• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1851-1863
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• 2014

Many countries are increasing their investments in smart grid technology to enhance energy efficiency, address climate change, and trigger a green energy revolution. In addition to these goals, Korea also seeks to promote national competitiveness, prepare for the growth of the renewable energy industry, and export industrialization through its strategic promotion of the smart grid. Given its inherent representativeness for Korean implementation of the smart grid and its growth potential, Jeju Island was selected by the Korean government as the site for smart grid testing in June 2009. This paper presents a new design for the electricity market and an operational scheme for testing Smart Electricity Services in the Jeju smart grid demonstration project. The Jeju smart grid test-bed electricity market is constructed on the basis of day-ahead and real-time markets to provide two-way electricity transaction environments. The experience of the test-bed market operation shows that the competitive electricity market can facilitate the smart grid deployment in Korea by allowing various demand side resources to be active market players.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.1-7
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• 2010

Recently, smart grid has been considered a very important new energy delivery technology, and one that can help ensure a cleaner environment by making use of information and communication technology (ICT) in countries around the world. The many technological benefits smart grid offers is expected to bring about a huge change in the electric energy supply chain. In particular, smart grid with advanced ICT is likely to allow market agents to participate in the decision-making process in the restructured electricity industry, easily facilitating Homeostatic Utility Control. In this paper, we examine smart grid as a market externality, and then illustrate issues from the commercial market perspective as it relates to electricity market design. Finally, our paper identifies some of the impacts of smart grid on electricity market design, which may possibly be incorporated into the evolution of the electricity market, thus ensuring market efficiency.

• Bulletin of the Korea Photovoltaic Society
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• v.6 no.1
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• pp.8-20
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• 2020

Nepal has huge potential of hydro and other renewable energy resources including solar energy. However, only 70% of the total population have access to electricity despite the long history of hydropower development in the country. Still more than 37% population in rural areas and around 73% population in Karnali Province, one of the least developed provinces, are living without access to electricity despite taking several initiatives and implementing various policies by government supporting electrification in off-grid rural areas. Government together with donors and private sector has extensively been promoting the off-grid solar photovoltaic (PV) echnology in un-electrified areas to increase electricity access. So far, more than 900,000 households in rural areas of Nepal are getting electricity from stand-alone solar PV systems. However, there are many challenges including financial, technical, institutional, and governance barriers in Nepal. This study based on extensive review of literatures and author's own long working experiences in renewable energy sector in Nepal, shares the best practices and lessons of off-grid solar PV for increasing access to electricity in rural areas of Nepal. This study suggests that flexible financial instruments, financial innovations, bundling of PV systems for concentrating energy loads, adopting standards process, local capacity building, and combination of technology, financing and institutional aspects are a key for enhancing effectiveness of solar PV technology in rural areas of Nepal.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2826-2832
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• 2012

A smart grid is an electric-power grid that employs a new information technology.This technology makes it possible to exchange real-time energy information between suppliers and consumers, finally resulting in high energy efficiency. The energy management system in smart grid provides up to date information on electricity consumption as well as dynamic electricity price to consumers of smart grid system. However, the existing energy management systems only focus on pricing system, for example, real-time electricity prices. In this paper, we try to improve the existing energy management system and propose the energy management system that mainly focuses on the efficiency of electricity consumption. In the proposed management system, PMU(Phasor Measurement Units) installed in switchboards gathers electricity data in a real time. We also propose to use data mining method, which is applied to analyzed electricity data for improving energy efficiency. Also, the proposed energy management system is designed to efficiently control the electricity between PMU and management system in case of a shortage of electricity or surplus electricity.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1821-1830
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• 2018

With the wide application of intelligent household appliances, the optimization of electricity behavior has become an important component of home-based intelligent electricity. In this study, a multi-objective optimization model in an intelligent electricity environment is proposed based on economy and comfort. Firstly, the domestic consumer's load characteristics are analyzed, and the operating constraints of interruptible and transferable electrical appliances are defined. Then, constraints such as household electrical load, electricity habits, the correlation minimization electricity expenditure model of household appliances, and the comfort model of electricity use are integrated into multi-objective optimization. Finally, a continuous search multi-objective particle swarm algorithm is proposed to solve the optimization problem. The analysis of the corresponding example shows that the multi-objective optimization model can effectively reduce electricity costs and improve electricity use comfort.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.3
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• pp.391-395
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• 2016

In the last few decades, global electricity consumption has dramatically increased and has become drastically fluctuating and uncertain causing blackout. Due to the unexpected peak electricity demand, we need significant electricity supply. The solutions to these problems are smart grid system which is envisioned as future power system. Smart grid system can reduce electricity peak demand and induce effective electricity consumption through various price policies, demand response (DR) control methodologies, and state-of-the-art smart equipments in order to optimize electricity resource usage in an intelligent fashion. Demand response (DR) is one of the key technologies to enable smart grid. In this paper, we propose greedy technique for demand response smart grid system. The proposed scheme focuses on minimizing electricity bills, preventing system blackout and sacrificing user convenience.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1701-1710
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• 2012

Recently, it has been found that it is important to use a smart grid to reduce greenhouse-gas emissions worldwide. A smart grid is a digitally enabled electrical grid that gathers, distributes, and acts on information regarding the behavior of all participants (suppliers and consumers) to improve the efficiency, importance, reliability, economics, and sustainability of electricity services. The smart grid technology uses two-way communication, where users can monitor and limit the electricity consumption of their home appliances in real time. Likewise, power companies can monitor and limit the electricity consumption of home appliances for stabilization of the electricity supply. However, if information regarding the measured electricity consumption of a user is leaked, serious privacy issues may arise, as such information may be used as a source of data mining of the electricity consumption patterns or life cycles of home residents. In this paper, we propose a data transaction protocol for privacy protection in a smart grid. In addition, a power company cannot decrypt an encrypted home appliance ID without the user's password.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.97-115
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• 2022

Smart power grid is a user friendly system that transforms the traditional electric grid to the one that operates in a co-operative and reliable manner. Demand Response (DR) is one of the important components of the smart grid. The DR programs enable the end user participation by which they can communicate with the electricity service provider and shape their daily energy consumption patterns and reduce their consumption costs. The increasing demands of electricity owing to growing population stresses the need for optimal usage of electricity and also to look out alternative and cheap renewable sources of electricity. The solar and wind energy are the promising sources of alternative energy at present because of renewable nature and low cost implementation. The proposed work models a smart home with renewable energy units. The random nature of the renewable sources like wind and solar energy brings an uncertainty to the model developed. A stochastic dual descent optimization method is used to bring optimality to the developed model. The proposed work is validated using the simulation results. From the results it is concluded that proposed work brings a balanced usage of the grid power and the renewable energy units. The work also optimizes the daily consumption pattern thereby reducing the consumption cost for the end users of electricity.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.33 no.4
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• pp.278-287
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• 2009

Plug-in hybrid electric vehicle (PHEV) with capability of being recharged from the power-grid will reduce oil consumption. Also, the PHEV will affect the utility operations by adding additional electricity demand for charging. In this research, the power-grid impact by demand of PHEV charging is presented and the optimal charging control strategy for utility operators is proposed with simulated data. The penetration of PHEV is assumed to be 50% in the circumstances of Korean passenger car market and Korean power-grid market limitedly. To obtain smooth load shape and utilize the surplus electricity in power-grid at midnight and dawn, the peak of charging demand should be controlled to be located before 4:00 a.m., and the time slot which can supply the electricity power to PHEV should be allowed between 1:00 a.m.$\sim$7:00 a.m.

• Journal of the Korean Solar Energy Society
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• v.38 no.4
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• pp.55-66
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• 2018

nZEH (net-Zero Energy House) is defined as a self-sufficient energy building where the sum of energy output generated from new & renewable energy system and annual energy consumption is zero. The electricity generated by new & renewable energy system with the form of distributed generation is preferentially supplied to electrical demand, and surplus electricity is transmitted back to grid. Due to the recent expansion of houses with photovoltaic system and the nZEH mandatory by 2025, the rapid increase of distributed generation is expected. Which means, we must prepare for an electricity-power accident and stable electricity supply. Also electricity charges have to be reduce and the grid-connected should be operated efficiently. The introduction of ESS is suggested as a solution, so the analysis of the load matching and grid interaction is required to optimize ESS design. This study analyzed the load matching and grid interaction by expected consumption behavior using actual data measured in one-minute intervals. The experiment was conducted in three nZEH with photovoltaic system, called all-electric houses. LCF (Load Cover Factor), SCF (Supply Cover Factor) and $f_{grid}$ (Grid Interaction Index) were evaluated as an analysis indicator. As a result, LCF, SCF and $f_{grid}$ of A house were 0.25, 0.23 and 0.27 respectively; That of B house were 0.23, 0.23, 0.19, and that of C were 0.20, 0.19, 0.27 respectively.