Because of the difficulties of biomass fuel collection and unstable supply in the actual operation of the integrated energy system (IES) centered on biomass cogeneration units, this paper proposes an IES planning method that considers the price elasticity of biomass supply. Firstly, according to the biomass purchase price elasticity theory, a straw collection cost model is established and introduced into the two-level planning of the industrial park IES. Secondly, the upper-level model uses equipment planning capacity and straw purchase price as optimization variables to pursue the minimum annualized comprehensive cost; the lower-level model uses equipment hourly output and straw consumption as optimization variables to pursue the minimum annual operating cost that takes into account the price flexibility of straw collection costs, and the particle swarm optimization and the CPLEX toolbox are used to solve the problem. Finally, a typical industrial park in Jilin Province is taken as an example to verify that the proposed planning method is more suitable for the actual situation of the project and has a more superior economic efficiency.

In order to solve the problems of inefficient energy utilization and high energy cost in the production process of protected agricultural industrial park, a multi-objective optimization scheduling method for integrated energy system (IES) of protected agricultural industrial park is proposed, which takes into account the local consumption of wind and solar energy. Firstly, the output model of integrated energy system of protected agricultural industrial park including renewable energy such as wind, solar and biogas power is established. Then, considering various constraints, taking the minimum energy consumption and maximum consumption rate of wind and solar energy as the objectives, the improved multi-objective sine-cosine algorithm based on reverse learning is used to solve the problem. Finally, the integrated energy system of a protected agricultural industrial park is taken as an example. The results show that the optimal scheduling method can effectively reduce the daily operation cost of the park and improve the local consumption rate of wind and solar energy.

Based on the deficiency that elementary particle swarm optimization is easy to fall into local optimum, an improved simplified particle swarm optimization algorithm based on levy flight（LISPSO） is proposed. The Simplified Particle Swarm Optimization（SPSO） discards the velocity in the updated formula and its evolution direction is only controlled by the position. Firstly, on the basis of simplified particle swarm optimization, the position of each particle is dynamically updated by using the nonlinear decreasing inertia weight with randomness. Secondly, the algorithm integrates Levy flight based on similarity and aggregation analysis. The higher the similarity between particles and the optimal particles, or the higher the concentration of particles, the greater the probability that the particle update the position with Levy flight, which can effectively help paticles to jump out of the local optimum. The 11 test function are simulated by matlab. The results show that the improved algorithm has significant improvement in solving accuracy and convergence speed. In addition, LISPSO is applied to solve min-max-min problem, and the experimental results show that the improved algorithm is obviously superior to other comparison algorithms in solving effect.