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This paper argues that a fit-for-purpose model and datasets are necessary to generate transition pathways for the electricity generation sector at the subnational level. We present the methodology, data, and results focusing at a sub-national level, the state of West Bengal in India. The approach can be generalized for any region with necessary customization. By utilizing high-resolution spatio-temporal input datasets, this study proposes a power sector capacity expansion model to compute three sets of transitional scenarios and one set of the current-as-usual scenario. These scenarios consider sub-national energy carrier-resource constraints and are solved to identify the most economically cost efficient future transition pathway for the electricity sector in West Bengal. Based on the least-cost solution, computations determine the optimal energy mix, operations, investments, and emissions for alternative scenarios. The results show that integrating demand-side flexibility (DSF) as a balancing option can lead to transformative outcomes. Compared to the current capacity expansion trend (ScenCA), adopting a thermal mix renewable scenario with intraday load-shifting (ScenTMDSF) could reduce 77% of CO2 emissions by 2040. This does not necessitate early retirement of existing thermal power plants, total investment increases by 13% compared to ScenCA. Without DSF as a balancing option, an additional 26% investment is required compared to the current-as-usual scenario for 2040. Transitioning to 100% renewable energy (ScenREN) requires 30% more investment, early retirement of 5.34 GW of thermal capacity, and nearly 2.7 times more storage battery capacity. These numbers help in understanding the magnitude of the financial resource and kind of technological need for the developing countries not only from the point of view of equitable climate action from burden sharing and just transition principles but also provides practical example of need for redirecting global capital for creating global good through subnational scale actions.

Sourish Chatterjee, Joyashree Roy, Arijit Mukherjee, Oleg Lugovoy and Anupam Debsarkar,

“Power sector transition plan of a coal-rich region in India with high-resolution spatio-temporal data based model,” Energy for Sustainable Development, Volume 83, 2024,101560, ISSN 0973-0826, https://doi.org/10.1016/j.esd.2024.101560

This study undertakes a detailed intercomparison of four opensource electricity system capacity expansion models—Temoa, Switch, GenX, and USENSYS—to examine their suitability for guiding U.S. power sector decarbonization policies. We isolate the effects of model-specific differences on policy outcomes and investment decisions by harmonizing empirical inputs via PowerGenome and systematically defining “scenarios” (policy conditions) and “configurations” (model setup choices). Our framework allows each model to be tested on identical assumptions for policy, technology costs, and operational constraints, thus distinguishing results that arise from data inputs or configuration versus inherent model structure. Key findings highlight that, when harmonized, models produce very similar capacity portfolios under each current policies and net-zero configuration, with less than 1% difference in system costs for most configurations. This agreement across models allows us to examine the impact of configuration choices. For example, configurations that assume unit commitment constraints or economic retirement of generators reveal the difference in investment decisions and system costs that arise from these modeling choices, underscoring the need for clear scenario and configuration definitions in policy guidance. Through this study, we identify critical structural assumptions that influence model outcomes and demonstrate the advantages of a standardized approach when using capacity expansion models. This work offers a valuable benchmark and identifies a few key modeling choices for policymakers, which ultimately will enhance transparency and reliability in modeling efforts to inform the clean energy transition for clean energy planning.

Greg Schivley [1], Michael Blackhurst [2], Patricia Hidalgo-Gonzalez [3], Jesse Jenkins [1], Oleg Lugovoy [4] , Qian Luo [1] , Michael J. Roberts [5] , Rangrang Zheng [5] , Cameron Wade [6] , Matthias Fripp [7] 

[1] Princeton University, Princeton, NJ, USA 

[2] Carnegie Mellon University, Pittsburgh, PA, USA 

[3] University of California San Diego, La Jolla, CA, USA

[4] Optimal Solutions LLC, Richmond, VA, USA

[5] University of Hawai’i, Honolulu, HI, USA

[6] Sutubra Research Inc., Halifax, Nova Scotia, Canada

[7] Environmental Defense Fund

 https://doi.org/10.1016/j.esd.2024.101560

The study "PyPSA-BD: A customized model to explore decarbonized energy transition for developing country", authored by Joyashree Roy and Firuz Ahamed Nahid from the MCET Bangladesh team, introduces PyPSA-BD—an advanced adaptation of the PyPSA-Earth model. This tool is designed to analyze, with high spatial and temporal resolution, the challenges and opportunities for transitioning to a decarbonized energy system in Bangladesh. Using 2019 as a reference year, the model projects an increase in installed capacity from 18.94 GW to 281.52 GW by 2050, a reduction in generation costs to 7.63 BDT/kWh, the creation of 6.7 million jobs, and the use of 3690.85 square kilometers of land.

The study emphasizes that achieving these goals will require annual investments equivalent to 1.99% of Bangladesh’s 2023 GDP starting in 2025 and highlights the potential of PyPSA-BD as an open-source tool for policymakers and researchers in developing sustainable energy scenarios tailored to the needs of developing countries.

For more details, access the full study here:

 https://www.sciencedirect.com/science/article/pii/S1755008424001194.

About The South and South-East Asia Multidisciplinary Applied Research Network on Transforming Societies of Global South (SMARTS) is the research center at the Asian Institute of Technology (AIT), Thailand: https://smartscenter.ait.ac.th/ 

This study evaluated a potential transition of India’s power sector to 100% wind and solar energy sources. Applying a macro-energy IDEEA (Indian Zero Carbon Energy Pathways) model to 32 regions and 114 locations of potential installation of wind energy and 60 locations of solar energy, we evaluated a 100% renewable power system in India as a concept. We considered 153 scenarios with varying sets of generating and balancing technologies to evaluate each intermittent energy source separately and their complementarity. Our analysis confirms the potential technical feasibility and long-term reliability of a 100% renewable system for India, even with solar and wind energy only. Such a dual energy source system can potentially deliver fivefold the annual demand of 2019. The robust, reliable supply can be achieved in the long term, as verified by 41 years of weather data. The required expansion of energy storage and the grid will depend on the wind and solar energy structure and the types of generating technologies. Solar energy mostly requires intraday balancing that can be achieved through storage or demand-side flexibility. Wind energy is more seasonal and spatially scattered, and benefits from the long-distance grid expansion for balancing. The complementarity of the two resources on a spatial scale reduces requirements for energy storage. The demand-side flexibility is the key in developing low-cost supply with minimum curtailments. This can be potentially achieved with the proposed two-level electricity market where electricity prices reflect variability of the supply. A modelled experiment with price signals demonstrates how balancing capacity depends on the price levels of guaranteed and flexible types of loads, and therefore, can be defined by the market.