Distributed robust optimization method for flexible resources of power system

Abstract

The invention discloses a distributed robust optimization method for flexible resources of a power system. The method comprises steps of obtaining cost expectation and constraint conditions of power supply energy sources in the power system, and establishing a flexible resource robust optimization model; converting a flexible resource robust optimization model into a plurality of single-target optimization problems by adopting the duality method; and solving a plurality of single-target optimization problems to obtain a power system flexible resource distributed robust optimization scheme. Themethod is advantaged in that problems existing in flexible resource optimization in the prior art can be solved, the purpose of minimizing the total cost is achieved, moreover, the calculation burdencan be relieved, data privacy can be protected, and system stability can be improved.

Description

technical field [0001] The invention relates to the technical field of power systems, in particular to a distributed robust optimization method for flexible resources of a power system. Background technique [0002] In order to solve the serious environmental problems caused by the massive use of fossil fuels and meet the growing energy demand in an environmentally friendly manner, the current solution is to integrate a large proportion of renewable energy into the power system. However, the uncertain and unpredictable nature of renewable energy such as wind energy and photovoltaic power generation poses great challenges to the dispatch and operation of power systems. Therefore, there is an urgent need for dispatchable flexible resources, such as conventional generators, energy storage systems, and deferrable loads, to maintain the power supply-demand balance between fluctuating renewable energy generation and time-varying load demands. [0003] Existing research results sh...

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Problems solved by technology

The problem with deterministic optimization is that it is impossible to take into account the characteristics of renewable energy such as uncertainty and volatility in the optimization, and it is easy to cause an imbalance between supply and demand in actual situations, resulting in greater economic losses; while centralized optimization May bring huge computational pressure, because a large amount of information needs to be collected from all distributed generating units and loads, moreover, when the power system scale is large, with centralized optimization method, real-time communication becomes inconvenient and difficult

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