EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Optimization of small hydropower systems in water distribution networks through evolutionary algorithms and water demand forecasting

Robert Sitzenfrei1, Lukas Schartner1,2, and Martin Oberascher1
Robert Sitzenfrei et al.
  • 1University Innsbruck, Faculty of Engineering Sciences, Department of Infrastructure Engineering, Innsbruck, Austria (
  • 2Ingenieurbüro Kirchebner Ziviltechnikergesellschaft m.b.H., 6020 Innsbruck, Austria.

The transition from fossil fuel to renewable energies represents the central challenge of the early 21st century. In this context, small hydro power systems (SHPS) can be implemented in water distribution networks (WDNs) to use pressure and drinking water surplus for hydropower production. However, inflow to SHPS is normally controlled based on the available water volume after ensuring a reliable drinking water supply and considering a fire-fighting reserve. Hence, the hydropower generation in WDNs has to be in accordance with its primary tasks. The challenge now is to optimally use the available pressure and water surplus for hydropower production while at the same time reliably fulfilling drinking water constraints.

In this work, future predictions of daily water demand are added into the control strategy of SHPS to optimize the operation. The control procedure of a SHPS is optimized by means of an evolutionary algorithm in combination with Monte-Carlo sampling. This is done for different categorized water demand and water source data in order to maximize profit while ensuring the WDNs reliable operation. Further, water demand forecasts of varying quality are evaluated in combination with previously optimized and categorized SHPS control-sets. For case study, a real WDN of an Alpine municipality is hypothetically retrofitted with a controllable SHPS. Different types of SHPS and turbine characterises are investigated using amount of hydropower production, more specifically profitability, as performance indicator.

While in literature, optimization is usually performed based on representative days (e.g., average day demand), long-term simulations over 10 years are used in this work. Therefore, a sufficient supply pressure in all water demand nodes in the WDN is ensured during this period. This results in a significant lower but more realistic estimation of potential benefits. The results also show, that after optimizing the SHPS location and device size, an additional potential increase of yearly profit of 1.1% can be achieved in the long-term operation of a Pelton turbine by considering water demand forecasts.

How to cite: Sitzenfrei, R., Schartner, L., and Oberascher, M.: Optimization of small hydropower systems in water distribution networks through evolutionary algorithms and water demand forecasting, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12372,, 2021.

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