A CVaR-robust-based multi-objective optimization model and three-stage solution algorithm for a virtual power plant considering uncertainties and carbon emission allowances

被引:85
作者
Ju, Liwei [1 ,3 ]
Tan, Qinliang [1 ,5 ]
Lu, Yan [2 ]
Tan, Zhongfu [1 ,4 ]
Zhang, Yuxie [1 ]
Tan, Qingkun [1 ]
机构
[1] North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
[2] State Grid Jibei Elect Econ Res Inst, Beijing 100045, Peoples R China
[3] China Univ Petr, Acad Chinese Energy Strategy, Beijing 102249, Peoples R China
[4] Beijing Energy Dev Res Base, Beijing 102206, Peoples R China
[5] North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R China
基金
美国国家科学基金会;
关键词
Virtual power plant; Multi-objective; CVaR; Robust optimization; DISTRIBUTED ENERGY-RESOURCES; BIDDING STRATEGIES; STORAGE-SYSTEM; WIND POWER;
D O I
10.1016/j.ijepes.2018.12.012
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In order to make full use of distribute energy resources and decrease the abandoned energy of clean energy, the paper aggregates wind power plant (WPP), photovoltaic power generation (PV), biomass power generation (BPG), energy storage system (ESS), conventional gas turbines (CGT) and flexible load into a virtual power plant (VPP). Firstly, the basic structure and uncertainty factors of VPP operation are analyzed, and the output model of power sources is proposed. Then, a multi-objective optimization model is proposed with three objective functions, namely, the maximum operation revenue, the minimum operation risk and the minimum carbon emissions. Furthermore, the robust optimization theory is applied to construct a risk aversion model by converting the constraint conditions into stochastic constraint conditions with uncertainty factors. Thirdly, a three-stage solution algorithm is put forward for the proposed multi-objective model including payoff table establishment, fuzzy linearization and objective weight calculation. Finally, the modified IEEE30 node system is chosen as simulation system. The results show: (1) VPP could utilize the complementary nature of different distributed energy sources to balance the revenue, risk and carbon emission. (2) Robust optimization theory and conditional value at risk could describe the uncertainty risk, and, the prediction accuracy needs to be improved for controlling the operation risk. (3) The risk attitude of decision makers would affect VPP scheduling scheme, the less uncertainty lead to greater risk when confidence degree <= 0.85 and robust coefficient >= 0.95 (risk extreme aversion). (3) Price-based demand response (PBDR) could smooth load demand curve and the maximum total emission allowances (MTEA) can highlight the environmentally friendly characteristics, which could improve more grid-connected space of clean energy and achieve the optimal VPP operation. Therefore, the proposed multi-objective model could obtain higher economic benefit and achieve lower carbon emissions while rationally controlling risks, which could be taken as an reliable decision support for decision makers.
引用
收藏
页码:628 / 643
页数:16
相关论文
共 32 条
[1]  
[Anonymous], EUROPEAN VIRTUAL FUE
[2]  
[董文略 Dong Wenlue], 2015, [电力系统自动化, Automation of Electric Power Systems], V39, P75
[3]   Optimal Bidding Strategies for Thermal and Generic Programming Units in the Day-Ahead Electricity Market [J].
Heredia, F. -Javier ;
Rider, Marcos J. ;
Corchero, Cristina .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2010, 25 (03) :1504-1518
[4]   Event-based scheduling of industrial technical virtual power plant considering wind and market prices stochastic behaviors - A case study in Iran [J].
Hooshmand, Rahmat-Allah ;
Nosratabadi, Seyyed Mostafa ;
Gholipour, Eskandar .
JOURNAL OF CLEANER PRODUCTION, 2018, 172 :1748-1764
[5]   Analyzing the performance of clean development mechanism for electric power systems under uncertain environment [J].
Jin, S. W. ;
Li, Y. P. ;
Huang, G. H. ;
Nie, S. .
RENEWABLE ENERGY, 2018, 123 :382-397
[6]   Multi-objective stochastic scheduling optimization model for connecting a virtual power plant to wind-photovoltaic-electric vehicles considering uncertainties and demand response [J].
Ju, Liwei ;
Li, Huanhuan ;
Zhao, Junwei ;
Chen, Kangting ;
Tan, Qingkun ;
Tan, Zhongfu .
ENERGY CONVERSION AND MANAGEMENT, 2016, 128 :160-177
[7]   Multi-objective operation optimization and evaluation model for CCHP and renewable energy based hybrid energy system driven by distributed energy resources in China [J].
Ju, Liwei ;
Tan, Zhongfu ;
Li, Huanhuan ;
Tan, Qingkun ;
Yu, Xiaobao ;
Song, Xiaohua .
ENERGY, 2016, 111 :322-340
[8]   A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind-photovoltaic-energy storage system considering the uncertainty and demand response [J].
Ju, Liwei ;
Tan, Zhongfu ;
Yuan, Jinyun ;
Tan, Qingkun ;
Li, Huanhuan ;
Dong, Fugui .
APPLIED ENERGY, 2016, 171 :184-199
[9]  
Kasaei MJ, 2003, J ENERGY RESOUR TECH, V139
[10]   Optimal management of renewable energy sources by virtual power plant [J].
Kasaei, Mohammad Javad ;
Gandomkar, Majid ;
Nikoukar, Javad .
RENEWABLE ENERGY, 2017, 114 :1180-1188