Generalized shared energy storage optimization configuration method and system based on fuzzy chance-constrained programming

Abstract

The application discloses a generalized shared energy storage optimization configuration method and system based on fuzzy chance constrained programming, relates to the technical field of electric power, considers the operation characteristics of various generalized energy storage resources, and respectively models the energy storage, wherein the base station energy storage model needs to consider communication load communication quality and base station power supply reliability, the air conditioner model needs to consider user comfort, and the electric vehicle charging station model needs to consider user driving characteristics, so that the existing large number of idle multi-energy generalized energy storage resources are fully tapped, and the generalized shared energy storage optimization configuration model considering multiple uncertainties is designed. The shared energy storage operator and the user group form a cooperative alliance by transferring the energy storage use right, the generalized shared energy storage optimization configuration model is decomposed into two sub-problems of alliance energy consumption cost minimization and internal payment negotiation based on Nash bargaining theory, and the risk brought by the parameter uncertainty of the user group source and load output and virtual energy storage is quantified based on the fuzzy chance constrained programming theory.

Description

Technical Field

[0001] This invention relates to the field of power technology, specifically to a method and system for optimizing the allocation of generalized shared energy storage based on fuzzy chance constraint programming. Background Technology

[0002] Currently, research on the optimal allocation of shared energy storage faces development barriers. On the one hand, existing research on resource allocation for shared energy storage only considers investing in and constructing conventional centralized energy storage or leasing conventional distributed energy storage, without taking into account the advantages of integrating idle, broadly defined distributed energy storage resources on the user side. On the other hand, current research on the optimal allocation of shared energy storage rarely considers the uncertainty of user demand. Furthermore, the introduction of such broadly defined energy storage resources will inevitably lead to time-varying characteristics in energy storage devices that were originally fixed parameters, ultimately resulting in uncertainty for both the suppliers and demanders of shared energy storage. How to quantify the impact of multiple uncertainties and optimize and customize the scale of energy storage based on this is also a key challenge that remains to be solved. Summary of the Invention

[0003] To address the shortcomings mentioned in the background art, the present invention aims to provide a method and system for optimizing the configuration of generalized shared energy storage based on fuzzy chance constraint programming. From the perspective of a shared energy storage operator (SESO), this invention achieves the economically optimal configuration goal of the shared energy storage operator by constructing centralized energy storage and leasing generalized energy storage resources such as base station backup energy storage, air conditioning, electric vehicle charging stations, and conventional distributed energy storage. Furthermore, it utilizes fuzzy chance constraint programming theory to quantify the impact of uncertainties in user demand and predict the adjustable potential of virtual energy storage on the configuration scheme.

[0004] The objective of this invention can be achieved through the following technical solution: a method for optimizing the allocation of generalized shared energy storage based on fuzzy chance constraint programming, characterized in that the method includes the following steps:

[0005] The system receives various generalized energy storage resources and inputs their operational characteristics into a pre-established generalized energy storage model to obtain individual models of various generalized energy storage resources. These generalized energy storage resources include base station backup energy storage, air conditioning, electric vehicle charging stations, and conventional energy storage.

[0006] By aggregating various individual models of generalized energy storage, a generalized shared energy storage resource aggregation model is obtained;

[0007] The annual fixed investment cost and annual variable net operating income of the shared energy storage operator are received and, combined with the constraints of the generalized shared energy storage resource aggregation model, are input into the pre-established optimized configuration model of the shared energy storage operator to obtain the first-level model.

[0008] The user's interaction fee is received and combined with existing power grid constraints, then input into a pre-established user optimization operation model to obtain a secondary model;

[0009] Based on Nash bargaining theory, the first-level and second-level models are transformed and solved to output a generalized shared energy storage optimal configuration model;

[0010] The system receives uncertainty information about wind power, photovoltaic power, and load from users within the user's network, as well as uncertainty information about the charging and discharging power limits of the virtual energy storage aggregation model for air conditioning and electric vehicle charging stations. This information is then input into the generalized shared energy storage optimization configuration model to obtain a three-level model.

[0011] The three-level model is transformed and solved using fuzzy chance-constrained programming theory to obtain a generalized shared energy storage optimization configuration model that considers multiple uncertainties. The optimization configuration results of the generalized shared energy storage are then output using this model.

[0012] Preferably, the operational characteristics of the various generalized energy storage resources include:

[0013] The base station energy storage model needs to consider communication load, communication quality, and base station power supply reliability; the air conditioning model needs to consider user comfort; and the electric vehicle charging station model needs to consider user driving characteristics.

[0014] Preferably, the objective function of the shared energy storage operator's optimized configuration model is as follows:

[0015] The goal is to maximize the annual net income of SESO, including annual fixed input costs and annual variable operating net income, with the annual variable operating net income taking into account the daily optimization period.

[0016]

[0017] In the formula, For SESO's annual net income; This refers to the annual fixed input costs for SESO; SESO's annual variable net operating income; , These are decision variables.

[0018] Preferably, the annual fixed investment costs included in the shared energy storage operator's optimized configuration model are: the configuration cost of centralized energy storage and the annual fixed lease cost of generalized energy storage resources; the annual variable net operating revenue includes: the variable compensation cost of generalized energy storage resources, the interaction cost with the upper-level distribution network, the interaction cost of energy storage usage rights, and the network transmission cost.

[0019] Preferably, the constraints of the shared energy storage operator's optimized configuration model include: power balance constraints, centralized energy storage aggregation model constraints, virtual energy storage constraints, and energy storage capacity start-end constraints.

[0020] Preferably, the objective function of the user-optimized operation model is as follows:

[0021]

[0022] In the formula, The annual energy cost for user i; The cost of interaction between user i and the upper-level distribution network on the s-th typical day; The interaction fee for the s-th typical daily user i to purchase the right to use energy storage from SESO; The network transmission fee payable for energy transfer between the s-th typical daytime user i and SESO.

[0023] Preferably, the costs included in the user-optimized operation model are: the interaction costs between the user and the upper-level distribution network, the interaction costs between the user and SESO for leasing the right to use energy storage, and the network transmission costs.

[0024] The constraints of the user-optimized operation model include power balance constraints and power purchase and sale constraints from the grid.

[0025] Preferably, the process of transforming and solving the first-level and second-level models based on Nash bargaining theory to output the generalized shared energy storage optimal configuration model is as follows:

[0026]

[0027] In the formula, , The points at which negotiations break down for SESO and User i respectively are taken as the annual net revenue of SESO and the annual energy cost of User i when they do not participate in the shared energy storage model. ; , These represent the increased benefits for SESO and user i after participating in shared energy storage, respectively.

[0028] Since the Nash bargaining model is a non-convex nonlinear optimization problem, it cannot be solved directly. It is equivalently transformed into two sub-problems: minimizing the energy consumption cost of the alliance and internal payment bargaining. Sub-problem 1 is used to solve for the optimal energy storage configuration scheme of SESO and the amount of energy storage usage rights interaction between SESO and each user; sub-problem 2 is used to solve for the energy storage usage rights interaction cost between SESO and each user.

[0029] (1) Subproblem 1: Minimize the energy cost of the coalition

[0030] We can find the maximum and minimum values ​​using the arithmetic mean-geometric mean inequality: For any When it is a non-negative number, When the inequality holds true, equality holds. Therefore, when the Nash bargaining model reaches its maximum value, the following constraint is satisfied:

[0031]

[0032] because And the point at which negotiations broke down The above formula can be transformed into:

[0033]

[0034] The objective function of the Nash bargaining cooperation configuration model between SESO and user groups can be equivalently expressed as:

[0035]

[0036] because Since is a constant and the ln function is a monotonically increasing function, the above equation can be further transformed into:

[0037]

[0038] definition , , representing SESO's annual net revenue and user i's annual energy cost, excluding payments for internal energy storage usage rights, respectively:

[0039]

[0040]

[0041] Substituting the definition into the equation yields the objective function for subproblem 1:

[0042]

[0043] (2) Sub-problem 2: Internal payment negotiation

[0044] Based on the solution results of the charging and discharging quantities between each user and SESO in sub-problem 1, the contribution of each user to the alliance is calculated, which is the bargaining power. The sum of the bargaining powers of SESO and each user satisfies:

[0045]

[0046] Then user i's bargaining power is:

[0047]

[0048] In the formula, The bargaining power of user i in the s-th typical day reflects the user's contribution. The more interaction a user has with SESO, the greater their contribution to the alliance and social welfare. The bargaining power of the SESO on the s-th typical day is related to the market competition situation and the SESO's preferences;

[0049] The optimal solution obtained based on subproblem 1 and Substituting this into the Nash bargaining model that includes bargaining power, we get:

[0050]

[0051] Taking the logarithm of the above equation yields subproblem 2:

[0052]

[0053] By solving subproblem 2, the interaction fees for energy storage usage rights between SESO and each user can be obtained, and thus the annual net revenue of SESO and the annual energy cost of each user can be obtained.

[0054] Preferably, the generalized shared energy storage optimization configuration model considering multiple uncertainties includes:

[0055] (1) Fuzzy opportunity constraints related to user wind power, photovoltaic power, and load

[0056] When confidence level At that time, its clear equivalence class is:

[0057]

[0058] In the formula, The confidence levels corresponding to uncertainties in wind power, photovoltaics, and load; , , These are the predicted values ​​for the corresponding fuzzy variables, { }、{ }、{ } are the proportional coefficients of the trapezoidal membership degrees of the corresponding fuzzy variables;

[0059] (2) Fuzzy chance constraints related to virtual energy storage parameters

[0060] When confidence level , At that time, its clear equivalence class is:

[0061]

[0062]

[0063] In the formula, , These represent the confidence levels corresponding to the uncertainties in the virtual energy storage aggregation models for air conditioners and electric vehicles, respectively. , , , These are the predicted values ​​for the corresponding fuzzy variables, { }、{ } are the proportional coefficients of the trapezoidal membership degrees of the corresponding fuzzy variables.

[0064] Secondly, in order to achieve the above objectives, this invention discloses a generalized shared energy storage optimization configuration system based on fuzzy chance-constrained programming, comprising:

[0065] Receiving module: Used to receive various generalized energy storage resources, input the operating characteristics of various generalized energy storage resources into a pre-established generalized energy storage model, and obtain individual models of various generalized energy storage resources. Among them, various generalized energy storage resources include base station backup energy storage, air conditioning, electric vehicle charging stations and conventional energy storage.

[0066] Aggregation module: Used to aggregate multiple individual models of generalized energy storage to obtain a generalized shared energy storage resource aggregation model;

[0067] The first-level input module is used to receive the annual fixed investment cost and annual variable net operating income of the shared energy storage operator, and combine them with the constraints of the generalized shared energy storage resource aggregation model, and input them into the pre-established optimized configuration model of the shared energy storage operator to obtain the first-level model.

[0068] Secondary input module: Used to receive user interaction fees, combine them with existing power grid constraints, and input them into a pre-established user optimization operation model to obtain the secondary model;

[0069] Output Solver Module: Used to transform and solve the Level 1 and Level 2 models based on Nash bargaining theory, and output the generalized shared energy storage optimal configuration model;

[0070] The third-level input module is used to receive uncertainty information about wind power, photovoltaic power, and load from users within the user's network, as well as uncertainty information about the charging and discharging power limits of the virtual energy storage aggregation model for air conditioning and electric vehicle charging stations. This information is then input into the generalized shared energy storage optimization configuration model to obtain the third-level model.

[0071] The optimization configuration module is used to transform and solve the three-level model based on the fuzzy chance-constrained programming theory, so as to obtain a generalized shared energy storage optimization configuration model that considers multiple uncertainties. The module then outputs the generalized shared energy storage optimization configuration results using the generalized shared energy storage optimization configuration model that considers multiple uncertainties.

[0072] The beneficial effects of this invention are:

[0073] This invention considers the operational characteristics of various generalized energy storage resources and models them separately. The base station energy storage model needs to consider communication load, communication quality, and base station power supply reliability; the air conditioning model needs to consider user comfort; and the electric vehicle charging station model needs to consider user driving characteristics. This allows for the full utilization of a large number of existing idle multi-energy generalized energy storage resources. This invention designs a generalized shared energy storage optimization configuration model that considers multiple uncertainties. Shared energy storage operators and user groups form a cooperative alliance by transferring the right to use energy storage. Based on Nash bargaining theory, the generalized shared energy storage optimization configuration model is decomposed into two sub-problems: minimizing the alliance's energy consumption cost and internal payment bargaining. Based on a deterministic model, fuzzy chance-constrained programming theory is used to quantify the risks brought about by the uncertainty of user group source and load output and virtual energy storage parameters. Attached Figure Description

[0074] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0076] Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0077] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0078] like Figure 1 As shown, the method for optimizing the allocation of generalized shared energy storage based on fuzzy chance-constrained programming is characterized by the following steps:

[0079] The system receives various generalized energy storage resources and inputs their operational characteristics into a pre-established generalized energy storage model to obtain individual models of various generalized energy storage resources. These generalized energy storage resources include base station backup energy storage, air conditioning, electric vehicle charging stations, and conventional energy storage.

[0080] By aggregating various individual models of generalized energy storage, a generalized shared energy storage resource aggregation model is obtained;

[0081] The annual fixed investment cost and annual variable net operating income of the shared energy storage operator are received and, combined with the constraints of the generalized shared energy storage resource aggregation model, are input into the pre-established optimized configuration model of the shared energy storage operator to obtain the first-level model.

[0082] The user's interaction fee is received and combined with existing power grid constraints, then input into a pre-established user optimization operation model to obtain a secondary model;

[0083] Based on Nash bargaining theory, the first-level and second-level models are transformed and solved to output a generalized shared energy storage optimal configuration model;

[0084] The system receives uncertainty information about wind power, photovoltaic power, and load from users within the user's network, as well as uncertainty information about the charging and discharging power limits of the virtual energy storage aggregation model for air conditioning and electric vehicle charging stations. This information is then input into the generalized shared energy storage optimization configuration model to obtain a three-level model.

[0085] The three-level model is transformed and solved using fuzzy chance-constrained programming theory to obtain a generalized shared energy storage optimization configuration model that considers multiple uncertainties. The energy storage optimization configuration result is then output using this model.

[0086] It needs further explanation that, in the specific implementation process, step one: establishing a generalized energy storage model

[0087] A. Base station energy storage modeling

[0088] To fully explore the adjustable potential of base station backup energy storage, a method for calculating the adjustable capacity of base station backup energy storage based on hibernation technology is designed.

[0089] (1) Service traffic and power consumption model of a single base station

[0090] Consider the construction of a certain area One communication base station, The set of communication users; the set of communication base stations is The set of communication users is .

[0091] 1) Business Model

[0092] The traffic volume of base stations within the designated area is independent of each other and varies periodically. The traffic volume model for a single base station is as follows:

[0093]

[0094] In the formula, The traffic volume of a single base station during time period t; , , , These are parameters related to base station traffic volume. By adjusting these parameters, traffic volume models for different base stations can be obtained.

[0095] 2) Power consumption model

[0096] Base station power consumption includes static power consumption and dynamic power consumption. Dynamic power consumption is related to real-time traffic volume. Base station power consumption model:

[0097]

[0098] In the formula, Let m be the power consumption of base station m; This represents the working state of base station m, with 1 indicating active state and 0 indicating dormant state. This represents the static power consumption of base station m when it is in an active state. The maximum transmit power of base station m. The linearity coefficient between power consumption and traffic volume; The dynamic power consumption of the base station in active state at time t; This represents the power consumption of base station m when it is in sleep mode.

[0099] (2) Base station hibernation model

[0100] 1) User association

[0101] Definition: The association matrix between users and base stations , This represents the association between base station m and user n. This indicates that user n is connected to base station m; otherwise, it is 0.

[0102] 2) The signal-to-interference-plus-noise ratio (SIR) of base station m and user n is:

[0103]

[0104]

[0105] In the formula, The signal-to-interference-plus-noise ratio (SIR) between base station m and user n; The transmission loss between base station m and user n. Let m be the transmission power from base station m to user n; For user n, receive power from base station m; For user n, receive interference power from base station j; Noise power; The signal propagation fading coefficient; Let m be the distance between base station m and user n; This is the path loss coefficient.

[0106] 3) Transmission rate and communication quality

[0107] Transmission rate:

[0108]

[0109] Communication quality constraints:

[0110] or

[0111] In the formula, B is the signal transmission rate between base station m and user n; B is the system bandwidth. Minimum transmission rate limit; This is the minimum signal-to-interference-plus-noise ratio (SINR) limit. Users can connect to the corresponding base station when communication quality requirements are met.

[0112] (3) Backup energy storage model for communication base stations considering backup demand and hibernation strategy

[0113]

[0114] In the formula, C1 represents the maximum number of users that base station m can serve; C2 represents the base station's operating status and the association status between the base station and users; C3 guarantees that a user is associated with at most one base station; C4 guarantees that the number of users served by the base station does not exceed the base station's capacity limit; C5 guarantees that the base station associated with a user is in a non-dormant state; C6 guarantees the user's communication quality; and C7 guarantees that the user's interruption probability is less than [a certain value]. C7 ensures backup power for base stations; Rated energy storage capacity for base stations.

[0115] (4) The standby energy storage unit model for communication base stations is as follows:

[0116]

[0117] Charge and discharge power constraints:

[0118]

[0119] Battery capacity constraints:

[0120]

[0121] In the formula, The remaining power of the backup energy storage of base station m during time period t; , These represent the charging and discharging power of the base station's backup energy storage during time period t; , The upper and lower limits of the energy storage capacity of the energy storage device in base station m; , These are the charging and discharging efficiencies of the backup energy storage at base station m; , , , This indicates the upper and lower limits of the charging and discharging power of the backup energy storage at base station m. , The states of the standby energy storage of base station m during time period t are represented by Boolean variables.

[0122] (5) The energy storage aggregation model for backup power supply in communication base stations is as follows:

[0123] . .

[0124] make , , We can obtain:

[0125]

[0126]

[0127]

[0128] The parameter calculation method is as follows:

[0129]

[0130] In the formula, The remaining power of the base station energy storage aggregation model during time period t; , These represent the charging and discharging power of the base station energy storage aggregation model during time period t; , These are the upper and lower limits of the energy storage capacity of the base station energy storage aggregation model, respectively. , These are the charging and discharging efficiencies of the base station energy storage aggregation model, respectively, and the average charging and discharging efficiency of the base station energy storage group is taken. , , , These represent the upper and lower limits of the charging and discharging power for the base station energy storage aggregation model; , These represent the charging and discharging states of the base station energy storage aggregation model, and are Boolean variables.

[0131] B. Virtual Energy Storage Modeling for Air Conditioning

[0132] (1) FTCL continuity model

[0133]

[0134]

[0135]

[0136] In the formula, The power consumption of the FTCL continuous model; , These are the equivalent heat capacity and equivalent thermal resistance of the room with air conditioner j, respectively. , The indoor and outdoor temperatures of room j under air conditioning during time period t are respectively; Let J be the energy efficiency ratio of the air conditioner. Let j be the rated power of the air conditioner.

[0137] Set the temperature for the air conditioner. The temperature dead zone width during air conditioner operation; , These represent the upper and lower limits of the indoor temperature when the air conditioner is operating normally.

[0138] (2) Baseline power of FTCL

[0139] When there is no external control signal, the FTCL operating temperature is The corresponding baseline power as follows:

[0140]

[0141] In the formula, Let J be the baseline power of the air conditioner.

[0142] (3) FTCL Virtual Energy Storage Unit Model

[0143] Will and The difference is defined as the charging and discharging power of the FTCL virtual energy storage model, and the energy storage capacity of the virtual energy storage model is also considered. Rated capacity The definition is as follows:

[0144]

[0145]

[0146] The FTCL virtual energy storage model is derived as follows:

[0147]

[0148]

[0149]

[0150] The parameter calculation method is as follows:

[0151]

[0152] In the formula, The rated capacity of the FTCL virtual energy storage j-cell model; The remaining power of the FTCL virtual energy storage j-cell model during time period t; , These represent the charging and discharging power of the FTCL virtual energy storage j-cell model during time period t, both of which are positive values. This represents the upper limit of the energy storage capacity of the FTCL virtual energy storage j-cell model. , These represent the upper limits of charge and discharge power for the FTCL virtual energy storage j-cell model, respectively. , These represent the charge and discharge states of the FTCL virtual energy storage j-cell model, both of which are Boolean variables. , , These are the relevant conversion parameters for the FTCL virtual energy storage j-cell model.

[0153] (4) FTCL Virtual Energy Storage Aggregation Model

[0154] To represent the aggregated virtual energy storage model for air conditioning, the following operations are performed:

[0155] Define the energy storage capacity, rated capacity, and charge / discharge power of the FTCL virtual energy storage aggregation model:

[0156]

[0157] In summary, the FTCLs virtual energy storage aggregation model can be rewritten as follows:

[0158]

[0159]

[0160]

[0161] The parameter calculation method is as follows:

[0162]

[0163] In the formula, The rated capacity of the FTCL virtual energy storage aggregation model; The remaining electricity of the FTCL virtual energy storage aggregation model in time period t; , These represent the charging and discharging power of the FTCL virtual energy storage aggregation model during time period t; This represents the upper limit of the storage capacity of the FTCL virtual energy storage aggregation model. , These represent the upper and lower limits of the charging and discharging power of the FTCL virtual energy storage aggregation model, respectively. , These represent the charge and discharge states of the FTCL virtual energy storage aggregation model, both of which are Boolean variables. This represents the total number of virtual energy storage units in FTCL. , These are the relevant conversion parameters for the FTCL virtual energy storage aggregation model.

[0164] C. Virtual Energy Storage Modeling for Electric Vehicle Charging Stations

[0165] (1) Virtual energy storage unit model for electric vehicles

[0166]

[0167]

[0168] In the formula, , These are the charging and discharging power of electric vehicles, respectively. The time period during which electric vehicles arrive at the charging station; The expected timeframe for electric vehicles to leave the charging station.

[0169]

[0170] In the formula, This refers to the grid connection status of the electric vehicle during time period t, i.e., whether it is within the charging station.

[0171]

[0172]

[0173]

[0174] The specific calculation methods for each parameter are as follows:

[0175]

[0176] In the formula, Let be the remaining electricity of the virtual energy storage unit model of electric vehicle j during time period t; , These represent the charging and discharging power of the virtual energy storage unit model of electric vehicle j during time period t; , The upper and lower limits of the energy storage capacity of the virtual energy storage unit model of electric vehicle j during time period t; , These represent the upper and lower limits of the charging and discharging power of the virtual energy storage unit model of electric vehicle j during time period t; The change in electricity consumption at the charging station during time period t is caused by the change in the grid connection status of electric vehicle j. , These represent the remaining battery power of electric vehicle j when it arrives at and leaves the charging station, respectively. , These represent the upper and lower limits of the electric vehicle's energy storage capacity during time period t; , These represent the upper limits of electric vehicle charging and discharging power during time period t.

[0177] (2) Virtual energy storage model for electric vehicle charging stations

[0178] Electric vehicle charging stations (EVCS), as natural aggregate controllers for electric vehicles, exhibit the following correlations between their energy storage model time-period coupling constraints and electric vehicle group parameters:

[0179]

[0180] For the sake of simplicity, this paper only considers the modeling of a single EVCS. Therefore, the energy storage capacity of the EVCS virtual energy storage model is... Rated capacity and charging / discharging power , The definition is as follows:

[0181]

[0182] By utilizing Minkowski methods and mapping the constraint space of the individual model to the constraint space of the aggregated model, the details of each electric vehicle variable are eliminated. The virtual energy storage aggregated model of a single EVCS is as follows:

[0183]

[0184]

[0185]

[0186] The parameter calculation method is as follows:

[0187]

[0188] In the formula, , , These represent the remaining energy and charging / discharging power of the EVCS virtual energy storage model during time period t. , The upper and lower limits of the energy storage capacity of the EVCS virtual energy storage model during time period t; , , These represent the upper and lower limits of the charging and discharging power of the EVCS virtual energy storage model during time period t; The change in EVCS due to the change in the grid connection status of electric vehicles during time period t; , These are the charging and discharging efficiencies of the EVCS virtual energy storage model, respectively, and the average charging and discharging efficiency of the electric vehicle fleet is taken. The number of electric vehicle clusters in EVCS during the entire scheduling cycle; , , , represent the charge and discharge states of the EVCS virtual energy storage model during time period t, and are Boolean variables.

[0189] D. Conventional Energy Storage Modeling

[0190] The individual and aggregate models for conventional centralized and distributed energy storage are consistent, as shown below:

[0191]

[0192]

[0193]

[0194] In the formula, , , , , , , , , , , These are parameters for a conventional energy storage model.

[0195] Step 2: Establish an optimized configuration model for shared energy storage operators

[0196] (1) Objective function

[0197] The objective is to maximize the annual net return of SESO, including annual fixed input costs and annual variable operating net income. The annual variable operating net income is optimized using a daily period.

[0198]

[0199] In the formula, For SESO's annual net income; This refers to the annual fixed input costs for SESO; SESO's annual variable net operating income; , These are decision variables.

[0200] 1) Annual fixed input costs:

[0201]

[0202] In the formula, The configuration cost of centralized energy storage; , , , The annual fixed lease costs are for base station energy storage, air conditioning, electric vehicle charging stations, and distributed energy storage, respectively; decision variables. Configuration power including centralized energy storage With capacity .

[0203] Configuration cost of centralized energy storage:

[0204]

[0205] In the formula, The initial investment cost is the equivalent annual value. Annual operating and maintenance costs.

[0206]

[0207]

[0208]

[0209] In the formula, This is the conversion factor for equivalent annual values; d represents the lifespan of the energy storage; d represents the discount rate. , These represent the unit power and unit capacity construction cost of centralized energy storage, respectively. This represents the annualized operation and maintenance cost per unit power of centralized energy storage.

[0210] Annual fixed lease cost of generalized energy storage resources:

[0211]

[0212] In the formula, , , , These are the average annual fixed rental cost coefficients per unit capacity for base station energy storage, air conditioning, electric vehicle charging stations, and distributed energy storage, respectively. , , , These represent the upper limits of the leased capacity for each type of generalized energy storage.

[0213] 2) Annual variable net operating income

[0214]

[0215] In the formula, S represents the number of typical days, and s represents the s-th typical day. The number of days for each typical day; The total cost of SESO's interaction with the power grid on the s-th typical day; The cost of energy storage usage rights interaction between SESO and user groups on the s-th typical day; The sum of the variable compensation costs for base station energy storage, air conditioning, electric vehicle charging stations and distributed energy storage on the s-th typical day; The network transmission cost is for the s-th typical day.

[0216] Decision variables Including the centralized energy storage charging and discharging power during time period t on the s-th typical day. , The charging and discharging power of the base station energy storage aggregation model , The charging and discharging power of the air conditioning virtual energy storage aggregation model , The charging and discharging power of the virtual energy storage aggregation model of electric vehicle charging stations , The charging and discharging power of the distributed energy storage aggregation model , The power that SESO buys and sells to the grid , .

[0217] Variable compensation costs for generalized energy storage resources:

[0218]

[0219]

[0220] In the formula, T is an optimization period, taken as 24 hours. The unit time period is 1 hour; , , , These are the variable compensation costs for the backup energy storage, air conditioning, electric vehicle charging station, and distributed energy storage of the base station on the s-th typical day, respectively. Based on experience, it is believed that they have a quadratic function relationship with the amount of energy used. , , , and , , , These are the coefficients of the primary and secondary terms of the compensation cost for changes in various generalized energy storage resources, respectively.

[0221] Interaction costs with the upstream distribution network:

[0222]

[0223] In the formula, , These are the prices for purchasing electricity from the grid and selling electricity, respectively, with the electricity price for selling electricity being the same as the price charged to the grid.

[0224] Energy storage usage right interaction fee:

[0225] Users need to pay a rental fee to SESO to obtain the right to use energy storage. During this period, they can also trade electricity. Taking all factors into account, the interaction fee for the right to use energy storage is as follows:

[0226]

[0227] In the formula, The total number of users with energy storage charging and discharging needs; , These represent the charging and discharging power of user i after purchasing the right to use energy storage during time period t on the s-th typical day, i.e., the charging demand and the discharging demand; , The electricity prices for users selling and purchasing electricity from SESO during the t-hour period of the s-th typical day should be between the grid's electricity purchase and sales prices. The unit price for leasing the right to use energy storage during time period t on the s-th typical day.

[0228] Network transmission fees:

[0229]

[0230] In the formula, The unit price for network transmission fees is determined by the distribution network operator and then issued to the user and SESO.

[0231] (2) Constraints

[0232] 1) Power balance constraints:

[0233]

[0234] 2) Constraints of the centralized energy storage aggregation model:

[0235]

[0236]

[0237]

[0238] Centralized energy storage also needs to be subject to energy storage rate constraints:

[0239]

[0240] In the formula, , , These represent the remaining energy and the maximum and minimum allowable state of charge coefficients of the centralized energy storage system during time period t on the s-th typical day. , These are the charging and discharging efficiencies of centralized energy storage, respectively. , These represent the charging and discharging states of centralized energy storage during time period t on the s-th typical day. This refers to the energy storage capacity ratio.

[0241] 3) Virtual energy storage constraints mentioned above

[0242] 4) Energy storage capacity constraints from start to finish

[0243]

[0244] In the formula, , , , , , , , , , These represent the remaining electricity of each generalized energy storage resource at the end of time period T and the beginning of time period T, respectively, on the s-th typical day.

[0245] Step 3: Establish a user-optimized operation model

[0246] (1) Objective function

[0247] Assuming user i has solar power, wind power, and load, and the desired total annual energy cost is minimized, the objective function is as follows:

[0248]

[0249] In the formula, The annual energy cost for user i; The cost of interaction between user i and the upper-level distribution network on the s-th typical day; The interaction fee for the s-th typical daily user i to purchase the right to use energy storage from SESO; The network transmission fee payable for energy transfer between the s-th typical daytime user i and SESO.

[0250] Interaction costs between the user and the upstream distribution network:

[0251]

[0252] In the formula, , These represent the power purchased from and sold to the grid by user i during time period t on the s-th typical day.

[0253] Interaction fees for users leasing energy storage usage rights from SESO:

[0254]

[0255] Network transmission fees:

[0256]

[0257] (2) Constraints

[0258] 1) Power balance constraints:

[0259]

[0260] In the formula, , , These represent the wind power, solar power output, and load of user i during time period t on the s-th typical day.

[0261] 2) Constraints on purchasing and selling electricity from the grid:

[0262]

[0263] In the formula, , These are the status bits of user i and the power grid during time period t on the s-th typical day, and are both Boolean variables; The upper limit of the power purchased and sold by user i and the power grid.

[0264] Step 4: Establish a generalized shared energy storage optimal allocation model based on Nash bargaining theory

[0265]

[0266] In the formula, , The points at which negotiations break down for SESO and User i respectively are taken as the annual net revenue of SESO and the annual energy cost of User i when they do not participate in the shared energy storage model. ; , These represent the increased benefits for SESO and user i after participating in shared energy storage, respectively.

[0267] Since the Nash bargaining model is a non-convex nonlinear optimization problem, it cannot be solved directly. It is equivalently transformed into two sub-problems: minimizing the energy consumption cost of the alliance and internal payment bargaining. Sub-problem 1 is used to solve for the optimal energy storage configuration scheme of SESO and the amount of energy storage usage rights interaction between SESO and each user; sub-problem 2 is used to solve for the energy storage usage rights interaction cost between SESO and each user.

[0268] (1) Subproblem 1: Minimize the energy cost of the coalition

[0269] We can find the maximum and minimum values ​​using the arithmetic mean-geometric mean inequality: For any When it is a non-negative number, When the inequality holds true, equality holds. Therefore, when the Nash bargaining model reaches its maximum value, the following constraint is satisfied:

[0270]

[0271] because And the point at which negotiations broke down The above formula can be transformed into:

[0272]

[0273] The objective function of the Nash bargaining cooperation configuration model between SESO and user groups can be equivalently expressed as:

[0274]

[0275] because Since is a constant and the ln function is a monotonically increasing function, the above equation can be further transformed into:

[0276]

[0277] definition , , representing SESO's annual net revenue and user i's annual energy cost, excluding payments for internal energy storage usage rights, respectively:

[0278]

[0279]

[0280] Substituting the definition into the equation yields the objective function for subproblem 1:

[0281]

[0282] (2) Sub-problem 2: Internal payment negotiation

[0283] Based on the solution results of the charging and discharging quantities between each user and SESO in sub-problem 1, the contribution of each user to the alliance is calculated, which is the bargaining power. The sum of the bargaining powers of SESO and each user satisfies:

[0284]

[0285] Then user i's bargaining power is:

[0286]

[0287] In the formula, The bargaining power of user i in the s-th typical day reflects the user's contribution. The more interaction a user has with SESO, the greater their contribution to the alliance and social welfare. The bargaining power of the SESO on the s-th typical day is related to the market competition situation and the SESO's preferences;

[0288] The optimal solution obtained based on subproblem 1 and Substituting this into the Nash bargaining model that includes bargaining power, we get:

[0289]

[0290] Taking the logarithm of the above equation yields subproblem 2:

[0291]

[0292] By solving subproblem 2, the interaction fees for energy storage usage rights between SESO and each user can be obtained, and thus the annual net revenue of SESO and the annual energy cost of each user can be obtained.

[0293] Step 5: Establish a generalized shared energy storage optimal configuration model that considers multiple uncertainties.

[0294] Considering the impact of multiple uncertainties, including the uncertainties of wind power, solar power, and load within the user's own system, and the uncertainties of the charging and discharging power limits of the virtual energy storage aggregation model for air conditioning and electric vehicle charging stations, a trapezoidal membership parameter is used to describe the uncertainties involved.

[0295] (1) Fuzzy opportunity constraints related to user wind power, photovoltaic power, and load

[0296] When confidence level At that time, its clear equivalence class is:

[0297]

[0298] In the formula, The confidence levels corresponding to uncertainties in wind power, photovoltaics, and load; , , These are the predicted values ​​for the corresponding fuzzy variables, { }、{ }、{ } are the proportional coefficients of the trapezoidal membership degrees of the corresponding fuzzy variables.

[0299] (2) Fuzzy chance constraints related to virtual energy storage parameters

[0300] When confidence level , At that time, its clear equivalence class is:

[0301]

[0302]

[0303] In the formula, , These represent the confidence levels corresponding to the uncertainties in the virtual energy storage aggregation models for air conditioners and electric vehicles, respectively. , , , These are the predicted values ​​for the corresponding fuzzy variables, { }、{ } are the proportional coefficients of the trapezoidal membership degrees of the corresponding fuzzy variables.

[0304] On the other hand, such as Figure 2 As shown, the present invention also discloses a generalized shared energy storage optimization configuration system based on fuzzy chance-constrained programming, comprising:

[0305] Receiving module: Used to receive various generalized energy storage resources, input the operating characteristics of various generalized energy storage resources into a pre-established generalized energy storage model, and obtain individual models of various generalized energy storage resources. Among them, various generalized energy storage resources include base station backup energy storage, air conditioning, electric vehicle charging stations and conventional energy storage.

[0306] Aggregation module: Used to aggregate multiple individual models of generalized energy storage to obtain a generalized shared energy storage resource aggregation model;

[0307] The first-level input module is used to receive the annual fixed investment cost and annual variable net operating income of the shared energy storage operator, and combine them with the constraints of the generalized shared energy storage resource aggregation model, and input them into the pre-established optimized configuration model of the shared energy storage operator to obtain the first-level model.

[0308] Secondary input module: Used to receive user interaction fees, combine them with existing power grid constraints, and input them into a pre-established user optimization operation model to obtain the secondary model;

[0309] Output Solver Module: Used to transform and solve the Level 1 and Level 2 models based on Nash bargaining theory, and output the generalized shared energy storage optimal configuration model;

[0310] The third-level input module is used to receive uncertainty information about wind power, photovoltaic power, and load from users within the user's network, as well as uncertainty information about the charging and discharging power limits of the virtual energy storage aggregation model for air conditioning and electric vehicle charging stations. This information is then input into the generalized shared energy storage optimization configuration model to obtain the third-level model.

[0311] The optimization configuration module is used to transform and solve the three-level model based on the fuzzy chance-constrained programming theory, so as to obtain a generalized shared energy storage optimization configuration model that considers multiple uncertainties. The module then outputs the generalized shared energy storage optimization configuration results using the generalized shared energy storage optimization configuration model that considers multiple uncertainties.

[0312] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0313] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

Claims

1. A method for optimizing the allocation of generalized shared energy storage based on fuzzy chance-constrained programming, characterized in that, The method includes the following steps: The system receives various generalized energy storage resources and inputs their operational characteristics into a pre-established generalized energy storage model to obtain individual models of various generalized energy storage resources. These generalized energy storage resources include base station backup energy storage, air conditioning, electric vehicle charging stations, and conventional energy storage. By aggregating various individual models of generalized energy storage, a generalized shared energy storage resource aggregation model is obtained; The annual fixed investment cost and annual variable net operating income of the shared energy storage operator are received and, combined with the constraints of the generalized shared energy storage resource aggregation model, are input into the pre-established optimized configuration model of the shared energy storage operator to obtain the first-level model. The objective function of the shared energy storage operator's optimal configuration model is as follows: The goal is to maximize the annual net income of SESO, including annual fixed input costs and annual variable operating net income, with the annual variable operating net income taking into account the daily optimization period. In the formula, For SESO's annual net income; For SESO's annual fixed input costs; SESO's annual variable net operating income; , For decision variables; The user's interaction fee is received, combined with existing power grid constraints, and input into a pre-established user optimization operation model to obtain a secondary model; The objective function for optimizing the user's operating model is as follows: In the formula, The annual energy cost for user i; The cost of interaction between user i and the upper-level distribution network on the s-th typical day; The interaction fee for the s-th typical daily user i to purchase the right to use energy storage from SESO; The network transmission fee payable for energy transfer between the s-th typical daytime user i and SESO; Based on Nash bargaining theory, the first-level and second-level models are transformed and solved to output a generalized shared energy storage optimal configuration model; The system receives uncertainty information about wind power, photovoltaic power, and load from users within the user's network, as well as uncertainty information about the charging and discharging power limits of the virtual energy storage aggregation model for air conditioning and electric vehicle charging stations. This information is then input into the generalized shared energy storage optimization configuration model to obtain a three-level model. The three-level model is transformed and solved using fuzzy chance-constrained programming theory to obtain a generalized shared energy storage optimization configuration model that considers multiple uncertainties. The optimization configuration results of the generalized shared energy storage are then output using this model.

2. The method for optimal allocation of generalized shared energy storage based on fuzzy chance-constrained programming according to claim 1, characterized in that, The operational characteristics of the various generalized energy storage resources include: the base station energy storage model needs to consider the communication load, communication quality, and base station power supply reliability; the air conditioning model needs to consider user comfort; and the electric vehicle charging station model needs to consider user driving characteristics.

3. The method for optimal allocation of generalized shared energy storage based on fuzzy chance-constrained programming according to claim 1, characterized in that, The annual fixed investment costs included in the optimized configuration model for shared energy storage operators are: the configuration cost of centralized energy storage and the annual fixed leasing cost of generalized energy storage resources; the annual variable net operating revenue included in the optimized configuration model for shared energy storage operators are: the variable compensation cost of generalized energy storage resources, the interaction cost with the upper-level distribution network, the interaction cost of energy storage usage rights, and the network transmission cost; the constraints of the optimized configuration model for shared energy storage operators include: power balance constraints, centralized energy storage aggregation model constraints, virtual energy storage constraints, and energy storage capacity start-end constraints.

4. The method for optimal allocation of generalized shared energy storage based on fuzzy chance-constrained programming according to claim 1, characterized in that, The user-optimized operation model includes the following costs: interaction costs between the user and the upstream distribution network, interaction costs between the user and the SESO for leasing energy storage usage rights, and network transmission costs; the constraints of the user-optimized operation model include power balance constraints and power purchase and sale constraints from the grid. Interaction costs between the user and the upstream distribution network In the formula, , These represent the electricity purchased from the grid and the electricity sold by user i during time period t on the s-th typical day. Interaction fees for users leasing energy storage usage rights from SESO Network transmission fees 。 5. The method for optimal allocation of generalized shared energy storage based on fuzzy chance-constrained programming according to claim 1, characterized in that, The process of transforming and solving the first-level and second-level models based on Nash bargaining theory to obtain the generalized shared energy storage optimal configuration model is as follows: In the formula, , The points at which negotiations break down for SESO and User i respectively are taken as the annual net revenue of SESO and the annual energy cost of User i when they do not participate in the shared energy storage model. ; , These represent the increased benefits for SESO and user i after participating in shared energy storage, respectively. Since the Nash bargaining model is a non-convex nonlinear optimization problem, it cannot be solved directly. It is equivalently transformed into two sub-problems: minimizing the energy consumption cost of the alliance and internal payment bargaining. Sub-problem 1 is used to solve for the optimal energy storage configuration scheme of SESO and the amount of energy storage usage rights interaction between SESO and each user; sub-problem 2 is used to solve for the energy storage usage rights interaction cost between SESO and each user. (1) Subproblem 1: Minimize the energy cost of the coalition We can find the maximum and minimum values ​​using the arithmetic mean-geometric mean inequality: For any When it is a non-negative number, When the inequality holds true, equality holds. Therefore, when the Nash bargaining model reaches its maximum value, the following constraint is satisfied: because And the point at which negotiations broke down The above formula can be transformed into: The objective function of the Nash bargaining cooperation configuration model between SESO and user groups can be equivalently expressed as: because Since is a constant and the ln function is a monotonically increasing function, the above equation can be further transformed into: definition , , representing SESO's annual net revenue and user i's annual energy cost, excluding payments for internal energy storage usage rights, respectively: Substituting the definition into the equation yields the objective function for subproblem 1: (2) Sub-problem 2: Internal payment negotiation Based on the solution results of the charging and discharging quantities between each user and SESO in sub-problem 1, the contribution of each user to the alliance is calculated, which is the bargaining power. The sum of the bargaining powers of SESO and each user satisfies: Then user i's bargaining power is: In the formula, The bargaining power of user i in the s-th typical day reflects the user's contribution. The more interaction a user has with SESO, the greater their contribution to the alliance and social welfare. The bargaining power of the SESO on the sth typical day is related to the market competition situation and the SESO's preferences; The optimal solution obtained based on subproblem 1 and Substituting this into the Nash bargaining model that includes bargaining power, we get: Taking the logarithm of the above equation yields subproblem 2: By solving subproblem 2, the interaction fees for energy storage usage rights between SESO and each user can be obtained, and thus the annual net revenue of SESO and the annual energy cost of each user can be obtained.

6. The method for optimal allocation of generalized shared energy storage based on fuzzy chance-constrained programming according to claim 1, characterized in that, The generalized shared energy storage optimization configuration model that considers multiple uncertainties includes: (1) Fuzzy opportunity constraints related to user wind power, photovoltaic power, and load When confidence level At that time, its clear equivalence class is: In the formula, The confidence levels corresponding to uncertainties in wind power, photovoltaics, and load; , , These are the predicted values ​​for the corresponding fuzzy variables, { }、{ }、{ } are the proportional coefficients of the trapezoidal membership degrees of the corresponding fuzzy variables; (2) Fuzzy chance constraints related to virtual energy storage parameters When confidence level , At that time, its clear equivalence class is: In the formula, , These represent the confidence levels corresponding to the uncertainties in the virtual energy storage aggregation models for air conditioners and electric vehicles, respectively. , , , These are the predicted values ​​for the corresponding fuzzy variables, { }、{ } are the proportional coefficients of the trapezoidal membership degrees of the corresponding fuzzy variables.

7. A generalized shared energy storage optimization configuration system based on fuzzy chance constraint programming, employing the generalized shared energy storage optimization configuration method based on fuzzy chance constraint programming as described in claim 1, characterized in that... include: Receiving module: Used to receive various generalized energy storage resources, input the operating characteristics of various generalized energy storage resources into a pre-established generalized energy storage model, and obtain individual models of various generalized energy storage resources. Among them, various generalized energy storage resources include base station backup energy storage, air conditioning, electric vehicle charging stations and conventional energy storage. Aggregation module: Used to aggregate multiple individual models of generalized energy storage to obtain a generalized shared energy storage resource aggregation model; The first-level input module is used to receive the annual fixed investment cost and annual variable net operating income of the shared energy storage operator, and combine them with the constraints of the generalized shared energy storage resource aggregation model, and input them into the pre-established optimized configuration model of the shared energy storage operator to obtain the first-level model. Secondary input module: Used to receive user interaction fees, combine them with existing power grid constraints, and input them into a pre-established user optimization operation model to obtain the secondary model; Output Solver Module: Used to transform and solve the Level 1 and Level 2 models based on Nash bargaining theory, and output the generalized shared energy storage optimal configuration model; The third-level input module is used to receive uncertainty information on wind power, photovoltaic power, and load from users within the user's network, as well as uncertainty information on the charging and discharging power limits of the virtual energy storage aggregation model for air conditioning and electric vehicle charging stations. This information is then input into the generalized shared energy storage optimization configuration model to obtain the third-level model. The optimization configuration module is used to transform and solve the three-level model based on the fuzzy chance-constrained programming theory, so as to obtain a generalized shared energy storage optimization configuration model that considers multiple uncertainties. The module then outputs the generalized shared energy storage optimization configuration results using the generalized shared energy storage optimization configuration model that considers multiple uncertainties.

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