• Journal of Communications and Networks
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• v.14 no.6
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• pp.662-671
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• 2012

Plug-in hybrid electric vehicles (PHEVs) will be widely used in future transportation systems to reduce oil fuel consumption. Therefore, the electrical energy demand will be increased due to the charging of a large number of vehicles. Without intelligent control strategies, the charging process can easily overload the electricity grid at peak hours. In this paper, we consider a smart charging and discharging process for multiple PHEVs in a building's garage to optimize the energy consumption profile of the building. We formulate a centralized optimization problem in which the building controller or planner aims to minimize the square Euclidean distance between the instantaneous energy demand and the average demand of the building by controlling the charging and discharging schedules of PHEVs (or 'users'). The PHEVs' batteries will be charged during low-demand periods and discharged during high-demand periods in order to reduce the peak load of the building. In a decentralized system, we design an energy cost-sharing model and apply a non-cooperative approach to formulate an energy charging and discharging scheduling game, in which the players are the users, their strategies are the battery charging and discharging schedules, and the utility function of each user is defined as the negative total energy payment to the building. Based on the game theory setup, we also propose a distributed algorithm in which each PHEV independently selects its best strategy to maximize the utility function. The PHEVs update the building planner with their energy charging and discharging schedules. We also show that the PHEV owners will have an incentive to participate in the energy charging and discharging game. Simulation results verify that the proposed distributed algorithm will minimize the peak load and the total energy cost simultaneously.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.269-283
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• 2021

The government policies and initiatives to guarantee sustainable energy and clean environmental conditions contributed to the introduction of green technology electricity appliances in the market. This study sought to determine the physiological and socio-economics-demographic factors driving residential electricity consumers to use green technology electricity appliances, mainly solar PV, smart meter, electric vehicle, and battery storage technology. By understanding consumer intention, the investors of solar PV, battery storage, electric vehicle, and smart meter can estimate the demand and upscale the market for the corresponding products. For that purpose, the intention to use the solar PV, smart meter, electric vehicle, and battery storage function is developed by utilizing the combination of the theory of planned behavior, technology acceptance, and reasoning action. A reliable and valid structured online questionnaire and stepwise multiple regression are used to identify the possible factors that drive consumer behavior intention. The results show that the social influence, knowledge on RE, and perceived price significantly influence residential consumers' willingness to adopt the technologies offered. The findings of this study suggest that the involvement of NGOs, public figures, and citizens' cooperation are all necessary to spread information about the government's objectives and support Malaysia's present energy and environmental policies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.3
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• pp.437-443
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• 2009

In recent years, electric power systems have been experiencing a rapid change due to the increasing electricity market. For the effective use of power system under the deregulated environment, it is important to make a fast and accurate calculation of the maximum available transfer capability (ATC) from a supply point to a demand point. In this paper, the purpose of this research is to calculate ATC fast and accurately for securing the stability of system and raising the efficiency as a result of anticipating transmission congestion according to transmission open access progressed in the future under the regulated environment of electricity market. In this paper, a study utilized a relation of the potential energy and energy function by which calculated CCT and then utilized a relation of PEBS for transient stability ATC calculation. In this paper, ATC was calculated as RPF and Energy Function method and calculation results of each method was compared. Contingence ranking method decided the weak bus by the Eigenvalues of Jacobian matrix and overloading branches by PI-index. As a result, a study proved the fast and accurate ATC calculation method considering transient stability suggested in this paper. Through the case study using New England 39 bus system, it is confirmed that the proposed method can be used for real time operation and the planning of electric market.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1135-1143
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• 2016

A private company's 1,000 MW coal-fired power plant will be the first coal-fired power plant that was included in the 5th 'Basic Plan on Electricity Demand and Supply' (2010). Now it is facing the task to abide by the RPS(Renewable Portfolio Standard) policy after commercial operation. If they fail to supply the necessary REC (Renewable Energy Certificate) mandated by the RPS policy, they are subject to be fined by the government and forced to modify the cost function to reflect the burden. Eventually the company's coal-fired power plant will be forced to reduce generation to maximize profit because the amount of electricity generated by the power plant and the REC obligation is positively correlated. This paper analyzed the change of cost function of private coal-fired power plant according to the introduction of RPS policy from the viewpoint of private company, and finally proposed the optimal generation to maximize the profit of private coal-fired power plant under the current RPS policy.

• Advances in Energy Research
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• v.6 no.1
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• pp.1-15
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• 2019

The objective of the study is to examine the relationship between water resources, energy demand, food production, and environmental pollutants in selected SAARC nations, namely, Bangladesh, India, Pakistan, and Sri Lanka, during the period of 1990-2016. The results show that water, energy, and food (WEF) resources substantially affected air quality in the form of high mass carbon emissions, fossil fuel energy demand, methane discharges, nitrous oxide emissions, and greenhouse gas emissions in these countries. Food production and food deficit largely increase $CO_2$ emissions due to unsustainable production and malnutrition, while land use under cereal production increases $CH_4$ and $N_2O$ emissions. Electricity production escalates $CO_2$ emissions and fossil emissions across countries. The results support the carbon EKC hypothesis, while monotonic increasing function exists in case of fossil fuel energy. The study emphasizes the need to ensure environmental sustainability agenda by adopting cleaner production technologies in WEF resources.

• Journal of Energy Engineering
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• v.5 no.1
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• pp.87-92
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• 1996

This paper formulates dynamic optimization model for Time-Of-Use Rates when a electric power system consists of three generators and a rating period is divided into three sub-periods. We use Pontryagin's Maximum Principle to derive optimal price and investment policy. Particularly the cross-price elasticities of demand are considered in the objective function. We get the following results. First, the price is equal to short-run marginal cost when the capacity is sufficient. However, if the capacity constraint is active, the capacity cost is included in the price. Therefore it is equal to the long-run marginal cost. Second, The length of rating period affects allocation of capacity cost for each price. Third, the capacity investment in dynamic optimization is proportional to the demand growth rate of electricity. However the scale of investment is affected by not only its own demand growth rate but also that of other rating period.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.760-762
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• 1998

Due to the reinforcement of government's DSM(Demand Side Management) policy. Solid State Meter was introduced in Korea since 1993 and it is applied to the high voltage customer exceeding 100kW in order to equalize daily load curve. In recent days, KEPCO has a Plan to use the Solid State Meter which has a data recording and remote meter-reading function for low voltage customer to introduce the real-time pricing system and reduce peak power in the near future. So, this paper suggests the specification and function of Solid State Meter.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.568-579
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• 2018

Virtual power plants can be regarded as systems that have entered the network after restructure of power systems. In fact, these plants are a set of consumers capable of consuming and generating power. In response to widespread implementation of plug-in hybrid electric vehicles, further investigation of energy management in this type of power plants seems to be of great value. In effect, these vehicles are able to receive and inject power from/into the network. Hence, study of the effects of these vehicles on management of virtual power plants seems to be illuminative. In this paper, management of power consumption/generation in virtual power plants has been investigated in the presence of hybrid electric vehicles. The objective function of virtual power plants problem management is to minimize the overall costs including not only the costs of energy production in power generation units, fuels, and degradation of batteries of vehicles, but also the costs of purchasing electricity from the network. Furthermore, the constraints on the operational of plants, loads and hybrid vehicles, level of penalty for greenhouse gas emissions ($CO_2$ and $NO_x$) produced by power plants and vehicles, and demand response to the immediate price of market have all been attended to in the present study. GAMS/Cplex software system and sample power system have been employed to pursue computer implementation and simulation.

• Journal of Energy Engineering
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• v.11 no.1
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• pp.59-66
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• 2002

The vertically integrated power industry was divided into six generation companies and one market operator, where electricity trading was launched at power exchange. In this environment, the profits of each generation companies are guaranteed according to utilizing strategies of their own generation equipments. This paper presents on generator maintenance scheduling and efficient bidding strategies for generation equipments through the calculation of the contract and the application of each generator cost function based on the past demand forecasting error and market operating data.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.135-150
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• 2012

This paper presents a portfolio model for a long-term power generation mix problem. The proposed portfolio model evaluates generation mix by considering the tradeoffs between the expected cost for power generation and its variability. Unlike conventional portfolio models measuring variance, we introduce Conditional Value-at-Risk (CVaR) in designing the variability with aims to considering events that are enormously expensive but are rare such as nuclear power plant accidents. Further, we consider uncertainties associated with future electricity demand, fuel prices and their correlations, and capital costs for power plant investments. To obtain an objective generation by each energy source, we employ the sample average approximation method that approximates the stochastic objective function by taking the average of large sample values so that provides asymptotic convergence of optimal solutions. In addition, the method includes Monte Carlo simulation techniques in generating random samples from multivariate distributions. Applications of the proposed model and method are demonstrated through a case study of an electricity industry with nuclear, coal, oil (OCGT), and LNG (CCGT) in South Korea.