CN117595261B - Optical storage micro-grid energy management strategy optimization method and device and electronic equipment - Google Patents
Optical storage micro-grid energy management strategy optimization method and device and electronic equipment Download PDFInfo
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Abstract
The invention provides an energy management strategy optimization method and device for an optical storage micro-grid and electronic equipment. The method comprises the following steps: acquiring energy storage battery data, photovoltaic system data, load data, electricity selling prices and electricity purchasing prices in different time periods in an optical storage micro-grid; establishing a charging constraint condition, a charging and discharging power constraint condition, an energy storage battery degradation cost constraint condition, a photovoltaic output power constraint condition and a benefit constraint condition of an energy storage battery according to energy storage battery data, photovoltaic system data, load data, selling electricity prices and purchasing electricity prices in different time periods in the optical storage micro-grid; the method comprises the steps of inputting charge and discharge power of an energy storage battery, photovoltaic output power, preset time periods, electricity selling prices in different time periods, electricity purchasing prices and degradation cost of the energy storage battery into a neural network model; under the constraint, the maximum profit value is targeted, and the energy management strategy of the optical storage micro-grid is obtained. The invention can maximize the economic benefit of the system, promote the power-assisted new energy power station and quickly finish the structural transformation of the electric power system.
Description
Technical Field
The invention relates to the technical field of micro-grid control, in particular to an optical storage micro-grid energy management strategy optimization method and device and electronic equipment.
Background
Development of micro-grids with energy storage technology is a necessary condition for the transformation of propulsion energy structures. However, because renewable energy sources have characteristics of intermittence, volatility, uncertainty and the like, the balance of the electric power process is difficult to ensure. The energy storage technology can improve the absorption proportion of renewable energy sources, reduce the impact on a power grid and improve the flexibility, economy and safety of a power system. Therefore, the problems brought by renewable energy sources can be effectively solved through the energy storage technology.
At present, a great deal of researches are made on energy optimization scheduling of optical storage micro-grid, wind-solar storage micro-grid and various distributed power stations with energy storage systems at home and abroad; on the one hand, the energy optimization regulation strategy at the present stage is mainly based on the side of a large power grid, and is mainly matched with peak regulation, frequency regulation, peak clipping, valley filling and the like of the side of the large power grid, so that the main energy storage project is built by official property investment at present, and meanwhile, a plurality of data used at the present stage belong to encrypted data (such as power dispatching data) of an electric power company, and a common user cannot obtain the encrypted data; on the other hand, in the current stage, in order to improve economic benefit or match peak regulation and frequency modulation, deep charge and discharge operation is carried out on the energy storage battery, so that the damage of the service life of the battery is greatly accelerated, and the phenomenon that the income is reduced or even the phenomenon that the cost cannot be returned in the service life period of the energy storage battery is caused.
Therefore, a method for promoting the boost new energy power station and rapidly completing the structural transformation of the boost power system while ensuring the safe low-carbon operation of the system and maximizing the economic benefit is needed.
Disclosure of Invention
The embodiment of the invention provides an energy management strategy optimization method and device for an optical storage micro-grid and electronic equipment, and aims to solve the problems that the micro-grid is limited to a large grid side and the economic benefit is low.
In a first aspect, an embodiment of the present invention provides a method for optimizing an energy management policy of an optical storage micro grid, including:
acquiring energy storage battery data, photovoltaic system data, load data, electricity selling prices and electricity purchasing prices in different time periods in an optical storage micro-grid; the energy storage battery data comprise rated power of the energy storage battery, battery parameters, current residual power, construction cost and recovery price;
establishing a charging constraint condition of the energy storage battery according to the rated electric quantity and a preset electric quantity value of the optical storage micro-grid; establishing a charge-discharge power constraint condition of the energy storage battery according to the discharge constraint condition, the current residual electric quantity and the rated electric quantity; establishing degradation cost constraint conditions of the energy storage battery according to the current charge and discharge current, construction cost, recovery price and battery parameters;
Establishing photovoltaic output power constraint conditions according to photovoltaic system data and load data;
establishing a profit constraint condition of the optical storage micro-grid according to the energy storage battery data, the photovoltaic system data, the load data, and the selling electricity prices and the purchasing electricity prices of different time periods;
the method comprises the steps of inputting charge and discharge power of an energy storage battery, photovoltaic output power, preset time periods, electricity selling prices in different time periods, electricity purchasing prices and degradation cost of the energy storage battery into a neural network model; and under the constraints of a discharging constraint condition, a charging and discharging power constraint condition of the energy storage battery, a degradation cost constraint condition of the energy storage battery, a photovoltaic output power constraint condition and a benefit constraint condition, the maximum benefit value is taken as a target, and the energy management strategy of the optical storage micro-grid is obtained.
In one possible implementation, establishing the energy storage battery degradation cost constraint according to the present charge-discharge current, construction cost, recovery price and battery parameters includes:
determining a weight coefficient according to the battery parameters and the current charge and discharge current;
and establishing the constraint condition of the degradation cost of the energy storage battery according to the current charge and discharge current, the construction cost, the recovery price and the weight coefficient.
In one possible implementation manner, the battery parameters include a default operating temperature of the battery, activation energy in a charging and discharging process, charging and discharging conversion efficiency, internal resistance, standard charging and discharging current, maximum sustainable charging and discharging current, number of times of charging and discharging under a rated working condition, current health state, optimal charging and discharging depth, number of times of charging and discharging under a maximum sustainable charging and discharging current condition and maximum safe charging and discharging current;
The battery parameters, the current charge and discharge current and the weight coefficient satisfy the following relations:
wherein,is a weight coefficient; />The charging and discharging times are the times of charging and discharging under the rated working condition; />Is a standard continuous discharge current; />Is the optimal charge and discharge depth; />Is the activation energy in the charge and discharge process; />The value is (1, 2) for the compensation coefficient;the charge-discharge conversion efficiency is achieved; />Is insideResistance; />Is the default operating temperature; />Is the current of charge and discharge; />Is a standard charging current; />Is the standard discharge current; />The number of times of charging and discharging can be set under the condition of maximum sustainable charging and discharging current; />A standard continuous charging current; />Is the maximum sustainable charging current; />Is the maximum sustainable discharge current; />Is the current health status; />Is the maximum safe charge-discharge current; />The battery charge and discharge time is the battery charge and discharge time under the condition of single power unchanged.
In one possible implementation, the energy storage cell degradation cost constraint is:
F Cell =y(a)×I cell ×t tt ×（F z -F h ）
wherein F is Cell Cost for battery degradation; y (a) is a weight coefficient; i cell T is the current of charge and discharge tt F is the charge and discharge time of the battery under the condition of single power invariance z For construction cost, F h To recycle prices.
In one possible implementation, the benefit constraints are:
Wherein,is a benefit value; t is the peak-valley electricity price or peak-valley period adjustment period of the power grid; t is the current period, and each period is 0.5h; />Discharging returns to the energy storage system; />Charging the energy storage system with a return; />Discharging benefits for the photovoltaic system; />The period is adjusted for the peak-valley electricity price of the power grid; st is the price number of electric power selling at different time periods; />Discharging power of the energy storage battery; />Electricity selling prices for different time periods; />The load regulation weight is used for discharging the energy storage system; />The period is adjusted for the peak-valley electricity price of the power grid; />The price number is purchased for the electric power of different time periods; />Charging power for the energy storage battery; />Electricity purchase prices at different time periods; />The load regulation weight is used for charging the energy storage system; />And outputting power for the photovoltaic.
In one possible implementation, the charge and discharge power, the photovoltaic output power, the preset time period, the electricity selling price, the electricity purchasing price and the degradation cost of the energy storage battery are input into a neural network model; under the constraints of a discharging constraint condition, a charging and discharging power constraint condition of an energy storage battery, a degradation cost constraint condition of the energy storage battery, a photovoltaic output power constraint condition and a benefit constraint condition, the maximum benefit value is taken as a target, and an energy management strategy of the optical storage micro-grid is obtained, which comprises the following steps:
Normalizing the charge and discharge power, the photovoltaic output power, the preset time period, the selling electricity prices of different time periods, the purchasing electricity prices and the degradation cost of the energy storage battery to obtain a matrix X;
wherein,；
wherein,the method is characterized by comprising charging and discharging power of an energy storage battery, photovoltaic output power, preset time periods, electricity selling prices in different time periods, electricity purchasing prices and degradation cost of the energy storage battery; m is the sample in each feature;
inputting the matrix X into a neural network model, and obtaining the matrix X corresponding to the maximum photovoltaic micro-grid system gain value under the constraints of a discharging constraint condition, a charging and discharging power constraint condition of an energy storage battery, a degradation cost constraint condition of the energy storage battery, a photovoltaic output power constraint condition and a gain constraint condition according to a set activation function;
and obtaining an energy management strategy of the optical storage micro-grid according to the matrix X corresponding to the maximum optical storage micro-grid system profit value.
In one possible implementation, the charging constraints of the energy storage battery are:
0≤Q E ≤（Q Ee -Q loadmin ）
wherein Q is E The energy storage battery can charge and discharge electric quantity; q (Q) Ee Is rated power; q (Q) loadmin The method comprises the steps of (1) presetting an electric quantity value for an optical storage micro-grid;
the constraint conditions of the charge and discharge power of the energy storage battery are as follows:
P Emax =（Q E -SOE t ×Q Ee ）/T Btime
wherein P is Emax SOE for charging and discharging power of energy storage battery t The current residual electric quantity; t (T) Btime Is the current charge or discharge duration.
In a second aspect, an embodiment of the present invention provides an optical storage micro-grid energy management policy optimization device, including:
the acquisition module is used for acquiring energy storage battery data, photovoltaic system data, load data, electricity selling prices and electricity purchasing prices in different time periods in the optical storage micro-grid; the energy storage battery data comprise rated power of the energy storage battery, battery parameters, current residual power, current charge and discharge current, construction cost and recovery price;
the building module is used for building a charging constraint condition of the energy storage battery according to the rated electric quantity and a preset electric quantity value of the optical storage micro-grid; establishing a charge-discharge power constraint condition of the energy storage battery according to the discharge constraint condition, the current residual electric quantity and the rated electric quantity; establishing degradation cost constraint conditions of the energy storage battery according to the current charge and discharge current, construction cost, recovery price and battery parameters;
the building module is also used for building photovoltaic output power constraint conditions according to the photovoltaic system data and the load data;
the building module is also used for building income constraint conditions of the optical storage micro-grid according to the energy storage battery data, the photovoltaic system data, the load data, the selling electricity prices and the purchasing electricity prices of different time periods;
The optimization module is used for inputting the charge and discharge power, the photovoltaic output power, the preset time period, the selling electricity price, the purchasing electricity price and the degradation cost of the energy storage battery into the neural network model; and under the constraints of a discharging constraint condition, a charging and discharging power constraint condition of the energy storage battery, a degradation cost constraint condition of the energy storage battery, a photovoltaic output power constraint condition and a benefit constraint condition, the maximum benefit value is taken as a target, and the energy management strategy of the optical storage micro-grid is obtained.
In a third aspect, an embodiment of the present invention provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to the first aspect or any one of the possible implementations of the first aspect, when the computer program is executed by the processor.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above in the first aspect or any one of the possible implementations of the first aspect.
The embodiment of the invention provides a method and a device for optimizing an energy management strategy of an optical storage micro-grid and electronic equipment, wherein data required by the method and the device only adopt data which can be acquired by a common user, and the method and the device are beneficial to promoting the development of the optical storage micro-grid on a user side, and the data specifically comprise the following steps: energy storage battery data, photovoltaic system data, load data, electricity selling prices and electricity purchasing prices in different time periods; the energy storage battery data comprise rated power of the energy storage battery, battery parameters, current residual power, current charge and discharge current, construction cost and recovery price. Corresponding constraint conditions are established according to the acquired data, charging and discharging power of the energy storage battery, photovoltaic output power, preset time periods, electricity selling prices in different time periods, electricity purchasing prices and degradation cost of the energy storage battery are input into the neural network model, so that the neural network model aims at the maximum profit value under the constraint of the constraint conditions, and an optimal light storage micro-grid energy management strategy is determined. When the energy management strategy of the optical storage micro-grid is determined, the influence of different charge and discharge control strategies on the aging degree of the battery is considered, the degradation cost of the battery is recorded into the energy management strategy calculation, and the benefit maximization in the life cycle of the battery is truly realized. The embodiment of the invention can ensure the safe low-carbon operation of the system and maximize the economic benefit, and simultaneously promote the boosting new energy power station and quickly finish the structure transformation of the boosting power system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the related technical descriptions will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an implementation of an energy management policy optimization method for an optical storage micro grid according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a typical structure of a customer-side-view micro-grid system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a typical configuration of a customer-side-view storage micro-grid system provided by an embodiment of the present invention;
fig. 4 is an optimization flow chart of an optimization method of an energy management strategy of an optical storage micro-grid according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an optical storage micro-grid energy management strategy optimizing device according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.
Fig. 1 is a flowchart of an implementation of an energy management policy optimization method for an optical storage micro grid according to an embodiment of the present invention. As shown in fig. 1:
step 110: acquiring energy storage battery data, photovoltaic system data, load data, electricity selling prices and electricity purchasing prices in different time periods in an optical storage micro-grid; the energy storage battery data comprise rated power of the energy storage battery, battery parameters, current residual power, construction cost and recovery price.
In this embodiment, because the existing optimization scheme of the energy management strategy of the optical storage micro-grid is based on the large grid side, the optimization scheme focuses on matching with peak regulation, frequency regulation, peak load shifting and the like of the large grid side, so that the main energy storage project is built by official property investment at present, and meanwhile, many data used in the prior art belong to encrypted data (such as power dispatching data) of an electric company, so that a common user cannot obtain the encrypted data. That is, the light storage polarity of the common user access at the present stage is not high. Aiming at the technical problem, the embodiment aims to provide an optical storage micro-grid energy management strategy optimization method based on a user side so as to improve the enthusiasm of private enterprises for developing optical storage micro-grids.
Fig. 2 is a schematic diagram of a typical structure of a customer-side optical storage micro grid system according to an embodiment of the present invention, and the following description will explain the present embodiment with reference to fig. 2:
in order to achieve the purpose of improving the enthusiasm of private enterprises for developing the optical storage micro-grid, the data provided by the embodiment are all data of an optical storage micro-grid system which can be obtained by common users. The photovoltaic storage micro-grid system of the common user generally comprises a photovoltaic power station, an electrochemical energy storage power station, a load and a power distribution network thereof, and the conventional electrochemical energy storage micro-grid system generally comprises a roof photovoltaic, an electrochemical energy storage power station, the load and the power distribution network thereof. Specifically, the user's equipment is connected between the distribution network and the micro-grid bus through a common connection point (Point of Common Coupling, PCC). And a plurality of system loads, a plurality of photovoltaic inverters (roof photovoltaic), a plurality of energy storage converters (electrochemical energy storage power stations) and the like are connected to the micro-grid bus, so that the electricity utilization stability of a user is ensured.
Correspondingly, the data related to the user, such as energy storage battery data, photovoltaic system data, load data, electricity selling price and electricity purchasing price in different time periods, chargeable times under a rated working condition, chargeable and dischargeable times under a maximum sustainable charge and discharge current working condition and the like in the optical storage micro-grid can be obtained in real time; the photovoltaic system data may include, among other things, photovoltaic system power rating, illumination intensity, and the like. The energy storage battery data comprises rated power of the energy storage battery, battery parameters, current residual power, construction cost, recovery price and the like.
In this embodiment, the battery parameters may be obtained by querying the specification of the energy storage battery, or directly provided by a battery manufacturer, or further obtained by a battery management system (Battery Management System, BMS), and specifically, the battery parameters may include a default operating temperature of the battery, activation energy in a charging and discharging process, charging and discharging conversion efficiency, internal resistance, standard charging and discharging current, maximum sustainable charging and discharging current, number of times of charging and discharging under a rated working condition, current health state, optimal charging and discharging depth, number of times of charging and discharging under a maximum sustainable charging and discharging current condition, maximum safe charging and discharging current, and so on.
Step 120: establishing a charging constraint condition of the energy storage battery according to the rated electric quantity and a preset electric quantity value of the optical storage micro-grid; establishing a charge-discharge power constraint condition of the energy storage battery according to the discharge constraint condition, the current residual electric quantity and the rated electric quantity; and establishing the degradation cost constraint condition of the energy storage battery according to the current charge and discharge current, the construction cost, the recovery price and the battery parameters.
In this embodiment, in the optical storage type micro-grid at the user side, when the power distribution network fails, the energy storage system needs to ensure that an important load in the micro-grid system continuously operates for a period of time, so that the maximum chargeable and dischargeable electric quantity of the system of the energy storage system is not the rated electric quantity, and accordingly, the charging constraint condition of the energy storage battery (energy storage system) can be as follows:
0≤Q E ≤（Q Ee -Q loadmin ）
In which Q E The energy storage battery can charge and discharge electric quantity; q (Q) Ee The rated electric quantity is a fixed value; q (Q) loadmin The preset electric quantity value of the optical storage micro-grid is the minimum electric quantity required for guaranteeing important operation load, and is determined by a micro-grid system. Wherein Q is Ee The rated voltage of the energy storage battery is multiplied by the rated capacity of the energy storage battery.
Accordingly, the charge-discharge power constraint condition of the energy storage battery may be:
P Emax =（Q E -SOE t ×Q Ee ）/T Btime
wherein P is Emax SOE for charging and discharging power of energy storage battery t The current residual electric quantity; t (T) Btime Is the current charge or discharge duration. The method provided in this embodiment requires iterative update during optimization, so SOE t The percentage data of the current residual capacity of the energy storage battery before each iteration.
In this embodiment, in order to truly realize the benefit maximization in the life cycle of the battery, the influence of different charge-discharge control strategies on the aging degree of the battery needs to be fully considered, the degradation cost of the energy storage battery is recorded into the calculation of the energy management strategy, and correspondingly, when the strategy optimization is performed, the constraint condition of the degradation cost of the energy storage battery needs to be established. Specifically, the energy storage cell degradation cost constraint condition may be established by:
Determining a weight coefficient according to the battery parameters and the current charge and discharge current;
and establishing the constraint condition of the degradation cost of the energy storage battery according to the current charge and discharge current, the construction cost, the recovery price and the weight coefficient.
In this embodiment, the related parameters are normalized first, and the physical units in the calculation process are removed. Then, a weight coefficient is determined according to the battery parameter and the present charge-discharge current. The weight coefficient is used for representing the weight of the current charge-discharge current of the energy storage battery and the battery aging relation. After the weight coefficient is determined, the degradation cost constraint condition of the energy storage battery is established according to the current charge and discharge current, the construction cost, the recovery price and the weight coefficient.
In a specific embodiment, the battery parameters, the present charge-discharge current, and the weight coefficient satisfy the following relationship:
in the method, in the process of the invention,is a weight coefficient; />The charging and discharging times are the times of charging and discharging under the rated working condition; />Is a standard continuous discharge current; />Is the optimal charge and discharge depth; />Is the activation energy in the charge and discharge process；/>The value is (1, 2) for the compensation coefficient;the charge-discharge conversion efficiency is achieved; />Is internal resistance; />Is the default operating temperature; />Is the current of charge and discharge; / >Is a standard charging current; />Is the standard discharge current; />The number of times of charging and discharging can be set under the condition of maximum sustainable charging and discharging current; />A standard continuous charging current; />Is the maximum sustainable charging current; />Is the maximum sustainable discharge current; />Is the current health status; />Is the maximum safe charge-discharge current; />The battery charge and discharge time is the battery charge and discharge time under the condition of single power unchanged. Wherein (1)>The battery charge and discharge temperature is measured and controlled by the BMS to be obtained in the optimal charge and discharge temperature interval, and is generally set to 25 ℃; r and->Other data may be provided by the battery manufacturer as measured by the BMS.
In addition, in the present embodiment, if the charge-discharge current is presentWhen the maximum sustainable charge-discharge current is exceeded, the safety of the battery can be judged to be reduced, so that the regulation and control of the energy storage load are not performed any more, an alarm is sent out, and a worker is reminded to discard the battery. In addition, the operation and maintenance cost and the degradation cost of photovoltaic, load and the like in the optical storage micro-grid are not influenced by a regulation and control strategy, so that the optical storage micro-grid is not treated; the service life of the energy storage battery is greatly influenced by factors such as the number of discharge times, the environment and the like, and the aging speed of the energy storage battery is different due to different regulation strategies, so that the degradation cost of the battery caused by different regulation strategies is different, and the influence of the regulation strategies on the battery is required to be considered when the benefit is calculated.
In one particular embodiment, the energy storage cell degradation cost constraint may be:
F Cell =y(a)×I cell ×t tt ×（F z -F h ）
wherein F is Cell Cost for battery degradation; y (a) is a weight coefficient; i cell T is the current of charge and discharge tt F is the charge and discharge time of the battery under the condition of single power invariance z For construction cost, F h To recycle prices.
Step 130: and establishing photovoltaic output power constraint conditions according to the photovoltaic system data and the load data.
In this embodiment, the photovoltaic output power constraint condition may be established by load data, photovoltaic system rated power, illumination intensity, temperature, humidity, conversion rate of the photovoltaic cell, and the like in the photovoltaic system data.
Step 140: and establishing a benefit constraint condition of the optical storage micro-grid according to the energy storage battery data, the photovoltaic system data, the load data, and the selling electricity prices and the purchasing electricity prices of different time periods.
In one particular embodiment, the benefit constraint may be:
wherein,is a benefit value; t is the peak-valley electricity price or peak-valley period adjustment period of the power grid; t is the current period, and each period is 0.5h; />Discharging returns to the energy storage system; />Charging the energy storage system with a return; />Discharging benefits for the photovoltaic system; />The period is adjusted for the peak-valley electricity price of the power grid; st is the price number of electric power selling at different time periods ；/>Discharging power of the energy storage battery; />Electricity selling prices for different time periods; />The load regulation weight is used for discharging the energy storage system; />The period is adjusted for the peak-valley electricity price of the power grid; />The price number is purchased for the electric power of different time periods; />Charging power for the energy storage battery; />Electricity purchase prices at different time periods; />The load regulation weight is used for charging the energy storage system; />And outputting power for the photovoltaic.
Step 150: the method comprises the steps of inputting charge and discharge power of an energy storage battery, photovoltaic output power, preset time periods, electricity selling prices in different time periods, electricity purchasing prices and degradation cost of the energy storage battery into a neural network model; and under the constraints of a discharging constraint condition, a charging and discharging power constraint condition of the energy storage battery, a degradation cost constraint condition of the energy storage battery, a photovoltaic output power constraint condition and a benefit constraint condition, the maximum benefit value is taken as a target, and the energy management strategy of the optical storage micro-grid is obtained.
In this embodiment, in order to obtain an energy management policy of the optical storage micro-grid, first, charge-discharge power data of an energy storage battery is extracted according to a charge-discharge power constraint condition of the energy storage battery; extracting degradation cost data of the energy storage battery according to the degradation cost constraint condition of the energy storage battery; and extracting photovoltaic output power data according to the photovoltaic output power constraint condition. Normalizing the charge and discharge power, the photovoltaic output power, the preset time period, the selling electricity prices of different time periods, the purchasing electricity prices and the degradation cost of the energy storage battery to obtain a matrix X;
Wherein,；
in the method, in the process of the invention,the method is characterized by comprising charging and discharging power of an energy storage battery, photovoltaic output power, preset time periods, electricity selling prices in different time periods, electricity purchasing prices and degradation cost of the energy storage battery; m is the sample in each feature. In this embodiment, the charge and discharge power of the energy storage battery may be represented by the same column of data in the matrix, "+" represents charge, and "-" represents discharge.
Illustratively, assume n=6, x 1 Representing charge and discharge power, X, of an energy storage battery 2 Representing the output power of photovoltaic, X 3 Representing a preset time period, X 4 Showing electricity selling price and X in different time periods 5 Representing electricity purchase price and X in different time periods 6 Representing the degradation cost of the energy storage battery; x is determined according to the constraint conditions or the value ranges corresponding to the items 1m =0、1、2、3……m；X 2m =0、1、2、3……m；X 3m =0、1、2、3……m；X 4m =0、1、2、3……m；X 5m =0、1、2、3……m；X 6m =0, 1, 2, 3 … … m; then the first time period of the first time period,=/>。
the above examples are merely for illustration, and are not intended to limit the present embodiment in any way, and the numerical values adopted in each item in actual application need to be set according to actual situations.
Then, the matrix X is used as an input layer of the neural network model, and the set activation function is as follows. Under the constraints of a discharging constraint condition, a charging and discharging power constraint condition of an energy storage battery, a degradation cost constraint condition of the energy storage battery, a photovoltaic output power constraint condition and a benefit constraint condition, calculating each group X in a matrix X by using a neural network model n Corresponding F M Value (optical storage micro grid system gain value) and from m F M The maximum value is F Mmax (maximum optical storage microgrid system benefit value). Thereafter, neural network model output F Mmax X in the corresponding matrix X n 。
After X is obtained n After that, according to X n And the data of photovoltaic, energy storage, load power and the like in the optical storage micro-grid system are regulated and controlled according to the optical storage micro-grid energy management strategy.
Exemplary, table 1 is F Mmax X in the corresponding input matrix X n . Specifically as shown in table 1:
table 1F Mmax X in the corresponding input matrix X n
| Time | Charging power of energy storage battery | Discharge power of energy storage battery | Photovoltaic output power |
| t=1 | 0.2PEmax | 0 | 0 |
| t=2 | 0.2PEmax | 0 | 0 |
| t=3 | 0.2PEmax | 0 | 0 |
| t=4 | 0.2PEmax | 0 | 0 |
| t=5 | 0 | 0 | 0 |
| t=6 | 0 | 0 | 0 |
| t=7 | 0 | 0 | 0.5PGmax |
| t=8 | 0 | 0.2PEmax | PGmax |
| t=9 | 0 | 0.2PEmax | PGmax |
| t=10 | 0 | 0.2PEmax | PGmax |
| t=11 | 0 | 0.2PEmax | PGmax |
| t=12 | 0 | 0.2PEmax | PGmax |
| t=13 | 0.5PEmax | 0 | PGmax |
| t=14 | 0.5PEmax | 0 | PGmax |
| t=15 | 0 | 0 | PGmax |
| t=16 | 0 | 0 | PGmax |
| t=17 | 0 | 0 | PGmax |
| t=18 | 0 | 0 | PGmax |
| t=19 | 0 | 0.25PEmax | 0.5PGmax |
| t=20 | 0 | 0.25PEmax | 0 |
| t=21 | 0 | 0.25PEmax | 0 |
| t=22 | 0 | 0.25PEmax | 0 |
| t=23 | 0 | 0 | 0 |
| t=24 | 0.2PEmax | 0 | 0 |
The data in Table 1 are obtained in 24 hours a day, and F is calculated from the input matrix X Mmax X is then outputted n Correspondingly, according to X n The energy management strategy of the optical storage micro-grid is as follows:
regarding the charge power of the energy storage battery: the charging power of the maximum energy storage battery is 0.2 times of the charging power of the maximum energy storage battery from 1 point to 4 points in the morning, the charging power of the maximum energy storage battery is 0.5 times of the charging power of the maximum energy storage battery from 13 points to 14 points, the charging power of the maximum energy storage battery is 0.2 times of the charging power of the maximum energy storage battery from 24 points, and the charging power of the rest time period is 0.
Regarding the discharge power of the energy storage battery: the discharge power of the maximum energy storage battery is 0.2 times from 8 points to 12 points, 0.25 times from 19 points to 22 points, and the discharge power of the rest time period is 0.
Regarding the photovoltaic output power: 7 and 19 are discharged at 0.5 times maximum photovoltaic output power; 8-18-point photovoltaic is discharged at the maximum output power, and the photovoltaic output power in the rest period is 0.
In this embodiment, since the power grid selling and purchasing price time period and the price are fixed, the example table is not displayed, but the power grid selling and purchasing price time period and the price are required to be considered to be fixed when the regulation and control are actually performed; the above examples are merely for illustration, and are not intended to limit the present embodiment in any way, and the values taken by each item in actual application need to be set according to the actual output situation.
FIG. 3 is a schematic diagram of a typical configuration of a customer-side-view storage micro-grid system provided by an embodiment of the present invention; as shown in fig. 3:
the dispatching data network can be divided into an A network and a B network in the user side optical storage micro-grid; the A network and the B network are two identical networks, the connected devices are identical, and the purpose of using the double networks is to ensure the reliability of the networks. When one of the networks is disconnected due to abnormality, the whole system can keep normal operation by the other network. In particular, the optical storage micro grid system is equipped with a remote access server, which enables a computer to be connected in the internet to the remote access server in the local area network, thereby acquiring resources in the local area network.
The WEB subsystem is connected with the A network and the B network simultaneously through the gateway workstation; the communication subsystem, the MGC (Migration Center) server, the SCADA (Supervisory Control And Data Acquisition) server, the historian server, the data acquisition subsystem and the attendant subsystem are respectively and simultaneously connected with the A network and the B network.
The WEB subsystem comprises a WEB server, a firewall, an MIS (Management Information System ) system and the like; the WEB subsystem and the gateway workstation are connected with forward and reverse safety isolation equipment, and the gateway workstation is provided with an IDS (Intrusion Detection System ) probe. The WEB subsystem can provide a network connected with the world, so that acquired data can be conveniently uploaded to the processing equipment, and the processing equipment can issue the acquired management strategy to a corresponding optical storage micro-grid; meanwhile, the WEB subsystem can also prevent malicious detection or system intrusion.
The communication subsystem comprises a communication server, a router, an electric power data network and a superior dispatching system, and can ensure communication between the optical storage micro-grid and the processing equipment.
The MGC server can realize multi-scene coverage, meet various migration requirements, and realize cloud edge end coordination between a plurality of optical storage micro-grids and a power distribution network.
The SCADA server can realize remote collection of various data in the optical storage micro-grid and monitor whether the optical storage micro-grid has defects or faults.
The historian server can store the acquired data and the energy management strategy of the optical storage micro-grid obtained each time.
The data acquisition subsystem comprises a data acquisition server, a GPS (Global Positioning System ) and a data acquisition device. The data acquisition subsystem can position corresponding equipment position information in the optical storage micro-grid and acquire various required data in the optical storage micro-grid.
The attendant subsystem comprises a simulation screen, a double-screen attendant workstation, an alarm workstation and a large screen. The on-duty subsystem can simulate and display various data in the collected optical storage micro-grid, meanwhile, whether deviation exists between the simulated data and the detected actual data or not is compared in real time, and when the obtained data and the simulated data have larger deviation, alarm information is sent out to remind a worker to check the corresponding optical storage micro-grid or the optical storage micro-grid system.
Fig. 4 is an optimization flow chart of an optimization method of an energy management strategy of an optical storage micro-grid according to an embodiment of the present invention; as shown in fig. 4:
in this embodiment, in order to ensure that the electricity consumption of the user is stable, the economic benefit of the user is maximized. The first need is to acquire relevant data related to the user, wherein the relevant data can comprise energy storage battery data, photovoltaic system data, load data, electricity selling prices and electricity purchasing prices of different periods and the like in the optical storage micro-grid. And secondly, initializing the acquired related data, namely establishing constraint conditions including a discharge constraint condition, a charge-discharge power constraint condition, an energy storage battery degradation cost constraint condition, a photovoltaic output power constraint condition and a benefit constraint condition of the optical storage micro-grid according to the related data. Thirdly, taking data such as charge and discharge power of the energy storage battery, photovoltaic output power, preset time periods, electricity selling prices in different time periods, electricity purchasing prices, degradation cost of the energy storage battery and the like as input into a neural network model, wherein the neural network model aims at solving the maximum benefit of the optical storage micro-grid. Fourth, the neural network model outputs the input data corresponding to the maximum benefit. Performing adjustment control according to the output data to obtain an energy management strategy of the optical storage micro-grid, namely a most profitable control strategy; and running the optical storage micro grid energy management strategy.
Reference may be made to the other embodiments described above for a portion of this embodiment that is not described in detail.
In summary, the data required by the embodiment only adopt the data which can be acquired by the common user, so that the enthusiasm of private enterprises for developing the optical storage micro-grid can be improved. When strategy regulation is carried out, the influence of different charge and discharge control strategies on the battery aging degree is fully considered, the battery degradation cost is recorded into energy management strategy calculation, and the benefit maximization in the battery life cycle is truly realized. And the system is arranged at the user side to maximize the economic benefit of the system operation, an optimal economic regulation strategy is constructed, and the energy storage activity is fully exerted by considering the change of the time-of-use electricity price and the selling and purchasing electricity price and the system energy supply and demand relationship, so as to realize the benefit maximization. For example, the average value (0.736 yuan/Kwh) of peak-valley electricity price difference in the upper half year of the Hebei province of 2023 and the average construction cost (5 yuan/wh) of the optical storage micro-grid system of 10 months of 2023 are calculated, and the cycle of the construction and recovery cost of the system is 5-7 years compared with that of the existing optical storage micro-grid regulation strategy. Finally, the method provided by the embodiment of the invention has strong applicability, the control strategy is a periodic control strategy, frequent calculation is not needed in the operation period, and the system operation resources are not occupied; if the existing micro-grid energy management system cannot be upgraded, a control strategy can be calculated by running on a calculation end and then the control strategy is imported into the micro-grid energy management system. The embodiment of the invention can maximize the economic benefit of the user on the premise of ensuring the electricity utilization stability of the user.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The following are device embodiments of the invention, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 5 shows a schematic structural diagram of an optical storage micro grid energy management policy optimizing device according to an embodiment of the present invention, and for convenience of explanation, only a portion relevant to the embodiment of the present invention is shown, which is described in detail below:
as shown in fig. 5, the optical storage micro grid energy management strategy optimization device 5 includes:
the acquisition module 51 is used for acquiring energy storage battery data, photovoltaic system data, load data, electricity selling prices and electricity purchasing prices in different time periods in the optical storage micro-grid; the energy storage battery data comprise rated power of the energy storage battery, battery parameters, current residual power, current charge and discharge current, construction cost and recovery price;
the establishing module 52 is configured to establish a charging constraint condition of the energy storage battery according to the rated power and a preset power value of the optical storage micro-grid; establishing a charge-discharge power constraint condition of the energy storage battery according to the discharge constraint condition, the current residual electric quantity and the rated electric quantity; establishing degradation cost constraint conditions of the energy storage battery according to the current charge and discharge current, construction cost, recovery price and battery parameters;
The establishing module 52 is further configured to establish a photovoltaic output power constraint condition according to the photovoltaic system data and the load data;
the establishing module 52 is further configured to establish a profit constraint condition of the optical storage micro-grid according to the energy storage battery data, the photovoltaic system data, the load data, the electricity selling prices and the electricity purchasing prices in different time periods;
the optimizing module 53 is configured to input the charge and discharge power of the energy storage battery, the photovoltaic output power, the preset time period, the selling electricity prices of different time periods, the purchasing electricity price and the degradation cost of the energy storage battery into the neural network model; and under the constraints of a discharging constraint condition, a charging and discharging power constraint condition of the energy storage battery, a degradation cost constraint condition of the energy storage battery, a photovoltaic output power constraint condition and a benefit constraint condition, the maximum benefit value is taken as a target, and the energy management strategy of the optical storage micro-grid is obtained.
In one possible implementation, the establishing module 52 is specifically configured to:
determining a weight coefficient according to the battery parameters and the current charge and discharge current;
and determining the degradation cost constraint condition of the energy storage battery according to the current charge and discharge current, the construction cost, the recovery price and the weight coefficient.
In one possible implementation manner, the battery parameters include a default operating temperature of the battery, activation energy in a charging and discharging process, charging and discharging conversion efficiency, internal resistance, standard charging and discharging current, maximum sustainable charging and discharging current, number of times of charging and discharging under a rated working condition, current health state, optimal charging and discharging depth, number of times of charging and discharging under a maximum sustainable charging and discharging current condition and maximum safe charging and discharging current;
The battery parameters, the current charge and discharge current and the weight coefficient satisfy the following relations:
wherein,is a weight coefficient; />The charging and discharging times are the times of charging and discharging under the rated working condition; />Is a standard continuous discharge current; />Is the optimal charge and discharge depth; />Is the activation energy in the charge and discharge process; />The value is (1, 2) for the compensation coefficient;the charge-discharge conversion efficiency is achieved; />Is internal resistance; />Is the default operating temperature; />Is the current of charge and discharge; />Is a standard charging current; />Is the standard discharge current; />The number of times of charging and discharging can be set under the condition of maximum sustainable charging and discharging current; />A standard continuous charging current; />Is the maximum sustainable charging current; />Is the maximum sustainable discharge current; />Is the current health status; />Is the maximum safe charge-discharge current; />The battery charge and discharge time is the battery charge and discharge time under the condition of single power unchanged.
In one possible implementation, the energy storage cell degradation cost constraint is:
F Cell =y(a)×I cell ×t tt ×（F z -F h ）
wherein F is Cell Cost for battery degradation; y (a) is a weight coefficient; i cell T is the current of charge and discharge tt F is the charge and discharge time of the battery under the condition of single power invariance z For construction cost, F h To recycle prices.
In one possible implementation, the benefit constraints are:
/>
Wherein,is a benefit value; t is the peak-valley electricity price or peak-valley period adjustment period of the power grid; t is the current period, and each period is 0.5h; />Discharging returns to the energy storage system; />Charging the energy storage system with a return; />Discharging benefits for the photovoltaic system; />The period is adjusted for the peak-valley electricity price of the power grid; st is the price number of electric power selling at different time periods; />Discharging power of the energy storage battery; />Electricity selling prices for different time periods; />The load regulation weight is used for discharging the energy storage system; />The period is adjusted for the peak-valley electricity price of the power grid; />The price number is purchased for the electric power of different time periods; />Charging power for the energy storage battery; />Electricity purchase prices at different time periods; />The load regulation weight is used for charging the energy storage system; />And outputting power for the photovoltaic.
In one possible implementation, the optimization module 43 is specifically configured to:
normalizing the charge and discharge power, the photovoltaic output power, the preset time period, the selling electricity prices of different time periods, the purchasing electricity prices and the degradation cost of the energy storage battery to obtain a matrix X;
wherein,；
wherein,the method is characterized by comprising charging and discharging power of an energy storage battery, photovoltaic output power, preset time periods, electricity selling prices in different time periods, electricity purchasing prices and degradation cost of the energy storage battery; m is the sample in each feature;
Inputting the matrix X into a neural network model, and obtaining the matrix X corresponding to the maximum photovoltaic micro-grid system gain value under the constraints of a discharging constraint condition, a charging and discharging power constraint condition of an energy storage battery, a degradation cost constraint condition of the energy storage battery, a photovoltaic output power constraint condition and a gain constraint condition according to a set activation function;
and obtaining an energy management strategy of the optical storage micro-grid according to the matrix X corresponding to the maximum optical storage micro-grid system profit value.
In one possible implementation, the charging constraints of the energy storage battery are:
0≤Q E ≤（Q Ee -Q loadmin ）
wherein Q is E The energy storage battery can charge and discharge electric quantity; q (Q) Ee Is rated power; q (Q) loadmin The method comprises the steps of (1) presetting an electric quantity value for an optical storage micro-grid;
the constraint conditions of the charge and discharge power of the energy storage battery are as follows:
P Emax =（Q E -SOE t ×Q Ee ）/T Btime
wherein P is Emax SOE for charging and discharging power of energy storage battery t The current residual electric quantity; t (T) Btime Is the current charge or discharge duration.
Fig. 6 is a schematic diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 6, the electronic device 6 of this embodiment includes: a processor 60, a memory 61 and a computer program 62 stored in said memory 61 and executable on said processor 60. The processor 60, when executing the computer program 62, implements the steps of the respective embodiments of the optical storage micro grid energy management strategy optimization method described above, such as steps 110 through 150 shown in fig. 1. Alternatively, the processor 60 may perform the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules shown in fig. 5, when executing the computer program 62.
Illustratively, the computer program 62 may be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used to describe the execution of the computer program 62 in the electronic device 6. For example, the computer program 62 may be partitioned into the modules shown in FIG. 5.
The electronic device 6 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The electronic device 6 may include, but is not limited to, a processor 60, a memory 61. It will be appreciated by those skilled in the art that fig. 6 is merely an example of the electronic device 6 and is not meant to be limiting as the electronic device 6 may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may further include an input-output device, a network access device, a bus, etc.
The processor 60 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 61 may be an internal storage unit of the electronic device 6, such as a hard disk or a memory of the electronic device 6. The memory 61 may be an external storage device of the electronic device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the electronic device 6. The memory 61 is used for storing the computer program and other programs and data required by the electronic device. The memory 61 may also be used for temporarily storing data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other manners. For example, the apparatus/electronic device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the foregoing embodiment, or may be implemented by instructing related hardware by a computer program, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of the energy management policy optimization method embodiment of each optical storage micro grid when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (8)
1. An optical storage micro-grid energy management strategy optimization method is characterized by comprising the following steps:
acquiring energy storage battery data, photovoltaic system data, load data, electricity selling prices and electricity purchasing prices in different time periods in an optical storage micro-grid; the energy storage battery data comprise rated power of the energy storage battery, battery parameters, current residual power, construction cost and recovery price;
establishing a charge-discharge constraint condition of an energy storage battery according to the rated electric quantity and a preset electric quantity value of the optical storage micro-grid; establishing a charge-discharge power constraint condition of the energy storage battery according to the charge-discharge constraint condition, the current residual electric quantity and the rated electric quantity; establishing an energy storage battery degradation cost constraint condition according to the current charge and discharge current, the construction cost, the recovery price and the battery parameters;
Establishing photovoltaic output power constraint conditions according to the photovoltaic system data and the load data;
establishing a benefit constraint condition of the optical storage micro-grid according to the energy storage battery data, the photovoltaic system data, the load data, and the electricity selling price and the electricity purchasing price of different time periods;
inputting charge and discharge power of an energy storage battery, the photovoltaic output power, a preset time period, electricity selling prices, electricity purchasing prices and degradation cost of the energy storage battery in different time periods into a neural network model; under the constraints of the discharging constraint condition, the charging and discharging power constraint condition of the energy storage battery, the degradation cost constraint condition of the energy storage battery, the photovoltaic output power constraint condition and the benefit constraint condition, the maximum benefit value is taken as a target, and the energy management strategy of the optical storage micro grid is obtained;
wherein the establishing the constraint condition of the degradation cost of the energy storage battery according to the current charge and discharge current, the construction cost, the recovery price and the battery parameter comprises the following steps:
determining a weight coefficient according to the battery parameters and the current charge and discharge current;
establishing a degradation cost constraint condition of the energy storage battery according to the current charge and discharge current, the construction cost, the recovery price and the weight coefficient;
The battery parameters comprise default working temperature of the battery, activation energy in the charging and discharging process, charging and discharging conversion efficiency, internal resistance, standard charging and discharging current, maximum sustainable charging and discharging current, chargeable and discharging times under rated working conditions, current health state, optimal charging and discharging depth, chargeable and discharging times under the maximum sustainable charging and discharging current condition and maximum safe charging and discharging current;
the battery parameter, the present charge-discharge current, and the weight coefficient satisfy the following relationship:
wherein,the weight coefficient is the weight coefficient; />The chargeable and dischargeable times under the rated working condition are obtained; />Is a standard continuous discharge current; />The optimal charge and discharge depth is set; />Is the activation energy in the charge and discharge process; />The value is (1, 2) for the compensation coefficient; />The charge-discharge conversion efficiency is the same; />Is the internal resistance; />-said default operating temperature; />The current is the current charging and discharging current; />Is a standard charging current; />Is the standard discharge current; />The number of times of charging and discharging can be set under the maximum sustainable charging and discharging current condition; />A standard continuous charging current; />Is the maximum sustainable charging current; />Is the maximum sustainable discharge current; / >Is the current state of health; />The maximum safe charge-discharge current; />The battery charge and discharge time is the battery charge and discharge time under the condition of single power unchanged.
2. The method of optimizing an energy management strategy of an optical storage micro grid according to claim 1, wherein the energy storage battery degradation cost constraint condition is:
F Cell =y(a)×I cell ×t tt ×（F z -F h ）
wherein F is Cell Cost for degradation of the battery; y (a) is the weight coefficient; i cell T is the current of the current charge and discharge tt F is the charge and discharge time of the battery under the condition of single power invariance z For the construction cost, F h For the recycle price.
3. The method of optimizing an energy management strategy for a micro-grid for light storage of claim 1, wherein the yield constraint condition is:
wherein,is a benefit value; t is the peak-valley electricity price or peak-valley period adjustment period of the power grid; t is the current period, and each period is 0.5h; />Discharging returns to the energy storage system; />Charging the energy storage system with a return; />Discharging benefits for the photovoltaic system; />The period is adjusted for the peak-valley electricity price of the power grid; st is the price number of electric power selling at different time periods; />Discharging power for the energy storage battery; />Electricity selling prices for different time periods; />The load regulation weight is used for discharging the energy storage system; / >The period is adjusted for the peak-valley electricity price of the power grid; />The price number is purchased for the electric power of different time periods; />Charging power for the energy storage battery; />Electricity purchase prices at different time periods; />The load regulation weight is used for charging the energy storage system; />And outputting power to the photovoltaic.
4. The method according to claim 1, wherein the charging and discharging power of the energy storage battery, the photovoltaic output power, a preset time period, the electricity selling prices, the electricity purchasing prices and the degradation cost of the energy storage battery in different time periods are input into a neural network model; under the constraints of the discharging constraint condition, the charging and discharging power constraint condition of the energy storage battery, the degradation cost constraint condition of the energy storage battery, the photovoltaic output power constraint condition and the benefit constraint condition, the maximum benefit value is targeted, and the light storage micro-grid energy management strategy is obtained, which comprises the following steps:
normalizing the charge and discharge power of the energy storage battery, the photovoltaic output power, the preset time period, the electricity selling price, the electricity purchasing price and the degradation cost of the energy storage battery in different time periods to obtain a matrix X;
wherein,；
wherein,the method is characterized by comprising charging and discharging power of the energy storage battery, photovoltaic output power, a preset time period, electricity selling prices, electricity purchasing prices and degradation cost of the energy storage battery in different time periods; m is the sample in each feature;
Inputting the matrix X into a neural network model, and obtaining the matrix X corresponding to the maximum photovoltaic micro-grid system gain value under the constraints of the discharging constraint condition, the charging and discharging power constraint condition of the energy storage battery, the degradation cost constraint condition of the energy storage battery, the photovoltaic output power constraint condition and the gain constraint condition according to a set activation function;
and obtaining the energy management strategy of the optical storage micro grid according to the matrix X corresponding to the maximum optical storage micro grid system gain value.
5. The method of optimizing an energy management strategy of an optical storage micro grid according to claim 1, wherein the charge and discharge constraint conditions of the energy storage battery are as follows:
0≤Q E ≤（Q Ee -Q loadmin ）
wherein Q is E The energy storage battery can charge and discharge electric quantity; q (Q) Ee Is the rated electric quantity; q (Q) loadmin A preset electric quantity value for the optical storage micro-grid;
the constraint conditions of the charge and discharge power of the energy storage battery are as follows:
P Emax =（Q E -SOE t ×Q Ee ）/T Btime
wherein P is Emax SOE for the charge and discharge power of the energy storage battery t The current residual electric quantity is the current residual electric quantity; t (T) Btime Is the current charge or discharge duration.
6. An optical storage micro-grid energy management strategy optimization device, characterized by comprising:
the acquisition module is used for acquiring energy storage battery data, photovoltaic system data, load data, electricity selling prices and electricity purchasing prices in different time periods in the optical storage micro-grid; the energy storage battery data comprise rated power of the energy storage battery, battery parameters, current residual power, current charge and discharge current, construction cost and recovery price;
The establishing module is used for establishing the charge and discharge constraint conditions of the energy storage battery according to the rated electric quantity and the preset electric quantity value of the optical storage micro-grid; establishing a charge-discharge power constraint condition of the energy storage battery according to the charge-discharge constraint condition, the current residual electric quantity and the rated electric quantity; establishing an energy storage battery degradation cost constraint condition according to the current charge and discharge current, the construction cost, the recovery price and the battery parameters;
the building module is further used for building photovoltaic output power constraint conditions according to the photovoltaic system data and the load data;
the building module is further used for building the income constraint conditions of the optical storage micro-grid according to the energy storage battery data, the photovoltaic system data, the load data, the electricity selling prices and the electricity purchasing prices in different time periods;
the optimization module is used for inputting the charge and discharge power of the energy storage battery, the photovoltaic output power, a preset time period, the electricity selling price, the electricity purchasing price and the degradation cost of the energy storage battery in the neural network model; under the constraints of the discharging constraint condition, the charging and discharging power constraint condition of the energy storage battery, the degradation cost constraint condition of the energy storage battery, the photovoltaic output power constraint condition and the benefit constraint condition, the maximum benefit value is taken as a target, and the energy management strategy of the optical storage micro grid is obtained;
The building module is specifically configured to:
determining a weight coefficient according to the battery parameters and the current charge and discharge current;
establishing a degradation cost constraint condition of the energy storage battery according to the current charge and discharge current, the construction cost, the recovery price and the weight coefficient;
the battery parameters comprise default working temperature of the battery, activation energy in the charging and discharging process, charging and discharging conversion efficiency, internal resistance, standard charging and discharging current, maximum sustainable charging and discharging current, chargeable and discharging times under rated working conditions, current health state, optimal charging and discharging depth, chargeable and discharging times under the maximum sustainable charging and discharging current condition and maximum safe charging and discharging current;
the battery parameter, the present charge-discharge current, and the weight coefficient satisfy the following relationship:
wherein,the weight coefficient is the weight coefficient; />The chargeable and dischargeable times under the rated working condition are obtained; />Is a standard continuous discharge current; />The optimal charge and discharge depth is set; />Is the activation energy in the charge and discharge process; />The value is (1, 2) for the compensation coefficient; />The charge-discharge conversion efficiency is the same; />Is the internal resistance; />-said default operating temperature; />The current is the current charging and discharging current; / >Is a standard charging current; />Is the standard discharge current; />The number of times of charging and discharging can be set under the maximum sustainable charging and discharging current condition; />A standard continuous charging current; />Is the maximum sustainable charging current; />Is the maximum sustainable discharge current; />Is the current state of health; />The maximum safe charge-discharge current; />The battery charge and discharge time is the battery charge and discharge time under the condition of single power unchanged.
7. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of the preceding claims 1 to 5 when the computer program is executed.
8. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any of the preceding claims 1 to 5.
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