• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.414-424
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• 2015

In this research, we consider a smart grid network of electricity with multiple consumers connected to a monopolistic provider. Each consumer can be informed the real time price changes through the smart meter and updates his consumption schedule to minimize the energy consumption expenditures by which the required power demand should be satisfied under the given real time pricing scheme. This real-time decision making problem has been recently studied through game-theoretic approach. The present paper contributes to the existing literature by incorporating storage appliance into the set of available household appliances which has somewhat distinctive functions compared to other types of appliances and would be regarded to play a significant role in energy consumption scheduling for the future smart grid. We propose a game-theoretic algorithm which could draw the optimal energy consumption scheduling for each household appliances including storage. Results on simulation data showed that the storage contributed to increase the efficiency of energy consumption pattern in the viewpoint of not only individual consumer but also whole system.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.743-744
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• 2011

Currently, the major issue in power system is construction of smart grid. Unlike conventional electricity grids, smart grid allows two-way communication between electricity suppliers and consumers, as well as enabling more dispersed generation and storage of power. The AMI(advanced metering infrastructure) system is the core technology of smart grid and this paper deals with the design methodology of AMI System using PLC(power line communication) networks.

• Journal of Power Electronics
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• v.9 no.6
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• pp.919-928
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• 2009

Microgrids (MGs) are typically comprised of distributed generators (DGs) including renewable energy sources (RESs), storage devices and controllable loads, which can operate in either interconnected or isolated mode from the main distribution grid. This paper introduces a novel dynamic programming (DP) approach to MG optimization which takes into consideration the coordination of energy supply in terms of heat and electricity. The DP method has been applied successfully to several cases in power system operations. In this paper, a special emphasis is placed on the uncontrollability of RESs, the constraints of DGs, and the application of demand response (DR) programs such as directed load control (DLC), interruptible/curtaillable (I/C) service, and/or demand-side bidding (DSB) in the deregulated market. Finally, in order to illustrate the optimization results, this approach is applied to a couple of examples of MGs in a certain configuration. The results also show the maximum profit that can be achieved.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.6
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• pp.55-62
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• 2007

Actual states of energy utilizations were investigated and analyzed on three representative rural villages in Chungcheongnam-Do. Rural residents were almost using the ondol boiler as a heating facility and oil(diesel, kerosene) and electricity(night thermal-storage power service) as a heating energy. There were a few households using briquette or firewood in a fuel hole with Korean hypocaust. Most of their cooking facilities were gas ranges using LPG. The most popular hot-water supply system was an oil boiler and the next was an electricity boiler. The amount of energy use in a rural household generally showed 20,000 to 40,000 won/month of the electric power rate, 400 to 800 liter/year of the oil and 60 to 120 kg/year of the LPG. Prompt measures should be taken to promote the spread of new and renewable energy such as solar heat, biomass and wind power, etc.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1369-1375
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• 2017

This paper presents an optimal operation scheduling algorithm for a smart home with energy storage system, electric vehicle and distributed generation. The proposed algorithm provides the optimal charge and discharge schedule of the EV and the ESS. In minimizing the electricity costs of the smart home, it considers not only the cost of energy purchase from the grid but also the life cost of batteries. The life costs of batteries are calculated based on the relation between the depth of discharge and life time of battery. As the life time of battery depends on the charge and discharge pattern, optimal charge and discharge schedule should consider the life cost of batteries especially when there is more than one battery with different technical characteristics. The proposed algorithm can also be used for optimal selection of size and type of battery for a smart home.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.51-59
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• 2018

Despite the advantages of energy arbitrage using energy storage systems (ESSs), the high cost of ESSs has not attracted storage owners for the arbitrage. However, as the costs of ESS have decreased and the price volatility of the electricity market has increased, many studies have been conducted on energy arbitrage using ESSs. In this study, the existing two-period model is modified in consideration of the ESS cost and risk-free contracts. Optimal investment strategies that maximize the sum of external effects caused by price changes and arbitrage profits are formulated by market participants. The optimal amounts of ESS investment for three types of investors in three different market structures are determined with game theory, and strategies in the form of the mixed-complementarity problem are solved by using the PATH solver of GAMS. Results show that when all market participants can participate in investment simultaneously, only customers invest in ESSs, which means that customers can obtain market power by operating their ESSs. Attracting other types of ESS investors, such as merchant storage owners and producers, to mitigate market power can be achieved by increasing risk-free contracts.

• Nuclear Engineering and Technology
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• v.50 no.3
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• pp.488-493
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• 2018

The Firebrick Resistance-Heated Energy Storage (FIRES) concept developed by the Massachusetts Institute of Technology aims to enhance profitability of the nuclear power industry in the next decades. Studies carried out at Massachusetts Institute of Technology already provide estimates of the potential revenue from FIRES system when it is applied to industrial heat supply, the likely first application. Here, we investigate the possibility of operating a power plant (PP) with a fluoride-salt-cooled high-temperature reactor and a closed Brayton cycle. This variant offers features such as enhanced nuclear safety as well as flexibility in design of the PP but also radically changes the way of operating the PP. This exploratory study provides estimates of the revenue generated by FIRES in addition to the nominal revenue of the stand-alone fluoride-salt-cooled high-temperature reactor, which are useful for defining an initial design. The electricity price data is based on the day-ahead markets of Germany/Austria and the United States (Iowa). The proposed method derives from the equation of revenue introduced in this study and involves simple computations using MatLab to compute the estimates. Results show variable economic potential depending on the host grid but stress a high profitability in both regions.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.87-106
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• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.

• Transactions of the Korean hydrogen and new energy society
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• v.28 no.2
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• pp.156-165
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• 2017

This study quantitatively assessed the environmental impacts of fuel cell (FC) systems by performing life cycle assessment (LCA) and analyzed their energy efficiencies based on energy return on investment (EROI) and electrical energy stored on investment (ESOI). Molten carbonate fuel cell (MCFC) system and polymer electrolyte membrane fuel cell (PEMFC) system were selected as the fuel cell systems. Five different paths to produce hydrogen ($H_2$) as fuel such as natural gas steam reforming (NGSR), centralized naptha SR (NSR(C)), NSR station (NSR(S)), liquified petroleum gas SR (LPGSR), water electrolysis (WE) were each applied to the FCs. The environmental impacts and the energy efficiencies of the FCs were compared with rechargeable batteries such as $LiFePO_4$ (LFP) and Nickel-metal hydride (Ni-MH). The LCA results show that MCFC_NSR(C) and PEMFC_NSR(C) have the lowest global warming potential (GWP) with 6.23E-02 kg $CO_2$ eq./MJ electricity and 6.84E-02 kg $CO_2$ eq./MJ electricity, respectively. For the impact category of abiotic resource depletion potential (ADP), MCFC_NGSR(S) and PEMFC_NGSR(S) show the lowest impacts of 7.42E-01 g Sb eq./MJ electricity and 7.19E-01 g Sb eq./MJ electricity, respectively. And, the energy efficiencies of the FCs are higher than those of the rechargeable batteries except for the case of hydrogen produced by WE.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.381-389
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• 2020

This study analyzed the optimal electricity cost of a 100% renewable energy source (RES) based system. Especially energy storage system (EES) and supplementary biopower system as well as photovoltaic (PV) and wind power component were included in the proposed RES-based system to overcome the intermittence of RESs and to efficiently balance energy supply and demand. To comparatively analyze the levelized cost of electricity (LCOE) of different RES-based systems, six scenarios were developed according to the involved RESs: PV, wind, PV/wind, PV/biopower, wind/biopower, and PV/wind/biopower systems. We then applied the proposed systems to build a 100% RES-based system in Jeju Island, Korea. As a result, the single component based system, PV and wind power system of 0.18 and 0.28 $/kWh, respectively, cannot compete with the economics of existing electricity grid. However, the optimal LCOE of the hybrid system where PV and wind power are used as main supply options and biopower as supplementary option was identified to be 0.08 $/kWh, which can compete with the economics of an existing electricity grid.