<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Renewable Energy | YuxiaDing's homepage</title><link>https://yuxiading.github.io/tags/renewable-energy/</link><atom:link href="https://yuxiading.github.io/tags/renewable-energy/index.xml" rel="self" type="application/rss+xml"/><description>Renewable Energy</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 01 Jun 2026 00:00:00 +0000</lastBuildDate><image><url>https://yuxiading.github.io/media/icon_hu_982c5d63a71b2961.png</url><title>Renewable Energy</title><link>https://yuxiading.github.io/tags/renewable-energy/</link></image><item><title>Electricity Market Dynamics under Renewable Integration</title><link>https://yuxiading.github.io/projects/electricity-market-dynamics/</link><pubDate>Mon, 01 Jun 2026 00:00:00 +0000</pubDate><guid>https://yuxiading.github.io/projects/electricity-market-dynamics/</guid><description>&lt;p&gt;This project conducts an integrated time-series analysis of the DE-AT-LU day-ahead electricity market using 32,849 hourly observations from 2015 to 2018. It examines electricity-load forecasting, renewable-energy integration, price dynamics, and market volatility.&lt;/p&gt;
&lt;p&gt;An enhanced SARIMAX model incorporates renewable generation, calendar effects, and Fourier harmonics, reducing the out-of-sample MAPE from 18.73% to 11.45%. VAR models, Granger-causality tests, impulse-response functions, and forecast-error variance decomposition are used to study dynamic interactions among wind generation, solar generation, electricity load, and day-ahead prices.&lt;/p&gt;
&lt;p&gt;The project also develops an ARX(7)-GARCH(1,1)-skew-t model to capture persistent volatility, heavy tails, and asymmetric downside risk. The analysis indicates that supply-demand fundamentals explain 28.8% of 20-day price uncertainty.&lt;/p&gt;</description></item></channel></rss>