Stochastic-Metaheuristic Model for Multi-Criteria Allocation of Wind Energy Resources in Distribution Network Using Improved Equilibrium Optimization Algorithm

被引:11
|
作者
Alanazi, Abdulaziz [1 ]
Alanazi, Mohana [2 ]
Nowdeh, Saber Arabi [3 ]
Abdelaziz, Almoataz Y. [4 ]
Abu-Siada, Ahmed [5 ]
机构
[1] Northern Border Univ, Coll Engn, Dept Elect Engn, Ar Ar 73222, Saudi Arabia
[2] Jouf Univ, Coll Engn, Dept Elect Engn, Sakaka 72388, Saudi Arabia
[3] Inst Res Sci, Power & Energy Grp, Johor Baharu 81310, Malaysia
[4] Future Univ Egypt, Fac Engn & Technol, Cairo 11835, Egypt
[5] Curtin Univ, Sch Elect Engn Elect & Comp Engn Discipline, Comp & Math Sci, Perth, WA 6102, Australia
关键词
distribution network; wind turbine; multi-objective allocation; stochastic-metaheuristic model; improved equilibrium optimization algorithm; DISTRIBUTION-SYSTEMS; GENERATION ALLOCATION; RECONFIGURATION; OPERATION; LOAD;
D O I
10.3390/electronics11203285
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, a stochastic-meta-heuristic model (SMM) for multi-criteria allocation of wind turbines (WT) in a distribution network is performed for minimizing the power losses, enhancing voltage profile and stability, and enhancing network reliability defined as energy not-supplied cost (ENSC) incorporating uncertainty of resource production and network demand. The proposed methodology has been implemented using the SMM, considering the uncertainty modeling of WT generation with Weibull probability distribution function (PDF) and load demand based on the normal PDF and using a new meta-heuristic method named the improved equilibrium optimization algorithm (IEOA). The traditional equilibrium optimization algorithm (EOA) is modeled by the simple dynamic equilibrium of the mass with proper composition in a control volume in which the nonlinear inertia weight reduction strategy is applied to improve the global search capability of the algorithm and prevent premature convergence. First, the problem is implemented without considering the uncertainty as a deterministic meta-heuristic model (DMM), and then the SMM is implemented considering the uncertainties. The results of DMM reveal the better capability of the IEOA method in achieving the lowest losses and the better voltage profile and stability and the higher level of the reliability in comparison with conventional EOA, particle swarm optimization (PSO), manta ray foraging optimization (MRFO) and spotted hyena optimization (SHO). The results show that in the DMM solving using the IEOA, traditional EOA, PSO, MRFO, and SHO, the ENSC is reduced from $3223.5 for the base network to $632.05, $636.90, $638.14, $635.67, and $636.18, respectively, and the losses decreased from 202.68 kW to 79.54 kW, 80.32 kW, 80.60 kW, 80.05 kW and 80.22 kW, respectively, while the network minimum voltage increased from 0.91308 p.u to 0.9588 p.u, 0.9585 p.u, 0.9584 p.u, 0.9586 p.u, and 0.9586 p.u, respectively, and the VSI improved from 26.28 p.u to 30.05 p.u, 30.03 p.u, 30.03 p.u, 30.04 p.u and 30.04 p.u; respectively. The results of the SMM showed that incorporating uncertainties increases the losses, weakens the voltage profile and stability and also reduces the network reliability. Compared to the DMM, the SMM-based problem is robust to prediction errors caused by uncertainties. Therefore, SMM based on existing uncertainties can lead to correct decision-making in the conditions of inherent-probabilistic changes in resource generation and load demand by the network operator.
引用
收藏
页数:20
相关论文
共 50 条
  • [21] Reservoir Optimization for Energy Production Using a New Evolutionary Algorithm Based on Multi-Criteria Decision-Making Models
    Ehteram, Mohammad
    Karami, Hojat
    Farzin, Saeed
    WATER RESOURCES MANAGEMENT, 2018, 32 (07) : 2539 - 2560
  • [22] A multi-objective improved horse herd optimizer based on convex lens imaging for stochastic optimization of wind energy resources in distribution networks considering reliability and uncertainty
    Duan, Fude
    Basem, Ali
    Jasim, Dheyaa J.
    Eslami, Mahdiyeh
    Okati, Mustafa
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [23] Reactive power optimization for distribution network system with wind power based on improved multi-objective particle swarm optimization algorithm
    Kuang, Honghai
    Su, Fuqing
    Chang, Yurui
    Kai, Wang
    He, Zhiyi
    ELECTRIC POWER SYSTEMS RESEARCH, 2022, 213
  • [24] Improved self-adaptive differential evolution algorithm for reactive power optimization of smart distribution network with wind energy
    Li, Timing
    Yuan, Rongxiang
    Deng, Xiangtian
    INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2016, 26 (12): : 2744 - 2758
  • [25] Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm
    Mumtahina, Umme
    Alahakoon, Sanath
    Wolfs, Peter
    ENERGIES, 2025, 18 (02)
  • [26] Optimal capacity allocation of wind-light-water multi-energy complementary capacity based on improved multi-objective optimization algorithm
    Wang, Ying
    Liu, Jiajun
    FRONTIERS IN ENERGY RESEARCH, 2023, 10
  • [27] Optimal Allocation and Planning of Distributed Power Generation Resources in a Smart Distribution Network Using the Manta Ray Foraging Optimization Algorithm
    Vahid, Masoud Zahedi
    Ali, Ziad M.
    Najmi, Ebrahim Seifi
    Ahmadi, Abdollah
    Gandoman, Foad H.
    Aleem, Shady H. E. Abdel
    ENERGIES, 2021, 14 (16)
  • [28] Energy Saving Maximization of Balanced and Unbalanced Distribution Power Systems via Network Reconfiguration and Optimum Capacitor Allocation Using a Hybrid Metaheuristic Algorithm
    Mohamed, Mohamed Abd-El-Hakeem
    Ali, Ziad M.
    Ahmed, Mahrous
    Al-Gahtani, Saad E.
    ENERGIES, 2021, 14 (11)
  • [29] Optimal Reconfiguration of Distribution Network Considering Stochastic Wind Energy and Load Variation Using Hybrid SAMPSO Optimization Method
    Sellami, Raida
    Khenissi, Imene
    Guesmi, Tawfik
    Alshammari, Badr M.
    Alqunun, Khalid
    Alshammari, Ahmed S.
    Tlijani, Kamel
    Neji, Rafik
    SUSTAINABILITY, 2022, 14 (18)
  • [30] Robust Allocation and Scheduling of Electric Parkings and Wind Resources in Distribution Networks Using Information Gap Decision Theory and Improved Flow Direction Algorithm
    Arabahmadi, Neda
    Ebrahimi, Reza
    Ghanbari, Mahmood
    INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2024, 2024 (01)