| Title |
A Study on the Economic Load Dispatch in Microgrid Considering Uncertainty of Photovoltaic Using Probability Distribution |
| Authors |
임승민(Seung-Min Lim) ; 송진솔(Jin-Sol Song) ; 박우근(Woo-Geun Park) ; 김철환(Chul-Hwan Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.8.1139 |
| Keywords |
Deep Learning; Economic Load Dispatch; Particle Swarm Optimization; Photovoltaic; Uncertainty |
| Abstract |
From the Kyoto Protocol until Paris Climate Change Accord, the interest in the eco-friendly energy topic has been gradually emerging. According to this trend, renewable energy resources accounted for 33% of the world's power generation utilities, and the cumulative size is 2,378 [GW]. As a result of this trend, the domestic Renewable Energy (RE) market is also sustainable growth along with the government’s 2050 Carbon Neutralization strategy. This change in the system requests for an Economic Load Dispatch (ELD) in a state where both demand and supply change from the conventional ELD, which is used to control power generation as demand changes. In this paper, we propose an ELD that considers the intermittency and uncertainty of the Photovoltaic (PV) system. To verify the efficiency of the proposed ELD method, we calculate the expected results through one of the probabilistic methods, using Monte Carlo (MC) simulation and the Probability distribution model. Finally, by determining the optimal generation through Particle Swarm Optimization (PSO) algorithms that are used to consider the input and output characteristics of generators in ELD, we calculate the economic operating profit based on the expected results. |