Investigating the Optimal DOD and Battery Technology for Hybrid Energy Generation Models in Cement Industry Using HOMER Pro

The cement industry is a major energy consumer, with most of its costs associated with fuel and energy requirements. While traditional thermal power plants generate electricity, they are both harmful and inefficient. In this study, battery depth of discharge (DOD) is evaluated for four different battery technologies in the context of the cement industry. The battery technologies evaluated are lead-acid (LA), lithium-ion (Li-ion), vanadium redox (VR), and nickel-iron (Ni-Fe). Five cement plants in Pakistan are considered, including Askari Cement Plant, Wah (ACPW), Bestway Cement Plant, Kalar Kahar (BCPKK), Bestway Cement Plant, Farooqia (BCPF), Bestway Cement Plant, Hattar (BCPH), and DG Cement Plant, Chakwal (DGCPC). Four hybrid energy generation models (HEGMs) were proposed using the HOMER pro software. HEGM-1 combines a diesel generator, photovoltaic system, converter, and battery system, while HEGM-2 consists of a photovoltaic system, converter, and battery system. HEGM-3 is a grid-connected version of HEGM-1 and HEGM-4 is the grid-connected version of HEGM-2. A reference base model using only grid connection is also considered. A multi-criteria decision analysis (MCDA) was performed using a cumulative objective function (COF) that includes net present cost (NPC), levelized cost of energy (LCOE), and greenhouse gas (GHG) emissions. The main objective was to maximize COF while minimizing NPC, LCOE, and GHG emissions using optimal battery technology and DOD. The results indicate that VR is the most optimal battery technology, with a DOD of 10% achieved in DGCPC using HEGM-3. This results in a 61.49% reduction in NPC, 78.62% reduction in LCOE, and 84.00% reduction in GHG emissions compared to the base model.

Abbreviations

AbbreviationExpansion

LA	lead-acid.
Li-ion	lithium ion.
VR	vanadium redox.
Ni-Fe	nickle iron.
MCDA	multi-criteria decision analysis.
COF	Cumulative objective function.
HEGMs	hybrid energy generation models.
LF	load following.
CC	cycle charging.
DOD	depth of discharge.
NPC	net present cost.
LCOE	levelized cost of electricity.
GHG	greenhouse gas.

SECTION I. Introduction

In recent years, there has been a gradual rise in the percentage of renewable energy in the global energy system. This shift aims to meet the targets of reducing carbon emissions and achieving carbon neutrality while also addressing the environmental challenges associated with the overconsumption of non-renewable energy sources. However, the integration of intermittent renewable energy sources has had a notable impact on the security and stability of power systems [1]. Numerous studies have been conducted on this topic [2] to address these issues and increase power system flexibility. An effective and practical approach to increasing electricity flexibility from the network side depends on the amount of current passing through batteries; they can be charged at varying rates. As the charging and discharge currents increase, both the aging rate and the level of operation intensify concurrently [3]. In [4] the authors define the problems associated with energy storage and battery technologies. The charging and discharging of a battery constitute one cycle, and repeated cycles result in a decrease in the battery’s capacity over time. To measure a battery’s cycle life, the industry typically employs a benchmark of 80 cycles until total capacity is reached [5]. Additionally, there are two methods for describing the level of charge in a battery [6]. Table 1 shows the comparison of different battery technologies. The term “state of charge” is an example of a description used to indicate the level of charge in a cell. If the cell is fully charged, it is said to have 100% SOC. Another term used is “depth of discharge” (DOD), which refers to the amount of charge that has been used up when the cell is discharged to 100% DOD. A cell with 100% SOC and 0% DOD is equivalent. To calculate the depth of discharge, the percentage of the battery’s total capacity that has been depleted, one divides the fully charged battery’s capacity by its nominal capacity and expresses the result as a percentage [7], [8].

TABLE 1 Comparison of Different Battery Technology

The researchers in [18], The article examines the impact of over-discharging on lithium-ion batteries with two distinct types of anodes: a graphite-based anode and an LTO-based anode model. The study involved testing cells at various DODs to determine how it affects cell performance and the critical parameters that contribute to irreversible cell failure. To optimize the efficiency of battery energy storage systems, the best scheduling problem should factor in the cost of battery degradation while accounting for equivalent cycles over the optimization time horizon and the effect of depth of discharge on degradation [19]. In [20], cycles with variable DOD were considered, and two distinct formulations were used to implement the deterioration model. A specific battery aging model with tentative verification for microgrid applications is still lacking, and one typical practice is establishing a fixed lifetime of 10 or 20 years [21].

Nonetheless, the battery aging models used in microgrid assessment and control references are primarily simplified to be linearly related to DOD and cycle quantities [22], [23], [24]. In [25], researchers present a feasible model for optimal scheduling with DOD and temperature parameters, neglecting C-rate, while ignoring the quantitative impact of charge/discharge operations. The authors in [26] provide a quadratic term to function battery degradation, such as film growth by current (the C-rate) but do not include a DOD factor. In [27], the research conducts a comprehensive examination of literature specific to the domain and presents a cost model that assigns weights to the outcomes and parameters of popular studies. Nonetheless, the study does not provide a detailed discussion of the various assumptions and experimental setups of the cited models. The most crucial aspect is the creation of a battery state of health (SOH) prediction model that is optimization-oriented and validated through experimentation in microgrid settings, which is still lacking. A comparative analysis of related works is presented in Table 2.

TABLE 2 Review Summary of Literature

By switching to cleaner energy sources, the issue of rising global surface temperatures could be addressed by lowering atmospheric carbon dioxide (CO2) levels [33]. This paper proposes an embedded battery impedance measurement system based on an LC resonant tank for battery management systems (BMS). The system is designed to measure the battery’s internal temperature [34]. This paper presents a battery management system that integrates battery impedance spectroscopy (BIS) using fewer inductor and switch components than existing methods [35]. These RESs have the potential to offer everyone, regardless of where they live, clean energy that they can control. HESS is created by combining renewable energy sources (RESs) with conventional generators based on petroleum derivatives. As a result, they can address the issue of inconsistent and inconsistent RES supply. HESS frameworks outperform single energy sources regarding the dependability, control, and value [36]. In [37] the Battery Energy Storage System (BESS) is a commonly used energy storage technology in independent microgrids. However, due to the fluctuating output power of renewable energy sources like PV and Wind Turbine (WT), the lifespan of the BESS may be diminished. The most crucial factor in implementing HES may be their flawless planning and preparation. Each microgrid level can be improved to provide optimal operating conditions for all models. A framework plan may be revised to examine the best configurations for a single target capability or multiple objectives.

On the other hand, multi-objective advancement computers are required when employing at least two target abilities. Two examples of such goals are increasing the framework’s effectiveness and reducing its cost. Various strategies and procedures can be used to achieve the best possible plan for definite improvement issues [38]. A few tools for the coming new age include fluffy rationale, hereditary calculations, and molecule swarm enhancement. However, multiple cycles are carried out for conventional approaches, such as linear programming [39]. In order to provide power to a distant island town, the research explores several off-grid hybrid renewable energy system (HRES) combinations [40]. An approach for creating hybrid power producing systems is provided in this research. The Modified Electric System Cascade Analysis approach serves as its foundation [41]. This study examined an affordable hybrid solution that satisfies residential community need for the finest storage technology in India’s underdeveloped areas [42].

A. Research Gap

The topic of optimal design for HEGM-based power systems in terms of reliability and economic considerations has been extensively explored in previous research. The importance of energy storage technologies, specifically battery technologies and their depth of discharge (DOD), in renewable generation-based power systems has been emphasized in these studies. However, it has been observed that the literature mainly focuses on the use of lead acid (LA) and lithium-ion (Li-ion) batteries, and there is a lack of investigation into other battery technologies in HEGM-based systems, particularly in the cement industry, and to evaluate the economic viability on different battery DOD levels.

B. Contributions

• The aim is to develop an efficient model for both standalone and grid connected HEGMs in the cement industry of Pakistan. The focus will be on battery technologies and DOD levels.

• The performance of the proposed HEGMs will be evaluated by analyzing load profiles of 5 cement industries and relevant geographical resource data.

• A multi-criteria decision analysis (MCDA) will be carried out by creating a cumulative objective function (COF) that considers the minimization of the NPC, LCOE, and greenhouse gas emissions (GHG emissions) simultaneously. The four different battery technologies, namely LA, Li-ion, vanadium redox (VR), and nickel-iron (Ni-Fe), will be compared in the proposed HEGMs.

• As per the authors’ best knowledge, the proposed study, which considers the minimization of NPC, LCOE, and GHG emissions with a focus on battery technologies and optimal DOD levels, has not been investigated before in the cement industry of Pakistan.

In the rest of the paper, the MCDA for HEGMs based on battery technologies is performed in four test cases:

Case-1: LA

Case-2: Li-ion

Case-3: VR

Case-4: Ni-Fe

Similarly, five cement plants are considered test sites:

Cement Plant-1: Askari Cement Plant, Wah (ACPW).

Cement Plant-2: Bestway Cement Plant, Kalar Kahar (BCPKK).

Cement Plant-3: Bestway Cement Plant, Farooqia (BCPF).

Cement Plant-4: Bestway Cement Plant, Hattar (BCPH).

Cement Plant-5: DG Cement Plant, Chakwal (DGCPC).

SECTION II. Methodology

This energy-producing system involved the use of HEGMs, and the methodology of the study is illustrated in Figure 1.

• To strengthen the microgrid, Homer Pro software was utilized, which required inputs such as load profile, equipment data, and environmental factors like sun irradiance.

• The resources that were selected formed the basis of the analysis, and techno-economic evaluations were conducted at various levels to determine the optimal solutions for minimizing NPC, LCOE, and GHG emissions.

• The enumerative optimization method was employed by HOMER to assess each combination that failed to meet the requirements, and the best options were compiled and ranked based on the factors considered.

FIGURE 1.

Methodology framework of the hybrid microgrid design.

A. Optimization Problem

There are two simulating tools the difference is HOMER Basic is a free, simplified version of software that can be used for basic modeling of renewable energy systems. It includes many of the same features as HOMER Pro but has some limitations. For example, it can only model systems with up to three power sources and does not include some of the more advanced features of HOMER Pro. The main difference between HOMER Basic and HOMER Pro is the level of complexity and detail that each version can handle. HOMER Basic is suitable for basic modeling of small renewable energy systems, while HOMER Pro is better suited for larger, more complex systems that require advanced optimization and financial analysis tools.

HOMER Pro employs a derivative-free optimizer. The mixed integer linear programming (MILP) optimization framework aims to identify the most suitable connections between plants, utilities, and modifications to achieve optimal resource efficiency, minimal environmental impact, minimal total cost, or other defined objectives [43]. The optimal solution is chosen by HOMER Pro after the user inputs many parameters [44]. HOMER Pro ranks the best option based on the objective. Using HOMER Pro, researchers can also relate various generator-storage unit combinations’ scientific and financial facets by considering MCDA best result obtained based on the minimization of NPC, LCOE, and GHG emissions.

B. Objectives

Several factors must be considered when determining the optimal configuration for a power system. One of the key considerations is the levelized cost of electricity (LCOE), which varies depending on the type of power generation used. In recent years, the LCOE for non-dispatchable renewable energy sources such as wind and solar has become comparable to that of conventional fossil fuel generators in many parts of the world [45]. Among the considered criteria are minimizing NPC, LCOE, and GHG emissions. Multi-criteria decision analysis is used to determine the ideal composition of the microgrid. A cumulative objective function (COF) was considered, as shown in Equation (1).

$$\begin{equation*} \text {COF}\,\,=\,\,[W_{1(\text {NPC})} +W_{2(\text {LCOE})} +W_{3(\text {GHG}\,\text {Emissions})}] \tag{1}\end{equation*}$$ where W₁ is 33.33%, W₂ is 33.34%, and W₃ is 33.33%. Equation (2) used to make these parameters unitless, as the objective parameters have different units. $$\begin{equation*} \text {Normalize}\_{}{\text {value}}=\,\,\frac {\text {Basevalue}\,-\text {Actualvalue}}{\text {Basevalue}} \tag{2}\end{equation*}$$ Normalize_value is the unit less value, Basevalue is the value of the base case considered in this study, and Actualvalue is the value to be normalized. Equation (2) is utilized for each parameter independently, and the COF for each HEGM is calculated using Equation (1).

As Equation (2) illustrates, if Actualvalue exceeds Basevalue, Normalize_value becomes negative and COF. Minimum COF results in maximum NPC, LCOE, and GHG emissions, and vice versa.

1) Net Present Cost

The numerous continuing cost combinations are comparable to the NPC that support experienced during its practical life, with a reduction of the improvement value at that point. The costs that are remembered for the net present cost are the costs that are shown in Equation (3) by reference [46] for initial expenditure, replacement cost, activity, and maintenance cost.

The total NPC is determined using the formula below:

$$\begin{equation*} \text {NPC}=\,\,\frac {C_{ann.\,tot}}{\text {CRF}\left ({{iR_{proj}} }\right)} \tag{3}\end{equation*}$$ Here, Cann, tot = Annualized cost. i = Interest rate (Annual). R_proj = Project lifetime. CRF (.) = Capital recovery factor.

2) Levelized Cost of Energy

The predetermined shaped framework delivers per KWh of energy. Equation (4) from [46] is used by HOMER to determine the ideal COE for a standalone system.

$$\begin{equation*} \text {LCOE}\,\,=\,\,\frac {C_{\,ann\,.\,tot}}{E_{prim} \,+\,E_{def} \,+\,E_{grid\,.\,sales}} \tag{4}\end{equation*}$$ The total deferrable load is represented by E_def, while the entire primary load is indicated by E_prim. The yearly cost is denoted by $\text{C}_{\mathrm {ann. tot}}$ and the amount of energy supplied to the grid per year is represented by E_grid sales.

3) GHG Emissions

The production of hazardous gas emissions from energy generation is dependent on the type of power resources utilized. The quantity of CO2 released per kWh is dependent on the type of fuel utilized, leading to constant changes. Along with CO2, the generation of each kWh also results in the production of 1.34 g of nitrogen oxides and 2.74 g of carbon dioxide. Notably, HEGM-2 was found to not emit any nitrogen oxides (NO), sulfur dioxide (SO2), carbon monoxide (CO), unburned hydrocarbons (UHC), or carbon dioxide (CO2).

C. Hybrid Energy Generation Models Designing

Four HEGMs were developed using the Homer Pro software for the techno-economic analysis. A photovoltaic (PV), converters, a battery system, and generators for top load needs are all part of the suggested system. The four types of HEGMs developed for this study are shown in Figure 2 and base model in Figure 3. These are the options provided for each HEGM that are the most cost-effective and sensible. Each HEGM has its gains and limits to meet the necessary load requirements.

FIGURE 2.

Schematic diagram of HEGMs (a) HEGM-1 (b) HEGM-2 (c) HEGM-3 (d) HEGM-4.

FIGURE 3.

Schematic diagram of base-model.

1) PV

The standard flat-plate photovoltaic is utilized in the manufactured variations. Generic PV panels have an efficiency of 14% and a lifespan of 25 years. Under normal operating conditions, the module’s output power is calculated using Equation (5) [47].

$$\begin{equation*} P_{pv}=f_{pv} x Y_{pv} x I_{T} \mathord {\left /{ {\vphantom {I_{T} I_{S}}} }\right. } I_{S} \tag{5}\end{equation*}$$ Ppv is the abbreviation for the PV panels’ meager power output in kW. The term “total incident radiation” (measured in kWh/m2) is IT. IS = 1000 W/m2; The reduction factor, also known as f_pv, is determined by energy loss caused by splices and long wiring distances.

2) Battery Storage System

Tests were conducted on four battery types, namely the Hoppecke 24 OPzS 3000-Vented lead-acid (Case-1), the Blue Ion 2.0 lithium-ion battery (Case-2), the redT vanadium redox battery (Case-3), and the Iron Edison nickel-iron battery (Case-4), to determine which one is most suitable for use in hybrid systems. Equation (6) [48] depicts the DOD expression, and Table 3 contains the cost of the components used in this study and Table 4 contains essential information about the chosen batteries.

$$\begin{equation*} \text {DOD}\left ({\% }\right)\,=\,100\left [{ {\frac {1}{\text {Q}}\int \limits _{0}^{1} {\text {i}(\text {t})\,\text {dt}}} }\right] \tag{6}\end{equation*}$$ The depth of discharge is calculated by dividing the maximum battery capacity (Q) by the load current (i(t)). Contrary to the DOD, SOC can be related to: “DOD = 1 – SOC”.

TABLE 3 Components Cost [49]

TABLE 4 Specifications of Induced Batteries [51]

3) Converter

To utilize the HEGMs effectively, the Homer Pro software employs a universal system converter that works with both rectifier and inverter modes. During periods of no solar or wind power, such as at night or on cloudy days, the converter operates exclusively in inverter mode. When enough renewable energy is available to charge the battery system, the converter only operates in its rectifier mode. The converter has an efficiency rating of 95%.

The power converter’s maximum capacity for converting DC to AC is determined by the effectiveness and selection of the inverter (Pl, s (t)). In [50], it is expressed as Equation (7):

$$\begin{equation*} P_{l,s} \left ({t }\right)\,=\,P_{input} \left ({t }\right)\,\times \,\,\eta _{conv} \tag{7}\end{equation*}$$ where P_input(t) stands for the converter’s input power and $\eta _{\mathrm {conv}}$ for its efficiency.

4) Diesel Generator

A standard small-size generator is used in the design and simulation processes in HEGM-1 and 3. The generator is adapted to meet its requirements by the Homer Pro program. A connection between the rated power of a diesel generator (DGen) and its output can be seen in Equation (8) from [52]. Table 4 contains the cost of components used in this study.

$$\begin{equation*} \text {PGD}=\,\eta _{\text {Diesel}} \,\times \,\text {NDG}\,\times \,\text {PGD},\,\text {N} \tag{8}\end{equation*}$$ The total number of diesel generators that are identical is represented as NDG, while the combined output of these generators is referred to as PDG. The productivity of the generator is denoted by the symbol $\eta$ .

5) Grid

The conventional grid power is used to supply electricity to the site, and when the load demand is not met by renewable sources, an AC power backup plant is utilized. The integrated grid system with net metering (NM) allows surplus power to be obtained from proposed HEGMs, which can be sold back to the grid through a billing mechanism. This approach encourages the use of cost-effective power generation from renewable sources and enables small and medium-sized consumers to acquire credits for excess electricity generation. In developed countries, customers generating electricity from renewable sources receive a fiscal incentive called a feed-in tariff (FIT), where the energy provider receives payment through a fixed unit price for the electricity generated. The proposed system’s unit price with NM is calculated using NEPRA’s retail electricity price for off-peak hours at 0.250 $\$$ /kWh [53].

D. Site Area

The cement manufacturing plants mentioned in this study are situated in Pakistan, with the following coordinates: ACPW at 33.8170° N, 72.7238° E, BCPKK at 32.7185° N, 72.7761° E, BCPF at 33.8282° N, 72.8337° E, BCPH at 33.8481° N, 72.8679° E, and DGCPC at 32.7344° N, 72.8100° E. Figure 4 depicts the geographical locations of these cement plant.

FIGURE 4.

The geographical locations of cement plants.

1) Load Profile

The total power demand of five cement industries has been recorded. Specifically, the ACPW industry requires a load profile of 18MW, while the BCPKK, BCPF, and BCPH industries have an average demand of 34MW, 37MW, and 18MW, respectively. The average load for the DGPC industry has been observed to be 31MW. In Figure 5, energy is utilized per day from [54].

FIGURE 5.

Daily load profile of the plants.

2) Energy Resource Assessment

The location of the area has a significant potential for energy resources. In Pakistan, there are a lot of data about the prospect of solar power. When determining their resource potential, the prospective locations’ geographical location has been considered. The places that were chosen to have a lot of potential for sun-oriented travel. Figure 6 provides an overview of the facilities under consideration’s annual ambient temperatures. It demonstrates that the ACPW is 22.85 °C. BCPKK has a yearly average ambient temperature of 17.80 C°, while BCPF and BCPH have annual averages of 22.75 C°. DGCPC has a yearly average ambient temperature of 24.21 C°. ACPW’s annual definite photovoltaic power yield is 1620 kWh/kWp, while BCPKK has an explicit photovoltaic power output of 1631 kWh/kWp. BCPF and BCPH generate 1621 kWh/kWp, while DGCPC generates 1651 kWh/kWp, as indicated in the study.

FIGURE 6.

Ambient temperature of the plants.

The annual daily radiation levels in kWh/m2 are depicted in Figure 7, with ACPW and BCPKK having average yearly values of approximately 4.91 kWh/m2/day, and BCPF and BCPH having values of 4.89 kWh/m2/day. Meanwhile, DGCPC records values of 5.027 kWh/m2/day. The average daily solar radiation in Pakistan is 5.0 kWh/m2/day, which implies that there is significant potential for the development of solar-powered chargers. Sun-radiation data can be estimated using available web-based data sets such as World-weather-online, a NASA website, and a book of sun-radiation-oriented maps [55].

FIGURE 7.

Annual daily radiation and clearness index.

SECTION III. Results

To determine the most efficient solution, Homer Pro software was used to analyze the NPC, LCOE, and GHG emissions data previously mentioned in this section. The output was customized by considering the COF. By doing so, authors scale down the system’s result for better understanding. Base-Model results are shown in Table 5.

TABLE 5 Base-Model Objective Parameters

A. Case-1 (LA)

Tables 6–​10 shows the result of the cumulative objective function. From the table, each HEGM has different values on different DOD levels. Maximum COF has the most negligible value of NPC, LCOE, and GHG Emissions for Case-1.

TABLE 6 COF of ACPW Case-1

TABLE 7 COF of BCPKK Case-1

TABLE 8 COF of BCPF Case-1

TABLE 9 COF of BCPH Case-1

TABLE 10 COF of DGCPC Case-1

B. Case-2 (Li-Ion)

Tables 11–​15 shows the result of the cumulative objective function. From the table, each HEGM has different values on different DOD levels. Maximum COF has the most negligible value of NPC, LCOE, and GHG Emissions for Case-2.

TABLE 11 COF of ACPW Case-2

TABLE 12 COF of BCPKK Case-2

TABLE 13 COF of BCPF Case-2

TABLE 14 COF of BCPH Case-2

TABLE 15 COF of DGCPC Case-2

C. Case-3 (VR)

Tables 16–​20 shows the result of the cumulative objective function. From the table, each HEGM has different values on different DOD levels. Maximum COF has the most negligible value of NPC, LCOE, and GHG Emissions for Case-3.

TABLE 16 COF of ACPW Case-3

TABLE 17 COF of BCPKK Case-3

TABLE 18 COF of BCPF Case-3

TABLE 19 COF of BCPH Case-3

TABLE 20 COF of DGCPC Case-3

D. Case-4 (Ni-Fe)

Tables 21–​25 shows the result of the cumulative objective function. From the table, each HEGM has different values on different DOD levels. Maximum COF has the most negligible value of NPC, LCOE, and GHG Emissions for Case-4.

TABLE 21 COF of ACPW Case-4

TABLE 22 COF of BCPKK Case-4

TABLE 23 COF of BCPF Case-4

TABLE 24 COF of BCPH Case-4

TABLE 25 COF of DGCPC Case-4

E. Graphical Representation

From Tables 5–​25, HEGM-3 is the most optimal model. Table 26 shows the comparison of the optimal solution with the base model. Figure 8 shows the Graphical representation of the optimal case and optimal DOD for each selected plant.

TABLE 26 Comparison of Optimal Solutions From the Base Model

FIGURE 8.

Optimal DOD and battery type for each plant.

Table 27 shows the comparison of excess energy. The excess energy provided by HOMER is considered as the original value, while the reduction is determined by comparing this value to that of other HEGMs. The comparison is based on a previous study, presented in [56].

TABLE 27 Comparison of Excess Energy [56]

SECTION IV. Discussion

For ACPW, when battery discharge is from 10% to 95%, COF varies from 3.12549598 to 73.658400 and the most optimal solution is HEGM-3 for Case-3 having NPC of USD 205,501,300, LCOE USD 0.0530640/kWh and GHG Emissions is 17,392,196 kg/year at the optimal DOD of 40%.

For BCPKK, when battery discharge from 10% to 95% COF varies from 1.244651 to 73.755313, the most optimal solution is HEGM-3 for Case-3 having NPC of USD 390,028,000, LCOE USD 0.05294655/kWh and GHG Emissions are 32,034,909 kg/year at the optimal DOD of 10%.

BCPF COF varies from 3.125829 to 73.393698, and an optimal solution is HEGM-3 for Case-3 with USD 430,640,400 NPC, LCOE is USD 0.0546779/kWh, and GHG emissions are 39,547,271 kg/year at the optimal DOD of 20%.

In the case of BCPH, COF varies from 3.252383 to 72.563919, and an optimal solution is HEGM-3 for Case-3 with USD 205,355,000 NPC, LCOE is USD 0.0533465/kWh, and GHG emissions are 18,098,903 kg/year at the optimal DOD of 10%.

In the case of DGCPC, COF varies from 1.711659 to 74.698037, and an optimal solution is HEGM-3 for Case-3 with USD 344,564,100 NPC, LCOE is USD 0.0513028/kWh and GHG emissions are 27,638,652kg/year at the optimal DOD of 10%.

The LCOE varies from case to case, with the lowest being in DGCPC at USD 0.0513028/kWh and the highest in BCPF at USD 0.0546779/kWh. LCOE also varies between cases, with the lowest in DGCPC at USD 0.0513028/kWh and the highest in BCPF at USD 0.0546779/kWh. The optimal DOD also varies, with the lowest being in BCPKK at 10% and the highest in ACPW at 40%. The NPC also varies, with the lowest in BCPH at USD 205,355,000 and the highest in BCPF at USD 430,640,400. The greenhouse gas emissions (GHG) also vary, with the lowest in BCPH at 18,098,903 kg/year and the highest in BCPF at 39,547,271 kg/year. Overall, the results suggest that the HEGM-3 solution for Case-3 that is vanadium redox is the most optimal in terms of cost and emissions across all cases.

The internet of energy (IoE) will be the vital themes in future. In this context, the features mining, event-driven processing, and optimization tools will be advantageous in terms of real-time compression, power consumption reduction, and computing performance [57], [58]. Additionally, the assimilation of these tools can enhance the performance of the suggested method [59]. Future research can be conducted to assess the feasibility of using these tools with the recommended methodology.

SECTION V. Conclusion

This study aims to determine the optimal battery technology and Depth of Discharge (DOD) for five cement plants by considering a cumulative objective function (COF) that combines NPC, LCOE, and GHG emissions. The objective is to minimize COF. It was found that DOD significantly affects battery performance. Four battery technologies - lead acid (LA), lithium-ion (Li-ion), vanadium redox (VR), and nickel-iron (Ni-Fe) - were evaluated.

The optimal values of NPC, LCOE, and GHG emissions for the HEGMs were determined using HOMER Pro with the given DOD values. The overall energy cost primarily depends on the battery’s DOD, and changing the DOD will result in a change in the overall cost for the HEGMs.

The results concluded that Case-3, that is, vanadium redox battery is the most optimal battery technology.

• HEGM-3 is the most optimal solution for each plant, with COF ranging from 72.563919 to 74.698037.

• NPC ranges from USD 205,355,000 to 430,640,400.

• LCOE USD 0.0513028/kWh to 0.0546779/kWh.

• GHG Emissions are 17,392,196 kg/year to 39,547,271.

• The optimal DOD is 10, 20, and 40%.

The cement plant DGCPC that employs HEGM-3 has been found to be the most effective, with a 61.49% reduction in NPC, 78.62% decrease in LCOE, and an 84.00% reduction in GHG emissions when compared to the base model.

Future cost savings can be obtained by utilizing more renewable resources. It is possible for microgrids to utilize a blockchain-based energy contract system for their energy transactions. These microgrids have the potential to serve as decentralized generators within the structure of a smart grid.

There are some factors that the researchers can focus. PV Tracker, minimum load ratio, include other renewable resources and other latest battery technologies can be changed or used to further minimize the objectives. The authors put these in the future direction of the study.

ACKNOWLEDGMENT

The authors are thankful to the Riphah International University, CESI LINEACT, Effat University, Bahria University, and Najran University for the technical support. They are thankful to the Najran university for providing financial support under the Grant number (NU/RG/SERC/12/7). Also thanks to the Effat University for financially supporting this project under the grant number (UC#9/12June2023/7.1-21(4)7).