Techno-economic and environmental analysis of hybrid energy system for industrial sector of Pakistan

Abstract

The industrial sector of Pakistan is currently facing severe load-shedding, which ultimately affects its unit production. The greater dependency on conventional energy resources (Thermal, Nuclear, etc.) results in higher production costs and environmental pollution. A sustainable, cost-effective, and environment-friendly solution can help the industrial growth of Pakistan. This article proposes an optimal hybrid energy system (HES) for the industrial sector of Pakistan to overcome the mentioned challenges. The proposed HES is developed in HOMER Pro. Three different energy cases (Case I: Existing energy system including a utility grid and diesel generator, Case II: On-grid Biogas system, and Case III: On-grid PV system with batteries) are considered for the Gourmet food Industry in the Sundar Industrial estate, Pakistan. The Load profile of the selected site was calculated through on-site visits and data provided by the designated utility grid feeder. The analysis shows that Case III is more effective than other cases, indicating reduced Net Present Cost (NPC), Cost of Energy (COE), and Operating Cost (OC) to $ 19.2 million, $0.034/kWh, and $ 573,371/year respectively. Moreover, the On-grid PV system with batteries (Case III) provides an environmentally friendly solution by reducing 63.82% $\:C{O}_{2}$ by and 62.22% $\:{NO}_{x}$. Comparing the sensitivity analysis for various grid sell-back prices ($0/kWh, $0.043/kWh, $0.061/kWh, and $0.09/kWh), Case III is more cost-effective than Case II. The revenue generation in Case III is $128,499.41/yr, considering the supply of excess electricity into nearby small industrial loads at $0.065/kWh, this indicates that installing optimal HES in industries will not only help in overall cost reduction but also support in mitigating environmental pollution and load shedding.

Introduction

Industrial growth is vital in determining a nation’s economic prosperity. The expansion and development of the industrial sector contribute significantly to job creation, technological advancement, and economic productivity¹. The success of industries is closely linked to the availability of a continuous supply of cheap electricity which is paramount for the smooth operation of industrial production. Interruptions in electrical supply can lead to disruptions in production, increased costs, and hampered economic growth^2,3. Therefore, a sustainable and cost-efficient energy configuration is crucial to support the industrial sector, ensuring reliable and cost-effective operations.

The Industrial Sector of Pakistan heavily relies on the Utility Grid (UG) as its primary energy source, which is supplemented by even more expensive diesel generators (DGs)⁴. Pakistan’s energy mix is composed of 61% thermal energy, as illustrated in Fig. 1, most of the conventional energy sources utilize fossil fuels for energy production. These fossil fuels are imported by Pakistan at high rates which have caused the country’s trade deficit to widen and increased electricity prices for consumers^5,6. Frequent load shedding presents another challenge, forcing industries to rely on more costlier DG operations. Consequently, the reliance on conventional energy sources has resulted in the energy sector being responsible for the largest share of the country’s greenhouse gas emissions, comprising 76.1% $\:\text{C}{\text{O}}_{2}$Of production⁷. This contradicts Pakistan’s commitments to its nationally determined contribution (NDC) under the United Nations sustainable development agenda for 2030, which aims to incorporate 30% Renewable energy sources in its energy mix^8,9.

Figure 1

Pakistan energy generation⁶.

The transition from conventional energy generation to inexpensive and environmentally friendly renewable energy generation may solve the problems faced by the industrial sector of Pakistan. Despite having one of the largest potentials for renewable energy generation, including a solar power generation capacity of 3000 MW, wind energy generation capacity of 346,000 MW, and annual biomass production of 230 billion tons, Pakistan’s economy is not stable enough to facilitate a quick transition^{10,11,12,13,14}. It’s important to note that making a significant shift to renewable energy would require transitioning the entire industrial energy usage to renewable sources. However, because renewable energy sources are intermittent, it may be challenging to meet the energy needs of industries year-round. To address this issue, a hybrid energy system that combines renewable and conventional energy sources could provide a solution¹⁵. The approach outlined above allows for a smoother transition while optimizing costs and carbon credits. By gradually increasing the use of renewable energy over time, industries can shift from conventional sources to renewable ones. A hybrid energy system utilizes various energy sources to ensure a consistent and economical energy supply. The reason for this is that when one of the energy sources falls short to meet the energy requirement of the system the other sources function to meet the load. This makes it most suitable for deployment in Pakistan’s industrial sector, where costly and unstable energy has negatively impacted the sector, driving up production costs and causing the nation to experience record inflation¹⁶.

Deploying a hybrid energy system will not only reduce dependency on costly and unreliable conventional energy resources but also provide various benefits to Pakistan’s industrial sector. The net present cost, cost of energy, and operating cost can all be considerably reduced by combining conventional and renewable energy sources such as wind and solar offer lower fuel prices¹⁷. Fuel expenses can be decreased overall by merging these renewable energy sources with conventional sources. In addition, the hybrid energy system guarantees an uninterrupted energy supply to industry. It enhances revenue generation by utilizing the excess energy produced during high renewable energy production, which can further improve the cost of the entire hybrid energy system.

Organization of research work

This article is organized in the following manner. First, a detailed literature review is conducted to find the research gaps in existing literature and investigate viable solutions for energy crises in the industrial sector of Pakistan. Then methodology chapter is presented, detailing each step followed in designing of solution for the problem identified. Lastly, a thorough analysis of the results will be presented by exposing the system to various economic and environmental factors.

Literature review

Remarkable research has been conducted globally for techno-economic and environmental analysis of hybrid energy systems to find the potential economic and environmental benefits of installing hybrid energy systems in place of conventional energy systems considering various technical and economic factors. Their finding reveals that incorporating renewable energy sources in the energy mix minimizes the net present cost, cost of energy, and environmental contaminations. Some of these research works are summarized below.

An optimum islanded hybrid energy system consisting of solar PV, wind turbine, DG, and battery storage was designed for Barishal and Chattogram divisions in Bangladesh. In this analysis, Homer Pro software was used to compare the results of five different optimization strategies to determine the most suitable component combination for optimal microgrid operation, ensuring optimum NPC, COE, and $\:C{O}_{2}$Missions. The power system performance and feasibility analysis were performed on MATLAB/SIMULINK to evaluate the frequency and voltage response of the proposed system. The findings highlight that incorporating renewable sources in microgrids leads to reduced cost and environmental impacts with the “Load Following” strategy identified as the most effective in terms of cost, environment, and stable power system response¹⁸. Similarly, a fourth phase was developed for power assessment of off-grid solar PV, wind DG, DG, and battery systems was developed for a community in Egypt. The result showed that the proposed system yields optimum cost and emissions as compared to the system with only DGs¹⁴.

The economic and environmental benefits of implementing an on-grid solar system were presented for charging of electric vehicles in King Abdullah University, Azad Jammu, and Kashmir District of Pakistan. This research introduces a new approach by integrating solar and battery technology in existing conventional systems for charging EVs. Two models were presented: an on-grid PV system without a battery and an on-grid PV system with a battery. The size of each component was estimated using Homer Pro software. The result of both models was compared with the existing energy system, grid-based EV charging. The findings revealed that integrating solar PV reduces dependence on the grid, reduces the cost of energy, and decreases greenhouse gas emissions. Additionally, the system with batteries demonstrated the best results in terms of cost and environmental benefits. Using batteries reduces grid dependence by charging during low demand and discharging during high demand¹⁹.

A recent study was conducted for the electrification of remote communities in Newfoundland, Canada. The study focused on often-neglected thermal load by designing a combined heat and power (CHP) microgrid that can serve both electric and thermal loads and reduce the community’s reliance on costlier DG operations. The model was prepared using MS Excel, Polysun, BEOPT, and Homer Pro. The simulation revealed that the proposed model consisting of solar thermal, PV, wind turbine, fuel cell, and hydroelectric storage was most effective in reducing the reliance on fuel and yields minimum NPC and carbon emissions. This study explores the use of hydrogen electrolysis as storage, by expanding its size to store electricity in the form of hydrogen and selling it to nearby municipalities²⁰. Another study explored the benefits of CHP hybrid energy system for the healthcare system by demonstrating its potential for enhancing energy resilience, sustainability, and cost-effectiveness²¹.

A techno-economic and environmental analysis of a hybrid energy system was conducted for rural electrification of a community in Nigeria. The proposed model consists of solar PV, wind turbine, DG, battery storage, and converters. The proposed model was presented with both on-grid and off-grid scenarios. The optimal system configuration for solar PV, wind turbine, and DG remains the same for both scenarios except for battery storage. In the off-grid scenario, battery storage units were used to store excess energy and utilize it when needed. In an on-grid scenario, the battery storage units are replaced with grid extension to utilize the revenue generation potential of the system by selling it back to the grid. Sensitivity analysis was performed to demonstrate the flexibility of hybrid energy systems for both scenarios. It was observed that the on-grid scenario has the least cost and emission as compared to the off-grid system as it can further optimize the cost by selling the excess electricity back to the grid and reducing emissions²².

The economic feasibility of an on-grid wind, biogas hybrid energy system for a dairy farm in Nova Scotia, Canada was investigated. This analysis was conducted to address the limited economic viability of an on-grid system under a community feed-in tariff (COMFIT) scheme. For this study, different turbine sizes were considered with a fixed-capacity biogas generator to find the most optimum configuration for three different locations in Nova Scotia. The hybrid energy system was analyzed under two different scenarios. Base scenario without any incentive and scenario with COMFIT incentive scheme. The result revealed that implementing the COMFIT scheme further reduces the NPC of the system for all locations²³. Similarly, the revenue generation potential of a hybrid energy system was investigated for two airports in Bangladesh. The findings indicate that employing on-grid PV not only optimizes the emissions but also has the potential to generate revenue by selling excess electricity to nearby loads²⁴.

A hybrid energy system was modeled for educational institutions in India to reduce reliance on conventional grids for energy consumption²⁵. In this analysis, solar PV, DG, and battery storage configurations were considered for both off-grid and on-grid scenarios. The proposed system was analyzed under two dispatch strategies “Load Following” and “Cycle charging”. The findings of the research indicate that the on-grid system was a more economically optimal configuration in terms of cost and emissions as it can sell excess energy back to the grid improving its economic viability and reducing dependency on DGs.

Another study investigates the techno-economic feasibility of two hybrid energy system scenarios for electrification and hydrogen production in Maroua, Cameroon for three different consumer sizes: large medium, and small²⁶. These scenarios were solar PV, fuel cell, electrolyzer, and biogas (Scenario 1), and solar PV, battery, fuel cell, electrolyzer, and biogas generator. The simulations revealed that scenario 2 had the optimum levelized cost of energy and levelized cost of hydrogen. Furthermore, the emissions were substantially reduced.

In Pakistan, significant research has been conducted to design and develop hybrid energy systems. Solar solar-powered reversed osmosis drinking water plant was designed to meet the requirement of drinking water for Pakistan²⁷. HOMER software is used for techno-economic analysis of this system. The simulation results showed that there was no carbon dioxide emission in the environment. A hybrid energy system consisting of an on-grid solar PV, DG, and battery system is designed in HOMER for laptop manufacturing industries. The research aimed to investigate the possibilities of integrating solar within the existing UG and DG network by optimizing the system for the least LCOE, NPC, and carbon emission while increasing the renewable power penetration. The authors presented a standalone hybrid energy system model for five cement plants in Pakistan²⁸. Multi-criteria decision analysis was carried out, based on objectives including optimization of net present cost, the levelized cost of electricity, and greenhouse gas emissions. Four hybrid energy models were tested, and it was found that solar PV, DG configuration was more optimal based on net present cost while solar PV, fuel cell and solar PV, battery model was more effective for carbon-free system²⁹. A hybrid energy system (HES) was designed on HOMER for three different locations in Pakistan. The system is optimized using a combined dispatch approach that considers different load types, batteries, controllers, wind, solar, and diesel generation. Utilizing NPC and LCOE, feasibility is assessed while carefully considering financial stability and contrasting it with different dispatch strategies³⁰. Similarly, HOMER was used to model off-grid hybrid energy systems for provincial capitals in Pakistan. Different system combinations were tested, and the solar/wind/diesel/battery hybrid system was found to be the most effective in terms of carbon emissions and performance³¹. An islanded solar PV, wind turbine, DG and battery hybrid energy system was designed to cater to the energy demand of remote communities in Pakistan. Homer was used to analyze the proposed system based on LCOE, NPC, and emissions. The results revealed that PV, wind turbine, and battery were the most economical solutions in terms of LCOE³². An on-grid hybrid energy system was proposed for the University of Azad Jammu and Kashmir, King Abdullah campus, Muzaffarabad. The results of the proposed energy system were compared with the results of the existing energy system. It revealed that an optimized on-grid PV Hybrid Energy system with battery backup is more efficient than all the other configurations with optimum COE³³.

The Literature work cited above is summarized in Table 1. Despite major contributions in previous studies, there has been very little effort made to design a hybrid energy system for the industrial sector of Pakistan. Few studies that were conducted in the industrial scenario of Pakistan mainly focused on optimizing the cost and emission aspects of techno-economic analysis, neglecting the fact that the most optimal system for the industrial sector is the one that minimizes the cost and has the potential to generate revenue. Revenue generation is the goal of the industrial sector. Additionally, the industrial load depends on its production process which varies seasonally based on customer demand. This variability in industrial load throughout the year was also neglected by previous research works, which can significantly influence the feasibility of such systems. Furthermore, the intermittent nature of PV energy was not leveraged for profitability. As PV systems generate maximum energy during times of high solar irradiance, and in the case of large industrial loads, the excess electricity produced by PV in large quantities cannot be saved by batteries or sold to utility grids due to limitations imposed by the government. This research work aims to fill these gaps by performing a techno-economic analysis of the hybrid energy system through a detailed comparison of three cases, Case I: Existing energy system consisting of utility grid and DGs, Case II: on-grid biogas system, and Case III: on-grid PV system with batteries to not only optimize cost but also maximize the revenue generation potential of industry. Furthermore, the Cases are designed considering the industrial load variation as subject to seasonal variations based on the production rate of the industry.

Table 1 Summarized related research.

Research contribution

This article brings a new look to the existing literature by proposing a hybrid energy system for the Gourmet food Industry, at Sundar Industrial Estate, Lahore. In contrast to previous research work, this research aims to investigate a reliable, cost-effective, and environmentally friendly hybrid energy system designed for industrial load using Homer Pro software. Various design considerations, such as component selection, blackouts, inflation rates, and grid purchases are considered to create a simulation that mirrors real-time assessment.

The main contributions and novelties of this research are as follows:

• Annual Load profile of selected industry is formulated by the data provided by utility bill and on-site assessment to model industrial load variation concerning production rate.

• Three different cases will be developed based on the configuration of the hybrid energy system. Case I: Existing energy system, Case III: on-grid Biogas system, and Case III: on-grid PV system will be discussed to find the most economically and environmentally friendly configuration for selected industry.

• Cost-benefit analysis is performed to determine the most optimal configuration that leads to cost savings for the entire project’s lifetime.

• Sensitivity analysis is performed on grid sell-back prices to determine the relationship between grid sell-back price and the overall cost of the system over the project’s lifetime.

• The revenue generation potential of the proposed hybrid energy system, that is obtained after comparison for least NPC, COE, and $\:C{O}_{2}$, will be evaluated by selling it to nearby industrial load.

• This techno-economic analysis will benefit Pakistan’s industrial sector by presenting a detailed analysis of the hybrid energy sector for industrial energy needs. This analysis will encourage policymakers to make new policies that incentivize renewable energy production in industrial sectors and grasp the attention of stakeholders by exploring the potential revenue generation of employing hybrid energy systems in industries.

Methodology

There are various software’s developed for designing hybrid energy systems, each having its advantages. PVsyst, RETScreen, MATLAB/Simulink, and Homer Pro are a few most widely used software. But Homer Pro stands out for its ability to comprehensively design, optimize, and analyze complex hybrid energy systems. These advantages stem from Homer Pro’s comprehensive modeling methodology. In contrast to other software, Homer Pro is highly adept at managing a variety of renewable energy sources and system configurations for large-scale hybrid energy systems with numerous components^34,35,36,37. Homer Pro also provides users with a more advanced analysis by handling broader component selection, complex load profiles, a variety of sensitivity variables, and renewable resource data. Additionally, Homer Pro can simulate a large set of load data to perform simulations for an entire year. These characteristics of this software make it much better for designing a system for varying loads^38,39,40.

In this research work, due to the reasons mentioned above Homer Pro is used to design, simulate, compare, and analyze three cases of hybrid energy configurations for the Gourmet Food Industry, Sundar Industrial Estate. The entire framework of methodology is presented in Fig. 2. The methodology initiates the selection of the site and collection of load data. The climate conditions are then assessed to find the available resources on the selected site. For this, metrological data like solar irradiance, and temperature are collected using National Renewable Energy Laboratory (NREL) database. Biomass data is then evaluated by evaluation of biowaste production data available online for the selected location. Economics plays a vital role in the feasibility of the system, so some important economic variables like the inflation rate and discount rate are adjusted. The component specification and size along with its economic parameters are then discussed and evaluated under various contributing factors like solar irradiance, temperature, state of charge, efficiency, and fuel price through comprehensive mathematical modeling. Three different cases consisting of different configurations namely: Case I: existing energy system where utility grid serves as primary energy source backed up by five DG’s, Case II: on-grid Biogas system, and Case III: on-grid PV system with batteries are then designed and analyzed based on NPC, COE, OC, and greenhouse gas emissions. Homer Pro is then used to compare the results of each case to find the configuration that yields the most optimum results in terms of NPC, COE, and $\:C{O}_{2}$emissions. A sensitivity analysis is then performed to test the adaptability of systems under varying economic variables. Based on the results of the most optimum configuration a revenue generation model will be presented to investigate the economic advantages of the proposed system.

Figure 2

Methodology flowchart.

Demographics and load profile of selected site

The study is conducted in the Gourmet Industry, situated within the Sundar Industrial Estate in Lahore. Sundar Industrial Estate spans 1753 acres of land and accommodates over 500 active factories, employing more than 80,000 individuals. Sundar Industries has its own dedicated 132 kVA grid station which is utilized by small and large industrial businesses at selected sites. This analysis selects the Gourmet Food Factory, a large-scale industry in Sundar Industrial Estate. The specific location of the selected industry is indicated in Fig. 3. The monthly energy consumption data for Gourmet Foods Factory was obtained from the utility bills and on-site visits. The load Profile is categorized into average load and energy consumptions during off-peak hours and peak hours of an industrial facility for each month was calculated considering the power factor of 0.94, obtained from the utility bill, as shown in Table 2. The load profile reveals that the total average daily energy consumption of the industry was 41,935 kWh, with an average peak load of 8264.71 KW. As Gourmet Food Factory falls under industrial loads, most of its operations occur between 9 AM and 5 PM due to which a large load can be observed during this period as shown in Fig. 4. Whereas, during non-operating hours, the demand decreases substantially due to interruptions or breaks in industrial production. The low demand during non-operating hours is contributed by other activities in industrial facilities such as call center operation, residential facilities for employees, administration office, and floor lights. Moreover, as industrial load depends on the rate of production of goods, it is subjected to monthly variation as shown in Fig. 5. Production increases during April, May, and June contribute to a high average industrial load during operating hours of 8264.809 KW in June. Whereas, due to low production in January and February, the demand remains relatively low, 1689.34 KW in January, due to the low rate of production of goods.

Figure 3

Geospatial coordinates of Gourmet food industry, Lahore (31°18’10"N 74°10’16” E) using Google Earth web⁴¹.

Table 2 Monthly load profile of the Gourmet food industry.

Figure 4

Scaled monthly load profile of Gourmet food factory.

Figure 5

Average monthly load demand of Gourmet food factory.

Renewable resource assessment

In this section, two of the most abundant resources available at the site will be briefly discussed.

Solar irradiance

The monthly and annual solar irradiance data is gathered from Solar Atlas and is summarized in Table 3. Where the global horizontal irradiance is 73.98% higher than the Standard Testing Condition of 1000 kWh/$\:{m}^{2}$. Furthermore, the location has a PV potential of around 1506.5 kWh of energy for every kW of installed PV. The optimum tilt angle of the solar panel for the location is 28 degrees. The solar irradiance is at its maximum in May and at its minimum in December due to the high daily radiation, as illustrated in Fig. 6.This shows that the selected site has immense solar potential, which can be utilized to its maximum by installing a solar module at the optimum tilt angle.

Table 3 Solar irradiance data of selected site.

Figure 6

Average monthly solar irradiance and clearness index.

Biomass resource

In Pakistan, the potential for generating electricity from biomass resources holds great promise, particularly in the Sundar Industrial Estate. As an agricultural economy with over 60% of its population engaged in agriculture, the country possesses significant biomass resources. Lahore, the second-largest city in Pakistan, generates approximately 7150 tons of municipal solid waste daily⁴². This waste comprises various components, with biodegradable materials, nylon plastic bags, textiles, diapers, and paper ranking in descending order, as illustrated in Fig. 7. Sundar Industrial Zone, with a workforce of nearly 80,000 individuals, actively manages its solid waste through designated dustbins categorized into Biodegradable, Recyclable, and Hazardous bins, each distinguished by specific color coding. The Sundar Industrial Estate’s solid waste management department oversees the collection and transportation of industrial waste. On average, the industrial zone produces 5–6 tons of waste daily, which is then deposited in government-designated landfills⁴³. The conversion of this waste into biogas presents a valuable opportunity to generate electrical energy.

Figure 7

Physical Composition (average) of waste produced in Lahore⁴².

Selection of components

In this study, Case I involves formulating the existing energy system, while two hybrid energy system models are designed and formulated based on geographical location and the availability of renewable energy resources. The proposed hybrid energy system models in this study consist of Case II, which is an on-grid biogas system with batteries, and Case III, an on-grid PV system with batteries. Due to the high cost associated with diesel generators, particularly during load-shedding hours attributed to surging fuel prices, hybrid energy systems are designed to provide a more reliable and cost-effective alternative, aiming to replace the diesel generator option.

Case I: existing energy system

Gourmet food is one of the largest industries in Pakistan and like any other industry, it needs a continuous supply of energy for which it mainly relies on a 132KV grid allocated for the sunder industrial estate by Lahore Electric Supply Company. This makes the industry highly dependent on the main grid. However, the unprecedented blackouts and load shedding have forced the industry to rely on backup diesel generators during the utility cut-off. The price of diesel has increased drastically around the globe which has affected the industrial sector of Pakistan as the price of fuel has reached approximately $0.9/ltr. The existing energy system consists of a utility grid and 5 units of 1 MW generators. The schematic diagram of the existing energy system is shown in Fig. 8. The system is linked to the grid via an AC bus and a transformer. Five diesel generators, each with a capacity of 1 MW, are interconnected with the AC bus to provide direct electrical power to the load in the event of blackouts. These generators are designed to operate solely during blackouts, operating only when the grid fails to supply power to the industry. The technical specifications of the components in the existing energy system are detailed in Table 4.

Figure 8

Schematic diagram of existing energy system (case I).

Table 4 Components specification of existing energy system components (case I).

Case II: on grid biogas system

As previously indicated, biomass stands out as one of the most abundant resources in Pakistan. The waste generated can produce biogas, presenting a more cost-effective alternative to diesel for operational purposes. The pricing of a biogas generator is contingent upon its size and rating. The integration of a biogas gasifier can convert a diesel generator into a biogas generator. Consequently, this integration results in a cost increase ranging from 10 to 30% of the total cost of the original diesel generator. Table 5 outlines the size and specifications of an on-grid biogas system with batteries, with the fuel price for biogas set at $0.25/kg. The schematic diagram of the on-grid biogas system is depicted in Fig. 9. In this configuration, the load is attached to the grid through a transformer and AC bus, while the biogas generator is connected to the AC bus. During operating hours, when the cost of energy produced per unit of biogas is notably lower than that of the grid, the biogas system supplies the load if the system is subjected to irregular situations the utility grid is set up as a backup source. During non-operating hours when the load drops significantly, the utility grid is utilized to serve the load.

Table 5 Components specification of on-grid biogas system (case II).

Figure 9

Schematic diagram of on-grid biogas system (case II).

Case III: on-grid PV with batteries

The on-grid PV system with batteries is gaining prominence in Pakistan due to the vast potential of solar energy. Solar energy stands out as one of the most economical energy resources globally, attributed to its minimal operating costs. The selection of PV modules in this system is determined based on the most economical market values, considering derating factors and the maximum power output of the modules. Table 6 contains specifications of the on-grid PV system with batteries. The schematic diagram of this system is depicted in Fig. 10. In this setup, the industrial load is connected to the grid via a transformer and AC bus, while the PV module is linked to the DC bus alongside the 9.07kWh, LGChem Resu- Li-ion batteries. A bi-directional auto-size converter having 95% efficiency serves to interconnect the AC and DC buses. During operational hours, the load is powered by an array of 330 W PV modules and any excess electricity generated is stored in the batteries. The bi-directional converter converts the DC output from the DC bus to supply the load connected to the AC bus. The purpose of using a small backup in this configuration is to provide limited backup power during short outages and fluctuations during peak hours that can potentially compromise grid stability. PV systems are highly intermittent, and their generation fluctuates throughout the day, leading to sudden drops in energy supply. This causes grid instability if not managed properly. Furthermore, these fluctuations can cause deviations in grid frequency. Employing a small backup can mitigate these challenges by smoothening out minor fluctuations, offering some stability benefits⁴⁴.

Table 6 Components specification of on-grid PV with batteries (case III).

Figure 10

Schematic diagram of on-grid PV system with batteries.

Mathematical modelling

PV modelling

PV works by converting the light energy of the sun into electrical energy. The PV panels are connected to the DC bus beyond which it is converted to AC power with the help of a converter can be modeled using a mathematical model that describes their electrical behavior. This model is used to calculate the amount of electricity that can be generated by solar panels.

$$\:Ppv=Ypv\:\times\:fpv\:\left(\frac{GT}{GT,STC}\right)[1+\alpha\:p\:\left[\:Tc-Tc,STC\right]$$

(1)

Here, Ypv represents the nominal capacity of the PV panels, indicating their maximum power output, fpv value denotes the derating factor, GT refers to the amount of solar radiation that reaches the surface of the PV panels, GT, STC signifies the solar radiation reaching the PV panel surface under standard testing conditions, αp represents the temperature coefficient of power, Tc stands for the temperature of the PV cell itself and Tc, STC represents the temperature of the PV cell under standard testing conditions.

Diesel generator

Diesel generators serve as a backup source for industrial operations. Renewable energy sources are highly dependent on environmental conditions that influence power generation at different levels. Furthermore, the backup provided by batteries is incapable of operating on higher load demand. Thus, a diesel generator is utilized as a backup source in a hybrid power system. Fuel curve and linear correlation are computed in Homer Pro. The quantity of fuel utilized to generate electricity is obtained from the equation given below.

$$\:{F}_{d}=\left(a.{T}_{d}+b.{P}_{d}\right)$$

(2)

Where, $\:{\text{F}}_{\text{d}}$ = Fuel consumption rate of diesel generator (L/h), a is the coefficient of fuel intercept (L/kWh), $\:{\text{T}}_{\text{d}}$ is the Diesel Generator capacity (KW), b is the fuel slope coefficient (L/kWh), and $\:{\text{P}}_{\text{d}}$ is the diesel generator output

Biogas generator

Biogas gasifiers can be used to produce gas from a biomass resource. In this study, the gasification process by thermochemical means is used. The gasification ratio and lower heating value (LHV) considered in this study are 0.8 kg/kg and 23.3 MJ/kg. The amount of output power from a biogas generator can be calculated by using Eq. (3)⁴⁵.

$$\:{P}_{BG}\left(t\right)=\left(\frac{{N}_{gen}}{{F}_{1}}\right)(\frac{{\eta\:}_{gas}\:.\:{H}_{a}\:.bio\left(t\right)}{{H}_{gas}}-{F}_{0}{P}_{e})$$

(3)

Where, $\:{F}_{0}$ is the intercept coefficient, $\:{N}_{gen}$ is the number of generators, $\:{\eta\:}_{gas}$ is the efficiency of the biogas generator, $\:{F}_{1}$is the slope, $\:{H}_{a}$is the Lower Heating value of biogas, $\:{H}_{gas}$is the heating value of fuel gas per unit volume, bio(t) is the biomass input into the biogas system at a specific time, and Pe represents the price of electrical or thermal energy produced by the biogas system.

Battery storage system

In this study, a battery energy storage system is used for storing excess electricity produced by the system and utilizing it for reliable and efficient operation due to the unpredictable nature of RE resources. Li-ion battery provides much more flexibility in terms of its lifecycle and high charge/discharge efficiency. Equations (4) and (5) are used to evaluate the state of charge of the battery storage system during discharging and charging.

$$\:{\text{E}}_{\text{b}}\left(\text{t}+1\right)={\text{E}}_{\text{b}}\left(\text{t}\right).\left(1-{\upsigma\:}\right)-\left(\frac{{\text{E}}_{\text{t}}\:\left(\text{t}\right)}{{\upeta\:}\text{c}\text{n}\text{v}}-{\text{E}}_{\text{g}}\left(\text{t}\right)\right).{\upeta\:}\text{B}\text{D}$$

(4)

$$\:{\text{E}}_{\text{b}}\left(\text{t}+1\right)={\text{E}}_{\text{b}}\left(\text{t}\right).\left(1-{\upsigma\:}\right)+\left({\text{E}}_{\text{g}}\left(\text{t}\right)-\frac{{\text{E}}_{\text{t}}\:\left(\text{t}\right)}{{\upeta\:}\text{c}\text{n}\text{v}}\right).{\upeta\:}\text{B}\text{C}$$

(5)

Where, $\:{\text{E}}_{\text{t}}\:\left(\text{t}\right)$, $\:{\text{E}}_{\text{g}}\:\left(\text{t}\right)$ are energy demand and power produced, $\:{\upeta\:}\text{B}\text{D}$ and $\:{\upeta\:}\text{B}\text{C}$ are discharge and charge efficiency of the battery, σ is the self-discharge of the battery which is set to be 0 for this research, $\:\eta\:cnv$ is the converter efficiency, $\:{E}_{b}\left(t\right)$ is restricted by minimum and maximum storage capacity. $\:E{b}_{min}$ and $\:E{b}_{max}$are stated in Eq. (6)⁴⁶.

$$\:E{b}_{min}\le\:Eb\left(t\right)\le\:Ebmax\:\:\:where\:\:E{b}_{min}=\left(1-DOD\right).Ebmax$$

(6)

DOD represents the depth of discharge of the battery which relies on the battery technology.

Converter

A converter refers to a device that is used to convert electrical energy from one form to another. In this study, A converter is placed to provide interconnection between AC and DC Bus. As PV and battery generate output in the form of a DC signal converter plays a major role by converting DC to AC signal for the load to operate. The efficiency of the converter is given in Eq. (7).

$$\:{\upeta\:}\text{c}\text{n}\text{v}=\frac{{\text{P}}_{\text{o}\text{u}\text{t}\text{p}\text{u}\text{t}}}{{\text{P}}_{\text{i}\text{n}\text{p}\text{u}\text{t}}}$$

(7)

Where, $\:{\text{P}}_{\text{o}\text{u}\text{t}\text{p}\text{u}\text{t}}$ is the output power of the converter and $\:{\text{P}}_{\text{i}\text{n}\text{p}\text{u}\text{t}}$ is represented as the input power of the converter.

Results and discussion

In this segment, we employ Homer Pro software to simulate three cases for the Gourmet Food Industry in Sundar Industrial Estate, Lahore. Case I is the existing energy system installed at the industrial site which comprises a utility grid supported by five 1 MW DG generators. Case II involves utilizing a Biogas generator as the primary energy source during operational hours, with the Utility grid serving as a backup energy source. Whereas in Case III, the industrial load predominantly depends on Photovoltaic generation during operational hours, and batteries are incorporated to store excess energy generated by Photovoltaic systems. In this scenario, the Utility grid comes into play when both PV and batteries fail to meet the load requirements. Each component of the respective model has been selected by taking real-time market values and specifications to make the simulation more realistic. In addition to this, the existing energy system model is also simulated based on the load profile and on-site evaluation. The main purpose of simulating the existing energy system on HOMER is to make analysis more accurate by comparing the results of the existing energy system and two HES. After comparing the results, the system has the most optimum cost including operating cost, net present cost, cost of energy, and initial present cost is selected as the proposed system for the location. Moreover, a sensitivity analysis is conducted to analyze the performance of proposed HES on various grid sell-back prices without making significant changes to the design configuration.

Size configuration

The size of each component used in all three configurations is shown in Table 7, where the size of each component is obtained after simulations of each case using Homer Pro. Here it can be seen that the sizes of components are fixed and do not change after Homer simulation in Case I and Case II. However, simulations of Case III show an optimized design configuration for meeting the industrial load. Optimizing total PV capacity to 13,542 KW and inverter capacity of 7,875 KW for optimal design configuration of Case III. Battery size is fixed due to economic considerations that will be discussed in the next subsection.

Table 7 Size configuration of energy system for three cases.

Economic analysis

Case I: existing energy system

The current energy infrastructure comprises a utility grid supported by five units of 1 MW diesel generators. These diesel generators are configured to align with the real-time parameters of the established setup implemented by Gourmet Foods. Given the industry’s predominant reliance on energy sourced from the utility grid, the Cost of Energy (COE) is heavily contingent on the power procured from the utility grid, as depicted in Fig. 11. Notably, during the summer season, the industry experiences peak energy consumption due to heightened product demand and the operation of essential devices such as air conditioning. The annual expenditure on energy purchased from the grid amounts to a substantial sum of $ 1.3 million/year, rendering the existing energy system economically burdensome. Moreover, the diesel generators come into operation only in the event of a power supply disruption from the utility grid, assuming a daily 2-hour load-shedding scenario. To assess the system’s performance under these conditions, simulations are conducted using HOMER Pro software. The cost implications of the existing energy system are shown in Table 8, where the NPC, COE, and operating cost are indicated as $34.2 million, $0.104/kWh, and $1.58 million/year, respectively. The escalation of COE from $0.09/kWh to $0.1 Rs/kWh is attributed to the fuel consumption of the diesel generators.

Figure 11

Monthly power purchased ($) from grid in case I.

Table 8 Cost of existing energy system (case I).

Case II: on-grid biogas system

The diesel generators in Case I are replaced by biogas generators. This change is made because biogas is cheaper at $0.25/kg, which is a lot less than the $0.91/ltr cost of diesel. Consequently, the biogas generators are utilized for electricity generation serving the industrial during operating hours. The main grid is only engaged when limitations arise in the biomass generators’ capacity to meet the demand. During non-operational hours, the utility grid is strategically utilized to meet the load. The utilization of biogas generators as the primary energy source not only enhances system economics but also diminishes the industrial load’s dependence on the utility grid to 4,452,789 kWh, as depicted in Fig. 12. Where it can be observed that the monthly grid purchases are reduced significantly, and the annual expenditure has dropped by 67.06% to $423,604.8. The simulation outcomes for the on-grid biogas system are presented in Table 9. Where the costs associated with the system decrease substantially by substituting DGs with biogas generators, a 16.5% reduction in Net Present Cost (NPC), 16.5% reduction in Cost of Energy (COE), and 17% reduction in Operating Cost (OC) as shown in Fig. 13. These reductions are the result of replacing expensive diesel operations during load-shedding hours with more cost-effective biogas operations during operating hours.

Figure 12

Monthly power purchased ($) from grid in case II.

Table 9 Cost of on-grid biogas system (case II).

Figure 13

NPC ($), COE ($/kWh) and OC ($/yr) comparison (case I and case II).

Case III: on-grid PV system with batteries

Diesel operation during load-shedding hours has substantial operational costs. To decrease the operational costs of generation during load-shedding hours, a more convenient and cost-efficient energy system is required. On-grid PV hybrid energy system is one of the most viable options. A small backup is provided to serve the load during off-peak hours. The site selected for industrial load mainly operates during the daytime from 9 am to 5 pm, this time is most suitable for energy generation through PV modules. Thus, during operating hours, the load mainly runs on the energy generated by PV modules. The excess energy is stored in batteries to satisfy small loads during off-peak hours. Here the utility grid serves as backup which operates only when the energy generated from PV modules and batteries fails to satisfy the load. As the load mainly runs on PV modules the industrial load dependency on energy produced by the utility grid drops to 5,617,994 kWh as shown in Fig. 14. Where it can be observed that the monthly grid purchases are reduced significantly, and the annual expenditure has dropped by 60.79% to $504,206. The results of on-grid HES with batteries are shown in Table 10. The results indicate that the optimum system reduces NPC by 43.8%, COE by 67.4% and OC by 63.3% as illustrated in Fig. 15. This indicates that the on-grid PV system with batteries is a more cost-effective option, attributed to its low Operation and Maintenance (O&M) costs and reduced dependency on fossil fuels.

Figure 14

Monthly power purchased ($) from grid in case III.

Table 10 Cost of on-grid PV system with batteries (case III).

Figure 15

NPC ($), COE ($/kWh) and OC ($/yr) comparison (case I and case III).

Cost-benefit analysis

The cost-benefit analysis is performed to evaluate the savings of Case II and Case III to determine the most economically feasible configuration that can minimize yearly expenditures. Case I is considered the base case, according to which the overall savings of both cases will be evaluated. Table 11 presents the nominal cash flow of all the cases. Here, Case I has the lowest capital cost; however, due to annual expenditures in terms of RC, O&M, and FC, this system is not an economically viable option. Case II on the other hand has relatively low annual expenditure and significantly higher salvage value which decreases the annual expenses greatly and saves up to 30% overall expenses., Despite having the highest CC, Case III outperforms other cases as it decreases the overall annual expenses by 43%, making it the most cost-efficient configuration.

Table 11 Annualized cash flow of each case.

Environmental analysis

The combustion of fossil fuels and biomass for electricity generation releases pollutants such as carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxides (NO_x), sulfur dioxide (SO₂), and unburned hydrocarbons into the atmosphere. To identify the optimal power system configuration, an environmental assessment is necessary. This study explores three cases: Case I and Case II involve combustible energy resources, while Case III relies predominantly on electricity generated from photovoltaic (PV) sources. Homer Pro is employed to assess the environmental impact of these three systems and compare their outcomes to determine the most environmentally friendly design with the least greenhouse gas (GHG) emissions. The simulation results are presented in Fig. 16. Where Case I exhibits the highest annual emissions reaching nearly 9.81x$\:{10}^{6}$ kg/yr of CO₂, 3.9x$\:{10}^{3}$ kg/yr of CO, 2x$\:{10}^{4}$ kg/yr of NO_x. This high emission level is attributed to its dependence on the utility grid, which relies on the combustion of fossil fuels for electricity generation, along with the use of diesel backup generators during load-shedding hours. Diesel generators generally contribute to higher emissions due to factors such as the composition of diesel fuel, incomplete combustion, and high combustion temperatures. In Case II, the diesel generation is replaced with biogas generation, resulting in a slight decrease in GHG emissions. Replacing DGs with biogas generators reduces CO₂ by 18.7%, CO by 30.2%, and NO_x by 32.3%. This reduction is attributed to the more economical energy generation provided by biogas generators during operating hours, irrespective of load-shedding hours. Based on the results of the simulation, it is evident that Case III outperforms previous cases, yielding minimum emissions. The simulation reveals that Case III reduces CO₂ by 63.82%, and NO_x by 62.22%. It’s important to highlight that photovoltaic (PV) is considered an emission-free energy source and it does not require any internal combustion of fuel for energy generation, thus there are no CO emissions in Case III. However, emissions related to CO₂ and NO_x in the context of Case III, where the utility grid operates as a backup source only when both the PV and batteries fail to satisfy the load, are attributed to the utility grid. The utility grid relies on fossil fuels for energy generation, and it becomes a source of GHG emissions during its operation as a backup in this specific scenario.

Figure 16

GHG emission contribution of each case.

Based on the preceding discussion, it is evident that Case III outperforms Case I and II, yielding significantly reduced costs. Additionally, with an approximate renewable energy penetration of 80%, Case II effectively decreases GHG emissions.

Sensitivity analysis

Variations in grid sell-back rates have a substantial impact on system costs, prompting an in-depth analysis of the effects on Cases II and III in this section. Case I is excluded from this assessment due to its reliance on the Utility Grid to meet industrial load demands. Conversely, Cases II and III possess the capability to generate surplus electricity during periods of low demand, which can be sold back to the utility grid. The performance of both Case II and Case III is scrutinized under varying grid sell-back prices—specifically, at $0/kWh, $0.043/kWh, $0.061/kWh, and $0.09/kWh—while maintaining their configurations without significant alterations. These sell-back prices are taken to provide a brief explanation of the current energy policies of the government that defined the criteria for setting the grid sell-back price to $0.043 Rs/kWh. Changing these prices can influence the cost of a hybrid energy system. The simulation results are illustrated in Figs. 17 and 18. It is revealed that Case III consistently outperforms Case II. As the grid sell-back price increases, there is a significant reduction in the Net Present Cost (NPC), Cost of Energy (COE), and Operation and Maintenance (O&M) costs associated with Case III. Notably, when the sell-back price aligns with the grid power price of $0.09/kWh, all costs linked to the system become negative. The NPC drops to -$2.26 million, COE drops to -$0.004/kWh, and O&M cost drops to -$427,670/yr. This signifies that the system can generate revenue, effectively offsetting its costs. Such a scenario unfolds when the revenue from selling electricity exceeds the expenses tied to the system.

Figure 17

Effect of changing grid sellback rates on NPC ($), COE ($/kWh), and OC ($/yr) (case II).

Figure 18

Effect of changing grid sellback rates on NPC ($), COE ($/kWh) and OC ($/yr) (case III).

Utilizing excess electricity

Photovoltaic modules frequently generate more electricity than necessary during sunny days compared to the connected loads. Managing this surplus electricity becomes crucial, and options include storing it in batteries, feeding it back into the grid, or facing wastage if not effectively utilized. In a previous section, we conducted a thorough comparison between an on-grid biogas system and an on-grid PV system to meet industrial load demands. The on-grid PV system emerged as the preferred choice for the location, demonstrating superior cost-effectiveness and environmental advantages. However, it’s important to acknowledge the intermittent nature of the PV system, resulting in a significant excess of electricity generation. Addressing this surplus can involve increasing the size of batteries, though this adjustment comes with associated costs, as outlined in Table 12. Specifically, enlarging the battery string to 200 leads to a 16.9% rise in NPC, an 8.36% rise in COE, and an 8.07% uptick in O&M costs. Therefore, strategic decisions must be made regarding the trade-offs between enhanced energy storage and the associated system expenditures. Furthermore, the grid has limitations that prohibit it from accepting electricity beyond certain predefined limits. To utilize the excess electricity benefit from a more convenient method is presented in Fig. 19. Here, the excess energy that is produced by photovoltaics that exceeds the storage capacity of batteries and limitations of the grid is diverted to nearby small industrial loads. This configuration will amplify the revenue generation potential of the on-grid PV system. Moreover, the increase in the price of excess energy plays a pivotal role in increasing the revenue-generating capacity of the generating industry. Figure 20 illustrates the revenue generation over different excess energy prices, $0.029/kWh, $0.05/kWh, and $0.065. These prices are selected to show the potential of revenue generation of the industry based on different excess electricity pricing by the government over the past 6 years. The objective of this analysis is to demonstrate the revenue generation potential of employing hybrid energy systems in industries so that policymakers and industrial stakeholders collectively incentivize and implement renewable energy in their energy mix. At an excess electricity price of $0.029/kWh system generates revenue of $57,070.97 per year which can contribute more towards industrial savings and improve the economic viability of the proposed system. Additionally, as the excess electricity price increases the revenue generation prospects of the proposed system substantially increase which reaches its maximum potential when the excess energy price is set at $0.065/kWh, the system can generate an annual revenue of nearly $128,499.41, this model extends its benefits to nearby small industrial loads. The energy supplied by the system proves to be more reliable than that from the utility grid, further underscoring the overall advantages of this approach.

Table 12 Effect of different battery sizes on the cost of on-grid PV system.

Figure 19

Revenue generation model.

Figure 20

Annual revenue generation.

Conclusion

Pakistan is currently facing worst energy crisis, which severely affects the industrial operations and economic growth. Addressing the energy crisis requires great efforts of policymakers and the government in formulating and implementing policies to incentivize renewable energy generation for the industrial sector of Pakistan. In this study, two hybrid energy systems were designed and compared to find the most economical and environment-friendly configuration for the Gourmet food industry, in Sundar Industrial Estate, Pakistan. A sensitivity analysis is also performed to give insight to policymakers on the performance of both hybrid energy systems for various grid sell-back prices. Furthermore, the revenue generation capability of an on-grid PV system with batteries by selling surplus electricity to nearby industrial loads was analyzed. The study systems were developed in Homer Pro for techno-economic and environmental analysis. The following findings are drawn from the simulation results.

• The Existing energy system (Case I) consumes about 358,053,749kWh of electricity annually. The net present cost, cost of energy, operating cost, and fuel costs associated with the existing energy system are $34 million, $0.104/kWh, $1.58 million/yr, and $265,061/yr respectively.

• The on-grid Biogas system (Case II) consumes about 4,452,789 kWh of electricity annually. The net present cost, cost of energy, operating cost, and fuel costs associated with on-grid Biogas system are $28.5 million, $0.0868/kWh, $1.3 million/yr, and $702,159/yr respectively. The annual saving of Case II is $271,207.

• The on-grid PV system with batteries (Case III) consumes about 5,617,994 kWh of electricity annually. The net present cost, cost of energy, operating cost, and fuel costs associated with the on-grid PV system are $19.2 million, $0.034/kWh, $573,371/yr, and negligible fuel cost respectively. The annual saving of Case III is $703,674. Making it a more economical system. Furthermore, Case III yields optimum GHG emissions with 60% reduction in overall GHG emissions.

• Sensitivity analysis is performed to give an insight into the performance of HES models: Case II and Case III, under different grid sellback prices. Where it was revealed that Case III outperforms Case II, as on Increasing grid sell-back rate there is a drastic reduction in costs associated with the system. At a grid sellback price of $0.09/kWh, which matches the grid purchase price, the costs become negative which indicates that revenue generated from the system exceeds all the expenses associated with it.

• Revenue generation of the system under Case III is analyzed by selling the excess electricity to nearby small industrial load. Where it was observed that on increasing the excess energy price to $0.065/kWh the on-grid PV was able to generate a large $143,553.04 revenue annually which was not possible with an on-grid biogas system.

The finding of this research aligns with Pakistan’s national commitment to increase renewable energy share in the energy mix and mitigate the challenges faced by the industrial sector due to load shedding to increase industrial productivity. This research work was conducted to propose an optimal hybrid energy system for the industrial sector of Pakistan. However, an optimal energy management system was not designed for the proposed system. In the future, an optimal hybrid energy system with an energy management system will be developed to control the energy flow between components and maximize the efficiency of the proposed system.