Retrofit Planning and Flexible Operation of Coal-Fired Units Using Stochastic Dual Dynamic Integer Programming

The continuous growth of wind power requires greater power system operational flexibility owing to the variability and uncertainty of wind power generation. Retrofitting existing coal-fired units is a demonstrated cost-effective method for improving system flexibility. This study proposes a decision...

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Vydáno v:IEEE transactions on power systems Ročník 39; číslo 1; s. 1 - 16
Hlavní autoři: Li, Zhuangzhuang, Yang, Ping, Zhao, Zhuoli, Lai, Loi Lei
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Benders decomposition
Coal-fired power plants
Coal-fired units
Costs
Decision support systems
Flexibility
flexibility retrofit
flexible operation
Integer programming
Load modeling
Mixed integer
Nonlinear programming
Operating costs
Operational problems
Planning
renewable energy sources
Retrofitting
stochastic dual dynamic integer programming
Stochastic processes
System effectiveness
Unit commitment
Wind farms
Wind forecasting
Wind power
Wind power generation
ISSN:0885-8950, 1558-0679
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Shrnutí:The continuous growth of wind power requires greater power system operational flexibility owing to the variability and uncertainty of wind power generation. Retrofitting existing coal-fired units is a demonstrated cost-effective method for improving system flexibility. This study proposes a decision support tool that provides optimal investment decisions to enhance power system operational flexibility. A novel flexibility retrofit planning (FRP) model is proposed in which the embedded operational problem is a stochastic mixed-integer nonlinear programming (SMINLP) unit commitment problem. In the operational unit commitment model, novel mixed-integer nonlinear formulations are proposed to represent the coal-fired unit attribute (minimum power output, startup and shutdown times, and maximum ramp rate) modifications after retrofitting. A new solution method based on the Benders decomposition (BD) and stochastic dual dynamic integer programming (SDDIP) algorithms is proposed to solve the proposed FRP model. The modified IEEE 39-bus, 57-bus, and 118-bus test systems were used to verify the effectiveness of the proposed model and solution method. The results demonstrate that the proposed FRP model can balance the retrofitting and operational costs to minimize the total cost and accommodate more wind generation. The proposed solution method is computationally efficient for solving FRP models.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2023.3243093