Multi-objective optimization method and system for client-side multi-energy system

Abstract

The invention discloses a multi-objective optimization method and system for a client-side multi-energy system, and the method comprises the steps: obtaining the attribute information of energy equipment in the client-side multi-energy system, and obtaining the prediction information of a client electrical load, a cooling and heating load and renewable energy output in the multi-energy system; constructing a decision variable and a constraint condition of an optimization problem according to the obtained attribute information and prediction information; based on the decision variable and the constraint condition, constructing each single target function considering economy, energy saving performance and environmental protection performance; and setting a decision maker preference weight for each single objective function, then constructing a decision multi-objective optimization function, and performing solving to obtain a final optimal solution. According to the method, a linear weighted sum method is improved, so that the importance degree of each component can be truly reflected; meanwhile, the method considers three objectives of economy, carbon emission and energy conservation, comprises a decision maker preference input module, a source-network-load-storage multi-energy optimization model and a linear optimization problem processing module, and can meet the selection requirements of different decision makers for optimization objectives.

Description

Technical field[0001]The invention belongs to the technical field of client-side multi-energy systems, and in particular relates to a client-side multi-energy system multi-object optimization method considering the preference of decision makers.Background technique[0002]Compared with the traditional single energy system, the integrated energy system contains multiple links such as source, grid, load, and storage, covering multiple energy sources such as electricity-gas-cooling-heating. Its energy coupling is strong and its operation mechanism is complex. Carry out multi-energy collaborative optimization research. The important purpose of multi-energy collaborative optimization is to make full and reasonable use of various energy sources and equipment operating capabilities, reduce the comprehensive operating costs of multi-energy systems, and achieve efficient and clean use of energy. So this is a multi-objective optimization research process.[0003]The current multi-objective optimi...

AI Technical Summary

Problems solved by technology

The modern linear programming algorithm is essentially derived from the simplex method proposed in the last century. It has the characteristics of fast solution speed and stable results. Its disadvantage is that the solution problem must be linear

The linear weighted sum method can make each component approach its optimal value according to the degree of importance, so as to give the decision maker a specific solution. The question of degree of influence

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