Modeling Demand-Responsive Pricing for India’s Power Sector: A Scenario-Based Analysis Using the Switch Electricity Planning Model

Demand response (DR) is increasingly positioned in India as a low-cost flexibility option to manage peak demand, support the integration of variable renewables, and enable time-of-day (ToD) pricing. Yet policy relevance depends on how DR is represented in planning models and whether it can materially improve reliability and costs under realistic constraints. This report evaluates three DR formulations within the switch planning model optimization framework using a stylized India 2030 test system. The model is implemented as a single load zone (NORTH) with hourly operations represented by a 24-hour profile scaled to a typical year under a fixed, predetermined supply portfolio (coal with CCS, gas with CCS, hydro, solar, wind, nuclear, biomass, batteries, and pumped hydro). Reliability is represented through an unserved-energy slack variable penalized for lost load, used here as a reliability-enforcing modeling parameter. CO₂ is priced at $15/tCO₂. We compare (1) fixed or inelastic demand, (2) bounded energy-neutral load shifting with a flexible-load module (5% daily and 3% annual shiftable shares), and (3) endogenous price-responsive demand using a quadratic-utility function with an explicit DR program cost, allowing economically optimal curtailment in scarcity hours. Under the fixed-demand baseline, the system is structurally supply-constrained, yields 99.37 TWh/yr of energy not served (ENS), non-served energy share (NENS) of 13.67%, and a peak shortfall of 35.99 GW. Flexible loads improve reliability only partially (ENS 74.08 TWh/yr; NENS 10.19%; peak shortfall 30.65 GW), indicating that limited energy-neutral shifting cannot resolve multi-hour adequacy deficits. Price-responsive elastic demand produces the largest improvement (ENS 20.34 TWh/yr; NENS 2.80%; peak shortfall 9.14 GW) by allowing economically optimal demand reduction in scarcity hours. However, its objective is welfare-based and not directly comparable to cost-only cases. Overall, the results suggest that DR benefits are highly sensitive to modeling choices and are constrained when adequacy is dominated by firm capacity limits. For policy, ToD pricing and DR programs should be assessed alongside complementary adequacy measures, and reporting should include reliability, costs, emissions, and explicit storage behavior metrics to avoid overstating flexibility impacts under scarcity-driven outcomes. These scenario results should be interpreted as estimates of technical DR potential under stylized response assumptions rather than forecasts of achievable real-world uptake. In practice, participation barriers, behavioral frictions, metering limitations, and institutional constraints would likely reduce the magnitude of observed impacts.