An Intelligent Energy Optimization Strategy for the Multi-Energy Microgrid with the Carbon Trading under the Step Price

It is promising to reduce carbon emission by incor-porating carbon trading into the multi-energy microgrid which can exploit the complementary advantages among various energies. Currently, most carbon trading uses the fixed price, limiting the prohibition effect on carbon emission. Introducing the step price can bring the indifferentiable characteristic of the cost function, which is difficult to solve. To address the issue, in this paper, an energy optimization strategy is proposed for the multi-energy microgrid based on the intelligent meta-heuristic algorithm. Firstly, the models of the electric, heat, gas devices, and energy converters are constructed. Then the energy optimization problem for multiple energies with the carbon trading under step price is formulated. Afterward, an improved grey wolf optimization algorithm with fast convergence is utilized to reduce the operation cost under the lowest carbon emission. The Simulations conducted on the real-world data verify the effectiveness of the proposed strategy.