CN116799838A - On-line energy storage charge and discharge control method and medium for demand prediction and electronic equipment - Google Patents
On-line energy storage charge and discharge control method and medium for demand prediction and electronic equipment Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0068—Battery or charger load switching, e.g. concurrent charging and load supply
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
The application provides an online energy storage charge and discharge control method for demand prediction, a medium and electronic equipment, wherein the method comprises the following steps: collecting electricity consumption data of a user, and obtaining electricity consumption power and corresponding electricity price; obtaining the maximum charging power and the maximum discharging power of an energy storage battery; configuring energy of an energy storage battery, and starting and ending energy storage state of charge values; constructing a mixed integer linear programming model and constraint conditions for optimizing the charge and discharge power of the energy storage battery; and constructing an online real-time charge-discharge decision optimization model based on the charge power, the discharge power and the predicted demand of the energy storage battery, and controlling the charge and discharge of the energy storage battery. According to the application, the maximum demand is predicted through the mixed integer linear programming model, the actual energy storage state and the electricity consumption requirement are combined, and then the real-time decision charge-discharge control strategy is decided through the online real-time charge-discharge decision optimization model, so that the technical problem that the energy storage capacity and the demand cannot be effectively controlled in the prior art is effectively solved.
Description
Technical Field
The application relates to the technical field of storage and charging control, in particular to the technical field of storage and charging configuration methods and models.
Background
The prior patent: the patent publication number is CN110148957A, the application discloses a demand control method, device and system based on an energy storage system. The demand control method comprises the steps of firstly obtaining the load real-time demand of a target user, the load real-time power and the state of charge of the energy storage system, and controlling the energy storage system, so that the demand reduction effect is improved. When the method needs to set a first preset demand value and a second preset demand value, but errors may exist in the set two preset values, and errors may exist in the stored energy control charging and discharging power.
The prior patent: the patent publication No. CN111898805A, the patent name is a capacity configuration method, device, configuration equipment and storage medium of an energy storage system, and the method, device, configuration equipment and storage medium are used for providing electric charge records and load information under the condition of executing two electric charge manufacturing processes, taking the electric quantity data and the load information as set objective functions of annual total cost of energy storage participation demand management, and providing secondary planning solution for the objective functions of annual total cost of energy storage participation demand management. However, the model built by the patent is a quadratic programming algorithm and is relatively complex, and meanwhile, the data required by the patent is historical electricity consumption data of the past year, so that the time is too long in actual energy storage capacity configuration.
Therefore, how to manage the energy demand is a technical problem to be solved by those skilled in the art, aiming at the problem that there is a constraint between the energy storage capacity and the maximum demand.
Disclosure of Invention
In view of the above drawbacks of the prior art, the present application aims to provide an online energy storage charging and discharging control method, medium and electronic device for demand prediction, which are used for solving the technical problem that the energy storage capacity and the demand cannot be effectively controlled in the prior art.
To achieve the above and other related objects, the present application provides an online energy storage charge and discharge control method for demand prediction, the method comprising: collecting electricity consumption data of a user, and acquiring electricity consumption power and corresponding electricity price based on the electricity consumption data; obtaining the maximum charging power and the maximum discharging power of an energy storage battery; configuring the energy of the energy storage battery, and initializing and ending an energy storage state of charge value; constructing a mixed integer linear programming model for optimizing the charge and discharge power of the energy storage battery based on the power consumption and the corresponding electricity price, and constructing constraint conditions for the mixed integer linear programming model based on the maximum charge power and the maximum discharge power of the energy storage battery, the energy of the energy storage battery, and the initial energy storage state of charge value and the final energy storage state of charge value; and calculating the charging power and the discharging power of the energy storage battery based on the mixed integer linear programming model, predicting the required quantity, constructing an online real-time charging and discharging decision optimization model based on the charging power and the discharging power of the energy storage battery and the predicted required quantity, and controlling the charging and discharging of the energy storage battery.
In an embodiment of the present application, one expression mode of the mixed integer linear programming model is as follows:
where T represents a period after equally dividing the history period T, N represents the number of periods,indicating the load of the t period except the energy storage battery, < >>Indicating the charging power of the energy storage battery in the t-th period, < >>Represents the discharge power of the energy storage battery in the t period, C t Time-of-use electricity rates representing the T-th period, Δt representing the time of each of the N periods in the history period T, Δt=t/N representing the electricity charge per unit of electricity demand, P M Representing the maximum demand over the historical time period.
In an embodiment of the application, the constraint includes any one or more of the following combinations:
SOC 1 ＝SOC Start ；
SOC N ＝SOC End ；
wherein ,indicating the charging power of the energy storage battery in the t-th period, < >>Represents the discharge power of the energy storage battery in the t period, P C,Max Represents the maximum charging power of the energy storage battery, P D,Max Indicating the maximum discharge power of the energy storage battery, x t and yt Shaping variable of 0-1, SOC t-1 State of charge value, SOC, of energy storage battery representing the t-1 th period t State of charge value of energy storage battery representing the t-th period, S min Representing the minimum state of charge value of the energy storage battery, S max Representing the maximum state of charge value, SOC, of an energy storage battery 1 State of charge value, SOC, of the energy storage battery at t=1 Start Representing initial stored state of charge value, SOC N Representing the state of charge value, SOC, of the energy storage battery at t=n End Represents the end stored state of charge value, E represents the energy of the energy storage battery, +.>Representing the power supplied by the grid, P M Represents the maximum capacity of the transformer, u represents the maximum capacity threshold multiple that the transformer can carry, +.>Representing the total power value recorded at intervals over the past period, P M For the total power in the periodMaximum value of>Lower limit value representing maximum demand, +.>The upper limit value of the maximum demand is indicated.
In an embodiment of the present application, an expression form of the online real-time charge-discharge decision optimization model is:
wherein ,xt+1 Expressed as decision variables, pi t Expressed as a policy function, SOC t Representing state of charge value, C, of the energy storage battery during the t-th period t The time-of-use electricity price of the t-th period is represented,indicating the load of the t period except the energy storage battery, < >>Expressed as the predicted optimal maximum demand.
In one embodiment of the present application, the decision variable x t+1 Represented as a charging strategy; one expression of the objective function of the online real-time charge-discharge decision optimization model is as follows:
constraint conditions of the online real-time charge-discharge decision optimization model are as follows:
SOC min ≤SOC t ≤SOC max ；
SOC min ≤SOC t+1 ≤SOC max ；
wherein ,SOCt+1 Representing the state of charge value, P, of the energy storage battery in the t+1th period C,Max Represents the maximum power of charging, P D,Max Indicating the maximum power of the discharge.
In one embodiment of the present application, the decision variable x t+1 Represented as a discharge strategy; one expression of the objective function of the online real-time charge-discharge decision optimization model is as follows:
constraint conditions of the online real-time charge-discharge decision optimization model are as follows:
SOC min ≤SOC t ≤SOC max ；
SOC min ≤SOC t+1 ≤SOC max ；
in one embodiment of the present application, the decision variable x t+1 Represented as an instantaneous load tracking strategy; one expression of the objective function of the online real-time charge-discharge decision optimization model is as follows:
constraint conditions of the online real-time charge-discharge decision optimization model are as follows:
SOC min ≤SOC t ≤SOC max ；
SOC min ≤SOC t+1 ≤SOC max ；
in one embodiment of the present application, the decision variable x t+1 Expressed as a do not fill and do not put strategy: in the price reduction stage, one expression of the objective function of the online real-time charge-discharge decision optimization model is as follows:
constraint conditions of the online real-time charge-discharge decision optimization model are as follows:
to achieve the above and other related objects, the present application also provides a storage medium storing program instructions which, when executed, implement the steps of the online energy storage charge-discharge control method for demand prediction as described above.
To achieve the above and other related objects, the present application also provides an electronic device including a memory for storing a computer program; a processor for running the computer program to implement the steps of the online energy storage charge and discharge control method for demand prediction as described above.
As described above, the online energy storage charge and discharge control method, medium and electronic equipment for demand prediction have the following beneficial effects:
according to the application, the maximum demand is predicted through the mixed integer linear programming model, the actual energy storage state and the electricity consumption requirement are combined, and then the real-time decision charge-discharge control strategy is decided through the online real-time charge-discharge decision optimization model, so that the technical problem that the energy storage capacity and the demand cannot be effectively controlled in the prior art is effectively solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an online energy storage charging and discharging control method for demand prediction according to an embodiment of the application;
FIG. 2 is a block diagram showing the input and output of a mixed integer linear programming model in an on-line energy storage charge-discharge control method for demand prediction according to an embodiment of the present application;
FIG. 3 is a graph showing the net load power in an example of an on-line energy storage charge-discharge control method for demand prediction in accordance with one embodiment of the present application;
FIG. 4 is a block diagram of an online real-time charge-discharge decision optimization model in an online energy storage charge-discharge control method for demand prediction according to an embodiment of the present application;
FIG. 5 is a control logic diagram of on-line optimization of an on-line energy storage charge and discharge control method for demand prediction in accordance with an embodiment of the present application;
FIG. 6 is a graph showing load power after optimization in an example of an on-line energy storage charge-discharge control method for demand prediction in accordance with an embodiment of the present application;
FIG. 7 is a schematic diagram showing the results of online optimization of an online energy storage charge/discharge control method for demand prediction according to an embodiment of the present application;
fig. 8 shows a functional block diagram of an electronic device in an embodiment of the application.
Description of element reference numerals
101. Electronic equipment
1001. Processor and method for controlling the same
1002. Memory device
S100 to S500 steps
Detailed Description
Other advantages and effects of the present application will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present application with reference to specific examples. The application may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
The embodiment aims to provide an online energy storage charge and discharge control method, medium and electronic equipment for demand prediction, which are used for solving the technical problem that the energy storage capacity and the demand cannot be effectively controlled in the prior art.
The online energy storage charging and discharging control method for demand prediction in the embodiment relates to the technical field of building energy storage and charging control, and is applied to a micro-grid system forming a system power supply, equipment power consumption and an energy storage device.
The online energy storage charge and discharge control method for demand prediction provides an optimal energy storage configuration method and a maximum demand prediction model, and the method of predicting first and then deciding in real time is provided by predicting the maximum demand for a period of time in the future first, combining the actual energy storage state and the electricity demand, and deciding in real time the charge and discharge control strategy.
The principle and the implementation of the on-line energy storage charge and discharge control method, medium and electronic device for demand prediction of the present application will be described in detail below, so that those skilled in the art can understand the on-line energy storage charge and discharge control method, medium and electronic device for demand prediction without creative labor.
The embodiment provides an online energy storage charging and discharging control method for demand prediction. Specifically, as shown in fig. 1, the online energy storage charging and discharging control method for demand prediction in the present embodiment includes the following steps S100 to S500.
Step S100, collecting electricity consumption data of a user, and obtaining electricity consumption power and corresponding electricity price based on the electricity consumption data;
step S200, obtaining the maximum charging power and the maximum discharging power of the energy storage battery;
step S300, configuring energy of the energy storage battery, and initializing and ending an energy storage state of charge value;
step S400, constructing a mixed integer linear programming model for optimizing the charge and discharge power of the energy storage battery based on the power consumption and the corresponding electricity price, and constructing constraint conditions for the mixed integer linear programming model based on the maximum charge power and the maximum discharge power of the energy storage battery, the energy of the energy storage battery, and the initial energy storage state of charge value and the final energy storage state of charge value;
and S500, calculating the charge power and the discharge power of the energy storage battery based on the mixed integer linear programming model, predicting the demand, constructing an online real-time charge-discharge decision optimization model based on the charge power and the discharge power of the energy storage battery and the predicted demand, and controlling the charge and discharge of the energy storage battery.
The following describes in detail steps S100 to S400 of the online energy storage charge and discharge control method for demand prediction of the present embodiment.
And step S100, collecting electricity consumption data of a user, and acquiring electricity consumption power and corresponding electricity price based on the electricity consumption data.
In this embodiment, for example, two months of electricity consumption data of a user are collected, the corresponding relationship between the electricity consumption power of the user and time is obtained according to the collected electricity consumption data of the user, and the corresponding electricity price is obtained by inquiry.
Step S200, obtaining the maximum charging power and the maximum discharging power of the energy storage battery.
Step S300, configuring energy of the energy storage battery, initiating an energy storage state of charge value and ending the energy storage state of charge value.
And step S400, constructing a mixed integer linear programming model for optimizing the charge and discharge power of the energy storage battery based on the power consumption and the corresponding electricity price, and constructing constraint conditions for the mixed integer linear programming model based on the maximum charge power and the maximum discharge power of the energy storage battery, and the energy of the energy storage battery, wherein the initial energy storage state of charge value and the final energy storage state of charge value.
In this embodiment, several different energy storage capacities may be simulated according to actual power demand, and the above mixed integer linear programming model is used to solve the optimal yield and the optimal required power.
In this embodiment, one expression mode of the mixed integer linear programming model is as follows:
where T represents a period after equally dividing a history period T (for example, 31 days of one month), N represents the number of periods,indicating the load of the t period except the energy storage battery, < >>Indicating the charging power of the energy storage battery in the t-th period, < >>Represents the discharge power of the energy storage battery in the t period, C t Time-of-use electricity rates representing the T-th period, Δt representing the time of each of N periods in the history period T, Δt=t/N representing the electricity charge per unit demand, for example Δt=15 min, t=31 days, then n=2976, p M Representing the maximum demand over the historical time period.
In this embodiment, the constraint includes any one or more of the following combinations:
SOC 1 ＝SOC Start ；
SOC N ＝SOC End ；
wherein ,indicating the charging power of the energy storage battery in the t-th period, < >>Represents the discharge power of the energy storage battery in the t period, P C,Max Represents the maximum charging power of the energy storage battery, P D,Max Indicating the maximum discharge power of the energy storage battery, x t and yt Shaping variable of 0-1, SOC t-1 State of charge value, SOC, of energy storage battery representing the t-1 th period t State of charge value of energy storage battery representing the t-th period, S min Representing the minimum state of charge value of the energy storage battery, S max Representing the maximum state of charge value, SOC, of an energy storage battery 1 State of charge value, SOC, of the energy storage battery at t=1 Start Representing initial stored state of charge value, SOC N Representing the state of charge value, SOC, of the energy storage battery at t=n End Represents the end stored state of charge value, E represents the energy of the energy storage battery, +.>Representing the power supplied by the grid, P M Represents the maximum capacity of the transformer, u represents the maximum capacity threshold multiple that the transformer can carry, +.>Representing the total power value recorded at intervals over the past period, P M For the total power in this period ∈>Maximum value of>Lower limit value representing maximum demand, +.>The upper limit value of the maximum demand is indicated.
And S500, calculating the charge power and the discharge power of the energy storage battery based on the mixed integer linear programming model, predicting the demand, constructing an online real-time charge-discharge decision optimization model based on the charge power and the discharge power of the energy storage battery and the predicted demand, and controlling the charge and discharge of the energy storage battery.
In the embodiment, on the basis of prediction, an online decision energy storage charging and discharging decision is built again.
In this embodiment, an expression form of the online real-time charge-discharge decision optimization model is as follows:
wherein ,xt+1 Expressed as decision variables, pi t Expressed as a policy function, SOC t Representing state of charge value, C, of the energy storage battery during the t-th period t The time-of-use electricity price of the t-th period is represented,indicating the load of the t period except the energy storage battery, < >>Expressed as the predicted optimal maximum demand. Policy function pi t Relying on past history data and predicted optimal maximum demand +.>x t+1 Is a decision variable, i.e. the charge or discharge power. The x is t+1 The decision variable is an online optimization control strategy and is divided into four categories, namely a charging decision, a discharging decision, a non-charging and non-discharging decision and an instantaneous load tracking decision. I.e. x t+1 Different objective functions and constraint conditions are provided for different scenes.
On the premise of meeting the requirement of not increasing the demand, the energy storage battery needs to be charged, and the optimization model of the problem is to calculate the maximum total powerAnd (3) the problem of chemical conversion. In the present embodiment, the decision variable x t+1 When the charge strategy is represented, one expression of the objective function of the online real-time charge-discharge decision optimization model is as follows:
constraint conditions of the online real-time charge-discharge decision optimization model are as follows:
SOC min ≤SOC t ≤SOC max ；
SOC min ≤SOC t+1 ≤SOC max ；
wherein ,SOCt+1 Representing the state of charge value, P, of the energy storage battery in the t+1th period C,Max Represents the maximum power of charging, P D,Max Indicating the maximum power of the discharge.
At the time of electricity consumption peak, in order to meet the electricity consumption requirement of a time user, the energy storage battery needs to be discharged, and an optimization model of the problem is to solve the problem of total power minimization. The objective function of the model is opposite to the charging strategy, and the constraint conditions are the same. In the present embodiment, the decision variable x t+1 Expressed as a discharge strategyOne expression of the objective function of the online real-time charge-discharge decision optimization model is as follows:
constraint conditions of the online real-time charge-discharge decision optimization model are as follows:
SOC min ≤SOC t ≤SOC max ；
SOC min ≤SOC t+1 ≤SOC max ；
at peak electricity consumption, in order to prevent the electricity consumption requirement from being too high, an optimization model of the problem is to solve a problem of total power minimization. In the present embodiment, the decision variable x t+1 When the model is expressed as an instantaneous load tracking strategy, one expression of the objective function of the online real-time charge-discharge decision optimization model is as follows:
constraint conditions of the online real-time charge-discharge decision optimization model are as follows:
SOC min ≤SOC t ≤SOC max ；
SOC min ≤SOC t+1 ≤SOC max ；
in the flat stage, neither charging nor discharging is required, in this embodiment, the decision variable x t+1 When the online real-time charging and discharging strategy is represented as a charging and discharging strategy, in the price reduction stage, one expression of the objective function of the online real-time charging and discharging decision optimization model is as follows:
constraint conditions of the online real-time charge-discharge decision optimization model are as follows:
therefore, the online energy storage charge and discharge control method for demand prediction in the embodiment can be used for calculating the maximum demand power value under the corresponding energy storage system and the optimal yield under the scene of calculating different energy storage capacities, so that the optimal energy storage capacity can be selected, and the maximum demand of the next month can be predicted. On the basis of 'prediction first', according to the predicted maximum demand, charging and discharging decisions are carried out on actual stored energy in real time on line, but the total cost is reduced, and better economic benefit is achieved.
In order for those skilled in the art to further understand the online energy storage charge and discharge control method for demand prediction of the present embodiment, the following describes an example of application of the present application.
In this embodiment, a micro-grid system is simulated through a custom scenario, where the scenario used is in a self-service building, forming a micro-grid for system power, equipment power and energy storage.
And (3) utilizing a daily load curve in three months, a time-of-use electricity price simulation optimization model and converting the cost into a daily value. The parameters are as follows:
1) The simulation time was 90 days, 24 hours per day, and the time interval was 15min.
2) The commercial and industrial time-of-use electricity prices in Shanghai are shown in Table 1.
TABLE 1 time-of-use electricity price
| Time period of | Electricity price (Yuan/kWh) |
| 0-7 hours | 0.316 |
| At 7-10 hours | 0.678 |
| 10-15 hours | 1.106 |
| 15-18 hours | 0.678 |
| 18-21 times | 1.106 |
| 21-24 hours | 0.678 |
The energy of the energy storage battery is set according to the actual situation, and the initial energy storage state of charge value and the final energy storage state of charge value are as follows in the parameters of table 2.
Table 2 parameter setting table
In the prediction stage, the block diagrams of the input and output of the mixed integer linear programming model are shown in fig. 2:
model input: based on historical load data of a month before the region. The real-time electricity price data comprises user load power data, and adopts industrial and commercial electricity prices, for example, the power data of 2976 moments are obtained every 15 minutes by comprehensively considering 31 days of the first month. In one specific example, the data is a graph of the payload power of a bright group for two months of a year, as shown in FIG. 3, showing the correspondence of the payload power to time, with the payload peak of the previous month reaching 1954kW.
Model output: according to 2 input data, namely the net load power data, real-time electricity price data and simultaneously considering the maximum required power value, the model finally outputs the charge and discharge power corresponding to 2976 times of the storage battery and an optimal month maximum required amount (namely the predicted maximum required amount P of the next month) M )。
In the real-time decision stage, the block diagram of the online real-time charge-discharge decision optimization model is shown in figure 4
Model input: optimal P predicted by last month M And the current time of day, the time of day electricity price and the payload power. Model output: charge-discharge power level at the next time.
The online optimization control strategy is divided into four types, namely intelligent charging, intelligent discharging, no charging and no discharging, and instantaneous load tracking, in the practical application scene, as shown in fig. 5, the time-of-use electricity price has three electricity price states of valley price, flat price and high price, and in order to reduce the real-time demand and pursue the economic benefit of energy storage, in general, intelligent charging and instantaneous load tracking should be performed in valley price stage; the charging and discharging should not be performed in the low price stage, the intelligent charging and instantaneous load tracking should be performed in the high price stage.
In order to describe a scheme of clear prediction and then on-line decision, it is assumed that when an energy storage system of 800kW is configured in a building in the place, a load power curve optimized by the mixed integer linear programming model is shown in fig. 6, and it can be seen from fig. 6 that the maximum required power value is 1764kW, so that economic benefits brought by maximum required power value and energy storage peak clipping and valley filling are reduced.
By utilizing the historical data of the previous month, the energy storage system can be reasonably selected according to the algorithm, the optimal demand of the next month can be predicted, and the online optimization control is required in the next month after the prediction.
In order to verify the feasibility of the algorithm, the embodiment uses data of the month of february as the prediction of the optimal required power, uses the data of the month of february as a test set, and controls the charge and discharge of the energy storage on line in real time. Assuming an energy storage system of 800kWh, the maximum demand in February is predicted according to the resultsThe result of the online energy storage charging and discharging decision control in february is shown in fig. 7.
As shown in fig. 8, the present embodiment provides an electronic apparatus 101, the electronic apparatus 101 including: a processor 1001 and a memory 1002; the memory 1002 is for storing a computer program; the processor 1001 is configured to execute a computer program stored in the memory 1002 to cause the electronic device 101 to execute the steps of the online energy storage charge and discharge control method for demand prediction as in embodiment 1. Since the specific implementation process of the steps of the online energy storage charge/discharge control method for demand prediction is described in embodiment 1, no further description is given here.
The processor 1001 is (Central Processing Unit ). The memory 1002 is connected to the processor 1001 through a system bus and performs communication with each other, the memory 1002 is configured to store a computer program, and the processor 1001 is configured to run the computer program, so that the processor 1001 performs the online energy storage charge and discharge control method for demand prediction. The memory 1002 may include a random access memory (Random Access Memory, simply referred to as RAM), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
In addition, the present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by the processor 1001, implements the steps in the above-described online energy storage charge and discharge control method for demand prediction. The above-mentioned online energy storage charging and discharging control method for demand prediction has been described in detail, and will not be described in detail herein.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by computer program related hardware. The aforementioned computer program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
In summary, the maximum demand is predicted through the mixed integer linear programming model, the actual energy storage state and the electricity consumption requirement are combined, and then the charge and discharge control strategy is decided in real time through the online real-time charge and discharge decision optimization model, so that the technical problem that the energy storage capacity and the demand cannot be effectively controlled in the prior art is effectively solved. Therefore, the application effectively overcomes various defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles of the present application and its effectiveness, and are not intended to limit the application. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the application. Accordingly, it is intended that all equivalent modifications and variations of the application be covered by the claims of this application, which are within the skill of those skilled in the art, be included within the spirit and scope of this application.
Claims (10)
1. An online energy storage charge-discharge control method for demand prediction is characterized in that: the method comprises the following steps:
collecting electricity consumption data of a user, and acquiring electricity consumption power and corresponding electricity price based on the electricity consumption data;
obtaining the maximum charging power and the maximum discharging power of an energy storage battery;
configuring the energy of the energy storage battery, and initializing and ending an energy storage state of charge value;
constructing a mixed integer linear programming model for optimizing the charge and discharge power of the energy storage battery based on the power consumption and the corresponding electricity price, and constructing constraint conditions for the mixed integer linear programming model based on the maximum charge power and the maximum discharge power of the energy storage battery, the energy of the energy storage battery, and the initial energy storage state of charge value and the final energy storage state of charge value;
and calculating the charging power and the discharging power of the energy storage battery based on the mixed integer linear programming model, predicting the required quantity, constructing an online real-time charging and discharging decision optimization model based on the charging power and the discharging power of the energy storage battery and the predicted required quantity, and controlling the charging and discharging of the energy storage battery.
2. The online energy storage charge and discharge control method for demand prediction according to claim 1, characterized in that: the expression mode of the mixed integer linear programming model is as follows:
where T represents a period after equally dividing the history period T, N represents the number of periods,indicating the load of the t period except the energy storage battery, < >>Indicating the charging power of the energy storage battery in the t-th period, < >>Represents the discharge power of the energy storage battery in the t period, C t Time-of-use electricity rates representing the T-th period, Δt representing the time of each of the N periods in the history period T, Δt=t/N representing the electricity charge per unit of electricity demand, P M Representing the maximum demand over the historical time period.
3. The on-line stored energy charge-discharge control method for demand prediction according to claim 1 or 2, characterized in that: the constraints include any one or a combination of the following:
SOC 1 ＝SOC Start ；
SOC N ＝SOC End ；
wherein ,indicating the charging power of the energy storage battery in the t-th period, < >>Represents the discharge power of the energy storage battery in the t period, P C,Max Represents the maximum charging power of the energy storage battery, P D,Max Indicating the maximum discharge power of the energy storage battery, x t and yt Shaping variable of 0-1, SOC t-1 State of charge value, SOC, of energy storage battery representing the t-1 th period t State of charge value of energy storage battery representing the t-th period, S min Representing the minimum state of charge value of the energy storage battery, S max Representing the maximum state of charge value, SOC, of an energy storage battery 1 State of charge value, SOC, of the energy storage battery at t=1 Start Representing initial stored state of charge value, SOC N Representing t=State of charge value, SOC, of the energy storage battery at N End Represents the end stored state of charge value, E represents the energy of the energy storage battery, +.>Representing the power supplied by the grid, P M Represents the maximum capacity of the transformer, u represents the maximum capacity threshold multiple that the transformer can carry, +.>Representing the total power value recorded at intervals over the past period, P M For the total power in this period ∈>Maximum value of>Lower limit value representing maximum demand, +.>The upper limit value of the maximum demand is indicated.
4. The online energy storage charge and discharge control method for demand prediction according to claim 3, wherein: the expression form of the online real-time charge-discharge decision optimization model is as follows:
wherein ,xt+1 Expressed as decision variables, pi t Expressed as a policy function, SOC t Representing state of charge value, C, of the energy storage battery during the t-th period t The time-of-use electricity price of the t-th period is represented,indicating the t period divided by the stored energyLoad outside the battery, ">Expressed as the predicted optimal maximum demand.
5. The on-line energy storage charge-discharge control method for demand prediction according to claim 4, characterized in that: decision variable x t+1 Represented as a charging strategy; one expression of the objective function of the online real-time charge-discharge decision optimization model is as follows:
constraint conditions of the online real-time charge-discharge decision optimization model are as follows:
SOC min ≤SOC t ≤SOC max ；
SOC min ≤SOC t+1 ≤SOC max ；
wherein ,SOCt+1 Representing the state of charge value, P, of the energy storage battery in the t+1th period C,Max Represents the maximum power of charging, P D,Max Indicating the maximum power of the discharge.
6. The on-line energy storage charge-discharge control method for demand prediction according to claim 5, characterized in that: decision variable x t+1 Represented as a discharge strategy; one expression of the objective function of the online real-time charge-discharge decision optimization model is as follows:
constraint conditions of the online real-time charge-discharge decision optimization model are as follows:
SOC min ≤SOC t ≤SOC max ；
SOC min ≤SOC t+1 ≤SOC max ；
7. the on-line energy storage charge-discharge control method for demand prediction according to claim 5, characterized in that: decision variable x t+1 Represented as an instantaneous load tracking strategy; one expression of the objective function of the online real-time charge-discharge decision optimization model is as follows:
constraint conditions of the online real-time charge-discharge decision optimization model are as follows:
SOC min ≤SOC t ≤SOC max ；
SOC min ≤SOC t+1 ≤SOC max ；
8. the on-line energy storage charging for demand prediction as in claim 2The discharge control method is characterized in that: decision variable x t+1 Expressed as a do not fill and do not put strategy: in the price reduction stage, one expression of the objective function of the online real-time charge-discharge decision optimization model is as follows:
constraint conditions of the online real-time charge-discharge decision optimization model are as follows:
9. a storage medium storing program instructions, characterized by: the program instructions, when executed, implement the steps of the on-line stored energy charge-discharge control method for demand prediction as claimed in any one of claims 1 to 8.
10. An electronic device, characterized in that: comprising a memory for storing a computer program; a processor for executing the computer program to implement the steps of the on-line energy storage charge and discharge control method for demand prediction as claimed in any one of claims 1 to 8.
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| CN117394403B (en) * | 2023-12-07 | 2024-03-29 | 深圳市伟鹏世纪科技有限公司 | Big data analysis-based intelligent control system for charging and discharging of energy storage power supply |
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