CN116862192A - Policy information generation method and device and related equipment - Google Patents
Policy information generation method and device and related equipment Download PDFInfo
- Publication number
- CN116862192A CN116862192A CN202310926605.2A CN202310926605A CN116862192A CN 116862192 A CN116862192 A CN 116862192A CN 202310926605 A CN202310926605 A CN 202310926605A CN 116862192 A CN116862192 A CN 116862192A
- Authority
- CN
- China
- Prior art keywords
- battery
- target
- cabinet
- model
- period
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 51
- 230000008569 process Effects 0.000 claims abstract description 15
- 230000008859 change Effects 0.000 claims description 96
- 238000006243 chemical reaction Methods 0.000 claims description 92
- 238000012549 training Methods 0.000 claims description 37
- 238000012545 processing Methods 0.000 claims description 22
- 238000004590 computer program Methods 0.000 claims description 12
- 238000007781 pre-processing Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000004140 cleaning Methods 0.000 claims description 6
- 230000009466 transformation Effects 0.000 claims description 6
- 230000004927 fusion Effects 0.000 claims description 5
- 230000009467 reduction Effects 0.000 claims description 5
- 230000005611 electricity Effects 0.000 abstract description 58
- 238000004891 communication Methods 0.000 description 8
- 230000006399 behavior Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 4
- 238000010606 normalization Methods 0.000 description 4
- 125000003275 alpha amino acid group Chemical group 0.000 description 3
- 150000001875 compounds Chemical class 0.000 description 3
- 230000003111 delayed effect Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000000513 principal component analysis Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 101000822695 Clostridium perfringens (strain 13 / Type A) Small, acid-soluble spore protein C1 Proteins 0.000 description 1
- 101000655262 Clostridium perfringens (strain 13 / Type A) Small, acid-soluble spore protein C2 Proteins 0.000 description 1
- 101000655256 Paraclostridium bifermentans Small, acid-soluble spore protein alpha Proteins 0.000 description 1
- 101000655264 Paraclostridium bifermentans Small, acid-soluble spore protein beta Proteins 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000012508 change request Methods 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000011946 reduction process Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The disclosure provides a policy information generation method, a policy information generation device and related equipment, and relates to the technical field of virtual power plants, wherein the method comprises the following steps: acquiring a plurality of historical electricity changing information corresponding to a plurality of electricity changing cabinets one by one; constructing a strategy model according to a plurality of historical battery changing information, wherein the model input of the strategy model is a scheduling strategy, the scheduling strategy comprises target parameters of each battery changing cabinet in a plurality of battery changing cabinets, the target parameters comprise a first subparameter and a second subparameter, the first subparameter is used for indicating the lowest number of available batteries corresponding to the battery changing cabinet, the second subparameter is used for indicating the lowest capacity of the available batteries corresponding to the battery changing cabinet, and the model output of the strategy model is the total charging cost of the plurality of battery changing cabinets; and solving and calculating the strategy model to obtain target strategy information, wherein the charging total cost corresponding to the target strategy information is the minimum value of a plurality of charging total costs output by the strategy model. The utility model discloses an electric charge expenditure in the operation process of trading the electricity cabinet can be reduced.
Description
Technical Field
The disclosure relates to the technical field of virtual power plant scheduling, in particular to a strategy information generation method, a strategy information generation device and related equipment.
Background
The time-sharing electricity price mechanism is arranged to guide users to use electricity in a staggered mode and orderly use the electricity, and ensure balanced operation of power supply and demand.
At present, in order to adapt to the rapid development of the takeaway industry, a plurality of battery change cabinets are correspondingly built in each place, and in application, the fact that the charging and discharging strategies of the battery change cabinets lack unified regulation and control is found, so that the electric charge paid by the battery change cabinets in the operation process is high.
Disclosure of Invention
The disclosure aims to provide a policy information generation method, a policy information generation device and related equipment, which are used for solving the technical problem that the electricity fee expenditure of a battery-changing cabinet in the operation process is high.
In a first aspect, an embodiment of the present disclosure provides a policy information generating method, including:
acquiring a plurality of historical power conversion information corresponding to a plurality of power conversion cabinets one by one, wherein the plurality of historical power conversion information comprises historical power conversion times and historical power conversion power corresponding to the power conversion cabinets;
constructing a strategy model according to the historical power exchange information, wherein the model input of the strategy model is a scheduling strategy, the scheduling strategy comprises target parameters of each power exchange cabinet in the plurality of power exchange cabinets, the target parameters comprise a first subparameter and a second subparameter, the first subparameter is used for indicating the lowest number of available batteries of the corresponding power exchange cabinet, the second subparameter is used for indicating the lowest capacity of the available batteries of the corresponding power exchange cabinet, and the model output of the strategy model is the total charging cost of the plurality of power exchange cabinets;
And solving and calculating the strategy model to obtain target strategy information, wherein the charging total cost corresponding to the target strategy information is the minimum value of a plurality of charging total costs output by the strategy model.
In one embodiment, the constructing a policy model from the plurality of historical battery exchange information includes:
training an initial model according to the plurality of historical battery replacement information to obtain a demand model;
predicting the battery quantity requirement and the battery capacity requirement of each of the plurality of battery change cabinets in a target period according to the requirement model;
and constructing the strategy model according to the battery quantity requirement and the battery capacity requirement of each battery-changing cabinet in the plurality of battery-changing cabinets in a target period, wherein the minimum quantity indicated by the first subparameter is not less than the battery quantity indicated by the battery quantity requirement, and the minimum capacity indicated by the second subparameter is not less than the battery capacity indicated by the battery capacity requirement.
In one embodiment, the training the initial model according to the plurality of historical battery replacement information to obtain the demand model includes:
performing data preprocessing on the plurality of historical information exchange messages to obtain a target data set;
Normalizing the target data set to obtain a training data set;
and training the initial model according to the training data set to obtain the demand model.
In one embodiment, the performing data preprocessing on the plurality of historical information for information of the historical information for obtaining a target data set includes:
performing data cleaning on the plurality of historical heat exchange messages to obtain a first data set;
performing feature transformation on the first data set to obtain a second data set;
and performing dimension reduction processing on the second data set to obtain the target data set.
In one embodiment, the target data set includes a battery usage amount and a battery usage capacity of the corresponding power cabinet in a first period, and a battery usage amount and a battery usage capacity of the corresponding power cabinet in a second period, wherein the first period and the second period are any two adjacent periods within a time period corresponding to the target data set, and the second period is located after the first period;
the normalizing process is performed on the target data set to obtain a training data set, including:
Carrying out data fusion on the battery use quantity and the battery use capacity of the target battery change cabinet in the first period and the battery to-be-used quantity and the battery to-be-used capacity in the second period to obtain the battery change demand data of the target battery change cabinet, wherein the target battery change cabinet is any battery change cabinet in the plurality of battery change cabinets;
and carrying out per unit processing on the power conversion requirement data of the target power conversion cabinet to obtain a power conversion requirement per unit value of the target power conversion cabinet, wherein the training data set comprises the power conversion requirement per unit value of the target power conversion cabinet.
In one embodiment, the solving the policy model to obtain the target policy information includes:
solving and calculating the strategy model based on preset constraint conditions to obtain target strategy information;
wherein the constraint includes at least one of:
a first sub-condition for constraining a maximum power change of a target power change cabinet, wherein the target power change cabinet is any power change cabinet of the plurality of power change cabinets;
a second sub-condition for constraining upper and lower limits of battery capacity of the target battery-change cabinet;
a third sub-condition for constraining a minimum number of available batteries of the target battery-change cabinet.
In one embodiment, the model output of the policy model is calculated according to the electricity price of the target electricity changing cabinet in a target period and the electricity demand of the target period, wherein the target electricity changing cabinet is any one of the plurality of electricity changing cabinets, the target period is any period in a scheduling period corresponding to the plurality of electricity changing cabinets, and the electricity demand of the target electricity changing cabinet in the target period is determined according to the difference between the available battery number of the target electricity changing cabinet in the target period and the minimum number indicated by the first subparameter.
In a second aspect, an embodiment of the present disclosure further provides a policy information generating apparatus, including:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a plurality of pieces of historical power conversion information corresponding to a plurality of power conversion cabinets one by one, and the plurality of pieces of historical power conversion information comprise historical power conversion times and historical power conversion power corresponding to the power conversion cabinets;
the modeling module is used for constructing a strategy model according to the historical power change information, wherein the model input of the strategy model is a scheduling strategy, the scheduling strategy comprises target parameters of each power change cabinet in the power change cabinets, the target parameters comprise a first subparameter and a second subparameter, the first subparameter is used for indicating the lowest number of available batteries of the corresponding power change cabinet, the second subparameter is used for indicating the lowest capacity of the available batteries of the corresponding power change cabinet, and the model output of the strategy model is the total charging cost of the power change cabinets;
And the calculation module is used for solving and calculating the strategy model to obtain target strategy information, wherein the charging total cost corresponding to the target strategy information is the minimum value of a plurality of charging total costs output by the strategy model.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, including a processor, a memory, and a computer program stored on the memory and executable on the processor, where the computer program when executed by the processor implements the steps of the policy information generating method described above.
In a fourth aspect, the embodiments of the present disclosure further provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the policy information generating method described above.
In the embodiment of the disclosure, a policy model is built through the historical power conversion times and the historical power conversion power of the power conversion cabinets, namely, the association between the scheduling policies of the available batteries of the plurality of power conversion cabinets and the total charging cost of the plurality of power conversion cabinets is built, and then the target policy information corresponding to the lowest total charging cost is obtained through solving and calculating, so that the expenditure of electricity charge in the operation process of the power conversion cabinets can be reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are needed in the description of the embodiments of the present disclosure will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
Fig. 1 is a flowchart of a policy information generating method provided in an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a power conversion behavior of a power conversion cabinet according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a policy information generating device according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
The following description of the technical solutions in the embodiments of the present disclosure will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
An embodiment of the present disclosure provides a policy information generating method, as shown in fig. 1, including:
and 101, acquiring a plurality of historical power conversion information corresponding to the plurality of power conversion cabinets one by one.
The plurality of historical power change information comprises historical power change times and historical power change power corresponding to the power change cabinet.
It should be noted that the power conversion cabinet disclosed by the disclosure is specifically a power conversion device which is connected to a virtual power plant and meets preset conditions, wherein the preset conditions include: the power conversion equipment is directly powered by mains supply, and the power conversion equipment is controllable by the virtual power plant;
the controllable power conversion device of the virtual power plant can be understood as follows: the virtual power plant can realize the regulation and control of the available battery quantity, the available battery capacity, whether the electric power supply is connected to the electric power supply for charging and other data of the electric power exchange equipment through an open control interface of the electric power exchange equipment.
The above-mentioned power changing device is understood to be a device provided with at least one charging compartment, wherein,
the battery can be placed in the charging cabin, and when the placed battery meets the charging condition, the battery can be charged through a charging circuit arranged in the charging cabin;
And when the battery placed in the charging cabin meets the taking condition, a user can take out the battery from the charging cabin.
The historical power change times at least comprise times of successful power change of the power change cabinet in each historical period and times of delayed power change, the successful power change behavior indicates a behavior that a user takes out a battery from the power change cabinet in the corresponding historical period, and the delayed power change behavior indicates a behavior that the user initiates a power change request in the corresponding historical period but does not take out the battery from the power change cabinet.
The historical battery change power comprises load power of the battery change cabinet for battery charging in each historical period.
And 102, constructing a strategy model according to the plurality of historical information exchange messages.
The model input of the strategy model is a scheduling strategy, the scheduling strategy comprises target parameters of each of the plurality of battery change cabinets, the target parameters comprise a first sub-parameter and a second sub-parameter, the first sub-parameter is used for indicating the minimum number of available batteries of the corresponding battery change cabinet, the second sub-parameter is used for indicating the minimum capacity of the available batteries of the corresponding battery change cabinet, and the model output of the strategy model is the total charging cost of the plurality of battery change cabinets.
Specifically, based on target parameters of the target power conversion cabinet, the minimum number and the minimum capacity of available batteries of the target power conversion cabinet in each period of the scheduling period can be determined, wherein the target power conversion cabinet is any power conversion cabinet in the plurality of power conversion cabinets.
Wherein the minimum number and minimum capacity of available batteries for the target battery closet at each time period in the scheduling cycle should be understood as: and the target power change cabinet is required to meet the power change requirement in each period of the scheduling period.
The charging setting of the target battery-changing cabinet can be as follows:
when the actual number of the available batteries of the target battery changing cabinet in a certain period is greater than or equal to the minimum number set in the period, any battery in the battery changing cabinet is not charged, wherein the battery with the residual battery capacity greater than the minimum capacity set in the period in the target battery changing cabinet is the available battery of the target battery changing cabinet in the period;
when the actual number of the available batteries of the target battery-changing cabinet in a certain period is smaller than the set minimum number in the period, the target battery-changing cabinet is sequenced from large to small based on the residual capacity of the unavailable batteries (other batteries except the available batteries) in the target battery-changing cabinet, and the target battery-changing cabinet is charged in sequence according to the sequence numbers until the actual number of the available batteries of the target battery-changing cabinet in the period is equal to the set minimum number in the period, and the charging is correspondingly ended.
For example: if the battery 1, the battery 2, the battery 3 and the battery 4 are placed in the target battery changing cabinet, the residual capacity of the battery 1 is 80%, the residual capacity of the battery 2 is 80%, the residual capacity of the battery 3 is 20% and the residual capacity of the battery 4 is 50%;
when the minimum number of available batteries set by the target battery-changing cabinet in a certain period is 3 and the minimum capacity of the available batteries is 70%, the target battery-changing cabinet charges the battery 4 (more than the residual electric quantity of the battery 3) in the period, and the charging is finished when the residual electric quantity of the battery 4 is recovered to 70%, so as to meet the battery-changing requirement of the target battery-changing cabinet in the period.
In application, the virtual power plant can acquire cost information which is issued by the power grid and used for indicating the electricity price of each period in the scheduling period, can calculate the power to be charged and supplemented and the corresponding charging cost of each period in the scheduling period according to the scheduling strategy and the cost information, and can obtain the model output corresponding to the scheduling strategy by calculating the charging cost corresponding to each period in the scheduling period through summation.
And 103, solving and calculating the strategy model to obtain target strategy information.
And the charging total cost corresponding to the target strategy information is the minimum value of a plurality of charging total costs output by the strategy model.
In the embodiment, a policy model is built through the historical power conversion times and the historical power conversion power of the power conversion cabinets, namely, the association between the scheduling policies of the available batteries of the plurality of power conversion cabinets and the total charging cost of the plurality of power conversion cabinets is built, and then the corresponding target policy information when the total charging cost is the lowest is obtained through solving and calculating, so that the electricity charge expenditure in the operation process of the power conversion cabinets can be reduced.
In one embodiment, the constructing a policy model from the plurality of historical battery exchange information includes:
training an initial model according to the plurality of historical battery replacement information to obtain a demand model;
predicting the battery quantity requirement and the battery capacity requirement of each of the plurality of battery change cabinets in a target period according to the requirement model;
and constructing the strategy model according to the battery quantity requirement and the battery capacity requirement of each battery-changing cabinet in the plurality of battery-changing cabinets in a target period, wherein the minimum quantity indicated by the first subparameter is not less than the battery quantity indicated by the battery quantity requirement, and the minimum capacity indicated by the second subparameter is not less than the battery capacity indicated by the battery capacity requirement.
In the embodiment, the initial model is trained to obtain the demand model for predicting the power conversion requirement of each power conversion cabinet in the target period, so that the accuracy of the subsequently constructed strategy model is improved, and the calculated target strategy information is more accurate and reliable.
In an example, the minimum number indicated by the first sub-parameter may be equal to the number of batteries indicated by the battery number requirement, and the minimum capacity indicated by the second sub-parameter may be equal to the battery capacity indicated by the battery capacity requirement, so as to further reduce the total charging cost corresponding to the calculated target policy information.
In another example, the minimum number indicated by the first sub-parameter may be determined according to the number of batteries indicated by the battery number requirement and a preset number fluctuation value, and the minimum capacity indicated by the second sub-parameter may be determined according to the battery capacity indicated by the battery capacity requirement and a preset capacity fluctuation value, which may further improve the fault tolerance of the method in application, reduce the duration of the case that the available battery in the battery-changing cabinet is 0, and ensure the use experience of the user of the battery-changing cabinet.
The plurality of historical power transfer information may also include, for example, an address of each of the plurality of power transfer cabinets, a maximum number of charging cabins, a source of power (mains direct/mains supply), a maximum charging power, and the like.
In one embodiment, the training the initial model according to the plurality of historical battery replacement information to obtain the demand model includes:
Performing data preprocessing on the plurality of historical information exchange messages to obtain a target data set;
normalizing the target data set to obtain a training data set;
and training the initial model according to the training data set to obtain the demand model.
In the embodiment, abnormal data in a plurality of pieces of historical power conversion information are cleaned or repaired in a data preprocessing mode, so that the data reliability of a data set for initial model training is ensured; after that, the target data set is normalized, so that the power change data of the power change cabinets with different specifications and different models can be normalized and unified, the data reliability of the data set for initial model training is further improved, and the prediction performance of the demand model obtained through training is more accurate and reliable.
In one embodiment, the performing data preprocessing on the plurality of historical information for information of the historical information for obtaining a target data set includes:
performing data cleaning on the plurality of historical heat exchange messages to obtain a first data set;
performing feature transformation on the first data set to obtain a second data set;
and performing dimension reduction processing on the second data set to obtain the target data set.
In the embodiment, through sequentially executing data cleaning, feature transformation and dimension reduction processing, data interference caused by abnormal data can be avoided, and the reliability of a subsequent training process is improved; the data size of the training data can be simplified by eliminating invalid features, and the efficiency of the subsequent training process is improved.
By way of example, the above data cleaning process may be applied to identify null values, continuous constant values, abnormal step values, outliers, etc. in the historical battery information, and repair or delete the identified abnormal values.
The above feature transformation process may be performed by taking log, squaring, multiplying, adding, and the like, for example.
For example, principal component analysis (Principal Component Analysis, PCA) may be applied, or an embedding method may be used to implement the aforementioned dimension reduction process, thereby achieving the goal of rejecting invalid data.
In one embodiment, the target data set includes a battery usage amount and a battery usage capacity of the corresponding power cabinet in a first period, and a battery usage amount and a battery usage capacity of the corresponding power cabinet in a second period, wherein the first period and the second period are any two adjacent periods within a time period corresponding to the target data set, and the second period is located after the first period;
The normalizing process is performed on the target data set to obtain a training data set, including:
carrying out data fusion on the battery use quantity and the battery use capacity of the target battery change cabinet in the first period and the battery to-be-used quantity and the battery to-be-used capacity in the second period to obtain the battery change demand data of the target battery change cabinet, wherein the target battery change cabinet is any battery change cabinet in the plurality of battery change cabinets;
and carrying out per unit processing on the power conversion requirement data of the target power conversion cabinet to obtain a power conversion requirement per unit value of the target power conversion cabinet, wherein the training data set comprises the power conversion requirement per unit value of the target power conversion cabinet.
In this embodiment, through a data fusion manner, the number and the capacity of the batteries of the target battery-changing cabinet which are successfully changed in each period and the number and the capacity of the batteries of which the battery-changing is delayed in the period are synthesized, and normalization processing is performed through a per unit value calculation manner, so that a demand curve corresponding to a demand model obtained through training can more accurately reflect the actual battery-changing demand of the battery-changing cabinet in each period, and further the reliability of a policy model constructed later is improved.
It should be noted that the time period corresponding to the target data set may be understood as the aforementioned scheduling period.
Exemplary, as shown in FIG. 2, t is in three adjacent periods of t-1, t, t+1 1 、t 2 The starting time and the ending time of the t time period are respectively, and the starting time of each time period is the ending time of the last time periodTime; in fig. 2, the dashed lines (1) and (2) are each used to indicate the time span of a user who arrives at the power exchange station in the period t and succeeds in the power exchange, except that the dashed line (1) indicates that the start time of the power exchange behavior of the user is a certain time in the period t-1, and the dashed line (2) indicates that the start time of the power exchange behavior of the user is a certain time in the period before the period t-1, and in addition, the dashed line (3) is used to indicate the time span of a user who arrives at the power exchange station in the period t but delays the power exchange (such user can finish the power exchange at the earliest time in the period t+1). The electricity consumption requirement of each electricity changing cabinet in each period can be obtained by counting the electricity changing times and the electricity changing quantity corresponding to the broken lines (1), (2) and (3) in each period, and the electricity changing cabinet can be converted into one of the input characteristics of the input initial model through per unit value calculation.
As illustrated in fig. 2, the per unit processing of the power conversion requirement data of the target power conversion cabinet may be:
And taking the maximum electricity consumption requirement of the target electricity changing cabinet as the reference requirement of the target electricity changing cabinet, and dividing the electricity consumption requirement of the target electricity changing cabinet in each period by the reference requirement of the target electricity changing cabinet to obtain the per unit value of the user requirement of the target electricity changing cabinet in each period.
It should be noted that, in some embodiments, the super-parameter tuning of the initial model may be completed by using a bayesian tuning method, and the training of the initial model may be completed by using a lightweight gradient lifting machine learning (Light Gradient Boosting Machine, lightGBM).
In one embodiment, the model output of the policy model is calculated according to the electricity price of the target electricity changing cabinet in a target period and the electricity demand of the target period, wherein the target electricity changing cabinet is any one of the plurality of electricity changing cabinets, the target period is any period in a scheduling period corresponding to the plurality of electricity changing cabinets, and the electricity demand of the target electricity changing cabinet in the target period is determined according to the difference between the available battery number of the target electricity changing cabinet in the target period and the minimum number indicated by the first subparameter.
Illustratively, the functional representation of the policy model described above may be as shown in equation (1):
In the formula (1), B is the available cabin space (the cabin space which can be controlled by a virtual power plant and can normally take and put batteries) in the current period in the power exchange cabinet, and the total number is B; t is the peak clipping and valley filling self-scheduling time period number, and Deltat is the time period resolution (namely the time length spanned by each time period);charging price (unit: yuan/MWh) of the battery-changing cabinet in period t is +.>The power is charged for each bunk b (determined based on the difference between the remaining amount of battery in the bunk and the lowest capacity of the available battery).
In one embodiment, the solving the policy model to obtain the target policy information includes:
solving and calculating the strategy model based on preset constraint conditions to obtain target strategy information;
wherein the constraint includes at least one of:
a first sub-condition for constraining a maximum power change of a target power change cabinet, wherein the target power change cabinet is any power change cabinet of the plurality of power change cabinets;
a second sub-condition for constraining upper and lower limits of battery capacity of the target battery-change cabinet;
a third sub-condition for constraining a minimum number of available batteries of the target battery-change cabinet.
In the embodiment, through the setting of constraint conditions, the situation that the calculated scheduling strategy exceeds the actual scheduling capability of the battery-changing cabinet when the strategy model performs optimal solution calculation is avoided, so that the reliability of target strategy information obtained through final calculation is ensured.
Illustratively, the first sub-condition may be as shown in equation (2):
in the formula (2), the amino acid sequence of the compound,the charging state of the available cabin bit b in the battery changing cabinet in the period t is set to be 1 during charging, otherwise, the charging state is set to be 0; />An upper power limit is charged for the single pod.
The second sub-condition may be as shown in formula (3):
in the formula (3), the amino acid sequence of the compound,for charging efficiency of available bunk b in battery-changing cabinet E b，t For the battery energy status of the available bunk b in the battery change cabinet during time period t,E b 、/>the upper and lower limits of the battery energy of the available cabin b in the battery changing cabinet.
The third sub-condition described above may be as shown in equations (4) and (5):
E threshhold -E b，t ≤(1-i b，t )·M (4)
in the formula (4), E threshold Single cabin borrowable power threshold, i, set for battery change cabinet operator b，t In order to obtain a borrowable state of the available cabin b in the battery changing cabinet in a period t, if the borrowable electric quantity threshold is met, i b，t =1, m is the maximum value.
In the formula (5), the amino acid sequence of the compound,the minimum number of borrowable cabins in the period t is required for the power exchange cabinet.
For ease of understanding, examples are illustrated below:
by applying the method disclosed by the disclosure, the virtual power plant can calculate and obtain target strategy information, the target strategy information is issued to a plurality of power change cabinets which are connected to the virtual power plant and can be controlled by the virtual power plant, the plurality of power change cabinets operate based on the charging strategy of the target strategy information, so that each power change cabinet is enabled to carry out full charge processing of all batteries (each battery of each power change cabinet is fully charged) in a power price valley period, each power change cabinet is enabled to carry out basic charge processing of part of the batteries in a power price peak period (each power change cabinet is enabled to only provide available batteries which can meet basic power change requirements and is not additionally charged), an orderly charging strategy is deployed on the basis of not influencing power change service, and energy price preference is obtained through peak clipping and valley filling, so that overall power charge is reduced.
For example, when the target battery-changing cabinet includes 2 batteries, the full battery charging can be performed in the electricity price valley period, so that the target battery-changing cabinet includes 2 batteries all charged to 100% of the electricity, and when the electricity price peak period, if the basic battery-changing requirement of the target battery-changing cabinet requires at least one battery with 70% of the electricity, the battery-changing cabinet does not charge when there is 100% of the electricity in 1 battery (one rechargeable battery is borrowed), and only when the remaining full battery is borrowed (when the remaining electricity of 2 batteries is less than 70% of the electricity), the battery-changing cabinet charges one of the battery-lacking batteries, and charges the battery with more remaining electricity preferentially, and charges only to 70% of the electricity, that is, the charging is ended.
Referring to fig. 3, fig. 3 is a policy information generating device provided in an embodiment of the present disclosure, and as shown in fig. 3, the policy information generating device 300 includes:
the obtaining module 301 is configured to obtain a plurality of historical power conversion information corresponding to a plurality of power conversion cabinets one to one, where the plurality of historical power conversion information includes a historical power conversion number and a historical power conversion power corresponding to the power conversion cabinet;
a modeling module 302, configured to construct a policy model according to the plurality of historical battery packs, where a model input of the policy model is a scheduling policy, the scheduling policy includes a target parameter of each battery pack of the plurality of battery packs, the target parameter includes a first sub-parameter for indicating a minimum number of available batteries for the corresponding battery pack and a second sub-parameter for indicating a minimum capacity of available batteries for the corresponding battery pack, and a model output of the policy model is a total cost of charging of the plurality of battery packs;
And the calculating module 303 is configured to perform solution calculation on the policy model to obtain target policy information, where the total charging cost corresponding to the target policy information is a minimum value among a plurality of total charging costs output by the policy model.
In one embodiment, the modeling module 302 includes:
the training sub-module is used for training the initial model according to the plurality of historical battery replacement information to obtain a demand model;
the prediction sub-module is used for predicting the battery quantity requirement and the battery capacity requirement of each battery exchange cabinet in the plurality of battery exchange cabinets in a target period according to the requirement model;
and the modeling module is used for building the strategy model according to the battery number requirement and the battery capacity requirement of each battery-changing cabinet in the plurality of battery-changing cabinets in the target period, wherein the minimum number indicated by the first subparameter is not less than the battery number indicated by the battery number requirement, and the minimum capacity indicated by the second subparameter is not less than the battery capacity indicated by the battery capacity requirement.
In one embodiment, the training sub-module comprises:
the preprocessing unit is used for preprocessing the data of the plurality of historical information exchange messages to obtain a target data set;
The normalization unit is used for performing normalization processing on the target data set to obtain a training data set;
and the training unit is used for training the initial model according to the training data set to obtain the demand model.
In one embodiment, the preprocessing unit is specifically configured to:
performing data cleaning on the plurality of historical heat exchange messages to obtain a first data set;
performing feature transformation on the first data set to obtain a second data set;
and performing dimension reduction processing on the second data set to obtain the target data set.
In one embodiment, the target data set includes a battery usage amount and a battery usage capacity of the corresponding power cabinet in a first period, and a battery usage amount and a battery usage capacity of the corresponding power cabinet in a second period, wherein the first period and the second period are any two adjacent periods within a time period corresponding to the target data set, and the second period is located after the first period;
the normalization unit is specifically configured to:
carrying out data fusion on the battery use quantity and the battery use capacity of the target battery change cabinet in the first period and the battery to-be-used quantity and the battery to-be-used capacity in the second period to obtain the battery change demand data of the target battery change cabinet, wherein the target battery change cabinet is any battery change cabinet in the plurality of battery change cabinets;
And carrying out per unit processing on the power conversion requirement data of the target power conversion cabinet to obtain a power conversion requirement per unit value of the target power conversion cabinet, wherein the training data set comprises the power conversion requirement per unit value of the target power conversion cabinet.
In one embodiment, the calculating module 303 is specifically configured to:
solving and calculating the strategy model based on preset constraint conditions to obtain target strategy information;
wherein the constraint includes at least one of:
a first sub-condition for constraining a maximum power change of a target power change cabinet, wherein the target power change cabinet is any power change cabinet of the plurality of power change cabinets;
a second sub-condition for constraining upper and lower limits of battery capacity of the target battery-change cabinet;
a third sub-condition for constraining a minimum number of available batteries of the target battery-change cabinet.
In one embodiment, the model output of the policy model is calculated according to the electricity price of the target electricity changing cabinet in a target period and the electricity demand of the target period, wherein the target electricity changing cabinet is any one of the plurality of electricity changing cabinets, the target period is any period in a scheduling period corresponding to the plurality of electricity changing cabinets, and the electricity demand of the target electricity changing cabinet in the target period is determined according to the difference between the available battery number of the target electricity changing cabinet in the target period and the minimum number indicated by the first subparameter.
The policy information generating device 300 provided in the embodiments of the present disclosure can implement each process in the embodiments of the method, and in order to avoid repetition, a detailed description is omitted here.
According to an embodiment of the disclosure, the disclosure further provides an electronic device, a readable storage medium.
Fig. 4 illustrates a schematic block diagram of an example electronic device 400 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 4, the apparatus 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a Read-Only Memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access Memory (Random Access Memory, RAM) 403. In RAM 403, various programs and data required for the operation of device 400 may also be stored. The computing unit 401, ROM 402, and RAM 403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Various components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, etc.; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408, such as a magnetic disk, optical disk, etc.; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 401 may be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 401 include, but are not limited to, a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphic Process Unit, GPU), various dedicated artificial intelligence (Artificial Intelligence, AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (Digital Signal Processing, DSP), and any suitable processors, controllers, microcontrollers, etc. The computing unit 401 performs the respective methods and processes described above, for example, a policy information generating method. For example, in some embodiments, the policy information generation method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 400 via the ROM 402 and/or the communication unit 409. When the computer program is loaded into RAM 403 and executed by computing unit 401, one or more steps of the policy information generating method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the policy information generating method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated circuitry, field programmable gate arrays (Field-Programmable Gate Array, FPGA), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), application specific standard products (Application Specific Standard Product, ASSP), system On Chip (SOC), complex programmable logic devices (Complex Programmable Logic Device, CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. A policy information generating method, the method comprising:
acquiring a plurality of historical power conversion information corresponding to a plurality of power conversion cabinets one by one, wherein the plurality of historical power conversion information comprises historical power conversion times and historical power conversion power corresponding to the power conversion cabinets;
constructing a strategy model according to the historical power exchange information, wherein the model input of the strategy model is a scheduling strategy, the scheduling strategy comprises target parameters of each power exchange cabinet in the plurality of power exchange cabinets, the target parameters comprise a first subparameter and a second subparameter, the first subparameter is used for indicating the lowest number of available batteries of the corresponding power exchange cabinet, the second subparameter is used for indicating the lowest capacity of the available batteries of the corresponding power exchange cabinet, and the model output of the strategy model is the total charging cost of the plurality of power exchange cabinets;
And solving and calculating the strategy model to obtain target strategy information, wherein the charging total cost corresponding to the target strategy information is the minimum value of a plurality of charging total costs output by the strategy model.
2. The method of claim 1, wherein said constructing a policy model from said plurality of historical battery change information comprises:
training an initial model according to the plurality of historical battery replacement information to obtain a demand model;
predicting the battery quantity requirement and the battery capacity requirement of each of the plurality of battery change cabinets in a target period according to the requirement model;
and constructing the strategy model according to the battery quantity requirement and the battery capacity requirement of each battery-changing cabinet in the plurality of battery-changing cabinets in a target period, wherein the minimum quantity indicated by the first subparameter is not less than the battery quantity indicated by the battery quantity requirement, and the minimum capacity indicated by the second subparameter is not less than the battery capacity indicated by the battery capacity requirement.
3. The method of claim 2, wherein training the initial model based on the plurality of historical battery change information to obtain the demand model comprises:
Performing data preprocessing on the plurality of historical information exchange messages to obtain a target data set;
normalizing the target data set to obtain a training data set;
and training the initial model according to the training data set to obtain the demand model.
4. The method of claim 3, wherein the performing data preprocessing on the plurality of historical information to obtain the target data set includes:
performing data cleaning on the plurality of historical heat exchange messages to obtain a first data set;
performing feature transformation on the first data set to obtain a second data set;
and performing dimension reduction processing on the second data set to obtain the target data set.
5. The method of claim 3, wherein the target data set includes a number of battery uses and a battery usage capacity for the corresponding battery cabinet in a first period of time and a number of battery uses and a battery usage capacity for the corresponding battery cabinet in a second period of time, wherein the first period of time and the second period of time are any two adjacent periods of time within a time period corresponding to the target data set, and the second period of time is subsequent to the first period of time;
The normalizing process is performed on the target data set to obtain a training data set, including:
carrying out data fusion on the battery use quantity and the battery use capacity of the target battery change cabinet in the first period and the battery to-be-used quantity and the battery to-be-used capacity in the second period to obtain the battery change demand data of the target battery change cabinet, wherein the target battery change cabinet is any battery change cabinet in the plurality of battery change cabinets;
and carrying out per unit processing on the power conversion requirement data of the target power conversion cabinet to obtain a power conversion requirement per unit value of the target power conversion cabinet, wherein the training data set comprises the power conversion requirement per unit value of the target power conversion cabinet.
6. The method of claim 1, wherein the solving the policy model to obtain target policy information comprises:
solving and calculating the strategy model based on preset constraint conditions to obtain target strategy information;
wherein the constraint includes at least one of:
a first sub-condition for constraining a maximum power change of a target power change cabinet, wherein the target power change cabinet is any power change cabinet of the plurality of power change cabinets;
A second sub-condition for constraining upper and lower limits of battery capacity of the target battery-change cabinet;
a third sub-condition for constraining a minimum number of available batteries of the target battery-change cabinet.
7. The method of claim 1, wherein the model output of the policy model is calculated according to a power consumption price of a target power conversion cabinet in a target period and a power consumption requirement of the target period, wherein the target power conversion cabinet is any power conversion cabinet in the plurality of power conversion cabinets, the target period is any period in a scheduling period corresponding to the plurality of power conversion cabinets, and the power consumption requirement of the target power conversion cabinet in the target period is determined according to a difference between an available battery number of the target power conversion cabinet in the target period and a minimum number indicated by the first subparameter.
8. A policy information generating apparatus, the apparatus comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a plurality of pieces of historical power conversion information corresponding to a plurality of power conversion cabinets one by one, and the plurality of pieces of historical power conversion information comprise historical power conversion times and historical power conversion power corresponding to the power conversion cabinets;
the modeling module is used for constructing a strategy model according to the historical power change information, wherein the model input of the strategy model is a scheduling strategy, the scheduling strategy comprises target parameters of each power change cabinet in the power change cabinets, the target parameters comprise a first subparameter and a second subparameter, the first subparameter is used for indicating the lowest number of available batteries of the corresponding power change cabinet, the second subparameter is used for indicating the lowest capacity of the available batteries of the corresponding power change cabinet, and the model output of the strategy model is the total charging cost of the power change cabinets;
And the calculation module is used for solving and calculating the strategy model to obtain target strategy information, wherein the charging total cost corresponding to the target strategy information is the minimum value of a plurality of charging total costs output by the strategy model.
9. An electronic device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, which when executed by the processor performs the steps of the method according to any one of claims 1 to 7.
10. A readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the steps of the method according to any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310926605.2A CN116862192A (en) | 2023-07-26 | 2023-07-26 | Policy information generation method and device and related equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310926605.2A CN116862192A (en) | 2023-07-26 | 2023-07-26 | Policy information generation method and device and related equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116862192A true CN116862192A (en) | 2023-10-10 |
Family
ID=88218998
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310926605.2A Pending CN116862192A (en) | 2023-07-26 | 2023-07-26 | Policy information generation method and device and related equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116862192A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113452056A (en) * | 2021-07-26 | 2021-09-28 | 北京市腾河智慧能源科技有限公司 | Charging control method and system of battery exchange cabinet, equipment and storage medium |
| CN113459871A (en) * | 2021-05-21 | 2021-10-01 | 蓝谷智慧(北京)能源科技有限公司 | Battery charging method and device for battery replacement station, storage medium and electronic equipment |
| CN114066198A (en) * | 2021-11-09 | 2022-02-18 | 上海有个机器人有限公司 | Robot cluster scheduling method, system, electronic device, medium, and program product |
| CN114448044A (en) * | 2022-01-28 | 2022-05-06 | 山东大学 | Bidirectional quick-charging ordered charging and discharging method and system for power changing station |
| CN114954105A (en) * | 2022-04-06 | 2022-08-30 | 北京胜能能源科技有限公司 | Battery replacement method and device, electronic equipment and storage medium |
| US20230162087A1 (en) * | 2021-11-19 | 2023-05-25 | Beijing Baidu Netcom Science Technology Co., Ltd. | Federated learning method, electronic device, and storage medium |
-
2023
- 2023-07-26 CN CN202310926605.2A patent/CN116862192A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113459871A (en) * | 2021-05-21 | 2021-10-01 | 蓝谷智慧(北京)能源科技有限公司 | Battery charging method and device for battery replacement station, storage medium and electronic equipment |
| CN113452056A (en) * | 2021-07-26 | 2021-09-28 | 北京市腾河智慧能源科技有限公司 | Charging control method and system of battery exchange cabinet, equipment and storage medium |
| CN114066198A (en) * | 2021-11-09 | 2022-02-18 | 上海有个机器人有限公司 | Robot cluster scheduling method, system, electronic device, medium, and program product |
| US20230162087A1 (en) * | 2021-11-19 | 2023-05-25 | Beijing Baidu Netcom Science Technology Co., Ltd. | Federated learning method, electronic device, and storage medium |
| CN114448044A (en) * | 2022-01-28 | 2022-05-06 | 山东大学 | Bidirectional quick-charging ordered charging and discharging method and system for power changing station |
| CN114954105A (en) * | 2022-04-06 | 2022-08-30 | 北京胜能能源科技有限公司 | Battery replacement method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10205317B2 (en) | Management of grid-scale energy storage systems for multiple services | |
| US9196010B2 (en) | Resource cost optimization system, method, and program | |
| CN110266031B (en) | Energy storage grid-connected charging and discharging capacity control method and device, server and storage medium | |
| JP6168060B2 (en) | Power management method, power management apparatus and program | |
| CN116911076B (en) | Toughness support simulation method and device for power distribution network by multiple micro-grids and electronic equipment | |
| CN115513984A (en) | Method and device for determining day-ahead charging and discharging power of energy storage system and storage medium | |
| CN116862192A (en) | Policy information generation method and device and related equipment | |
| CN115742781A (en) | Battery charging method and device, electronic equipment and storage medium thereof | |
| CN110175728A (en) | Electric automobile charging station dispatching device based on gradient projection involution form interior point method | |
| CN115347586A (en) | New energy station energy storage optimal configuration system and method based on multi-constraint multi-objective optimization | |
| CN112803427A (en) | Regional power grid dispatching method, system, terminal and storage medium based on demand | |
| CN116961188B (en) | Scheduling method and device of base station power supply, electronic equipment and readable storage medium | |
| CN113555887B (en) | Power grid energy control method and device, electronic equipment and storage medium | |
| CN116961188A (en) | Scheduling method and device of base station power supply, electronic equipment and readable storage medium | |
| CN117767289A (en) | Determination method and device for micro-grid energy scheduling, electronic equipment and storage medium | |
| CN106529989A (en) | Server adaptive pricing strategy for green data center | |
| CN117650551A (en) | New energy source outgoing energy storage capacity accumulation type configuration method, device, equipment and medium | |
| CN117767374A (en) | New energy source outgoing energy storage capacity decreasing configuration method, device, equipment and medium | |
| CN116544918A (en) | Scheduling method, device, equipment and storage medium | |
| CN116845907A (en) | Micro-grid source load scheduling method, micro-grid source load scheduling system, electronic equipment and medium | |
| CN115733160A (en) | Energy storage system planning curve optimization method and device, storage medium and electronic equipment | |
| CN115833104A (en) | Power distribution network control method and device under uncertain factors | |
| CN117217495A (en) | Energy scheduling method and device, electronic equipment and storage medium | |
| CN117559498A (en) | Rural energy station regulation and control method, device, equipment and storage medium | |
| CN115714379A (en) | Electricity price and carbon price related index prediction method and device based on electricity and hydrogen energy storage coordination |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |