• Journal of Internet Computing and Services
• /
• v.24 no.2
• /
• pp.47-56
• /
• 2023

Hydrogen energy is an eco-friendly energy that produces heat and electricity with high energy efficiency and does not emit harmful substances such as greenhouse gases and fine dust. In particular, smart hydrogen energy is an economical, sustainable, and safe future smart hydrogen energy service, which means a service that stably operates based on 'data' by digitally integrating hydrogen energy infrastructure. In this paper, in order to implement a data-based hydrogen charging station demand forecasting model, three hydrogen charging stations (Chuncheon, Sokcho, Pyeongchang) installed in Gangwon-do were selected, supply and demand data of hydrogen charging stations were secured, and 7 machine learning and deep learning algorithms were used. was selected to learn a model with a total of 27 types of input data (weather data + demand for hydrogen charging stations), and the model was evaluated with root mean square error (RMSE). Through this, this paper proposes a machine learning-based hydrogen charging station energy demand prediction model for optimal hydrogen energy supply and demand.

• Transactions of the Korean hydrogen and new energy society
• /
• v.34 no.5
• /
• pp.398-412
• /
• 2023

The Korean government is actively promoting the hydrogen industry as a key driver of economic growth. This commitment is evident in the 2019 hydrogen economy activation roadmap and the 2021 basic plan for hydrogen economy implementation. This study quantitatively analyzes the economic impact of the hydrogen economy using input-output analysis based on the Bank of Korea's 2019 input-output table, projecting its size by 2050. Four parts dealt with production-inducing, value-added creation, employment-inducing, and wage-inducing based on a demand-driven model. The results reveal that transportation had the most remarkable economic effect throughout the hydrogen economy, and production was the least. The hydrogen economy is projected to reach 71.2 trillion won by 2050.

• Journal of Communications and Networks
• /
• v.14 no.6
• /
• pp.662-671
• /
• 2012

Plug-in hybrid electric vehicles (PHEVs) will be widely used in future transportation systems to reduce oil fuel consumption. Therefore, the electrical energy demand will be increased due to the charging of a large number of vehicles. Without intelligent control strategies, the charging process can easily overload the electricity grid at peak hours. In this paper, we consider a smart charging and discharging process for multiple PHEVs in a building's garage to optimize the energy consumption profile of the building. We formulate a centralized optimization problem in which the building controller or planner aims to minimize the square Euclidean distance between the instantaneous energy demand and the average demand of the building by controlling the charging and discharging schedules of PHEVs (or 'users'). The PHEVs' batteries will be charged during low-demand periods and discharged during high-demand periods in order to reduce the peak load of the building. In a decentralized system, we design an energy cost-sharing model and apply a non-cooperative approach to formulate an energy charging and discharging scheduling game, in which the players are the users, their strategies are the battery charging and discharging schedules, and the utility function of each user is defined as the negative total energy payment to the building. Based on the game theory setup, we also propose a distributed algorithm in which each PHEV independently selects its best strategy to maximize the utility function. The PHEVs update the building planner with their energy charging and discharging schedules. We also show that the PHEV owners will have an incentive to participate in the energy charging and discharging game. Simulation results verify that the proposed distributed algorithm will minimize the peak load and the total energy cost simultaneously.

• Journal of the Architectural Institute of Korea Planning & Design
• /
• v.34 no.7
• /
• pp.21-29
• /
• 2018

This study aimed to find and suggest the purpose of future plans, critical items on designing and Related standards to amend construction standards for domestic energy saving and environmentally friendly housings. It would also activate and increase the supplies of passive house minimizing the problems on current apartment housings in winter and reducing heating energy which brings fine dust pollution. After defining the standard model of Korean apartment housings(reference model), this study calculated the amount of heating energy demand per unit area annually as applying the yearly changed standards from 2008 to 2017 to existing standard model. It turned out that applying 2017 construction standards of energy saving and eco-friendly housings to reference model has saved up to 75% of heating energy demand comparing to the one applied 2008's. However, it still could not solve the fundamental problems such as winter fungus, condensation, freezing, freeze and burst, and insulation weakness space, and could not be free from fine dust particles. To solve them, this study suggested improved standards adding critical items on design related to outside insulation, cut off the heat-bridge, enforce air-tightness and heat change efficiency on heat recovery ventilator. As a result, it has reduced more than 10% of heat demand from 2017. It would be more than 90% of savings from 2008 if we make the amount of heat loss be zero on heat bridge on designing stage in the future.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.5
• /
• pp.1709-1718
• /
• 2017

This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The prime objective of the DASO is the minimization of the daily production cost in power systems with high penetration scenarios of variable generation. Furthermore, energy storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO). DASO simulation results are used as the base starting-point values in the hour-ahead online rolling RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid flexibility, to balance out the deviation between predicted and actual net load demand values. Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind forecasting.

• JOURNAL OF ELECTRICAL WORLD
• /
• no.4 s.136
• /
• pp.12-17
• /
• 1988

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.53 no.3
• /
• pp.187-197
• /
• 2004

This paper presents a window-based load management system (LMS) developed as a decision-making tool in the competitive electricity market The developed LMS can help the users to monitor system load patterns, analyze their past energy consumption and schedule for the future energy consumption. The LMS can also provide the effective information on real-time energy/cost monitoring, consumed energy/cost analysis, demand schedule and cost-savings. Therefore. this LMS can be used to plan the optimal demand schedule and consumption strategy.

• KIEAE Journal
• /
• v.10 no.6
• /
• pp.21-26
• /
• 2010

This study is to suggest basic data for the policy on the energy demand and management of an U-based city compliant with characteristics of regions and districts in future city by conducting research on the energy consumption and management of an U-based city, considering city energy demand characteristics focused on the center district of Daegu metropolitan city. U-based city energy consumption and management solution is considered to be effective in establishing the guideline of environment-friendly urban architecture planning as well as the assessment of energy consumption characteristics in a city.

• Journal of Korea Technology Innovation Society
• /
• v.14 no.4
• /
• pp.736-760
• /
• 2011

The importance of new renewable energy is emphasized not only new growth engine but also the key solution for the exhaustion problem of fossil energy and environment problem. For the steady growth of new renewable energy industry, securing related labor force is an essential factor. In this study, the status on labor force of new renewable energy industry was identified and forecasted the labor force demand of new renewable energy in 2015 by reflecting the industrial growth outlook on the new renewable energy. For the quantitative analysis methodology, the stock approach of Bureau of Labor Statistics (BLS) of the United States was applied. Also by performing survey on the experts, the opinions of experts on supply and demand of new renewable energy labor force or worker training programs have been gathered. As a result of study, it has been analyzed that nearly 20% annual growth rate will be shown as the labor force demand in the field of new renewable energy industry increases from 14,100 people in 2010 to 33,200 people in 2015. In the survey on experts, we could find that a plan for supplying labor force must be prepared promptly in order to accomplish new renewable energy supply objectives and industrial growth objectives by our country in the future as the supply of new renewable energy labor force is currently insufficient. Also, it has been analyzed that the effort for deciding the proper new renewable energy labor force training program standard will be necessary. This study result could be used as a material of labor force training plan for the steady growth of new renewable energy industry in the future.

• Current Photovoltaic Research
• /
• v.5 no.3
• /
• pp.100-104
• /
• 2017

This paper analyzes the correlation between Net Benefit Test (NBT) and System marginal price (SMP), which has a significant impact on the allocation of demand response (DR) resources in resource scheduling and commitment (RSC) process, based on the performance data of the demand resource market which has been established in 2014. Demand resources compete with generation resources in the RSC process, and it is prescribed to use demand resources only when net benefit occurs. Analysis result shows that the larger the SMP than the Net Benefit Threshold Price (NBTP), the more the winning bid of demand response resource was. It is interpreted that the introduction of NBT in DR market is justified. The demand resource market has been steadily growing. It is required to expand the scope of resources up to the small-sized DR, and to expand the functionalities of demand resources not only in the current energy market but also in the reserve market in the future. In order for that, institutional improvements are required.