Regional integrated energy system equipment capacity optimization method based on improved NSGA-III

Abstract

The invention relates to a regional integrated energy system equipment capacity optimization method based on improved NSGA-III. The method comprises the following steps: establishing a regional integrated energy system model composed of photovoltaic, wind power, CCHP and electric energy storage equipment; from the perspective of a carbon target, determining a target function and constraint conditions of the regional integrated energy system model; the method comprises the following steps: constructing an improved NSGA-III algorithm based on a self-adaptive SBX-NBX hybrid crossover operator; the method comprises the following steps: simulating cooling, heating and power loads of a research area, collecting annual temperature, solar radiation intensity and wind speed data, and dividing the whole year into three typical days of summer, winter and transition season by adopting a K-means clustering method; and by utilizing an improved NSGA-III algorithm, according to a target function and constraint conditions of the model, in combination with typical daily data of a research region, solving capacity configuration of the regional integrated energy system equipment, and obtaining an optimal capacity configuration scheme. Compared with the prior art, the method provided by the invention can accurately and stably solve the optimal scheme for the equipment capacity configuration of the low-carbon regional integrated energy system.

Description

technical field [0001] The invention relates to the technical field of regional comprehensive energy system optimization, in particular to a method for optimizing the capacity of regional comprehensive energy system equipment based on improved NSGA-III. Background technique [0002] Facing my country's "dual carbon" strategic needs of "carbon peaking" and "carbon neutrality", research on energy supply side reform is the main direction to achieve carbon goals, and the introduction of clean energy multi-energy coordinated regional integrated energy system (Regional Integrated Energy System) , RIES) has now become a new development direction. Due to the instability of renewable energy equipment such as wind turbines and photovoltaics, the impact of peak-to-valley electricity prices, restrictions on the installed capacity of each equipment, and operational constraints on the system configuration scheme, this makes the multi-energy coupling and complementary regional integrated en...

AI Technical Summary

Problems solved by technology

However, the crossover operator of the basic NSGA-III algorithm is realized by Simulated Binary crossover (SBX), which has problems such as low search efficiency and easy to fall into local optimum.

[0003] In addition, the regional comprehensive energy system with multi-energy synergy and complementarity is complex, and there are still some problems to be explored in the current research

On the one hand, some studies construct single-objective programming models, or use linear weighting methods to simplify multi-objective models, and the planning results are relatively single, and there is less consideration of the system's low-carbon binding goals; on the other hand, due to the complexity of the system model, Some models do not comprehensively consider the instability of renewable energy equipment such as wind turbines and photovoltaics, and ignore the influence of peak and valley electricity prices, limitations on the installed capacity of each equipment, and operational constraints on the system configuration scheme

In addition, some studies use the traditional swarm intelligence algorithm to solve the model optimization, the convergence speed is slow and it is easy to fall into local optimum

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