AC microgrid intelligent energy management method and system

Abstract

The present invention provides an AC microgrid intelligent energy management method and system. The scheme includes obtaining weather forecast data, rated operating parameters and operating status parameters through sensors, Internet crawling and real-time settings; calculating wind power forecast values ​​and photovoltaic forecast values ​​at future times; calculating power balance control losses, load benefits, energy loss rates and carbon emission losses; obtaining local area settings, and calculating the first, second, and third optimal control coefficients and the first, second, and third x local optimal control coefficients; obtaining the target optimal control set, determining the control mode, and selecting the control coefficient according to the control mode, and controlling the proportion of charging, discharging and thermal power in real time; and automatically performing curve display and digital display. The scheme realizes new energy management of the corresponding microgrid by providing automatic optimal control based on carbon emissions and maximum utilization of new energy.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy power generation technology, and more specifically, to an AC microgrid intelligent energy management method and system. Background Art

[0002] The energy in microgrids is mainly renewable energy, including photovoltaic, wind energy and DC batteries with bidirectional energy feeding; in addition to renewable energy, it also includes traditional thermal power generation and hydropower generation. Since microgrids can operate both in isolated islands and in grid connection, they are applicable in many scenarios, and even ensure that the power system containing microgrid units can still supply power to important users in severe weather conditions or special periods such as military wars and severe natural disasters.

[0003] Prior to the technology of the present invention, the existing energy management methods of microgrids focused more on how to improve the power supply reliability and power quality requirements for the loads. However, with the continuous development of power technology, while pursuing higher and better safety, the economic efficiency of operation should also be fully considered. How to maximize the utilization of new energy and make the microgrid of the overall system or local system achieve economic optimization is a difficult problem that needs to be solved. Summary of the invention

[0004] In view of the above problems, the present invention proposes an AC microgrid intelligent energy management method and system, which realizes new energy management of the corresponding microgrid by providing automatic optimal control based on carbon emissions and maximum utilization of new energy.

[0005] According to a first aspect of an embodiment of the present invention, an AC microgrid intelligent energy management method is provided.

[0006] In one or more embodiments, preferably, the AC microgrid intelligent energy management method includes:

[0007] Obtain weather forecast data, rated operating parameters and operating status parameters through sensors, Internet crawling and real-time settings;

[0008] Calculate the wind power forecast value and photovoltaic forecast value at future time;

[0009] Setting the initial values ​​and update ranges of the first control coefficient, the second control coefficient, and the third control coefficient, and calculating the power balance control loss, load benefit, energy loss rate, and carbon emission loss;

[0010] Obtaining a local area setting method, and calculating a first optimal control coefficient, a second optimal control coefficient, a third optimal control coefficient, and a first x local optimal control coefficient, a second x local optimal control coefficient, and a third x local optimal control coefficient;

[0011] Obtain the target optimal control set, determine the control mode, and select the control coefficient according to the control mode to control the proportion of charging, discharging and thermal power in real time;

[0012] Automatically display curves and numbers based on the system's optimal economic index and all x-th local optimal economic indexes;

[0013] The obtaining of the local area setting mode and calculating the first optimal control coefficient, the second optimal control coefficient, the third optimal control coefficient and the first x local optimal control coefficient, the second x local optimal control coefficient and the third x local optimal control coefficient specifically include:

[0014] Extract all currently calculated power balance control losses, load benefits, energy loss rates, and carbon emission losses as calculation intermediate data, and extract the calculation intermediate data in the x-th local area;

[0015] According to the calculated intermediate data in the xth local area, all the xth local optimal economic indexes are calculated using the sixth calculation formula;

[0016] Obtaining a preset local area weighting coefficient, and using the seventh calculation formula to calculate all of the system's optimal economic indexes;

[0017] Calculate the first optimal control coefficient, the second optimal control coefficient, and the third optimal control coefficient using the eighth calculation formula;

[0018] Calculate the first x local optimal control coefficient, the second x local optimal control coefficient, and the third x local optimal control coefficient using the ninth calculation formula;

[0019] Packing all the optimal control coefficients and the x local optimal control coefficients to generate the target optimal control set;

[0020] The sixth calculation formula is:

[0021] J x =S load -YH(1+N loss )-C loss -DS

[0022] Wherein, YH is the power supply prediction value, J x is the xth local optimal economic index, S load is the load benefit, N loss is the energy loss rate, C loss is the carbon emission loss, DS is the power balance control loss;

[0023] The seventh calculation formula:

[0024]

[0025] Among them, J is the optimal economic index of the system, W x is the weighting coefficient of the xth local area, and m is the total number of local areas;

[0026] The eighth calculation formula:

[0027] (k 1z , k 2z , k 3z ) = Argmin J (k1, k2, k3)

[0028] Among them, k 1z is the first optimal control coefficient, k 2z is the second optimal control coefficient, k 3z is the third optimal control coefficient; k1 is the first control coefficient K1, k2 is the second control coefficient K2, and k3 is the third control coefficient K3; Argmin represents the parameter value of a function when it achieves the minimum value in its domain; the J(k1, k2, k3) represents the function of J (k1, k2, k3) in the seventh calculation formula, so the independent variable of J is (k1, k2, k3); Argmin J(k1, k2, k3) represents the parameter value of (k1, k2, k3) that makes J minimum; the parameter values ​​of (k1, k2, k3) that make J minimum are (k1, k2, k3) respectively. 1z , k 2z , k 3z ), which can then be stored in the target optimal control set;

[0029] The ninth calculation formula:

[0030] (k 1x , k 2x , k 3x )=Argmin J x (k1, k2, k3)

[0031] Among them, k 1x is the first x local optimal control coefficient, k 2x is the second x local optimal control coefficient, k 3x is the third x local optimal control coefficient; k1 is the first control coefficient K1, k2 is the second control coefficient K2, k3 is the third control coefficient K3; J x (k1, k2, k3) represents the x local J in the sixth calculation formula x is a function of (k1, k2, k3), so J x The independent variables are (k1, k2, k3); Argmin J x (k1, k2, k3) represents thex The smallest (k1, k2, k3) parameter value; extract the obtained J x The smallest (k1, k2, k3) parameter values ​​are (k 1x , k 2x , k 3x ), and then according to (k 1x , k 2x , k 3x ) Use the first to sixth calculation formulas to update the local optimal economic index.

[0032] In one or more embodiments, preferably, the obtaining of weather forecast data, rated operating parameters and operating status parameters by means of sensors, Internet crawling and real-time settings specifically includes:

[0033] Collect the current operating status parameters in real time through sensors;

[0034] According to the operating status parameters, real-time calculation of wind power and photovoltaic information in the current system is performed in real time to obtain real-time photovoltaic data, real-time wind power data, and real-time system power data;

[0035] Obtain the weather forecast data corresponding to the current moment by crawling the web;

[0036] The rated operating parameters of the current system are obtained based on the input of the operator.

[0037] In one or more embodiments, preferably, the calculating of the wind power prediction value and the photovoltaic prediction value at the future moment specifically includes:

[0038] Obtaining the weather forecast data and wind power real-time operation data;

[0039] Obtaining the weather forecast data and photovoltaic real-time operation data;

[0040] Calculating the wind power forecast value at a future time according to all the wind power real-time operation data and the weather forecast data;

[0041] The photovoltaic forecast value at a future time is calculated based on all the photovoltaic real-time operation data and the weather forecast data.

[0042] In one or more embodiments, preferably, the setting of the initial values ​​and update ranges of the first control coefficient, the second control coefficient, and the third control coefficient, and the calculation of the power balance control loss, the load benefit, the energy loss rate, and the carbon emission loss specifically include:

[0043] Setting a first control coefficient update range, and randomly selecting a first control coefficient initial value within the first control coefficient update range;

[0044] Obtain the maximum value of the discharge power per unit time, and calculate the power balance loss using the first calculation formula;

[0045] Calculating the energy loss rate using a second calculation formula according to the total power of all power sources and the total power of all loads;

[0046] Setting the second control coefficient update range, randomly selecting the second control coefficient initial value within the second control coefficient update range, and calculating the load benefit in combination with the third calculation formula;

[0047] Setting the third control coefficient update range, and randomly selecting the third control coefficient initial value within the third control coefficient update range;

[0048] Obtaining the total thermal power capacity, the wind power forecast value, and the photovoltaic forecast value, setting the hydropower ratio coefficient, and calculating the power forecast value in combination with the fourth calculation formula;

[0049] Obtaining the estimated fluctuation ratio and the carbon emission loss coefficient, and calculating the carbon emission loss using the fifth calculation formula;

[0050] The first calculation formula is:

[0051] DS=K1*H max

[0052] Wherein, DS is the power balance control loss, K1 is the first control coefficient, H max is the maximum value of the discharge power per unit time;

[0053] The second calculation formula is:

[0054] N loss =(D all -D load ) / D all

[0055] Among them, N loss is the energy loss rate, D all is the total power of all power supplies, D load is the total power of all loads;

[0056] The third calculation formula is:

[0057]

[0058] Among them, S load is the load benefit, L i is the weighting coefficient of the i-th load, W i is the power of the i-th load, K2 is the second control coefficient, and n is the total number of loads;

[0059] The fourth calculation formula is:

[0060] YH=K3*S h +Fmax+Gmax+YH*K WATER

[0061] Then we can get:

[0062] Among them, YH is the power supply prediction value, Z load is the weighted index of the total load power, K3 is the third control coefficient, S h is the total thermal power capacity, Fmax is the wind power forecast value, Gmax is the photovoltaic forecast value, K WATER is the hydropower ratio coefficient;

[0063] The fifth calculation formula is:

[0064] C loss =YH*(1-P b )*(1-K WATER )C k

[0065] Wherein, YH is the power supply prediction value, C loss is the carbon emission loss, P b is the estimated volatility ratio, C k is the carbon emission loss coefficient.

[0066] In one or more embodiments, preferably, the obtaining of the target optimal control set, determining the control mode, and selecting the control coefficient according to the control mode to control the proportion of charging, discharging and thermal power in real time specifically includes:

[0067] Obtain the target optimal control set, extract the first optimal control coefficient, the second optimal control coefficient, the third optimal control coefficient and the first x local optimal control coefficient, the second x local optimal control coefficient, and the third x local optimal control coefficient;

[0068] Determine a current control mode, and determine a real-time first control coefficient, a real-time second control coefficient, and a real-time third control coefficient according to the control mode;

[0069] According to the first real-time control coefficient, the duty cycle of the discharge controller at the current moment is determined to control the current discharge speed in real time;

[0070] According to the second real-time control coefficient, controlling the charging power within the current range;

[0071] According to the third real-time control coefficient, the capacity input ratio of the thermal power plants within the current range is controlled and adjusted.

[0072] In one or more embodiments, preferably, the automatic curve display and digital display according to the system optimal economic index and all x-th local optimal economic indexes specifically include:

[0073] Generate first time series data according to the optimal economic index of the system;

[0074] Generate the x+1th time series data according to the xth local optimal economic index;

[0075] Automatically display all time series data as online display curves;

[0076] The load benefit, the energy loss rate and the carbon emission loss are displayed in numerical form.

[0077] According to a second aspect of an embodiment of the present invention, an AC microgrid intelligent energy management system is provided.

[0078] In one or more embodiments, preferably, an AC microgrid intelligent energy management system is applied to the intelligent energy management method as described above, and the system includes:

[0079] Data acquisition module, used to obtain weather forecast data, rated operating parameters and operating status parameters through sensors, Internet crawling and real-time settings;

[0080] Data prediction module, used to calculate the wind power prediction value and photovoltaic prediction value at future time;

[0081] An energy loss calculation module is used to set the initial values ​​and update ranges of the first control coefficient, the second control coefficient, and the third control coefficient, and calculate the power balance control loss, load benefit, energy loss rate, and carbon emission loss;

[0082] A control generation module, used to obtain a local area setting mode, and calculate a first optimal control coefficient, a second optimal control coefficient, a third optimal control coefficient, and a first x local optimal control coefficient, a second x local optimal control coefficient, and a third x local optimal control coefficient;

[0083] The control execution module is used to obtain the target optimal control set, determine the control mode, and select the control coefficient according to the control mode to control the proportion of charging, discharging and thermal power in real time;

[0084] The application display module is used to automatically display curves and numbers based on the system optimal economic index and all x-th local optimal economic indexes.

[0085] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0086] According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the methods described in the first aspect of the embodiment of the present invention.

[0087] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0088] The present invention forms a corresponding control instruction set by performing data collection, data prediction, and data processing, and then utilizes the execution of effective control instructions to enable energy to be adaptively managed based on new energy.

[0089] In the embodiment of the present invention, the microgrid system is regulated at multiple levels at the regional and system levels on the premise of ensuring maximum utilization of new energy and optimal carbon emissions of the entire system, thereby improving energy utilization and economy in the microgrid.

[0090] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0091] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0093] Figure 1 It is a flow chart of an AC microgrid intelligent energy management method according to an embodiment of the present invention.

[0094] Figure 2 It is a flowchart of obtaining weather forecast data, rated operating parameters and operating status parameters by means of sensors, Internet crawling and real-time settings in an AC microgrid intelligent energy management method according to an embodiment of the present invention.

[0095] Figure 3 The present invention is a flowchart of calculating wind power prediction values ​​and photovoltaic prediction values ​​at future times in an AC microgrid intelligent energy management method according to an embodiment of the present invention.

[0096] Figure 4 It is a flow chart of setting the initial values ​​and update ranges of the first control coefficient, the second control coefficient, and the third control coefficient in an AC microgrid intelligent energy management method according to an embodiment of the present invention, and calculating the power balance control loss, load benefit, energy loss rate, and carbon emission loss.

[0097] Figure 5 It is a flowchart of obtaining a local area setting method in an AC microgrid intelligent energy management method according to an embodiment of the present invention, and calculating the first, second, and third optimal control coefficients and the first, second, and third x local optimal control coefficients.

[0098] Figure 6 The present invention is a flowchart of obtaining a target optimal control set, determining a control mode, selecting a control coefficient according to the control mode, and controlling the proportion of charging, discharging and thermal power in real time in an AC microgrid intelligent energy management method according to an embodiment of the present invention.

[0099] Figure 7 It is a flow chart for automatically performing curve display and digital display according to the system optimal economic index and all x-th local optimal economic indexes in an AC microgrid intelligent energy management method according to an embodiment of the present invention.

[0100] Figure 8 It is a structural diagram of an AC microgrid intelligent energy management system according to an embodiment of the present invention.

[0101] Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0102] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0103] 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0104] The energy in microgrids is mainly renewable energy, including photovoltaic, wind energy and DC batteries with bidirectional energy feeding; in addition to renewable energy, it also includes traditional thermal power generation and hydropower generation. Since microgrids can operate both in isolated islands and in grid connection, they are applicable in many scenarios, and even ensure that the power system containing microgrid units can still supply power to important users in severe weather conditions or special periods such as military wars and severe natural disasters.

[0105] Prior to the technology of the present invention, the existing energy management methods of microgrids focused more on how to improve the power supply reliability and power quality requirements for the loads. However, with the continuous development of power technology, while pursuing higher and better safety, the economic efficiency of operation should also be fully considered. How to maximize the utilization of new energy and make the microgrid of the overall system or local system achieve economic optimization is a difficult problem that needs to be solved.

[0106] In an embodiment of the present invention, a method and system for intelligent energy management of an AC microgrid are provided. The solution realizes new energy management of the corresponding microgrid by providing automatic optimal control based on carbon emissions and maximum utilization of new energy.

[0107] According to a first aspect of an embodiment of the present invention, an AC microgrid intelligent energy management method is provided.

[0108] Figure 1 It is a flow chart of an AC microgrid intelligent energy management method according to an embodiment of the present invention.

[0109] In one or more embodiments, preferably, the AC microgrid intelligent energy management method includes:

[0110] S101, obtaining weather forecast data, rated operating parameters and operating status parameters through sensors, Internet crawling and real-time settings;

[0111] S102, calculating the wind power prediction value and photovoltaic prediction value at the future time;

[0112] S103, setting initial values ​​and update ranges of the first control coefficient, the second control coefficient, and the third control coefficient, and calculating power balance control loss, load benefit, energy loss rate, and carbon emission loss;

[0113] S104, obtaining a local area setting mode, and calculating the first, second, and third optimal control coefficients and the first, second, and third x local optimal control coefficients;

[0114] S105, obtaining a target optimal control set, determining a control mode, and selecting a control coefficient according to the control mode to control the proportion of charging, discharging, and thermal power in real time;

[0115] S106. Automatically display curves and numbers based on the system optimal economic index and all x-th local optimal economic indexes.

[0116] In the real-time example of the present invention, a corresponding control instruction set is formed by performing data collection, data prediction, and data processing, and then effective control instructions are executed to complete multi-level simultaneous regulation of the microgrid system at the regional and system levels while ensuring maximum utilization of new energy and optimal carbon emissions for the entire system, thereby improving energy utilization and economy in the microgrid.

[0117] Figure 2 It is a flowchart of obtaining weather forecast data, rated operating parameters and operating status parameters by means of sensors, Internet crawling and real-time settings in an AC microgrid intelligent energy management method according to an embodiment of the present invention.

[0118] like Figure 2 As shown, in one or more embodiments, preferably, the weather forecast data, rated operating parameters and operating status parameters are obtained by means of sensors, Internet crawling and real-time settings, specifically including:

[0119] S201, collecting the current operating status parameters in real time through sensors;

[0120] S202, performing real-time calculations on wind power and photovoltaic information in the current system according to the operating status parameters, and obtaining real-time photovoltaic data, real-time wind power data, and real-time system power data;

[0121] S203, obtaining the weather forecast data corresponding to the current time by web crawling;

[0122] S204: Obtain rated operating parameters of the current system according to input from the operator.

[0123] In the embodiment of the present invention, in order to perform online analysis, energy management and operation control, a large amount of data needs to be collected. These data include weather data, rated parameters and real-time parameters. These parameters can be effectively obtained through the above method, and finally basic data can be extracted for subsequent analysis.

[0124] Figure 3The present invention is a flowchart of calculating wind power prediction values ​​and photovoltaic prediction values ​​at future times in an AC microgrid intelligent energy management method according to an embodiment of the present invention.

[0125] like Figure 3 As shown, in one or more embodiments, preferably, the calculating of the wind power prediction value and the photovoltaic prediction value at the future moment specifically includes:

[0126] S301, obtaining the weather forecast data and wind power real-time operation data;

[0127] S302, obtaining the weather forecast data and photovoltaic real-time operation data;

[0128] S303, calculating the wind power forecast value at a future time according to all the wind power real-time operation data and the weather forecast data;

[0129] S304: Calculate the photovoltaic prediction value at a future time according to all the photovoltaic real-time operation data and the weather forecast data.

[0130] In the embodiment of the present invention, in order to set all control parameters online under the premise of maximizing wind power and photovoltaic power, wind power prediction values ​​and photovoltaic prediction values ​​at future moments are firstly performed based on real-time data and historical data.

[0131] Figure 4 It is a flow chart of setting the initial values ​​and update ranges of the first control coefficient, the second control coefficient, and the third control coefficient in an AC microgrid intelligent energy management method according to an embodiment of the present invention, and calculating the power balance control loss, load benefit, energy loss rate, and carbon emission loss.

[0132] like Figure 4 As shown, in one or more embodiments, preferably, the setting of the initial values ​​and update ranges of the first control coefficient, the second control coefficient, and the third control coefficient, and the calculation of the power balance control loss, the load benefit, the energy loss rate, and the carbon emission loss specifically include:

[0133] S401, setting a first control coefficient update range, and randomly selecting a first control coefficient initial value within the first control coefficient update range;

[0134] S402, obtaining the maximum value of the discharge power per unit time, and calculating the power balance loss using the first calculation formula;

[0135] S403, calculating the energy loss rate using a second calculation formula according to the total power of all power sources and the total power of all loads;

[0136] S404, setting the second control coefficient update range, randomly selecting the second control coefficient initial value within the second control coefficient update range, and calculating the load benefit in combination with the third calculation formula;

[0137] S405, setting the third control coefficient update range, and randomly selecting the third control coefficient initial value within the third control coefficient update range;

[0138] S406, obtaining the total thermal power capacity, the wind power prediction value, and the photovoltaic prediction value, setting the hydropower ratio coefficient, and calculating the power prediction value in combination with the fourth calculation formula;

[0139] S407, obtaining an estimated fluctuation ratio and a carbon emission loss coefficient, and calculating the carbon emission loss using a fifth calculation formula;

[0140] The first calculation formula is:

[0141] DS=K1H max

[0142] Wherein, DS is the power balance control loss, K1 is the first control coefficient, H max is the maximum value of the discharge power per unit time;

[0143] The second calculation formula is:

[0144] N loss =(D all -D load ) / D all

[0145] Among them, N loss is the energy loss rate, D all is the total power of all power supplies, D load is the total power of all loads;

[0146] The third calculation formula is:

[0147]

[0148] Among them, S load is the load benefit, L i is the weighting coefficient of the i-th load, W i is the power of the i-th load, K2 is the second control coefficient, and n is the total number of loads;

[0149] The fourth calculation formula is:

[0150] YH=K3S h +Fmax+Gmax+YHK WATER

[0151] Wherein, YH is the power supply prediction value, K3 is the third control coefficient, S h is the total thermal power capacity, Fmax is the wind power forecast value, Gmax is the photovoltaic forecast value, K WATER is the hydropower ratio coefficient;

[0152] The fifth calculation formula is:

[0153] C loss =YH(1-P b )(1-K WATER )C k

[0154] Among them, C loss is the carbon emission loss, P b is the estimated volatility ratio, C k is the carbon emission loss coefficient.

[0155] In the embodiment of the present invention, in order to maximize wind power generation and photovoltaic power generation, the maximum predicted values ​​of wind power and photovoltaic power generation are utilized, and multiple parameters are calculated in combination with a preset range. These parameters include power balance control loss, load revenue, energy loss rate and carbon emission loss. These parameters can directly provide data support for subsequent regional optimization and system optimization. This time, during the calculation process, the impact of new energy volatility on system operation is fully considered, and carbon emission losses are combined to conduct a truly comprehensive online loss assessment, and information resources are fully collected and calculated.

[0156] Figure 5 It is a flowchart of obtaining a local area setting method in an AC microgrid intelligent energy management method according to an embodiment of the present invention, and calculating the first, second, and third optimal control coefficients and the first, second, and third x local optimal control coefficients.

[0157] like Figure 5 As shown, in one or more embodiments, preferably, the obtaining of the local area setting mode and the calculation of the first, second, and third optimal control coefficients and the first, second, and third x local optimal control coefficients specifically include:

[0158] S501, extracting all currently calculated power balance control losses, load benefits, energy loss rates, and carbon emission losses as calculation intermediate data, and extracting the calculation intermediate data in the x-th local area;

[0159] S502, calculating all x-th local optimal economic indexes using the sixth calculation formula according to the calculation intermediate data in the x-th local area;

[0160] S503, obtaining a preset local area weighting coefficient, and calculating all the system optimal economic indexes using the seventh calculation formula;

[0161] S504, calculating the first, second and third optimal control coefficients using the eighth calculation formula;

[0162] S505, using the ninth calculation formula to calculate the first, second, and third x local optimal control coefficients;

[0163] S506, packaging all the optimal control coefficients and the x local optimal control coefficients to generate the target optimal control set;

[0164] The sixth calculation formula is:

[0165] J x =S load -YH(1+N loss )-C loss -DS

[0166] Among them, J x is the xth local optimal economic index, S load is the load benefit, N loss is the energy loss rate, C loss is the carbon emission loss, DS is the power balance control loss;

[0167] The seventh calculation formula:

[0168]

[0169] Among them, J is the optimal economic index of the system, W x is the weighting coefficient of the xth local area, and m is the total number of local areas;

[0170] The eighth calculation formula:

[0171] (k 1z ,k 2z ,k 3z ) = Argmin J (k1, k2, k3)

[0172] Among them, k 1z ,k 2z ,k 3z are the first, second and third optimal control coefficients respectively;

[0173] The ninth calculation formula:

[0174] (k 1x ,k 2x ,k 3x )=Argmin J x(k1, k2, k3)

[0175] Among them, k 1x ,k 2x ,k 3x are the first, second and third x local optimal control coefficients respectively.

[0176] In an embodiment of the present invention, during the operation of the eighth calculation formula, a clear training range is required. Therefore, the training range specifically only includes the first control coefficient update range, the second control coefficient update range, and the third control coefficient update range; in the previous calculation, all ranges were selected and determined, but if the test is repeated without limit, the number of calculations will approach infinity. Therefore, when calculating all the power balance control losses, load benefits, energy loss rates and carbon emission losses as intermediate data for calculation, it is necessary to set an update step size, which is generally set to 5% of the length of the first control coefficient update range, the second control coefficient update range, and the third control coefficient update range as the update step size; after obtaining the x local control coefficient and the optimal control coefficient, you can proceed.

[0177] Figure 6 The present invention is a flowchart of obtaining a target optimal control set, determining a control mode, selecting a control coefficient according to the control mode, and controlling the proportion of charging, discharging and thermal power in real time in an AC microgrid intelligent energy management method according to an embodiment of the present invention.

[0178] like Figure 6 As shown, in one or more embodiments, preferably, the obtaining of the target optimal control set, determining the control mode, and selecting the control coefficient according to the control mode to control the proportion of charging, discharging and thermal power in real time specifically includes:

[0179] S601, obtaining the target optimal control set, extracting the first, second, third optimal control coefficients and the first, second, third x local optimal control coefficients;

[0180] S602, determining a current control mode, and determining a real-time first control coefficient, a real-time second control coefficient, and a real-time third control coefficient according to the control mode;

[0181] S603, determining the duty cycle of the discharge controller at the current moment according to the first real-time control coefficient, and controlling the current discharge speed in real time;

[0182] S604, controlling the charging power within the current range according to the second real-time control coefficient;

[0183] S605. According to the third real-time control coefficient, the capacity input ratio of the thermal power plants within the current range is controlled and adjusted.

[0184] In the embodiment of the present invention, the charging process, the discharging process and the thermal power generation are controlled. In this control process, the most important core is that these areas are flexible to control and are more stable equipment, unlike new energy sources that have certain intermittency and instability. Therefore, in the previous algorithm, the maximum investment ratio of new energy power generation in the region and the optimal economic operation are guaranteed. According to some pre-set operating modes, the current control mode is determined. Under different control modes, the corresponding first control coefficient, second control coefficient and third control coefficient in the corresponding target optimal control set are different. Therefore, online optimal control can be effectively performed in this way.

[0185] Figure 7 It is a flow chart for automatically performing curve display and digital display according to the system optimal economic index and all x-th local optimal economic indexes in an AC microgrid intelligent energy management method according to an embodiment of the present invention.

[0186] like Figure 7 As shown, in one or more embodiments, preferably, the automatic curve display and digital display according to the system optimal economic index and all x-th local optimal economic indexes specifically include:

[0187] S701, generating first time series data according to the optimal economic index of the system;

[0188] S702, generating x+1th time series data according to the xth local optimal economic index;

[0189] S703, automatically displaying an online display curve based on all the time series data;

[0190] S704: Display in digital form the load benefit, the energy loss rate, and the carbon emission loss.

[0191] In the embodiment of the present invention, in order to be able to display and show the energy control effect of the current microgrid system online, online analysis of all data is implemented to obtain the final real-time changes in the operating status of the entire system level and local area level.

[0192] According to a second aspect of an embodiment of the present invention, an AC microgrid intelligent energy management system is provided.

[0193] Figure 8 It is a structural diagram of an AC microgrid intelligent energy management system according to an embodiment of the present invention.

[0194] In one or more embodiments, preferably, the AC microgrid intelligent energy management system includes:

[0195] The data acquisition module 801 is used to obtain weather forecast data, rated operating parameters and operating status parameters through sensors, Internet crawling and real-time settings;

[0196] The data prediction module 802 is used to calculate the wind power prediction value and the photovoltaic prediction value at the future moment;

[0197] The energy loss calculation module 803 is used to set the initial values ​​and update ranges of the first control coefficient, the second control coefficient, and the third control coefficient, and calculate the power balance control loss, load benefit, energy loss rate, and carbon emission loss;

[0198] The control generation module 804 is used to obtain the local area setting mode and calculate the first, second, and third optimal control coefficients and the first, second, and third x local optimal control coefficients;

[0199] The control execution module 805 is used to obtain the target optimal control set, determine the control mode, and select the control coefficient according to the control mode to control the proportion of charging, discharging and thermal power in real time;

[0200] The application display module 806 is used to automatically display curves and numbers according to the system optimal economic index and all x-th local optimal economic indexes.

[0201] In the real-time example of the present invention, a corresponding control instruction set is formed by performing data collection, data prediction, and data processing, and then effective control instructions are executed to complete multi-level simultaneous regulation of the microgrid system at the regional and system levels while ensuring maximum utilization of new energy and optimal carbon emissions for the entire system, thereby improving energy utilization and economy in the microgrid.

[0202] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0203] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided. Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Fig. 9The electronic device shown is a general microgrid energy management device, which includes a general computer hardware structure, which includes at least a processor 901 and a memory 902. The processor 901 and the memory 902 are connected via a bus 903. The memory 902 is suitable for storing instructions or programs executable by the processor 901. The processor 901 can be an independent microprocessor or a collection of one or more microprocessors. Thus, the processor 901 executes the instructions stored in the memory 902, thereby executing the method flow of the embodiment of the present invention as described above to realize the processing of data and the control of other devices. The bus 903 connects the above-mentioned multiple components together, and at the same time connects the above-mentioned components to the display controller 904 and the display device and the input / output (I / O) device 905. The input / output (I / O) device 905 can be a mouse, a keyboard, a modem, a network interface, a touch input device, a somatosensory input device, a printer, and other devices known in the art. Typically, the input / output device 905 is connected to the system through an input / output (I / O) controller 906.

[0204] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0205] The present invention forms a corresponding control instruction set by performing data collection, data prediction, and data processing, and then utilizes the execution of effective control instructions to enable energy to be adaptively managed based on new energy.

[0206] In the embodiment of the present invention, the microgrid system is regulated at multiple levels at the regional and system levels on the premise of ensuring maximum utilization of new energy and optimal carbon emissions of the entire system, thereby improving energy utilization and economy in the microgrid.

[0207] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0208] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0209] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0210] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0211] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An AC microgrid intelligent energy management method, characterized in that: The method includes: Obtain weather forecast data, rated operating parameters and operating status parameters through sensors, Internet crawling and real-time settings; Calculate the wind power forecast value and photovoltaic forecast value at future time; Setting the initial values ​​and update ranges of the first control coefficient, the second control coefficient, and the third control coefficient, and calculating the power balance control loss, load benefit, energy loss rate, and carbon emission loss; Obtaining a local area setting method, and calculating a first optimal control coefficient, a second optimal control coefficient, a third optimal control coefficient, and a first x local optimal control coefficient, a second x local optimal control coefficient, and a third x local optimal control coefficient; Obtain the target optimal control set, determine the control mode, and select the control coefficient according to the control mode to control the proportion of charging, discharging and thermal power in real time; Automatically display curves and numbers based on the system's optimal economic index and all x-th local optimal economic indexes; The obtaining of the local area setting mode and calculating the first optimal control coefficient, the second optimal control coefficient, the third optimal control coefficient and the first x local optimal control coefficient, the second x local optimal control coefficient and the third x local optimal control coefficient specifically include: Extract all currently calculated power balance control losses, load benefits, energy loss rates, and carbon emission losses as calculation intermediate data, and extract the calculation intermediate data in the x-th local area; According to the calculated intermediate data in the xth local area, all the xth local optimal economic indexes are calculated using the sixth calculation formula; Obtaining a preset local area weighting coefficient, and using the seventh calculation formula to calculate all of the system's optimal economic indexes; Calculate the first optimal control coefficient, the second optimal control coefficient, and the third optimal control coefficient using the eighth calculation formula; Calculate the first x local optimal control coefficient, the second x local optimal control coefficient, and the third x local optimal control coefficient using the ninth calculation formula; Packing all the optimal control coefficients and x local optimal control coefficients to generate the target optimal control set; The sixth calculation formula is: J x =S load -YH(1+N loss )-C loss -DS Among them, YH is the power supply prediction value, J x is the xth local optimal economic index, S load is the load benefit, N loss is the energy loss rate, C loss is the carbon emission loss, DS is the power balance control loss; The seventh calculation formula: Among them, J is the optimal economic index of the system, W x is the weighting coefficient of the xth local area, and m is the total number of local areas; The eighth calculation formula: (k 1z ,k 2z ,k 3z )=Argmin J(k1,k2,k3) Among them, k 1z is the first optimal control coefficient, k 2z is the second optimal control coefficient, k 3z is the third optimal control coefficient; k1 is the first control coefficient K1, k2 is the second control coefficient K2, and k3 is the third control coefficient K3; Argmin represents the parameter value of a function when it achieves the minimum value in its domain; the J(k1, k2, k3) represents the function of J (k1, k2, k3) in the seventh calculation formula, so the independent variable of J is (k1, k2, k3); Argmin J(k1, k2, k3) represents the parameter value of (k1, k2, k3) that makes J minimum; the parameter values ​​of (k1, k2, k3) that make J minimum are (k1, k2, k3) respectively. 1z , k 2z , k 3z ), which can then be stored in the target optimal control set; The ninth calculation formula: (k 1x ,k 2x ,k 3x )=Argmin J x (k1,k2,k3) Among them, k 1x is the first x local optimal control coefficient, k 2x is the second x local optimal control coefficient, k 3x is the third x local optimal control coefficient; k1 is the first control coefficient K1, k2 is the second control coefficient K2, k3 is the third control coefficient K3; J x (k1, k2, k3) represents the x local J in the sixth calculation formula x is a function of (k1, k2, k3), so J x The independent variables are (k1, k2, k3); Argmin J x (k1, k2, k3) represents the x The smallest (k1, k2, k3) parameter value; extract the obtained J x The smallest (k1, k2, k3) parameter values ​​are (k 1x , k 2x , k 3x ), and then according to (k 1x , k 2x , k 3x ) Use the first to sixth calculation formulas to update the local optimal economic index.

2. The AC microgrid intelligent energy management method according to claim 1, characterized in that: The weather forecast data, rated operating parameters and operating status parameters are obtained by sensors, Internet crawling and real-time settings, specifically including: Collect the current operating status parameters in real time through sensors; According to the operating status parameters, real-time calculation of wind power and photovoltaic information in the current system is performed in real time to obtain real-time photovoltaic data, real-time wind power data, and real-time system power data; Obtain the weather forecast data corresponding to the current moment by crawling the web; The rated operating parameters of the current system are obtained based on the input of the operator.

3. The AC microgrid intelligent energy management method according to claim 1, characterized in that: The calculation of the wind power forecast value and the photovoltaic forecast value at the future time specifically includes: Obtaining the weather forecast data and wind power real-time operation data; Obtaining the weather forecast data and photovoltaic real-time operation data; Calculating the wind power forecast value at a future time according to all the wind power real-time operation data and the weather forecast data; The photovoltaic forecast value at a future time is calculated based on all the photovoltaic real-time operation data and the weather forecast data.

4. The AC microgrid intelligent energy management method according to claim 1, characterized in that: The setting of the initial values ​​and update ranges of the first control coefficient, the second control coefficient, and the third control coefficient, and the calculation of the power balance control loss, the load benefit, the energy loss rate, and the carbon emission loss specifically include: Setting a first control coefficient update range, and randomly selecting a first control coefficient initial value within the first control coefficient update range; Obtain the maximum value of the discharge power per unit time, and calculate the power balance loss using the first calculation formula; Calculating the energy loss rate using a second calculation formula according to the total power of all power sources and the total power of all loads; Setting the second control coefficient update range, randomly selecting the second control coefficient initial value within the second control coefficient update range, and calculating the load benefit in combination with the third calculation formula; Setting the third control coefficient update range, and randomly selecting the third control coefficient initial value within the third control coefficient update range; Obtaining the total thermal power capacity, the wind power forecast value, and the photovoltaic forecast value, setting the hydropower ratio coefficient, and calculating the power forecast value in combination with the fourth calculation formula; Obtaining the estimated fluctuation ratio and the carbon emission loss coefficient, and calculating the carbon emission loss using the fifth calculation formula; The first calculation formula is: DS=K1*H max Wherein, DS is the power balance control loss, K1 is the first control coefficient, H max is the maximum value of the discharge power per unit time; The second calculation formula is: N loss =(D all -D load ) / D all Among them, N loss is the energy loss rate, D all is the total power of all power supplies, D load is the total power of all loads; The third calculation formula is: Among them, S load is the load benefit, Z load is the weighted index of the total load power, L i is the weighting coefficient of the ith load, W i is the power of the i-th load, K2 is the second control coefficient, and n is the total number of loads; The fourth calculation formula is: YH=K3*S h +F max +G max +YH*K WATER Then we can get: Wherein, YH is the power supply prediction value, K3 is the third control coefficient, S h is the total thermal power capacity, Fmax is the wind power forecast value, Gmax is the photovoltaic forecast value, K WATER is the hydropower ratio coefficient; The fifth calculation formula is: C loss =YH*(1-P b )*(1-K WATER )C k Wherein, YH is the power supply prediction value, C loss is the carbon emission loss, P b is the estimated volatility ratio, C k is the carbon emission loss coefficient.

5. The AC microgrid intelligent energy management method according to claim 1, characterized in that: The obtaining of the target optimal control set, determining the control mode, and selecting the control coefficient according to the control mode to control the proportion of charging, discharging and thermal power in real time specifically includes: Obtain the target optimal control set, extract the first optimal control coefficient, the second optimal control coefficient, the third optimal control coefficient and the first x local optimal control coefficient, the second x local optimal control coefficient, and the third x local optimal control coefficient; Determine a current control mode, and determine a real-time first control coefficient, a real-time second control coefficient, and a real-time third control coefficient according to the control mode; According to the first real-time control coefficient, the duty cycle of the discharge controller at the current moment is determined to control the current discharge speed in real time; According to the second real-time control coefficient, controlling the charging power within the current range; According to the third real-time control coefficient, the capacity input ratio of the thermal power plants within the current range is controlled and adjusted.

6. The AC microgrid intelligent energy management method according to claim 1, characterized in that: The automatic curve display and digital display according to the system optimal economic index and all the x-th local optimal economic indexes specifically include: Generate first time series data according to the optimal economic index of the system; Generate the x+1th time series data according to the xth local optimal economic index; Automatically display all time series data as online display curves; The load benefit, the energy loss rate and the carbon emission loss are displayed in numerical form.

7. An AC microgrid intelligent energy management system, the system is applied to the intelligent energy management method as claimed in claim 1, characterized in that: The system includes: Data acquisition module, used to obtain weather forecast data, rated operating parameters and operating status parameters through sensors, Internet crawling and real-time settings; Data prediction module, used to calculate wind power prediction value and photovoltaic prediction value at future time; An energy loss calculation module is used to set the initial values ​​and update ranges of the first control coefficient, the second control coefficient, and the third control coefficient, and calculate the power balance control loss, load benefit, energy loss rate, and carbon emission loss; A control generation module, used to obtain a local area setting mode, and calculate a first optimal control coefficient, a second optimal control coefficient, a third optimal control coefficient, and a first x local optimal control coefficient, a second x local optimal control coefficient, and a third x local optimal control coefficient; The control execution module is used to obtain the target optimal control set, determine the control mode, and select the control coefficient according to the control mode to control the proportion of charging, discharging and thermal power in real time; The application display module is used to automatically display curves and numbers based on the system optimal economic index and all x-th local optimal economic indexes.

8. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 7 when executed by a processor.

9. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1-7.

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