Risk Constrained Self-Scheduling of AA-CAES Facilities in Electricity and Heat Markets: A Distributionally Robust Optimization Approach

被引:5
|
作者
Li, Zhiao [1 ]
Chen, Laijun [2 ]
Wei, Wei [1 ]
Mei, Shengwei [1 ]
机构
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] Qinghai Univ, New Energy Photovolta Ind Res Ctr, Xining 810016, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
Resistance heating; Mathematical model; Cogeneration; Reservoirs; Job shop scheduling; Uncertainty; Turbines; Advanced adiabatic compressed air energy storage (AA-CAES); conditional value at risk (CVaR); distributionally robust optimization (DRO); heat market; self-scheduling; Stackelberg game; AIR ENERGY-STORAGE; WIND POWER; SYSTEM; MANAGEMENT; PROGRAM; UNITS;
D O I
10.17775/CSEEJPES.2020.06130
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Advanced adiabatic compressed air energy storage (AA-CAES) has the advantages of large capacity, long service time, combined heat and power generation (CHP), and does not consume fossil fuels, making it a promising storage technology in a low-carbon society. An appropriate self-scheduling model can guarantee AA-CAES's profit and attract investments. However, very few studies refer to the cogeneration ability of AA-CAES, which enables the possibility to trade in the electricity and heat markets at the same time. In this paper, we propose a multi-market self-scheduling model to make full use of heat produced in compressors. The volatile market price is modeled by a set of inexact distributions based on historical data through & oslash;-divergence. Then, the self-scheduling model is cast as a robust risk constrained program by introducing Stackelberg game theory, and equivalently reformulated as a mixed-integer linear program (MILP). The numerical simulation results validate the proposed method and demonstrate that participating in multi-energy markets increases overall profits. The impact of uncertainty parameters is also discussed in the sensibility analysis.
引用
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页码:1159 / 1167
页数:9
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