CN109285016A - Distributed photovoltaic power generation pricing method - Google Patents
Distributed photovoltaic power generation pricing method Download PDFInfo
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- CN109285016A CN109285016A CN201710601186.XA CN201710601186A CN109285016A CN 109285016 A CN109285016 A CN 109285016A CN 201710601186 A CN201710601186 A CN 201710601186A CN 109285016 A CN109285016 A CN 109285016A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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Abstract
The invention discloses a distributed photovoltaic power generation pricing method, which comprises the following steps: inputting the total electricity cost a; acquiring geographic information, and calculating the sunrise time Ta and the sunset time Tb of the next day; calculating a power load function by using a neural network algorithm; the neural network used by the neural network algorithm is a feedback type neural network with 3 layers and 10 neurons in each layer; obtaining a check table of the electric load and the pricing at a preset time T1; obtaining pricing P2 according to the checking table at a preset time T2 and outputting the pricing; according to the technical scheme, the electricity utilization behavior of the user is influenced through a real-time pricing mechanism, so that the electricity energy configuration is more reasonable to use, and the waste of clean energy is reduced.
Description
Technical field
The present invention relates to power fields, and in particular, to a kind of distributed photovoltaic power generation pricing method.
Background technique
Currently, electricity charge pricing mechanism is generally according to the flat regular price of peak valley.Although this mechanism is simple, can not send out
The effectiveness of user demand side elasticity is waved, can't be had with the variation of photovoltaic power generation quantity so as to cause user power utilization load too big
Fluctuation, causes the waste of clean energy resource to a certain extent.How the variation and user power utilization of pricing mechanism are reasonably utilized
The interaction of behavior, so that electric power energy configuration use is rationally, it is the major issue of industry concern.
Summary of the invention
For overcome the deficiencies in the prior art, the purpose of the present invention is to provide one kind can be real using user power utilization behavior
The pricing method of existing distributed photovoltaic power generation.
To solve the above problems, the technical solution adopted in the present invention is as follows:
A kind of distributed photovoltaic power generation pricing method, step include:
S1: input totality degree electricity cost a；
S2: obtaining geography information, calculates second day sunrise moment Ta and sunset moment Tb；
S3: power load function is calculated using neural network algorithm；The neural network that the neural network algorithm uses is 3
Layer, the feedback neural network of every layer of 10 neuron；
S4: the inspection table of power load and price is obtained in predetermined time T1；
S5: price P2 is obtained according to the inspection table in predetermined time T2 and exports the price；
S6: judging whether current time Tn is equal to sunset moment Tb, if so, returning to S2；If it is not, returning to S4.
For by Real-Time Pricing mechanism influence user electricity consumption behavior so that electric power energy configuration use more close
Reason, reduces the waste of clean energy resource, and inventor realizes this technical purpose by the scheme of the real-time change mechanism of formulation electricity price.
Since photovoltaic power generation is related to solar energy, the present invention was preferably selected within the two moment of sunrise to sunset, carried out electricity price
Real-time change regulation.Then, power load function calculated by neural network algorithm respectively, obtain power load at the T1 moment
With the inspection table of price, and the T2 moment according to the inspection table obtain fix a price and the price is exported.Method is also
Including a judgment step, i.e., judge whether current time is the sunset moment after output price, if it is not, then repeating to determine
Valence calculates step, if so, returning to the sunrise of acquisition second day, sunset moment, waits price calculating and output in second day.
Wherein, the photovoltaic project totality degree electricity cost a refers to the cost of manufacture electric power.
Preferably, the geodata inputted in the S2 includes local longitude, latitude and elevation data.
It should be noted that in the technical scheme, sunrise moment Ta and sunset moment Tb determine what price calculated in real time
Beginning and end.Therefore it needs in advance to calculate sunrise moment and sunset moment.And it is used in the technical program and passes through ground
Information is managed to calculate the mode at sunrise moment and sunset moment.As more specific preferred embodiment, the geography information includes working as
Longitude, latitude and the elevation data on ground.
Preferably, the S3 the following steps are included:
S31: obtaining data, including the first period set of prices X1, the second period set of prices X2, time interval Time, corresponding
Temperature Temperature and corresponding load Load；
S32: using X1, X2, Time and Temperature as neural network input layer, it is input to hidden layer neuron；
S33: Load is used as to output layer inspection result in array, and result is reversely inputted back hidden layer, is learned again
It practises；
S34: after the input of all initial data, X1, X2, Time, Temperature is formed by computer self learning and made
For the corresponding Load function of independent variable, i.e. Load=f (X1, X2, Time, Temperature).
It should be noted that the effect for obtaining Load function is to form one certainly by neural network computing self-teaching
Variable is the first period price, the second period price, moment, temperature, and dependent variable is the functional form of load, is facilitated below to defeated
The calculating of price out.
Wherein, the first period set of prices X1 refers to the set of the first period price.The set of first period price
Refer to the set for the first period price that historical data is stored.When the first period price is referred to as input is calculated
Price when quarter；The second period set of prices X2 refers to the set of the second period price.The set of second period price refers to
Be the second period price that historical data is stored set.The second period price is referred to as calculating output time
When price.Specifically, the price such as when moment Tm is Pm, then is Pn in the price that the Tn moment exports after operation, then
Pm belongs to the first period price, and Pn belongs to the second period price；And in input value of the Pn at Tn moment as operation next time,
Then in operation next time, Pn belongs to the first period price；In the second period of the Pl that the Tl moment calculates second of operation thus
Price, and so on.So, the set for the first period price being used as in these all operations is X1, is made in all operations
For the set of the first period price be X2.
Time interval Time refers to the time interval between price operation twice.
Corresponding temperature Temperature refers to environment temperature when electricity consumption, the i.e. outdoor temperature in the place.
Preferably, the S4 the following steps are included:
S41: acquiring data, including current time electricity price P1, T2 when reaching T1, and temperature Temperature spends electric cost
Electricity price a, T2 moment bulk power grid power purchase price b；
S42: setting certain step value for price P2, and section is [a, b], will own (P1, P2, T2,
Temperature the Load function for) substituting into S3, is calculated and draws the relationship inspection table of P2 and Load.
It is further preferred that the price P2 step-length is set as 0.001 yuan.
It should be noted that the effect for calculating and obtaining the relationship inspection table of P2 and Load is to lead in subsequent step
Load value is crossed to find corresponding P2 value as Spot Price and exported.
It should be noted that current time price P1 refers to the power supply price at current time.When initial, currently
Moment price refers to the price that power supply bureau initially provides；And after first time operation, present price refers to previous moment
Present price of the price exported after calculating as second of operation.
It should be noted that the present invention considers the discontinuous variation of price, therefore minimum unit step-length is set as 0.001
Member so needs to calculate number no more than 1000 in total, so that method is actually more quickly accurate.
Preferably, the S5 the following steps are included:
S51: it is acquired when reaching T2 and determines currently practical generated output P；
S52: it is poor that Load all in inspection table are made with currently practical generated output P respectively, and it is the smallest to select absolute value
Load value, and corresponding P2 is found by inspection table.
Preferably, the T2=T1+5min.
Preferably, the initial value of T1 is=Ta-5min.
It should be noted that being spaced five minutes time to being checked from calculating inspection table, advantageously ensure that
Operational capability.
Ta-5min, i.e. first five minute at sunrise moment are set by T1, it is ensured that the sunrise moment can calculate input electricity price
P2。
Compared with prior art, the beneficial effects of the present invention are:
1, distributed photovoltaic power generation pricing method of the invention, the real-time change mechanism by formulating electricity price influence user's
Electricity consumption behavior so that electric power energy configuration using more reasonable, reduce the waste of clean energy resource.
2, distributed photovoltaic power generation pricing method of the invention, is calculated using neural network learning, so that result is more smart
Really.
The above description is only an overview of the technical scheme of the present invention, in order to better understand the technical means of the present invention,
And it can be implemented in accordance with the contents of the specification, and in order to allow above and other objects, features and advantages of the invention can
It is clearer and more comprehensible, special below to lift preferred embodiment, detailed description are as follows.
Specific embodiment
For further illustrate the present invention to reach the technical means and efficacy that predetermined goal of the invention is taken, below to according to
According to a specific embodiment of the invention, structure, feature and its effect, detailed description are as follows:
A kind of distributed photovoltaic power generation pricing method, step include:
S1: input totality degree electricity cost a；
S2: obtaining geography information, calculates second day sunrise moment Ta and sunset moment Tb；
S3: power load function is calculated using neural network algorithm；The neural network that the neural network algorithm uses is 3
Layer, the feedback neural network of every layer of 10 neuron；
S4: the inspection table of power load and price is obtained in predetermined time T1；
S5: price P2 is obtained according to the inspection table in predetermined time T2 and exports the price；
S6: judging whether current time Tn is equal to sunset moment Tb, if so, returning to S2；If it is not, returning to S4.
As one of specific embodiment, the geodata inputted in the S2 includes local longitude, latitude
And elevation data.
In the present embodiment, the S3 the following steps are included:
S31: obtaining data, including the first period set of prices X1, the second period set of prices X2, time interval Time, corresponding
Temperature Temperature and corresponding load Load；
S32: using X1, X2, Time and Temperature as neural network input layer, it is input to hidden layer neuron；
S33: Load is used as to output layer inspection result in array, and result is reversely inputted back hidden layer, is learned again
It practises；
S34: after the input of all initial data, X1, X2, Time, Temperature is formed by computer self learning and made
For the corresponding Load function of independent variable, i.e. Load=f (X1, X2, Time, Temperature).
In the present embodiment, the S4 the following steps are included:
S41: acquiring data, including current time electricity price P1, T2 when reaching T1, and temperature Temperature spends electric cost
Electricity price a, T2 moment bulk power grid power purchase price b；
S42: setting certain step value for price P2, and section is [a, b], will own (P1, P2, T2,
Temperature the Load function for) substituting into S3, is calculated and draws the relationship inspection table of P2 and Load.
As one of preferred embodiment, the price P2 step-length is set as 0.001 yuan.
In the present embodiment, the S5 the following steps are included:
S51: it is acquired when reaching T2 and determines currently practical generated output P；
S52: it is poor that Load all in inspection table are made with currently practical generated output P respectively, and it is the smallest to select absolute value
Load value, and corresponding P2 is found by inspection table.
Specifically, it is assumed that a=0.05, b=0.055, step-length are 0.001 yuan, obtained table are as follows:
| P2 | 0.051 | 0.052 | 0.053 | 0.054 | 0.055 |
| Load | 200 | 300 | 400 | 500 | 600 |
Assuming that current generated output P=210, then it makees with Load poor respectively, obtains the minimum Load=of absolute value
200, corresponding P2 is 0.051, then exporting P2 is 0.051.
As one of preferred embodiment, to guarantee operational capability, the T2=T1+5min.
In the case, the initial value of T1 is=Ta-5min, i.e. first five minute at sunrise moment, it is ensured that the sunrise moment
Input electricity price P2 can be calculated.
The above embodiment is only the preferred embodiment of the present invention, and the scope of protection of the present invention is not limited thereto,
The variation and replacement for any unsubstantiality that those skilled in the art is done on the basis of the present invention belong to institute of the present invention
Claimed range.
Claims (8)
1. a kind of distributed photovoltaic power generation pricing method, which is characterized in that its step includes:
S1: input totality degree electricity cost a；
S2: obtaining geography information, calculates second day sunrise moment Ta and sunset moment Tb；
S3: power load function is calculated using neural network algorithm；The neural network that the neural network algorithm uses is 3 layers,
The feedback neural network of every layer of 10 neuron；
S4: the inspection table of power load and price is obtained in predetermined time T1；
S5: price P2 is obtained according to the inspection table in predetermined time T2 and exports the price；
S6: judging whether current time Tn is equal to sunset moment Tb, if so, returning to S2；If it is not, returning to S4.
2. distributed photovoltaic power generation pricing method as described in claim 1, which is characterized in that the geographical number inputted in the S2
According to longitude, latitude and the elevation data for including locality.
3. distributed photovoltaic power generation pricing method as described in claim 1, which is characterized in that the S3 the following steps are included:
S31: data, including the first period set of prices X1, the second period set of prices X2, time interval Time, corresponding temperature are obtained
Spend Temperature and corresponding load Load；
S32: using X1, X2, Time and Temperature as neural network input layer, it is input to hidden layer neuron；
S33: Load is used as to output layer inspection result in array, and result is reversely inputted back hidden layer, is relearned；
S34: after the input of all initial data, X1, X2, Time, Temperature is formed by computer self learning and are used as from change
Measure corresponding Load function, i.e. Load=f (X1, X2, Time, Temperature).
4. distributed photovoltaic power generation pricing method as claimed in claim 3, which is characterized in that the S4 the following steps are included:
S41: acquiring data, including current time electricity price P1, T2 when reaching T1, and temperature Temperature spends electric cost electricity price
A, T2 moment bulk power grid power purchase price b；
S42: setting certain step value for price P2, and section is [a, b], and all (P1, P2, T2, Temperature) is substituted into
The Load function of S3 is calculated and draws the relationship inspection table of P2 and Load.
5. distributed photovoltaic power generation pricing method as claimed in claim 4, which is characterized in that the price P2 step-length is set as
0.001 yuan.
6. distributed photovoltaic power generation pricing method as claimed in claim 4, which is characterized in that the S5 the following steps are included:
S51: it is acquired when reaching T2 and determines currently practical generated output P；
S52: it is poor that Load all in inspection table are made with currently practical generated output P respectively, selects the smallest Load value of absolute value,
And corresponding P2 is found by inspection table.
7. such as distributed photovoltaic power generation pricing method as claimed in any one of claims 1 to 6, which is characterized in that the T2=T1+
5min。
8. distributed photovoltaic power generation pricing method as claimed in claim 7, which is characterized in that the initial value of T1 is=Ta-
5min。
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113570267A (en) * | 2021-08-02 | 2021-10-29 | 福州万山电力咨询有限公司 | Method and terminal for determining spontaneous self-use proportion of distributed photovoltaic power generation |
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