Configuration and capacity optimization method used for distributed energy system technology selection

Abstract

The invention provides a configuration and capacity optimization method used for distributed energy system technology selection. The method comprises the following steps that: (1) analyzing the load of a distributed energy system; (2) analyzing the resources of a place where the distributed energy system is positioned; (3) selecting one type of technology selection; and (4) adopting a mixed integer linear programming method to meet energy balance constraints, energy production equipment constraints and energy storage equipment constraints, and calculating the configuration and the capacity of lowest annual average cost. By use of the configuration and capacity optimization method used for the distributed energy system technology selection, the mixed integer linear programming algorithm is adopted to design the distributed energy system, the synthesis solving of a continuous design variable and a discrete design variable is both given consideration, the optimal modeling of various different distributed energy technologies can be realized, the optimal modeling flexibility and the solving flexibility of the system are improved, the optimal solving problem of the large time scale is effectively solved, the solving accuracy of the design variable is guaranteed, system design objectivity and optimal result accuracy are guaranteed, and a user is assisted in realizing the design of the distributed energy system.

Description

technical field [0001] The invention relates to the field of distributed energy, in particular to a configuration and capacity optimization method for technology selection of distributed energy systems. Background technique [0002] There are many types of distributed energy technologies, including renewable energy generation technologies such as photovoltaics and wind turbines, and traditional combined cooling, heating, and power technologies based on micro-combustion engines, gas turbines, and gas internal combustion engines, as well as energy storage batteries, electric vehicles, New distributed energy application technologies such as cold storage / heat storage. In the design process of equipment selection and capacity allocation, various technologies include not only continuous design variables, such as photovoltaic installation area and battery capacity, but also discrete design variables, such as fan capacity and number of units, CHP capacity and number of units. Such ...

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Problems solved by technology

like figure 1 As shown, it is a flow chart of distributed energy system technology selection, which includes continuous equipment and discrete equipment, and considers all links from resources to loads, including resources, energy production, energy storage, energy conversion, and loads. Each link has ni possible choices, the overall design possible scheme has at most 2 n1*n2*n3*n4*n5 It is difficult for the user to determine the technology and capacity when designing the system. In addition, when the time scale is introduced into the design, the scale of the optimization problem will expand rapidly. If one hour is used as the minimum time scale, considering the time scale in a year With the change of energy supply and demand, the scale of the problem will be enlarged by 8760 times, and the design calculation volume is huge

[0003] Existing methods for the design of distributed energy systems cannot meet the needs of solving various types of design variables and large-time design scale problems in actual distributed energy systems, and there are problems such as modeling difficulties and solving difficulties. For different types of distributed energy The accuracy of the system design is poor, and the optimization effect is uneven

[0004] Existing planning software lacks effective modeling methods, or greatly simplifies the complexity of the system through a simple modeling process, ignoring the fact that there are both discrete and continuous variables in the system design, and it cannot consider solving large-scale problems , but there are different types of technologies in the actual energy system, the nature of variables is different, and the time scale of planning is also different, which makes the accuracy of the design scheme poor

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