For the market participants (i.e., energy consumers and prosumers) in a microgrid to acquire profits via trading surplus electricity, it is essential to determine an appropriate trading price of electricity. Therefore, this study developed a business feasibility evaluation model to predict the optimal trading price of electricity that maximizes the profits of both the market participants participating in the Peer-to-Peer (P2P) electricity trading, by reflecting the structure of electricity market in South Korea. The residential areas located in the seven metropolitan cities in South Korea (Seoul, Incheon, Daejeon, Daegu, Ulsan, Busan, and Gwangju) were selected for the model application. The main findings from the model application are as follows. First, the annual electricity generation of the solar photovoltaic (PV) panel was highest in Daegu (5,541 kWh) and lowest in Seoul (3,569 kWh). In addition, the electricity generation was generally shown to be higher in spring (March–May) and relatively lower in summer and winter. Second, the estimated annual maximum profit of the energy prosumer was highest in Daegu (US$995.5) and lowest in Seoul (US$638.1). Furthermore, it was determined to be beneficial to the energy prosumers to reduce their self-use rate to the extent possible. By using the developed business feasibility evaluation model, decision makers, including specialists and non-specialists, can determine the optimal trading price of electricity and whether to participate in the market of P2P electricity trading.
Bibliographical noteFunding Information:
This research was supported by a grant ( 20CTAP-C15188002 ) from Technology Advancement Research Program (TARP) funded by Ministry of Land, Infrastructure and Transport of Korean government.
This study aimed to apply LCOE in cases without government incentives while considering that the incentives offered by MOTIE are decreasing every year (Korea New & Renewable Energy Center, 2019), and considering the uncertainty of sustainable financial support in the future. As a result, the LCOE of the 3 kW-capacity solar PV panel can be derived using Eq. (1) (refer to Table 2).The future research aims to derive the optimal trading price of electricity in the case of the actual enactment of P2P electricity trading that further reflects the reality. Here, it is believed that the energy consumers and prosumers, P2P electricity trading brokers, and KEPCO will participate in the market of P2P electricity trading, and other issues concerning P2P electricity trading (e.g., grid usage fees, trading fees, installation expenses, etc.) should also be considered. In addition, the physical effects on the electricity grid due to the diffusion of distributed generation and P2P electricity trading are also important issues. If these factors are considered, the optimal trading price of electricity derived in this study may change; thus, it is expected that the estimated maximum profit and estimated costs of electricity savings for the energy consumer and prosumer will decrease. In addition, according to the results of this study, it was found that it is advantageous for energy prosumers to reduce self-use electricity as much as possible. However, in terms of social interests, it is beneficial to increase the self-use rate of the energy prosumer to reduce the energy load on the centralized generation. Therefore, in future studies, the optimal trading price of electricity should be presented considering all market participants, not limited to energy consumers and prosumers, considering not only the monetary value (i.e., economic profit) considered in this study but also the non-monetary value (i.e., environmental profit, social profit).This research was supported by a grant (20CTAP-C15188002) from Technology Advancement Research Program (TARP) funded by Ministry of Land, Infrastructure and Transport of Korean government.
© 2021 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
- Environmental Science(all)
- Strategy and Management
- Industrial and Manufacturing Engineering