Two-stage operation optimization method for distributed system

Abstract

The invention discloses a two-stage operation optimization method for a distributed system, and the method comprises the steps of predicting the hourly cooling and heating load change of a typical day based on historical operation data, and dividing the typical day into a working period and a non-working period; solving an average value of hourly load values in the working time period and the non-working time period, and taking the average value as a load reference value for total load optimization of the first stage in each time period; optimizing a load reference value by taking maximization of the total benefit of system operation as a target to obtain a total load optimal distribution scheme with the maximum benefit, and taking the total load optimal distribution scheme as an initial distribution scheme in the current time period; calculating a load difference value between the hourly predicted load and the load reference value in each time period of the typical day, and carrying out second-stage micro-increasing optimization distribution on the load difference value to obtain a micro-increasing optimization scheme; and increasing the distribution quantity of the micro-increasing part based on the initial distribution scheme to obtain a final operation distribution scheme. According to the invention, the coordination of the distributed enterprise in two aspects of economy and operation feasibility is realized.

Description

technical field [0001] The invention relates to the technical field of operation optimization of cooling and heating loads of distributed systems, in particular to a two-stage operation optimization method for distributed systems. Background technique [0002] The distributed system is based on the combined supply of cooling, heating and electricity. It has the characteristics of high efficiency, cleanliness and flexibility, and realizes the cascade utilization of energy to directly meet the various needs of users. Production equipment and co-production equipment of lithium bromide units with different prime movers as the core. Different from the domestic gas-fired cooling, heating and power distributed energy system from scratch at that time, more and more practitioners have learned about related technologies, and more and more enterprise projects are experiencing the economic development of distributed systems. sex optimization problem. [0003] During the operation of t...

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Problems solved by technology

[0003] During the operation of the distributed energy supply system, the cooling and heating loads vary greatly, which has a strong load randomness. On the one hand, due to the lack of forecasting of the operating load in the distributed system, each device cannot perform ideal changes at each time. Operating under working conditions, the system itself has a variety of operating modes with multiple devices. If the main device is not properly matched and starts and stops frequently, the feasibility of operation is very low; on the other hand, the deviation of system load distribution continues to increase, and the main device The operating efficiency is reduced, the system is difficult to meet the design energy utilization rate requirements, and the economic benefits of system operation will be greatly reduced

[0004] The current existing technology is aimed at the research on the operation optimization strategy of the enterprise distributed system. Some researchers evaluate the price system of each equipment to carry out the equivalent micro-increase optimization strategy of a single equipment to ensure the feasibility of equipment scheduling, but they cannot The economic benefits under the operation plan can be effectively improved. Although the traditional technical means can ensure the economy of the system operation, the accuracy of the model is very high, and because the distribution of the model is carried out hourly, the load change range There will be a large number of equipment operation adjustments during relatively large periods of time, and there will also be unreasonable plans such as multi-investment and withdrawal of units. It is difficult to guarantee the feasibility of equipment scheduling, and the difficulty of operation cannot be confirmed.

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