Charging pile-containing regional energy network management method based on MACPSO algorithm

Abstract

The invention provides a large-scale charging pile-containing regional energy network economic optimization management method based on an MACPSO algorithm, and belongs to the field of energy network management methods. The method comprises the following steps of counting the travel data of regional electric vehicles, and establishing a travel model; counting the working data of the charging pilesin the region to establish a probability distribution model of the electric vehicles, and sampling to calculate an estimator; establishing an electric vehicle response scheduling model through the travel model, the probability distribution model and the estimator; and establishing an energy network economic optimization model through the electric vehicle response scheduling model. According to thepresent invention, the energy network economic optimization model is solved through the MACPSO algorithm, so that on the premise that the regional power generation meets the power consumption load requirement, the optimal economic operation management cost and environmental pollution treatment cost are obtained through calculation, the economic management is conducted on the regional power, and on the premise that the load power consumption requirement in the region is guaranteed, the optimal planned output of the controllable power generation units in the regional energy network is realized.

Description

technical field [0001] The invention relates to the field of energy network management, in particular to a method for managing an energy network in a region containing charging piles based on a MACPSO algorithm. Background technique [0002] As the country vigorously promotes clean energy power generation, the installed capacity of distributed energy is increasing. As an effective technical method for managing distributed power generation, regional energy grids have received continuous attention from the industry. The new energy regional energy network uses various distributed intermittent and random clean energy such as wind, light, and natural gas in the region to generate electricity. The joint operation of multiple distributed energy sources can achieve multi-energy complementarity. And optimize the capacity allocation of distributed power generation units to achieve a dynamic balance between the power generated by the power supply in the region and the energy load. [...

AI Technical Summary

Problems solved by technology

[0004] The existing regional energy economic optimization management methods do not consider the travel characteristics of large-scale electric vehicle owners and the joint scheduling of large-scale electric vehicles as mobile energy storage units and controllable power generation units in regional energy grids. Uncertain and qualitative factors such as the implementation plan are difficult to judge and comprehensively predict, and it is impossible to objectively and accurately optimize the output of each controllable power generation unit in the regional energy network

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