网络物理电力系统的强大优化研究,考虑到风力发电的不确定性和合关系
Jiuling Dong1, Zilong Song1, Yuanshuo Zheng2
1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Entropy (Basel, Switzerland)
|September 27, 2024
概括
本研究提出了一项强有力的优化策略,以提高电力系统对风力发电不确定性和网络合的弹性. 新模型显著减少了负载减排,并在中断期间提高了网络存活率.
科学领域:
- 电气工程 电气工程
- 复杂的网络 复杂的网络
- 优化理论 优化理论
背景情况:
- 电力系统面临的挑战是间歇性风力发电和电力和通信网络之间的复杂合.
- 现有的模型往往无法充分解决这些因素对系统稳定性的综合影响.
研究的目的:
- 制定一个双层混合整数强大的优化策略,以提高电力系统的弹性.
- 减轻风力发电不确定性和电力通信合对系统稳定性和可靠性的影响.
主要方法:
- 使用复杂的网络理论构建了一个合网络模型,以表示电力和通信系统之间的相互依赖.
- 开发了一个双层强大的优化模型,在上层具有模糊的机会约束 (负载减排最小化) 和在下层具有拓函数受约束的交通模型 (交通负载减排最小化).
- 使用经过修改的IEEE 39总线测试系统进行案例研究和模拟,包括随机攻击场景.
主要成果:
- 拟议的策略通过优化风力发电波动下的直流电流来有效地减少负载减排.
- 下层模型通过最大限度地减少流量负载减排来提高信息网络的效率.
- 与高压条件下的基线模型相比,模拟显示负载损失比率减少了23.6%,节点存活率改善了5.3%.
结论:
- 双层强大的优化策略显著提高了对风力发电不确定性和网络合的电力系统弹性.
- 综合方法为管理复杂的电力和通信网络交互提供了一个强大的框架.
- 研究结果为设计更可靠,更稳定的未来电网提供了宝贵的见解.
更多相关视频
09:04A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
Published on: June 1, 2022
3.0K
10:36Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
Published on: November 3, 2023
1.4K
相关概念视频
01:24Wind Turbine Machine Models
110
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
110
01:17Turbine-Governor Control
178
Turbine-governor control is crucial for maintaining power system stability by balancing turbine mechanical power output with electrical load demand. This mechanism ensures that generator frequency and rotor speed are within acceptable limits during load variations. Turbine-generator units store kinetic energy due to their rotating masses; this energy is released to meet the load requirement when the load increases. The electrical torque of turbines rises to meet the demand, whereas the...
178
01:23Maximum Power Flow and Line Loadability
97
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
97
01:24Fast Decoupled and DC Powerflow
178
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
178
01:10Propagation of Uncertainty from Systematic Error
490
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
490
00:59Propagation of Uncertainty from Random Error
656
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
656