Optimal Clean Energy Resource Allocation in Balanced and Unbalanced Operation of Sustainable Electrical Energy Distribution Networks

被引:0
作者
Kumar, Abhinav [1 ]
Kumar, Sanjay [1 ]
Sinha, Umesh Kumar [1 ]
Bohre, Aashish Kumar [2 ]
Saha, Akshay Kumar [3 ]
机构
[1] Natl Inst Technol Jamshedpur, Dept Elect, Jamshedpur 831014, India
[2] Natl Inst Technol, Dept Elect Egg, Durgapur 713209, India
[3] Univ KwaZulu Natal, Discipline Elect Elect & Comp Engn, ZA-4041 Durban, South Africa
关键词
distributed clean energy resources; active power loss index; reactive power loss index; voltage index; balanced and unbalanced distribution systems; soft computing techniques; LOAD FLOW SOLUTION; LEARNING-BASED OPTIMIZATION; DISTRIBUTION-SYSTEMS; VOLTAGE REGULATION; OPTIMAL PLACEMENT; GENERATION; DG; RECONFIGURATION; ALGORITHM; COUNTRIES;
D O I
10.3390/en17184572
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
Electric power is crucial for economic growth and the overall development of any country. The efficient planning of distribution system is necessary because all the consumers mainly rely on the distribution network to access the power. This paper focuses on addressing distribution system challenges and meeting consumers' fundamental needs, such as achieving an improved voltage profile and minimizing costs within an environmentally sustainable framework. This work addressed the gap in the existing research by analysing the performance of both balanced and unbalanced systems within the same framework, specifically using the IEEE 33-bus and IEEE 118-bus test systems. Unlike prior studies that focused solely on either balanced or unbalanced systems, this work redistributed balanced loads into three-phase unequal unbalanced loads to create a more challenging unbalanced distribution network. The primary objective is to compare the effects of balanced and unbalanced loads on system the performances and to identify strategies for mitigating unbalanced load issues in each phase. Six optimization methods (PSO, TLBO, JAYA, SCA, RAO, and HBO) were employed to minimize losses, voltage variations, and other multi-objective function factors. Additionally, the study compared the cost of energy loss (CEL), emission factors, costs associated with distributed clean energy resources (DCER), and active and reactive power losses. Phase angle distortions due to unbalanced loads were also analysed. The results showed that among the optimization techniques tested (PSO, TLBO, JAYA, SCA, RAO, and HBO), the HBO method proved to be the most effective for the optimal allocation of distributed clean energy resources, yielding the lowest PFMO values and favourable outcomes across the technical, economic, and environmental parameters.
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页数:52
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共 92 条
  • [1] Abdelhay A.S., 2011, Electric Distribution Systems
  • [2] Optimal power flow using particle swarm optimization
    Abido, MA
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2002, 24 (07) : 563 - 571
  • [3] The Impact of Distributed Energy Storage on Distribution and Transmission Networks' Power Quality
    Adewumi, Olurotimi Babatunde
    Fotis, Georgios
    Vita, Vasiliki
    Nankoo, Daniel
    Ekonomou, Lambros
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (13):
  • [4] Two-Stage Optimization Strategy for Solving the VVO Problem Considering High Penetration of Plug-In Electric Vehicles to Unbalanced Distribution Networks
    Aljohani, Tawfiq Masad
    Saad, Ahmed
    Mohammed, Osama A.
    [J]. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2021, 57 (04) : 3425 - 3440
  • [5] Modeling and open source implementation of balanced and unbalanced harmonic analysis in radial distribution networks
    Antic, Tomislav
    Thurner, Leon
    Capuder, Tomislav
    Pavic, Ivica
    [J]. ELECTRIC POWER SYSTEMS RESEARCH, 2022, 209
  • [6] Arrilliaga J., 1985, Power System Harmonics
  • [7] Impact of harmonic limits on PV penetration levels in unbalanced distribution networks considering load and irradiance uncertainty
    Barutcu, Ibrahim Cagri
    Karatepe, Engin
    Boztepe, Mutlu
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2020, 118
  • [8] Bohre Aashish Kumar, 2015, Advances in Artificial Intelligence, DOI 10.1155/2015/297436
  • [9] Bohre A.K., 2021, P 2021 1 INT C POW E, P1
  • [10] Bohre A.K., 2015, Electr. Comput. Eng. Int. J. (ECIJ), V4, P15, DOI [10.14810/ecij.2015.4202, DOI 10.14810/ECIJ.2015.4202]