Operation regulation and control method, device and equipment of micro-grid and medium

Abstract

The invention discloses an operation regulation and control method, device and equipment of a micro-grid and a medium, and belongs to the technical field of micro-grids, and the method comprises the steps: determining operation constraint conditions of an energy storage system and an electric vehicle in a target micro-grid, obtaining a first boundary condition and a second boundary condition, and determining operation constraint conditions of a power grid on the target micro-grid, obtaining a third boundary condition; setting a target mathematical model according to the power demand of the user corresponding to the target micro-grid; taking the target mathematical model as a target function, taking the first boundary condition, the second boundary condition and the third boundary condition as constraint conditions, and determining optimal operation parameters of the target microgrid by using a genetic algorithm; and according to the optimal operation parameters of the target micro-grid, the operation states of a household electricity load, a photovoltaic system, an energy storage system and an electric vehicle are regulated and controlled. Through the method, the oscillation phenomenon of the output power of the power grid can be avoided, and safe and stable operation of the whole power system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrids, and in particular to a method, device, equipment and medium for controlling operation of a microgrid. Background Art

[0002] With the continuous growth of electricity demand, the peak load regulation pressure of the power grid is increasing. In this technical context, further integrating the power consumption mechanism of distributed microgrid groups will not only help to absorb renewable energy, but also promote the stable development of the power market. A household microgrid is a small power system built in a residential environment. The use of a household microgrid can not only improve the utilization rate of renewable energy, but also participate in the regulation of the power grid through demand response and other methods, and improve the stability of the power system during operation.

[0003] In the existing operation and control methods of household microgrids, the actual output power of the power grid and the actual needs of various devices in the household microgrid are usually not considered. The charging and discharging rate of the energy storage device in the household microgrid is only controlled according to the pre-set charging and discharging parameters to meet the power demand of various electrical devices in the household microgrid. However, in this control method, the actual output power of the power grid does not match the required power of each device in the household microgrid, which will cause the output power of the power grid to oscillate and affect the safe and stable operation of the entire power system. At present, there is no more effective solution to this technical problem. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a microgrid operation control method, device, equipment and medium to solve the technical problem in the prior art that when the operation state of the microgrid is controlled, the grid output power will oscillate and affect the safe and stable operation of the power system. The specific scheme is as follows:

[0005] In order to solve the above technical problems, the present invention provides an operation control method of a microgrid, comprising:

[0006] According to the operation data of the target microgrid, the operation constraints of the energy storage system and the electric vehicle in the target microgrid are respectively determined to obtain the first boundary condition and the second boundary condition, and the operation constraints of the power grid on the target microgrid are determined to obtain the third boundary condition; wherein the target microgrid includes a household power load, a photovoltaic system, the energy storage system and the electric vehicle;

[0007] Setting a target mathematical model according to the electricity demand of the user corresponding to the target microgrid;

[0008] Taking the target mathematical model as the objective function, taking the first boundary condition, the second boundary condition and the third boundary condition as constraint conditions, and using a genetic algorithm to determine the optimal operating parameters of the target microgrid;

[0009] The operating states of the household power load, the photovoltaic system, the energy storage system and the electric vehicle are regulated according to the optimal operating parameters of the target microgrid.

[0010] Preferably, the operation constraints of the energy storage system and the electric vehicle in the target microgrid are determined respectively according to the operation data of the target microgrid to obtain the first boundary condition and the second boundary condition, including:

[0011] Determine the operation constraint conditions of the energy storage system in the target microgrid according to the energy storage hourly calculation model, the energy storage hourly constraint model, the energy storage daily constraint model and the energy storage power constraint model to obtain the first boundary condition;

[0012] The expression of the hourly calculation model of energy storage is:

[0013] ;

[0014] In the formula, The energy storage system is The charge state at the moment, For the energy storage system The charge state corresponding to when is equal to zero, is the nominal capacity of the energy storage system, The energy storage system is The charging status parameters at the moment, The energy storage system is The discharge state parameters at the moment, is the charging efficiency of the energy storage system, is the discharge efficiency of the energy storage system, The energy storage system is Energy storage power at the moment;

[0015] The expression of the energy storage hourly constraint model is:

[0016] ;

[0017] In the formula, The energy storage system is The charge state at the moment, is the lower limit of SOC in the energy storage system, is the upper limit value of SOC in the energy storage system;

[0018] The expression of the energy storage daily constraint model is:

[0019] ;

[0020] In the formula, For the energy storage system The charge state corresponding to when is equal to zero, For the energy storage system The charge state corresponding to 24 o'clock;

[0021] The expression of the energy storage power constraint model is:

[0022] ;

[0023] In the formula, is the output power of the converter in the energy storage system, is the maximum output power of the converter in the energy storage system.

[0024] Preferably, the operation constraints of the energy storage system and the electric vehicle in the target microgrid are determined respectively according to the operation data of the target microgrid to obtain the first boundary condition and the second boundary condition, including:

[0025] Determine the operation constraint conditions of the electric vehicle in the target microgrid according to the first power model, the second power model and the third power model to obtain the second boundary condition;

[0026] The expression of the first power model is:

[0027] , ;

[0028] In the formula, For the The total power exchanged between all electric vehicles in the target microgrid and the grid at the moment, is the maximum power of a single electric vehicle charging station, is the hour during which the electric vehicle is charging or discharging with the power grid, is the number of electric vehicles in the target microgrid that are being charged or discharged from the grid, The time period during which all electric vehicles in the target microgrid are charging or discharging with the grid;

[0029] The expression of the second power model is:

[0030] ;

[0031] In the formula, For the The total power exchanged between all electric vehicles in the target microgrid and the grid at the moment, The energy required to fully charge all electric vehicles in the target microgrid;

[0032] The expression of the third power model is:

[0033] , ;

[0034] In the formula, For the The total power exchanged between all electric vehicles in the target microgrid and the grid at the moment, is the hour during which the electric vehicle is charging or discharging with the power grid, is the number of electric vehicles in the target microgrid that are being charged or discharged from the grid.

[0035] Preferably, the determining the operation constraint condition of the power grid on the target microgrid to obtain the third boundary condition includes:

[0036] Determine the operation constraint condition of the power grid on the target microgrid according to the power balance model to obtain the third boundary condition;

[0037] The expression of the power balance model is:

[0038] ;

[0039] In the formula, The target microgrid is The power exchanged with the grid at any moment, For the photovoltaic system in The predicted power generation value at the time, The household electricity load is The power demand at any time, The energy storage system is The energy storage power at the moment, For the electric vehicle The power of the moment.

[0040] Preferably, the target mathematical model is set according to the power demand of the user corresponding to the target microgrid, including:

[0041] Setting a first mathematical model according to the exchange power between the target microgrid and the power grid, and / or setting a second mathematical model according to the electricity cost of the target microgrid;

[0042] Wherein, the expression of the first mathematical model is:

[0043] ;

[0044] In the formula, The target microgrid is the power exchanged with the grid at any moment;

[0045] The expression of the second mathematical model is:

[0046] ;

[0047] In the formula, For the The purchase status parameters at the moment, For the Selling status parameters at the moment, For the The electricity purchase price at the time, For the The electricity sales price at the moment.

[0048] Preferably, the target mathematical model is used as the objective function, the first boundary condition, the second boundary condition and the third boundary condition are used as constraint conditions, and a genetic algorithm is used to determine the optimal operating parameters of the target microgrid, including:

[0049] Taking the first mathematical model as the objective function, taking the first boundary condition, the second boundary condition and the third boundary condition as constraint conditions, and using the genetic algorithm to determine the optimal operating parameters of the target microgrid, a first operating parameter group is obtained;

[0050] Taking the second mathematical model as the objective function, if the target microgrid does not need to respond to the peak power demand of the power grid, taking the first boundary condition, the second boundary condition and the third boundary condition as constraint conditions, and using the genetic algorithm to determine the optimal operating parameters of the target microgrid, to obtain a second operating parameter group;

[0051] Taking the second mathematical model as the objective function, if the target microgrid needs to respond to the peak power demand of the power grid, taking the first boundary condition, the second boundary condition, the third boundary condition and the peak constraint model as constraint conditions, and using the genetic algorithm to determine the optimal operating parameters of the target microgrid, to obtain a third operating parameter group;

[0052] Wherein, the expression of the peak constraint model is:

[0053] ;

[0054] In the formula, The target microgrid is The power exchanged with the grid at any moment, is the peak power allowed by the power grid;

[0055] The first mathematical model and the second mathematical model are used as objective functions, the first boundary condition, the second boundary condition and the third boundary condition are used as constraint conditions, and the genetic algorithm is used to determine the optimal operating parameters of the target microgrid to obtain a fourth operating parameter group.

[0056] Preferably, it also includes:

[0057] Determine the electricity cost of the target microgrid according to the first operating parameter group, the second operating parameter group, the third operating parameter group, and the fourth operating parameter group, respectively, to obtain a first electricity cost, a second electricity cost, a third electricity cost, and a fourth electricity cost;

[0058] The target microgrid has the lowest electricity cost, and the best operation plan of the target microgrid is determined according to the first electricity cost, the second electricity cost, the third electricity cost and the fourth electricity cost.

[0059] In order to solve the above technical problems, the present invention also provides an operation control device of a microgrid, comprising:

[0060] A boundary setting module, used to determine the operation constraints of the energy storage system and the electric vehicle in the target microgrid according to the operation data of the target microgrid, obtain the first boundary condition and the second boundary condition, and determine the operation constraints of the power grid on the target microgrid to obtain the third boundary condition; wherein the target microgrid includes a household power load, a photovoltaic system, the energy storage system and the electric vehicle;

[0061] A model setting module, used to set a target mathematical model according to the power demand of the user corresponding to the target microgrid;

[0062] a parameter optimization module, used to determine the optimal operating parameters of the target microgrid using the target mathematical model as the objective function, the first boundary condition, the second boundary condition and the third boundary condition as the constraint conditions, and using a genetic algorithm;

[0063] A state control module is used to regulate the operating states of the household power load, the photovoltaic system, the energy storage system and the electric vehicle according to the optimal operating parameters of the target microgrid.

[0064] In order to solve the above technical problem, the present invention further provides an electronic device, comprising:

[0065] Memory for storing computer programs;

[0066] The processor is used to implement the steps of the microgrid operation control method disclosed above when executing the computer program.

[0067] In order to solve the above technical problems, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a microgrid operation control method disclosed above are implemented.

[0068] Beneficial effects: In the operation and control method of the microgrid provided by the present invention, first, the operation constraints of the energy storage system and the electric vehicle in the target microgrid are determined respectively according to the operation data of the target microgrid, and the first boundary condition and the second boundary condition are obtained, and the operation constraints of the power grid on the target microgrid are determined to obtain the third boundary condition; wherein, the target microgrid includes household electricity loads, photovoltaic systems, energy storage systems and electric vehicles; then, the target mathematical model is set according to the electricity demand of the user corresponding to the target microgrid; thereafter, the target mathematical model is used as the objective function, and the first boundary condition, the second boundary condition and the third boundary condition are used as the constraint conditions, and the optimal operation parameters of the target microgrid are determined by using a genetic algorithm; finally, the operation status of the household electricity load, the photovoltaic system, the energy storage system and the electric vehicle in the target microgrid is controlled according to the optimal operation parameters of the target microgrid.

[0069] Compared with the prior art, in the process of using this method to regulate the operating status of household power loads, photovoltaic systems, energy storage systems and electric vehicles in the target microgrid, not only the demand response of the power grid is considered, but also the actual needs of various types of equipment in the target microgrid. At the same time, since the genetic algorithm has good global search capabilities, parallelism, adaptability and robustness, therefore, through this method, in the process of regulating various types of equipment in the target microgrid, the power supply of the power grid can be adapted to the actual needs of various types of equipment in the target microgrid, thereby avoiding the oscillation phenomenon of the power output of the power grid and ensuring the safe and stable operation of the entire power system.

[0070] Correspondingly, the microgrid operation control device, equipment and medium provided by the present invention also have the above-mentioned beneficial effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0072] Figure 1 A flow chart of a microgrid operation control method provided by an embodiment of the present invention;

[0073] Figure 2 A flowchart of another microgrid operation control method provided by an embodiment of the present invention;

[0074] Figure 3 It is the hourly power output curve diagram of the photovoltaic system, the energy storage system and the electric vehicle in the target microgrid after target optimization in the first operation scenario;

[0075] Figure 4 is an hourly power output curve diagram of the photovoltaic system, the energy storage system and the electric vehicle in the target microgrid after target optimization under the second operation scenario;

[0076] Figure 5 It is the hourly power output curve diagram of the photovoltaic system, the energy storage system and the electric vehicle in the target microgrid after target optimization under the third operation scenario;

[0077] Figure 6 1 is an hourly power output curve diagram of the photovoltaic system, the energy storage system and the electric vehicle in the target microgrid after target optimization under the fourth operation scenario;

[0078] Figure 7 is an hourly power output curve diagram of the household power load in the target microgrid in the initial state, the household power load after target optimization, and the power exchanged between the household power load and the grid after target optimization in the first operation scenario;

[0079] Figure 8 is an hourly power output curve diagram of the household power load in the target microgrid in the initial state, the household power load after target optimization, and the power exchanged between the household power load and the grid after target optimization in the second operation scenario;

[0080] Fig. 9 is an hourly power output curve diagram of the household power load in the target microgrid in the initial state, the household power load after target optimization, and the power exchanged between the household power load and the grid after target optimization under the third operation scenario;

[0081] Fig.10 is an hourly power output curve diagram of the household power load in the target microgrid in the initial state, the household power load after target optimization, and the power exchanged between the household power load and the grid after target optimization under the fourth operation scenario;

[0082] Fig.11 This is a schematic diagram of hourly electricity prices and transaction electricity prices;

[0083] Fig.12 A bar diagram showing the daily peak power output of the power grid under the initial operation situation, the first operation situation, the second operation situation, the third operation situation and the fourth operation situation;

[0084] Fig.13 A bar diagram showing the daily electricity costs required for the power grid under the initial operation condition, the first operation condition, the second operation condition, the third operation condition and the fourth operation condition;

[0085] Fig.14 A bar diagram showing the daily electricity cost savings of the target microgrid under the initial operation condition, the first operation condition, the second operation condition, the third operation condition and the fourth operation condition;

[0086] Fig.15 A structural diagram of an operation control device of a microgrid provided by an embodiment of the present invention;

[0087] Fig.16 A structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0088] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0089] See also Figure 1 , Figure 1 An operation control method of a microgrid provided in an embodiment of the present invention includes:

[0090] Step S11: determining the operating constraints of the energy storage system and the electric vehicle in the target microgrid according to the operating data of the target microgrid, obtaining the first boundary condition and the second boundary condition, and determining the operating constraints of the power grid on the target microgrid, obtaining the third boundary condition; wherein the target microgrid includes household power loads, photovoltaic systems, energy storage systems and electric vehicles;

[0091] Step S12: setting a target mathematical model according to the power demand of the user corresponding to the target microgrid;

[0092] Step S13: taking the target mathematical model as the target function, taking the first boundary condition, the second boundary condition and the third boundary condition as the constraint conditions, and using the genetic algorithm to determine the optimal operating parameters of the target microgrid;

[0093] Step S14: regulating the operating states of household power loads, photovoltaic systems, energy storage systems and electric vehicles according to the optimal operating parameters of the target microgrid.

[0094] In this embodiment, a microgrid operation and control method is provided, which can not only make the power supply of the power grid adapt to the actual needs of various types of equipment in the target microgrid, but also avoid the oscillation of the power output of the power grid and ensure the safe and stable operation of the entire power system.

[0095] In this method, the operating constraints of the energy storage system and electric vehicles in the target microgrid are first determined based on the operating data of the target microgrid; wherein the target microgrid is equipped with household electricity loads, photovoltaic systems, energy storage systems and electric vehicles.

[0096] It is understandable that the energy storage system will not only be limited by the output power of the converter during operation, but also by its stored energy. The operation data of the target microgrid contains all the operation data of the energy storage system and the attribute characteristic data corresponding to the energy storage system. Therefore, the operation constraint conditions of the energy storage system in the target microgrid can be determined based on the operation data of the target microgrid, thereby obtaining the first boundary condition.

[0097] During operation, electric vehicles are not only limited by the charging power of the charging pile, but also by the output power of the power grid. The operation data of the target microgrid contains all the operation data of the electric vehicle and the attribute characteristic data corresponding to the electric vehicle. Therefore, the operation constraint conditions of the electric vehicle in the target microgrid can be determined based on the operation data of the target microgrid, thereby obtaining the second boundary condition.

[0098] Since the various electrical devices in the target microgrid do not need to be powered by the power grid at all times, and the output power of the power grid is not constant, the operating constraints of the power grid on the target microgrid can be determined by monitoring the output power data of the power grid and the operating data of various devices in the target microgrid, thereby obtaining the third boundary condition.

[0099] After that, the target mathematical model is set according to the electricity demand of the user corresponding to the target microgrid, so as to achieve the purpose of using the target mathematical model to characterize the user's electricity demand. Specifically, in practical applications, when using the target microgrid, the user may sometimes aim to minimize the electricity cost, thereby achieving economic optimization, or sometimes aim to minimize the exchange of power with the grid, thereby reducing the impact of household power demand on the grid.

[0100] After the first boundary condition, the second boundary condition, the third boundary condition and the target mathematical model are determined, the target mathematical model can be used as the objective function, and the first boundary condition, the second boundary condition and the third boundary condition can be used as constraints, and a genetic algorithm can be used to find the optimal operating parameters of the target microgrid from the operating parameters of the target microgrid.

[0101] Specifically, when the operating parameters of the target microgrid are used to determine the optimal operating parameters of the target microgrid, a set of candidate solutions will be randomly generated according to the operating parameters of the target microgrid to form an initial population; then, the replicated individuals will be selected according to the fitness, and crossover and mutation will be performed to introduce new candidate solutions. At the same time, the fitness evaluation, selection, crossover and mutation steps are repeated until the termination condition is met and the optimal operating parameters of the target microgrid are found.

[0102] When the genetic algorithm is used to determine the optimal operating parameters of the target microgrid, it is equivalent to finding the optimal operating parameters corresponding to each device in the target microgrid. At this time, according to the optimal operating parameters of the target microgrid, the operating status of household power loads, photovoltaic systems, energy storage systems and electric vehicles in the target microgrid can be controlled in real time.

[0103] Obviously, in the process of using this method to regulate the operating status of household power loads, photovoltaic systems, energy storage systems and electric vehicles in the target microgrid, not only the demand response of the power grid is considered, but also the actual needs of various types of equipment in the target microgrid. At the same time, since the genetic algorithm has good global search capabilities, parallelism, adaptability and robustness, therefore, through this method, in the process of regulating various types of equipment in the target microgrid, the power supply of the power grid can be adapted to the actual needs of various types of equipment in the target microgrid, thereby avoiding the oscillation phenomenon of the power output of the power grid and ensuring the safe and stable operation of the entire power system.

[0104] Based on the above embodiment, this embodiment further illustrates and optimizes the technical solution. As a preferred implementation mode, the above steps: determining the operation constraints of the energy storage system and the electric vehicle in the target microgrid according to the operation data of the target microgrid, and obtaining the first boundary condition and the second boundary condition, include:

[0105] Determine the operation constraint conditions of the energy storage system in the target microgrid according to the energy storage hourly calculation model, the energy storage hourly constraint model, the energy storage daily constraint model and the energy storage power constraint model, and obtain the first boundary condition;

[0106] Among them, the expression of the hourly calculation model of energy storage is:

[0107] ;

[0108] In the formula, For the energy storage system The charge state at the moment, For energy storage systems The charge state corresponding to when is equal to zero, is the nominal capacity of the energy storage system, For the energy storage system The charging status parameters at the moment, For the energy storage system The discharge state parameters at the moment, is the charging efficiency of the energy storage system, is the discharge efficiency of the energy storage system, For the energy storage system Energy storage power at the moment;

[0109] The expression of the hourly constraint model of energy storage is:

[0110] ;

[0111] In the formula, For the energy storage system The charge state at the moment, is the lower limit of SOC in the energy storage system, is the upper limit of SOC in the energy storage system;

[0112] The expression of the energy storage daily constraint model is:

[0113] ;

[0114] In the formula, For energy storage systems The charge state corresponding to when is equal to zero, For energy storage systems The charge state corresponding to 24 o'clock;

[0115] The expression of energy storage power constraint model is:

[0116] ;

[0117] In the formula, is the output power of the converter in the energy storage system, is the maximum output power of the converter in the energy storage system.

[0118] In this embodiment, the operating constraints of the energy storage system in the target microgrid are specifically described. The first boundary condition (i.e., the operating constraints of the energy storage system) can be described by four models: an energy storage hourly calculation model, an energy storage hourly constraint model, an energy storage daily constraint model, and an energy storage power constraint model.

[0119] It should be noted that in the above model, The unit is kwh, The unit is kwh. When the energy storage system is in charging state, is a positive number. When is 1, when 1 when , and 0 otherwise.

[0120] For the convenience of description, Formula 1, Formula 2, Formula 3 and Formula 4 are used here to represent the energy storage hourly calculation model, energy storage hourly constraint model, energy storage daily constraint model and energy storage power constraint model respectively. Among them, Formula 1 represents the calculation of the energy storage system from zero to The ratio of the charging capacity / discharging capacity to the nominal capacity of the energy storage system at the time is added to the SOC (State of Charge) of the energy storage system at the initial time to obtain the energy storage system at time Formula 2 indicates that the SOC of the energy storage system during operation should be within the allowable SOC range. If the deviation is large, the daily operation strategy of the target microgrid will be greatly different. , which will greatly reduce the flexibility of the energy storage daily constraint model. Therefore, in order to ensure the flexibility of the energy storage daily constraint model, it is necessary to limit , and thus we get formula 3. Formula 4 shows that during the charging or discharging process of the energy storage system, its allowable power range is limited by the maximum output power of the converter in the energy storage system. .

[0121] It should be noted that in practical applications, and Determined by the rated power and capacity of the energy storage system installed in the target microgrid. and Determined by the equipment parameters of the energy storage system installed in the target microgrid. For daily The initial value of SOC at 0 o'clock can be set by the user when running this solution for the first time. For example, if the time-of-use electricity price valley period is after 0 o'clock, If the time-of-use electricity price exists before 0 o'clock Hours of valley time, then ,but, No more than When running this plan the next day, For the previous day SOC of the energy storage system. and Set by the equipment parameters of the energy storage system, usually, , .

[0122] Obviously, the technical solution provided by this embodiment is equivalent to using mathematical models to describe the first boundary condition. Through these mathematical models, various constraints of the energy storage system can be qualitatively and quantitatively analyzed, thereby making the setting result of the first boundary condition more accurate and reliable.

[0123] Based on the above embodiment, this embodiment further illustrates and optimizes the technical solution. As a preferred implementation mode, the above steps: determining the operation constraints of the energy storage system and the electric vehicle in the target microgrid according to the operation data of the target microgrid, and obtaining the first boundary condition and the second boundary condition, include:

[0124] Determine the operation constraint conditions of the electric vehicle in the target microgrid according to the first power model, the second power model and the third power model to obtain the second boundary condition;

[0125] Among them, the expression of the first power model is:

[0126] , ;

[0127] In the formula, For the The total power exchanged between all electric vehicles and the grid in the target microgrid at the moment, is the maximum power of a single electric vehicle charging station, The hours when the electric vehicle is charging or discharging from the grid. is the number of electric vehicles in the target microgrid that are charging or discharging with the grid, It is the time period when all electric vehicles in the target microgrid are charging or discharging with the grid;

[0128] The expression of the second power model is:

[0129] ;

[0130] In the formula, For the The total power exchanged between all electric vehicles and the grid in the target microgrid at the moment, The energy required to fully charge all EVs in the target microgrid;

[0131] The expression of the third power model is:

[0132] , ;

[0133] In the formula, For the The total power exchanged between all electric vehicles and the grid in the target microgrid at the moment, The hours when the electric vehicle is charging or discharging from the grid. is the number of electric vehicles in the target microgrid that are charging or discharging with the grid.

[0134] In this embodiment, the operating constraints of electric vehicles in the target microgrid are specifically described. The second boundary condition (i.e., the operating constraints of electric vehicles) can be described by three models: the first power model, the second power model, and the third power model. It should be noted that and The unit is kw. When the electric vehicle is in charging state, Is a positive number. The unit is kwh.

[0135] For the convenience of description, Formula 5, Formula 6 and Formula 7 are used here to represent the first power model, the second power model and the third power model respectively. Among them, Formula 5 indicates that during the charging or discharging time period, the power of electric vehicles in the target microgrid shall not exceed the maximum power of a single charging pile multiplied by the number of vehicles being charged or discharged with the power grid. Formula 6 indicates that by the end of the charging and discharging time period between the electric vehicles in the target microgrid and the power grid, all electric vehicles are fully charged. That is, at the end of the charging or discharging time period, the SOC of each electric vehicle in the target microgrid reaches 100%. Formula 7 indicates that in one day, except for During other time periods, the power exchanged between electric vehicles and the grid is zero.

[0136] It should be noted that in actual applications, since users have more time to charge electric vehicles during the night off-peak hours, electric vehicles are usually charged using 7kw and 3.5kw AC slow charging piles. It is set by the rated power of the charging pile used by the user's home. The number of electric vehicles that need to be charged for user households. The number of hours during the off-peak period. Determined by the parameters of the electric vehicle used by the user's family.

[0137] Obviously, through the technical solution provided in this embodiment, a mathematical model can be used to describe the operating constraints of the electric vehicle, thereby making the operating constraints of the electric vehicle more credible.

[0138] Based on the above embodiment, this embodiment further illustrates and optimizes the technical solution. As a preferred implementation method, the above steps: determining the operation constraint conditions of the power grid on the target microgrid, and obtaining the third boundary condition, include:

[0139] Determine the operation constraints of the power grid on the target microgrid according to the power balance model, and obtain the third boundary condition;

[0140] Among them, the expression of the power balance model is:

[0141] ;

[0142] In the formula, The target microgrid is The power exchanged with the grid at all times, For photovoltaic systems The predicted power generation value at the time, For household electricity load The power demand at any time, For the energy storage system The energy storage power at the moment, For electric vehicles The power of the moment.

[0143] In this embodiment, the operating constraints of the power grid on the target microgrid are specifically described. Specifically, the power balance model is used to describe the operating constraints of the power grid on the target microgrid. In the power balance model, , , , and The unit is kw.

[0144] If the power balance model is expressed by Formula 8, then Formula 8 indicates that the power grid and the target microgrid are The exchange power of the photovoltaic system at the moment The power generation forecast at the time minus the household power load, energy storage system and electric vehicle The power required at all times.

[0145] It should be noted that in practical applications, the power generation of a photovoltaic system is related to the rated power, light intensity and ambient temperature of the photovoltaic system. The power generation prediction model of the photovoltaic system used in this application is as follows:

[0146] ;

[0147] In the formula, is the rated power of the PV system, which is determined by the installed PV capacity in the target microgrid. For the The solar radiation intensity at each moment is obtained from the meteorological forecast data of the district or county where the target microgrid is located. is the solar radiation intensity under standard conditions, obtained from the photovoltaic system design parameters. is the relationship coefficient between component power and temperature, which is obtained from the photovoltaic system design parameters. is the photovoltaic operating temperature under standard conditions, which is obtained from the photovoltaic system design parameters. For the The hourly ambient temperature of the photovoltaic system is obtained from the meteorological forecast data of the district or county where the target microgrid is located.

[0148] Obviously, in this embodiment, by using Formula 8 to constrain the grid's operating state of each device in the target microgrid, the power supply and demand balance between the grid and each device in the target microgrid can be ensured.

[0149] Based on the above embodiment, this embodiment further illustrates and optimizes the technical solution. As a preferred implementation method, the above steps: setting a target mathematical model according to the power demand of the user corresponding to the target microgrid includes:

[0150] Setting a first mathematical model according to the exchange power between the target microgrid and the power grid, and / or setting a second mathematical model according to the electricity cost of the target microgrid;

[0151] Among them, the expression of the first mathematical model is:

[0152] ;

[0153] In the formula, The target microgrid is The power exchanged with the grid at all times;

[0154] The expression of the second mathematical model is:

[0155] ;

[0156] In the formula, For the The purchase status parameters at the moment, For the Selling status parameters at the moment, For the The electricity purchase price at the time, For the The electricity sales price at the moment.

[0157] In this embodiment, when setting the target mathematical model according to the electricity demand of the user corresponding to the target microgrid, two mathematical models are set, one is to set the first mathematical model according to the exchange power between the target microgrid and the power grid, and the other is to set the second mathematical model according to the electricity cost of the target microgrid.

[0158] If Formula 9 and Formula 10 are used to represent the first mathematical model and the second mathematical model respectively, then the first mathematical model represents that the target microgrid exchanges power with the grid to the minimum, and its purpose is to reduce the impact of changes in household power demand where the target microgrid is located on the grid, while the second mathematical model represents the benefits obtained by considering the target microgrid, and its purpose is to minimize the electricity cost required by the target microgrid.

[0159] Since minimizing power exchange between the target microgrid and the power grid and minimizing the electricity cost required by the target microgrid are the two most common desired goals for users of household microgrids in real life, when the first mathematical model and / or the second mathematical model is used to characterize the electricity demand of users of the target microgrid, the universality of the technical solution provided in this application in practical applications can be improved.

[0160] As a preferred implementation, the above steps: taking the target mathematical model as the objective function, taking the first boundary condition, the second boundary condition and the third boundary condition as the constraint conditions, and using the genetic algorithm to determine the optimal operating parameters of the target microgrid include:

[0161] Taking the first mathematical model as the objective function, taking the first boundary condition, the second boundary condition and the third boundary condition as the constraint conditions, and using the genetic algorithm to determine the optimal operating parameters of the target microgrid, a first operating parameter group is obtained;

[0162] Taking the second mathematical model as the objective function, if the target microgrid does not need to respond to the peak power demand of the power grid, the first boundary condition, the second boundary condition and the third boundary condition are taken as constraint conditions, and the genetic algorithm is used to determine the optimal operating parameters of the target microgrid to obtain the second operating parameter group;

[0163] Taking the second mathematical model as the objective function, if the target microgrid needs to respond to the peak power demand of the power grid, the first boundary condition, the second boundary condition, the third boundary condition and the peak constraint model are taken as constraint conditions, and the genetic algorithm is used to determine the optimal operating parameters of the target microgrid to obtain a third operating parameter group;

[0164] The expression of the peak constraint model is:

[0165] ;

[0166] In the formula, The target microgrid is The power exchanged with the grid at all times, is the peak power allowed by the power grid;

[0167] The first mathematical model and the second mathematical model are used as objective functions, the first boundary condition, the second boundary condition and the third boundary condition are used as constraint conditions, and the optimal operating parameters of the target microgrid are determined by using a genetic algorithm to obtain a fourth operating parameter group.

[0168] In this embodiment, when the first mathematical model and / or the second mathematical model is used to characterize the electricity demand of the target microgrid users, the following four cases can be used to find the optimal operating parameters of the target microgrid.

[0169] The first case is to use the first mathematical model as the objective function, and the first boundary condition, the second boundary condition and the third boundary condition as the constraint conditions, and use the genetic algorithm to determine the optimal operating parameters of the target microgrid, thereby obtaining the first operating parameter group. In this case, it is equivalent to minimizing the power exchange between the target microgrid and the power grid, and its purpose is to achieve the self-sufficiency of the internal equipment of the microgrid as much as possible, so as to prevent the power fluctuation of the target microgrid from affecting and interfering with the operation of the power grid. At this time, the target microgrid does not participate in the demand response during the peak load period of the power grid, and the demand power of the target microgrid will not be restricted.

[0170] The second case is to use the second mathematical model as the objective function. If the target microgrid does not need to respond to the peak power demand of the power grid, the first boundary condition, the second boundary condition and the third boundary condition are used as constraints, and the genetic algorithm is used to determine the optimal operating parameters of the target microgrid, thereby obtaining the second operating parameter group. In this case, it is equivalent to taking the lowest electricity cost of the target microgrid as the target, and in this process, the target microgrid will not participate in the demand response during the peak load period of the power grid, nor will it limit the power demand of the target microgrid.

[0171] The third case is to use the second mathematical model as the objective function. If the target microgrid needs to respond to the peak electricity demand of the power grid, the first boundary condition, the second boundary condition, the third boundary condition and the peak constraint model are used as constraints, and a genetic algorithm is used to determine the optimal operating parameters of the target microgrid, thereby obtaining the third operating parameter group. In this case, it is equivalent to taking the lowest electricity cost of the target microgrid as the goal, and in this process, the target microgrid will also participate in the demand response during the peak load period of the power grid. Through this setting method, not only can the electricity cost of the target microgrid be minimized, but the target microgrid can also participate in the demand response during the peak load period of the power grid.

[0172] The fourth case is to use the first mathematical model and the second mathematical model as the objective function, and use the first boundary condition, the second boundary condition and the third boundary condition as the constraint conditions, and use the genetic algorithm to determine the optimal operating parameters of the target microgrid, thereby obtaining the fourth operating parameter group. In this case, it is equivalent to finding the optimal parameters of the target microgrid during operation with the goal of minimizing the power exchange between the target microgrid and the grid and minimizing the electricity cost required by the target microgrid.

[0173] It should be noted that in this application, Determined by the peak limits of the power grid and the user's home.

[0174] See also Figure 2 , Figure 2 A flowchart of another microgrid operation control method provided by an embodiment of the present invention, the method comprising:

[0175] Step S101: obtaining the operating parameters of the target microgrid and generating an initial population;

[0176] Step S102: Determine whether the maximum number of iterations has been reached, if so, execute step S103; if not, execute step S104;

[0177] Step S103: outputting the optimal operating parameters of the target microgrid;

[0178] Step S104: Generate a parent population;

[0179] Step S105: select replicated individuals according to fitness, and perform crossover and mutation to introduce a new population;

[0180] Step S106: Execute formula 1 to formula 8;

[0181] Step S107: If the target microgrid takes minimizing the power exchange with the grid as the objective function, then Formula 9 is executed, and step S108 is executed;

[0182] Step S108: determine whether the current calculation result of the genetic algorithm meets the pre-set constraints and objective function. If so, generate a new generation of population and continue to execute step S102; if not, continue to execute step S014;

[0183] Step S109: If the target microgrid takes the lowest electricity cost as the objective function, then Formula 10 is executed to determine whether the target microgrid needs to respond to the peak electricity demand of the power grid; if so, after executing Formula 11, step S108 is executed; if not, step S108 is executed;

[0184] Step S110: If the target microgrid takes minimizing power exchange with the grid and minimizing electricity cost as the objective function, then Formula 9 and Formula 10 are executed, and then step S108 is continued.

[0185] It should be noted that, in this embodiment, Formulas 1 to 8 respectively represent the energy storage hourly calculation model, the energy storage hourly constraint model, the energy storage daily constraint model, the energy storage power constraint model, the first power model, the second power model, the third power model and the power balance model, while Formulas 9 to 11 respectively represent the first mathematical model, the second mathematical model and the peak constraint model.

[0186] When the target microgrid takes minimizing power exchange with the grid as the objective function or minimizing electricity cost as the objective function, in fact, one expected goal is set, and when the target microgrid takes minimizing power exchange with the grid and minimizing electricity cost as the objective function, in fact, two expected goals are set at the same time. The number of goals set in the objective function will also affect the convergence speed of the genetic algorithm. In this application, the goals in the objective function are basically divided into two categories. This setting method can also improve the convergence speed and operation speed of the genetic algorithm.

[0187] Obviously, through the technical solution provided in this embodiment, the optimal operating parameters corresponding to the target microgrid under various expected electricity demands can be calculated using a genetic algorithm.

[0188] As a preferred embodiment, the method further comprises:

[0189] Determine the electricity cost of the target microgrid according to the first operating parameter group, the second operating parameter group, the third operating parameter group, and the fourth operating parameter group, respectively, to obtain a first electricity cost, a second electricity cost, a third electricity cost, and a fourth electricity cost;

[0190] The goal is to minimize the electricity cost of the target microgrid, and the best operation plan of the target microgrid is determined according to the first electricity cost, the second electricity cost, the third electricity cost and the fourth electricity cost.

[0191] In this embodiment, when the first operating parameter group, the second operating parameter group, the third operating parameter group and the fourth operating parameter group are calculated, it is equivalent to determining the optimal operating parameters of the target microgrid under the above four situations. In this case, in order to determine the best operating plan of the target microgrid in the subsequent process, the electricity cost of the target microgrid can also be determined according to the first operating parameter group, the second operating parameter group, the third operating parameter group and the fourth operating parameter group, and thus the first electricity cost, the second electricity cost, the third electricity cost and the fourth electricity cost are obtained.

[0192] After that, the target microgrid's electricity cost is minimized, and the optimal operation plan of the target microgrid is determined based on the first electricity cost, the second electricity cost, the third electricity cost, and the fourth electricity cost. That is, the operating condition of the target microgrid corresponding to the lowest electricity cost is the optimal operation plan of the target microgrid. In the optimal operation plan of the target microgrid, not only the operating constraints in the genetic algorithm are restricted, but also the objective function in the genetic algorithm is restricted. That is, according to the optimal operation plan of the target microgrid, it can be determined under what electricity demand the users of the target microgrid need to have the lowest electricity cost.

[0193] Obviously, through the technical solution provided in this embodiment, the optimal operation scheme of the target microgrid in the subsequent operation process can also be inferred based on the optimal operation parameters of the target microgrid calculated by the genetic algorithm.

[0194] In order to enable those skilled in the art to more clearly and understandably implement the implementation principle of the technical solution provided by the present application, the aforementioned technical solution is described in detail herein through an application scenario embodiment.

[0195] In this embodiment, it is assumed that the target microgrid is equipped with an energy storage system, a photovoltaic system, a household power load and an electric car. Among them, the power of the energy storage system is 12kw, the capacity is 24kwh, the SOC range is 20%~100%, the initial value of SOC is 50%, and the charging and discharging efficiency of the energy storage system is 0.95. The installed capacity of the photovoltaic system is 15kw, the maximum power of a single electric vehicle charging pile is 7kw, the amount of electricity required to fully charge an electric vehicle is 30kwh, and the charging and discharging period of the electric vehicle is 0~8 hours during the valley period of the power grid. The peak power allowed by the power grid is 10kw. The population size of the genetic algorithm is 80, the termination number is 500, the crossover probability is 0.9, the mutation rate is 0.01, and the tolerance is 1*10 -12 .

[0196] Specifically, the above parameters can be set as follows:

[0197] , , , , , , , , , .

[0198] See also Figures 3 to 6 , Figure 3 It is the hourly power output curve diagram of the photovoltaic system, the energy storage system and the electric vehicle in the target microgrid after target optimization in the first operation scenario; Figure 4 is an hourly power output curve diagram of the photovoltaic system, the energy storage system and the electric vehicle in the target microgrid after target optimization under the second operation scenario; Figure 5 It is the hourly power output curve diagram of the photovoltaic system, the energy storage system and the electric vehicle in the target microgrid after target optimization under the third operation scenario; Figure 6 Hourly power output curves of the photovoltaic system, energy storage system and electric vehicle after target optimization in the target microgrid under the fourth operating scenario.

[0199] exist Figures 3 to 6 In the PV system, if the output power is positive, it means that the PV system is generating electricity. If the output power of an electric vehicle is positive, it means that the electric vehicle is charging, and if the output power of an electric vehicle is negative, it means that the electric vehicle is discharging.

[0200] See also Figures 7 to 10 , Figure 7 is an hourly power output curve diagram of the household power load in the target microgrid in the initial state, the household power load after target optimization, and the power exchanged between the household power load and the grid after target optimization in the first operation scenario; Figure 8 is an hourly power output curve diagram of the household power load in the target microgrid in the initial state, the household power load after target optimization, and the power exchanged between the household power load and the grid after target optimization in the second operation scenario; Fig. 9 is an hourly power output curve diagram of the household power load in the target microgrid in the initial state, the household power load after target optimization, and the power exchanged between the household power load and the grid after target optimization under the third operation scenario; Fig.10 Hourly power output curves of household power loads in the target microgrid under the fourth operating scenario in the initial state, the household power loads after target optimization, and the power exchanged between the household power loads and the grid after target optimization.

[0201] exist Figures 7 to 10In the calculation, if the power corresponding to the household power load in the initial state is positive, it means that the household power load is using electricity. If the power exchanged between the household power load and the grid after target optimization is positive, it means that the household power load is sending electricity to the grid. If the power exchanged between the household power load and the grid after target optimization is negative, it means that the household power load is using electricity.

[0202] See also Fig.11 , Fig.11 This is a schematic diagram of hourly electricity prices and transaction prices. Fig.11 It can be seen that the electricity price during the peak period of the power grid is 0.85 yuan / kwh, and the electricity price during the trough period of the power grid is one-third of the electricity price during the peak period of the power grid. Excluding the normal period, the power grid's electricity sales price is 60% of the electricity price during the peak period of the power grid.

[0203] See also Figure 12 to Figure 14 , Fig.12 Schematic diagram of the daily peak power output of the power grid under the initial operation scenario, the first operation scenario, the second operation scenario, the third operation scenario and the fourth operation scenario. Fig.13 A bar diagram showing the daily electricity costs required for the power grid under the initial operation condition, the first operation condition, the second operation condition, the third operation condition and the fourth operation condition; Fig.14 It is a bar diagram showing the daily electricity cost savings of the target microgrid under the initial operation condition, the first operation condition, the second operation condition, the third operation condition and the fourth operation condition.

[0204] It should be noted that in Figures 3 to 10 as well as Figure 12 to Figure 14 In the first operating scenario, the target microgrid takes minimizing power exchange with the grid as the optimization goal to find the optimal operating parameters of the target microgrid. The second operating scenario refers to the target microgrid taking the lowest electricity cost as the optimization goal, and in this process, the target microgrid will not participate in the demand response during the peak-shaving period of the grid to find the optimal operating parameters of the target microgrid. The third operating scenario refers to the target microgrid taking the lowest electricity cost as the optimization goal, and in this process, the target microgrid will participate in the demand response during the peak-shaving period of the grid to find the optimal operating parameters of the target microgrid. The fourth operating scenario refers to the target microgrid minimizing power exchange with the grid and taking the lowest electricity cost as the optimization goals to find the optimal operating parameters of the target microgrid. And Figure 12 to Figure 14 The initial operation situation in the target microgrid refers to the situation where only photovoltaic systems, household electricity loads and electric vehicles are installed, and no energy storage system is installed.

[0205] In addition, Figure 12 to Figure 14In the figure, initial situation, situation 1, situation 2, situation 3 and situation 4 are used to represent the initial operation situation, the first operation situation, the second operation situation, the third operation situation and the fourth operation situation respectively.

[0206] As can be seen from the above figures, in the first operating situation of the target microgrid, the energy storage system is mainly used to charge electric vehicles during the valley period of the power grid, and thereby reduce the charging power of electric vehicles relying on the power grid for charging. At this time, the peak power of the target microgrid purchasing electricity from the power grid is 10.1kw. The energy storage system will store the excess power generated by the photovoltaic system at noon and discharge it at night (the peak period of power consumption of the power grid), thereby improving the utilization rate of renewable energy. In the initial operation situation, the electricity cost of the target microgrid is 51.11 yuan / day, and the electricity cost of the target microgrid after target optimization is 40.07 yuan / day. In this case, although the objective function of the genetic algorithm is not the lowest electricity cost of the target microgrid, the target microgrid minimizes the exchange of power with the power grid, which can still save 22% of the electricity cost compared to the initial operation situation.

[0207] In the second operation scenario, since the target microgrid does not limit the power of electricity exchange with the grid, the peak power of the target microgrid purchased from the grid is 19.4kw, and the electric vehicles are charged at the maximum power during the off-peak period of the grid. The energy storage system is charged during the off-peak period of the grid and stores the excess power generated by the photovoltaic system during the day to cope with the household load power demand during the peak period of the grid, which can reduce the power purchased during the peak period of the grid and improve the utilization rate of renewable energy. After the target optimization, the electricity cost required by the target microgrid is the lowest among the four operation scenarios, which is 37.73 yuan / day, saving 26% of the electricity cost compared with the initial operation scenario.

[0208] In the third operation scenario of the target microgrid, the peak power of the power grid is no more than 10kw, and the electricity cost required by the target microgrid is 43.57 yuan / day, which is relatively high, saving 15% of the electricity cost compared to the initial operation scenario. Since the user participates in the demand response of the power grid during the peak power consumption, the user should also obtain additional economic compensation from the power grid.

[0209] In the fourth operation scenario, the target microgrid requires 44.12 yuan / day of electricity, which is the highest among the four operation scenarios. Compared with the initial operation scenario, the electricity cost is saved by 14%. At this time, the peak power purchased by the target microgrid from the grid is 14.8kw.

[0210] See also Fig.15 , Fig.15 A structural diagram of a microgrid operation control device provided by an embodiment of the present invention, the device comprising:

[0211] The boundary setting module 21 is used to determine the operation constraints of the energy storage system and the electric vehicle in the target microgrid according to the operation data of the target microgrid, obtain the first boundary condition and the second boundary condition, and determine the operation constraints of the power grid on the target microgrid to obtain the third boundary condition; wherein the target microgrid includes household power loads, a photovoltaic system, the energy storage system and the electric vehicle;

[0212] A model setting module 22, used to set a target mathematical model according to the power demand of the user corresponding to the target microgrid;

[0213] A parameter optimization module 23, used to use the target mathematical model as the objective function, the first boundary condition, the second boundary condition and the third boundary condition as constraint conditions, and use a genetic algorithm to determine the optimal operating parameters of the target microgrid;

[0214] The state control module 24 is used to regulate the operating states of the household power load, the photovoltaic system, the energy storage system and the electric vehicle according to the optimal operating parameters of the target microgrid.

[0215] Preferably, the boundary setting module 21 includes:

[0216] A first setting unit is used to determine the operation constraint conditions of the energy storage system in the target microgrid according to the energy storage hourly calculation model, the energy storage hourly constraint model, the energy storage daily constraint model and the energy storage power constraint model to obtain the first boundary condition;

[0217] The expression of the hourly calculation model of energy storage is:

[0218] ;

[0219] In the formula, The energy storage system is The charge state at the moment, For the energy storage system The charge state corresponding to when is equal to zero, is the nominal capacity of the energy storage system, The energy storage system is The charging status parameters at the moment, The energy storage system is The discharge state parameters at the moment, is the charging efficiency of the energy storage system, is the discharge efficiency of the energy storage system, The energy storage system is Energy storage power at the moment;

[0220] The expression of the energy storage hourly constraint model is:

[0221] ;

[0222] In the formula, The energy storage system is The charge state at the moment, is the lower limit of SOC in the energy storage system, is the upper limit value of SOC in the energy storage system;

[0223] The expression of the energy storage daily constraint model is:

[0224] ;

[0225] In the formula, For the energy storage system The charge state corresponding to when is equal to zero, For the energy storage system The charge state corresponding to 24 o'clock;

[0226] The expression of the energy storage power constraint model is:

[0227] ;

[0228] In the formula, is the output power of the converter in the energy storage system, is the maximum output power of the converter in the energy storage system.

[0229] Preferably, the boundary setting module 21 includes:

[0230] A second setting unit is used to determine the operation constraint conditions of the electric vehicle in the target microgrid according to the first power model, the second power model and the third power model to obtain the second boundary condition;

[0231] The expression of the first power model is:

[0232] , ;

[0233] In the formula, For the The total power exchanged between all electric vehicles in the target microgrid and the grid at the moment, is the maximum power of a single electric vehicle charging station, is the hour during which the electric vehicle is charging or discharging with the power grid, is the number of electric vehicles in the target microgrid that are being charged or discharged from the grid, The time period during which all electric vehicles in the target microgrid are charging or discharging with the grid;

[0234] The expression of the second power model is:

[0235] ;

[0236] In the formula, For the The total power exchanged between all electric vehicles in the target microgrid and the grid at the moment, The energy required to fully charge all electric vehicles in the target microgrid;

[0237] The expression of the third power model is:

[0238] , ;

[0239] In the formula, For the The total power exchanged between all electric vehicles in the target microgrid and the grid at the moment, is the hour during which the electric vehicle is charging or discharging with the power grid, is the number of electric vehicles in the target microgrid that are being charged or discharged from the grid.

[0240] Preferably, the boundary setting module 21 includes:

[0241] A third setting unit, configured to determine the operation constraint condition of the power grid on the target microgrid according to the power balance model, and obtain the third boundary condition;

[0242] The expression of the power balance model is:

[0243] ;

[0244] In the formula, The target microgrid is The power exchanged with the grid at any moment, For the photovoltaic system in The predicted power generation value at the time, The household electricity load is The power demand at any time, The energy storage system is The energy storage power at the moment, For the electric vehicle The power of the moment.

[0245] Preferably, the model setting module 22 includes:

[0246] A model setting unit, configured to set a first mathematical model according to the exchange power between the target microgrid and the power grid, and / or set a second mathematical model according to the electricity cost of the target microgrid;

[0247] Wherein, the expression of the first mathematical model is:

[0248] ;

[0249] In the formula, The target microgrid is the power exchanged with the grid at any moment;

[0250] The expression of the second mathematical model is:

[0251] ;

[0252] In the formula, For the The purchase status parameters at the moment, For the Selling status parameters at the moment, For the The electricity purchase price at the time, For the The electricity sales price at the moment.

[0253] Preferably, the parameter optimization module 23 includes:

[0254] A first optimization unit is used to use the first mathematical model as an objective function, the first boundary condition, the second boundary condition and the third boundary condition as constraint conditions, and use the genetic algorithm to determine the optimal operating parameters of the target microgrid to obtain a first operating parameter group;

[0255] a second optimization unit, configured to use the second mathematical model as an objective function, and if the target microgrid does not need to respond to the peak power demand of the power grid, use the first boundary condition, the second boundary condition and the third boundary condition as constraint conditions, and use the genetic algorithm to determine the optimal operating parameters of the target microgrid, so as to obtain a second operating parameter group;

[0256] a third optimization unit, configured to use the second mathematical model as an objective function, and if the target microgrid needs to respond to the peak power demand of the power grid, use the first boundary condition, the second boundary condition, the third boundary condition and the peak constraint model as constraint conditions, and use the genetic algorithm to determine the optimal operating parameters of the target microgrid, so as to obtain a third operating parameter group;

[0257] Wherein, the expression of the peak constraint model is:

[0258] ;

[0259] In the formula, The target microgrid is The power exchanged with the grid at any moment, is the peak power allowed by the power grid;

[0260] The fourth optimization unit is used to use the first mathematical model and the second mathematical model as objective functions, the first boundary condition, the second boundary condition and the third boundary condition as constraints, and use the genetic algorithm to determine the optimal operating parameters of the target microgrid to obtain a fourth operating parameter group.

[0261] Preferably, it also includes:

[0262] a cost calculation module, configured to determine the electricity cost of the target microgrid according to the first operating parameter group, the second operating parameter group, the third operating parameter group, and the fourth operating parameter group, respectively, to obtain a first electricity cost, a second electricity cost, a third electricity cost, and a fourth electricity cost;

[0263] A scheme selection module is used to take the lowest electricity cost of the target microgrid as the goal, and determine the best operation scheme of the target microgrid according to the first electricity cost, the second electricity cost, the third electricity cost and the fourth electricity cost.

[0264] An operation control device for a microgrid provided in an embodiment of the present invention has the beneficial effects of the operation control method for a microgrid disclosed above.

[0265] See also Fig.16 , Fig.16 A structural diagram of an electronic device provided by an embodiment of the present invention, the electronic device comprising:

[0266] A memory 31, used for storing computer programs;

[0267] The processor 32 is used to implement the steps of the microgrid operation control method disclosed above when executing the computer program.

[0268] An electronic device provided by an embodiment of the present invention has the beneficial effects of the aforementioned microgrid operation control method disclosed above.

[0269] Correspondingly, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a microgrid operation and control method disclosed above are implemented.

[0270] A computer-readable storage medium provided in an embodiment of the present invention has the beneficial effects of the aforementioned microgrid operation control method disclosed above.

[0271] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0272] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0273] The above is a detailed introduction to the operation and control method, device, equipment and medium of a microgrid provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A microgrid operation control method, characterized in that: include: According to the operation data of the target microgrid, the operation constraints of the energy storage system and the electric vehicle in the target microgrid are respectively determined to obtain the first boundary condition and the second boundary condition, and the operation constraints of the power grid on the target microgrid are determined to obtain the third boundary condition; wherein the target microgrid includes a household power load, a photovoltaic system, the energy storage system and the electric vehicle; Setting a target mathematical model according to the electricity demand of the user corresponding to the target microgrid; Taking the target mathematical model as the objective function, taking the first boundary condition, the second boundary condition and the third boundary condition as constraint conditions, and using a genetic algorithm to determine the optimal operating parameters of the target microgrid; The operating states of the household power load, the photovoltaic system, the energy storage system and the electric vehicle are regulated according to the optimal operating parameters of the target microgrid.

2. The operation control method of a microgrid according to claim 1, characterized in that: The step of respectively determining the operating constraints of the energy storage system and the electric vehicle in the target microgrid according to the operating data of the target microgrid to obtain the first boundary condition and the second boundary condition includes: Determine the operation constraint conditions of the energy storage system in the target microgrid according to the energy storage hourly calculation model, the energy storage hourly constraint model, the energy storage daily constraint model and the energy storage power constraint model to obtain the first boundary condition; The expression of the hourly calculation model of energy storage is: ； In the formula, The energy storage system is The charge state at the moment, For the energy storage system The charge state corresponding to when is equal to zero, is the nominal capacity of the energy storage system, The energy storage system is The charging status parameters at the moment, The energy storage system is The discharge state parameters at the moment, is the charging efficiency of the energy storage system, is the discharge efficiency of the energy storage system, The energy storage system is Energy storage power at the moment; The expression of the energy storage hourly constraint model is: ； In the formula, The energy storage system is The charge state at the moment, is the lower limit of SOC in the energy storage system, is the upper limit value of SOC in the energy storage system; The expression of the energy storage daily constraint model is: ； In the formula, For the energy storage system The charge state corresponding to when is equal to zero, For the energy storage system The charge state corresponding to 24 o'clock; The expression of the energy storage power constraint model is: ； In the formula, is the output power of the converter in the energy storage system, is the maximum output power of the converter in the energy storage system.

3. The operation control method of a microgrid according to claim 1, characterized in that: The step of respectively determining the operating constraints of the energy storage system and the electric vehicle in the target microgrid according to the operating data of the target microgrid to obtain the first boundary condition and the second boundary condition includes: Determine the operation constraint conditions of the electric vehicle in the target microgrid according to the first power model, the second power model and the third power model to obtain the second boundary condition; The expression of the first power model is: ， ； In the formula, For the The total power exchanged between all electric vehicles in the target microgrid and the grid at the moment, is the maximum power of a single electric vehicle charging station, is the hour during which the electric vehicle is charging or discharging with the power grid, is the number of electric vehicles in the target microgrid that are being charged or discharged from the grid, The time period during which all electric vehicles in the target microgrid are charging or discharging with the grid; The expression of the second power model is: ； In the formula, For the The total power exchanged between all electric vehicles in the target microgrid and the grid at the moment, The energy required to fully charge all electric vehicles in the target microgrid; The expression of the third power model is: ， ； In the formula, For the The total power exchanged between all electric vehicles in the target microgrid and the grid at the moment, is the hour during which the electric vehicle is charging or discharging with the power grid, is the number of electric vehicles in the target microgrid that are being charged or discharged from the grid.

4. The operation control method of a microgrid according to claim 1, characterized in that: The determining of the operation constraint condition of the power grid on the target microgrid to obtain the third boundary condition includes: Determine the operation constraint condition of the power grid on the target microgrid according to the power balance model to obtain the third boundary condition; The expression of the power balance model is: ； In the formula, The target microgrid is The power exchanged with the grid at any moment, For the photovoltaic system in The predicted power generation value at the time, The household electricity load is The power demand at any time, The energy storage system is The energy storage power at the moment, For the electric vehicle The power of the moment.

5. The operation control method of a microgrid according to claim 1, characterized in that: The target mathematical model is set according to the power demand of the user corresponding to the target microgrid, including: Setting a first mathematical model according to the exchange power between the target microgrid and the power grid, and / or setting a second mathematical model according to the electricity cost of the target microgrid; Wherein, the expression of the first mathematical model is: ； In the formula, The target microgrid is the power exchanged with the grid at any moment; The expression of the second mathematical model is: ； In the formula, For the The purchase status parameters at the moment, For the Selling status parameters at the moment, For the The electricity purchase price at the time, For the The electricity sales price at the moment.

6. A microgrid operation control method according to claim 5, characterized in that: The method of using the target mathematical model as the objective function, using the first boundary condition, the second boundary condition and the third boundary condition as constraint conditions, and using a genetic algorithm to determine the optimal operating parameters of the target microgrid includes: Taking the first mathematical model as the objective function, taking the first boundary condition, the second boundary condition and the third boundary condition as constraint conditions, and using the genetic algorithm to determine the optimal operating parameters of the target microgrid, a first operating parameter group is obtained; Taking the second mathematical model as the objective function, if the target microgrid does not need to respond to the peak power demand of the power grid, taking the first boundary condition, the second boundary condition and the third boundary condition as constraint conditions, and using the genetic algorithm to determine the optimal operating parameters of the target microgrid, to obtain a second operating parameter group; Taking the second mathematical model as the objective function, if the target microgrid needs to respond to the peak power demand of the power grid, taking the first boundary condition, the second boundary condition, the third boundary condition and the peak constraint model as constraint conditions, and using the genetic algorithm to determine the optimal operating parameters of the target microgrid, to obtain a third operating parameter group; Wherein, the expression of the peak constraint model is: ； In the formula, The target microgrid is The power exchanged with the grid at any moment, is the peak power allowed by the power grid; The first mathematical model and the second mathematical model are used as objective functions, the first boundary condition, the second boundary condition and the third boundary condition are used as constraint conditions, and the genetic algorithm is used to determine the optimal operating parameters of the target microgrid to obtain a fourth operating parameter group.

7. A microgrid operation control method according to claim 6, characterized in that: Also includes: Determine the electricity cost of the target microgrid according to the first operating parameter group, the second operating parameter group, the third operating parameter group, and the fourth operating parameter group, respectively, to obtain a first electricity cost, a second electricity cost, a third electricity cost, and a fourth electricity cost; The target microgrid has the lowest electricity cost, and the best operation plan of the target microgrid is determined according to the first electricity cost, the second electricity cost, the third electricity cost and the fourth electricity cost.

8. A microgrid operation control device, characterized in that: include: A boundary setting module, used to determine the operation constraints of the energy storage system and the electric vehicle in the target microgrid according to the operation data of the target microgrid, obtain the first boundary condition and the second boundary condition, and determine the operation constraints of the power grid on the target microgrid to obtain the third boundary condition; wherein the target microgrid includes a household power load, a photovoltaic system, the energy storage system and the electric vehicle; A model setting module, used to set a target mathematical model according to the power demand of the user corresponding to the target microgrid; a parameter optimization module, used to determine the optimal operating parameters of the target microgrid using the target mathematical model as the objective function, the first boundary condition, the second boundary condition and the third boundary condition as the constraint conditions, and using a genetic algorithm; A state control module is used to regulate the operating states of the household power load, the photovoltaic system, the energy storage system and the electric vehicle according to the optimal operating parameters of the target microgrid.

9. An electronic device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of a microgrid operation control method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the microgrid operation control method according to any one of claims 1 to 7 are implemented.

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