Hydropower as Flexibility Provider: Modeling Approaches and Numerical Analysis

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1 Hydropower as Flexibility Provider: Modeling Approaches and Numerical Analysis Andrew Hamann, Prof. Gabriela Hug Power Systems Laboratory, ETH Zürich February 8, 2017 Future Electric Power Systems and the Energy Transition Champéry, Switzerland Prof. Gabriela Hug

2 US Pacific Northwest www. transmission.bpa.gov Prof. Gabriela Hug

3 Contributions 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? Prof. Gabriela Hug

4 Mid-Columbia hydropower system Located on the Columbia River in Washington, USA Seven dams with approximately 13 to 14 GW of capacity Average flow is several thousand m 3 /s Travel times are tens of minutes (strongly coupled) Surface areas are tens of km 2 17 entities with a stake in at least one of the dams Operating under a coordination agreement signed in 1997 Prof. Gabriela Hug

5 Map of the Columbia River Basin Prof. Gabriela Hug

6 Map of the Mid-Columbia Prof. Gabriela Hug

7 Pacific Northwest power system 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) Prof. Gabriela Hug

8 Wind farms in the Pacific Northwest Prof. Gabriela Hug

9 Wind farms in the Pacific Northwest Mid-Columbia Columbia River Gorge Prof. Gabriela Hug

10 Real-time hydropower optimization 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 , Jan Prof. Gabriela Hug

11 Hydraulic model Prof. Gabriela Hug

12 Hydraulic model (forebay elevation) Forebay elevation uses a linear rule curve (i.e., surface area is assumed to be constant) Prof. Gabriela Hug

13 Hydraulic model (tailrace elevation) Tailrace elevation modeled using a linear function of turbine flow, spill, and downstream forebay elevation Prof. Gabriela Hug

14 Hydraulic model (hydraulic coupling) Water needs to travel a certain amount of time before arriving in the downstream reservoir Prof. Gabriela Hug

15 Modeling hydropower generation Each one of these sections is a linear function of h and q i The total discharge is then the sum of all the q i variables minus their lower limits, e.g. the contribution of q 2 is this point minus this point (for a given h) Prof. Gabriela Hug

16 Modeling hydropower generation Prof. Gabriela Hug

17 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 Prof. Gabriela Hug

18 Validation of the hydropower optimization algorithm 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 % increase in system hydraulic potential % increase in stored energy 3. Turbine ramping was reduced 4. Forebays were kept full without unnecessary spill 5. All system constraints were observed Prof. Gabriela Hug

19 Hydro-wind coordination problem Firming wind generation schedules can be used to mitigate variability and forecast uncertainty 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. Prof. Gabriela Hug

20 Hydro-wind coordination problem Firming wind generation schedules can be used to mitigate variability and forecast uncertainty We propose a coordination scheme in which hydropower 1. Meets the aggregate generation requests of plant stakeholders 2. Satisfies the net load from the wind producer due to the firming of generation schedules Hydro load Wind generation Wind load Net system load Generation requested from stakeholders Gross wind generation Scheduled wind generation Hydro load + wind load wind generation Use wind/load curtailments to maintain system feasibility Prof. Gabriela Hug

21 Hydro-wind coordination problem Formulation is almost identical to the general real-time hydropower optimization problem 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 Penalize wind and load curtailment Prof. Gabriela Hug

22 Hydro-wind coordination case study Consider high/low flow scenarios and different firming periods (multi-day, daily, peak, hourly, moving average) Preliminary study with two goals 1. Understand the behavior of the hydro-wind coordination problem 2. 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 Prof. Gabriela Hug

23 Simulation scenarios (flow) 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) Prof. Gabriela Hug

24 Simulation scenarios (generation) 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 Prof. Gabriela Hug

25 Simulation results w. Wind These figures show generation when firming wind for on-peak and off-peak periods 1. 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) Prof. Gabriela Hug

26 Estimating power capacity 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 Prof. Gabriela Hug

27 Power capacity results PP : charge PP + : discharge Discharge capacity was limited in the high flow scenario Charge capacity was limited in the low flow scenario Prof. Gabriela Hug

28 Estimating energy capacity 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 Prof. Gabriela Hug

29 Energy capacity results 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) Prof. Gabriela Hug

30 Conclusions 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 Prof. Gabriela Hug

31 Thank you! Questions? Comments?

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33 Swiss Electric Power Generation Run-of-River Hydro Hydro with Storage 56.4% Switzerland 2014: Total Production: 69.6 TWh Nuclear Power Thermal Power Total Consumption: 57.5 TWh 37.9% 4% Losses (Transmission and Pumping) Net Export 33

34 Prof. Gabriela Hug

35 Prof. Gabriela Hug