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Contract No. DE-AC36-08GO28308

Quantifying the Operational 

Benefits of Conventional and 

Advanced Pumped Storage 

Hydro on Reliability and 

Efficiency 

Preprint 

I. Krad and E. Ela

National Renewable Energy Laboratory

V. Koritarov

Argonne National Laboratory 

To be presented at the IEEE Power and Energy Society 

General Meeting 

National Harbor, Maryland

July 27–31, 2014

Conference Paper

NREL/CP-5D00-60806

July 2014 
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Quantifying the Operational Benefits of Conventional 

and Advanced Pumped Storage Hydro on Reliability 

and Efficiency

Ibrahim Krad

Erik Ela

National Renewable Energy Laboratory

Golden, CO – USA

ibrahim.krad@nrel.gov, erik.ela@nrel.gov

Vladimir Koritarov

Argonne National Laboratory

Lemont, IL – USA

Koritarov@anl.gov

Abstract—Pumped storage hydro (PSH) plants have significant 

potential in providing reliability and efficiency benefits in future 

electric power systems. New PSH technologies, like adjustablespeed PSH, have also been introduced and can present further 

benefits. An understanding of these benefits on systems with 

high penetrations of variable generation (VG) is a primary 

focus. This paper will demonstrate and quantify some of the 

reliability and efficiency benefits afforded by pumped storage 

hydro plants utilizing the Flexible Energy Scheduling Tool for 

Integrating Variable generation (FESTIV), an integrated power 

system operations tool which evaluates both reliability and 

production costs. A description about the FESTIV tool and how 

it simulates PSH operations at multiple timescales will be given. 

Impacts of PSH on area control error, production costs, and 

system operation are quantified on a high VG scenario in the 

Balancing Area of Northern California. We also perform a study 

on how advanced PSH can provide a fast form of regulation to 

improve reliability and potentially reduce costs. 

Index Terms—area control error (ACE), automatic generation 

control (AGC), electricity markets, pumped storage hydro 

(PSH), variable generation (VG)

I. INTRODUCTION

The benefits of energy storage systems are desirable and 

well documented. They can help reduce production costs by 

providing power during expensive peak periods, while 

purchasing the power and storing it during cheap off-peak 

periods. They can provide numerous types of active power 

control support including contingency reserve, primary 

frequency control, automatic generation control, and load 

following. It can also provide benefits for reducing capacity 

needs, congestion management, and voltage and reactive 

power support. The response time, synchronization time, and 

ability to provide energy as both a generator and a load give

energy storage unique qualities for both improving reliability 

and reducing production costs. Currently, the most common 

form of utility-scale energy storage is pumped storage hydro 

(PSH). 

PSH first began gaining popularity in the 1970s in 

response to a sharp rise in natural gas and oil prices. In the 

USA, the Power Plant and Industrial Fuel Use Act was 

enacted which would limit the amount of oil and natural gas 

that can be consumed via new power plants [1]. The 

construction of new PSH plants was justified by comparing 

the net cost of a PSH plant and an equivalently sized fossil 

fuel plant [1-3]. This method disregarded the operational 

benefits that PSH can provide and failed to provide a level 

comparison. The financial justification of PSH was based on 

the potential for energy arbitrage. As a result, the allure of 

PSH has slowly diminished over the years due to falling oil 

and gas prices and improved thermal generator operating 

characteristics. However, by incentivizing and recognizing the 

other benefits afforded by PSH, namely their ability to aid in 

system reliability, there is potential for PSH to again return to 

the forefront of emerging grid technologies.

There has been some research performed in an attempt to 

better model PSH plants. The authors of [4] developed a 

mixed integer linear programming (MIP) model of PSH that 

considers the operating characteristics of PSH such as ramp 

transition constraints and pumped-storage operating mode 

constraints. They also introduce a method to model the head 

effect through approximations to capture the relationship 

between power, volume of the water, and the flow of the 

water. The authors of [5] developed an aggregate hydro plant, 

mixed-integer model that considers minimum on and off 

times, unit availability constraints, start-up constraints, change 

of water flow limit constraints, water flow constraints, 

reservoir balance constraints, reservoir volume limits, and 

reservoir spill constraints.

Conventional PSH units typically utilized synchronous 

machines to generate electricity. As a result, the generators’ 

speed is fixed at the corresponding synchronous frequency. 

Adjustable-speed PSH can utilize a doubly-fed induction 

machine (DFIM) rather than the synchronous generator. As a 

result, the speed of the generator can be varied and a power 

This work was supported by the U.S. Department of Energy under Contract 

No. DE-AC36-08-GO28308 with the National Renewable Energy Laboratory. 

1

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electronics converter can be used to control the output power 

[6].

There has been considerable research performed in an 

attempt to demonstrate the value of PSH in facilitating the 

penetration of variable generation such as wind and solar. The 

authors of [7] indicate that new PSH plants could significantly 

improve grid reliability while reducing the need for new 

thermal generation in areas with high levels of wind and solar 

generators. The authors of [8] devise a co-optimized 

coordination of wind power and PSH. They develop a 

stochastic MIP-based solution method that minimizes 

expected operating costs and corrective action costs. Wind 

forecast uncertainties and component outages are treated as 

stochastic variables. The authors of [9] investigated the use of 

PSH in small, islanded systems with high wind penetration. 

Their study showed that PSH is particularly valuable in such 

scenarios due to their ability to provide primary frequency 

control. The authors of [10] develop an operating strategy of a 

hybrid wind-hydro system with the goal of ensuring wind 

generation output for 24 hours. The operational strategy is 

determined via a 24-hour stochastic, operational profit 

maximization optimization problem incorporating the 

operational constraints of the wind-hydro system. The authors 

conclude that by co-optimizing wind and PSH, operational 

profits can be increased anywhere from 12% to 22% 

depending on the deviation penalty level.

Today’s electricity markets may not be designed in a way 

that would allow market regions to obtain all the benefits that 

energy storage owners can provide. Potential ways that ISOs 

and RTOs could extract more of the benefits and avoid 

limitations are described in [11]. For example, some entities 

argued in the past that in some wholesale electricity markets, 

some market participants may have faced undue 

discrimination in the way that frequency regulation was 

procured. The Federal Energy Regulatory Commission 

(FERC) acknowledged that previous compensation methods 

for frequency regulation did not recognize the performance 

benefit of faster-ramping and more accurate resources. In 

order to correct this, FERC Order 755 requires all system and 

transmission operators to pay resources based on their actual 

performance, including a capacity payment that covers 

opportunity costs and a performance payment that reflects the 

generator’s ability to follow the control signal [12]. 

Traditionally, resources were sent a smoothed, low frequency 

control signal for frequency control, but now it may be 

beneficial to allow certain generators that have the ability to 

follow the unfiltered or high-frequency control signal. 

Power systems will become more susceptible to variability 

and uncertainty as the amount of VG installed increases. 

Variability can be seen as the expected changes in system 

variables while uncertainty is the unexpected changes in 

system variables [13]. Variability and uncertainty occurs at 

multiple timescales and it is important to understand these 

characteristics vary at different timescales. As more and more 

VG is installed, net load forecasting can become less accurate 

and system ramping events can become more prevalent. As a 

result, systems must adapt and become more flexible in order 

to maintain reliability at least cost. Due to their fast ramping 

and response time, PSH can be a useful tool in mitigating 

these problems.

The rest of this paper is organized as follows: section II 

introduces the model used, section III describes the test system 

and assumptions used, section IV describes the results of the 

simulations, and section V concludes the paper.

II. FESTIV

This study utilizes the Flexible Energy Scheduling Tool 

for Integration of Variable generation (FESTIV) developed by 

the National Renewable Energy Laboratory [14]. This model 

mimics the behavior of system operations at multiple 

timescales including the day-ahead security-constrained unit 

commitment (DASCUC), real-time security-constrained unit 

commitment (RTSCUC) and economic dispatch (RTSCED), 

and automatic generation control (AGC). 

The main feature of this model is its ability to simulate the 

system at multiple time scales by interconnecting the different 

forms of operational scheduling. This feature allows for the 

investigation of the variability and uncertainty of variable 

generation at fine time scales. It provides both cost and 

reliability information about the system. First, the day-ahead 

security constrained unit commitment is solved and the dayahead market is cleared. When the real time operations begin, 

the system periodically solves the real time security 

constrained unit commitment and the real time security 

constrained economic dispatch and clears the real time market 

accordingly. Upon completing the economic dispatch, the 

model employs a rule based automatic generation control 

algorithm that corrects the system area control error (ACE). 

The security constrained unit commitment problems are 

solved using mixed integer programming while the economic 

dispatch problem is formulated as a linear programming 

problem. The optimizations include typical operational 

constraints such as generator start-up times, minimum run 

times, minimum down times, generator ramp rates, minimum 

and maximum generation levels, as well as transmission line 

loading constraints. The model also schedules different types 

of reserves, and deploys them at the appropriate time 

including contingency spin reserves, regulation reserves, and 

flex reserves. The timing parameters of all the different 

models are configurable. The model is built in MATLAB and 

leverages the General Algebraic Modeling System (GAMS) 

using the CPLEX mixed integer optimization solver [15-16]. 

The outputs of each model are fed as inputs to the 

subsequent models while maintaining a consistent time 

reference. The end result is a harmonized, operational 

simulation of the power system across multiple temporal 

resolutions that accounts for both system costs as well as 

system reliability. The model produces the total system 

production costs as well as several reliability performance 

metrics including CPS2 violations, the standard deviation of 

ACE as well as the Absolute ACE in Energy (AACEE) which 

is the accumulated area control error over time provided in 

megawatt-hours. A more detailed discussion of the model can 

be found in [17].

In this study, a number of enhancements were made to the 

FESTIV model to incorporate conventional and advanced 

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PSH at multiple operational timescales. These enhancements 

included variable efficiency levels, energy limits, pumping 

limitations in the security constrained unit commitment 

(SCUC) model, the security constrained economic dispatch 

(SCED) model, and the AGC model, ancillary service 

provision while pumping, and efficient exchange of energy 

levels between day-ahead and real-time. These enhancements 

can be seen in more detail in [18].

III. SIMULATION

Utilizing the National Renewable Energy Laboratory’s 

Western Wind and Solar Integration Study data set, a test 

system based on the Balancing Area of Northern California 

(BANC) was developed. This system was deemed large 

enough to produce meaningful results and included significant 

variable generation penetration so as to adequately capture the 

benefits PSH can provide. The test system was then simulated 

for two individual weeks. One week was chosen due to its 

being the system peak period in July. The second week was 

chosen as a high variable generation output period in April. A 

brief system overview is provided in Table I. 

TABLE I – OVERVIEW OF SYSTEM DETAILS

Peak Load 4207 MW

Total Installed Capacity 6975 MW

Total Installed Wind Capacity 1738 MW

Total Installed Solar Capacity 325 MW

Total Conventional Hydro Capacity 2509 MW

Total Combined Cycle Capacity 1482 MW

Total Combustion Turbine Capacity 372 MW

The day-ahead unit commitment problem was solved 

every 24 hours for the next 24 hours with hourly time steps. 

The real time unit commitment was solved every 15 minutes 

for the next three hours with 15 minute time steps. The realtime economic dispatch was solved every five minutes for the 

next 60 minutes with five minute time steps. The automatic 

generation control was solved every four second interval. 

In order to simulate the advanced, adjustable-speed 

pumped storage hydro plants, the operating characteristics 

shown in table II were used. The main difference of 

adjustable-speed and conventional PSH is that the minimum 

pumping output of conventional PSH is equal to its maximum 

pumping output of 133 MW. As a result, the adjustable-speed 

PSH is able to regulate its active power output while pumping 

while the conventional PSH cannot.

Table II – SUMMARY OF PSH OPERATING CHARACTERISTICS

Maximum Pumping Output [MW] 133

Maximum Generating Output [MW] 133

Minimum Pumping Output [MW] 79.8

Minimum Generating Output [MW] 39.9

Minimum Pumping Time [hours] 0

Minimum Generating Time [hours] 0

Time to Start Pumping [minutes] 15

Time to Start Generating [minutes] 15

Pumping Ramp Rate [MW/s] 7

Generating Ramp Rate [MW/s] 7

Pumping Efficiency 0.80472

Total Maximum Storage Capacity [MWh] 5000

The reserve schedules were determined based on the 

methodology employed in phase 2 of the Western Wind and 

Solar Integration Study. The requirements take into account 

the needs that wind and solar forecast uncertainty have on

reserve requirements. Wind generators are assumed to have

short term persistence forecasts and solar generators are 

assumed to use a cloudy, persistence forecast (i.e., assumes the 

current cloudiness will remain but the daily ramp up and down 

are factored in). These requirements are then added to the base 

requirements to obtain the total system reserve requirements. 

The types of reserves considered are spinning, non-spinning, 

regulation up, regulation down, and flexibility reserves. An in 

depth discussion on these reserves can be found in [13]. 

The spin and non-spin reserves were taken as 3% of the 

system load. The regulation reserves were taken as the 

geometric sum of 1% of load and the additional requirements 

due to the additional wind and solar generators. The flexibility 

reserves were taken as the geometric sum of the solar and 

wind hour-ahead forecast errors covering 70% of the 

distribution. More details on the methodology used to 

determine these reserves can be found in [19]. The flexibility 

reserves were held in the unit commitment problems and 

dispatched in the economic dispatch problem. This is because 

the flexibility reserves were viewed as products deployed 

across dispatch intervals to assist the system operator. 

Three different scenarios were simulated. Scenario one is 

the base case scenario that does not include any pumped 

storage hydro plants. Scenario two includes a conventional, 

single-speed pumped storage hydro plant consisting of three 

units. Scenario three includes an advanced, adjustable-speed 

pumped storage hydro plant consisting of three units. 

IV. RESULTS

The costs of each scenario are shown in figure III. In order 

to ensure an even comparison, any inadvertent interchange or 

deviation from the scheduled final storage level is either 

purchased or sold. As a result, fair comparisons between the 

scenarios can be made. Notice that by adding PSH (scenarios 

2 and 3) the total production cost is reduced.

TABLE III – COST RESULTS

April July

Scenario 1 $3.449M $5.394M

Scenario 2 $3.169M $5.101M

Scenario 3 $3.032M $5.021M

A summary of the reliability results is shown table IV.

TABLE IV – SUMMARY OF RELIABILITY RESULTS

April

Scenario 1 Scenario 2 Scenario 3

CPS2 Violations 49 47 45

CPS2 Score 0.951 0.953 0.955

AACEE [MWh] 2582.78 2619.72 2644.19

sigma ACE [MW] 23.80 25.10 23.00

July

Scenario 1 Scenario 2 Scenario 3

CPS2 Violations 40 16 15

CPS2 Score 0.960 0.984 0.985

AACEE [MWh] 3201.00 2736.00 2593.00

sigma ACE [MW] 29.30 21.30 20.20

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As is evident from tables III and IV, there are benefits to 

total production cost and reliability when either a conventional 

or adjustable-speed pumped storage hydro plant is added to 

the system. In April, total production costs were reduced by 

8% and 12% by adding single-speed PSH and adjustablespeed PSH respectively. In July, the total production costs 

were reduced by approximately 5% and 7% by adding singlespeed PSH and adjustable-speed PSH respectively. 

In April, while the number of CPS2 violations decreased, 

the AACEE and the standard deviation of the ACE remained 

relatively unchanged. This could be due to the fact that the 

introduction of the PSH units could allow for more inflexible 

units that are cheaper to be committed. In July, there were 

substantial improvements in all reliability metrics. Since the 

system load is much higher, other flexible units were likely 

still needed. These reliability improvements may also be the 

result of the PSH being asked to regulate approximately 22% 

of the time while in April, they were only asked to regulate

approximately 13% of the time. This could be due to the fact 

there are significant amounts of installed conventional hydro 

units in the system that are quite flexible themselves. So the 

PSH benefit is not as noticeable during times of low loading.

Fig. 1 shows a portion of the realized generation schedule 

of an advanced, adjustable-speed PSH unit. Notice that the 

PSH is able to regulate while in pumping mode. This is 

important since the single-speed PSH cannot regulate while 

pumping (Fig 2a, Fig 2c).

50 55 60 65 70 -150

-100

-50

0

50

100

150

Hour

MW

Unit 1

Unit 2

Unit 3

Figure 1 – Realized schedule for 3 adjustable-speed PSH units 

Table V shows that amount of energy consumed and 

produced by the PSH plant for both the single-speed plant 

(scenario 2) and the adjustable-speed plant (scenario 3).

Table V – AMOUNT OF ENERGY CONSUMED AND PRODUCED BY PSH IN MWH

April July 

Gen Pump % Gen Gen Pump % Gen

Scenario 2 11,951 15,328 78.0% 17,543 21,823 80.4% 

Scenario 3 11,736 16,029 73.2% 16,606 20,897 79.5% 

If the final reservoir level exactly matched the initial 

reservoir level, the ratio of energy generating to pumping

would exactly match the round-trip efficiency of the PSH, in 

this case 80.4%. Only the July simulation of scenario 2 

reflected this. The PSH generated more in July than in April. 

This is most likely due to the increased load in July resulting 

with a higher energy cost. In general, the adjustable-speed 

PSH plant would pump more than it generated (i.e. the percent 

that it pumped as a percentage of its generation is less than its 

round trip efficiency). This is interesting because intuition 

would suggest that since the adjustable-speed PSH can change 

its pumping production, it would not need to pump more than 

it generates. However, since the adjustable-speed PSH can 

regulate in pumping mode, the optimization utilizes their 

pumping ability more often. This is evident in the fact that the 

adjustable-speed PSH were selected to regulate more, 48% 

more in April and 36% more in July.

Figure 2 – Realized output of three PSH generators in (a) April scenario 2 (b) 

April scenario 3 (c) July scenario 2 and (d) July scenario 3 

The use of PSH was also able to curb the use of expensive 

thermal units. For the week in April, the conventional, singlespeed PSH plants were able to reduce the energy production of 

the thermal units by approximately 14%. The advanced, 

adjustable-speed PSH were able to reduce it by approximately 

15%. In July, both the single-speed PSH and the adjustablespeed PSH reduced the energy production of thermal units by 

approximately 4%.

Similar results can be seen in a system without any 

variable generation (VG) as summarized in Table VI.

Table VI – APRIL SIMULATION RESULTS OF A SYSTEM WITHOUT VG

Scenario 1 Scenario 2 Scenario 3

Total Cost $6.226M $5.582M $5.616M

CPS2 Violations 3 1 0

CPS2 Score 99.7 0.999 1

AACEE [MWh] 2112 2268 2176

Sigma ACE [MW] 16.7 17.5 16.8

It is evident from Table VI that even in a system without 

any VG, PSH plants can still provide benefits to the system. In 

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this case, the adjustable-speed PSH was able to reduce the 

total system production costs by approximately 10%. Both 

scenarios successfully reduced the number of CPS2 violations 

while the absolute ACE and the standard deviation of the ACE 

where slightly increased. Since there isn’t any VG, the only 

variability and uncertainty comes from the load. 

Table VII shows the reliability improvements affordable 

by adjustable-speed PSH tracking the raw ACE signal.

Table VII – SUMMARY OF SCENARIO 4 BENEFITS

April July

Scenario 3 Scenario 4 Scenario 3 Scenario 4

Cost $3.032M $2.941M $5.021M $4.924M

CPS2 Violations 45 44 15 14

AACEE [MWh] 2644.19 1992.00 2593.00 1233.00

sigma ACE [MW] 23.00 20.00 20.20 12.17

In terms of CPS2 violations, there is not any significant 

improvement by allowing the PSH to follow the raw ACE 

signal. However, in terms of AACEE and σACE, there is a 

significant improvement. In April, the AACEE is reduced by 

approximately 25%, and by 48% in July. This means that in 

general, the magnitude of the ACE is reduced significantly 

throughout the study period. The standard deviation of the 

ACE is also reduced considerably meaning that the ACE 

becomes less volatile if the PSH plants are allowed to track the 

unfiltered ACE signal. These operational improvements could 

yield financial benefits to PSH owners through market rules 

that have been implemented to meet FERC Order 755. 

Allowing the PSH plants to track the unfiltered ACE 

signal resulted in further reducing the total production costs. 

Note that these costs only reflect the production costs and do 

not include wear-and-tear costs or other cycling costs. By 

allowing the PSH to track the unfiltered ACE signal, it 

reduces the ACE to zero much more quickly than in scenario 

3. As a result, other thermal units are not regulating as much

and can be operated closer to their optimal loading point. As a 

result, the overall system operates more efficiently and the 

total production cost is reduced.

V. CONCLUSION

This paper explored these benefits of PSH and quantified 

the total production cost savings and reliability impacts on 

CPS2 violations, AACEE, and the standard deviation of the 

ACE. PSH plants can provide both production cost savings 

and reliability improvements for systems with significant VG. 

They provide much more than just energy arbitrage, including 

numerous ancillary services and, especially adjustable-speed 

PSH, system reliablitiy improvements. PSH was able to 

reduce total production costs in all scenarios when compared 

to the system without PSH. The adjustable-speed PSH was 

able to improve reliability, especially during high load 

periods. Both types of PSH were able to reduce the number of 

CPS2 violations. In a system without VG, the PSH were able 

to provide more production cost savings rather than reliablity 

improvements. Adjustable-speed PSH was able to 

signficantly improve reliablity metrics by following the 

unfiltered ACE control signal. Further research should be 

pursued to better model conventional and advanced PSH, and 

how best to extract and quantify their potential benefits. 

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5

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