• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1251-1260
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• 2013

This paper presents the optimal scheduling of hourly consumption in a residential community (community, neighborhood, etc.) based on real-time electricity price. The residential community encompasses individual residential loads, communal (shared) loads, and local generation. Community-aggregated loads, which include residential and communal loads, are modeled as fixed, adjustable, shiftable, and storage loads. The objective of the optimal load scheduling problem is to minimize the community-aggregated electricity payment considering the convenience of individual residents and hourly community load characteristics. Limitations are included on the hourly utility load (defined as community-aggregated load minus the local generation) that is imported from the utility grid. Lagrangian relaxation (LR) is applied to decouple the utility constraint and provide tractable subproblems. The decomposed subproblems are formulated as mixed-integer programming (MIP) problems. The proposed model would be used by community master controllers to optimize the utility load schedule and minimize the community-aggregated electricity payment. Illustrative optimal load scheduling examples of a single resident as well as an aggregated community including 200 residents are presented to show the efficiency of the proposed method based on real-time electricity price.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.331-338
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• 2005

In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS). In competitive power markets, electricity prices are determined by the balance between demand and supply through electric power exchanges or by bilateral contracts. Therefore, in decision makings, it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling approach, firstly, electricity prices over the targeted period are forecasted based on Artificial Neural Network (ANN) and also a newly proposed aggregated bidding curve. Secondary, the maintenance scheduling is formulated as a combinatorial optimization problem with a novel objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss by Maintenance (OLM) is adopted to maximize the profit of generation companies (GENCOS). Thirdly, the combinatorial optimization maintenance scheduling problem is solved by using Reactive Tabu Search in the light of the objective functions and forecasted electricity prices. Finally, the proposed maintenance scheduling is applied to a practical test power system to verify the advantages and practicability of the proposed method.

• Health Policy and Management
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• v.30 no.2
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• pp.164-177
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• 2020

This study aims to compare the experience of selected countries in operating separate payment system for new healthcare technology and to find implications for price setting in Korea. We analyzed the related reports, papers, laws, regulations, and related agencies' online materials from five selected countries including the United States, Japan, Taiwan, Germany, and France. Each country has its own additional payment system for new technologies: transitional pass-through payment and new technology ambulatory payment classification for outpatient care and new technology add-on payment for inpatient care (USA), an extra payment for materials with new functions or new treatment (C1, C2; Japan), an additional payment system for new special treatment materials (Taiwan), a short-term extra funding for new diagnosis and treatment (NUB; Germany), and list of additional payments for new medical devices (France). The technology should be proven safe and effective in order to get approval for an additional payment. The price is determined by considering the actual cost of providing the technology and the cost of existing similar technologies listed in the benefits package. The revision cycle of the additional payment is 1 to 4 years. The cost or usage is monitored during that period and then integrated into the existing fee schedule or removed from the list. We conclude that it is important to set the explicit criteria to select services eligible for additional payment, to collect and analyze data to assess eligibility and to set the payment, to monitor the usage or cost, and to make follow-up measures in price setting for new health technologies in Korea.

• Journal of the Korean association of regional geographers
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• v.14 no.5
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• pp.553-572
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• 2008

International financial crises of the mid-l990s are widely perceived as the trigger that prompted recognition of the need for a new data quality management by the OECD, IMF, Eurostat and individual statistical agency. The official statistics improvements schedule in Korea was launched in 1996 as part of a broader internationally-agreed-upon initiative to strengthen transparency and promote good governance practices. These new initiatives are based on the Total Quality Management(TQM) movement and other management frameworks broadening the concept of quality beyond the traditional statistician's concepts of data quality. This paper aims to evaluate the statistics quality of Land Price Index. Evauation Method is the National Statistical Office's Data Quality Management System which focus on accuracy, timeliness, relevance, accessibility, comparability, serviceability, efficiency.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.11B
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• pp.709-717
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• 2007

Pricing a network service aims for congestion control of the network as well as economic efficiency. A monopolistic supplier providing users with a network service on a shared link needs a pricing schedule that maximizes revenue under the link's bandwidth constraint and guarantees the bandwidth purchased by the users. In that case, nonlinear pricing is an efficient scheme which meets both requirements. This study reviews how nonlinear pricing can be applied to the network service under the constraint and shows that the nonlinear pricing may result in a fixed unit price of bandwidth as linear pricing when demand characteristics of the users follow a power law. Also, the way how the provider with incomplete information on the demand distribution seeks for the optimal pricing from the degree of the network congestion is introduced and the relationship between the development direction of the Internet and internet pricing is considered based on the results of the study.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2227-2235
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• 2018

Under the current policies and compensation rules in South Korea, Photovoltaic (PV) generation supplier can maximize the profit by combining PV generation with Energy Storage System (ESS). However, the existing operational strategy of ESS is not able to maximize the profit due to the limitation of ESS capacity. In this paper, new ESS scheduling algorithm is introduced by utilizing the System Marginal Price (SMP) and PV generation forecasting to maximize the profits of PV generation supplier. The proposed algorithm determines the charging time of ESS by ranking the charging schedule from low to high SMP when PV generation is more than enough to charge ESS. The discharging time of ESS is determined by ranking the discharging schedule from high to low SMP when ESS energy is not enough to maintain the discharging. To compensate forecasting error, the algorithm is updated every hour to apply the up-to-date information. The simulation is performed to verify the effectiveness of the proposed algorithm by using actual PV generation and ESS information.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.5
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• pp.7-13
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• 2011

The climate change due to the increased consumption with fossil fuel and rise of the oil price have been serious global issues. Automobile industry consumes 30% of the oil every year and causes air pollution and global warming by the exhaust emissions and carbon dioxide ($CO_2$). The demand of two-wheeled vehicle increases every year due to the parking and traffic problem caused by the increased automobiles in the urban area. Approximately 50,000,000 two-wheeled vehicles were produced in 2008. The development and sales of the hybrid two-wheeled vehicle industry become active due to its increased market demands. In this paper, the change of the motor and battery efficiency, driving distance, hill climbing ability with the change of the motor capacity was analyzed. Simulation of the peculiarities in urban driving schedule(World-wide Motorcycle Test Cycle(WMTC), Manhattan driving schedule), constant speed(10 km/h, 35 km/h) of small electronic two-wheeled vehicle was also carried out. Through the simulation result, appropriate capacities of the motor and battery for urban driving was acquired.

• 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.

• Journal of the Korea Institute of Building Construction
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• v.13 no.3
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• pp.263-271
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• 2013

Change orders have been widespread in both the private and public construction sectors. In particular, in the case of G2B (Government to Business) contracts, adjustment of contract price and/or schedule extension is a frequent occurrence due to change orders. To uncover the causes of change orders and suggest an appropriate strategy, this study analyzed 296 cases of change orders in military facility construction projects from 2008 to 2010. The analysis revealed that the major causes of change orders are users' additional requirements (28.38%), a change of finishing materials (23.99%), and change of footing type (17.57%), in that order. Building on the results of this analysis, the authors suggest plans for practical improvements. Specific recommendations include 1) reflect user requirements at the early stage, 2) minimize the use of additional budget due to change orders, and 3) reduce the process and time for contract amendment, among others. The results of this study may provide significant implications to those involved in military construction projects, particularly project owners (i.e., the Ministry of Defense) and contractors.

• Korean Journal of Construction Engineering and Management
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• v.7 no.2 s.30
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• pp.171-180
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• 2006

Change is inevitable and is a reality of construction projects. Most construction contracts include change clauses and allowing contractors an equitable adjustment to the contract price and duration caused by change. However, the actions of a contractor can cause a loss of productivity and furthermore can result in disruption of the whole project because of a cumulative or ripple effect. Because of its complicated nature, it becomes a complex issue to determine the cumulative impact (ripple effect) caused by single or multiple change orders. Furthermore, owners and contractors do not always agree on the adjusted contract price for the cumulative impact of the changes. A number of studies have attempted to quantify the impact of change orders on project costs and schedule. Many of these attempted to develop regression models to quantify the loss. However, regression analysis has shortcomings in dealing with many qualitative or noisy input data. This study develops ANN models to classify and quantify the labor productivity losses that are caused by the cumulative impact of change orders. The results show that ANN models give significantly improved performance compared to traditional statistical models.