Determining the Peer-to-Peer electricity trading price and strategy for energy prosumers and consumers within a microgrid

Applied Energy 261 (2020) 114335

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Determining the Peer-to-Peer electricity trading price and strategy for energy prosumers and consumers within a microgrid Jongbaek An, Minhyun Lee, Seungkeun Yeom, Taehoon Hong

T

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Department of Architecture and Architectural Engineering, Yonsei University, Seoul 03722, Republic of Korea

H I GH L IG H T S

electricity trading price and strategy is proposed for prosumers and consumers. • P2P minimum and maximum electricity trading prices are calculated in South Korea. • The P2P trading prices were calculated based on the market participation conditions. • The maximum of US$0.27/kWh profit can be obtained through P2P electricity trading. • AProsumers and consumers can find optimal P2P trading partners for maximum profits. •

A R T I C LE I N FO

A B S T R A C T

Keywords: Energy prosumer Peer to Peer electricity trading Distributed solar generation Solar photovoltaic system Electricity trading price Levelized cost of electricity

A successful Peer-to-Peer (P2P) electricity trading within a microgrid requires a P2P electricity trading price and strategy that enable both energy prosumers and consumers to obtain profits. Therefore, this study aims to propose a P2P electricity trading strategy based on the minimum and maximum electricity trading prices for energy prosumers and consumers that ensure their profitability, by considering the actual electricity market structure in South Korea. Towards this end, the minimum and maximum electricity trading prices for energy prosumers and consumers were calculated based on the market participation conditions and electricity trading scenarios established in this study. By matching energy prosumers and consumers based on the calculated minimum and maximum electricity trading prices, a P2P electricity trading strategy was ultimately proposed. As a result, the minimum (i.e., US$0.05–0.34/kWh) and maximum (i.e., US$0.09–0.32/kWh) electricity trading prices increased as the monthly electricity consumption of energy prosumers and consumers increased and the self-consumption and electricity purchase rates decreased. Consequently, the profitable electricity trading scenarios increased as the monthly electricity consumption was lower and the self-consumption rate was higher for energy prosumers, and as the monthly electricity consumption was higher and the electricity purchase rate was lower for energy consumers. In particular, the P2P electricity trading can provide maximum profits to energy prosumers and consumers when the monthly electricity consumption of energy prosumers is 200 kWh, the monthly electricity consumption of energy consumers is 500 kWh, and the electricity purchase rate is 20%. Based on the findings of this study, it is possible not only to determine profitable P2P electricity trading prices to market participants but also to establish an optimal P2P electricity trading strategy by matching energy prosumers and consumers that can ensure them with maximum profits.

1. Introduction With the increasing worldwide interest in the environment, sustainable development is emerging as an important goal in the energy sector, including the electric power industry. To efficiently achieve sustainable development, the energy sector currently suggested various action plans, such as the transmission integration, promotion of

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distributed generation, reorganization of the energy market, and expansion of the renewable energy supply [1]. In particular, as renewable energy accounts for 26.5% of the global electricity demand as of 2017, it is expected to play an important role in facilitating distributed generation [2]. Meanwhile, energy security and electric power supply has become an important issue with the increasing demand for electricity due to

Corresponding author at: Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea. E-mail address: [email protected] (T. Hong).

https://doi.org/10.1016/j.apenergy.2019.114335 Received 21 August 2019; Received in revised form 27 November 2019; Accepted 8 December 2019 0306-2619/ © 2019 Elsevier Ltd. All rights reserved.

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Nomenclature

KEPCO KPX LCC LCOE O&M P2P PV SMP

Abbreviations ECOS EEA EPSIS KEEI IEA

Economic Statistics System European Environment Agency Electric Power Statistics Information System Korea Energy Economic Institute International Energy Agency

Korea Electric Power Corporation Korea Power Exchange Life Cycle Cost Levelized Cost of Electricity Operation and Maintenance Peer to Peer Photovoltaic System Marginal Price

regarding P2P electricity trading suggested the future direction of the P2P electricity trading market as an initial step in the exploration of P2P electricity trading. Diestelmeier et al. [9] introduced a blockchain technology in the electricity sector and presented a political analysis on the definition of the energy prosumer’s responsibility and the assurance of financial incentives as the energy consumer is transformed into the energy prosumer. Park and Yong [10] argued that to facilitate P2P electricity trading, its impact on the grid, fairly sharing the increased distribution network costs, and the adaptation of the existing centralized power supply system to the new electricity trading environment should be well addressed and considered. Zhang et al. [11] suggested that as P2P electricity trading depends on the availability of local energy sources, a hierarchical system, a local energy market, and a sophisticated technology or system, are necessary for the balanced distributed networks. Second, as the concept of P2P electricity trading has become familiar to the public, there has been many studies that analyzed the P2P electricity trading method using various concepts and models. The P2P electricity trading models can be classified into the auction-based model [12–16], the bilateral contract-based model [17–19], and the decentralized model through blockchain [20,21] according to their trading mechanism. In the auction-based model, the energy consumer makes a bid for the surplus electricity through the platform, and the energy prosumer sells the surplus electricity to the energy consumer with the highest bid. The main purpose of this model is to maximize the profit of the energy prosumer. The bilateral contractbased model exhibits electricity trading procedures similar to those of the auction-based model, but it can be said to be a more open P2P electricity trading model because the bid price of the energy consumer is open to all bidders. The bilateral contract-based model is also a model that preferentially pursues the profit of the energy prosumer. The decentralized model through blockchain is a system that uses the growing blockchain technology for a safe and decentralized P2P electricity trading, with various advantages, such as decentralization, security, and anonymity. To analyze these P2P electricity trading models, previous studies have been actively conducted especially using a game theory [13,22–25]. Leong et al. [13] proposed a bidding solution using a Bayesian game theory for fair and efficient P2P electricity trading. Zhang et al. [25] examined methods of reducing the electricity trade between the microgrid and the utility grid using the game theory based on the auction-based model for efficient P2P electricity trading. Third, only few studies have investigated or actually determined the P2P electricity trading prices for energy prosumers and consumers. Liu et al. [14] formulated a model for estimating the electricity selling price for energy prosumers, and proposed a P2P electricity trading algorithm based on the electricity demand and response which can maximize the profit of the energy prosumer. Most of these previous studies regarding the P2P electricity trading method and price, however, used a real-time P2P electricity trading method in minutes or hours, and have focused on proposing a trading method rather than determining the trading price. Likewise, while various research and business endeavors have been made in relation to P2P electricity trading, there are challenges in revitalizing the actual market. Under different electricity market conditions, not only the structure of the electricity market but also the

rapid population growth and high electricity consumption. To resolve this issue, increasing not only renewable energy supply but also centralized power plants have been proposed as the solutions. The construction of the large-scale power plants required for centralized generation, however, often faces various difficulties due to the following environmental and economic reasons. The opposition from the local residents and environmental organizations has led to frequent cancellations of and changes in the governmental plans to construct new power plants. Moreover, the construction of new plants requires the additional installation of transmission towers and lines, and considerable costs are incurred for the installation and maintenance of such facilities [1]. Under these circumstances, distributed generation is emerging as a viable alternative to solve the problems of the conventional centralized power generation system, and to secure electric power supply with clean energy. Distributed generation refers to any method that generates electricity at or near where it will be used, and it can reduce the transmission losses and ensure a stable supply of electricity because the transmission distance is shorter than that in the centralized power generation system [3]. In addition, clean energy such as solar, wind, and hydroelectric power is generally used as the energy sources of distributed generation, thereby helping promote the sustainable development of the energy sector. To facilitate such distributed generation, the concept of an energy prosumer has emerged, which refers to one who both produces and consumes energy. The emergence of this energy prosumer concept and a paradigm shift in the energy sector has led to an increase in the demand for the flexibility of electricity consumption by the consumers. This is because if the electricity produced by the energy prosumer is greater than that consumed by it, a large amount of surplus electricity can be generated due to the inconsistency of the electricity generation and consumption at a certain point of time, unless an energy storage system is installed. In this case, the energy prosumer can sell the surplus electricity through net metering with Korea Electric Power Corporation (KEPCO), or can reduce the capacity of the installed distributed generation system. Reducing the system capacity, however, can lower the economic efficiency from the perspective of the energy prosumer, and if the surplus electricity, which is transmitted back to the grid through net metering, cannot be consumed by the nearby consumer, large amounts of transmission loss occur, and this results in inefficient use of electricity. To address these problems, Peer-to-Peer (P2P) electricity trading has emerged as an alternative to selling surplus electricity [4]. P2P electricity trading not only allows energy prosumers benefit from selling the surplus electricity to the energy consumers in the same power grid, but can also maximize the advantages of the distributed generation, such as minimizing the transmission losses and stabilizing the electricity supply [5]. Leading countries such as the United Kingdom [6], Netherlands [7], and Germany [8] have developed P2P electricity trading platforms that allow direct trading of electricity between the energy prosumer and consumer, and have put these into effect. As the electricity market conditions are different for each country, however, the trading methods also vary. To facilitate P2P electricity trading, previous studies have been conducted from various perspectives [9–26]. First, the previous studies 2

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(refer to Table 1) [5]. As shown in Table 1 and Eqs. (1) and (2), the residential progressive electricity tariffs in South Korea are charged to the customers based on their monthly electricity consumption; the demand charge is imposed at a fixed price according to the monthly electricity consumption; and the energy charge is calculated by applying different electricity rates for each tariff zone according to the monthly electricity consumption. For example, a household using 450 kWh electricity per month is subject to a demand charge of US$6.13; and the energy charge of US$0.08/kWh is applied with respect to the first 200 kWh of the monthly electricity consumption, US$0.16/kWh to the next 200 kWh, and US$0.24/kWh to the remaining 50 kWh. In addition to this, a 10% value-added tax (VAT) and a 3.7% electricity industry foundation fund is added to the bill. Finally, the amount of US$75.19 (i.e., demand charge [US$6.13] + energy charge [US$60] + VAT [US$6.61] + electricity industry foundation fund [US$2.45] = US$75.19) is charged to the customer (refer to Eqs. (1) and (2)).

methods of imposing the electricity bill vary by country, and even the P2P electricity trading platform operating in the same country has a variety of objectives, network sizes, and P2P layers [11]. In this context, it is difficult to apply the generalization model or previous studies based on the real-time P2P electricity trading method in a certain time unit (usually, minutes and hours) to localized electricity markets with different characteristics. Therefore, it is important to consider the application conditions and targets according to the characteristic of the electricity market in a region where the P2P electricity trading will take place. In particular, as the electricity bill is calculated and charged on a monthly basis in South Korea, it is difficult to apply the existing realtime P2P electricity trading method to the South Korean electricity market. Therefore, there is a need to devise a new method for analyzing the economic feasibility of P2P electricity trading on a monthly basis in countries like South Korea. Unlike in developed countries like the United States and those comprising the European Union where research and business related to P2P electricity trading is actively underway, however, the concept of the energy prosumer has not yet been fully established in the East Asian countries, including South Korea, and there is still a lack of studies on P2P electricity trading related policies and platforms [26,27]. In 2016, South Korea put in place a prosumer electricity trading system to allow energy prosumers with a renewable energy system (e.g., solar photovoltaic (PV) system) to sell surplus electricity directly to energy consumers. The system, however, is still in the demonstration stage and has yet to be properly operated with a fixed electricity trading price plan [26]. To overcome the aforementioned challenges, this study aims to determine the minimum and maximum electricity trading prices that guarantee profitability of the energy prosumer and consumer, and thus to establish a P2P electricity trading strategy as a preliminary research to successfully introduce P2P electricity trading in South Korea. Towards this end, the conditions for energy prosumers and consumers to participate in the P2P electricity trading market were defined considering the actual electricity market structure and billing system in South Korea, and the minimum and maximum electricity trading prices were calculated based on the various electricity trading scenarios established in this study. In addition, it proposed a P2P electricity trading strategy by matching energy prosumers and consumers based on their minimum and maximum electricity trading prices. To proceed with this study, analysis was conducted on the residential building with a solar PV system located in Seoul for the following reasons: (i) target region: Seoul with the highest population in South Korea (i.e., 20% of the total population) [28]; (ii) target facility: residential building with the highest potential benefit from P2P electricity trading due to the progressive electricity tariffs; and (iii) target energy system: a solar PV system which accounts for 37.2% (5835 MW) of the cumulative capacity of the renewable energy system in South Korea, as of 2017 [29].

PElec = CDemand + CEnergy

(1)

BElec = PElec + VAT + FElec = PElec + (0.1 × PElec ) + (0.037 × PElec )

(2)

where PElec stands for the electricity price (US$), CDemand stands for the demand charge (US$), CEnergy stands for the energy charge (US$), BElec stands for the electricity bill (US$), FElec stands for the electricity industry foundation fund (US$). Based on the aforementioned electricity billing system in South Korea, profits and electricity bill savings from installing the solar PV system and generating electricity from it can be calculated. In South Korea, there are three types of electricity trading method (i.e., revenue model) with different characteristics for energy prosumers installing solar PV system for self-consumption purposes (refer to Table 2). First, energy prosumers who install solar PV system can benefit from netmetering operated by KEPCO since 2012. Energy prosumers will first self-consume the electricity generated from their solar PV system and transfer surplus electricity to the macro-grid through net-metering. Then, the electricity bill can be reduced by subtracting the amount of electricity sent back to the macro-grid from the monthly electricity consumption of the energy prosumer. Second, energy prosumers who install solar PV system can also benefit from power purchase agreement (PPA) by selling surplus electricity at the system marginal price (SMP) through a contract with KEPCO (additional profit can be made by selling renewable energy certificate (REC)). However, PPA requires at least 10 kW of solar PV system to be installed, which can only sell up to 50% of electricity generation. Moreover, selling surplus electricity at the SMP, which is the wholesale price, does not guarantee a higher profit than net metering for energy prosumers, which makes net metering the most common and preferred electricity trading method for self-consumption purposes [3]. Finally, prosumer electricity trading system is an emerging trading method that aims to generate revenue through the saving of electricity bills and sales of surplus electricity. Unlike net-metering and PPA, surplus electricity can be sold directly to nearby energy consumers or even prosumers as a form of P2P trading in

2. Materials and methods 2.1. Step 1: Factors affecting the electricity trading price 2.1.1. Electricity billing system To calculate the minimum and maximum electricity trading prices that guarantee profitability of the energy prosumers and consumers, there should be an understanding of the electricity billing system in South Korea. Due to the global oil crisis that took place in 1973, the South Korean government began to apply progressive tariffs to residential electricity rates with the aim of saving energy and protecting the lower-income class. After the reform of the electricity rates in December 2016, the progressive electricity tariffs were basically divided into three tariff zones (except for the super users) according to electricity consumption, as shown in Table 1. The progressive rates are tripled for the lowest and highest tariff zones. In addition, relaxed progressive tariffs is temporarily applied in July and August, as a large amount of electricity is consumed for cooling during the summer season

Table 1 Residential progressive electricity tariffs in South Korea. Classification

Tariff zone 1 Tariff zone 2 Tariff zone 3 Super user

Progressive tariff zone General

Summera

Under 200kWh 201–400kWh 401–1000kWh Over 1000kWh

Under 300kWh 301–500kWh 501–1000kWh

Demand charge (US $/household)

Energy charge (US $/kWh)

0.76 1.34 6.13 6.13

0.08 0.16 0.24 0.60

Note: VAT (10% of the electricity price) and the electricity industry foundation fund (3.7% of the electricity price) is charged separately; The exchange rate (KRW/US$) is 1191.50 won to a U.S. dollar (as of 23 May 2019). a Relaxed progressive tariff zone for July and August. 3

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Table 2 Different characteristics of three electricity trading method for energy prosumers. Classification Electricity trading method (Revenue model)

System size Profit structure

Description Net metering

PPA

Prosumer electricity trading system

Less than 1000 kW Electricity bill saving

From 10 kW to 1000 kW Electricity bill saving Selling surplus electricity Selling REC

Less than 1000 kW Electricity bill saving Selling surplus electricity

prosumer electricity trading system, making it highly likely to be implemented in the future. 2.1.2. Levelized cost of electricity (LCOE) The levelized cost of electricity (LCOE) for the solar PV system should be considered to calculate the minimum electricity trading price that guarantees profitability of the energy prosumer. LCOE is a concept which can make it easier and more convenient to compare the economic efficiency levels of different generating technologies. In general, it means the average electricity generation cost over the useful life of the energy system; so, LCOE can be used to set a minimum trading price for the generated electricity to make the energy system economically feasible. The LCOE of the solar PV system can be calculated using the following Eq. (3) [30,31]. T

LCOEPV =

•

•

ICt + OMCt + RCt (1 + r )t

∑t = 1 LCCPV = T LCEGPV ∑t = 1

AEG × (1 − d)t (1 + r )t

(3)

•

where LCOEPV stands for the levelized cost of electricity of the solar PV system (US$/kWh), LCCPV stands for the life cycle cost of the solar PV system (US$), LCEGPV stands for the life cycle electricity generation of the solar PV system (kWh), ICt stands for the installation cost in year t (US$), OMCt stands for the operation and maintenance cost in year t (US $), RCt stands for the replacement cost in year t (US$), AEG stands for the annual electricity generation (kWh), T stands for the useful life of the solar PV system (year), r stands for the real discount rate, d stands for the annual degradation rate. First, to estimate the life cycle electricity generation of the solar PV system, the clean-energy management software RETScreen was used to calculate the annual electricity generation of the solar PV system. The RETScreen software is a clean-energy simulation software developed by the Canadian government that is recognized as a highly useful tool based on the evaluations of International Energy Agency (IEA) and European Environment Agency (EEA) [32]. To calculate the annual electricity generation of the solar PV system using RETScreen, the installation conditions of the solar PV system were set as follows (refer to Table 3):

Second, the life cycle cost (LCC) of the solar PV system was calculated to estimate the LCOE of the solar PV system. To calculate the LCC of the solar PV system, the following assumptions on LCC analysis were determined (refer to Table 4):

• Analysis point: The analysis point was set at 2019 to reflect the most recent solar PV market conditions. Analysis period: The analysis period was set to 25 years based on the • useful life of the solar PV system [37–39]. • Real discount rate: The real discount rate was calculated based on the •

• Region: As the weather conditions (e.g., solar radiation) vary de-

• •

[3]. As this study conducts an analysis on the trading of surplus electricity between the energy prosumers and consumers, it was assumed that the solar PV system was installed for the self-consumption purpose. Tracking option, orientation, and tilt angle: In this study, an energy simulation was performed for the solar PV system at a fixed tilt as the tracking option. Accordingly, it was assumed that the solar PV panel was facing south at a tilt angle of 33°, which is the optimal orientation and tilt angle that can maximize the electricity generation of the solar PV system [33,34]. Installed capacity: In South Korea, the installed capacity of the residential solar PV system is typically 3 kW, but it can be set to 6 kW if the monthly electricity consumption is more than 600 kWh [35]. To receive a subsidy payment from the government, however, the installed capacity per household should be 3 kW or less [36]. Therefore, considering the above two points, the installed capacity of the solar PV system was set to 3 kW, and as such, the inverter capacity was also assumed to be 3 kW. Degradation rate: According to the previous studies, the efficiency of the solar PV system decreases by about 20% during its useful life of 25 years [37–39]. Therefore, considering this, the annual degradation rate was assumed to be 0.8%.

pending on the region where the solar PV system is installed, the electricity generation of the solar PV system is also different for each region. In this study, an analysis was conducted on Seoul, which has the largest population in South Korea (i.e., 20% of the total population) [27,28]. Therefore, energy simulation was performed using the meteorological data of Seoul in South Korea, which are provided by RETScreen. Facility type: In this study, an analysis was conducted on residential buildings with a large potential benefit from P2P electricity trading due to the progressive electricity tariffs [27]. Installation purpose: The profit structure of the energy prosumer changes according to the two different purposes of installing the solar PV system; the energy prosumer who install the system for the self-consumption purpose directly consumes the electricity generated from the system in that building and any surplus electricity is sold back to the grid; however, the system installed for the electricity business purpose generates electricity for selling purpose only

collected data: (i) the nominal interest and inflation rates were collected from Economic Statistics System (ECOS) of Bank of Korea; and (ii) the SMP was collected from Electric Power Statistics Information System (EPSIS) [40]. Installation cost: The installation cost was set at US$893/kW based on the data from HAEZOOM, a popular solar PV-related service provider in South Korea [35]. In the case of the residential solar PV system with a capacity up to 3 kW installed for the self-consumption purpose, a subsidy payment of US$470/kW is available from the

Table 3 Assumptions for the installation conditions of the solar PV system. Classification Region Facility type Installation purpose System

Inverter Degradation rate

4

Description Country City

Tracking option Orientation Tilt angle Capacity Capacity

South Korea Seoul Residential Self-consumption Fixed South (0°) 33° 3 kW 3 kW 0.8% per year for 25 years

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Besides the local electricity market structure of South Korea, the local electricity market trends are also considered in determining the P2P electricity trading price. Although energy storage system (ESS) and electric vehicles can be one of the factors that may affect the dynamics of energy prosumers and consumers, ESS and electric vehicles are not considered in this study. In the case of ESS, the chemical ESS installation capacity is the world’s second largest, but it is mainly installed in large-scale power plants and public enterprises with system capacity over 200 kWh. The share of residential and small-scale commercial ESS (less than 200 kWh) accounts for only 14% of the total installation [43]. In addition, the number of registered cars in Seoul is about 3,120,000, but the number of electric vehicles is 11,580, which accounts for only 3% of the total number of cars [44]. Therefore, in order to reflect the characteristics of the local electricity market trends in South Korea, this study does not consider any variations associated with installation of ESS and possession of electric vehicles for calculating the P2P electricity trading price between energy prosumers and consumers.

Table 4 Assumptions for LCC analysis. Classification

Description

Analysis point Analysis period Real discount rate

2019 25 years 0.91% 3.17% US$893/kW US$470/kW 1% of the installation cost 1 year 9.5% of the installation cost 13 year

Installation cost O&M cost Replacement cost

Interest Electricity Unit cost Subsidy O&M rate O&M cycle Replacement rate Replacement cycle

Note: The exchange rate (KRW/US$) is 1191.50 won to a U.S. dollar (as of 23 May 2019).

South Korean government [36].

• Operation and maintenance cost (O&M cost) and replacement cost: The O&M and replacement costs were set at 1% (every year) and 9.5% (every 13 years) of the installation cost, respectively, based on the previous studies [38,39,41]. The O&M cost refers to the annual O& M cost of the solar PV system, and the replacement cost is the replacement cost of the inverter.

2.2.1. Market participation conditions for energy prosumers To calculate the minimum electricity trading price that ensures profitability of the energy prosumer, the market participation conditions were established which can bring profits to the energy prosumer through P2P electricity trading. From the energy prosumer’s perspective, two requirements should be met to expect profits from the sale of surplus electricity through P2P electricity trading: (i) the profit from the sale of surplus electricity through P2P electricity trading should be higher than that from the sale of surplus electricity by means of other electricity trading methods (e.g., net metering); and (ii) the generation cost of the solar PV system installed in the building should be made up for. First, for the energy prosumer to expect profits from the sale of surplus electricity through P2P electricity trading rather than through other electricity trading methods, the profit from P2P electricity trading should be higher than that from net metering (i.e., electricity trading method for self-consumption purposes); thus, the following Eq. (4) should be satisfied. To calculate and compare the profits of energy prosumer with two different electricity trading methods (i.e., P2P electricity trading and net metering) in Eq. (4), the electricity bill for the entire electricity consumption of the energy prosumer (B (ECprosumer)), indicating the electricity bill that can be charged to a household if he or she does not install a solar PV system, was compared to that for each of the electricity trading method. Accordingly, in Eq. (4), the left side represents the profit from P2P electricity trading whereas the right side is the profit from net metering.

The above assumptions were applied to calculate the LCOE of the solar PV system. As a result, the LCOE was calculated at US$0.02/kWh (with subsidy) and US$0.05/kWh (without subsidy) depending on the subsidy payment. In a report titled “Study on the Estimation of Levelized Cost of Electricity by Generation Source,” published by Korea Energy Economic Institute (KEEI), the LCOE of the utility-scale solar PV system for the electricity business purpose was estimated at US$0.11/ kWh, as of 2017 [42]. In this study, however, the LCOE of the residential solar PV system was set at US$0.05/kWh considering the continuous drop in the solar PV panel prices, the difference in the assumptions for LCC analysis (e.g., additional expenses such as the site rental costs for the utility-scale solar PV system), and the uncertainty of subsidy payments. 2.2. Step 2: Market participation conditions Before defining the P2P electricity trading market participation conditions for energy prosumers and consumers, it is necessary to comprehensively understand the local electricity market structure and landscape in South Korea. Unlike other countries, the electricity market in South Korea is run exclusively by a public enterprise called KEPCO. KEPCO purchases electricity generated by six subsidiary power generation companies and various private power generation companies from the Korea Power Exchange (KPX) through the transmission and distribution network and sells it to retail customers. That is, not only all transmission and distribution lines and related facilities in South Korea are installed and operated by KEPCO, but also KEPCO is in charge of the retail sales [5]. Accordingly, the prosumer electricity trading system, which is currently in progress as a pilot project in South Korea, is also operated by KEPCO as the system uses the transmission and distribution network provided by KEPCO. In this prosumer electricity trading system, additional costs such as the transmission and distribution network charges are not considered separately as the retail customers are already connected to and paying for the use of grid provided by KEPCO. In addition, a 3.7% electricity industry foundation fund which is a type of tax payment in South Korea’s electricity billing system imposed on electricity purchased from KEPCO [5] is also not considered in the electricity trading between individuals. Therefore, in order to reflect the characteristics of the local electricity market structure in South Korea, other additional costs but VAT are not considered in this study for calculating the P2P electricity trading price between energy prosumers and consumers.

B (ECprosumer ) − B (ECgrid ) + Pmin × (EG − ECself ) ⩾ B (ECprosumer ) − B (ECgrid − (EG − ECself ))

(4)

where B(x) stands for the electricity bill for electricity consumption × (US$), ECprosumer stands for the monthly total electricity consumption of the energy prosumer (kWh) (ECprosumer = ECgrid + ECself), ECgrid stands for the electricity consumption from the grid (kWh), ECself stands for the self-consumed electricity (kWh), EG stands for the electricity generation of the solar PV system (kWh), and Pmin stands for the minimum electricity trading price for the energy prosumer (US$/kWh). Based on Eq. (4), the minimum price of surplus electricity to be sold through P2P electricity trading can be summarized as in Eq. (5). As shown in Eq. (5), the minimum unit price for P2P electricity trading from the energy prosumer’s perspective can be calculated based on the electricity consumption from the grid (ECgrid) and the surplus electricity to be sold to the energy consumer (EG-ECself).

Pmin ⩾

B (ECgrid ) − B (ECgrid − (EG − ECself )) (EG − ECself )

(5)

Second, in addition to the above conditions, economic feasibility of the energy prosumer should be guaranteed by making up for the 5

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Criterion 2 to establish the electricity trading scenario for both the energy prosumer and consumer. This is because although the energy prosumer or consumer trade the same amount of electricity, the electricity bills and profits based on the progressive electricity tariffs are calculated differently for both of the energy prosumer and consumer, respectively, according to their monthly electricity consumption. Therefore, different minimum and maximum electricity trading prices should be determined depending on the monthly electricity consumption of the energy prosumer and consumer, respectively. Third, the self-consumption rate was defined as Criterion 3 to establish the electricity trading scenario from the energy prosumer’s perspective, and the electricity purchase rate from the energy consumer’s perspective. The self-consumption rate is the portion of the energy prosumer’s monthly total electricity consumption that is directly covered by the electricity generated from the solar PV system. As this affects the amount of surplus electricity that can be sold, different minimum electricity trading prices should be determined according to the self-consumption rate. The electricity purchase rate is the portion of the energy prosumer’s monthly total electricity consumption that is covered by the electricity purchased from the energy prosumer through P2P electricity trading. As this determines the amount of electricity to be purchased, different maximum electricity trading prices should be determined according to the electricity purchase rate. As a result, by considering Criterion 1 (12 months and 2 seasons, respectively), Criterion 2 (five variations, respectively), and Criterion 3 (nine variations, respectively), this study can create 540 electricity trading scenarios for energy prosumers (12 months × 5 variations × 9 variations) and 90 electricity trading scenarios for energy consumers (2 seasons × 5 variations × 9 variations).

generation cost of the solar PV system installed in the building. Therefore, the minimum electricity trading price for the energy prosumer should be higher than the LCOE of the solar PV system calculated in step 1.2. By considering the aforementioned two conditions, the minimum electricity trading price that can make the energy prosumer decide to participate in the P2P electricity trading market is finally defined as in Eq. (6).

Pmin ⩾ max ⎜⎛LCOEPV , ⎝

B (ECgrid ) − B (ECgrid − (EG − ECself )) ⎞ (EG − ECself )

⎟

⎠

(6)

where Pmin includes a 10% of VAT (Pmin = P2P electricity trading price + 0.1*P2P electricity trading price). 2.2.2. Market participation conditions for energy consumers To calculate the maximum electricity trading price that guarantees profitability of the energy consumer, the market participation conditions were established which can bring profits to the energy consumer through P2P electricity trading. To expect profits from the purchase of electricity through P2P electricity trading from the energy consumer’s perspective, the electricity bill from purchasing a part of the electricity consumption from the energy prosumer through P2P electricity trading should be lower than that from purchasing electricity fully from KEPCO; thus, the following Eq. (7) should be satisfied. In Eq. (7), the left side represents the electricity bill when purchasing partial electricity through P2P electricity trading whereas the right side is the electricity bill when purchasing all the electricity from KEPCO.

B (ECconsumer − EP 2P ) + Pmax × EP 2P ⩽ B (ECconsumer )

(7)

where ECconsumer stands for the monthly total electricity consumption of the energy consumer (kWh), EP2P stands for the electricity purchased from the energy prosumer through the P2P electricity trading (kWh), and Pmax stands for the maximum electricity trading price for the energy consumer (US$/kWh). here, ECconsumer > EP2P. Based on Eq. (7), the maximum price of electricity to be purchased through P2P electricity trading can be summarized as in Eq. (8). As shown in Eq. (8), the maximum unit price for P2P electricity trading from the energy consumer’s perspective can be calculated based on the monthly total electricity consumption of the energy consumer (ECconsumer) and the electricity to be purchased from the energy prosumer through the P2P electricity trading (EP2P).

Pmax ⩽ B

(ECconsumer ) − B (ECconsumer − EP 2P ) EP 2P

2.3.2. Calculating the minimum and maximum electricity trading price Based on the market participation conditions established in Step 2 and the electricity trading scenarios created in Step 3.1, this study calculated the minimum and maximum electricity trading price for energy prosumers and consumers. First, the minimum electricity trading price was calculated for all the 540 electricity trading scenarios for energy prosumers with a combination of 12 months (Criterion 1), five monthly electricity consumption variations (Criterion 2), and nine self-consumption rate variations (Criterion 3). Second, the maximum electricity trading price was calculated for all the 90 electricity trading scenarios for energy consumers with a combination of two seasons (Criterion 1), five monthly electricity consumption variations (Criterion 2), and nine electricity purchase rate variations (Criterion 3).

(8)

where Pmax includes a 10% of VAT (Pmax = P2P electricity trading price + 0.1*P2P electricity trading price).

2.3.3. Matching energy prosumers and consumers for electricity trading Based on the minimum electricity trading prices for 540 scenarios and the maximum electricity trading prices for 90 scenarios calculated in step 3.2, this study matched the energy prosumers and consumers for profitable P2P electricity trading. If there is no consideration for profitable P2P electricity trading, a total of 24,300 potential electricity trading scenarios can be created through a combination of 540

2.3. Step 3: Scenario analysis 2.3.1. Creating electricity trading scenarios To establish electricity trading scenarios for calculating the minimum and maximum electricity trading prices that guarantee profitability of the energy prosumer and consumer, this study defined three criteria according to the following two perspectives: (i) the energy prosumer’s perspective; and (ii) the energy consumer’s perspective (refer to Table 5). First, month was defined as Criterion 1 to establish the electricity trading scenario from the energy prosumer’s perspective, and season from the energy consumer’s perspective. For the energy prosumer, as the electricity generation of the solar PV system varies by month, different minimum electricity trading prices should be determined depending on the month. On the other hand, for the energy consumer, different maximum electricity trading prices should be determined depending on the season, as the progressive electricity tariffs and the electricity bills are different for the general and summer seasons. Second, the monthly electricity consumption was defined as

Table 5 Criteria for establishing electricity trading scenarios. Classification

Energy consumer Season General and Summer (2 seasons)

Criterion 1

Variable Range

Month Jan.-Dec. (12 months)

Criterion 2

Variable Range

Monthly electricity consumption 200−600kWh (interval: 100kWh)

Criterion 3

Variable Range

Self-consumption rate 10–90% (interval: 10%)

Electricity purchase rate 10–90% (interval: 10%)

540

90

The number of scenarios

6

Energy prosumer

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First, the minimum electricity trading price (US$0.05–0.34/kWh) for energy prosumers generally increased with increasing monthly electricity consumption (refer to Table 7 and Figs. 1 and 2). This is because there has been a steep increase in electricity bill as the monthly electricity consumption of the energy prosumer increases due to the electricity bill system in South Korea, which is characterized by progressive electricity tariffs; thus, profits can be expected only when the surplus electricity is sold at a high electricity trading price. Second, the minimum electricity trading price for energy prosumers generally decreased as the self-consumption rate increased (refer to Table 7 and Figs. 1 and 2). The reason for this is the difference in billing system between net metering and P2P electricity trading. In net metering, the electricity bill is offset based on the electricity generation of the energy prosumer, and the profits are the same irrespective of the self-consumption rate. On the other hand, in P2P electricity trading, as the amount of self-consumed electricity increases, the progressive tariff zone applied to the electricity bill is lowered, so the profit becomes higher. Consequently, as the self-consumption rate increases, profits can be expected even with a low electricity trading price. Due to the drop in the tariff zone, there is a self-consumption rate (i.e., monthly electricity consumption of (i) 300 kWh: 33%; (ii) 400 kWh: 50% [25% in the summer season]; (iii) 500 kWh: 20 and 60% [40% in the summer season]; and (iv) 600 kWh: 33% [17% in the summer season]), at which the minimum electricity trading prices greatly decrease according to the monthly electricity consumption. This is because if a certain selfconsumption rate is reached due to the amount of electricity that the energy prosumer produces, the electricity bill decreases as the progressive tariff zone is lowered by a step. In this case, profits can be expected even with a low electricity trading price. Exceptionally, when the monthly electricity consumption is 400 kWh and 500 kWh, the minimum electricity trading prices have rather slightly increased for the summer season with an increase of self-consumption rate from 30% to 40% and for the general season with an increase of self-consumption rate from 20% to 50%, respectively. This is because surplus electricity decreases as the self-consumption rate of the energy prosumer increases, whereas the profits for the energy prosumer does not increase significantly due to no drop in the tariff zone. Third, as a comparison of the seasonal patterns, the minimum electricity trading price for energy prosumers gradually decreased as the self-consumption rate increased for both the general and summer seasons. In most cases, the minimum electricity trading prices were higher in the general season than in the summer season. This is because as the energy prosumer with a monthly electricity consumption of 200–300 kWh or 400–500 kWh benefits from the relaxed progressive tariffs in the summer season, profits can be expected with a lower electricity trading price. To specifically explain the changes in the minimum electricity trading price for energy prosumers by month or season, this study analyzed how the minimum electricity trading price changes by month and self-consumption rate in the case where the monthly electricity consumption is 400 kWh, which accounts for the highest percentage of the total residential electricity consumption and electricity bill (refer to Fig. 3) [45]. As a result, the minimum electricity trading price for energy prosumers generally decreased as the electricity generation increased (refer to Fig. 3). This is because the higher the electricity generation is, the more the surplus electricity that can be sold under the same conditions (i.e., the same monthly electricity consumption or self-consumption rate). In this case, profit can be

electricity trading scenarios for energy prosumers and 90 electricity trading scenarios for energy consumers. By matching energy prosumers and consumers where both of them can be profitable through P2P electricity trading based on the calculated minimum and maximum electricity trading prices, this study defined this match to be a profitable P2P electricity trading. In this regard, this study ultimately presents an electricity trading strategy that ensures profitability of energy prosumers and consumers as follows: (i) energy prosumers search for energy consumers who are capable of P2P electricity trading at prices higher than the minimum electricity trading prices; and (ii) energy consumers search for energy prosumers who are capable of P2P electricity trading at prices lower than the maximum electricity trading prices. Accordingly, decision on the matching of energy prosumers and consumers based on the minimum and maximum electricity trading prices can be divided into three criteria, as follows (refer to Table 6). First, if the minimum electricity trading price for the energy prosumer is higher than the maximum electricity trading price for the energy consumer, the energy prosumer should sell its surplus electricity at a price higher than the maximum electricity trading price to the energy consumer for a profitable P2P electricity trading; whereas the energy consumer should purchase the electricity at a price lower than the minimum electricity trading price from the energy prosumer for a profitable P2P electricity trading. Therefore, this P2P electricity trading is regarded as non-profitable for both the energy prosumer and consumer. Second, if the minimum electricity trading price for the energy prosumer is the same as the maximum electricity trading price for the energy consumer, P2P electricity trading is regarded as possible but neither the prosumer nor the consumer has profit or loss. Finally, if the minimum electricity trading price for the energy prosumer is lower than the maximum electricity trading price for the energy consumer, P2P electricity trading is regarded as profitable for both the energy prosumer and consumer at a price within the range of the minimum and maximum electricity trading prices. In 24,300 potential electricity trading scenarios by matching energy prosumers and consumers, an energy prosumer indicates the person who sells surplus electricity and an energy consumer indicates the person who purchases surplus electricity. In a case where the energy prosumers do not have enough onsite electricity generation to supply their own electricity demand, even energy prosumers can become energy consumers who purchase surplus electricity from other energy prosumers and the same mechanism can be applied to match them. 3. Results and discussion This study aimed to determine the minimum and maximum electricity trading prices that allow energy prosumers and consumers to trade surplus electricity, and to find potential P2P electricity trading partners. The calculation results of the minimum and maximum electricity trading prices for energy prosumers and consumers can be analyzed as follows: (i) analysis of the electricity trading price for energy prosumers and consumers; and (ii) analysis of the electricity trading strategy for energy prosumers and consumers. 3.1. Analysis of the electricity trading price for energy prosumers and consumers 3.1.1. Minimum electricity trading price for energy prosumers Table 7 shows the calculation results of the minimum electricity trading prices that allow energy prosumers to profit from P2P electricity trading with respect to their 540 electricity trading scenarios according to the month, monthly electricity consumption rate, and selfconsumption rate. Figs. 1 and 2 are graphs showing the trends in the average minimum electricity trading price according to the monthly electricity consumption and self-consumption rates in the general (i.e., from January to June and from September to December) and summer (i.e., July and August) seasons, respectively.

Table 6 Decision criteria on matching energy prosumers and consumers.

7

Criteria

Decision

Minimum price for prosumer > Maximum price for consumer Minimum price for prosumer = Maximum price for consumer Minimum price for prosumer < Maximum price for consumer

Non-profitable Possible Profitable

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Table 7 The analysis results of the minimum electricity trading prices for energy prosumers. MECa

SCRb

Minimum electricity trading prices (US$/kWh) Jan.

Feb.

Mar.

Apr.

May.

Jun.

Jul.

Aug.

Sep.

Oct.

Nov.

Dec.

200 kWh

10% 20% 30% 40% 50% 60% 70% 80% 90%

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.06 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

0.06 0.06 0.05 0.05 0.05 0.05 0.05 0.05 0.05

300 kWh

10% 20% 30% 40% 50% 60% 70% 80% 90%

0.14 0.13 0.12 0.09 0.09 0.09 0.09 0.09 –

0.12 0.11 0.10 0.07 0.07 0.06 0.05 0.05 0.05

0.10 0.09 0.08 0.05 0.05 0.05 0.05 0.05 0.05

0.09 0.08 0.07 0.05 0.05 0.05 0.05 0.05 0.05

0.09 0.08 0.07 0.05 0.05 0.05 0.05 0.05 0.05

0.11 0.10 0.09 0.06 0.06 0.05 0.05 0.05 0.05

0.11 0.11 0.11 0.09 0.09 0.09 0.09 0.09 –

0.09 0.10 0.10 0.07 0.07 0.07 0.06 0.05 0.05

0.11 0.11 0.10 0.07 0.06 0.06 0.05 0.05 0.05

0.11 0.10 0.09 0.06 0.06 0.05 0.05 0.05 0.05

0.14 0.13 0.13 0.09 0.09 0.09 0.09 –c –

0.14 0.14 0.13 0.09 0.09 0.09 0.09 – –

400 kWh

10% 20% 30% 40% 50% 60% 70% 80% 90%

0.18 0.18 0.17 0.17 0.09 0.09 – – –

0.16 0.16 0.16 0.15 0.09 0.09 0.09 – –

0.15 0.15 0.14 0.13 0.09 0.09 0.09 0.09 –

0.14 0.14 0.13 0.12 0.08 0.08 0.08 0.06 0.05

0.14 0.14 0.13 0.12 0.08 0.08 0.07 0.06 0.05

0.16 0.15 0.15 0.14 0.09 0.09 0.09 – –

0.14 0.13 0.12 0.13 0.09 0.09 – – –

0.13 0.12 0.11 0.12 0.09 0.09 0.09 – –

0.16 0.16 0.15 0.15 0.09 0.09 0.09 – –

0.16 0.15 0.15 0.14 0.09 0.09 0.09 – –

0.18 0.19 0.19 0.19 0.09 – – – –

0.19 0.20 0.20 0.22 0.09 – – – –

500 kWh

10% 20% 30% 40% 50% 60% 70% 80% 90%

0.23 0.18 0.18 0.18 0.18 – – – –

0.22 0.18 0.18 0.18 0.18 – – – –

0.21 0.18 0.18 0.18 0.18 0.09 – – –

0.20 0.17 0.17 0.17 0.16 0.09 0.09 – –

0.20 0.17 0.17 0.17 0.16 0.09 0.09 – –

0.23 0.19 0.20 0.20 0.22 0.09 – – –

0.16 0.15 0.14 0.09 0.09 – – – –

0.15 0.14 0.13 0.09 0.09 – – – –

0.24 0.20 0.21 0.22 0.26 0.09 – – –

0.23 0.19 0.20 0.21 0.23 0.09 – – –

0.23 0.18 0.18 0.18 – – – – –

0.24 0.18 0.18 0.18 – – – – –

600 kWh

10% 20% 30% 40% 50% 60% 70% 80% 90%

0.27 0.27 0.27 0.18 – – – – –

0.26 0.25 0.24 0.18 – – – – –

0.24 0.24 0.22 0.18 0.18 – – – –

0.24 0.23 0.22 0.18 0.18 0.18 – – –

0.24 0.23 0.22 0.18 0.18 0.18 – – –

0.25 0.24 0.23 0.18 0.18 – – – –

0.23 0.18 0.18 0.18 – – – – –

0.22 0.18 0.18 0.18 – – – – –

0.25 0.25 0.24 0.18 0.18 – – – –

0.25 0.24 0.23 0.18 0.18 – – – –

0.28 0.29 0.30 – – – – – –

0.29 0.30 0.34 – – – – – –

Note: The exchange rate (KRW/US$) is 1191.50 won to a U.S. dollar (as of 23 May 2019). a MEC refers to the monthly electricity consumption. b SCR refers to the self-consumption rate. c Non-tradable due to the lack of surplus electricity generated by the energy prosumer.

to the progressive electricity tariffs in South Korea, as in the minimum electricity trading price for energy prosumers. That is, there has been a sharp increase in the electricity bill as the monthly electricity consumption of the energy consumer increases, and thus, profits can be expected even when the electricity is purchased at a high electricity trading price (i.e., up to US$0.32/kWh). Second, the maximum electricity trading price for energy consumers generally decreased as the electricity purchase rate increased (refer to Table 8 and Figs. 4 and 5). The reason for this is that as the amount of purchased electricity increases, the progressive tariff zone applied to the electricity bill is lowered, so the electricity bill savings increase dramatically. Consequently, as the electricity purchase rate increases, profits can be expected only when the electricity is purchased at a low electricity trading price. Similar to the minimum electricity trading price for energy prosumers, there is an electricity purchase rate (i.e., monthly electricity consumption of (i) 300 kWh: 33%; (ii) 400 kWh:

expected even when the surplus electricity is sold at a low electricity trading price. 3.1.2. Maximum electricity trading price for energy consumers Table 8 shows the calculation results of the maximum electricity trading prices that allow energy consumers to profit from P2P electricity trading with respect to their 90 electricity trading scenarios by season, monthly electricity consumption, and electricity purchase rate. Figs. 4 and 5 are graphs showing the trends in the maximum electricity trading price according to the monthly electricity consumption and electricity purchase rates in the general (i.e., from January to June and from September to December) and summer (i.e., July and August) seasons, respectively. First, the maximum electricity trading price (US$0.09–0.32/kWh) for energy consumers generally increased with increasing monthly electricity consumption (refer to Table 8 and Figs. 4 and 5). This is due 8

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Fig. 1. The average minimum electricity trading price according to the monthly electricity consumption and self-consumption rate for the general season.

3.2. Analysis of the electricity trading strategy for energy prosumers and consumers

50% [25% in the summer season]; (iii) 500 kWh: 20 and 60% [40% in the summer season]; and (iv) 600 kWh: 33% [17% in the summer season]), at which the maximum electricity trading prices greatly decrease by monthly electricity consumption. This is because if a certain electricity purchase rate is reached by purchasing electricity from the energy prosumer, the electricity bill decreases as the progressive tariff zone is lowered by a step. In this case, profits can be expected even with a high electricity trading price. Third, as a comparison of the seasonal patterns, the maximum electricity trading price for energy consumers gradually decreased as the electricity purchase rate increased both in the general and summer seasons. In most cases, the maximum electricity trading prices were higher in the general season than in the summer season. This is because as the energy consumer with a monthly electricity consumption of 200–300 kWh or 400–500 kWh benefits from the relaxed progressive tariffs in the summer season, profits can be expected only when the surplus electricity is purchased at a lower electricity price. Accordingly, in the general season, it shows similar maximum electricity trading price ranges when the monthly electricity consumption is 300 and 400 kWh and 500 and 600 kWh, respectively; whereas in the summer season, it shows similar maximum electricity trading price ranges when the monthly electricity consumption is 200 and 300 kWh and 400 and 500 kWh, respectively.

3.2.1. Macroscopic analysis of electricity trading strategy for energy prosumers and consumers By matching energy prosumers and consumers based on the minimum electricity trading prices for 540 scenarios and the maximum electricity trading prices for 90 scenarios, the electricity trading strategy for energy prosumers and consumers was analyzed. Table 9 presents the results of the comprehensive analysis of the matching of energy prosumers and consumers for all the months of the year, resulting in 24,300 potential electricity trading scenarios; it also shows the number of profitable electricity trading scenarios for each month as well as the electricity generation of the solar PV system and the average minimum and maximum electricity trading prices. As a result, it was analyzed that May exhibits the lowest average minimum electricity trading price (i.e., US$0.10/kWh) in the general season, resulting in the largest number of profitable electricity trading scenarios (i.e., 1515 scenarios, 74.81%) among the 12 months of the year. Between the summer months July and August when the relaxed progressive tariffs are applied, August exhibits a lower average minimum electricity trading price (i.e., US$0.09/kWh), showing a larger number of profitable electricity trading scenarios (i.e., 1165 scenarios, 57.53%). On the

Fig. 2. The average minimum electricity trading price according to the monthly electricity consumption and self-consumption rate for the summer season. 9

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Fig. 3. The minimum electricity trading price according to month and self-consumption rate for energy prosumers with monthly electricity consumption of 400 kWh.

Although the energy prosumer can expect profits even from a low electricity trading price (i.e., at least US$0.05/kWh) in these scenarios, he or she can sell surplus electricity at a high electricity trading price (i.e., up to US$0.32/kWh). Therefore, a profit of up to US$0.27/kWh can be obtained if surplus electricity is sold at the maximum electricity trading price. Likewise, although the energy consumer can expect profits even from a high electricity trading price (i.e., up to US$0.32/ kWh), he or she can purchase surplus electricity at a low electricity trading price (i.e., at least US$0.05/kWh). Thus, a profit of up to US $0.27/kWh can be obtained if surplus electricity is purchased at the minimum electricity trading price. On the other hand, the energy consumer whose monthly electricity consumption is 200 kWh cannot expect much profit (i.e., up to US$0.04/kWh) because there is almost no difference between the minimum and maximum electricity trading prices (refer to the blue-colored area in Figs. 6 and 7). Compared to December, the majority of scenarios were capable of profitable P2P electricity trading in May due to a high electricity generation of the solar PV system (i.e., 374 kWh) and the low minimum electricity trading prices (i.e., US$0.10/kWh on average; refer to Table 9) Second, as shown in Fig. 8, the scenarios of the summer season (i.e., August) show the largest difference between the minimum and maximum electricity trading prices when energy prosumers consuming 200 kWh electricity per month are matched to the energy consumers consuming 600 kWh electricity with a 20% electricity purchase rate (refer to the red-colored area in Fig. 8); these are the cases from which the maximum profits are expected. Although the energy prosumer can expect profits even from a low electricity trading price (i.e., at least US $0.05/kWh), he or she can sell surplus electricity at a high electricity trading price (i.e., up to US$0.30/kWh). Therefore, a profit of up to US $0.25/kWh can be obtained if surplus electricity is sold at the maximum electricity trading price. Likewise, although the energy consumer can expect profits even from a high electricity trading price (i.e., up to US$0.30/kWh), he or she can purchase surplus electricity at a low electricity trading price (i.e., at least US$0.05/kWh). Thus, a profit of up to US$0.25/kWh can be obtained if surplus electricity is purchased at the minimum electricity trading price. On the other hand, the energy consumer whose monthly electricity consumption is 200–300 kWh cannot expect much profit (i.e., up to US$0.07/kWh) because there is almost no difference between the minimum and maximum electricity trading prices (refer to the blue-colored area in Fig. 8).

contrary, among the 12 months of the year, December shows the highest average minimum electricity trading price (i.e., US$0.14/kWh), resulting in the lowest number of profitable electricity trading scenarios (i.e., 869 scenarios, 42.91%). Given that a considerable number of potential electricity trading scenarios (i.e., 24,300) is presented if all the months of the year are included, this study conducted a detailed analysis on three typical months, which were selected based on the number of profitable electricity trading scenarios as above: (i) the electricity trading strategy in general (i.e., May and December); and (ii) the electricity trading strategy in summer (i.e., August). Figs. 6–8 are matrices showing the analysis results of the matching of energy prosumers and consumers for profitable P2P electricity trading in the general (i.e., May and December) and summer (i.e., August) seasons, respectively. In Figs. 6–8, the scenarios colored in blue to red indicates profitable P2P electricity trading, whereas the scenarios colored in white indicates non-tradable (due to the lack of surplus electricity generated by the energy prosumer) or non-profitable P2P electricity trading. Profitable electricity trading scenarios are displayed in darker red when the difference between the minimum and maximum electricity trading prices is larger; this indicates a higher possibility to expect greater profits for both the energy prosumer and consumer. According to the analysis results of the electricity trading prices for energy prosumers and consumers and Figs. 6–8, the lower the monthly electricity consumption and the higher the self-consumption rate is for the energy prosumer, and the higher the monthly electricity consumption and the lower the electricity purchase rate is for the energy consumer, more cases are capable of profitable P2P electricity trading. That is, the lower the monthly electricity consumption and the higher the self-consumption rate is, the lower the minimum electricity trading prices for energy prosumers that guarantee their profitability; the higher the monthly electricity consumption and the lower the electricity purchase rate is, the higher the maximum electricity trading prices for energy consumers that ensure their profitability. With a lower minimum electricity trading prices for energy prosumers and a higher maximum electricity trading prices for energy consumers, it becomes easier to find a potential partner that can engage in profitable P2P electricity trading. First, as shown in Figs. 6 and 7, the scenarios of the general season (i.e., May and December) show the largest difference between the minimum and maximum electricity trading prices when energy prosumers consuming 200–300 kWh electricity per month are matched to the energy consumers consuming 500 kWh electricity with a 20% electricity purchase rate (refer to the red-colored area in Figs. 6 and 7); these are the cases from which the maximum profits are expected.

3.2.2. Microscopic analysis of electricity trading strategy for energy prosumers and consumers To find potential partners who can engage in profitable P2P 10

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electricity trading with the price range indicated by the bars colored in grayscale. The use of Fig. 9 not only allows the energy prosumer to find the energy consumer that ensures their profitability according to the self-consumption rate but also helps identify the range of profitable P2P electricity trading prices. For example, if an energy prosumer whose monthly electricity consumption is 400 kWh engages in P2P electricity trading with a consumer who has a 30% electricity purchase rate among the energy consumers whose monthly electricity consumption is 500 kWh (refer to Part (A) in Fig. 9), he or she can sell surplus electricity at the widest range of trading prices (i.e., US$0.05–0.32/kWh). On the other hand, if an energy prosumer whose monthly electricity consumption is 400 kWh and whose self-consumption rate ranges from 10 to 40% engages in P2P electricity trading with an energy consumer whose monthly electricity consumption is 200 kWh (refer to Part (B) in Fig. 9), he or she decides not to sell surplus electricity due to the expected loss from the sale because the maximum electricity trading price (i.e., up to US$0.09/kWh) is lower than the minimum electricity trading price (i.e., at least US$0.12/kWh). Fig. 10 shows the profitable P2P electricity trading prices from the perspective of the energy consumer whose monthly electricity consumption is 400 kWh, according to the monthly electricity consumption and self-consumption rates of energy prosumer. In Fig. 10, the red lines indicate the minimum electricity trading prices of energy prosumers according to their monthly electricity consumption and self-consumption rate, whereas the black and gray dotted lines indicate the maximum electricity trading prices of energy consumers according to their electricity purchase rate. If the red line is below the black and gray dotted lines, the energy consumer is capable of profitable P2P electricity trading with the price range indicated by the bars colored in grayscale. The use of Fig. 10 not only allows the energy consumer to find the energy prosumer that ensures their profitability according to the electricity purchase rate but also helps identify the range of profitable P2P electricity trading prices. For example, if an energy consumer whose monthly electricity consumption is 400 kWh engages in P2P electricity trading with an energy consumer whose monthly electricity consumption is 200 kWh (refer to Part (A) in Fig. 10), he or she can purchase surplus electricity at the widest range of trading prices (i.e., US$0.05–0.20/kWh). On the other hand, if an energy consumer engages in P2P electricity trading with an energy prosumer whose monthly electricity consumption is 600 kWh and whose self-consumption rate ranges from 10 to 30% (refer to Part (B) in Fig. 10), he or she decides not to purchase surplus electricity due to the expected loss from the purchase because the minimum electricity trading price (i.e., at least US$0.22/kWh) is higher than the maximum electricity trading price (i.e., up to US$0.20/kWh).

Table 8 The analysis results of the maximum electricity trading prices for energy consumers. MECa

EPRb

Maximum electricity trading prices (US$/kWh) General season

Summer season

200 kWh

10% 20% 30% 40% 50% 60% 70% 80% 90%

0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.08

0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.08

300 kWh

10% 20% 30% 40% 50% 60% 70% 80% 90%

0.18 0.18 0.18 0.20 0.18 0.16 0.15 0.15 0.13

0.09 0.09 0.09 0.12 0.11 0.11 0.11 0.10 0.10

400 kWh

10% 20% 30% 40% 50% 60% 70% 80% 90%

0.18 0.18 0.18 0.18 0.20 0.18 0.17 0.16 0.15

0.18 0.18 0.17 0.15 0.16 0.15 0.14 0.13 0.13

500 kWh

10% 20% 30% 40% 50% 60% 70% 80% 90%

0.27 0.32 0.27 0.25 0.24 0.24 0.22 0.20 0.19

0.18 0.18 0.18 0.18 0.16 0.16 0.15 0.15 0.14

600 kWh

10% 20% 30% 40% 50% 60% 70% 80% 90%

0.27 0.27 0.27 0.28 0.26 0.24 0.24 0.22 0.21

0.27 0.30 0.26 0.24 0.23 0.21 0.20 0.18 0.17

Note: The exchange rate (KRW/US$) is 1191.50 won to a U.S. dollar (as of 23 May 2019). a MEC refers to the monthly electricity consumption. b EPR refers to the electricity purchase rate.

3.3. Analysis of the estimated profit from P2P electricity trading for energy prosumers and consumers Based on the potential electricity trading scenarios established in 3.2.1, the maximum estimated profit from P2P electricity trading for energy prosumers and consumers are presented in Tables 10 and 11, respectively. First, as shown in Table 10, monthly maximum profit from P2P electricity trading for energy prosumers increases as they produce more electricity from the installed solar PV system. This is because, as electricity generation increases, surplus electricity available for sale increases. Meanwhile, monthly maximum estimated profit from P2P electricity trading for energy prosumers increases as their monthly electricity consumption increases. This is because, while the installed capacity of the solar PV system is the same as 3 kW, the increase of monthly electricity consumption of energy prosumer causes not only a decrease of the surplus electricity that can be sold but also an increase of the minimum electricity trading price which lowers the benefits that can be obtained from P2P electricity trading. As a result, energy prosumers with monthly electricity consumption from 200 to 600 kWh can expect a profit of up to US$ 201.2–885.8 per year if they install a 3 kW

electricity trading as well as the price range for such trading, a microscopic analysis was conducted to present an electricity trading strategy from the perspectives of the individual energy prosumer and consumer. Fig. 9 shows the profitable P2P electricity trading prices from the perspective of the energy prosumer according to the monthly electricity consumption and electricity purchase rates of the energy consumer; here, the monthly electricity consumption of the energy prosumer is 400 kWh, which accounts for the highest percentage of the total residential electricity consumption and electricity bill. In Fig. 9, the red lines indicate the maximum electricity trading prices of energy consumers according to their monthly electricity consumption and electricity purchase rate, whereas the black and gray dotted lines indicate the minimum electricity trading prices of energy prosumers according to their self-consumption rate. If the red line is above the black and gray dotted lines, the energy prosumer is capable of profitable P2P 11

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Fig. 4. The maximum electricity trading price according to the monthly electricity consumption and electricity purchase rate for the general season.

unconsidered constraints due to the market structure and social reality have been summarized in Table 12. These factors can be important in determining the P2P electricity trading price because considering these factors may result in lower economic performance of P2P electricity trading compared to the maximum estimated profit proposed in this study.

solar PV system. Second, as shown in Table 11, monthly maximum profit from P2P electricity trading for energy consumers increases as they consume more electricity per month. This is because, as monthly electricity consumption increases, progressive tariff zone increases. However, if the monthly electricity consumption is greater than 500 kWh, the maximum estimated profit for energy consumers decreases because the highest progressive tariff zone is applied when the monthly electricity consumption is over 400 kWh. In the summer season, the maximum estimated profit for energy consumers increased even at monthly electricity consumption of 600 kWh because of the change in the range of the progressive tariff zone (refer to Table 1). As a result, energy consumers with monthly electricity consumption from 200 to 600 kWh can expect a profit of up to US$ 121.9–828.3 per year if they purchase surplus electricity from energy prosumers. In this study, various financial constraints for energy prosumers and consumers to participate in the P2P electricity trading market were considered to derive the P2P electricity trading price based on the electricity billing system in South Korea. Although this study fully reflected the characteristics of the local electricity market in South Korea, where KEPCO is monopolizing, some of the physical constraints such as additional costs for communication infrastructure and transmission losses were not considered. These physical constraints along with other

4. Conclusion This study aims to establish a P2P electricity trading strategy based on electricity trading prices that enable both the energy prosumers and consumers to gain profits. Towards this end, the conditions in which the energy prosumer and consumer participate in the P2P electricity trading market were defined, and the minimum and maximum electricity trading prices were calculated based on the electricity trading scenarios. In addition, an analysis was conducted on a total of 24,300 potential electricity trading scenarios. If P2P electricity trading is to be actually performed on the energy prosumers with the residential solar PV system in Seoul, South Korea, the minimum and maximum electricity trading prices and electricity trading strategy for the energy prosumers and consumers would be calculated as follows.

• Analysis

of the electricity trading price for energy prosumers and

Fig. 5. The maximum electricity trading price according to the monthly electricity consumption and electricity purchase rate for the summer season. 12

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number of profitable electricity trading scenarios (1515 scenarios, 74.81%), and the most active trading is expected to be done in this month. On the other hand, December exhibits the smallest number of profitable electricity trading scenarios (869 scenarios, 42.91%), and trading is not likely to occur in this month. In this regard, the profitable electricity trading scenarios increase as the energy prosumer’s monthly electricity consumption decreased while the selfconsumption rate increased, and the energy consumer’s monthly electricity consumption increased while the electricity purchase rate decreased. Therefore, it was found that an energy prosumer is more likely to obtain higher profits when selling surplus electricity to an energy consumer with high monthly electricity consumption, whereas an energy consumer is highly likely to purchase electricity at a low electricity trading price when purchasing it from an energy prosumer with low monthly electricity consumption.

Table 9 The analysis results of matching energy prosumers and consumers for all the months. Month

Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.

Potential electricity trading scenarios

Profitable electricity trading scenarios

Nc

Nc

%d

2025 2025 2025 2025 2025 2025 2025 2025 2025 2025 2025 2025

1051 1186 1310 1485 1515 1254 1030 1165 1228 1237 880 869

51.90 58.57 64.69 73.33 74.81 61.93 50.86 57.53 60.64 61.09 43.46 42.91

Electricity generation (kWh)

Average of Pmina (US $/kWh)

Average of Pmaxb (US $/kWh)

256 290 344 370 374 320 253 284 302 314 238 224

0.13 0.11 0.11 0.10 0.10 0.12 0.10 0.09 0.12 0.12 0.13 0.14

0.19 0.19 0.19 0.19 0.19 0.19 0.15 0.15 0.19 0.19 0.19 0.19

This study is expected to serve as a preliminary research to introduce P2P electricity trading in South Korea with the following contributions: (i) it presents a marginal price that enables both energy prosumers and consumers to obtain profits through P2P electricity trading; (ii) it allows to establish a self-consumption or electricity purchase strategy for profitable P2P electricity trading according to the monthly electricity consumption by matching energy prosumers and consumers on a monthly basis; and (iii) it allows energy prosumers and consumers to find optimal trading partners suitable for their conditions. Furthermore, it is expected that the methodology used in this study will help energy prosumers evaluate economic feasibility of an energy system and establish an optimal energy trading strategy when adopting different types of P2P energy trading in the future.

a Pmin refers to the minimum electricity trading price for the energy prosumer. b Pmax refers to the maximum electricity trading price for the energy consumer. c N refers to the number of scenarios for each month. d % refers to the percentage of scenarios for each month.

•

consumers: From the perspective of the energy prosumer, the minimum electricity trading prices ranged from US$0.05/kWh to US $0.34/kWh according to the month, monthly electricity consumption, and self-consumption rate. The prices were higher as the monthly electricity consumption increased while the self-consumption rate decreased. From the perspective of the energy consumer, the maximum electricity trading prices ranged from US$0.09/kWh to US$0.32/kWh according to the month, monthly electricity consumption, and electricity purchase rate. The prices were higher as the monthly electricity consumption increased while the electricity purchase rate decreased. This is because (i) the electricity generation of the solar PV system and the seasonal progressive tariffs in the general and summer seasons vary depending on the month; and (ii) the progressive tariff zone applied according to the monthly electricity consumption, self-consumption rate, and electricity purchase rate is subject to change. Analysis of the electricity trading strategy for energy prosumers and consumers: Among the 12 months of the year, May shows the largest

CRediT authorship contribution statement Jongbaek An: Methodology, Software, Validation, Writing - original draft. Minhyun Lee: Methodology, Software, Writing - review & editing, Supervision. Seungkeun Yeom: Investigation, Visualization, Resources. Taehoon Hong: Writing - review & editing, Visualization, Project administration, Funding acquisition.

Declarartion of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Fig. 6. The analysis results of matching energy prosumers and consumers in the general season (May). 13

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Fig. 7. The analysis results of matching energy prosumers and consumers in the general season (December).

Fig. 8. The analysis results of matching energy prosumers and consumers in the summer season (August).

Fig. 9. The profitable P2P electricity trading price for energy prosumers.

14

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Fig. 10. The profitable P2P electricity trading price for energy consumers. Table 10 The maximum estimated profit from P2P electricity trading for energy prosumers. MECa

200 300 400 500 600

kWh kWh kWh kWh kWh

Maximum estimated profit for energy prosumers (US$) Jan.

Feb.

Mar.

Apr.

May.

Jun.

Jul.

Aug.

Sep.

Oct.

Nov.

Dec.

Total

63.8 41.9 31.4 22.3 10.2

73.0 48.2 36.4 27.1 11.9

87.6 70.2 51.9 34.3 22.7

94.6 78.6 59.2 40.4 26.4

95.7 79.9 60.5 41.3 27.0

81.1 62.5 46.3 28.7 19.3

57.4 42.9 34.7 28.3 15.8

65.1 51.8 41.2 34.8 19.5

76.3 56.7 42.1 24.5 16.7

79.5 60.6 44.9 24.6 18.5

58.1 37.7 27.2 17.0 7.6

53.6 34.4 24.0 17.7 5.6

885.8 665.4 499.8 341.0 201.2

Note: The exchange rate (KRW/US$) is 1191.50 won to a U.S. dollar (as of 23 May 2019). a MEC refers to the monthly electricity consumption of energy prosumer. Table 11 The maximum estimated profit from P2P electricity trading for energy consumers. MECa

200 300 400 500 600

kWh kWh kWh kWh kWh

Maximum estimated profit for energy consumers (US$) Jan.

Feb.

Mar.

Apr.

May.

Jun.

Jul.

Aug.

Sep.

Oct.

Nov.

Dec.

Total

8.8 35.4 35.4 63.8 52.9

10.1 40.5 40.5 73.0 60.5

12.1 48.6 48.6 87.6 72.6

13.1 52.5 52.5 94.6 78.4

13.2 53.1 53.1 95.7 79.3

11.2 45.0 45.0 81.1 67.2

8.6 16.0 29.7 30.4 57.4

9.9 18.3 33.7 34.6 65.1

10.5 42.3 42.3 76.3 63.2

11.0 44.1 44.1 79.5 65.8

7.3 31.8 31.9 58.1 48.0

6.1 29.0 29.0 53.6 44.1

121.9 456.6 485.8 828.3 754.5

Note: The exchange rate (KRW/US$) is 1191.50 won to a U.S. dollar (as of 23 May 2019). a MEC refers to the monthly electricity consumption of energy consumer.

References

Table 12 Unconsidered constraints for determining the P2P electricity trading price. Classification

Reasons for inapplicability

Unconsidered factors

Physical constraint

Social reality

Energy storage system Electricity vehicle Transmission losses Communication infrastructure

Estimation difficulty Economic constraint

Market structure

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