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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 4
Hydrol. Earth Syst. Sci., 18, 1359–1368, 2014
https://doi.org/10.5194/hess-18-1359-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Practice and strategies for managing water conflicts between...

Hydrol. Earth Syst. Sci., 18, 1359–1368, 2014
https://doi.org/10.5194/hess-18-1359-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 08 Apr 2014

Research article | 08 Apr 2014

Portfolio optimisation for hydropower producers that balances riverine ecosystem protection and producer needs

X. A. Yin, Z. F. Yang, and C. L. Liu X. A. Yin et al.
  • State Key Laboratory of Water Environmental Simulation, School of Environment, Beijing Normal University, Beijing, China

Abstract. In deregulated electricity markets, hydropower portfolio design has become an essential task for producers. The previous research on hydropower portfolio optimisation focused mainly on the maximisation of profits but did not take into account riverine ecosystem protection. Although profit maximisation is the major objective for producers in deregulated markets, protection of riverine ecosystems must be incorporated into the process of hydropower portfolio optimisation, especially against a background of increasing attention to environmental protection and stronger opposition to hydropower generation. This research seeks mainly to remind hydropower producers of the requirement of river protection when they design portfolios and help shift portfolio optimisation from economically oriented to ecologically friendly. We establish a framework to determine the optimal portfolio for a hydropower reservoir, accounting for both economic benefits and ecological needs. In this framework, the degree of natural flow regime alteration is adopted as a constraint on hydropower generation to protect riverine ecosystems, and the maximisation of mean annual revenue is set as the optimisation objective. The electricity volumes assigned in different electricity submarkets are optimised by the noisy genetic algorithm. The proposed framework is applied to China's Wangkuai Reservoir to test its effectiveness. The results show that the new framework could help to design eco-friendly portfolios that can ensure a planned profit and reduce alteration of the natural flow regime.

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