Multi-objective optimal operation of industrial microgrids based on stochastic optimization

Renewable energy is the trend for future energy development, and energy transition in industrial parks, as significant sources of carbon emissions, is becoming increasingly essential. Industrial microgrids serve as an effective pathway for industrial parks to achieve their carbon neutrality goals. To fully leverage the role of industrial parks in carbon reduction, this study proposes a multi-objective optimization model based on stochastic optimization, considering a stepped carbon trading mechanism, specifically addressing the economic operation of industrial microgrids. The model consists of three components: the industrial microgrid model, the stepped carbon trading model, and the economic optimization scheduling model that converts carbon emissions into carbon emission costs. The volatility of renewable energy output is thoroughly considered, employing Latin hypercube sampling to generate random scenarios and perform reductions. A stochastic optimization algorithm is used to analyze typical scenarios. The results indicate that the scheduling strategy effectively utilizes renewable energy output, significantly reduces system carbon emissions, while ensuring a certain level of economic benefits for the system.