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Integrated energy system equipment capacity optimization method based on improved particle swarm optimization

An integrated energy system and improved particle swarm technology, applied in computing, computing models, data processing applications, etc., can solve problems such as easy to fall into local optimum, unstable convergence speed, poor dispersion of solutions, etc., to achieve improved search Excellent accuracy and stability, preventing premature convergence of the algorithm, and preventing the loss of population diversity

Pending Publication Date: 2022-05-06
TONGJI UNIV
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Problems solved by technology

[0004] However, when dealing with complex and high-dimensional optimization problems, the existing PSO algorithm has problems such as weak search ability, poor dispersion of solutions, unstable convergence speed, and easy to fall into local optimum.

Method used

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  • Integrated energy system equipment capacity optimization method based on improved particle swarm optimization
  • Integrated energy system equipment capacity optimization method based on improved particle swarm optimization
  • Integrated energy system equipment capacity optimization method based on improved particle swarm optimization

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Embodiment

[0039] Such as figure 1 As shown, a comprehensive energy system equipment capacity optimization method based on the improved particle swarm optimization algorithm includes the following steps:

[0040] S1. Establish a comprehensive energy system model composed of photovoltaic, wind power, CCHP and electric energy storage equipment;

[0041] S2. Determine the objective function and constraint conditions of the integrated energy system model, wherein the objective function of the integrated energy system model is specifically to minimize the initial investment cost;

[0042] The constraints of the comprehensive energy system model include equipment operating power constraints and electric cooling and heating power constraints;

[0043] S3. Based on reverse learning and elite promotion, construct an improved dynamic multi-population speed-free particle swarm optimization algorithm. The specific working process of the improved dynamic multi-population speed-free particle swarm al...

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Abstract

The invention relates to an integrated energy system equipment capacity optimization method based on an improved particle swarm algorithm. The method comprises the following steps: establishing an integrated energy system model composed of photovoltaic, wind power, CCHP and electric energy storage equipment; determining an objective function and constraint conditions of the integrated energy system model; based on reverse learning and elite promotion, constructing an improved dynamic multi-population speed-item-free particle swarm algorithm; solving the capacity configuration of the integrated energy system equipment by utilizing an improved dynamic multi-population speed-item-free particle swarm algorithm in combination with a target function and a constraint condition of the integrated energy system model to obtain an optimal capacity configuration scheme. Compared with the prior art, the method has the advantages that by improving the particle swarm optimization, the position updating process of the particles can be effectively simplified, the particles can be prevented from missing the globally optimal solution, and therefore the equipment capacity configuration optimal scheme of the comprehensive energy system can be rapidly, accurately and stably solved.

Description

technical field [0001] The invention relates to the technical field of equipment capacity optimization of an integrated energy system, in particular to a method for optimizing the equipment capacity of an integrated energy system based on an improved particle swarm algorithm. Background technique [0002] The traditional energy system mainly adopts the independent energy supply mode, and the multi-energy collaborative integrated energy system that introduces clean energy has become a new development direction. Since the energy supply of renewable energy equipment is highly random, this makes the multi-energy coupling and complementary energy system more complex, and the optimization of system equipment capacity has become a hot research topic. Many practical engineering optimization problems can be abstracted into mathematical expressions of multimodal functions, and the particle swarm optimization algorithm is a stochastic optimization method based on social behavior simula...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/00G06Q50/06
CPCG06Q10/04G06N3/006G06Q50/06
Inventor 戴毅茹曾依浦
Owner TONGJI UNIV
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