• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.8
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• pp.1074-1079
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• 2013

Demand response(DR) is potential generation alternative to improve the reliability indices of system and load points. However, when demand resources scheduled in DR market fail to reduce demand, it can create new problems associated with maintaining a reliable supply. In this paper, a reliability model of demand resource is constructed considering customers' behaviors in the same form as conventional generation units, where availability and unavailability are associated with the simple two-state model. As a result, the generalized reliability model of demand resources is represented by multi-state model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.154-158
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• 1997

This paper proposes a new resource allocation system where overall performance can be improved using production smoothing method. In economic point of view, market price is determined by the market mechanism that is subject to the law of demand and supply. Similarly, agents determine whether to allocate tasks to machines by profit and loss or not. In existing resource allocation system, tasks are exclusively allocated to agents with better manufacturing conditions, because they are evaluated by the only currency. But in the proposed resource allocation system, agents are evaluated by not only a currency but also machine specifications. Hereby, the production smoothing is achieved and we expect to improve system performance In this study, we propose a resource allocation system with consideration of Production Smoothing.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.165-172
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• 2018

This paper presents a structure of the retail electricity market based on the local pool with LDERP(Local Distributed Energy Resource Provider) for small energy prosumers. LDERP is an operator for the proposed market, which conducts performance measurement and settlement by the distribution plan determined through contract with participants. The trading process is designed similarly to the negawatt market. In the case study, the many-sided conditions of the proposed market are comparatively analyzed with the existing prosumer programs. The results demonstrate the effectiveness of the proposed framework in determining the purpose of market operating for the benefit of participants according to the various situations.

• Environmental and Resource Economics Review
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• v.9 no.4
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• pp.747-771
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• 2000

This paper reports the results of a study to elicit willingness to pay (WTP) for changes in health risks from exposure to As, Pb, THM in tap water using experimental market method. The experimental market method, compared with other non-market valuation methods, allows us to use incentive compatible demand revealing scheme, to acquire market-like experience through repetitive auctions, and to incorporate learning process by providing new information during the session. Participants seemed to utilize the objective risk information in a 'rational' manner, and to change their WTP bids accordingly. Moreover they were able to reduce the 'ambiguity' in risk perception processes when objective risk probabilities provided are quite different from their subjective perceptions. Nonetheless, anchoring effects appeared to be still persistent in spite of market-like experience and learning opportunity. And implicit values entailed by WTP bid/risk tradeoffs indicate a wide variation in values across alternative risk reductions and overrated responses to very small risk reductions.

• Korean System Dynamics Review
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• v.5 no.1
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• pp.49-71
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• 2004

Demand-side Management can be defined as'any utility activity aimed at modifying customers' use of energy to produce desired changes in the utility's load shape'. Customers benefit by being able to control energy costs and improve quality of life and become more productive. Utilities benefit from DSM's value as a resource that enhances asset utilization and reduces both fuel costs and environmental emissions. The scope of DSM includes load management through rate schedules and conservation by improving energy effciency and using electricity consumption effectively. This paper study the DSM resource evaluation and customer behavior analysis todesign the DSM Program plan in response to customer needs. We develop basic system dynamics model to analysis the customer behavior based on a survey research. The DSM Program participants in the Hi- efficiency Inverter, Electric motor and efficient lighting applicancies operating by Conservation program 2002 become the survey objects. DSM resource evaluation evaluate firstt the distribution potentialities of each machine and then forecast the degree of diffusion. We apply the system dynamic approach to simulate the dynamic DSM market situation at the domestic beginning. This model will give the energy Planner the opportunity to create different scenarios for DSM program planning. Also it will lead to increased understanding of the dynamic DSM market

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.789-795
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• 2014

In a demand response (DR) market run by independent system operators (ISOs), load aggregators are important market participants who aggregate small retail customers through various DR programs. A load aggregator can minimize the allocation cost by efficiently allocating its demand response resources (DRRs) considering retail customers' characteristics. However, the uncertain response behaviors of retail customers can influence the allocation strategy of its DRRs, increasing the economic risk of DRR allocation. This paper presents a risk-based DRR allocation method for the load aggregator that takes into account not only the physical characteristics of retail customers but also the risk due to the associated response uncertainties. In the paper, a conditional value-at-risk (CVaR) is applied to deal with the risk due to response uncertainties. Numerical results are presented to illustrate the effectiveness of the proposed method.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.5
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• pp.275-285
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• 2006

This paper proposes an efficient market mechanism-based resource transaction model for grid computing. This model predicts the next resource demand of users and suggests reasonable resource price for both of customers and resource providers. This model increases resource transactions between customers and resource providers and reduces the average of transaction response times from resource providers. For prediction accuracy improvement of resource demands and suggestion of reasonable resource price, this model introduces a statistics-based prediction model and a price decision model of microeconomics. For performance evaluating, this paper measures resource demand prediction accuracy rate of users, response time of resource transaction, the number of resource transactions, and resource utilization. With 87.45% of reliable prediction accuracy, this model works on the less 72.39% of response time than existing resource transaction models in a grid computing environment. The number of transactions and the resource utilization increase up to 162.56% and up to 230%, respectively.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.195-209
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• 2016

IoT (Internet of Things) is a collective term referring to application services that provide information through sensors/devices connected to the internet. The real world application of IoT is expanding fast along with growing number of sensors/devices. However, since IoT application relies on vertical combination of sensors/devices networks, information sharing within IoT services remains unresolved challenge. Consequently, IoT sensors/devices demand high construction and maintenance costs, rendering the creation of new IoT services potentially expensive. One solution is to launch an IoT open market for information sharing similar to that of App Store for smart-phones. Doing so will efficiently allow novel IoT services to emerge across various industries, because developers can purchase licenses to access IoT resources directly via an open market. Sharing IoT resource information through an open market will create an echo-system conducive for easy utilization of resources and communication between IoT service providers, resource owners, and developers. This paper proposes the new business model of IoT open market for information sharing, and the requirements for ensuring security and standardization of open markets.

• Environmental and Resource Economics Review
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• v.17 no.2
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• pp.287-311
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• 2008

This paper is designed to examine how the global timber market impacted by climate change would affect global warming through the carbon flux of forests. For this purpose, we integrated the modified TSM 2000 and the extended TCM in order to simulate the projection of net carbon release of forests from 1995 to 2085. On the basis of the simulation results under normal demand growth scenario, we identified that the global timber market impacted by climate change ameliorates the atmospheric carbon about 3.60% of carbon dioxide concentration in 1990 over 90 years. This implies that the global timber market impacted by climate change has a negative feedback effect on global warming over 90 years. For sensitivity analysis, we performed these simulation procedure under high demand growth scenario and very high demand growth scenario.

• Journal of the Korea Convergence Society
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• v.8 no.1
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• pp.19-23
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• 2017

Energy is a vital resource for the economic growth and the social development for any country. As the industry becomes more sophisticated and the economy more grows, the electricity demand is increasing. So forecasting electricity demand is an important for electricity suppliers. Forecasting electricity demand makes it possible to distribute electricity demand. As the market for Negawatt market began to grow in Korea from 2014, the prediction of electricity consumption demand becomes more important. Moreover, power consumption forecasting provides a way for demand management to be directly or indirectly participated by consumers in the electricity market. We use Genetic Algorithms to predict the energy demand of the fishing industry in Jeju Island by using GDP, per capita gross national income, value add, and domestic electricity consumption from 1999 to 2011. Genetic Algorithm is useful for finding optimal solutions in various fields. In this paper, genetic algorithm finds optimal parameters. The objective is to find the optimal value of the coefficients used to predict the electricity demand and to minimize the error rate between the predicted value and the actual power consumption values.