• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5712-5728
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• 2017

Recently, there are many studies, that considering green wireless cellular networks, have taken the energy consumption of the base station (BS) into consideration. In this work, we first introduce an energy consumption model of multi-mode sharing BS powered by multiple energy sources including renewable energy, local storage and power grid. Then communication load requests of the BS are transformed to energy demand queues, and battery energy level and worst-case delay constraints are considered into the virtual queue to ensure the network QoS when our objective is to minimize the long term electricity cost of BSs. Lyapunov optimization method is applied to work out the optimization objective without knowing the future information of the communication load, real-time electricity market price and renewable energy availability. Finally, linear programming is used, and the corresponding energy efficient scheduling policy is obtained. The performance analysis of our proposed online algorithm based on real-world traces demonstrates that it can greatly reduce one day's electricity cost of individual BS.

• Environmental and Resource Economics Review
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• v.21 no.1
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• pp.93-127
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• 2012

We estimate how much KEPCO can save their loss and how much social welfare can be increased by applying the real-time pricing instead of current regulated retail price in the electricity market in order to analyze the problem of the regulated retail price which is fixed below the marginal cost. We estimate the demand functions of peak time and off-peak time in summer (June to August) and winter (December to February). We construct the supply function based on hourly step-wise linear marginal cost functions, too. We find that the increase of social welfare will be 67 billion won in summer if the fixed retail price is changed into the real-time pricing scheme. The total 705 billion won will be transferred from consumer surplus to producer surplus and the rest (67 billion won) will be saved from the reduction of deadweight loss among KEPCO's loss. In winter, the increase of social surplus will be 225 billion won and 1,174 billion won of KEPCO's loss will be transferred from consumer surplus. As a result, we conclude that the regulation of the retail price in the electricity market induces the social welfare loss and KEPCO suffers a huge loss.

• 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.

• Environmental and Resource Economics Review
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• v.16 no.1
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• pp.99-127
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• 2007

When the margin between available capacity and demand is thin in a liberalized electricity market, prices rise steeply and system reliability is threatened. The principal response to these circumstances is often an assumption that price spikes and electricity shortages are the result of a failure to build sufficient new supplying facilities. It is, of course, often the case that additional investments in generation and network facilities would improve reliability, and such investments are often needed. But focusing on additional generation and transmission facilities for restoring balance to the grid overlooks the essential fact that reliability is a function of the relationship between supply and demand, imposing unnecessary costs on electric system. When the relationship is out of balance, the search for solutions must consider not only investments supply-side resources but also cost-effective demand-side resources such as accelerated load management, efficiency measures, and price-responsive load programs. Integrating demand resources into electricity markets can add enormous value to the electric system, widening the capacity margin, lowering costs and enhancing system reliability at the same time. This paper studies several challenges now facing electricity markets: demand-side management-especially, economic effects of demand response, potential reliability problems, market and system operation, CBP market improvements and so on. The paper concludes with a series of policy recommendations in five areas: (i) The Effects of efficient improvement to incorporate demand responses and demand-side resources into modem electricity markets, (ii) Fosteing price based demand response and (iii) improving incentive based demand response, (iv) strengthen demand response analysis and valuation, (v) integrating demand response into resource planning and adopting enabling technologies.

• IE interfaces
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• v.10 no.1
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• pp.237-248
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• 1997

The purpose of this study is to make an economic analysis of power plant utilities by examining electricity generating costs with environmental consideration. Economic growth has caused pollutant emission, and subsequent environmental pollution has been identified as a very real limit to sustainable development. Considering the enormous role of electricity in the national economy, it is thus very important to study the effect of environmental regulations on the electricity sector. Because power utilities need large investments during construction, operation and maintenance, and also require much construction lead time. Economic analysis is the very important process in the electric system expansion planning. In this study, the levelized generation cost method is used in comparing economic analysis of power plant utilities. Among the pollutants discharged of the electricity sector, this study principally deals with the control activities related only to $CO_2$, and $NO_2$, since the control cost of $SO_2$, and TSP (Total Suspended Particulates) is already included in the construction cost of utilities. The cost of electricity generation in a coal-fired power plant is compared with one in an LNG combined cycle power plant. Moreover this study surveys the sensitivity of fuel price, interest rate and carbon tax. In each case, this sensitivity can help to decide which utility is economically justified in the circumstance of environmental regulations.

• The Journal of Society for e-Business Studies
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• v.17 no.2
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• pp.105-114
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• 2012

Recently, global warming, energy shortage, and environmental disruption have been serious problems in every nation. It became more and more important to reduce the emission of CO2 and to use of energy efficiently. Smart grid was also introduced using the rapidly developing information technology. It deployed the mutual communication concept between customers and the suppliers in the electricity supply. There were increasing demands to adopt the smart meter and to present incentive for efficient energy usage in many developed countries. The objective of this research was to develop the optimal real time pricing model which maximized the profit of the power retailer and reduced the usage of energy. The simulation study was given to show the usefulness of the model. Simulation considered the customer demand response rate and price elasticity rate. The price elasticity rate was compared in the condition of fixed value according to time and variable value according to the customers. The optimal price model could maximize the profit of the power retailer and reduce the energy usage of the consumers.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.291-297
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• 2010

A system called demand response programs (DRP) is being introduced among various countries owing to the lack of new generation capacity and the higher fuel generation cost. It is a program which provides for the end-users to select their consumption of electricity by recognizing the value of their consumption in real time. That is, Demand Response can be defined as the changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity or other signals. It is expected that the effects of DRP are preventing price spike, improving supply reliability and social welfare and increasing option of customers. Considering the customer's thermal comfort zone, this paper determines the most profitable combination of optimal incentives and amounts of load reduction for a retailer to maximize profits according to predicted outdoor temperatures while implementing DRP.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.414-424
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• 2015

In this research, we consider a smart grid network of electricity with multiple consumers connected to a monopolistic provider. Each consumer can be informed the real time price changes through the smart meter and updates his consumption schedule to minimize the energy consumption expenditures by which the required power demand should be satisfied under the given real time pricing scheme. This real-time decision making problem has been recently studied through game-theoretic approach. The present paper contributes to the existing literature by incorporating storage appliance into the set of available household appliances which has somewhat distinctive functions compared to other types of appliances and would be regarded to play a significant role in energy consumption scheduling for the future smart grid. We propose a game-theoretic algorithm which could draw the optimal energy consumption scheduling for each household appliances including storage. Results on simulation data showed that the storage contributed to increase the efficiency of energy consumption pattern in the viewpoint of not only individual consumer but also whole system.

• Environmental and Resource Economics Review
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• v.30 no.1
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• pp.107-128
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• 2021

It has been recognized that implementing the marginal price mechanism to CBP is not acceptable due to the lack of revenue of the marginal generators. This study shows that it is not the problem of marginal price mechanism but the structural problems originated by the suspension of restructuring, the technical limits of RSC program and inaccuracy of the generation cost estimation method. This study explains the method to calculate the cost function in operating modes of the CC generators and proposes the modeling for the CC generators in RSC program. To implementing the cost function in operating modes could give an opportunity to change the price setting mechanism from average to marginal cost. The price setting mechanism based on the marginal cost will be one of the main points to provide the right price signals and to introduce a real-time and A/S markets to prepare the energy transition era.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.3
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• pp.144-151
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• 2005

This paper presents a forecasting technique of the short-term marginal price (SMP) using an Artificial Neural Network (ANN). The SW forecasting is a very important element in an electricity market for the optimal biddings of market participants as well as for market stabilization of regulatory bodies. Input data are organized in two different approaches, time-axis and day-axis approaches, and the resulting patterns are used to train the ANN. Performances of the two approaches are compared and the better estimate is selected by a composition rule to forecast the SMP. By combining the two approaches, the proposed composition technique reflects the characteristics of hourly, daily and seasonal variations, as well as the condition of sudden changes in the spot market, and thus improves the accuracy of forecasting. The proposed method is applied to the historical real-world data from the Korea Power Exchange (KPX) to verify the effectiveness of the technique.