Power systems are planned such that they have adequate generation capacity to meet the demand according to a defined reliability target. The landscape increase in the penetration of Renewable Energy (wind and PV solar), a variable and uncertain resource, has led to a number of challenges in planning and operation of power systems. A key metric for generation adequacy is the ‘capacity value’ of generation. The RE cannot fully replace the capacity resources which are despatchable generators that are used to meet the peak demand. For example, Wind power is considered as an ‘Energy Resource’ but not a ‘Capacity Resource’. If it has some capacity value for reliability planning purposes, then it should be viewed as a bonus. This article brings out a preferred method for calculation of Capacity Value of Renewable Energy (Wind and PV Solar) and pertinent issues surrounding it...

In parallel with the radical changes that are happening in the structure of Electrical Industry, there is dramatic increase in the amount of Renewable Energy being integrated with the existing Electricity Grids. This development is driven by the desire to reduce harmful Green House Gas Emissions. Renewable Energy Sources, such as Wind and PV Solar Energy have characteristics of Variable Generation (VG) which are unique compared to those of Conventional Generation from Thermal and Hydro Plants.

Some of the characteristics of VG

VG increases the variability and uncertainty of the  “net load ” (load minus VG ) because its power availability  changes with time due to the changing source(e.g., wind speeds and solar irradiance) and also the available Power from VG sources cannot be perfectly predicted in all time horizons.

VG has near zero or negative variable costs because of the free source of its fuel and generation based subsidies. It means that VG is operated at its maximum available capacity limit during majority of times.

VG is primarily an energy resource that has limited capacity value relative to its rated capacity because the periods of high energy output may not correspond to times of peak demand  or risk of insufficient generation.

VG characteristics have impacted the Electricity Markets in many ways because Markets were initially designed without the notion of such largescale penetration of RE.

Impacts of VG

VG reduces the average Marginal Pricing at various Locations (LMPs) in the system affecting the revenue stream of other resources that depend on revenues from the Energy markets. It means, energy schedules from the other sources are reduced. If these sources are still required to be available (to meet long term reliability requirements) they will become capacity -based resources rather than energy -based. They may have to rely on forward capacity markets to earn revenues needed to remain in the market.

With the increased variability and uncertainty, VG increases the need for flexibility in the system. If there is no way to incentivise for this flexibility when needed, potential reliability issues will occur.

With increasing levels of VG, more operating reserve may be needed which will increase the demand and (therefore) the clearing price for these ancillary services.

When VG replaces synchronous frequency-responsive power plants (such as inertia response units) and when the VG is not equipped with technology to provide such frequency response, supplemental actions are needed to ensure sufficient frequency-response is made available.

The summary of these impacts leads to two Challenges to be further explored. These are are:

Resource Adequacy and Revenue Sufficiency

Do the markets provide sufficient revenues to cover all costs?  Insufficient revenues may lead to an unreliable system when those resources choose to leave the market.

Availability of Sufficient Level of Flexibility In The System

Improper utilization of existing flexibility can lead to degradation of reliability and efficiency. Insufficient Flexibility to meet the changing ‘net load’ results in energy imbalance. Proper Market designs are therefore necessary to ensure long term reliability to recover the costs to remain in the market and ensure sufficient flexibility is available in the system to balance the variability and uncertainty of VG.

Definitions of a few related metrics[1]

Reliability Metrics

  • LOLP – Loss of load probability. The probability of generation not meeting the demand.
  • LOLE – Loss of load expectancy. (LOLP multiplied by a unit of time.) Commonly used target- 1day failure in 10 years. (0.1day / year )
  • RESOURCE ADEQUACY- aggregate generator availability, which is in turn a function of total capacity that is not experiencing a forced outage.
  • EFOR – equivalent forced outage rate.

= (FOH+EFDH) / (SH+FOH) where

SH=service hours; FOH= full forced outage hours, EFDH= equivalent de-rated outage hours.

This rate describes unit’s failure rate

  • ELCC- Effective Load- carrying Capability. This metric describes the contribution that a given generator or group of generators makes towards resource adequacy. In general, thermal generation will have an ELCC value (in MW) close to its rated capacity. ELCC can be approximated by the unforced capacity of the unit, calculated as (1-EFORd) x Capacity; where EFORd – (Equivalent forced outage rate demand,) -the probability that the unit will fail partially or fully when needed.
  • ELCC of VG is significantly lower than its nameplate capacity.
  • CAPACITY CREDIT of any power plant is defined as a measure of the ability of the plant to contribute to the peak demand of the system. It is the ratio of

Firm power capability/ rated output

It is also defined as the statistical probability of the plant being available at the time of peak demand.  Wind energy in large networks can provide firm capacity – roughly equal to the capacity factor measured during peak demand periods.

  • VOLL- value of lost load. VOLL evaluates the potential cost of supply shortages where load is involuntarily curtailed. It is useful to determine scarcity pricing  in the energy market.
  • FLEXIBILITY- the ability of a resource whether any component or collection of components of the power system, to respond to the known and unknown changes of power system conditions at various operational time scales.
  • Forward Capacity Markets. These markets look ahead to ensure that enough capacity is available to meet load in peak periods.

Long term resource adequacy and revenue sufficiency

Consumers cannot choose the level of their individual electric reliability (currently there is no way to differentiate reliability among customers on the same feeder) and also are insulated from time – sensitive price swings happening at the Bulk system level. These are the two primary demand-side flaws that impact the way the electricity markets are designed. Reliability targets in power systems exist because of these demand flaws.

Thus, bridging the gap between reliability and markets became challenging. Resource adequacy is not based on true market outcome. Instead it is based on long term Reliability Norm defined by the Policy maker/Regulator. In the systems with significant levels of VG, additional questions need to be addressed.

How the VG characteristics influence reliability calculations and resource adequacy targets?

Does the resource have the right flexibility attributes to handle grid operation?

Resource adequacy calculations

Resource adequacy must be considered to ensure that the electricity supply is sufficient to serve the load that appears largely as inelastic demand. It is a probabilistic problem.

LOLP is a well-known probabilistic method used in power system reliability calculations. In modern interconnected systems more common reliability metric used is LOLE. If the calculated LOLE is higher than the target, alternative resources are added until the computed LOLE reaches the target LOLE. Traditionally LOLE is computed using a single data per day, chosen from the peak hour of the day (assuming peak load remains all day). To calculate the daily LOLE each Generator’s capacity and forced outage rate (FOR) are used in a mathematical convolution with forecast demand values. This approach explicitly considers the generator meeting the load on a statistically expected basis.

For example, a 100 MW thermal unit with FOR of 0.1 would have a higher statistically expected output than another 100MW unit with FOR of 0.2. Thus, the convolution of multiple units with differing capacities and FORs forms the basis for the reliability calculation and related metric.

Another reliability Metric also generally used in adequacy calculations is Planning Reserve Margin (PRM). PRM is defined as the percentage by which the installed capacity exceeds the annual peak demand. PRM metric is not very useful in systems with large penetration of VG.

Capacity value of wind power and PV solar

The hourly production data (time- synchronized data with load) replaces the use of conventional Generator capacity and FOR values in the reliability calculations of VG. ELCC method is built on the fundamental reliability metric LOLE /LOLH. This approach can be applied to conventional generation and also to VG. ELCC represents the additional load that can be served by the resource holding long term reliability constant. ELCC is also the basis for assigning the Capacity credits for making payments in the System with markets. ELCC of wind power plants typically ranges from 5% to 40%. One simplified method to calculate the capacity value (capacity credit) of wind power is to define a peak time period and calculate the capacity factor during that period. Capacity credit is also defined as the statistical probability of the plant being available at the time of peak demand. A wind plant of 1000MW installed capacity is expected to displace around 300 MW of thermal plant which corresponds to a capacity credit of 0.3. A thermal power plant has a capacity credit of 0.75. A nuclear plant has a capacity credit of 0.85.

Computation of ELCC value of wind power

Modelling wind power as a two state model as is done in the case of a conventional thermal unit is not appropriate as it is highly variable. As noted above, the relationship between wind power and the load is the key factor to be captured in the calculations.

As shown in the diagram (Illustration from NERC) curve[2] is the target reliability curve for a LOLE of 1d/10y.Curve (1) shows the relationship between the level of peak load that can be served and the LOLE. At the target Reliability level a 10 GW load can be served. As the curve shows, a lower load will have a higher Reliability level and higher load lower reliability. When a new generator (a VG unit) is added to this system the reliability curve shifts to right (curve 2) and the distance of this shift depends on a combination of system and generator attributes. The additional load which can be served while maintaining original level of reliability is 150 MW (horizontal distance of the two curves measured at the target level of reliability. Therefore, the new generator has a capacity value (ELCC or Capacity credit) of 150 MW. The mathematical formulations are:

Annual daily LOLE can be calculated as

P [ ] denotes the probability function (LOLP), N is the number of days in the year, Ci is the available capacity in the day and Li is daily forecast peak load.

Reliability after new capacity of VG is added to the system

gi is the rated power output from the VG added.

ELCC of the VG added is the additional system load that can be served at a specified level of risk LOLE.

Calculating the ELCC (Capacity Value) of the VG unit added shall be the value of ΔCi that satisfies above equation. Many simplified approaches are developed as shortcuts and can be found in the literature.[3]

Computation of capacity value of PV solar[4]

Many different approaches to calculate the capacity values have been used by utilities and system operators. ELCC and Peak Period Capacity Factor (PPCF) are the two approaches generally used to calculate the Capacity value of PV Solar power. ELCC method which is based on LOLE reliability is considered most accurate. However, it requires complicated calculations and large amount of generation and load data. As shown in the figure below the ELCC of a PV Solar source with installed capacity of 149 MW is calculated as 85.55 MW (57.41%). In the example studied, ELCC is tested for five study cases representing different levels of PV penetration. The diminishing pattern of Capacity values with increase in penetration can be noticed.[4]

Impact of increasing level of RE on flexibilisation

Besides variability and uncertainty VG increases the need for flexibility on the system. VG is not a synchronous resource, nor is it inherently responsive to grid frequency. However, VG is displacing resources that do all these. Primary frequency response and synchronous inertia are not currently part of ancillary services market, but they will be necessary in future as VG penetration increases. It is possible that VG can create controls to provide these services itself, but it all depends on incentives and whether revenues from providing this service will justify VG installing these capabilities.

Flexibility attributes can be grouped into two categories.

  • Operating range (difference between maximum and minimum stable output). Larger operating range suggests more flexibility.
  • Rate of change from one state to other state – including ramping up and ramping down, number of starts, shutdown time etc. A high rate change per minute denotes more flexible than low rate of change. Quick start units that can ramp up quickly are more flexible than slow-start units with low ramp rates.

The first step in obtaining flexibility from market participants is to have a mechanism that allows the market operator to commit the resource and despatch the resource’s output when it is needed. It is important to incentivise suppliers to offer their full flexibility into the market.


Many of the tools necessary to mitigate the challenges faced by the system operator while operating the system with large scale integration of renewables already exist, but the market structures need to be properly designed to incentivise the resources to offer their full capabilities, efficient use of the resources or to elicit sufficient investment. Variability and uncertainty are not new to the system operator. However, such large scale integration of renewables may provoke the system planners to further investigate into:

  • Improved methods to calculate resource adequacy requirements
  • Improved scheduling strategies that can meet the increased variability and uncertainty of VG and extract correct amount of flexibility
  • Improved methods to streamline the pricing aspects of Ancillary services as applicable to wholesale electricity markets.


[1] E. Ela, M. Milligan et al, “Evolution of Wholesale Electricity Market Design
with increasing levels of Renewable Generation” NREL Technical Report NREL/
TP-5D00-61765-September 2014
[2] Jaquelin Cochran et al, “Market Evolution: Wholesale Electricity Market
Design for 21stCentury Power Systems “ NREL Technical Report
NREL/TP-6A20-57477 October 2013
[3] Andrew Keane et al, “Capacity Value of Wind Power” IEEE Power and Energy
Society, May 2014
[4] S. Lu et al, “Capacity Value of PV and Wind Generation in the NV (NEVADA,
USA) Energy System” Pacific Northwest National Laboratory- PNNL-22117
[5] R. Billington, Allan, R.N. (1996). “Reliability Evaluations of Power Systems.
New York: Plenum Press

Dr. K. S. Gandhi possesses B.E. (Electrical) from Andhra University College of Engineering, M. Tech. (Electrical Power Systems) from JNTU, Hyderabad and Ph.D. from JNTU, Hyderabad. He has also undergone training programmes at CEGB, U.K. and ASCI, Hyderabad. With more than 50 years of experience gathered from reputed organisations like AP State Electricity Board, BHEL, NTPC, ESCI and others; an FIE and a Life Member of the Institution of Plant Engineers (IIPE), he is now a guest faculty for M.Tech (EPS) batches of a reputed engineering college at Hyderabad.

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