• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 추계학술대회 논문집 전력기술부문
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• pp.146-148
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• 2001

This paper discusses the optimal sizes of BESS. The goal must be optimized electricity charge of the customers-side with choosing the time-of-use rates. Therefore the cost is minimized by BESS installed the customers-side. Feasible ROR that means the ratio of capital costs to economic effect owned the optimal BESS sizes is determined the suitable domestic condition based on the battery cost and power converter system cost. Payback period times can be presented by BESS through the ROR. Multi-Pass Dynamic Programming(MPDP) algorithm is applied to the customer for the optimal sizes determination in this paper. It is to solve the optimal solution under the constraints. To investigate the efficiencies of the constraints, it is applied the typical load curve to the high-voltage customer owned Time-Of-Use(TOU) whether BESS is installed or not. Well, The result is obtained that feasible BESS sizes can be achieved the suitable customers-side of meter through the ROR.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 A
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• pp.315-317
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• 2001

This paper studies for the optimal operation of BESS. The goal must be optimized electricity charge of the customer sides owned time-of-use rates in this paper. Therefore, the least of cost is caused by BESS installation, Multi-Pass Dynamic Programming (MPDP) algorithm is applied to the customer for the optimal operation determination in this paper. It is to solve the optimal solution under the constraints. No matter how become one stage in general, problem is divided into several stage in series in this algorithm. Regardless of the decision step, MPDP is only accomplished based on the state of stage in the present. To investigate the efficiencies of the algorithm, it is applied the typical load curve to the cutomer owned Time-Of-Use(TOU). Result shows that the maximun economic benefits of the battery energy storage system can be achieved by the purposed algorithm.

• 에너지공학
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• 제29권3호
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• pp.74-85
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• 2020

본 논문은 PEVs가 배전계통 모선에 연계되었을 때 PEVs 보급 시나리오별로 PEVs 일간 충전 패턴에 따라 배전계통 모선별 PEVs 충전 일부하곡선을 산정하여 배전계통 모선별 과부하 영향 평가를 하기 위한 알고리즘을 제시하였다. 제안한 알고리즘은 첫째 배전계통 모선별 가구 수 산출을 위한 PEVs 대수 산출, 둘째 PEVs 운행 특성을 고려한 PEVs 충전시작시간 확률밀도 함수 산출, 셋째 PEVs 보급시나리오별로 배터리 특성을 반영한 해당 모선별 PEVs 충전 일부하곡선을 산출하였다. 넷째 산출된 해당 모선별 PEVs 충전 일부하곡선과 기존 일부 하곡선을 합산하여 PEVs 보급시나리오별로 해당 모선의 과부하 영향 평가를 시행하였다. 추가로 제안된 알고리즘에 대해 배전계통 모선별 과부하 영향 평가 검증을 위해 한국 동탄 신도시의 배전계통 회선의 해당 모선(아파트, 단독주택 지역)을 대상으로 사례 검토를 실시하였다.

• Journal of Electrical Engineering and Technology
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• 제9권3호
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• pp.1125-1131
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• 2014

Application of residential demand response (DR) programs are currently realized up to a limited extent due to customers' difficulty in manually responding to the time-differentiated prices. As a solution, this paper proposes an automatic home load management (HLM) framework to achieve the household minimum payment as well as meet the operational constraints to provide customer's comfort. The projected HLM method controls on/off statuses of responsive appliances and the charging/discharging periods of plug-in hybrid electric vehicle (PHEV) and battery storage at home. This paper also studies the impacts of different time-varying tariffs, i.e., time of use (TOU), real time pricing (RTP), and inclining block rate (IBR), on the home load management (HLM). The study is effectuated in a smart home with electrical appliances, a PHEV, and a storage system. The simulation results are presented to demonstrate the effectiveness of the proposed HLM program. Peak of household load demand along with the customer payment costs are reported as the consequence of applying different pricings models in HLM.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2015년도 제46회 하계학술대회
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• pp.205-206
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• 2015

전력소비는 꾸준한 증가를 보이고 있지만 연료가격의 증가와 환경 및 사회적 문제로 발전설비를 늘리는 것은 제한적이다. 따라서 발전설비를 추가하지 않고 전력수급의 안정화를 꾀하기 위해 최근 소비자가 전력시장에 참여하는 수요반응이 주목을 받고 있다. 본 논문은 수용가의 전기 사용료 절감 측면에서 수요반응을 분석해 보았다. 먼저 수용가의 부하패턴을 분석하는 기준을 제시하고 그에 따라 현재 우리나라에서 시행하고 있는 대표적인 수요반응인 계시별 요금제(TOU, Time of Use)에 따라 부하 사용패턴을 재조정함으로써 오는 전기 사용료 감소량과 부하율을 계산하였다.

• 전기학회논문지
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• 제64권11호
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• pp.1535-1542
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• 2015

An increasing number of electric vehicles (EVs) in power system affects its reliability in various aspects. Especially under high EV penetration level, new generating units are required to satisfy system's adequacy criterion. Wind power generation is expected to take the major portion of the new units due to environmental and economic issues. In this paper, the system reliability is analyzed using Loss of Load Expectation (LOLE) and Expected Energy Not Served (EENS) under each and both cases of increasing wind power generation and EVs. A probabilistic multi-state modeling method of wind turbine generator under various power output for adequate reliability evaluation is presented as well. EVs are modeled as loads under charging algorithm with Time-Of-Use (TOU) rates in order to incorporate EVs into hour-to-hour yearly load curve. With the expected load curve, the impact of EVs on the system adequacy is analyzed. Simulations show the reliability evaluation of increasing wind power capacity and number of EVs. With this method, system operator becomes capable of measuring appropriate wind power capacity to meet system reliability standard.

• 전기학회논문지
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• 제62권1호
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• pp.21-28
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• 2013

The paper proposes an optimal sizing method of a customer's battery energy storage system (BESS) which aims at managing the electricity demand of the customer to minimize electricity cost under the time of use(TOU) pricing. Peak load limit of the customer and charging and discharging schedules of the BESS are optimized on annual basis to minimize annual electricity cost, which consists of peak load related basic cost and actual usage cost. The optimal scheduling is used to assess the maximum cost savings for all sets of candidate capacities of BESS. An optimal size of BESS is determined from the cost saving curves via capacity of BESS. Case study uses real data from an apartment-type factory customer and shows how the proposed method can be employed to optimally design the size of BESS for customer demand management.

• Journal of Electrical Engineering and Technology
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• 제11권4호
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• pp.810-819
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• 2016

Uncoordinated charging of large-scale electric vehicles (EVs) will have a negative impact on the secure and economic operation of the power system, especially at the distribution level. Given that the charging load of EVs can be controlled to some extent, research on the optimal charging control of EVs has been extensively carried out. In this paper, two possible smart charging scenarios in China are studied: centralized optimal charging operated by an aggregator and decentralized optimal charging managed by individual users. Under the assumption that the aggregators and individual users only concern the economic benefits, new load peaks will arise under time of use (TOU) pricing which is extensively employed in China. To solve this problem, a simple incentive mechanism is proposed for centralized optimal charging while a rolling-update pricing scheme is devised for decentralized optimal charging. The original optimal charging models are modified to account for the developed schemes. Simulated tests corroborate the efficacy of optimal scheduling for charging EVs in various scenarios.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2157-2177
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• 2024

Demand response (DR) refers to the customers' active reaction with respect to the changes of market pricing or incentive policies. DR plays an important role in improving network reliability, minimizing operational cost and increasing end users' benefits. Hence, the integration of DR in the microgrid (MG) management is gaining increasing popularity nowadays. This paper proposes a day-ahead MG scheduling framework in conjunction with DR and investigates the impact of DR in optimizing load profile and reducing overall power generation costs. A linear responsive model considering time of use (TOU) price and incentive is developed to model the active reaction of customers' consumption behaviors. Thereafter, a novel multi-swarm sine cosine algorithm (MSCA) is proposed to optimize the total power generation costs in the framework. In the proposed MSCA, several sub-swarms search for better solutions simultaneously which is beneficial for improving the population diversity. A cooperative learning scheme is developed to realize knowledge dissemination in the population and a competitive substitution strategy is proposed to prevent local optima stagnation. The simulation results obtained by the proposed MSCA are compared with other meta-heuristic algorithms to show its effectiveness in reducing overall generation costs. The outcomes with and without DR suggest that the DR program can effectively reduce the total generation costs and improve the stability of the MG network.

• 전기학회논문지
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• 제67권10호
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• pp.1278-1285
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• 2018

For saving electricity bill, energy storage system(ESS) is being installed in factories, public building and commercial building with a Time-of-Use(TOU) tariff which consists of demand charge(KRW/kW) and energy charge(KRW/kWh). However, both of peak reduction and ESS special tariff are not considered in an analysis of initial cost payback period(ICPP) on ESS. Since it is difficult to reflect base rate by an amount of uncertain peak demand reduction during mid-peak and on-peak periods in the future days. Therefore, the ICPP on ESS can be increased. Based on this background, this paper presents the advanced analysis method for the ICPP on ESS. In the proposed algorithm, the representative days of monthly electricity consumption pattern for the amount of peak reduction can be found by the k­means clustering algorithm. Moreover, the total expected energy costs of representative days are minimized by optimal daily ESS operation considering both peak reduction and the special tariff through a mixed-integer linear programming(MILP). And then, the amount of peak reduction becomes a value that the sum of the expected energy costs for 12 months is maximum. The annual benefit cost is decided by the amount of annual peak reduction. Two simulation cases are considered in this study, which one only considers the special tariff and another considers both of the special tariff and amount of peak reduction. The ICPP in the proposed method is shortened by 18 months compared to the conventional method.