期刊
APPLIED ENERGY
卷 250, 期 -, 页码 389-403出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2019.04.090
关键词
Multienergy hybrid system; Deep learning; Cascade reservoirs; Coordinated operation strategy
资金
- National Key RAMP
- D Program of China [2017YFB0902200]
- Science and Technology Project of State Grid Corporation of China [5228001700CW]
The high penetration of variable renewable energy sources (RESs) has greatly increased the difficulty in power system scheduling and operation. To fully utilize the complementary characteristics of various RESs, a stochastic optimization model considering the strong regulation capacity of cascade hydropower stations and the uncertainty of wind and photovoltaic (PV) power is presented. Based on the improved generative adversarial networks, the spatial and temporal correlation characteristics between wind farms and PV plants are accurately captured via measured data. Due to the nonlinear features of the hydroelectric plants, linearization methods are adopted to reformulate the original model into a standard mixed integer linear programming (MILP) formulation. Then, the model is solved with a proposed two-stage approach, in which a heuristic algorithm is used to solve the first-stage unit commitment optimization. The cascade hydraulic connection and time delay of the water flow are established in the second stage to exploit the considerably controllable adjustment capability of hydropower generation. A renewable energy base in southwest China is chosen as a detailed case study. The simulation results reveal the potential of the large-scale application of only a hydro-wind-solar hybrid system to satisfy the power transmission demand with the guidance of the coordinated operation strategy, and the performance of the hybrid system can be further enhanced with high-quality scenarios from the proposed deep neural network.
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