Optimization scheduling of integrated energy system for agricultural park considering uncertainty of energy and loads

As the fundamental energy unit for future agricultural production, agricultural parks integrate clean energy sources such as biomass, photovoltaic, wind power, and greenhouse technology. However, the output of clean energy and the energy consumption of agricultural parks are affected by various random factors. The traditional scheduling model, which independently supplies energy to each type of load, cannot meet the requirements for stable economic operation of agricultural parks in the future. This paper proposes an optimal scheduling method for the integrated energy system in agricultural parks, which takes into account the random characteristics of both energy and load. It first utilizes Chance-Constrained Programming theory, incorporating random variables into the optimization model through probability inequalities. This model enhances the flexibility of energy supply in integrated energy systems with a certain level of confidence. Furthermore, combined with the search strategy of the improved Particle Swarm Optimization algorithm, the convergence speed of the model is enhanced. The results show that the proposed method can effectively improve the robustness and economic efficiency of the integrated energy system in agricultural parks.