For decades, Pakistan's electricity supply has fluctuated between surplus and deficit. Recently, the country has achieved surplus generation capacity, leading to significant circular debt due to high capacity payments. Additionally, daily and seasonal demand variations contribute to this surplus during off-peak times, which has drastically increased the Weighted Average Cost of Generation (WACG). To address this, there is a need to find suitable, non-seasonal, flexible loads that do not increase daily load peaks. A dynamic, temporally variable tariff model is essential to encourage generation capacity utilization during off-peak hours. This approach benefits both energy consumers and the power sector: consumers get better rates, while the power sector improves its load factor by using excess capacity during off-peak times. To address this challenge, we developed a dynamic, dashboard-based Time-of-Use (ToU) pricing tool. The tool adjusts tariff rates, making them higher during peak hours to incentivize off-peak electricity usage. Importantly, the tool ensures tariffs remain above the basket price of electricity, preventing any additional burden on the national budget. The tool's effectiveness was demonstrated through various scenarios. For example, shifting just 5% of peak load to off-peak hours in August could save the power sector 17.18 million rupees. Similarly, shifting power plant operations to off-peak hours could generate 93.5 million rupees per month in emissions reduction value for October. CO2 emissions would also decrease from 5413.08 metric tons to 5044.30 metric tons with a 5% demand shift, aligning with the UN Sustainable Development Goals. This load-shifting method could help reduce circular debt and carbon emissions. By lowering the WACG, the power sector can cut capacity payments, reduce debt, and provide revenue to generation companies. Our ToU pricing tool offers affordable off-peak rates, benefiting consumers, power generation entities, and the government. Finally, we recommend strategies to maximize the benefits of this mechanism.

The Power School of Center for Excellence (PSCE) has been instrumental in advancing professional development within the energy sector through structured programs and specialized trainings. Programs Offered: Structured and Customized Professional Development Program (PDP): This comprehensive 12-month program equipped new entrants in the power sector with an in-depth understanding of the electric utility business. Participants gained essential skills to tackle challenges in the evolving sustainable energy landscape. Continuous Development Program (CDP): This ongoing program allowed professionals to return to PSCE for tailored training courses based on current industry needs, ensuring participants remained updated on advancements in their fields. Train the Trainers Program: In collaboration with public and private universities, this initiative expanded knowledge sharing nationwide and set up satellite facilities to enhance research capabilities. Specialized Trainings: Competitive Trading Bilateral Contract (CTBCM) Program: Focused on electricity market deregulation, this training emphasized how competition enhances energy efficiency. Electricity Market Program: This comprehensive training covered all aspects of electricity generation, including licensing and procurement. Forecasting Training: Participants learned to analyze trends and load demand for informed investment decisions. Generation and Transmission Planning Trainings: These programs provided insights into effective electricity generation and transmission planning while considering environmental impacts. Through these initiatives, PSCE has significantly enhanced the skills and knowledge of energy sector professionals in Pakistan

The rapid expansion of renewable energy is reshaping the global energy sector, with wind power playing a critical role in this transformation. In Pakistan, regions like Sindh and Balochistan hold vast potential for wind energy. However, the unpredictable and intermittent nature of wind presents challenges for its integration into the power grid, requiring effective energy management strategies. Accurate forecasting of wind power generation is crucial to address these issues, ensuring a steady and reliable energy supply. This project seeks to meet the need for precise wind power forecasting in Pakistan by utilizing Long Short-Term Memory (LSTM) neural networks. These networks excel in time series prediction due to their ability to capture long-term patterns and handle nonlinear data relationships. A key feature of this initiative is its focus on developing a forecasting system capable of operating with limited data. Many parts of Pakistan lack comprehensive meteorological information, posing challenges for traditional forecasting models. The LSTM-based approach, however, is designed to work with sparse data, learning from available information and generalizing patterns for accurate predictions. This project aims to contribute to more reliable wind power forecasting, enhancing renewable energy integration into Pakistan's national grid and advancing the country’s transition toward sustainable energy sources.

Short-Term Load Forecasting (STLF) is a critical tool for managing energy markets. STLF utilizes various inputs to generate accurate load forecasts for specific regions. Several techniques are available for STLF, including statistical methods like multiple regression, exponential smoothing, iterative reweighted least squares, adaptive load forecasting, and stochastic time series. Additionally, artificial intelligence-based techniques such as fuzzy logic, genetic programming, deep neural networks, random forests, and support vector machines have been employed, along with knowledge-based methods like case-based reasoning. However, no single technique is universally suitable for every region's forecasting needs. Therefore, a hybrid model that combines multiple techniques is necessary to achieve accurate predictions. The tool developed by LEI uses automated inputs such as historical demand data and weather forecasts. Manual inputs can also be added for better accuracy, including significant events like Ramadan, elections, holidays, and schedules for offices and schools. The tool is customizable, allowing it to be tailored to the specific needs of Distribution Companies (DISCOs) or specific areas within them. Currently, the tool is being tested on offline historical load data. Based on this data alone, it provides a Mean Absolute Percentage Error (MAPE) of 7% to 1%, depending on the season. With additional inputs, the tool is expected to reduce its daily MAPE to 0.5% to 3%. LUMS' Energy Informatics Group (EIG) has deployed a team of developers and data scientists for this project.

This electricity outlook report presents the findings from the independent dispatch model developed by the LUMS Energy Institute (LEI) for Pakistan’s power system. It critically examines the Indicative Generation Capacity Expansion Plan (IGCEP) 2021 by NTDC, offering a thorough analysis of the business-as-usual trajectory outlined in IGCEP 2021. The aim is to provide readers with a clear understanding of Pakistan's power system. The report covers nine financial years, from 2021-22 (FY22) to 2029-30 (FY30), aligning with the timeline in IGCEP 2021. The modeling process utilizes the in-house LUMS Power Dispatch Model (LPDM), which simulates the NTDC system to replicate the IGCEP 2021 scenario. Inputs and assumptions are based on IGCEP 2021, with updates from the latest available data from relevant organizations. The results include analyses of capacity and energy balance, fuel-based dispatch, capacity and energy payments, the impact on power purchase prices, and projected fuel quantities. Additionally, nine alternative scenarios have been developed and simulated using LPDM, exploring the effects of changes in key variables. This report provides a summary of key takeaways and conclusions from the IGCEP 2021 scenario, as well as insights from the alternative scenarios. However, this summary only scratches the surface. Readers are encouraged to review the full report for a comprehensive understanding, especially when considering the alternative scenarios.

This project aimed to improve energy efficiency and conservation in government buildings across Pakistani municipalities. It involved installing energy monitoring systems that provided high-resolution data on energy use with one-minute precision. This data was crucial for conducting detailed energy audits and analysing consumption patterns to identify inefficiencies and develop strategies for improvement. The project also included extensive training for municipal staff on energy management systems (EnMS), demand-side management (DSM) techniques, and practical energy-saving measures. The goal was to equip staff with the skills to implement effective energy conservation practices. Additionally, the project focused on creating policy recommendations to enhance energy management in government buildings. By combining data analysis, training, and policy development, the project aimed to improve energy practices, promote sustainable use, and reduce overall consumption in the public sector.

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.