Day-ahead optimal dispatch considering demand response compensation and carbon trading under uncertain environment

Area of Science:

• Energy Systems Engineering
• Environmental Economics
• Optimization Theory

Background:

• Balancing energy supply and demand while minimizing environmental impact is crucial.
• Uncertainty in new energy generation and load forecasting poses challenges for grid optimization.
• Integrating demand-side management and carbon pricing mechanisms can enhance grid efficiency and sustainability.

Purpose of the Study:

• To develop a low-carbon economic optimization dispatch model.
• To incorporate demand response (DR) and carbon trading mechanisms for energy saving and emission reduction.
• To address uncertainties in energy systems using fuzzy methods.

Main Methods:

• Analysis of demand response (DR) economic principles and compensation models for different load types.
• Development of a reward-punishment laddered carbon trading model for emission reduction.
• Construction of an optimization model minimizing DR compensation, carbon trading, and operation costs.
• Application of the triangular fuzzy method to handle uncertainty in forecasting.

Main Results:

• The proposed model effectively integrates demand response and carbon trading for synergistic low-carbon effects.
• The optimization objective successfully minimizes combined costs related to DR, carbon trading, and system operations.
• Simulation and analysis confirm the economic and low-carbon benefits of the proposed dispatch model.

Conclusions:

• The integrated model provides a viable strategy for achieving energy saving and emission reduction goals.
• The approach effectively manages uncertainties in new energy and load forecasting.
• The study demonstrates the economic feasibility and environmental advantages of the proposed optimization dispatch model.