PDF(4121 KB)
Bi-Level Game Optimal Operation of Integrated Energy System Considering Carbon Trading and Green Certificates Uncertainty
GONG Yongguo, ZENG Linjun, CHEN Jian, YAN Qin, LI Yuhao, XU Jiani
Electric Power Construction ›› 2025, Vol. 46 ›› Issue (12) : 119-130.
PDF(4121 KB)
PDF(4121 KB)
Bi-Level Game Optimal Operation of Integrated Energy System Considering Carbon Trading and Green Certificates Uncertainty
[Objective] Integrated energy systems （IESs） face multiple challenges in carbon and green certificate trading markets. Existing scheduling models have difficulty in balancing the dynamics of economic returns with low-carbon objectives and lack effective quantification methods for unstructured uncertainty. An optimal scheduling strategy for IES intervals that takes into account the uncertainties of green certificates and carbon trading is proposed，and a master-slave game mechanism is combined to coordinate conflicts of interest with multiple actors to enhance economic outcomes and minimize the overall carbon footprint of the system. [Methods] First，a quantitative model of uncertainty in carbon trading and green certificate markets is constructed based on interval mathematics to characterize the range of fluctuation of green certificate trades and carbon emissions in terms of the number of intervals to avoid relying on a probability distribution. Second，a master-slave game framework is established for an IES operator and an energy supplier，and a two-layer interval optimization model is designed. The upper layer dynamically optimizes an energy pricing strategy to maximize the revenue of the IES operator，and the lower layer optimizes the unit output to maximize the revenue of the energy supplier. Furthermore，the particle swarm algorithm was applied to nest the CPLEX solver to efficiently search for an equilibrium solution to the game. The performance of the model was verified through a multi-scenario comparative analysis. [Results] Simulation results show that the proposed two-layer interval optimization model increased the revenue of the integrated energy system operator and energy supplier by 2.3 % and 5.1 %，respectively，and reduced the cost of carbon emissions by 12.7 %. Narrow interval optimization combined with the dynamic pricing strategy significantly mitigated the impact of fluctuations in green certificates and carbon trading on the system，which verifies the model's economic efficacy and synergistic capability to optimize for minimal carbon consumption. [Conclusions] By integrating interval optimization and the master-slave game，the proposed model balances the conflicting interests of IESs effectively and improves the low-carbon economics of the system by taking the uncertainty of carbon trading and green certificate markets into consideration.
green certificate trading / carbon emission trading / integrated energy system（IES） / interval optimization / master- slave game / uncertainty
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A park-integrated energy system (PIES) is the key to optimizing and upgrading energy systems and reducing carbon emissions in the power industry. How parks participate in the electricity-carbon-green certificate market by formulating trading strategies, improving overall efficiency, and reducing system carbon emissions is key to the rapid development of parks in the future open market environment. This study is based on the noncooperative game model of three parties in a park, constructing a power market operation framework of green electricity day-ahead market, conventional energy day-ahead market, and real-time market, and introducing a green certificate-carbon emission equivalent offset mechanism to achieve a connection between green certificate trading and carbon trading. A park comprehensive energy system day-ahead optimization scheduling model that considers the coupling of the electricity-carbon-green certificate market was proposed. Finally, the improved particle swarm optimization algorithm and alternating direction multiplier method are utilized to solve the nonlinear model and verified in a typical park in Shanghai. The results indicated that the proposed method could effectively reduce carbon emissions in the park, and the proportion of green electricity in the total electricity consumption of the park increased by 6.4%.
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