• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.85-92
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• 2018

Reductions of the electricity charge are achieved by demand management of the load. The demand management method of the load using ESS involves peak shifting, which shifts from a high demand time to low demand time. By shifting the load, the peak load can be lowered and the energy charge can be saved. Electricity charges consist of the energy charge and the basic charge per contracted capacity. The energy charge and peak load are minimized by Linear Programming (LP) and Quadratic Programming (QP), respectively. On the other hand, each optimization method has its advantages and disadvantages. First, the LP cannot separate the efficiency of the ESS. To solve these problems, the charge and discharge efficiency of the ESS was separated by Mixed Integer Linear Programming (MILP). Nevertheless, both methods have the disadvantages that they must assume the reduction ratio of peak load. Therefore, QP was used to solve this problem. The next step was to optimize the formula combination of QP and LP to minimize the electricity charge. On the other hand, these two methods have disadvantages in that the charge and discharge efficiency of the ESS cannot be separated. This paper proposes an optimization method according to the situation by analyzing quantitatively the advantages and disadvantages of each optimization method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.9
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• pp.1174-1180
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• 2018

In December 2017, the government announced plans to increase the current proportion of renewable energy from 7% to 20% by 2030 through a plan called the Renewable Energy 3020 Implementation Plan. Therefore, the demand for installation of photovoltaic(PV), wind turbine(WT) and battery energy storage system(BESS) is expected to increase. In particular, the system combined with energy storage system(ESS) is expected to take up a large portion since PV and WT can receive high renewable energy certificates(REC) weights when combined with ESS. In this study, we calculate the optimal capacity of the power conditioning system(PCS) and the BESS by comparing the economical efficiency and maximize the efficiency of the PV-BESS system in which the PV and the BESS are connected. By analyzing the system marginal price(SMP) and REC, it maximize profits through application of REC weight 5.0 and optimal charge-discharge scheduling according to the SMP changes.

• Proceedings of the KIPE Conference
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• 2012.11a
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• pp.33-34
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• 2012

As the distributed power system with PV and ESS is highlighted to be one of the most prominent structure to replace the traditional electric power system, power flow scheduling is expected to bring better system efficiency. Optimal energy management system (EMS) where the power from PV and the grid is managed in time-domain using ESS needs an optimization process. In this paper, main optimization method is implemented using dynamic programming (DP). To overcome the drawback of DP in which ideal future information is required, prediction stage precedes every EMS execution. A simple auto-regressive moving-average (ARMA) forecasting followed by a PI-controller updates the prediction data. Assessment of the on-line optimal EMS scheme has been evaluated on several cases.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.157-163
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• 2019

K-BEMS System was introduced to reduce peak load and to save total energy of the 120 buildings that KEPCO headquarter and branch offices use. K-BEMS system is composed of PV, battery, and hybrid PCS. In this system, ESS, PV, lighting is used to save building energy based on demand prediction. Currently, neural network technique for short past data is applied to demand prediction, and fixed scheduling method by operator for ESS charging/discharging is used. To enhance this system, KEPCO research institute has carried out this K-BEMS research project for 3 years since January 2016. As the result of this project, we developed new real-time highly reliable building demand prediction technique with error free and optimized automatic ESS charging/discharging technique. Through several field test, we can certify the developed algorithm performance successfully. So we will describe the details in this paper.

• 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.

• Transactions of the Korean hydrogen and new energy society
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• v.34 no.2
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• pp.91-99
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• 2023

Participation in power trading using surplus power is considered a business model active in the domestic energy trade market, but it is limited only if the legal requirements according to the type, capacity, and use of the facilities to be applied for are satisfied. The hydrogen residential demonstration model presented in this paper includes solar power, energy storage system (ESS), fuel cell, and water electrolysis facilities in electrical facilities for private use with low-voltage power receiving system. The concept of operations strategy for this model focuses on securing the energy self-sufficiency ratio of the entire system, securing economic feasibility through the optimal operation module installed in the energy management system (EMS), and securing the stability of the internal power balancing issue during the stand-alone mode. An electric facility configuration method of a hydrogen residential complex demonstrated to achieve this operational goal has a structure in which individual energy sources are electrically connected to the main bus, and ESS is also directly connected to the main bus instead of a renewable connection type to perform charging/discharging operation for energy balancing management in the complex. If surplus power exists after scheduling, participation in power trading through reverse transmission parallel operation can be considered to solve the energy balancing problem and ensure profitability. Consequentially, this paper reviews the legal regulations on participation in electric power trading using surplus power from hydrogen residential models that can produce and consume power, gas, and thermal energy including hybrid distributed power sources, and suggests action plans.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.225-234
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• 2015

Nowadays a Virtual Power Plant (VPP) represents an aggregation of distributed energy resource such as Distributed Generation (DG), Combined Heat and Power generation (CHP), Energy Storage Systems (ESS) and load in order to operate as a single power plant by using Information and Communication Technologies, ICT. The VPP has been developed and verified based on a single virtual plant platform which is connected with a number of various distributed energy resources. As the VPP's distributed energy resources increase, so does the number of data from distributed energy. Moreover, it is obviously inefficient in the aspects of technique and cost that a virtual plant platform operates in a centralized manner over widespread region. In this paper the concept of the large-scale VPP which can reduce a error probability of system's load and increase the robustness of data exchange among distributed energy resources will be proposed. In addition, it can directly control and supervise energy resource by making small size's virtual platform which can make a optimal resource scheduling to consider of variable and sensitive load in the large-scale VPP. It makes certain the result is verified by simulation.

• 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).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.10
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• pp.1390-1396
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• 2013

The paper estimated the reasonable market price of lead-acid battery energy storage system (BESS) intended for demand management of electricity customers. As time-of-use (TOU) tariffs have extended to a larger number of customers and gaps in the peak and off-peak rates have gradually risen, deployment of BESS has been highly needed. However, immature engineering techniques, lack of field experiences and high initial investment cost have been barriers to opening up ESS markets. This paper assessed electricity cost that BESS operation could save for customers and, based on the possible cost savings, estimated reasonable prices at which BESSs could become a more prospective option for demand management of customers. Battery scheduling was optimized to maximize the electricity cost savings that BESS would possibly achieve under TOU tariffs conditions. Basic economic factors such as payback period and return on investment were calculated to determine reasonable market prices. Actual load data of 12 industrial customers were used for case studies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.393-398
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• 2015

City or Community-scale Energy Management System(CEMS) is used to reduce the total energy consumed in the city by arranging the energy resources efficiently at the planning stage and controlling them economically at the operating stage. Of the operational functions of the CEMS, generation forecasting of renewable energy resources is an essential feature for the effective supply scheduling. This is because it can develop daily operating schedules of controllable generators in the city (e.g. diesel turbine, micro-gas turbine, ESS, CHP and so on) in order to minimize the inflow of the external power supply system, considering the amount of power generated by the uncontrollable renewable energy resources. This paper is written to introduce numerical models for photo-voltaic power generation prediction based on the weather forecasting information. Unlike the conventional methods using the average radiation or average utilization rate, the proposed models are developed for CEMS applications using the realtime weather forecast information provided by the National Weather Service.