CN118074196A - Intelligent distribution method for energy of unstable power supply - Google Patents
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Abstract
The invention relates to the technical field of power distribution systems, in particular to an intelligent distribution method of unstable power supply energy, which comprises the following steps: constructing a power generation power sequence, a voltage stabilizing power sequence and a battery power sequence at each sampling moment to obtain a power generation power curve, a voltage stabilizing power curve and a battery power curve corresponding to each sampling moment; determining a curve sequence projection coefficient and a curve sequence difference coefficient of each sampling time in the generated power sequence, and further determining a wind power fluctuation disorder coefficient of each sampling time; calculating the electric field bearing output conversion coefficient at each sampling moment to determine the self-adaptive inertia weight at each iteration at each sampling moment; and carrying out energy distribution on the unstable point power supply wind power plant confluence station according to the self-adaptive inertia weight and the particle swarm optimization algorithm. The invention can realize reasonable distribution of the energy of the unstable power supply, maintain the system stability of the unstable power supply and improve the energy distribution efficiency of the unstable power supply.
Description
Technical Field
The application relates to the technical field of power distribution systems, in particular to an intelligent distribution method for energy of an unstable power supply.
Background
The electric energy is the most widely applied energy source in the labor life of people, and the currently used electric energy mainly comes from fossil energy, hydropower, photoelectricity, wind power and the like. Where photovoltaic and wind capacitance are subject to meteorological conditions and cannot provide a stable voltage and power that one needs, commonly referred to as an unstable power supply. How to fully utilize the energy of an unstable power supply is always a problem which needs to be solved by people.
For a long time, the energy of the unstable power supply can be indirectly used by the power grid through a switching voltage stabilizing circuit and a storage battery. When the output power of the unstable power supply is larger than the minimum limiting power of the power grid, the switching voltage stabilizing circuit is in an off state, and the unstable power supply is connected with the power grid through the switching voltage stabilizing circuit to provide electric energy for the power grid; and conversely, when the power is smaller than the minimum limit power of the power grid, the voltage stabilizing circuit is switched to a closed state, weak electric quantity which does not meet the power grid requirement is stored in the storage battery, and the storage battery discharges outwards. In addition, if the output power of the unstable power supply exceeds the maximum limit power of the power grid, the unstable power supply supplies power to the power grid and charges a storage battery. However, the maximum and minimum limiting power of the power grid is related to the power grid load, the storage capacity of the storage battery and the output power of the unstable power supply, and the unstable power supply is easy to cause fluctuation of the power grid voltage and reduction of the energy utilization rate of the unstable power supply due to an improper limiting value, so that an optimization algorithm is required to adjust, and the optimal limiting parameter is found. However, when using the conventional Particle Swarm Optimization (PSO) algorithm, the solution is easy to fall into a local optimal solution, and the global optimal point is difficult to be accessed.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent distribution method of unstable power energy to solve the existing problems.
The invention discloses an intelligent distribution method of unstable power supply energy, which adopts the following technical scheme:
an embodiment of the invention provides an intelligent distribution method of unstable power supply energy, which comprises the following steps:
acquiring output power of an unstable power supply wind power station converging station, a switching voltage stabilizing circuit and a storage battery at each sampling moment, constructing a power generation power sequence, a voltage stabilizing power sequence and a battery power sequence at each sampling moment, and respectively fitting to obtain a power generation power curve, a voltage stabilizing power curve and a battery power curve corresponding to each sampling moment;
Determining a curve sequence projection coefficient of each sampling time in the power generation sequence based on the distribution condition of output power on the power generation curve at each sampling time; determining a curve sequence difference coefficient of each sampling time in the power generation power sequence based on the difference between the output power of the substation at each sampling time in the power generation power sequence at each sampling time and the output power on the power generation power curve; determining wind power fluctuation disorder coefficients at all sampling moments based on maximum values and average values in the power generation power sequences at all sampling moments, curve sequence projection coefficients and curve sequence difference coefficients at all sampling moments in the power generation power sequences; determining the actual power generation amount and the actual output electric quantity of the confluence station at each sampling moment based on the power generation power curve, the stabilized voltage power curve and the battery power curve at each sampling moment; determining the storage electric quantity of the storage battery at each sampling moment based on the SOC values of the storage battery at the starting and ending moments in the power generation sequence at each sampling moment; based on the actual generated energy, the actual output electric quantity, the stored electric quantity and the wind power fluctuation disorder coefficient, and combining the relation among the generated power sequence, the stabilized voltage power sequence and the battery power sequence at each sampling moment, determining the electric field bearing output conversion coefficient at each sampling moment; determining self-adaptive inertia weight of each sampling moment in each iteration based on the electric field bearing output conversion coefficient of each sampling moment, the iteration times of a particle swarm optimization algorithm and the mean value and variance of a power generation sequence;
and carrying out energy distribution on the unstable point power supply wind power plant confluence station according to the self-adaptive inertia weight and the particle swarm optimization algorithm.
Further, the constructing the generated power sequence, the stabilized voltage power sequence and the battery power sequence at each sampling time includes:
And forming the output power of the wind power station convergence station at a plurality of sampling moments before each sampling moment into a power generation power sequence at each sampling moment, and acquiring a voltage stabilizing power sequence and a battery power sequence at each sampling moment by adopting a construction method of the power generation power sequence aiming at the output power of the switch voltage stabilizing circuit and the storage battery.
Further, the determining the curve sequence projection coefficient of each sampling time in the power generation sequence based on the distribution condition of the output power on the power generation curve of each sampling time comprises:
Fitting the power generation power sequences according to the power generation power sequences at each sampling time to obtain a corresponding power generation power curve, and intercepting curve segments between each sampling time and the next adjacent sampling time on the power generation power curve; acquiring a line segment formed by connecting each sampling time in a power generation sequence with the output power of the next adjacent sampling time;
and respectively taking each sampling time and the next adjacent sampling time as the lower limit and the upper limit of an integral function, integrating the absolute value of the difference value between the curve segment and the line segment, and taking the integral result as the curve sequence projection coefficient of each sampling time in the generated power sequence.
Further, the determining the curve sequence difference coefficient of each sampling time in the generated power sequence includes:
And acquiring an absolute value of a difference value between the output power of each sampling time in the power generation sequence and a fitting value of each sampling time on a corresponding power generation curve, and determining the minimum value of the absolute value of each sampling time and the adjacent next sampling time in the power generation sequence as a curve sequence difference coefficient of each sampling time in the power generation sequence.
Further, the determining the wind power fluctuation disorder coefficient of each sampling time comprises:
Acquiring average values of sum values of curve sequence projection coefficients and curve sequence difference coefficients of all sampling moments except the sampling moments in the power generation power sequences of the sampling moments;
calculating the difference between the maximum value and the average value of the power generation sequence at each sampling moment;
and taking the result of the product of the average value and the difference value as a wind power fluctuation disorder coefficient at each sampling moment.
Further, the determining the actual power generation amount and the actual output power of the bus station at each sampling time based on the generated power curve, the stabilized power curve and the battery power curve at each sampling time includes:
counting the starting time and the ending time corresponding to the power generation power sequences at each sampling time, and calculating the integral result of the power generation power curve at each sampling time from the starting time to the ending time as the actual power generation amount of the confluence station at each sampling time;
and determining a sequence constructed by all summation results after summing corresponding positions in the stabilized voltage power sequence and the battery power sequence at each sampling time as a summation value sequence, and obtaining the actual output electric quantity at each sampling time by adopting a calculation method of actual electric generation quantity for the summation value sequence.
Further, the determining the storage capacity of the storage battery at each sampling time based on the SOC values of the storage battery at the start and end times in the generated power sequence at each sampling time includes:
calculating the difference value between the SOC values of the storage battery at the starting time and the ending time in the power generation sequence at each sampling time, and counting the theoretical total power storage amount of the storage battery;
When the difference value is greater than or equal to 0, taking the product of the difference value and the theoretical total power storage amount as the storage electric quantity of the storage battery at the corresponding sampling moment; and otherwise, taking the ratio of the theoretical total power storage amount to the absolute value of the difference value as the storage electric quantity of the storage battery at the corresponding sampling moment.
Further, the determining the electric field bearing output conversion coefficient of each sampling time includes:
acquiring the accumulated sum of the actual output electric quantity of the collecting station and the storage electric quantity of the storage battery at each sampling moment;
Calculating the DTW distance between the sum value sequence and the generated power sequence at each sampling moment, obtaining the product of the actual generated energy of the confluence station at each sampling moment and the wind power fluctuation disorder coefficient, and calculating the sum value of the product, the DTW distance and the numerical value larger than zero;
and taking the ratio of the accumulated sum to the sum value as an electric field bearing output conversion coefficient at each sampling moment.
Further, the determining the adaptive inertia weight at each iteration of each sampling moment includes:
Respectively obtaining the mean value and the variance of the power generation sequence at each sampling moment;
Taking the opposite number of the electric field bearing output conversion coefficient at each sampling moment as an index of an exponential function taking a natural constant as a base number, and obtaining the product of the variance and the calculation result of the exponential function as a molecule;
Sequentially marking each iteration of the sampling moment from the number 1 according to the ascending order of the numerical value, obtaining the sum value of the numerical value and the number 1 of the iteration times, calculating the logarithm of the sum value taking a natural constant as a base, and obtaining the result of adding a preset numerical value larger than zero to the product of the average value and the logarithm as a denominator;
and taking the ratio of the numerator to the denominator as the self-adaptive inertia weight of each iteration at each sampling moment.
Further, the energy distribution of the unstable point power supply wind power station substation according to the adaptive inertia weight and particle swarm optimization algorithm comprises the following steps:
the output power of the confluence station, the switching voltage stabilizing circuit and the storage battery at each sampling moment, the SOC value of the storage battery and the self-adaptive inertia weight are used as the input of a particle swarm optimization algorithm, and the maximum and minimum limiting power of the optimization at each sampling moment is obtained;
If the output power of the wind power plant bus station is lower than the minimum limiting power, only charging a storage battery, and transmitting electric quantity to a power grid through the storage battery;
If the output power of the wind power plant bus station is between the minimum limiting power and the maximum limiting power, transmitting electric quantity to the power grid through a switch voltage stabilizing circuit;
And if the output power of the wind power plant bus station is higher than the maximum limiting power, the storage battery is charged and the electric quantity is transmitted to the power grid through the switch voltage stabilizing circuit.
The invention has at least the following beneficial effects:
According to the invention, the output power of the wind power station is collected, a power generation power sequence and a power generation power curve are constructed, and the wind power fluctuation disorder coefficient is obtained by combining the information of the sequence value and the curve of adjacent sampling time, so that the stability of the wind power station in power generation at the current sampling time is reflected;
Meanwhile, the invention combines the generated power actually received by the power grid and the SOC value of the storage battery to obtain the electric field bearing output conversion coefficient, reflects the energy conversion condition of the wind power field at the current sampling moment, and finally obtains the self-adaptive inertia weight at each iteration according to the mean value and the variance of the generated power sequence. The method solves the defects that the fixed inertia weight in the traditional particle swarm optimization PSO algorithm is easy to fall into a local optimal solution and difficult to approach a global optimal point, can adaptively adjust the inertia weight according to the stability of the wind power generation of the wind power plant, and outputs the optimal maximum and minimum limiting power, thereby realizing the intelligent distribution of the energy of the unstable power supply of the wind power plant and improving the energy utilization rate of the unstable power supply.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of an intelligent distribution method of unstable power energy provided by the invention;
Fig. 2 is a schematic diagram of a maximum and minimum limiting power extraction flow optimal for each sampling time.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description refers to specific implementation, structure, characteristics and effects of an intelligent distribution method of unstable power energy according to the invention by combining the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the intelligent distribution method of the unstable power supply energy provided by the invention with reference to the accompanying drawings.
The invention provides an intelligent distribution method of energy of an unstable power supply, in particular to an intelligent distribution method of energy of an unstable power supply, referring to fig. 1, comprising the following steps:
Example 1
In this embodiment, wind power generation is taken as an example, and wind power generation energy can be directly transmitted to a power grid or stored in a storage battery, so that the utilization rate of an unstable power supply is improved and the stability of the unstable power supply is ensured through reasonable and intelligent distribution of wind power generation energy of a confluence station.
And S001, acquiring power signals of all nodes in the unstable power supply system through a power sensor, and preprocessing data.
In the embodiment, taking a wind turbine generator deployed in a wind farm in a certain place as an example, electric energy output by a plurality of wind generators is transmitted to a converging station through a collecting circuit, and distribution of an unstable power supply is realized through a wind farm management system. In order to improve the energy utilization rate of the unstable power supply, the intelligent distribution of energy is realized. For this purpose, the output power of the bus station is obtained by a power sensor, denoted by a, which is the output energy of wind power generation, and the output powers of the switching regulator circuit and the storage battery to the power grid are respectively recorded as B and C by the power sensor, and the SOC of the storage battery is obtained by an ampere-hour integration method. The ampere-hour integration method is a well-known technique, and the description of this embodiment is omitted.
In order to acquire real-time data of the wind power generation system, the present embodiment sets the sampling interval of the power sensor and the battery state of charge to 1s, taking the output power of the bus station as an example,Where a (i) represents the output power of the bus station at the ith sampling time, N A represents the total number of sampling times (uninterrupted sampling is performed during operation of the wind turbine), and a is the sequence of the output power of the bus station at all sampling times, and is denoted as the output power sequence. Because data loss may exist in the process of sensor acquisition, in order to reduce the influence of the missing value on subsequent analysis, the embodiment adopts a KNN nearest neighbor algorithm for filling. The KNN nearest neighbor algorithm is a known technique, and this embodiment is not described in detail.
Step S002, a power generation power sequence and a power generation power curve are constructed, a wind power fluctuation disorder coefficient is obtained by utilizing the difference of output power at each sampling time in the power generation power sequence and the power generation power curve, the output power of a switching voltage stabilizing circuit and a storage battery and the SOC value of the storage battery are further combined to bear the output conversion coefficient of an electric field, the self-adaptive inertia weight of each iteration is calculated, and the optimal maximum and minimum limiting power is obtained through a particle swarm optimization algorithm.
Because the generated energy of the unstable power supply has the characteristics of instability and uncontrollable, under the condition that the generated energy of the wind power station is higher than the power grid demand in the actual process and the storage battery is full at the moment, the situation of discarding electricity is often generated in order to maintain the stability of the power grid, and meanwhile, when the generated energy of the wind power station is too small, the charging standard of the storage battery cannot be met, and the smaller electric quantity of the part can be discarded in order to protect the normal operation of the storage battery. In addition, the switching regulator circuit and the storage battery also need to consume a part of energy during energy conversion, wherein the efficiency of the switching regulator circuit is generally 95%, and the conversion efficiency of the storage battery is 85%. Therefore, maximum and minimum limited power is effectively balanced according to the power generation capacity of the wind power plant, the state of a storage battery and the load demand of a power grid, and the maximum and effective utilization of unstable energy sources of the wind power plant is realized.
In the conventional particle swarm optimization algorithm PSO, the inertial weight controls the movement of particles in the search space, and the speed of the particles in updating iteration is determined. However, the conventional PSO usually adopts fixed random inertial weights, and does not consider the distribution state of data, so that the optimization structure is easily trapped into a locally optimal solution, and the capability of global searching and local searching cannot be effectively balanced. Therefore, according to the output power of the bus station, the switch voltage stabilizing circuit and the storage battery and the SOC state of the battery, the inertia weight in the PSO is dynamically adjusted, the algorithm is prevented from falling into a local optimal solution, and the stability and the robustness of the algorithm are improved.
Because the generated energy of the wind power plant is influenced by uncontrollable factors such as wind speed, the electric quantity output by the wind power plant has certain instability, and the stability degree of the output of the wind power plant is a main influencing factor for influencing the unstable power supply system to adjust the maximum and minimum limiting power. Therefore, aiming at the output power sequence A of the wind power station substation, tau sampling moments are selected forward by taking the current sampling moment as a time end point to construct a power generation power sequence of the current sampling moment, and the power generation power sequence A is usedThe generated power sequence at the i-th sampling time, which is the end time in the generated power sequence, is shown, and τ=60 is set in this embodiment. And filling the sensor with a linear interpolation method when the sampling time is smaller than tau in the initial stage of sensor sampling, and filling the sequence length into tau. The linear interpolation method is a known technique, and the description of this embodiment is omitted.
Taking the generated power sequence at the ith sampling moment as an example, the generated power sequence is calculatedAs the input of the nonlinear least square method, fitting to obtain a generating power curve, and using/>Each generated power sequence corresponds to a generated power curve. The nonlinear least square method is a known technique, and the present embodiment is not described in detail. And the line segment used for constructing the output power of two adjacent sampling moments in the power generation sequence/>K represents the kth sampling time in the generated power sequence at the ith sampling time. The wind power fluctuation disorder coefficient is obtained by combining the generated power sequence and the generated power curve:
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Where a k denotes a curve sequence projection coefficient at a kth sampling time in the generated power sequence, k and k+1 denote kth and k+1 sampling times in the generated power sequence, respectively, Representing curve segments intercepted by the kth and the kth+1 sampling moments in the power generation curve corresponding to the ith sampling moment, wherein tau is the sequence length,/>A line segment which is formed by the output power corresponding to the kth and the (k+1) th sampling time in the generating power sequence is represented, and d t represents integration of time t;
beta k represents a curve sequence difference coefficient at the kth sampling time in the generated power sequence, min () represents a minimum value calculated therein, And/>Respectively represent the output power corresponding to the kth and the (k+1) th sampling time in the power generation sequence at the ith sampling time,/>And/>Respectively representing fitting values of the kth sampling moment and the (k+1) th sampling moment in the generated power curve corresponding to the ith sampling moment;
gamma i denotes the disturbance factor of the wind power fluctuation at the ith sampling instant, max () denotes the maximum value in the calculation sequence, From the generated power sequence at the i-th sampling time, E () represents the calculated sequence average.
If the wind farm is unstable in power generation, the power generation sequence will have larger fluctuation. At this time, the fitting of the generated power curve is poor, so that the enclosed area surrounded by the connecting line of the curve and the adjacent sampling points of the sequence is larger, and the value of the projection coefficient alpha k of the obtained curve sequence is larger. Meanwhile, the difference value between two adjacent sampling moments and the corresponding value of the generated power curve is increased, and the value of the curve sequence difference coefficient beta k is increased. In addition, as the fluctuation of the power generation power sequence is larger, the difference between the maximum value and the average value of the power generation power sequence is larger, and finally, the value of the wind power fluctuation disorder coefficient gamma i is larger. In contrast, if the wind farm is stable in power generation, the fluctuation of the output power of the confluence station is relatively consistent, the power generation power curves of the power generation power sequences are basically overlapped, and finally the value of the wind power fluctuation disorder coefficient gamma i is reduced.
The wind power fluctuation disorder coefficient gamma can be used for measuring the stable condition of wind power plant power generation in a short period of time determined at a single sampling moment, and the stable degree of wind power plant power generation has a certain degree of influence on the states of a switch voltage stabilizing circuit and a storage battery. In order to further measure the stability of wind power generation and the influence degree on a wind power system, a switching voltage stabilizing circuit and the output power of a storage battery are used for constructing a voltage stabilizing power sequence and a battery power sequence in the same way as the power generation power sequence respectivelyAnd/>The same fitting results in a regulated power curve/>And battery power curve/>. Thereby obtaining the electric field bearing output conversion coefficient:
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In the method, in the process of the invention, Representing the actual power generation of the bus station at the ith sampling moment,/>And/>Respectively representing a start time and an end time corresponding to the generated power sequence at the ith sampling time,/>Representing a generated power curve corresponding to the ith sampling moment, and d t represents integrating the time t;
Representing the actual output power of the bus station at the ith sampling instant,/> Respectively representing a regulated power curve and a battery power curve corresponding to the ith sampling moment;
epsilon i represents the stored charge of the battery at the ith sample time, And/>The SOC values of the storage battery at the starting time and the ending time in the generated power sequence at the ith sampling time are respectively represented, W represents the theoretical total storage amount of the storage battery, and the theoretical total storage amount is obtained through a storage battery specification table;
η i represents the electric field-carrying output conversion coefficient at the i-th sampling instant, 、/>And/>Respectively representing a generated power sequence, a stabilized voltage power sequence and a battery power sequence at the ith sampling time,/>For a sequence constructed by summing corresponding positions in the regulated power sequence and the battery power sequence, DTW () represents the DTW distance of the calculated sequence,/>Representing denominator adjustment parameters, empirically set/>。
If the whole power generation of the wind power plant is unstable, the maximum value and the smaller value with more abnormality of the power generation sequence cannot be converted by the wind power unstable power supply system to generate the condition of power abandoning. The generated energy and the actual output electric quantity are obtained by integrating the power curve, and the actual generated energy in the wind power plant is obtained at the moment due to the electricity discarding condition caused by the unstable fluctuation of the generated electricityAnd the actual output power/>The deviation value of (2) is large. And meanwhile, the storage capacity SOC of the battery is combined to obtain the storage capacity of the battery. Although the switch voltage stabilizing circuit and the storage battery are isolated, the instability of the generated energy of the wind power plant can cause the actual output power of the bus station to generate fluctuation different from the generated power, so that/>The value of (3) increases, ultimately resulting in a smaller value for the electric field carrying output transformation coefficient η i. On the contrary, if the whole power generation of the wind farm is stable, the output power of the bus station is only stored and discharged into the power grid through the switch voltage stabilizing circuit or the storage battery, no electricity discarding condition exists, and the conversion efficiency is high at the moment, namely the value of the electric field bearing output conversion coefficient eta i is high.
The coupling relation between the actual power generation amount and the actual output power of the wind power plant can be measured through the electric field bearing output conversion coefficient eta. If the power generation is more stable, the coupling relation between the two is better, and the value of the electric field bearing output conversion coefficient eta is larger; conversely, if the power generation is less stable, the coupling relationship between the two is poorer, and the value of the electric field load output conversion coefficient η is smaller. Therefore, dynamic adjustment of inertia weight is realized by using a PSO algorithm, and optimal distribution of maximum limiting power and minimum limiting power in the wind power unstable power supply system to wind power generation is realized. Combining the electric field bearing output conversion coefficient to obtain the self-adaptive inertia weight:
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In the method, in the process of the invention, Representing adaptive inertial weights at the ith sample time, the nth iteration,/>The generated power sequence at the ith sampling moment, E () represents the calculated sequence mean value, sigma () represents the calculated sequence variance, exp () represents an exponential function based on a natural number E, eta i represents the electric field bearing output conversion coefficient at the ith sampling moment, ln () represents a logarithmic function based on the natural number E, and d represents the d-th iteration in the particle swarm optimization algorithm,/>Representing denominator adjustment parameters, arranged empirically。
When the power generation of the wind power plant is unstable, the ratio of the variance to the mean value of the power generation sequence is larger, which indicates that the variation coefficient of the power generation sequence is larger at the moment, meanwhile, the value of the load output conversion coefficient eta i of the wind power plant is smaller due to the influence of the fluctuation of the power generation sequence, and finally, the initial value of the inertia weight is larger. Because the fluctuation of the sequence is increased, the particle searching area is enlarged, global information is considered more, and the global optimal solution is found conveniently. If the wind farm is stable in power generation, the initial value of the obtained inertia weight is smaller, and the smaller inertia weight is adopted at the moment to facilitate algorithm convergence and improve algorithm stability. Meanwhile, as the iteration times increase, the value of ln (1+d) increases, so that the inertia weight gradually decreases along with the iteration times, and the stability and the robustness of the algorithm are improved.
In this embodiment, the output power of the bus station, the output power of the switching regulator circuit and the storage battery at each sampling time, the SOC value of the battery and the adaptive inertia weight are used as inputs of the particle swarm optimization PSO algorithm, the initial particle count is set to 30, the maximum iteration number is 50, the maximum energy efficiency of the power plant is used as an objective function in this embodiment, that is, the ratio of the actual received electric quantity of the power grid to the output electric quantity of the bus station is used as an objective function, the implementer can set the objective function of the optimization process by himself, this embodiment does not limit, and the particle swarm optimization PSO algorithm outputs the maximum limiting power optimal at each sampling timeAnd minimizing limiting power/>. The PSO algorithm and the optimization process are known techniques, and the description of the embodiment is omitted. The optimal maximum limited power and minimum limited power extraction flow chart at each sampling time is shown in fig. 2.
Therefore, the optimal maximum limiting power and the optimal minimum limiting power can be obtained by traversing each sampling time, the intelligent distribution of the generated energy of the wind power plant is realized according to the maximum limiting power and the minimum limiting power, the service life of a storage battery is prolonged, and the energy utilization rate and the output stability of the unstable power supply of wind power generation are improved.
And step S003, intelligent distribution is carried out on the energy of the unstable power supply based on the maximum and minimum limiting power combined with the particle swarm optimization algorithm.
According to the method, the maximum limiting power and the minimum limiting power of each sampling time can be obtained, and based on the maximum limiting power and the minimum limiting power, the energy of the wind power station bus station at each sampling time is reasonably distributed, and the specific distribution is as follows:
If the output power of the wind power plant bus station is lower than the minimum limiting power, only charging a storage battery, and transmitting electric quantity to a power grid through the storage battery;
If the output power of the wind power plant bus station is between the minimum limiting power and the maximum limiting power, transmitting electric quantity to the power grid through a switch voltage stabilizing circuit;
And if the output power of the wind power plant bus station is higher than the maximum limiting power, the storage battery is charged and the electric quantity is transmitted to the power grid through the switch voltage stabilizing circuit.
By adopting the method and the process of the embodiment, the intelligent and reasonable distribution of the energy of the unstable power supply can be realized.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment mainly describes differences from other embodiments.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; the technical solutions described in the foregoing embodiments are modified or some of the technical features are replaced equivalently, so that the essence of the corresponding technical solutions does not deviate from the scope of the technical solutions of the embodiments of the present application, and all the technical solutions are included in the protection scope of the present application.
Claims (10)
1. An intelligent distribution method for energy of an unstable power supply is characterized by comprising the following steps:
acquiring output power of an unstable power supply wind power station converging station, a switching voltage stabilizing circuit and a storage battery at each sampling moment, constructing a power generation power sequence, a voltage stabilizing power sequence and a battery power sequence at each sampling moment, and respectively fitting to obtain a power generation power curve, a voltage stabilizing power curve and a battery power curve corresponding to each sampling moment;
Determining a curve sequence projection coefficient of each sampling time in the power generation sequence based on the distribution condition of output power on the power generation curve at each sampling time; determining a curve sequence difference coefficient of each sampling time in the power generation power sequence based on the difference between the output power of the substation at each sampling time in the power generation power sequence at each sampling time and the output power on the power generation power curve; determining wind power fluctuation disorder coefficients at all sampling moments based on maximum values and average values in the power generation power sequences at all sampling moments, curve sequence projection coefficients and curve sequence difference coefficients at all sampling moments in the power generation power sequences; determining the actual power generation amount and the actual output electric quantity of the confluence station at each sampling moment based on the power generation power curve, the stabilized voltage power curve and the battery power curve at each sampling moment; determining the storage electric quantity of the storage battery at each sampling moment based on the SOC values of the storage battery at the starting and ending moments in the power generation sequence at each sampling moment; based on the actual generated energy, the actual output electric quantity, the stored electric quantity and the wind power fluctuation disorder coefficient, and combining the relation among the generated power sequence, the stabilized voltage power sequence and the battery power sequence at each sampling moment, determining the electric field bearing output conversion coefficient at each sampling moment; determining self-adaptive inertia weight of each sampling moment in each iteration based on the electric field bearing output conversion coefficient of each sampling moment, the iteration times of a particle swarm optimization algorithm and the mean value and variance of a power generation sequence;
and carrying out energy distribution on the unstable point power supply wind power plant confluence station according to the self-adaptive inertia weight and the particle swarm optimization algorithm.
2. The method for intelligently distributing energy from an unstable power source according to claim 1, wherein said constructing a generated power sequence, a regulated power sequence and a battery power sequence for each sampling instant comprises:
And forming the output power of the wind power station convergence station at a plurality of sampling moments before each sampling moment into a power generation power sequence at each sampling moment, and acquiring a voltage stabilizing power sequence and a battery power sequence at each sampling moment by adopting a construction method of the power generation power sequence aiming at the output power of the switch voltage stabilizing circuit and the storage battery.
3. The method for intelligently distributing energy from an unstable power source according to claim 1, wherein determining the projection coefficients of the power generation sequence for each sampling time based on the distribution of the output power on the power generation curve for each sampling time comprises:
Fitting the power generation power sequences according to the power generation power sequences at each sampling time to obtain a corresponding power generation power curve, and intercepting curve segments between each sampling time and the next adjacent sampling time on the power generation power curve; acquiring a line segment formed by connecting each sampling time in a power generation sequence with the output power of the next adjacent sampling time;
and respectively taking each sampling time and the next adjacent sampling time as the lower limit and the upper limit of an integral function, integrating the absolute value of the difference value between the curve segment and the line segment, and taking the integral result as the curve sequence projection coefficient of each sampling time in the generated power sequence.
4. The method for intelligently distributing energy from an unstable power source according to claim 3, wherein said determining a coefficient of variation of the sequence of curves for each sampling instant in the sequence of generated power comprises:
And acquiring an absolute value of a difference value between the output power of each sampling time in the power generation sequence and a fitting value of each sampling time on a corresponding power generation curve, and determining the minimum value of the absolute value of each sampling time and the adjacent next sampling time in the power generation sequence as a curve sequence difference coefficient of each sampling time in the power generation sequence.
5. The method for intelligently distributing energy from an unstable power source according to claim 1, wherein determining the disturbance factor of wind power fluctuation at each sampling time comprises:
Acquiring average values of sum values of curve sequence projection coefficients and curve sequence difference coefficients of all sampling moments except the sampling moments in the power generation power sequences of the sampling moments;
calculating the difference between the maximum value and the average value of the power generation sequence at each sampling moment;
and taking the result of the product of the average value and the difference value as a wind power fluctuation disorder coefficient at each sampling moment.
6. The method for intelligently distributing the energy of the unstable power supply according to claim 1, wherein the determining the actual power generation amount and the actual output power amount of the bus station at each sampling time based on the generated power curve, the stabilized power curve and the battery power curve at each sampling time comprises:
counting the starting time and the ending time corresponding to the power generation power sequences at each sampling time, and calculating the integral result of the power generation power curve at each sampling time from the starting time to the ending time as the actual power generation amount of the confluence station at each sampling time;
and determining a sequence constructed by all summation results after summing corresponding positions in the stabilized voltage power sequence and the battery power sequence at each sampling time as a summation value sequence, and obtaining the actual output electric quantity at each sampling time by adopting a calculation method of actual electric generation quantity for the summation value sequence.
7. The method for intelligently distributing energy from an unstable power source according to claim 1, wherein the determining the storage capacity of the storage battery at each sampling time based on the SOC values of the storage battery at the start and end time in the generated power sequence at each sampling time comprises:
calculating the difference value between the SOC values of the storage battery at the starting time and the ending time in the power generation sequence at each sampling time, and counting the theoretical total power storage amount of the storage battery;
When the difference value is greater than or equal to 0, taking the product of the difference value and the theoretical total power storage amount as the storage electric quantity of the storage battery at the corresponding sampling moment; and otherwise, taking the ratio of the theoretical total power storage amount to the absolute value of the difference value as the storage electric quantity of the storage battery at the corresponding sampling moment.
8. The method for intelligently distributing energy from an unstable power source according to claim 6, wherein said determining the electric field bearing output conversion coefficient at each sampling instant comprises:
acquiring the accumulated sum of the actual output electric quantity of the collecting station and the storage electric quantity of the storage battery at each sampling moment;
Calculating the DTW distance between the sum value sequence and the generated power sequence at each sampling moment, obtaining the product of the actual generated energy of the confluence station at each sampling moment and the wind power fluctuation disorder coefficient, and calculating the sum value of the product, the DTW distance and the numerical value larger than zero;
and taking the ratio of the accumulated sum to the sum value as an electric field bearing output conversion coefficient at each sampling moment.
9. The method of claim 1, wherein determining the adaptive inertial weights for each iteration of each sampling instant comprises:
Respectively obtaining the mean value and the variance of the power generation sequence at each sampling moment;
Taking the opposite number of the electric field bearing output conversion coefficient at each sampling moment as an index of an exponential function taking a natural constant as a base number, and obtaining the product of the variance and the calculation result of the exponential function as a molecule;
Sequentially marking each iteration of the sampling moment from the number 1 according to the ascending order of the numerical value, obtaining the sum value of the numerical value and the number 1 of the iteration times, calculating the logarithm of the sum value taking a natural constant as a base, and obtaining the result of adding a preset numerical value larger than zero to the product of the average value and the logarithm as a denominator;
and taking the ratio of the numerator to the denominator as the self-adaptive inertia weight of each iteration at each sampling moment.
10. The method for intelligently distributing energy from an unstable point power source according to claim 7, wherein said energy distribution from an unstable point power source wind farm junction according to an adaptive inertial weight in combination with a particle swarm optimization algorithm comprises:
the output power of the confluence station, the switching voltage stabilizing circuit and the storage battery at each sampling moment, the SOC value of the storage battery and the self-adaptive inertia weight are used as the input of a particle swarm optimization algorithm, and the maximum and minimum limiting power of the optimization at each sampling moment is obtained;
If the output power of the wind power plant bus station is lower than the minimum limiting power, only charging a storage battery, and transmitting electric quantity to a power grid through the storage battery;
If the output power of the wind power plant bus station is between the minimum limiting power and the maximum limiting power, transmitting electric quantity to the power grid through a switch voltage stabilizing circuit;
And if the output power of the wind power plant bus station is higher than the maximum limiting power, the storage battery is charged and the electric quantity is transmitted to the power grid through the switch voltage stabilizing circuit.
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