To address the challenges of large loads and frequent sudden load fluctuations during peak hours in highway energy microgrid,an intraday scheduling decision-making optimization model for wind/solar/hydrogen storage highway microgrid was proposed based on the improved Pareto algorithm,to ensure the power peak shaving and valley filling in the microgrid during the intraday operation cycle,help the power system maximize the consumption of wind/solar unit output,and achieve the complementary and optimal utilization of different types of energies.The objective function was constructed by using the lowest intraday operating cost,the lowest carbon emission,and the highest wind/solar consumption rate of the microgrid system as the main criteria.In view of various constraints such as the electric power balance,wind/solar energy output,hydrogen storage energy,and interaction with the external grid,the output strategy was formulated according to the policy of preferential consumption of renewable energy,and the optimal scheduling decision-making results of the microgrid in the region were output.To verify the validity,accuracy,and practicality of the proposed model,the meteorological data and electric load data of Jili Lake section of Xinjiang S21 Highway were analyzed.Research results show that the wind/solar/hydrogen storage highway microgrid system constructed based on the proposed optimization model can effectively improve the consumptions of wind/solar energies.Compared with the traditional Pareto algorithm and multi-objective particle swarm optimization algorithm,the improved Pareto algorithm reduces the total intraday operating cost of the system by 8.5％and 3.7％under the same microgrid structure,increases the renewable energy consumption rate by 3.6％and 10.1％,and lowers the carbon emission by 14.4％and 23.9％,respectively.Thus,the wind/solar/hydrogen storage intraday scheduling decision-making optimization model based on the improved Pareto algorithm can improve the reliability of the wind/solar/hydrogen storage system while ensuring the smooth operation of the highway microgrid system.5 tabs,5 figs,30 refs.
highway engineeringhighway microgridhybrid energy storagerenewable energy consumptionmulti-objective optimizationimproved Paretolow-carbon scheduling
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