Optimizing Load Dispatch using Artificial Neural Networks and Fractional Weighted Models

Summary

This paper proposes an innovative approach combining Artificial Neural Networks with a Fractional Weighted Model for load dispatch optimization. The hybrid method enables more accurate, adaptive prediction and dynamic load allocation, helping to balance demand, reduce transmission losses, and strengthen grid stability under varying operating conditions.

Abstract

The paper describes a protocol that will optimize the real-time load data collected from regional load dispatch centres in India with a hybrid Artificial Neural Network (ANN) and Fractional Weighted Load Dispatch (FWLD) approach. The flow is data input, fractional calculation (using fractional calculus to incorporate alpha memory and non-locality of time), ANN module training, and making real-time dispatch decisions. The referred model, S,T,D,C,T (Supply, Transmission, Demand, Cost, and Time) efficiently tracks real-time fluctuations of S,T,D,C,T to optimize energy allocation. This method outperforms traditional ones in load distribution and flexibility with grid oscillations. Experimental studies have reported the values of Mean Squared Error (MSE) of 245.80 MW² during training, 260.95 MW² during testing, with Root Mean Squared Errors (RMSE) of 15.68 MW and 16.15 MW during testing; Mean Absolute Percentage Error (MAPE) values also were recorded, being less than 10.35%, showing better predictability. This combination of ANN learning and fractional weighting methods gives more stability, efficiency, and scalability for present power system optimization.

Introduction

Load dispatch optimization is an essential problem in contemporary power grids, involving optimal utilization of generation capacity to fulfill demand at the lowest cost and stability. Conventional methods struggle to deal with the dynamic nature of power systems; therefore, computational methods have been developed. This paper integrates Artificial Neural Networks (ANNs) with Fractional Weighted Load Dispatch (FWLD) to optimize the power system. It has been demonstrated in recent studies that ANN-based models can be efficient for short-term electrical load forecasting. Recent works have made inroads into fields relating to applications of ANNs and their variants in f....

Protocol

Fractional mathematical model for power dispatch

The present study derives the FWLD model via fractional differential equations for optimal power distribution. The Caputo fractional derivative accounts for memory effects in the system to obtain a more accurate understanding of power variation over time. We also present numerical solutions via the Grünwald-Letnikov (GL) method, which is suitable for discretizing fractional order models in power grids²⁵.

Model overview

The FWLD model aims to optimize power dispatch strategies by i....

Results

Model training, performance evaluation, and error analysis

Here, we describe the process of model building, performance analysis, and error analysis for the predictive weather load dispatch model. This study aims to select appropriate features and train the machine learning model, evaluate its performance, and examine its limitations.

Feature s.......

Discussion

The incorporation of machine learning in FWLD has proven to bring noteworthy advancements in energy optimization, enabling accurate and dynamic load distribution. The findings reveal that machine learning models, especially ANNs, can accurately forecast energy demand patterns, minimizing total system inefficiencies. Through the use of historical and real-time data, the models improve the accuracy of load dispatch decisions, resulting in more stable grid operation. Concerning conventional techniques, machine learning-base.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Cloud-based Python Runtime (Colab)	Current (2024)	https://colab.research.google.com/drive/1TpfkodoyzoO2m4Aq7nIih1aT5_zUydZo#scrollTo=wE7HaH-V0VQM
Matplotlib	3.5	matplotlib.org
NumPy	1.22	numpy.org