A multi-microgrid is a system consisting of various regional microgrids that utilize locally available renewable energy sources, supplemented by storage backups, to meet their own electrical demands. This concept focuses on enhancing energy reliability and sustainability within specific regions. On the other hand, a virtual power plant (VPP) offers a platform for renewable energy resources to engage in peer-to-peer (P2P) energy transactions and trading between different generation and load centres. The VPP integrates and optimizes the use of distributed energy resources across multiple microgrids. In this project, the VPP operates through three layers. The first layer involves infrastructure planning and optimization, focusing on the design and integration of microgrids. The second layer represents the multi-VPPs layer, functioning as a 3-level energy market where different microgrids interact for energy trading. The third layer re-evaluates economic parameters, assessing the impact of multiple VPPs on the broader energy market. The VPP's primary role is to meet the energy demands of local microgrids by enabling energy trading between those with surplus energy and those with deficits. This system ensures efficient energy distribution and reduces reliance on external energy sources. Simultaneously, the VPP coordinates with neighbouring VPPs at the regional energy market level and with the larger grid through the wholesale energy market. This project is vital for creating a long-term roadmap for energy management and policy optimization. It provides valuable insights for policymakers and government entities, helping them develop strategies for sustainable energy systems. By promoting the use of renewable resources and enabling efficient energy trading, this project supports the transition to a more resilient and eco-friendly energy infrastructure.

A multi-microgrid is a system consisting of various regional microgrids that utilize locally available renewable energy sources, supplemented by storage backups, to meet their own electrical demands. This concept focuses on enhancing energy reliability and sustainability within specific regions. On the other hand, a virtual power plant (VPP) offers a platform for renewable energy resources to engage in peer-to-peer (P2P) energy transactions and trading between different generation and load centres. The VPP integrates and optimizes the use of distributed energy resources across multiple microgrids. In this project, the VPP operates through three layers. The first layer involves infrastructure planning and optimization, focusing on the design and integration of microgrids. The second layer represents the multi-VPPs layer, functioning as a 3-level energy market where different microgrids interact for energy trading. The third layer re-evaluates economic parameters, assessing the impact of multiple VPPs on the broader energy market. The VPP's primary role is to meet the energy demands of local microgrids by enabling energy trading between those with surplus energy and those with deficits. This system ensures efficient energy distribution and reduces reliance on external energy sources. Simultaneously, the VPP coordinates with neighbouring VPPs at the regional energy market level and with the larger grid through the wholesale energy market. This project is vital for creating a long-term roadmap for energy management and policy optimization. It provides valuable insights for policymakers and government entities, helping them develop strategies for sustainable energy systems. By promoting the use of renewable resources and enabling efficient energy trading, this project supports the transition to a more resilient and eco-friendly energy infrastructure.

In today's rapidly evolving world, large language models (LLMs) like ChatGPT, Google Bard, and Large Language Model Meta AI (LLaMA) have become powerful tools for knowledge extraction and generation through quick user prompts. However, a significant challenge with these models is their tendency to provide generic answers that lack domain-specific relevance. This limitation can hinder their effectiveness in specialized fields, such as energy consumption analysis, where precise insights are crucial for informed decision-making. This project aims to develop an Assistance Program for Large-Language Models (APLM) that enables these models to deliver domain-specific insights in the field of energy consumption. The primary objective is to enhance the practical applicability of ChatGPT by tailoring its responses to meet the unique needs of energy users, analysts, and decision-makers. The proposed model addresses this challenge by refining energy-related prompts, allowing ChatGPT to generate insights and recommendations that are both comprehensive and deeply rooted in the specifics of energy consumption. This approach ensures that the responses provided are not only accurate but also highly relevant to the field. By focusing on precise energy consumption patterns and relevant data, the APLM bridges the gap between generic responses and the detailed analysis required for effective energy management. This advancement will make LLMs more valuable tools for professionals in the energy sector, offering them the specific insights they need to make well-informed decisions. In conclusion, the APLM project seeks to improve the utility of LLMs in specialized fields, ensuring they can provide the precise, domain-specific information necessary for real-world applications, particularly in the critical area of energy consumption.

Pakistan’s power sector has faced significant challenges for many years. Although the country has sufficient power generation capacity, the transmission and distribution systems are not resilient enough to ensure reliable electricity supply. Distribution companies (DISCOs) struggle with maintaining their assets, leading to frequent outages and power failures that affect the country’s financial stability. This project aimed to develop a GIS-enabled tool for the preventive maintenance of distribution system assets. The tool offers a dynamic view of the distribution grid, allowing DISCOs to perform system updates and reconfigurations before assets become overloaded and potentially disrupt the entire grid. The GIS-based software helps monitor asset usage, enhance system reliability, and reduce operating costs. The tool is designed to spatially analyse network congestion, assess site feasibility, and address power congestion issues. With this tool, power companies can plan, build, monitor, and manage their distribution networks more effectively. A geodatabase is also being developed to maintain and manage accurate distribution asset data, including substations, lines, and transformers. This database will help identify patterns of distribution, demand, consumption, and losses. By evaluating power demand scenarios, electricity can be supplied more efficiently to targeted areas. GIS-based mapping of energy demand patterns, using physical data incorporated into the GIS database, is a crucial component of the project. This approach aims to improve the overall management and efficiency of the distribution network.