Integrating active demand into the distribution system using metaheuristic techniques

被引:0
作者
Obando-Paredes, Edgar Dario [1 ,2 ]
Lopez-Garcia, Dahiana [1 ]
Carvajal-Quintero, Sandra X. [1 ]
机构
[1] Univ Nacl Colombia, Fac Engn & Architecture, Dept Elect Engn Elect & Comp Sci, Grp E3P, Manizales, Colombia
[2] Univ Cooperat Colombia, Fac Engn, Eslinga Grp, Pasto, Colombia
来源
JOURNAL OF ENGINEERING-JOE | 2024年 / 2024卷 / 11期
关键词
demand response; distributed systems; distribution networks; distribution networks planning; dynamic modelling; energy management; electrical and electronics engineering; ENERGY MANAGEMENT-SYSTEM; SIDE MANAGEMENT; SOLAR-RADIATION; RESOURCES; IMPACT; TECHNOLOGIES; ALGORITHMS; PREDICTION; NETWORKS;
D O I
10.1049/tje2.70005
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Integrating non-conventional renewable energy sources into distribution systems, alongside data science and enabling technological infrastructures, presents significant challenges, particularly in managing active demand. The rapid evolution of the electric energy system and increasing electricity demand highlight the need for reliable tracking and predictive methods to manage Distributed Energy Resources and digital infrastructure. These methods are essential for advancing carbon neutrality, democratizing environmental sustainability, and improving energy efficiency. Effective active demand monitoring requires understanding the transactional system concept, including digital infrastructure and decentralized demand. Although metaheuristic techniques are increasingly important in demand response integration, much research focuses on specific techniques rather than providing a comprehensive view of dynamic transaction integration for active demand. Technological advancements, like smart meters and communication systems, are shifting from basic consumption measurement to active customer participation. This article reviews key concepts in electrical distribution systems, such as active demand, DERs, and transactive systems. It examines prevalent metaheuristic techniques, emphasizing their role in integrating and predicting active demand and DER behaviors. Additionally, the study presents a methodology serving as a roadmap for efficient DER integration and the transition to active demand and transactive electricity systems, addressing gaps in the current literature.
引用
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页数:18
相关论文
共 105 条
  • [11] Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review
    Antonopoulos, Ioannis
    Robu, Valentin
    Couraud, Benoit
    Kirli, Desen
    Norbu, Sonam
    Kiprakis, Aristides
    Flynn, David
    Elizondo-Gonzalez, Sergio
    Wattam, Steve
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2020, 130 (130)
  • [12] Active demand response with electric heating systems: Impact of market penetration
    Arteconi, Alessia
    Patteeuw, Dieter
    Bruninx, Kenneth
    Delarue, Erik
    D'haeseleer, William
    Helsen, Lieve
    [J]. APPLIED ENERGY, 2016, 177 : 636 - 648
  • [13] Demand-side management and European environmental and energy goals: An optimal complementary approach
    Bergaentzle, Claire
    Clastres, Cedric
    Khalfallah, Haikel
    [J]. ENERGY POLICY, 2014, 67 : 858 - 869
  • [14] Biggar DR, 2014, ECONOMICS OF ELECTRICITY MARKETS, P1, DOI 10.1002/9781118775745
  • [15] Distributed energy resources on distribution networks: A systematic review of modelling, simulation, metrics, and impacts
    Caballero-Pena, Juan
    Cadena-Zarate, Cristian
    Parrado-Duque, Alejandro
    Osma-Pinto, German
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2022, 138
  • [16] Cerero R., 2011, 2011 IEEE POW EN SOC, P1
  • [17] Chebotareva G., 2021, E3S Web of Conferences
  • [18] From demand response to transactive energy: state of the art
    Chen, Sijie
    Liu, Chen-Ching
    [J]. JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2017, 5 (01) : 10 - 19
  • [19] Blockchains and Smart Contracts for the Internet of Things
    Christidis, Konstantinos
    Devetsikiotis, Michael
    [J]. IEEE ACCESS, 2016, 4 : 2292 - 2303
  • [20] Daneshvar M., 2018, OPERATION DISTRIBUTE, P349