使用机器学习和模糊多目标改进的开普勒优化算法优化分布网络中的基于光伏/风能/电池的微电网
Fude Duan1, Mahdiyeh Eslami2, Mohammad Khajehzadeh3
1School of Intelligent Transportation, Nanjing Vocational College of Information Technology, Nanjing, 210000, Jiangsu, China.
Scientific reports
|June 10, 2024
概括
这项研究利用预测数据优化了混合微电网,表明,虽然预测数据稍微增加了成本,但电池使用显著减少了分布网络中的能量损失和电压偏差.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 优化算法 优化算法
背景情况:
- 混合微电网 (HMG) 集成光伏 (PV),风能和电池储能 (BES) 对于现代配电网络至关重要.
- 优化HMG包括平衡能量损耗,电压稳定性和购买电力成本,特别是使用预测数据时.
研究的目的:
- 开发和应用一个模糊的多目标框架,用于在33个总线的配送网络中优化HMG (PV/WT/BES).
- 使用预测数据,尽量减少能源损失,电压振荡和购买电力成本.
- 为了评估电池放电深度对HMG性能的影响.
主要方法:
- 开发了一种多目标改进的开普勒优化算法 (MOIKOA),结合了开普勒定律,混乱地图和FDMT.
- 一个多层感知人造神经网络 (MLP-ANN) 用于预测太阳辐射,风速,温度和负载.
- 实施了三个优化场景:真实数据,预测数据和不同的电池放电深度.
主要成果:
- 与传统的开普勒,PSO和GA算法相比,MOIKOA表现出更高的性能.
- 使用预测数据进行优化 (MLP-ANN) 导致能源损耗增加了3.50%,电压偏差增加了2.33%,购买电力成本与实际数据相比增加了1.98%.
- 电池放电深度的增加增加了BES的参与,从而减少了网络能量损失和电压偏差.
结论:
- MOIKOA框架有效地优化了HMG用于分销网络,平衡了多个目标.
- 虽然预测数据引入了微小的成本增加,但其与MLP-ANN等先进算法的集成是可行的.
- 电池储能在减轻HMG运营影响和提高网络稳定性方面发挥着至关重要的作用.
更多相关视频
10:36Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
Published on: November 3, 2023
1.5K
05:30Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
536
相关概念视频
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
01:21Distributed Loads: Problem Solving
642
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
642
01:24Fast Decoupled and DC Powerflow
187
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:
187
01:20Maxwell-Boltzmann Distribution: Problem Solving
1.5K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.5K
01:17Ampere-Maxwell's Law: Problem-Solving
617
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
617
01:26The Power Flow Problem and Solution
200
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...
200