Sarkar Tushnik, Gupta Sabyasachi, Paul Chandan, Dutta Susanta, Roy Provas Kumar, Bhattacharya Anagha, Tejani Ghanshyam G, Mousavirad Seyed Jalaleddin
Department of Electrical Engineering, Dr. B. C. Roy Engineering College, Durgapur, India.
Department of Electrical Engineering, NIT Mizoram, Aizawl, India.
Sci Rep. 2025 Jun 4;15(1):19594. doi: 10.1038/s41598-025-02636-1.
On IEEE 30, 57, 118 & 300-bus experimental networks, this work aims to solve the optimal reactive power dispatch (ORPD) problem. Initially, the conventional network is countered, and subsequently, renewable energy sources (RESs) such as wind power (WP), solar photovoltaic (PV) sources, and hydro power (HP) are combined with the traditional network. This study examines both single and multiple type objective functions (OFs). The Objectives include lowering active power loss (APL), lowering aggregated voltage deviation (AVD), lowering the voltage stability index (VSI), lowering reactive power loss and concurrently lowering AVD, APL & VSI. There are five test modules that comprise a total of 30 cases. Cases 5-8 and 13-30 are being conducted using STATCOM in conjunction with the test setup. The Driving Training Based Optimization (DTBO) method has been used to achieve the goals, and its performance has been compared to that of other optimization algorithms that have been reported in recent ORPD studies. Both stable load demand and uncertain changing load demand scenarios are included in the study. Appropriate probability density functions (PDF) are employed to estimate the uncertain WP, PV source, HP, and load demand. Uncertain scenarios with variable load demand, wind speed (WS), solar irradiance (SI), and water flow rate (WFR) are created using Monte Carlo simulations (MCS). Based on a range of studied cases, the experiment results show that the DTBO has a significantly stronger ability to solve ORPD challenges than the optimization methods discovered in the most recent ORPD literature. The usage of STATCOM improves power network performance for the ORPD issue, which is another significant finding. From simulation results it has been observed that for IEEE 30 bus the average power loss (APL) is 4.5086 MW, utilizing STATCOM the APL is reduced by 5.3% MW, with integrating renewable sources the APL is reduced 41%, and for both STATCOM and renewable sources (RESs) system it decreases to 43.6%. Hence, STATCOM and RES help to reduce the power losses using DTBO approach. Furthermore, average voltage deviation (AVD) improved by 97.4 % with incorporating STATCOM-RESs. Voltage stability index (VSI) improved by 26.9% with scheduling STATCOM and renewable sources (RESs). For the multi-objective situation APL & AVD both simultatiously improved to 5.0701(MW) & 0.1221 (p.u.), respectively, with incorporating STATCOM and RESs using DTBO. Voltage deviation converges at 40 iterations for with STATCOM but for without STATCOM it takes 80 iterations to converge. Similarly for voltage stability index with STATCOM converge 4 iterations earlier rather than without STATCOM system. Again for large scale IEEE 57 bus system The DTBO approach incorporating STATCOM and RESs provided optimal results. So, for IEEE 30, 57, 118 & 300 bus systems DTBO proves its superiority and robustness satisfactorily. From simulation results it has been observed that for IEEE 30 bus the average power loss (APL) is 4.5086 MW, utilizing STATCOM the APL is reduced by 5.3% MW, with integrating renewable sources the APL is reduced 41%, and for both STATCOM and renewable sources (RESs) system it decreases to 43.6%. Hence, STATCOM and RES help to reduce the power losses using DTBO approach. Furthermore, average voltage deviation (AVD) improved by 97.4 % with incorporating STATCOM-RESs. Voltage stability index (VSI) improved by 26.9% with scheduling STATCOM and renewable sources (RESs). For the multi-objective situation APL & AVD both simultatiously improved to 5.0701(MW) & 0.1221 (p.u.), respectively, with incorporating STATCOM and RESs using DTBO. Voltage deviation converge at 40 iterations for with STATCOM but for without STATCOM it takes 80 iterations to converge. So, for IEEE 30, 57, 118 & 300 bus systems DTBO proof its superiority and robustness satisfactorily.
在IEEE 30、57、118和300节点实验网络上，这项工作旨在解决最优无功功率调度（ORPD）问题。首先，应对传统网络，随后，将诸如风力发电（WP）、太阳能光伏（PV）电源和水力发电（HP）等可再生能源与传统网络相结合。本研究考察了单目标和多目标函数（OFs）。目标包括降低有功功率损耗（APL）、降低综合电压偏差（AVD）、降低电压稳定指标（VSI）、降低无功功率损耗并同时降低AVD、APL和VSI。有五个测试模块，总共包括30个案例。案例5 - 8和13 - 30正在使用静止同步补偿器（STATCOM）结合测试装置进行。基于驱动训练的优化（DTBO）方法已被用于实现这些目标，并且其性能已与近期ORPD研究中报道的其他优化算法的性能进行了比较。该研究包括稳定的负荷需求和不确定变化的负荷需求场景。采用适当的概率密度函数（PDF）来估计不确定的WP、PV电源、HP和负荷需求。使用蒙特卡罗模拟（MCS）创建具有可变负荷需求、风速（WS）、太阳辐照度（SI）和水流速率（WFR）的不确定场景。基于一系列研究案例，实验结果表明，DTBO解决ORPD挑战的能力比近期ORPD文献中发现的优化方法显著更强。使用STATCOM改善了针对ORPD问题的电网性能，这是另一个重要发现。从仿真结果可以观察到，对于IEEE 30节点系统，平均功率损耗（APL）为4.5086兆瓦，使用STATCOM时APL降低了5.3%兆瓦，整合可再生能源时APL降低了41%，对于同时采用STATCOM和可再生能源（RESs）的系统，APL降至43.6%。因此，STATCOM和RES有助于使用DTBO方法降低功率损耗。此外，结合STATCOM - RESs时，平均电压偏差（AVD）提高了97.4%。调度STATCOM和可再生能源（RESs）时，电压稳定指标（VSI）提高了26.9%。对于多目标情况，使用DTBO结合STATCOM和RESs时，APL和AVD分别同时改善到5.0701（兆瓦）和0.1221（标幺值）。对于有STATCOM的情况，电压偏差在40次迭代时收敛，但对于没有STATCOM的情况，需要80次迭代才能收敛。同样，对于有STATCOM的电压稳定指标，比没有STATCOM的系统提前4次迭代收敛。再次，对于大规模IEEE 57节点系统，结合STATCOM和RESs的DTBO方法提供了最优结果。所以，对于IEEE 30、57、118和300节点系统，DTBO令人满意地证明了其优越性和鲁棒性。从仿真结果可以观察到，对于IEEE 30节点系统，平均功率损耗（APL）为4.5086兆瓦，使用STATCOM时APL降低了5.3%兆瓦，整合可再生能源时APL降低了41%，对于同时采用STATCOM和可再生能源（RESs）系统，APL降至43.6%。因此，STATCOM和RES有助于使用DTBO方法降低功率损耗。此外，结合STATCOM - RESs时，平均电压偏差（AVD）提高了97.4%。调度STATCOM和可再生能源（RESs）时，电压稳定指标（VSI）提高了26.9%。对于多目标情况，使用DTBO结合STATCOM和RESs时，APL和AVD分别同时改善到5.0701（兆瓦）和0.1221（标幺值）。对于有STATCOM的情况，电压偏差在40次迭代时收敛，但对于没有STATCOM的情况，需要80次迭代才能收敛。所以，对于IEEE 30、57、118和300节点系统，DTBO令人满意地证明了其优越性和鲁棒性。
