Hydropower as Flexibility Provider - HEVs · 1. Understand the behavior of the hydro- wind...

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Andrew Hamann, Prof. Gabriela HugPower Systems Laboratory, ETH ZürichFebruary 8, 2017Future Electric Power Systems and the Energy TransitionChampéry, Switzerland

02.08.2017Prof. Gabriela Hug 1

Hydropower as Flexibility Provider:Modeling Approaches and Numerical Analysis

|| 02.08.2017Prof. Gabriela Hug 2

US Pacific Northwest

www. transmission.bpa.gov

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Data based modeling of hydro power system Quadratic optimization problem formulation Case study using Mid-Columbia River data

Questions: How much efficiency can be gained using an MPC

based optimization scheme? If a flexible run-of-river hydropower system was a

battery, what kind of battery would it be?


Contributions

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Located on the Columbia River in Washington, USA Seven dams with approximately 13 to 14 GW of capacity Average flow is several thousand m3/s Travel times are tens of minutes (strongly coupled) Surface areas are tens of km2

17 entities with a stake in at least one of the dams Operating under a coordination agreement signed in 1997


Mid-Columbia hydropower system


Map of the Columbia River Basin


Map of the Mid-Columbia

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Hydro is dominant in Washington, Oregon, and Idaho Significant exports to California, but balancing must

happen on a regional basis Bonneville Power Administration (BPA) already uses its

hydropower plants to balance hourly variability (for a fee)


Pacific Northwest power system


Wind farms in the Pacific Northwest


Wind farms in the Pacific Northwest

Columbia River Gorge

Mid-Columbia

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MPC controller to minimize discharged water Weights calculated according to the hydraulic head of each plant

Constraints Turbine discharge and turbine ramping Spill and spill ramping Reservoir and tailrace elevation Time-delayed hydraulic coupling Power balance (system load)

Generation is modeled using a piecewise planar function 5-minute optimization interval and 3-hour receding horizon

“Real-time optimization of the Mid-Columbia hydropower system”, IEEE Trans. Power Syst., vol. 32, no. 1, pp. 157-165, Jan. 2017


Real-time hydropower optimization


Hydraulic model


Hydraulic model (forebay elevation)

Forebay elevation uses a linear rule curve

(i.e., surface area is assumed to be

constant)


Hydraulic model (tailrace elevation)

Tailrace elevation modeled using a linear function of turbine flow, spill, and downstream forebay elevation


Hydraulic model (hydraulic coupling)

Water needs to “travel” a certain amount of

time before arriving in the downstream

reservoir


Modeling hydropower generation

Each one of these sections is a linear function of h and qi

The total discharge is then the sum of all

the qi variables minus their lower

limits, e.g. the contribution of q2 is this point minus this point (for a given h)


Modeling hydropower generation


Objective Function

Minimize weighted turbine discharge and spill

Change in effective hydraulic head is a

function of discharge, surface area, and

efficiency

We want to transfer water from large surface

forebays to small surface forebays to maximize

system H/K

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Tested/simulated for 5 days in March 2013 (medium flow) Objective function performed as desired Piecewise linear HPF approximation performed well

compared to a simple linear model

1. 0.6% increase in system hydraulic potential2. 0.3% increase in stored energy3. Turbine ramping was reduced4. Forebays were kept full without unnecessary spill5. All system constraints were observed


Validation of the hydropower optimization algorithm

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This figure shows wind generation and wind load when wind generation is firmed for on-peak and off-peak periods. Wind load and wind generation are energy neutral.


Hydro-wind coordination problem

Firming wind generation schedules can be used to mitigate variability and forecast uncertainty

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Firming wind generation schedules can be used to mitigate variability and forecast uncertainty

We propose a coordination scheme in which hydropower1. Meets the aggregate generation requests of plant stakeholders2. Satisfies the net load from the wind producer due to the firming of

generation schedulesHydro load Generation requested from stakeholdersWind generation Gross wind generationWind load Scheduled wind generationNet system load Hydro load + wind load – wind generation

Use wind/load curtailments to maintain system feasibility



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Formulation is almost identical to the general real-time hydropower optimization problem



Penalize wind and load curtailment

Introduce a new term for wind and load curtailment

Power balance is equal to hydro load + scheduled

wind gen – wind gen, and accounts for wind and

load curtailments

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Consider high/low flow scenarios and different firming periods (multi-day, daily, peak, hourly, moving average)

Preliminary study with two goals1. Understand the behavior of the hydro-wind coordination problem2. Estimate the battery-like properties of the Mid-Columbia

5-minute Mid-Columbia hydropower data from July 2012 (high flow) and September 2012 (low flow)

5-minute BPA wind generation data from July 2012 We only consider the five municipal hydropower plants,

with total generation capacity on the order of 4 to 4.5 GW


Hydro-wind coordination case study

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In the high flow scenario, inflow was fairly flat and exceeded turbine capacity (spill was unavoidable)

In the low flow case, inflow had an obvious diurnal pattern and was below turbine capacity (little to no spill)


Simulation scenarios (flow)

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In the high flow scenario, generation was flat and there was little to no excess generation capacity

In the low flow scenario, generation was constrained only during peak hours


Simulation scenarios (generation)

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These figures show generation when firming wind for on-peak and off-peak periods1. Primary cause of

curtailment: Not enough power capacity

2. Secondary cause of curtailment: Not enough storage capacity

3. Lack of ramping capacity was generally not an issue

4. More losses if firming for longer periods, due to wind curtailments (more spill)


Simulation results

w. Wind

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How much capacity did the hydropower system provide with 99% availability?

Analyzed the discrepancy between requested power (i.e., net wind load signal) and delivered power


Estimating power capacity

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Discharge capacity was limited in the high flow scenario Charge capacity was limited in the low flow scenario


Power capacity results𝑃𝑃−: charge𝑃𝑃+: discharge

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If an ideal battery mimicked the balancing performance of the hydropower system, what would its state-of-charge look like?

This calculation ignores any charge or discharge “losses”


Estimating energy capacity

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Energy storages given above are in GWh Longer firming periods require more energy storage Even when firming wind energy across long periods, the

energy storage capacity required is only a portion of available Mid-Columbia water storage (~70 GWh)


Energy capacity results

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Based on this preliminary study, the Mid-Columbia system can be reasonably said to be a battery with (at least) Power capacity of several hundred MW Energy capacity of several GWh Round-trip conversion efficiency of approximately 60-90%

Run-of-river hydropower plants could be effective at firming wind generation on hourly timescales

Flexible run-of-river hydro may be as valuable as load following batteries as baseload electricity generation


Conclusions

Thank you! Questions? Comments?

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Swiss Electric Power Generation

Run-of-River Hydro Hydro with Storage

Nuclear Power Thermal Power

56.4%

Switzerland 2014:Total Production:

69.6 TWh

Total Consumption:57.5 TWh

37.9%

Losses(Transmission and Pumping)

Net Export4%



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