands out as the best option to meet the load demand during the period of the highest grid cost, compared to the possibility of using the DG and the grid itself. However, the purely financial perspective leaves gaps regarding the environmental impacts that these technologies can provide. When comparing the diesel generator group and the battery energy storage system, significant differences are observed in terms of emissions and environmental impacts.

For the case under analysis, CO₂ emissions related to DG total 67.32 tons of CO₂-equivalent per year, a significantly high and harmful value to the environment. Furthermore, the DG emits other harmful atmospheric pollutants, such as CO₂ (30.56 kg/year), PM (1.39 kg/year), and NO_x (145.94 kg/year), contributing to the degradation of air quality and public health issues (Table 3).

In contrast to the DG, the BESS presents itself as a greener and more sustainable option. It does not emit CO₂ during its operation, contributing to a substantial reduction in greenhouse gas emissions. This environmental benefit is converted into financial gains with carbon credits, totaling USD 350.9 per year, which can be considered an additional return when opting for this technology.

In terms of financial analysis, it is verified that BESS also stands out in terms of total cost, OPEX, and LCOS. Although the DG presents a slightly superior RADP, the BESS excels in RAOP, indicating a higher average operational profit.

Thus, balancing environmental and financial considerations, the BESS stands as the most favorable option for the case. It not only minimizes the environmental impact by significantly reducing greenhouse gas emissions and other pollutants, but also shows financial performance.

5. Conclusions

This article explores the comparison between the use of diesel generators and BESS, focusing on their application in Brazil. Diesel generators have been identified to be widely used on the Brazilian market to meet increasing demand, especially in the industry. However, this solution has limitations, such as high fuel costs, negative environmental impacts, and a lack of operational flexibility.

The current climate scenario highlights the imperative need for sustainable energy choices. In this context, BESS emerges as a prominent alternative in stark contrast to the traditional options, especially in terms of CO₂ emissions. This article presents an analysis using data from a real microgrid in operation and then simulating the use of DG in place of BESS to meet the same load during the high-cost energy period that occurs every day. From the results obtained, it is found that DG is responsible for emitting a substantial 67.32 tons of CO₂ equivalent annually, a striking contrast to BESS, which does not contribute to CO₂ emissions, resulting in a carbon credit of USD 350.9.

The environmental impact of DG is exacerbated by the emission of other pollutants, such as CO₂, PM, and NO_x, totaling 30.56 kg/year, 1.39 kg/year, and 145.94 kg/year, respectively. These further contribute to environmental degradation, presenting significant challenges in terms of air quality and public health. The DG carbon credit, valued at USD 5.91, is significantly less than that of BESS, further highlighting the environmental advantages of the latter.

Beyond environmental considerations, financial analysis reveals critical insights. The total cost over a 20-year life cycle is lower for BESS (USD 1,551,486.50) compared to DG (USD 1,562,643.80), which is substantially lower than that of the Electric Grid (USD 2,722,137.23). In the case of the grid, only the cost of energy is observed, and its growth is considered over 20 years. These data, combined with an LCOS of USD 0.38 (lower than the LCOE of DG and the grid), position BESS as an economically viable option, ensuring energy efficiency at a lower cost.

Among the metrics analyzed, RADP, an indication of the necessary discharge price for equilibrium, is comparatively lower for BESS (USD 0.38/kWh), implying that it requires a lower average discharge price to cover all associated costs. This is complemented by a higher RAOP (USD 0.11/kWh), suggesting a higher average operational profit for BESS compared to DG and the Electric Grid. The RAPS for BESS is also the lowest, indicating that BESS requires a smaller price spread to reach the breakeven point.

Based on the results obtained in this study, it is recommended that policymakers and other stakeholders in the energy sector consider the potential of BESS as a viable and sustainable alternative to diesel generators. Implementing policies and incentives that favor the use of energy storage technologies, such as BESS, could accelerate the transition to cleaner and more sustainable energy sources. It is important to highlight the need for clear and comprehensive regulation that guarantees the safety, reliability, and accessibility of these technologies for all users of the electrical system.

In conclusion, the article unequivocally demonstrates that BESS not only meets contemporary environmental imperatives by minimizing CO₂ emissions and other pollutants, but also stands out as an energetically efficient and economically viable option. Commitment to sustainability, combined with tangible economic benefits, emphasizes BESS as a superior strategic investment choice, promoting not only environmental health, but also ensuring an optimized financial return over time. For future work, it is recommended to use a longer period of data analysis for more accurate validation of the results.

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Sustainbly

Comparing the Financial and Environmental Impact of Battery Energy Storage Systems and Diesel Generators on Microgrids

1. Introduction

Contributions

2. Materials and Methods

2.1. Financial Analysis Methods

2.2. Environmental Analysis Method

3. Case Study

4. Discussion of Results

5. Conclusions

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1. Introduction

Contributions

2. Materials and Methods

2.1. Financial Analysis Methods

2.2. Environmental Analysis Method

3. Case Study

4. Discussion of Results

5. Conclusions

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