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미세먼지와 기상정보 기반의 AHP 분석을 통하여 태양광 발전소 최적입지선정에 대한 사례연구
이건주,이기현,강성우 대한안전경영과학회 2017 대한안전경영과학회지 Vol.19 No.4
Solar energy has been known as a successful alternative energy source, however it requires a large area to build power generation facilities compared to other energy sources such as nuclear power. Weather factors such as rainy weather or night time impact on solar power generation because of lack of insolation and sunshine. In addition, solar power generation is vulnerable to external elements such as changes in temperature and fine dust. There are four seasons in the Republic of Korea hereby variations of temperature, insolation and sunshine are broad. Currently factors that cause find dust are continuously flowing in to Korea from abroad. In order to build a solar power plant, a large area is required for a limited domestic land hereby selecting the optimal location for the plant that maximizes the efficiency of power generation is necessary. Therefore, this research analyze the optimal site for solar power generation plant by implementing analytic hierarchy process based on weather factors such as fine dust. In order to extract weather factors that impact on solar power generation, this work conducts a case study which includes a correlation analysis between weather information and power generation.
S. F. Syed Vasiyullah,S. G. Bharathidasan 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.1
In restructured power system, Generation Companies (GENCOs) has an opportunity to sell power and reserve in power market to earn profi t by market clearing process. Defi ning unit commitment problem in a competitive environment to maximize the profi t of GENCOs while satisfying all the network constraints is called Profi t Based Unit Commitment problem (PBUC). The main contribution of this paper is modeling and inclusion of Market Clearing Price (MCP) in PBUC problem. In Day market, MCP is determined by market operator which provides maximum social welfare for both GENCOs and Consumers.On other hand this paper proposes a novel combination of solution methodology: Improved Pre-prepared power demand (IPPD) table and Analytical Hierarchy method (AHP) for solving the optimal day ahead scheduling problem as an another contribution. In this method, the status of unit commitment is obtained by IPPD table and AHP provides an optimal solution to PBUC problem. Minimizing total operating cost of thermal units to provide maximum profi t to GENCOs is called an optimal day ahead scheduling problem. Also it will be more realistic to redefi ne this problem to include multiple distributed resources and Electric vehicles with energy storage. Because of any uncertainties or fl uctuation of renewable energy resources (RESs), Electric vehicles (EV) can be used as load, energy sources and energy storage. This would reduce cost, emission and to improve system power quality and reliability. So output power of solar (P S ), wind output power (P W ) and Electric Vehicles power (P EV ) are modeled and included into day ahead scheduling problem.The proposed methodology is tested on a standard thermal unit system with or without RESs and EVs. Cost and emission reduction in a smart grid by maximum utilization of EVs and RESs are presented in this literature. It is indicated that the proposed method provides maximum profi t to GENCOs when compared to other methodologies such as Memory Management Algorithm, Improved Particle Swarm Optimization (PSO), Muller method, Gravitational search algorithm etc.