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.
| Original language | English |
|---|---|
| Pages (from-to) | 1139-1145 |
| Number of pages | 7 |
| Journal | Transactions of the Korean Institute of Electrical Engineers |
| Volume | 70 |
| Issue number | 8 |
| DOIs | |
| State | Published - Aug 2021 |
Keywords
- Deep Learning
- Economic Load Dispatch
- Particle Swarm Optimization
- Photovoltaic
- Uncertainty