Supporting materials

Downloads

Download the full PDF

Full Report (PDF)

Methodology

Using the certainty equivalent method to calculate risk premium

The certainty equivalent method quantifies the risk premium by comparing projected cash flows under different risk scenarios, such as average (P50) and conservative (P90) estimates, to determine the additional compensation required for uncertainty in outcomes.

To illustrate this methodology, we calculate the risk premium for a potential shortfall in the capacity utilization factor (CUF) due to weather-related variability, such as cloudy conditions or reduced sunshine. Historical data provides a benchmark for CUF variability; for example, a solar project in Rajasthan might experience CUF values ranging between 18% and 22%.

Steps to Calculate Risk Premium:

Identify the scenarios:

Average scenario (e.g., P50): In this scenario, the expected cash flow is based on the average CUF observed under expected/average regional conditions. For instance, if the average CUF in Rajasthan is 20%, this value is used to estimate the expected revenue of the solar project in the average scenario.

Conservative scenario (e.g., P90): In this scenario, the expected cash flow is adjusted downward to reflect more risky conditions, applying a “haircut” to account for uncertainty. This represents the certainty equivalent cash flow, or the guaranteed level of income an investor would expect in a worst-case scenario for CUF. For instance, if the CUF in Rajasthan under a P90 scenario is 18%, this value is used to estimate the expected revenue of the solar project under conservative assumptions, ensuring a more risk-averse financial projection.

The distribution of Capacity Utilization Factor (CUF) can be modelled as a normal distribution, where the most likely values cluster around the mean (20%), while extreme cases (18% or 22% CUF) fall toward the distribution tails. Identifying the P90 estimate involves quantifying tail risks in financial modelling, helping to assess the likelihood of worst-case scenarios, where CUF drops to 18% or lower.

A holistic risk assessment must account for all potential risks, identified through literature reviews, stakeholder surveys, or expert consultations. Developing such risk scenarios relies on historical data to understand past trends and events. However, when data availability is limited, computational techniques such as Monte Carlo simulations can be employed to construct probabilistic risk scenarios, offering a structured approach to quantify uncertainties.

Determine risk premium using certainty equivalent:

The difference in percentage terms between the two levels of cash flows—average (P50) and conservative (P90) scenarios—discounted to their present value, represents the risk premium for a specific phenomenon. In this case, it quantifies the additional compensation an investor might require if they perceive CUF shortfall as a significant risk, such as due to an anticipated increase in cloudy weather or fog.

Adding this risk premium to the base case (average scenario) results in the risk-adjusted discount rate, which reflects the higher cost of financing associated with greater uncertainty. This adjustment ensures that investors account for potential revenue volatility when making financing decisions.

Risk premia are typically expressed in basis points (bps)—a standard unit of measurement in financial markets used to indicate percentage changes in financial instruments. One basis point equals 1/100th of 1%, or 0.01%

While the methodology for assessing risk premiums appears objective and data-driven, it is inherently influenced by prevailing market conditions, which are often dynamic, as well as investor perceptions based on comparable projects. For instance, an investor with greater confidence in project performance might use P75 values for the return spectrum rather than P90. However, to maintain methodological consistency, this analysis employs P90 as the conservative scenario, which may lead to some risk premiums appearing higher than common industry estimates.

The certainty equivalent method can also be employed to compute the weighted average cost of capital (WACC) by accounting for all potential risks starting from a base rate or risk-free rate, a process known as the cost build-up method. This method can also be used to attribute various risks as a proportion of the WACC to understand the proportional magnitude of different types of risks and the uncertainty that they bring with them. A more detailed explanation of the certainty equivalent approach can be found here.

Calculating risk premium due to curtailment

Generators face revenue uncertainty due to curtailment, which occurs when grid constraints or reduced off-take limit energy dispatch. However, due to limited historical data, the distribution of possible curtailment is estimated using a Monte Carlo simulation, allowing for a structured assessment of curtailment risk.

In accordance with SECI tender specifications, generators are compensated for curtailments exceeding 175 hours of reduced off-take and 175 hours of grid unavailability. However, curtailment below this threshold remains an uncompensated risk, potentially impacting project revenues. To model this impact, we assume a probabilistic distribution that accounts for the hours of uncompensated curtailment, enabling a data-driven estimation of risk exposure.

Step-by-Step Process:

Simulation Parameters: The simulation is conducted with 1,000 runs to account for variability in curtailment. For each run, a normal distribution is assumed to model curtailment events for both grid unavailability and reduced off-take hours.
Modelling Grid Unavailability: Hours of grid unavailability are randomly selected from a normal distribution with a range of 0 to 175 hours. This distribution is centred at 0, with a standard deviation designed to approximate the range up to 175 hours, capturing the likelihood of different curtailment periods.
Modelling Reduced Off-Take: Hours of reduced off-take are similarly selected using a normal distribution between 0 and 175 hours. Additionally, the extent of generation reduction during these periods follows a normal distribution, with reductions ranging from 10% to 100% of maximum possible generation.
Curtailment Calculation: The simulation sums the curtailed generation for each run and calculates the curtailment percentage as the curtailed generation divided by the total possible generation in a year. This percentage reflects the extent of reduction in energy output due to curtailment.
Statistical Analysis: The results of the 1,000 Monte Carlo runs are used to derive the P90 and P50 values, representing the 90th and 50th percentiles of curtailment percentages, respectively.

Calculating the risk due to DSM penalties

Deviation Settlement Mechanism (DSM) penalties could pose a significant financial risk to solar generators, affecting revenue stability and investment confidence. This analysis quantifies the impact of DSM penalties by examining historical generation data and simulating penalties under existing and future DSM regulations.

Data collection & preprocessing

Historical 15-minute generation data from solar plants in northern and western regions was analysed, including actual generation values, scheduled generation, and deviation records over multiple years.

Penalty Calculation

Annual DSM penalties were computed as a percentage of total revenue under:

Case 1 (Pre-2024 DSM Rules): Baseline penalties with P50 (expected) and P90 (conservative) levels, capturing uncertainty in penalties even without regulatory changes (Risk 1).
Case 2 (Future DSM Rules): Stricter DSM frameworks (2024, 2026, and beyond) applied to the same deviation data to estimate penalties under progressively tighter DSM regulations (Risk 2).

The cumulative effect of potential revenue losses due to Risk 1 and Risk 2 is assessed.

Calculating the risk due to FDRE projects

Ember’s FDRE modelling focuses on optimising FDRE capacity from a developer’s perspective to meet tender specifications. The model ensures that the FDRE configuration meets demand profile while incorporating penalties into the optimisation process. This means that the final FDRE capacity is structured to optimise cost and revenue, and may optimise to pay some amount of penalty i.e. is designed to meet a lower demand fulfilment ratio.

Beyond meeting demand, the model accounts for oversizing, where excess generation beyond the contracted capacity is sold in the wholesale market. To estimate expected revenue from these surplus sales, the model uses historical Day-Ahead Market (DAM) electricity prices, mapping the distribution of revenue realisation over different years. By analysing the volume and timing of excess energy sales, the model helps in understanding how much developers can earn from the open market.

Additionally, the modelling assesses the risk of price cannibalisation, particularly due to increased solar penetration in the Indian Energy Exchange (IEX) DAM. Through styled scenarios, the analysis explores how higher levels of solar generation could depress market prices, potentially reducing the revenue from excess energy sales.

Finally, FDRE capacity may be designed for a lower demand fulfilment ratio (DFR) and may be designed with an expected level of penalties, However, uncertainties related to solar and wind generation variability and performance issues in battery storage could lead to higher-than-expected penalties. To assess this risk, we simulate this for multiple hourly annual profiles for solar and wind generation and incorporate assumptions on battery cost deviations. This allows us to estimate the distribution of actual penalties under different scenarios. For instance, if the expected DFR was 75%, these uncertainties could lower it to 69% at the P90 level, resulting in higher penalty costs than initially projected.

A note on power contracting process in India

The renewable energy procurement process in India is a complex interplay involving multiple stakeholders, including the Solar Energy Corporation of India (SECI), state Discoms (distribution companies), renewable energy developers, and state electricity regulators. The step-by-step process is explained below:

SECI collects renewable energy demand from state Discoms, aligning these requirements with their Renewable Purchase Obligation (RPO) targets. Based on this aggregated demand, SECI issues tenders for renewable energy projects, inviting bids from developers. Developers submit their bids, and the winning bidders are selected based on competitive criteria, such as the lowest tariff offered.
Following the tendering process, SECI negotiates Power Sale Agreements (PSAs) with state Discoms to secure the sale of electricity generated by the projects. These agreements outline key terms, including pricing, quantities, and the duration of power purchase. PSAs must be approved by state electricity regulators to ensure compliance with state policies and safeguard consumer interests.
Once PSAs are approved, SECI signs Power Purchase Agreements (PPAs) with the winning renewable energy developers. These agreements formalize the procurement terms and offer financial security, enabling developers to secure financing and begin project execution. However, delays in PSA approvals can directly postpone PPA signing, disrupting project timelines and increasing risks for developers.

Acknowledgements

Contributors

Special thanks to Abhishek Jain (VP, Investment Cell, O2 Power) , Satyadeep Jain (Director – Equity Research, Ambit Private Limited) and Nihit Kumar (DGM, Investment Cell, O2 Power) for their support in reviewing this article and providing guidance. Thanks to Jivan Zhen Thiru and Reynaldo Dizon for their contributions to the report’s illustrations. Thanks to Shiyao Zhang, Sachin Sreejith and Ardhi Arsala Rahmani for their communications support.