Energy storage optimal configuration method and system considering endogenous and endogenous dual uncertainty

Abstract

The invention relates to an energy storage optimal configuration technology, in particular to an energy storage optimal configuration method and system considering endogenous and endogenous dual uncertainty, and the method comprises the steps: taking the sum of the minimum energy storage configuration cost, the minimum energy storage loss cost and the minimum system scheduling cost as a target function, and building an energy storage configuration optimization model; determining constraint conditions; an optimal solution of a decision-making dependency uncertainty robust problem is realized through a dual column constraint generation method, and an energy storage planning decision-making scheme is obtained through two-stage transformation and CPLEX solver calling. According to the method, the exogenous uncertainty of new energy and the endogenous uncertainty of energy storage are comprehensively considered, the energy storage life attenuation characteristic is considered, the energy storage planning configuration method is constructed, and the energy storage operation life is guaranteed while the system dispatching economy and the energy storage planning economy are improved. The problems that a traditional column constraint generation algorithm cannot adapt to the worst working condition considering the endogenous uncertainty problem and is difficult to determine are solved, and the economic dispatching level of the system is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage optimization configuration, and in particular to an energy storage optimization configuration method and system taking into account dual uncertainties of endogenous and exogenous energy storage. Background Art

[0002] The high proportion of uncertain access to new energy brings severe challenges to the analysis of power balance of power system. The economic dispatch of the system is also subject to the exogenous uncertainty of new energy and presents a high operating risk. Scientific planning of energy storage is conducive to smoothing the fluctuation of new energy, improving the utilization efficiency of new energy, greatly improving the risk of power imbalance, and optimizing the economic operation level of the system. However, the frequent charging and discharging of energy storage is not conducive to ensuring the safety of the full life operation of energy storage. The life of energy storage is subject to frequent actions and decreases rapidly. The existing energy storage attenuation model only considers the charging and discharging actions and ignores the ambient temperature and activation performance, which shows certain limitations. At the same time, the energy storage configuration only considers the exogenous uncertainty of new energy, while ignoring the endogenous uncertainty of energy storage devices subject to the absorption capacity of new energy, resulting in a relatively conservative energy storage planning scheme. Furthermore, in the energy storage robust planning model taking into account endogenous uncertainty, new energy is no longer at the extreme boundary under the worst working conditions, and the decision-making results of endogenous uncertainty of energy storage are highly dependent on the decision-making scheme of new energy, which directly leads to the inability of the column constraint generation algorithm to obtain the optimal solution. Summary of the invention

[0003] The present invention provides an energy storage optimization configuration method and system taking into account dual uncertainties of endogenous and exogenous factors, so as to solve the defects of insufficient flexibility on the grid side and node side in the prior art, and realize the application of energy storage to improve the flexibility of system operation.

[0004] The present invention provides an energy storage optimization configuration method taking into account both endogenous and exogenous uncertainties, comprising: An optimization model for energy storage configuration is established with the objective function of minimizing the sum of energy storage configuration cost, energy storage loss cost and system dispatch cost. Identify constraints; The optimal solution of the decision-dependent uncertain robust problem is achieved through the dual column constraint generation method, and the energy storage planning decision plan is obtained through two-stage transformation and CPLEX solver call.

[0005] According to an energy storage optimization configuration method taking into account both endogenous and exogenous uncertainties provided by the present invention, establishing an optimization model for energy storage configuration includes the following steps: Step 1.1 Thermal power unit operating costs for,

[0006] In the formula, For the crew i The operating cost coefficient, For the running time, For the node i A collection of connected generator sets, For the crew i exist t Active power in the time period; Step 1.2 Energy storage investment cost for,

[0007] In the formula, is the energy storage capacity, is the unit cost coefficient of power; is the energy storage capacity, is the energy storage power; Step 1.3 Levelized loss cost of energy storage for,

[0008] In the formula, is the levelized loss cost coefficient of energy storage; Energy storage capacity attenuation caused by a single charge and discharge operation , The threshold for energy storage retirement; Step 1.4 Objective function for,

[0009] In the formula, is the operating cost of the thermal power unit, is the energy storage investment cost, The levelized loss cost of energy storage.

[0010] According to an energy storage optimization configuration method taking into account dual uncertainties of endogenous and exogenous factors provided by the present invention, the constraints include: 2.1) Unit active output constraints:

[0011] In the formula, For the crew i The upper limit of the active contribution, For the crew i The lower bound of the active contribution, For the crew i exist t Active power in the time period; 2.2) Unit power balance constraints:

[0012] In the formula, is the charging power of the energy storage, is the discharge power of the energy storage, Abandoned electricity for new energy is the load shedding amount, is the node active load, Make real contribution to new energy; 2.3) Load shedding constraints:

[0013] In the formula, is the load abandonment amount, is the load level; 2.4) Constraints on abandoned electricity from new energy sources:

[0014] In the formula, Abandoned electricity for new energy It is the actual output value of new energy; 2.5) Energy storage configuration constraints:

[0015] In the formula, , are binary variables, representing the charging and discharging states of energy storage; is the energy storage capacity, The maximum configuration capacity of energy storage is is the energy storage power, is the maximum power of energy storage, is the charging power of the energy storage, is the discharge power of energy storage; The big M method is introduced to relax the above equation, and the above equation is updated as follows:

[0016] In the formula, M is the penalty coefficient, is the auxiliary variable introduced; 2.6) Energy storage system operation constraints:

[0017] In the formula, For Node i Power-on energy storage t The charging speed at all times, For Node i Power-on energy storage t The discharge speed at the moment; is the charging efficiency of the electric energy storage, is the discharge efficiency of the electric energy storage; is the charge of the energy storage, is the maximum charging and discharging speed of energy storage; 2.7) Energy storage life decay model:

[0018] In the formula, is the apparent activation energy; A is the Arrhenius constant; R is the molar gas constant; T is the thermodynamic temperature; z is the power law factor, is the charging efficiency of the electric energy storage, is the discharge efficiency of the electric energy storage; Assume that the energy storage SOC error conforms to the segmented normal distribution, that is:

[0019] In the formula, For the expectation , the variance is Normal distribution of 2.8) Exogenous uncertainty constraints on new energy:

[0020] In the formula, is a 0-1 variable; It is the uncertain fluctuation range of new energy. Forecast level of new energy.

[0021] According to a method for optimizing energy storage configuration taking into account both endogenous and exogenous uncertainties provided by the present invention, obtaining an energy storage planning decision plan includes the following steps: Step 3.1 Establish a robust energy storage planning model that takes into account endogenous uncertainties. Its mathematical compact model is:

[0022] Where: is the objective function, For variables The set of feasible solutions of is the set of robust feasible solutions; and is the coefficient vector; , , , and are coefficient matrices; is the second stage decision variable; is the set of endogenous uncertainties; is the slack variable; The dual C&CG method is used for solution; Step 3.2: Establish the main problem model;

[0023] In the formula, k is the number of iterations; For the k The worst condition in the iteration; and are dual variables, is the introduced binary variable, K For iterating over a collection; b is the coefficient vector and its transpose, , , , and are coefficient matrices, , and are decision variables, and is the optimal solution of the main problem in the k+1th iteration, and is the decision variable describing the uncertain parameters; Step 3.3: Build sub-problem model:

[0024] In the formula, and All are dual variables; Step 3.4 Two-stage loop iteration; 1) Initialization: K=0; 2) Solve the main problem and obtain a feasible solution, ; In the formula, is the optimal solution of the main problem for the kth iteration, , , , is the decision variable in the first iteration , , and The value of , , , is the decision variable in the kth iteration , , and The value of 3) Substitute into the subproblem and get the optimal solution of the subproblem , set up ; 4) If 0, the algorithm terminates and outputs the optimal solution , otherwise jump to the next step; 5) Create variables , and add the following cut constraints to the main problem:

[0025] 6) Number of iterations k=k+1.

[0026] The present invention also provides an energy storage optimization configuration system taking into account both endogenous and exogenous uncertainties, the system comprising: The energy storage configuration module is used to establish an optimization model for energy storage configuration by minimizing the sum of energy storage configuration cost, energy storage loss cost and system dispatch cost as the objective function; The constraint condition module is used to establish various constraints with the objective function of minimizing the sum of energy storage configuration cost, energy storage loss cost and system dispatch cost; The energy storage planning module is used to achieve the optimal solution of the decision-dependent uncertain robust problem through the dual column constraint generation method, and obtain the energy storage planning decision plan through two-stage transformation and CPLEX solver call.

[0027] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the energy storage optimization configuration method taking into account the dual uncertainties of endogenous and exogenous energy as described in any one of the above methods is implemented.

[0028] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described energy storage optimization configuration methods taking into account both endogenous and exogenous uncertainties.

[0029] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned energy storage optimization configuration methods taking into account both endogenous and exogenous uncertainties.

[0030] Compared with the prior art, the present invention has the following beneficial effects: The present invention combines energy storage planning with energy storage life decay, realizes the mutual unification of energy storage planning and operation, and helps to significantly increase the operating life of energy storage equipment and improve the utilization efficiency of energy storage resources while ensuring the economy of the planning scheme. Based on the Arrhenius activation formula, the present invention constructs an energy storage life decay model that takes into account multiple influencing factors, which is conducive to more accurate description of the energy storage life decay characteristics. At the same time, the endogenous uncertainty of energy storage planning is comprehensively considered, and the energy storage planning results have better economy, improving the economic level of energy storage configuration schemes. The present invention proposes a dual column constraint generation and solution method that is suitable for taking into account endogenous and exogenous dual uncertainties. It breaks through the problem that the traditional column constraint generation algorithm cannot meet the feasibility problem and optimization problem of the worst working condition taking into account endogenous uncertainty, and improves the solution efficiency of the decision-making scheme while obtaining the global optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 It is a two-stage solution flow chart based on the dual column constraint generation algorithm provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0034] There are relatively many studies on flexible resource regulation to improve system operation flexibility, mainly focusing on flexible loads, demand-side response and energy storage. Relatively speaking, energy storage has a better regulatory effect, but faces the problem of high investment. At the same time, in the existing energy storage planning methods, only the exogenous uncertainty of new energy is considered, while the endogenous uncertainty of energy storage planning schemes is ignored, that is, the energy storage planning scheme depends on the new energy decision-making scheme, resulting in a relatively conservative planning scheme. This embodiment focuses on the endogenous uncertainty of energy storage, revolves around energy storage planning, comprehensively considers the exogenous uncertainty of new energy, the endogenous uncertainty of energy storage and the energy storage life attenuation characteristics, and establishes an energy storage robust planning model and solution method based on a dual column constraint generation algorithm, breaking through the problem that the traditional column constraint generation algorithm cannot adapt to the problem of the worst working condition that takes into account the endogenous uncertainty problem and is difficult to determine. The simulation results show that the proposed planning scheme has better configuration energy efficiency, greatly reduces the conservatism of the scheme, effectively improves the operating life of the energy storage equipment, and optimizes the system economic dispatch level.

[0035] This embodiment is implemented by the following technical solution, a method for optimizing energy storage configuration taking into account dual uncertainties of endogenous and exogenous factors, comprising the following steps: S1: Establish an optimization model for energy storage configuration by minimizing the sum of energy storage configuration cost, energy storage loss cost and system dispatch cost as the objective function; S1.1. Operating costs of thermal power units :

[0036] In the formula, For the crew i The operating cost coefficient, For the running time, For the node i A collection of connected generator sets, For the crew i exist t Active power in the time period; S1.2. Energy storage investment cost :

[0037] In the formula, is the energy storage capacity, is the unit cost coefficient of power; is the energy storage capacity, is the energy storage power; S1.3. Levelized loss cost of energy storage :

[0038] In the formula, is the levelized loss cost coefficient of energy storage; Energy storage capacity attenuation caused by a single charge and discharge operation , The threshold for energy storage retirement; S1.4. Objective function :

[0039] In the formula, is the operating cost of the thermal power unit, is the energy storage investment cost, The levelized loss cost of energy storage.

[0040] S2: Constraints; S2.1、Active power output constraints of the unit:

[0041] In the formula, For the crew i The upper limit of the active contribution, For the crew i The lower bound of the active contribution, For the crew i exist t Active power in the time period; S2.2, Unit power balance constraints:

[0042] In the formula, is the charging power of the energy storage, is the discharge power of the energy storage, Abandoned electricity for new energy is the load shedding amount, is the node active load, Make real contribution to new energy; S2.3, Load shedding constraints:

[0043] In the formula, is the load abandonment amount, is the load level; S2.4、Renewable energy curtailment constraints:

[0044] In the formula, Abandoned electricity for new energy It is the actual output value of new energy; S2.5, Energy storage configuration constraints:

[0045] In the formula, , are binary variables, representing the charging and discharging states of energy storage; is the energy storage capacity, The maximum configuration capacity of energy storage is is the energy storage power, is the maximum power of energy storage, is the charging power of the energy storage, is the discharge power of energy storage; There is a nonlinear variable group in the above formula and , introduce the big M method to relax the above formula, and the above formula is updated as follows:

[0046] In the formula, M is the penalty coefficient, is the auxiliary variable introduced; S2.6. Energy storage system operation constraints:

[0047] In the formula, For Node i Power-on energy storage t The charging speed at all times, For Node i Power-on energy storage t The discharge speed at the moment; is the charging efficiency of the electric energy storage, is the discharge efficiency of the electric energy storage; is the charge of the energy storage, is the maximum charging and discharging speed of energy storage; S2.7, Energy storage life decay model:

[0048] Where: is the apparent activation energy; A is the Arrhenius constant; R is the molar gas constant; T is the thermodynamic temperature; z is the power law factor, is the charging efficiency of the electric energy storage, is the discharge efficiency of the electric energy storage; S2.8. Endogenous uncertainty constraints of energy storage: The inherent uncertainty of energy storage is mainly manifested in the SOC change caused by the charging and discharging action of energy storage, and SOC is subject to the impact of the reduction of energy storage life. The energy storage SOC capacity level has a certain error. At present, the SOC state estimation assumes that the energy storage SOC error conforms to the segmented normal distribution, that is:

[0049] In the formula, For the expectation , the variance is Normal distribution of S2.9. New energy exogenous uncertainty constraints:

[0050] In the formula, is a 0-1 variable; It is the uncertain fluctuation range of new energy. Forecast level of new energy.

[0051] S3: The optimal solution of the decision-dependent uncertain robust problem is achieved through the dual column constraint generation method, and the final energy storage planning decision plan is obtained through two-stage transformation and CPLEX solver call.

[0052] S3.1. Establish a robust energy storage planning model taking into account endogenous uncertainties. Its mathematical compact model is as follows:

[0053] Where: is the objective function, For variables The set of feasible solutions of is the set of robust feasible solutions; and is the coefficient vector; , , , and are coefficient matrices; is the second stage decision variable; is the set of endogenous uncertainties; is the slack variable; The above min-max-min structure problem can be solved by the traditional method by introducing the column and constraint generation method (C&CG). By dual transformation of the max-min problem, the above robust planning model is transformed into a min-max two-stage problem, and the decision plan is obtained by iteration of the main and sub-problems. However, the existence of endogenous uncertainty in energy storage means that the solution obtained by the main problem may not meet the constraints of the sub-problem. The main reason is that the endogenous uncertainty will cause the uncertain parameters under the worst working conditions to no longer be at a critical level. Therefore, it is necessary to improve the algorithm to overcome the shortcomings of the traditional C&CG method and obtain the optimal decision of the planning scheme. This embodiment introduces the dual C&CG method to solve it.

[0054] S3.2, establish the main problem model;

[0055] In the formula, k is the number of iterations; For the k The worst condition in the iteration; and are dual variables, is the introduced binary variable, K For iterating over a collection; b is the coefficient vector and its transpose, , , , and are coefficient matrices, , and are decision variables, and is the optimal solution of the main problem in the k+1th iteration, and is the decision variable describing the uncertain parameters; S3.3. Establish sub-problem model:

[0056] In the formula, and All are dual variables; S3.4, two-stage loop iteration, such as Figure 1 As shown; 1: Initialization: k=0; 2: Solve the main problem and obtain a feasible solution: ; In the formula, is the optimal solution of the main problem for the kth iteration, , , , is the decision variable in the first iteration , , and The value of , , , is the decision variable in the kth iteration , , and The value of 3: Substitute into the subproblem and get the optimal solution of the subproblem , set up ; 4: If 0, the algorithm terminates and outputs the optimal solution , otherwise jump to the next step; 5: Create variables , and add the following cut constraints to the main problem,

[0057] 6: Number of iterations k=k+1.

[0058] This embodiment is based on the regulation characteristics of energy storage, combined with the Arrhenius activation formula, comprehensively considers the exogenous uncertainty of new energy and the endogenous uncertainty of energy storage, takes into account the energy storage life decay characteristics, and constructs an energy storage planning configuration method. While improving the system scheduling economy and energy storage planning economy, it guarantees the operating life of energy storage, which is an important guarantee for using energy storage to improve the flexibility of system operation.

[0059] The following describes an energy storage optimization configuration system that takes into account both endogenous and exogenous uncertainties provided by this embodiment, including: The energy storage configuration module is used to establish an optimization model for energy storage configuration by minimizing the sum of energy storage configuration cost, energy storage loss cost and system dispatch cost as the objective function; The constraint condition module is used to establish various constraints with the objective function of minimizing the sum of energy storage configuration cost, energy storage loss cost and system dispatch cost; The energy storage planning module is used to achieve the optimal solution of the decision-dependent uncertain robust problem through the dual column constraint generation method, and obtain the energy storage planning decision plan through two-stage transformation and CPLEX solver call.

[0060] Figure 2 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 2 As shown, the electronic device may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus. The processor may call the logic instructions in the memory to execute the energy storage optimization configuration method taking into account the dual uncertainties of endogenous and exogenous.

[0061] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, 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, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0062] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the energy storage optimization configuration method that takes into account the dual uncertainties of endogenous and exogenous energy provided by the above methods.

[0063] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the energy storage optimization configuration method taking into account both endogenous and exogenous dual uncertainties provided by the above methods.

[0064] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0065] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing energy storage configuration taking into account both endogenous and exogenous uncertainties, characterized in that: include: An optimization model for energy storage configuration is established with the objective function of minimizing the sum of energy storage configuration cost, energy storage loss cost and system dispatch cost. Identify constraints; The optimal solution of the decision-dependent uncertain robust problem is achieved through the dual column constraint generation method, and the energy storage planning decision plan is obtained through two-stage transformation and CPLEX solver call.

2. The energy storage optimization configuration method taking into account the dual uncertainties of endogenous and exogenous energy storage according to claim 1 is characterized in that: Establishing an optimization model for energy storage configuration includes the following steps: Step 1.1 Thermal power unit operating costs for, In the formula, For the crew i The operating cost coefficient, For the running time, For the node i A collection of connected generator sets, For the crew i exist t Active power in the time period; Step 1.2 Energy storage investment cost for, In the formula, is the energy storage capacity, is the unit cost coefficient of power; is the energy storage capacity, is the energy storage power; Step 1.3 Levelized loss cost of energy storage for, In the formula, is the levelized loss cost coefficient of energy storage; Energy storage capacity attenuation caused by a single charge and discharge operation , The threshold for energy storage retirement; Step 1.4 Objective function for, in, is the operating cost of the thermal power unit, is the energy storage investment cost, The levelized loss cost of energy storage.

3. The energy storage optimization configuration method taking into account both endogenous and exogenous uncertainties according to claim 1 is characterized in that: The constraints include: 2.1) Unit active output constraints: In the formula, For the crew i The upper limit of the active contribution, For the crew i The lower bound of the active contribution, For the crew i exist t Active power in the time period; 2.2) Unit power balance constraints: in, is the charging power of the energy storage, is the discharge power of the energy storage, Abandoned electricity for new energy is the load shedding amount, is the node active load, Make real contribution to new energy; 2.3) Load shedding constraints: In the formula, is the load abandonment amount, is the load level; 2.4) Constraints on abandoned electricity from new energy sources: In the formula, Abandoned electricity for new energy It is the actual output value of new energy; 2.5) Energy storage configuration constraints: In the formula, , are binary variables, representing the charging and discharging states of energy storage; is the energy storage capacity, The maximum configuration capacity of energy storage is is the energy storage power, is the maximum power of energy storage, is the charging power of the energy storage, is the discharge power of energy storage; The big M method is introduced to relax the above equation, and the above equation is updated as follows: In the formula, M is the penalty coefficient, is the auxiliary variable introduced; 2.6) Energy storage system operation constraints: In the formula, For Node i Power-on energy storage t The charging speed at all times, For Node i Power-on energy storage t The discharge speed at the moment; is the charging efficiency of the electric energy storage, is the discharge efficiency of the electric energy storage; is the charge of the energy storage, is the maximum charging and discharging speed of energy storage; 2.7) Energy storage life decay model: In the formula, is the apparent activation energy; A is the Arrhenius constant; R is the molar gas constant; T is the thermodynamic temperature; z is the power law factor, is the charging efficiency of the electric energy storage, is the discharge efficiency of the electric energy storage; Assume that the energy storage SOC error conforms to the segmented normal distribution, that is: In the formula, For the expectation , the variance is Normal distribution of 2.8) Exogenous uncertainty constraints on new energy: In the formula, is a 0-1 variable; It is the uncertain fluctuation range of new energy. Forecast level of new energy.

4. The energy storage optimization configuration method taking into account both endogenous and exogenous uncertainties according to claim 1 is characterized in that: Obtaining an energy storage planning decision solution includes the following steps: Step 3.1 Establish a robust energy storage planning model that takes into account endogenous uncertainties. Its mathematical compact model is: Where: is the objective function, For variables The set of feasible solutions of is the set of robust feasible solutions; and is the coefficient vector; , , , and are coefficient matrices; is the second stage decision variable; is the set of endogenous uncertainties; is the slack variable; The dual C&CG method is used for solution; Step 3.2: Establish the main problem model; Where: k is the number of iterations; For the k The worst condition in the iteration; and are dual variables, is the introduced binary variable, K For iterating over a collection; b is the coefficient vector and its transpose, , , , and are coefficient matrices, , and are decision variables, and is the optimal solution of the main problem in the k+1th iteration, and is the decision variable describing the uncertain parameters; Step 3.3: Build sub-problem model: In the formula, and All are dual variables; Step 3.4 Two-stage loop iteration; 1) Initialization: K=0; 2) Solve the main problem and obtain a feasible solution, ； In the formula, is the optimal solution of the main problem for the kth iteration, , , , is the decision variable in the first iteration , , and The value of , , , is the decision variable in the kth iteration , , and The value of 3) Substitute into the subproblem and get the optimal solution of the subproblem , set up ; 4) If 0, the algorithm terminates and outputs the optimal solution , otherwise jump to the next step; 5) Create variables , and add the following cut constraints to the main problem: 6) Number of iterations k=k+1.

5. An energy storage optimization configuration system taking into account both endogenous and exogenous uncertainties, characterized in that: The system is used to implement the energy storage optimization configuration method taking into account the dual uncertainties of endogenous and exogenous as described in any one of claims 1 to 4, comprising: The energy storage configuration module is used to establish an optimization model for energy storage configuration by minimizing the sum of energy storage configuration cost, energy storage loss cost and system dispatch cost as the objective function; The constraint condition module is used to establish various constraints with the objective function of minimizing the sum of energy storage configuration cost, energy storage loss cost and system dispatch cost; The energy storage planning module is used to achieve the optimal solution of the decision-dependent uncertain robust problem through the dual column constraint generation method, and obtain the energy storage planning decision plan through two-stage transformation and CPLEX solver call.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the energy storage optimization configuration method taking into account the dual uncertainties of endogenous and exogenous energy as described in any one of claims 1 to 4 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the energy storage optimization configuration method taking into account the dual uncertainties of endogenous and exogenous energy as described in any one of claims 1 to 4 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the energy storage optimization configuration method taking into account the dual uncertainties of endogenous and exogenous energy as described in any one of claims 1 to 4 is implemented.

Images