Optimization method of hybrid energy storage capacity configuration of microgrid
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A hybrid energy storage and capacity allocation technology, applied in photovoltaic power generation, wind power generation, energy storage, etc., can solve the problems of slow genetic optimization and closed competition
Inactive Publication Date: 2019-06-18
GUANGDONG UNIV OF TECH
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Many scholars have carried out beneficial explorations in two aspects of genetic operations and multi-objective optimization methods, and proposed various improved genetic multi-objective optimization algorithms to solve the problems of slow genetic optimization and closed competition.
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Embodiment 1
[0061] An optimization method for microgrid hybrid energy storage capacity configuration, such as figure 1 , including the following steps:
[0062] S1: Obtain historical data and real-time data of the microgrid in previous years, and construct a microgrid data matrix;
[0063] S2: Normalize random variables;
[0064] S3: Build a microgrid energy storage capacity configuration optimization model;
[0065] S4: Use the FCM-improved multi-objective genetic algorithm to optimize the energy storage capacity configuration of the microgrid.
[0066] In step S1, the historical data and real-time data of previous years include wind power generation power, photovoltaic power generation power, battery output power, super capacitor output power, and load demand power.
[0067] In step S1, the microgrid data matrix is constructed as follows:
[0068]
[0069] In the formula, x Di1 is the wind power in the microgrid, x Di2 is the photovoltaic power, x Di3 output power for the ba...
Embodiment 2
[0108] An optimization method for microgrid hybrid energy storage capacity configuration, such as figure 1 , including the following steps:
[0109] S1: Obtain historical data and real-time data of the microgrid in previous years, and construct a microgrid data matrix;
[0110] S2: Normalize random variables;
[0111] S3: Build a microgrid energy storage capacity configuration optimization model;
[0112] S4: Use the FCM-improved multi-objective genetic algorithm to optimize the energy storage capacity configuration of the microgrid.
[0113] In step S1, the historical data and real-time data of previous years include wind power generation power, photovoltaic power generation power, battery output power, super capacitor output power, and load demand power.
[0114] In step S1, the microgrid data matrix is constructed as follows:
[0115]
[0116] In the formula, x Di1 is the wind power in the microgrid, x Di2 is the photovoltaic power, x Di3 output power for the ba...
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Abstract
The present invention discloses an optimization method of hybrid energy storage capacity configuration of a microgrid. The method comprises the following steps of: S1: obtaining historical data in previous years and real-time data of a microgrid to construct a microgrid data matrix; S2: performing normalization processing of random variable; S3: constructing a microgrid energy storage capacity configuration optimization model; and S4: performing microgrid energy storage capacity configuration optimization by employing the multi-target genetic algorithm improved by FCM. The method provided by the invention employs the fuzzy C clustering and Euclidean distance selection intersection parent methods to combine with an adaptive crossover and mutation operator to greatly improve the diversity ofthe genetic algorithm population and avoid falling into local optimum. The optimization method of hybrid energy storage capacity configuration of the microgrid facilitates obtaining of an optimal solution set of the hybrid energy storage capacity configuration to allow decision makers to conveniently make a reasonable final selection and provide technical supports for stable operation and the economical efficiency of the microgrid.
Description
technical field [0001] The invention relates to the field of microgrid energy storage configuration, and more particularly, to an optimization method for microgrid hybrid energy storage capacity configuration. Background technique [0002] The inherent volatility, randomness and uncontrollability of intermittent power sources such as wind power and photovoltaic power generation lead to their large-scale grid-connected applications, which will cause hidden dangers to the safety and stability of the interconnection and operation of large power grids, and bring about power grid frequency regulation and reserve capacity planning. It is a great challenge and affects the electrical characteristics such as power flow distribution, dynamic characteristics and power quality of the power grid. [0003] The introduction of energy storage technology provides a convenient and effective way to improve the grid-connected application of intermittent energy generation. The power bidirection...
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