A multi-time scale rolling optimization framework for low-carbon operation of CCHP microgrids with demand response...

Area of Science:

• Energy Systems Engineering
• Artificial Intelligence in Energy
• Sustainable Energy Development

Background:

• Microgrids are crucial for low-carbon societies, but optimal operation is hindered by equipment response rates, load prediction errors, and multi-energy device complexities.
• Integrating carbon trading and demand response (DR) presents opportunities for enhanced microgrid performance and sustainability.
• Challenges in microgrid operation include managing diverse equipment response times and intricate energy interdependencies.

Purpose of the Study:

• To propose a robust optimization framework for Combined Cooling, Heating, and Power (CCHP) microgrid systems.
• To address operational challenges including response rate disparities, load prediction inaccuracies, and complex device coupling.
• To minimize operating costs while integrating demand response and carbon trading mechanisms.

Main Methods:

• A two-layer rolling optimization framework with multi-time scale scheduling was developed for CCHP microgrids.
• Wind and photovoltaic power generation were predicted using a CNN-ATT-BiLSTM model, compared against CNN, BiLSTM, and CNN-LSTM.
• A multi-time scale optimization model was established with operating cost minimization as the objective function.

Main Results:

• The CNN-ATT-BiLSTM model demonstrated superior performance in predicting renewable energy generation compared to baseline models.
• The proposed optimization framework successfully coordinated multi-temporal resolutions to meet cooling, heating, and power demands.
• Comparative analysis of four operational scenarios highlighted the cost-effectiveness of integrating DR and carbon trading.

Conclusions:

• The developed framework effectively mitigates stochastic supply-demand fluctuations in CCHP microgrids.
• The study validates the simultaneous satisfaction of diverse energy demands through coordinated multi-temporal scheduling.
• The integration of demand response and carbon trading significantly enhances the economic and sustainable operation of microgrids.