CN113762650A - Optimization method and system for distributed prediction of power grid - Google Patents
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Abstract
The invention discloses an optimization method and system for a distributed prediction power grid, which comprises the following steps: step S1, monitoring power supply parameters provided by each distributed power supply in the power distribution network for the load of the power distribution network in real time, and calculating the contribution degree of the distributed power supplies based on the power supply parameters; and S2, predicting the contribution of the distributed power supply at a future moment, and constructing a distribution model for the contribution of the distributed power supply in the power distribution network, wherein the distribution model is used for performing coordinated distribution on the contribution of the distributed power supply in the power distribution network so that the distributed power supply can meet the load power demand of the power distribution network and reduce the power consumption cost and improve the cleaning benefit at the same time. The method dynamically adjusts the distributed power sources until the predicted value of the contribution degree obtained by the neural network at the future moment is consistent with the regulation reference, so that the optimal scheduling of all the distributed power sources in the power distribution network is realized, and the power distribution network meets the requirements of reliability and low carbon at the same time.
Description
Technical Field
The invention relates to the technical field of power grid optimization, in particular to a distributed prediction power grid optimization method and system.
Background
With increasing importance placed on energy crisis, environmental protection, global climate change and other problems of countries in the world, development and utilization of renewable energy have become a global common consensus and concerted action. In recent years, renewable energy power generation technology is mature day by day, and renewable energy power generation grid connection is developed rapidly. Meanwhile, electric vehicles are widely researched and utilized due to the advantages of energy conservation, environmental protection and air pollution prevention. The scale access and application of the distributed power supply and the electric automobile are inevitable trends and future important characteristics of the sustainable development of the intelligent power distribution network. At present, the characteristics of intermittency, randomness and fluctuation of the output of the distributed power supply are one of bottlenecks which restrict the consumption and the efficient utilization of the distributed power supply. Random grid connection and disordered charging of large-scale electric vehicles can generate peak loads and increase network loss. The power distribution network has normalized uncertainty and volatility due to the strong fluctuation power supply and the strong random load, great challenges are brought to safe and reliable operation of the intelligent power distribution network, and higher requirements are brought to coordination optimization control of the intelligent power distribution network.
The invention discloses a distributed power supply coordinated optimization control method and system based on power distribution network running state data and topology data and electric vehicle charging state data, and predicts source load power to obtain a prediction result.
The above prior art can improve the utilization level of the distributed power source to a certain extent, but still has certain defects, such as: the power supply sequence is determined qualitatively, but the adjustment quantity of the distributed power supply is difficult to grasp quantitatively, that is, the adjustment accuracy of the distributed power supply cannot be controlled, so that the planning reliability of the distributed power supply is low.
Disclosure of Invention
The invention aims to provide a distributed prediction power grid optimization method and system to solve the technical problem that the planning reliability of a distributed power supply is low because the adjustment quantity of the distributed power supply is difficult to quantitatively master, namely the adjustment accuracy of the distributed power supply cannot be controlled in the prior art.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a distributed prediction power grid optimization method comprises the following steps:
step S1, monitoring power supply parameters provided by each distributed power supply in the power distribution network for the load of the power distribution network in real time, and calculating contribution degrees of the distributed power supplies based on the power supply parameters, wherein the contribution degrees are used as measurement indexes of supply degrees of the distributed power supplies to total power supply parameters of the load of the power distribution network, and the power supply parameters are power parameter data representing the input of the distributed power supplies to the power distribution network when the distributed power supplies are connected to the power distribution network;
step S2, predicting the contribution of the distributed power sources at a future moment, and constructing a distribution model for the contribution of the distributed power sources in the power distribution network, wherein the distribution model is used for performing coordinated distribution on the contribution of the distributed power sources in the power distribution network so that the distributed power sources can meet the load power demand of the power distribution network and reduce the power consumption cost and improve the cleaning benefit at the same time;
and step S3, taking the distribution value of the contribution degree obtained by the distribution model at a future moment as an adjustment reference, and dynamically adjusting the distributed power supply until the predicted value of the contribution degree obtained by the neural network at the future moment is consistent with the adjustment reference, so as to realize the optimal scheduling of all distributed power supplies in the power distribution network, so that the power distribution network meets the reliability requirement and simultaneously meets the low-carbon requirement.
As a preferable aspect of the present invention, the calculating the contribution degree of the distributed power supply based on the power supply parameter includes:
sequentially calculating the output power of each distributed power supply according to the power supply parameters of the distributed power supplies;
monitoring power consumption parameters of loads at each load of the power distribution network, calculating the input power of each load according to the power consumption parameters of the loads, summing the input powers of all the loads to obtain the total input power of the loads of the power distribution network, wherein the power consumption parameters are represented by the power consumption parameter data which are input into the power distribution network and output from the power distribution network to the loads;
calculating the ratio of the output power of each distributed power supply to the total input power in turn as the contribution degree of each distributed power supply, wherein the calculation formula of the contribution degree is as follows:
in the formula (I), the compound is shown in the specification,
characterized by the contribution of the ith distributed power source at time t,
characterized by the output power of the ith distributed power source at time t,
characterization ofFor the input power of the jth load at time t, N is characterized as the total number of loads, and i, j is a metering constant with no material meaning.
As a preferred aspect of the present invention, the predicting the contribution degree of the distributed power source at a future time includes:
the contribution degree of the distributed power supply at the time t
Inputting the data into an LSTM long-short term memory network, outputting the data to obtain a predicted value of the contribution degree of the distributed power supply at the future time t +1
To predict the contribution of the distributed power source at a future moment; m is characterized as the total number of distributed power sources;
the training mode of the LSTM long-short term memory network is set as the reverse transmission mode of seq2 seq.
As a preferred aspect of the present invention, the building a distribution model for the contribution degree of the distributed power source in the power distribution network includes:
setting a power optimal value and a power lowest value for the distributed power supply, wherein the power optimal value is used for representing an output power optimal value for full-load operation of all loads, and the power lowest value is used for representing an output power lowest value for full-load operation of all loads;
quantifying the power consumption cost of the operation of the power distribution network based on the power generation cost of the distributed power supply to serve as an economic evaluation index;
quantifying the environmental cost of the operation of the power distribution network based on the pollutant emission of the distributed power supply as a cleanness evaluation index;
and searching and solving in the range from the optimal power value to the lowest power value to obtain an evaluation value, wherein the evaluation value is characterized in that an economic evaluation index and a clean evaluation index are used for jointly representing the evaluation index of the running good and bad state of the power distribution network.
As a preferred scheme of the invention, the economic evaluation index is obtained based on the power generation cost quantification of the distributed power supply, and the economic evaluation index comprises the following steps:
the method comprises the steps of obtaining the electricity production cost of each distributed power supply, summing the electricity production cost of all the distributed power supplies to obtain an economic evaluation index, wherein the economic evaluation index is calculated according to the formula:
in the formula (I), the compound is shown in the specification,
characterized by an economic evaluation index value at time t,
characterized by the electricity production unit cost of the ith distributed power supply,
，
characterized by the output power of the ith distributed power source at time t,
characterized by the temporal duration at the instant t,
characterizing the instantaneous power of the ith distributed power supply at time t, and M characterizing the total number of distributed power supplies;
the instantaneous time length at the time t
And if the quantization is 1 unit time, updating the calculation formula of the economic evaluation index into:
as a preferred scheme of the invention, the method for quantifying and obtaining the cleaning evaluation index based on the pollutant discharge amount of the distributed power supply comprises the following steps:
the method comprises the steps of obtaining pollutants generated by each distributed power supply, pollutant yield and pollutant treatment unit price cost, and calculating a cleaning evaluation index according to the pollutants, the pollutant yield and the pollutant treatment unit price cost, wherein a calculation formula of the cleaning evaluation index is as follows:
in the formula (I), the compound is shown in the specification,
characterized by a cleaning evaluation index value at time t,
contaminant processing Unit price characterized by the kth contaminant
，
，
Characterized by the output power of the ith distributed power source at time t,
characterized by the temporal duration at the instant t,
characterized by the instantaneous power of the ith distributed power source at time t,
characterised by being generating instantaneous electric energy
The correlation coefficient of the kth pollutant yield is generated,
the method is characterized in that the yield of the kth pollutant generated by the ith distributed power supply at the moment t, M is characterized in that the total number of the distributed power supplies, n is characterized in that the total number of pollutant types, k is a metering constant and has no substantial meaning;
the instantaneous time length at the time t
And if the quantization is 1 unit time, updating the calculation formula of the economic evaluation index into:
as a preferable embodiment of the present invention, the using of the economic evaluation index and the cleanliness evaluation index to jointly form the evaluation merit value includes:
carrying out weighted summation on the economic evaluation index and the clean evaluation index to obtain a merit evaluation value, wherein the calculation formula of the merit evaluation value is as follows:
in the formula (I), the compound is shown in the specification,
characterized by a merit value at time t,
，
，
、
are respectively economic evaluation indexes
And cleanliness evaluation index
The weight coefficient of (2).
As a preferred embodiment of the present invention, the calculating, by the assignment model, the assignment value of the contribution degree obtained at a future time includes:
at a future time t +1, the distributed power supply will be powered on
Of the output power
Carrying out value search from the optimal power value to the lowest power value to obtain the maximum evaluation value, and corresponding to the maximum evaluation value
Formula for calculating degree of contribution
Obtaining distributed power supply through medium computation
Assigned value of contribution degree
The assigned value of the contribution degree
As a reference for the adjustment, among others,
characterized by an assigned value of the output power of the ith distributed power source at a future time instant t +1,
characterized by the rated power of the jth load at a future time instant t +1,
a distribution value characterized as a contribution degree to the ith distributed power source by the distribution model at a future time t + 1;
and solving by adopting a genetic algorithm.
As a preferred aspect of the present invention, dynamically adjusting a distributed power source until a predicted value of the contribution obtained by a neural network at a future time is consistent with an adjustment reference includes:
according to the predicted value of the contribution degree of the distributed power supply at the future time t +1
And calculating the predicted value of the output power of the distributed power supply at the future time t +1 by using a calculation formula of the contribution degree
；
Predicting value of output power of distributed power supply at future time t +1
Adjusting the power supply parameter until the predicted value
Distribution value of output power of distributed power supply
And equality is carried out so as to enable the predicted value of the contribution degree to be consistent with the regulation reference.
As a preferred aspect of the present invention, the present invention provides an optimization system according to the optimization method of a distributed prediction power grid, including:
the data acquisition module is used for monitoring power supply parameters provided by each distributed power supply in the power distribution network for the load of the power distribution network and power consumption parameters of the load of the power distribution network in real time;
the data processing module is used for calculating the contribution degree of the distributed power supply based on the power supply parameters, predicting the contribution degree of the distributed power supply at a future moment, and constructing a distribution model for the contribution degree of the distributed power supply in the power distribution network;
and the optimization and regulation module is used for taking the distribution value of the contribution degree obtained by the distribution model at a future moment as a regulation reference and dynamically regulating the distributed power supply until the predicted value of the contribution degree obtained by the neural network at the future moment is consistent with the regulation reference.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, the distribution model of the contribution rate of the distributed power supply is built, the distribution value of the contribution rate obtained by the distribution model at the future moment is used as the regulation reference, the distributed power supply is dynamically regulated until the predicted value of the contribution rate obtained by the neural network at the future moment is consistent with the regulation reference, so that the optimal scheduling of all the distributed power supplies in the power distribution network is realized, the power distribution network meets the reliability requirement and simultaneously meets the low-carbon requirement, the purpose of quantitative optimal scheduling is achieved, and the accuracy and controllability of the optimal scheduling are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a flowchart of an optimization method for a distributed prediction power grid according to an embodiment of the present invention;
fig. 2 is a block diagram of an optimization system according to an embodiment of the present invention.
The reference numerals in the drawings denote the following, respectively:
1-a data acquisition module; 2-a data processing module; and 3, optimizing and adjusting the module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present invention provides a method for optimizing a distributed prediction power grid, which includes the following steps:
step S1, monitoring power supply parameters provided by each distributed power supply in the power distribution network for the load of the power distribution network in real time, and calculating contribution degrees of the distributed power supplies based on the power supply parameters, wherein the contribution degrees are used as measurement indexes of supply degrees of the distributed power supplies to total power supply parameters of the load of the power distribution network, and the power supply parameters are electric parameter data representing the input of the distributed power supplies to the power distribution network after being accessed to the power distribution network;
calculating the contribution degree of the distributed power supply based on the power supply parameters, comprising the following steps:
sequentially calculating the output power of each distributed power supply according to the power supply parameters of the distributed power supplies;
monitoring power consumption parameters of loads at each load of the power distribution network, calculating the input power of each load according to the power consumption parameters of the loads, summing the input powers of all the loads to obtain the total input power of the loads of the power distribution network, wherein the power consumption parameters are represented by power consumption parameter data which are obtained by connecting the loads to the power distribution network and outputting the loads from the power distribution network;
calculating the ratio of the output power and the total input power of each distributed power supply in turn as the contribution degree of each distributed power supply, wherein the calculation formula of the contribution degree is as follows:
in the formula (I), the compound is shown in the specification,
characterized by the contribution of the ith distributed power source at time t,
characterized by the output power of the ith distributed power source at time t,
characterized by the input power of the jth load at time t, N by the total number of loads, and i, j by a metering constant, with no material implication.
The power supply parameters include but are not limited to the voltage and the current transmitted to the power distribution network line, the electricity consumption parameters include but are not limited to the voltage and the current transmitted from the power distribution network to the load end, and the electricity consumption of the power distribution network load is from the combined action of all the distributed power sources, so the power of the power distribution network load is completely converted from the power of the distributed power sources, the contribution degree of a single distributed power source to the power distribution network is measured, and the contribution degree can be measured by the ratio of the output power of the distributed power source to the sum of the input power of all the loads in the power distribution network, namely the output power transmitted by the distributed power source to the power distribution network accounts for the sum of the input power of all the loads, the larger the specific gravity is, the larger the supply degree of the distributed power source to the power distribution network load is, the higher the contribution degree is, and conversely, the smaller the specific gravity is the supply degree of the distributed power source to the power distribution network load is, the lower the contribution.
Step S2, predicting the contribution of the distributed power sources at a future moment, and constructing a distribution model for the contribution of the distributed power sources in the power distribution network, wherein the distribution model is used for performing coordinated distribution on the contribution of the distributed power sources in the power distribution network so that the distributed power sources can reduce power consumption cost and improve cleaning benefit while meeting the power consumption requirement of loads of the power distribution network;
the method for predicting the contribution degree of the distributed power source at the future moment comprises the following steps:
contribution degree of distributed power supply at time t
Inputting the data into an LSTM long-short term memory network, outputting the data to obtain a predicted value of the contribution degree of the distributed power supply at the future time t +1
The contribution degree of the distributed power supply at the future moment is predicted;
the training mode of the LSTM long-short term memory network is set as the reverse transmission mode of seq2 seq.
The method comprises the following steps of constructing a distribution model for the contribution degree of a distributed power supply in a power distribution network, wherein the distribution model comprises the following steps:
setting a power optimal value and a power lowest value for the distributed power supply, wherein the power optimal value is used for representing an output power optimal value for full-load operation of all loads, and the power lowest value is used for representing an output power lowest value for full-load operation of all loads;
quantifying the power consumption cost of the operation of the power distribution network based on the power generation cost of the distributed power supply to serve as an economic evaluation index;
quantifying the environmental cost of the operation of the power distribution network based on the pollutant emission of the distributed power supply as a cleanness evaluation index;
and searching and solving in the range from the optimal power value to the lowest power value to obtain an evaluation value, wherein the evaluation value is characterized in that an economic evaluation index and a clean evaluation index are used for jointly representing the running quality state of the power distribution network.
The economic evaluation index and the clean evaluation index are set for measuring the cost and the pollution condition of the power distribution network, the higher the economic evaluation index is, the worse the running state of the power distribution network is, the lower the economic evaluation index is, the better the running state of the power distribution network is, the higher the clean evaluation index is, the worse the running state of the power distribution network is, the lower the clean evaluation index is, the better the running state of the power distribution network is, and the running target of the power distribution network is to keep a better running state, namely, to pursue the lower economic evaluation index and the lower clean evaluation index, so that the output power of the distributed power supply corresponding to the maximum evaluation value is obtained by searching the evaluation value formed by the economic evaluation index and the clean evaluation index and is taken as the expected distributed power supply dispatching state of the power distribution network, and the optimal running state of the power distribution network is kept in real time.
The method for obtaining the economic evaluation index based on the power generation cost quantification of the distributed power supply comprises the following steps:
the method comprises the steps of obtaining the electricity production cost of each distributed power supply, summing the electricity production cost of all the distributed power supplies to obtain an economic evaluation index, wherein the economic evaluation index is calculated according to the formula:
in the formula (I), the compound is shown in the specification,
characterized by an economic evaluation index value at time t,
characterized by the electricity production unit cost of the ith distributed power supply,
，
characterized by the output power of the ith distributed power source at time t,
characterized by the temporal duration at the instant t,
characterizing the instantaneous power of the ith distributed power supply at time t, and M characterizing the total number of distributed power supplies;
the instantaneous time length at the time t
And if the quantization is 1 unit time, updating the calculation formula of the economic evaluation index into:
the method for quantifying the pollutant discharge amount based on the distributed power supply to obtain the cleaning evaluation index comprises the following steps:
the method comprises the steps of obtaining pollutants generated by each distributed power supply, pollutant yield and pollutant treatment unit price cost, and calculating a cleaning evaluation index according to the pollutants, the pollutant yield and the pollutant treatment unit price cost, wherein the calculation formula of the cleaning evaluation index is as follows:
in the formula (I), the compound is shown in the specification,
characterized by a cleaning evaluation index value at time t,
a contaminant processing unit price characterized as the kth contaminant,
，
，
characterized by the output power of the ith distributed power source at time t,
characterized by the temporal duration at the instant t,
characterized by the instantaneous power of the ith distributed power source at time t,
characterised by being generating instantaneous electric energy
The correlation coefficient of the kth pollutant yield is generated,
the method is characterized in that the yield of the kth pollutant generated by the ith distributed power supply at the moment t, M is characterized in that the total number of the distributed power supplies, n is characterized in that the total number of pollutant types, k is a metering constant and has no substantial meaning;
the instantaneous time length at the time t
And if the quantization is 1 unit time, updating the calculation formula of the economic evaluation index into:
the economic evaluation index and the clean evaluation index are jointly used for forming an evaluation merit value, and the evaluation merit value comprises the following steps:
carrying out weighted summation on the economic evaluation index and the clean evaluation index to obtain a merit evaluation value, wherein the calculation formula of the merit evaluation value is as follows:
in the formula (I), the compound is shown in the specification,
characterized by a merit value at time t,
，
，
，
、
are respectively economic evaluation indexes
And cleanliness evaluation index
The weight coefficient of (2).
Calculating the distribution value of the contribution degree obtained at the future moment by the distribution model, wherein the distribution value comprises the following steps:
at a future time t +1, the distributed power supply will be powered on
Of the output power
Carrying out value search in the range from the optimal power value to the lowest power value to obtain the maximum evaluation value, and corresponding to the maximum evaluation value
Formula for calculating degree of contribution
Obtaining distributed power supply through medium computation
Assigned value of contribution degree
The assigned value of the contribution degree
As a reference for the adjustment, among others,
characterized by an assigned value of the output power of the ith distributed power source at a future time instant t +1,
characterized by the rated power of the jth load at a future time instant t +1,
a distribution value characterized as a contribution degree to the ith distributed power source by the distribution model at a future time t + 1;
and solving the search by adopting a genetic algorithm.
And step S3, taking the distribution value of the contribution degree obtained by the distribution model at the future time as an adjustment reference, and dynamically adjusting the distributed power sources until the predicted value of the contribution degree obtained by the neural network at the future time is consistent with the adjustment reference, so that optimal scheduling of all the distributed power sources in the power distribution network is realized, and the power distribution network meets the reliability requirement and simultaneously meets the low-carbon requirement.
The dynamic adjustment of the distributed power supply is carried out until the predicted value of the contribution degree obtained by the neural network at the future moment is consistent with the regulation reference, and the method comprises the following steps:
according to the predicted value of the contribution degree of the distributed power supply at the future time t +1
And calculating the predicted value of the output power of the distributed power supply at the future time t +1 by using a calculation formula of the contribution degree
；
Predicting value of output power of distributed power supply at future time t +1
Adjusting the power supply parameter until the predicted value
Distribution value of output power of distributed power supply
And equality is carried out so as to enable the predicted value of the contribution degree to be consistent with the regulation reference.
Calculating the distributed power supply at the future time t +1 by using the evaluation merit value calculation formula
Of the output power
Thus, it can be seen that in order to maintain the optimal operation state of the power distribution network at the future time t +1, the output power of the distributed power supply needs to be set to be
However, since the output power of the distributed power supply at the future time t +1 can be predicted from the output power of the distributed power supply at the time t without human intervention, if the predicted output power of the distributed power supply at the future time t +1 (the predicted value in the present embodiment) is compared with the output power of the distributed power supply at the future time t +1
If the distribution values of the output power are inconsistent, the power distribution network cannot keep the optimal operation state at the future time t +1, at this time, human intervention is needed to be performed on the distributed power supply, the distributed power supply is adjusted to enable the output power to be the same as the distribution values, and the power distribution network at the future time t +1 is adjusted to the optimal operation state again.
As shown in fig. 2, based on the optimization method of the distributed prediction power grid, the invention provides an optimization system, which includes:
the data acquisition module 1 is used for monitoring power supply parameters provided by each distributed power supply in the power distribution network for the load of the power distribution network and power consumption parameters of the load of the power distribution network in real time;
the data processing module 2 is used for calculating the contribution degree of the distributed power supply based on the power supply parameters, predicting the contribution degree of the distributed power supply at a future moment, and constructing a distribution model for the contribution degree of the distributed power supply in the power distribution network;
and the optimization and adjustment module 3 is used for taking the distribution value of the contribution degree obtained by the distribution model at the future moment as an adjustment reference, and dynamically adjusting the distributed power supply until the predicted value of the contribution degree obtained by the neural network at the future moment is consistent with the adjustment reference.
According to the method, the distributed model of the contribution rate of the distributed power sources is constructed, the distribution value of the contribution degree obtained by the distributed model at the future time is used as the regulation reference, the distributed power sources are dynamically regulated until the predicted value of the contribution degree obtained by the neural network at the future time is consistent with the regulation reference, so that the optimal scheduling of all the distributed power sources in the power distribution network is realized, the power distribution network meets the reliability requirement and simultaneously meets the low-carbon requirement, the purpose of quantitative optimal scheduling is achieved, and the accuracy and controllability of the optimal scheduling are improved.
The above embodiments are merely exemplary embodiments of the present application and are not intended to limit the present application. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (10)
1. A distributed prediction power grid optimization method is characterized by comprising the following steps:
step S1, monitoring power supply parameters provided by each distributed power supply in the power distribution network for the load of the power distribution network in real time, and calculating contribution degrees of the distributed power supplies based on the power supply parameters, wherein the contribution degrees are used as measurement indexes of supply degrees of the distributed power supplies to total power supply parameters of the load of the power distribution network, and the power supply parameters are power parameter data representing the input of the distributed power supplies to the power distribution network when the distributed power supplies are connected to the power distribution network;
step S2, predicting the contribution of the distributed power sources at a future moment, and constructing a distribution model for the contribution of the distributed power sources in the power distribution network, wherein the distribution model is used for performing coordinated distribution on the contribution of the distributed power sources in the power distribution network so that the distributed power sources can meet the load power demand of the power distribution network and reduce the power consumption cost and improve the cleaning benefit at the same time;
and step S3, taking the distribution value of the contribution degree obtained by the distribution model at a future moment as an adjustment reference, and dynamically adjusting the distributed power supply until the predicted value of the contribution degree obtained by the neural network at the future moment is consistent with the adjustment reference, so as to realize the optimal scheduling of all distributed power supplies in the power distribution network, so that the power distribution network meets the reliability requirement and simultaneously meets the low-carbon requirement.
2. The method of claim 1, wherein the step of optimizing the decentralized predictive power grid comprises: the calculating the contribution degree of the distributed power supply based on the power supply parameter includes:
sequentially calculating the output power of each distributed power supply according to the power supply parameters of the distributed power supplies;
monitoring power consumption parameters of loads at each load of the power distribution network, calculating the input power of each load according to the power consumption parameters of the loads, summing the input powers of all the loads to obtain the total input power of the loads of the power distribution network, wherein the power consumption parameters are represented by the power consumption parameter data which are input into the power distribution network and output from the power distribution network to the loads;
calculating the ratio of the output power of each distributed power supply to the total input power in turn as the contribution degree of each distributed power supply, wherein the calculation formula of the contribution degree is as follows:
in the formula，
Characterized by the contribution of the ith distributed power source at time t,
characterized by the output power of the ith distributed power source at time t,
characterized by the input power of the jth load at time t, N by the total number of loads, and i, j by a metering constant, with no material implication.
3. The method of claim 2, wherein the step of optimizing the decentralized predictive power grid comprises: predicting the contribution of the distributed power source at a future time, comprising:
the contribution degree of the distributed power supply at the time t
Inputting the data into an LSTM long-short term memory network, outputting the data to obtain a predicted value of the contribution degree of the distributed power supply at the future time t +1
To predict the contribution of the distributed power source at a future moment; m is characterized as the total number of distributed power sources;
the training mode of the LSTM long-short term memory network is set as the reverse transmission mode of seq2 seq.
4. The method of claim 3, wherein the step of optimizing the decentralized predictive power grid comprises: the method for constructing the distribution model for the contribution degree of the distributed power sources in the power distribution network comprises the following steps:
setting a power optimal value and a power lowest value for the distributed power supply, wherein the power optimal value is used for representing an output power optimal value for full-load operation of all loads, and the power lowest value is used for representing an output power lowest value for full-load operation of all loads;
quantifying the power consumption cost of the operation of the power distribution network based on the power generation cost of the distributed power supply to serve as an economic evaluation index;
quantifying the environmental cost of the operation of the power distribution network based on the pollutant emission of the distributed power supply as a cleanness evaluation index;
and searching and solving in the range from the optimal power value to the lowest power value to obtain an evaluation value, wherein the evaluation value is characterized in that an economic evaluation index and a clean evaluation index are used for jointly representing the evaluation index of the running good and bad state of the power distribution network.
5. The method of claim 4, wherein the step of optimizing the decentralized predictive power grid comprises: the method for obtaining the economic evaluation index based on the power generation cost quantification of the distributed power supply comprises the following steps:
the method comprises the steps of obtaining the electricity production cost of each distributed power supply, summing the electricity production cost of all the distributed power supplies to obtain an economic evaluation index, wherein the economic evaluation index is calculated according to the formula:
in the formula (I), the compound is shown in the specification,
characterized by an economic evaluation index value at time t,
characterized by the electricity production unit cost of the ith distributed power supply,
，
characterized by the output power of the ith distributed power source at time t,
characterized by the temporal duration at the instant t,
characterizing the instantaneous power of the ith distributed power supply at time t, and M characterizing the total number of distributed power supplies;
the instantaneous time length at the time t
And if the quantization is 1 unit time, updating the calculation formula of the economic evaluation index into:
6. the method of claim 5, wherein the step of optimizing the decentralized predictive power grid comprises: the method for quantifying the pollutant discharge amount based on the distributed power supply to obtain the cleaning evaluation index comprises the following steps:
the method comprises the steps of obtaining pollutants generated by each distributed power supply, pollutant yield and pollutant treatment unit price cost, and calculating a cleaning evaluation index according to the pollutants, the pollutant yield and the pollutant treatment unit price cost, wherein a calculation formula of the cleaning evaluation index is as follows:
in the formula (I), the compound is shown in the specification,
characterized by a cleaning evaluation index value at time t,
a contaminant processing unit price characterized as the kth contaminant,
，
，
characterized by the output power of the ith distributed power source at time t,
characterized by the temporal duration at the instant t,
characterized by the instantaneous power of the ith distributed power source at time t,
characterised by being generating instantaneous electric energy
The correlation coefficient of the kth pollutant yield is generated,
the method is characterized in that the yield of the kth pollutant generated by the ith distributed power supply at the moment t, M is characterized in that the total number of the distributed power supplies, n is characterized in that the total number of pollutant types, k is a metering constant and has no substantial meaning;
the instantaneous time length at the time t
And if the quantization is 1 unit time, updating the calculation formula of the economic evaluation index into:
7. the method of claim 6, wherein the using of the economic evaluation index and the clean evaluation index together to form the evaluation merit value comprises:
carrying out weighted summation on the economic evaluation index and the clean evaluation index to obtain a merit evaluation value, wherein the calculation formula of the merit evaluation value is as follows:
in the formula (I), the compound is shown in the specification,
characterized by a merit value at time t,
，
，
，
、
are respectively economic evaluation indexes
And cleanliness evaluation index
The weight coefficient of (2).
8. The method of claim 7, wherein calculating the distribution value of the contribution degree obtained at a future time by a distribution model comprises:
at a future time t +1, the distributed power supply will be powered on
Of the output power
Carrying out value search from the optimal power value to the lowest power value to obtain the maximum evaluation value, and corresponding to the maximum evaluation value
Formula for calculating degree of contribution
Obtaining distributed power supply through medium computation
Assigned value of contribution degree
The assigned value of the contribution degree
As a reference for the adjustment, among others,
characterized by an assigned value of the output power of the ith distributed power source at a future time instant t +1,
characterized by the rated power of the jth load at a future time instant t +1,
a distribution value characterized as a contribution degree to the ith distributed power source by the distribution model at a future time t + 1;
and solving by adopting a genetic algorithm.
9. The method according to claim 8, wherein dynamically adjusting the distributed power source until the predicted value of the contribution obtained by the neural network at a future time is consistent with a regulation reference comprises:
according to the predicted value of the contribution degree of the distributed power supply at the future time t +1
And calculating the predicted value of the output power of the distributed power supply at the future time t +1 by using a calculation formula of the contribution degree
；
Predicting value of output power of distributed power supply at future time t +1
Adjusting the power supply parameter until the predicted value
Distribution value of output power of distributed power supply
And equality is carried out so as to enable the predicted value of the contribution degree to be consistent with the regulation reference.
10. An optimization system of the optimization method of the decentralized prediction power grid according to any one of claims 1 to 9, characterized by comprising:
the data acquisition module is used for monitoring power supply parameters provided by each distributed power supply in the power distribution network for the load of the power distribution network and power consumption parameters of the load of the power distribution network in real time;
the data processing module is used for calculating the contribution degree of the distributed power supply based on the power supply parameters, predicting the contribution degree of the distributed power supply at a future moment, and constructing a distribution model for the contribution degree of the distributed power supply in the power distribution network;
and the optimization and regulation module is used for taking the distribution value of the contribution degree obtained by the distribution model at a future moment as a regulation reference and dynamically regulating the distributed power supply until the predicted value of the contribution degree obtained by the neural network at the future moment is consistent with the regulation reference.
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