Simulated-annealing and conic optimization based power distribution network operation optimization method

Abstract

Disclosed is a simulated-annealing and conic optimization based power distribution network operation optimization method. The method includes: inputting basic parameters and information according to a selected power distribution system; according to the basic parameters and the information, establishing a mathematical model of power distribution network operation optimization problem concurrent with an interconnection switch and an intelligent soft switch and setting rotating cone constraints according to the network structure and scale to form a conic optimization model of the power distribution network operation optimization model concurrent with the interconnection switch and the intelligent soft switch; according to the basic principle of a simulated annealing method and network topological constraints, randomly generating a feasible switch-state combination, and performing calculation on the cone optimization model of the power distribution network operation optimization problem concurrent with the interconnection switch and the intelligent soft switch by adopting a cone optimization tool; outputting a calculation result and analyzing the optimal calculation condition by the aid of the simulated annealing principle. States of the interconnection switch in the power distribution system and transmission power of an SNOP (soft normally open point) are considered from the economic perspective of operation of the power distribution system, and the optimum switch combination of the interconnection switch in the power distribution network and the optimum transmission power of the SNOP are determined.

Description

technical field [0001] The invention relates to a distribution network operation optimization algorithm. In particular, it relates to a distribution network operation optimization method based on simulated annealing and cone optimization. Background technique [0002] With the widespread application of technologies such as distributed power generation, energy storage, demand-side response, and electric vehicles, the distribution network has transformed into a new energy system that integrates multiple roles such as production, transmission, storage, and distribution of electric energy. The core of smart grid technology development. The smart distribution network will actively optimize and control the operation of distributed power sources, energy storage, demand-side response resources, electric vehicles, reactive power compensation equipment, smart switches, etc., which will completely change the planning, Design and operation mode to form a new operation mode. As the ma...

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Problems solved by technology

[0005] Although the above methods or technologies have certain application effects, they also have obvious shortcomings. For example, although the traditional mathematical optimization method can theoretically perform global optimization, it inevitably has the problem of "curse of dimensionality" in practical application. , the calculation time often shows an explosive surge; the heuristic optimization algorithm requires a polynomial time bound in terms of time complexity, and the calculation speed is fast, but the optimal solution obtained either lacks the optimality in the mathematical sense or is only a local optimal solution ; Although the final solution searched by the stochastic optimization method has nothing to do with the initial solution, it is necessary to reset its control parameters, number of populations, iteration times, etc.

Heuristic and stochastic methods are mostly suitable for solving integer programming problems, but for distribution network operation optimization problems where tie switches and SNOPs coexist, mathematics is essentially a mixed integer nonlinear optimization problem, so traditional mathematical optimization methods and heuristic algorithms are very important for solving On this type of problem, the speed or accuracy cannot meet the requirements at the same time.

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