CN117236530B - Power energy optimization method based on 4G/5G short sharing power wireless communication - Google Patents
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Abstract
The application relates to the technical field of power optimization configuration, in particular to a power consumption optimization method based on 4G/5G short shared power wireless communication, which comprises the following steps: predicting a predicted load sequence of the next energy utilization period according to the historical load sequence of the target user; calculating the electricity consumption dependency and the congestion degree of each energy consumption moment; initializing an operation state vector of an energy storage battery in a target user; constructing an objective function based on the running state vector, the predicted load sequence, and the electricity consumption dependency degree and the congestion degree of each energy consumption moment; and solving the objective function, and controlling the running state of the energy storage battery in the objective user according to the running state vector corresponding to the minimum value of the objective function. According to the technical scheme, the operation state of the energy storage battery can be accurately controlled, the stability of the power system is improved while the electricity cost is reduced, and the power consumption can be optimized.
Description
Technical Field
The present disclosure relates generally to the field of power optimization configuration, and in particular, to a power consumption optimization method based on 4G/5G short-sharing power wireless communication.
Background
Along with the continuous development of artificial intelligence technology, more and more users enter an intelligent home stage, so that the electricity consumption demand of a demand side in a power system is increased, in order to meet the self electricity consumption of the users, the users can deploy energy storage batteries in own families, schools or factories, and can supply power to electric equipment when the power system cannot meet the self demand. Meanwhile, with the development of the intelligent power grid and the 4G/5G wireless communication technology, the electric power system can share electric power data in real time through the 4G/5G wireless communication technology, so that the intelligent power grid is energized, and the development of the intelligent power grid is greatly promoted.
Currently, a patent application document with publication number of CN116865282A discloses a power optimization scheduling method, and user privacy protection quantification is determined according to the power consumption of a flexible load and the power consumption of an energy storage battery; establishing an electric power optimization scheduling model taking the user privacy protection quantification as an element and the minimized electricity cost as an optimization target; and carrying out optimal solution on the power optimal scheduling model to obtain an optimal scheduling strategy of the flexible load and the energy storage battery, and carrying out optimal scheduling on the flexible load and the energy storage battery according to the optimal scheduling strategy.
However, the method adopts the energy storage battery to disguise the actual electricity consumption condition, so that the electricity consumption mode of the user becomes fuzzy, and the electricity consumption privacy information of the user is actively protected; but neglecting the influence of the running state of the energy storage battery on the electricity cost and the stability of the power system, the stability of the power system can not be improved while the electricity cost is reduced, and the power consumption can not be optimized accurately.
Disclosure of Invention
In order to solve the technical problems in the prior art, the application provides an electric power energy consumption optimization method based on 4G/5G short shared electric power wireless communication, which can realize accurate control on the running state of an energy storage battery and improve the stability of an electric power system while reducing the electricity consumption cost.
The invention provides a power consumption optimization method based on 4G/5G short shared power wireless communication, which is used for controlling the running state of an energy storage battery in a target user and comprises the following steps: predicting a predicted load sequence of a next energy utilization period according to a historical load sequence of a target user, wherein the predicted load sequence comprises a predicted load of each energy utilization moment of the target user in the next energy utilization period, and the predicted load comprises the total power of all electric equipment except an energy storage battery; calculating the electricity consumption dependency degree of each energy consumption moment based on the historical load sequences of the target users, receiving the historical load sequences of other users through 4G/5G wireless communication, and calculating the congestion degree of each energy consumption moment based on the historical load sequences of other users and the target users; initializing an operation state vector of an energy storage battery in the target user, wherein the operation state vector comprises an operation state of the energy storage battery at each energy utilization time in a next energy utilization period, the operation state is charging power or power supply power of the energy storage battery, the charging power is not less than 0, and the power supply power is not more than 0; constructing an objective function based on the running state vector, the predicted load sequence, and the electricity consumption dependency and the congestion degree of each energy consumption moment, wherein the objective function satisfies the relation:
wherein,for predicting the energy consumption moment in the load sequence>Predicted load of +.>For the energy utilization time in the operating state vector +.>Is->For the number of energy consumption moments in one energy consumption period,/->For energy use time->Is->And->Energy utilization time ∈>Electricity consumption dependency and congestion level, +.>Representing the moment of computing energy +.>Time of energy consumption->Between which are locatedVariance of->Representing the moment of computing energy +.>Time of energy consumption->Between (I)>Variance of->As a sign function +.>The value of the objective function is taken; and solving the objective function by utilizing an optimizing algorithm, taking a corresponding running state vector when the value of the objective function reaches the minimum value as a target running state, and controlling the running state of the energy storage battery in the target user in the next energy utilization period based on the target running state.
In some embodiments, the historical load sequence of the target user includes an average load value for each energy usage time of the target user over the current energy usage period and the historical energy usage period.
In some embodiments, the predicting the predicted load sequence for the next energy usage period based on the historical load sequence for the target user comprises: and taking the historical load sequence of the target user as a predicted load sequence of the next energy utilization period.
In some embodiments, the predicting the predicted load sequence for the next energy usage period based on the historical load sequence for the target user comprises: acquiring a current load sequence of a current energy utilization period, wherein the current load sequence comprises a load value of each energy utilization moment of the target user in the current energy utilization period; calculating a predicted load sequence of the next energy utilization period based on the historical load sequence and the current load sequence, wherein the energy utilization time in the predicted load sequenceThe predicted load of (2) satisfies the relation:
wherein,for the energy utilization time in the history load sequence +.>Average load value of>For the energy utilization time in the current load sequence +.>Load value of>For forgetting coefficient, < >>For the energy consumption moment in the predicted load sequence>Is used for predicting the load of the vehicle.
In some embodiments, calculating the power usage dependency for each power usage time based on the historical load sequence of the target user includes: calculating the average value of all average load values in the historical load sequence as a first demarcation value; subtracting the first demarcation value from the average load value of the energy utilization time to obtain a first deviation value of the energy utilization time; calculating the electricity consumption dependency of the energy consumption time based on the first deviation value, wherein the electricity consumption dependency of the energy consumption time satisfies the relation:
wherein,for energy use time->First deviation value of ∈d->For the maximum value of the first deviation values for all energy usage moments, and (2)>For energy use time->The electricity consumption dependence of (2); the value range of the electricity utilization dependency is [ -1,1]。
In some embodiments, calculating the congestion level for each energy usage time based on the historical load sequences of other users and the target user includes: calculating the historical load sequence sum of the other users and the target user to obtain a fusion load sequence, wherein the fusion load sequence comprises fusion loads of each energy utilization moment in an energy utilization period; calculating the average value of all fusion loads in the fusion load sequence as a second boundary value; subtracting the second threshold from a fusion load of the energy utilization time to obtain a second deviation value of the energy utilization time, and calculating the congestion degree of the energy utilization time based on the second deviation value, wherein the congestion degree of the energy utilization time satisfies a relation:
wherein,for energy use time->Second deviation value of ∈d->For the maximum value of the second deviation values for all energy consumption moments, and (2)>For energy use time->The value range of the crowding degree is [ -1,1]。
In some embodiments, the energy usage time isElectric charge->The calculation method of (1) comprises the following steps: the energy utilization time in a plurality of energy utilization periods is +.>Average value of electric charge of (2) as energy utilization time +.>Electric charge->Wherein the plurality of energy usage periods includes a current energy usage period and a set number of energy usage periods prior to the current energy usage period.
According to the power consumption optimization method based on 4G/5G short shared power wireless communication, firstly, the predicted load of each energy consumption moment of a target user in the next energy consumption period is predicted; receiving historical load sequences of other users in real time through a 4G/5G wireless communication network, and calculating the crowding degree and electricity consumption dependence degree of each energy consumption moment according to the historical load sequences of the target users and the other users, wherein the electricity consumption dependence degree is used for reflecting the electricity consumption habit of the target users, and the crowding degree considers the electricity consumption habits of all users and is used for reflecting the peak period and the valley period of the load; further, an objective function is constructed based on the predicted load, the crowding degree of each energy utilization moment and the electricity utilization dependency, four aspects of minimizing load fluctuation, minimizing electricity utilization cost, minimizing running state fluctuation and maximizing running state rationality of the electric power system are used as optimization targets of the objective function, the running state of the energy storage battery in the next energy utilization period in a target user is controlled according to the running state vector corresponding to the minimum value of the objective function, the running state of the energy storage battery is accurately controlled, the electricity utilization cost is reduced, meanwhile, the stability of the electric power system is improved, and the energy utilization optimization of electric power is realized.
Further, in the objective function, the load fluctuation of the power system is minimized, so that the harmonic wave generated in the power system by the load fluctuation is avoided, and the stability of the power system is ensured; the electricity cost is minimized, and the cost of the electricity energy is ensured to be minimum; the fluctuation of the running state is minimized, so that the damage to the energy storage battery caused by the severe fluctuation of the running state is avoided; the rationality of the running state can effectively control the charging and power supply states of the energy storage battery.
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The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present application will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. Several embodiments of the present application are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar or corresponding parts and in which:
fig. 1 is a flow chart of a power consumption optimization method based on 4G/5G short shared power wireless communication according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
It should be understood that when the terms "first," "second," and the like are used in the claims, specification, and drawings of this application, they are used merely for distinguishing between different objects and not for describing a particular sequential order. The terms "comprises" and "comprising," when used in the specification and claims of this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The utility model provides a power consumption optimization method based on 4G/5G short shared power wireless communication, which is used for controlling the running state of an energy storage battery in a target user, determining the running state of each energy consumption moment of the energy storage battery in the next power consumption period, and further realizing the accurate control of the energy storage battery. In an application scenario, a power supply area includes a plurality of users, each user corresponds to an intelligent ammeter, and the intelligent ammeter can be used for controlling the running state of an energy storage battery in the user; meanwhile, the intelligent electric meters of a plurality of users can realize short-distance real-time sharing of electric power data in a power supply area through 4G/5G wireless communication, and a data base is provided for optimizing the electric power consumption; the user may be a home, a school, an enterprise, or a factory, which is not limited in this application. The 4G/5G wireless communication supports both 4G wireless communication and 5G wireless communication.
Referring to fig. 1, a flowchart of a power consumption optimization method based on 4G/5G short-sharing power wireless communication according to an embodiment of the present application is shown. The order of the steps in the flow diagrams may be changed, and some steps may be omitted, according to different needs.
S11, predicting a predicted load sequence of the next energy utilization period according to a historical load sequence of a target user, wherein the predicted load sequence comprises a predicted load of each energy utilization moment of the target user in the next energy utilization period, and the predicted load comprises the total power of all electric equipment except an energy storage battery.
In one embodiment, the target user is any one of the power supply areas, and the historical load sequence of the target user includes the current energy utilization period and an average load value of each energy utilization time in the historical energy utilization period, wherein the average load value is an average value of total power of all electric equipment except the energy storage battery.
In an alternative embodiment, the historical load sequence of the target user may be taken as the predicted load sequence for the next energy usage period.
In another alternative embodiment, the predicting the predicted load sequence of the next energy usage period based on the historical load sequence of the target user includes: acquiring a current load sequence of a current energy utilization period, wherein the current load sequence comprises a load value of each energy utilization moment of the target user in the current energy utilization period; calculating a predicted load sequence of the next energy utilization period based on the historical load sequence and the current load sequence, wherein the energy utilization time in the predicted load sequenceThe predicted load of (2) satisfies the relation:
wherein,for the energy utilization time in the history load sequence +.>Average load value of>For the energy utilization time in the current load sequence +.>Load value of>For forgetting coefficient, < >>For the energy consumption moment in the predicted load sequence>Is used for predicting the load of the vehicle. Wherein the value range of the forgetting coefficient is 0-1, and in the embodiment of the application, the forgetting coefficient is +.>。
It is understood that the predicted load is a predicted value of the total power of all consumers except the energy storage battery.
Illustratively, if the energy usage period is one day and the energy usage time is recorded as one energy usage time per hour, the predicted load sequence, the current load sequence and the predicted load sequence each include 24 values.
Thus, the predicted load of each energy utilization time of the target user in the next energy utilization period is obtained.
And S12, calculating the electricity consumption dependence of each energy consumption moment based on the historical load sequences of the target users, receiving the historical load sequences of other users through 4G/5G wireless communication, and calculating the congestion degree of each energy consumption moment based on the historical load sequences of other users and the target users.
In one embodiment, the historical load sequence of the target user can reflect the energy usage habit of the target user, for example, if the energy usage moment in the historical load sequenceThe average load value of (2) is the maximum, the target user is at the energy utilization time +>Maximum power consumption of (a) target user at power consumption time +.>The power consumption dependence of (2) is the largest.
Specifically, calculating the electricity consumption dependency of each energy consumption moment based on the historical load sequence of the target user includes: calculating the average value of all average load values in the historical load sequence as a first demarcation value; subtracting the first demarcation value from the average load value of the energy utilization time to obtain a first deviation value of the energy utilization time; calculating the electricity consumption dependency of the energy consumption time based on the first deviation value, wherein the electricity consumption dependency of the energy consumption time satisfies the relation:
wherein,for energy use time->First deviation value of ∈d->For the maximum value of the first deviation values for all energy usage moments, and (2)>For energy use time->The electricity consumption dependence of (2); the value range of the electricity utilization dependency is [ -1,1]。
In one embodiment, one power supply area includes a plurality of users, and the power consumption habits of different users are different, so that the congestion degree of each power consumption moment in one power consumption period can be calculated based on the historical load sequences of other users and target users.
When calculating the congestion degree of each energy utilization moment, the short-distance real-time sharing of the historical load sequences of other users in the power supply area is needed to be realized through 4G/5G wireless communication, namely, the historical load sequences of other users can be received at the target user.
Specifically, calculating the degree of congestion at each energy usage time based on the historical load sequences of other users and target users includes: calculating the historical load sequence sum of the other users and the target user to obtain a fusion load sequence, wherein the fusion load sequence comprises fusion loads of each energy utilization moment in an energy utilization period; calculating the average value of all fusion loads in the fusion load sequence as a second boundary value; subtracting the second threshold from a fusion load of the energy utilization time to obtain a second deviation value of the energy utilization time, and calculating the congestion degree of the energy utilization time based on the second deviation value, wherein the congestion degree of the energy utilization time satisfies a relation:
wherein,for energy use time->Second deviation value of ∈d->For the maximum value of the second deviation values for all energy consumption moments, and (2)>For energy use time->The value range of the crowding degree is as follows[-1,1]. It can be understood that the greater the congestion level of the energy use time, the greater the probability that the energy use time belongs to the electricity use peak period.
Thus, the accurate quantification of the crowding degree and the electricity consumption dependency degree of each energy consumption moment is realized, wherein the electricity consumption dependency degree is used for reflecting the electricity consumption habit of a target user; the congestion degree considers the electricity utilization habits of all users and is used for reflecting the peak period and the valley period of the load.
S13, initializing an operation state vector of the energy storage battery in the target user, wherein the operation state vector comprises an operation state of each energy utilization time of the energy storage battery in a next energy utilization period, the operation state is charging power or power supply power of the energy storage battery, the charging power is not less than 0, and the power supply power is not more than 0.
In one embodiment, the operation state vector of the energy storage battery in the target user includes an operation state of each energy utilization time of the energy storage battery in a next energy utilization period, and the operation state is charging power or power supply power of the energy storage battery. And randomly generating the running state of each energy utilization time in the running state vector, and finishing the initialization of the running state vector of the energy storage battery in the target user.
When the energy storage battery is in a charging state, the running state of the energy storage battery is the charging power which is not less than 0; when the energy storage battery is in a power supply state, the running state of the energy storage battery is the power supply power which is not more than 0; when the charging power or the supplying power is equal to 0, the energy storage battery is in a non-working state.
It will be appreciated that in the operational state vector, when operational stateAbove 0, it means that the energy storage battery is at power +.>Charging; when operating state->When smaller than 0, the energy storage battery is at power +.>Supplying power to other electric equipment; when operating state->And when the energy storage battery is equal to 0, the energy storage battery is in a non-working state.
S14, constructing an objective function based on the running state vector, the predicted load sequence, and the electricity consumption dependency degree and the congestion degree of each energy consumption moment.
In one embodiment, after the initialization of the operation state vector of the energy storage battery in the target user is completed, four aspects of power system load fluctuation minimization, electricity cost minimization, operation state fluctuation minimization and operation state rationality maximization are taken as targets to construct a target function.
Specifically, the objective function satisfies the relation:
wherein,for predicting the energy consumption moment in the load sequence>Predicted load of +.>For the energy utilization time in the operating state vector +.>Is->For the number of energy consumption moments in one energy consumption period,/->For energy use time->Is->And->Energy utilization time ∈>Electricity consumption dependency and congestion level, +.>Representing the moment of computing energy +.>Time of energy consumption->Between which are locatedVariance of->Representing the moment of computing energy +.>Time of energy consumption->Between (I)>Variance of->As a sign function +.>Take the value of the objective function.
Wherein, inIn (I)>For the target user the energy consumption time in the next energy consumption cycle +.>Predicted load of +.>The energy storage battery for the target user is charged in the next charging cycle at the moment +.>Is a running state of (2); when the energy storage battery is in a charged state (+)>) When the energy storage power supply obtains electric energy from the electric power system, the energy utilization time is increased>Predicted load of +.>Can be regarded as the energy utilization time of the target user in the next energy utilization period>Is a power system load of (1); when the energy storage battery is in the power supply state (+)>) When the energy storage power supply supplies electric energy for other electric equipment, the energy consumption moment is reduced>Predicted load of +.>Can be regarded as the energy utilization time of the target user in the next energy utilization period>Is of the power of (2)System load; thus, the energy consumption time is->Time of energy consumption->Between (I)>Variance of->Can be used to reflect the power system load fluctuations, the smaller the value of which is indicative of the smaller the power system load fluctuations.
At the position ofIn (I)>Energy consumption time for the next energy consumption period>The power obtained by the target user from the power system is the load of the power system; />Energy consumption time for the next energy consumption period>Is used for controlling the electric charge of the electric car,can be used for reflecting the electricity consumption cost, and the smaller the value is, the smaller the electricity consumption cost is.
Wherein, the energy utilization time isElectric charge->The calculation method of (1) comprises the following steps: the energy utilization time in a plurality of energy utilization periods is +.>Average value of electric charge of (2) as energy utilization time +.>Electric charge->. Wherein the plurality of energy usage periods includes a current energy usage period and a set number of energy usage periods preceding the current energy usage period. The value of the set number is 10.
At the position ofIn (I)>Energy consumption time for the operating state vector of the energy storage battery>And energy use time->The change of the running state between the two is used for reflecting the energy utilization time +.>Is subject to fluctuation of the operation state of the device; then the variance isThe fluctuation of the running state in the next energy utilization period can be reflected, and the smaller the value is, the smaller the running state fluctuation is.
At the position ofIn (I)>For energy use time->The larger the average value of the crowding degree and the electricity consumption dependency is, the larger the numerical value is, which indicates the energy consumptionTime->The greater the degree of congestion and the greater the power that the target user needs to consume, in order to meet the electricity demand of the target user, when +.>The larger the energy storage power supply should be in the power supply state, in other words, the more the energy storage power supply is in the power supply state +.>The closer to the maximum value 1, the greater the running state rationality; when the energy storage power supply is in a charging state, the energy storage power supply is in a charging state>The closer to the minimum value-1, the greater the operating state rationality. When the energy storage power supply is in a charging state, the energy storage power supply is in a charging state>When the energy storage power supply is in a power supply state, the energy storage power supply is in a power supply state>The method comprises the steps of carrying out a first treatment on the surface of the Therefore(s)>The method can be used for reflecting the rationality of the running state, and the smaller the value is, the larger the rationality of the running state is indicated.
Wherein,as a sign function, satisfy the relation:
thus, the construction of the objective function is completed, and the four aspects of minimizing the load fluctuation of the power system, minimizing the electricity cost, minimizing the fluctuation of the running state and maximizing the rationality of the running state are constrained, so that the load fluctuation of the power system is minimized, the harmonic wave generated by the load fluctuation in the power system is avoided, and the stability of the power system is ensured; the electricity cost is minimized, and the cost of the electricity energy is ensured to be minimum; the fluctuation of the running state is minimized, so that the damage to the energy storage battery caused by the severe fluctuation of the running state is avoided; the rationality of the running state can effectively control the charging and power supply states of the energy storage battery.
And S15, solving the objective function by utilizing an optimizing algorithm, taking a corresponding running state vector when the value of the objective function reaches the minimum value as a target running state, and controlling the running state of the energy storage battery in the target user in the next energy utilization period based on the target running state.
In one embodiment, the running state vector is continuously updated by using an optimizing algorithm, so that the value of the objective function is changed; when the value of the objective function reaches the minimum value, the running state vector at the moment is used as a target running state, and the running state of the energy storage battery in the target user in the next energy utilization period is controlled based on the target running state, so that the power energy utilization optimization is realized.
Wherein the optimizing algorithm is any existing optimizing algorithm such as a particle swarm algorithm, a gradient descent method or a genetic algorithm.
According to the power consumption optimization method based on 4G/5G short shared power wireless communication, firstly, the predicted load of each energy consumption moment of a target user in the next energy consumption period is predicted; receiving historical load sequences of other users in real time through a 4G/5G wireless communication network, and calculating the crowding degree and electricity consumption dependence degree of each energy consumption moment according to the historical load sequences of the target users and the other users, wherein the electricity consumption dependence degree is used for reflecting the electricity consumption habit of the target users, and the crowding degree considers the electricity consumption habits of all users and is used for reflecting the peak period and the valley period of the load; further, an objective function is constructed based on the predicted load, the crowding degree of each energy utilization moment and the electricity utilization dependency, four aspects of minimizing load fluctuation, minimizing electricity utilization cost, minimizing running state fluctuation and maximizing running state rationality of the electric power system are used as optimization targets of the objective function, the running state of the energy storage battery in the next energy utilization period in a target user is controlled according to the running state vector corresponding to the minimum value of the objective function, the running state of the energy storage battery is accurately controlled, the electricity utilization cost is reduced, meanwhile, the stability of the electric power system is improved, and the energy utilization optimization of electric power is realized.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the claims. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (4)
1. The power consumption optimizing method based on 4G/5G short shared power wireless communication is characterized by comprising the following steps of:
predicting a predicted load sequence of a next energy utilization period according to a historical load sequence of a target user, wherein the predicted load sequence comprises a predicted load of each energy utilization moment of the target user in the next energy utilization period, and the predicted load comprises the total power of all electric equipment except an energy storage battery;
calculating the electricity consumption dependence of each energy utilization moment based on the historical load sequences of the target users, receiving the historical load sequences of other users through 4G/5G wireless communication, and calculating the congestion degree of each energy utilization moment based on the historical load sequences of other users and the target users, wherein the electricity consumption dependence is used for reflecting the electricity consumption habit of the target users; the crowding degree considers the electricity utilization habit of all users and is used for reflecting the peak period and the valley period of the load;
the historical load sequence of the target user comprises an average load value of each energy utilization time of the target user in the current energy utilization period and the historical energy utilization period;
calculating the electricity consumption dependency of each energy consumption moment based on the historical load sequence of the target user comprises:
calculating the average value of all average load values in the historical load sequence as a first demarcation value; subtracting the first demarcation value from the average load value of the energy utilization time to obtain a first deviation value of the energy utilization time; calculating the electricity consumption dependency of the energy consumption time based on the first deviation value, wherein the electricity consumption dependency of the energy consumption time satisfies the relation:
wherein,for energy use time->First deviation value of ∈d->For the maximum value of the first deviation values for all energy usage moments, and (2)>For energy use time->The electricity consumption dependence of (2); the value range of the electricity utilization dependency is [ -1,1]；
Calculating the degree of congestion at each energy usage time based on the historical load sequences of other users and the target user includes:
calculating the historical load sequence sum of the other users and the target user to obtain a fusion load sequence, wherein the fusion load sequence comprises fusion loads of each energy utilization moment in an energy utilization period; calculating the average value of all fusion loads in the fusion load sequence as a second boundary value; subtracting the second threshold from a fusion load of the energy utilization time to obtain a second deviation value of the energy utilization time, and calculating the congestion degree of the energy utilization time based on the second deviation value, wherein the congestion degree of the energy utilization time satisfies a relation:
wherein,for energy use time->Second deviation value of ∈d->For the maximum value of the second deviation values for all energy consumption moments, and (2)>For energy use time->The value range of the crowding degree is [ -1,1]；
Initializing an operation state vector of an energy storage battery in the target user, wherein the operation state vector comprises an operation state of the energy storage battery at each energy utilization time in a next energy utilization period, the operation state is charging power or power supply power of the energy storage battery, the charging power is not less than 0, and the power supply power is not more than 0;
constructing an objective function based on the running state vector, the predicted load sequence, and the electricity consumption dependency and the congestion degree of each energy consumption moment, wherein the objective function satisfies the relation:
wherein,for predicting the energy consumption moment in the load sequence>Predicted load of +.>For the energy utilization time in the operating state vector +.>Is->For the number of energy consumption moments in one energy consumption period,/->For energy use time->Is->And->Energy utilization time ∈>Electricity consumption dependency and congestion level, +.>Representing the moment of computing energy +.>At the time of energy utilization/>Between which are locatedVariance of->Representing the moment of computing energy +.>Time of energy consumption->Between (I)>Variance of->As a sign function +.>The value of the objective function is taken;
and solving the objective function by utilizing an optimizing algorithm, taking a corresponding running state vector when the value of the objective function reaches the minimum value as a target running state, and controlling the running state of the energy storage battery in the target user in the next energy utilization period based on the target running state.
2. The power consumption optimization method based on 4G/5G short-sharing power wireless communication according to claim 1, wherein predicting the predicted load sequence of the next consumption cycle according to the historical load sequence of the target user comprises:
and taking the historical load sequence of the target user as a predicted load sequence of the next energy utilization period.
3. The power consumption optimization method based on 4G/5G short-sharing power wireless communication according to claim 1, wherein predicting the predicted load sequence of the next consumption cycle according to the historical load sequence of the target user comprises:
acquiring a current load sequence of a current energy utilization period, wherein the current load sequence comprises a load value of each energy utilization moment of the target user in the current energy utilization period;
calculating a predicted load sequence of the next energy utilization period based on the historical load sequence and the current load sequence, wherein the energy utilization time in the predicted load sequenceThe predicted load of (2) satisfies the relation:
wherein,for the energy utilization time in the history load sequence +.>Average load value of>For the energy utilization time in the current load sequence +.>Load value of>For forgetting coefficient, < >>For the energy consumption moment in the predicted load sequence>Is used for predicting the load of the vehicle.
4. The power consumption optimization method based on 4G/5G short shared power wireless communication according to claim 1, wherein the power consumption time isElectric charge->The calculation method of (1) comprises the following steps:
the energy utilization time in a plurality of energy utilization periods is setAverage value of electric charge of (2) as energy utilization time +.>Electric charge->Wherein the plurality of energy usage periods includes a current energy usage period and a set number of energy usage periods prior to the current energy usage period.
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