• JOURNAL OF ELECTRICAL WORLD
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• s.432
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• pp.27-34
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• 2012

• KIPE Magazine
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• v.16 no.5
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• pp.39-43
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• 2011

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.593-602
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• 2020

Recently, as research on smart farms has been actively conducted, systems for efficiently cultivating crops have been introduced and various energy systems using renewable energy such as solar, geothermal and wind power generation have been proposed to save the energy. In this paper, we propose a new and renewable energy convergence system for crops that provides energy independence and improved crop cultivation environment. First, we present LPWA-based communication node and gateway for ICT-based data collection. Then we propose an integrated monitoring server that collects energy data, crop growth data, and environmental data through a communication node and builds it as big data to perform optimal energy management that reflects the characteristics of the environment for cultivating crops. The proposed system is expected to contribute to the production of low-cost, high-quality crops through the fusion of renewable energy and smart farms.

• Transactions on Electrical and Electronic Materials
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• v.18 no.2
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• pp.111-118
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• 2017

The increased diversity of different types of energy sources requires moving towards smart distribution networks. This paper proposes a probabilistic DG (distributed generation) units planning model to determine technology type, capacity and location of DG units while simultaneously allocating ESS (energy storage systems) based on pre-determined capacities. This problem is studied in a wind integrated power system considering loads, prices and wind power generation uncertainties. A suitable method for DG unit planning will reduce costs and improve reliability concerns. Objective function is a cost function that minimizes DG investment and operational cost, purchased energy costs from upstream networks, the defined cost to reliability index, energy losses and the investment and degradation costs of ESS. Electrical load is a time variable and the model simulates a typical radial network successfully. The proposed model was solved using the DICOPT solver under GAMS optimization software.

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.116-124
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• 2015

Smart grids propose new solutions for electricity consumers as a means to help them use energy in an efficient way. In this paper, we consider the demand-side management issue that exists for a group of consumers (houses) that are equipped with renewable energy (wind turbines) and storage units (battery), and we try to find the optimal scheduling for their home appliances, in order to reduce their electricity bills. Our simulation results prove the effectiveness of our approach, as they show a significant reduction in electricity costs when using renewable energy and battery storage.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1053_1054
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• 2009

A significant number of renewable energy systems have been connected to the grids as supplement power source. The renewable energy systems require control algorithm to maintain the power-supply reliability and quality. This paper proposes a novel control algorithm for smart Power Conditioning System (PCS) with harmonics and reactive power compensation. The smart PCS is used to feed Photovoltaic (PV) power to utility and compensate harmonics and reactive power at the same time. The experimentation is carried out on the proposed grid-connected PV generation system, and controlled by digital signal processor. The grid-connected PV generation system injects PV energy into the grid and performs as Active Filter (AF) and Static Synchronous Compensator (STATCOM) without additional devices. The experiment results show that the proposed control algorithm is effective for smart PCS with harmonics and reactive power compensation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2377-2398
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• 2021

In the recent modernized world, utilization of natural resources (renewable & non-renewable) is increasing drastically due to the sophisticated life style of the people. The over-consumption of non-renewable resources causes pollution which leads to global warming. Consequently, government agencies have been taking several initiatives to control the over-consumption of non-renewable natural resources and encourage the production of renewable energy resources. In this regard, we introduce an IoT powered integrated framework called as green home architecture (GHA) for green score calculation based on the usage of natural resources for household purpose. Green score is a credit point (i.e.,10 pts) of a family which can be calculated once in a month based on the utilization of energy, production of renewable energy and pollution caused. The green score can be improved by reducing the consumption of energy, generation of renewable energy and preventing the pollution. The main objective of GHA is to monitor the day-to-day usage of resources and calculate the green score using the proposed green score algorithm. This algorithm gives positive credits for economic consumption of resources and production of renewable energy and also it gives negative credits for pollution caused. Here, we recommend a green score based tax calculation system which gives tax exemption based on the green score value. This direct beneficiary model will appreciate and encourage the citizens to consume fewer natural resources and prevent pollution. Rather than simply giving subsidy, this proposed system allows monitoring the subsidy scheme periodically and encourages the proper working system with tax exemption rewards. Also, our GHA will be used to monitor all the household appliances, vehicles, wind mills, electricity meter, water re-treatment plant, pollution level to read the consumption/production in appropriate units by using the suitable sensors. These values will be stored in mass storage platform like cloud for the calculation of green score and also employed for billing purpose by the government agencies. This integrated platform can replace the manual billing and directly benefits the government.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.93-104
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• 2024

Demand response is a strategy that encourages customers to adjust their consumption patterns at times of peak demand with the aim to improve the reliability of the power grid and minimize expenses. The integration of renewable energy sources into smart grids poses significant challenges due to their intermittent and unpredictable nature. Demand response strategies, coupled with reinforcement learning techniques, have emerged as promising approaches to address these challenges and optimize grid operations where traditional methods fail to meet such kind of complex requirements. This research focuses on investigating the application of reinforcement learning algorithms in demand response for renewable energy integration. The objectives include optimizing demand-side flexibility, improving renewable energy utilization, and enhancing grid stability. The results emphasize the effectiveness of demand response strategies based on reinforcement learning in enhancing grid flexibility and facilitating the integration of renewable energy.

• Journal of the Korean Solar Energy Society
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• v.40 no.5
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• pp.13-22
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• 2020

Day ahead forecast is necessary for the electricity market to stabilize the electricity penetration. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for longer than 12 hours forecast horizon. Korea Meteorological Administration operates the UM-LDAPS model to produce the 36 hours forecast of hourly total irradiance 4 times a day. This study interpolates the hourly total irradiance into 15 minute instantaneous irradiance and then compare them with observed solar irradiance at four ground stations at 1 minute resolution. Numerical weather prediction model employed here was produced at 00 UTC or 18 UTC from January to December, 2018. To compare the statistical model for the forecast horizon less than 3 hours, smart persistent model is used as a reference model. Relative root mean square error of 15 minute instantaneous irradiance are averaged over all ground stations as being 18.4% and 19.6% initialized at 18 and 00 UTC, respectively. Numerical weather prediction is better than smart persistent model at 1 hour after simulation began.