Method for optimizing energy dispatching of combined cooling, heating and power supply microgrid system

Abstract

The invention discloses a method for optimizing energy dispatching of a combined cooling, heating and power supply microgrid system, which comprises the steps of 1 acquiring a state vector of the system at the current decision-making moment according to measurement data and a prediction result; and 2 dividing all decision-making periods into an optimized non-learning period and a learning period,determining a principle of fixing power based on heat or fixing heat based on power to control the operation of the system according to the electricity price in the non-learning period, and calculating the average cost of the operation in the period of time; and obtaining an optimal Q-value table according to a reinforcement learning algorithm and the average cost of the non-learning period in thelearning period, and thus acquiring an optimizing strategy. The method can fully considers statistical characteristics and uncertain characteristics of random variables, thereby improving the actualoperation economy of the system.

Description

technical field [0001] The invention relates to the field of optimal dispatching of a combined cooling, heating and power supply microgrid system, and specifically relates to an energy scheduling optimization method for a combined cooling, heating and power supply microgrid system. Background technique [0002] Due to the energy crisis and environmental pressure, energy conservation and emission reduction has become an increasingly important research topic in various fields. The combined cooling, heating and power technology micro-grid system can not only increase the penetration rate of renewable energy, but also use some of the waste heat generated in the power generation process to meet the cooling and heating needs of users, realize the step-by-step utilization of energy, and save transmission costs at the same time Meet diverse energy needs. [0003] However, since the cooling, heating and power cogeneration microgrid system involves the matching of supply and demand o...

AI Technical Summary

Problems solved by technology

However, due to the fact that the penetration rate of renewable energy is getting higher and higher, and the user load demand is increasing, the randomness in the system cannot be ignored; the other is to transform the randomness problem into a deterministic problem through forecasting or statistical methods solution, but the disadvantage is that the optimization strategy obtained by this method does not consider the impact of the unpredictability of the environment and load changes in actual conditions

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