A Multi-objective Optimal Scheduling Method for Distribution Networks Containing Distributed Power
A distributed power supply, multi-objective optimization technology, applied in the direction of electrical components, circuit devices, AC network circuits, etc., can solve problems such as non-convergence, insufficient performance of local search or global search, easy to fall into local optimum, etc.
Active Publication Date: 2018-11-20
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +2
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For multi-objective optimization algorithms, mainly multi-objective particle swarm optimization algorithm, multi-objective genetic algorithm and multi-objective swarm search algorithm, etc., they have the problem of insufficient local search or global search performance, that is, they do not converge or easily fall into local optimum
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[0077] In order to clearly illustrate the technical features of this solution, the present invention will be described in detail below through specific implementation modes and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the present invention. To simplify the disclosure of the present invention, components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and / or letters in different instances. This repetition is for the purpose of simplicity and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and processes are omitted herein to avoid unnecessarily limiting the...
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The invention provides a multi-objective optimization scheduling method for a distribution network with a distributed power supply. The method comprises the steps that S1 a multi-objective distribution network model with the minimum deviation of the maximum system node voltage, the minimum network loss of each distribution network branch and the maximum distributed power supply access is established; S2 flow equation constraints, DG active power upper limit constraints, node voltage constraints and other constraints are determined; and S3 a group animal self-learning mechanism is introduced, and a multi-objective group search algorithm based on multi-group self-learning is established to optimize distribution network scheduling. According to the invention, the multi-objective distribution network model is established by using the maximum DG net in capacity and the minimum system network loss and voltage deviation as objective functions; a flow equation and DG capacity limit are used as constraints; various types of DG access are comprehensively considered; and a multi-group self-learning group search algorithm based on a self-learning group search algorithm is used to optimize the established multi-objective distribution network model.
Description
technical field [0001] The invention relates to a multi-objective optimization scheduling method for a distribution network including distributed power sources, and belongs to the technical field of distribution network scheduling. Background technique [0002] In recent years, in response to the challenges of energy, environmental protection and climate change, low-carbon renewable energy has been vigorously developed, and the direct connection of new energy to the distribution network in the form of distributed power is the future development trend. The distribution network is a power grid that sends electric energy to the user side directly or after step-down. It is extremely important to study the system structure and operation after a large number of distributed power sources are connected. [0003] There are many indicators to be optimized in the distribution network optimization problem including distributed power generation, such as voltage deviation, network loss, m...
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IPC IPC(8): H02J3/38
CPCH02J3/381H02J2203/20
Inventor 苏建军张林利李立生孙勇邵志敏张世栋李建修王昕刘合金樊迪孙树敏张婉婕李广磊王青李文杰郭新胜田野
Owner ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY