Energy storage configuration strategy optimization method and system

Abstract

An energy storage configuration strategy optimization method and system. The method comprises: acquiring historical output data and predicted output data of distributed renewable energy (S11); constructing a distributed renewable energy uncertainty model on the basis of the historical output data and predicted output data of the distributed renewable energy and Wasserstein divergence (S12); acquiring a fuzzy set of the distributed renewable energy, and associating the distributed renewable energy uncertainty model with the fuzzy set (S13); and constructing an energy storage configuration strategy optimization model on the basis of a distributed robust optimization algorithm (S14).

Description

Optimization method and system for energy storage configuration strategy

[0001] The present application claims priority to the Chinese patent application No. 202410453346.0, filed on April 16, 2024, with the Chinese Patent Office, the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the technical field of energy storage configuration strategy, for example, to an optimization method and system for energy storage configuration strategy. BACKGROUND

[0003] With the rapid development of new energy, the concept of "energy internet", "carbon peak and carbon neutralization" is proposed, and the form of power system is facing great changes. High proportion of new energy access will become a typical feature of new power system. New power system also faces new problems such as new energy consumption, which brings great challenges to the safe and stable operation of power system. Energy storage is considered to be an effective way to solve these problems. Through charging and discharging behavior, energy storage devices can realize energy time shift, breaking the limitation that generation and consumption need to be synchronized in traditional power system.

[0004] With the development of technology, distributed renewable energy is further controlled, and distributed renewable energy has uncertainty. The commonly used method to deal with uncertainty includes stochastic programming, which is difficult to accurately and effectively deal with the uncertainty of distributed renewable energy output. SUMMARY

[0005] The present application provides an optimization method and system for energy storage configuration strategy, which introduces Wasserstein divergence to construct a distributed renewable energy uncertainty model based on historical output data, predicted output data and Wasserstein divergence of distributed renewable energy, so as to control the distributed renewable energy uncertainty model, and then further combine with distributed robust optimization algorithm to construct an energy storage configuration strategy optimization model, thereby effectively dealing with the uncertainty of distributed renewable energy output, making the energy storage configuration strategy more reasonable, and effectively balancing the dispatching economy and robustness of power system.

[0006] The present application provides an optimization method for energy storage configuration strategy, applied to an energy storage scenario. The optimization method for energy storage configuration strategy comprises:

[0007] Obtaining historical output data and predicted output data of distributed renewable energy;

[0008] Constructing a distributed renewable energy uncertainty model based on historical output data, predicted output data and Wasserstein divergence of distributed renewable energy;

[0009] Obtain fuzzy sets of distributed renewable energy and associate the distributed renewable energy uncertainty model with the fuzzy sets;

[0010] An energy storage configuration strategy optimization model is constructed based on the distributed robust optimization algorithm.

[0011] Optionally, the obtaining of historical output data and predicted output data of distributed renewable energy includes:

[0012] locating distributed renewable energy;

[0013] Obtain historical output data and predicted output data of distributed renewable energy based on distributed renewable energy.

[0014] Optionally, constructing a distributed renewable energy uncertainty model based on historical output data, predicted output data, and Wasserstein divergence of distributed renewable energy includes:

[0015] Freeze historical and predicted output data of distributed renewable energy;

[0016] Collect Wasserstein divergence and correlate it with historical and predicted output data of distributed renewable energy sources;

[0017] A distributed renewable energy uncertainty model is constructed based on the historical output data, predicted output data and Wasserstein divergence of distributed renewable energy.

[0018] Optionally, the constructing of a distributed renewable energy uncertainty model based on historical output data of distributed renewable energy, predicted output data, and Wasserstein divergence further includes:

[0019] The prediction error of the i-th output power of the distributed renewable energy unit is a random variable δ i , in practical applications, δ i The true probability distribution of It is impossible to extract, but the forecast error of the limited historical sample (N sam is the number of historical samples) can provide information about Reliable probability information to extract an empirical distribution

[0020] Where, d k express The Dirac measure of .

[0021] Optionally, the constructing the distributed renewable energy uncertainty model based on the historical output data, the predicted output data and the Wasserstein divergence further comprises:

[0022] centering the fuzzy set to ensure good properties, the Wasserstein divergence is used to accurately describe the distance between

[0023] wherein, is a random variable of the empirical distribution J is the joint distribution of is the distance between two random variables.

[0024] Optionally, the obtaining the fuzzy set of the distributed renewable energy and associating the distributed renewable energy uncertainty model with the fuzzy set further comprises:

[0025] defining the fuzzy set based on the distributed renewable energy uncertainty model;

[0026] fixing the distributed renewable energy uncertainty model and the fuzzy set;

[0027] associating the distributed renewable energy uncertainty model with the fuzzy set.

[0028] Optionally, the obtaining the fuzzy set of the distributed renewable energy and associating the distributed renewable energy uncertainty model with the fuzzy set further comprises:

[0029] the fuzzy set may be expressed as:

[0030] wherein, may be regarded as a Wasserstein ball with a radius of and a ball center, which is specifically expressed as:

[0031] wherein, is the confidence level of δ i ; η is an auxiliary variable; is the average value of the historical prediction error; may be obtained by the bisection search method.

[0032] ​​​​Optionally, the storage configuration strategy optimization model is constructed based on the distribution robust optimization algorithm, including:

[0033] The storage-related cost and the grid operation cost are collected;

[0034] The distribution robust optimization algorithm is associated based on the storage-related cost and the grid operation cost, and the storage configuration strategy optimization model is constructed.

[0035] Optionally, the storage configuration strategy optimization model constructed based on the distribution robust optimization algorithm further includes:

[0036] The storage configuration strategy optimization model takes the minimum total planning cost of the system as an objective function:

[0037] The objective function is as follows: min C ESD +C ope ;

[0038] In the formula, C ESD is the storage-related cost; and C ope is the grid operation cost.

[0039] Optionally, the storage configuration strategy optimization system is applied to the above-mentioned storage configuration strategy optimization method, and the storage configuration strategy optimization system includes:

[0040] An acquisition module is set to acquire historical output data and predicted output data of the distributed renewable energy;

[0041] A construction module is set to construct a distributed renewable energy uncertainty model based on the historical output data and the predicted output data of the distributed renewable energy and the Wasserstein divergence;

[0042] An association module is set to acquire a fuzzy set of the distributed renewable energy, and associate the distributed renewable energy uncertainty model with the fuzzy set;

[0043] An optimization module is set to construct a storage configuration strategy optimization model based on a distribution robust optimization algorithm.

[0044] In the embodiment of the present application, by the method in the embodiment of the present application, the historical output data and the predicted output data of the distributed renewable energy are obtained; the distributed renewable energy uncertainty model is constructed based on the historical output data and the predicted output data of the distributed renewable energy and the Wasserstein divergence; the fuzzy set of the distributed renewable energy is obtained, and the distributed renewable energy uncertainty model and the fuzzy set are associated; the energy storage configuration strategy optimization model is constructed based on the distributed robust optimization algorithm, at this time, the Wasserstein divergence is introduced, so as to construct the distributed renewable energy uncertainty model based on the historical output data and the predicted output data of the distributed renewable energy and the Wasserstein divergence, thereby controlling the distributed renewable energy uncertainty model, and then further combining with the distributed robust optimization algorithm, thereby constructing the energy storage configuration strategy optimization model, and then effectively processing the uncertainty of the output of the distributed renewable energy, making the energy storage configuration strategy more reasonable, and effectively balancing the dispatching economy and the robustness of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the embodiments or the related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0046] FIG. 1 is a flow diagram of the optimization method of the energy storage configuration strategy in the embodiment of the present application;

[0047] FIG. 2 is a flow diagram of S12 in the optimization method of the energy storage configuration strategy in the embodiment of the present application;

[0048] FIG. 3 is a flow diagram of S13 in the optimization method of the energy storage configuration strategy in the embodiment of the present application;

[0049] FIG. 4 is a flow diagram of S14 in the optimization method of the energy storage configuration strategy in the embodiment of the present application;

[0050] FIG. 5 is a schematic diagram of the dispatching strategy in the optimization method of the energy storage configuration strategy in the embodiment of the present application;

[0051] FIG. 6 is a structural composition schematic diagram of the optimization system of the energy storage configuration strategy in the embodiment of the present application;

[0052] FIG. 7 is a hardware diagram of an electronic device according to an example embodiment. DETAILED DESCRIPTION

[0053] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0054] Embodiment

[0055] Referring to FIGS. 1-7, an energy storage configuration strategy optimization method is applied to a magnetic induction sensor. The energy storage configuration strategy optimization method comprises:

[0056] Step S11: Obtain historical output data and predicted output data of distributed renewable energy.

[0057] Step S12: Construct a distributed renewable energy uncertainty model based on the historical output data and the predicted output data of the distributed renewable energy and a Wasserstein divergence.

[0058] Step S13: Obtain a fuzzy set of the distributed renewable energy, and associate the distributed renewable energy uncertainty model with the fuzzy set.

[0059] Step S14: Construct an energy storage configuration strategy optimization model based on a distributed robust optimization algorithm.

[0060] In the embodiments of the present application, the historical output data and the predicted output data of the distributed renewable energy are obtained by the method in the embodiments of the present application. The distributed renewable energy uncertainty model is constructed based on the historical output data and the predicted output data of the distributed renewable energy and the Wasserstein divergence. The fuzzy set of the distributed renewable energy is obtained, and the distributed renewable energy uncertainty model is associated with the fuzzy set. The energy storage configuration strategy optimization model is constructed based on the distributed robust optimization algorithm. At this time, the Wasserstein divergence is introduced to facilitate the construction of the distributed renewable energy uncertainty model based on the historical output data and the predicted output data of the distributed renewable energy and the Wasserstein divergence, so as to control the distributed renewable energy uncertainty model, and then further combined with the distributed robust optimization algorithm to construct the energy storage configuration strategy optimization model, thereby effectively processing the uncertainty of the distributed renewable energy output, making the energy storage configuration strategy more reasonable, and effectively balancing the dispatching economy and robustness of the power system.

[0061] In step S11, the historical output data and the predicted output data of the distributed renewable energy are obtained.

[0062] In the embodiment of the present application, the distributed renewable energy is located; the distributed renewable energy historical output data and the predicted output data are obtained based on the distributed renewable energy.

[0063] At this time, the distributed renewable energy is located, and the distributed renewable energy is controlled in order to obtain the distributed renewable energy historical output data and the predicted output data based on the distributed renewable energy, so as to introduce the distributed renewable energy historical output data and the predicted output data in order to further process the distributed renewable energy historical output data and the predicted output data.

[0064] In step S12, the distributed renewable energy uncertainty model is constructed based on the distributed renewable energy historical output data, the predicted output data and the Wasserstein divergence;

[0065] In the specific implementation process of the present application, the specific steps can be:

[0066] S121: The distributed renewable energy historical output data and the predicted output data are fixed;

[0067] S122: The Wasserstein divergence is collected, and the Wasserstein divergence is associated with the distributed renewable energy historical output data and the predicted output data;

[0068] S123: The distributed renewable energy uncertainty model is constructed according to the distributed renewable energy historical output data, the predicted output data and the Wasserstein divergence.

[0069] In the embodiment of the present application, the distributed renewable energy historical output data and the predicted output data are fixed in order to process the distributed renewable energy historical output data and the predicted output data in the next step. At this time, the Wasserstein divergence is collected, and the Wasserstein divergence is associated with the distributed renewable energy historical output data and the predicted output data, so as to process the Wasserstein divergence, the distributed renewable energy historical output data and the predicted output data as a whole, construct the distributed renewable energy uncertainty model according to the distributed renewable energy historical output data, the predicted output data and the Wasserstein divergence, and ensure the distributed renewable energy uncertainty model, and realize the subsequent processing of the distributed renewable energy uncertainty model.

[0070] Further, the distributed renewable energy uncertainty model is constructed based on the distributed renewable energy historical output data, the predicted output data and the Wasserstein divergence, and the method further comprises:

[0071] The prediction error of the i-th output power of the distributed renewable energy unit is a random variable δ i , in practical applications, δ i The true probability distribution of It is impossible to extract, but the forecast error of the limited historical sample (N sam is the number of historical samples) can provide information about Reliable probability information to extract an empirical distribution

[0072] Where, d k express The Dirac measure of .

[0073] In addition, the distributed renewable energy uncertainty model constructed based on the historical output data, the predicted output data and the Wasserstein divergence of distributed renewable energy further includes:

[0074] by Construct fuzzy sets for the centers To ensure Has good properties and is accurately described using Wasserstein divergence and the distance between them;

[0075] Where, For the empirical distribution A random variable; J is and The joint distribution of is the distance between two random variables, and the 1-norm is used due to its superior numerical traceability in distributionally robust optimization problems.

[0076] In step S13, a fuzzy set of distributed renewable energy is obtained, and the distributed renewable energy uncertainty model is associated with the fuzzy set;

[0077] During the specific implementation of this application, the specific steps may be:

[0078] S131: Defining fuzzy sets based on the uncertainty model of distributed renewable energy;

[0079] S132: Fixed-grid distributed renewable energy uncertainty model and fuzzy sets;

[0080] S133: Uncertainty models and fuzzy sets for associated distributed renewable energy sources.

[0081] In the embodiments of the present application, the fuzzy set is defined based on the distributed renewable energy uncertainty model, and the fuzzy set is introduced to facilitate the formation of the corresponding constraint of the fuzzy set, at this time, the rated distributed renewable energy uncertainty model and the fuzzy set are defined; the distributed renewable energy uncertainty model and the fuzzy set are associated.

[0082] Further, the fuzzy set of the distributed renewable energy is acquired, and the distributed renewable energy uncertainty model and the fuzzy set are associated, and further comprising:

[0083] Fuzzy set Can be expressed as:

[0084] In the formula, Can be regarded as a Wasserstein ball with a radius of And a ball center Specifically expressed as follows:

[0085] In the formula, Is the confidence level of δ i ; η is an auxiliary variable; Is the average value of the historical prediction error. Can be obtained by the bisection search method.

[0086] S14: constructing an energy storage configuration strategy optimization model based on a distribution robust optimization algorithm;

[0087] In the specific implementation process of the present application, the specific steps can be:

[0088] S141: collecting energy storage related costs and power grid operation costs;

[0089] S142: associating a distribution robust optimization algorithm based on the energy storage related costs and the power grid operation costs, and constructing an energy storage configuration strategy optimization model;

[0090] At this time, the energy storage related costs and the power grid operation costs are collected to facilitate the introduction of the energy storage related costs and the power grid operation costs, so as to control the energy storage related costs and the power grid operation costs, so as to associate the distribution robust optimization algorithm based on the energy storage related costs and the power grid operation costs, and construct the energy storage configuration strategy optimization model, realizing the processing of the energy storage configuration strategy optimization model.

[0091] Further, the energy storage configuration strategy optimization model based on the distribution robust optimization algorithm further comprises: the energy storage configuration strategy optimization model takes the minimum system total planning cost as the objective function:

[0092] The objective function is as follows: min C ESD +C​ope ;

[0093] Where C ESD is the energy storage related cost; C ope The cost of grid operation.

[0094] Energy storage related costs C ESD The calculation method of C is as follows: ESD =C inv +C main,ESD -C res (1) R=(1+r) -1 (5)

[0095] Where, formula (1) indicates that the energy storage related costs are composed of the energy storage investment cost C inv , Energy storage operation and maintenance cost C main,ESD and the residual value of the energy storage device C res composition; is the unit capacity investment cost of energy storage; M ESD The investment and construction capacity of energy storage devices; is the unit power investment cost of energy storage equipment; is the maximum charge and discharge power of the energy storage device; N year is the total number of years for the planning period; is the maintenance cost per unit power of the energy storage equipment; t represents the dispatching time; T is the total number of dispatching times in a year; is the energy storage power at time t; L ESD is the number of energy storage devices in service at the end of the planning period; δ ESD is the net residual value rate of energy storage equipment; T ESD is the total number of years that the shared energy storage will operate from configuration to the end of the planning period; R is the present value coefficient, and r is the discount rate.

[0096] Grid operating cost C ope The calculation method is as follows: C ope =C buy +C pun +C main,RES +C GT (6)

[0097] Where C buy is the external electricity purchase cost; C pun Penalty cost for curtailing renewable energy; C main,RES is the maintenance cost of renewable energy units; C GT The cost of balancing the uncertainty of distributed renewable energy output for gas turbines. The calculation method of each cost is as follows:

[0098] Where, Real-time electricity price of the system purchasing electricity from the outside at time t; Electric power purchased by the system from the outside at time t; Abandoned electricity cost of photovoltaic, wind power and biomass energy, respectively; Abandoned electricity power of photovoltaic, wind power and biomass energy at time t, respectively; Unit power maintenance cost of photovoltaic, wind power and biomass energy, respectively; Actual consumption power of photovoltaic, wind power and biomass energy at time t, respectively; S is a probability distribution that distributed new energy output prediction error may conform to; is a fuzzy set of historical sample probability distribution of distributed new energy output prediction error based on Wasserstein distance; ES is mathematical expectation; is the actual output of the i-th gas turbine; and is the gas turbine coefficient of the balance error, both greater than 0; Δt is the scheduling time interval.

[0099] The constraint conditions of the energy storage configuration strategy optimization model include energy storage device configuration and operation constraints, renewable energy operation constraints and consumption level constraints, power balance constraints, electricity purchase constraints, and distributed robust opportunity constraints, as follows:

[0100] (1) Energy storage device configuration constraint

[0101] In the formula, is the upper limit value of the energy storage configuration capacity and power.

[0102] (2) Energy storage device operation constraint

[0103] 1) Charge and discharge constraint

[0104] In the formula, is the charging power of the energy storage device at time t; is the discharging power of the energy storage device at time t; is the upper limit value of the charge and discharge power of the energy storage device; are the charge and discharge state variables of the energy storage device at time t, both are 0-1 integer variables.

[0105] 2) State of charge constraint γ min M ESD ≤S t ≤γ max M ESD (17) S 0 =S T(18)

[0106] where S t is the SOC value of the energy storage device at time t; η los , η cha , η dis are the energy loss coefficient, charging efficiency and discharging efficiency of the energy storage device, respectively; Δt is the time interval between adjacent dispatching times, set as 1 h; γ min , γ max are the proportion coefficients of the upper and lower limits of the SOC of the energy storage device to its total capacity, respectively.

[0107] (3) Renewable energy operation constraints

[0108] where i = 1, 2, 3 represent photovoltaic, wind power and biomass energy, respectively; P i,min , P i,max represent the upper and lower limits of the output of the renewable energy unit; is the total predicted power of the renewable energy unit at time t; is the actual consumption power of the renewable energy unit at time t; is the power abandoned by the renewable energy unit at time t.

[0109] (4) Renewable energy consumption level constraints

[0110] where is the consumption rate of the three types of renewable energy at time t; is the specified minimum consumption rate of the three types of renewable energy.

[0111] (5) Power balance constraints

[0112] where is the load of the system at time t; is the actual output of the i-th GT; is the planned output of the i-th GT; is the distribution coefficient of the i-th GT participating in balancing the output error; δ t is the output prediction error vector of the DG; e is a column vector with all elements being 1.

[0113] (6) Power purchase constraints

[0114] where is the upper limit of the power purchased by the power system from the outside.

[0115] (7) Distributed robust chance constraints

[0116] wherein ε 1,i and ε 2,i are the confidence parameters of the two distributionally robust chance constraints, respectively; Ω GT is the set of gas turbines; P GT,imax , P GT,imin are the upper and lower bounds of the gas turbine output; are the upper and lower bounds of the gas turbine ramping.

[0117] To verify the effectiveness and accuracy of the proposed energy storage configuration strategy optimization method and system based on the Wasserstein distributionally robust algorithm (W-D), a certain power distribution system is taken as an example, and it is assumed that the system can purchase power from the upper-level power grid, is configured with photovoltaic, wind power and biomass energy, and is provided with a gas turbine to balance errors. The energy storage investment and operation related parameters are shown in Table 1.

[0118] The rationality of the energy storage configuration strategy optimization method based on the Wasserstein distributionally robust algorithm is analyzed, and the energy storage configuration results are listed in Table 2, and the system dispatching strategy under a typical operating day is shown in FIG. 1. As can be seen from FIG. 1, after the energy storage device is configured, the energy storage occurs charging and discharging at several time points in a day. For example, at the time when the renewable energy output is large and the load level is low (such as before 9:00), the energy storage device absorbs the excess power of the wind and light renewable energy through charging; and at the time when the renewable energy output is small and the load level is high (such as from 10:00 to 15:00), the energy storage device discharges the power stored at other time points to meet the load demand in the system, thereby effectively improving the renewable energy consumption rate of the system.

[0119] In order to make a comparison, a model without configuring energy storage is set for comparison, and the annual renewable energy consumption rate and the overall cost of the system are measured, and the results are shown in Tables 3 and 4. Through the results of Tables 3 and 4, it can be found that the rationality of the energy storage configuration strategy optimization method based on the Wasserstein distributionally robust algorithm proposed in the present application not only effectively improves the consumption rate of distributed renewable new energy, but also effectively reduces the operation cost of the system.

[0120] Table 1 Energy storage investment and operation parameters

[0121] Table 2 Energy storage configuration results based on the Wasserstein distributionally robust algorithm

[0122] Table 3 Comparison of system renewable energy consumption rate

[0123] Table 4 Comparison of system operation cost

[0124] In the embodiment of the present application, the distributed renewable energy historical output data and the predicted output data are acquired; the distributed renewable energy uncertainty model is constructed based on the distributed renewable energy historical output data, the predicted output data and the Wasserstein divergence; the fuzzy set of the distributed renewable energy is acquired, and the distributed renewable energy uncertainty model and the fuzzy set are associated; the energy storage configuration strategy optimization model is constructed based on the distribution robust optimization algorithm, at this time, the Wasserstein divergence is introduced so as to construct the distributed renewable energy uncertainty model based on the distributed renewable energy historical output data, the predicted output data and the Wasserstein divergence, thereby controlling the distributed renewable energy uncertainty model, and then further combining with the distribution robust optimization algorithm, thereby constructing the energy storage configuration strategy optimization model, and then effectively processing the uncertainty of the distributed renewable energy output, making the energy storage configuration strategy more reasonable, and effectively balancing the dispatching economy and the robustness of the power system.

[0125] Embodiment

[0126] Please refer to FIG. 6, which is a structural composition schematic diagram of the energy storage configuration strategy optimization system in the embodiment of the present application.

[0127] As shown in FIG. 6, the energy storage configuration strategy optimization system comprises:

[0128] The acquisition module 21 is configured to acquire the distributed renewable energy historical output data and the predicted output data.

[0129] The construction module 22 is configured to construct the distributed renewable energy uncertainty model based on the distributed renewable energy historical output data, the predicted output data and the Wasserstein divergence.

[0130] The association module 23 is configured to acquire the fuzzy set of the distributed renewable energy, and associate the distributed renewable energy uncertainty model and the fuzzy set.

[0131] The optimization module 24 is configured to construct the energy storage configuration strategy optimization model based on the distribution robust optimization algorithm.

[0132] Embodiment

[0133] Please refer to FIG. 7, the electronic device 40 according to this embodiment of the present application will be described below with reference to FIG. 7. FIG. 7 shows only one example of the electronic device 40, and should not impose any limitation on the function and use range of the embodiment of the present application.

[0134] As shown in FIG. 7, the electronic device 40 is in the form of a general-purpose computing device. Components of the electronic device 40 can include, but are not limited to, the at least one processing unit 41, the at least one storage unit 42, and a bus 43 that connects the various system components, including the storage unit 42 and the processing unit 41.

[0135] The storage unit stores programming code that can be executed by the processing unit 41 to cause the processing unit 41 to perform the steps described in the "Embodiment Methods" section above with respect to the various example embodiments of the present application.

[0136] The storage unit 42 can include a readable medium in the form of volatile storage such as random access memory (RAM) 421 and / or cache memory 422, and can further include non-volatile storage such as read only memory (ROM) 423.

[0137] The storage unit 42 can also include a program / utility 424 having a set of program modules 425, including but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which can give rise to an implementation of a network environment in one or a combination of the examples.

[0138] The bus 43 can represent one or more of several types of bus structures, including a storage bus or bus controller, a peripheral bus, a graphics bus (e.g., an Accelerated Graphics Port, or AGP bus) and a local bus using any of a variety of bus architectures.

[0139] The electronic device 40 can also communicate with one or more external devices 46 such as a keyboard or pointing device, a Bluetooth device, etc.; one or more devices that enable a user to interact with the electronic device 40; and / or one or more devices (e.g., a router, a modem, a server, etc.) that enable the electronic device 40 to communicate with one or more other computing devices. Such communication can occur via an input / output (I / O) interface 44. Still yet, the electronic device 40 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or the Internet) through a network adapter 45. As depicted in FIG. 7, the network adapter 45 can communicate with the other components of the electronic device 40 through the bus 43. It should be understood that, although not shown explicitly in FIG. 7, other hardware and / or software components could be used in conjunction with the electronic device 40. Such components include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0140] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0141] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Furthermore, the computer program instructions are stored therein, and when the computer executes the computer program instructions, the computer executes the above methods.

Claims

1. A method for optimizing energy storage configuration strategy, applied to an energy storage scenario. The optimization method of the energy storage configuration strategy comprises the following steps: acquire historical output data and predicted output data of the distributed renewable energy source; construct a distributed renewable energy source uncertainty model based on the historical output data and the predicted output data of the distributed renewable energy source and a Wasserstein divergence; acquire a fuzzy set of the distributed renewable energy source and associate the distributed renewable energy source uncertainty model with the fuzzy set; construct an energy storage configuration strategy optimization model based on a distribution robust optimization algorithm.

2. The optimization method of energy storage configuration strategy according to claim 1, wherein, The acquisition of the historical output data and the predicted output data of the distributed renewable energy source comprises the following steps: locate the distributed renewable energy source; acquire the historical output data and the predicted output data of the distributed renewable energy source based on the distributed renewable energy source.

3. The optimization method of energy storage configuration strategy according to claim 2, wherein, The construction of the distributed renewable energy source uncertainty model based on the historical output data and the predicted output data of the distributed renewable energy source and the Wasserstein divergence comprises the following steps: frame the historical output data and the predicted output data of the distributed renewable energy source; collect the Wasserstein divergence and associate the Wasserstein divergence with the historical output data and the predicted output data of the distributed renewable energy source; construct the distributed renewable energy source uncertainty model according to the historical output data, the predicted output data and the Wasserstein divergence of the distributed renewable energy source.

4. The optimization method of energy storage configuration strategy according to claim 3, wherein, The construction of the distributed renewable energy source uncertainty model based on the historical output data and the predicted output data of the distributed renewable energy source and the Wasserstein divergence further comprises the following steps: The prediction error of the ith output power of the distributed renewable energy unit is a random variable δ i In practical applications, the true probability distribution of δ i ​ is not extractable, but limited historical sample prediction error (N sam For historical sample sizes) can provide insight into reliable probabilistic information to extract an empirical distribution In the formula, d k denotes a Dirac measure.

5. The optimization method of energy storage configuration strategy according to claim 4, wherein, The construction of the distributed renewable energy source uncertainty model based on the historical output data and the predicted output data of the distributed renewable energy source and the Wasserstein divergence further comprises the following steps: With Fuzzy set construction for center To ensure Good properties, accurately described using the Wasserstein divergence and distance between; In the formulae, For empirical distribution a random variable; J is and the joint distribution of X and Y; a distance between two random variables.

6. The optimization method of energy storage configuration strategy according to claim 5, wherein, The acquisition of the fuzzy set of the distributed renewable energy source and the association of the distributed renewable energy source uncertainty model with the fuzzy set comprise the following steps: define the fuzzy set based on the distributed renewable energy source uncertainty model; frame the distributed renewable energy source uncertainty model and the fuzzy set; associate the distributed renewable energy source uncertainty model with the fuzzy set.

7. The optimization method of energy storage configuration strategy according to claim 6, wherein, The acquisition of the fuzzy set of the distributed renewable energy source and the association of the distributed renewable energy source uncertainty model with the fuzzy set further comprise the following steps: fuzzy set may be expressed as: In the formulae, may be considered as a circle of radius With Wasserstein balls with barycenter, Specifically represented as follows: In the formulae, for a confidence level of δ i η is an auxiliary variable; is the average of the historical prediction errors; The fuzzy set can be obtained by an equal-division search method.

8. The optimization method of energy storage configuration strategy according to claim 7, wherein, The construction of the energy storage configuration strategy optimization model based on the distribution robust optimization algorithm comprises the following steps: collect energy storage related costs and power grid operation costs; associate the distribution robust optimization algorithm with the energy storage related costs and the power grid operation costs and construct the energy storage configuration strategy optimization model.

9. The optimization method of energy storage configuration strategy according to claim 8, wherein, The construction of the energy storage configuration strategy optimization model based on the distribution robust optimization algorithm further comprises the following steps: The energy storage configuration strategy optimization model takes the minimum total system planning cost as an objective function: The objective function is as follows: min C ESD +C ope ; In the formula, C ESD is the energy storage related cost; C ope is the grid operation cost. 10.An energy storage configuration strategy optimization system, applied to the energy storage configuration strategy optimization method according to any one of claims 1-9, comprising: an acquisition module configured to acquire historical output data and predicted output data of a distributed renewable energy source; The construction module is configured to construct a distributed renewable energy uncertainty model based on distributed renewable energy historical output data, predicted output data, and a Wasserstein divergence; The association module is configured to obtain a fuzzy set of the distributed renewable energy, and associate the distributed renewable energy uncertainty model with the fuzzy set; The optimization module is configured to construct an energy storage configuration strategy optimization model based on a distribution robust optimization algorithm.

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