Demonstration Study of Voltage Control of DC Grid Using Energy Management System Based DC Applications

Kim, Ju–Yong; Kim, Hongjoo; Cho, Jintae; Cho, Youngpyo; Cho, Yoonsung; Kim, Suk-Cheol

doi:10.3390/en13174551

Cited by 1 publication

(1 citation statement)

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“…However, as the distribution system changes due to distributed power sources such as photovoltaic (PV) generation, wind turbine generation, and the energy storage system (ESS), the concept of the distribution system is changing [1,2]. In other words, while stably supplying electricity generated from large-scale power plants to consumers was important in the past, the acceptance of distributed power sources near demand sides and flexibility for new distribution systems such as microgrids and DC distribution systems are gradually emerging as a new issue in the distribution system [3,4]. Additionally, despite the load concentration phenomenon due to urbanization and the limitations of new distribution facilities, consumers continuously want to receive a high-quality and reliable power supply.…”

Section: Introductionmentioning

confidence: 99%

A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

et al. 2022

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The complexity and uncertainty of the distribution system are increasing as the connection of distributed power sources using solar or wind energy is rapidly increasing, and digital loads are expanding. As these complexity and uncertainty keep increasing the investment cost for distribution facilities, optimal distribution planning becomes a matter of greater focus. This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO’s distribution planning and proposed a mid- to long-term load forecasting method based on ensemble learning. After selecting optimal input variables required for the load forecasting model through correlation analysis, individual forecasting models were selected, which enabled the derivation of the optimal combination of ensemble load forecast models. This paper additionally offered an improved load forecasting model that considers the characteristics of each distribution line for enhancing the mid- to long-term distribution line load forecasting process for distribution planning. The study verified the performance of the proposed method by comparing forecasting values with actual values.

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Section: Introductionmentioning

confidence: 99%