通过准确的功率负载预测优化水电计划:一个实际的案例研究
Guangqin Huang1, Ming Tan1, Zhihang Meng2,3
1Guizhou Wujiang River Navigation Authority, Tongren, 565100, Guizhou, China.
Heliyon
|April 4, 2024
概括
本研究介绍了一种水力发电调度模型,该模型整合了电力负载预测和优化,使得即使有延迟数据,也可以有效地调度电网. 该模型有效地平衡了发电和导航需求.
科学领域:
- * 工程 * 工程师 *
- * 环境科学 环境科学
- * 运营研究 运营研究
背景情况:
- * 连接到电网的水电站面临着由于负载数据延迟而造成的发电不均和效益分配的挑战.
- *当前的调度模型在实时负载信息无法获得时,难以实现低于最佳的调度.
研究的目的:
- *为水电站开发一种新的调度模型,该模型结合了功率负载预测和双重目标优化.
- * 解决延迟负载数据的问题,并提高网联水电运营的调度效率.
- * 为了实现水电发电和航行要求之间的和平衡.
主要方法:
- *对各种功率负载预测模型的评估,确定卷积神经网络门式递归单元 (CNN-GRU) 是最准确的.
- * 将预测的功率负载数据集成到一个增强的精英非主导排序遗传算法 (GA-NSGA-II).
- *利用拟议的目标函数,优化水电站排放流.
主要成果:
- *CNN-GRU模型实现了高预测准确性,其R平方为0.991和RMSE为0.026.
- *基于预测负载值的调度显示与实际负载值相比差异最小 (5%以内),证明了实际有效性.
- *优化的调度成功地平衡了水电发电和船舶航行需求,在现实世界的案例研究中.
结论:
- * 开发的模型为水电站调度提供了有效的解决方案,即使负载数据不完整或延迟.
- *该方法有效地解决了连接到电网的水电运营中的实际挑战.
- * 实现了优化发电和无障碍导航的双重好处,展示了该模型的实际应用.
更多相关视频
06:04Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
Published on: February 14, 2025
342
11:53Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
13.0K
相关概念视频
01:24Fast Decoupled and DC Powerflow
191
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:
191
01:30Control of Power Flow
266
There are several methods to control power flow in power systems:
266
01:26The Power Flow Problem and Solution
211
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk, phase angle δk, real power Pk, and reactive power Qk. Two of these four variables are inputs, while the...
211
01:23Maximum Power Flow and Line Loadability
107
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.
107
01:28Load-frequency control
162
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
162
01:25Multimachine Stability
151
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
151