| dc.description.abstract | As global awareness of the devastating impacts of climate change is growing, nations are uniting to address the imperative of decarbonization. The electricity sector, known for its high carbon intensity, accounts for the largest share of global greenhouse gas emissions, making it a critical focus for decarbonization efforts. The shift from fossil fuel-based power generation to carbon-free alternatives like renewables is paramount for achieving deep decarbonization. However, integrating renewable energy technologies in the electricity grid presents its own set of challenges due to their reliance on natural forces and intermittent generation patterns. Despite these challenges, the world is increasingly integrating renewable energy sources into their generation mix leading to electricity system transitions. The new challenges posed by nature dependent renewable energy like intermittency, variability, uncertainty and low-reliability have led to significant supply-demand gaps in terms of temporal mismatches (times of peak generation and peak demand) and quantity mismatches (high supply with low demand leading to generation curtailment, and low supply with high demand leading to consumption curtailment). Hence, the question posed to the electricity planners is – “How to match or balance “dynamic supply” with “dynamic demand” at every timeframe for effective operation of the overall electricity system with such challenges?”. The present research responds to this question by solving multiple problem contexts using a model-based approach.
The above objective is achieved by developing and validating a generic linear programming-based optimization model that takes into account interventions on the supply-side, the demand-side, and the integration of both in the context of transitioning electricity systems. In the first phase, we conducted a comprehensive systematic literature review, examining existing literature related to electricity transition planning models and its applications. In the second phase, we developed and validated an optimization model that dynamically matches demand with supply at least cost while considering multiple supply-side options and demand projections. Here, the technical, economic, and environmental feasibilities of electricity system transitions are assessed. In the third phase, using the model, multiple scenarios, spanning different timeframes and varying renewable energy integration levels, are developed and evaluated. In the fourth phase, the above model is expanded to integrate demand-side interventions to study their effectiveness in reducing renewable generation curtailment and battery storage requirement, and inturn increasing capacity utilization. Finally, in the last phase, we leveraged the insights gained from the above research in formulating and recommending appropriate policies and decision inputs for the key stakeholders. | en_US |
