Comparative Economic Analysis of Solar PV and Reused EV Batteries in the Residential Sector of Three Emerging Countries—The Philippines, Indonesia, and Vietnam

Abstract

An emerging problem associated with the increased global demand for electric vehicles (EVs) is the post-use of lithium-ion batteries installed in them. Discarded batteries maintain 70–80% of their performance; thus, they are highly valuable recycling resources. Accordingly, technologies that complement the intermittency of renewable energy by integrating discarded EV batteries into battery energy storage systems (BESSs) are receiving attention. Here, the economic feasibility of a residential solar photovoltaic (PV) + reused BESS (RBESS) integrated system in three emerging countries (Philippines, Indonesia, and Vietnam) was analyzed by comparing its performance with that of diesel power generation and central grid-supplied power. The proposed system had a higher economic feasibility than diesel power generation (55.9% lower LCOE) but a lower economic feasibility than the central grid-supplied power (282.7% higher LCOE) in all three countries. Additionally, we conducted a sensitivity analysis by incorporating the investment cost, government subsidy, and social cost of greenhouse gas emissions. In conclusion, the Philippines is the best country for grid parity with the integrated system, following Indonesia and Vietnam. This study examined both the economic and social benefits of the proposed system as a countermeasure to climate change and the virtuous resource cycle.

1. Introduction

The distribution of electric vehicles (EVs) has expanded owing to the increasing global interest in climate change response and eco-friendly means of transportation. According to a report by the International Energy Agency [1], the cumulative number of global EVs in 2020 increased by 43% compared with that of previous years; under the current policy maintenance scenario, an annual growth of 30% is expected by 2030. Therefore, the development and demonstration of discarded battery technology is gaining traction, and the technological development of reusing discarded batteries as energy storage systems (ESSs) has inspired the growth of a new industry. Batteries account for at least 40% of the cost of EV components [2]. Considering factors such as driving stability and vehicle durability, battery replacement is required after completion of a certain driving distance and period. As discarded lithium-ion EV batteries retain 70–80% of their performance, they can be recycled and reused [3]. Reusing discarded EV batteries as battery energy storage systems (BESSs) equipped with a battery management system (BMS) is an efficient technique. Therefore, because of the growing EV market, the dissemination of BESSs through discarded battery reuse has been highly evaluated in terms of the economic and environmental aspects of efficient resource use. EV distribution has predominantly expanded in China and other advanced countries [1]. However, EV markets in India and Brazil, two representative emerging economies, are growing rapidly. The Indian EV market grew to USD 5.47 billion in 2020 and is expected to reach USD 17.01 billion by 2026. It is continuously growing at a compounded annual growth rate of 23.47% for the forecast period (2021–2026). The Indian government has launched multiple initiatives to promote the manufacturing and adoption of EVs, reduce emissions pertaining to international conventions, and develop e-mobility in response to rapid urbanization [4]. In addition, according to the Brazilian Electric Vehicle Association, 19,745 EVs were sold in Brazil in 2020, an increase of 66.5% since the previous year. In 2021, 35,000 EV units were sold in Brazil, an increase of approximately 88% compared with the previous year [5].

The EV industry is also expected to grow in the emerging markets of ASEAN countries owing to their recent rapid economic growth. The ASEAN EV market grew to USD 498.93 million in 2021 and is expected to reach USD 2665.3 million by 2027 [6]. Notwithstanding the current insufficient infrastructure for EV dissemination in most ASEAN countries, the market potential is high. In emerging ASEAN countries, the automobile market is dominated by two-wheeled vehicles instead of four-wheelers. However, the four-wheeled vehicle market is expected to expand gradually owing to rapid economic growth. Particularly, in response to climate change, the EV market is expected to actively expand its supply by introducing various new technologies from advanced countries and mature-market construction environments instead of gasoline vehicles. Accordingly, discarded batteries obtained from dismantled EVs in ASEAN countries will require more efficient utilization.

The main uses of a BESS are frequency adjustment, load response, and renewable energy power storage. In particular, to increase the efficiency of large intermittent renewable energy generation, the stability of the power supply in connection with renewable energy generation needs to be improved. Among ASEAN countries, Indonesia, the Philippines, and Vietnam are countries that have effectively applied this function. Indonesia and the Philippines, as representative archipelagic states, are increasing national investment to expand distributed power generation through renewable energy, such as solar and wind, in the islands and rural regions effectively. In Vietnam, a curtailment problem has emerged because of the rapid increase in the amount of renewable energy generation. In addition, as these three countries have a high power loss rate in the central power grid due to old transmission and distribution networks, countermeasures are needed. Thus, the supply of mini-grids for home use is expanding in these three countries, with the goal of reducing transmission and distribution losses, along with a stable and eco-friendly power supply. Hence, we expect a high social utility for the solar PV + RBESS system introduced in the present study.

Table 1. Demographics and electricity supply status of ASEAN countries.

Country	Population *	Number of Islands **	Access to Electricity (%) *	Urban Population (%) *	Transmission and Distribution Losses (% of Output) *	Accumulated Solar Photo-voltaic (PV) Capacity, 2021 (MWh) ***
Brunei	441,532	33	100	79	6	1
Cambodia	16,946,446	82	86.4	25	23	208
Indonesia	276,361,788	18,307	96.9	57	9	172
Laos	7,379,358	0	100	37	N/A	22
Malaysia	32,776,195	878	100	78	6	1,493
Myanmar	54,806,014	800+	70.4	31	20	84
Philippines	111,046,910	7641	96.8	48	9	1,048
Singapore	5,453,566	63	100	100	2	329
Thailand	69,950,844	1430	100	52	6	2,988
Vietnam	98,168,829	4000	100	38	9	16,504

Note: The three target countries were highlighted in colors; * Developed from data from [7]; ** Developed from data from [8,9,10]; *** Developed from data from [11].

illustrates why these three countries represent the ASEAN member states, especially in terms of population, number of islands, and electrification.

Furthermore, these three countries are very open to international exchanges, actively attract international project finance more than other ASEAN countries, and have recently shown rapid growth (Figure 1). Thus, they have immense potential to actively support and attract investment for the EV and battery market, which is emerging as a significant global industry.

Therefore, the present study aimed to analyze the economic feasibility of an ESS integrated system using discarded EV batteries (reused battery energy storage system, RBESS) in three emerging Asian countries (Indonesia, the Philippines, and Vietnam) that are actively developing new and renewable energy systems for the rapid growth of EV markets.

This system is particularly meaningful as a sustainable development business model that pursues the virtuous cycle of resources through the reuse of waste products and a carbon reduction response to climate change through the supply of renewable energy.

2. Literature Review

2.1. Literature Related to the BESS and Renewable Energy Integrated System

With the growth of the EV market, the use of discarded batteries is receiving global attention as a new industry. The use of discarded EV batteries with the integration of renewable energy sources such as solar and wind has been demonstrated to be an efficient application [13]. Europe, with its leading industries, has been conducting research on the economic feasibility of disseminating ESS integrated renewable energy generation systems to compensate for the intermittent power generation problems of solar and wind energy. Table 2 summarizes the main findings from the related literature.

Many studies investigating the economic feasibility of producing BESSs using lithium-ion batteries to determine its potential as a new industry have mainly focused on developed countries such as Europe [14,15,16,17,18,19,20,21], Australia [22], and North America [23]. The studies used factors such as the electricity price and photovoltaic (PV) and BESS facility cost in each country as key indicators, and the results showed different economic feasibility. Additionally, the economic feasibility of a country depends on the selected indicators [14,15,16]. Therefore, using suitable and practical indicators is essential for accurate economic prediction.

Most studies on the economic feasibility of RBESSs have focused on developed countries [24,25,26,27,28]. However, Schopfer [21] reported that China, although it is not a developed country yet, leads research and investment in this area. The break-even point changes mainly depending on the power price, battery pack price, or the size of the system [24,25,27,29]. Tong et al. [26] demonstrates that RBESSs can realize higher economic efficiency than BESSs by changing these conditions. Heymans et al. [28] suggested electricity price adjustment and incentive provision as policy approaches to improving and maturing the industry’s economic feasibility.

Table 2. Summary of research related to economic analysis of renewable energy + energy storage system (ESS) integrated systems. Developed with permission from Elsevier, 2022 [30].

Author	Battery Type	Main Inputs	Economic Output Parameters	Base Locations	Application Sector	Main Conclusion
Merei et al. [14]	Lithium-ion batteries	Size and cost of PV system and battery energy storage system (BESS), etc.	Annuity costs	Germany	PV Commercial	A decrease in battery cost to 200 €/kWh could be profitable
Stephan et al. [15]	Lithium-ion batteries	Application combination, etc.	Net present value (NPV)	Germany	PV Residential, commercial & industry (C & I)	Batteries could be attractive for investors even now if non-market barriers impeding the combination of applications were removed
Linssen et al. [16]	Lithium-ion batteries	Load profile, etc.	Break-even price	Germany	PV Residential	Break-even price of BESS ranges from 900 to 1200 €/kWh
Silva et al. [17]	Lithium-ion batteries	Size of PV module and BESS, Electricity price, etc.	Levelized cost of electricity (LCOE)	Belgium	PV Residential	Compared with lead-acid batteries, lithium-ion batteries offer a strong LCOE reduction
Vieira et al. [18]	Lithium-ion batteries	Cost of BESS, interest rate, etc.	The energy bill, NPV	Portugal	PV Residential	PV with BESS could reduce 87.2% energy bill
Uddin et al. [19]	Lithium-ion batteries	Battery degradation costs, etc.	NPV, internal rate of return (IRR), payback period (PBP)	The United Kingdom	PV Residential	Integration of batteries yielded no added benefits
Litjens et al. [20]	Lithium-ion batteries	Storage capacity, electricity tariffs, etc.	Annual cash flow and storage revenue	Netherlands	PV Residential and C & I	Combining increasing self-consumption contribution rate and frequency restoration reserves can yield better economic performance
Schopfer et al. [21]	Lithium-ion batteries	Cost of PV and BESS, etc.	NPV	Switzerland	PV Residential	A price decrease in BESS to 250–500 €/kWh can be profitable
Javeed et al. [22]	Lithium-ion batteries	Electricity price, feed-in-tariff (FiT), time of use (TOU), etc.	Cost of energy (COE), Net present cost (NPC)	Australia	PV Residential	The optimal capacities of rooftop PV and BESS were 9 kWh and 6 kWh, respectively; the TOU-Flat option for the PV-BESS configuration achieved the lowest NPC and COE
Tervo et al. [23]	Lithium-ion batteries	Size of PV module and BESS, etc.	LCOE	USA	PV Residential	Pairing BESS with PV system can improve the economics and performance
Gur et al. [24]	Reused electric vehicle (EV) batteries	Electricity price, FiT, battery cost, etc.	NPV	European countries	PV Residential and C & I	Investment in C & I sector is more favorable than that in residential sector. FiT and electricity price has different effects on economic feasibility by country
Song et al. [25]	Reused EV batteries	Size of battery, battery degradation, battery cost, energy price, etc.	Accumulated profits	USA, Denmark	Wind farm Commercial	Provided the current prices of wind energy and lithium-ion batteries, reusing batteries was not profitable for the studied wind farms
Tong et al. [26]	Reused EV batteries	Size of BESS, etc.	NPV, PBP	Portugal	PV Residential	Reused battery energy storage system (RBESS) outperforms a brand-new BESS
Saez-de-Ibarra et al. [27]	Reused EV batteries	Electricity generation, consumption, electricity price, etc.	Cost analysis	Spain	PV Residential	The optimal capacity value of the storage system is 1.825 kWh. With this optimal value, the battery pack could be designed based on the battery cell characteristics.
Heymans et al. [28]	Reused EV batteries	TOU price, battery size, charging/discharging period, etc.	Cost analysis, load-levelling	Canada	Residential	Electricity prices and fiscal incentives appear to have a major impact on the viability of the 2nd life battery investment
Lee et al. [29]	Reused EV batteries	Size of battery, energy price, subsidy, battery capacity, etc.	LCOE	South Korea	PV C & I	A levelized cost of electricity was 0.31 USD kWh. The range of optimized purchase costs was 2 679-70 927, 3 786-100 234, and 5 747-152 162 USD according to 5, 10, and 20 years of the remaining lifetime of the battery, respectively. The repurposing of the used battery was infeasible.
Bai et al. [30]	Reused EV batteries	Electricity price, FiT battery cost, etc.	NPV	China	PV Residential and C & I	Only when the price of RBESS decreases to 200 CNY/kWh, nationwide profitability could be achieved
Umam et al. [31]	Pumped hydro energy storage (PHES), lithium-ion battery	Capital and operation cost, fixed-charge rate, electricity production, etc.	LCOE	Indonesia	C & I	PV + BESS system has the lowest economic feasibility compared with only PV and PHES
Lozano et al. [32]	Lithium-ion batteries	Initial and operation cost, renewable energy fraction, etc.	LCOE, PBP, return on investment	Philippines	PV, diesel, Residential,	To secure 24-h electricity access, solar PV + diesel + BESS is favorable
Tsai et al. [33]	Lithium-ion batteries	Initial and operation cost, renewable energy fraction, etc.	COE, NPC	Philippines	PV, wind, diesel, Residential	The lowest COE obtained is 0.2539 $/kWh in PV-wind-diesel hybrid energy system

Table 2. Summary of research related to economic analysis of renewable energy + energy storage system (ESS) integrated systems. Developed with permission from Elsevier, 2022 [30].

Author	Battery Type	Main Inputs	Economic Output Parameters	Base Locations	Application Sector	Main Conclusion
Merei et al. [14]	Lithium-ion batteries	Size and cost of PV system and battery energy storage system (BESS), etc.	Annuity costs	Germany	PV Commercial	A decrease in battery cost to 200 €/kWh could be profitable
Stephan et al. [15]	Lithium-ion batteries	Application combination, etc.	Net present value (NPV)	Germany	PV Residential, commercial & industry (C & I)	Batteries could be attractive for investors even now if non-market barriers impeding the combination of applications were removed
Linssen et al. [16]	Lithium-ion batteries	Load profile, etc.	Break-even price	Germany	PV Residential	Break-even price of BESS ranges from 900 to 1200 €/kWh
Silva et al. [17]	Lithium-ion batteries	Size of PV module and BESS, Electricity price, etc.	Levelized cost of electricity (LCOE)	Belgium	PV Residential	Compared with lead-acid batteries, lithium-ion batteries offer a strong LCOE reduction
Vieira et al. [18]	Lithium-ion batteries	Cost of BESS, interest rate, etc.	The energy bill, NPV	Portugal	PV Residential	PV with BESS could reduce 87.2% energy bill
Uddin et al. [19]	Lithium-ion batteries	Battery degradation costs, etc.	NPV, internal rate of return (IRR), payback period (PBP)	The United Kingdom	PV Residential	Integration of batteries yielded no added benefits
Litjens et al. [20]	Lithium-ion batteries	Storage capacity, electricity tariffs, etc.	Annual cash flow and storage revenue	Netherlands	PV Residential and C & I	Combining increasing self-consumption contribution rate and frequency restoration reserves can yield better economic performance
Schopfer et al. [21]	Lithium-ion batteries	Cost of PV and BESS, etc.	NPV	Switzerland	PV Residential	A price decrease in BESS to 250–500 €/kWh can be profitable
Javeed et al. [22]	Lithium-ion batteries	Electricity price, feed-in-tariff (FiT), time of use (TOU), etc.	Cost of energy (COE), Net present cost (NPC)	Australia	PV Residential	The optimal capacities of rooftop PV and BESS were 9 kWh and 6 kWh, respectively; the TOU-Flat option for the PV-BESS configuration achieved the lowest NPC and COE
Tervo et al. [23]	Lithium-ion batteries	Size of PV module and BESS, etc.	LCOE	USA	PV Residential	Pairing BESS with PV system can improve the economics and performance
Gur et al. [24]	Reused electric vehicle (EV) batteries	Electricity price, FiT, battery cost, etc.	NPV	European countries	PV Residential and C & I	Investment in C & I sector is more favorable than that in residential sector. FiT and electricity price has different effects on economic feasibility by country
Song et al. [25]	Reused EV batteries	Size of battery, battery degradation, battery cost, energy price, etc.	Accumulated profits	USA, Denmark	Wind farm Commercial	Provided the current prices of wind energy and lithium-ion batteries, reusing batteries was not profitable for the studied wind farms
Tong et al. [26]	Reused EV batteries	Size of BESS, etc.	NPV, PBP	Portugal	PV Residential	Reused battery energy storage system (RBESS) outperforms a brand-new BESS
Saez-de-Ibarra et al. [27]	Reused EV batteries	Electricity generation, consumption, electricity price, etc.	Cost analysis	Spain	PV Residential	The optimal capacity value of the storage system is 1.825 kWh. With this optimal value, the battery pack could be designed based on the battery cell characteristics.
Heymans et al. [28]	Reused EV batteries	TOU price, battery size, charging/discharging period, etc.	Cost analysis, load-levelling	Canada	Residential	Electricity prices and fiscal incentives appear to have a major impact on the viability of the 2nd life battery investment
Lee et al. [29]	Reused EV batteries	Size of battery, energy price, subsidy, battery capacity, etc.	LCOE	South Korea	PV C & I	A levelized cost of electricity was 0.31 USD kWh. The range of optimized purchase costs was 2 679-70 927, 3 786-100 234, and 5 747-152 162 USD according to 5, 10, and 20 years of the remaining lifetime of the battery, respectively. The repurposing of the used battery was infeasible.
Bai et al. [30]	Reused EV batteries	Electricity price, FiT battery cost, etc.	NPV	China	PV Residential and C & I	Only when the price of RBESS decreases to 200 CNY/kWh, nationwide profitability could be achieved
Umam et al. [31]	Pumped hydro energy storage (PHES), lithium-ion battery	Capital and operation cost, fixed-charge rate, electricity production, etc.	LCOE	Indonesia	C & I	PV + BESS system has the lowest economic feasibility compared with only PV and PHES
Lozano et al. [32]	Lithium-ion batteries	Initial and operation cost, renewable energy fraction, etc.	LCOE, PBP, return on investment	Philippines	PV, diesel, Residential,	To secure 24-h electricity access, solar PV + diesel + BESS is favorable
Tsai et al. [33]	Lithium-ion batteries	Initial and operation cost, renewable energy fraction, etc.	COE, NPC	Philippines	PV, wind, diesel, Residential	The lowest COE obtained is 0.2539 $/kWh in PV-wind-diesel hybrid energy system

As ASEAN countries are gradually increasing renewable energy generation based on abundant sources of renewable energy such as solar and wind energy, research on the application of renewable energy and the BESS integrated system is also in its initial stage. Umam et al. [31] compared the economic feasibility of solar PV alone, the solar PV and lithium-ion BESS integrated system, and pumped hydro energy storage (PHES) in Indonesia and found that the economic feasibility of the solar PV and BESS integrated system is currently the lowest. Erlina et al. [34] simulated the applicability of a wind farm and the BESS integrated system using lithium-ion batteries on Buton Island, Baubau, Southeast Sulawesi, Indonesia, and found that this system achieved load leveling and peak shaving effects. Rangaraju et al. [35] reviewed the effectiveness and limitations of the national policy to activate the solar PV and BESS linkage system as a countermeasure against the chronic curtailment and aging of transmission and distribution facilities in Vietnam. The system’s eco-friendliness, load responsiveness, independence, and power supply stability were found to be high, especially in the residential sector. Additionally, the authors emphasized that the fire risk of batteries, post-use treatment methods, and high initial equipment costs need improvement. Lozano et al. [32] and Tsai et al. [33] designed an optimized power system for a stable power supply in Pangan-an and Batan Islands, rural areas of the Philippines. They suggested that the most stable and economical power supply can be achieved when the region’s diesel generator, the existing power generation source, is connected with renewable energy sources such as solar PV, wind turbines, and the BESS.

It is evident from the literature review that, despite the rapid increase in the use of renewable energy such as solar energy and the distribution of EVs worldwide, most studies on the possibility of solar PV + RBESS integrated systems have been conducted mainly in Europe and developed countries. In Indonesia, the Philippines, and Vietnam, such hybrid system-related research is in its infancy even though the government is taking the lead in using renewable energy and disseminating EVs. In particular, research on the RBESS is lacking. Therefore, the present study is significant in leading the techno–economic and socio–economic impact analysis of the PV + RBESS system in emerging economies.

2.2. Studies Using HOMER Software

Looking at recent research trends, the most frequently used indicators for analyzing the economic feasibility of renewable energy generation and RBESS integrated systems are energy prices (electricity rates), feed-in-tariff (FiT), system facility costs, and government subsidies. As the levels differ significantly by region, the economic feasibilities are also different. Therefore, it is essential to configure factors that objectively reflect the situation in the system application area.

To achieve the objectives of the present study, the Hybrid Optimization Model for Multiple Energy Resources (HOMER) Pro was adopted to evaluate projects through economical techniques and implement various energy sources and systems in an integrated manner. In addition, HOMER can determine the optimal system configuration and operation method according to changes in the power source and system network. HOMER Pro was developed by Dr. Peter Lilienthal and the National Renewable Energy Laboratory (NREL) in the US as a research tool to perform economic analysis using data from the US NASA database [36]. HOMER derives an economically optimized system by inputting the climatic factors of the applied area after setting all the energy sources, power generation types, and scales. It has been widely used to design energy-generation systems that combine various new and renewable energy sources. HOMER evaluates an hourly simulation of thousands of possible system configurations [33] and simplifies the evaluating designs of off-grid and grid-connected systems for various applications. Currently, HOMER is frequently used to implement an optimal power generation system in islands and rural areas where it is difficult to supply and use energy efficiently.

Yang et al. [37], Masud [38], and Marneni et al. [39] developed cost-optimal models of an independent hybrid power generation system, including renewable energy sources such as wind and solar power, diesel generators as emergency backup systems, and fuel cells and battery banks, in rural areas such as islands and mountainous areas. HOMER can also be effectively used to compare the economic feasibility of applied technologies between countries. Sampath Kumar et al. [40] compared the economic feasibility of a micro-grid using solar PV in India and Botswana.

The BESS is also an applicable technology for implementing an optimal system in the HOMER program, and numerous studies have recently been conducted on this. The BESS can be connected to an on-grid, off-grid, and micro-grid in various ways to design a power generation system according to its shape and purpose. The studies conducted in Indonesia, the Philippines, and Vietnam introduced earlier [30,33,34] used the HOMER program for economic feasibility analysis of renewable energy + BESS systems. Araújo et al. [41] designed a system that minimizes the grid load loss and maximizes the efficiency by connecting an on-grid PV-BESS to an EV fast-charging station.

Table 3. Summary of research related to economic analysis of renewable energy power generation systems using HOMER.

Author	Applied Sources	Base Locations	Application Sector	Main Conclusion
Yang et al. [37]	Solar PV, wind, diesel	South Korea	Stand-alone hybrid power systems	Considering economic feasibility and carbon emission reduction, a system comprising diesel 40 kWh, solar PV 40 kW, wind 7.5 kW, and battery 700 kW is the most optimal
Masud [38]	Solar PV, wind	Nigeria	Residential micro-grid	A stand-alone PV/wind system was feasible in the rural communities of Sokoto with 100% pollution-free energy system
Sampath Kumar et al. [40]	Solar PV wind	India, Botswana	Micro-grid	Solar PV on-grid model had the lowest net present cost and was suitable for both the localities
Araújo et al. [41]	Solar PV + BESS	Brazil	Fast EV charging station	Through the optimization model, a system that supplies 88.9% of energy through PV + BESS and only 11.1% through grid can be implemented
Lee et al. [42]	Solar PV, wind	South Korea	Commercial building (grid-connected, stand-alone model)	In the case of the stand-alone model, the optimal combination is 30 kW PV, 50 kW FC, and 30 kW battery; in case of grid-connected model, only the grid was found to be optimal
Eum et al. [43]	Solar PV + BESS	South Korea	Residential	When the price of balcony-PV and residential BESS with a power capacity of 1.2 kW PV was 50% (915,000 won) lower than its current price (1,830,000 won), the electric power charge reduced by 34.2% (NPC 10,207,600 won) and the payback period was 4.09 years

summarizes the recent trends and applications of research related to the economic analysis of renewable energy power generation systems using HOMER.

3. Methodology

3.1. Study Outline

The present study selected the Philippines, Indonesia, and Vietnam, representing emerging countries of the ASEAN region, as target markets for residential solar PV + BESS facilities. Moreover, we compared this facility with the most commonly used residential power sources in the three countries currently, such as diesel power generation and power supply through the central power grid. Various factors that differ among these countries, such as meteorological resources (solar insolation), electricity rates (for household use), and renewable energy supply policies, can lead to different economic results. We analyzed the data using a simulation-based research methodology. The technical–financial conditions that can provide the lowest cost while satisfying the electricity demand of each country’s households were derived through an economic analysis (comparison with existing power supply sources).

The study flow was as follows:

• The load pattern and daily average load category representing the household electricity demand were defined;
• The specifications of the proposed system (PV + RBESS) and the first base system (Base 1: Off-grid diesel generator) were set;
• Meteorological data of major cities in the three countries were collected and analyzed (Manila (Philippines), Jakarta (Indonesia), and Hanoi (Vietnam));
• In the first step of the economic analysis, the proposed and first base systems were compared in terms of operational performance and financial results;
• A second economic analysis was performed by comparing the proposed and second base systems (Base 2: Buying electricity from the central power grid);
• A sensitivity analysis was conducted to examine the financial impact on the levelized cost of electricity (LCOE) by adjusting the investment amount and including the social costs;
• Based on the results of the sensitivity analysis, a feasible range of grid parity was defined to equate the cost of the proposed system with the second base system (on-grid);
• Finally, the possible time and conditions for the PV + RBESS market as a business model were analyzed in the three countries.

Figure 2 provides a schematic diagram of the research framework.

To respond to the electric power load of specific household subjects in the study, (1) Base 1 (diesel generator) and Base 2 (central power grid) systems, which were the existing sources, and (2) the proposed system (solar PV + RBESS + inverter (DC/AC conversion)) were evaluated using technical and financial comparative analysis; the model composition for each type is illustrated in Figure 3.

In this study, the discounted cash flow (DCF) method was used according to the cash flows calculated for the base systems: diesel generators (off-grid), central grid (on-grid), and the proposed system (PV + RBESS). The DCF method calculates future cash flow values by reflecting the time value of money. The minimum attractive rate of return (MIRR) means the lowest (lower limit) rate among the expected returns of investment. Here, we applied this to the discount rate. An investment project is economically feasible if its net present value (NPV) using the rate of return is positive. Therefore, the discount rate comprises “procurement rate + minimum expected margin”. In this study, the average discount rate of the three countries in 2020, 8% per year [44], was applied uniformly. For accuracy, the discount rate was applied differentially as an opportunity cost by country. However, the main focus of the study was the direct nature/social difference rather than the indirect financial factor (discount rate). Therefore, we drove policy implications by fixing other factors and examining how the economic feasibility changes according to government policies (investment subsidies to reduce investment costs, electricity rate system, carbon emission cost, etc.). The LCOE, which is a helpful indicator for quantifying the energy prices generated by renewable energy projects, was derived for each system. LCOE is usually measured as the total cost incurred during the operating period divided by the total energy output, which is the minimum electricity price required to reach the break-even point over the entire project life. In this study, the economic feasibility was determined by comparing the LCOE of each of the three types of power supply systems. The LCOE derivation formula for each design is as follows.

1.

Base 1: Diesel generation (Off-grid)

$$LCOE=\frac{C_i+{{\displaystyle \sum }}_1^n\frac{\left(O\&M_y+F_y\right)}{{\left(1+i\right)}^n} }{{{\displaystyle \sum }}_1^n\frac{G_y}{{\left(1+i\right)}^n}}$$

(1)

i = Discount rate;

C_i = Initial cost;

O & M_y = Annual operation and management cost;

F_y = Annual fuel cost;

G_y = Annual power generation.

The LCOE of Base 1 is the power generation cost per unit (kWh) calculated by dividing the sum of the initial diesel power generation facility cost, the annual maintenance cost, and fuel cost during the 30-year facility operation period by the sum of the annual power generation. In this case, a discount rate was applied to reflect the present value of cash flows during the operating period.

2.

Base 2: On-grid

$$LCOE=\frac{{{\displaystyle \sum }}_1^n\frac{E_{tariff}}{{\left(1+i\right)}^n}}{{{\displaystyle \sum }}_1^n\frac{E{C}_y}{{\left(1+i\right)}^n}}$$

(2)

EC_y = Annual energy consumption;

E_tariff = Annual energy usage price.

Base 2 LCOE shows the cost of electricity per unit (kWh) calculated by dividing paid electricity fee by electricity consumption for 30 years. The central power grid does not require an initial facility installation cost, which is not reflected in the above equation. The discount rate was the same as Base 1.

3.

Proposed system: Solar PV + RBESS integrated system

$$LCOE=\frac{C_i+{{\displaystyle \sum }}_1^n\frac{\left(O\&M_{PV}+O\&M_{BESS}\right)}{{\left(1+i\right)}^n}+I_{rp}+BES{S}_{rp}}{{{\displaystyle \sum }}_1^n\frac{G_y}{{\left(1+i\right)}^n}}$$

(3)

O & M_PV = Annual PV operation and management cost;

O & M_BESS = Annual BESS operation and management cost;

I_rp = Inverter replacement cost;

BESS_rp = BESS replacement cost.

The LCOE of the proposed system is the power generation cost per unit (kWh) calculated by dividing the sum of the initial installation cost of solar PV + RBESS integrated system, the annual maintenance cost of PV + RBESS for 30 years, and the cost of the RBESS that is replaced every 10 years by the sum of the annual power generation for 30 years. The discount rate was applied in the same way as in the previous two cases. By comparing the LCOE of the three systems, we evaluated and compared the economic feasibility of each system.

3.2. Electricity Demand

The daily power load per household may differ among the three countries depending on their individual conditions. However, the same daily load pattern (profile) can apply to the residential sector. Therefore, we used the household load pattern provided by HOMER Pro, which showed a decrease in power demand in the order of evening, day, morning, and night (Figure 4).

The load pattern supplied by the HOMER system (baseline) is based on an average daily load of 11.26 kWh/day. Although the same household load pattern was used in this study, the daily load curve of the target household was derived by scaling the figure to the average daily load within the analysis category. In addition, we assumed monthly electricity demand fluctuations and seasonal load variability with the setting of the difference between the monthly average load and peak load. Even if the load pattern and average daily load remain the same, the load category that can satisfy the combined power generation/storage system specification (equipped capacity) may differ among the three countries because of the difference in environmental conditions (solar radiation potential). Therefore, each load category was economically analyzed by dividing the average daily load into eight types (5–12 kWh/day).

Table 4. Scaled standard load pattern.

Load Pattern	Baseline	Scaled
Daily average (kWh/day)	11.26	5	6	7	8	9	10	11	12
Average (kW)	0.47	0.21	0.25	0.29	0.33	0.38	0.42	0.46	0.50
Peak (kW)	2.09	0.93	1.11	1.30	1.49	1.67	1.86	2.04	2.23
Load Factor	0.22	0.22	0.22	0.22	0.22	0.22	0.22	0.22	0.22

compares each load data value and the reference baseline. Finally, we designed the method to determine the daily load resulting in cost minimization for each of the three countries.

Furthermore, we used HOMER to find the conditions in which the PV + RBESS system with the same specification can be optimized (cost minimization) in each county. We obtained the most optimal level (daily average load) of each country’s household within a range of 5–12 kWh. Therefore, we regarded the optimal load of the three countries as an endogenous variable, which can be applied in the sensitivity analysis. Our main aim was to find a suitable clean energy motivation scheme (electricity tariff, investment subsidy) and environmental regulation (carbon cost) under the determined optimal daily load in each country.

3.3. Electricity Supply

3.3.1. Technical and Financial Specifications of the Solar PV System

We designed a 3 kW-capacity residential solar PV model in a solar panel constructed with crystalline polysilicon as a basic specification. For the cost composition figures for each capacity, the US home solar system in the US NREL [45] report was applied. The detailed specifications are listed in

Table 5. Specifications of applicable PV system.

System Configuration	Specification (Unit)
Capacity	3 (kW)
Lifetime	30 (year)
Derating factor	90 (%)
Array structure	Fixed array
Ground reflectance	20 (%)
System cost (total amount/W)	1.2 (USD/W)	3600 (USD)
Module	0.33	990
BOS	0.32	960
Installation	0.55	1650
Operation and management (O & M) cost	0.012 (USD/W)

.

3.3.2. Technical and Financial Specifications of the RBESS Facilities

Discarded batteries from EVs were reused as the energy storage component, which was combined with the PV solar system (PV + BESS). The total battery capacity of Hyundai’s representative EV “Kona” in the market is 54 kWh from five battery modules. Therefore, the capacity of one battery module is 10.8 kWh, which can be separated (reused) and equipped with a BMS for commercialization as an ESS linked to the solar PV generation system, as proposed in our system. The method involves selecting a product with technical characteristics closest to one module of a discarded battery among the various ESS products sold in the market and downgrading a few technical specifications, such as efficiency and service life.

To satisfy the technical specifications of the proposed storage system, we selected a commercial ESS product as a proxy model to adjust the efficiency and service life. Therefore, we adopted the technical specifications of the proxy model for the RBESS system, except for the battery performance (

Table 6. Specifications of the RBESS (after efficiency adjustment).

System Configuration	Specification (Unit)	Note
Capacity	9.8 (kWh)
Roundtrip efficiency	76.8 (%)	80% of that of a new product
Lifetime	10 (year)	Replacement every ten years
Nominal voltage	48 (V)
Nominal capacity	189 (Ah)
Maximum charge current	119 (A)
Maximum discharge current	119 (A)

). The efficiency of the ESS product used for the analysis was set at 80% of that of the new product, and the round-trip efficiency of the battery in the proposed system was reduced to 76.8% (the round-trip efficiency of the new product was 96%) [46]. The significant determinants of the lifetime of a lithium-ion battery are its maximum depth of discharge, the associated number of cycles, and its capacity rating [15]. Moreover, the energy rating and lifetime of the battery consider the load and PV-supply characteristics of the grid-connected transformer [15]. The lifetime of RBESSs used by previous studies for solar PV systems ranged from 5–20 years [28,30,31,33,34]. In the present study, the RBESS lifetime was set as 10 years; thus, it would need to be replaced every 10 years (two times in total) during the entire operation period of the proposed system.

Because the RBESS used in the proposed system is commercially unavailable currently, its price was set at USD 4000 in accordance with the current market price of products with similar specifications. Then, using sensitivity analysis at the final stage of this study, the RBESS LCOE level for achieving grid parity was derived.

3.3.3. Technical and Financial Specifications of Base 1 System

The Base 1 system (diesel generator, off-grid) specifications were set to automatically calculate its capacity during the HOMER Pro optimization simulation process (Autosize Genset); the detailed specifications are listed in

Table 7. Specifications of the applied diesel generator.

System Configuration	Specification (Unit)
Capacity	2.3 (kW)
Initial cost	300 (USD/kW)/Total USD 690
Lifetime	15,000 (h)
O & M cost	0.03 (USD/h)
Diesel fuel price	0.8 (USD/L) *

Note: * Reflecting the trend of retail diesel prices in each country as of November 2021, the price was set at USD 0.828, 0.916, and 1.042 in Vietnam, Indonesia, and the Philippines, respectively [47].

. Through optimization, the diesel generator capacity was automatically determined to be 2.3 kW in all three countries, and the total initial investment was calculated to be USD 690.

3.3.4. Solar Radiation Data

Large cities in the three countries were selected as the locations for installing and operating the proposed system. Solar radiation information was collected from the US NASA meteorological database and used for our analysis (

Table 8. Average annual solar radiation in the three countries.

Month	Solar Radiation (kWh/m2/day)
	Philippines (Manila)	Indonesia (Jakarta)	Vietnam (Hanoi)
Jan	5.23	4.25	2.49
Feb	5.93	4.24	2.86
Mar	6.76	4.72	3.66
Apr	7.16	4.76	4.07
May	6.20	4.67	4.59
Jun	5.32	4.58	4.67
Jul	4.87	4.82	4.60
Aug	4.41	5.21	4.56
Sep	4.81	5.50	4.39
Oct	5.04	5.20	3.79
Nov	4.91	4.67	3.39
Dec	4.73	4.45	3.02
Average	5.45	4.76	3.84

Source: Developed from data obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program [48].

). According to this database, the annual average solar radiation (kWh/m²/day) is 5.45 in Manila, Philippines, 4.76 in Jakarta, Indonesia, and 3.84 in Hanoi, Vietnam, which can be considered the power generation potential in the proposed system.

4. Results

4.1. Economic Feasibility: Comparison with Base Systems

4.1.1. Comparison with Base 1 System

Economic Analysis Result of the Proposed System

For diesel generators (off-grid) and the proposed system (PV + RBESS), the economic feasibility was analyzed using the DCF method for cash flows. The proposed power generation/storage system for homes comprised 3 kW of solar power, 9.8 kWh of the BESS using discarded batteries from EVs, and a DC/AC inverter (capacity is automatically determined during the optimization process). As the lifetime of the solar module was set at 30 years, the proposed system (PV + ESS) was also consistently operated for the same period. With the average daily load range set from a minimum of 5 to a maximum of 12 kWh/day, (1) the applicable load categories of the proposed system and (2) the optimal proposed system configuration that minimizes the LCOE are described in

Table 9. Comparative economic feasibility analysis for Base 1 system.

Load Category (kWh/day)	Philippines			Indonesia			Vietnam
	Base 1 LCOE (USD)	PV + RBESS LCOE (USD)	IRR (%)	Base 1 LCOE (USD)	PV + RBESS LCOE (USD)	IRR (%)	Base 1 LCOE (USD)	PV + RBESS LCOE (USD)	IRR (%)
5	1.26	0.592	29.0	1.26	0.592	29	1.26	0.593	29
6	1.08	0.495	29.9	1.08	0.495	30	1.08	0.498	30
7	0.953	0.424	30.8	0.953	0.425	31
8	0.857	0.373	31.7	0.857	0.377	31
9	0.783	0.334	32.5
10	0.724
11	0.675
12	0.635

.

Operational Performance of Base 1 System (Load Dependent)

Because of the different optimal daily average loads of the proposed system in the three countries, the power generation, fuel consumption, and capacity factor of the Base 1 system were also different. The comparative operating performance of diesel generators based on the optimal daily average load in each country is presented in

Table 10. Comparison of Base 1 system performance (based on the optimal daily average load in the three countries).

System Operation	Philippines	Indonesia	Vietnam
Optimal daily average load (kWh/day)	9	8	6
Annual power generation (kWh)	3285	2920	2190
Diesel consumption (L)	1901	1815	1643
Average power generation efficiency (%)	17.6	16.3	13.5
Capacity factor (%)	16.3	14.5	10.9
Power generation fixed cost (USD/h)	0.218	0.218	0.218
Power generation marginal cost (USD/kWh)	0.189	0.189	0.189

.

Operational Performance of Solar PV in the Proposed System (Load Independent)

Although the same PV facilities (3 kW capacity) were installed in the three countries to simulate the power generation performance, the amount of PV power generation in each country was different, as illustrated in

Table 11. Comparison of solar PV performance in the three countries.

System Operation	Philippines	Indonesia	Vietnam
Total power generation (kWh/year)	5552	4716	3889
Daily average power generation (kWh/day)	15.2	12.9	10.7
Annual power generation hour (h/year)	4331	4406	4365
Capacity factor (%)	21.1	17.9	14.8
LCOE (USD/kWh)	0.0641	0.0754	0.0915

.

Operational Performance of the RBESS in the Proposed System (Load Dependent)

Even when the ESS (capacity 9.07 kWh) was installed and operated similarly in the three countries, its charging and discharging capacities varied according to differences in load and power generation. The comparative operational performance under the optimal daily average load standards of each country is presented in

Table 12. Comparison of the RBESS operation performance in the three countries.

System Operation	Philippines	Indonesia	Vietnam
Annual charging (kWh)	1933	1762	1328
Annual discharging (kWh)	1489	1359	1026
Annual throughput (kWh)	1699	1550	1171
Annual loss (kWh)	449	410	309
Usable nominal capacity (kWh)	6.35	6.35	6.35

. The annual charge/discharge levels also differed according to the solar radiation conditions.

Figure 5 shows the simulated result. The BESS responded stably to the change in the amount of PV power generation and the corresponding load. The minimum remaining amount of the BESS was always set to exceed 50% of the capacity in all three countries. The simulation of the state of charge (the charging/discharging status) of the BESS in each country was within this allowance range for the entire operation period.

Comprehensive Economic Results

In the Philippines, the proposed system (PV + RBESS) could supply stable power up to an average daily load of 9 kWh. The system was optimized (LCOE was USD 0.334, which is the minimum value) at a 9 kWh/day load, and 32.5% of the internal rate of return (IRR) was expected from this system compared with that from diesel generators. Indonesia was estimated to have a minimum LCOE of USD 0.377 at a lower load of 8 kWh/day (31% IRR compared with that from diesel), and Vietnam was estimated to have a minimum LCOE of USD 0.498 at the lowest load of 6 kWh/day (30% IRR compared with that from diesel).

Table 13. LCOE comparison with diesel consumption at optimal average daily load.

Economic Comparison Indicators	Philippines		Indonesia		Vietnam
	Base 1	PV + RBESS (Proposed)	Base 1	PV + RBESS (Proposed)	Base 1	PV + RBESS (Proposed)
Optimal daily load category	9 kWh/day		8 kWh/day		6 kWh/day
NPC (USD)	28,963	11,110	28,187	11,158	26,636	11,059
Initial investment (USD)	690	7814	690	7850	690	7775
Annual O & M (USD/year)	2511	292.80	2442	293.81	2305	291.71
LCOE (USD/kWh)	0.783	0.334	0.857	0.377	1.08	0.498

and Figure 6 present detailed analysis figures comparing the optimal daily average load costs.

4.1.2. Comparison with Base 2 System

Current Household Electricity Rates in the Three Countries

A household purchases electricity through the central power grid in real time without any additional investment and pays a monthly bill based on the electricity rate. Therefore, the average household electricity cost per kilowatt-hour is the LCOE of the Base 2 system. In this study, we assumed that the average daily power load is within the range of 5–9 kWh; therefore, considering 30 days per month, the electricity consumption in the electricity rate table was in the range of 150–270 kWh. As described in

Table 14. Average electricity rates in the three countries.

Country	Rate Range (kWh)	Rate (USD)	Average (kWh)	Note/Reference
Philippines	100	0.147	0.168	Based on usage/[49]
	200	0.182
	300	0.189
Indonesia	Equal rate over 1300 VA	0.101	0.101	Based on the power section/[50]
Vietnam	101–200	0.089	0.087	Progressive pay-as-you-go system, the initial section is 0.074 kWh/[51]
	201–300	0.112

, the average electricity cost per kWh was calculated as 0.168, 0.10, and 0.087 USD/kWh for the Philippines, Indonesia, and Vietnam, respectively.

Economic Analysis Result of the Proposed System

Applying the same comparisons between the proposed system (PV + RBESS) and the Base 2 system as with the Base 1 system, the proposed system was found to be less economical than the Base 2 system in all three countries.

Table 15. LCOE comparison with Base 2 system at optimal daily average load.

Economic Comparison Indicators	Philippines		Indonesia		Vietnam
	Base 2	PV + RBESS (Proposed)	Base 2	PV + RBESS (Proposed)	Base 2	PV + RBESS (Proposed)
Optimal average daily load	9 kWh/day		8 kWh/day		6 kWh/day
NPC (USD)	6213	11,110	3320	11,158	2145	11,059
Initial investment (USD)	-	7814	-	7850	-	7775
Annual O & M (USD/year)	551.88	292.80	294.92	293.81	190.53	291.71
LCOE (USD/kWh)	0.168	0.334	0.101	0.377	0.087	0.498

and Figure 7 present a detailed cost comparison analysis for optimal daily average load.

5. Sensitivity Analysis: Finding the Even Point with Base 2 (Grid Parity)

5.1. Calculation of LCOE by Adjusting the Investment Amount of the Proposed System (PV + RBESS)

Although consumers choose the alternative power supply with the lowest cost burden, other factors such as the quality of power supply (stability, reduced blackout potential) and environmental factors pertaining to the power supply source (power generation source, reduction in greenhouse gases (GHGs)) can also influence their choice. Sensitivity analysis that considers social costs, such as GHG emission costs, has not been conducted in previous studies. The social cost of GHG emissions is a global issue that is becoming increasingly important and is difficult to predict because of its high volatility. Therefore, examining how these social costs affect the results of the economic analysis and what policy implications they derive is necessary. We calculated the conditions required for the proposed system (PV + RBESS) to yield the same cost (LCOE) as that of the Base 2 system through sensitivity analysis. The results of the sensitivity analysis can be used to estimate the size of the subsidies/incentives required to support policies for the dissemination of the proposed system.

During the 30-year operational period of the PV + RBESS, the PV system (3 kW) was not replaced. However, it was assumed that the RBESS was replaced every 10 years (initial installation and two replacements). Therefore, in the sensitivity analysis, adjustments in the investment amounts for the solar power system and the RBESS were set separately and as downward adjustment coefficients, as shown in

Table 16. Adjustment coefficient of the investments.

Adjustment Coefficient	Solar PV (3 KW) Investment (USD)	BESS (9.07 kWh) Investment (USD)
0.1	360	400
0.2	720	800
0.3	1080	1200
0.4	1440	1600
1	3600	4000

.

Table 17. LCOE sensitivity to investment adjustment in the three countries (USD).

Adjustment Coefficient		LCOE (USD/kWh)
		0.1			0.2			0.3			0.4			1
		P	I	V	P	I	V	P	I	V	P	I	V	P	I	V
BESS (9.07 kWh)	0.1	0.055	0.063	0.080	0.066	0.076	0.096	0.077	0.088	0.112	0.088	0.010	0.129	0.152	0.173	0.226
	0.2	0.075	0.086	0.110	0.086	0.098	0.126	0.097	0.110	0.143	0.108	0.123	0.159	0.173	0.196	0.256
	0.3	0.095	0.109	0.141	0.106	0.121	0.157	0.117	0.133	0.173	0.128	0.147	0.189	0.193	0.218	0.287
	0.4	0.116	0.132	0.171	0.126	0.144	0.187	0.137	0.156	0.203	0.148	0.168	0.219	0.213	0.241	0.317
	1	0.237	0.226	0.352	0.247	0.280	0.369	0.258	0.292	0.385	0.269	0.304	0.401	0.334	0.377	0.498

Note: LCOE of the Base 2 system is 0.168, 0.101, and 0.087 USD/kWh in the Philippines, Indonesia, and Vietnam, respectively. Values below the LCOE of the Base 2 system for each of the three target countries were highlighted in colors.

presents the LCOE of the proposed systems in the three countries calculated by mapping the solar PV and RBESS investments according to the adjustment coefficient.

In the case of the Philippines, to achieve an LCOE of 0.168 USD/kWh (LCOE of the Base 2 system), the RBESS investment amount must be (1) adjusted to USD 400 (downgraded by USD 3600) or (2) adjusted to USD 1600 (downgraded by USD 2400) + solar power of USD 1440 (downgraded by USD 2160) to satisfy the parity condition. Therefore, to secure the economic feasibility of the proposed system, grid parity can be achieved by providing a subsidy for dissemination diffusion which preserves a certain portion of the investment (USD 3600–4560).

In contrast, in the case of Indonesia, to achieve an LCOE of 0.101 USD/kWh (LCOE of the Base 2 system), the RBESS investment amount must be adjusted downward to (1) USD 400 (downgraded by USD 3600) + solar PV to USD 1440 (downgraded by USD 2160) or (2) to USD 800 (downgraded by USD 3200) + solar PV to USD 720 (downgraded by USD 2880) to satisfy the parity condition. To secure the economic feasibility of the proposed system, a supply diffusion subsidy that preserves a certain portion of the investment must be provided at the level of USD 5760–6080 to achieve grid parity, which is a poorer business proposition than that of the Philippines.

Finally, in the case of Vietnam, to achieve an LCOE of 0.087 USD/kWh (obtained from the as-is on-grid system), the RBESS investment amount should decrease by USD 400 (downgraded by USD 3600) and solar PV investment amount should decrease by a USD 360 (downgraded by USD 3240). To ensure the economic feasibility of the proposed system, grid parity can be achieved if a supply diffusion subsidy of USD 6840, which preserves 90% of the total investment, is provided. This is the poorest business condition among the three countries.

5.2. Calculation of LCOE by Including the Social Costs (GHG Emissions)

If the GHG emissions are included for each power mix, the calculated social cost is added to the Base 2 system and the economic gap between the two alternatives (Base 2 and the proposed systems) will be narrowed. Sensitivity analysis was performed to determine the extent to which the social cost unit price should be set for each country to achieve grid parity. After converting the GHG emission factor to CO₂ ton using the IPCC standard value (2006 IPCC Guidelines), the emission values for the Philippines, Indonesia, and Vietnam were calculated to be 0.683, 0.734, and 0.597 CO₂ ton/MWh, respectively. Therefore, the sensitivity is expected to increase in the following order: Indonesia, the Philippines, and Vietnam. CO₂, CH₄, and N₂O, the main GHGs, were reflected in the calculated emission coefficient, and the coefficient value was converted into LCOE. The social cost per ton was set at USD 20, 40, and 60, and a sensitivity analysis was performed for each country to achieve the grid parity level.

Table 18. Changes in LCOE of the Base 2 system (on-grid) after incorporating social costs (sensitivity analysis).

Economic Comparison Indicators		Philippines	Indonesia	Vietnam
Emission coefficient (CO2 ton/MWh)		0.683	0.734	0.597
Optimal average load (kWh/day)		9	8	6
Annual electricity consumption (kWh)		3285	2920	2190
Proposed system (PV + RBESS) LCOE (USD/kWh)		0.334	0.377	0.498
LCOE After Incorporating a Social cost (USD/ CO2 ton)	0	0.168	0.101	0.087
	20	0.182	0.116	0.0989
	40	0.195	0.130	0.111
	60	0.209	0.145	0.123

indicates that after incorporating a social cost of 60 USD/CO₂ ton, the LCOE of the proposed system is still 59.8%, 160%, and 304.8% higher for the Philippines, Indonesia, and Vietnam, respectively, than that of the on-grid system.

5.3. Calculation of Grid Parity Level by Adjusting Investment + Social Costs

Incorporating social costs will eventually create the same price effect as an electricity rate hike; thus, an additional electricity rate increase should ultimately be considered to achieve grid parity with the exclusion of investment subsidies. Therefore, parity conditions were analyzed by simultaneously considering both the social cost and downward adjustment of investment amount (subsidy support). The results are presented in

Table 19. Calculation of grid parity level in the three countries: social cost + investment/subsidy.

Social Cost (USD/CO2 Ton)	LCOE of Base 2 System (USD)			Total Cost (Initial Investment + Subsidy) = USD 7600
				Initial Investment of Proposed System (PV + RBESS) for Parity (USD)			Required Subsidy (USD)
	P	I	V	P	I	V	P	I	V
0	0.168	0.101	0.087	4000–4500	1800–2100	1000–1300	3100–3600	5500–5800	6300–6600
20	0.182	0.116	0.0989	4500–5000	2100–2300	1300–1400	2600–3100	5300–5500	6200–6300
40	0.195	0.13	0.111	5000–5200	2300–2600	1400–1600	2400–2600	5000–5300	6000–6200
60	0.209	0.145	0.123	5200–5400	2600–2900	1600–1700	2200–2400	4700–5000	5900–6000

and Figure 8.

As previously assumed, the initial installation cost of the proposed system in the three countries was same: USD 7600. However, since the LCOE of the Base 2 system is different according to differences in solar radiation, optimal average load, and residential electricity rate in each country, the conditions for achieving grid parity are also different. In the Philippines, to make the proposed system (PV + RBESS) competitive with the Base 2 system, even after the inclusion of social costs, subsidies to offset the investment amount of USD 2200–3600 are required (the government’s purpose of encouraging diffusion). This subsidy amounts to 29–47% of the total investment in the proposed system. Similarly, subsidies for an investment of USD 4700–5800 (62–76% of the total investment of the proposed system) are required in Indonesia. Finally, in Vietnam, subsidies for investment in the range of USD 5900–6600 (78–87% of the total investment of the proposed system) are required. When USD 60/CO₂ ton was applied compared with when social cost was not reflected, the subsidy showed a difference as low as 9% (Vietnam) to as high as 18% (Philippines).

The Philippines market was found to be the most advantageous for investment. This is because the Philippines has the highest residential electricity rate and the strongest solar radiation among the three countries, which have positive effects on the economic feasibility of the proposed system (

Table 20. Economic feasibility ranking of the proposed system in the three countries.

Country	Solar Radiation (kWh/m2/day)	Optimal Average Load (kWh/day)	Residential Electricity Rate (USD/kWh)	GHG Emission Coefficient (CO2 ton/MWh)	Economic Feasibility Ranking
Philippines	5.45	9	0.168	0.683	1
Indonesia	4.76	8	0.101	0.734	2
Vietnam	3.84	6	0.087	0.597	3

).

These three countries have typical island and mountainous regions and high transmission and distribution loss rates due to frequent blackouts and old infrastructure. In 2020, the number of power outages in the Philippines was 4, and the average duration was 4.6 h, that of Indonesia was 2.9 and 4.5, and that of Vietnam was 10.8 and 21.4, respectively [52]. Gorman [53] suggested an empirical model to derive the value of lost load (VoLL). He improves on prior work by distinguishing applications of the VoLL metric between cost-benefit analyses that inform centralized, reliability-enhancing infrastructure investments and decentralized rationing decisions made during the electric supply shortage. The annualized VoLL is as shown in the equation below:

$$OC=h\times l\times f$$

(4)

$$VoLL=\frac{A+OC-B}{h\times l}$$

(5)

OC = operating costs (USD/yr);

h = operational hours during outages (h/yr);

l = average load (kW);

f = fuel costs ($/kWh);

A = annualized capital cost (USD/yr);

B = annual bill savings (USD/yr).

Using the above equation, the power outage cost of each system (Base 1, 2, proposed system) used in this study ca n be calculated and compared. In addition, social costs, such as reduced labor productivity caused by power outages, generate additional shadow costs. However, calculating the shadow cost was beyond our study scope. We hope to supplement this in our future research.

6. Conclusions

This study analyzed the economic feasibility of a business model for introducing a residential power generation/storage system combining solar PV and reused EV batteries (RBESS) in emerging countries such as the Philippines, Indonesia, and Vietnam.

The Philippines and Indonesia are representative island countries that comprise numerous islands. Therefore, these countries have extremely high potential to replace existing power supply sources (diesel power generation) in rural island regions. However, to respond to the increased electricity demand in a short period, Vietnam has disseminated solar-power-centered renewable energy, which is easy to construct in a short time by actively attracting foreign direct investment. However, large-scale curtailment frequently occurs owing to an aging transmission and distribution infrastructure. Therefore, in all three countries, a new alternative is required to replace the existing diesel generators in rural regions with poor electricity supply conditions.

In the first analysis, the results proved that the proposed system (PV + RBESS) is economically advantageous over the Base 1 system (diesel) in all three countries. In the second economic analysis, we evaluated the feasibility of an alternative system that can significantly reduce the grid dependence on electricity by installing and operating PV + BESS facilities for households that purchase electricity from the grid. In particular, as the number and duration of power outages are high in all three countries, the demand for power generation storage facilities for self-consumption is expected to increase in the future preferentially over the power grid in terms of power quality. As expected, the results of the second economic analysis revealed that the economic feasibility of the proposed system (PV + RBESS) was lower in all three countries than that of the Base 2 system (on-grid). To compensate for this shortcoming, the cost of GHG emissions corresponding to the individual electricity mixes of the three countries was considered as an added social cost of the Base 2 system. However, even after incorporating a cost of 60 USD/CO₂ ton of emissions, the economic feasibility of the proposed system was still inferior to that of Base 2.

The policy implications are as follows. The economic feasibility of the proposed system will increase if the investment cost for the households wishing to install the system is reduced through policies (investment subsidies) to support the spread of renewable energy in response to the new climate regime. In addition, environmental policy schemes such as imposing social costs on carbon emissions and adjusting electricity rates can induce investment by improving economic feasibility. In the future, the number of discarded EV batteries will significantly increase and solar energy investment costs will consistently decrease. Therefore, we can expect higher economic feasibility than the current situation. In particular, establishing standardization for reuse as ESSs from the manufacturing stage of EV batteries can bring effects such as simplifying the reuse procedure and reducing costs, thereby enhancing the price competitiveness of discarded battery reuse products. Moreover, we can assume that several households in southeast Asian countries have a lower electricity demand (load) than that set in this study. Therefore, the proposed system can be applied to one household and multiple households (2–3 households) through joint generation, storage, and electricity consumption to improve economic feasibility.

In the rural areas of emerging countries, off-grid distributed power is more advantageous for stable and efficient power supply than on-grid. However, renewable energy, as the primary distributed power resource, has the problem of intermittent power generation, and ESSs are essential to solving this issue. Using the RBESS can be the most cost-effective alternative. Thus, the novelty of this study lies in its evaluation of the technical and economic feasibility of a virtuous cycle model that reuses EV batteries instead of new products in the three emerging countries. Through this, we can determine in advance the countries in which this system would be most effective.

Furthermore, this study aimed to build a sustainable development business model that pursues not only carbon reduction as a response to climate change through the supply of renewable energy, but also a virtuous cycle of resources through the reuse of waste products. In particular, this study has preemptive value because it proposes an energy-technology international cooperation business model that can be implemented in emerging countries with active EV markets in the future by utilizing discarded batteries from developed countries where the EV market is already active. ASEAN countries are actively engaged in international cooperation with neighboring countries based around the Pacific Ocean geopolitically. Recently, starting with the entry of the Regional Comprehensive Economic Partnership (RCEP) in 2011, ASEAN countries have been actively cooperating with major economies in the world, including Korea, China, and Japan, as well as northeast Asia, Australia, and New Zealand. These countries are also currently leading the global EV industry. They are trying to utilize the discarded battery industry as a new growth engine, along with the rapidly increasing EV industry. Therefore, ASEAN countries, including the Philippines, Indonesia, and Vietnam, can act as economic cooperation partners in this vast market, and can serve as a center for responding to global climate change and establishing a system for virtuous cycle of resources.

This study has the following limitations. First, we used a product of a specific brand (LG RESU10) as a proxy model and reflected the product specifications. Since we cannot standardize the specifications and performance level of discarded batteries, we assumed 80% efficiency of the new product. Second, we focused on analyzing the economic differences of each country with the controllable policy-related variables (electricity rate system, investment subsidies, and carbon emission costs). Therefore, we applied the same discount rate to the three countries because we wanted to prevent each country’s different financial conditions from impacting the financial result. We expect that future research will reflect the detailed economic situation of each country in the related economic analysis. Third, this study aims to prove the economic feasibility of reusing discarded batteries from the perspective of a techno–economic approach with policy implications. Therefore, it lacks the details of the technical application, especially the degradation of the reused battery. Even though many parameters, such as charging/discharging power, operating temperature, and usage duty ratio, impact the level of degradation, we could not apply them to the present analysis because of system limitations. However, we plan to supplement the subsidy impact in the sensitivity analysis with the Monte-Carlo simulation in future studies.