Real-time optimization of a hydropower cascade using a linear modeling approach

Hamann, Andrew; Hug, Gabriela

doi:10.1109/pscc.2014.7038354

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…A deterministic optimization algorithm that uses inflow scenarios as possible future realizations of the inflow calculates the optimal releases at each site (the set of water releases at all sites is referred to as a decision). In the optimization algorithm, a piecewise linear approximation of the hydropower function of powerhouses is used (Hamann and Hug, 2014), which linearizes the optimization model. One problem with managing reservoirs is that the optimization algorithms attempt to empty the reservoirs at the end of the forecast window because the future value of water in the reservoir is zero unless otherwise specified.…”

Section: Current Operational Settingmentioning

confidence: 99%

Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

Arsenault

Côté

2019

Hydrol. Earth Syst. Sci.

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Abstract. This paper presents an analysis of the effects of biased extended streamflow prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon test bed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data were fed to the hydrological model, and the synthetic streamflow subsequently generated was considered to be a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method's response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test bed in which the entire system is precisely and accurately modelled. The ensemble climate data forecasts, the hydrological modelling and ESP forecast generation, optimization model, and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focussed on one hydropower system both with and without minimum baseload constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore place the reservoir levels at greater risk to maximize short-term profit. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider to be optimal trajectories.

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Section: Current Operational Settingmentioning

confidence: 99%

Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

Arsenault

Côté

2019

Hydrol. Earth Syst. Sci.

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“…powerhouses is used (Hamann and Hug, 2014), which linearizes the optimization model. One problem with managing reservoirs is that the optimization algorithms attempt to empty the reservoirs at the end of the forecast window because the future value of water in the reservoir is zero unless otherwise specified.…”

Section: Current Operational Settingmentioning

confidence: 99%

Analysis of the effects of biases in ESP forecasts on electricity production in hydropower reservoir management

Arsenault

Côté

2018

Preprint

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Abstract. This paper presents an analysis of the effects of biased Extended Streamflow Prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon testbed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data was fed to the hydrological model and the synthetic streamflow thus generated was considered as a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method’s response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test-bed in which the entire system is precisely and accurately modeled. The ensemble climate data forecasts, the hydrological modeling and ESP forecast generation, optimization model and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focused on one hydropower system both with and without minimum base load constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore increases the odds of forced spillage by attempting to maximize short-term profit by keeping a higher net head. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider optimal trajectories.

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“…Such an MPC approach is used in [5], for hybrid photovoltaic-storage systems participation in the energy market, and in [6] for frequency control of microgrids operating in islanded mode. RTMPC is also employed in [7] for real-time control of a CHPS. However, RTMPC does not guarantee the optimization of the economic objectives in market-based problems.…”

Section: Introductionmentioning

confidence: 99%

Economic model predictive control for the energy management problem of a virtual power plant including resources at different voltage levels

Santosuosso,

Camal,

di Giorgio

et al. 2023

27th International Conference on Electricity Distribution (CIRED 2023)

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Real-time optimization of a hydropower cascade using a linear modeling approach

Cited by 11 publications

References 19 publications

Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

Analysis of the effects of biases in ESP forecasts on electricity production in hydropower reservoir management

Economic model predictive control for the energy management problem of a virtual power plant including resources at different voltage levels

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