Journal
APPLIED ENERGY
Volume 281, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2020.116136
Keywords
Climate change; Deep learning; Hydropower pinch analysis; Optimal water management; Renewable energy recovery; Water/energy nexus
Categories
Funding
- Korea Research Fellowship Program through the National Research Foundation of Korea (NRF) - Ministry of Science and ICT [2019H1D3A1A02071051]
- National Research Foundation (NRF) - Korea government (MSIT) [2017R1E1A1A3070713]
- Korea Ministry of Environment (MOE) as Graduate School specialized in Climate Change
- National Research Foundation of Korea [2019H1D3A1A02071051] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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A novel supply-side management approach for multi-purpose hydro reservoirs is proposed, utilizing the water/energy nexus concept and a hydropower pinch analysis. Graphical tools such as hydropower composite curves are used to facilitate numerical analysis, while smart algorithms predict the effects of climate change on downstream energy and water sinks. The results demonstrate successful prediction and increased hydroelectricity production and water savings in the case study of the Karkheh reservoir.
A novel, smart, supply-side management approach is proposed for optimal operation of multi-purpose hydro reservoirs using the water/energy nexus concept and introducing a hydropower pinch analysis (HyPoPA). The nexus among water and energy sources and sinks are considered to develop hydropower composite curves, grand composite curves, and continuous composite curves. These graphical tools are accompanied by hydropower cascade tables to facilitate numerical analysis of conservation and recovery of water resources at high resolution. The minimum hydro storage is targeted, and three management cases are determined by obtaining the surplus (or deficit) head of a hydro reservoir in successive operational years in unreliable, reliable, and self-sufficient cases. The effects of climate change are predicted using smart algorithms to manage varying downstream energy and water sinks under optimal conditions by HyPoPA. Two prediction scenarios are developed to mimic annual operation and online monitoring cases using advanced neural networks. Karkheh hydro reservoir serves as a case study to verify smart HyPoPA. The results showed that the sources were successfully predicted employing a hybrid long short-term memory and gated recurrent unit network in 2018 (R-2 = 97.3%, MAPE = 15.9%), which was a dry year when reservoir water levels fell in a non-reliable case with deficit head. The Karkheh reservoir produced 37.2 GWh more hydroelectricity and saved 1.66 billion m(3) of water after meeting all water requirements using the smart HyPoPA in the target year.
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