• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1511-1518
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• 2017

Renewable energy hybrid systems look into the process of choosing the finest arrangement of components and their sizing with suitable operation approach to deliver effective, consistent and cost effective energy source. This paper presents hybrid renewable energy system (HRES) solar photovoltaic, downdraft biomass gasifier, and fuel cell based generation system. HRES electrical power to supply the electrical load demand of academic research building sited in $23^{\circ}12^{\prime}N$ latitude and $77^{\circ}24^{\prime}E$ longitude, India. Fuzzy logic programming discover the most effective capital and replacement value on components of HRES. The cause regarding fuzzy logic rule usage on HOMER pro (Hybrid optimization model for multiple energy resources) software program finds the optimum performance of HRES. HRES is designed as well as simulated to average energy demand 56.52 kWh/day with a peak energy demand 4.4 kW. The results shows the fuel cell and battery bank are the most significant modules of the HRES to meet load demand at late night and early morning hours. The total power generation of HRES is 23,794 kWh/year to the supply of the load demand is 20,631 kWh/year with 0% capacity shortage.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.804-806
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• 2005

In this paper, a new probabilistic generation modeling method which can address the characteristics of changed electricity industry is proposed. The major contribution of this paper can be captured in the development of a probabilistic generation modeling considering generator maintenance outage and in the classification of market demand into multiple demand clusters for the applications to electricity markets. Conventional forced outage rates of generators are conceptually combined with maintenance outage of generators and, consequently, effective outage rates of generators are new iy defined in order to properly address the probabilistic characteristic of generation in electricity markets. Then, original market demands are classified into several distinct demand clusters, which are defined by the effective outage rates of generators and by the inherent characteristic of the original demand. We have found that generators have different effective outage rates values at each classified demand cluster, depending on the market situation. From this, therefore, it can be seen that electricity markets can also be classified into several groups which show similar patterns and that the fundamental characteristics of power systems can be more efficiently analyzed in electricity markets perspectives, for this classification can be widely applicable to other technical problems in power systems such as generation scheduling, power flow analysis, price forecasts, and so on.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.10
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• pp.963-968
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• 2016

This paper proposes a new method reducing the maximum demand power of substations at urban railway by using transformer with OLTC(:On Load Tap Changer). Most of the domestic urban railway is rectified by a diode scheme, and supplies the electric vehicles in dc 1500[v]. Because the substations are connected in parallel, if an input voltage of a substation is increased, then the voltage of rectifiers is also increased, and which leads to an increase in the maximum demand of the substation. Simulation results show that increment of maximum demand power can significantly be limited using the method proposed in this paper.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.222-228
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• 2017

The aims of the demand response market are stabilization of the power supply and improving of the reliability of the power system. The various applications of the energy storage system (ESS) in the railway systems are studied and implemented to raise the energy efficiency. It is one of the most important how to determine the obligation reduction capacity (ORC) in participation to the demand response market because it has an influence on the profit extremely. In this paper, when participating to the demand response market with demands in the urban railway, we calculated the available ORC and economically evaluated ESS based on the real load data.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.6
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• pp.17-22
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• 2013

Fuzzy linear regression method has been used for short-term load forecasting of the special day in the previous researches. However, considerable load forecasting errors would be occurring if a special day is located on Saturday or Monday. In this paper, a new load forecasting method for the consecutive holidays is proposed with the consideration of the power demand variation rate. In the proposed method, a exponential smoothing model reflecting temperature is used to short-term load forecasting for Sunday during the consecutive holidays and then the loads of the special day during the consecutive holidays is calculated using the hourly power demand variation rate between the previous similar consecutive holidays. The proposed method is tested with 10 cases of the consecutive holidays from 2009 to 2012. Test results show that the average accuracy of the proposed method is improved about 2.96% by comparison with the fuzzy linear regression method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.4
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• pp.513-522
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• 2019

We calculate the benefit of distributed combined heat power generators from avoiding a transmission expansion cost by building distributed generators near electricity demand centers. We determine a transmission expansion plan by solving a mixed integer linear problem, where we modify capacities of existing transmission lines and build new transmission lines. We calculate the benefit by comparing the sum of generation and transmission expansion costs with or without distributed generators through two simulation frames. In the first frame, for the current demand, we substitute existing distributed generators for non-distributed generators and measure an additional cost to balance the generation and demand. In the second frame, for increased future demand, we compare the cost to invest only in distributed generators to the cost to invest only in non-distributed generators. As a result, we show that the distributed generators have at least 5.8 won/kWh of the benefit from avoiding the transmission expansion cost.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.35-42
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• 2021

In order to produce electric vehicle demand forecasting information, which is an important element of the plan to expand charging facilities for electric vehicles, a model for predicting electric vehicle demand was proposed using Exponential Smoothing. In order to establish input data for the model, the monthly power demand of cities and counties was applied as independent variables, monthly electric vehicle charging stations, monthly electric vehicle charging stations, and monthly electric vehicle registration data. To verify the accuracy of the electric vehicle power demand prediction model, we compare the results of the statistical methods Exponential Smoothing (ETS) and ARIMA models with error rates of 12% and 21%, confirming that the ETS presented in this paper is 9% more accurate as electric vehicle power demand prediction models. It is expected that it will be used in terms of operation and management from planning to install charging stations for electric vehicles using this model in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2398-2421
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• 2020

In third world countries like Pakistan, the production of electricity has been quickly reduced in past years due to rely on the fossil fuel. According to a survey conducted in 2017, the overall electrical energy capacity was 22,797MW, since the electrical grids have gone too old, therefore the efficiency of grids, goes down to nearly 17000MW. Significant addition of fossil fuel, hydro and nuclear is 64.2%, 29% and 5.8% respectively in the total electricity production in Pakistan. In 2018, the demand crossed 20,223MW, compared to peak generation of 15,400 to 15,700MW as by the Ministry of Water and Power. Country faces a deficit of almost 4000MW to 5000MW for the duration of 2019 hot summer term. Focus on one aspect considering Demand Side Management (DSM) cannot oversea the reduction of gap between power demand and customer supply, which eventually leads to the issue of load shedding. Hence, a scheduling scheme is proposed in this paper called RPSMDSM that is based on selection of those appliances that need to be only Turned-On, on priority during peak hours consuming minimum energy. The Home Energy Management (HEM) system is integrated between consumer and utility and bidirectional flow is presented in the scheme. During peak hours of electricity, the RPSMDSM is capable to persuade less power consumption and accomplish productivity in load management. Simulations show that RPSMDSM scheme helps in scheduling the electricity loads from peak price to off-peak price hours. As a result, minimization in electricity cost as well as (Peak-to-Average Ratio) PAR are accomplished with sensible waiting time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.915-922
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• 2017

Demand Response Market (DR Market) has risen as one of the key solutions to address the growth and fluctuation of electricity consumptions. In Korea, DR market has been in operation since 2014, where the focus has been mainly on large-scale loads. Small-scale DR market, however, is becoming increasingly important because small power consumers' contribution to the national power consumption has been increasing and because small loads tend to show large fluctuations. Furthermore, small-scale DR can improve social awareness on energy issues which can bring additional impacts. In this paper, we provide the findings from a small-scale consumer DR pilot. The pilot was conducted in the summer of 2016 on over 5,000 small-scale users in Korea, and smartphone applications were used in the pilot. The effectiveness of small-scale DR Market is analyzed and addressed, and the results indicate a promising future of small-scale DR Market.