Energy Economic Dispatch for Photovoltaic–Storage via Distributed Event-Triggered Surplus Algorithm

This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices. This method integrates features including photovoltaic (PV) systems, energy storage coupling, varied energy roles, and energy supply and demand dynamics. The system model is developed by co...

Full description

Saved in:
Bibliographic Details
Published in:Energy engineering Vol. 121; no. 9; pp. 2621 - 2637
Main Authors: Liu, Kaicheng, Liang, Chen, Wu, Naiyue, Dong, Xiaoyang, Yu, Hui
Format: Journal Article
Language:English
Published: Atlanta Tech Science Press 2024
Subjects:
Algorithms
Energy
Energy sources
Graph theory
Normal distribution
Photovoltaic cells
Production costs
Smart grid
ISSN:1546-0118, 0199-8595, 1546-0118
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices. This method integrates features including photovoltaic (PV) systems, energy storage coupling, varied energy roles, and energy supply and demand dynamics. The system model is developed by considering energy devices as versatile units capable of fulfilling various functionalities and playing multiple roles simultaneously. To strike a balance between optimality and feasibility, renewable energy resources are modeled with considerations for forecasting errors, Gaussian distribution, and penalty factors. Furthermore, this study introduces a distributed event-triggered surplus algorithm designed to address the economic dispatch problem by minimizing production costs. Rooted in surplus theory and finite time projection, the algorithm effectively rectifies network imbalances caused by directed graphs and addresses local inequality constraints. The algorithm greatly reduces the communication burden through event triggering mechanism. Finally, both theoretical proofs and numerical simulations verify the convergence and event-triggered nature of the algorithm.
ISSN:1546-0118
0199-8595
1546-0118
DOI:10.32604/ee.2024.050001