Energy storage configuration optimization method, device, equipment and storage medium for multiple microgrids

Abstract

The present invention discloses a method, device, equipment and storage medium for optimizing energy storage configuration of multiple microgrids. The method predicts the hydropower output data, wind power output data and photovoltaic output data of multiple microgrids at future moments based on the historical status data of all renewable energy sources in the multiple microgrids; predicts the power load data of multiple microgrids at future moments based on the historical power load data of the multiple microgrids; then constructs a power fluctuation optimization function based on the renewable energy output data and the power load data, and constructs a cost optimization function based on the shared energy storage data of the multiple microgrids; finally, iterates the target optimization function using preset microgrid constraints until the cost optimization function and the power fluctuation optimization function reach minimum values, and obtains the target shared energy storage data, so as to treat multiple microgrids as an equivalent microgrid for the purpose of comprehensive configuration of energy storage, avoid disorder in energy storage charging and discharging behavior, reduce waste of energy storage resources, and improve comprehensive operation efficiency.

Description

Technical Field

[0001] The present invention relates to the field of power system automation technology, and in particular to a method, device, equipment and storage medium for optimizing energy storage configuration of multiple microgrids. Background Art

[0002] As the penetration rate of renewable energy in microgrids continues to increase, in order to make up for the inherent defects of renewable energy in terms of random volatility, the charging and discharging characteristics of energy storage are used to smooth out system power fluctuations and improve the absorption capacity of renewable energy, thereby reducing the frequency of power fluctuation impacts of microgrids on their access systems.

[0003] At present, the energy storage demand for smoothing microgrid power fluctuations is related to the power fluctuation status of the microgrid. The smoothing status of microgrid power fluctuations is mainly reflected by the mean square error of net load (i.e. load minus new energy output), and the configuration of microgrid energy storage is optimized with the minimum net load mean square error and the minimum energy storage cost as the configuration target. However, when multiple microgrids are connected to the same distribution network, since each microgrid independently configures energy storage, the energy storage charging and discharging behavior in multiple microgrids becomes disordered, resulting in a waste of energy storage resources. Summary of the invention

[0004] The present invention provides a method, device, equipment and storage medium for optimizing energy storage configuration of multiple microgrids to solve the current technical problem of waste of energy storage resources in multiple microgrids.

[0005] In order to solve the above technical problems, in a first aspect, the present invention provides a method for optimizing energy storage configuration of multiple microgrids, comprising:

[0006] Based on the historical status data of all renewable energy sources in multiple microgrids, the renewable energy output data of multiple microgrids at future moments is predicted. The renewable energy output data includes hydropower output data, wind power output data and photovoltaic output data.

[0007] Based on the historical power load data of multiple microgrids, predict the power load data of multiple microgrids at future times;

[0008] Constructing a target optimization function, which includes a power fluctuation optimization function based on renewable energy output data and power load data, and a cost optimization function based on shared energy storage data of multiple microgrids;

[0009] Using the preset microgrid constraints, the target optimization function is iterated until the cost optimization function and the power fluctuation optimization function reach the minimum value, and the target shared energy storage data corresponding to the target optimization function is obtained. The target shared energy storage data is used to configure energy storage for multiple microgrids in the future.

[0010] As a preferred method, based on the historical status data of all renewable energy sources in the multi-microgrids, the renewable energy output data of the multi-microgrids at a future moment is predicted, including:

[0011] Obtain historical inflow data for all hydropower stations in multiple microgrids;

[0012] For each hydropower station, the target inflow data of the hydropower station at a future time is predicted based on the historical inflow data using the weighted average method;

[0013] Using the preset hydropower output calculation formula, the hydropower output data of multiple microgrids at future times is calculated according to the target inflow data.

[0014] As a preferred method, based on the historical status data of all renewable energy sources in the multi-microgrids, the renewable energy output data of the multi-microgrids at a future moment is predicted, including:

[0015] Obtain historical wind speed data for all wind farms in multiple microgrids;

[0016] For each wind farm, the target wind speed data of the wind farm at a future time is predicted based on the historical wind speed data using the weighted average method;

[0017] Using the preset wind power output calculation formula, according to the target wind speed data, the wind power output data of multiple microgrids at future times is calculated.

[0018] As a preferred method, based on the historical status data of all renewable energy sources in the multi-microgrids, the renewable energy output data of the multi-microgrids at a future moment is predicted, including:

[0019] Obtain historical sunshine intensity data for all photovoltaic power stations in multiple microgrids;

[0020] For each photovoltaic power station, the target sunshine intensity data of the photovoltaic power station at the future time is predicted by using the weighted average method based on the historical sunshine intensity data;

[0021] Using the preset photovoltaic output calculation formula, the photovoltaic output data of multiple microgrids at future times is calculated according to the target sunshine intensity data.

[0022] Preferably, based on the historical power load data of multiple microgrids, the power load data of multiple microgrids at a future time is predicted, including:

[0023] Obtain historical power load data of all power users in multiple microgrids;

[0024] For each electricity user, based on historical electricity load data, predict the probability value of the electricity user's electricity load being within a preset load range at a future time;

[0025] The preset load calculation formula is used to calculate the power load data of each power user at a future moment according to the probability value corresponding to each power user.

[0026] Preferably, the expression of the cost optimization function is:

[0027] F1=min[C in +C op ];

[0028] in,

[0029]

[0030] C in is the investment and construction cost of shared energy storage equipment, C op is the operation and maintenance cost of the shared energy storage equipment, C p is the unit power cost of the shared energy storage device, P max is the rated power of the shared energy storage device; C s is the unit capacity cost of shared energy storage equipment, S total is the power capacity of the shared energy storage device, g is the discount rate, N y is the full life cycle of the shared energy storage device, k is the number of replacements of the shared energy storage device, n is the total number of replacements of the shared energy storage device in the full life cycle, C pom is the unit power operation and maintenance cost of the shared energy storage equipment; C som is the unit capacity operation and maintenance cost of the shared energy storage equipment, and W(t) is the annual charge and discharge capacity of the shared energy storage equipment.

[0031] Preferably, the expression of the power fluctuation optimization function is:

[0032]

[0033] in, is the hydropower output data of all hydropower stations in the i-th microgrid at time t; is the wind power output data of all wind farms in the i-th microgrid at time t; is the photovoltaic output data of all photovoltaic power stations in the i-th microgrid at time t; Share the output data of the energy storage device for the i-th microgrid at time t; is the power load data of the i-th microgrid at time t; is the hydropower output data of all hydropower stations in the i-th microgrid at time t-1; is the wind power output data of all wind farms in the i-th microgrid at time t-1; is the photovoltaic output data of all photovoltaic power stations in the i-th microgrid at time t-1; Share the output data of the energy storage device for the i-th microgrid at time t-1; is the power load data of the i-th microgrid at time t-1.

[0034] In a second aspect, the present invention provides a device for optimizing energy storage configuration of multiple microgrids, comprising:

[0035] The first prediction module is used to predict the new energy output data of the multiple microgrids at a future time based on the historical status data of all new energy sources in the multiple microgrids. The new energy output data includes hydropower output data, wind power output data and photovoltaic output data.

[0036] The second prediction module is used to predict the power load data of multiple microgrids at a future time based on the historical power load data of multiple microgrids;

[0037] A construction module is used to construct a target optimization function, which includes a power fluctuation optimization function based on new energy output data and power load data, and a cost optimization function based on shared energy storage data of multiple microgrids;

[0038] The iteration module is used to iterate the target optimization function by using the preset microgrid constraints until the cost optimization function and the power fluctuation optimization function reach the minimum value, and obtain the shared energy storage configuration data corresponding to the target optimization function. The target shared energy storage data is used to configure energy storage for multiple microgrids in the future.

[0039] In a third aspect, the present invention provides a computer device comprising a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the method for optimizing energy storage configuration of multiple microgrids as in the first aspect is implemented.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for optimizing energy storage configuration of multiple microgrids as in the first aspect.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention predicts the hydropower output data, wind power output data and photovoltaic output data of multiple microgrids at future moments based on the historical status data of all renewable energy sources in multiple microgrids, so as to regard multiple microgrids as an equivalent microgrid, thereby considering the output conditions of all renewable energy sources in multiple microgrids after being connected to the distribution network, facilitating the comprehensive configuration of energy storage and avoiding disorder in energy storage charging and discharging behavior; and predicts the power load data of multiple microgrids at future moments based on the historical power load data of multiple microgrids, so as to consider the power load data closest to the power consumption conditions of each user in the future; then, based on the renewable energy output data and the power load data, a power fluctuation optimization function is constructed, and based on the shared energy storage data of multiple microgrids, a cost optimization function is constructed, so as to consider the shared energy storage cost and the power fluctuation smoothing effect of the multi-dimensional power grid; finally, the target optimization function is iterated by using the preset microgrid constraint conditions, until the cost optimization function and the power fluctuation optimization function reach the minimum value, and the target shared energy storage data corresponding to the target optimization function is obtained, and the minimum shared energy storage cost and the minimum power fluctuation are used as optimization indicators, so as to reduce the waste of energy storage resources and improve the comprehensive operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of a flow chart of a method for optimizing energy storage configuration of multiple microgrids shown in an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of the structure of a device for optimizing energy storage configuration of multiple microgrids shown in an embodiment of the present invention;

[0045] Figure 3 The figure is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] 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.

[0047] Please refer to Figure 1 , Figure 1 The flowchart of a method for optimizing energy storage configuration of multiple microgrids provided in an embodiment of the present invention is shown in FIG. The method for optimizing energy storage configuration of multiple microgrids in an embodiment of the present invention can be applied to computer devices, including but not limited to smart phones, laptops, tablet computers, desktop computers, physical servers, and cloud servers. Figure 1 As shown, the energy storage configuration optimization method of multiple microgrids in this embodiment includes steps S101 to S104, which are described in detail as follows:

[0048] Step S101, based on the historical status data of all renewable energy sources in the multi-microgrid, predicting renewable energy output data of the multi-microgrid at a future time, wherein the renewable energy output data includes hydropower output data, wind power output data and photovoltaic output data.

[0049] In this step, the historical status data includes but is not limited to the historical inflow data of each hydropower station in the multi-microgrid, the historical wind speed data of each wind farm and the historical sunshine intensity data of each photovoltaic power station. The new energy output data represents the power generation power of the multi-microgrid when it is connected to the distribution network, through the use of new energy to generate electricity and output electricity to the distribution network.

[0050] Optionally, a first artificial intelligence model is constructed, the first artificial intelligence model is trained through historical state data, and the trained first artificial intelligence model is used to predict the new energy output data of multiple microgrids at future moments, and the first artificial intelligence model includes but is not limited to models such as long short-term memory neural network and convolutional neural network. It is understandable that a corresponding first artificial intelligence model can be constructed for each historical state data.

[0051] Optionally, a first linear fitting model is constructed to obtain the historical power generation law of new energy by performing linear fitting on the historical state data, thereby predicting the new energy output data of multiple microgrids at future times.

[0052] Step S102: predicting the power load data of the multi-microgrid at the future time based on the historical power load data of the multi-microgrid.

[0053] In this step, optionally, a second artificial intelligence model is constructed, the second artificial intelligence model is trained through historical electricity load data, and the trained second artificial intelligence model is used to predict the new energy output data of multiple microgrids at future times. The second artificial intelligence model includes but is not limited to models such as long short-term memory neural networks and convolutional neural networks.

[0054] Optionally, a second linear fitting model is constructed to obtain the historical electricity consumption patterns of electricity users by performing linear fitting on the historical electricity load data, thereby predicting the electricity load data of multiple microgrids at future times.

[0055] Step S103, constructing a target optimization function, wherein the target optimization function includes a power fluctuation optimization function constructed based on the new energy output data and the power load data, and a cost optimization function constructed based on the shared energy storage data of the multiple microgrids.

[0056] In this step, the shared energy storage data includes but is not limited to the construction cost data and operation and maintenance cost data of the shared energy storage equipment; the power fluctuation optimization function is used to characterize the power fluctuation of multiple microgrids after the output of new energy and shared energy storage equipment, and the cost optimization function is used to characterize the cost of the construction and operation and maintenance of shared energy storage equipment. It can be understood that the output data of the shared energy storage equipment is the data to be configured, and the different output data of the shared energy storage equipment will affect the power fluctuation of multiple microgrids and the shared energy storage cost. Therefore, the minimum power fluctuation and the minimum shared energy storage cost are used as optimization indicators of the target optimization function to simultaneously achieve the effect of smoothing power fluctuations and reducing energy storage resource consumption.

[0057] Optionally, power fluctuation optimization function = (new energy output data at this moment + shared energy storage output data - power load data) - (new energy output data at the last moment + shared energy storage output data - power load data). Cost optimization function = construction cost data of shared energy storage equipment + operation and maintenance cost data.

[0058] Step S104, using preset microgrid constraints, iterate the target optimization function until the cost optimization function and the power fluctuation optimization function reach a minimum value, and obtain the target shared energy storage data corresponding to the target optimization function, and the target shared energy storage data is used to perform energy storage configuration on the multiple microgrids at the future moment.

[0059] In this step, the multi-objective particle swarm algorithm iterates the target optimization function according to the preset microgrid constraints. When the cost optimization function and the power fluctuation optimization function both reach the minimum value, since the new energy output data has been determined in step S101, the output data of the shared energy storage device at the future moment (i.e., the target shared energy storage data) can be obtained according to the minimum value of the power fluctuation optimization function and the new energy output data. Therefore, energy storage configuration is performed for multiple microgrids at the future moment according to the output data to achieve the effect of smoothing power fluctuations and reducing energy storage resource consumption at the same time.

[0060] Optionally, the preset microgrid constraint conditions include:

[0061] (1) Microgrid power balance constraints:

[0062]

[0063]

[0064]

[0065]

[0066] P iis the active power injected into the i-th microgrid; is the active power generated by the i-th microgrid photovoltaic power station; is the active power generated by the i-th microgrid wind farm; The active power injected into the i-th microgrid energy storage system; is the active load power of the i-th microgrid; The charging power of the energy storage system for the i-th microgrid in the t-th time period; is the discharge power of the energy storage system of the i-th microgrid in the t-th time period; u is the state coefficient, generally, u=[0,1].

[0067] (2) Line flow constraints:

[0068] P ij ≤α ij P ij，max ;

[0069] P ij is the transmission power between transmission lines ij; P ij，max is the maximum transmission power between transmission lines ij; α ij is the load rate constraint parameter between transmission lines ij. Generally, α ij =(0,1].

[0070] If all transmission lines in the entire network use the same constraint value, then α ij =α, the corresponding above formula can be simplified to:

[0071] P ij ≤αP ij，max .

[0072] (3) Unit output constraints:

[0073]

[0074]

[0075]

[0076] is the actual output of the i-th microgrid hydropower station; is the minimum output value of the i-th microgrid hydropower station; is the maximum output value of the i-th microgrid hydropower station; is the actual output of the ith microgrid photovoltaic power station; is the minimum output value of the ith microgrid photovoltaic power station; is the maximum output value of the i-th microgrid photovoltaic power station; is the actual output of the i-th microgrid wind farm; is the minimum output value of the i-th microgrid wind farm; is the maximum output value of the i-th microgrid wind farm.

[0077] (4) Phase angle constraint:

[0078]

[0079] θ i，j is the actual phase angle of the jth node in the i-th microgrid; is the minimum value allowed for the phase angle at the jth node of the i-th microgrid; is the minimum allowed phase angle at the jth node of the i-th microgrid.

[0080] (5) Energy storage charging and discharging power constraints:

[0081]

[0082]

[0083] is the rated value of the energy storage converter of the i-th microgrid.

[0084] 6) Energy storage charge state constraints:

[0085]

[0086]

[0087] is the state of charge of the energy storage in the t-1th time period of the i-th microgrid; is the minimum state of charge of the energy storage of the i-th microgrid; is the maximum state of charge of the energy storage of the i-th microgrid; is the charging efficiency of the i-th microgrid energy storage; is the discharge efficiency of the energy storage of the i-th microgrid.

[0088] In some embodiments, Figure 1 Based on the illustrated embodiment, step S101 includes:

[0089] Obtaining historical inflow flow data of all hydropower stations in the multi-microgrid;

[0090] For each of the hydropower stations, using a weighted average method, based on the historical inflow flow data, predict the target inflow flow data of the hydropower station at the future moment;

[0091] The preset hydropower output calculation formula is used to calculate the hydropower output data of the multi-microgrid at the future moment according to the target inflow flow data.

[0092] In this embodiment, within the time period T, the inflow flow of the ath hydropower station in the microgrid changes according to the step interval value: And satisfy Q i <Q j (m=1,2,...,N Q , n=1,2,...,N Q ,m<n,N Q is the number of intervals of step-change in the inflow flow of the hydropower station). a In (Q m , Q n The probability of ] is:

[0093]

[0094] in

[0095] The weighted average method is used to calculate the interval inflow of the a-th hydropower station in the microgrid. The interval inflow of the a-th hydropower station in the microgrid at time t is:

[0096]

[0097] The power generation of the ath hydropower station in the i-th microgrid at time t is:

[0098]

[0099] is the proportional coefficient of the power generation of the hydropower station.

[0100] In some embodiments, Figure 1 Based on the illustrated embodiment, step S101 includes:

[0101] Obtaining historical wind speed data of all wind farms in the multi-microgrid;

[0102] For each of the wind farms, using a weighted average method, based on the historical wind speed data, predict the target wind speed data of the wind farm at the future moment;

[0103] The wind power output data of the multi-microgrid at the future moment is calculated by using a preset wind power output calculation formula according to the target wind speed data.

[0104] In the embodiment, within the time period T, the wind speed of the bth wind farm in the microgrid is The interval changes, and v m <v n (m=1,2,...,N v, n=1,2,...,N v ,m<n,N v is the number of intervals of wind speed step change in the wind farm). The wind speed of the bth wind farm in the microgrid is The probabilities of equal interval changes are:

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] where v in 、v out It is the cut-in wind speed and cut-out wind speed of the wind turbine.

[0111] The weighted average method is used to calculate the wind speed of the bth wind farm in the microgrid. The wind speed of the bth wind farm in the microgrid at time t is:

[0112]

[0113] The wind power of the bth wind farm in the i-th microgrid at time t is:

[0114]

[0115] A is the swept area of ​​the wind wheel, C p is the power coefficient of the fan, and ρ is the air density.

[0116] In some embodiments, Figure 1 Based on the illustrated embodiment, step S101 includes:

[0117] Obtaining historical sunshine intensity data of all photovoltaic power stations in the multi-microgrid;

[0118] For each of the photovoltaic power stations, using a weighted average method, based on the historical sunshine intensity data, predict the target sunshine intensity data of the photovoltaic power station at the future moment;

[0119] The photovoltaic output data of the multi-microgrid at the future moment is calculated by using a preset photovoltaic output calculation formula according to the target sunshine intensity data.

[0120] In this embodiment, within the time period T, the sunshine intensity of the cth photovoltaic power station in the microgrid changes according to the following interval values: And satisfy E PVi <E PVj (m=1,2,...,N PV , n=1,2,...,N PV ,m<n,N PV is the relationship between the number of intervals of step-change in the sunshine intensity of the photovoltaic power station. The sunshine intensity E of the cth photovoltaic power station in the microgrid PV In (E PVi , E PVj The probability of ] is:

[0121]

[0122] The weighted average method is used to calculate the sunshine intensity of the cth photovoltaic power station in the microgrid. The sunshine intensity of the cth photovoltaic power station in the microgrid at time t is:

[0123]

[0124] The power generation of the cth photovoltaic power station in the i-th microgrid at time t is:

[0125]

[0126] η is the conversion efficiency of the photovoltaic cell (%); S is the area of ​​the photovoltaic panel (m 2 );t0 is the temperature of the working environment of the photovoltaic panel (℃).

[0127] In some embodiments, Figure 1 Based on the illustrated embodiment, step S102 includes:

[0128] Obtaining historical power load data of all power users in the multi-microgrid;

[0129] For each of the electricity users, based on the historical electricity load data, predict the probability value that the electricity load of the electricity user will be within a preset load range at a future moment;

[0130] The preset load calculation formula is used to calculate the power load data of each power user at a future moment according to the probability value corresponding to each power user.

[0131] In this embodiment, within the time period T, the historical load power of the i-th microgrid user changes according to the following interval values: And satisfy P Lm <P Ln (m=1,2,...,N L , n=1,2,...,N L ,m<n,N LThe historical load power P of the i-th microgrid user is L In (P Li , P Lj The probability of ] is:

[0132]

[0133] The weighted average method is used to calculate the real-time load of the i-th microgrid power user at time t. The specific calculation method is:

[0134]

[0135] is the load of the i-th microgrid power user at time t.

[0136] In some embodiments, Figure 1 Based on the embodiment shown, the expression of the cost optimization function is:

[0137] F1=min[C in +C op ];

[0138] in,

[0139]

[0140] C in is the investment and construction cost of shared energy storage equipment, C op is the operation and maintenance cost of the shared energy storage equipment, C p is the unit power cost of the shared energy storage device, P max is the rated power of the shared energy storage device; C s is the unit capacity cost of shared energy storage equipment, S total is the power capacity of the shared energy storage device, g is the discount rate, N y is the full life cycle of the shared energy storage device, k is the number of replacements of the shared energy storage device, n is the total number of replacements of the shared energy storage device in the full life cycle, C pom is the unit power operation and maintenance cost of the shared energy storage equipment; C som is the unit capacity operation and maintenance cost of the shared energy storage equipment, and W(t) is the annual charge and discharge capacity of the shared energy storage equipment.

[0141] In some embodiments, Figure 1 Based on the embodiment shown, the expression of the power fluctuation optimization function is:

[0142]

[0143] in, is the hydropower output data of all hydropower stations in the i-th microgrid at time t; is the wind power output data of all wind farms in the i-th microgrid at time t; is the photovoltaic output data of all photovoltaic power stations in the i-th microgrid at time t; Share the output data of the energy storage device for the i-th microgrid at time t; is the power load data of the i-th microgrid at time t; is the hydropower output data of all hydropower stations in the i-th microgrid at time t-1; is the wind power output data of all wind farms in the i-th microgrid at time t-1; is the photovoltaic output data of all photovoltaic power stations in the i-th microgrid at time t-1; Share the output data of the energy storage device for the i-th microgrid at time t-1; is the power load data of the i-th microgrid at time t-1.

[0144] The calculation formula of the hydropower power of the i-th microgrid at time t is:

[0145]

[0146] is the power generation capacity of the ath hydropower station in the ith microgrid; A is the number of hydropower stations in the ith microgrid.

[0147] The wind power calculation formula of the i-th microgrid at time t is:

[0148]

[0149] is the power generation power of the bth wind farm in the ith microgrid; B is the number of wind farms in the ith microgrid.

[0150] The calculation formula for the photovoltaic power generation of the i-th microgrid at time t is:

[0151]

[0152] is the power generation power of the cth photovoltaic power station in the ith microgrid; C is the number of photovoltaic power stations in the ith microgrid.

[0153] In order to implement the energy storage configuration optimization method of multiple microgrids corresponding to the above method embodiment, so as to achieve the corresponding functions and technical effects. Figure 2 , Figure 2The structural block diagram of a multi-microgrid energy storage configuration optimization device provided by an embodiment of the present invention is shown. For the convenience of explanation, only the parts related to this embodiment are shown. The multi-microgrid energy storage configuration optimization device provided by an embodiment of the present invention includes:

[0154] A first prediction module 201 is used to predict the new energy output data of the multi-microgrid at a future time based on the historical status data of all new energy sources in the multi-microgrid, wherein the new energy output data includes hydropower output data, wind power output data and photovoltaic output data;

[0155] A second prediction module 202, configured to predict the power load data of the multi-microgrid at the future time based on the historical power load data of the multi-microgrid;

[0156] A construction module 203 is used to construct a target optimization function, wherein the target optimization function includes a power fluctuation optimization function constructed based on the new energy output data and the power load data, and a cost optimization function constructed based on the shared energy storage data of the multi-microgrid;

[0157] The iteration module 204 is used to iterate the target optimization function by using preset microgrid constraints until the cost optimization function and the power fluctuation optimization function reach a minimum value, and obtain the shared energy storage configuration data corresponding to the target optimization function. The target shared energy storage data is used to perform energy storage configuration on the multiple microgrids at the future moment.

[0158] In some embodiments, the first prediction module 201 is specifically used to:

[0159] Obtaining historical inflow flow data of all hydropower stations in the multi-microgrid;

[0160] For each of the hydropower stations, using a weighted average method, based on the historical inflow flow data, predict the target inflow flow data of the hydropower station at the future moment;

[0161] The preset hydropower output calculation formula is used to calculate the hydropower output data of the multi-microgrid at the future moment according to the target inflow flow data.

[0162] In some embodiments, the first prediction module 201 is specifically used to:

[0163] Obtaining historical wind speed data of all wind farms in the multi-microgrid;

[0164] For each of the wind farms, using a weighted average method, based on the historical wind speed data, predict the target wind speed data of the wind farm at the future moment;

[0165] The wind power output data of the multi-microgrid at the future moment is calculated by using a preset wind power output calculation formula according to the target wind speed data.

[0166] In some embodiments, the first prediction module 201 is specifically used to:

[0167] Obtaining historical sunshine intensity data of all photovoltaic power stations in the multi-microgrid;

[0168] For each of the photovoltaic power stations, using a weighted average method, based on the historical sunshine intensity data, predict the target sunshine intensity data of the photovoltaic power station at the future moment;

[0169] The photovoltaic output data of the multi-microgrid at the future moment is calculated by using a preset photovoltaic output calculation formula according to the target sunshine intensity data.

[0170] In some embodiments, the second prediction module 202 is specifically used to:

[0171] Obtaining historical power load data of all power users in the multi-microgrid;

[0172] For each of the electricity users, based on the historical electricity load data, predict the probability value that the electricity load of the electricity user will be within a preset load range at a future moment;

[0173] The preset load calculation formula is used to calculate the power load data of each power user at a future moment according to the probability value corresponding to each power user.

[0174] In some embodiments, the cost optimization function is expressed as:

[0175] F1=min[C in +C op ];

[0176] in,

[0177]

[0178] C in is the investment and construction cost of shared energy storage equipment, C op is the operation and maintenance cost of the shared energy storage equipment, C p is the unit power cost of the shared energy storage device, P max is the rated power of the shared energy storage device; C s is the unit capacity cost of shared energy storage equipment, S total is the power capacity of the shared energy storage device, g is the discount rate, N yis the full life cycle of the shared energy storage device, k is the number of replacements of the shared energy storage device, n is the total number of replacements of the shared energy storage device in the full life cycle, C pom is the unit power operation and maintenance cost of the shared energy storage equipment; C som is the unit capacity operation and maintenance cost of the shared energy storage equipment, and W(t) is the annual charge and discharge capacity of the shared energy storage equipment.

[0179] In some embodiments, the power fluctuation optimization function is expressed as:

[0180]

[0181] in, is the hydropower output data of all hydropower stations in the i-th microgrid at time t; is the wind power output data of all wind farms in the i-th microgrid at time t; is the photovoltaic output data of all photovoltaic power stations in the i-th microgrid at time t; Share the output data of the energy storage device for the i-th microgrid at time t; is the power load data of the i-th microgrid at time t; is the hydropower output data of all hydropower stations in the i-th microgrid at time t-1; is the wind power output data of all wind farms in the i-th microgrid at time t-1; is the photovoltaic output data of all photovoltaic power stations in the i-th microgrid at time t-1; Share the output data of the energy storage device for the i-th microgrid at time t-1; is the power load data of the i-th microgrid at time t-1.

[0182] The above-mentioned multi-microgrid energy storage configuration optimization device can implement the multi-microgrid energy storage configuration optimization method of the above-mentioned method embodiment. The options in the above-mentioned method embodiment are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present invention can refer to the contents of the above-mentioned method embodiment, and will not be repeated in this embodiment.

[0183] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present invention. Figure 3 As shown, the computer device 3 of this embodiment includes: at least one processor 30 ( Figure 3 Only one is shown in the figure) a processor, a memory 31, and a computer program 32 stored in the memory 31 and executable on the at least one processor 30, and when the processor 30 executes the computer program 32, the steps in any of the above method embodiments are implemented.

[0184] The computer device 3 may be a computing device such as a smart phone, a tablet computer, a desktop computer, a cloud server, etc. The computer device may include but is not limited to a processor 30 and a memory 31. Those skilled in the art will understand that Figure 3 It is only an example of computer device 3 and does not constitute a limitation on computer device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0185] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0186] In some embodiments, the memory 31 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 31 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 3. Further, the memory 31 may also include both an internal storage unit of the computer device 3 and an external storage device. The memory 31 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0187] In addition, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0188] An embodiment of the present invention provides a computer program product. When the computer program product is run on a computer device, the computer device implements the steps in the above-mentioned method embodiments when executing the computer device.

[0189] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0190] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0191] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing energy storage configuration of multiple microgrids, characterized in that: include: Based on the historical status data of all renewable energy sources in the multi-microgrid, predict the renewable energy output data of the multi-microgrid at a future time, wherein the renewable energy output data includes hydropower output data, wind power output data and photovoltaic output data; Based on the historical power load data of the multiple microgrids, predicting the power load data of the multiple microgrids at the future moment; Constructing a target optimization function, wherein the target optimization function includes a power fluctuation optimization function constructed based on the new energy output data and the power load data, and a cost optimization function constructed based on the shared energy storage data of the multiple microgrids; Using preset microgrid constraints, the target optimization function is iterated until the cost optimization function and the power fluctuation optimization function reach a minimum value, and the target shared energy storage data corresponding to the target optimization function is obtained, and the target shared energy storage data is used to perform energy storage configuration on the multi-microgrid at the future moment; The expression of the cost optimization function is: ； in, ; ； To share the investment and construction costs of energy storage equipment, To share the operation and maintenance costs of energy storage equipment, is the unit power cost of the shared energy storage device, is the rated power of the shared energy storage device; is the unit capacity cost of shared energy storage equipment, To share the power capacity of energy storage equipment, is the discount rate, For the full life cycle of shared energy storage equipment, is the number of replacements of shared energy storage equipment, is the total number of replacements of shared energy storage equipment during its entire life cycle, The unit power operation and maintenance cost of the shared energy storage equipment; is the unit capacity operation and maintenance cost of the shared energy storage equipment, is the annual charge and discharge capacity of the shared energy storage device; The expression of the power fluctuation optimization function is: ； in, For the Microgrids in Hydropower output data of all hydropower stations at all times; For the Microgrids in Wind power output data of all wind farms at all times; For the Microgrids in Photovoltaic output data of all photovoltaic power stations at all times; For the Microgrids in Share the output data of energy storage equipment at all times; For the Microgrids in Electricity load data at all times; For the Microgrids in Hydropower output data of all hydropower stations at all times; For the Microgrids in Wind power output data of all wind farms at all times; For the Microgrids in Photovoltaic output data of all photovoltaic power stations at all times; For the Microgrids in Share the output data of energy storage equipment at all times; For the Microgrids in The electricity load data at the moment.

2. The method for optimizing energy storage configuration of multiple microgrids according to claim 1, characterized in that: The method of predicting the new energy output data of the multi-microgrid at a future time based on the historical status data of all new energy sources in the multi-microgrid includes: Obtaining historical inflow flow data of all hydropower stations in the multi-microgrid; For each of the hydropower stations, using a weighted average method, based on the historical inflow flow data, predict the target inflow flow data of the hydropower station at the future moment; The preset hydropower output calculation formula is used to calculate the hydropower output data of the multi-microgrid at the future moment according to the target inflow flow data.

3. The method for optimizing energy storage configuration of multiple microgrids according to claim 1, characterized in that: The method of predicting the new energy output data of the multi-microgrid at a future time based on the historical status data of all new energy sources in the multi-microgrid includes: Obtaining historical wind speed data of all wind farms in the multi-microgrid; For each of the wind farms, using a weighted average method, based on the historical wind speed data, predict the target wind speed data of the wind farm at the future moment; The preset wind power output calculation formula is used to calculate the wind power output data of the multi-microgrid at the future moment according to the target wind speed data.

4. The method for optimizing energy storage configuration of multiple microgrids according to claim 1, characterized in that: The method of predicting the new energy output data of the multi-microgrid at a future time based on the historical status data of all new energy sources in the multi-microgrid includes: Obtaining historical sunshine intensity data of all photovoltaic power stations in the multi-microgrid; For each of the photovoltaic power stations, using a weighted average method, based on the historical sunshine intensity data, predict the target sunshine intensity data of the photovoltaic power station at the future moment; The photovoltaic output data of the multi-microgrid at the future moment is calculated by using a preset photovoltaic output calculation formula according to the target sunshine intensity data.

5. The method for optimizing energy storage configuration of multiple microgrids according to claim 1, characterized in that: The predicting of the power load data of the multi-microgrid at the future time based on the historical power load data of the multi-microgrid includes: Obtaining historical power load data of all power users in the multi-microgrid; For each of the electricity users, based on the historical electricity load data, predict the probability value that the electricity load of the electricity user will be within a preset load range at a future moment; The preset load calculation formula is used to calculate the power load data of each power user at a future moment according to the probability value corresponding to each power user.

6. A device for optimizing energy storage configuration of multiple microgrids, characterized in that: include: A first prediction module is used to predict the new energy output data of the multi-microgrid at a future time based on the historical status data of all new energy sources in the multi-microgrid, wherein the new energy output data includes hydropower output data, wind power output data and photovoltaic output data; A second prediction module, configured to predict the power load data of the multi-microgrid at the future moment based on the historical power load data of the multi-microgrid; A construction module, used to construct a target optimization function, wherein the target optimization function includes a power fluctuation optimization function constructed based on the new energy output data and the power load data, and a cost optimization function constructed based on the shared energy storage data of the multi-microgrid; An iteration module, used to iterate the target optimization function by using preset microgrid constraints until the cost optimization function and the power fluctuation optimization function reach a minimum value, and obtain shared energy storage configuration data corresponding to the target optimization function, wherein the target shared energy storage data is used to perform energy storage configuration for the multi-microgrid at the future moment; The expression of the cost optimization function is: ； in, ; ； To share the investment and construction costs of energy storage equipment, To share the operation and maintenance costs of energy storage equipment, is the unit power cost of the shared energy storage device, is the rated power of the shared energy storage device; is the unit capacity cost of shared energy storage equipment, To share the power capacity of energy storage equipment, is the discount rate, For the full life cycle of shared energy storage equipment, is the number of replacements of shared energy storage equipment, is the total number of replacements of shared energy storage equipment during its entire life cycle, The unit power operation and maintenance cost of the shared energy storage equipment; is the unit capacity operation and maintenance cost of the shared energy storage equipment, is the annual charge and discharge capacity of the shared energy storage device; The expression of the power fluctuation optimization function is: ； in, For the Microgrids in Hydropower output data of all hydropower stations at all times; For the Microgrids in Wind power output data of all wind farms at all times; For the Microgrids in Photovoltaic output data of all photovoltaic power stations at all times; For the Microgrids in Share the output data of energy storage equipment at all times; For the Microgrids in Electricity load data at all times; For the Microgrids in Hydropower output data of all hydropower stations at all times; For the Microgrids in Wind power output data of all wind farms at all times; For the Microgrids in Photovoltaic output data of all photovoltaic power stations at all times; For the Microgrids in Share the output data of energy storage equipment at all times; For the Microgrids in The electricity load data at the moment.

7. A computer device, characterized in that: It includes a processor and a memory, the memory is used to store a computer program, and when the computer program is executed by the processor, it implements the energy storage configuration optimization method of multiple microgrids as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the energy storage configuration optimization method for multiple microgrids as described in any one of claims 1 to 5.

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