• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1852-1863
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• 2018

Direct current(DC) systems have recently attracted attention due to the increase in DC loads and distributed generations, such as renewable energy sources. Among these technologies, there has been much research into DC distribution systems or DC microgrids. Within this body of research, the main topics have been about optimum control and operation methods in terms of improving power efficiency. When DC systems are controlled and operated using power electronic devices such as converters, it is necessary to design and analyze them by considering the power electronics sections. For this reason, we propose a scalable DC system analysis algorithm, which considers various system configurations depending on the operating mode and location of the converter. The algorithm consists of power flow fault current calculations, and the results of the algorithm can be used for designing DC systems. The algorithm is implemented using MATLAB with defined input and output data. The verification of the algorithm is mainly performed using ETAP software, and the accuracy of the algorithm analysis can be confirmed through the results.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.6 no.2
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• pp.1-7
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• 2010

The objective of this study is to find the optimal control algorithm of a hybrid Plant, which is combined by renewable energy plant of the GSHP(Geothermal Source Heat Pump) and the normal plant (Chiller, boiler). The work presented in this study was carried out in the EnergyPlus(Version 2.0). The EnergyPlus was modified in order to simulate the hybrid plant. The plant system was controlled by the load-range-based operation in which schemes select a user specified set of equipment for each user specified range of a particular simulation condition. In the use of the load-range-based operation, four kind of control cases were defined and simulated in order to obtain the optimal control algorithm of the hybrid plant. The result showed that the Case 2 was the optimal control algorithm which used the GSHP as a base operating plant and the normal plant as an assistant operating plant. Even though the normal plant was operated in full load and the renewable energy plant of the GSHP was operated in partial load, the annual energy consumption of the normal plant was larger than that of the GSHP plant.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1158-1168
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• 2020

The distance vector-hop wireless sensor node location method is one of typical range-free location methods. In distance vector-hop location method, if a wireless node A can directly communicate with wireless sensor network nodes B and C at its communication range, the hop count from wireless sensor nodes A to B is considered to be the same as that form wireless sensor nodes A to C. However, the real distance between wireless sensor nodes A and B may be dissimilar to that between wireless sensor nodes A and C. Therefore, there may be a discrepancy between the real distance and the estimated hop count distance, and this will affect wireless sensor node location error of distance vector-hop method. To overcome this problem, it proposes a wireless sensor network node location method by modifying the method of distance estimation in the distance vector-hop method. Firstly, we set three different communication powers for each node. Different hop counts correspond to different communication powers; and so this makes the corresponding relationship between the real distance and hop count more accurate, and also reduces the distance error between the real and estimated distance in wireless sensor network. Secondly, distance difference between the estimated distance between wireless sensor network anchor nodes and their corresponding real distance is computed. The average value of distance errors that is computed in the second step is used to modify the estimated distance from the wireless sensor network anchor node to the unknown sensor node. The improved node location method has smaller node location error than the distance vector-hop algorithm and other improved location methods, which is proved by simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1721-1726
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• 2011

Recently, interest in renewable energy is increased due to exhaustion of fossil fuel and environmental pollution all over the world, therefore the solar power generation using solar energy is many researched. The solar power generation is required solar tracking control and high concentration solar thermal collector because generation performance is depended on concentrator efficiency. This paper proposes high efficiency solar power generation with two-axis tracking control using dish-type solar thermal collector that has excellent thermal collector performance and tracking algorithm that can be accurately tracked solar position. This paper proves validity through analysis with accuracy of tracking algorithm and generating efficiency.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1586-1592
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• 2017

Voltage sourced converter (VSC) based direct-current (DC) grid has the ability to control power flow flexibly and securely, thus it has become one of the most valid approaches in aspect of large-scale renewable power generation, oceanic island power supply and new urban grid construction. To solve the optimal power flow (OPF) problem in DC grid, an adaptive particle swarm optimization (PSO) algorithm based on fuzzy control theory is proposed in this paper, and the optimal operation considering both power loss and voltage quality is realized. Firstly, the fuzzy membership curve is used to transform two objectives into one, the fitness value of latest step is introduced as input of fuzzy controller to adjust the controlling parameters of PSO dynamically. The proposed strategy was applied in solving the power flow issue in six terminals DC grid model, and corresponding results are presented to verify the effectiveness and feasibility of proposed algorithm.

• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.255-258
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• 2007

In this paper, a PV multi-string PCS with a grid-connection is proposed. An improved MPPT algorithm for the PV multi-string PCS is suggested. Each PV string has its own MPP tracker and the proposed MPPT algorithm prevents LMPP tracking due to power ripple. In the PV PCS with single-phase inverter has a large current ripple at twice the grid frequency. The current ripple reduction algorithm without external component is suggested. Also, this paper proposes a simple control method to achieve sharing of the PV string voltage and current among the interleaved parallel boost converters. All algorithms and controllers are implemented on a single-chip microcontroller. Experimental results obtained on a 3kW prototype show high performance of the proposed PV multi-string PCS.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1331-1338
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• 2011

This paper proposes a method for frequency control of islanded microgrid with battery energy storage system. For frequency control of islanded microgrid, battery energy storage system uses a phase locked loop algorithm with positive sequence components for a fast frequency estimation. Microgrid is a power system with small inertia because it has small capacity generators and inverter systems for renewable energy. So, Islanded microgrid's frequency varies fast and large as small generation and load changes. To reduce frequency variation of islanded microgrid, it needs a device with fast frequency response. For fast frequency response, a fast frequency tracking is important. To show the validation of proposed fast frequency tracking algorithm, battery energy storage system with proposed algorithm is tested in microgrid pilot plant.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1483-1493
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• 2018

Recently, more power facilities are needed to cope with the increasing electric demand. However, the additional construction of generators, transmission and distribution installations is not easy because of environmental problems and citizen's complaints. Under this circumstance, a microgrid system with distributed renewable resources emerges as an alternative of the traditional power systems. Moreover, the configuration of power system changes with more DC loads and more DC installations. This paper is written to introduce an idea of a genetic algorithm-based solution to determine the optimal capacity of the distributed generators depending on the types of system configuration: AC-link, DC-link and Hybrid-link types. In this paper, photovoltaic, wind turbine, energy storage system and diesel generator are considered as distributed generators and the feasibility of the proposed algorithm is verified by comparing the calculated capacity of each distributed resource with HOMER simulation results for 3 types of system configuration.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.229-256
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• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.