A Multi-time Scale Optimal Operation Method for Combined Cooling, Heating and Power System

Abstract

The invention discloses a multi-time-scale optimization operation method for a combined cooling, heating and power system, which takes the comprehensive optimization of the operating cost, energy utilization rate, and emission of the combined cooling, heating and power system as the optimization target, and establishes a static state of the electric energy of the combined cooling, heating and power system. Optimizing the model; taking t as the power optimization period, predicting the power generation output and electric load of renewable energy sources; and using the particle swarm optimization algorithm to solve the static optimization model of power in the combined cooling, heating and power system to determine the output of each equipment in the combined cooling, heating and power system ;Taking the comprehensive optimization of operating cost, energy utilization rate, and emission of combined cooling, heating and power system as the optimization goal, and considering the inertia of heat / cold energy flow, a dynamic optimization model of heat / cold energy based on heat storage is established; For the cold energy flow optimization cycle, the dynamic programming method is used to solve the dynamic optimization model, and the output heat power and heat storage power of the generator set are determined.

Description

technical field [0001] The disclosure belongs to the field of energy technology, and specifically relates to a multi-time-scale dynamic scheduling strategy and operation method for a new energy combined cooling, heating and power supply system. Background technique [0002] As one of the forms of distributed energy supply, the combined cooling, heating and power system has developed rapidly in recent years. A typical CCHP system is composed of a power generation system, a waste heat recovery system and an auxiliary system. Through optimal dispatching of energy, it can realize cascade utilization of energy while meeting the user's various load demands of cooling, heating and electricity, and effectively improve the efficiency of energy utilization. To achieve the goals of energy saving, environmental protection and economic benefits. The combined cooling, heating and power system itself is a complex system of multiple energy sources (cold, heat, and electricity). In recent ...

AI Technical Summary

Problems solved by technology

However, the disadvantages of static optimization on the same time scale are: due to the large difference in thermoelectric time scales, there are big problems in using the same time scale

If the matching slow reference scale optimization is adopted according to the change speed of thermal energy, it will not be able to make an appropriate and timely response to the fast time scale electric energy; if the matching fast reference scale optimization is adopted according to the change speed of electric energy, the thermal energy The difference between slow time scale dynamics is almost zero, resulting in a serious loss of significant figures and huge errors; if an intermediate time scale controller is used, due to the significant difference between the fast and slow time scales of electric heating, the processing of fast and slow dynamics will encounter similar pathological characteristics at the same time

[0004] To sum up, at present, it is impossible to make an appropriate and timely response to the electric energy with a fast time scale, the significant loss is serious, and a huge error is generated. Due to the significant difference between the fast and slow time scales of electric heating, the processing of fast and slow dynamics will encounter similar problems at the same time. The problem of pathological characteristics, there is still no effective solution

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