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

• 한국태양에너지학회 논문집
• /
• 제33권3호
• /
• pp.67-74
• /
• 2013

These days, although thermal energy is decreasing, electric energy is increasing in building. Also, it is very important to research and distribute BIPV(Building Integrated photovoltaic) because our society consider electricity more significant than other energy in building. Therefore, in this paper, our research team analyzed difference between BIPV yield and building energy consumption through experimental research. As a result, yearly building energy consumption was 104,602.4kWh and BIPV yield was 105,267kWh. And then, totally counterbalanced time took up 26%, reduced electric load time took up 16%. In other words, peak load could be reduced up to 42% by BIPV. As a result, yearly building energy consumption was 104,602.4kWh and BIPV yield was 105,267kWh. And then, totally counterbalanced time took up 26%, reduced electric load time took up 16%. In other words, peak load could be reduced up to 42% by BIPV.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1993년도 하계학술대회 논문집 B
• /
• pp.1088-1090
• /
• 1993

This paper shows efficient peak demand control method in office buildings. With a rapid growth of national economics and living standard, electrical energy consumption markedly increased. Expecially, it is increased electrical energy comsumption in the office buildings and thus an energy conservation through efficient use of electricity became more important. From the data of electric equipment capacity and electric power consumption for 96 buildings, current levels of demand factor and a growth trend of peak loads by office buildings were surveyed and analyzed. In addition the efficient peak demand control method in office buildings were studied.

• 한국가스학회지
• /
• 제17권1호
• /
• pp.42-48
• /
• 2013

한반도의 지구 온난화는 세계에서 가장 빠른 속도로 진행되고 있고, 이상기후 현상도 날로 심화되고 있다. 따라서 하절기의 혹서와 동절기의 혹한으로 인한 전력수요가 급증함에 따라 우리나라의 전력 수급상황은 매우 어려운 상황에 있다. 현재 천연가스는 현존하는 화석연료 중 온실가스 배출이 가장 적어서 공급이 확산되고 있다. 천연가스냉방은 전력피크 완화는 물론, 발전소 추가 건설비용의 절감, 천연가스 저장설비의 효율적 운영 및 분산형 전원으로서의 역할수행도 가능한 장점이 있다. 따라서 본 연구는 전력 부하관리의 대안으로서 천연가스냉방의 경제성을 분석하였다.

• 산업경영시스템학회지
• /
• 제37권4호
• /
• pp.63-71
• /
• 2014

As power consumption increases, more power utilities are required to satisfy the demand and consequently results in tremendous cost to build the utilities. Another issue in construction of power utilities to meet the peak demand is an inefficiency caused by surplus power during non-peak time. Therefore, most power company considers power demand management with time-based electricity rate policy which applies different rate over time. This paper considers an optimal machine operation problem under the time-based electricity rates. In TOC (Theory of Constraints), the production capacities of all machines are limited to one of the bottleneck machine to minimize the WIP (work in process). In the situation, other machines except the bottleneck are able to stop their operations without any throughput loss of the whole manufacturing line for saving power utility cost. To consider this problem three integer programming models are introduced. The three models include (1) line shutdown, (2) block shutdown, and (3) individual machine shutdown. We demonstrate the effectiveness of the proposed IP models through diverse experiments, by comparing with a TOC-based machine operation planning considered as a current model.

• 전기전자학회논문지
• /
• 제25권2호
• /
• pp.388-398
• /
• 2021

본 논문에서는 전기요금 절감용 ESS를 활용한 Particle swarm optimization(PSO) 기반 Peak shaving 제어 방법을 제안한다. 제안한 방식은 실제 부하와 예상되는 부하의 소비를 비교하여 피크 절감을 위해 ESS의 추가 유효전력값을 계산하여 입력을 더한다. 또한 추가로 증가시킨 유효전력을 보상하기 위해, 유효전력을 할당하는 과정을 수행하며 유효전력 할당치가 피크 부하에 영향을 주지 않도록 유효전력 할당 지점에 예상되는 부하의 평균을 최소화하는 최적화 해를 PSO를 통해 찾는다. 제안한 방식의 성능 검증을 위해 실제 부하 데이터와 예측 알고리즘을 반영하여 예측 오차가 적은 경우와 큰 경우의 사례 연구를 수행하였다. 사례 연구 수행 결과 제안한 방식을 전기요금 절감을 위한 충·방전 제어 방식과 같이 수행한 경우 예측 오차가 큰 경우에도 성공적으로 피크 부하 절감을 수행하였으며, 17.8%의 피크 부하 절감 효과와 6.02%의 전기요금 절감 효과를 보였다.

• International Journal of Advanced Culture Technology
• /
• 제4권1호
• /
• pp.1-9
• /
• 2016

The demand for energy in Thailand has been continually increasing as the economic and social country grows. Approximately 60% of Thailand's primary energy is imported, mostly petroleum products. In 2008 Thailand's total energy consumption was 80,971 ktoe and the net price of energy imported was up to 1,161 billion Baht which is equivalent to 12.8% of GDP at the current price. The energy consumption or energy demand has been growing at an annual compounded growth rate of 6.42% and the peak electric power demand and electricity consumption was recorded at 22,568 MW and 148,264 GWh and grew at a rate of 7.0% and 7.5% per annum during the period from 1989 to 2008. The gross agriculture production in 2008 was recorded at 135.4 Mt which represents agriculture residue for energy at 65.73 Mt, which is equivalent to energy potential of about 561.64 PJ or 13,292 ktoe an increase in average of 5.59% and 5.44% per year respectively. The agricultural residues can converted to 15,600 GWh/year or 1,780 MW of power capacity. So, if government sector plan to install small biomass gasification for electricity generation 200 kW for Community. The residue agricultural is available for 8,900 plants nationwide. The small biomass power generation for electricity generation not only to reduce the energy imports, it also makes the job and income for people in rural areas as well. This paper's aim is to report the energy situation in Thailand and has studied 5 main agricultural products with high residue energy potential namely sugarcane, paddy, oil palm, cassava, and maize appropriate for small electricity production. These agricultural products can be found planted in many rural areas throughout Thailand. Finally, discuss the situation, methods and policies which the government uses to promote small private power producers supplying electricity into the grid.

• 한국대기환경학회지
• /
• 제28권4호
• /
• pp.466-476
• /
• 2012

This study calculated monthly and sectoral (for industry, energy industry, transport, residential, commercial and public sectors) greenhouse gas (GHG) emissions of Seoul, Korea from 1999 until 2009 with following the IPCC 2006 Guideline for National Greenhouse Gas Inventories through an analysis on available monthly data of fossil fuel and electricity consumption for the period. The time series analysis showed that GHG emissions had significant cyclical pattern season by season with the highest peak in August and the lowest peak in January throughout the period. The analysis on monthly and sectoral energy consumption showed that residential, commercial and public sectors had emitted about 65% of total GHG emissions of Seoul and had consumed more energy in winter for heating. About 30% GHG of Seoul was emitted from transport sector but its monthly energy consumption showed irregular pattern and it consumed 80% petroleum (in 2009) of Seoul. Hopefully together with further study on this subject, it is expected that this study can be used as basic data for various research regarding Greenhouse gas baseline emission, energy consumption pattern and estimation for future GHG emission of Seoul.

• 전기학회논문지P
• /
• 제64권2호
• /
• pp.74-78
• /
• 2015

With the improvement of living standards and economic development, electricity consumption continues to grow. The electricity is a special energy which is hard to store, so its supply must be consistent with the demand. The objective of electricity demand forecasting is to make best use of electricity energy and provide balance between supply and demand. Hence, it is very important work to forecast electricity demand with higher precision. So, various forecasting methods have been developed. They can be divided into five broad categories such as time series models, regression based model, artificial intelligence techniques and fuzzy logic method without considering special-day effects. Electricity demand patterns on holidays can be often idiosyncratic and cause significant forecasting errors. Such effects are known as special-day effects and are recognized as an important issue in determining electricity demand data. In this research, we developed the power demand forecasting method using ELM(Extreme Learning Machine) for special day, particularly, lunar new year and Chuseok holiday.

• 전기학회논문지
• /
• 제65권7호
• /
• pp.1128-1134
• /
• 2016

Demand response provides customer load reductions based on high market prices or system reliability conditions. One type of demand response, price-based program, induces customers to respond to changes in product rates. However, there are large-scale general and industrial customers that have difficulty changing their energy consumption patterns, even with rate changes, due to their electricity demands being commercial and industrial. This study proposes an in-house pricing model for large-scale general and industrial customers, particularly those with multiple business facilities, for self-regulating demand-side management and cost reduction. The in-house pricing model charges higher rates to customers with lower load factors by employing peak to off-peak ratios in order to reduce maximum demand at each facility. The proposed scheme has been applied to real world and its benefits are demonstrated through an example.