1. Introduction

2. Literature Review

3. Materials and Methods

4. Results and Discussion

4.1. Peer-to-Peer Energy Trading Model by Blockchain Technology

This model implements a solar microgrid system in a rural area in Gumelar, Banyumas, Central Java. The model generates a smart contract that facilitates grid users to buy, sell, and trade electricity through a mobile application. The program examines the impact of the model on users compared to previous electricity access methods, as well as the operation and billing techniques of the mini-grid. At the end of the program, the system is handed over to the local community, and its management is handed over to a cooperative formed to operate sustainably and integrate it with broader community service delivery to maximize sustainable development impact. The ultimate goal is to reach the entire microgrid industry. The users of the model are focusing on low-income households that struggle to access affordable energy and receive minimal fuel subsidies. Additionally, the model aims to assist communities with access to the grid but are experiencing frequent power outages, occurring at least twice a week for approximately 2 hours each time. By examining the impact on these areas, the model seeks to gain a better understanding of the potential for expansion in Indonesia, considering the adaptability of the core technology to various scenarios and operational cases.

Figure 1. Peer-to-Peer Energy Trading System Design in Gumelar.

Figure 1. Peer-to-Peer Energy Trading System Design in Gumelar.

4.2. Implementation of a Peer-to-Peer Energy Trading Model Using Blockchain Technology in Central Java

The study involved prosumers from 10 households in Gumelar District who were connected to a single energy supply distributed by solar panels through a battery system. Originally, the plan was to install solar panels on each participating house's roof. However, this plan was changed, and the solar panels were arranged in a community setup placed in the Gumelar Office, to improve the network. This decision was made due to potential political and structural issues that might arise from roof installation and a technical considerations regarding the installation of cables and the overall system cost.

Figure 2. Implementation of a peer-to-peer energy trading model using blockchain technology. In Gumelar, Banyumas, Central Java

Figure 2. Implementation of a peer-to-peer energy trading model using blockchain technology. In Gumelar, Banyumas, Central Java

4.3. Stakeholders’ Perceptions

The model trial was conducted twice: (a) from December 15, 2023, to January 13, 2024, and (b) from January 15 to February 13, 2024. A perception test was carried out on 10 prosumers in Gumelar District. They were asked to complete a questionnaire and then participate in a semi-structured interview to ensure that all questions were answered properly and correctly. The questionnaire filled out by the prosumers and expert users was then tested for validity and reliability. The results were analyzed using four methods: (a) IFE/EFE Matrix, (b) IE Matrix, (c) SWOT Matrix, and (d) SPACE Matrix to assess the results and their suitability with each other. Through the analysis of these various matrices, the stakeholder's perceptions will be assessed to understand the potential development of P2P energy trading using Blockchain technology and the factors that affect it.

Figure 3. Participants Profile.

Figure 3. Participants Profile.

The survey participants were predominantly male, accounting for 70% of the total respondents (7 people). There were three female respondents, comprising 30% of the total. Notably, these three women were using an electricity buying and selling application on behalf of their absent husbands. The highest number of respondents, four people (40%), fell within the 46-50 age range, followed by three people (30%) in the over 50 age range. Additionally, two people (20%) were in the 41-45 age range, while a person (10%) fell in the 36-40 age range. It's important to mention that there are few young people in Gumelar District (aged 18-35) as most individuals in this age range work as Indonesian Migrant Workers abroad. The majority have a high school education, comprising 70%. Additionally, there is a person (10%) with an elementary school (SD), junior high school (SMP), and bachelor's degree (Sarjana). It is noteworthy that although most of them have a high school education, on average they are former Indonesian Migrant Workers who have worked abroad. Their level of technological literacy and software usage is quite promising. Providing training to each of these individuals should not take long. According to the survey, the majority of respondents, five people (50%), are self-employed. Additionally, two respondents work as housewives. The following categories are private employees, retirees, and others, represented by a person (10%). It's worth noting that many of the respondents were former Indonesian Migrant Workers abroad who, upon returning to Indonesia, tended to start their businesses or become small entrepreneurs contributing to their respective regional economies. The respondents have varying income levels. 10% of respondents earn below Rp. 2,000,000, while another 10% earn above Rp. 4,000,000. The majority, which is 20% of the respondents, earn between Rp. 2,100,000 and Rp. 3,500,000, with two people falling into this category.

4.5. Internal Factors Evaluation Analysis

Identification of the internal factors of this model involves assessing its strengths and weaknesses, assigning them weights and ratings, and then processing them to obtain a score on the IFE Matrix. The IFE matrix identification is detailed in the table below.

Table 1. Internal Factors Evaluation Matrix.

Table 1. Internal Factors Evaluation Matrix.

No.	Internal Factors	Weight	Rating	Score
	Strengths
1	The model gives you the freedom to choose to generate and sell electricity.	0.03	3.70	0.10
2	The model can improve your ability to manage electricity.	0.03	3.20	0.08
3	The model can send electricity from areas with excess to areas with electricity shortages.	0.02	3.40	0.08
4	The model creates clarity and freedom in direct electricity buying and selling (without intermediaries).	0.02	3.70	0.09
5	The model eliminates PLN's monopoly as an electricity provider.	0.03	3.30	0.09
6	The model makes it easy to manage your electricity usage and buying and selling.	0.02	3.60	0.09
7	The model increases your electricity savings.	0.02	3.70	0.09
8	The model is environmentally friendly and has pollution-free electricity.	0.03	3.40	0.09
9	The model eliminates centralized electricity management in urban areas.	0.02	2.70	0.07
10	The model will become a solution for remote communities with difficulty accessing electricity and finances.	0.03	3.50	0.09
11	The model creates transparency such as the amount of electricity, duration, usage, and selling prices.	0.03	3.50	0.10
12	You are used to using the internet or Smartphone as a tool to implement the model.	0.02	3.60	0.06
13	The requirements for installing the model are easy to become a producer or consumer of electricity.	0.03	3.40	0.09
14	The electricity usage rate on the model is more stable.	0.02	2.60	0.06
15	The model complements the existing PLN electricity.	0.02	3.30	0.08
	Subtotal of Strengths	0.37		1.26
	Weakness
1	Lack of knowledge to implement the model.	0,02	3,10	0,07
2	The model produces limited electrical energy during the day.	0,02	2,40	0,05
3	The high cost of installing technology in the model.	0,02	2,90	0,07
4	There are no examples of other areas implementing this model.	0,02	3,70	0,09
5	The electricity-generated model is not sufficient for household needs or sales.	0,02	2,90	0,06
6	The regional data center does not regulate household electricity usage.	0,02	3,30	0,08
7	You find it difficult to adapt to using the digital application/platform model.	0,02	3,10	0,06
8	The installation of the model is subject to high costs.	0,02	2,10	0,05
9	The sun does not shine brightly all year round in your area.	0,02	1,60	0,04
10	The installation of the model requires a large place/room.	0,02	2,20	0,04
11	The model is only suitable for lower-middle class society who live in areas far from the city center or rural.	0.02	1.20	0.02
12	You do not mind the instability of the electricity received.	0.02	2.80	0.06
13	Your area finds it difficult to implement the model because of the large electricity needs.	0.02	1.80	0.04
14	The electricity-generated model cannot be produced continuously.	0.02	1.80	0.04
15	The electricity generated from the model tends to be small and large expenditures.	0.02	3.30	0.07
16	There are no regional regulations regarding the implementation of the model.	0.03	3.50	0.09
17	Your motivation for buying and selling electricity from this program is personal gain.	0.02	2.90	0.06
18	The uncertainty of the model is both from the electricity generated by the seller and that requested by the buyer.	0.02	2.70	0.06
19	The costs set by the model are only suitable for some groups of electricity buyers.	0.02	3.00	0.07
20	There is no legal regulation to protect model users.	0.02	2.90	0.07
21	The electricity availability model is small, so wait to become a user.	0.02	3.30	0.07
22	The more model users there are, the more difficult it will be to manage.	0.02	1.50	0.03
23	The function of the model used is still not optimal and has many shortcomings.	0.02	3.50	0.07
24	The residential area has little internet network, and few people have smartphones.	0.02	1.90	0.04
25	The internet connection in your place of residence is poor (weak).	0.02	2.60	0.06
26	The electricity generated from the model is only for household scale.	0.02	2.80	0.06
27	The electricity price from the old model will become more expensive over time.	0.02	2.50	0.05
28	Implementing the model will make it difficult for PLN electricity users, especially for PLN customers.	0.02	2.40	0.05
29	Do not have electrical knowledge and skills.	0.02	2.50	0.05
	Subtotal of Weaknesses	0.63		1.66
	Total (Strengths + Weaknesses)	1.00		2.92

There are fifteen significant strength factors and twenty-nine key weakness factors that influence this model. The IFE matrix indicates a score of 2.92. According to the IFE Matrix, the main strength factor crucial to this model is the freedom to generate and sell electricity (0.1). This factor introduces transparency in the electricity usage and sales by disclosing the amount, duration, and price of electricity (0.1). This internal advantage is highly influential in driving the adoption of this model by stakeholders. The concept of transparency is at the core of Blockchain technology, enabling public participation in the electricity trading process without fear of deception due to the recording of all transactions in the application. Regarding weakness factors, the IFE matrix suggests that this model is primarily suitable for the lower middle class residing in rural or remote areas (0.02). Additionally, the intermittent electricity production to meet the substantial demand is a major weakness (0.03). Furthermore, the weak and relatively expensive internet network, low penetration of sophisticated mobile phones, and complex system management contribute to a negative impact on the model's adoption. Currently, P2P energy trading using blockchain technology remains at the level of simulation and has not involved large prosumers (more than 100 people). The prospect of simulations involving numerous participants and advanced technology is indeed intriguing.

4.5. External Factors Evaluation Matrix Analysis

Identification of external strategy factors includes opportunity and threat factors that influence the strategy of this model. The results are then processed to obtain a score on the EFE matrix.

Table 2. External Factors Evaluation Matrix.

Table 2. External Factors Evaluation Matrix.

No.	External Factors	Weight	Rating	Score
	Opportunities
1	The model creates electricity distribution.	0.03	3.30	0.11
2	The model increases public awareness and cooperation in using environmentally friendly electricity (pollution-free).	0.03	3.50	0.12
3	The model opens up new business opportunities.	0.03	3.50	0.11
4	The model becomes additional income.	0.03	3.40	0.12
5	The model opens up opportunities for new business investment.	0.03	3.40	0.12
6	The model increases community income.	0.03	3.30	0.10
7	The model managed efficiently by the community.	0.03	3.50	0.11
8	The model sends electricity and stores electricity usage data via smartphones.	0.03	3.40	0.11
9	The community is empowered in business and socially with the model.	0.03	3.40	0.10
10	The model provides electricity for public facilities (mosques, roads, etc.).	0.03	3.30	0.10
11	The model creates benefits at the community level (RT/RW).	0.03	3.40	0.10
12	The model fosters public interest in using renewable energy (environmentally friendly).	0.03	3.50	0.11
13	The model helps solve the problem of the limited reach of the PLN electricity network.	0.03	3.30	0.09
14	The model used can create employment opportunities for the community.	0.03	3.50	0.11
15	The behavior of the community working together in meeting electricity needs makes it easier to implement the model.	0.03	3.50	0.10
16	The electricity sales system to PLN increases the use of the model.	0.03	3.20	0.09
17	PLN's high electricity load increases public interest in using the model.	0.03	3.40	0.10
18	PLN's plan to reduce its business area is an opportunity for the community to switch to the model.	0.03	3.20	0.09
19	The limitations of fossil fuels can increase the use of the model.	0.03	3.40	0.10
	Subtotal of Opportunities	0.58		1.97
	Threats
1	Weather conditions have a major impact on electricity production in the model.	0.03	3.00	0.09
2	There are no laws and regulations governing the transfer of PLN electricity customers to the model.	0.03	2.80	0.09
3	Concerns about the security and confidentiality of personal data, if they become model users.	0.03	2.20	0.06
4	No government assistance or private investment in the model.	0.03	2.10	0.06
5	The danger of customer data theft reduces interest in using the model	0.03	2.30	0.06
6	The model is highly dependent on the availability of smartphones and internet connections.	0.03	2.60	0.08
7	The installation of the model must not exceed the size of PLN's installed power.	0.03	2.20	0.06
8	The procedures for trading (buying and selling) electricity from the model are not properly regulated.	0.03	1.70	0.06
9	The implementation of the model involves many parties.	0.03	1.60	0.04
10	PLN's free electricity service program for public facilities hinders the implementation of the model.	0.03	2.10	0.05
11	Bad relationships between neighbors can hinder the model.	0.03	2.10	0.06
12	The wasteful behavior of people using electricity can threaten the sustainability of the implementation of the model.	0.02	2.50	0.06
13	The losses of people who have installed PLN electricity infrastructure hinder the implementation of the model.	0.03	1.40	0.04
14	People who fail to pay for electricity hinder the development of the model.	0.03	1.80	0.05
	Subtotal of Threats	0.39		0.86
	Total (Opportunities + Threats)	0.97		2.83

There are 19 key opportunity factors and 14 threat factors that significantly influence this model. The IFE matrix results indicate a total score of 2.83. According to the EFE Matrix, the opportunity factors for the program is the increase in public awareness and cooperation in using environmentally friendly, pollution-free electricity (0.12). Furthermore, there is an opportunity for additional income (0.12) and new business investment (0.12). This particular opportunity factor is crucial as it can expand market share and generate public interest in the model. Regarding threat factors, the EFE matrix indicates that the model faces significant threats from the implementation aspect involving multiple parties (0.04). Additionally, there is a potential threat in the form of losses for state utilities such as PLN or individuals who have already established electricity infrastructure if this program implemented on a large scale (0.04). A threat poses to the sustainability and profitability of PLN's state utility business and could impede the implementation of this model.

4.6. Internal-External Matrix Analysis

Figure 4. Internal-External Matrix.

Figure 4. Internal-External Matrix.

For the internal and external results using the IFE-EFE matrix, we obtained an IFE matrix score of 2.92 and an EFE matrix score of 2.83. This places the model in quadrant V in the IE matrix. IE matrix theory, the recommended strategy for quadrants III, V, and VII is the hold and maintain strategy. Based on stakeholder assessments, the suggested strategies for this model are market penetration and product development [34]. Market penetration could involve increasing the number of prosumers from 10 to 100 or 1000, or expanding to one district, or province at a time to assess the effectiveness of systems. Encouraging existing prosumers to buy and sell electricity more frequently is also recommended.

To expand the market share of prosumer products, product development can focus on creating high-selling items. For instance, offering solar panels to urban communities with higher incomes through a program involving PLN as an intermediary, similar to the trial conducted by "Tenaga National Berhad" (TNB), a Malaysian state-owned electricity utility company, can be beneficial. This approach allows the sale of environmentally friendly electricity at a premium price to specific consumer groups. It's important to view this program as a complement to PLN's existing system, rather than a direct competitor. Furthermore, addressing the current issue of power wheeling, where PLN's transmission and distribution networks can be jointly utilized, with a business model like this could be a viable solution. Despite PLN's current reluctance to utilize the concept of power wheeling, this approach holds promise.

4.7. SWOT Analysis

To assess stakeholder perceptions of this model, a SWOT analysis was conducted. The aim is to identify both internal and external aspects of the P2P Blockchain program, mapping out potential opportunities and challenges. This involves taking stock of factors influencing the model within the planning strategy, serving as a foundation for determining necessary corrective actions for future developments. The study's SWOT analysis involved comparing factors impacting the model, comprising Strengths (S), Weaknesses (W), Opportunities (O), and Threats (T). The detailed mapping of SWOT factors, derived from the results of brainstorming with expert users as stakeholders, is presented in the following table. The basic concept of SWOT analysis was developed by assessing weighted scores using EFE/IFE analysis. Stakeholders from the government, private sector, academic, non-governmental organizations, and the community act as assessors. They assign a weight of 0.00 to 1.00 to each aspect of SWOT. Each factor (internal/external) is then summed to produce a weight of 1. Once the criteria are weighted, the next step is to rate them to indicate their level of importance (1=less important, 2=quite important, 3=important, and 4=very important). The weighting value is then multiplied by the specified rating. The position of the SWOT quadrant is determined by calculating each factor (internal/external) to produce a diagram indicating the program's future quadrant position. The quantitative SWOT analysis uses results from the IFE and EFE matrix approaches obtained in the previous analysis. Based on the IFE matrix, the total S-W score is -0.40, and the total O-T score is 0.31. The detailed results of the IFE and EFE matrix analysis are presented in the following table.

Table 3. IFE-EFE Matrix Analysis.

Table 3. IFE-EFE Matrix Analysis.

Internal Factor	Skor		External Factor	Skor
Strength (S)	1.26		Opportunities (O)	1.97
Weakness (W)	1.66		Threats (T)	1.66
Total (S-W)	-0.40		Total (O-T)	0.31

Based on the analysis results, this model located at the coordinate point (-0.40, 0.31), placing it in quadrant II. This indicates that the model should apply the STABILITY strategy, with a specific focus on the "Selective Maintenance Strategy" for improvement. This involves internal consolidation to address weaknesses and sustain accomplishments. The goal of the Stability strategy is to maintain the current situation by leveraging opportunities and addressing weaknesses.

Figure 5. SWOT Matrix.

Figure 5. SWOT Matrix.

This model enables equitable electricity distribution in remote and marginalized areas. Despite PLN's claim that Indonesia's electrification rate is 99%, sporadic power outages persist in Java, including in the Gumelar District. The program aims to achieve two objectives: first, to increase the adoption of this model in energy-deprived areas by focusing on energy quality, reliability, sufficiency, affordability, community acceptance, environmental feasibility, and the multiple socioeconomic benefits of energy access; and second, to allow PLN to act as an intermediary, supplying clean electricity at a competitive price, when targeting urban or industrial areas.

4.8. SPACE Matrix Analysis

The SPACE (Strategic Position and Action Evaluation) Matrix consists of a 2×2 table with four quadrants: aggressive, conservative, defensive, and competitive strategies. The matrix's axes are determined by internal factors such as Financial Strength (FS) and Competitive Advantage (CA), as well as by external factors including Environmental Stability (ES) and Industry Strength (IS). The financial strength factor evaluates all indicators of an organization's financial capability. The environmental stability and industry strength factors analyze their respective content and material. The strategy can formulated as follows: Environmental stability (ES), represented by the highest score of market entry barriers (-4), is a significant concern. The Constitutional Court, through a judicial review decision, annulled Article 10 Paragraph 2 and Article 11 Paragraph 1 of the 2009 Law on Electricity, which further confirms that PLN has control over electricity. The coordination of electricity provision and distribution remains with the government, specifically through state owned enterprise (BUMN) operating in the electricity sector (i.e., PLN) and unbundling. Protective regulations act as the primary barrier hindering the development of this program. And PLN's support for renewable energy, such as solar power, is currently inadequate. The core of this program revolves around the installation of solar panels. PLN's policy prohibiting the direct sale of electricity from solar panels to PLN, in addition to the uncompetitive prices offered to IPP solar panels, pose significant challenges. Moreover, the high initial cost and lengthy payback period deter consumer adoption of solar panels, thus prolonging the decision-making process.

The competitive advantage (CA) of controlling suppliers is rated (-3). This advantage stems from PLN's monopoly over the sale and purchase of electricity, enabling it to offer affordable and uninterrupted electricity to the community by leveraging fossil fuels and an extensive network. The superiority of technology and product quality holds a rated (-3) also. An existing advantage lies in the transparent implementation of a program that allows the community to buy, sell, and track energy transactions, thereby promoting the transition from fossil fuels to clean energy through used solar panels. As for financial strength (FS), the working capital is rated at (+5) due to the substantial initial investment required, balanced by low operational costs and reliance on solar energy for electricity generation. This program is also beneficial for remote areas and only necessitates an application and a smart meter tool for distribution. The return on investment (ROI) similarly rated at (+5). Despite the initial investment, the program's benefits for the community, particularly in areas not covered by PLN electricity, and its positive impact on the environment and society expected to surpass the initial investment value. Regarding industrial strength (IS), financial stability is rated at (+5) owing to the P2P electricity regulation buying and selling program, ensuring the sustainability of the independent system, community empowerment, and environmental benefits from solar energy use.

X-axis = Average CA score + Average IS score = 1

Y-axis = Average FS score + Average ES score = -0.3

Therefore, the coordinate point xy = (1, - 0.3).

Figure 6. SPACE Matrix.

Figure 6. SPACE Matrix.

Based on the SPACE matrix, it is evident that the vector line points towards the COMPETITIVE quadrant (bottom right) of the matrix. Suggests that the model holds the potential for a competitive advantage in its evolving activity type. Consequently, it inferred that the model is well-positioned to capitalize on internal strengths to leverage external opportunities, address internal weaknesses, and mitigate external threats. Collaborating with PLN is a crucial aspect of the widespread adoption of this model, enabling it to operate in areas where the PLN network doesn't reach. The stakeholder perception analysis used the IFE-EFE matrix, IE matrix, SWOT matrix, and SPACE matrix. The conclusion is that the P2P energy trading model, supported by blockchain technology, can grow and evolve by meeting various existing requirements. Additionally, strategic steps for future development, including collaboration with PLN, have been clearly outlined.

4.9. Sustainability Policy Strategies to Model Implementation

The sustainability policy strategies in the model use the SWOT matrix approach. This involves describing the various external and internal factors that impact the model's sustainability, based on EFAS (External Factors Analysis Summary) and IFAS (Internal Factors Analysis Summary) conclusions. The SWOT matrix helps to depict how the model can adapt to external opportunities and threats by leveraging its strengths and addressing its weaknesses.

Table 4. SWOT Matrix Alternatife Strategy.

Table 4. SWOT Matrix Alternatife Strategy.

No.	S-O Strategy	Rank
1	Collaboration with PLN to help meet the electricity needs of remote areas.	1
2	Collaboration with PLN, which has an extensive network.	2
3	Improve the transparency and accountability system for solar panel electricity sales and purchase transactions.	3
No.	W-O Strategy
1	Regulations are needed that support independent electricity sales, especially in areas without PLN networks.	1
2	Increase the number of users of this model en masse.	2
3	Increase public awareness to want to use electricity from this model to improve their family's economy.	3
No.	S-T Strategy
1	Carried out on remote or massive targets.	1
2	The available of internet network technology in unreachable areas (Starlink).	2
3	Increase socialization to the community about the economic benefits of this model.	3
No.	W-T Strategy
1	Investors with a high social entrepreneurship vision are needed to support this model.	1
2	Increase community behavior to use this model.	2
3	Improve the interface system (UI/UX) to make it easier.	3

To determine the priority of each alternative strategy, the researcher utilized the Quantitative Strategic Planning Matrix (QSPM), a highly suitable tool for prioritizing crucial internal, external, and competitive information essential for crafting an effective strategic plan. Then, the decision-making stage relies on the key indicators or factors outlined in the input stage, IFAS and EFAS. These factors prepared an alternative strategy in the second stage (matching stage). Following the QSPM assessment of each alternative strategy, they are sorted from the largest to the smallest value to identify a priority strategy for implementation. A total of 12 vital strategies need to be executed to effectively promote the P2P energy trading model using blockchain technology for systematic development in society. The strategy priorities are shown in the table.

4.10. Peer-to-Peer Energy Trading Model Strategy Priorities

The model is considered economically feasible. On the other hand, the model can effectively reduce CO₂ due to the use of solar panels. Furthermore, this model will work more effectively if it can build cooperation with PLN, which is legally the only utility company in Indonesia that has the largest electricity network. For the initial stage, the Regulatory sandbox model will be an ideal forum to start a dialogue and experiment between regulators and innovators to learn and exchange insights about testing a product before it is suitable for use in the wider community. A trial like this was conducted by "Tenaga Nasional Berhad" (TNB), a Malaysian state-owned electricity utility company, in 2019 on a P2P energy trading model using blockchain technology in collaboration with the renewable energy authority SEDA (Sustainable Energy Development Authority). Thus, this might be an entry point in collaborating with PLN as the electricity sold is environmentally friendly energy and is sold at a higher price to certain consumers by involving PLN as part of its business model. This program should not be considered a competitor to PLN but rather as a complement to the existing PLN system. The energy trading model's twelve priority strategies are represented using the triangular relationship between environmental, social, and economic dimensions, which are interconnected and mutually influential.

Table 5. Peer-to-Peer Energy Trading Model Strategy Priorities.

Table 5. Peer-to-Peer Energy Trading Model Strategy Priorities.

No.	Strategies	Total	Annotation	Priority
1	Collaboration with PLN to help meet the electricity needs of remote areas.	1.92	S-O	1
2	Collaboration with PLN, which has an extensive network.	1.75	S-O	2
3	Regulations are needed that support independent electricity sales, especially in areas without PLN networks.	1.67	W-O	3
4	Investors with a high social entrepreneurship vision are needed to support this model.	1.47	W-T	4
5	Carried out on remote or massive targets.	1.44	S-T	5
6	Increase socialization to the community about the economic benefits of this model.	1.32	S-T	6
7	Improve the transparency and accountability system for solar panel electricity sales and purchase transactions.	1.28	S-O	7
8	Increase community behavior to use this model.	1.27	W-T	8
9	Increase the number of users of this model en masse.	1.26	W-O	9
10	Increase public awareness to want to use electricity from this model to improve their family's economy.	1.15	W-O	10
11	The available of internet network technology in unreachable areas.	0.59	S-T	11
12	Improve the interface system (UI/UX) to make it easier.	0.23	W-T	12

5. Conclusions

References

1. International Renewable Energy Agency. Renewable capacity statistics 2019. IRENA, Abu Dhabi, 2019. [https://www.irena.org/].
2. Soto, E.A.; Bosman, L.B.; Wollega, E.; Leon-salas, W.D. Peer-to-peer energy trading: A review of the literature. Applied Energy 2021, 283, 116268. [Google Scholar] [CrossRef]
3. Tushar, W.; Yuen, C.; Mohsenian-Rad, H.; Saha, T.; Poor, H.V.; Wood, K.L. Transforming energy networks via peer-to-peer energy trading: the potential of game- theoretic approaches. IEEE Signal Processing Magazine 2018, 35, 4, 90-111. [https://ieeexplore.ieee.org/document/8398582].
4. Mengelkamp, E.; Garttner, J.; Rock, K. ; Kessler, S; Orsini, L. ; Weinhardt, C. Designing microgrid energy markets: a case study: the Brooklyn microgrid. Applied Energy 2018, 210, 870–880. [Google Scholar] [CrossRef]
5. Zhang, C.; Wu, J.; Zhou, Y.; Cheng, M.; Long, C. Peer-to-peer energy trading in a microgrid. Applied Energy 2018, 220, 1–12. [Google Scholar] [CrossRef]
6. Zhou, Y.; Wu, J.; Long, C. Evaluation of peer-to-peer energy sharing mechanisms based on a multiagent simulation framework. Applied Energy 2018, 222, 993–1022. [Google Scholar] [CrossRef]
7. Andoni, M.; Robu, V.; Flynn, D.; Abram, S.; Geach, D.; Jenkins, D.; McCallum, P.; Peacock, A. Blockchain technology in the energy sector: a systematic review of challenges and opportunities. Renewable and Sustainable Energy Reviews 2019, 100, 143–174. [Google Scholar] [CrossRef]
8. Khan, I. Drivers, enablers, and barriers to prosumerism in Bangladesh: A sustainable solution to energy poverty? . Energy Research & Social Science 2019, 55, 82–92. [Google Scholar] [CrossRef]
9. Krause, M.J.; Tolaymat, T. Quantification of energy and carbon costs for mining cryptocurrencies. Nature Sustainability 2018, 1, 711-718. [https://www.nature.com/articles/s41893-018-0152-7].
10. Bauwens, T. Analyzing the determinants of the size of investments by community renewable energy members: findings and policy implications from Flanders. Energy Policy 2019, 129, 841–852. [Google Scholar] [CrossRef]
11. Radtke, J. A closer look inside collaborative action: civic engagement and participation in community energy initiatives. People, Place and Policy 2014, 8, 235-248. [https://ppp-online.org/].
12. Hackbarth, A.; Lobbe, S. Attitudes, preferences, and intentions of German households concerning participation in peer-to-peer electricity trading. Energy Policy 2020, 138, 111238. [Google Scholar] [CrossRef]
13. Reuter, E.; Loock, M. Empowering Local Electricity Markets: A survey study from Switzerland, Norway, Spain and Germany. Responsibility & Sustainability, University of St. Gallen 2017. [https://sustainability.unisg.ch/].
14. Wilkinson, S.; Hojckova, K.; Eon, C.; Morrison, G.M.; Sanden, B. Is peer-to-peer electricity trading empowering users? : evidence on motivations and roles in a prosumer business model trial in Australia. Energy Research & Social Science 2020, 66, 101500. [Google Scholar] [CrossRef]
15. Palm, J.; Tengvard, M. Motives for and barriers to household adoption of small-scale production of electricity: Examples from Sweden. Sustainability: Science, Practice and Policy 2017, 7, 6–15. [Google Scholar] [CrossRef]
16. Wittenberg, I.; Matthies, E. Solar policy and practice in Germany: How do residential households with solar panels use electricity? . Energy Research & Social Science 2016, 21, 199–211. [Google Scholar] [CrossRef]
17. Dahono, P.A. Indonesia supergrid: A key to 100% Renewable. School of Electrical Engineering and Informatics, Bandung Institute of Technology 2021.
18. International Renewable Energy Agency. Innovation landscape brief: Peer-to-peer electricity trading, IRENA, Abu Dhabi, 2020. [https://www.irena.org/].
19. Morstyn, T.; Farrell, N.; Darby, S.J.; McCulloch, M.D. Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants. Nature Energy 2018, 3, 94-101. [https://www.nature.com/articles/s41560-017-0075-y].
20. Berka, A.L.; Creamer, E. Taking stock of the local impacts of community owned renewable energy: a review and research agenda. Renewable and Sustainable Energy Reviews 2017, 82, 3, 3400–3419. [Google Scholar] [CrossRef]
21. Marnay, C.; Chatzivasileiadis, S.; Abbey, C.; Iravani, R.; Joos, G.; Lombardi, P.A.; Mancarella, P.; Appen, J.V. Microgrid evolution roadmap. International Symposium on Smart Electric Distribution Systems and Technologies (EDST) 2015, 139–44. [Google Scholar] [CrossRef]
22. Ton, D.T.; Smith, M.A. The US Department of Energy's microgrid initiative. The Electricity Journal 2012, 25, 8, 84–94. [Google Scholar] [CrossRef]
23. Kamel, R.M.; Chaouachi, A.; Nagasaka, K. Carbon emissions reduction and power losses saving besides voltage profiles improvement using microgrids. Low Carbon Economy 2010, 1, 1. [Google Scholar] [CrossRef]
24. Mihaylov, M.; Jurado, S.; Avellana, N.; Razo-Zapata, I; Van Moffaert, K.; Canadas, A.; Arco, L.; Grau, I.; Nowe, A. SCANERGY. A scalable and modular system for energy trading between prosumers. International Conference on Autonomous Agents and Multiagent Systems 2015. [https://nrgcoin.org/].
25. Lasseter, R.H., Paigi, P. (2004). Microgrid: A conceptual solution. IEEE 35th Annual Power Electronics Specialists Conference 2004, 6, 4285. [CrossRef]
26. Katiraei, F.; Iravani, M.R. Power management strategies for a microgrid with multiple distributed generation units. IEEE Transactions on Power Systems, 2016, 21, 4, 1821–1831. [Google Scholar] [CrossRef]
27. Nosratabadi, S.M.; Hooshmand, R.A.; Gholipour. E. A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems. Renewable and Sustainable Energy Reviews 2017, 67, 341-363. [CrossRef]
28. Stadler. M.; Cardoso, G.; Mashayekh, S.; Forget, T.; DeForest, N.; Agarwal, A.; Schonbein, A. Value streams in microgrids: a literature review. Applied Energy 2016, 162, 980–989. [CrossRef]
29. Palizban, O.; Kauhaniemi, K.; Guerrero, J.M. Microgrids in active network management Part I: Hierarchical control, energy storage, virtual power plants, and market participation. Renewable and Sustainable Energy Reviews 2014, 36, 428–439. [Google Scholar] [CrossRef]
30. Dimeas, A.L.; Hatziargyriou, N.D. Operation of a multiagent system for microgrid control. IEEE Transactions on Power Systems 2005, 20, 3, 1447–1455. [Google Scholar] [CrossRef]
31. Mihaylov, M.; Jurado, S; Avellana, N.; Van Moffaert, K.; de Abril, I.M.; Nowe, A. NRGcoin: Virtual currency for trading of renewable energy in smart grids. 11th International Conference on the European Energy Market 2014. [CrossRef]
32. Fell, M.J. Social impacts of peer-to-peer energy trading: a rapid realist review protocol. Centre for Research into Energy Demand Solutions, Environmental Change Institute, University of Oxford 2019. [https://www.creds.ac.uk/].
33. Institute of People's Business and Economics. Renewable Energi Project Development. IBEKA 2021. [https://ibeka.or.id/solar-photovoltaic/].
34. David, F.R.; David, F.R.; David, M.E. Strategic Management: A Competitive Advantage Approach, Concepts & Cases, 17th ed. Harlow Pearson Education 2023.
35. Sun, Z.; Tavakoli, S.; Khalilpour, K.; Voinov, A.; Marshall, J.P. Barriers to Peer-to-Peer Energy Trading Networks: A Multi-Dimensional PESTLE Analysis. Sustainability 2024, 16, 1517. [Google Scholar] [CrossRef]
36. Almarzooqi, A.H.; Osman, A.H.; Shabaan, M.; Nassar, M. An Exploratory Study of the Perception of Peer-to-Peer Energy Trading within the Power Distribution Network in the UAE. Sustainability 2023, 15, 4891. [Google Scholar] [CrossRef]
37. Gupta, J.; Jain, S.; Chakraborty, S.; Panchenko, V.; Smirnov, A.; Yudaev, I. Advancing Sustainable Energy Transition: Blockchain and Peer-to-Peer Energy Trading in India’s Green Revolution. Sustainability 2023, 15, 13633. [Google Scholar] [CrossRef]
38. Rahman, M.; Chowdhury, S.; Shorfuzzaman, M.; Hossain, M.K.; Hammoudeh, M. Peer-to-Peer Power Energy Trading in Blockchain Using Efficient Machine Learning Model. Sustainability 2023, 15, 13640. [Google Scholar] [CrossRef]
39. Shan, S.; Yang, S.; Becerra, V.; Deng, J.; Li, H. A Case Study of Existing Peer-to-Peer Energy Trading Platforms: Calling for Integrated Platform Features. Sustainability 2023, 15, 16284. [Google Scholar] [CrossRef]