Transmission Expansion Planning Considering the Impact of Distributed Generation

被引:3
作者
Matute, Nelson E. [1 ]
Torres, Santiago P. [1 ]
Castro, Carlos A. [2 ]
机构
[1] Univ Cuenca, Dept Elect Elect & Telecommun Engn, Cuenca, Ecuador
[2] Univ Estadual Campinas, Dept Syst & Energy, BR-13083852 Sao Paulo, Brazil
来源
PROCEEDINGS OF 2019 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES EUROPE (ISGT-EUROPE) | 2019年
关键词
AC model; artificial fish swarm algorithm; distributed generation; electric power systems; expansion planning; particle swarm; transmission;
D O I
10.1109/isgteurope.2019.8905460
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Distributed Generation (DG) is a very important alternative to the traditional approach of centralized generation and plays a major role not only in electric distribution systems but also in transmission systems. The incidence of DG in the electrical system (sub-transmission and/or distribution) could defer the addition of new transmission circuits and reduce transmission network losses, representing potential economical savings. This paper studies the economic impact of DG on the Transmission Expansion Planning (TEP) problem including also the cost of transmission network losses. A long-term deterministic static transmission expansion planning using the mathematical AC model is presented. DG is modeled as the summation of each type of small-scale generation technology concentrated in the load node. The proposed TEP approach provides information on the optimal combination of transmission circuits and DG in load nodes. The problem, formulated using the AC model, corresponds to a full non convex, non-linear mixed-integer programming (MINLP) problem. Performance comparisons between Particle Swarm Optimization (PSO) and Artificial Fish Swarm Algorithm (AFSA), to solve the problem, are shown. Garver 6 - bus and IEEE 24 - bus test systems are used to evaluate this TEP approach.
引用
收藏
页数:6
相关论文
共 12 条
  • [1] Transmission Network Investment With Distributed Energy Resources and Distributionally Robust Security
    Alvarado, Diego
    Moreira, Alexandre
    Moreno, Rodrigo
    Strbac, Goran
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2019, 34 (06) : 5157 - 5168
  • [2] Probabilistic transmission expansion planning considering distributed generation and demand response programs
    Hejeejo, Rashid
    Qiu, Jing
    [J]. IET RENEWABLE POWER GENERATION, 2017, 11 (05) : 650 - 658
  • [3] [李晓磊 Li Xiao lei], 2003, [电路与系统学报, Journal of circuits and systems], V8, P1
  • [4] Mendez V. H., 2005, THESIS
  • [5] Rathgeb C, 2013, INT CONF BIOMETR
  • [6] Power system transmission network expansion planning using AC model
    Rider, M. J.
    Garcia, A. V.
    Romero, R.
    [J]. IET GENERATION TRANSMISSION & DISTRIBUTION, 2007, 1 (05) : 731 - 742
  • [7] Rider M. J., 2006, THESIS
  • [8] Simon D., 2013, Evolutionary Optimization Algorithms: Biologically-Inspired and Population-Based Approaches to Computer Intelligence
  • [9] Expansion planning for smart transmission grids using AC model and shunt compensation
    Torres, Santiago P.
    Castro, Carlos A.
    [J]. IET GENERATION TRANSMISSION & DISTRIBUTION, 2014, 8 (05) : 966 - 975
  • [10] Zhao J. H., 2010, INVESTIGATING IMPACT