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    Optimal Hedging for Hydropower Operation and End-of-Year Carryover Storage Values

    Source: Journal of Water Resources Planning and Management:;2019:;Volume ( 145 ):;issue: 004
    Author:
    Jian Wang; Chuntian Cheng; Xinyu Wu; Jianjian Shen; Rui Cao
    DOI: 10.1061/(ASCE)WR.1943-5452.0001046
    Publisher: American Society of Civil Engineers
    Abstract: Hedging rules for hydropower operation provide optimal decisions to hedge against extreme system loss from an energy shortfall in the future at the price of a small current loss. Aiming at determining optimal end-of-year carryover storage decisions, this paper develops a hedging model for hydropower operation and proposes a corresponding numerical solving procedure to derive optimal hedging rules. The overall solving procedure involves generating possible future inflow sequences, optimizing the hedging model with nonlinear programming (NLP) technique, and plotting the hedging rule with the optimal results in condition–decision forms. Results from a case study demonstrate the correctness and feasibility of the proposed model and procedure. Performance comparison between the generated hedging rule and standard operating policy (SOP) reveals that (1) in a dry period, hedging rules could provide optimal end-of-year carryover storage, which helps achieve more benefit and suffer less system loss compared with SOP; (2) forecast uncertainty greatly affects the operation for it reduces the benefit and increases the system loss; and (3) when water is abundant, hedging becomes trivial and the generated operating rules provide similar benefit and system loss compared with SOP. The proposed procedure and the implications could be applied to determine more rational end-of-year carryover storage decisions for hydropower operation considering inflow uncertainty.
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      Optimal Hedging for Hydropower Operation and End-of-Year Carryover Storage Values

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    contributor authorJian Wang; Chuntian Cheng; Xinyu Wu; Jianjian Shen; Rui Cao
    date accessioned2019-03-10T11:51:09Z
    date available2019-03-10T11:51:09Z
    date issued2019
    identifier other%28ASCE%29WR.1943-5452.0001046.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4254379
    description abstractHedging rules for hydropower operation provide optimal decisions to hedge against extreme system loss from an energy shortfall in the future at the price of a small current loss. Aiming at determining optimal end-of-year carryover storage decisions, this paper develops a hedging model for hydropower operation and proposes a corresponding numerical solving procedure to derive optimal hedging rules. The overall solving procedure involves generating possible future inflow sequences, optimizing the hedging model with nonlinear programming (NLP) technique, and plotting the hedging rule with the optimal results in condition–decision forms. Results from a case study demonstrate the correctness and feasibility of the proposed model and procedure. Performance comparison between the generated hedging rule and standard operating policy (SOP) reveals that (1) in a dry period, hedging rules could provide optimal end-of-year carryover storage, which helps achieve more benefit and suffer less system loss compared with SOP; (2) forecast uncertainty greatly affects the operation for it reduces the benefit and increases the system loss; and (3) when water is abundant, hedging becomes trivial and the generated operating rules provide similar benefit and system loss compared with SOP. The proposed procedure and the implications could be applied to determine more rational end-of-year carryover storage decisions for hydropower operation considering inflow uncertainty.
    publisherAmerican Society of Civil Engineers
    titleOptimal Hedging for Hydropower Operation and End-of-Year Carryover Storage Values
    typeJournal Paper
    journal volume145
    journal issue4
    journal titleJournal of Water Resources Planning and Management
    identifier doi10.1061/(ASCE)WR.1943-5452.0001046
    page04019003
    treeJournal of Water Resources Planning and Management:;2019:;Volume ( 145 ):;issue: 004
    contenttypeFulltext
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