Optimal operation method of hydropower station group on the basis of orthogonal dimensionality reduction search algorithm

Abstract

The invention relates to the field of reservoir operation, and discloses an optimal operation method of a hydropower station group on the basis of an orthogonal dimensionality reduction search algorithm. The optimal operation method is characterized by comprising the following steps: carrying out encoding design to the hydropower station group by adopting a one-dimensional real number matrix, and obtaining dimensionality n; according to a state discrete number Q and the dimensionality n, constructing a corresponding orthogonal table; obtaining an initial operation process of each hydropower station by a conventional optimal operation method, and calculating to obtain an initial search step length by the state discrete number; according to the current state Z1 and the search step length of each hydropower station as well as the selected orthogonal table, constructing an orthogonal test scheme set, checking the corresponding state of each power station in each orthogonal test scheme, and correcting to a feasible region if border crossing happens; and calculating a target function value of each test scheme by adopting a penalty function method, and selecting an optimal test scheme Z2, wherein Z1 is caused to be equal to Z2 if a Z2 target function value is better than a Z1 target function. The invention has the advantages of being simple and clear in principle, few in calculation parameters, high in convergence rate, high in robustness and the like, and can be used for the optimal operation of a hydropower system and the optimal configuration of water resources.

Description

technical field [0001] The invention relates to an optimal scheduling method for hydropower station groups based on an orthogonal dimensionality reduction search algorithm, which is an optimal scheduling method for water resources and belongs to the technical field of optimal scheduling for water resources and hydropower station groups. technical background [0002] As of the end of 2013, the total installed capacity of hydropower in my country exceeded 280 million kW, forming the largest and most complex interconnected hydropower system in the world. Taking China Southern Power Grid as an example, there are currently more than 100 large and medium-sized hydropower stations under centralized management and dispatch. Huge hydropower systems involve massive space-time constraints that restrict each other and complex and changeable comprehensive utilization requirements, leading to explosive growth in computing scale, and the problem of dimensionality disaster has become increasi...

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Problems solved by technology

[0002] As of the end of 2013, the total installed capacity of hydropower in my country exceeded 280 million kW, forming the largest and most complex interconnected hydropower system in the world. Taking China Southern Power Grid as an example, there are currently more than 100 large and medium-sized hydropower stations under centralized management and dispatch. Huge hydropower systems involve massive space-time constraints that restrict each other and complex and changeable comprehensive utilization requirements, leading to explosive growth in computing scale, and the problem of dimensionality disaster has become increasingly prominent, bringing unprecedented new problems and challenges to dispatching management departments at all levels

Traditional optimization methods such as stepwise optimization algorithm, discrete differential dynamic programming (Discrete Differential Dynamic Program, DDDP), dynamic programming successive approximation method and other dynamic programming improved algorithms achieve dimensionality reduction by reducing the number of system stages or states, but when dealing with large-scale hydropower systems When optimizing scheduling problems, we still face the curse of dimensionality caused by too many state combinations; intelligent algorithms such as Genetic Algorithm (GA) and particle swarm optimization have been widely used in the research field of hydropower optimal scheduling in recent years, but there are common parameters There are many problems, such as premature convergence, and the calculation results are greatly affected by the problem scale and constraint processing methods, which are limited in practical engineering applications. Therefore, it is urgent to study a new and feasible optimal scheduling method suitable for large-scale and complex hydropower systems.

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