Research on Day-ahead Dispatch of Electricity-heat Integrated Energy System Based on Improved PSO Algorithm

Shuai, Xuanyue; Wang, Xiuli; Li, Kaiying; Wang, Bangyan; Yuan, Shengqi; Zhang, Liang

doi:10.1051/e3sconf/202020402008

Cited by 3 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Genetic algorithms and particle swarm algorithms are two algorithms that are often used in energy planning projects [10][11][12]. However, research has demonstrated that using a pure genetic algorithm or a particle swarm approach to determine the best solution for energy planning is problematic.…”

Section: Introductionmentioning

confidence: 99%

A Novel Energy Planning Scheme Based on PGA Algorithm and Its Application

Tang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In order to actively respond to the “14th Five-Year Plan,” the PGA algorithm is used to develop a new energy planning strategy in this paper. The project can make full use of my country’s abundant renewable energy resources, encourage energy conservation and reduction of emissions, improve the energy structure’s low-carbon level, support the development of smart green energy, and achieve ecological civilization construction. This solution can show users how much greenhouse gas emissions can be reduced through some environmental changes, as well as the basic issues of meeting the future energy needs. It can display the benefits, costs, and emissions data under different scenarios in the future and use the scenario demonstration method to show energy planning to make energy data more vivid. It allows people, technicians, and decision makers to understand what will happen to China’s carbon emissions over time in the next 15 years. This paper innovatively combines a particle swarm optimization algorithm with a genetic algorithm and designs a PGA algorithm for path optimization. In terms of carbon emission reduction, comparative trials demonstrate that the PGA algorithm’s path optimization is 58.06 percent greater than the genetic algorithm; In terms of cost, the PGA algorithm’s path optimization is 15.72% less expensive than the genetic algorithm’s. This article provides a reference path for selecting the best results for future energy planning schemes and provides a new strategy for the “14th Five-Year” energy plan.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Energy Planning Scheme Based on PGA Algorithm and Its Application

Tang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Cantor: A Novel Dynamic Source‐Grid‐Load‐Storage Dispatching Model for Multiple Objectives in a Regional‐Level Power System

Du,

Guo

2024

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

The alignment of the real‐time power supply–demand side becomes a crucial issue in the regional‐level power system due to the growing complexity of source‐grid‐load‐storage scenarios. However, traditional dispatching schemes with inflexible time points are hardly adaptive to the unpredictability of renewable energy generation and multiple‐type load demands. To address this issue, we propose Cantor, a novel dynamic source‐gird‐load‐storage dispatching model for multiple objectives in a regional‐level power system. Cantor incorporates two techniques: (1) An identification algorithm of dispatching time points (IDTP) is designed to satisfy the requirement of power supply–demand alignment dynamically. (2) The latest Twin Delayed Deep Deterministic Policy Gradient decision algorithm is improved to calculate optimal dispatching results in different IDTPs. Furthermore, our case studies highlight Cantor's effective adaptation to complex scenarios in a regional‐level power system dispatching, outperforming state‐of‐the‐art models in power supply reliability, environmental protection, and economic costs, as well as model‐solving efficiency. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

show abstract