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Verilog Course Team is a Electronic Design Services (EDS) for VLSI / EMBEDDED and MATLAB,delivering a wide variety of end- to -end services , including design , development , & testing for customers around the world . With proven expertise across multiple domains such as Consumer Electronics Market , Infotainment,Office Automation,Mobility and Equipment Controls. Verilog Course Team is managed by Engineers /Professionals possessing significant industrial experience across various application domains and engineering horizontals.Our engineers have expertise across a wide range of technologies, to the engineering efforts of our clients.Leveraging standards based components and investments in dedicated test lab infrastructure , we offer innovative ,flexible and cost-effective Services and Solutions.

09/10/2026

🌟 𝗔 𝗡𝗢𝗩𝗘𝗟 𝗗𝗘𝗦𝗘𝗥𝗧 𝗦𝗖𝗢𝗥𝗣𝗜𝗢𝗡 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗔𝗟𝗚𝗢𝗥𝗜𝗧𝗛𝗠 𝗙𝗢𝗥 𝗢𝗣𝗧𝗜𝗠𝗔𝗟 𝗣𝗟𝗔𝗖𝗘𝗠𝗘𝗡𝗧 𝗔𝗡𝗗 𝗦𝗜𝗭𝗜𝗡𝗚 𝗢𝗙 𝗗𝗜𝗦𝗧𝗥𝗜𝗕𝗨𝗧𝗘𝗗 𝗚𝗘𝗡𝗘𝗥𝗔𝗧𝗢𝗥𝗦 🌟

📄 𝗗𝗢𝗪𝗡𝗟𝗢𝗔𝗗 𝗧𝗛𝗘 𝗧𝗘𝗖𝗛𝗡𝗜𝗖𝗔𝗟 𝗗𝗢𝗖𝗨𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡 𝗙𝗥𝗢𝗠
https://drive.google.com/file/d/14zUrgkcdUYRrHzEjbb734fAlX0jhrQhu/view?usp=sharing

📌 𝗩𝗜𝗗𝗘𝗢 𝗗𝗘𝗦𝗖𝗥𝗜𝗣𝗧𝗜𝗢𝗡

This video presents a MATLAB-based implementation of the **𝗗𝗲𝘀𝗲𝗿𝘁 𝗦𝗰𝗼𝗿𝗽𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗗𝗦𝗢) 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺** for the optimal placement and sizing of Distributed Generators (DGs) in electrical power distribution systems.

The primary objective is to improve distribution network performance by reducing active power losses, enhancing voltage profiles, and optimizing operating costs while satisfying essential power-system operating constraints.

🔬 𝗞𝗘𝗬 𝗙𝗘𝗔𝗧𝗨𝗥𝗘𝗦

✅ 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗗𝗚 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗦𝗶𝘇𝗶𝗻𝗴 – Identifies suitable locations and capacities for distributed generators.

✅ 𝗧𝗲𝗺𝗽𝗲𝗿𝗮𝘁𝘂𝗿𝗲-𝗕𝗮𝘀𝗲𝗱 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 – Searches the solution space to identify promising candidate configurations.

✅ 𝗖𝗼𝘀𝗶𝗻𝗲-𝗕𝗮𝘀𝗲𝗱 𝗘𝘅𝗽𝗹𝗼𝗶𝘁𝗮𝘁𝗶𝗼𝗻 – Refines candidate solutions around promising regions.

✅ 𝗕𝗲𝘀𝘁-𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗦𝗲𝗮𝗿𝗰𝗵 – Improves the quality of candidate solutions throughout the optimization process.

✅ 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 – Considers DG location and sizing limits, bus-voltage limits, generator constraints, transformer tap limits, and shunt-capacitor limits where applicable.

⚡ 𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘 𝗙𝗨𝗡𝗖𝗧𝗜𝗢𝗡𝗦

🔹 Active Power Loss Minimization
🔹 Voltage Deviation Minimization
🔹 Fuel Cost Optimization
🔹 Emission Cost Optimization
🔹 Combined Objective Optimization

🖥️ 𝗠𝗔𝗧𝗟𝗔𝗕 𝗜𝗠𝗣𝗟𝗘𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡

The framework integrates the DSO optimization procedure with power-flow analysis, fitness evaluation, constraint checking, greedy selection, global-best updating, and convergence monitoring. Candidate solutions are evaluated using the implemented load-flow and objective-function modules.

The IEEE test system can be used to investigate the performance of the proposed optimization framework under the selected network configuration and operating conditions.

🎯 𝗘𝗫𝗣𝗘𝗖𝗧𝗘𝗗 𝗕𝗘𝗡𝗘𝗙𝗜𝗧𝗦

✔️ Reduced active power losses
✔️ Improved voltage profile
✔️ More effective DG allocation
✔️ Better utilization of distributed generation
✔️ Potential reductions in operating and emission costs, depending on the objective formulation and simulation results

📚 𝗥𝗘𝗦𝗘𝗔𝗥𝗖𝗛 𝗔𝗣𝗣𝗟𝗜𝗖𝗔𝗧𝗜𝗢𝗡𝗦

This work is relevant to researchers, postgraduate students, electrical engineers, and power-system optimization specialists working on distributed generation planning, evolutionary computation, renewable energy integration, and MATLAB-based power-system analysis.

👍 𝗟𝗶𝗸𝗲, 𝗦𝗵𝗮𝗿𝗲, 𝗮𝗻𝗱 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 for more MATLAB tutorials, optimization algorithms, and power-system research implementations.

💬 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 below with your questions, suggestions, or research interests.

🔖 𝗛𝗔𝗦𝗛𝗧𝗔𝗚𝗦

𝗟𝗜𝗞𝗘 👍 | 𝗦𝗛𝗔𝗥𝗘 🔄 | 𝗖𝗢𝗠𝗠𝗘𝗡𝗧 💬 | 𝗦𝗨𝗕𝗦𝗖𝗥𝗜𝗕𝗘 🔔

𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠
𝗖𝗵𝗲𝗻𝗻𝗮𝗶 – 𝗜𝗡𝗗𝗜𝗔 🇮🇳
📱 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽: +𝟵𝟭 𝟳𝟵𝟬 𝟰𝟱𝟲 𝟴 𝟰𝟱𝟲

#𝗖𝗢𝗩𝗜𝗗𝗢𝗔 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 #𝗗𝗚𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 #𝗗𝗚𝗦𝗶𝘇𝗶𝗻𝗴 #𝗣𝗼𝘄𝗲𝗿𝗦𝘆𝘀𝘁𝗲𝗺𝘀 #𝗣𝗼𝘄𝗲𝗿𝗙𝗮𝗰𝘁𝗼𝗿 #𝗠𝗔𝗧𝗟𝗔𝗕 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝗦𝘆𝘀𝘁𝗲𝗺 #𝗣𝗼𝘄𝗲𝗿𝗟𝗼𝘀𝘀 #𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 #𝗜𝗘𝗘𝗘𝟯𝟯𝗕𝘂𝘀 #𝗜𝗘𝗘𝗘𝟲𝟵𝗕𝘂𝘀

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘:
𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

📧 𝗘𝗠𝗔𝗜𝗟:
𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

▶️ 𝗬𝗢𝗨𝗧𝗨𝗕𝗘 – 𝗠𝗔𝗜𝗡 𝗖𝗛𝗔𝗡𝗡𝗘𝗟

▶️ 𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗥𝗢𝗝𝗘𝗖𝗧𝗦 𝗖𝗛𝗔𝗡𝗡𝗘𝗟

08/10/2026

𝗠𝗔𝗧𝗟𝗔𝗕 𝗙𝗥𝗔𝗠𝗘𝗪𝗢𝗥𝗞 𝗔𝗡𝗗 𝗙𝗨𝗡𝗖𝗧𝗜𝗢𝗡𝗔𝗟 𝗢𝗥𝗚𝗔𝗡𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗢𝗙 𝗧𝗛𝗘 𝗢𝗡𝗘-𝗧𝗢-𝗢𝗡𝗘 𝗕𝗔𝗦𝗘𝗗 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗘𝗥 𝗙𝗢𝗥 𝗢𝗣𝗧𝗜𝗠𝗔𝗟 𝗗𝗚 𝗣𝗟𝗔𝗖𝗘𝗠𝗘𝗡𝗧 𝗔𝗡𝗗 𝗦𝗜𝗭𝗜𝗡𝗚

📄 𝗗𝗢𝗪𝗡𝗟𝗢𝗔𝗗 𝗧𝗛𝗘 𝗧𝗘𝗖𝗛𝗡𝗜𝗖𝗔𝗟 𝗗𝗢𝗖𝗨𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡 𝗙𝗥𝗢𝗠
https://drive.google.com/file/d/1TzQ0jAFTQ9rY1l8uMx1QpOgvdjsHsDI7/view?usp=sharing

This video presents a modular 𝗠𝗔𝗧𝗟𝗔𝗕 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 for optimal 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗗𝗚) placement and sizing in radial distribution systems. The framework integrates the 𝗢𝗻𝗲-𝘁𝗼-𝗢𝗻𝗲 𝗕𝗮𝘀𝗲𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿, multi-objective evaluation, 24-hour load modelling, radial load flow, constraint handling, Pareto-based solution management, and results generation.

𝗙𝗥𝗔𝗠𝗘𝗪𝗢𝗥𝗞 𝗦𝗧𝗥𝗨𝗖𝗧𝗨𝗥𝗘

📁 𝟬𝟭_𝗠𝗔𝗜𝗡
📄 𝗧𝗢𝗣_𝗥𝗨𝗡.𝗺 — Main ex*****on script. Initializes the study, defines system and optimization parameters, loads network/load data, executes the optimizer, and reports the results.

📁 𝟬𝟮_𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡
📄 𝗠𝗢_𝗢𝗢𝗕𝗢_𝗗𝗚.𝗺 — Implements the Multi-Objective One-to-One Based Optimizer for generating and improving candidate DG configurations. It performs candidate comparison, solution updates, constraint handling, repair, Pareto-dominance evaluation, and non-dominated archive management.

📁 𝟬𝟯_𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘𝗦
📄 𝗘𝗩𝗔𝗟𝗨𝗔𝗧𝗘_𝗗𝗚.𝗺
📄 𝗠𝗨𝗟𝗧𝗜_𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘_𝗗𝗚.𝗺

These modules evaluate each DG configuration and calculate the main objectives:

🔹 𝗘𝗻𝗲𝗿𝗴𝘆 𝗟𝗼𝘀𝘀 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 — minimizes total energy loss over the 24-hour period.
🔹 𝗩𝗼𝗹𝘁𝗮𝗴𝗲 𝗗𝗲𝘃𝗶𝗮𝘁𝗶𝗼𝗻 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 — keeps bus voltages close to the nominal value.
🔹 𝗗𝗚 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗖𝗼𝘀𝘁 — represents the economic aspect of DG deployment.

📁 𝟬𝟰_𝗟𝗢𝗔𝗗_𝗙𝗟𝗢𝗪
📄 𝗥𝗔𝗗𝗜𝗔𝗟_𝗟𝗢𝗔𝗗_𝗙𝗟𝗢𝗪.𝗺 — Performs radial distribution-system load flow using the 𝗯𝗮𝗰𝗸𝘄𝗮𝗿𝗱-𝗳𝗼𝗿𝘄𝗮𝗿𝗱 𝘀𝘄𝗲𝗲𝗽 method. It calculates bus voltages, branch currents, power losses, and load-flow convergence.

📁 𝟬𝟱_𝗟𝗢𝗔𝗗_𝗠𝗢𝗗𝗘𝗟
📄 𝗕𝗨𝗜𝗟𝗗_𝗟𝗢𝗔𝗗_𝗣𝗥𝗢𝗙𝗜𝗟𝗘.𝗺
📄 𝗖𝗥𝗘𝗔𝗧𝗘_𝗖𝗔𝗦𝗘_𝗗𝗔𝗧𝗔.𝗺
📄 𝗖𝗥𝗘𝗔𝗧𝗘_𝗦𝗘𝗖𝗧𝗢𝗥_𝗣𝗥𝗢𝗙𝗜𝗟𝗘𝗦.𝗺

This module generates 𝟮𝟰-𝗵𝗼𝘂𝗿 𝘁𝗶𝗺𝗲-𝘃𝗮𝗿𝘆𝗶𝗻𝗴 load profiles for different sectors, including residential, industrial, commercial, and school loads.

📁 𝟬𝟲_𝗡𝗘𝗧𝗪𝗢𝗥𝗞
📄 𝗚𝗘𝗧_𝗡𝗘𝗧𝗪𝗢𝗥𝗞_𝗗𝗔𝗧𝗔.𝗺
📄 𝗟𝗜𝗡𝗘_𝗗𝗔𝗧𝗔.𝗺

Provides the electrical network information required for the optimization and load-flow calculations, including bus, branch, load, topology, and line parameters.

📁 𝟬𝟳_𝗥𝗘𝗦𝗨𝗟𝗧𝗦
📁 𝗙𝗜𝗚𝗨𝗥𝗘𝗦
📁 𝗧𝗔𝗕𝗟𝗘𝗦

Stores convergence curves, voltage profiles, loss profiles, DG locations and sizes, Pareto solutions, numerical tables, and other graphical results.

𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗪𝗢𝗥𝗞𝗙𝗟𝗢𝗪

𝟭️⃣ Initialize the MATLAB study and system parameters.
𝟮️⃣ Load the distribution-network data.
𝟯️⃣ Generate 24-hour sector-specific load profiles.
𝟰️⃣ Generate candidate DG placement and sizing solutions.
𝟱️⃣ Repair infeasible solutions and enforce constraints.
𝟲️⃣ Perform radial backward-forward sweep load flow.
𝟳️⃣ Calculate energy loss, voltage deviation, and DG cost.
𝟴️⃣ Evaluate solution feasibility and Pareto dominance.
𝟵️⃣ Apply the One-to-One Based Optimizer update process.
🔟 Maintain the non-dominated solution archive.
1️⃣1️⃣ Repeat the optimization for the specified iterations.
1️⃣2️⃣ Report and save the final Pareto-optimal DG solutions.

𝗖𝗢𝗡𝗦𝗧𝗥𝗔𝗜𝗡𝗧𝗦

The framework considers important operational and planning constraints, including voltage limits, DG activation, individual DG capacity limits, unique DG locations, active DG count, total DG capacity, and load-flow convergence.

𝗞𝗘𝗬 𝗢𝗨𝗧𝗖𝗢𝗠𝗘

The proposed modular MATLAB framework integrates 𝗗𝗚 𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁, 𝗗𝗚 𝘀𝗶𝘇𝗶𝗻𝗴, 𝟮𝟰-𝗵𝗼𝘂𝗿 𝗹𝗼𝗮𝗱 𝗺𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴, 𝗿𝗮𝗱𝗶𝗮𝗹 𝗹𝗼𝗮𝗱 𝗳𝗹𝗼𝘄, 𝗺𝘂𝗹𝘁𝗶-𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝗣𝗮𝗿𝗲𝘁𝗼 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀, and systematic result reporting in a single research framework.

𝗞𝗘𝗬𝗪𝗢𝗥𝗗𝗦

𝗟𝗜𝗞𝗘 👍 | 𝗦𝗛𝗔𝗥𝗘 🔄 | 𝗖𝗢𝗠𝗠𝗘𝗡𝗧 💬 | 𝗦𝗨𝗕𝗦𝗖𝗥𝗜𝗕𝗘 🔔

𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠
𝗖𝗵𝗲𝗻𝗻𝗮𝗶 – 𝗜𝗡𝗗𝗜𝗔 🇮🇳
📱 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽: +𝟵𝟭 𝟳𝟵𝟬 𝟰𝟱𝟲 𝟴 𝟰𝟱𝟲

#𝗖𝗢𝗩𝗜𝗗𝗢𝗔 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 #𝗗𝗚𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 #𝗗𝗚𝗦𝗶𝘇𝗶𝗻𝗴 #𝗣𝗼𝘄𝗲𝗿𝗦𝘆𝘀𝘁𝗲𝗺𝘀 #𝗣𝗼𝘄𝗲𝗿𝗙𝗮𝗰𝘁𝗼𝗿 #𝗠𝗔𝗧𝗟𝗔𝗕 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝗦𝘆𝘀𝘁𝗲𝗺 #𝗣𝗼𝘄𝗲𝗿𝗟𝗼𝘀𝘀 #𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 #𝗜𝗘𝗘𝗘𝟯𝟯𝗕𝘂𝘀 #𝗜𝗘𝗘𝗘𝟲𝟵𝗕𝘂𝘀

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘:
𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

📧 𝗘𝗠𝗔𝗜𝗟:
𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

▶️ 𝗬𝗢𝗨𝗧𝗨𝗕𝗘 – 𝗠𝗔𝗜𝗡 𝗖𝗛𝗔𝗡𝗡𝗘𝗟

▶️ 𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗥𝗢𝗝𝗘𝗖𝗧𝗦 𝗖𝗛𝗔𝗡𝗡𝗘𝗟

07/10/2026

𝗢𝗣𝗧𝗜𝗠𝗔𝗟 𝗔𝗟𝗟𝗢𝗖𝗔𝗧𝗜𝗢𝗡 𝗢𝗙 𝗖𝗔𝗣𝗔𝗖𝗜𝗧𝗢𝗥𝗦 𝗔𝗡𝗗 𝗗-𝗦𝗧𝗔𝗧𝗖𝗢𝗠𝗦 𝗙𝗢𝗥 𝗣𝗢𝗪𝗘𝗥 𝗟𝗢𝗦𝗦 𝗠𝗜𝗡𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗔𝗡𝗗 𝗖𝗢𝗦𝗧 𝗥𝗘𝗗𝗨𝗖𝗧𝗜𝗢𝗡 𝗜𝗡 𝗥𝗔𝗗𝗜𝗔𝗟 𝗗𝗜𝗦𝗧𝗥𝗜𝗕𝗨𝗧𝗜𝗢𝗡 𝗦𝗬𝗦𝗧𝗘𝗠𝗦 𝗨𝗦𝗜𝗡𝗚 𝗧𝗛𝗘 𝗙𝗘𝗥𝗧𝗜𝗟𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗔𝗟𝗚𝗢𝗥𝗜𝗧𝗛𝗠 (𝗙𝗢𝗔)

📄 𝗗𝗢𝗪𝗡𝗟𝗢𝗔𝗗 𝗧𝗛𝗘 𝗧𝗘𝗖𝗛𝗡𝗜𝗖𝗔𝗟 𝗗𝗢𝗖𝗨𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡 𝗙𝗥𝗢𝗠
https://drive.google.com/file/d/1HcAiEWejZYRKLONYTLkfl7EOdG1Tq--3/view?usp=sharing

This video presents a MATLAB-based optimization framework for the 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝘀𝗶𝘇𝗶𝗻𝗴 of Capacitors and D-STATCOMs in the 𝗜𝗘𝗘𝗘 𝟯𝟯-𝗕𝘂𝘀 𝗥𝗮𝗱𝗶𝗮𝗹 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺.

The proposed approach uses the 𝗙𝗲𝗿𝘁𝗶𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 (𝗙𝗢𝗔) to search for suitable device locations and reactive-power ratings while evaluating network losses, voltage performance, and installation cost.

𝗦𝗬𝗦𝗧𝗘𝗠 𝗖𝗢𝗡𝗙𝗜𝗚𝗨𝗥𝗔𝗧𝗜𝗢𝗡

🔹 IEEE 33-Bus Radial Distribution System
🔹 Capacitor Units: 2
🔹 D-STATCOM Units: 3
🔹 Capacitor Size: 10–500 kVAr
🔹 D-STATCOM Size: 100–1000 kVAr
🔹 Capacitor Location: Bus 2–33
🔹 D-STATCOM Location: Bus 2–33
🔹 Population Size: 10
🔹 Optimization Iterations: 100
🔹 Voltage Limits: 0.90–1.05 p.u.

𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗗𝗘𝗖𝗜𝗦𝗜𝗢𝗡 𝗩𝗔𝗥𝗜𝗔𝗕𝗟𝗘𝗦

The optimization simultaneously determines:

𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝗼𝗿𝘀
• Optimal installation locations
• Optimal reactive-power ratings

𝗗-𝗦𝗧𝗔𝗧𝗖𝗢𝗠𝘀
• Optimal installation locations
• Optimal reactive-power ratings

The candidate solution therefore contains capacitor locations, capacitor sizes, D-STATCOM locations, and D-STATCOM sizes.

𝗙𝗢𝗔 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗣𝗥𝗢𝗖𝗘𝗦𝗦

The MATLAB implementation performs the following steps:

① Generate the initial population
② Define capacitor and D-STATCOM locations and ratings
③ Evaluate each candidate using distribution load flow
④ Check operating and placement constraints
⑤ Calculate the fitness value
⑥ Generate new candidate solutions using FOA
⑦ Retain the best solution
⑧ Store the best fitness at every iteration
⑨ Continue until the specified iteration limit
⑩ Evaluate the final optimized network

The resulting convergence curve shows the evolution of the best objective value during the optimization process.

𝗣𝗢𝗪𝗘𝗥 𝗟𝗢𝗦𝗦 𝗔𝗡𝗗 𝗩𝗢𝗟𝗧𝗔𝗚𝗘 𝗔𝗡𝗔𝗟𝗬𝗦𝗜𝗦

The optimized configuration is compared with the base case using:

✓ Total active-power loss
✓ Total reactive-power loss
✓ Minimum bus voltage
✓ Maximum bus voltage
✓ Voltage-profile characteristics
✓ Linewise active-power loss
✓ Linewise reactive-power loss

The analysis provides a numerical comparison of the distribution-system operating condition before and after compensation.

𝗖𝗢𝗦𝗧 𝗘𝗩𝗔𝗟𝗨𝗔𝗧𝗜𝗢𝗡

The study also evaluates the economic component of the optimized solution.

𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝗼𝗿 𝗖𝗼𝘀𝘁 = Capacitor Rating × Capacitor Cost Coefficient

𝗗-𝗦𝗧𝗔𝗧𝗖𝗢𝗠 𝗖𝗼𝘀𝘁 = D-STATCOM Rating × D-STATCOM Cost Coefficient

𝗧𝗼𝘁𝗮𝗹 𝗖𝗼𝘀𝘁 = Capacitor Cost + D-STATCOM Cost

The MATLAB program reports the individual and combined installation costs together with the electrical performance results.

━━━━━━━━━━━━━━━━━━━━━━

𝗠𝗔𝗧𝗟𝗔𝗕 𝗥𝗘𝗦𝗨𝗟𝗧𝗦

The Command Window provides detailed optimization information, including:

🔹 Iteration number
🔹 Best fitness value
🔹 Fitness improvement
🔹 Optimal capacitor locations
🔹 Optimal capacitor ratings
🔹 Optimal D-STATCOM locations
🔹 Optimal D-STATCOM ratings
🔹 Active-power loss
🔹 Reactive-power loss
🔹 Minimum and maximum bus voltage
🔹 Capacitor cost
🔹 D-STATCOM cost
🔹 Total installation cost
🔹 Final FOA fitness

𝗚𝗥𝗔𝗣𝗛𝗜𝗖𝗔𝗟 𝗥𝗘𝗦𝗨𝗟𝗧𝗦 include the voltage-profile comparison, active-power-loss profile, reactive-power-loss profile, and FOA convergence curve.

━━━━━━━━━━━━━━━━━━━━━━

𝗞𝗘𝗬 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 𝗢𝗨𝗧𝗖𝗢𝗠𝗘𝗦

🎯 Capacitor placement and sizing
🎯 D-STATCOM placement and sizing
🎯 FOA implementation in MATLAB
🎯 Distribution-system load-flow integration
🎯 Constraint handling
🎯 Power-loss minimization
🎯 Voltage-profile analysis
🎯 Cost evaluation
🎯 Optimization convergence analysis
🎯 Base-case versus optimized-case comparison

━━━━━━━━━━━━━━━━━━━━━━

𝗔𝗣𝗣𝗟𝗜𝗖𝗔𝗧𝗜𝗢𝗡𝗦

The framework can be extended to distribution-network reconfiguration, distributed-generation placement, renewable-energy integration, reactive-power compensation, voltage optimization, loss minimization, and other power-system optimization problems.

━━━━━━━━━━━━━━━━━━━━━━

𝗞𝗘𝗬𝗪𝗢𝗥𝗗𝗦

𝗟𝗜𝗞𝗘 👍 | 𝗦𝗛𝗔𝗥𝗘 🔄 | 𝗖𝗢𝗠𝗠𝗘𝗡𝗧 💬 | 𝗦𝗨𝗕𝗦𝗖𝗥𝗜𝗕𝗘 🔔

𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠
𝗖𝗵𝗲𝗻𝗻𝗮𝗶 – 𝗜𝗡𝗗𝗜𝗔 🇮🇳
📱 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽: +𝟵𝟭 𝟳𝟵𝟬 𝟰𝟱𝟲 𝟴 𝟰𝟱𝟲

#𝗖𝗢𝗩𝗜𝗗𝗢𝗔 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 #𝗗𝗚𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 #𝗗𝗚𝗦𝗶𝘇𝗶𝗻𝗴 #𝗣𝗼𝘄𝗲𝗿𝗦𝘆𝘀𝘁𝗲𝗺𝘀 #𝗣𝗼𝘄𝗲𝗿𝗙𝗮𝗰𝘁𝗼𝗿 #𝗠𝗔𝗧𝗟𝗔𝗕 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝗦𝘆𝘀𝘁𝗲𝗺 #𝗣𝗼𝘄𝗲𝗿𝗟𝗼𝘀𝘀 #𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 #𝗜𝗘𝗘𝗘𝟯𝟯𝗕𝘂𝘀 #𝗜𝗘𝗘𝗘𝟲𝟵𝗕𝘂𝘀

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘:
𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

📧 𝗘𝗠𝗔𝗜𝗟:
𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

▶️ 𝗬𝗢𝗨𝗧𝗨𝗕𝗘 – 𝗠𝗔𝗜𝗡 𝗖𝗛𝗔𝗡𝗡𝗘𝗟

▶️ 𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗥𝗢𝗝𝗘𝗖𝗧𝗦 𝗖𝗛𝗔𝗡𝗡𝗘𝗟

06/10/2026

𝗢𝗣𝗧𝗜𝗠𝗔𝗟 𝗣𝗟𝗔𝗖𝗘𝗠𝗘𝗡𝗧 𝗔𝗡𝗗 𝗦𝗜𝗭𝗜𝗡𝗚 𝗢𝗙 𝗙𝗢𝗨𝗥 𝗧𝗬𝗣𝗘𝗦 𝗢𝗙 𝗗𝗜𝗦𝗧𝗥𝗜𝗕𝗨𝗧𝗘𝗗 𝗚𝗘𝗡𝗘𝗥𝗔𝗧𝗜𝗢𝗡 𝗨𝗦𝗜𝗡𝗚 𝗖𝗢𝗩𝗜𝗗𝗢𝗔-𝗕𝗔𝗦𝗘𝗗 𝗗𝗚 𝗣𝗟𝗔𝗖𝗘𝗠𝗘𝗡𝗧 𝗔𝗡𝗗 𝗦𝗜𝗭𝗜𝗡𝗚 𝗙𝗢𝗥 𝗥𝗔𝗗𝗜𝗔𝗟 𝗗𝗜𝗦𝗧𝗥𝗜𝗕𝗨𝗧𝗜𝗢𝗡 𝗦𝗬𝗦𝗧𝗘𝗠𝗦

📄 𝗗𝗢𝗪𝗡𝗟𝗢𝗔𝗗 𝗧𝗛𝗘 𝗧𝗘𝗖𝗛𝗡𝗜𝗖𝗔𝗟 𝗗𝗢𝗖𝗨𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡 𝗙𝗥𝗢𝗠
https://drive.google.com/file/d/1QHBOfmm_YvqBqgCmPeUOtVB8_a4kX2lO/view?usp=sharing

This video presents a detailed MATLAB-based research framework for the 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝘀𝗶𝘇𝗶𝗻𝗴 𝗼𝗳 𝗳𝗼𝘂𝗿 𝘁𝘆𝗽𝗲𝘀 𝗼𝗳 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗗𝗚) in radial distribution systems. The proposed methodology uses the 𝗖𝗼𝗿𝗼𝗻𝗮𝘃𝗶𝗿𝘂𝘀 𝗗𝗶𝘀𝗲𝗮𝘀𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 (𝗖𝗢𝗩𝗜𝗗𝗢𝗔) to determine suitable DG locations and DG sizes while satisfying specified electrical and operational constraints.

𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘

The main objective is to simultaneously determine 𝗗𝗚 𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻𝘀, 𝗗𝗚 𝘀𝗶𝘇𝗲𝘀, and the effect of different 𝗗𝗚 𝗽𝗼𝘄𝗲𝗿-𝗳𝗮𝗰𝘁𝗼𝗿 𝗰𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿𝗶𝘀𝘁𝗶𝗰𝘀 on distribution-system operation and active-power losses. The formulation represents a 𝗺𝗶𝘅𝗲𝗱-𝗶𝗻𝘁𝗲𝗴𝗲𝗿 𝗻𝗼𝗻𝗹𝗶𝗻𝗲𝗮𝗿 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 problem because DG locations are discrete variables and DG sizes are numerical decision variables.

𝗙𝗢𝗨𝗥 𝗗𝗚 𝗣𝗢𝗪𝗘𝗥-𝗙𝗔𝗖𝗧𝗢𝗥 𝗧𝗬𝗣𝗘𝗦

🔵 𝗧𝘆𝗽𝗲-𝟭 𝗗𝗚 — Unity power factor (pf = 1.0): 𝗣𝗚 is positive, 𝗤𝗚 = 0. This represents active-power-only injection.

🟢 𝗧𝘆𝗽𝗲-𝟮 𝗗𝗚 — Lagging power factor (pf = 0.85): 𝗣𝗚 is positive, 𝗤𝗚 is positive. This DG supplies both active and reactive power.

🟠 𝗧𝘆𝗽𝗲-𝟯 𝗗𝗚 — Pure reactive power (pf = 0): 𝗣𝗚 = 𝟬, 𝗤𝗚 is positive. This model represents purely reactive-power injection.

🔴 𝗧𝘆𝗽𝗲-𝟰 𝗗𝗚 — Leading power factor (pf = −0.85): 𝗣𝗚 is positive, 𝗤𝗚 is negative. This DG supplies active power while absorbing reactive power.

𝗗𝗚 𝗣𝗢𝗪𝗘𝗥 𝗠𝗢𝗗𝗘𝗟𝗟𝗜𝗡𝗚

The reactive-power contribution is obtained from the power-factor relationship 𝗸ₚ𝒇 = 𝘁𝗮𝗻(𝗮𝗰𝗼𝘀(𝗽𝗳)). For Types 1, 2 and 4, 𝗣𝗚 = 𝗦 and 𝗤𝗚 = 𝗸ₚ𝒇𝗦. For Type 3, 𝗣𝗚 = 𝟬 and 𝗤𝗚 = 𝗦. This allows the same optimization framework to represent different active/reactive-power characteristics.

𝗗𝗘𝗖𝗜𝗦𝗜𝗢𝗡 𝗩𝗔𝗥𝗜𝗔𝗕𝗟𝗘𝗦

The COVIDOA decision vector is represented as 𝗫 = [𝗟₁, 𝗟₂, …, 𝗟ₙᴅɢ | 𝗦₁, 𝗦₂, …, 𝗦ₙᴅɢ], where 𝗟ᵢ represents the location of the ith DG and 𝗦ᵢ represents its size. The substation/slack bus is excluded from the DG candidate locations.

𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘 𝗙𝗨𝗡𝗖𝗧𝗜𝗢𝗡

The principal objective is to minimize total 𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗼𝘄𝗲𝗿 𝗹𝗼𝘀𝘀:

𝗙(𝗫) = 𝗣ₗₒₛₛ

The total loss is evaluated from the branch currents and branch resistances using 𝗣ₗₒₛₛ = Σ 𝗥ₖ|𝗜ₖ|².

𝗖𝗢𝗡𝗦𝗧𝗥𝗔𝗜𝗡𝗧𝗦

𝗩𝗼𝗹𝘁𝗮𝗴𝗲: 𝟬.𝟵𝟬 ≤ 𝗩ⱼ ≤ 𝟭.𝟬𝟱 𝗽.𝘂.

𝗗𝗚 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: 𝟮 ≤ 𝗟ᵢ ≤ 𝗡, with integer-valued locations.

𝗨𝗻𝗶𝗾𝘂𝗲 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻𝘀: No two DG units are assigned to the same bus.

𝗗𝗚 𝗦𝗶𝘇𝗶𝗻𝗴: Each DG size remains within the specified minimum and maximum limits.

𝗣𝗼𝘄𝗲𝗿 𝗕𝗮𝗹𝗮𝗻𝗰𝗲: Aggregate DG active and reactive-power contributions are restricted relative to system demand.

𝗟𝗼𝘀𝘀 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁: The candidate solution must produce active-power loss lower than the base-case active-power loss.

𝗠𝗔𝗧𝗟𝗔𝗕 𝗜𝗠𝗣𝗟𝗘𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡

The complete framework is implemented in 𝗠𝗔𝗧𝗟𝗔𝗕 and integrates the main program, COVIDOA optimization process, DG modelling, radial distribution load flow, constraint checking, convergence analysis, and final result evaluation.

The implementation can report 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗗𝗚 𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻𝘀, 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗗𝗚 𝘀𝗶𝘇𝗲𝘀, 𝗯𝗮𝘀𝗲-𝗰𝗮𝘀𝗲 𝗮𝗰𝘁𝗶𝘃𝗲 𝗹𝗼𝘀𝘀, 𝗗𝗚-𝗰𝗮𝘀𝗲 𝗮𝗰𝘁𝗶𝘃𝗲 𝗹𝗼𝘀𝘀, 𝗮𝗰𝘁𝗶𝘃𝗲-𝗹𝗼𝘀𝘀 𝗿𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻, 𝗿𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗼𝘄𝗲𝗿 𝗹𝗼𝘀𝘀, 𝗺𝗶𝗻𝗶𝗺𝘂𝗺/𝗺𝗮𝘅𝗶𝗺𝘂𝗺 𝗯𝘂𝘀 𝘃𝗼𝗹𝘁𝗮𝗴𝗲, 𝗳𝗲𝗮𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝘀𝘁𝗮𝘁𝘂𝘀, and 𝗰𝗼𝗻𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲 𝗰𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿𝗶𝘀𝘁𝗶𝗰𝘀.

𝗧𝗘𝗦𝗧 𝗦𝗬𝗦𝗧𝗘𝗠𝗦

The methodology is structured for standard radial distribution-system analysis and can be applied to systems such as the 𝗜𝗘𝗘𝗘 𝟯𝟯-𝗕𝘂𝘀 and 𝗜𝗘𝗘𝗘 𝟲𝟵-𝗕𝘂𝘀 radial distribution systems.

𝗥𝗘𝗦𝗘𝗔𝗥𝗖𝗛 𝗦𝗜𝗚𝗡𝗜𝗙𝗜𝗖𝗔𝗡𝗖𝗘

The framework integrates 𝗗𝗚 𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁, 𝗗𝗚 𝘀𝗶𝘇𝗶𝗻𝗴, 𝗽𝗼𝘄𝗲𝗿-𝗳𝗮𝗰𝘁𝗼𝗿-𝗯𝗮𝘀𝗲𝗱 𝗗𝗚 𝗺𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴, 𝗿𝗮𝗱𝗶𝗮𝗹 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗹𝗼𝗮𝗱 𝗳𝗹𝗼𝘄, 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁 𝗰𝗵𝗲𝗰𝗸𝗶𝗻𝗴, and 𝗖𝗢𝗩𝗜𝗗𝗢𝗔 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 into a unified MATLAB framework for investigating DG integration in radial distribution networks.

𝗞𝗘𝗬𝗪𝗢𝗥𝗗𝗦

𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 | 𝗗𝗚 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 | 𝗗𝗚 𝗦𝗶𝘇𝗶𝗻𝗴 | 𝗖𝗢𝗩𝗜𝗗𝗢𝗔 | 𝗣𝗼𝘄𝗲𝗿 𝗙𝗮𝗰𝘁𝗼𝗿 | 𝗥𝗮𝗱𝗶𝗮𝗹 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 | 𝗣𝗼𝘄𝗲𝗿 𝗟𝗼𝘀𝘀 | 𝗠𝗔𝗧𝗟𝗔𝗕 | 𝗜𝗘𝗘𝗘 𝟯𝟯-𝗕𝘂𝘀 | 𝗜𝗘𝗘𝗘 𝟲𝟵-𝗕𝘂𝘀 | 𝗣𝗼𝘄𝗲𝗿 𝗦𝘆𝘀𝘁𝗲𝗺 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻

#𝗖𝗢𝗩𝗜𝗗𝗢𝗔 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 #𝗗𝗚𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 #𝗗𝗚𝗦𝗶𝘇𝗶𝗻𝗴 #𝗣𝗼𝘄𝗲𝗿𝗦𝘆𝘀𝘁𝗲𝗺𝘀 #𝗣𝗼𝘄𝗲𝗿𝗙𝗮𝗰𝘁𝗼𝗿 #𝗠𝗔𝗧𝗟𝗔𝗕 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝗦𝘆𝘀𝘁𝗲𝗺 #𝗣𝗼𝘄𝗲𝗿𝗟𝗼𝘀𝘀 #𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 #𝗜𝗘𝗘𝗘𝟯𝟯𝗕𝘂𝘀 #𝗜𝗘𝗘𝗘𝟲𝟵𝗕𝘂𝘀

⚠️ 𝗗𝗜𝗦𝗖𝗟𝗔𝗜𝗠𝗘𝗥

This video presents a research-oriented MATLAB implementation and the numerical results obtained under the stated modelling assumptions, constraints, and simulation parameters. Results may vary with network data, initialization, optimization settings, load-flow implementation, and independent optimization runs.

📌 𝗜𝗳 𝘆𝗼𝘂 𝗳𝗶𝗻𝗱 𝘁𝗵𝗶𝘀 𝘃𝗶𝗱𝗲𝗼 𝘂𝘀𝗲𝗳𝘂𝗹:

👍 𝗟𝗶𝗸𝗲
🔔 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲
💬 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 𝗬𝗼𝘂𝗿 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
📢 𝗦𝗵𝗮𝗿𝗲 𝗪𝗶𝘁𝗵 𝗢𝘁𝗵𝗲𝗿 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀

𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠
𝗖𝗵𝗲𝗻𝗻𝗮𝗶 – 𝗜𝗡𝗗𝗜𝗔 🇮🇳
📱 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽: +𝟵𝟭 𝟳𝟵𝟬 𝟰𝟱𝟲 𝟴 𝟰𝟱𝟲

#𝗖𝗢𝗩𝗜𝗗𝗢𝗔 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 #𝗗𝗚𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 #𝗗𝗚𝗦𝗶𝘇𝗶𝗻𝗴 #𝗣𝗼𝘄𝗲𝗿𝗦𝘆𝘀𝘁𝗲𝗺𝘀 #𝗣𝗼𝘄𝗲𝗿𝗙𝗮𝗰𝘁𝗼𝗿 #𝗠𝗔𝗧𝗟𝗔𝗕 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝗦𝘆𝘀𝘁𝗲𝗺 #𝗣𝗼𝘄𝗲𝗿𝗟𝗼𝘀𝘀 #𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 #𝗜𝗘𝗘𝗘𝟯𝟯𝗕𝘂𝘀 #𝗜𝗘𝗘𝗘𝟲𝟵𝗕𝘂𝘀

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘:
𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

📧 𝗘𝗠𝗔𝗜𝗟:
𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

▶️ 𝗬𝗢𝗨𝗧𝗨𝗕𝗘 – 𝗠𝗔𝗜𝗡 𝗖𝗛𝗔𝗡𝗡𝗘𝗟

▶️ 𝗠𝗔𝗧𝗟𝗔𝗕 𝗣𝗥𝗢𝗝𝗘𝗖𝗧𝗦 𝗖𝗛𝗔𝗡𝗡𝗘𝗟

05/10/2026

𝗙𝗨𝗭𝗭𝗬-𝗔𝗦𝗦𝗜𝗦𝗧𝗘𝗗 𝗣𝗔𝗥𝗧𝗜𝗖𝗟𝗘 𝗦𝗪𝗔𝗥𝗠 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗙𝗢𝗥 𝗢𝗣𝗧𝗜𝗠𝗔𝗟 𝗗𝗚 𝗣𝗟𝗔𝗖𝗘𝗠𝗘𝗡𝗧 𝗔𝗡𝗗 𝗦𝗜𝗭𝗜𝗡𝗚 – 𝗜𝗘𝗘𝗘 𝟱𝟳-𝗕𝗨𝗦 𝗦𝗬𝗦𝗧𝗘𝗠

📄 𝗗𝗢𝗪𝗡𝗟𝗢𝗔𝗗 𝗧𝗛𝗘 𝗧𝗘𝗖𝗛𝗡𝗜𝗖𝗔𝗟 𝗗𝗢𝗖𝗨𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡 𝗙𝗥𝗢𝗠

https://drive.google.com/file/d/1aDVC-3YbOqo3X638rcMH6l5obsI5IjjL/view?usp=sharing

🔬 𝗣𝗥𝗢𝗝𝗘𝗖𝗧 𝗢𝗩𝗘𝗥𝗩𝗜𝗘𝗪

This research presents a 𝗙𝘂𝘇𝘇𝘆-𝗔𝘀𝘀𝗶𝘀𝘁𝗲𝗱 𝗣𝗮𝗿𝘁𝗶𝗰𝗹𝗲 𝗦𝘄𝗮𝗿𝗺 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗙𝗔𝗣𝗦𝗢) framework for the optimal placement and sizing of Distributed Generation (DG) units in the 𝗜𝗘𝗘𝗘 𝟱𝟳-𝗕𝘂𝘀 𝗽𝗼𝘄𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺.

The proposed methodology integrates 𝗳𝘂𝘇𝘇𝘆 𝗺𝗲𝗺𝗯𝗲𝗿𝘀𝗵𝗶𝗽-𝗯𝗮𝘀𝗲𝗱 𝗗𝗚 𝘀𝘂𝗶𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 with PSO to identify suitable DG locations and capacities while considering network operating conditions.

⚡ 𝗣𝗥𝗢𝗕𝗟𝗘𝗠 𝗙𝗢𝗥𝗠𝗨𝗟𝗔𝗧𝗜𝗢𝗡

The optimization considers:

🔹 DG placement in the IEEE 57-bus system
🔹 DG active-power sizing
🔹 DG power-factor characteristics
🔹 Active power-loss minimization
🔹 Voltage Deviation Index (VDI) minimization
🔹 Fuzzy DG Suitability Index (DGSI)
🔹 DG capacity limitation
🔹 Voltage operating constraints
🔹 AC Newton–Raphson load-flow analysis

🎯 𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘 𝗙𝗨𝗡𝗖𝗧𝗜𝗢𝗡

The optimization objective combines normalized network loss, voltage deviation, fuzzy suitability, and DG size:

𝗙 = 𝘄₁𝗳ₗₒₛₛ + 𝘄₂𝗳ᵥ𝗗𝗜 + 𝘄₃𝗳𝒻𝘂𝘇𝘇𝘆 + 𝘄₄𝗳𝒹𝗴𝒔𝒾𝘇𝘦 + 𝗣ₚₑₙₐₗₜᵧ

The adopted weights are:

𝗪𝗟𝗢𝗦𝗦 = 𝟬.𝟰𝟬
𝗪𝗩𝗗𝗜 = 𝟬.𝟮𝟱
𝗪𝗙𝗨𝗭𝗭𝗬 = 𝟬.𝟮𝟬
𝗪𝗗𝗚 𝗦𝗜𝗭𝗘 = 𝟬.𝟭𝟱

🧠 𝗙𝗔𝗣𝗦𝗢 𝗠𝗘𝗧𝗛𝗢𝗗𝗢𝗟𝗢𝗚𝗬

The PSO search process is enhanced using fuzzy suitability information to support the optimization process.

The implementation includes:

✔️ Fuzzy DG Suitability Index
✔️ Adaptive PSO inertia mechanism
✔️ Particle position and velocity updating
✔️ DG-location handling
✔️ DG-capacity constraints
✔️ Voltage-feasibility checking
✔️ Newton–Raphson AC load flow
✔️ Multiple candidate evaluations
✔️ Best-solution tracking and convergence analysis

📊 𝗦𝗜𝗠𝗨𝗟𝗔𝗧𝗜𝗢𝗡 𝗣𝗔𝗥𝗔𝗠𝗘𝗧𝗘𝗥𝗦

🔹 𝗦𝘆𝘀𝘁𝗲𝗺: IEEE 57-Bus
🔹 𝗕𝗮𝘀𝗲 𝗠𝗩𝗔: 100 MVA
🔹 𝗡𝘂𝗺𝗯𝗲𝗿 𝗼𝗳 𝗗𝗚 𝗨𝗻𝗶𝘁𝘀: 2
🔹 𝗗𝗚 𝗥𝗮𝘁𝗶𝗻𝗴: 10–100 MW per unit
🔹 𝗠𝗮𝘅𝗶𝗺𝘂𝗺 𝗗𝗚 𝗣𝗲𝗻𝗲𝘁𝗿𝗮𝘁𝗶𝗼𝗻: 20%
🔹 𝗩𝗼𝗹𝘁𝗮𝗴𝗲 𝗟𝗶𝗺𝗶𝘁: 0.94–1.06 p.u.
🔹 𝗣𝗦𝗢 𝗣𝗼𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻: 30
🔹 𝗣𝗦𝗢 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: 100
🔹 𝗣𝗦𝗢 𝗖₁: 1.8
🔹 𝗣𝗦𝗢 𝗖₂: 1.8
🔹 𝗜𝗻𝗲𝗿𝘁𝗶𝗮 𝗪𝗲𝗶𝗴𝗵𝘁: 0.90 → 0.40
🔹 𝗡𝗥 𝗧𝗼𝗹𝗲𝗿𝗮𝗻𝗰𝗲: 1×10⁻⁹

📈 𝗥𝗘𝗣𝗢𝗥𝗧𝗘𝗗 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗥𝗘𝗦𝗨𝗟𝗧

The reported FAPSO solution identifies DG placement at:

𝗗𝗚 𝗕𝘂𝘀𝗲𝘀: 𝟰𝟵 𝗮𝗻𝗱 𝟯𝟰

𝗗𝗚 𝗦𝗶𝘇𝗲𝘀:
🔹 Bus 49 → 90.7866 MW
🔹 Bus 34 → 28.1168 MW
🔹 Total DG Capacity → 118.9034 MW

𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲:

📌 Active Power Loss → 18.7714 MW
📌 Minimum Voltage → 0.9646 p.u.
📌 Maximum Voltage → 1.0545 p.u.
📌 VDI → 0.016582
📌 Fuzzy DGSI → 0.683732
📌 Objective Fitness → 0.577955
📌 Voltage Constraint Violation → 0
📌 Feasible Solution → YES
📌 Reported Active-Loss Reduction → 32.63%
📌 Reported VDI Reduction → 30.43%

📉 𝗥𝗘𝗦𝗨𝗟𝗧 𝗔𝗡𝗔𝗟𝗬𝗦𝗜𝗦

The optimized DG configuration demonstrates the effect of coordinated DG location and capacity selection on network active-power losses and voltage performance. The resulting solution satisfies the specified voltage limits and provides an improved voltage profile compared with the corresponding base-case evaluation.

💻 𝗦𝗢𝗙𝗧𝗪𝗔𝗥𝗘 𝗔𝗡𝗗 𝗜𝗠𝗣𝗟𝗘𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡

The complete implementation is developed in 𝗠𝗔𝗧𝗟𝗔𝗕 𝗥𝟮𝟬𝟮𝟱𝗯 with locally implemented:

✔️ IEEE 57-bus network data
✔️ Y-bus construction
✔️ Newton–Raphson load flow
✔️ Fuzzy DG suitability calculation
✔️ Particle Swarm Optimization
✔️ Constraint handling
✔️ Performance evaluation
✔️ Convergence plotting

📚 𝗥𝗘𝗦𝗘𝗔𝗥𝗖𝗛 𝗥𝗘𝗟𝗘𝗩𝗔𝗡𝗖𝗘

This project is suitable for research and academic studies related to:

🔸 Distributed Generation Placement
🔸 Power-System Optimization
🔸 Renewable/DG Integration
🔸 Voltage Profile Improvement
🔸 Power-Loss Reduction
🔸 Fuzzy Optimization
🔸 Particle Swarm Optimization
🔸 AC Power-Flow Analysis
🔸 IEEE Benchmark Power Systems
🔸 MATLAB-Based Power-System Research

🎓 𝗙𝗢𝗥 𝗣𝗛𝗗 / 𝗠.𝗧𝗘𝗖𝗛 / 𝗣𝗚 𝗥𝗘𝗦𝗘𝗔𝗥𝗖

The implementation can serve as a foundation for further investigation involving multi-objective optimization, uncertainty modelling, renewable DG integration, sensitivity analysis, comparative metaheuristic studies, and larger IEEE benchmark systems.

⚠️ 𝗗𝗜𝗦𝗖𝗟𝗔𝗜𝗠𝗘𝗥

This video presents a research-oriented MATLAB implementation and the numerical results obtained under the stated modelling assumptions, constraints, and simulation parameters. Results may vary with network data, initialization, optimization settings, load-flow implementation, and independent optimization runs.

📌 𝗜𝗳 𝘆𝗼𝘂 𝗳𝗶𝗻𝗱 𝘁𝗵𝗶𝘀 𝘃𝗶𝗱𝗲𝗼 𝘂𝘀𝗲𝗳𝘂𝗹:

👍 𝗟𝗶𝗸𝗲
🔔 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲
💬 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 𝗬𝗼𝘂𝗿 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
📢 𝗦𝗵𝗮𝗿𝗲 𝗪𝗶𝘁𝗵 𝗢𝘁𝗵𝗲𝗿 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠
𝗖𝗵𝗲𝗻𝗻𝗮𝗶 – 𝗜𝗡𝗗𝗜𝗔 🇮🇳
📱 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽: +𝟵𝟭 𝟳𝟵𝟬 𝟰𝟱𝟲 𝟴 𝟰𝟱𝟲

#𝗙𝗔𝗣𝗦𝗢 #𝗣𝗦𝗢 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 #𝗗𝗚𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 #𝗗𝗚𝗦𝗶𝘇𝗶𝗻𝗴 #𝗜𝗘𝗘𝗘𝟱𝟳𝗕𝘂𝘀 #𝗠𝗔𝗧𝗟𝗔𝗕 #𝗣𝗼𝘄𝗲𝗿𝗦𝘆𝘀𝘁𝗲𝗺 #𝗙𝘂𝘇𝘇𝘆𝗟𝗼𝗴𝗶𝗰 #𝗣𝗼𝘄𝗲𝗿𝗟𝗼𝘀𝘀𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 #𝗩𝗼𝗹𝘁𝗮𝗴𝗲𝗣𝗿𝗼𝗳𝗶𝗹𝗲 #𝗣𝗵𝗗𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 #𝗣𝗼𝘄𝗲𝗿𝗦𝘆𝘀𝘁𝗲𝗺𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘
https://www.verilogcourseteam.com/request-form

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘:
𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

📧 𝗘𝗠𝗔𝗜𝗟:
𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

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

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

04/10/2026

𝗜𝗘𝗘𝗘 𝟲𝟵-𝗕𝗨𝗦 𝗗𝗚 𝗣𝗟𝗔𝗖𝗘𝗠𝗘𝗡𝗧 𝗔𝗡𝗗 𝗦𝗜𝗭𝗜𝗡𝗚 𝗨𝗦𝗜𝗡𝗚 𝗧𝗛𝗘 𝗛𝗬𝗕𝗥𝗜𝗗 𝗚𝗔–𝗣𝗦𝗢–𝗔𝗖𝗢 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 𝗙𝗥𝗔𝗠𝗘𝗪𝗢𝗥𝗞

📄 𝗗𝗢𝗪𝗡𝗟𝗢𝗔𝗗 𝗧𝗛𝗘 𝗧𝗘𝗖𝗛𝗡𝗜𝗖𝗔𝗟 𝗗𝗢𝗖𝗨𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡 𝗙𝗥𝗢𝗠

https://drive.google.com/file/d/1ZO_I4JnfXtRrfp6Qg_RacOdGCKe768uz/view?usp=sharing

𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗶𝘀 𝘃𝗶𝗱𝗲𝗼

𝗧𝗵𝗶𝘀 𝘃𝗶𝗱𝗲𝗼 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝘀 𝗮 𝗵𝘆𝗯𝗿𝗶𝗱 𝗚𝗲𝗻𝗲𝘁𝗶𝗰 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺–𝗣𝗮𝗿𝘁𝗶𝗰𝗹𝗲 𝗦𝘄𝗮𝗿𝗺 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻–𝗔𝗻𝘁 𝗖𝗼𝗹𝗼𝗻𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗚𝗔–𝗣𝗦𝗢–𝗔𝗖𝗢) 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗗𝗚) 𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝘀𝗶𝘇𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝗜𝗘𝗘𝗘 𝟲𝟵-𝗯𝘂𝘀 𝗿𝗮𝗱𝗶𝗮𝗹 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺.

𝗧𝗵𝗲 𝗺𝗮𝗶𝗻 𝗴𝗼𝗮𝗹 𝗶𝘀 𝘁𝗼 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲 𝘄𝗵𝗲𝗿𝗲 𝗗𝗚 𝘂𝗻𝗶𝘁𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗽𝗹𝗮𝗰𝗲𝗱 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗹𝗮𝗿𝗴𝗲 𝘁𝗵𝗲𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲, 𝘄𝗵𝗶𝗹𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝗶𝗻𝗴 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻-𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲.

𝗛𝗬𝗕𝗥𝗜𝗗 𝗚𝗔–𝗣𝗦𝗢–𝗔𝗖𝗢 𝗙𝗥𝗔𝗠𝗘𝗪𝗢𝗥𝗞

🔹 𝗚𝗔 — 𝗚𝗹𝗼𝗯𝗮𝗹 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻
The Genetic Algorithm searches a broad solution space to identify promising combinations of DG locations and DG sizes.

🔹 𝗣𝗦𝗢 — 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗥𝗲𝗳𝗶𝗻𝗲𝗺𝗲𝗻𝘁
Particle Swarm Optimization further refines the candidate solutions generated by the preceding search stage.

🔹 𝗔𝗖𝗢 — 𝗙𝗶𝗻𝗮𝗹 𝗦𝗲𝗮𝗿𝗰𝗵
Ant Colony Optimization is used as the final optimization stage to intensify the search around promising solutions.

𝗧𝗵𝗲 𝗼𝘃𝗲𝗿𝗮𝗹𝗹 𝗳𝗹𝗼𝘄 𝗶𝘀:

𝗜𝗡𝗜𝗧𝗜𝗔𝗟 𝗣𝗢𝗣𝗨𝗟𝗔𝗧𝗜𝗢𝗡
⬇️
𝗚𝗔 𝗚𝗟𝗢𝗕𝗔𝗟 𝗦𝗘𝗔𝗥𝗖𝗛
⬇️
𝗣𝗦𝗢 𝗦𝗢𝗟𝗨𝗧𝗜𝗢𝗡 𝗥𝗘𝗙𝗜𝗡𝗘𝗠𝗘𝗡𝗧
⬇️
𝗔𝗖𝗢 𝗙𝗜𝗡𝗔𝗟 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡
⬇️
𝗢𝗣𝗧𝗜𝗠𝗔𝗟 𝗗𝗚 𝗟𝗢𝗖𝗔𝗧𝗜𝗢𝗡𝗦 𝗔𝗡𝗗 𝗦𝗜𝗭𝗘𝗦

𝗗𝗚 𝗣𝗟𝗔𝗖𝗘𝗠𝗘𝗡𝗧 𝗔𝗡𝗗 𝗦𝗜𝗭𝗜𝗡𝗚

𝗧𝗵𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:

🔸 𝗗𝗚 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻 — the selected bus number for each DG unit.

🔸 𝗗𝗚 𝗦𝗶𝘇𝗲 — the active-power rating of each DG unit.

🔸 𝗗𝗚 𝗥𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝗣𝗼𝘄𝗲𝗿 — determined according to the specified DG operating power factor when applicable.

𝗘𝗮𝗰𝗵 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗶𝘀 𝘀𝘂𝗯𝗷𝗲𝗰𝘁𝗲𝗱 𝘁𝗼 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗹𝗼𝗮𝗱-𝗳𝗹𝗼𝘄 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘁𝗼 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲 𝘁𝗵𝗲 𝗲𝗳𝗳𝗲𝗰𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗗𝗚 𝗶𝗻𝘀𝘁𝗮𝗹𝗹𝗮𝘁𝗶𝗼𝗻 𝗼𝗻 𝘁𝗵𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸.

𝗣𝗢𝗪𝗘𝗥 𝗦𝗬𝗦𝗧𝗘𝗠 𝗘𝗩𝗔𝗟𝗨𝗔𝗧𝗜𝗢𝗡

𝗧𝗵𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲𝘀 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲 𝗗𝗚 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝘂𝘀𝗶𝗻𝗴 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗹𝗼𝗮𝗱-𝗳𝗹𝗼𝘄 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀.

𝗞𝗲𝘆 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:

🔹 𝗔𝗰𝘁𝗶𝘃𝗲 𝗣𝗼𝘄𝗲𝗿 𝗟𝗼𝘀𝘀
🔹 𝗥𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝗣𝗼𝘄𝗲𝗿 𝗟𝗼𝘀𝘀
🔹 𝗩𝗼𝗹𝘁𝗮𝗴𝗲 𝗣𝗿𝗼𝗳𝗶𝗹𝗲
🔹 𝗔𝘃𝗲𝗿𝗮𝗴𝗲 𝗩𝗼𝗹𝘁𝗮𝗴𝗲 𝗗𝗲𝘃𝗶𝗮𝘁𝗶𝗼𝗻
🔹 𝗩𝗼𝗹𝘁𝗮𝗴𝗲 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆
🔹 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝗻𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲

𝗠𝗔𝗧𝗟𝗔𝗕 𝗜𝗠𝗣𝗟𝗘𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡

𝗧𝗵𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗶𝘀 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝗶𝗻 𝗠𝗔𝗧𝗟𝗔𝗕 𝗳𝗼𝗿 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗗𝗚 𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝘀𝗶𝘇𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗶𝘀 𝗰𝗼𝘂𝗽𝗹𝗲𝗱 𝘄𝗶𝘁𝗵 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗹𝗼𝗮𝗱-𝗳𝗹𝗼𝘄 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝘀𝗼 𝘁𝗵𝗮𝘁 𝗲𝗮𝗰𝗵 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲 𝗗𝗚 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻 𝗰𝗮𝗻 𝗯𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲𝗱 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲.

𝗧𝗵𝗲 𝗵𝘆𝗯𝗿𝗶𝗱 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗶𝘀 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗯𝗶𝗻𝗲 𝗯𝗿𝗼𝗮𝗱 𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗶𝘁𝗵 𝘀𝘂𝗯𝘀𝗲𝗾𝘂𝗲𝗻𝘁 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗿𝗲𝗳𝗶𝗻𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗳𝗶𝗻𝗮𝗹 𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝗻𝘁𝗲𝗻𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻.

𝗪𝗛𝗔𝗧 𝗧𝗛𝗜𝗦 𝗩𝗜𝗗𝗘𝗢 𝗖𝗢𝗩𝗘𝗥𝗦

✅ 𝗜𝗘𝗘𝗘 𝟲𝟵-𝗕𝘂𝘀 𝗥𝗮𝗱𝗶𝗮𝗹 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺
✅ 𝗗𝗚 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
✅ 𝗗𝗚 𝗦𝗶𝘇𝗶𝗻𝗴 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
✅ 𝗛𝘆𝗯𝗿𝗶𝗱 𝗚𝗔–𝗣𝗦𝗢–𝗔𝗖𝗢 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆
✅ 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗟𝗼𝗮𝗱 𝗙𝗹𝗼𝘄
✅ 𝗣𝗼𝘄𝗲𝗿 𝗟𝗼𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
✅ 𝗩𝗼𝗹𝘁𝗮𝗴𝗲 𝗣𝗿𝗼𝗳𝗶𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
✅ 𝗩𝗼𝗹𝘁𝗮𝗴𝗲 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
✅ 𝗖𝗼𝗻𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
✅ 𝗠𝗔𝗧𝗟𝗔𝗕 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻

𝗥𝗘𝗦𝗘𝗔𝗥𝗖𝗛 𝗙𝗢𝗖𝗨𝗦

𝗧𝗵𝗶𝘀 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗶𝘀 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝘁𝗼 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝗻 𝗽𝗼𝘄𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻, 𝗿𝗮𝗱𝗶𝗮𝗹 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀, 𝗵𝘆𝗯𝗿𝗶𝗱 𝗺𝗲𝘁𝗮𝗵𝗲𝘂𝗿𝗶𝘀𝘁𝗶𝗰 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀, 𝗹𝗼𝘀𝘀 𝗺𝗶𝗻𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝘃𝗼𝗹𝘁𝗮𝗴𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀.

𝗧𝗵𝗲 𝗚𝗔–𝗣𝗦𝗢–𝗔𝗖𝗢 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝘀 𝗮 𝗰𝗼𝗺𝗯𝗶𝗻𝗲𝗱 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗿 𝘀𝗲𝗮𝗿𝗰𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝗺𝗶𝘅𝗲𝗱 𝗱𝗶𝘀𝗰𝗿𝗲𝘁𝗲–𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗗𝗚 𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝘀𝗶𝘇𝗶𝗻𝗴 𝘀𝗽𝗮𝗰𝗲.

𝗦𝗨𝗕𝗦𝗖𝗥𝗜𝗕𝗘 𝗙𝗢𝗥 𝗠𝗢𝗥𝗘 𝗥𝗘𝗦𝗘𝗔𝗥𝗖𝗛 𝗖𝗢𝗡𝗧𝗘𝗡𝗧

𝗙𝗼𝗿 𝗺𝗼𝗿𝗲 𝗠𝗔𝗧𝗟𝗔𝗕 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, 𝗽𝗼𝘄𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝗵𝘆𝗯𝗿𝗶𝗱 𝗺𝗲𝘁𝗮𝗵𝗲𝘂𝗿𝗶𝘀𝘁𝗶𝗰 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀, 𝗗𝗚 𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁, 𝗗𝗚 𝘀𝗶𝘇𝗶𝗻𝗴, 𝗹𝗼𝗮𝗱-𝗳𝗹𝗼𝘄 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀, 𝗮𝗻𝗱 𝗽𝗼𝘄𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘃𝗶𝗱𝗲𝗼𝘀, 𝗳𝗼𝗹𝗹𝗼𝘄 𝗮𝗻𝗱 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲.

𝗞𝗘𝗬𝗪𝗢𝗥𝗗𝗦:

#𝗜𝗘𝗘𝗘𝟲𝟵𝗕𝘂𝘀 #𝗗𝗚𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 #𝗗𝗚𝗦𝗶𝘇𝗶𝗻𝗴 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 #𝗚𝗔𝗣𝗦𝗢𝗔𝗖𝗢 #𝗛𝘆𝗯𝗿𝗶𝗱𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 #𝗚𝗲𝗻𝗲𝘁𝗶𝗰𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 #𝗣𝗦𝗢 #𝗔𝗖𝗢 #𝗣𝗼𝘄𝗲𝗿𝗦𝘆𝘀𝘁𝗲𝗺 #𝗣𝗼𝘄𝗲𝗿𝗦𝘆𝘀𝘁𝗲𝗺𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 #𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝗡𝗲𝘁𝘄𝗼𝗿𝗸 #𝗠𝗔𝗧𝗟𝗔𝗕 #𝗟𝗼𝗮𝗱𝗙𝗹𝗼𝘄 #𝗗𝗚𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 #𝗦𝗺𝗮𝗿𝘁𝗚𝗿𝗶𝗱

💻 𝗥𝗘𝗤𝗨𝗘𝗦𝗧 𝗠𝗔𝗧𝗟𝗔𝗕 𝗦𝗢𝗨𝗥𝗖𝗘 𝗖𝗢𝗗𝗘
https://www.verilogcourseteam.com/request-form

🌐 𝗪𝗘𝗕𝗦𝗜𝗧𝗘:
𝗩𝗘𝗥𝗜𝗟𝗢𝗚 𝗖𝗢𝗨𝗥𝗦𝗘 𝗧𝗘𝗔𝗠

📧 𝗘𝗠𝗔𝗜𝗟:
𝗶𝗻𝗳𝗼@𝘃𝗲𝗿𝗶𝗹𝗼𝗴𝗰𝗼𝘂𝗿𝘀𝗲𝘁𝗲𝗮𝗺.𝗰𝗼𝗺

📱 𝗪𝗛𝗔𝗧𝗦𝗔𝗣𝗣:
+𝟵𝟭 𝟳𝟵𝟬𝟰𝟱𝟲𝟴𝟰𝟱𝟲

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

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

03/10/2026

𝗠𝗨𝗟𝗧𝗜-𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘 𝗨𝗡𝗖𝗘𝗥𝗧𝗔𝗜𝗡𝗧𝗬-𝗔𝗪𝗔𝗥𝗘 𝗦𝗜𝗡𝗘 𝗖𝗢𝗦𝗜𝗡𝗘 𝗔𝗟𝗚𝗢𝗥𝗜𝗧𝗛𝗠 | 𝗣𝗩–𝗕𝗘𝗦𝗦–𝗩𝟮𝗛 𝗦𝗠𝗔𝗥𝗧 𝗛𝗢𝗠𝗘 𝗘𝗡𝗘𝗥𝗚𝗬 𝗠𝗔𝗡𝗔𝗚𝗘𝗠𝗘𝗡𝗧

📄 𝗗𝗢𝗪𝗡𝗟𝗢𝗔𝗗 𝗧𝗛𝗘 𝗧𝗘𝗖𝗛𝗡𝗜𝗖𝗔𝗟 𝗗𝗢𝗖𝗨𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡 𝗙𝗥𝗢𝗠
https://drive.google.com/file/d/13RrpQSHkYHIVnreyzonmFRMj369evCJ-/view?usp=sharing

𝗣𝗥𝗢𝗝𝗘𝗖𝗧 𝗢𝗩𝗘𝗥𝗩𝗜𝗘𝗪

This project presents a 𝗠𝘂𝗹𝘁𝗶-𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 𝗨𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆-𝗔𝘄𝗮𝗿𝗲 𝗦𝗶𝗻𝗲 𝗖𝗼𝘀𝗶𝗻𝗲 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 (𝗦𝗖𝗔) for intelligent energy management of a residential smart home integrating 𝗣𝗩, 𝗕𝗘𝗦𝗦, 𝗘𝗩/𝗩𝟮𝗛, 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗿𝗶𝗰𝗶𝗻𝗴, and 𝗗𝗲𝗺𝗮𝗻𝗱 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲.

The proposed framework considers energy-management decisions under realistic uncertainty in 𝗣𝗩 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻, 𝗵𝗼𝘂𝘀𝗲𝗵𝗼𝗹𝗱 𝗹𝗼𝗮𝗱, 𝗲𝗹𝗲𝗰𝘁𝗿𝗶𝗰𝗶𝘁𝘆 𝗽𝗿𝗶𝗰𝗲, 𝗘𝗩 𝗮𝗿𝗿𝗶𝘃𝗮𝗹/𝗱𝗲𝗽𝗮𝗿𝘁𝘂𝗿𝗲, and 𝗘𝗩 𝘁𝗿𝗶𝗽-𝗲𝗻𝗲𝗿𝗴𝘆 requirements.

𝗠𝗨𝗟𝗧𝗜-𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘𝗦

🔹 𝗙𝟭 – 𝗘𝗹𝗲𝗰𝘁𝗿𝗶𝗰𝗶𝘁𝘆 𝗖𝗼𝘀𝘁 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
🔹 𝗙𝟮 – 𝗣𝗲𝗮𝗸-𝘁𝗼-𝗔𝘃𝗲𝗿𝗮𝗴𝗲 𝗥𝗮𝘁𝗶𝗼 (𝗣𝗔𝗥) 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻
🔹 𝗙𝟯 – 𝗖𝗢₂ 𝗘𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻
🔹 𝗙𝟰 – 𝗕𝗘𝗦𝗦 𝗗𝗲𝗴𝗿𝗮𝗱𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘀𝘁 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
🔹 𝗙𝟱 – 𝗣𝗩 𝗦𝗲𝗹𝗳-𝗖𝗼𝗻𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝗠𝗮𝘅𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻

𝗞𝗘𝗬 𝗦𝗬𝗦𝗧𝗘𝗠 𝗙𝗘𝗔𝗧𝗨𝗥𝗘𝗦

🔹 𝟮𝟰-𝗵𝗼𝘂𝗿 𝗦𝗺𝗮𝗿𝘁-𝗛𝗼𝗺𝗲 𝗘𝗻𝗲𝗿𝗴𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁
🔹 𝗣𝗩 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗨𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴
🔹 𝗕𝗮𝘁𝘁𝗲𝗿𝘆 𝗘𝗻𝗲𝗿𝗴𝘆 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 (𝗕𝗘𝗦𝗦)
🔹 𝗘𝗩 𝗖𝗵𝗮𝗿𝗴𝗶𝗻𝗴 𝗮𝗻𝗱 𝗩𝗲𝗵𝗶𝗰𝗹𝗲-𝘁𝗼-𝗛𝗼𝗺𝗲 (𝗩𝟮𝗛)
🔹 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗘𝗹𝗲𝗰𝘁𝗿𝗶𝗰𝗶𝘁𝘆 𝗣𝗿𝗶𝗰𝗶𝗻𝗴
🔹 𝗗𝗲𝗺𝗮𝗻𝗱-𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴
🔹 𝗕𝗮𝘁𝘁𝗲𝗿𝘆 𝗗𝗲𝗴𝗿𝗮𝗱𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘀𝘁
🔹 𝗖𝗢₂ 𝗘𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
🔹 𝗣𝗩 𝗦𝗲𝗹𝗳-𝗖𝗼𝗻𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
🔹 𝗣𝗮𝗿𝗲𝘁𝗼-𝗕𝗮𝘀𝗲𝗱 𝗠𝘂𝗹𝘁𝗶-𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
🔹 𝗠𝗼𝗻𝘁𝗲 𝗖𝗮𝗿𝗹𝗼 𝗥𝗼𝗯𝘂𝘀𝘁𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀

𝗨𝗡𝗖𝗘𝗥𝗧𝗔𝗜𝗡𝗧𝗬 𝗠𝗢𝗗𝗘𝗟𝗜𝗡𝗚

The framework incorporates uncertainty in 𝗣𝗩 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁, 𝗵𝗼𝘂𝘀𝗲𝗵𝗼𝗹𝗱 𝗱𝗲𝗺𝗮𝗻𝗱, 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗲𝗹𝗲𝗰𝘁𝗿𝗶𝗰𝗶𝘁𝘆 𝗽𝗿𝗶𝗰𝗲𝘀, 𝗘𝗩 𝗮𝗿𝗿𝗶𝘃𝗮𝗹/𝗱𝗲𝗽𝗮𝗿𝘁𝘂𝗿𝗲 𝘁𝗶𝗺𝗲𝘀, and 𝗘𝗩 𝘁𝗿𝗶𝗽-𝗲𝗻𝗲𝗿𝗴𝘆 requirements.

A 𝗿𝗶𝘀𝗸-𝗮𝗱𝗷𝘂𝘀𝘁𝗲𝗱 𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 formulation is used to account for both expected performance and variability.

𝗦𝗜𝗡𝗘 𝗖𝗢𝗦𝗜𝗡𝗘 𝗔𝗟𝗚𝗢𝗥𝗜𝗧𝗛𝗠

The proposed 𝗦𝗖𝗔 framework progressively controls exploration and exploitation using a decreasing control parameter. An 𝗮𝗿𝗰𝗵𝗶𝘃𝗲-𝗴𝘂𝗶𝗱𝗲𝗱 𝗹𝗲𝗮𝗱𝗲𝗿 mechanism maintains diverse non-dominated solutions, while 𝗺𝘂𝘁𝗮𝘁𝗶𝗼𝗻 and 𝗿𝗲𝗽𝗮𝗶𝗿 operations improve search diversity and constraint feasibility.

𝗩𝗔𝗟𝗜𝗗𝗔𝗧𝗜𝗢𝗡 𝗔𝗡𝗗 𝗥𝗢𝗕𝗨𝗦𝗧𝗡𝗘𝗦𝗦

The optimized solutions are evaluated using 𝗼𝘂𝘁-𝗼𝗳-𝘀𝗮𝗺𝗽𝗹𝗲 𝗠𝗼𝗻𝘁𝗲 𝗖𝗮𝗿𝗹𝗼 𝘂𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 to investigate robustness under conditions different from the optimization scenarios.

𝗥𝗘𝗦𝗘𝗔𝗥𝗖𝗛 𝗖𝗢𝗡𝗧𝗘𝗡𝗧

✔ 𝗦𝗺𝗮𝗿𝘁-𝗛𝗼𝗺𝗲 𝗘𝗻𝗲𝗿𝗴𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁
✔ 𝗣𝗩–𝗕𝗘𝗦𝗦 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻
✔ 𝗘𝗩/𝗩𝟮𝗛 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴
✔ 𝗗𝗲𝗺𝗮𝗻𝗱-𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
✔ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗿𝗶𝗰𝗶𝗻𝗴
✔ 𝗕𝗮𝘁𝘁𝗲𝗿𝘆 𝗗𝗲𝗴𝗿𝗮𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴
✔ 𝗠𝘂𝗹𝘁𝗶-𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝗮𝗿𝗲𝘁𝗼 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
✔ 𝗨𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆-𝗔𝘄𝗮𝗿𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
✔ 𝗠𝗼𝗻𝘁𝗲 𝗖𝗮𝗿𝗹𝗼 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻
✔ 𝗠𝗔𝗧𝗟𝗔𝗕 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻

𝗞𝗘𝗬𝗪𝗢𝗥𝗗𝗦

𝗦𝗶𝗻𝗲 𝗖𝗼𝘀𝗶𝗻𝗲 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺, 𝗦𝗖𝗔, 𝗠𝘂𝗹𝘁𝗶-𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝗦𝗺𝗮𝗿𝘁 𝗛𝗼𝗺𝗲 𝗘𝗻𝗲𝗿𝗴𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁, 𝗣𝗩, 𝗕𝗘𝗦𝗦, 𝗘𝗩, 𝗩𝟮𝗛, 𝗗𝗲𝗺𝗮𝗻𝗱 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲, 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗿𝗶𝗰𝗶𝗻𝗴, 𝗕𝗮𝘁𝘁𝗲𝗿𝘆 𝗗𝗲𝗴𝗿𝗮𝗱𝗮𝘁𝗶𝗼𝗻, 𝗥𝗲𝗻𝗲𝘄𝗮𝗯𝗹𝗲 𝗘𝗻𝗲𝗿𝗴𝘆, 𝗨𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴, 𝗣𝗮𝗿𝗲𝘁𝗼 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝗠𝗼𝗻𝘁𝗲 𝗖𝗮𝗿𝗹𝗼, 𝗠𝗔𝗧𝗟𝗔𝗕, 𝗦𝗺𝗮𝗿𝘁 𝗚𝗿𝗶𝗱.

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