Abstract
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.
Highlights
Since the global electricity reform process began in the 1980s (Zelner et al, 2009; Wang and Chen, 2012), and especially after the 1990s, market-oriented reforms in the electric power industry were implemented in many countries to allocate electricity more efficiently through market mechanisms (Cai et al, 2009, 2011; Williams and Dubash, 2004; Tsai, 2011; Wu, 2012)
Profit maximisation is the major objective for producers in deregulated markets, the need to reduce flow regime alteration must be incorporated into the process of hydropower portfolio optimisation for riverine ecosystem protection, especially with the background of increasing attention to environmental protection and stronger opposition to hydropower generation (Jager and Smith, 2008; Chen et al, 2012)
We extend previous research on hydropower portfolio optimisation and establish a framework to determine the optimal portfolio of a hydropower reservoir, accounting for both economic benefits and ecological needs
Summary
Since the global electricity reform process began in the 1980s (Zelner et al, 2009; Wang and Chen, 2012), and especially after the 1990s, market-oriented reforms in the electric power industry were implemented in many countries to allocate electricity more efficiently through market mechanisms (Cai et al, 2009, 2011; Williams and Dubash, 2004; Tsai, 2011; Wu, 2012). Hydropower producers usually own generation resources and are allowed to participate in any submarkets such as bilateral contract and spot markets (Karandikar et al, 2010; Ramos et al, 2010). Hydropower producers need to devise their own strategies for portfolio design (Shen and Yang, 2012). Bjørgan et al (1999) integrated the optimisation of future contract and power scheduling based on risk management in a static mean-variance framework, and the efficient frontier was used as a tool to identify a preferred contract portfolio. Using a continuous-time framework, Keppo (2002) proposed a model for optimal longterm electricity trading strategies and the associated production process by maximising production and terminal water reservoir level in the case of multi-reservoir hydropower systems. Using a continuous-time framework, Keppo (2002) proposed a model for optimal longterm electricity trading strategies and the associated production process by maximising production and terminal water reservoir level in the case of multi-reservoir hydropower systems. Fleten et al (2002) used a four-stage stochastic programming model with 256 scenarios for simultaneous risk
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