TY - JOUR
T1 - Stochastic Day-ahead operation of cascaded hydropower systems with Bayesian neural network-based scenario generation
T2 - A Portland general electric system study
AU - Liu, Yikui
AU - Chen, Xianbang
AU - Fan, Neng
AU - Zhao, Zhechong
AU - Wu, Lei
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/11
Y1 - 2023/11
N2 - Coordinating water usage of cascaded dams along a river to serve electric power generation and other non-power needs is challenging, due to the volatile and time-variate streamflow, the tight hydrologic couplings between the dams, and the strict regulation of individual dams. With this, cascaded hydropower system (CHS) operators, such as Portland General Electric (PGE), usually adopt empirical strategies to sidestep these difficulties, but at the expense of water usage inefficiency and economic losses. Motived by this, a stochastic optimization-based day-ahead operation model of CHSs is developed to determine scenario-independent hourly forebay levels of individual dams, which could assist operators in effectively utilizing available water resources against inflow uncertainties and maximizing the profit of hydropower. A common penstock effect model is also integrated to accurately capture the physical operating characteristics of CHSs. Moreover, a data-driven Bayesian neural network (BNN)-based scenario generation model is seamlessly connected to the stochastic day-ahead operation model for preparing water inflow scenarios of the impending operating day. Leveraging actual operation data of the PGE's Round Butte-Pelton CHS, numerical simulations demonstrate that the day-ahead operation schedules out of the proposed approach could deliver more efficient water usage and higher economic benefits than the PGE's current practice.
AB - Coordinating water usage of cascaded dams along a river to serve electric power generation and other non-power needs is challenging, due to the volatile and time-variate streamflow, the tight hydrologic couplings between the dams, and the strict regulation of individual dams. With this, cascaded hydropower system (CHS) operators, such as Portland General Electric (PGE), usually adopt empirical strategies to sidestep these difficulties, but at the expense of water usage inefficiency and economic losses. Motived by this, a stochastic optimization-based day-ahead operation model of CHSs is developed to determine scenario-independent hourly forebay levels of individual dams, which could assist operators in effectively utilizing available water resources against inflow uncertainties and maximizing the profit of hydropower. A common penstock effect model is also integrated to accurately capture the physical operating characteristics of CHSs. Moreover, a data-driven Bayesian neural network (BNN)-based scenario generation model is seamlessly connected to the stochastic day-ahead operation model for preparing water inflow scenarios of the impending operating day. Leveraging actual operation data of the PGE's Round Butte-Pelton CHS, numerical simulations demonstrate that the day-ahead operation schedules out of the proposed approach could deliver more efficient water usage and higher economic benefits than the PGE's current practice.
KW - Bayesian neural network
KW - Hydropower
KW - Scenario generation
KW - Stochastic optimization
UR - http://www.scopus.com/inward/record.url?scp=85163544662&partnerID=8YFLogxK
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U2 - 10.1016/j.ijepes.2023.109327
DO - 10.1016/j.ijepes.2023.109327
M3 - Article
AN - SCOPUS:85163544662
SN - 0142-0615
VL - 153
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
M1 - 109327
ER -