Double-layer-optimization-based optimized operation management method for electrical-thermal-storage boiler by microgrid

Abstract

The invention proposes a double-layer-optimization-based optimized operation management method for an electrical-thermal-storage boiler by a microgrid. The method comprises: a photovoltaic output prediction model, a wind power output prediction model, a micro gas turbine model, a diesel engine model, and a storage battery model are constructed, and a thermal load demand model is constructed by a method of improving a similar day so as to improve the precision of a thermal load demand prediction model; a double-layer optimization model is established, so that the wind curtailment and light curtailment values of the system can be reduced under the circumstance that the integrated cost of the system is minimized, wherein an energy management system is introduced into the lower layer and a centralized energy management system is introduced into the upper layer; and an objective function is solved by using an adaptive quantum chaos genetic algorithm to obtain outputs of all units in a microgrid system precisely, so that the high economic benefits and the high renewable energy source utilization rate can be realized under the circumstance that the electrical load and thermal load demands in the system are satisfied. Therefore, energy dispatching values of all units in the system can be calculated scientifically.

Description

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AI Technical Summary

Benefits of technology

The technical effect of this patented technology allows an efficient use of green resources during peak hours when there are high loads on the grid due to fluctuating wind or solar generation. This helps maintain stable performance over time without sacrificing more expensive equipment like batteries used in generating heat from fossil fuel sources.

Problems solved by technology

This patents describes two technical problem addressed by this patented innovation: how best operate and manage distributed electrical generators (EGS) systems while reducing their emissions from airplanets or other sources such as nuclear reactors. Current methods involve simplifying assumptions about EGR flow rates based solely upon mass consumptions and waste gas production levels without considering factors like loads being used at different locations within the network. These limitations lead to poor performance and potential issues related to overfitting models during implementation.

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