Auxiliary decision method for recovering group multiattitude of power system

Abstract

The invention discloses an auxiliary decision method for recovering the group multiattitude of a power system. With the method, the recovery scheme of black start, net rack recovery and a load recovery stage can be generated and a plurality of candidate recovery schemes are sorted; the generation process of the recovery scheme is divided into three steps so as to ensure that the method is self-adaptively changed into a scheme by taking the safety of the system as an optimizing object when the recovery scheme by taking the recovery time as the optimizing object is infeasible; different important degrees of a decider can be considered for the provided group polymerization function and the requirement on the independence between the number and the attribute of the scheme is not strict; the recovery object and the recovery scheme of the recovery object can be determined on line according to the recovery condition of the current power grid and the flexibility and the feasibility of the recovery auxiliary decision of the system can be improved; and the auxiliary decision method is suitable for assisting in dispatchers dispatched to a province to recover the partition independent parallel recover stage at the initial stage of the system and the auxiliary decision of each subsystem when the serialization is recovered can provide the effective supplementation for the off-line preplan.

Description

technical field [0001] The invention belongs to the technical field of power system operation and control, and in particular relates to a multi-attribute decision-making method for power system restoration groups. Background technique [0002] The main goal of power system restoration is the safe and rapid restoration of generating units, substations, transmission lines and loads in the system. Due to the complex characteristics of the power system itself and the increasing scale, many factors such as system operation status, equipment operation, recovery time and safety operation constraints need to be considered during the system restoration process, which includes a large number of analysis and verification, and scheduling Therefore, the system recovery problem belongs to the category of nonlinear optimization and is a typical semi-structured problem. It is difficult to establish a mathematical model for accurate solution. It has complex systems, multi-objectives, multi-v...

AI Technical Summary

Problems solved by technology

Aiming at the problem of unit recovery sequence, the AHP is mainly used to determine the unit recovery sequence, but when there are many candidate schemes or evaluation attributes, it is difficult for the AHP to ensure the consistency of the judgment matrix and the objectivity of the evaluation results; for the grid recovery sequence However, it mainly uses artificial intelligence or graph theory algorithms to determine the target skeleton network and restoration path sequence, but it mainly focuses on the charging capacitance of the line and the amount of load restoration in the solution process, and lacks detailed analysis of restoration time, branch power flow, operating overvoltage, and industrial Comprehensive consideration of frequency overvoltage, self-excitation, equipment operation success rate, system safety and stability, etc.

In fact, these factors are very likely to affect the recovery speed, and even make the recovery scheme unfeasible; for the load recovery problem, the main research is the load recovery capability under the system security and stability constraints, but there is a lack of analysis of recovery time, load importance, system security and Stable Comprehensive Coordination

[0004] In addition, there is currently no suitable algorithm that can analyze and evaluate unit characteristics and their importance, load characteristics and their importance, recovery time, and equipment operation success rate according to the system recovery progress, and give decision values, and there is no algorithm that can be based on The results of safety verification and various evaluation decision values ​​provide a theoretical method for the final restoration target decision. Therefore, for these aspects, the power sector can only give the final decision based on experience on the basis of safety verification.

Obviously, it is difficult to ensure the quickness of system recovery in such a solution, and recovery may be delayed due to inexperience or poor consideration of unit and load characteristics, and even lead to recovery failure

[0005] In order to assist the emergency command after the blackout, most network provincial companies have formulated recovery plans or used case-based reasoning to guide the recovery process. It brings errors to the recovery simulation, and the system is in abnormal operating conditions during the recovery process. Frequent equipment operations, load changes and a large number of uncertain factors will significantly increase the probability of large disturbances to the system. The disturbance ability is weak, and the state may exceed the limit in the actual recovery process, and even the system collapses. It is difficult to ensure that the actual recovery process is carried out in accordance with the plan or case. The development and maintenance workload is greatly increased

[0006] One of the inventors of the present invention in the Chinese doctoral dissertation full-text database, the doctoral dissertation of "Research on Auxiliary Decision-Making Method for Power System Restoration and System Development" in 2010, in the second chapter, proposed to adopt the multi-attribute utility method to optimize the grid restoration scheme. The method can comprehensively consider multiple factors that affect system security and recovery speed to obtain the ranking of schemes, but the multi-attribute utility method requires strict independence between attributes, and considers that the substitution rate between attributes is equal, which may make some schemes with poor attributes become the best. In terms of recovery strategy, plan generation and auxiliary decision-making, it does not consider how the system recovers during the black start and load recovery stages; in the process of optimizing decision-making using the ideal solution in Chapter 3, it does not consider when multiple decision-makers make joint decisions. The difference in importance between the two is not close to the actual situation; the path search algorithm proposed in Chapter 4 does not consider how to deal with the candidate scheme with the shortest recovery time as the target is not feasible, nor does it consider the suboptimal solution with the shortest recovery time as the target. How to use the program

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