• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.577-579
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• 2022

최근 대두된 환경문제로 인해 다양한 재생 에너지의 실리적인 활용 방법에 귀추가 주목되고 있다. 특히 '그린뉴딜', 'K-RE100' 등 정부 주도의 정책으로 태양광 발전 시장 규모가 확대되면서, 소규모 발전 사업자의 태양광 발전 참여율도 매년 증가 추세를 보이고 있다. 이로 인해 소규모 발전 사업자의 수익을 산정하는 전력 중개 시스템의 태양광 발전 예측은 에너지 시장의 핵심요소로 부각되었다. 하지만 전력 중개용 태양광 발전 예측에는 기후의 간헐성으로 인한 예측 정확도 감소, 소규모 발전 사업자의 개인정보 보호 등 제약이 존재한다. 이 논문에서는 전력 중개용 태양광 발전 예측의 제약을 해소하고, 전력 중개 활성화를 지원키 위한 CNN-LSTM 기반 연합학습 기법을 제안한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.735-742
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• 2021

Recently, renewable energy-related power generation facilities have been surging due to the government's "Renewable Energy 3020", "Green New Deal", "2050 Carbon Neutrality" and "K-RE100" policies. Most renewable energy facilities are small and distributed, making it difficult to manage efficiently, and small distributed resources less than 1MW are having a hard time with participating in the market due to the limited sales and avoidance of trading. In particular, the intermittency of renewable energy has a significant impact on the stability of the power grid. The government is seeking to address volatility and intermittency issues through 'small distributed resource brokerage trading, and to expand the systematic resourceization and acceptability of heterogeneous large and small distributed resources. In this work, we intend to apply an AI-based power generation prediction model to a distributed resource brokerage trading system so that it can be utilized as a foundation platform for pioneering new energy business markets.

• New & Renewable Energy
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• v.18 no.3
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• pp.32-42
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• 2022

Rapid spread of intermittent renewable energy has amplified the instability and uncertainty of power systems. The Korea Power Exchange (KPX) promoted efficient management by opening the power brokerage market in 2019. By combining small-scale intermittent renewable energy with a flexible facility through the power brokerage market, the KPX aims to develop a virtual power plant system that will allow the conversion of existing intermittent renewable energy into collective power plants. However, the participation rate of renewable power owners in the power brokerage market is relatively low because other markets such as the small solar power contract market or the Korea Electric Power Corporation power purchase agreement are more profitable. In this study, we used a choice experiment to determine the attributes affecting the participation rate in the power brokerage market for 113 renewable power owners and estimate the value of the power brokerage market. According to the estimation results, a low smart meter installation cost, low profit variations, long contract periods, and few clearances increased the probability of participation. Moreover, the average value of the power brokerage market was estimated to be 2.63 million KRW per power owner.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.55-61
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• 2022

In order to reduce greenhouse gases, the main culprit of global warming, the United Nations signed the Climate Change Convention in 1992. Korea is also pursuing a policy to expand the supply of renewable energy to reduce greenhouse gas emissions. The expansion of renewable energy development using solar power led to the expansion of wind power and solar power generation. The expansion of renewable energy development, which is greatly affected by weather conditions, is creating difficulties in managing the supply and demand of the power system. To solve this problem, the power brokerage market was introduced. Therefore, in order to participate in the power brokerage market, it is necessary to predict the amount of power generation. In this paper, the prediction system was used to analyze the Yonchuk solar power plant. As a result of applying solar insolation from on-site (Model 1) and the Korea Meteorological Administration (Model 2), it was confirmed that accuracy of Model 2 was 3% higher. As a result of comparative analysis of the DNN and RNN models, it was confirmed that the prediction accuracy of the DNN model improved by 1.72%.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.76-81
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• 2019

This paper is a review of the certification system required for various energy prosumer business models, including P2P energy trading and participation in small demand response programs, which are based on reliable production and consumption certification. One of the most important parameter in energy trading is ensuring the reliability of trading account balancing. Therefore, we studied to use big data pattern analysis based blockchain smart contract between trading partners to make its tradings are more reliable. For this purpose big data analysis system collected from the IoT AMI and a production authentication system using a private blockchain network linked with the AMI is discussed, using the blockchain smart contract are also suggested. Futhermore, energy trading system concept and business models are introduced.

• Journal of IKEEE
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• v.26 no.3
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• pp.355-363
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• 2022

Korea is pursuing a plan to switch and expand energy sources with a focus on renewable energy with the goal of becoming carbon neutral by 2050. As the instability of energy supply increases due to the intermittent nature of renewable energy, accurate prediction of the amount of renewable energy generation is becoming more important. Therefore, the government has opened a small-scale power brokerage market and is implementing a system that pays settlements according to the accuracy of renewable energy prediction. In this paper, a prediction model was implemented using a statistical model and an artificial intelligence model for the prediction of solar power generation. In addition, the results of prediction accuracy were compared and analyzed, and the revenue from the settlement amount of the renewable energy generation forecasting system was estimated.

• Environmental and Resource Economics Review
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• v.30 no.2
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• pp.237-268
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• 2021

As variable renewable sources rapidly increase due to the Energy Transition plan, integration cost of renewable sources to the power system is rising sharply. The increase in variable renewable energy reduces the capacity factor of existing traditional power capacity, and this undermines the efficiency of the overall power supply, and demand resources are drawing attention as a solution. In this study, we analyzed how much electric vehicle demand resouces, which has great potential among other demand resources, can reduce power supply costs if it is used as a flexible resource for renewable generation. As a methodology, a stochastic form of power system optimization model that can effectively reflect the volatile characteristics of renewable generation is used to analyze the cost induced by renewable energy and the benefits offered by electric vehicle demand resources. The result shows that virtual power plant-based direct control method has higher benefits than the time-of-use tariff, and the higher the proportion of renewable energy is in the power system, the higher the benefits of electric vehicle demand resources are. The net benefit after considering commission fee for aggregators and battery wear-and-tear costs was estimated as 67% to 85% of monthly average fuel cost under virtual power plant with V2G capability, and this shows that a sufficient incentive for market participation can be offered when a rate system is applied in which these net benefits of demand resources are effectively distributed to consumers.