India’s 2030 decarbonisation commitment is ambitious but necessary, especially considering that it is the world’s third-largest emitter of greenhouse gases. Towards this, the country has set several targets, such as installing 500 gigawatts (GW) of renewable energy (RE) capacity and achieving a 30% sales share for electric vehicles (EVs) by 2030. While all Indian states are expected to contribute to these national targets, the RE resource potential varies considerably across states. The consistent growth in EV penetration is expected to increase overall electricity demand. Along with this, the country is witnessing the launch of various policies for strengthening its decarbonisation efforts, supporting cleaner energy generation, and ensuring grid stability. Prominent among these are the Resource Adequacy (RA) Guidelines and the Carbon Credit Trading Scheme (CCTS). These developments necessitate that the Indian states—particularly those with limited RE resources—assess how the EV growth, the RE targets, and the evolving regulatory framework will impact their power generation sector.

Uttar Pradesh (UP)—the second-largest electricity-consuming state and home to India’s largest EV fleet—has a relatively low RE potential of 33 GW. In financial year (FY) 2023–24, the energy demand of UP was 150 billion units (BU), with a peak demand of 28 GW. Almost 90% of the state’s electricity demand is met through coal-based plants, resulting in 137 million tons of annual carbon-dioxide (CO2) emissions. UP’s energy requirement is expected to reach 229 BU by FY 2029-30, with a peak demand of 40 GW. States like UP need to plan ahead for procuring RE-based power from other states for meeting their growing demand (including that from EVs), complying with RA guidelines, and utilising the opportunities under CCTS.

In this context, we executed a generation planning analysis of UP for FY 2029-30, using an open-source software GridPath. First, we estimated the number of EVs that will ply on the roads of UP by that year and calculated the additional electricity required for charging them. This EV-charging demand was then combined with the state's projected electricity demand. Finally, the quantum of RE and energy storage systems (ESS) required for meeting this overall electricity demand was identified, assuming two carbon-tax scenarios: (i) low carbon-tax scenario (USD 10 per ton of CO₂); and (ii) high carbon-tax scenario (USD 50 per ton of CO₂).

Our study finds that under a low carbon tax, UP would require 36.3 GW of RE and 21.9 GW of ESS capacity by FY 2029-30 to meet its demand. At a higher carbon tax, RE generation could meet 50% of the state's electricity needs, achieving a 10% reduction in CO2 emissions, as compared to the FY 2023-24 level. The above capacity addition proposed by the study aligns with the RA guidelines and could generate 7.8–10 million carbon credits, translating into a revenue of USD 78–105 million for the state of UP.

The study offers valuable insights, especially into dispatch strategies, thereby equipping states to develop robust generation-capacity-expansion plans. It also demonstrates the use of open-source modelling tools for planners. For instance, GridPath, which offers flexibility of inputs, can incorporate emerging policy instruments like RA and CCTS and thus strengthen a state’s strategy for optimising capacity additions to meet its growing electricity demand.