Estimation of the usage fee for peer-to-peer electricity trading platform: The case of South Korea

Energy Policy 136 (2020) 111050

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Estimation of the usage fee for peer-to-peer electricity trading platform: The case of South Korea Juyong Lee, Youngsang Cho * Department of Industrial Engineering, College of Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-gu, Seoul, 03722, South Korea

A R T I C L E I N F O

A B S T R A C T

Keywords: P2P electricity trading platform Energy prosumer Willingness-to-pay Contingent valuation method Spike model

The peer-to-peer (P2P) electricity trading platform (ETP) is an electricity-sharing system by which individuals can sell surplus electricity or purchase electricity to meet a deficit in a regional power network. South Korea has allowed energy prosumers to engage in electricity trading from 2016 and is seeking ways to vitalize the elec­ tricity trading market. This study aims to estimate the willingness to pay (WTP) of respondents for the monthly usage of P2P ETP by using the contingent valuation method. Of the 1000 Korean respondents, 639 were willing to pay a fee for using the platform. The estimated WTP is KRW 5958.86 (USD 5.50) per month. Moreover, the estimated WTP is higher in respondents who have more background knowledge of the P2P ETP and who pay higher electricity fees. The estimated WTP is lower than the usage fees of existing ETPs in other countries, which is primarily due to Korea’s low electricity prices. Furthermore, the estimation results show that enhancing consumer awareness of P2P trading and introducing market strategies based on electricity usage should be considered by electricity trading companies and regulators to enhance P2P ETP usage.

1. Introduction Reducing greenhouse gas emissions has emerged as one of the most prominent global environmental issues. Therefore, the development and introduction of renewable energy are actively pursued globally. In response to the growing interest in renewable energy, new business models in the electricity industry have been launched worldwide, with the gradual expansion of distributed generation (also known as micro­ grid) based on the energy storage system (ESS) and photovoltaic power generation. These small-scale energy systems have enabled energy consumers to become energy producers or sellers. With energy con­ sumers starting to generate renewable energy at their homes, new forms of energy products and energy markets—such as solar panels, batteries, smart home applications, and energy trading services—are emerging (Parag and Sovacool, 2016). Energy prosumers—who have emerged due to the improvement in the economic feasibility of distributed resources and changes in con­ sumer perceptions due to the technological advances—are causing a paradigm shift in the electricity industry (Shandurkova et al., 2012; Marques and Nixon, 2013). The word “prosumer” is a compound word derived from “producer” and “consumer.” The concept of energy pro­ sumers was first introduced by McLuhan and Nevitt in their book Take

Today, which argues that consumers can become producers through the development of power generation technology (Nevitt and McLuhan, 1972). Energy prosumers participate in the electricity market through generation aggregators or platforms and sell surplus electricity through an electricity trading platform. Similarly, neighbors who have higher electricity bills or a higher electricity demand can buy the electricity that the prosumers sell on the platform. This intermediary platform is called a peer-to-peer (P2P) electricity trading platform (ETP). P2P electricity trading refers to the end consumer playing the role of an energy prosumer who produces electricity and trades their surplus electricity with other consumers in the power grid. There are established P2P ETPs—such as Piclo in the United Kingdom, Sonnencommunity in Germany, and Vandebron in the Neth­ erlands—that are commercialized for mediating the electricity trading between prosumers and consumers. The United States launched a pilot ETP commercialization business through the Brooklyn Microgrid proj­ ect. South Korea has also been promoting the electricity trading business between neighbors in a similar microgrid since March 2016 and is seeking ways to energize the electricity trading market. Korea is still in the process of reviewing the feasibility of commercializing P2P ETPs and the existing studies on the subject focus mainly on technical issues, such as the application of blockchain or big data technology in the platform

* Corresponding author. E-mail addresses: [email protected] (J. Lee), [email protected] (Y. Cho). https://doi.org/10.1016/j.enpol.2019.111050 Received 4 June 2019; Received in revised form 19 September 2019; Accepted 12 October 2019 0301-4215/© 2019 Elsevier Ltd. All rights reserved.

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Energy Policy 136 (2020) 111050

(Yoo et al., 2017; Kang et al., 2018; Lee et al., 2018). However, to the best of our knowledge, there have been no studies that investigate the financial aspects of P2P electricity trading such as usage fees of the trading platform, even though a large investment is essential for installing the platform. Based on this, the main purpose of this study is two-fold; (1) to estimate the usage fee1 for a P2P ETP by determining the willingness-to-pay (WTP) of potential users and (2) to derive future government policies and company strategies for activating and growing P2P ETP businesses. The remainder of this study is organized as follows: Section 2 de­ scribes the concept and background of P2P ETPs and reviews previous studies related to the subject. Section 3 explains the data and method­ ology used in this study, while Section 4 presents the estimation results and the discussion. Section 5 provides the main findings, implications, and limitations of the study.

The Brooklyn Microgrid is a photovoltaic energy trading platform that was launched as a demonstration project in Brooklyn, New York, in the United States in April 2017 (Mengelkamp et al., 2018). Fifty resi­ dents of Brooklyn took part in the project, installing solar (or photo­ voltaic) panels and selling surplus energy. Prosumers can choose whether to trade the energy generated by the photovoltaic panels on a peer-to-peer basis, store the surplus electricity in an on/offline storage device, or use it in the home. The Brooklyn Microgrid gains profit from the sales of energy storage units and commission from electricity trading. Table 1 summarizes the characteristics of the existing P2P ETPs discussed in this section. Unlike the above countries with established and commercialized P2P ETPs, Korea’s P2P ETP market is still in its early stages. In 2016, Korea launched a demonstration project of P2P electricity trading with phys­ ical infrastructure such as solar panels and ESS in two small towns to foster energy prosumers’ business and activate the P2P electricity trade through new trading platforms. However, this project is still in the early stage of examining the feasibility of P2P ETP, and has therefore not been promoted actively in Korea. Currently, Korean energy prosumers who have registered electricity trading businesses are trading mostly in electricity generated by photovoltaic panels. The transactions are with the Korea Electric Power Corporation (KEPCO). There are several other possible energy sources for P2P electricity trading, such as wind power and biomass; however, photovoltaic-based electricity trading is the main source that is currently being promoted in Korea. However, photovoltaic power generation has characteristics of intermittent availability of time for generation and high generation cost, compared with traditional generation methods. This is one of the reasons the P2P power trading market is not actively operated in Korea. The prosumers and consumers are less likely to participate in P2P electricity trading as the average electricity charge is lower than that of photovoltaic generation cost.2 To achieve grid parity by lowering the generation cost of renewable energy to the level of the average electricity charge, the Korean government is planning to invest KRW 140 trillion (USD 129 billion) in renewable energy R&D and related facilities by 2030 and to invest KRW 1.2 trillion (USD 1.11 bil­ lion)—approximately 62.1% of the Electrical Industry Foundation

2. Background 2.1. Peer-to-peer electricity trading platform (P2P ETP) In P2P electricity trading, energy prosumers’ trading partners are neighbors who want to buy electricity from someone other than the electricity sales company. Prosumers, therefore, get sales revenue by selling surplus electricity to their neighbors, and this transaction is performed via a trading platform. P2P ETPs are being developed glob­ ally based on technological improvements such as the application of blockchain and big data. In several countries—such as the United Kingdom, the Netherlands, Germany, and the United States—pilot businesses are already in operation or being commercialized. Vandebron is a Dutch web-based electricity trading platform launched in April 2014 (Vandebron, 2019). Prosumers introduce their power generation modes, residential areas, and trading conditions on the platform and once the consumer has selected the prosumer they want to trade with, the transaction takes place within 2 min. In December 2018, Vandebron had approximately 100,000 members and prosumers on the platform selling electricity generated by wind, biomass, and solar energy. Vandebron has a profit structure based on platform usage fees, charging a membership fee of USD 12 per month for both consumers and prosumers. In the United Kingdom, Piclo was launched in October 2015 under sponsorship of the United Kingdom Department of Energy and Climate Change (DECC) and the energy fund of Nominet Trust (Piclo, 2019). Piclo matches energy consumers and prosumers every 30 min using electric meter data, power generation costs, and consumer preference information. Both consumers and prosumers can use Piclo online ser­ vices through computers and smartphones, and electricity trading takes place when the consumers’ desired prices and the prosumers’ trans­ action conditions are both met. Piclo is managed and supported by the government and venture capital investment and there is no fee for using the Piclo platform. Sonnencommunity is Germany’s renewable energy trading platform and was established in 2016 (KEMRI, 2016; Sonnenbatterie, 2019). Sonnencommunity combines energy trading and battery storage tech­ nology, providing surplus power and services that can be stored in batteries and for emergencies. This means that prosumers can save their surplus electricity to the battery and use it when needed or sell it again at a future date. Unlike other platforms, where the prosumer presents prices directly, Sonnencommunity adopts a fixed-price system and consumers pay 23 cents per kWh to the prosumers. Platform users pay a fee of EUR 19.99 per month and additional costs—such as transaction fees—are not incurred.

Table 1 Existing cases of P2P ETPs. P2P ETP (Country, Launch year)

Characteristics

Monthly usage fee

Vandebron (Netherlands, 2014)

- Consumers select prosumers directly and deal at a price set by the prosumers. - Transaction takes place in 2 min. - Web and App based platform - Platform matches energy consumers and prosumers every 30 min. - Fixed-price system (23 cents/kWh) - Stable power supply with battery storage technology - Able to sell, store, and use photovoltaic energy that prosumers generate - Provide solar panels and on/ offline energy storage device

- USD 12

Piclo (UK, 2015)

Sonnencommunity (Germany, 2016) Brooklyn Microgrid (USA, 2017)

- Free - government and venture capital investment - EUR 19.99

- Free - commissions of electricity trading

(Source: Vandebron, 2019; Piclo, 2019; Sonnencommunity, 2019; Mengelkamp et al., 2018)

1 In this study, “usage fee” is the amount payable for the right to access the grid and trade electricity on the platform. The payment for using P2P ETP in­ cludes transaction fees and platform maintenance costs and does not include power generation facilities and ESS construction costs.

2 In Korea, the average electricity charge of households in 2017 was KRW 110/kwh (EPSIS, 2019). Conversely, the average cost of photovoltaic genera­ tion in 2017 was KRW 182/kwh (Song et al., 2018).

2

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Energy Policy 136 (2020) 111050

Fund—in renewable energy businesses in 20193 (Park and Koo, 2018).

initial bid amount that has a significant influence on the WTP estimation is difficult (Ready and Hu, 1995). Despite these drawbacks, most recent studies utilized the DC format because of its evident merits: high response rates, low biases, and a low likelihood of yielding unreliable WTP measures (Hanemann, 1984; Oerlemans et al., 2016). The DC format can be categorized into a single-bounded dichoto­ mous choice (SBDC) proposed by Bishop and Heberlein (1979) and double-bounded dichotomous choice (DBDC) proposed by Hanemann (1985). The DBDC format asks a respondent a yes/no question twice, whereas the SBDC format asks just one yes/no question. When the respondent answers “yes” to an initial bid, a follow-up question with a second bid—higher than the initial bid—is presented. If the respondent responds with “no” to the initial bid, the second bid—lower than the initial bid—is presented. The DBDC format is preferred in many studies, due to the SBDC format having only one question and therefore being statistically inefficient compared to the DBDC format. However, the DBDC format can potentially have compliance problems or rejection problems4 (Cameron and Quiggin, 1994). To overcome the disadvan­ tages of both the SBDC and DBDC formats, Cooper et al. (2002) proposed the one and one-half bounded dichotomous choice (OOHBDC) format, in which ranges are presented to respondents for both the lower bid and the upper bid. In the OOHBDC format, one of the two bids is randomly presented to the respondents as an initial bid. If the respondent answers “yes” to the upper bid or “no” to the lower bid, the survey ends. Whereas, if a respondent presents answer of “yes” (“no”) to the lower (upper) bid, a follow-up question with the upper (lower) bid is presented. If the respondent answers “no” (“yes”) to the follow-up question, the survey ends. This study applied the OOHBDC format to estimate WTP, as this format has the advantages of reducing both inefficiency and bias (Cooper et al., 2002).

2.2. Literature review concerning P2P ETPs Technical feasibility studies concerning P2P ETPs have been actively conducted. Werth et al. (2015) conducted a conceptual study applying nanogrid—a technologically smaller and simpler version of micro­ grid—in the distributed energy network and the application was proven to be valid on a conceptual level. K. Zhang et al. (2018) proposed a P2P ETP model in a microgrid by applying the game theory and simulated electricity trading with their model. The simulation results showed that P2P electricity trading improves the balance between power generation and consumption and the more peers, the easier it is to reach that equilibrium. Di Silvestre et al. (2018) proposed an experimental sce­ nario of a blockchain-based microgrid ETP. They discovered that the superposition of electricity trading in the microgrid causes power losses in every branch and expressed the need for technological alternatives to address this issue. Alam et al. (2019) proposed an optimal trading model integrating a demand-side management (DSM) system with the P2P ETP. The energy cost is increased under unrestrained trading conditions. However, when the proposed demand size adjusting algorithm is applied, the total energy cost of the peers can be minimized. There are few studies on the economic and social aspects of P2P ETP—such as charging schemes or consumer preferences—compared to the number of studies on technical aspects. There are some studies, however, on consumer preference for renewable energy-based policy or market expansion. Bertsch et al. (2016) analyzed public acceptance of the renewable energy grid expansion policy in Germany using survey questionnaires. They found that public acceptance improves as the amount of renewable energy sources and the level of education on en­ ergy increases. There are also studies concerning the activation of the microgrid or P2P electricity market, but they were all conducted through literature and case review (Franke et al., 2005; Sousa et al., 2019). To our knowledge, there are no studies that carried out empirical analyses on the subject. To successfully introduce and operate a P2P ETP, a WTP estimation concerning the platform usage from the perspective of consumer preference is needed.

3.2. WTP estimation model In this study, we use the utility difference model proposed by Hanemann (1984). The utility of the respondent with income (m) and characteristics (S) for the state of the non-market goods (J) is expressed by the indirect utility function (u). Here, J ¼ 0 means that P2P ETP is not introduced and J ¼ 1 means that P2P ETP is introduced and available in the contingent market. The indirect utility function can then be expressed by the observable deterministic part (VðJ; m; SÞ) and the un­ observable stochastic part (εj ) as follows:

3. Methodology 3.1. The one and one-half bounded dichotomous choice contingent valuation method

(1)

uðJ; m; SÞ ¼ VðJ; m; SÞ þ εj

This study uses a contingent valuation method (CVM) to estimate the monthly usage fee for a P2P ETP. CVM is a survey-based method and one of the most widely-used approaches for measuring the value of nonmarket goods. In the electricity industry, CVM has in the past been utilized to estimate the WTP of consumers concerning the acceptance of green energy sources, measuring the social value of nuclear energy, and avoiding blackouts (Nomura and Akai, 2004; Stigka et al., 2014; Kim et al., 2015). In a CVM survey, a specially designed questionnaire sets up a hy­ pothetical situation representing changes in non-market goods and re­ spondents answer how much they are willing to pay for the market change. There are several survey formats suitable for eliciting the WTP of respondents in CVM, including open-ended (OE), payment card (PC), and dichotomous choice (DC) formats (Boyle et al., 1996; Welsh and Poe, 1998). The DC format is an approach that gives respondents a “yes” or “no” choice for the amounts of bids presented by an interviewer and the WTP is estimated through econometric analyses such as logit or probit model. The DC format is costly since a large number of obser­ vations must be obtained to ensure reliability. Moreover, setting an

where εj is independent and identically distributed with a zero mean. If the respondent replies “yes” to the question “Are you willing to pay A for using P2P ETP?” they maximize the utility by willingly paying amount A. The probability that the respondent answers “yes” can be expressed as: Prf}yes}g ¼ PrfΔVðAÞ � ηg ¼ Fη ½ΔVðAÞ�

(2)

where η ¼ ε0 ε1 and Fη ð ⋅ Þ represents the cumulative distribution function (CDF) of η. For a respondent whose WTP (denoted as C) exceeds or equals the bid presented, the probability of answering “yes” can be expressed as: Prf}yes}g ¼ PrfC � Ag � 1

GC ðAÞ

(3)

where Gc ðAÞ is defined as the CDF of C. Therefore, we can derive the following relationship from equations (2) and (3): 4 The compliance problem is when respondents answer “yes” to satisfy in­ terviewers’ expectations although they actually wanted to answer “no.” The rejection problem is when respondents answer “no’ unconditionally (or without thinking) because the repeated questions are troublesome to them.

3 The yearly average exchange rate in February 2018 was 1083.29 KRW/USD (IMF, 2019).

3

J. Lee and Y. Cho

Fη ½ΔVðAÞ� � 1

Energy Policy 136 (2020) 111050

benefits of using the platform: (1) additional revenue from sales of surplus electricity; (2) a reduction in energy wastage by purchasing only lacking electricity; (3) getting information concerning power genera­ tion, transactions, and usage from the platform provider. We then explained to the respondents that financial investments such as the installation of an ESS and power generation facilities are necessary for using P2P ETP and these investments can be satisfied by electricity trading through the platform. In the survey, the payment vehicle for using the P2P ETP was the monthly platform usage fee per household. Generally, the payment vehicle in CVM studies on public or environ­ mental goods is an additional payment of taxes, such as income tax. However, the P2P ETP is a platform for electricity trading, which is different from general public or environmental goods. Therefore, using income tax as a payment vehicle is not appropriate for this study. This is because the direct consumer benefits from the P2P ETP are limited to platform users and do not apply to the general public on a national level. We therefore set the usage fee for the P2P ETP as a payment vehicle. Several existing CVM studies on the electricity industry have used usage fees (bills) as payment vehicles in a similar way (Yoo and Kwak, 2009; Kim et al., 2015). Moreover, as the direct consumer benefits are not derived when the transaction is stopped, we assumed that the payments are made monthly for the period of the platform’s use. Furthermore, we explained to the respondents that their incomes are limited and they should be used for a variety of purposes. Prior to the actual survey, we conducted an online pilot survey with 500 respondents to determine the initial bid sets for the actual survey. The upper and lower 5% outliers from the pilot test were excluded to reduce bias and we determined five sets of values for the initial bids – KRW (1000; 5000), (5000; 10,000), (10,000; 15,000), (15,000; 20,000), (20,000; 25,000). The actual survey was conducted by a specialized market survey company, Gallop Korea, by utilizing one-on-one face-toface interviews in March 2018. For the actual survey, 1000 respondents were selected by applying stratified sampling to reflect the population of South Korea.5 The demographic characteristics of the survey re­ spondents are described in Table 2. The ten bid sets were allocated to 1000 respondents at a ratio similar to each other. Table 3 presents the distribution of the responses corre­ sponding to each bid. When a lower bid was given as an initial bid, 177 out of 495 respondents (35.8%) answered “no-no.” When the upper bid was given as the initial bid, 184 out of 505 (36.4%) answered “no-nono.” The respondents who answered “no-no” or “no-no-no” represent a zero WTP. This indicates that 361 out of all respondents (36.1%) have zero WTP for using a P2P ETP. The zero WTP respondents’ intention can be categorized in two ways: (1) their WTP is actually zero; (2) they disagree with or are dis­ pleased with the survey topic and the presented bid. Concerning re­ spondents who are dissatisfied with the questionnaire and whose answers are zero, their actual WTP may not be zero. These answers are �ndez and del called protest WTP (Carson et al., 2003; Ramajo-Herna Saz-Salazar, 2012). In the survey, we presented an additional question to the zero WTP respondents, asking why the WTP is zero to distinguish the true zero responses from the protest WTP responses. Table 4 shows the responses concerning the reasons for their zero WTPs. We considered the respondents who selected “Not enough information” or “Against the electricity trading platform” as the protest WTP respondents. Of the 361 zero WTP respondents, 142 were the protest-WTP respondents who are 39.4% of the zero-WTP respondents. The protest-WTP respondents have

(4)

GC ðAÞ

When the upper and lower bids are presented to respondents, there are six possible outcomes: “yes,” “yes-yes,” “yes-no,” “no,” “no-yes,” and “no-no.” For convenience in the formula, we use the binary value indi­ cator variables (I) for each outcome as follows: 8 9 Y > > > > < I i ¼ 1ðrespondent i answers "yes"Þ = NY Iðupper bid as an initial bidÞ ¼ I i ¼ 1ðrespondent i answers "no yes"Þ > > > : I NN ¼ 1ðrespondent i answers "no no"Þ ​ > ; i

(5) and 8 9 YY > > > < I i ¼ 1ðrespondent i answers "yes yes"Þ > = YN Iðlower bid as an initial bidÞ ¼ I i ¼ 1ðrespondent i answers "yes no"Þ > > > > : I N ¼ 1ðrespondent i answers "no"Þ ; i (6) where 1 (…) is an indicator function with a value of 1 if the argument is true, and 0 otherwise. When the respondent answers “no” to a lower bid or “no-no” to an upper bid, the WTP of the respondent may be less than the lower bid or zero. To determine whether the respondent’s WTP is zero or at least not €m (1997). For re­ zero, we adopt the spike model proposed by Kristro spondents who answer “no” to a lower bid or “no-no” to an upper bid, an additional question was presented: “Are you willing to pay just KRW 1 or more for using the P2P ETP?” Based on this, the value indicator variables can be added as follows: ( ) I NNY’ ¼ 1ðrespondentianswers "no no yes"Þ i Iðupper bid asaninitial bidÞ¼ NNN’ Ii ¼ 1ðrespondent ianswers "no no no"Þ (7) and ! I lower bid as an initial bid ¼

(

)

IiNY’ ¼ 1 respondent i answers }no

yes}Þ

IiNN’ ¼ 1 respondent i answers }no

no}Þ (8)

Possible answers for the additional question are denoted as Y’ and N’. The WTP is zero when the respondent answers “no-no-no” or “no-no” and the WTP is positive but less than the lower bid when the respondent answers “no-no-yes” or “no-yes.” From the eight indicator variables, the log likelihood function for the OOHBDC spike model is as follows: ( � � �� � � � �� ) NY N X I Yi þ I YY ln 1 GC AUi þ I YN ln GC AUi GC ALi i i þ Ii � � � � � ln L¼ ln GC ALi GC ð0Þ þ I NNN’ ln½GC ð0Þ� þ I NNY’ þ I NY’ þ I NN’ i i i i i¼1 (9) Here, assuming that the respondent’s WTP follows the logistic CDF, GC ðAÞ has the following functional form with parameter estimates a and b: 8 < ½1 þ expða bAÞ� 1 if A > 0 GC ðAÞ ¼ ½1 þ expðaÞ� 1 (10) if A ¼ 0 : 0 if A < 0 The spike is defined as ½1 þ expðaÞ� as ð1 =bÞln½1 þ expðaÞ�.

1

and the mean WTP is calculated

5 In this study, we used proportionate probability stratified sampling, which is the most widely-used method for national-level surveys. The proportions of respondents (households) were allocated according to the national population composition ratio by “region.” The sample was then extracted according to demographic composition ratios such as “age” and “gender.” By utilizing this stratified sampling process, we were able to obtain a representative sample of Korea.

3.3. Survey design and data descriptions We designed the OOHBDC questionnaire to estimate the monthly WTP for using P2P ETP. We started the questionnaire by describing the backgrounds and basic concepts of the energy prosumers and the concept of P2P ETP to the respondents. We also presented the possible 4

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Energy Policy 136 (2020) 111050

priori, the coefficient of the bid amount is negative. We can therefore interpret that a higher bid amount is less likely to elicit a “yes” response. The estimated WTP to use the P2P ETP is KRW 5958.86 (USD 5.50) per month and statistically significant at the 1% level. Additionally, to calculate the confidence intervals of the estimated WTP, we conduct the Monte Carlo simulation, a parametric bootstrap approach proposed by Krinsky and Robb (1986). First, we repeatedly generate 5000 individual parameter values using the estimated coefficients and the variance-covariance matrix and then we calculate 5000 mean WTPs. After listing the calculated WTPs in order of magnitude, we obtain the 95% and 99% confidence intervals by omitting 2.5% and 0.5% at both ends. The 95% and 99% confidence intervals are presented in the last two rows of Table 5. To analyze the effects of the respondents’ socio-economic charac­ teristics on the WTP for using a P2P ETP, we estimated the model with covariates. The estimation results are presented in Table 6. Among the covariates, gender, age, level of education, number of family members, and income do not have statistically significant effects on WTP. In other words, most of demographic characteristics have no statistically signif­ icant relations with the WTP for using P2P ETP. Several studies have analyzed that electricity demand has income elasticity (Fatai et al., 2003; Zachariadis and Pashourtidou, 2007; Kim et al., 2015), but our result show that WTP for using P2P ETP is not income elastic. On the other hands, region, electricity fee, and background knowledge about P2P ETPs are statistically significant at the 1% level. The estimated coefficients of the variables “fee” and “knowledge” are positive, which indicates that respondents who pay higher electricity fees or have more background knowledge concerning P2P ETP are willing to pay more for

Table 2 Characteristics of survey respondents. Characteristic

Group

Number of respondents

Ratio (%)

Gender

Male Female 20–29 30–39 40–49 50–59 60–69 Metropolitan Area Non-metropolitan Area Under 9 10–29 30–49 50–69 70–89 90–109 Over 110 Under 299 300–399 400–499 500–699 Over 700 Lower than highschool Higher than University/college 1 2 3 4 More than 5

507 493 184 201 233 229 153 715 285

50.7 49.3 18.4 20.1 23.2 22.9 15.3 71.5 28.5

2 100 488 348 41 14 7 165 190 232 277 136 452

0.2 10.0 48.8 34.8 4.1 1.4 0.7 16.5 19.0 23.2 27.7 13.6 45.2

548

54.8

76 165 264 437 58

7.6 16.5 26.4 43.7 5.8

Age

Regiona Average monthly electricity bill per household (KRW 1000)

Average monthly income per household (KRW 10,000)

Education level (graduation)

Number of family members

Table 4 Reasons for zero-WTP.

Notes: a In South Korea, cities with populations of more than one million are referred to as “metropolitan cities” (Huh and Kim, 2003). We applied this criterion to organize the survey data concerning the different regions.

been generally excluded from the analysis data set because their re­ sponses do not represent their actual WTPs (Boyle and Bishop, 1988; Chien et al., 2005; Meyerhoff and Liebe, 2010). However, when the responses of the protest WTP respondents are excluded from the data set, the overall WTP can be overestimated (Strazzera et al., 2003). We therefore considered the WTP values of the protest WTP respondents as actual zero and included their responses in the data set.

Reasons mentioned for zero-WTP

Number of respondents

Ratio (%)

Cannot afford to pay the additional fee Should be provided free of charge Not enough information Do not care Against the electricity trading platform Total of reasons mentioned

33 96 106 90 36 361

9.1 26.6 29.4 24.9 10.0 100

Table 5 Estimation results (without covariates).

4. Results and discussion The estimation results of the OOHBDC spike model without cova­ riates are shown in Table 5. According to the result of the Wald test performed, the null hypothesis—which states that values of all co­ efficients in the estimated equation are zero—is statistically rejected at the 1% significance level, meaning that the estimated equation is sta­ tistically appropriate. All estimates are statistically significant at the 1% level. The number of zero WTP responses is 361 out of the 1000 re­ spondents and the estimated value of the spike is 0.3849, which virtually coincides with the actual proportion of zero WTP responses. We can therefore say that the spike is estimated well. Additionally, as expected a

Variables

Coefficient estimates

Constant Bid Spike Log-likelihood Wald statistic (p-value) WTP (KRW)

0.4687 (7.02)a 0.1602 ( 27.41)a 0.3849 (24.34)a 1298.484 592.51 (0.000) 5958.86 (24.34)a 5483.40–6464.08 5335.22–6639.39

Mean 95% Confidence interval 99% Confidence interval

Notes: The unit of bid is KRW 1000 for convenience. Values in parenthesis except Wald statistic are t-statistics. a Significant at the 1% level.

Table 3 Distribution of responses in the survey. Bid amount (KRW)

Lower bid is given as an initial bid

Lower 1000 5000 10,000 15,000 20,000 Total (%)

“yes-yes” 26 15 8 12 8 69 (13.9)

Upper 5000 10,000 15,000 20,000 25,000

“yes-no” 32 20 11 9 4 76 (15.4)

“no-yes” 17 36 46 37 37 173 (34.9)

Upper bid is given as an initial bid “no-no” 24 29 36 42 46 177 (35.8)

Total 99 100 101 100 95 495 (100)

5

“yes” 36 26 12 7 10 91 (18.0)

“no-yes” 22 16 6 11 8 63 (12.5)

“no-no-yes” 13 25 42 42 45 167 (33.1)

“no-no-no” 29 36 39 45 35 184 (36.4)

Total 100 103 99 105 98 505 (100)

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Energy Policy 136 (2020) 111050

monthly WTP for consumers using the CVM. The estimated WTP is USD 5.50 per household per month, and the national-level WTP is USD 1.46 billion per year. Based in the estimation results, we derive some impli­ cations concerning the introduction and vitalization of a P2P ETP system in Korea. First, the estimated WTP of the P2P ETP in this study (USD 5.50) is less than half of the monthly ETP usage fees in the Netherlands (USD 12) and Germany (EUR 19.99; USD 22.53). This result may be due to low consumers’ incentives for electricity trading through the P2P ETP due to uncertainties concerning the infrastructure construction costs and the risks related to implementing new electricity trading methods. However, the low incentives and high risks for P2P ETP trading are also attributed to the current electricity price in Korea. As the current elec­ tricity price does not appropriately reflect the various external and so­ cial costs, consumers are not motivated to adopt new technologies and systems despite their advantages and potential benefits. We therefore propose that the main underlying cause of the low WTP for P2P ETP is the low electricity price in Korea. Unlike the other countries, the elec­ tricity price in Korea is set not by the market, but by the government6 (Park and Hong, 2014) and the Korean government has maintained relatively low electricity price as a priority to create a stable electricity supply and demand. Korea’s electricity supply is therefore based on providing lower-cost energy sources such as coal and nuclear power preferentially.7 Due to the government’s decades-long policy of main­ taining low electricity prices, Korean consumers are accustomed to the low price and thus have less interest in improving energy efficiency or enhancing renewable and eco-friendly energy generation (Kim and Shin, 2016; Kim and Cho, 2017). Furthermore, the price of secondary energy, electricity, has been lower than that of primary energy in Korea until recently. This means that the electricity price system of Korea is “dis­ torted.” The lower WTP of Koreans (compared to other countries) can therefore be understood based on the historical context and current circumstances in Korea. To implement strategies and policies for acti­ vating P2P ETP, normalization of the current electricity price scheme of Korea should be premised preferentially. For the normalization of the distorted electricity price scheme, we propose that the Korean govern­ ment needs to comprehensively reflect the energy calories and social costs of electricity—such as GHG emissions and particulate mat­ ters—and secure fairness in the tax regime across all energy sources. Second, the results show that respondents living in non-metropolitan cities are willing to pay more for using a P2P ETP. Renewable energy facilities such as photovoltaic panels and ESSs and microgrid networks are relatively easier to construct in non-metropolitan cities than in metropolitan cities. A higher WTP for non-metropolitan residents means higher acceptance of P2P ETP. Therefore, the high acceptance of po­ tential consumers and the relative ease of infrastructure establish­ ment—which refers to the cost-benefit efficiency of the nonmetropolitan prosumers—can have positive impacts on the activation of P2P ETP. Therefore, introducing a P2P electricity trading system in

Table 6 Estimation results (with covariates). Variables

Coefficient estimates

Constant Bid Gender Age Region Fee Knowledge Education Family Income Spike Log-likelihood Wald statistic (p-value) WTP (KRW)

0.5782 ( 0.93) 0.1663 ( 27.03)a 0.1580 (1.28) 0.0058 ( 1.10) 0.4007 ( 2.94)a 0.1174 (2.73)a 0.4380 (5.83)a 0.0117 (0.36) 0.0418 (0.60) 0.0443 ( 0.94) 0.3790 (23.71)a 1271.476 561.61 (0.000) 5835.97 (23.64)a 5362.21–6332.23 5204.48–6439.49

Mean 95% Confidence interval 99% Confidence interval

Notes: The unit of bid is KRW 1000 for convenience. Values in parenthesis except Wald statistic are t-statistics. a Significant at the 1% level.

using the P2P ETP. On the other hand, the estimated coefficient of the variable “region” is negative, indicating that households not in metro­ politan cities are willing to pay more for using P2P ETP than those who live in metropolitan cities. The average monthly WTP is estimated to be KRW 5835.97 (USD 5.39) per month and statistically significant at the 1% level. To sum up, respondents living in non-metropolitan areas, those paying more for electricity, and those that have more prior knowledge about ETPs are willing to pay a higher usage fee than those who do not. Expanding the household-level estimates to the entire population could be helpful in estimating the economic benefit of P2P ETPs at a national level. Since this study’s survey respondents were selected by stratified sampling to reflect the entire Korean population, the sample provides representative data for the total population. The estimated WTP for using P2P ETP is KRW 5958.86 (USD 5.50) per month. Therefore, the annual WTP is KRW 71,506.32 (USD 66.01) per house­ hold. The number of households paying their own electricity fees in South Korea is 22.08 million as of January 2019 (KOSIS, 2019). Therefore, the annual and national-level WTP is KRW 1.58 trillion (USD 1.46 billion). This was determined by multiplying the number of households with the annual WTP per household. 5. Conclusion and policy implications The reduction of greenhouse gas emissions to cope with climate change is emerging as one of the most critical environmental issues in the world and P2P ETP has become one of the most significant new forms of the energy market solutions in several countries such as the United Kingdom, Germany, Netherlands, and the United States. The South Korean government announced the “2030 New Energy Industry Diffusion Strategy” in 2015 to preemptively address the future energy system. The strategy included the proliferation of energy prosumers as part of the relaxation of the regulations as well as new business creation in the energy sector. Moreover, the Korean government has revised the “Guidelines for the transmission of small-scale renewable energy power generation” on March 2018, thereby laying the foundation for the entry of energy prosumers into the electric power trading market. To encourage and assist energy prosumers through new trading platforms and to activate the P2P electricity trade, in 2016, Korea has launched a demonstration project of P2P ETP targeting two small towns. However, the P2P ETP market in Korea is still in its early stages. This study estimates the usage fee—or WTP—of consumers for P2P ETP in South Korea. As Korea’s P2P ETP market is not yet activated and commercialized, we assume a virtual ETP market and estimate the

6 The electricity price adjustment process in Korea is as follows. When the KEPCO board of directors submits the electricity price adjustment plan to the Ministry of Trade, Industry and Energy (MOTIE), this plan is first discussed between MOTIE, the Ministry of Economy and Finance (MOEF), and the Korea Electricity Regulatory Commission (KOREC). After an agreement is reached and the price adjustment is sent to KEPCO, the result of the electricity price adjustment is implemented. KOREC is affiliated with MOTIE, and the members of KOREC consists of experts from various fields and institutions, such as uni­ versities, research institutes, private organizations, and legal entities. However, the final approval of the electricity price adjustment is provided by government. This procedure is completely different from the general market pricing mechanism. 7 In Korea, coal and nuclear power plants were built in large numbers during the process of achieving rapid industrialization and economic growth, which started in the late 1900s (Kim et al., 2011). In 2018, the share of electricity generation by energy source was 42.4% for coal power and 23.4% for nuclear power (KEEI, 2019).

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Energy Policy 136 (2020) 111050

non-metropolitan cities and gradually expanding it to other regions should be considered. Feldhiem, a small agricultural town in Germany, is Germany’s first energy self-sufficient village, generating 100% of its own electricity and contributing to regional economic revitalization through electricity sales (Young and Brans, 2017). In Korea, small-scale cities such as Chungnam and Jeonbuk are piloting energy self-sufficient village programs, but they are not yet generating 100% of their own electricity and electricity trading remains in the early stages of introduction. Third, the results showed that respondents who have more prior knowledge about P2P ETPs are willing to pay more for using the plat­ form. This means that enhancing consumer awareness can raise social acceptance of energy prosumers and P2P electricity trading. It is therefore necessary to gradually expand P2P ETP related policies and systems along with appropriate education and publicity, so that existing electricity consumers or energy prosumers who have traded only through KEPCO can enter the ETP as early adopters. In previous studies concerning new energy market (or technology) introduction, the WTP of consumers that have more prior knowledge about the market were higher and it has been suggested that enhancing consumers’ prior knowledge is a significant factor for raising social acceptance and preference for the market (Roche et al., 2010; Kim et al., 2018; Xie and Zhao, 2018). These existing studies analyzed various electricity-related industries and markets, such as green electricity and electric vehicles; our study, however, is the first—to our knowledge—to analyze the relationship between prior knowledge and WTP specifically concerning P2P ETP. Lastly, respondents paying higher electricity fees have higher WTP for using the P2P ETP. This indicates that the consumers with high electricity consumption have high acceptance and preference for ETPs, and their utilities for using the ETP are also high. This means that considering a strategy introducing P2P ETP to consumers with high electricity consumption preferentially and then gradually expanding the scope of the target users may be effective. This phased introduction of P2P ETP based on electricity consumption may mean that ETP com­ panies can secure financial resources effectively. The contributions of this study are twofold. First, from a consumer point of view, we successfully estimate the usage fee of P2P ETPs in Korea, even though this platform has not been activated yet. Previous studies on P2P ETPs have focused on the technical development and application of technologies for improving the efficiency of the platform but have not—to our knowledge—considered the consumer’s perspec­ tive. This study further proposes strategies for activating the P2P ETP market by including various socio-economic variables in the analysis. Second, we compare the estimated usage fee of this study with that of other countries where the P2P ETP market has been introduced and commercialized, and discover that consumer acceptance of the platform in Korea is lower than those of countries. The main cause of the low consumer acceptance in Korea is the distorted electricity market. To address this, we propose that the electricity price structure should be normalized to enable P2P ETP market vitalization. Despite these clear contributions, this study is subject to certain limitations. First, we estimated the usage fee of the P2P ETP by deter­ mining respondents’ willingness to pay for the presented bids based on the CVM questionnaire format. However, when determining whether to use the P2P ETP, consumers may consider the various technical and social attributes of the platform, such as functionality, convenience, and network externality. This means that the consumers’ WTP can vary depending on the quality or quantity of each attribute. Therefore, it is necessary for future studies to consider subdividing the attributes of the P2P ETP when designing WTP questionnaires and conducting a conjoint analysis. Additionally, micro- and macro-level cost-benefit studies on prosumers and the electricity market are necessary to estimate the appropriate usage fee that will enable optimal operation of the P2P ETP in the electricity market. A sensitivity analysis of the P2P ETP sub­ scription rate considering the usage fee level is necessary to introduce

and commercialize P2P ETP in the future. Second, many of the de­ mographic characteristics have no significant association with WTP for using P2P ETP. As we derived implications with only statistically sig­ nificant covariates, we did not interpret the other non-significant de­ mographic variables further. Further analyses are therefore necessary to discover why most of the demographic variables are not statistically significant and to determine whether this result is specific to Korea or whether it can be commonly observed in other countries. Third, we set up the contingent market of the P2P ETP based on the current status of Korea with its distorted electricity price scheme. Since the estimated WTP in this study reflects the current status of the Korean electricity market, the estimation result can vary as the market changes into the renewable energy base or the electricity price is determined by the market not by the government. Therefore, by setting up several sce­ narios for the future electricity market of Korea and performing WTP estimation for each scenario, more detailed information and different implications for introducing P2P ETP can be obtained. Lastly, for the introduction of the P2P ETP, the establishment of a reasonable pricing scheme is important in conjunction with the estimation of the consumer WTP. The usage fee can be set as a flat price, but it can also be set differently according to the users or the services offered. For example, the pricing schemes of the platform can be established based on (1) the amount of electricity usage, (2) the volume of electricity sales or pur­ chases, or (3) the quantity and quality of the functions or services pro­ vided. However, an appropriate pricing scheme for P2P ETP will be somewhat limited if derived only from our analysis results, as further studies are needed. There are several studies on the quality (or services) differentiated charging scheme in the electricity industry (Fumagalli et al., 2001; Woo et al., 2014; Kim et al., 2015; C. Zhang et al., 2018), but to the best of our knowledge, there is no study proposing charging schemes for P2P ETPs from the platform providers’ perspective. Declaration of interest None. Acknowledgements This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Appendix. OOHBDC CVM Questionnaire As a result of the pilot survey, the expected usage fee is between (Lower) to (Upper) KRW per month. Type A [Type A questions were posed to approximately half of the respondents.] Q1. Would you be willing to pay (Lower) KRW per month for using P2P ETP? (1) Yes → Go to Q2 (2) No → Go to Q3 Q2. If so, would you be willing to pay (Upper) KRW per month for using P2P ETP? (1) Yes → Survey ends (2) No → Survey ends Type B [Type B questions are posed to the other half of the respondents.] Q1. Would you be willing to pay (Upper) KRW per month for using P2P ETP? (1) Yes → Survey ends (2) No → Go to Q2 7

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Energy Policy 136 (2020) 111050

Q2. If so, would you be willing to pay (Lower) KRW per month for using P2P ETP?

Kim, C., Shin, J., 2016. Electricity pricing policy alternatives to control rapid electrification in Korea. J. Electr. Eng. Technol. 11 (2), 285–299. Kim, D., Kim, N., Kim, W., 2018. The effect of patent protection on firms’ market value: the case of the renewable energy sector. Renew. Sustain. Energy Rev. 82, 4309–4319. Kim, H., Shin, E.S., Chung, W.J., 2011. Energy demand and supply, energy policies, and energy security in the Republic of Korea. Energy Policy 39 (11), 6882–6897. Kim, K., Nam, H., Cho, Y., 2015. Estimation of the inconvenience cost of a rolling blackout in the residential sector: the case of South Korea. Energy Policy 76, 76–86. Kim, K., Cho, Y., 2017. Estimation of power outage costs in the industrial sector of South Korea. Energy Policy 101, 236–245. KOSIS (Korean Statistical Information Service), 2019. Number of Resident Registration Households by Administrative District [accessed 16.05.19]; Available from: htt p://kosis.kr/statHtml/statHtml.do?orgId¼101&tblId¼DT_1B040B3. Krinsky, I., Robb, A.L., 1986. On approximating the statistical properties of elasticities. Rev. Econ. Stat. 715–719. Kristr€ om, B., 1997. Spike models in contingent valuation. Am. J. Agric. Econ. 79 (3), 1013–1023. Lee, M., Hong, T., Jeong, K., Kim, J., 2018. A bottom-up approach for estimating the economic potential of the rooftop solar photovoltaic system considering the spatial and temporal diversity. Appl. Energy 232, 640–656. Marques, B., Nixon, K., 2013, September. The gamified grid: possibilities for utilising game-based motivational psychology to empower the Smart Social Grid. In: 2013 Africon. IEEE, pp. 1–5. Mengelkamp, E., G€ arttner, J., Rock, K., Kessler, S., Orsini, L., Weinhardt, C., 2018. Designing microgrid energy markets: a case study: the Brooklyn Microgrid. Appl. Energy 210, 870–880. Meyerhoff, J., Liebe, U., 2010. Determinants of protest responses in environmental valuation: a meta-study. Ecol. Econ. 70 (2), 366–374. Nevitt, B., McLuhan, M., 1972. Take Today: the Executive as Dropout. Harcourt Brace Jovanovich, New York. Nomura, N., Akai, M., 2004. Willingness to pay for green electricity in Japan as estimated through contingent valuation method. Appl. Energy 78 (4), 453–463. Oerlemans, L.A., Chan, K.Y., Volschenk, J., 2016. Willingness to pay for green electricity: a review of the contingent valuation literature and its sources of error. Renew. Sustain. Energy Rev. 66, 875–885. Parag, Y., Sovacool, B.K., 2016. Electricity market design for the prosumer era. Nat. energy 1 (4), 16032. Park, H., Hong, W.K., 2014. Korea‫ ׳‬s emission trading scheme and policy design issues to achieve market-efficiency and abatement targets. Energy Policy 75, 73–83. Park, R., Koo, M.G., 2018. South Korea’s renewable energy policy: coming together or drifting apart? J. Int. Area Stud. 25 (2), 61–85. Piclo, 2019. Building Software for a Smarter Energy Future [accessed 16.05.19]; Available from: https://piclo.energy/. Ramajo-Hern� andez, J., del Saz-Salazar, S., 2012. Estimating the non-market benefits of water quality improvement for a case study in Spain: a contingent valuation approach. Environ. Sci. Policy 22, 47–59. Ready, R.C., Hu, D., 1995. Statistical approaches to the fat tail problem for dichotomous choice contingent valuation. Land Econ. 491–499. Roche, M.Y., Mourato, S., Fischedick, M., Pietzner, K., Viebahn, P., 2010. Public attitudes towards and demand for hydrogen and fuel cell vehicles: a review of the evidence and methodological implications. Energy Policy 38 (10), 5301–5310. Shandurkova, I., Bremdal, B.A., Bacher, R., Ottesen, S., Nilsen, A., 2012. A Prosumer Oriented Energy Market. Developments and Future Outlooks for Smart Grid Oriented Energy Markets. A State-Of-The-Art Perspective, vol. 3. IMPROSUME Publication Series, Halden. Song, J., Seo, K., Kang, Y., 2018. Introduction of capacity charge system to expand PV market. Bull. Korea Photovolt. Soc. 4 (1), 35–41 (in Korean). Sonnenbatterie, 2019. What Is the SonnenCommunity [accessed 16.05.19]; Available from: https://sonnengroup.com/sonnencommunity/. Sousa, T., Soares, T., Pinson, P., Moret, F., Baroche, T., Sorin, E., 2019. Peer-to-peer and community-based markets: a comprehensive review. Renew. Sustain. Energy Rev. 104, 367–378. Stigka, E.K., Paravantis, J.A., Mihalakakou, G.K., 2014. Social acceptance of renewable energy sources: a review of contingent valuation applications. Renew. Sustain. Energy Rev. 32, 100–106. Strazzera, E., Scarpa, R., Calia, P., Garrod, G.D., Willis, K.G., 2003. Modelling zero values and protest responses in contingent valuation surveys. Appl. Econ. 35 (2), 133–138. Vandebron, 2019. Duurzame Energie Van Nederlandse Bodem [accessed 16.05.19]; Available from: https://vandebron.nl. Welsh, M.P., Poe, G.L., 1998. Elicitation effects in contingent valuation: comparisons to a multiple bounded discrete choice approach. J. Environ. Econ. Manag. 36 (2), 170–185. Werth, A., Kitamura, N., Tanaka, K., 2015. Conceptual study for open energy systems: distributed energy network using interconnected DC nanogrids. IEEE Trans. Smart Grid 6 (4), 1621–1630. Woo, C.K., Ho, T., Shiu, A., Cheng, Y.S., Horowitz, I., Wang, J., 2014. Residential outage cost estimation: Hong Kong. Energy Policy 72, 204–210. Xie, B.C., Zhao, W., 2018. Willingness to pay for green electricity in Tianjin, China: based on the contingent valuation method. Energy Policy 114, 98–107. Yoo, S.H., Kwak, S.Y., 2009. Willingness to pay for green electricity in Korea: a contingent valuation study. Energy Policy 37 (12), 5408–5416. Yoo, Y.S., Hwang, T., Kang, S., Newaz, S.S., Lee, I.W., Choi, J.K., 2017. October). Peer-topeer based energy trading system for heterogeneous small-scale DERs. In: 2017 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, pp. 813–816.

(1) Yes → Survey ends (2) No → Go to Q3 Q3. If so, would you willing to pay just 1 KRW or more per month for using P2P ETP? (1) Yes → Survey ends (2) No → Go to 4 Q4. What is your main reason for not being willing to pay? Please mark the most appropriate reason below. (1) (2) (3) (4) (5)

I cannot afford to pay for using P2P ETP. It should be provided free of charge. There is not enough information. I do not care about P2P ETP. I am against P2P ETP.

References Alam, M.R., St-Hilaire, M., Kunz, T., 2019. Peer-to-peer energy trading among smart homes. Appl. Energy 238, 1434–1443. Bertsch, V., Hall, M., Weinhardt, C., Fichtner, W., 2016. Public acceptance and preferences related to renewable energy and grid expansion policy: empirical insights for Germany. Energy 114, 465–477. Bishop, R.C., Heberlein, T.A., 1979. Measuring values of extramarket goods: are indirect measures biased? Am. J. Agric. Econ. 61 (5), 926–930. Boyle, K.J., Bishop, R.C., 1988. Welfare measurements using contingent valuation: a comparison of techniques. Am. J. Agric. Econ. 70 (1), 20–28. Boyle, K.J., Johnson, F.R., McCollum, D.W., Desvousges, W.H., Dunford, R.W., Hudson, S.P., 1996. Valuing public goods: discrete versus continuous contingentvaluation responses. Land Econ. 381–396. Cameron, T.A., Quiggin, J., 1994. Estimation using contingent valuation data from a" dichotomous choice with follow-up" questionnaire. J. Environ. Econ. Manag. 27 (3), 218–234. Carson, R.T., Mitchell, R.C., Hanemann, M., Kopp, R.J., Presser, S., Ruud, P.A., 2003. Contingent valuation and lost passive use: damages from the Exxon Valdez oil spill. Environ. Resour. Econ. 25 (3), 257–286. Chien, Y.L., Huang, C.J., Shaw, D., 2005. A general model of starting point bias in double-bounded dichotomous contingent valuation surveys. J. Environ. Econ. Manag. 50 (2), 362–377. Cooper, J.C., Hanemann, M., Signorello, G., 2002. One-and-one-half-bound dichotomous choice contingent valuation. Rev. Econ. Stat. 84 (4), 742–750. Di Silvestre, M.L., Gallo, P., Ippolito, M.G., Sanseverino, E.R., Zizzo, G., 2018. A technical approach to the energy blockchain in microgrids. IEEE Transactions on Industrial Informatics 14 (11), 4792–4803. EPSIS (Electric Power Statistics Information System) [accessed 16.05.19]; Available from: http://epsis.kpx.or.kr/epsisnew/selectEksaScfGrid.ajax?menuId¼060700. Fatai, K., Oxley, L., Scrimgeour, F.G., 2003. Modeling and forecasting the demand for electricity in New Zealand: a comparison of alternative approaches. Energy J. 75–102. Franke, M., Rolli, D., Kamper, A., Dietrich, A., Geyer-Schulz, A., Lockemann, P., Schmeck, H., Weinhardt, C., 2005, June. Impacts of distributed generation from virtual power plants. In: International Sustainable Development Research Conference, vol. 11. Fumagalli, E., Black, J.W., Ilic, M., Vogelsang, I., 2001, July. A reliability insurance scheme for the electricity distribution grid. In 2001 Power Engineering Society Summer Meeting. In: Conference Proceedings (Cat. No. 01CH37262), vol. 1. IEEE, pp. 261–266. Hanemann, W.M., 1984. Welfare evaluations in contingent valuation experiments with discrete responses. Am. J. Agric. Econ. 66 (3), 332–341. Hanemann, W.M., 1985. Some issues in continuous-and discrete-response contingent valuation studies. Northeast. J. Agric. Resour. Econ. 14(1204-2016-69798), 5. Huh, W.K., Kim, H., 2003. Information flows on the internet of Korea. J. Urban Technol. 10 (1), 61–87. IMF (International Monetary Fund), 2019 [accessed 16.05.19]; Available from: https ://www.imf.org/external/np/-fin/data/param_rms_mth.aspx. Kang, E.S., Pee, S.J., Song, J.G., Jang, J.W., 2018. April). A blockchain-based energy trading platform for smart homes in a microgrid. In: 2018 3rd International Conference on Computer and Communication Systems (ICCCS). IEEE, pp. 472–476. KEEI (Korea Energy Economics Institute), 2019. Energy Supply and Demand Brief 2019.02 (in Korean). KEMRI (KEPCO Management Research Institute), 2016. Overview of E-Prosumer and domestic/foreign trends. KEMRI Power Economics Review 2016–2017 (in Korean).

8

J. Lee and Y. Cho

Energy Policy 136 (2020) 111050

Young, J., Brans, M., 2017. Analysis of factors affecting a shift in a local energy system towards 100% renewable energy community. J. Clean. Prod. 169, 117–124. Zachariadis, T., Pashourtidou, N., 2007. An empirical analysis of electricity consumption in Cyprus. Energy Econ. 29 (2), 183–198.

Zhang, C., Wu, J., Zhou, Y., Cheng, M., Long, C., 2018. Peer-to-Peer energy trading in a Microgrid. Appl. Energy 220, 1–12. Zhang, K., Mao, Y., Leng, S., He, Y., Maharjan, S., Gjessing, S., Zhang, Y., Tsang, D.H., 2018. Optimal charging schemes for electric vehicles in smart grid: a contract theoretic approach. IEEE Trans. Intell. Transp. Syst. (99), 1–13.

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