Multi-energy microgrid optimal configuration method based on deep joint generation of source-load-temperature scenarios

Abstract

The present invention is a multi-energy micro-grid optimal configuration method based on the deep joint generation of source-load-temperature scenes, which is characterized in that it includes determining typical weather types and extreme temperature types, and carrying out source-load configuration for typical weather types and extreme weather types. ‑Temperature scene in-depth joint generation and scene reduction, establishment of multi-energy micro-grid system model, establishment of multi-energy micro-grid system optimal configuration model with the goal of the lowest total annualized cost, solves the problem of using different scene generation methods in the existing technology to carry out multiple When optimizing the configuration of the energy system, it is impossible to consider the coupling between new energy measurement and multi-energy demand side and the limited extreme scenarios that make it difficult to cover the potential demand for extreme energy consumption. It has the advantages of scientific and reasonable method, strong applicability, good effect, and can improve the economy and energy supply reliability of the multi-energy micro-grid system.

Description

Technical field [0001] The present invention relates to a multi-energy microgrid system, is a multi-energy microgrid optimization configuration method based on the depth of the source - load - temperature scenario, applied to the optimal configuration of multi-energy microgrid system. Background [0002] The coupling relationship of the equipment inside the multi-energy microgrid system is complex and non-linear, and unlike the traditional microgrid, the multi-energy microgrid system is more complex in optimizing the configuration. In addition, the main power generation equipment in the multi-energy microgrid system is mainly distributed renewable energy such as photovoltaic panels. Distributed renewable energy power generation devices are seriously affected by weather, have strong volatility and uncertainty, and there is a large mismatch in the timing of local user energy loads, which cannot meet user energy demand at all times。 And the local temperature will directly affect the...

AI Technical Summary

Problems solved by technology

However, most of the current research does not consider the extreme weather conditions (temperature) with less historical data, and the scenarios are limited and it is difficult to cover the potential demand for extreme energy consumption.

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