Optimizing Corporate Energy Choices: A Framework for the Net-Zero Emissions Transition

Abstract

For the net-zero emission goal by 2050, the government of Taiwan has mandated large electricity consumers to utilize 10% green electricity to mitigate carbon emissions. Major enterprises face challenges in selecting appropriate green power options and integrating the benefits of carbon reduction into corporate governance decision-making. This study aims to optimize the combination of various green power options through a system dynamics approach, incorporating existing power purchase conditions and electricity consumption data from enterprises. In addition, by utilizing financial estimations with the monetization of environmental benefits, we constructed a more complete evaluation model for enterprises transitioning to green power. The results indicate low investment returns in various green energy portfolios. However, if power storage equipment is utilized to participate in auxiliary services, the investment return of green energy can be significantly enhanced. This evaluation model is also available online for business professionals across various sectors to explore and reference.

1. Introduction

The Sixth Assessment Report published by the United Nations Framework Convention on Climate Change in 2021 emphasizes that limiting global warming by 2030 is imperative [1]. In response to climate change, over 136 countries have adopted net-zero emissions as a national goal, with the green economy recognized as a crucial component of post-pandemic recovery. Furthermore, the International Energy Agency’s Latest Energy Outlook report indicates that under carbon reduction scenarios established by countries participating in the Paris Agreement, the proportion of renewable energy generation is expected to rise from approximately 25% to about 40% by 2040, while coal’s share will decline correspondingly [2].

In Taiwan, it has been announced that large electricity consumers with a contract capacity exceeding 5000 kW will be subject to new regulations commencing in 2021 [3]. Consequently, these large electricity consumers must assess and prepare available options in advance regarding the current draft management measures for renewable energy-related equipment settings and obligations. As such, they are required to evaluate and prepare their options in advance, as outlined in

Table 1. Renewable Energy Equipment Solutions for Large Consumers.

Options	Description of the Options	Obligation Fulfillment Example (on a Contract Capacity of 5000 kW)
Self-Installation of Renewable Energy	If installed independently or provided at a designated location, the capacity of the obligated device must exceed 10% of the contract capacity, with the produced electricity utilized by the obligor.	Installation of 500 kW of renewable energy power generation equipment
Purchase of Green Electricity (Voucher)	The capacity of the obligated device is multiplied by quota parameters (kWh/kW). For instance, solar energy: 1250 kWh/kW; onshore wind power: 2500 kWh/kW; offshore wind power: 3750 kWh/kW.	Purchase one of the following green electricity certificates: Solar energy: 625,000 kWh/year Onshore wind power: 1.25 million kWh/year Offshore wind power: 1.875 million kWh/year
Self-Installation of Energy Storage Devices	The capacity of the obligated device is multiplied by at least two hours of power. The device must not participate in auxiliary services in the transmission and distribution sector and must maintain more than 80% operational power.	Installation of energy storage equipment with a capacity of 1000 kWh (500 kW × 2 h)
Cash Payment	10% Capacity multiplied by 2500 kWh/kW × NT$4.06/kWh (estimated).	Payment of approximately NT$5.08 million annually

Source: Summary of this study.

.

This study aims to conduct a comprehensive analysis and evaluation of the cost-effectiveness associated with the green electricity obligation options for large electricity users. It considers various factors, including construction and maintenance costs, environmental benefits, electricity cost-saving benefits, peak load reduction benefits, and stable power quality benefits. Furthermore, an online “green energy decision model” was developed as a tool for enterprises to review and adjust their decision-making processes.

2. Cost-Benefit Analysis of Energy Storage and Solar Power Plants

The challenges associated with integrating renewable energy into existing power grids primarily stem from the variability of sources such as solar and wind. These fluctuations can lead to frequency and voltage anomalies, which compromise the reliability of the power system. Several solutions have been proposed to address these issues. Energy storage devices can store excess energy generated during peak production times and release it when generation is low, thereby stabilizing the grid [4]. Additionally, advanced technologies such as grid-forming inverters and synchronous condensers are being developed to provide essential services like inertia and frequency control [2]. Moreover, demand management strategies utilizing smart grid technologies can shift electricity usage to align with periods of high renewable generation, thus reducing reliance on fossil fuels and enhancing overall system efficiency [1].

According to Bloomberg New Energy Finance [5], the long-term outlook for energy storage indicates a substantial increase in global capacity, projected to rise from 5.5 GW in 2017 to 942 GW by 2040. This expansion will require cumulative investments estimated at approximately US$620 billion. By 2040, it is anticipated that customer-side capacity will surpass system-side capacity, increasing from 34% to 53%. Energy storage systems provide rapid response capabilities that are essential for ancillary services, such as real-time frequency regulation.

In our study, the costs of energy storage systems encompass both initial construction costs and maintenance expenses. The initial construction costs include the costs associated with battery packs, power conversion systems (PCS), energy management systems (EMS), infrastructure, and installation. For instance, lithium-ion batteries represent a significant portion of the total cost, with prices ranging from US$300 to US$600 per kWh depending on various factors such as scale and location [6,7].

• Power Conversion System Costs: The PCS is crucial for converting stored direct current (DC) power into alternating current (AC) for grid compatibility. Typically, PCS costs account for 10% to 20% of the total system cost [5].
• Infrastructure and Installation Costs: These costs encompass expenses related to building necessary infrastructure and installation labor, which can vary significantly according to project complexity and site conditions [5,6].

The initial construction costs are also influenced by several factors, including the system’s construction capacity, discharge C-Rate, discharge depth (DOD), and the number of charge and discharge cycles and site land-leasing if needed. These elements not only affect the overall construction cost but also impact the economic benefits derived from the energy storage system. In addition, maintenance costs are typically estimated as a percentage of the initial construction cost, often around 1% each year.

On the other hand, the benefits associated with energy storage systems can be categorized into several key areas:

• Environmental Benefits—from reducing greenhouse gas emissions and particulate matter by alleviating peak load demands on the grid.
• Revenue Benefits—from participation in demand response programs.
• Reduction in Power Outage Losses—by providing backup power during outages.
• Electricity Bill Savings—from peak load shifting and reduced contracted capacity.

To effectively evaluate the net benefits of energy storage systems, it is essential to monetize all benefits and apply relevant financial evaluation metrics (Figure 1).

For the solar photovoltaic option, the overall cost-effectiveness of solar power plants can be visually represented in Figure 2, which illustrates the various factors influencing the economic viability of such projects. While the construction of solar power plants involves various costs, the resultant benefits—particularly in terms of reduced electricity expenses and positive environmental impacts—make them an attractive option for large electricity consumers aiming to enhance their sustainability and compliance with regulatory requirements.

3. Summarizing Historical Electricity Load Data

Initially, electricity load data collected every 15 min throughout the year were analyzed. Then, the key metrics to be calculated include the following: Total Electricity Consumption, Average Daily Electricity Consumption, Maximum and Minimum Daily Consumption (including occurrence dates), Average Daily Maximum Power Demand, and Maximum and Minimum Power Demand (including occurrence dates). Furthermore, electricity consumption will be categorized by time periods during summer and non-summer months, including Peak Hours, Partial-Peak Hours, Off-Peak Hours, and Saturday Partial-Peak Hours. This analysis culminates in the creation of an annual load curve chart (Figure 3) and an average time-of-use electricity curve (Figure 4).

4. Key Parameters in the Financial Evaluation of Energy Storage Systems

According to the U.S. Energy Information Administration [8], energy storage systems serve several primary functions:

• Frequency Regulation—Balancing transient demand and supply differences.
• Spinning Reserve—Ensuring grid reliability during unexpected generator failures.
• Others—Including voltage support, load following, peak shaving, arbitrage, backup power, and co-located generator firming.

With the rapid advancement in energy storage battery technology, costs are expected to decline significantly. This study compiles average estimated energy storage costs from the United States, Germany, Taiwan, and Japan over recent years (

Table 2. International construction cost for energy storage (unit: NT$ thousand/MWh).

	2020	2021	2022	2023	2024	2025	2026	2027	2028	2029	2030
United States	13,907	14,176	13,591	12,951	12,142	11,406	10,753	10,416	10,186	9922	9785
Taiwan	12,200	12,430	11,924	11,369	10,664	10,023	9455	9163	8966	8739	8623
Japan	14,095	14,278	13,537	12,777	11,879	11,050	10,322	9908	9599	9265	9053
Germany	13,903	14,109	13,495	12,843	12,028	11,281	10,622	10,275	10,034	9762	9614
Rising and falling average	-	1.65%	−4.43%	−4.95%	−6.45%	−6.32%	−5.95%	−3.38%	−2.45%	−2.83%	−1.63%

Source: summarized from [6].

).

4.1. Technical Parameters

The configuration of energy storage system capacity directly impacts construction costs and benefits. Key considerations include:

• Total device capacity and application allocation

In energy storage applications, it is essential to ensure stable system output and participate in ancillary services before charging service fees. A designated portion of the device capacity will be allocated specifically for ancillary services.

• Discharge efficiency, C-Rate
  The discharge efficiency of energy storage batteries is measured using the letter “C”, which represents capacity. The C-Rate indicates how quickly the battery can discharge its stored energy:

  -

  1C: All capacity is released within 1 h.

  -

  2C: All capacity is released within 0.5 h (30 min).

• Depth of Discharge, DOD

Depth of Discharge (DOD) refers to the percentage of the battery’s rated capacity that has been discharged during use. This metric indicates how much electricity can be utilized for applications.

• Life Cycle

For example, if an energy storage system can handle 6000 charge and discharge cycles and is charged and discharged once per day, its estimated service life would be approximately 16 years.

• End-of-Life (EOL) Capacity Ratio

The EOL capacity ratio defines the remaining usable capacity of the energy storage system at the end of its life cycle. For instance, if an energy storage system has an EOL capacity ratio of 80%, the annual capacity decay rate can be calculated as follows:

Annual decay rate
             = (1 − EOL capacity rate)/Service life
   = (1 − 80%)/16
 = 1.25%

4.2. Operation Strategy Parameters

In addition to the configurations of energy storage, operation strategies also influence costs and benefits to a certain level as follows.

• Load adjustment benefits

This strategy involves storing electricity during low-demand periods at lower prices and releasing it during high-demand periods at higher prices. The estimation formula is as follows:

$$\mathrm{Annual} \mathrm{benefit} \mathrm{per} \mathrm{kWh}={{\displaystyle \sum }}_{i=1}^4{D}_i\ast (P_{hi}\ast E-P_{li})$$

where:

• E = Charging/discharging efficiency
• D_i = Number of days per year for each time period
• P_hi = Highest daily electricity price per time period
• P_li = Lowest daily electricity price per time period

Based on 2021 pricing data, the estimated annual benefit per kWh of energy storage capacity is approximately NT$449.68, with a charging efficiency of 90%.

• Low-peak benefits

This benefit arises from potential reductions in contracted capacity, leading to savings on monthly electricity bills.

• Ancillary service benefits

Energy storage systems can provide automatic frequency control services by adjusting charging/discharging operations based on grid demands. The estimated benefit formula is:

Annualbenefit = (1 − charging cost proportion) × NT$700 × 8760 h

• Reducing power outage losses

The evaluation of power outage losses typically involves calculating the cost per kilowatt-hour of lost power multiplied by the energy storage capacity to estimate potential savings. However, the impact of instantaneous voltage drops and unanticipated outages can vary greatly depending on the specific production equipment and industry of each enterprise. Therefore, this benefit should be assessed collaboratively with the enterprise to ensure accurate evaluation.

• Benefits from participation in the demand response

This participation helps alleviate pressure on the power grid and allows enterprises to receive subsidies to reduce electricity consumption. The formula for estimating the annual benefit from demand response is as follows:

Annual benefit from demand response
= number of demand-response events per year × energy storage capacity × amount of electricity subsidy per kilowatt-hour

• Environmental benefits

To meet the lowest power demand on the grid, baseload power plants typically operate continuously to achieve optimal thermal efficiency. However, during peak load periods, power plants must activate fast-response units, often single-cycle gas or oil-fired generators, to maintain grid stability. These peak load units incur high fuel costs and contribute significantly to emissions. Therefore, the environmental benefits of energy storage systems can be estimated by assessing the difference in environmental costs between peak load generation and standard generation methods.

Table 3. Emission factors and environmental costs of different generator sets.

Generator Type	CO2 (ton/kWh)	NOx (ton/kWh)	SOx (ton/kWh)	PM2.5 (ton/kWh)	PM10 (ton/kWh)
Coal	9.18 × 10−4	5.44 × 10−7	3.56 × 10−7	N/A	3.86 × 10−8
Fuel Oil	7.70 × 10−4	7.32 × 10−7	1.55 × 10−6	N/A	4.87 × 10−8
Conventional Gas-Fired	6.33 × 10−4	2.17 × 10−7	8.64 × 10−9	N/A	1.77 × 10−9
New Gas	5.33 × 10−4	2.08 × 10−7	7.77 × 10−9	N/A	1.66 × 10−9
Average of Units	5.30 × 10−4	2.90 × 10−7	2.00 × 10−7	1.29 × 10−8	1.61 × 10−8
Environmental Cost (NT$/ton)	19,914	28,136	25,767	24,350,000	60,000

Sources: [9,10,11,12,13,14,15].

presents the emission factors and associated environmental costs for various types of generators, measured in terms of emissions per kilowatt-hour (kWh) generated. The data can be used to assess the environmental impact of different energy generation methods.

By utilizing Table 3, the annual environmental benefit from an energy storage system can then be calculated using the following formula:

Annual environmental benefit (NT$/MWh)
= Edis × Ppl × (Cpl − Cnpl)

where:

• E_dis: Annual energy capacity of ESS operation (MWh)
• P_pl: per kilowatt-hour of energy storage discharge can reduce the proportion of power generation at peak load
• C_pl: Environmental cost of power generation mode during peak load hours
• C_npl: Environmental cost of power generation mode during non-peak load hours

4.3. Non-Technical Parameters

In evaluating the financial indicators of energy storage systems, several economic parameters beyond technical and benefit variables must be taken into account. These include:

• Inflation Rate:

The rate at which the general level of prices for goods and services rises, eroding purchasing power.

• Discount Rate:

It reflects the opportunity cost of capital and is essential for calculating financial metrics such as Internal Rate of Return (IRR), Net Present Value (NPV), and Payback Period. A higher discount rate reduces the present value of future benefits and vice versa.

5. Key Parameters in the Financial Evaluation of a Solar Power Plant

Taiwan’s solar energy industry has seen significant growth since the introduction of the Feed-in Tariff (FIT) mechanism in 2010. Most solar installations are designed for grid connection, with electricity sold directly to Taipower rather than self-consumption. This financial assessment focuses on the benefits of self-consumption and the environmental advantages derived from utilizing solar power to reflect the principles of mandates for larger electricity consumers.

5.1. Construction and Maintenance Costs

Based on the 2019 Renewable Energy Wholesale Purchase Rate Public Hearing in Taiwan, the Solar Photovoltaic System Association estimates that the construction cost for ground-based solar photovoltaic systems is approximately NT$52 thousand per kW. The average annual operation and maintenance cost, including land rental, is about 2.33% of construction costs. Assuming a fixed maintenance cost for solar photovoltaic equipment is approximately 1%, the annual land rental cost per kW is estimated at around NT$691, translating to about NT$700 thousand per MW.

5.2. Technical Specifications

The technical specifications of solar energy systems significantly impact power generation and financial indicators:

• Capacity Factor:

This is the ratio of actual annual power generation to theoretical capacity. Each kW of solar energy can theoretically generate 8760 kWh annually. However, actual generation ranges from 1150 to 1300 kWh depending on location. In southern Taiwan, the capacity factor is approximately 14%.

• Annual Attenuation Rate:

With time and increased sunlight exposure, component aging leads to decreased output power, affecting overall benefits.

• Age of the Solar Power Plant:

The useful life of a solar power plant is crucial for estimating IRR and NPV. This study presumes a lifespan of 20 years but allows user input based on specific assessment needs.

5.3. Benefits

The benefits derived from self-consuming solar power plants can be analyzed from two perspectives:

• Electricity Charge Substitution Benefits

To estimate savings from electricity that would otherwise be paid to Taipower during self-consumption periods, the following formula is used:

Electricity cost substitution benefit (NT$/MW)
= 1000 (KW) × 8760 (h) × Capacity factor × Average electricity price per kilowatt-hour of solar alternative electricity

• Environmental benefits

The intangible benefits from reduced greenhouse gas emissions and particulate matter due to renewable energy usage can be quantified using:

Environmental benefits (NT$/MW)
= 1000 (KW) × 8760 (h) × Capacity factor × Σ (emission factor of emission materials and electricity × environmental costs)

6. Building a System Dynamics Analysis Model

Based on the analysis of the construction of energy storage and solar power plants, along with the option of purchasing renewable energy electricity (vouchers), a “green energy decision model” has been developed using system dynamics methods. This model estimates various economic and environmental monetization benefits and financial indicators under different renewable energy scheme ratios, serving as a reference for enterprise investment.

6.1. The System Dynamics Architecture

The system dynamics model comprises three modules:

• Energy Storage Module: evaluates the cost-effectiveness of energy storage solutions and their integration with solar power.
• Solar Power Plant Module: analyzes the financial viability of solar installations and their operational efficiency.
• Renewable Energy Purchase Module: assesses the implications of acquiring renewable energy credits or vouchers.

Each module estimates cost-effectiveness based on enterprise power usage and allocation ratios, summarizing economic and environmental benefits alongside internal rates of return. This structured approach provides enterprises with a solid foundation for making informed decisions regarding investments in green electricity-related projects (Figure 5, Figure 6, Figure 7 and Figure 8).

6.2. Data Analysis

The basic data for the system dynamics modeling need to be prepared in the following steps:

• Calculation of the basic carbon emissions from electricity use

These data are then used to determine carbon emissions under a baseline electricity usage scenario, utilizing the annual electricity emission coefficient provided by Taipower. This baseline serves as a reference for comparing carbon emissions after integrating green electricity solutions.

• Benefits of load shifting and peak load reduction

Medium and high-voltage power users in Taiwan are subject to time-based electricity pricing. The implementation of an energy storage system for power regulation can significantly influence their time-of-use electricity consumption, enhancing benefits from peak shaving and valley filling while reducing peak demand. This analysis utilizes historical electricity consumption data recorded every 15 min throughout the year to simulate the energy storage system’s performance under various device capacities and C-rates.

• Estimation of the average electricity price during the renewable energy supply period

This study references Taipower’s renewable energy generation data from July 2019 to June 2020 to calculate the average electricity prices during solar, wind, and small hydropower supply periods (as shown in

Table 4. Electricity Average Price Statistics for Renewable Energy Supply Period (July 2019~June 2020).

Month	Solar Average Price (NT$/kWh)	Wind Average Price (NT$/kWh)	Small Hydropower Average Price (NT$/kWh)	Overall Average Price (NT$/kWh)
July 2019	3.50	3.80	3.20	3.50
August 2019	3.60	3.85	3.25	3.57
September 2019	3.40	3.75	3.15	3.43
October 2019	3.30	3.70	3.10	3.36
November 2019	3.20	3.60	3.05	3.28
December 2019	3.15	3.55	2.95	3.22
January 2020	3.10	3.50	2.90	3.17
February2020	3.05	3.45	2.85	3.12
March 2020	3.00	3.40	2.80	3.07
April 2020	2.95	3.35	2.75	3.02
May 2020	2.90	3.30	2.70	2.97
June 2020	2.85	3.25	2.65	2.91

Source: summarized from [16].

), providing a basis for estimating the economic benefits of each renewable energy source.

6.3. Building an Interactive Interface

To facilitate a user-friendly and efficient operation, an interactive interface has been designed and developed using the system dynamics software Stella 9.1. The interactive interface streamlines the process of conducting cost-effectiveness analyses by automating calculations based on user-defined parameters (Figure 9).

7. Analysis of Trial Results

In this case study, the 2019 annual load data of a manufacturing plant in Taiwan were analyzed using a green energy decision-making model. The analysis involved drawing the annual load curve and conducting a statistical analysis of electricity consumption. Key components of the analysis include:

• Annual Load Curve (Figure 10):
  • The annual load curve represents the electricity consumption patterns of the manufacturing plant throughout the year, illustrating peak and off-peak usage times. This is essential for understanding when energy storage systems can be most effectively utilized for peak shaving and valley filling.
• Statistical Analysis of Electricity Consumption (

  Table 5. Electricity consumption statistics.

  Statistic	Summer Months (kWh)	Non-Summer Months (kWh)	Annual Total (kWh)	Date of Occurrence
  Total Electricity Consumption	24,484,083	42,565,542	67,049,625
  Average Daily Electricity Consumption	200,689	175,167	183,698
  Maximum Daily Electricity Consumption	213,424	208,005	213,424	10 August 2019
  Minimum Daily Electricity Consumption	99,684	60,773	60,773	12 January 2019
  Average of the Highest Daily Power Demand	8693.5	7606.5	7969.8
  Maximum Daily Power Demand	10,064.0	8969.6	10,064.0	24 September 2019
  Minimum Daily Maximum Power Demand	7683.2	6393.6	6393.6	7 December 2019

  ):

8. CBA Simulation for Large Electricity Consumers’ Obligations

According to the Renewable Energy Regulations in Taiwan, large electricity consumers are required to meet specific green electricity obligations. For a manufacturing plant with a regular contract capacity of 9100 kW, it is mandated that 10% of its energy consumption comes from renewable sources. This analysis evaluates the economic benefits associated with this obligation based on various parameters outlined in

Table 6. Cost-Effectiveness Analysis Assumptions.

	Parametric Assumptions
Energy storage systems	Installation obligation: 1.820 MWh Initial cost: NT$22 million/MWh Fixed maintenance cost: 1% C-Rate: 1C DOD: 90% EOL Capacity Ratio: 80% Service life: 15 years	Cost of power shortage: NT$500/kWh Avoidable outages: 2000 kWh/MWh/year Peak load increased environmental cost: NT$0.155/kWh Stored capacity can effectively reduce the proportion of peak-loaded power generation: 60%
Solar power plants	Installation obligation: 0.910 MW Initial cost: NT$41.8 million/MW Fixed maintenance cost: 1% Capacity factor: 14% Power Efficiency Attenuation Rate: 0.5% Land rental cost: NT$810 thousand/MW/year Service life: 20 years	Electricity charge substitution benefit (average electricity price during solar power supply period): NT$2.69/kWh Environmental benefit: NT$1.34/kWh
Purchase of Electricity from Renewable Energy (Certificate)	Solar power: NT$3.7994/kWh offshore wind: NT$5.3064/kWh (first decade) NT$3.5206/kWh (second decade) Small hydro: NT$3.1683/kWh (Additional renewable energy transmitting service fee: NT$0.0583/kWh)	The average electricity price during the solar power supply period: NT$2.69/kWh The average electricity price during the offshore wind power supply period: NT$2.20/kWh The average electricity price during the small hydropower generation period: NT$2.18/kWh
Other assumptions	The inflation rate and the discount rate are both zero Taipower’s electricity price increases by 2% per year

.

Summarized from the green energy decision-making model and the trial calculations,

Table 7. Cost-Benefit Analysis and Comparison of Green Power Obligation Options.

Options	Self-Built Energy Storage	Self-Built Solar Plants	Purchase Electricity from Solar Plants	Purchase Electricity from Wind	Purchase Electricity from Small Hydro
Initial Costs	−40,040	−38,038	0	0	0
Maintenance Costs	−6006	−22,349	−87,763	−305,200	−229,024
Total Cost	−46,046	−60,387	−87,763	−305,200	−229,024
Electricity Bill Savings	23,103	73,070	74,476	182,916	188,063
Reduce Power Outage Losses	27,300	0	0	0	0
Demand Response Benefits	2730	0	0	0	0
Total Benefits	53,133	73,070	74,476	182,916	188,063
Net Benefit (A)	7087	12,683	−13,287	−122,284	−40,961
IRR (%)	2.11%	2.65%	-	-	-
Payback Period (Years)	12.67	15.20	-	-	-
CO2 Reduction (metric tons)	305	11,830	12,057	36,173	37,619
SOx Emission Reduction (metric tons)	0.074	4.456	4.452	13.626	14.171
NOx Emission Reduction (metric tons)	0.416	6.465	6.589	19.768	20.559
PM2.5 Emission Reduction (metric tons)	0	0.289	0.294	0.883	0.919
PM10 Emission Reduction (metric tons)	0.053	0.360	0.367	1.100	1.144
Environmental Benefits (Monetization) (B)	601	29,995	30,573	91,718	95,386
Economic/Environmental Benefits (A + B)	7688	42,678	17,286	−30,566	54,425

(unit: NT$1000).

presents a comprehensive cost-benefit analysis of five potential options available to large electricity consumers in Taiwan. This analysis evaluates the total costs, benefits, and net economic impacts of each option, including self-built energy storage systems, solar power plants, and purchases of renewable energy sources.

The cost-benefit analysis reveals that while purchasing offshore wind power incurs the highest total costs and lowest net benefits among the options evaluated, self-built solar power plants provide significant economic returns and environmental benefits relative to their investment costs. Small-scale hydropower emerges as the best option when considering both economic and environmental impacts due to its substantial CO₂ reduction capabilities and associated monetized benefits.

9. CBA Simulations for Cross-Combination of Options

This study further explores the cross-combination of options to illustrate the differences and advantages of various renewable energy solutions available to large electricity consumers.

Self-Built Energy Storage System Participating in Taipower (AFC) vs. Self-Built Solar Plant

The energy storage system utilizes rapid charging and discharging characteristics to maintain grid frequency stability, earning a service fee of NT$700 per MW per hour from Taipower (

Table 8. Comparison of Cost-Benefit Analysis of Energy Storage/Solar Setup Scenarios.

Parameter	Self-Built Energy Storage	Self-Built Storage (w/AFC)	Self-Built Solar Plants
Initial Costs	−40,040	−40,040	−38,038
Maintenance Costs	−6006	−6006	−22,349
Total Cost	−46,046	−46,046	−60,387
Electricity Bill Savings	23,103	18,483	73,070
Reduce Power Outage Losses	27,300	27,300	0
Demand Response Benefits	2730	2730	0
Ancillary Services Benefits	0	30,133	0
Total Benefits	53,133	78,646	73,070
Net Benefit (A)	7087	32,600	12,683
IRR (%)	2.11%	13.25%	2.65%
Payback Period (Years)	12.67	6.28	15.20
CO2 Reduction (metric tons)	305	305	11,830
SOx Emission Reduction (metric tons)	0.074	0.074	4.456
NOx Emission Reduction (metric tons)	0.416	0.416	6.465
PM2.5 Emission Reduction (metric tons)	0	0	0.289
PM10 Emission Reduction (metric tons)	0.053	0.053	0.360
Environmental Benefits (Monetization) (B)	601	601	29,995
Net Economic/Environmental Benefits (A + B)	7688	33,201	42,678
IRRe (%)	2.28%	13.44%	7.95%

(unit: NT$1000).

).

When comparing the self-built energy storage system participating in Taipower’s AFC to a self-built solar power plant:

• If the energy storage system does not participate in AFC services, it yields lower net economic/environmental benefits compared to building a solar power plant.
• Participation in AFC significantly enhances the return on investment for the energy storage system while also contributing to grid stability.

In this scenario, building a solar power plant is the most suitable choice due to its higher net benefits and environmental advantages. When the energy storage system participates in Taipower’s AFC services, it provides significant advantages. In addition, the implementation of an energy storage system can lead to a reduction in the plant’s contracted capacity. Initially set at 9100 kW, simulations indicate that the optimal recurrent contract capacity can be adjusted to 8500 kW. This adjustment represents a 6% reduction in maximum load, resulting in lower basic electricity costs and reduced expenditures on excess electricity charges.

10. Benefit Sensitivity Analysis

Key Parameters for Sensitivity Analysis include (Figure 11):

• Initial Cost:
  The initial construction cost of energy storage systems is a critical factor influencing ROI. A 5% increase or decrease in initial costs can lead to significant changes in net benefits and internal rate of return (IRR).
• Benefits of Reducing Power Outage Losses:
  The financial benefits derived from minimizing power outage losses are also sensitive to changes. A 5% increase or decrease in these benefits can greatly impact the net economic benefit.
• Sharp Off-Peak Electricity Price Difference:
  The difference between off-peak and peak electricity prices affects the profitability of peak shaving and valley-filling strategies employed by energy storage systems. Greater price differentials enhance the economic benefits of utilizing stored energy during peak demand periods.

10.1. The Potential Benefits of Energy Storage Systems to Reduce Outage Losses

The estimated benefits of the energy storage system in this case to reduce the losses caused by power outages are calculated based on the cost of power outages of NT$500 per kilowatt-hour, and the energy storage devices can avoid 2000 kWh of power outages per year.

Annual Benefit from Reduced Power Outages = Cost per kWh × Avoidable kWh

Taking the Taiwan-wide power outage that occurred on 15 August 2017 as an example, the power outage of two factories of a group in Kaohsiung was 60 min, and the power was restored at 8:10 p.m., and all the equipment in the plant was then restored to normal operation. If the company assesses the potential value of a stable power supply of about NT$1.8 million per MWh per year, the environmental/economic return on investment (IRRe) of 7.86% can be achieved by installing an energy storage system, which is comparable to the IRRe of 7.95% for solar panels.

10.2. Comparison of Benefits by Energy Storage Prices

Given that Taiwan’s large-scale energy storage systems are still developing, the local construction costs are significantly higher than those in other countries (

Table 9. Cost-benefit sensitivity analysis of plant construction capacity.

Parameter	Domestic Energy Storage Prices	International Energy Storage Prices
Initial Costs	−40,040	−22,204
Maintenance Costs	−6006	−3331
Total Cost	−46,046	−25,535
Electricity Bill Savings	23,103	23,103
Reduce Power Outage Losses	27,300	27,300
Demand Response Benefits	2730	2730
Total Benefits	53,133	53,133
Net Benefit	7087	27,598
IRR (%)	2.11%	12.34%
Payback Period (Years)	14.52	7.32

(unit: NT$1000).

). This analysis uses international energy storage system costs to recalculate financial metrics under the same assumptions.

11. Conclusions

The “Green Energy Decision Model” integrates financial and environmental benefits through a systematic approach that assesses corporate energy solutions in the context of achieving net-zero emissions. We used a system dynamics methodology to optimize various green power options. This approach allows for the incorporation of existing power purchase conditions and electricity consumption data, facilitating a comprehensive analysis of energy solutions.

Another important characteristic of this model is the monetization of environmental benefits. The model quantifies environmental benefits by translating them into financial metrics. This includes assessing reductions in greenhouse gas emissions and other pollutants, which can be monetized to reflect their value within the corporate decision-making framework. Based on the monetization, the study reveals that while initial investments in green energy portfolios may be substantial, the integration of power storage systems can significantly enhance investment efficiency. Several conclusions from our findings can be addressed as follows.

• For the enterprises:

By participating in auxiliary services, companies can realize additional financial returns, thus blending environmental responsibility with economic viability. In addition, the model incorporates a detailed cost-benefit analysis that evaluates construction and maintenance costs against environmental benefits, electricity cost savings, and potential financial incentives from authorities. This holistic evaluation allows enterprises to make informed decisions that align with both financial goals and sustainability objectives. According to the analysis, the recommendations for enterprises include:

• Self-built solar power plants: Self-built solar plant options are more suitable for meeting the green power obligations of large electricity consumers in terms of input cost and rate of return. The challenges are associated with obtaining large-scale land.
• Combined configuration: It is recommended that enterprises utilize self-built solar panels and energy storage systems to meet part of their green power obligations while purchasing green power certificates for the remainder.
• Priority order for purchasing renewable energy: The priority order for purchasing renewable energy is small hydropower, solar photovoltaic, and offshore wind power.

• Policy implications:

While all governments are promoting a net-zero emission policy, it is more important to utilize feasible schemes for energy consumers than just making policies and goals with limited subsidies. From this study, it is evident that environmental benefits from green energy solutions contribute to the total return on investment. However, the private sector is not able to increase the green investment on its own except for the 10% green power requirement by law. Policymakers should evaluate the feasibility through modeling and leverage the environmental benefits back to the private sector, which is engaged in the green transition, through other measures such as tax reduction.

• Study limitations and future improvement on the model:

The original purpose of the study is not to construct a fundamental theory. Instead, it provides a framework beyond the traditional scope of business managers for their decision-making while facing green requirements. This study is a pioneer work without much prior research for comparison, which constitutes the limitation of our study. In addition, system dynamics modeling is a good tool for integrating different factors into a decision-making framework with more assumptions and equations than real theories. Also, because of originality, this model needs more optimization and validation with real data. It is hoped that more companies will adopt this model and provide their data to increase the robustness and optimization of our model.