CN107085774A - A kind of sale of electricity service platform method of evaluating performance and device - Google Patents
A kind of sale of electricity service platform method of evaluating performance and device Download PDFInfo
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Abstract
The invention discloses a kind of sale of electricity service platform method of evaluating performance and device, this method is according to the actual power consumption of each user in each examination cycle in default predetermined period and prediction power consumption, it is determined that the load prediction deviation ratio of each user；According to the load prediction deviation ratio of acquisition, the prediction power consumption of each user in each examination cycle, the second prediction total electricity consumption in the first prediction total electricity consumption and default predetermined period in each examination cycle determines load prediction precision；The lowest price of electricity is bought from electricity market according to each minimum transaction cycle in default predetermined period and the price and the actual total electricity consumption of all users of the electricity of all users is sold to, determine the optimal total gross profit in market, total gross profit according to the optimal total gross profit in market and in default predetermined period, determines power purchase strategy lean degree；According to load prediction precision and power purchase strategy lean degree, it is determined that evaluating the coefficient of performance accordingly.
Description
Technical field
The present invention relates to technical field of electric power, more particularly to a kind of sale of electricity service platform method of evaluating performance and device.
Background technology
With the propulsion of power system reform, sale of electricity company arises at the historic moment in electricity market, sale of electricity service platform performance
The level of profitability of sale of electricity company is directly affected, and the selection of very different sale of electricity service platform is to the greatly tired of sale of electricity company
Disturb.However, the method still without sale of electricity service platform performance evaluation in the market, it is only in load prediction precision measuring and calculating side
There are a small number of jejune algorithms in face, and these algorithms always use energy result generally be directed to unique user or multi-user, and uncomfortable
Close the measuring and calculating of multi-user's load prediction precision.
The performance of sale of electricity service platform can not be reflected completely merely with load prediction precision, and due to existing load
Prediction precision measuring method is that, always with energy situation, customer charge can be caused to predict positive negative bias during measuring and calculating based on all users
Difference is cancelled out each other, therefore, and existing measuring method is inaccurate objective, and the performance of sale of electricity service platform can not be evaluated exactly.
Therefore, how to improve the accuracy of sale of electricity service platform performance evaluation, be prior art urgent problem to be solved it
One.
The content of the invention
The invention discloses a kind of sale of electricity service platform method of evaluating performance and device, to improve sale of electricity service platform
The accuracy that can be evaluated.
The embodiments of the invention provide a kind of sale of electricity service platform method of evaluating performance, including：
For presetting each examination cycle in predetermined period, according to the actual electricity consumption of each user in the examination cycle
Amount, and each prediction power consumption of user, determine the load prediction deviation ratio of each user in the examination cycle；
According to the load prediction deviation ratio of each user in each examination cycle in described default predetermined period, Mei Yikao
The prediction power consumption of each user in nuclear cycle, the first prediction total electricity consumption and the default prediction week in each examination cycle
In phase second prediction total electricity consumption, it is determined that in described default predetermined period sale of electricity service platform load prediction precision；
The lowest price of electricity is bought from electricity market according to each minimum transaction cycle in described default predetermined period
With the price and the actual total electricity consumption of all users of the electricity for being sold to all users, determine in described default predetermined period
The optimal total gross profit in market, according to the optimal total gross profit in the market and total gross profit in described default predetermined period, it is determined that
The power purchase strategy lean degree of sale of electricity service platform in described default predetermined period；
According to the load prediction precision of sale of electricity service platform in described default predetermined period and power purchase strategy lean degree,
Determine the evaluation coefficient of performance of sale of electricity service platform.
Preferably, according to the load prediction precision and power purchase strategy of the sale of electricity service platform in described default predetermined period
Lean degree, determines the evaluation coefficient of performance of sale of electricity service platform, specifically includes:
The evaluation coefficient of performance of sale of electricity service platform is determined by following methods：
Z=X × Y
Wherein：Z represents the evaluation coefficient of performance of sale of electricity service platform；
X represents the load prediction precision of the sale of electricity service platform in default predetermined period；
Y represents the power purchase strategy lean degree of the sale of electricity service platform in default predetermined period.
Preferably, for presetting each examination cycle in predetermined period, according to the reality of each user in the examination cycle
Border power consumption, and each prediction power consumption of user, determine the load prediction deviation ratio of each user in the examination cycle, have
Body includes：
The load prediction deviation ratio of each user in the examination cycle is determined by the following method：
qj=QReal ji/QPre- ji-1
Wherein：qjRepresent the load prediction deviation ratio of i-th of user in j-th of examination cycle, i ∈ [1, n], n is represented
Total number of users amount, j ∈ [1, m], m represents the number in the examination cycle in default predetermined period；
QReal jiRepresent the actual power consumption of i-th of user in j-th of examination cycle；
QPre- jiRepresent the prediction power consumption of i-th of user in j-th of examination cycle.
Preferably, according to the load prediction deviation of each user in each examination cycle in described default predetermined period
Rate, the prediction power consumption of each user in each examination cycle, first in each examination cycle predicts total electricity consumption and described
The second prediction total electricity consumption in default predetermined period, it is determined that the load of sale of electricity service platform is pre- in described default predetermined period
Precision is surveyed, is specifically included：
The load prediction precision of the sale of electricity service platform in described default predetermined period is determined by the following method：
Wherein：X represents the load prediction precision of the sale of electricity service platform in default predetermined period；
QPre- jThe first prediction total electricity consumption in j-th of examination cycle is represented,
QIn advanceThe second prediction total electricity consumption in default predetermined period is represented,
Preferably, electricity is bought most from electricity market according to each minimum transaction cycle in described default predetermined period
Low price and the price and the actual total electricity consumption of all users of the electricity for being sold to all users, determine the default prediction
The optimal total gross profit in market in cycle, is specifically included：
The optimal total gross profit in market in described default predetermined period is determined by the following method：
Wherein：M represents the optimal total gross profit in market in default predetermined period；
K represents k-th of minimum transaction cycle in default predetermined period, and k ∈ [1, z], z is represented in default predetermined period
The number of minimum transaction cycle；
PUser kRepresent that sale of electricity service platform is sold to the price of the electricity of all users in k-th of minimum transaction cycle；
PMarket kRepresent that sale of electricity service platform buys the lowest price of electricity in k-th of minimum transaction cycle from electricity market
Lattice；
QReal kRepresent the actual total electricity consumption of all users in k-th of minimum transaction cycle.
Preferably, according to the optimal total gross profit in the market and total gross profit in described default predetermined period, it is determined that in institute
The power purchase strategy lean degree of sale of electricity service platform in default predetermined period is stated, is specifically included：
The power purchase strategy lean degree of the sale of electricity service platform in described default predetermined period is determined by the following method：
Y=N/M
Wherein：Y represents power purchase strategy lean degree of the sale of electricity service platform in default predetermined period；
N represents total gross profit in default predetermined period.
The embodiments of the invention provide a kind of sale of electricity service platform device for evaluating performance, including：
First determining unit, for for presetting each examination cycle in predetermined period, according to every in the examination cycle
The actual power consumption of individual user, and each user prediction power consumption, determine that the load of each user in the examination cycle is pre-
Survey deviation ratio；
Second determining unit, for the load according to each user in each examination cycle in described default predetermined period
Prediction deviation rate, the prediction power consumption of each user in each examination cycle, the first total electricity consumption of prediction in each examination cycle
The second prediction total electricity consumption in amount and described default predetermined period, it is determined that the sale of electricity service platform in described default predetermined period
Load prediction precision；
3rd determining unit, for being purchased according to each minimum transaction cycle in described default predetermined period from electricity market
Buy the lowest price of electricity and be sold to the price and the actual total electricity consumption of all users of the electricity of all users, determine institute
The optimal total gross profit in market in default predetermined period is stated, according to the optimal total gross profit in the market and in described default predetermined period
Total gross profit, it is determined that in described default predetermined period sale of electricity service platform power purchase strategy lean degree；
4th determining unit, for the load prediction precision according to the sale of electricity service platform in described default predetermined period
With power purchase strategy lean degree, the evaluation coefficient of performance of sale of electricity service platform is determined.
Preferably, the 4th determining unit, the evaluation specifically for determining sale of electricity service platform by following methods
Can coefficient：
Z=X × Y
Wherein：Z represents the evaluation coefficient of performance of sale of electricity service platform；
X represents the load prediction precision of the sale of electricity service platform in default predetermined period；
Y represents the power purchase strategy lean degree of the sale of electricity service platform in default predetermined period.
Preferably, first determining unit, specifically for for preset predetermined period in each examination cycle, according to
Following methods determine the load prediction deviation ratio of each user in the examination cycle：
qj=QReal ji/QPre- ji-1
Wherein：qjRepresent the load prediction deviation ratio of i-th of user in j-th of examination cycle, i ∈ [1, n], n is represented
Total number of users amount, j ∈ [1, m], m represents the number in the examination cycle in default predetermined period；
QReal jiRepresent the actual power consumption of i-th of user in j-th of examination cycle；
QPre- jiRepresent the prediction power consumption of i-th of user in j-th of examination cycle.
Preferably, second determining unit, specifically for determining by the following method in described default predetermined period
The load prediction precision of sale of electricity service platform：
Wherein：X represents the load prediction precision of the sale of electricity service platform in default predetermined period；
QPre- jThe first prediction total electricity consumption in j-th of examination cycle is represented,
QIn advanceThe second prediction total electricity consumption in default predetermined period is represented,
Preferably, the 3rd determining unit, specifically for determining by the following method in described default predetermined period
The optimal total gross profit in market：
Wherein：M represents the optimal total gross profit in market in default predetermined period；
K represents k-th of minimum transaction cycle in default predetermined period, and k ∈ [1, z], z is represented in default predetermined period
The number of minimum transaction cycle；
PUser kRepresent that sale of electricity service platform is sold to the price of the electricity of all users in k-th of minimum transaction cycle；
PMarket kRepresent that sale of electricity service platform buys the lowest price of electricity in k-th of minimum transaction cycle from electricity market
Lattice；
QReal kRepresent the actual total electricity consumption of all users in k-th of minimum transaction cycle.
Preferably, the 3rd determining unit, specifically for determining by the following method in described default predetermined period
The power purchase strategy lean degree of sale of electricity service platform：
Y=N/M
Wherein：Y represents power purchase strategy lean degree of the sale of electricity service platform in default predetermined period；
N represents total gross profit in default predetermined period.
Beneficial effects of the present invention include：
In sale of electricity service platform method of evaluating performance and device provided in an embodiment of the present invention, in default predetermined period
Each examination cycle, according to the actual power consumption of each user in the examination cycle and the prediction power consumption of each user, really
The load prediction deviation ratio of each user in the fixed examination cycle, further according in each examination cycle in default predetermined period
The load prediction deviation ratio of each user, each user's predicts power consumption, in each examination cycle in each examination cycle
The second prediction total electricity consumption in first prediction total electricity consumption and default predetermined period, it is determined that the sale of electricity clothes in default predetermined period
The load prediction precision of business platform, electricity is bought according to each minimum transaction cycle in default predetermined period from electricity market
Lowest price and be sold to all users electricity price and the actual total electricity consumption of all users, it is determined that default prediction
The optimal total gross profit in market in cycle, total gross profit according to the optimal total gross profit in market and in default predetermined period, it is determined that pre-
If the power purchase strategy lean degree of sale of electricity service platform in predetermined period, according in default predetermined period sale of electricity service platform it is negative
Lotus predicts precision and power purchase strategy lean degree, determines in the evaluation coefficient of performance of sale of electricity service platform, above-mentioned flow, based on pre-
If predetermined period internal loading predicts precision and power purchase strategy lean two dimensions of degree, automatic measurement ＆ calculation sale of electricity service platform performance demands
Number, substantially increases the accuracy of sale of electricity service platform performance evaluation.
Other features and advantages of the present invention will be illustrated in the following description, also, partly becomes from specification
Obtain it is clear that or being understood by implementing the present invention.The purpose of the present invention and other advantages can be by the explanations write
Specifically noted structure is realized and obtained in book, claims and accompanying drawing.
Brief description of the drawings
Accompanying drawing described herein is used for providing a further understanding of the present invention, constitutes the part of the present invention, this hair
Bright schematic description and description is used to explain the present invention, does not constitute inappropriate limitation of the present invention.In the accompanying drawings：
Fig. 1 be the embodiment of the present invention in, the implementation process diagram of sale of electricity service platform method of evaluating performance；
Fig. 2 be the embodiment of the present invention in, the structural representation of sale of electricity service platform device for evaluating performance.
Embodiment
In order to improve the accuracy of sale of electricity service platform performance evaluation, the present invention proposes a kind of sale of electricity service platform performance
Evaluation method and device.
The implementation principle of sale of electricity service platform method of evaluating performance provided in an embodiment of the present invention is：For default prediction week
Interim each examination cycle, according to the prediction electricity consumption of the actual power consumption of each user in the examination cycle and each user
Amount, determines the load prediction deviation ratio of each user in the examination cycle, further according to each examination week in default predetermined period
The load prediction deviation ratio of each user in phase, the prediction power consumption of each user, each examination week in each examination cycle
The second prediction total electricity consumption in the first prediction total electricity consumption and default predetermined period in phase, it is determined that in default predetermined period
The load prediction precision of sale of electricity service platform, is purchased according to each minimum transaction cycle in default predetermined period from electricity market
Buy the lowest price of electricity and be sold to the price and the actual total electricity consumption of all users of the electricity of all users, it is determined that in advance
If the optimal total gross profit in market in predetermined period, total gross profit according to the optimal total gross profit in market and in default predetermined period, really
The power purchase strategy lean degree of sale of electricity service platform in default predetermined period is scheduled on, according to flat in sale of electricity service in predetermined period of presetting
The load prediction precision and power purchase strategy lean degree of platform, are determined in the evaluation coefficient of performance of sale of electricity service platform, above-mentioned flow,
Based on default predetermined period internal loading prediction precision and power purchase strategy lean two dimensions of degree, automatic measurement ＆ calculation sale of electricity service platform
The coefficient of performance, substantially increases the accuracy of sale of electricity service platform performance evaluation.
In the running of whole power network, in order to ensure the economy of operation of power networks, generally require to enter network load
Row long-range forecasting, in order to ensure that power system can be run economical and efficient, the precision for improving load prediction is entirely being powered
Enterprise development in planning operation with playing an important role.The load prediction cycle, according to the difference of prediction target, can typically divide
For：Ultra-short term (within an hour), short-term (within 1~7 day), mid-term (within 1 year), long-term (within 10 years or longer time).
Wherein, the load prediction of ultra-short term is mainly adjusted and monitored to the quality of power supply and its stability, predicts whether electricity occur
Can quality events；Short-term load forecasting is mainly to be optimized to the process of operation of power networks, so that the economy to network system is transported
Row is controlled；Medium term load forecasting is mainly to determine that dispatching of power netwoks plan and main transformer maintenance project provide foundation；It is long-term negative
Lotus prediction mainly provides service to build new transformer station and power network reorganization and expansion.
It should be noted that predetermined period of load can be any of the above-described kind in the embodiment of the present invention, can be according to not
Same prediction target sets itself, is not construed as limiting herein.
Load prediction precision is one of important performance of sale of electricity service platform, in the embodiment of the present invention, by default prediction
Cycle is divided into several isometric examination cycles, based on each user, determines bearing for each user in each examination cycle
Lotus prediction deviation rate, then the load prediction precision in default predetermined period is calculated, meanwhile, introduce sale of electricity service platform power purchase plan
This parameter of lean degree is omited to determine the essence of sale of electricity service platform each trade market Purchasing combination strategy in default predetermined period
Beneficial degree；According to the load prediction precision of sale of electricity service platform and power purchase strategy lean degree in predetermined period is being preset, jointly
The sale of electricity service platform coefficient of performance is determined, the performance of the sale of electricity service platform is commented according to the sale of electricity service platform coefficient of performance
Valency.In the embodiment of the present invention, type of transaction can include but is not limited to following several：Annual trade market, season trade market,
Monthly trade market, day-ahead trading, hour trade market.
The preferred embodiments of the present invention are illustrated below in conjunction with Figure of description, it will be appreciated that described herein
Preferred embodiment is merely to illustrate and explain the present invention, and is not intended to limit the present invention, and in the case where not conflicting, this hair
The feature in embodiment and embodiment in bright can be mutually combined.
As shown in figure 1, it shows for the implementing procedure of sale of electricity service platform method of evaluating performance provided in an embodiment of the present invention
It is intended to, may comprise steps of：
S11, for preset predetermined period in each examination cycle, according to the actual use of each user in the examination cycle
Electricity, and each prediction power consumption of user, determine the load prediction deviation ratio of each user in the examination cycle.
When it is implemented, for presetting each examination cycle in predetermined period, from sale of electricity service platform reading this and examining
The actual power consumption of each user and the prediction power consumption of each user in nuclear cycle, being determined according to the above-mentioned data of reading should
The load prediction deviation ratio of each user in the examination cycle.
Specifically, for presetting each examination cycle in predetermined period, examination week can be determined by following formula
The load prediction deviation ratio of each user in phase：
qj=QReal ji/QPre- ji-1
Wherein：qjRepresent the load prediction deviation ratio of i-th of user in j-th of examination cycle, i ∈ [1, n], n is represented
Total number of users amount, j ∈ [1, m], m represents the number in the examination cycle in default predetermined period；
QReal jiRepresent the actual power consumption of i-th of user in j-th of examination cycle；
QPre- jiRepresent the prediction power consumption of i-th of user in j-th of examination cycle.
When it is implemented, examination the cycle be electricity market to the examination time limit of sale of electricity company purchase of electricity, can be but not limit
In 1 year, 1 month, 1 day or 1 hour.Default predetermined period includes m isometric examination cycles, it is assumed that predetermined period is 1 year,
1 examination cycle is 1 month, then the predetermined period includes 12 examination cycles.
It should be noted that in the embodiment of the present invention, predetermined period can voluntarily be set with the examination cycle according to actual conditions
It is fixed, it is not construed as limiting herein.
S12, the load prediction deviation ratio according to each user in each examination cycle in described default predetermined period, often
The prediction power consumption of each user in one examination cycle, first in each examination cycle predicts total electricity consumption and described default pre-
The second prediction total electricity consumption in the survey cycle, it is determined that the load prediction of sale of electricity service platform is accurate in described default predetermined period
Degree.
When it is implemented, according to the prediction electricity consumption of each user in each examination cycle obtained from sale of electricity service platform
Amount calculates the first prediction total electricity consumption of all users in each examination cycle, and according to the first prediction in each examination cycle
Total electricity consumption calculates the second prediction total electricity consumption in default predetermined period, wherein, the first prediction total electricity consumption is each examination
The prediction total electricity consumption of all users in cycle, the second total land used amount of prediction is the prediction of all users in default predetermined period
Total electricity consumption.
Specifically, calculate first by following formula and predict total electricity consumption and the second prediction total electricity consumption：
Wherein, QPre- jThe first prediction total electricity consumption in j-th of examination cycle is represented, n represents total number of users amount, QPre- jiRepresent
The prediction power consumption of i-th of user in j-th of examination cycle.
QIn advanceRepresent the second prediction total electricity consumption in default predetermined period.
Further, the load prediction that the sale of electricity service platform in default predetermined period is calculated according to the following equation is accurate
Degree：
Wherein：X represents the load prediction precision of the sale of electricity service platform in default predetermined period；
qjThe load prediction deviation ratio of i-th of user in j-th of examination cycle is represented, i ∈ [1, n], n represents that user is total
Quantity, j ∈ [1, m], m represents the number in the examination cycle in default predetermined period.
Alternatively, in above-mentioned formulaXjRepresent sale of electricity service platform in j-th of examination cycle
Load prediction precision.
S13, according to each minimum transaction cycle in described default predetermined period from electricity market buy electricity it is minimum
Price and the price and the actual total electricity consumption of all users of the electricity for being sold to all users, determine the default prediction week
The optimal total gross profit in market in phase, according to the optimal total gross profit in the market and total gross profit in described default predetermined period, really
It is scheduled on the power purchase strategy lean degree of sale of electricity service platform in described default predetermined period.
When it is implemented, obtaining each minimum transaction cycle in default predetermined period from sale of electricity service platform from electric power
The lowest price of market purchase electricity and be sold to all users electricity price, and all users actual total electricity consumption
Amount, the optimal total gross profit in market in default predetermined period is determined according to above-mentioned data.Wherein, transaction cycle is power market transaction
Frequency, such as can be 1 month, one day or 1 hour transaction once, minimum transaction cycle be electricity transaction minimum unit,
It it is 1 hour than the minimum transaction cycle in such as above-mentioned transaction cycle.
Specifically, the optimal total gross profit in market in default predetermined period can be calculated by following formula：
Wherein：M represents the optimal total gross profit in market in default predetermined period；
K represents k-th of minimum transaction cycle in default predetermined period, and k ∈ [1, z], z is represented in default predetermined period
The number of minimum transaction cycle；
PUser kRepresent that sale of electricity service platform is sold to the price of the electricity of all users in k-th of minimum transaction cycle；
PMarket kRepresent that sale of electricity service platform buys the lowest price of electricity in k-th of minimum transaction cycle from electricity market
Lattice；
PUser k-PMarket kRepresent the optimal gross profit of each type of transaction in k-th of minimum transaction cycle；
QReal kRepresent the actual total electricity consumption of all users in k-th of minimum transaction cycle.
Wherein, PMarket kRepresent that sale of electricity service platform buys electricity most in k-th of minimum transaction cycle from electricity market
Low price, is specifically referred to, the lowest price of All Activity type in k-th of minimum transaction cycle.For example, sale of electricity service platform
Bought within the minimum period from annual trade market 40% electricity, bought from monthly trade market 30% electricity, from small
When trade market buy 30% electricity, purchasing price is followed successively by:10000 yuan, 8000 yuan, 7500 yuan, then sale of electricity service platform exist
The lowest price for buying electricity from electricity market in k-th of minimum transaction cycle is the total price from hour trade market purchase,
That is PMarket k=7500 yuan, it is assumed that sale of electricity service platform is sold to the price of all user's electricity in k-th of minimum transaction cycle
PUser kFor 8500 yuan, then in k-th of minimum transaction cycle each type of transaction optimal gross profit PUser k-PMarket k=8500-7500=
1000 yuan.
Further, total gross profit in predetermined period is obtained, according to the optimal total gross profit in market and in default predetermined period
Interior total gross profit, it is determined that presetting the power purchase strategy lean degree of sale of electricity service platform in predetermined period.
Specifically, the power purchase strategy lean of the sale of electricity service platform in default predetermined period can be determined according to the following equation
Degree：
Y=N/M
Wherein：Y represents power purchase strategy lean degree of the sale of electricity service platform in default predetermined period；
N represents total gross profit in default predetermined period.
S14, basis the load prediction precision of sale of electricity service platform and power purchase strategy essence in described default predetermined period
Beneficial degree, determines the evaluation coefficient of performance of sale of electricity service platform.
When it is implemented, the evaluation coefficient of performance of sale of electricity service platform can be determined by following formula：
Z=X × Y
Wherein：Z represents the evaluation coefficient of performance of sale of electricity service platform；
X represents the load prediction precision of the sale of electricity service platform in default predetermined period；
Y represents the power purchase strategy lean degree of the sale of electricity service platform in default predetermined period.
Further, sale of electricity service platform performance is evaluated according to the evaluation performance index of sale of electricity service platform.Specific implementation
When, Z ∈ [0,1], if the sale of electricity service platform performance index is higher, the sale of electricity service platform performance is higher；If this is sold
Electric service platform performance index is lower, then the sale of electricity service platform performance is lower.If the i.e. sale of electricity service platform performance index
More level off to 1, then its performance is higher.
Sale of electricity service platform method of evaluating performance provided in an embodiment of the present invention, for presetting each examining in predetermined period
Nuclear cycle, according to the actual power consumption of each user in the examination cycle and the prediction power consumption of each user, determines the examination
The load prediction deviation ratio of each user in cycle, further according to each user in each examination cycle in default predetermined period
Load prediction deviation ratio, in each examination cycle each user prediction power consumption, in each examination cycle first prediction
The second prediction total electricity consumption in total electricity consumption and default predetermined period, it is determined that the sale of electricity service platform in default predetermined period
Load prediction precision, the lowest price of electricity is bought according to each minimum transaction cycle in default predetermined period from electricity market
The price and the actual total electricity consumption of all users of lattice and the electricity for being sold to all users, it is determined that in default predetermined period
The optimal total gross profit in market, total gross profit according to the optimal total gross profit in market and in default predetermined period, it is determined that in default prediction week
The power purchase strategy lean degree of sale of electricity service platform in phase, according to the load prediction essence of the sale of electricity service platform in default predetermined period
Accuracy and power purchase strategy lean degree, are determined in the evaluation coefficient of performance of sale of electricity service platform, above-mentioned flow, based on default prediction week
Phase internal loading prediction precision and power purchase strategy lean two dimensions of degree, the automatic measurement ＆ calculation sale of electricity service platform coefficient of performance, significantly
Improve the accuracy of sale of electricity service platform performance evaluation.
Based on same inventive concept, a kind of sale of electricity service platform device for evaluating performance is additionally provided in the embodiment of the present invention,
Because the principle that said apparatus solves problem is similar to above-mentioned sale of electricity service platform method of evaluating performance, therefore the reality of said apparatus
The implementation for the method for may refer to is applied, part is repeated and repeats no more.
As shown in Fig. 2 it is the structural representation of sale of electricity service platform device for evaluating performance provided in an embodiment of the present invention,
It can include：
First determining unit 21, for for presetting each examination cycle in predetermined period, according in the examination cycle
The actual power consumption of each user, and each prediction power consumption of user, determine the load of each user in the examination cycle
Prediction deviation rate；
Second determining unit 22, for according to the negative of each user in each examination cycle in described default predetermined period
Lotus prediction deviation rate, the prediction power consumption of each user in each examination cycle, the total use of the first prediction in each examination cycle
The second prediction total electricity consumption in electricity and described default predetermined period, it is determined that sale of electricity service is flat in described default predetermined period
The load prediction precision of platform；
3rd determining unit 23, for according to each minimum transaction cycle in described default predetermined period from electricity market
The price and the actual total electricity consumption of all users of the lowest price for buying electricity and the electricity for being sold to all users, it is determined that
The optimal total gross profit in market in described default predetermined period, according to the optimal total gross profit in the market and in described default predetermined period
Interior total gross profit, it is determined that in described default predetermined period sale of electricity service platform power purchase strategy lean degree；
4th determining unit 24, for basis, the load prediction of sale of electricity service platform is accurate in described default predetermined period
Degree and power purchase strategy lean degree, determine the evaluation coefficient of performance of sale of electricity service platform.
It is preferred that the 4th determining unit 24, the evaluation specifically for determining sale of electricity service platform by following methods
The coefficient of performance：
Z=X × Y
Wherein：Z represents the evaluation coefficient of performance of sale of electricity service platform；
X represents the load prediction precision of the sale of electricity service platform in default predetermined period；
Y represents the power purchase strategy lean degree of the sale of electricity service platform in default predetermined period.
It is preferred that first determining unit 21, specifically for for presetting each examination cycle in predetermined period, pressing
The load prediction deviation ratio of each user in the examination cycle is determined according to following methods：
qj=QReal ji/QPre- ji-1
Wherein：qjRepresent the load prediction deviation ratio of i-th of user in j-th of examination cycle, i ∈ [1, n], n is represented
Total number of users amount, j ∈ [1, m], m represents the number in the examination cycle in default predetermined period；
QReal jiRepresent the actual power consumption of i-th of user in j-th of examination cycle；
QPre- jiRepresent the prediction power consumption of i-th of user in j-th of examination cycle.
It is preferred that second determining unit 22, specifically for determining by the following method in described default predetermined period
The load prediction precision of interior sale of electricity service platform：
Wherein：X represents the load prediction precision of the sale of electricity service platform in default predetermined period；
QPre- jThe first prediction total electricity consumption in j-th of examination cycle is represented,
QIn advanceThe second prediction total electricity consumption in default predetermined period is represented,
It is preferred that the 3rd determining unit 23, specifically for determining by the following method in described default predetermined period
The optimal total gross profit in market：
Wherein：M represents the optimal total gross profit in market in default predetermined period；
K represents k-th of minimum transaction cycle in default predetermined period, and k ∈ [1, z], z is represented in default predetermined period
The number of minimum transaction cycle；
PUser kRepresent that sale of electricity service platform is sold to the price of the electricity of all users in k-th of minimum transaction cycle；
PMarket kRepresent that sale of electricity service platform buys the lowest price of electricity in k-th of minimum transaction cycle from electricity market
Lattice；
QReal kRepresent the actual total electricity consumption of all users in k-th of minimum transaction cycle.
It is preferred that the 3rd determining unit 23, specifically for determining by the following method in described default predetermined period
The power purchase strategy lean degree of interior sale of electricity service platform：
Y=N/M
Wherein：Y represents power purchase strategy lean degree of the sale of electricity service platform in default predetermined period；
N represents total gross profit in default predetermined period.
For convenience of description, above each several part is divided by function describes respectively for each module (or unit).Certainly, exist
Implement the function of each module (or unit) can be realized in same or multiple softwares or hardware during the present invention.
It should be understood by those skilled in the art that, embodiments of the invention can be provided as method, system or computer program
Product.Therefore, the present invention can be using the reality in terms of complete hardware embodiment, complete software embodiment or combination software and hardware
Apply the form of example.Moreover, the present invention can be used in one or more computers for wherein including computer usable program code
The computer program production that usable storage medium is implemented on (including but is not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of product.
The present invention is the flow with reference to method according to embodiments of the present invention, equipment (system) and computer program product
Figure and/or block diagram are described.It should be understood that can be by every first-class in computer program instructions implementation process figure and/or block diagram
Journey and/or the flow in square frame and flow chart and/or block diagram and/or the combination of square frame.These computer programs can be provided
The processor of all-purpose computer, special-purpose computer, Embedded Processor or other programmable data processing devices is instructed to produce
A raw machine so that produced by the instruction of computer or the computing device of other programmable data processing devices for real
The device for the function of being specified in present one flow of flow chart or one square frame of multiple flows and/or block diagram or multiple square frames.
These computer program instructions, which may be alternatively stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that the instruction being stored in the computer-readable memory, which is produced, to be included referring to
Make the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one square frame of block diagram or
The function of being specified in multiple square frames.
These computer program instructions can be also loaded into computer or other programmable data processing devices so that in meter
Series of operation steps is performed on calculation machine or other programmable devices to produce computer implemented processing, thus in computer or
The instruction performed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in individual square frame or multiple square frames.
, but those skilled in the art once know basic creation although preferred embodiments of the present invention have been described
Property concept, then can make other change and modification to these embodiments.So, appended claims are intended to be construed to include excellent
Select embodiment and fall into having altered and changing for the scope of the invention.
Obviously, those skilled in the art can carry out the essence of various changes and modification without departing from the present invention to the present invention
God and scope.So, if these modifications and variations of the present invention belong to the scope of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to comprising including these changes and modification.
Claims (12)
1. a kind of sale of electricity service platform method of evaluating performance, it is characterised in that including：
For presetting each examination cycle in predetermined period, according to the actual power consumption of each user in the examination cycle, and
The prediction power consumption of each user, determines the load prediction deviation ratio of each user in the examination cycle；
According to the load prediction deviation ratio of each user in each examination cycle in described default predetermined period, each examination week
In the prediction power consumption of each user in phase, the first prediction total electricity consumption and described default predetermined period in each examination cycle
Second prediction total electricity consumption, it is determined that in described default predetermined period sale of electricity service platform load prediction precision；
The lowest price of electricity is bought from electricity market according to each minimum transaction cycle in described default predetermined period and gone out
The price of the electricity of all users and the actual total electricity consumption of all users are sold to, the city in described default predetermined period is determined
The optimal total gross profit in field, according to the optimal total gross profit in the market and total gross profit in described default predetermined period, it is determined that described
The power purchase strategy lean degree of sale of electricity service platform in default predetermined period；
According to the load prediction precision of sale of electricity service platform in described default predetermined period and power purchase strategy lean degree, it is determined that
The evaluation coefficient of performance of sale of electricity service platform.
2. the method as described in claim 1, it is characterised in that according to the sale of electricity service platform in described default predetermined period
Load prediction precision and power purchase strategy lean degree, determine the evaluation coefficient of performance of sale of electricity service platform, specifically include:
The evaluation coefficient of performance of sale of electricity service platform is determined by following methods：
Z=X × Y
Wherein：Z represents the evaluation coefficient of performance of sale of electricity service platform；
X represents the load prediction precision of the sale of electricity service platform in default predetermined period；
Y represents the power purchase strategy lean degree of the sale of electricity service platform in default predetermined period.
3. the method as described in claim 1, it is characterised in that for presetting each examination cycle in predetermined period, according to
The actual power consumption of each user in the examination cycle, and each prediction power consumption of user, are determined every in the examination cycle
The load prediction deviation ratio of individual user, is specifically included：
The load prediction deviation ratio of each user in the examination cycle is determined by the following method：
qj=QReal ji/QPre- ji-1
Wherein：qjThe load prediction deviation ratio of i-th of user in j-th of examination cycle is represented, i ∈ [1, n], n represents that user is total
Quantity, j ∈ [1, m], m represents the number in the examination cycle in default predetermined period；
QReal jiRepresent the actual power consumption of i-th of user in j-th of examination cycle；
QPre- jiRepresent the prediction power consumption of i-th of user in j-th of examination cycle.
4. method as claimed in claim 3, it is characterised in that according in each examination cycle in described default predetermined period
The load prediction deviation ratio of each user, the prediction power consumption of each user in each examination cycle, in each examination cycle
The second prediction total electricity consumption in first prediction total electricity consumption and described default predetermined period, it is determined that in described default predetermined period
The load prediction precision of interior sale of electricity service platform, is specifically included：
The load prediction precision of the sale of electricity service platform in described default predetermined period is determined by the following method：
Wherein：X represents the load prediction precision of the sale of electricity service platform in default predetermined period；
QPre- jThe first prediction total electricity consumption in j-th of examination cycle is represented,
QIn advanceThe second prediction total electricity consumption in default predetermined period is represented,
5. the method as described in claim 1, it is characterised in that according to each minimum transaction week in described default predetermined period
Phase buys the lowest price of electricity from electricity market and is sold to the price and the reality of all users of the electricity of all users
Total electricity consumption, determines the optimal total gross profit in market in described default predetermined period, specifically includes：
The optimal total gross profit in market in described default predetermined period is determined by the following method：
Wherein：M represents the optimal total gross profit in market in default predetermined period；
K represents k-th of minimum transaction cycle in default predetermined period, and k ∈ [1, z], z represents the minimum in default predetermined period
The number of transaction cycle；
PUser kRepresent that sale of electricity service platform is sold to the price of the electricity of all users in k-th of minimum transaction cycle；
PMarket kRepresent that sale of electricity service platform buys the lowest price of electricity in k-th of minimum transaction cycle from electricity market；
QReal kRepresent the actual total electricity consumption of all users in k-th of minimum transaction cycle.
6. method as claimed in claim 5, it is characterised in that according to the optimal total gross profit in the market and in the default prediction
Total gross profit in cycle, it is determined that in described default predetermined period sale of electricity service platform power purchase strategy lean degree, specifically include：
The power purchase strategy lean degree of the sale of electricity service platform in described default predetermined period is determined by the following method：
Y=N/M
Wherein：Y represents power purchase strategy lean degree of the sale of electricity service platform in default predetermined period；
N represents total gross profit in default predetermined period.
7. a kind of sale of electricity service platform device for evaluating performance, it is characterised in that including：
First determining unit, for for presetting each examination cycle in predetermined period, according to each being used in the examination cycle
The actual power consumption at family, and each user prediction power consumption, determine that the load prediction of each user in the examination cycle is inclined
Rate；
Second determining unit, for the load prediction according to each user in each examination cycle in described default predetermined period
Deviation ratio, the prediction power consumption of each user in each examination cycle, in each examination cycle first prediction total electricity consumption and
The second prediction total electricity consumption in described default predetermined period, it is determined that in described default predetermined period sale of electricity service platform it is negative
Lotus predicts precision；
3rd determining unit, for buying electricity from electricity market according to each minimum transaction cycle in described default predetermined period
The price and the actual total electricity consumption of all users of the lowest price of amount and the electricity for being sold to all users, are determined described pre-
If the optimal total gross profit in market in predetermined period, according to the optimal total gross profit in the market and total in described default predetermined period
Gross profit, it is determined that in described default predetermined period sale of electricity service platform power purchase strategy lean degree；
4th determining unit, for according to the load prediction precision of sale of electricity service platform and purchase in described default predetermined period
Electric strategy lean degree, determines the evaluation coefficient of performance of sale of electricity service platform.
8. device as claimed in claim 7, it is characterised in that
4th determining unit, the evaluation coefficient of performance specifically for determining sale of electricity service platform by following methods：
Z=X × Y
Wherein：Z represents the evaluation coefficient of performance of sale of electricity service platform；
X represents the load prediction precision of the sale of electricity service platform in default predetermined period；
Y represents the power purchase strategy lean degree of the sale of electricity service platform in default predetermined period.
9. device as claimed in claim 7, it is characterised in that
First determining unit, specifically for for preset predetermined period in each examination cycle, by the following method really
The load prediction deviation ratio of each user in the fixed examination cycle：
qj=QReal ji/QPre- ji-1
Wherein：qjThe load prediction deviation ratio of i-th of user in j-th of examination cycle is represented, i ∈ [1, n], n represents that user is total
Quantity, j ∈ [1, m], m represents the number in the examination cycle in default predetermined period；
QReal jiRepresent the actual power consumption of i-th of user in j-th of examination cycle；
QPre- jiRepresent the prediction power consumption of i-th of user in j-th of examination cycle.
10. device as claimed in claim 9, it is characterised in that
Second determining unit, specifically for determining the sale of electricity service platform in described default predetermined period by the following method
Load prediction precision：
Wherein：X represents the load prediction precision of the sale of electricity service platform in default predetermined period；
QPre- jThe first prediction total electricity consumption in j-th of examination cycle is represented,
QIn advanceThe second prediction total electricity consumption in default predetermined period is represented,
11. device as claimed in claim 7, it is characterised in that
3rd determining unit, specifically for determining the optimal total hair in market in described default predetermined period by the following method
Profit：
Wherein：M represents the optimal total gross profit in market in default predetermined period；
K represents k-th of minimum transaction cycle in default predetermined period, and k ∈ [1, z], z represents the minimum in default predetermined period
The number of transaction cycle；
PUser kRepresent that sale of electricity service platform is sold to the price of the electricity of all users in k-th of minimum transaction cycle；
PMarket kRepresent that sale of electricity service platform buys the lowest price of electricity in k-th of minimum transaction cycle from electricity market；
QReal kRepresent the actual total electricity consumption of all users in k-th of minimum transaction cycle.
12. device as claimed in claim 11, it is characterised in that
3rd determining unit, specifically for determining the sale of electricity service platform in described default predetermined period by the following method
Power purchase strategy lean degree：
Y=N/M
Wherein：Y represents power purchase strategy lean degree of the sale of electricity service platform in default predetermined period；
N represents total gross profit in default predetermined period.
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| CN201710344402.7A CN107085774A (en) | 2017-05-16 | 2017-05-16 | A kind of sale of electricity service platform method of evaluating performance and device |
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| CN201710344402.7A CN107085774A (en) | 2017-05-16 | 2017-05-16 | A kind of sale of electricity service platform method of evaluating performance and device |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108108833A (en) * | 2017-11-22 | 2018-06-01 | 新奥泛能网络科技股份有限公司 | Load prediction precision assessment method and system |
| CN108694478A (en) * | 2018-07-09 | 2018-10-23 | 广西电网有限责任公司电力科学研究院 | A kind of sale of electricity risk indicator computational methods considering power quantity predicting deviation |
| CN109066661A (en) * | 2018-08-29 | 2018-12-21 | 新智能源系统控制有限责任公司 | A kind of sale of electricity Deviation Control Method and sale of electricity control system |
| CN113128867A (en) * | 2021-04-20 | 2021-07-16 | 四川瑞康智慧能源有限公司 | Electricity selling deviation management system and method |
-
2017
- 2017-05-16 CN CN201710344402.7A patent/CN107085774A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108108833A (en) * | 2017-11-22 | 2018-06-01 | 新奥泛能网络科技股份有限公司 | Load prediction precision assessment method and system |
| CN108694478A (en) * | 2018-07-09 | 2018-10-23 | 广西电网有限责任公司电力科学研究院 | A kind of sale of electricity risk indicator computational methods considering power quantity predicting deviation |
| CN109066661A (en) * | 2018-08-29 | 2018-12-21 | 新智能源系统控制有限责任公司 | A kind of sale of electricity Deviation Control Method and sale of electricity control system |
| CN109066661B (en) * | 2018-08-29 | 2020-08-18 | 新智能源系统控制有限责任公司 | Electricity selling deviation control method and electricity selling control system |
| CN113128867A (en) * | 2021-04-20 | 2021-07-16 | 四川瑞康智慧能源有限公司 | Electricity selling deviation management system and method |
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