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VerilogCourseTeam, 10. B Chowdry Nagar Main Road, Valasaravakkam, Chennai (2026)

08/09/2026

𝐌𝐔𝐋𝐓𝐈-𝐎𝐁𝐉𝐄𝐂𝐓𝐈𝐕𝐄 𝐏𝐒𝐎-𝐁𝐀𝐒𝐄𝐃 𝐎𝐏𝐓𝐈𝐌𝐀𝐋 𝐃𝐈𝐒𝐓𝐑𝐈𝐁𝐔𝐓𝐄𝐃 𝐆𝐄𝐍𝐄𝐑𝐀𝐓𝐎𝐑 𝐏𝐋𝐀𝐂𝐄𝐌𝐄𝐍𝐓 𝐀𝐍𝐃 𝐒𝐈𝐙𝐈𝐍𝐆 𝐔𝐍𝐃𝐄𝐑 𝟐𝟒-𝐇𝐎𝐔𝐑 𝐒𝐄𝐂𝐓𝐎𝐑-𝐒𝐏𝐄𝐂𝐈𝐅𝐈𝐂 𝐋𝐎𝐀𝐃 𝐕𝐀𝐑𝐈𝐀𝐓𝐈𝐎𝐍𝐒 𝐈𝐍 𝐑𝐃𝐒

📄 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝘁𝗵𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗹𝗶𝗻𝗸 𝗯𝗲𝗹𝗼𝘄.
https://drive.google.com/file/d/1wJ4LDMPgQ8PItPpLlLb0EACxCKZL1zXb/view?usp=sharing

🔹 𝐕𝐈𝐃𝐄𝐎 𝐎𝐕𝐄𝐑𝐕𝐈𝐄𝐖
This video presents a 𝐌𝐮𝐥𝐭𝐢-𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞 𝐏𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐒𝐰𝐚𝐫𝐦 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (𝐏𝐒𝐎)-𝐛𝐚𝐬𝐞𝐝 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 for optimal 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐨𝐫 (𝐃𝐆) 𝐩𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐬𝐢𝐳𝐢𝐧𝐠 in 𝐑𝐚𝐝𝐢𝐚𝐥 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 (𝐑𝐃𝐒) considering realistic 𝟐𝟒-𝐡𝐨𝐮𝐫 𝐬𝐞𝐜𝐭𝐨𝐫-𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐥𝐨𝐚𝐝 𝐯𝐚𝐫𝐢𝐚𝐭𝐢𝐨𝐧𝐬.

⚡ 𝐊𝐄𝐘 𝐅𝐄𝐀𝐓𝐔𝐑𝐄𝐒
• 𝐎𝐩𝐭𝐢𝐦𝐚𝐥 𝐃𝐆 𝐩𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐬𝐢𝐳𝐢𝐧𝐠
• 𝟐𝟒-𝐡𝐨𝐮𝐫 𝐭𝐢𝐦𝐞-𝐯𝐚𝐫𝐲𝐢𝐧𝐠 𝐥𝐨𝐚𝐝 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠
• 𝐑𝐞𝐬𝐢𝐝𝐞𝐧𝐭𝐢𝐚𝐥, 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥, 𝐂𝐨𝐦𝐦𝐞𝐫𝐜𝐢𝐚𝐥 𝐚𝐧𝐝 𝐒𝐜𝐡𝐨𝐨𝐥 𝐥𝐨𝐚𝐝 𝐩𝐫𝐨𝐟𝐢𝐥𝐞𝐬
• 𝐌𝐮𝐥𝐭𝐢-𝐨𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞 𝐏𝐒𝐎 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧
• 𝐑𝐚𝐝𝐢𝐚𝐥 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐥𝐨𝐚𝐝-𝐟𝐥𝐨𝐰 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬
• 𝐏𝐚𝐫𝐞𝐭𝐨-𝐨𝐩𝐭𝐢𝐦𝐚𝐥 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬

🎯 𝐎𝐏𝐓𝐈𝐌𝐈𝐙𝐀𝐓𝐈𝐎𝐍 𝐎𝐁𝐉𝐄𝐂𝐓𝐈𝐕𝐄𝐒
🔻 𝐌𝐢𝐧𝐢𝐦𝐢𝐳𝐞 𝟐𝟒-𝐡𝐨𝐮𝐫 𝐞𝐧𝐞𝐫𝐠𝐲 𝐥𝐨𝐬𝐬
📉 𝐌𝐢𝐧𝐢𝐦𝐢𝐳𝐞 𝐯𝐨𝐥𝐭𝐚𝐠𝐞 𝐝𝐞𝐯𝐢𝐚𝐭𝐢𝐨𝐧
💰 𝐌𝐢𝐧𝐢𝐦𝐢𝐳𝐞 𝐃𝐆 𝐢𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐜𝐨𝐬𝐭
⚙️ 𝐃𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐞 𝐨𝐩𝐭𝐢𝐦𝐚𝐥 𝐃𝐆 𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐬𝐢𝐳𝐞𝐬
🕐 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐬𝐲𝐬𝐭𝐞𝐦 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐨𝐯𝐞𝐫 𝟐𝟒 𝐡𝐨𝐮𝐫𝐬

🔬 𝐌𝐄𝐓𝐇𝐎𝐃𝐎𝐋𝐎𝐆𝐘
The framework integrates 𝐏𝐒𝐎-𝐛𝐚𝐬𝐞𝐝 𝐦𝐮𝐥𝐭𝐢-𝐨𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧, 𝐫𝐚𝐝𝐢𝐚𝐥 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐥𝐨𝐚𝐝-𝐟𝐥𝐨𝐰 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬, 𝟐𝟒-𝐡𝐨𝐮𝐫 𝐬𝐞𝐜𝐭𝐨𝐫-𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐥𝐨𝐚𝐝 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠, and 𝐏𝐚𝐫𝐞𝐭𝐨-𝐟𝐫𝐨𝐧𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬.

📊 The optimized solutions demonstrate the potential of intelligent DG allocation for improving 𝐩𝐨𝐰𝐞𝐫 𝐥𝐨𝐬𝐬, 𝐯𝐨𝐥𝐭𝐚𝐠𝐞 𝐩𝐫𝐨𝐟𝐢𝐥𝐞, 𝐚𝐧𝐝 𝐞𝐜𝐨𝐧𝐨𝐦𝐢𝐜 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 of radial distribution systems.

🔖 𝐊𝐄𝐘𝐖𝐎𝐑𝐃𝐒
𝐏𝐒𝐎 | 𝐌𝐎𝐏𝐒𝐎 | 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 | 𝐃𝐆 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 | 𝐃𝐆 𝐒𝐢𝐳𝐢𝐧𝐠 | 𝟐𝟒-𝐇𝐨𝐮𝐫 𝐋𝐨𝐚𝐝 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 | 𝐑𝐚𝐝𝐢𝐚𝐥 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐒𝐲𝐬𝐭𝐞𝐦 | 𝐏𝐚𝐫𝐞𝐭𝐨 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 | 𝐕𝐨𝐥𝐭𝐚𝐠𝐞 𝐏𝐫𝐨𝐟𝐢𝐥𝐞 | 𝐏𝐨𝐰𝐞𝐫 𝐋𝐨𝐬𝐬 | 𝐌𝐀𝐓𝐋𝐀𝐁

#𝐏𝐒𝐎 #𝐌𝐎𝐏𝐒𝐎 #𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 #𝐃𝐆𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 #𝐃𝐆𝐒𝐢𝐳𝐢𝐧𝐠 #𝐏𝐨𝐰𝐞𝐫𝐒𝐲𝐬𝐭𝐞𝐦 #𝐌𝐀𝐓𝐋𝐀𝐁 #𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘

🌐 𝐡𝐭𝐭𝐩𝐬://𝐰𝐰𝐰.𝐯𝐞𝐫𝐢𝐥𝐨𝐠𝐜𝐨𝐮𝐫𝐬𝐞𝐭𝐞𝐚𝐦.𝐜𝐨𝐦/𝐫𝐞𝐪𝐮𝐞𝐬𝐭-𝐟𝐨𝐫𝐦

📧 𝗘𝗠𝗔𝗜𝗟

𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

📱 𝗪𝗛𝗔𝗧𝗦𝗔𝗣𝗣

+𝟵𝟭 𝟳𝟵𝟬𝟰𝟱𝟲𝟴𝟰𝟱𝟲

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘

𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

🔗 𝗙𝗢𝗟𝗟𝗢𝗪 𝗨𝗦

𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸:
𝗩𝗲𝗿𝗶𝗹𝗼𝗴 𝗖𝗼𝘂𝗿𝘀𝗲 𝗧𝗲𝗮𝗺

𝗬𝗼𝘂𝗧𝘂𝗯𝗲 – 𝗠𝗮𝗶𝗻 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/

𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/verilogcourseteammatlabproject

07/09/2026

⚡ 𝗣𝗢𝗪𝗘𝗥 𝗟𝗢𝗦𝗦 𝗠𝗜𝗡𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 & 𝗖𝗢𝗦𝗧 𝗘𝗩𝗔𝗟𝗨𝗔𝗧𝗜𝗢𝗡 𝗢𝗙 𝗢𝗣𝗧𝗜𝗠𝗔𝗟 𝗖𝗔𝗣𝗔𝗖𝗜𝗧𝗢𝗥 𝗔𝗡𝗗 𝗗-𝗦𝗧𝗔𝗧𝗖𝗢𝗠 𝗔𝗟𝗟𝗢𝗖𝗔𝗧𝗜𝗢𝗡 𝗨𝗦𝗜𝗡𝗚 𝗣𝗔𝗥𝗧𝗜𝗖𝗟𝗘 𝗦𝗪𝗔𝗥𝗠 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 (𝗣𝗦𝗢) ⚡

📄 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝘁𝗵𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗹𝗶𝗻𝗸 𝗯𝗲𝗹𝗼𝘄.
https://drive.google.com/file/d/1KTmL3RgwnpqngJif49q50HhDe2PZJevz/view?usp=sharing

This project presents an 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 for minimizing power losses and improving the voltage profile of radial distribution systems through the optimal placement of 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝗼𝗿𝘀 𝗮𝗻𝗱 𝗗-𝗦𝗧𝗔𝗧𝗖𝗢𝗠𝘀.

🎯 𝗣𝗥𝗢𝗝𝗘𝗖𝗧 𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘𝗦
━━━━━━━━━━━━━━━━━━━━

🔹 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗲 𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗼𝘄𝗲𝗿 𝗹𝗼𝘀𝘀𝗲𝘀
🔹 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝘁𝗵𝗲 𝘃𝗼𝗹𝘁𝗮𝗴𝗲 𝗽𝗿𝗼𝗳𝗶𝗹𝗲
🔹 𝗗𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝗼𝗿 𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝘀𝗶𝘇𝗲𝘀
🔹 𝗗𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗗-𝗦𝗧𝗔𝗧𝗖𝗢𝗠 𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝘀𝗶𝘇𝗲𝘀
🔹 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗶𝗻𝘀𝘁𝗮𝗹𝗹𝗮𝘁𝗶𝗼𝗻 𝗰𝗼𝘀𝘁𝘀
🔹 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝘁𝗵𝗲 𝗯𝗮𝗹𝗮𝗻𝗰𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗽𝗼𝘄𝗲𝗿-𝗹𝗼𝘀𝘀 𝗿𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘁𝗼𝘁𝗮𝗹 𝗰𝗼𝘀𝘁

🧠 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗧𝗘𝗖𝗛𝗡𝗜𝗤𝗨𝗘

𝗣𝗮𝗿𝘁𝗶𝗰𝗹𝗲 𝗦𝘄𝗮𝗿𝗺 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗣𝗦𝗢) is used to determine the optimal locations and sizes of Capacitors and D-STATCOM devices in the radial distribution system.

⚙️ 𝗦𝗬𝗦𝗧𝗘𝗠 𝗔𝗡𝗔𝗟𝗬𝗦𝗜𝗦

➤ 𝗥𝗮𝗱𝗶𝗮𝗹 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴
➤ 𝗟𝗼𝗮𝗱-𝗙𝗹𝗼𝘄 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
➤ 𝗣𝗼𝘄𝗲𝗿-𝗟𝗼𝘀𝘀 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗶𝗼𝗻
➤ 𝗩𝗼𝗹𝘁𝗮𝗴𝗲-𝗣𝗿𝗼𝗳𝗶𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
➤ 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝗼𝗿 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻
➤ 𝗗-𝗦𝗧𝗔𝗧𝗖𝗢𝗠 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻
➤ 𝗣𝗦𝗢-𝗕𝗮𝘀𝗲𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
➤ 𝗖𝗼𝘀𝘁 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻

💰 𝗖𝗢𝗦𝗧 𝗘𝗩𝗔𝗟𝗨𝗔𝗧𝗜𝗢𝗡

The 𝗧𝗼𝘁𝗮𝗹 𝗖𝗼𝘀𝘁 is evaluated considering 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝗼𝗿 𝗖𝗼𝘀𝘁, 𝗗-𝗦𝗧𝗔𝗧𝗖𝗢𝗠 𝗖𝗼𝘀𝘁, installation, maintenance, and other relevant economic factors.

📊 𝗣𝗘𝗥𝗙𝗢𝗥𝗠𝗔𝗡𝗖𝗘 𝗣𝗔𝗥𝗔𝗠𝗘𝗧𝗘𝗥𝗦

✓ 𝗔𝗰𝘁𝗶𝘃𝗲 𝗣𝗼𝘄𝗲𝗿 𝗟𝗼𝘀𝘀
✓ 𝗠𝗶𝗻𝗶𝗺𝘂𝗺 𝗕𝘂𝘀 𝗩𝗼𝗹𝘁𝗮𝗴𝗲
✓ 𝗩𝗼𝗹𝘁𝗮𝗴𝗲 𝗣𝗿𝗼𝗳𝗶𝗹𝗲
✓ 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗗𝗲𝘃𝗶𝗰𝗲 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻𝘀
✓ 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗗𝗲𝘃𝗶𝗰𝗲 𝗦𝗶𝘇𝗲𝘀
✓ 𝗧𝗼𝘁𝗮𝗹 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗖𝗼𝘀𝘁
✓ 𝗣𝗦𝗢 𝗖𝗼𝗻𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲

🔬 𝗖𝗔𝗦𝗘 𝗦𝗧𝗨𝗗𝗜𝗘𝗦

① 𝗕𝗮𝘀𝗲 𝗥𝗮𝗱𝗶𝗮𝗹 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺
② 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝗼𝗿-𝗢𝗻𝗹𝘆 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻
③ 𝗗-𝗦𝗧𝗔𝗧𝗖𝗢𝗠-𝗢𝗻𝗹𝘆 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻
④ 𝗖𝗼𝗺𝗯𝗶𝗻𝗲𝗱 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝗼𝗿 + 𝗗-𝗦𝗧𝗔𝗧𝗖𝗢𝗠 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻

🌟 𝗞𝗘𝗬 𝗕𝗘𝗡𝗘𝗙𝗜𝗧𝗦

⚡ 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗣𝗼𝘄𝗲𝗿 𝗟𝗼𝘀𝘀𝗲𝘀
📈 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗩𝗼𝗹𝘁𝗮𝗴𝗲 𝗣𝗿𝗼𝗳𝗶𝗹𝗲
💡 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗥𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝗣𝗼𝘄𝗲𝗿 𝗖𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗶𝗼𝗻
🔧 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲
💰 𝗖𝗼𝘀𝘁-𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴
🎯 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻-𝗦𝘆𝘀𝘁𝗲𝗺 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴

🔔 𝗟𝗜𝗞𝗘 👍 • 𝗦𝗛𝗔𝗥𝗘 📢 • 𝗦𝗨𝗕𝗦𝗖𝗥𝗜𝗕𝗘 🔔

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘

🌐 𝐡𝐭𝐭𝐩𝐬://𝐰𝐰𝐰.𝐯𝐞𝐫𝐢𝐥𝐨𝐠𝐜𝐨𝐮𝐫𝐬𝐞𝐭𝐞𝐚𝐦.𝐜𝐨𝐦/𝐫𝐞𝐪𝐮𝐞𝐬𝐭-𝐟𝐨𝐫𝐦

📧 𝗘𝗠𝗔𝗜𝗟

𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

📱 𝗪𝗛𝗔𝗧𝗦𝗔𝗣𝗣

+𝟵𝟭 𝟳𝟵𝟬𝟰𝟱𝟲𝟴𝟰𝟱𝟲

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘

𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

🔗 𝗙𝗢𝗟𝗟𝗢𝗪 𝗨𝗦

𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸:
𝗩𝗲𝗿𝗶𝗹𝗼𝗴 𝗖𝗼𝘂𝗿𝘀𝗲 𝗧𝗲𝗮𝗺

𝗬𝗼𝘂𝗧𝘂𝗯𝗲 – 𝗠𝗮𝗶𝗻 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/

𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/verilogcourseteammatlabproject

06/09/2026

𝗥𝗜𝗦𝗞-𝗔𝗪𝗔𝗥𝗘 𝗠𝗨𝗟𝗧𝗜-𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘 𝗣𝗛𝗢𝗧𝗢𝗩𝗢𝗟𝗧𝗔𝗜𝗖-𝗘𝗟𝗘𝗖𝗧𝗥𝗜𝗖 𝗩𝗘𝗛𝗜𝗖𝗟𝗘 𝗦𝗖𝗛𝗘𝗗𝗨𝗟𝗜𝗡𝗚 𝗙𝗥𝗔𝗠𝗘𝗪𝗢𝗥𝗞 𝗨𝗦𝗜𝗡𝗚 𝗣𝗦𝗢

📄 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝘁𝗵𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗹𝗶𝗻𝗸 𝗯𝗲𝗹𝗼𝘄.
https://drive.google.com/file/d/1il4EX5nVK4UDLnEz5od7T1RwQe_pr9f4/view?usp=sharing

📌 𝐀𝐛𝐨𝐮𝐭 𝐓𝐡𝐢𝐬 𝐕𝐢𝐝𝐞𝐨
𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗮 𝗥𝗶𝘀𝗸-𝗔𝘄𝗮𝗿𝗲 𝗠𝘂𝗹𝘁𝗶-𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝗵𝗼𝘁𝗼𝘃𝗼𝗹𝘁𝗮𝗶𝗰-𝗘𝗹𝗲𝗰𝘁𝗿𝗶𝗰 𝗩𝗲𝗵𝗶𝗰𝗹𝗲 (𝗣𝗘𝗩) 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗨𝘀𝗶𝗻𝗴 𝗣𝗦𝗢, designed for intelligent EV charging and discharging in a renewable-energy microgrid.

𝗧𝗵𝗶𝘀 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗰𝗼𝗺𝗯𝗶𝗻𝗲𝘀 𝗣𝗮𝗿𝘁𝗶𝗰𝗹𝗲 𝗦𝘄𝗮𝗿𝗺 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗣𝗦𝗢) with 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗩𝗮𝗹𝘂𝗲-𝗮𝘁-𝗥𝗶𝘀𝗸 (𝗖𝗩𝗮𝗥) to manage uncertainty while optimizing multiple competing objectives.

⚡ 𝗞𝗲𝘆 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝘀:
• Risk-aware economic cost
• Grid stability
• Peak demand reduction
• Battery degradation cost
• Renewable-energy curtailment

🔋 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀:
• Photovoltaic (PV) energy integration
• Electric Vehicle (EV/PEV) scheduling
• Vehicle-to-Grid (V2G) operation
• Multi-objective optimization
• Risk-aware scheduling using CVaR
• Battery state-of-charge management
• Grid power balancing
• Renewable-energy utilization
• Scenario-based stochastic optimization

𝗧𝗵𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝘀 realistic EV constraints including SoC limits, charging/discharging power limits, vehicle availability windows, departure SoC requirements, and microgrid power balance.

𝗧𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝘀 𝟵 𝘃𝗲𝗵𝗶𝗰𝗹𝗲𝘀, a 𝟮𝟰-𝗵𝗼𝘂𝗿 scheduling horizon, 𝟭𝟰𝟰 time steps, and 𝟭,𝟮𝟵𝟲 continuous decision variables.

𝗧𝗵𝗶𝘀 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝘀 the mathematical formulation, objective functions, operational constraints, system parameters, and expected optimization results of the proposed PEV scheduling framework.

📌 𝗞𝗲𝘆𝘄𝗼𝗿𝗱𝘀:
Photovoltaic Scheduling, Electric Vehicle Scheduling, EV Charging, V2G, Particle Swarm Optimization, PSO, Multi-Objective Optimization, CVaR, Smart Grid, Renewable Energy, Solar PV, Battery Management, PEV Scheduling, Stochastic Optimization.

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘

🌐 𝐡𝐭𝐭𝐩𝐬://𝐰𝐰𝐰.𝐯𝐞𝐫𝐢𝐥𝐨𝐠𝐜𝐨𝐮𝐫𝐬𝐞𝐭𝐞𝐚𝐦.𝐜𝐨𝐦/𝐫𝐞𝐪𝐮𝐞𝐬𝐭-𝐟𝐨𝐫𝐦

📧 𝗘𝗠𝗔𝗜𝗟

𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

📱 𝗪𝗛𝗔𝗧𝗦𝗔𝗣𝗣

+𝟵𝟭 𝟳𝟵𝟬𝟰𝟱𝟲𝟴𝟰𝟱𝟲

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘

𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

🔗 𝗙𝗢𝗟𝗟𝗢𝗪 𝗨𝗦

𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸:
𝗩𝗲𝗿𝗶𝗹𝗼𝗴 𝗖𝗼𝘂𝗿𝘀𝗲 𝗧𝗲𝗮𝗺

𝗬𝗼𝘂𝗧𝘂𝗯𝗲 – 𝗠𝗮𝗶𝗻 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/

𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/verilogcourseteammatlabproject

05/09/2026

🔥 𝐌𝐔𝐋𝐓𝐈-𝐎𝐁𝐉𝐄𝐂𝐓𝐈𝐕𝐄 𝐎𝐏𝐓𝐈𝐌𝐈𝐙𝐀𝐓𝐈𝐎𝐍 𝐎𝐅 𝐃𝐈𝐒𝐓𝐑𝐈𝐁𝐔𝐓𝐄𝐃 𝐆𝐄𝐍𝐄𝐑𝐀𝐓𝐈𝐎𝐍 𝐏𝐋𝐀𝐂𝐄𝐌𝐄𝐍𝐓 𝐀𝐍𝐃 𝐒𝐈𝐙𝐈𝐍𝐆 𝐈𝐍 𝐑𝐀𝐃𝐈𝐀𝐋 𝐃𝐈𝐒𝐓𝐑𝐈𝐁𝐔𝐓𝐈𝐎𝐍 𝐍𝐄𝐓𝐖𝐎𝐑𝐊𝐒 𝐔𝐒𝐈𝐍𝐆 𝐖𝐇𝐀𝐋𝐄 𝐎𝐏𝐓𝐈𝐌𝐈𝐙𝐀𝐓𝐈𝐎𝐍 𝐀𝐋𝐆𝐎𝐑𝐈𝐓𝐇𝐌 | 𝐃𝐈𝐒𝐓𝐑𝐈𝐁𝐔𝐓𝐈𝐎𝐍 𝐋𝐎𝐂𝐀𝐓𝐈𝐎𝐍𝐀𝐋 𝐌𝐀𝐑𝐆𝐈𝐍𝐀𝐋 𝐏𝐑𝐈𝐂𝐈𝐍𝐆 (𝐃𝐋𝐌𝐏) 🌊🐋⚡

📌 𝐀𝐛𝐨𝐮𝐭 𝐓𝐡𝐢𝐬 𝐕𝐢𝐝𝐞𝐨

This video presents a 𝐦𝐮𝐥𝐭𝐢-𝐨𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 for optimal Distributed Generation (DG) placement and sizing in radial distribution networks using the 𝐖𝐡𝐚𝐥𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 (𝐖𝐎𝐀).

🎯 𝐒𝐞𝐯𝐞𝐧 𝐊𝐞𝐲 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬

🔹 𝐅₁ – 𝐀𝐜𝐭𝐢𝐯𝐞 𝐏𝐨𝐰𝐞𝐫 𝐋𝐨𝐬𝐬: Minimize network power losses
🔹 𝐅₂ – 𝐕𝐨𝐥𝐭𝐚𝐠𝐞 𝐃𝐞𝐯𝐢𝐚𝐭𝐢𝐨𝐧: Improve voltage profile
🔹 𝐅₃ – 𝐃𝐋𝐌𝐏 𝐃𝐢𝐬𝐩𝐞𝐫𝐬𝐢𝐨𝐧: Improve distribution locational marginal pricing characteristics
🔹 𝐅₄ – 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐂𝐨𝐬𝐭: Reduce overall operating/market cost
🔹 𝐅₅ – 𝐄𝐧𝐞𝐫𝐠𝐲 𝐍𝐨𝐭 𝐒𝐞𝐫𝐯𝐞𝐝 (𝐄𝐍𝐒): Improve supply reliability
🔹 𝐅₆ – 𝐂𝐨𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧: Minimize line congestion
🔹 𝐅₇ – 𝐄𝐦𝐢𝐬𝐬𝐢𝐨𝐧: Reduce CO₂ emissions 🌱

⚙️ 𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐲

The framework integrates 𝐀𝐂 𝐩𝐨𝐰𝐞𝐫 𝐟𝐥𝐨𝐰, 𝐃𝐋𝐌𝐏 𝐜𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐢𝐨𝐧, 𝐨𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞 𝐧𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧, 𝐜𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐭 𝐡𝐚𝐧𝐝𝐥𝐢𝐧𝐠, and WOA-based optimization to determine suitable DG locations and capacities.

📊 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐞𝐝

⚡ IEEE 33-Bus Radial Distribution System

📈 𝐊𝐞𝐲 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐀𝐫𝐞𝐚𝐬

✅ Optimal DG placement and sizing
✅ Reduced active power losses
✅ Improved voltage profile
✅ Improved DLMP characteristics
✅ Reduced operating cost
✅ Reduced congestion
✅ Improved reliability through ENS minimization
✅ Reduced environmental impact

🎓 𝐖𝐡𝐨 𝐒𝐡𝐨𝐮𝐥𝐝 𝐖𝐚𝐭𝐜𝐡?

👨‍🎓 Research Scholars
👨‍🔬 Power System Researchers
⚡ Electrical & Power Engineers
🏫 Engineering Students & Academicians
💻 MATLAB Optimization Researchers
🌱 Smart Grid & Renewable Energy Researchers

📌 This work is particularly useful for researchers working on 𝐃𝐆 𝐩𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭, 𝐫𝐚𝐝𝐢𝐚𝐥 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, 𝐃𝐋𝐌𝐏, 𝐦𝐮𝐥𝐭𝐢-𝐨𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧, 𝐖𝐎𝐀, 𝐌𝐀𝐓𝐋𝐀𝐁, 𝐚𝐧𝐝 𝐬𝐦𝐚𝐫𝐭 𝐠𝐫𝐢𝐝 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬.

👍 𝐋𝐢𝐤𝐞 | 💬 𝐂𝐨𝐦𝐦𝐞𝐧𝐭 | 🔄 𝐒𝐡𝐚𝐫𝐞 | 🔔 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞

#️⃣ #𝐖𝐡𝐚𝐥𝐞𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 #𝐖𝐎𝐀 #𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 #𝐃𝐆𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 #𝐃𝐋𝐌𝐏 #𝐌𝐮𝐥𝐭𝐢𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 #𝐏𝐨𝐰𝐞𝐫𝐒𝐲𝐬𝐭𝐞𝐦𝐬 #𝐒𝐦𝐚𝐫𝐭𝐆𝐫𝐢𝐝 #𝐌𝐀𝐓𝐋𝐀𝐁 #𝐈𝐄𝐄𝐄𝟑𝟑𝐁𝐮𝐬 #𝐑𝐞𝐧𝐞𝐰𝐚𝐛𝐥𝐞𝐄𝐧𝐞𝐫𝐠𝐲

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘

Request the MATLAB source code for academic, educational, and research purposes by completing the request form:
https://www.verilogcourseteam.com/request-form

📧 𝗘𝗠𝗔𝗜𝗟

𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

📱 𝗪𝗛𝗔𝗧𝗦𝗔𝗣𝗣

+𝟵𝟭 𝟳𝟵𝟬𝟰𝟱𝟲𝟴𝟰𝟱𝟲

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘

𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

🔗 𝗙𝗢𝗟𝗟𝗢𝗪 𝗨𝗦

𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸:
𝗩𝗲𝗿𝗶𝗹𝗼𝗴 𝗖𝗼𝘂𝗿𝘀𝗲 𝗧𝗲𝗮𝗺

𝗬𝗼𝘂𝗧𝘂𝗯𝗲 – 𝗠𝗮𝗶𝗻 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/

𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/verilogcourseteammatlabproject

04/09/2026

𝐀 𝐆𝐄𝐍𝐄𝐓𝐈𝐂 𝐀𝐋𝐆𝐎𝐑𝐈𝐓𝐇𝐌 𝐀𝐏𝐏𝐑𝐎𝐀𝐂𝐇 𝐓𝐎 𝐂𝐎𝐑𝐑𝐄𝐂𝐓𝐈𝐕𝐄 𝐓𝐂𝐒𝐂 𝐀𝐋𝐋𝐎𝐂𝐀𝐓𝐈𝐎𝐍 𝐔𝐍𝐃𝐄𝐑 𝐂𝐎𝐍𝐓𝐈𝐍𝐆𝐄𝐍𝐂𝐘 𝐂𝐎𝐍𝐃𝐈𝐓𝐈𝐎𝐍𝐒

📄 𝗗𝗢𝗪𝗡𝗟𝗢𝗔𝗗 𝗧𝗛𝗘 𝗧𝗘𝗖𝗛𝗡𝗜𝗖𝗔𝗟 𝗔𝗡𝗔𝗟𝗬𝗦𝗜𝗦 𝗔𝗡𝗗 𝗔𝗟𝗚𝗢𝗥𝗜𝗧𝗛𝗠𝗜𝗖 𝗗𝗢𝗖𝗨𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡 𝗙𝗥𝗢𝗠 𝗧𝗛𝗘 𝗟𝗜𝗡𝗞 𝗕𝗘𝗟𝗢𝗪:
https://drive.google.com/file/d/1Pn9ylKAxgrlO9MeNbYoR2IqyrgWsxlQs/view?usp=sharing

⚡ 𝐏𝐑𝐎𝐉𝐄𝐂𝐓 𝐎𝐕𝐄𝐑𝐕𝐈𝐄𝐖

This video presents a 𝐌𝐀𝐓𝐋𝐀𝐁-𝐁𝐀𝐒𝐄𝐃 𝐆𝐄𝐍𝐄𝐓𝐈𝐂 𝐀𝐋𝐆𝐎𝐑𝐈𝐓𝐇 (𝐆𝐀) 𝐅𝐑𝐀𝐌𝐄𝐖𝐎𝐑𝐊 for 𝐂𝐎𝐍𝐓𝐈𝐍𝐆𝐄𝐍𝐂𝐘 𝐃𝐄𝐓𝐄𝐂𝐓𝐈𝐎𝐍, 𝐒𝐄𝐂𝐔𝐑𝐈𝐓𝐘 𝐀𝐒𝐒𝐄𝐒𝐒𝐌𝐄𝐍𝐓, 𝐀𝐍𝐃 𝐂𝐎𝐑𝐑𝐄𝐂𝐓𝐈𝐕𝐄 𝐓𝐂𝐒𝐂 𝐀𝐋𝐋𝐎𝐂𝐀𝐓𝐈𝐎𝐍 in the 𝐈𝐄𝐄𝐄 𝟑𝟎-𝐁𝐔𝐒 𝐏𝐎𝐖𝐄𝐑 𝐒𝐘𝐒𝐓𝐄𝐌.

The proposed implementation combines 𝐀𝐂 𝐍𝐄𝐖𝐓𝐎𝐍–𝐑𝐀𝐏𝐇𝐒𝐎𝐍 𝐏𝐎𝐖𝐄𝐑 𝐅𝐋𝐎𝐖, 𝐆𝐄𝐍𝐄𝐑𝐀𝐓𝐎𝐑 𝐐-𝐋𝐈𝐌𝐈𝐓 𝐄𝐍𝐅𝐎𝐑𝐂𝐄𝐌𝐄𝐍𝐓, 𝐂𝐎𝐍𝐓𝐈𝐍𝐆𝐄𝐍𝐂𝐘 𝐒𝐂𝐑𝐄𝐄𝐍𝐈𝐍𝐆, and 𝐆𝐀-𝐁𝐀𝐒𝐄𝐃 𝐎𝐏𝐓𝐈𝐌𝐀𝐋 𝐓𝐂𝐒𝐂 𝐏𝐋𝐀𝐂𝐄𝐌𝐄𝐍𝐓 𝐀𝐍𝐃 𝐒𝐈𝐙𝐈𝐍𝐆.

🔹 𝐌𝐀𝐉𝐎𝐑 𝐅𝐄𝐀𝐓𝐔𝐑𝐄𝐒

✔️ 𝐈𝐄𝐄𝐄 𝟑𝟎-𝐁𝐔𝐒 𝐓𝐑𝐀𝐍𝐒𝐌𝐈𝐒𝐒𝐈𝐎𝐍 𝐒𝐘𝐒𝐓𝐄𝐌
✔️ 𝐀𝐂 𝐍𝐄𝐖𝐓𝐎𝐍–𝐑𝐀𝐏𝐇𝐒𝐎𝐍 𝐏𝐎𝐖𝐄𝐑 𝐅𝐋𝐎𝐖
✔️ 𝐆𝐄𝐍𝐄𝐑𝐀𝐓𝐎𝐑 𝐑𝐄𝐀𝐂𝐓𝐈𝐕𝐄 𝐏𝐎𝐖𝐄𝐑 𝐋𝐈𝐌𝐈𝐓 𝐄𝐍𝐅𝐎𝐑𝐂𝐄𝐌𝐄𝐍𝐓
✔️ 𝐆𝐄𝐍𝐄𝐑𝐀𝐓𝐎𝐑 𝐎𝐔𝐓𝐀𝐆𝐄 𝐀𝐍𝐀𝐋𝐘𝐒𝐈𝐒
✔️ 𝐓𝐑𝐀𝐍𝐒𝐌𝐈𝐒𝐒𝐈𝐎𝐍 𝐋𝐈𝐍𝐄 𝐎𝐔𝐓𝐀𝐆𝐄 𝐀𝐍𝐀𝐋𝐘𝐒𝐈𝐒
✔️ 𝐋𝐎𝐀𝐃 𝐆𝐑𝐎𝐖𝐓𝐇 𝐂𝐎𝐍𝐃𝐈𝐓𝐈𝐎𝐍𝐒
✔️ 𝐆𝐄𝐍𝐄𝐓𝐈𝐂 𝐀𝐋𝐆𝐎𝐑𝐈𝐓𝐇𝐌 (𝐆𝐀) 𝐎𝐏𝐓𝐈𝐌𝐈𝐙𝐀𝐓𝐈𝐎𝐍
✔️ 𝐎𝐏𝐓𝐈𝐌𝐀𝐋 𝐓𝐂𝐒𝐂 𝐁𝐑𝐀𝐍𝐂𝐇 𝐋𝐎𝐂𝐀𝐓𝐈𝐎𝐍
✔️ 𝐎𝐏𝐓𝐈𝐌𝐀𝐋 𝐓𝐂𝐒𝐂 𝐂𝐎𝐌𝐏𝐄𝐍𝐒𝐀𝐓𝐈𝐎𝐍 𝐋𝐄𝐕𝐄𝐋
✔️ 𝐕𝐎𝐋𝐓𝐀𝐆𝐄 𝐒𝐄𝐂𝐔𝐑𝐈𝐓𝐘 𝐀𝐍𝐀𝐋𝐘𝐒𝐈𝐒
✔️ 𝐀𝐂𝐓𝐈𝐕𝐄 𝐏𝐎𝐖𝐄𝐑 𝐋𝐎𝐒𝐒 𝐀𝐍𝐀𝐋𝐘𝐒𝐈𝐒
✔️ 𝐕𝐎𝐋𝐓𝐀𝐆𝐄 𝐃𝐄𝐕𝐈𝐀𝐓𝐈𝐎𝐍 𝐈𝐍𝐃𝐄𝐗 (𝐕𝐃𝐈)
✔️ 𝐒𝐘𝐒𝐓𝐄𝐌 𝐒𝐄𝐕𝐄𝐑𝐈𝐓𝐘 𝐄𝐕𝐀𝐋𝐔𝐀𝐓𝐈𝐎𝐍
✔️ 𝐏𝐑𝐄- 𝐀𝐍𝐃 𝐏𝐎𝐒𝐓-𝐂𝐎𝐑𝐑𝐄𝐂𝐓𝐈𝐎𝐍 𝐕𝐀𝐋𝐈𝐃𝐀𝐓𝐈𝐎𝐍

🧬 𝐆𝐀-𝐁𝐀𝐒𝐄𝐃 𝐓𝐂𝐒𝐂 𝐎𝐏𝐓𝐈𝐌𝐈𝐙𝐀𝐓𝐈𝐎𝐍

The 𝐆𝐄𝐍𝐄𝐓𝐈𝐂 𝐀𝐋𝐆𝐎𝐑𝐈𝐓𝐇𝐌 searches for the optimal TCSC configuration by simultaneously determining:

🔸 𝐓𝐂𝐒𝐂 𝐁𝐑𝐀𝐍𝐂𝐇 𝐋𝐎𝐂𝐀𝐓𝐈𝐎𝐍
🔸 𝐓𝐂𝐒𝐂 𝐂𝐎𝐌𝐏𝐄𝐍𝐒𝐀𝐓𝐈𝐎𝐍 𝐋𝐄𝐕𝐄𝐋

The GA evaluates candidate solutions using a multi-objective-inspired composite fitness formulation involving 𝐏𝐎𝐖𝐄𝐑 𝐋𝐎𝐒𝐒, 𝐕𝐎𝐋𝐓𝐀𝐆𝐄 𝐃𝐄𝐕𝐈𝐀𝐓𝐈𝐎𝐍, 𝐓𝐂𝐒𝐂 𝐂𝐎𝐒𝐓, 𝐆𝐄𝐍𝐄𝐑𝐀𝐓𝐈𝐎𝐍 𝐂𝐎𝐒𝐓, and 𝐒𝐘𝐒𝐓𝐄𝐌 𝐒𝐄𝐕𝐄𝐑𝐈𝐓𝐘.

⚙️ 𝐂𝐎𝐍𝐓𝐈𝐍𝐆𝐄𝐍𝐂𝐘 𝐀𝐍𝐀𝐋𝐘𝐒𝐈𝐒

The IEEE 30-bus system is examined under different stressed operating conditions, including:

🔹 𝐆𝐄𝐍𝐄𝐑𝐀𝐓𝐎𝐑 𝐎𝐔𝐓𝐀𝐆𝐄𝐒
🔹 𝐓𝐑𝐀𝐍𝐒𝐌𝐈𝐒𝐒𝐈𝐎𝐍 𝐋𝐈𝐍𝐄 𝐎𝐔𝐓𝐀𝐆𝐄𝐒
🔹 𝐋𝐎𝐀𝐃 𝐆𝐑𝐎𝐖𝐓𝐇 𝐂𝐎𝐍𝐃𝐈𝐓𝐈𝐎𝐍𝐒

For each contingency, the system is screened for 𝐕𝐎𝐋𝐓𝐀𝐆𝐄 𝐕𝐈𝐎𝐋𝐀𝐓𝐈𝐎𝐍𝐒, 𝐏𝐎𝐖𝐄𝐑-𝐅𝐋𝐎𝐖 𝐂𝐎𝐍𝐕𝐄𝐑𝐆𝐄𝐍𝐂𝐄, and 𝐁𝐑𝐀𝐍𝐂𝐇 𝐋𝐎𝐀𝐃𝐈𝐍𝐆. When a corrective action is required, the GA searches for an appropriate TCSC location and compensation level.

📊 𝐏𝐄𝐑𝐅𝐎𝐑𝐌𝐀𝐍𝐂𝐄 𝐄𝐕𝐀𝐋𝐔𝐀𝐓𝐈𝐎𝐍

The MATLAB framework provides graphical and numerical assessment of:

📈 𝐆𝐀 𝐂𝐎𝐍𝐕𝐄𝐑𝐆𝐄𝐍𝐂𝐄
📉 𝐏𝐎𝐖𝐄𝐑 𝐋𝐎𝐒𝐒 𝐂𝐎𝐌𝐏𝐀𝐑𝐈𝐒𝐎𝐍
📊 𝐕𝐎𝐋𝐓𝐀𝐆𝐄 𝐏𝐑𝐎𝐅𝐈𝐋𝐄𝐒
📊 𝐕𝐃𝐈 𝐂𝐎𝐌𝐏𝐀𝐑𝐈𝐒𝐎𝐍
📊 𝐂𝐎𝐍𝐓𝐈𝐍𝐆𝐄𝐍𝐂𝐘 𝐒𝐄𝐕𝐄𝐑𝐈𝐓𝐘
📍 𝐓𝐂𝐒𝐂 𝐏𝐋𝐀𝐂𝐄𝐌𝐄𝐍𝐓 𝐀𝐍𝐃 𝐒𝐈𝐙𝐈𝐍𝐆

🎯 𝐎𝐁𝐉𝐄𝐂𝐓𝐈𝐕𝐄

The proposed 𝐆𝐀-𝐁𝐀𝐒𝐄𝐃 𝐂𝐎𝐑𝐑𝐄𝐂𝐓𝐈𝐕𝐄 𝐓𝐂𝐒𝐂 𝐅𝐑𝐀𝐌𝐄𝐖𝐎𝐑𝐊 aims to identify suitable series compensation for improving post-contingency operating conditions while maintaining the underlying network topology.

The complete computational workflow is:

𝐈𝐄𝐄𝐄 𝟑𝟎-𝐁𝐔𝐒 𝐃𝐀𝐓𝐀 → 𝐁𝐀𝐒𝐄 𝐀𝐂 𝐏𝐎𝐖𝐄𝐑 𝐅𝐋𝐎𝐖 → 𝐂𝐎𝐍𝐓𝐈𝐍𝐆𝐄𝐍𝐂𝐘 𝐀𝐍𝐀𝐋𝐘𝐒𝐈𝐒 → 𝐕𝐈𝐎𝐋𝐀𝐓𝐈𝐎𝐍 𝐃𝐄𝐓𝐄𝐂𝐓𝐈𝐎𝐍 → 𝐆𝐀-𝐁𝐀𝐒𝐄𝐃 𝐓𝐂𝐒𝐂 𝐎𝐏𝐓𝐈𝐌𝐈𝐙𝐀𝐓𝐈𝐎𝐍 → 𝐂𝐎𝐑𝐑𝐄𝐂𝐓𝐈𝐕𝐄 𝐀𝐂 𝐏𝐎𝐖𝐄𝐑 𝐅𝐋𝐎𝐖 → 𝐏𝐎𝐒𝐓-𝐂𝐎𝐍𝐓𝐈𝐍𝐆𝐄𝐍𝐂𝐘 𝐕𝐀𝐋𝐈𝐃𝐀𝐓𝐈𝐎𝐍

📌 𝐊𝐄𝐘𝐖𝐎𝐑𝐃𝐒

#𝐌𝐀𝐓𝐋𝐀𝐁 #𝐆𝐀 #𝐆𝐞𝐧𝐞𝐭𝐢𝐜𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 #𝐓𝐂𝐒𝐂 #𝐅𝐀𝐂𝐓𝐒 #𝐏𝐨𝐰𝐞𝐫𝐒𝐲𝐬𝐭𝐞𝐦 #𝐈𝐄𝐄𝐄𝟑𝟎𝐁𝐮𝐬 #𝐂𝐨𝐧𝐭𝐢𝐧𝐠𝐞𝐧𝐜𝐲𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 #𝐅𝐚𝐮𝐥𝐭𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 #𝐕𝐨𝐥𝐭𝐚𝐠𝐞𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 #𝐏𝐨𝐰𝐞𝐫𝐅𝐥𝐨𝐰 #𝐅𝐀𝐂𝐓𝐒𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 #𝐓𝐂𝐒𝐂𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 #𝐏𝐨𝐰𝐞𝐫𝐒𝐲𝐬𝐭𝐞𝐦𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘:
𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

📧 𝗘𝗠𝗔𝗜𝗟:
𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

📱 𝗪𝗛𝗔𝗧𝗦𝗔𝗣𝗣:
+𝟵𝟭 𝟳𝟵𝟬𝟰𝟱𝟲𝟴𝟰𝟱𝟲

🌐 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗙𝗢𝗥𝗠:
https://www.verilogcourseteam.com/request-form

▶️ 𝗬𝗢𝗨𝗧𝗨𝗕𝗘 – 𝗠𝗔𝗜𝗡 𝗖𝗛𝗔𝗡𝗡𝗘𝗟:
https://www.youtube.com/

▶️ 𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗥𝗢𝗝𝗘𝗖𝗧𝗦 𝗖𝗛𝗔𝗡𝗡𝗘𝗟:
https://www.youtube.com/verilogcourseteammatlabproject

03/09/2026

𝗠𝗨𝗟𝗧𝗜-𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘 𝗦𝗧𝗢𝗖𝗛𝗔𝗦𝗧𝗜𝗖 𝗢𝗣𝗧𝗜𝗠𝗔𝗟 𝗚𝗘𝗡𝗘𝗥𝗔𝗧𝗜𝗢𝗡 𝗦𝗖𝗛𝗘𝗗𝗨𝗟𝗜𝗡𝗚 𝗪𝗜𝗧𝗛 𝗥𝗘𝗡𝗘𝗪𝗔𝗕𝗟𝗘 𝗨𝗡𝗖𝗘𝗥𝗧𝗔𝗜𝗡𝗧𝗬, 𝗖𝗩𝗮𝗥-𝗕𝗔𝗦𝗘𝗗 𝗥𝗜𝗦𝗞 𝗔𝗡𝗗 𝗕𝗔𝗧𝗧𝗘𝗥𝗬 𝗘𝗡𝗘𝗥𝗚𝗬 𝗦𝗧𝗢𝗥𝗔𝗚𝗘

📄 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝘁𝗵𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗹𝗶𝗻𝗸 𝗯𝗲𝗹𝗼𝘄.
https://drive.google.com/file/d/1zhPJ82ZKAf3GeChX8c7pDP_-Kp79Kz7R/view?usp=sharing

This design presents an advanced 𝟮𝟰-𝗵𝗼𝘂𝗿 𝘀𝘁𝗼𝗰𝗵𝗮𝘀𝘁𝗶𝗰 𝗺𝘂𝗹𝘁𝗶-𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 framework integrating 𝘀𝗼𝗹𝗮𝗿 𝗣𝗩, 𝘄𝗶𝗻𝗱 𝗽𝗼𝘄𝗲𝗿, 𝘁𝗵𝗲𝗿𝗺𝗮𝗹 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿𝘀 𝗮𝗻𝗱 𝗕𝗮𝘁𝘁𝗲𝗿𝘆 𝗘𝗻𝗲𝗿𝗴𝘆 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗕𝗘𝗦𝗦).

The proposed methodology models 𝗿𝗲𝗻𝗲𝘄𝗮𝗯𝗹𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗹𝗼𝗮𝗱 𝘂𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆 using stochastic scenarios and incorporates 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗩𝗮𝗹𝘂𝗲-𝗮𝘁-𝗥𝗶𝘀𝗸 (𝗖𝗩𝗮𝗥) for risk assessment under uncertain operating conditions.

🔹 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀:

✅ 𝟮𝟰-𝗛𝗼𝘂𝗿 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴
✅ 𝗠𝘂𝗹𝘁𝗶-𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 𝗘𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗮𝗻𝗱 𝗘𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗗𝗶𝘀𝗽𝗮𝘁𝗰𝗵
✅ 𝗦𝗼𝗹𝗮𝗿 𝗣𝗩 𝗮𝗻𝗱 𝗪𝗶𝗻𝗱 𝗣𝗼𝘄𝗲𝗿 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻
✅ 𝗥𝗲𝗻𝗲𝘄𝗮𝗯𝗹𝗲 𝗮𝗻𝗱 𝗟𝗼𝗮𝗱 𝗨𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴
✅ 𝗦𝘁𝗼𝗰𝗵𝗮𝘀𝘁𝗶𝗰 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻
✅ 𝗖𝗩𝗮𝗥-𝗕𝗮𝘀𝗲𝗱 𝗥𝗶𝘀𝗸 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁
✅ 𝗥𝗲𝘀𝗲𝗿𝘃𝗲 𝗔𝗱𝗲𝗾𝘂𝗮𝗰𝘆 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
✅ 𝗕𝗘𝗦𝗦 𝗖𝗵𝗮𝗿𝗴𝗶𝗻𝗴/𝗗𝗶𝘀𝗰𝗵𝗮𝗿𝗴𝗶𝗻𝗴 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴
✅ 𝗕𝗘𝗦𝗦 𝗦𝘁𝗮𝘁𝗲-𝗼𝗳-𝗖𝗵𝗮𝗿𝗴𝗲 (𝗦𝗢𝗖) 𝗖𝗼𝗻𝘁𝗿𝗼𝗹
✅ 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿 𝗥𝗮𝗺𝗽-𝗥𝗮𝘁𝗲 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻
✅ 𝗙𝘂𝗲𝗹 𝗮𝗻𝗱 𝗘𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗖𝗼𝘀𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
✅ 𝗘𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝗖𝗼𝘀𝘁 𝗮𝗻𝗱 𝗖𝗼𝘀𝘁 𝗩𝗮𝗿𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
✅ 𝗘𝗛𝗢-𝗕𝗮𝘀𝗲𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
✅ 𝗖𝗼𝗻𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀

📊 The MATLAB implementation evaluates 𝗳𝘂𝗲𝗹 𝗰𝗼𝘀𝘁, 𝗲𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀, 𝗿𝗲𝗻𝗲𝘄𝗮𝗯𝗹𝗲 𝗰𝘂𝗿𝘁𝗮𝗶𝗹𝗺𝗲𝗻𝘁, 𝗿𝗲𝘀𝗲𝗿𝘃𝗲 𝗮𝗱𝗲𝗾𝘂𝗮𝗰𝘆, 𝗿𝗮𝗺𝗽 𝗿𝗮𝘁𝗲, 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝗰𝗼𝘀𝘁, 𝗖𝗩𝗮𝗥 𝗿𝗶𝘀𝗸, 𝗮𝗻𝗱 𝗕𝗘𝗦𝗦 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲.

📌 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗿𝗲𝗮𝘀:
⚡ 𝗣𝗼𝘄𝗲𝗿 𝗦𝘆𝘀𝘁𝗲𝗺 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
🌞 𝗥𝗲𝗻𝗲𝘄𝗮𝗯𝗹𝗲 𝗘𝗻𝗲𝗿𝗴𝘆
🔋 𝗕𝗮𝘁𝘁𝗲𝗿𝘆 𝗘𝗻𝗲𝗿𝗴𝘆 𝗦𝘁𝗼𝗿𝗮𝗴𝗲
📈 𝗦𝘁𝗼𝗰𝗵𝗮𝘀𝘁𝗶𝗰 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
🛡️ 𝗖𝗩𝗮𝗥 𝗥𝗶𝘀𝗸 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
🤖 𝗠𝗲𝘁𝗮𝗵𝗲𝘂𝗿𝗶𝘀𝘁𝗶𝗰 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
💻 𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗼𝘄𝗲𝗿 𝗦𝘆𝘀𝘁𝗲𝗺 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻

#️⃣ #𝗠𝘂𝗹𝘁𝗶𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 #𝗘𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝗗𝗶𝘀𝗽𝗮𝘁𝗰𝗵 #𝗥𝗲𝗻𝗲𝘄𝗮𝗯𝗹𝗲𝗘𝗻𝗲𝗿𝗴𝘆 #𝗕𝗘𝗦𝗦 #𝗖𝗩𝗮𝗥 #𝗦𝘁𝗼𝗰𝗵𝗮𝘀𝘁𝗶𝗰𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 #𝗣𝗼𝘄𝗲𝗿𝗦𝘆𝘀𝘁𝗲𝗺 #𝗠𝗔𝗧𝗟𝗔𝗕 #𝗘𝗛𝗢 #𝗣𝗵𝗗𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 #𝗦𝗖𝗜 #𝗘𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝗗𝗶𝘀𝗽𝗮𝘁𝗰𝗵 #𝗘𝗻𝗲𝗿𝗴𝘆𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘

Request the MATLAB source code for academic, educational, and research purposes by completing the request form:

𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗙𝗢𝗥𝗠

📧 𝗘𝗠𝗔𝗜𝗟

𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

📱 𝗪𝗛𝗔𝗧𝗦𝗔𝗣𝗣

+𝟵𝟭 𝟳𝟵𝟬𝟰𝟱𝟲𝟴𝟰𝟱𝟲

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘

𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

🔗 𝗙𝗢𝗟𝗟𝗢𝗪 𝗨𝗦

𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸:
𝗩𝗲𝗿𝗶𝗹𝗼𝗴 𝗖𝗼𝘂𝗿𝘀𝗲 𝗧𝗲𝗮𝗺

𝗬𝗼𝘂𝗧𝘂𝗯𝗲 – 𝗠𝗮𝗶𝗻 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/

𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/verilogcourseteammatlabproject

02/09/2026

𝗠𝗨𝗟𝗧𝗜-𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗢𝗙 𝗗𝗜𝗦𝗧𝗥𝗜𝗕𝗨𝗧𝗘𝗗 𝗦𝗢𝗟𝗔𝗥 𝗣𝗩 𝗔𝗡𝗗 𝗘𝗟𝗘𝗖𝗧𝗥𝗜𝗖 𝗩𝗘𝗛𝗜𝗖𝗟𝗘 𝗖𝗛𝗔𝗥𝗚𝗜𝗡𝗚 𝗦𝗧𝗔𝗧𝗜𝗢𝗡𝗦 𝗪𝗜𝗧𝗛 𝗜𝗡𝗧𝗘𝗚𝗥𝗔𝗧𝗘𝗗 𝗩𝗢𝗟𝗧𝗔𝗚𝗘 𝗥𝗘𝗚𝗨𝗟𝗔𝗧𝗜𝗢𝗡 𝗙𝗢𝗥 𝗪𝗘𝗔𝗞 𝗥𝗨𝗥𝗔𝗟 𝗗𝗜𝗦𝗧𝗥𝗜𝗕𝗨𝗧𝗜𝗢𝗡 𝗙𝗘𝗘𝗗𝗘𝗥𝗦

📄 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝘁𝗵𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗹𝗶𝗻𝗸 𝗯𝗲𝗹𝗼𝘄.
https://drive.google.com/file/d/1Jao0uufsE8F5yJrRCc2T3h9IFyqh3FW_/view?usp=sharing

🔹 𝗩𝗜𝗗𝗘𝗢 𝗢𝗩𝗘𝗥𝗩𝗜𝗘𝗪
This video presents a 𝗠𝘂𝗹𝘁𝗶-𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝗮𝗿𝘁𝗶𝗰𝗹𝗲 𝗦𝘄𝗮𝗿𝗺 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗣𝗦𝗢) framework for coordinated planning of 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗦𝗼𝗹𝗮𝗿 𝗣𝗩, 𝗘𝗩 𝗖𝗵𝗮𝗿𝗴𝗶𝗻𝗴 𝗦𝘁𝗮𝘁𝗶𝗼𝗻𝘀 (𝗘𝗩𝗖𝗦), 𝗮𝗻𝗱 𝗢𝗻-𝗟𝗟𝗼𝗮𝗱 𝗧𝗮𝗽 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 (𝗢𝗟𝗧𝗖) voltage regulation.

⚡ 𝗞𝗘𝗬 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘𝗦:

🔸 𝗔𝗻𝗻𝘂𝗮𝗹 𝗘𝗻𝗲𝗿𝗴𝘆 𝗟𝗼𝘀𝘀 — Minimize
🔸 𝗔𝗻𝗻𝘂𝗮𝗹 𝗘𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗖𝗼𝘀𝘁 — Minimize
🔸 𝗔𝗩𝗗𝗜 — Minimize voltage deviation
🔸 𝗩𝗦𝗜 — Improve voltage stability
🔸 𝗘𝗘𝗡𝗦 — Improve supply reliability

🔧 𝗞𝗘𝗬 𝗙𝗘𝗔𝗧𝗨𝗥𝗘𝗦:

✅ IEEE 33-Bus Distribution System
✅ 24-Hour Load and Renewable-Generation Profiles
✅ Distributed Solar PV Integration
✅ EV Charging Station Allocation
✅ Integrated OLTC Voltage Regulation
✅ Voltage Limits: 𝟬.𝟵𝟯–𝟭.𝟬𝟱 𝗽.𝘂.
✅ Multi-Objective PSO Optimization
✅ Base-Case vs. Optimized Performance Analysis
✅ Technical, Economic, Voltage-Stability and Reliability Assessment
✅ MATLAB-Based Simulation and Publication-Quality Figures

📊 The proposed coordinated 𝗣𝗩–𝗘𝗩𝗖𝗦–𝗢𝗟𝗧𝗖 strategy demonstrates the potential to reduce distribution-system losses, improve voltage profiles, enhance voltage stability, and eliminate undervoltage violations in weak rural feeders.

🎓 This work is particularly relevant to researchers and engineers working in 𝗦𝗺𝗮𝗿𝘁 𝗚𝗿𝗶𝗱𝘀, 𝗥𝗲𝗻𝗲𝘄𝗮𝗯𝗹𝗲 𝗘𝗻𝗲𝗿𝗴𝘆, 𝗘𝗹𝗲𝗰𝘁𝗿𝗶𝗰 𝗩𝗲𝗵𝗶𝗰𝗹𝗲𝘀, 𝗣𝗼𝘄𝗲𝗿 𝗦𝘆𝘀𝘁𝗲𝗺 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀.

#𝗦𝗼𝗹𝗮𝗿𝗣𝗩 #𝗘𝗩𝗖𝗦 #𝗢𝗟𝗧𝗖 #𝗣𝗦𝗢 #𝗠𝘂𝗹𝘁𝗶𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 #𝗠𝗔𝗧𝗟𝗔𝗕 #𝗜𝗘𝗘𝗘𝟯𝟯𝗕𝘂𝘀 #𝗦𝗺𝗮𝗿𝘁𝗚𝗿𝗶𝗱 #𝗩𝗼𝗹𝘁𝗮𝗴𝗲𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 #𝗥𝗲𝗻𝗲𝘄𝗮𝗯𝗹𝗲𝗘𝗻𝗲𝗿𝗴𝘆 #𝗘𝗹𝗲𝗰𝘁𝗿𝗶𝗰𝗩𝗲𝗵𝗶𝗰𝗹𝗲𝘀 #𝗣𝗼𝘄𝗲𝗿𝗦𝘆𝘀𝘁𝗲𝗺 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝗦𝘆𝘀𝘁𝗲𝗺

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘

Request the MATLAB source code for academic, educational, and research purposes by completing the request form:

𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗙𝗢𝗥𝗠

📧 𝗘𝗠𝗔𝗜𝗟

𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

📱 𝗪𝗛𝗔𝗧𝗦𝗔𝗣𝗣

+𝟵𝟭 𝟳𝟵𝟬𝟰𝟱𝟲𝟴𝟰𝟱𝟲

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘

𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

🔗 𝗙𝗢𝗟𝗟𝗢𝗪 𝗨𝗦

𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸:
𝗩𝗲𝗿𝗶𝗹𝗼𝗴 𝗖𝗼𝘂𝗿𝘀𝗲 𝗧𝗲𝗮𝗺

𝗬𝗼𝘂𝗧𝘂𝗯𝗲 – 𝗠𝗮𝗶𝗻 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/

𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/verilogcourseteammatlabproject

01/09/2026

OPTIMAL ALLOCATION OF ROOFTOP PHOTOVOLTAIC DISTRIBUTED GENERATORS USING GLOBAL BALL SEARCH OPTIMIZATION IN NEPAL'S DEVDAHA DISTRIBUTION FEEDER

DESIGN DETAILS
The increasing integration of rooftop photovoltaic (PV) systems in modern distribution networks necessitates advanced optimization techniques to ensure efficient, stable, and reliable system operation. Improper siting and sizing of PV-based distributed generators (DGs) can increase power losses, deteriorate voltage profiles, and compromise overall network performance. This study proposes a novel Global Ball Search Optimization (GBSO) algorithm for the optimal allocation of rooftop PV-based distributed generators in the Devdaha feeder of Nepal's distribution network. The proposed algorithm employs a global ball search mechanism that dynamically balances exploration and exploitation by enabling candidate solutions to move within adaptive search regions, thereby enhancing global search capability while maintaining efficient local refinement.

The optimization problem is formulated as a multi-objective framework to minimize real power losses, voltage deviation, and operational cost while satisfying practical constraints, including bus voltage limits and DG capacity bounds. A comprehensive load-flow analysis is integrated into the optimization process to accurately evaluate system performance under varying operating conditions and ensure that all candidate solutions remain feasible.

Simulation results demonstrate that the proposed GBSO-based approach significantly improves voltage profiles and reduces network losses compared with conventional optimization algorithms. Moreover, GBSO exhibits superior convergence characteristics and enhanced solution quality owing to its adaptive search strategy and effective balance between global exploration and local exploitation. The global search and adaptive neighborhood refinement mechanisms improve population diversity, mitigate premature convergence, and enable the algorithm to consistently identify high-quality optimal solutions.

The optimal placement and sizing of rooftop PV-DGs obtained using GBSO result in a more uniform voltage profile, enhanced network reliability, and improved overall system stability while satisfying all operational constraints. Overall, the proposed GBSO-based framework provides a robust, efficient, and computationally effective solution for rooftop PV integration. Its strong optimization capability and consistent performance offer significant technical and economic benefits, making it well suited for modern and evolving power distribution systems, including Nepal's distribution network.

The multi-objective function,F(k)=min{w_1 f_1 (k)+w_2 f_2 (k)+w_3 (1/(f_3 (k)))}
Where,
w_1,w_2 ,w_3 represent the weighting factors.
The summation of weights should not exceed 1 here ω_1=0.3, ω_2=0.3, ω_3=0.3 considered f_1,〖 f〗_2, and f_(3 ) by providing equal weights for objectives.

PV MODEL (Reference Paper-1)
Generally, PV system consists of 280W PV modules. With this, it is possible to randomly size and site the PV systems across the network. The rand() function is used to generate random numbers between 0 and 1 which are used to determine the number of 280W PV modules to be placed on each target nodes using the below equation,
n_a=(rand(a))/(∑_(a=6)^nbus▒〖rand(a)〗)*k , P_PVa=n_a*P_PVm

SIMULATION RESULTS VALUES IN MATLAB COMMAND WINDOW
Total Active and Reactive Power Loss
VSI
AVDI
Optimal PV DG location and Size.
Minimum and Maximum Voltage @ Bus
Ex*****on Time

SIMULATION FIGURES
Voltage Profile
Power Loss
Convergence Graph

REFERENCES
Reference Paper-1: Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network with Randomly Distributed Rooftop Photovoltaic Systems
Author’s Name: W***y Stephen Tounsi Fokui, Michael J. Saulo, And Livingstone Ngoo
Source: IEEE
Year: 2021

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31/08/2026

🔬 𝗥𝗢𝗕𝗨𝗦𝗧 𝗣𝗔𝗥𝗧𝗜𝗖𝗟𝗘 𝗦𝗪𝗔𝗥𝗠 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡-𝗕𝗔𝗦𝗘𝗗 𝗣𝗜𝗗 𝗖𝗢𝗡𝗧𝗥𝗢𝗟 𝗙𝗢𝗥 𝗙𝗥𝗘𝗤𝗨𝗘𝗡𝗖𝗬 𝗥𝗘𝗚𝗨𝗟𝗔𝗧𝗜𝗢𝗡 𝗜𝗡 𝗠𝗨𝗟𝗧𝗜-𝗔𝗥𝗘𝗔 𝗜𝗡𝗧𝗘𝗥𝗖𝗢𝗡𝗡𝗘𝗖𝗧𝗘𝗗 𝗣𝗢𝗪𝗘𝗥 𝗦𝗬𝗦𝗧𝗘𝗠𝗦 𝗪𝗜𝗧𝗛 𝗥𝗘𝗡𝗘𝗪𝗔𝗕𝗟𝗘 𝗘𝗡𝗘𝗥𝗚𝗬 𝗔𝗡𝗗 𝗕𝗔𝗧𝗧𝗘𝗥𝗬 𝗦𝗧𝗢𝗥𝗔𝗚𝗘

📄 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝘁𝗵𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗹𝗶𝗻𝗸 𝗯𝗲𝗹𝗼𝘄.
https://drive.google.com/file/d/1LBEdfWknPquq6xGnThA0FLRq6S3dMOm9/view?usp=sharing

⚡ This research-oriented video presents a 𝗣𝗮𝗿𝘁𝗶𝗰𝗹𝗲 𝗦𝘄𝗮𝗿𝗺 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗣𝗦𝗢)-𝗯𝗮𝘀𝗲𝗱 𝗣𝗜𝗗 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 for load frequency regulation in a multi-area interconnected power system.

🔹 𝗞𝗘𝗬 𝗧𝗢𝗣𝗜𝗖𝗦 𝗖𝗢𝗩𝗘𝗥𝗘𝗗

✅ Multi-area interconnected power system
✅ PSO-based PID controller tuning
✅ Load Frequency Control (LFC)
✅ Photovoltaic (PV) generation
✅ Wind energy integration
✅ Thermal power generation
✅ Battery Energy Storage System (BESS)
✅ Frequency deviation regulation
✅ Tie-line power control
✅ Renewable-energy variability
✅ PID parameter optimization
✅ Robustness under different operating scenarios
✅ MATLAB/Simulink-based implementation

🎯 The proposed framework focuses on optimizing 𝗞𝗽, 𝗞𝗶, 𝗮𝗻𝗱 𝗞𝗱 controller parameters to improve frequency regulation and reduce undesirable oscillations caused by load disturbances and renewable-energy variability.

📊 The study considers multiple renewable operating conditions, including reduced PV generation, reduced wind generation, simultaneous renewable shortfalls, rapid renewable ramps, and stochastic disturbances.

💻 The implementation is intended for 𝗠𝗔𝗧𝗟𝗔𝗕/𝗦𝗶𝗺𝘂𝗹𝗶𝗻𝗸-based power-system research and can serve as a foundation for further investigation into intelligent control, renewable-rich grids, energy storage, and optimization-based frequency regulation.

📌 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗔𝗿𝗲𝗮𝘀

🔸 Smart Grids
🔸 Renewable Energy Systems
🔸 Load Frequency Control
🔸 Power System Stability
🔸 Battery Energy Storage
🔸 Intelligent Optimization
🔸 MATLAB/Simulink Research
🔸 Multi-Area Power Systems

⚠️ 𝗗𝗜𝗦𝗖𝗟𝗔𝗜𝗠𝗘𝗥: This content is provided for academic and research purposes. Actual implementation, parameter selection, simulation results, and controller performance should be independently verified before use.

#𝗣𝗦𝗢 #𝗣𝗜𝗗 #𝗟𝗼𝗮𝗱𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆𝗖𝗼𝗻𝘁𝗿𝗼𝗹 #𝗣𝗼𝘄𝗲𝗿𝗦𝘆𝘀𝘁𝗲𝗺 #𝗥𝗲𝗻𝗲𝘄𝗮𝗯𝗹𝗲𝗘𝗻𝗲𝗿𝗴𝘆 #𝗕𝗘𝗦𝗦 #𝗦𝗺𝗮𝗿𝘁𝗚𝗿𝗶𝗱 #𝗠𝗔𝗧𝗟𝗔𝗕 #𝗦𝗶𝗺𝘂𝗹𝗶𝗻𝗸 #𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻 #𝗣𝗼𝘄𝗲𝗿𝗦𝘆𝘀𝘁𝗲𝗺𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘

Request the MATLAB source code for academic, educational, and research purposes by completing the request form:

𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗙𝗢𝗥𝗠

📧 𝗘𝗠𝗔𝗜𝗟

𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

📱 𝗪𝗛𝗔𝗧𝗦𝗔𝗣𝗣

+𝟵𝟭 𝟳𝟵𝟬𝟰𝟱𝟲𝟴𝟰𝟱𝟲

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘

𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

🔗 𝗙𝗢𝗟𝗟𝗢𝗪 𝗨𝗦

𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸:
𝗩𝗲𝗿𝗶𝗹𝗼𝗴 𝗖𝗼𝘂𝗿𝘀𝗲 𝗧𝗲𝗮𝗺

𝗬𝗼𝘂𝗧𝘂𝗯𝗲 – 𝗠𝗮𝗶𝗻 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/

𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/verilogcourseteammatlabproject

30/08/2026

🚀 𝗠𝗨𝗟𝗧𝗜-𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘 𝗢𝗣𝗧𝗜𝗠𝗔𝗟 𝗗-𝗦𝗧𝗔𝗧𝗖𝗢𝗠 𝗦𝗜𝗧𝗜𝗡𝗚 𝗔𝗡𝗗 𝗦𝗜𝗭𝗜𝗡𝗚 𝗨𝗦𝗜𝗡𝗚 𝗧𝗜𝗔𝗡𝗝𝗜’𝗦 𝗛𝗢𝗥𝗦𝗘 𝗥𝗔𝗖𝗜𝗡𝗚 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 (𝗧𝗛𝗥𝗢)

📄 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝘁𝗵𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗹𝗶𝗻𝗸 𝗯𝗲𝗹𝗼𝘄.
https://drive.google.com/file/d/1EqOyEnalC52d0K5jDoQ2Dvi8J2sNjRSf/view?usp=sharing

🔹 𝗧𝗛𝗜𝗦 𝗩𝗜𝗗𝗘𝗢 𝗣𝗥𝗘𝗦𝗘𝗡𝗧𝗦 a 𝟮𝟰-𝗵𝗼𝘂𝗿 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹-𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 for optimal D-STATCOM placement and sizing in radial distribution systems using 𝗧𝗶𝗮𝗻𝗷𝗶’𝘀 𝗛𝗼𝗿𝘀𝗲 𝗥𝗮𝗰𝗶𝗻𝗴 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗧𝗛𝗥𝗢).

⚡ The MATLAB implementation considers 𝟮𝟰-𝗵𝗼𝘂𝗿 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲 𝗹𝗼𝗮𝗱 𝗱𝗲𝗺𝗮𝗻𝗱 and 𝗧𝗶𝗺𝗲-𝗼𝗳-𝗨𝘀𝗲 (𝗧𝗢𝗨) 𝘁𝗮𝗿𝗶𝗳𝗳𝘀.

🔹 𝗞𝗘𝗬 𝗙𝗘𝗔𝗧𝗨𝗥𝗘𝗦

⚡ 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗗-𝗦𝗧𝗔𝗧𝗖𝗢𝗠 𝗦𝗶𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗶𝘇𝗶𝗻𝗴
🐎 𝗧𝗶𝗮𝗻𝗷𝗶’𝘀 𝗛𝗼𝗿𝘀𝗲 𝗥𝗮𝗰𝗶𝗻𝗴 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗧𝗛𝗥𝗢)
🕒 𝟮𝟰-𝗛𝗼𝘂𝗿 𝗟𝗼𝗮𝗱-𝗙𝗹𝗼𝘄 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
💰 𝗧𝗶𝗺𝗲-𝗼𝗳-𝗨𝘀𝗲 𝗧𝗮𝗿𝗶𝗳𝗳 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻
📉 𝗔𝗰𝘁𝗶𝘃𝗲 𝗣𝗼𝘄𝗲𝗿-𝗟𝗼𝘀𝘀 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻
📊 𝗩𝗼𝗹𝘁𝗮𝗴𝗲-𝗣𝗿𝗼𝗳𝗶𝗹𝗲 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁
🔋 𝗥𝗲𝗮𝗰𝘁𝗶𝘃𝗲-𝗣𝗼𝘄𝗲𝗿 𝗖𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗶𝗼𝗻
📐 𝗙𝗩𝗦𝗜 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
✅ 𝗩𝗼𝗹𝘁𝗮𝗴𝗲 𝗮𝗻𝗱 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀
💵 𝗖𝗮𝗽𝗶𝘁𝗮𝗹 𝗮𝗻𝗱 𝗢&𝗠 𝗖𝗼𝘀𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
📈 𝗔𝗻𝗻𝘂𝗮𝗹𝗶𝘇𝗲𝗱 𝗘𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
💳 𝗣𝗮𝘆𝗯𝗮𝗰𝗸-𝗣𝗲𝗿𝗶𝗼𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
📊 𝗡𝗣𝗩 𝗮𝗻𝗱 𝗜𝗥𝗥 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
🔬 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗠𝗔𝗧𝗟𝗔𝗕 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻

🎯 The framework combines 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 and 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 to identify practical D-STATCOM configurations for improving radial distribution-system performance.

🔬 𝗔𝗣𝗣𝗟𝗜𝗖𝗔𝗧𝗜𝗢𝗡 𝗔𝗥𝗘𝗔𝗦

𝗗-𝗦𝗧𝗔𝗧𝗖𝗢𝗠 • 𝗥𝗮𝗱𝗶𝗮𝗹 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 • 𝗩𝗼𝗹𝘁𝗮𝗴𝗲 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 • 𝗥𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝗣𝗼𝘄𝗲𝗿 𝗖𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗶𝗼𝗻 • 𝗣𝗼𝘄𝗲𝗿 𝗟𝗼𝘀𝘀 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 • 𝗠𝗔𝗧𝗟𝗔𝗕 • 𝗠𝗲𝘁𝗮𝗵𝗲𝘂𝗿𝗶𝘀𝘁𝗶𝗰 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘

Request the MATLAB source code for academic, educational, and research purposes by completing the request form:

𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗙𝗢𝗥𝗠

📧 𝗘𝗠𝗔𝗜𝗟

𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

📱 𝗪𝗛𝗔𝗧𝗦𝗔𝗣𝗣

+𝟵𝟭 𝟳𝟵𝟬𝟰𝟱𝟲𝟴𝟰𝟱𝟲

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘

𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

🔗 𝗙𝗢𝗟𝗟𝗢𝗪 𝗨𝗦

𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸:
𝗩𝗲𝗿𝗶𝗹𝗼𝗴 𝗖𝗼𝘂𝗿𝘀𝗲 𝗧𝗲𝗮𝗺

𝗬𝗼𝘂𝗧𝘂𝗯𝗲 – 𝗠𝗮𝗶𝗻 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/

𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:
https://www.youtube.com/verilogcourseteammatlabproject

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