• ETRI Journal
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• v.45 no.6
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• pp.996-1006
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• 2023

Renewable energy is promoted massively to overcome problems that fossil fuel power plants generate. One popular renewable energy type that offers easy installation is a photovoltaic (PV) system. However, the energy harvested through a PV system is not optimal because influenced by exposure to solar irradiance in the PV module, which is constantly changing caused by weather. The maximum power point tracking (MPPT) technique was developed to maximize the energy potential harvested from the PV system. This paper presents the MPPT technique, which is operated on a new high-gain voltage DC/DC converter that has never been tested before for the MPPT technique in PV systems. Fuzzy logic (FL) was used to operate the MPPT technique on the converter. Conventional and adaptive perturb and observe (P&O) techniques based on variables step size were also used to operate the MPPT. The performance generated by the FL algorithm outperformed conventional and variable step-size P&O. It is evident that the oscillation caused by the FL algorithm is more petite than variables step-size and conventional P&O. Furthermore, FL's tracking speed algorithm for tracking MPP is twice as fast as conventional P&O.

• New & Renewable Energy
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• v.9 no.2
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• pp.51-57
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• 2013

The Grid code is being strengthen as increase of renewable energy ratio. Especially, the grid connection regulations are continuously being updated for stable operation of power grids. Static grid support and Dynamic grid support must make an accurate measure at Grid connected point because they needs control algorithm individually. It has to exactly measure voltage including switching ripple at the output of the inverter generating system. In addition, it is necessary to have an accurate voltage measurement when the situation rapidly changing the grid impedance is caused by the input of serial impedance of transformer and line impedance as well as Grid Fault Device. In this paper, We propose a new detection method of grid voltage to calculate accurately the r.m.s voltage of the grid connection point along the standard required by the low voltage regulation. We verified performance through simulation grid fault device.

• 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.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.370-373
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• 2005

In this research, we presented the regenerative braking algorithms considering fuel economy and charging oftenness, and also analyzed these algorithms. The first algorithm was the regenerative braking algorithm for the ideal recovery of kinetic energy. The HEV using this algorithm had high fuel economy, on the other hand frequent charging was occurred. The second algorithm was the regenerative braking algorithm for reduction of the charging oftenness. Using this algorithm, the HEV had the low charging oftenness and small loss of fuel economy.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.65-68
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• 2008

This paper proposes the parallel operation control algorithm of a power conditioning system (PCS) for a distributed Fuel Cell power generation system. A proposed control algorithm is made good a drawback of the conventional control algorithm. The controller must also supervise the total PCS operation while communicating with the fuel cell system controller. Simulation results are presented to performance of a proposed control algorithm for the PCS.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.4
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• pp.505-514
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• 2015

This paper proposes a probabilistic generation assessment model of renewable energy generators(REGs) considering uncertainty of resources, mainly focused on Wind Turbine Generator(WTG) and Solar Cell Generator(SCG) which are dispersed widely in South Korea The proposed numerical analysis method assesses the one day-ahead generation by combining equivalent generation characteristics function and probabilistic distribution function of wind speed(WS) and solar radiation(SR) resources. The equivalent generation functions(EGFs) of the wind and solar farms are established by grouping a lot of the farms appropriately centered on Weather Measurement Station(WMS). First, the EGFs are assessed by using regression analysis method based on typical least square method from the recorded actual generation data and historical resources(WS and SR). Second, the generation of the REGs is assessed by adding the one day-ahead resources forecast, announced by WMS, to the EGFs which are formulated as third order degree polynomials using the regression analysis. Third, a Renewable Energy Generation Assessment System(REGAS) including D/B of recorded actual generation data and historical resources is developed using the model and algorithm predicting one day-ahead power output of renewable energy generators.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1123-1146
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• 2023

In South Korea, there have been many studies on efficient building-energy management using renewable energy facilities in single zero-energy houses or buildings. However, such management was limited due to spatial and economic problems. To realize a smart zero-energy city, studying efficient energy integration for the entire city, not just for a single house or building, is necessary. Therefore, this study was conducted in the eco-friendly energy town of Chungbuk Innovation City. Chungbuk successfully realized energy independence by converging new and renewable energy facilities for the first time in South Korea. This study analyzes energy data collected from public buildings in that town every minute for a year. We propose a smart city building-energy management model based on the results that combine various renewable energy sources with grid power. Supervised learning can determine when it is best to sell surplus electricity, or unsupervised learning can be used if there is a particular pattern or rule for energy use. However, it is more appropriate to use reinforcement learning to maximize rewards in an environment with numerous variables that change every moment. Therefore, we propose a power distribution algorithm based on reinforcement learning that considers the sales of Energy Storage System power from surplus renewable energy. Finally, we confirm through economic analysis that a 10% saving is possible from this efficiency.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1287-1293
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• 2009

This paper presents an application of Harmony Search (HM) meta-heuristic optimization algorithm for optimal operation of microgrid system. The microgrid system considered in this paper consists of a wind turbine, a diesel generator, and a fuel cell. An one day load profile which divided 20 minute data and wind resource for wind turbine generator were used for the study. In optimization, the HS algorithm is used for solving the problem of microgrid system operation which a various generation resources are available to meet the customer load demand with minimum operating cost. The application of HS algorithm to optimal operation of microgrid proves its effectiveness to determine optimally the generating resources without any differences of load mismatch and having its nature of fast convergency time as compared to other optimization method.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.6
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• pp.507-514
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• 2013

In this paper, the proposed charging algorithm is converted from the charging mode to compensate the transient state in the solar battery charging system. The maximum power point tracking (MPPT) control methods and the various charging algorithms for the optimal battery charging are reviewed. The proposed algorithm has excellent transient characteristics compare to the previous algorithm by adding the optimal control method to compensate the transient state when the charging mode switches from the constant current mode to the constant voltage mode based on the conventional constant-current constant-voltage (CC-CV) charging algorithm. The effectiveness of the proposed method has been verified by simulations and experimental results.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.107-110
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• 2009

The photovoltaic power system is effected by atmospheric condition. Therefore, The maximum power point tracking(MPPT) algorithm of the Photovoltaic (PV) power system is needed for high efficiency. Many MPPT techniques have been considered in past, but In this paper, the author analyzes widely known P&O MPPT algorithm and ImP&O algorithm, and presents new MPPT algorithm complementing weaknesses of other two algorithms.