Back to Search Start Over

Optimal schedule for virtual power plants based on price forecasting and secant line search aided sparrow searching algorithm

Authors :
Hongbo Wu
Bo Feng
Peng Yang
Hongtao Shen
Hao Ma
Weile Kong
Xintong Peng
Source :
Frontiers in Energy Research, Vol 12 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

With a growing focus on the environment, the power system is evolving into a cleaner and more efficient energy supply infrastructure. Photovoltaic (PV) and storage are key assets for the power industry’s shift to sustainable energy. PV generation has zero carbon emission, and the integration of a substantial number of PV units is fundamentally important to decarbonize the power system. However, it also poses challenges in terms of voltage stability and uncertainty. Besides, the daily load and real-time price are also uncertain. As a prosumer, energy storage demonstrates the capacity to enhance accommodation and stability. The adoption of Virtual Power Plants (VPPs) emerges as a promising strategy to address these challenges, which allows the coordinated orchestration of PV systems and storage to participate power dispatch as a virtual unit. It further augments the flexibility of the power distribution system (PDS). To maximize the profit of VPP, a data-driven price forecasting method is proposed to extract useful information from historical datasets based on a novel LSTM-Transformer-combined neural network. Then, an improved sparrow searching algorithm (SSA) is proposed to schedule VPPs by combining the secant line search strategy. The numerical results, obtained from testing the model on IEEE 13-node and 141-node distribution systems, demonstrate the effectiveness and efficiency of the proposed model and methodology.

Details

Language :
English
ISSN :
2296598X
Volume :
12
Database :
Directory of Open Access Journals
Journal :
Frontiers in Energy Research
Publication Type :
Academic Journal
Accession number :
edsdoj.7b724be884a740c88da30aed6e4a01a6
Document Type :
article
Full Text :
https://doi.org/10.3389/fenrg.2024.1427614