New energy power generation plays an important role in adjusting energy structure, reducing environmental pollution, and promoting sustainable social development. However, new energy power generation represented by solar and wind power has significant volatility, meanwhile, the wind and solar curtailment caused by uncertainty reduce utilization rate. In this context, considering the impact of uncertainty in new energy generation on system scheduling and developing new algorithms that can efficiently solves such complex scheduling problems are necessary. Therefore, this study constructed a hybrid dynamic economic emission model (HDEED) with the new energy generation penalty mechanism, and proposed a novel multi-objective tunicate swarm algorithm (MTSA)-based solution method. First, based on the unit operation characteristic equations, considering wind and solar power uncertainty, the HDEED model with the new energy generation penalty mechanism was constructed. Second, on the basis of the single objective tunicate swarm algorithm, MTSA algorithm was proposed by adding the running operators, and the feasibility of the proposed algorithm was verified. The test results showed that the Pareto fronts obtained by MTSA algorithm presented good distribution and diversity compared with existing algorithms. Finally, the proposed model and algorithm were proved by modified test system, meanwhile, the impacts of clean energy power on system operation stability, economic and environmental benefits were discussed. The results revealed that the penalty costs caused by uncertainty were higher than the operation and maintenance costs of new energy power stations, which accounted for about 2.1% of the total costs. The outcomes obtained in this study contribute to improving the utilization rate of clean energy and promoting the sustainable development of energy system.

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