Research on Source-load Coordinated Dispatch Based on Multi-scenario Wind Power Consumption

Qian, Liang; Lin, Shunfu; Wang, Wei; Quach, Duc-Cuong

doi:10.1109/spies52282.2021.9633930

Cited by 3 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The primary problem affecting wind and solar power consumption is that it is difficult to accurately predict output. The uncertainty of wind and solar power output will be the key factor affecting the absorptive capacity of the power system [3]. To address the uncertainties of wind and solar power outputs, it is necessary to improve the unit commitment (UC) optimization decision.…”

Section: Introductionmentioning

confidence: 99%

Stochastic unit commitment based on energy‐intensive loads participating in wind and solar power consumption

Qian,

Lin,

Zhou

et al. 2024

IET Renewable Power Gen

Self Cite

View full text Add to dashboard Cite

The fluctuation and intermittency of wind and solar power outputs result in increased regulation pressure on thermal units in power systems. Adjustable energy‐intensive loads (such as electrolytic aluminium and steel plants) have great potential for participating in demand response (DR) programs with the goal of reducing thermal unit regulation pressure. This paper proposes an optimal scheduling method of unit commitment (UC) which gives consideration to energy‐intensive loads participating in wind and solar power consumption. The UC method adopts the nonparametric kernel density estimation method to model wind and solar power outputs and then uses the Frank‐Copula function to describe the correlation between the scenarios of wind and solar power outputs. A stochastic unit commitment (SUC) model introduces a chance‐constrained theory of a reserve coefficient to describe time‐variant scenarios on the basis of the deviation between the typical and simulative scenarios. The simulation results based on the IEEE 118‐bus system show that the energy‐intensive load in the SUC model can flexibly adjust and respond to changes in wind and solar power output, reduce the impact of the uncertainties of wind and solar power output, and promote the consumption of wind and solar power.

show abstract

Section: Introductionmentioning

confidence: 99%

Stochastic unit commitment based on energy‐intensive loads participating in wind and solar power consumption

Qian,

Lin,

Zhou

et al. 2024

IET Renewable Power Gen

Self Cite

View full text Add to dashboard Cite

show abstract

Optimal Dispatch of Multi-Energy Power System with High Energy Load Absorbing Blocked Wind and Photovoltaic Power

Jiang

Yao

Chen

2023

2023 6th International Conference on Energy, Electrical and Power Engineering (CEEPE)

View full text Add to dashboard Cite

Power System Dispatch Based on Improved Scenario Division with Physical and Data-Driven Features

Huang,

Cao,

Liang

et al. 2023

Energies

View full text Add to dashboard Cite

In power systems with high penetration of renewable energy, traditional physical model-based optimal dispatch methods suffer from modeling difficulties and poor adaptability, while data-driven dispatch methods, represented by reinforcement learning, have the advantage of fast decision making and reflecting long-term benefits. However, the performances of data-driven methods are much limited by the problem of distribution shift under insufficient power system scenario samples in the training. To address this issue, this paper proposes an improved scenario division method by integrating the power system’s key physical features and the data-driven variational autoencoder (VAE)-generated features. Next, based on the scenario division results, a multi-scenario data-driven dispatch model is established. The effectiveness of the proposed method is verified by a simulation conducted on a real power system model in a province of China.

show abstract

Research on Source-load Coordinated Dispatch Based on Multi-scenario Wind Power Consumption

Cited by 3 publications

References 1 publication

Stochastic unit commitment based on energy‐intensive loads participating in wind and solar power consumption

Stochastic unit commitment based on energy‐intensive loads participating in wind and solar power consumption

Optimal Dispatch of Multi-Energy Power System with High Energy Load Absorbing Blocked Wind and Photovoltaic Power

Power System Dispatch Based on Improved Scenario Division with Physical and Data-Driven Features

Contact Info

Product

Resources

About