MCET NETWORK

Effective management and strategic planning of a national-level power sector require comprehensive scenario analysis, leveraging official data from key power sector institutions. This research focuses on evaluating nine distinct scenarios for the Bangladesh power system with projections for the year 2050. The scenarios were developed using official datasets from the Bangladesh Power Development Board (BPDB) and Power Grid Company of Bangladesh (PGCB), ensuring an accurate representation of the country’s energy landscape.

The datasets incorporated into the analysis include detailed records of operational and planned power plants, projected power demand, transmission line capacities, baseload power plants, and comprehensive cost data. The cost data covers essential parameters such as capital expenditure, fixed and variable operational costs, and maintenance expenses, which are crucial for evaluating the economic feasibility of different energy pathways.

The SWITCH model was employed to simulate and compare these scenarios. Using the CPLEX solver, the model efficiently processed the data to produce outputs related to optimal generation capacity and dispatch, transmission expansion, and cost trajectories for the power sector. The outputs from these simulations have been systematically documented in CSV format, capturing critical insights into potential future developments of Bangladesh’s energy infrastructure. These results have been securely uploaded to the designated cloud storage (G-Drive) for easy access and review by stakeholders.

This analysis provides key insights into the strategic direction required to meet Bangladesh's future energy needs, which will be instrumental in shaping policy decisions, guiding investments, and facilitating the transition toward a decarbonized power sector in Bangladesh.

SMART-AIT team-maintained engagement with key stakeholders across both Bangladesh and Thailand. The team collaborated with of experts, governmental agencies, and power sector authorities in Thailand, whose insights were instrumental in refining the models and ensuring that the results align with national energy strategies.

• Director of the Metropolitan Electricity Authority (MEA): The Director's involvement was instrumental in validating the assumptions and data related to the Thai power sector. Their extensive experience in electricity market operations and grid management provided a solid foundation for refining the input parameters for Thailand's scenario analysis. Their guidance helped align the model outputs with the operational realities of the country's metropolitan electricity systems.

• SCADA Division, Provincial Electricity Authority (PEA): The representative from the SCADA Division provided valuable technical support and data pertaining to Thailand’s regional electricity network. Their insights into the SCADA (Supervisory Control and Data Acquisition) systems were crucial for understanding the real-time dynamics of power distribution and the operational challenges faced by the regional grid. This collaboration enabled the inclusion of granular data on electricity demand, supply, and system operation, enriching the model’s capacity to simulate realistic decarbonization pathways.

• Electricity Generating Authority of Thailand (EGAT): Further data validation and model calibration were achieved through the cooperation of EGAT, where the Governor of EGAT facilitated access to official power generation and demand data. This comprehensive dataset covered historical trends, current generation mixes, thereby ensuring that the modeled scenarios for Thailand were both data-driven and reflective of national energy strategies.

These collaborations were essential for obtaining accurate, high-resolution data and for validating key assumptions used in the energy system models. By engaging with these stakeholders, the AIT team was able to develop robust, country-specific decarbonization scenarios that align with national priorities and provide actionable insights for policymakers in both Bangladesh and Thailand.

Also, The Asian Institute of Technology (AIT) team, tasked with power sector decarbonization scenario analysis for Thailand under the MCET project, carried out the following activities in alignment with the project's objectives:

• Training: The team actively participated in discussions and training sessions focused on the development and customization of energy modeling tools such as Python for Power System Analysis (PyPSA), SWITCH, and EnergyRT. These sessions aimed to adapt these models to specific national requirements, conduct scenario analyses, and facilitate the dissemination of results for both Bangladesh and Thailand. The hands-on training enhanced the team's technical expertise, ensuring a deep understanding of these complex models and their applications in varying national contexts.

• Model Customization: Building upon the training, the SMART-AIT team successfully acquired the skills necessary to customize the PyPSA and SWITCH models to suit the characteristics of Bangladesh and Thailand's power sectors. This customization involved adjusting the models to represent regional and national electricity sector scenarios, allowing for detailed analysis of decarbonization pathways. The modifications considered factors such as energy demand projections, renewable energy and storage integration, and policy directives, facilitating a comprehensive understanding of the energy transition landscape in both countries.

• Expert Consultation: To ensure the accuracy and relevance of the model outputs, the team engaged in consultations with key national experts and governmental entities. In Thailand, consultations were held with organizations such as the Provincial Electricity Authority (PEA), the Metropolitan Electricity Authority (MEA), and the Electricity Generating Authority of Thailand (EGAT). Similarly, in Bangladesh, the team collaborated with the Bangladesh Power Development Board (BPDB), the Power Grid Company of Bangladesh (PGCB), and the Bangladesh Energy and Power Research Council (BEPRC). These consultations were critical for accessing granular data, validating model assumptions, and aligning the scenarios with national policies and regulations.