Optimal Scheduling of Micro-Energy Grid Based on Pareto Frontier Under Uncertainty and Pollutant Emissions

With the continuous improvement of micro-energy grid, in order to reduce carbon emissions and environmental pollution, a multi-objective optimization operation strategy considering the distributed energy generation uncertainty, environmental factors, and self-supply rate is proposed in this paper. First, to simulate the uncertainty of distributed energy, we used the three-parameter Weibull distribution to simulate the wind power. Then, for the load side, an innovative electric and thermal comprehensive demand response model is proposed. Different from the traditional micro energy network that only considers the heat energy balance, this paper also considers the thermal inertia of buildings to more accurately describe the characteristic of the heat load. Meanwhile, on the basis of economic factors, the abandon rate of distributed energy, environmental coefficient and self-sufficiency rate are also regarded as three objective functions. Finally, in order to obtaining a set of solutions at the Pareto front, the entropy-weighted TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is introduced to obtain the global optimal solution. Simulation study of multiple scenarios shows the effectiveness of the proposed method. Compared with the method that considers only economic costs, the pollutant emissions are reduced by 50kg and self-power supply rate is increased by 20%.