A Scatter Search Heuristic for the Optimal Location, Sizing and Contract Pricing of Distributed Generation in Electric Distribution Systems

被引:21
作者
Perez Posada, Andres Felipe [1 ]
Villegas, Juan G. [2 ]
Lopez-Lezama, Jesus M. [3 ]
机构
[1] Celsia SA ESP, Carrera 43A 1sur 143, Medellin 050022, Colombia
[2] Univ Antioquia, Dept Ind Engn, Supply Chains Management & Innovat Res Grp INCAS, 67th St,53-108, Medellin 050110, Colombia
[3] Univ Antioquia, Dept Elect Engn, Res Grp Efficient Energy Management GIMEL, 67th St,53-108, Medellin 050110, Colombia
来源
ENERGIES | 2017年 / 10卷 / 10期
关键词
bilevel programming; distributed generation (DG); scatter search (SS); evolutionary algorithms; PARTICLE SWARM OPTIMIZATION; SMART GRID TECHNOLOGIES; BEE COLONY ALGORITHM; GENETIC ALGORITHM; DISTRIBUTION NETWORKS; ENERGY-RESOURCES; VOLTAGE CONTROL; POWER; COMBINATION; HYBRID;
D O I
10.3390/en10101449
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
In this paper we present a scatter search (SS) heuristic for the optimal location, sizing and contract pricing of distributed generation (DG) in electric distribution systems. The proposed optimization approach considers the interaction of two agents: (i) the potential investor and owner of the DG, and (ii) the Distribution Company (DisCo) in charge of the operation of the network. The DG owner seeks to maximize his profits from selling energy to the DisCo, while the DisCo aims at minimizing the cost of serving the network demand, while meeting network constraints. To serve the expected demand the DisCo is able to purchase energy, through long-term bilateral contracts, from the wholesale electricity market and from the DG units within the network. The interaction of both agents leads to a bilevel programming problem that we solve through a SS heuristic. Computational experiments show that SS outperforms a genetic algorithm hybridized with local search both in terms of solution quality and computational time.
引用
收藏
页数:16
相关论文
共 40 条
  • [1] Review of optimization techniques applied for the integration of distributed generation from renewable energy sources
    Abdmouleh, Zeineb
    Gastli, Adel
    Ben-Brahim, Lazhar
    Haouari, Mohamed
    Al-Emadi, Nasser Ahmed
    [J]. RENEWABLE ENERGY, 2017, 113 : 266 - 280
  • [2] Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm
    Abu-Mouti, Fahad S.
    El-Hawary, M. E.
    [J]. IEEE TRANSACTIONS ON POWER DELIVERY, 2011, 26 (04) : 2090 - 2101
  • [3] Strategies to improve the voltage quality in active low-voltage distribution networks using DSO's assets
    Armendariz, Mikel
    Babazadeh, Davood
    Broden, Daniel
    Nordstroem, Lars
    [J]. IET GENERATION TRANSMISSION & DISTRIBUTION, 2017, 11 (01) : 73 - 81
  • [4] Arumuga M., 2016, INT J ADV ENG TECHNO, VVII, P668
  • [5] Reliability Evaluation of a Distribution Network with Microgrid Based on a Combined Power Generation System
    Bai, Hao
    Miao, Shihong
    Zhang, Pipei
    Bai, Zhan
    [J]. ENERGIES, 2015, 8 (02): : 1216 - 1241
  • [6] Smart grid technologies and applications
    Bayindir, R.
    Colak, I.
    Fulli, G.
    Demirtas, K.
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2016, 66 : 499 - 516
  • [7] A genetic algorithm for the set covering problem
    Beasley, JE
    Chu, PC
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 1996, 94 (02) : 392 - 404
  • [8] Buitrago L. F., 2014, THESIS
  • [9] Cataliotti A, 2016, IEEE T SMART GRID, V7, P889, DOI 10.1109/TSG.2015.2430891
  • [10] Optimal reactive power and voltage control in distribution networks with distributed generators by fuzzy adaptive hybrid particle swarm optimisation method
    Chen, Shuheng
    Hu, Weihao
    Su, Chi
    Zhang, Xiaoxu
    Chen, Zhe
    [J]. IET GENERATION TRANSMISSION & DISTRIBUTION, 2015, 9 (11) : 1096 - 1103