Capacity credits or the reliable contribution of a power source to meet peak demand, typically expressed as a percentage of its nameplate capacity, is being studied by policy planners. Recently, the Central Electricity Authority (CEA) published a draft discussion paper on the methodology for calculating capacity credit for renewable energy sources, including solar and wind. In the paper, the CEA highlights the importance of capacity credits for energy planning and grid stability while also suggesting using statistical methods, such as the top 10 per cent methodology, to analyse generation profiles during the top 10 per cent of demand hours.
Background
To recall, the CEA had issued the Electricity Amendment Rules in 2022 and the Resource Adequacy Guidelines in 2023. As per the CEA’s long-term national resource adequacy plan (LT-NRAP), planning reserve margin (PRM) benchmarks, capacity credits and peak demand contributions must be set for states, while distribution licensees must create 10-year distribution licensee resource adequacy plans to align with these guidelines.
Coincident peak refers to the contribution of various distribution utilities to the national peak, which varies by month and time of day. Rather than a single peak, the CEA states that the top 5-10 per cent of national demand hours are used to determine the coincident peak, which may not align with each utility’s individual peak demand. Under the Resource Adequacy Guidelines, each distribution licensee must plan to secure contracted capacities equal to their peak contribution plus the national PRM as outlined in the LT-NRAP. For resource adequacy requirements (RAR), they must demonstrate to the state electricity regulation commission (SERC)/joint electricity regulatory commission (JERC) a 100 per cent capacity tie-up for the first year and at least 90 per cent for the second year, using long-, medium- and short-term contracts. Additionally, through their LT-DRAP, distribution licensees must show their strategy to meet both peak demand and energy needs.
To measure coincident peak requirements, utilities can choose between the single peak method (which captures only the highest demand point) and the top 5 per cent peak method (which examines the top 5 per cent of demand hours for a more comprehensive analysis). The single peak method is beneficial for systems with minimal peak variations, and when time or computational resources are limited, offering a straightforward approach to peak assessment. However, it may not fully account for demand fluctuations and could underestimate capacity needs, especially in systems with solar and non-solar peaks.
The top 5 per cent peak method, preferred in systems with variable demand and high renewable energy integration, provides a more accurate picture by capturing multiple peaks across different times, enhancing reliability in resource adequacy planning. This method is particularly valuable in grids with significant renewable penetration, where the variability in solar and wind generation creates multiple peak times. While computationally more intensive, the top 5 per cent peak allows for a more resilient and adaptable system design that aligns with regulatory goals for precise demand forecasting.
Solar versus non-solar hours
In its paper, the CEA noted that the growing addition of solar capacity and shifting of agricultural loads to daytime hours have intensified the need to distinguish between solar (7 a.m. to 6 p.m.) and non-solar demand hours. The traditional top 5 per cent demand hour method does not fully address the variable nature of solar and wind power, as it focuses on peak hours without accounting for renewable energy’s time-dependent fluctuations. As solar demand increases, understanding the interplay between solar and non-solar peaks becomes crucial for resource adequacy planning.
For instance, during solar peak hours, solar power can contribute around 40 GW or 16 per cent of total demand; however, during non-solar peak hours, this contribution drops to zero, highlighting potential challenges for states reliant on solar power. Additionally, seasonal hydropower availability and intermittent wind generation emphasise the need for accurate resource adequacy planning across solar and non-solar hours to ensure reliable capacity. If distribution utilities were to secure capacity equal to the maximum coincident peak plus PRM, this could lead to overcapacity and underutilisation. Alternatively, basing requirements on average values could result in insufficient capacity during peak demand.
Capacity credits rationale and methodology for estimating them
While conventional sources have straightforward capacity credit calculations based on availability, renewable energy sources (such as solar and wind) pose challenges due to their intermittency and variability. Factors such as location, the DC/AC ratio for solar, ambient conditions, year-on-year generation variations and seasonal fluctuations complicate the determination of capacity credit for variable renewable energy (VRE) sources.
The capacity credits for each resource type can vary by region and resource age. As renewable energy capacity increases, determining accurate capacity credits for VRE becomes crucial to ensure a reliable power supply during disturbances or demand fluctuations.
The PRM is a critical metric in energy system planning, ensuring that sufficient generating capacity is available to meet peak demand and maintain system reliability. It represents the surplus capacity above peak demand levels, accounting for factors such as unexpected generation outages and demand fluctuations. PRM is determined based on reliability criteria such as loss of load probability and energy not served to ensure a high level of system reliability. The RAR ensures that distribution licensees meet the PRM, as determined by the CEA or through their own studies approved by the SERC/JERC.
Conventional power sources such as coal, gas and nuclear are reliable and dispatchable, with their capacity credit calculated by multiplying the installed capacity by the factor (1 minus the auxiliary power) and then multiplying the result by the availability factor. However, their availability can be affected by factors such as fuel supply, maintenance and forced outages.
For seasonal sources such as hydro, biomass and geothermal, capacity credit is based on past generation data, with hydro plants categorised into run-of-the-river and those with bondage–the latter providing higher output during peak hours due to stored water. Energy storage systems like pumped storage plants and battery energy storage systems can provide near-full capacity during peak demand but may face limitations due to storage constraints over prolonged periods.
The capacity credit of VRE sources such as solar and wind is influenced by factors such as location, temperature and weather, making it difficult to accurately assess due to their intermittent nature. In India, wind generation is highly variable and hard to forecast, whereas solar generation follows a more consistent pattern. System planners typically estimate VRE capacity credit by considering the top 100 hours or the top 10 per cent of demand hours.
However, the top 10 per cent methodology faces a significant limitation as it is time-dependent and does not adequately address the impact of solar and non-solar periods. To refine the estimation, a more comprehensive approach is required, which includes analysing the hourly or sub-hourly generation profiles of solar, wind and hydro sources over the past two to three years. Additionally, the hourly or sub-hourly national demand profiles for the same period should be considered. By filtering the top 10 per cent of demand during solar and non-solar hours, the median generation per MW of installed capacity for each source is then calculated for these periods to determine the capacity credit. This more granular analysis reflects the time-dependent nature of VRE sources and addresses the unique contributions of solar and wind power during different periods of the day.
While solar generation is relatively stable during the day, ranging from 20 GW to50 GW, it does not contribute significantly during non-solar hours. In contrast, wind power generation shows a weak negative correlation with national demand, meaning it typically provides higher availability during non-solar hours when solar generation is absent. However, due to its intermittency, wind power is less reliable during peak national demand periods, especially when demand is high and solar generation is low. This dynamic highlights the need to carefully consider the variability of wind and solar power when assessing their capacity credits and their role in maintaining grid reliability during peak demand times.
Wind power has significant potential to complement solar energy in meeting India’s energy needs, especially in states with favourable wind conditions. For example, in states such as Uttar Pradesh, where coincident peak demand is higher during non-solar hours, wind energy can provide a valuable contribution. The higher capacity credit of wind during these non-solar periods offers a promising solution to balance demand when solar generation is unavailable, enhancing grid reliability and supporting energy security.
Flexible demand response entities and hybrid resources are increasingly vital for India’s grid flexibility, balancing peak loads and supporting renewable integration, and are often preferred over standalone solar and wind. Estimating their capacity credits is complex due to diverse technology combinations and geographic locations, but advancements in forecasting and performance criteria in recent tenders help address these challenges. As VRE integration grows, its capacity credit may decrease due to demand, weather and environmental variations. The key strategies to support VRE reliability include pairing with storage, demand shifting, making thermal plant operations flexible and establishing capacity markets.
Conclusion
Capacity credits will be crucial in resource adequacy planning, evaluating each power source’s reliability during peak demand to ensure grid stability. By accurately assessing capacity credit, policymakers can effectively integrate traditional and renewable sources, maintaining a reliable, sustainable power system and offering them a realistic, resilient framework as VRE integration expands.
Aastha Sharma