• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1486-1492
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• 2016

This study presents the electrical load forecasting and error correction method using a real building load pattern, and the way to manage the energy storage system with forecasting results for economical load operation. To make a unique pattern of target load, we performed the Hierarchical clustering that is one of the data mining techniques, defined load pattern(group) and forecasted the demand load according to the clustering result of electrical load through the previous study. In this paper, we propose the new reference demand for improving a predictive accuracy of load demand forecasting. In addition we study an error correction method for response of load events in demand load forecasting, and verify the effects of proposed correction method through EMS scheduling simulation with load forecasting correction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.61-69
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• 2021

On-demand broadcast is an effective data dissemination technique in mobile computing environments. This paper explores the issues for scheduling multi-data queries in on-demand broadcast environments, and proposes a new broadcast scheduling scheme named CASS. The proposed scheme prioritizes queries by reflecting the characteristics of multi-data queries, and selects the data that has not been broadcast in the query for the longest time as the broadcast data according to the query priority. Simulation is performed to evaluate the performance of CASS. The simulation results show that the proposed scheme outperforms other schemes in terms of the average response time since it can show highly desirable characteristics in the aspects of query data adjacency and data conflict rate.

• Proceedings of the KIEE Conference
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• summer
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• pp.663-665
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• 2004

There are some demand response program which is Direct Load Control and so on in Korea. These are used to manage lack of power stability or shift peak time for shading load. It is very important not only using stability power system but controling and scheduling power system on the whole. Interruptible loads are essential resources to solve lack of energy and limit of constructing generator On recently days, Demand Response Program's reliability is recognized as ancillary or reserve service in many country. This paper presents a necessity to apply demand resource to our ancillary program. For this reason, it is introduce overseas ancillary program using load resource.

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.116-124
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• 2015

Smart grids propose new solutions for electricity consumers as a means to help them use energy in an efficient way. In this paper, we consider the demand-side management issue that exists for a group of consumers (houses) that are equipped with renewable energy (wind turbines) and storage units (battery), and we try to find the optimal scheduling for their home appliances, in order to reduce their electricity bills. Our simulation results prove the effectiveness of our approach, as they show a significant reduction in electricity costs when using renewable energy and battery storage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3663-3679
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• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.312-321
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• 2018

The Korean DR market proposes suppression of peak demand under reliability crisis caused a natural disaster or unexpected power plant accidents as well as saving power plant construction costs and expanding amount of reserve as utility's perspective. End-user is notified a DR event signal DR execution before one hour, and executes DR based on requested amount of load reduction. This paper proposes a DR energy management algorithm that can be scheduled the optimal operations of chiller system and ESS in the next day considering the TOU tariff and DR scheme. In this DR algorithm is divided into two scheduling's; day-ahead operation scheduling with temperature forecasting error and operation rescheduling on DR operation. In day-ahead operation scheduling, the operations of DR resources are scheduled based on the finite number of ambient temperature scenarios, which have been generated based on the historical ambient temperature data. As well as, the uncertainties in DR event including requested amount of load reduction and specified DR duration are also considered as scenarios. Also, operation rescheduling on DR operation day is proposed to ensure thermal comfort and the benefit of a COB owner. The proposed method minimizes the expected energy cost by a mixed integer linear programming (MILP).

• Advances in Energy Research
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• v.7 no.2
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• pp.85-100
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• 2020

Distributed energy resources (DERs) are essential for coping with growing multiple energy demands. A microgrid (MG) is a small-scale version of the power system which makes possible the integration of DERs as well as achieving maximum demand-side management utilization. Hence, this study focuses on the analysis of optimal power dispatch considering economic aspects in a multi-carrier microgrid (MCMG) with price-responsive loads. This paper proposes a novel time-based demand-side management in order to reshape the load curve, as well as preventing the excessive use of energy in peak hours. In conventional studies, energy consumption is optimized from the perspective of each infrastructure user without considering the interactions. Here, the interaction of energy system infrastructures is considered in the presence of energy storage systems (ESSs), small-scale energy resources (SSERs), and responsive loads. Simulations are performed using GAMS (General Algebraic modeling system) to model MCMG, which are connected to the electricity, natural gas, and district heat networks for supplying multiple energy demands. Results show that the simultaneous operation of various energy carriers, as well as utilization of price-responsive loads, lead to better MCMG performance and decrease operating costs for smart distribution grids. This model is examined on a typical MCMG, and the effectiveness of the proposed model is proven.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.529-530
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• 2015

스마트그리드 환경에서 증가하는 에너지 수요에 대응하기 위하여 수요 반응(Demand response, DR) 프로그램에 대한 연구가 활발히 진행 중에 있다. 특히, 전력 분야에서는 공급측 자원과 수요측 자원을 효율적으로 운영하기 위하여 수요 반응에 기반한 마이크로그리드 운영 기술이 요구되며, 추후 마이크로그리드의 실 시스템에 많은 도입이 예상된다. 본 논문에서는 TOU(Time-of-use)와 DLC(Direct load control)에 기반한 마이크로그리드의 최적 운영계획을 수립하고, 시뮬레이션을 통하여 제안된 수리적 모델의 타당성을 검토하고자 한다.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.3
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• pp.318-324
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• 2014

Recent years have witnessed a growing demand for a wide variety of high-performance industrial robots. In this paper, for accurate gain tuning of a 6-axis articulated industrial robot with reduced noise, a program routine for a dynamic signal analyzer (DSA) using the frequency response method will be programmed using $LabVIEW^{(R)}$. Then, robot transfer functions can be obtained experimentally using the frequency response method with the DSA program. Data from the robot transfer functions are transformed into Bode plots, based on which an optimal gain tuning will be executed. Gain tuning can enhance the response quality of the output signal for a given input signal during real-time control of the robot. The effectiveness of our proposed technique will be verified by implementation with a (lab-manufactured) 6-axis articulated industrial robot (hereinafter called "RS2") and comparison with the zero position gain tuning, as well as other positions.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1069-1078
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• 2018

Demand response (DR) programs give opportunity to consumers to manage their electricity bills. Besides, distribution system operator (DSO) is interested in using DR programs to obtain technical and economic benefits for distribution network. Since small consumers have difficulties to individually take part in the electricity market, an entity named demand response provider (DRP) has been recently defined to aggregate the DR of small consumers. However, implementing DR programs face challenges to fairly allocate benefits and payments between DRP and DSO. This paper presents a procedure for modeling the interaction between DRP and DSO based on a bilevel programming model. Both DSO and DRP behave from their own viewpoint with different objective functions. On the one hand, DRP bids the potential of DR programs, which are load shifting and load curtailment, to maximize its expected profit and on the other hand, DSO purchases electric power from either the electricity market or DRP to supply its consumers by minimizing its overall cost. In the proposed bilevel programming approach, the upper level problem represents the DRP decisions, while the lower level problem represents the DSO behavior. The obtained bilevel programming problem (BPP) is converted into a single level optimizing problem using its Karush-Kuhn-Tucker (KKT) optimality conditions. Furthermore, point estimate method (PEM) is employed to model the uncertainties of the power demands and the electricity market prices. The efficiency of the presented model is verified through the case studies and analysis of the obtained results.