New Multistage and Stochastic Mathematical Model for Maximizing RES Hosting Capacity-Part I: Problem Formulation

被引:118
作者
Santos, Sergio F. [1 ,2 ,3 ]
Fitiwi, Desta Z. [1 ,2 ,3 ]
Shafie-Khah, Miadreza [1 ,2 ,3 ]
Bizuayehu, Abebe W. [1 ,2 ,3 ]
Cabrita, Carlos M. P. [4 ]
Catalao, Joao P. S. [1 ,2 ,3 ]
机构
[1] Univ Porto, INESC TEC, P-4200465 Oporto, Portugal
[2] Univ Porto, Fac Engn, P-4200465 Oporto, Portugal
[3] Univ Lisbon, INESC ID, Inst Super Tecn, P-1049001 Lisbon, Portugal
[4] Univ Beira Interior, CISE Electromechatron Syst Res Ctr, P-6201001 Covilha, Portugal
关键词
Distributed generation; distribution network systems; energy storage systems; integrated planning; stochastic programming; variability and uncertainty; DISTRIBUTION NETWORK EXPANSION; DISTRIBUTED GENERATION PLACEMENT; POWER DISTRIBUTION NETWORKS; ENERGY-STORAGE SYSTEMS; SWARM OPTIMIZATION; RENEWABLE GENERATION; OPTIMAL ALLOCATION; LOSS MINIMIZATION; ALGORITHM METHOD; FUTURE-RESEARCH;
D O I
10.1109/TSTE.2016.2598400
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This two-part work presents a new multistage and stochastic mathematical model, developed to support the decision-making process of planning distribution network systems (DNS) for integrating large-scale "clean" energy sources. Part I is devoted to the theoretical aspects and mathematical formulations in a comprehensive manner. The proposed model, formulated from the system operator's viewpoint, determines the optimal sizing, timing, and placement of distributed energy technologies (particularly, renewables) in coordination with energy storage systems and reactive power sources. The ultimate goal of this optimization work is to maximize the size of renewable power absorbed by the system, while maintaining the required/standard levels of power quality and system stability at a minimum possible cost. From the methodological perspective, the entire problem is formulated as a mixed integer linear programming optimization, allowing one to obtain an exact solution within a finite simulation time. Moreover, it employs a linearized ac network model which captures the inherent characteristics of electric networks and balances well accuracy with computational burden. The IEEE 41-bus radial DNS is used to test validity and efficiency of the proposed model, and carry out the required analysis from the standpoint of the objectives set. Numerical results are presented and discussed in Part II of this paper to unequivocally demonstrate the merits of the model.
引用
收藏
页码:304 / 319
页数:16
相关论文
共 60 条
[1]   A Stochastic Optimization Approach to Rating of Energy Storage Systems in Wind-Diesel Isolated Grids [J].
Abbey, Chad ;
Joos, Geza .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2009, 24 (01) :418-426
[2]   Optimal allocation of stochastically dependent renewable energy based distributed generators in unbalanced distribution networks [J].
Abdelaziz, A. Y. ;
Hegazy, Y. G. ;
El-Khattam, Walid ;
Othman, M. M. .
ELECTRIC POWER SYSTEMS RESEARCH, 2015, 119 :34-44
[3]   Heuristic curve-fitted technique for distributed generation optimisation in radial distribution feeder systems [J].
Abu-Mouti, F. S. ;
El-Hawary, M. E. .
IET GENERATION TRANSMISSION & DISTRIBUTION, 2011, 5 (02) :172-180
[4]   Mathematical representation of radiality constraint in distribution system reconfiguration problem [J].
Ahmadi, Hamed ;
Marti, Jose R. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2015, 64 :293-299
[5]   On possibilistic and probabilistic uncertainty assessment of power flow problem: A review and a new approach [J].
Aien, Morteza ;
Rashidinejad, Masoud ;
Fotuhi-Firuzabad, Mahmud .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2014, 37 :883-895
[6]   Optimal Placement and Sizing Method to Improve the Voltage Stability Margin in a Distribution System Using Distributed Generation [J].
Al Abri, R. S. ;
El-Saadany, Ehab F. ;
Atwa, Yasser M. .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2013, 28 (01) :326-334
[7]   A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm [J].
Aman, M. M. ;
Jasmon, G. B. ;
Bakar, A. H. A. ;
Mokhlis, H. .
ENERGY, 2014, 66 :202-215
[8]   A Multiobjective Particle Swarm Optimization for Sizing and Placement of DGs from DG Owner's and Distribution Company's Viewpoints [J].
Ameli, Amir ;
Bahrami, Shahab ;
Khazaeli, Farid ;
Haghifam, Mahmood-Reza .
IEEE TRANSACTIONS ON POWER DELIVERY, 2014, 29 (04) :1831-1840
[9]  
[Anonymous], THESIS
[10]   Probabilistic approach for optimal allocation of wind-based distributed generation in distribution systems [J].
Atwa, Y. M. ;
El-Saadany, E. F. .
IET RENEWABLE POWER GENERATION, 2011, 5 (01) :79-88