Multi-time scale rolling micro-grid energy optimization management method

Abstract

The invention discloses a multi-time scale rolling micro-grid energy optimization management method. Firstly, two time scales are divided, the intra-day optimization aims at the minimum of the operating cost and the system network loss, the constraints of start, stop and operation of each distributed power supply are taken as constraints, and at the same time, a mathematical model for intra-day optimization is established in consideration with three load response costs and the constraints. The real-time optimization uses the intra-day optimization result as a reference, and the rolling optimization solution in the future finite time domain is performed by using a model prediction control algorithm. The method can comprehensively consider the constraints of various distributed power supplies and various load characteristics to ensure the safe and stable operation of a micro-grid, at the same time, considers the power consumption and improves the operating economic efficiency of the micro-grid.

Description

technical field [0001] The invention relates to the technical field of new energy power generation and smart grid, in particular to a multi-time scale rolling optimization micro-grid energy optimization management method considering demand response. Background technique [0002] With the gradual increase in the penetration rate of renewable energy, distributed power generation has gradually emerged in various countries. However, distributed power generation is decentralized and difficult to manage, so there is the emergence of microgrids. A microgrid is a small grid composed of distributed power sources such as photovoltaics, wind power, micro gas turbines, energy storage systems, and loads. Various distributed power sources have their own advantages and disadvantages, especially the intermittent power supply of renewable energy, which requires the micro-grid energy optimization management system to coordinate and optimize various distributed power sources to ensure that the...

AI Technical Summary

Problems solved by technology

Relevant scholars have proposed a method to consider the energy optimization management of the microgrid on the demand side, but only considered two types of loads, namely controllable and uncontrollable loads, and did not divide the controllable loads in detail, and did not consider that some loads can only be shifted Cannot cut power directly

Also does not consider the constraint that some loads cannot be stopped once started

Relevant scholars have proposed a multi-time-scale optimization method, but they only divide the time scale again and again into three periods: day-ahead, intra-day, and real-time, without dealing with the gap between the controllable distributed power output issued in advance and the actual active output. The problem of deviation

Images