Abstract
The proportion of renewable energy generation is expanding worldwide with the goal of reducing greenhouse gas. According to the 8th Basic Plan for Long-term Electricity Supply and Demand in South Korea, South Korea reduces traditional energy generation such as nuclear and coal plants and achieves 20% (58.5GW) of renewable energy generation by 2030. Wind Generating Resources (WGRs) are affected by meteorological variables such as temperature, wind speed and wind direction. Specifically, WGRs have uncertainty and variability issues depending on temporal and spatial characteristics. In this paper, we propose the probabilistic estimation of wind generating resources based on the spatiotemporal penetration scenarios for power grid expansion. The data of WGRs are analyzed based on clustering method considering the spatiotemporal penetration scenarios, and the potential scenarios are estimated using Monte Carlo simulation by selecting a representative power distribution probability for each cluster. The proposed estimation model of WGRs will play a key role to develop the hedging strategies of investment decision on power grid expansion planning with high wind power penetrations.
Highlights
The proportion of renewable energy production in each country is increasing in accordance with the decreased production costs of renewable energy sources, regulatory and policy obligations, and the goal of reducing pollutants emitted from fossil fuel power sources
This paper focuses on analyzing wind power generation patterns and estimating probabilistic wind power output scenarios using historical wind power output data for power grid expansion planning
The characteristics of the wind power output of South Korea were analyzed with the Davies–Bouldin index and k–means clustering according to the region and season; the probabilistic wind power output scenario was generated with a probability distribution and Monte Carlo simulations
Summary
The proportion of renewable energy production in each country is increasing in accordance with the decreased production costs of renewable energy sources, regulatory and policy obligations, and the goal of reducing pollutants (e.g. greenhouse gases) emitted from fossil fuel power sources. Unlike the existing deterministic system review, which analyzes the optimal solution for maintaining the balance of power supply and demand in a single scenario, the probabilistic scenarios estimated through Monte Carlo Simulation was used when analyzing the system to maintain the balance of power supply and demand This can be used to establish a stable power system operation plan considering the characteristics of variable renewable energy. The characteristics of the wind power output of South Korea were analyzed with the Davies–Bouldin index and k–means clustering according to the region and season; the probabilistic wind power output scenario was generated with a probability distribution and Monte Carlo simulations. METHOD The k–means clustering concept based on the Davies–Bouldin index and Monte Carlo simulations was applied to predict the probabilistic wind power generation scenarios. Facilities in the same cluster have high relevance, and facilities in different clusters have low relevance
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