Low-carbon operation optimization of integrated energy system considering CCS-P2G and multi-market interaction

Integrated energy system is crucial in realizing China’s “dual carbon” targets. Considering the carbon capture based electricity to gas and the interaction of multiple markets, this paper proposes a low-carbon operation optimization method of integrated energy system. In terms of market policy, a coupling trading mechanism for carbon trade and green certificates is established. This approach is intended to delve into the profound significance of utilizing green certificates in carbon emission reduction. In terms of equipment models, the coupling model of carbon capture equipment with coal-fired cogeneration unit, as well as power-to-gas equipment with renewable energy, is con-structed. In addition, this equipment model is introduced into the operation optimization scheduling of the comprehensive energy systems. A low-carbon economic operational strategy is further proposed to minimize the daily operational costs, by which the integrated energy system is eco-nomically, environmental protection optimized. To verify the effectiveness and feasibility of the proposed model, this paper sets up several comparison scenarios and conducts the simulations using GUROBI solver. The results show that the proposed strategy can effectively improve the uptake rate of renewable energy, reduce the carbon emission, improve the operation economy, and realize the complementary incentive effect between markets

CLSTM-AR-Based Multi-Dimensional Feature Fusion for Multi-Energy Load Forecasting

Integrated Energy Systems (IES) are an important way to improve the efficiency of energy, promote closer connections between various energy systems, and reduce carbon emissions. The transformation between electricity, heating, and cooling loads into each other makes the dynamic characteristics of multiple loads more complex and brings challenges to the accurate forecasting of multi-energy loads. In order to further improve the accuracy of IES short-term load forecasting, we propose the Convolutional Neural Network, the Long Short-Term Memory Network, and Auto-Regression (CLSTM-AR) combined with the multi-dimensional feature fusion (MFFCLA). In detail, CLSTM can extract the coupling and periodic characteristics implied in IES load data from multiple time dimensions. AR takes load data as the input to extract features of sequential auto-correlation over adjacent time periods. Then, the diverse and effective features extracted by CLSTM, LSTM, and AR can be fused using the multi-dimensional feature fusion technique. Ultimately, the model achieves the accurate prediction of multiple loads. In conclusion, compared with other forecasting models, the case study results show that MFFCLA has higher forecasting precision compared with the comparable model in the short-term multi-energy load forecasting performance of electricity, heating, and cooling. The accuracy of MFFCLA can help to optimize and dispatch IES to make better use of renewable energy

CLSTM-AR-Based Multi-Dimensional Feature Fusion for Multi-Energy Load Forecasting

Integrated Energy Systems (IES) are an important way to improve the efficiency of energy, promote closer connections between various energy systems, and reduce carbon emissions. The transformation between electricity, heating, and cooling loads into each other makes the dynamic characteristics of multiple loads more complex and brings challenges to the accurate forecasting of multi-energy loads. In order to further improve the accuracy of IES short-term load forecasting, we propose the Convolutional Neural Network, the Long Short-Term Memory Network, and Auto-Regression (CLSTM-AR) combined with the multi-dimensional feature fusion (MFFCLA). In detail, CLSTM can extract the coupling and periodic characteristics implied in IES load data from multiple time dimensions. AR takes load data as the input to extract features of sequential auto-correlation over adjacent time periods. Then, the diverse and effective features extracted by CLSTM, LSTM, and AR can be fused using the multi-dimensional feature fusion technique. Ultimately, the model achieves the accurate prediction of multiple loads. In conclusion, compared with other forecasting models, the case study results show that MFFCLA has higher forecasting precision compared with the comparable model in the short-term multi-energy load forecasting performance of electricity, heating, and cooling. The accuracy of MFFCLA can help to optimize and dispatch IES to make better use of renewable energy