Duan Fude, Basem Ali, Jasim Dheyaa J, Belhaj Salem, Eslami Mahdiyeh, Khajehzadeh Mohammad, Palani Sivaprakasam
School of Intelligent Transportation, Nanjing Vocational College of Information Technology, Nanjing, 210000, Jiangsu, China.
Faculty of Engineering, Warith Al-Anbiyaa University, Karbala, 56001, Iraq.
Sci Rep. 2024 Sep 6;14(1):20857. doi: 10.1038/s41598-024-71672-0.
Improving the reliability and power quality of unbalanced distribution networks is crucial for ensuring consistent and reliable electricity supply. In this research, multi-objective optimization of unbalanced distribution networks reconfiguration integrated with wind turbine allocation (MORWTA) is implemented considering uncertainties of networks load, and also wind power incorporating a stochastic framework. The multi-objective function is defined by the minimization of power loss, voltage sag (VS), total harmonic distortion (THD), voltage unbalance (VU), energy not-supplied (ENS), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and momentary average interruption frequency (MAIFI). A new improved dandelion optimizer (IDO) with adaptive inertia weight is recommended to counteract premature convergence to identify decision variables, including the optimal network configuration through opened switches and the best location and size of wind turbines in the networks. The stochastic problem is modeled using the 2m + 1 point estimate method (PEM) combined with K-means clustering, taking into account the mentioned uncertainties. The proposed stochastic methodology is implemented on three modified 33-bus, and unbalanced 25-, and 37-bus distribution networks. The results demonstrated that the MORWTA enhanced all study objectives in comparison to the base networks. The results also demonstrated that the IDO had superior capability to solve the deterministic- and stochastic-MORWTA in comparison to the conventional DO, grey wolf optimizer (GWO), particle swarm optimization (PSO), and arithmetic optimization algorithm (AOA) in terms of achieving greater objective value. Moreover, the results demonstrated that when the stochastic-MORWTA model is considered, the power loss, VS, THD, VU, ENS, SAIFI, SAIDI, and MAIFI are increased by 18.35%, 9.07%, 10.43%, 12.46%, 11.90%, 9.28%, 12.16% and 14.36%, respectively for 25-bus network, and also these objectives are increased by 12.21%, 10.64%, 12.37%, 9.82%, 14.30%, 12.65%, 12.63% and 13.89%, respectively for 37-bus network compared to the deterministic-MORWTA model, which is related to the defined uncertainty patterns.
提高不平衡配电网的可靠性和电能质量对于确保持续可靠的电力供应至关重要。在本研究中，考虑到网络负荷的不确定性以及纳入随机框架的风电，实施了与风力发电机组配置相结合的不平衡配电网重构多目标优化（MORWTA）。多目标函数通过最小化功率损耗、电压暂降（VS）、总谐波失真（THD）、电压不平衡（VU）、未供电能量（ENS）、系统平均停电频率指标（SAIFI）、系统平均停电持续时间指标（SAIDI）和瞬间平均停电频率（MAIFI）来定义。推荐一种具有自适应惯性权重的新型改进蒲公英优化器（IDO），以对抗过早收敛来识别决策变量，包括通过断开开关确定最优网络配置以及网络中风力发电机组的最佳位置和容量。考虑到上述不确定性，使用2m + 1点估计法（PEM）结合K均值聚类对随机问题进行建模。所提出的随机方法在三个修改后的33节点、不平衡的25节点和37节点配电网中实施。结果表明，与基础网络相比，MORWTA改善了所有研究目标。结果还表明，在实现更大目标值方面，与传统的蒲公英优化器（DO）、灰狼优化器（GWO）、粒子群优化算法（PSO）和算术优化算法（AOA）相比，IDO在解决确定性和随机MORWTA方面具有更强的能力。此外，结果表明，当考虑随机MORWTA模型时，对于25节点网络，功率损耗、VS、THD、VU、ENS、SAIFI、SAIDI和MAIFI分别增加了18.35%、9.07%、10.43%、12.46%、11.90%、9.28%、12.16%和14.36%，对于37节点网络，与确定性MORWTA模型相比，这些目标分别增加了12.21%、10.64%、12.37%、9.82%、14.30%、12.65%、12.63%和13.89%，这与定义的不确定性模式有关。
