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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (1): 177-188    DOI: 10.3785/j.issn.1008-973X.2021.01.021
    
Bi-level robust game optimal scheduling of regional comprehensive energy system
Xiao-zhu LI(),Wei-qing WANG*()
Engineering Research Center of Ministry of Education for Renewable Energy Generation and Grid Connection Technology, Xinjiang University, Urumqi 830047, China
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Abstract  

The two-stage optimal operation strategy model of hybrid energy storage, combined cooling, heating and power (CCHP) units and energy conversion device was analyzed based on the basic framework of regional integrated energy system (RIES) in order to improve the system economy and the grid connection capacity of large-scale connected renewable energy under the RIES with multiple energy complementary. Virtual energy plant (VEP) was used to stabilize the uncertainty of power generation and consumption. The robust theory was used to construct the uncertain aggregate to adjust the boundary flexibly, and conditional risk theory was introduced to construct the risk model of RIES energy management based on Copula-RCVaR. A multi-objective whale optimal algorithm based on filter technology was proposed to solve the above complex model. The influence of different renewable energy penetration rate and their cluster effect on the income result and operation strategy of RIES was analyzed. Results show that the profit of RIES can be increased by 1.9% by introducing VEP. The profit of RIES can be increased by 5.9% by selecting a reasonable confidence probability of the uncertain variables for load and source based on the premise of ensuring the stable operation.

Key words： regional integrated energy system (RIES)      robust theory      bi-level optimization      revenue and loss risk      multi-objective optimization algorithm     
Received: 19 May 2020      Published: 27 January 2021
CLC:  TM 73  
Corresponding Authors: Wei-qing WANG     E-mail: 272268272@qq.com;wq59@xiu.edu.cn
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Xiao-zhu LI
Wei-qing WANG
Cite this article:

Xiao-zhu LI,Wei-qing WANG. Bi-level robust game optimal scheduling of regional comprehensive energy system. Journal of ZheJiang University (Engineering Science), 2021, 55(1): 177-188.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.01.021     OR     http://www.zjujournals.com/eng/Y2021/V55/I1/177


区域综合能源系统两阶段鲁棒博弈优化调度

在区域综合能源系统的基本架构上，为了提升系统经济性与可再生能源并网能力，研究混合储能、冷热电联供机组（CCHP）、能量转换装置在多能互补下的两阶段优化运行模型. 利用虚拟能量厂（VEP），平抑发、用电不确定性；采用鲁棒理论，构建灵活调整边界的不确定合集；引入条件风险理论，构建考虑多种不确定关系耦合下基于Copula-RCVaR的能量管理风险模型. 针对上述模型特点，提出基于滤子技术的多目标鲸鱼算法进行求解. 分析不同可再生能源渗透率及集群效应对系统收益结果和运行策略的影响. 结果表明，引入虚拟能量厂可以提高利润1.9%，在保证稳定运行的前提下合理选择荷、源不确定变量的置信概率，可以提高利润5.9%.


关键词： 区域综合能源系统（RIES）,  鲁棒理论,  两阶段优化,  收益损失风险,  多目标优化算法 
Fig.1 Structure and operation of RIES
Fig.2 Flow chart of solution process
设备 ai bi ci Pmax /
kW 		Pmin /
kW 		${{T}}_{{\rm{GT}}}^{{\rm{on/off}}} $/
h 		${\rm{R}}_{{\rm{GTU/D}}}^{\rm{e}} $/
（kW·h?1）
GT 2.15 2.21 0.11 190 40 3 80
WHR 27.0 ?3.30 0.74 6000 0 ? ?
Tab.1 Parameter setting 1 of each device in CCHP
设备 η Cc /（美元·kW?1） Pmax /kW
GT ? ? ?
WHR ? 0.01674 ?
ABS 0.70 0.012 2000
ASR 3.08 0.015 2000
BO 0.85 ? 500
Tab.2 Parameter setting 2 of each device in CCHP
类型 参数 数值
VEP-e VEP-h VEP-c
LSI Pt 20% ? ?
LSI ξeLSI/（kW·h·美元?1） 0.7 ? ?
LSII Pt 30% ? ?
LSII ξeLSII/（kW·h·美元?1） 0.45 ? ?
LT Pt ? 25% 20%
LT ${P_{{\rm{L}}{{\rm{T}}_{\min ,t}}}} $ ? 0 0
LT $ {P_{ {\rm{L} }{ {\rm{T} }_{\max ,t} } } } $ ? 0.5 0.5
LT ξLT /（kW·h·美元?1） ? 0.4 0.4
ESS SOC/kW 250 500 500
ESS PESS/kW 100 200 200
ESS ρ, ηc, ηd 1%, 0.9, 0.9 ? ?
ESS SOCpu 0.2~0.9 ? ?
ESS ξESS /（kW·h·美元?1） 0.45 0.5 0.5
Tab.3 Parameter setting of VEP
Fig.3 Daily forecast of wind power output and load
Fig.4 Price trend chart
Fig.5 Relation of operating outside extreme scenario
算例 BRIES利润/(105美元) DVEP-e /MW DVEP-h /MW DVEP-c /MW CVEP-e /(103 美元) CVEP-h /(103 美元) CVEP-c /(103 美元) SVEP-e SVEP-h SVEP-c
算例1 8.62 6.00 8.54 2.23 2.87 4.75 1.91 0.81 0.61 0.71
算例2 8.53 ? 17.20 6.17 ? 5.56 1.97 ? 0.53 0.60
算例3 8.50 6.36 ? ? 3.04 ? ? 0.77 ? ?
算例4 8.46 ? ? ? ? ? ? ? ? ?
Tab.4 Operation result of each case
Fig.6 Optimization plan of VEP of each case
α/% 空间约束参数 BRIES /
（105美元） 		CVaRRIES POE /%
$\varGamma_{{\rm{W}},t} $ $\varGamma_{{\rm{L,}}t}^{\rm{e}} $
60 19.2 30.2 8.22 0.982 5 0.02
55.5 17.6 27.4 8.45 0.911 8 0.09
45 14.5 21.6 8.71 0.784 5 1.11
30 9.7 14.6 9.28 0.536 1 24.23
20 5.9 8.6 9.53 0.351 3 100.00
10 0.59 0.89 9.66 0.351 3 100.00
Tab.5 Results with different confidence probabilities
Fig.7 Pareto frontier for α=55.5%
Fig.8 Convergence for bi-level model （α=55.5%）
Fig.9 Probability relation of operating outside extreme for each case
Fig.10 Impact of spatial cluster benefits on profit and loss risk of RIES
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