A shared energy storage operation method, system, device, medium and product for multi-type user side flexibility resources

Abstract

The present application relates to the technical field of power market operation, and discloses a shared energy storage operation method, system, device, medium and product for multi-type user side flexibility resources. In the method, the shared energy storage operator uniformly obtains the leasing capacity declared by each resource subject, so that the dispersed resource demand is first uniformly obtained by the shared energy storage operator and included in the constraint condition, then the candidate alliance participated by each resource subject is evaluated in terms of income, so as to screen out the optimal alliance with the maximum comprehensive income and determine the actual leasing capacity of each subject, and then further develop energy scheduling under the capacity constraint. The scheme can fully utilize the complementarity between different resource subjects, improve the utilization efficiency of shared energy storage capacity and overall operation income, thereby effectively coordinating the collaborative optimization between resource subjects, and the benefit distribution relationship between resource subjects and the shared energy storage operator.

Description

Technical Field

[0001] This invention relates to the field of electricity market operation technology, and in particular to a shared energy storage operation method, system, equipment, medium and product for flexible resources on multiple user sides. Background Technology

[0002] Energy storage is a crucial technology for achieving dynamic and rapid matching of supply and demand, and a fundamental piece of equipment supporting new power systems. Among these, shared energy storage is a primary model for addressing the grid integration of new energy sources. From a sharing perspective, the operation modes of new energy power plants and shared energy storage can be categorized into co-construction and sharing, interactive sharing, and leasing shared energy storage models. In the leasing-based sharing model, ownership no longer belongs to the power plant; instead, it is invested in, operated, and managed by a third-party shared energy storage operator. Under this model, the shared energy storage operator aims to maximize profits through the rational allocation of leased capacity and the selection of optimal sharing strategies.

[0003] In existing technologies, leasing capacity based on the day-ahead power generation fluctuations of power plants reduces losses caused by volatility and increases the profits of energy storage operators. Introducing a dynamic capacity pricing mechanism not only improves energy storage utilization and increases operator profits but also reduces costs for producers and consumers. Planning methods based on capacity leasing and energy sharing not only enhance the photovoltaic carrying capacity of microgrids but also improve the economics of microgrid operators and shared energy storage operators. Therefore, the leased shared energy storage model separates power plants from energy storage operators, resolving the issues of unclear responsibilities and complex revenue distribution between power plants and shared energy storage through independent third-party investment. Furthermore, for new energy power plants, the leased shared energy storage model reduces the investment risks and costs of self-built energy storage and allows for more flexible adjustments to charging and discharging strategies. Thus, the leased shared energy storage model has become a research hotspot.

[0004] However, under the leasing and sharing energy storage model, the lack of transparency in information exchange between various types of user-side flexible resources and shared energy storage can easily lead to conflicts in the distribution of interests among multiple parties, making it difficult to effectively coordinate the collaborative optimization among resource entities and the interest distribution mechanism between resource entities and shared energy storage operators under conditions of information asymmetry. Summary of the Invention

[0005] In view of this, in order to solve the above-mentioned technical problems, the present invention provides a shared energy storage operation method, system, equipment, medium and product for flexible resources on multiple types of user sides.

[0006] The first aspect of this invention provides a shared energy storage operation method for flexible resources on multiple user sides, comprising:

[0007] Obtain the leased capacity declared by each resource entity to the shared energy storage operator;

[0008] Obtain all candidate alliances in which each resource entity participates, evaluate the revenue of each candidate alliance, and under the constraint of the rental capacity of each resource entity, select the optimal alliance with the highest revenue, and determine the actual rental capacity of each resource entity under the optimal alliance.

[0009] Under the constraint of the actual leased capacity of each resource entity in the optimal alliance, and with the goal of maximizing the total revenue of the shared energy storage operator, energy scheduling optimization is performed on each resource entity in the optimal alliance to obtain the energy scheduling scheme of each resource entity.

[0010] In one embodiment, the step of acquiring all candidate alliances in which each of the resource entities participates, evaluating the profitability of each candidate alliance, selecting the optimal alliance with the highest profitability under the constraint of the rental capacity of each of the resource entities, and determining the actual rental capacity of each resource entity under the optimal alliance includes:

[0011] Enumerate all non-empty subsets in the set of all resource entities participating in the shared energy storage system, and use each non-empty subset as the candidate alliance;

[0012] For each candidate alliance, the characteristic function value of the candidate alliance and the actual rental capacity of each resource entity under the candidate alliance are determined according to the rental capacity of each resource entity under the candidate alliance; wherein, the characteristic function value is the maximum total revenue value under the preset alliance operation constraints, which maximizes the total revenue of the candidate alliance.

[0013] The characteristic function values ​​of each candidate alliance are compared, and the candidate alliance corresponding to the largest characteristic function value is selected as the optimal alliance. The actual rental capacity of each resource entity in the optimal alliance is then determined.

[0014] In one embodiment, under the constraint of the actual leased capacity of each resource entity in the optimal alliance, and with the goal of maximizing the total operating revenue of the shared energy storage operator, energy scheduling optimization is performed on each resource entity in the optimal alliance to obtain an energy scheduling scheme for each resource entity, including:

[0015] Obtain the revenue and cost information of the shared energy storage operator, and construct an objective function to maximize the total revenue of the shared energy storage operator based on the revenue and cost information;

[0016] The operational constraints of the shared energy storage operator are determined. Based on the operational constraints of the shared energy storage operator and the constraints of the actual leased capacity of each resource entity, the objective function of maximizing total revenue is optimized and solved. The energy scheduling scheme of each resource entity is obtained based on the optimal solution.

[0017] In one embodiment, the method further includes:

[0018] Obtain the volatility preference type declared by each of the resource entities to the shared energy storage operator; wherein, the volatility preference type includes high volatility type and low volatility type;

[0019] A leasing attribute value is generated based on the volatility preference type; wherein the leasing attribute value is used to characterize the cost of the resource entity calling the shared energy storage system; wherein the leasing attribute value for the high volatility type is greater than the leasing attribute value for the low volatility type;

[0020] After executing the energy scheduling schemes of each of the resource entities, the call data of each of the resource entities to the shared energy storage system is obtained;

[0021] Based on the call data, the true fluctuation preference type of each resource subject is inferred using Bayesian method;

[0022] For each resource subject whose actual volatility preference type is high volatility, determine whether the volatility preference type declared by the resource subject is consistent with the actual volatility preference type;

[0023] If there is a discrepancy, the resource subject is marked as a false resource subject, and the rental attribute value of the false resource subject is re-evaluated according to the true fluctuation preference type. The re-evaluated rental attribute value is then increased to obtain the punitive rental attribute value of the false resource subject.

[0024] In one embodiment, the method further includes:

[0025] Obtain historical and predicted operational trajectory data for each of the resource entities;

[0026] Based on the historical operating trajectory data and the predicted operating trajectory data, a normalized volatility is determined; wherein, the normalized volatility is the ratio of the deviation between the historical operating trajectory data and the predicted operating trajectory data of the resource subject to the predicted operating trajectory data;

[0027] Determine whether the normalized volatility is greater than a preset deviation threshold;

[0028] If the normalized volatility is determined to be greater than the preset deviation threshold, then the resource entity is determined to be of a high volatility type.

[0029] If the normalized volatility is determined to be no greater than the preset deviation threshold, then the resource entity is determined to be of the low volatility type.

[0030] In one embodiment, the step of inferring the true fluctuation preference type of each resource subject using Bayesian method based on the call data includes:

[0031] Obtain the prior probability distribution of each resource subject in the true fluctuation preference type;

[0032] Based on the call data and the prior probability distribution, a likelihood function of the resource subject under different real fluctuation preference types is constructed;

[0033] The prior probability of the likelihood function is iteratively updated using Bayes' rule to obtain the posterior probability that each resource subject has a different real fluctuation preference type.

[0034] The true fluctuation preference type of each resource subject is determined based on the maximum value of the posterior probability.

[0035] Secondly, the present invention also provides a shared energy storage operation system for multiple types of user-side flexible resources, comprising:

[0036] The application information acquisition module is used to obtain the leased capacity applied for by each resource entity to the shared energy storage operator;

[0037] The alliance screening module is used to obtain all candidate alliances in which each resource entity participates, evaluate the benefits of each candidate alliance, screen out the optimal alliance with the highest benefit under the constraint of the rental capacity of each resource entity, and determine the actual rental capacity of each resource entity under the optimal alliance.

[0038] The energy scheduling optimization module is used to optimize the energy scheduling of each resource entity in the optimal alliance under the constraint of the actual leased capacity of each resource entity in the optimal alliance, with the goal of maximizing the total revenue of the shared energy storage operator, so as to obtain the energy scheduling scheme of each resource entity.

[0039] Thirdly, the present invention also provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, the computer program being executed by the processor causing the processor to perform the steps of the shared energy storage operation method for flexible resources for multiple types of users as described in the first aspect.

[0040] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the shared energy storage operation method for flexible resources on multiple user sides as described in the first aspect.

[0041] Fifthly, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the steps of the shared energy storage operation method for flexible resources on multiple user sides as described in the first aspect.

[0042] As can be seen from the above technical solutions, this invention obtains the leased capacity declared by each resource entity through a unified shared energy storage operator. This allows the dispersed resource demand to be uniformly acquired and incorporated into the constraints by the shared energy storage operator. Then, the profitability of candidate alliances among the resource entities is evaluated, thereby selecting the optimal alliance with the highest overall profitability and determining the actual leased capacity of each entity. Energy dispatch is then carried out under this capacity constraint, transforming the decision-making process from individual entity-specific decisions to multi-entity collaborative optimization decisions. Because both alliance selection and dispatch processes are based on maximizing profitability, this solution fully utilizes the complementarity between different resource entities, improving the utilization efficiency and overall operational profitability of shared energy storage capacity. It reduces capacity declaration deviations, adverse selection, and conflicting interests caused by information asymmetry, thus effectively coordinating collaborative optimization among resource entities and the distribution of benefits between resource entities and the shared energy storage operator. Attached Figure Description

[0043] 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, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 An application environment diagram for a shared energy storage operation method for flexible resources on multiple user sides, provided by an embodiment of the present invention;

[0045] Figure 2 A flowchart illustrating a shared energy storage operation method for multiple types of user-side flexibility resources, provided in an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of a shared energy storage operation framework for flexible resources for multiple types of users;

[0047] Figure 4 This is a schematic diagram of a multi-type user-side flexible resource sharing energy storage operation framework based on hybrid game theory.

[0048] Figure 5 A schematic diagram of the game tree between flexible resources on the multi-type user side and shared energy storage operators;

[0049] Figure 6a This is a schematic diagram of the power output of PV1 in the photovoltaic power station;

[0050] Figure 6b This is a schematic diagram of the power output of the PV2 photovoltaic power station;

[0051] Figure 6c This is a schematic diagram of the power output of PV3 in a photovoltaic power station.

[0052] Figure 7 A diagram showing the characteristic function values ​​and revenue comparisons of each alliance under predicted output;

[0053] Figure 8 A diagram comparing capacity leasing and usage at different stages;

[0054] Figure 9a This is a schematic diagram illustrating the trend of energy storage SOC changes;

[0055] Figure 9b This is a schematic diagram illustrating the trend of charging and discharging power changes.

[0056] Figure 10 Update the trend chart for operator beliefs;

[0057] Figure 11 This is a comparative diagram of fluctuation-type inference;

[0058] Figure 12 A diagram showing the comparison between the initial and final lease prices for each station;

[0059] Figure 13 This is a diagram illustrating the comparison of benefits under misjudgment.

[0060] Figure 14 A diagram illustrating the comparison of payoffs under different game modes;

[0061] Figure 15 This is a schematic diagram of the structure of a shared energy storage operation system for flexible resources on multiple user sides, provided by an embodiment of the present invention.

[0062] Figure 16 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0063] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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.

[0064] From the perspective of shared energy storage models, the operation modes of new energy power plants and shared energy storage can be divided into co-construction shared energy storage model, interactive shared energy storage model, and leasing shared energy storage model. In the leasing shared energy storage model, ownership no longer belongs to the power plant, but is invested in, operated, and managed by a third-party shared energy storage operator. Under this model, the goal of the shared energy storage operator is to maximize profits by rationally allocating leased capacity and selecting the optimal sharing strategy. Existing technologies lease capacity based on the day-ahead power generation fluctuations of the power plant, which reduces losses caused by fluctuations and increases the profits of the energy storage operator; the introduction of a dynamic capacity pricing mechanism not only improves energy storage utilization and increases operator profits but also reduces costs for prosumers; the planning method based on capacity leasing and energy sharing not only improves the photovoltaic carrying capacity of microgrids but also improves the economics of microgrid operators and shared energy storage operators. Therefore, the leasing shared model separates the power plant and the energy storage operator, and through independent third-party investment, solves the problems of unclear responsibilities and complex profit distribution between the power plant and shared energy storage. Meanwhile, for new energy power plants, the leasing and sharing model can reduce the investment risks and costs of building their own energy storage, and also allows for more flexible adjustments to charging and discharging strategies. Therefore, the leasing and sharing energy storage model has become a research hotspot.

[0065] In the leasing and sharing energy storage model, renewable energy power plants and shared energy storage operators, as independent stakeholders, exhibit a typical "cooperation-competition" characteristic in their decision-making. However, the current leasing and sharing energy storage model fails to fully consider the information asymmetry that arises between power plants and shared energy storage operators due to the unique private information they possess. This information asymmetry can lead to adverse selection and moral hazard, resulting in market failure and efficiency losses. Therefore, how to coordinate cooperation among various types of user-side flexible resources and competition between resources and operators under conditions of information asymmetry, and achieve synergy and incentive compatibility among all parties, is a critical technical bottleneck that urgently needs to be addressed in the design of current shared energy storage operation mechanisms.

[0066] The lack of transparency in information exchange between various user-side flexible resources and shared energy storage can easily lead to conflicts of interest among multiple parties, making it difficult to effectively coordinate collaborative optimization among resource entities and the interest distribution mechanism between resource entities and shared energy storage operators under conditions of information asymmetry.

[0067] To address the problems in existing technologies, such as opaque information exchange between various types of user-side flexible resources and shared energy storage, which easily leads to conflicts of interest among multiple parties and makes it difficult to achieve collaborative optimization among resource entities under information asymmetry, as well as the reasonable distribution of benefits between resource entities and shared energy storage operators, this application's embodiment addresses these issues. First, the shared energy storage operator uniformly obtains the leased capacity declared by each resource entity. This allows the dispersed resource demand to be uniformly acquired and incorporated into the constraints by the shared energy storage operator. Then, the profitability of candidate alliances participating in by each resource entity is evaluated, thereby selecting the optimal alliance with the highest overall profitability and determining the actual leased capacity of each entity. Energy dispatch is then carried out under this capacity constraint, transforming the decision-making process from individual entities to multi-entity collaborative optimization. Because both alliance selection and dispatch processes are based on maximizing profitability, this solution fully utilizes the complementarity between different resource entities, improving the utilization efficiency and overall operational profitability of shared energy storage capacity. It reduces capacity declaration deviations, adverse selection, and conflicts of interest distribution caused by information asymmetry, thus effectively coordinating collaborative optimization among resource entities and the distribution of benefits between resource entities and shared energy storage operators.

[0068] The shared energy storage operation method for various types of user-side flexible resources provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, each resource entity 101 communicates with the shared energy storage operator's server 102 via a network. The shared energy storage operator's server 102 executes a shared energy storage operation method for flexible resources on multiple user sides. This method includes: obtaining the leased capacity declared by each resource entity to the shared energy storage operator; obtaining all candidate alliances in which each resource entity participates, evaluating the revenue of each candidate alliance, and, under the constraint of the leased capacity of each resource entity, selecting the optimal alliance with the highest revenue, and determining the actual leased capacity of each resource entity under the optimal alliance; under the constraint of the actual leased capacity of each resource entity under the optimal alliance, optimizing the energy scheduling of each resource entity under the optimal alliance with the goal of maximizing the total revenue of the shared energy storage operator, to obtain the energy scheduling scheme of each resource entity.

[0069] Resource entity 101 may include, but is not limited to, new energy power plants, adjustable loads, electric vehicle clusters, and distributed energy storage systems.

[0070] Shared energy storage operators are the core hub connecting various flexible resources and the core organizers and coordinators of the shared energy storage model. The server 102 of the shared energy storage operator can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0071] like Figure 2As shown, this application provides a shared energy storage operation method for flexible resources on multiple user sides, which can be applied to... Figure 1 Taking the server 102 of the shared energy storage operator as an example, the explanation includes the following steps S1 to S3. Wherein:

[0072] Step S1: Obtain the leased capacity declared by each resource entity to the shared energy storage operator.

[0073] Before participating in the shared energy storage market, each resource entity must declare the capacity it intends to lease to the shared energy storage operator based on its own energy storage capacity. In the subsequent confirmation of actual leased capacity and energy dispatch, the actual leased capacity shall not exceed the upper limit of the leased capacity declared.

[0074] Step S2: Obtain all candidate alliances in which each resource entity participates, evaluate the benefits of each candidate alliance, and under the constraint of the rental capacity of each resource entity, select the optimal alliance with the highest benefit, and determine the actual rental capacity of each resource entity under the optimal alliance.

[0075] The candidate alliance is a non-empty subset generated by enumerating all combinations of resource entities. Each subset corresponds to a combination of resource entities, ensuring that all possibilities for alliance formation are covered.

[0076] The revenue of candidate alliances is obtained by solving a mixed-integer linear programming model with the goal of maximizing the total revenue of the alliance. The optimal alliance with the highest revenue is selected, and the actual rental capacity of each resource entity in the alliance is determined. The actual rental capacity of each resource entity is the optimal solution that maximizes the total revenue of the alliance, and strictly does not exceed the upper limit of its declared rental capacity.

[0077] Step S3: Under the constraint of the actual leased capacity of each resource entity in the optimal alliance, with the goal of maximizing the total revenue of the shared energy storage operator, energy scheduling optimization is performed on each resource entity in the optimal alliance to obtain the energy scheduling scheme of each resource entity.

[0078] The total revenue of the shared energy storage operator is the net revenue after deducting the energy storage degradation cost and operation and maintenance cost, taking into account the capacity leasing revenue and peak-valley arbitrage revenue of the shared energy storage operator. Under the constraint of the actual leased capacity of each resource entity, the energy scheduling of each resource entity under the optimal alliance is optimized to obtain the energy scheduling scheme of each resource entity. The energy scheduling scheme includes the charging and discharging power plan of each resource entity.

[0079] It should be noted that this embodiment of the application obtains the leased capacity declared by each resource entity through a unified shared energy storage operator. This allows the dispersed resource demand to be uniformly acquired by the shared energy storage operator and incorporated into the constraints. Then, the profitability of candidate alliances in which each resource entity participates is evaluated, thereby selecting the optimal alliance with the highest overall profitability and determining the actual leased capacity of each entity. Energy dispatch is then carried out under this capacity constraint, realizing a shift from individual entity decision-making to multi-entity collaborative optimization decision-making. Because both alliance selection and dispatch processes are based on maximizing profitability, this scheme can fully utilize the complementarity between different resource entities, improve the utilization efficiency of shared energy storage capacity and overall operational profitability, and reduce capacity declaration deviations, adverse selection, and conflicting interests caused by information asymmetry. This effectively coordinates collaborative optimization among resource entities and the distribution of interests between resource entities and the shared energy storage operator.

[0080] In some embodiments, all candidate alliances in which each resource entity participates are obtained, and the profitability of each candidate alliance is evaluated. Under the constraint of the rental capacity of each resource entity, the optimal alliance with the highest profitability is selected, and the actual rental capacity of each resource entity under the optimal alliance is determined, including:

[0081] Step S201: Enumerate all non-empty subsets in the set of all resource entities participating in the shared energy storage system, and use each non-empty subset as a candidate alliance.

[0082] A shared energy storage system is a shared energy storage control center operated by a shared energy storage operator. It can coordinate the charging and discharging behavior of various types of user-side flexible resources, and aggregate flexible resources such as new energy output, adjustable load response, and energy storage charging and discharging in real time to form a dispatchable virtual aggregate.

[0083] Preferably, the alliance combination enumeration is performed. Let N be the set of resource entities participating in the shared energy storage system, and enumerate all possible non-empty subsets. Each subset represents a candidate alliance, and collects the forecast curves of each resource entity within the alliance and the rental capacity declared in Phase 1.

[0084] Step S202: For each candidate alliance, determine the characteristic function value of the candidate alliance and the actual rental capacity of each resource entity based on the rental capacity of each resource entity under the candidate alliance; wherein, the characteristic function value is the maximum total revenue value under the preset alliance operation constraints, which maximizes the total revenue of the candidate alliance.

[0085] For each candidate alliance S, an optimization model is constructed with the objective of maximizing the total revenue of the alliance. This model is then solved using a mixed-integer linear programming solver to obtain the maximum total revenue value for that alliance. And the actual rental capacity of each resource entity.

[0086] The objective function of the optimization model, which aims to maximize the total revenue of the alliance, is:

[0087]

[0088] in, The total cooperative revenue of candidate alliance S, For the revenue from selling electricity to the grid by resource entity i, This refers to the energy sharing benefits that resource entity i receives from other resource entities i through shared energy storage. For the peak-shaving revenue of resource subject i. The payment cost from resource entity i to the shared energy storage operator. For operating and maintenance costs.

[0089] The maximum total profit value is obtained by solving the problem. Then, define the eigenfunction values ​​of the alliance S. The increase in total net revenue brought about by the cooperation of Alliance S is as follows:

[0090]

[0091] in, This refers to the revenue generated when resource entity i operates independently. It also compares the revenue of each candidate alliance. Select the largest The corresponding candidate alliance is the optimal alliance, and its corresponding alliance is adopted. This refers to the actual rental capacity of each resource entity.

[0092] The revenue generated when resource entity i operates independently is as follows:

[0093]

[0094] Among them, the revenue from selling electricity to the grid by resource entity i is:

[0095]

[0096] in, Let be the internet access price for resource subject i at time t. Let represent the internet access power of resource subject i at time t.

[0097] Among them, the energy sharing benefits that resource entity i receives from other resource entities i through shared energy storage are:

[0098]

[0099] in, Let be the electricity price at time t. The shared energy power provided by resource subject i to other resource subjects i through shared energy storage during time period t.

[0100] Among them, the peak-shaving revenue of resource entity i is:

[0101]

[0102] in, For peak power, For peak-shaving prices.

[0103] Among them, the payment cost of resource subject i is

[0104]

[0105] in, For resource entity i that has not joined the alliance, declare the fluctuation type and the frequency and intensity of actual call to leased capacity. Inconsistent penalty rental expenses, For resource entity i that has not joined the alliance, declare the fluctuation type and the frequency and intensity of actual call to leased capacity. Inconsistent penalty rental expenses, For resource entity i joining the alliance, declare the fluctuation type and the frequency and intensity of actual leased capacity calls. Consistent normal rental expenses, The resource entity i that joins the alliance declares the type and the frequency and intensity of actual calls to leased capacity. Inconsistent penalty rental expenses, The capacity leasing price for shared energy storage, Indicates the base penalty rate. For leased capacity at non-alliance stations, This refers to the leased capacity of the alliance's facilities.

[0106] Among them, the operation and maintenance cost of resource entity i is

[0107]

[0108] in, The unit operation and maintenance cost of resource subject i, This refers to the installed capacity of resource entity i, i.e., the actual leased capacity of resource entity i.

[0109] The constraints on alliance operation include leased capacity constraints, shared energy storage capacity constraints, power balance constraints, and operational status constraints.

[0110] Among these, the leasing capacity constraint means that the actual leasing capacity of each resource entity cannot exceed the upper limit of its declared leasing capacity, that is:

[0111]

[0112] In the formula, For resource entity i, the actual rental capacity. This refers to the maximum rental capacity declared by resource entity i.

[0113] The shared energy storage capacity constraint is that within a period T, the energy storage capacity leased by the alliance shall not exceed the rated capacity of the shared energy storage, that is:

[0114]

[0115] In the formula, The rated capacity for shared energy storage.

[0116] The power balance constraint is that when candidate consortium S operates at time t, the sum of its charging power and discharging power is 0, that is:

[0117]

[0118] in, Let i be the actual power generation of resource subject i at time t. Let represent the amount of electricity discharged by resource entity i through shared energy storage at time t. Let i be the amount of electricity consumed by resource subject i at time t. Let represent the amount of electricity that resource subject i shares at time t. The amount of electricity that resource subject i charges through shared energy storage at time t.

[0119] The operational state constraint allows different resource entities to perform charging and discharging operations simultaneously at the same time. Therefore, the operational state constraint is as follows:

[0120]

[0121] in, This is a charging symbol. For discharge indication, The charging and discharging power of the leased capacity of resource subject i at time t. This indicates that the leased capacity of the shared energy storage is being charged. , ; This indicates that the leased capacity of the shared energy storage is being discharged. , ; This indicates that the shared energy storage is not working. , At the same time and They cannot be established simultaneously.

[0122] Step S203: Compare the characteristic function values ​​of each candidate alliance, select the candidate alliance with the largest characteristic function value as the optimal alliance, and determine the actual rental capacity of each resource entity in the optimal alliance.

[0123] After obtaining the characteristic function values ​​of each candidate alliance, the characteristic function values ​​of each candidate alliance are compared, and the alliance with the maximum value is selected as the optimal alliance. If multiple alliances have the same characteristic function value, the alliance with more members is selected first to enhance the robustness of the system and the breadth of resource sharing. If there are still ties, the alliance with the smaller total rental capacity is selected to reduce coordination complexity. Finally, the actual rental capacity of each resource entity under the optimal alliance is determined.

[0124] In some embodiments, under the constraint of the actual leased capacity of each resource entity in the optimal alliance, and with the objective of maximizing the total revenue of the shared energy storage operator, energy scheduling optimization is performed on each resource entity in the optimal alliance to obtain the energy scheduling scheme for each resource entity, including:

[0125] Step S301: Obtain the revenue and cost information of the shared energy storage operator, and construct the objective function to maximize the total revenue of the shared energy storage operator based on the revenue and cost information.

[0126] The revenue information for shared energy storage operators includes capacity leasing revenue, penalty revenue from resource entity i, and peak-valley arbitrage revenue. The cost information includes degradation costs, operation and maintenance costs for shared energy storage operators.

[0127] Shared energy storage operators accumulate their long-term revenue and costs by integrating over a time period T. They also apply a probability-weighted approach to the impact of different site types on their revenue, ultimately summing the results across all user-side flexibility resources of various types. The shared energy storage operator's objective function for maximizing total revenue is expressed as follows:

[0128]

[0129] In the formula, For the total revenue of shared energy storage operators, The revenue for shared energy storage operators is typically the revenue paid through lease agreements with renewable energy power plants. To share the peak-valley arbitrage profits of energy storage operators. For the degradation costs of shared energy storage operators, This refers to the operating and maintenance costs for shared energy storage operators.

[0130] The revenue of shared energy storage operators is as follows:

[0131]

[0132] in, Revenue is paid for lease contracts for new energy power plants.

[0133] Among them, the peak-valley arbitrage profits of shared energy storage operators are:

[0134]

[0135] in, For peak and valley price differences, , These refer to the charging and discharging power during peak-valley trading of shared energy storage.

[0136] Among them, the energy storage degradation cost for shared energy storage operators is:

[0137]

[0138] in, For the degradation costs of shared energy storage, , These represent the charging and discharging power of resource subject i at time t, respectively.

[0140] The operation and maintenance costs of shared energy storage are as follows:

[0141]

[0142] in, The capacity maintenance cost rate for shared energy storage.

[0143] Step S302: Determine the operating constraints of the shared energy storage operator. Combining the operating constraints of the shared energy storage operator with the constraints of the actual leased capacity of each resource entity, optimize the objective function of maximizing total revenue. Based on the optimal solution, obtain the energy scheduling scheme of each resource entity, that is, the charging and discharging power plan of each resource entity at each time.

[0144] The constraint on the actual leased capacity of each resource entity is the capacity boundary of each resource entity in the optimal alliance, namely:

[0145]

[0146] In the formula, This refers to the shared energy storage capacity leased by resource entity i in the optimal alliance.

[0147] The operational constraints for shared energy storage operators include charging and discharging capacity constraints, consistency constraints, and operational state coupling constraints.

[0148] The charge / discharge capacity constraint is expressed as follows:

[0149]

[0150]

[0151]

[0152]

[0153] in, The capacity of the shared energy storage at time t is related to both the remaining capacity of the shared energy storage at the previous time and the charging and discharging power of the shared energy storage at the current time. , These represent the lower and upper limits of shared energy storage capacity, respectively. , These represent the minimum and maximum utilization rates of the shared energy storage capacity, respectively. To share the remaining capacity of energy storage from the previous moment, The charging and discharging power of the shared energy storage at time t, , These are the charging efficiency and discharging efficiency of shared energy storage, respectively.

[0154] Consistency constraint: Within a period T, the sum of the total charge and total discharge of shared energy storage should be 0, expressed as:

[0155]

[0156] In the formula, , Let be the charging power and discharging power of resource subject i at time t, respectively. The duration of a single time period. , These are the charging power and discharging power of the shared energy storage, respectively.

[0157] Operating constraints: Within one cycle T, the total charging and discharging power of the shared energy storage does not exceed the rated maximum charging and discharging power of the shared energy storage, and the shared energy storage does not simultaneously perform charging and discharging operations, i.e.:

[0158]

[0159]

[0160] In the formula, I represents the set of resources participating in shared energy storage. , These are the rated maximum charging power and rated maximum discharging power of the shared energy storage, respectively. , These are the charging and discharging state variables of resource subject i at time t, respectively, and their values ​​are 0 or 1. =1 indicates that charging is in progress during this period. =0 indicates no charging. =1 indicates that discharge is in progress during that period. =0 indicates no discharge.

[0161] By considering the operational constraints of shared energy storage operators and the actual leased capacity of each resource entity, a mathematical solver is used to optimize the objective function of maximizing total revenue, thereby obtaining the charging and discharging power planning strategies of each resource entity at each time point.

[0162] In some embodiments, the above method further includes:

[0163] Step S401: Obtain the volatility preference type declared by each resource entity to the shared energy storage operator; wherein, the volatility preference type includes high volatility type and low volatility type.

[0164] Among them, the volatility preference type declared by the resource entity to the shared energy storage operator is a subjective classification submitted after making its own judgment on its own volatility characteristics. This type of declaration directly affects its leasing pricing in the alliance. Due to the subjective declaration, there is a certain degree of strategic misrepresentation of its own volatility preference type. That is, the volatility preference type should be high volatility type, but it is deliberately declared as low volatility type in order to obtain lower leasing costs.

[0165] Step S402: Generate a leasing attribute value based on the volatility preference type; wherein, the leasing attribute value is used to characterize the cost of resource entities calling the shared energy storage system; wherein, the leasing attribute value of the high volatility type is greater than the leasing attribute value of the low volatility type.

[0166] Among them, the leasing attribute value is generated by differentiating the fluctuation preference type. This leasing attribute value not only reflects the physical fluctuation characteristics of the resource subject, but also represents the cost of the resource subject calling the shared energy storage system. The higher the leasing attribute value, the higher the unit capacity leasing cost.

[0167] Generally, low-fluctuation type resources have lower leasing attribute values ​​due to their low call frequency and small power fluctuations, which helps reduce the overall alliance operating costs. High-fluctuation type resources, although having higher application costs, rely more on energy storage regulation capabilities in actual operation, thus more easily triggering high-frequency, small-amplitude charge and discharge responses, thereby improving the utilization rate and cycle life matching of shared energy storage. Low-fluctuation type resources tend to have long-cycle, high-power charge and discharge operations, which are more in line with the energy dispatching needs of energy storage systems.

[0168] Step S403: After executing the energy scheduling scheme of each resource entity, obtain the call data of each resource entity to the shared energy storage system.

[0169] The call data includes call frequency and call intensity. Call frequency is defined as the proportion of the number of time periods in which shared energy storage is actually called out to the total number of time periods, while call intensity is the charge-discharge cycle depth.

[0170] Step S404: Based on the retrieved data, infer the true fluctuation preference type of each resource subject using Bayesian method.

[0171] This involves using data as observational evidence, updating posterior probabilities based on prior beliefs, inferring the true fluctuation type using Bayesian methods, and then calibrating the reporting bias.

[0172] Step S405: For each resource subject whose actual volatility preference type is high volatility, determine whether the volatility preference type declared by the resource subject is consistent with the actual volatility preference type.

[0173] Specifically, after obtaining the true volatility preference type of each resource subject, for resource subjects whose true volatility preference is high volatility, it is determined whether the volatility preference type declared by the resource subject is consistent with the true volatility preference type.

[0174] Step S406: If there is a discrepancy, mark the resource subject as a false resource subject, and re-verify the rental attribute value of the false resource subject according to the true fluctuation preference type. Then, adjust the re-verified rental attribute value upward to obtain the punitive rental attribute value of the false resource subject.

[0175] If the volatility preference type declared by a resource entity is inconsistent with the actual volatility preference type, it indicates that the resource entity has engaged in strategic underreporting. The declared type is lower than the actual volatility level in order to avoid higher leasing costs. In this case, the entity should be marked as a falsely reporting resource entity, and the leasing attribute value of the falsely reporting resource entity should be re-evaluated based on its actual high volatility characteristics. The re-evaluated leasing attribute value should be adjusted upward, with the adjustment amount being proportional to the difference between high and low volatility prices and the actual usage capacity, ensuring that the penalty is strictly matched with the degree of strategic deviation.

[0176] After the operator completes the settlement of leasing costs, it stores the observation data, posterior probabilities, and penalty records of this round into the database, which is used to update the prior beliefs and likelihood function parameters for the next round of the game.

[0177] In some embodiments, the process of obtaining the volatility preference type of a resource subject in the above method further includes: obtaining historical operating trajectory data and predicted operating trajectory data for each resource subject; determining normalized volatility based on the historical operating trajectory data and predicted operating trajectory data; wherein, normalized volatility is the ratio of the deviation between the historical operating trajectory data and predicted operating trajectory data of the resource subject to the predicted operating trajectory data; determining whether the normalized volatility is greater than a preset deviation threshold; if the normalized volatility is greater than the preset deviation threshold, then the resource subject is determined to be of a high volatility type; if the normalized volatility is not greater than the preset deviation threshold, then the resource subject is determined to be of a low volatility type.

[0178] Historical operating trajectory data is acquired in real time through the grid-connected power energy acquisition system, such as grid-connected power, while predicted operating trajectory data is generated based on time-series prediction models, such as the LSTM (Long Short-Term Memory) algorithm.

[0179] The normalized volatility is determined by using historical and predicted trading trajectory data:

[0180]

[0181] In the formula, Normalized volatility For grid-connected power, To predict grid-connected power generation.

[0182] Set preset deviation threshold ,when When resource subject i is of the high volatility type, then At that time, resource subject i is of the low volatility type.

[0183] In some embodiments, based on the invoked data, Bayesian methods are used to infer the true fluctuation preference type of each resource subject, including:

[0184] Step S4041: Obtain the prior probability distribution of each resource subject in the real fluctuation preference type.

[0185] Specifically, for different types of real fluctuation preferences, a prior probability is set for each type of real fluctuation preference. In the initial round, the prior probability can be set based on historical statistics or expert experience, such as 0.5. As the number of game rounds increases, the prior probability is dynamically updated to the posterior probability of the previous round.

[0186] Step S4042: Based on the call data and prior probability distribution, construct the likelihood function of the resource subject under different real fluctuation preference types.

[0187] The likelihood function describes the conditional probability of observing a specific call frequency and call intensity given a real type. This likelihood function is obtained by fitting historical call data and follows a normal or gamma distribution. Assuming that the call frequency X and call intensity Y are conditionally independent given a type θ, the joint likelihood function is:

[0188] P(X,Y|θ)=P(X|θ)·P(Y|θ)

[0189] In the formula, P(X|θ) is the conditional probability density function of call frequency X under type θ, and P(Y|θ) is the conditional probability density function of call intensity Y under type θ.

[0190] Step S4043: Use Bayes' rule to iteratively update the prior probability of the likelihood function to obtain the posterior probability of each resource subject having different real fluctuation preference types.

[0191] The logic of Bayesian update is posterior probability ∝ likelihood function × prior probability. First, the total probability of the observed data O is calculated as follows:

[0192] P(O)=P(O∣H)P(H)+P(O∣L)P(L)

[0193] In the formula, P(O|H) and P(O|L) are the likelihood values ​​for high and low volatility types, respectively, and P(H) and P(L) are the corresponding prior probabilities.

[0194] The Bayesian posterior process is as follows:

[0195] Posterior probability for high volatility types:

[0196] P(H∣O)=(P(O∣H)P(H)) / P(O)

[0197] Posterior probability for low volatility type:

[0198] P(L∣O)=(P(O∣L)P(L)) / P(O)

[0199] It is clear that P(H∣O)+P(L∣O)=1 is satisfied.

[0200] The posterior probability of each round will be used as the prior probability of the next round, thus achieving continuous calibration of beliefs.

[0201] Step S4044: Determine the true fluctuation preference type of each resource subject based on the maximum value of the posterior probability.

[0202] Specifically, when the difference between the posterior probabilities P(H∣O) and P(L∣O) is less than 0.05 for three consecutive rounds, the type identification is considered to have converged. If P(H∣O) > P(L∣O), then it is identified as a high-volatility type; otherwise, it is identified as a low-volatility type.

[0203] In some embodiments, shared energy storage operators establish transaction storage and supervision mechanisms with grid dispatch centers and power trading centers. They use blockchain or dedicated interfaces to hash-store application data, dispatch instructions, settlement bills, etc., to ensure immutability and accept security verification from the dispatch center and compliance review from the trading center. At the same time, operators convert the power charging and discharging schemes of each resource entity into dispatch instructions and send them to the grid dispatch center through standard communication protocols, which are then incorporated into the overall grid dispatch plan for execution. This achieves a closed loop of shared energy storage operation with transparent information, effective supervision, and coordinated dispatch.

[0204] In this embodiment, the Bayesian update-driven type identification and dynamic pricing strategy effectively solves the information asymmetry problem. Operators achieve complete and accurate identification of high / low fluctuation entities by observing the frequency and intensity of resource entity calls; the differentiated pricing mechanism imposes additional penalty costs on entities that falsely report their type, avoiding adverse selection and ensuring incentive compatibility.

[0205] Next, a numerical example will be used to further illustrate the shared energy storage operation method proposed in this application for flexible resources on multiple user sides. Suppose a certain area contains 3 new energy power stations (N1, N2, N3).

[0206] Combination Figure 3 As shown, at the system level, renewable energy power plants, as the main body of the system, generate electricity and connect to the grid, and participate in electricity market transactions through shared energy storage as an intermediary. Shared energy storage operators provide capacity leasing and energy sharing services to the power plant cluster and report their operating status to the dispatch center and renewable energy power plants respectively. The dispatch center, as the central hub for ensuring the real-time safety, stability and reliable operation of the power system, issues dispatch instructions to renewable energy power plants based on the operating status reported by the operators. Renewable energy power plants then call upon energy storage according to the dispatch instructions and the energy storage operating status. The trading center, as the center for organizing various electricity market operations and settlements, forms electricity prices and trading plans through market clearing mechanisms and supervises and records the process of power plants calling upon energy storage to participate in transactions.

[0207] At the operational level, new energy power plants form alliances based on predicted power generation and sell electricity to the grid, participating in multi-level power transactions that include grid connection, energy sharing between power plants, and peak shaving services. Shared energy storage operators build diversified revenue structures through leasing fees, penalty fees, and peak-valley arbitrage income, while also bearing the degradation costs and operation and maintenance responsibilities of energy storage equipment, and implementing 24-hour optimized charging and discharging scheduling. Ultimately, this achieves dynamic game equilibrium among the participating entities and maximizes the overall benefits of the system under conditions of information asymmetry.

[0208] In this system, shared energy storage is invested, constructed, and operated by a third-party independent operator. By integrating the spatiotemporal complementary characteristics of distributed renewable energy power generation resources, a two-tier service system of "capacity leasing - energy sharing" is constructed to provide full-scenario energy storage support for various types of user-side flexible resource entities.

[0209] In terms of capacity leasing, resource entities lease specific capacity quotas from shared energy storage operators based on their own fluctuation characteristics. This is used to achieve closed-loop control of power generation resources through "storing surplus electricity during periods of over-generation and supplementing electricity during periods of under-generation" or to optimize the operation of energy storage and load resources through "charging during off-peak hours and discharging during peak hours." This helps to smooth out intermittent fluctuations, improve grid connection stability, or reduce electricity costs.

[0210] The energy sharing mechanism further breaks down the energy storage resource boundaries of a single resource entity. That is, entities with excess power generation or low load can inject redundant power into the shared energy storage pool, while entities with insufficient power generation or high load can obtain power from the pool to fill the gap, forming a real-time energy mutual assistance network across entities. At the same time, the shared energy storage system provides peak shaving and frequency regulation auxiliary services to the group of resource entities by responding quickly to power commands, thereby strengthening the support capacity of the power system.

[0211] As the core coordinator and operational strategy maker of the system, shared energy storage operators realize dynamic power charging and discharging strategies and differentiated pricing mechanisms for resource entities participating in shared energy storage through Bayesian update mechanisms. While maximizing their own benefits, they incentivize resource entities to optimize charging and discharging strategies, promote the efficient allocation of shared energy storage resources and improve their economic efficiency throughout the entire life cycle, and provide a technology-economic synergy solution for large-scale renewable energy consumption.

[0212] A multi-type user-side flexible resource-sharing energy storage operation framework under hybrid game theory, such as Figure 4 As shown, a model for the interaction between various types of user-side flexible resource entities and shared energy storage operators is constructed to coordinate the conflicting interests of multiple parties.

[0213] By introducing a virtual participant, "Nature," through the Harsanyi transformation, the incomplete information dynamic game is transformed into a complete but imperfect information dynamic game, with the game tree as follows: Figure 5 As shown, the transmission process of the game tree is as follows:

[0214] Virtual participant N with prior probability and Randomly assign the real type to the resource subject That is, high volatility type or low volatility type;

[0215] A resource entity knows its true type and then chooses to send a declaration signal to the operator to explain its fluctuation type; different true types can send the same signal.

[0216] Operators set initial lease prices and capacity quotas based on the signals reported by the resource entities they observe, including those sent to stations within the alliance and those sent to non-alliance stations;

[0217] After the operator makes a decision, the resource entity executes the actual energy storage and charging / discharging power allocation, and obtains the allocation frequency and intensity of the resource entity after executing the signal response;

[0218] Based on observed call frequency and intensity, the operator updates its beliefs using Bayesian rules, infers the true type of the resource entity, and re-selects actions according to the true type, ultimately determining the final rental price and penalty, reaching one of the outcomes S1 to S8, thus completing this game.

[0219] The entire game tree fully illustrates the dynamic game process with incomplete information, from type assignment to settlement. The information set of resource subject i is... 、. , , These represent the action information "naturally" transmitted before the decision was made and the action information of the shared energy storage operator in the previous stage, respectively.

[0220] Next, this example uses three photovoltaic (PV) resource entities in a certain region, each with an installed capacity of 100MW, as typical representatives of various types of user-side flexibility resources. The actual operating data on a typical day is analyzed. The grid-connected power generation of each of the three PV resource entities is 450MWh, with working hours limited to 7:00-19:00 and zero power generation during non-working hours. The measured volatility rates are 13%, 8%, and 15%, respectively. Using 10% as the threshold for determining high and low volatility, PV resource entities 1 and 3 are classified as high-volatility entities, while PV resource entity 2 is classified as a low-volatility entity.

[0221] In the cooperative game phase between power plants, we assume complete information sharing, meaning that information such as power and price among the resource entities is fully disclosed. This invention selects three photovoltaic resource entities, thus resulting in five different strategic game models, as shown in Table 1.

[0222] Table 1. Strategic Game Model of Resource Subjects

[0223]

[0224] To meet the leasing needs of various resource entities, the shared energy storage system is configured to account for 30% of the installed capacity of the resource entity group, i.e., 90 / 180MWh, with a maximum charging and discharging power of 90MW and a charging and discharging efficiency of 95%. The SOC operating state is that it can be fully charged and discharged once a day. The parameter settings are shown in Table 2.

[0225] Table 2 Simulation Parameter Settings

[0226]

[0227] Depend on Figures 6a-6c It can be seen that the predicted power and actual power of each resource entity are generally consistent. Among them, entities 1 and 3 are highly volatile entities, with entity 1 experiencing a significant discrepancy between its actual output and grid connection plan due to unforeseen factors such as weather; entity 3 chooses to reduce grid connection during peak hours, thus reducing grid absorption pressure and increasing reserves for peak-shaving power in the evening peak; while entity 2 is a low-volatile entity, with relatively stable output. The overall trend of the predicted output and grid connection plan of each resource entity shows that the changes are consistent with their respective measured volatility rates.

[0228] In Phase 1, each resource entity selects its leased energy storage capacity based on the product of its predicted power generation and its own volatility, and reports its leased capacity to the consortium for optimization analysis in Phase 2. In Phase 2, the consortium uses a CPLEX solver to solve for the characteristic function values ​​under different consortia based on the leased capacity and predicted power output of each resource entity. Depending on the different characteristic function values ​​under different consortia, each resource entity selects the consortium with the largest characteristic function value as the optimal consortium and determines the optimal leased capacity under the optimal consortium.

[0229] Figure 6 shows a comparison of the characteristic function values ​​of each alliance with the revenue of each resource entity under the optimal alliance and its revenue when operating independently. Figure 7 It can be seen that the characteristic function value of the alliance formed by the three resource entities is the highest, at 0.92 million yuan. Therefore, the alliance is the optimal alliance under the predicted output in stage 2. Moreover, when the optimal alliance is the alliance, the income of each resource entity is higher than that of its independent operation, which satisfies the optimal solution of the cooperative game, indicating that the alliance is the optimal choice under the predicted output of each resource entity.

[0230] In Phase 3, the alliance formed by the resource entities is the optimal alliance from Phase 2, and the actual capacity leased by the alliance from operators is based on the leased capacity under the optimal alliance. The leased capacity of each resource entity under the optimal alliance and the leased capacity under the non-alliance alliance are as follows: Figure 8 As shown. By Figure 8It is known that in Phase 1, when each resource entity operates independently, the sum of leased capacity is 162 MWh. Under the optimal alliance in Phase 2, the sum of leased capacity is 116.7 MWh, while in Phase 3, the leased capacity actually used by the alliance is 114.1 MWh. Entities 1 and 3 are high-volatility entities, and while maintaining a consistent installed capacity, they often require more energy storage capacity than low-volatility entities to cope with the volatility of power generation. Therefore, entity 1, as an under-generating entity, needs to lease more capacity to cope with under-generating situations when operating independently. However, within the alliance, it can compensate for its own insufficient power generation through energy sharing with other over-generating entities, thus significantly reducing its required leased capacity and lowering leasing costs. Entity 3, as an over-generating entity, can not only participate in energy sharing within the alliance but also more flexibly use energy storage to store excess power generation. Entity 2, as a low-volatility entity, participates in energy sharing with its excess power generation, thus reducing leasing costs and increasing the benefits of energy sharing. This shows that through cooperation, the various resource entities not only reduced their actual leasing costs, but also significantly improved the utilization rate of energy storage, bringing the alliance's energy storage utilization rate to 97.7%.

[0231] During Phase 3, when the alliance schedules the charging and discharging of leased energy storage capacity based on actual output and grid connection plans, the SOC and charging / discharging power trends of the shared energy storage are shown in the following figures: Figure 9a and Figure 9b As shown.

[0232] Depend on Figure 9a and Figure 9b It is known that the operating hours of photovoltaic (PV) resource providers are 7:00-19:00. The peak power generation period is from 12:00-15:00, requiring charging of shared energy storage. The period from 17:00-19:00 is a period of power shortage, during which resource providers begin discharging shared energy storage. The peak shaving period for resource providers is from 18:00-19:00, requiring the release of a large amount of energy storage to complete peak shaving and grid connection plans and obtain peak shaving revenue. After the peak shaving period ends, the remaining energy storage of the resource providers is purchased by the operators. Simultaneously, during the peak power generation period of PV resource providers, operators purchase electricity from external PV resource providers to supplement remaining energy storage capacity. During the evening peak period from 20:00-22:00, this energy storage is discharged along with the purchased remaining energy storage from resource providers to obtain peak-valley arbitrage revenue.

[0233] During the process of various resource entities using shared energy storage to address "overstorage and undercapacity generation," the shared energy storage operator records the frequency and intensity of energy storage usage by each entity and uses this information for Bayesian inference, such as... Figure 10 As shown. By Figure 10It can be seen that photovoltaic resource entities 1 and 3 are both high-volatility entities, and the operators' belief inferences about entities 1 and 3 are close to 1, which is reflected in a higher frequency and intensity of energy storage. In addition, photovoltaic resource entity 2 is a low-volatility entity, so the operators' belief inferences about entity 2 are close to 0, which is reflected in a lower frequency and intensity of energy storage.

[0234] Based on the updated belief results from Bayesian inference, the operator's inference types regarding resource subjects are as follows: Figure 11 As shown.

[0235] Depend on Figure 11 It can be seen that the operator has achieved accurate identification of the true fluctuation characteristics of resource subjects through Bayesian inference. Compared with the declared type that depends on resource subjects, the type identification accuracy based on Bayesian inference is significantly improved, proving the effectiveness of the Bayesian belief update mechanism in asymmetric information environments.

[0236] After updating its beliefs about the true types of resource entities using Bayesian methods, the operator provides the final and initial lease prices for each resource entity based on the updated beliefs. Figure 12 As shown.

[0237] Depend on Figure 12 It can be seen that the rental price of entities 1 and 3, as high-volatility entities, is 120 yuan / MWh, while the rental price of photovoltaic entity 2, as a low-volatility entity, is 110 yuan / MWh. There is a 9% price premium for high-volatility entities compared to low-volatility entities. This reflects the quantitative pricing of uncertainty risk by energy storage operators. That is, the power output prediction deviation of high-volatility entities is larger, requiring energy storage systems to provide more frequent adjustment services, which increases equipment wear and operational complexity. On the other hand, the operator correctly identified the true type of entity 3 through Bayesian inference, which greatly reduced the uncertainty risk that it needs to bear.

[0238] If an operator misjudges the true type of the resource entity during the belief update phase, it may result in a final lease price that does not align with the resource entity's own volatility pattern, thereby impacting the operator's own revenue. Figure 13 As shown.

[0239] Depend on Figure 13 It can be seen that when the operator misjudged Entity 3 as a low-volatility entity, the actual lease price of Entity 3 dropped from RMB 120 / MWh to RMB 110 / MWh. As a result, the operator's daily loss for Entity 3 due to the misjudgment reached RMB 814.1, and the annual loss was RMB 297,000. The dynamic investment payback period increased from 9.25 years to 9.41 years, which is 0.16 years longer. The IRR dropped from 12.83% to 12.63%.

[0240] It is evident that misjudgment can cause significant economic losses to operators on the one hand, and indirectly "encourage" other resource entities to conceal their types of information on the other, thereby causing a crisis of trust between resource entities and operators, and even leading to the collapse of the entire system.

[0241] This invention introduces signal game theory and cooperative game theory models to demonstrate the effectiveness of the proposed method by considering the revenues of various resource entities and operators. Figure 14 As shown. The cooperative game model refers to a cooperative game among resource entities, but not a signal game with the operator; the signal game model refers to a game where resource entities do not cooperate, but only engage in signal games with the operator. This invention is a cooperative-signal game model.

[0242] Depend on Figure 14 It can be seen that the net profit of PV1 under the signal game model is approximately 60,000 yuan, while it increases to approximately 65,000 yuan under both the cooperative game and cooperative-signal game mechanisms. The net profit of PV2 increases from 75,000 yuan under the signal game model to approximately 80,000 yuan. The net profit of PV3 increases from 77,000 yuan under the signal game model to approximately 82,000 yuan and 81,000 yuan respectively. For operators, the profit under the signal game model is approximately 75,000 yuan, which increases to approximately 82,000 yuan and 83,000 yuan under the cooperative game and cooperative-signal game mechanisms respectively. Therefore, it is evident that the profit for both resource providers and operators under the signal game model is significantly lower than the other two scenarios. This is because under the signal game model, the inability of resource providers to share energy leads to significant light curtailment and consumes a large amount of energy storage capacity, preventing operators from taking advantage of peak-valley arbitrage. Therefore, the signal game model is not a good choice for either resource providers or operators. In the cooperative game model, resource entities can share energy and all information is completely open, which means that resource entities do not need to bear any risk costs. Therefore, in the cooperative game model, the total revenue of each resource entity is slightly higher than that in the cooperative-signal game model, but the operator's revenue has decreased significantly. The dynamic payback period has increased from 9.25 years to 9.36 years, and the IRR has decreased from 12.83% to 12.69%.

[0243] The above results indicate that the cooperative-signaling game-based operating mechanism not only satisfies incentive compatibility and achieves an overall improvement in social welfare, but also better reflects the reality of opaque information.

[0244] Table 3 shows the impact of different shared energy storage capacity configurations on system revenue and economics in a cooperative-information game scenario where the operator is not deceived.

[0245] Table 3. Comparative Analysis of Revenue under Different Shared Energy Storage Capacity Configurations

[0246]

[0247] As shown in Table 3, as the energy storage configuration ratio increases from 20% to 40% of the installed capacity, the daily net profits of resource entities PV1, PV2, and PV3 are RMB 65,000, RMB 80,000, and RMB 81,000, respectively. This indicates the stability of the resource entity's revenue without changing the grid connection plan and transaction rules, further verifying the stability of this model. Since the resource entity's leasing demand under the optimal alliance is 116 MWh, the leasable capacity of the shared energy storage operator must meet the alliance's leasing demand; otherwise, it will trigger the resource entity's exit. However, when the energy storage configuration is 20%, 60 / 120 MWh, which just meets the leasing demand, the operator's daily net profit is only RMB 44,000, the NPV is -RMB 17,090,400, the dynamic investment payback period is 15 years, and the IRR is 5.46%. This means that the operator cannot recover its investment because almost all of the operator's capacity is used to participate in the resource entity's over-storage and under-generation activities, leaving no surplus capacity for peak-valley arbitrage.

[0248] When the allocation ratio increases from 25% to 30%, the operator's net profit increases from 60,000 yuan to 83,000 yuan, NPV increases from 17.9358 million yuan to 52.962 million yuan, the dynamic investment payback period decreases from 11.9 years to 9.25 years, and the IRR increases from 10.02% to 12.83%. This demonstrates that with the increase in energy storage capacity, the operator's revenue significantly improves, reflecting the positive impact of economies of scale on operator profitability. When the allocation ratio is 35%, although the NPV increases to 87.9882 million yuan, the growth rate declines, indicating that the marginal benefit of increasing the capacity ratio begins to decrease. When the allocation ratio further increases to 40%, although the NPV increases from 87.9882 million yuan to 123.0144 million yuan, excessive capacity allocation not only increases investment costs but also makes the operator's revenue severely susceptible to market fluctuations, meaning that the operator needs to bear significant investment risks.

[0249] The results indicate that, under the cooperation-signaling game framework, there is a significant marginal benefit point for the configuration of shared energy storage capacity, which is about 25%-30% of the configuration ratio. Beyond this range, although the overall economic efficiency of the system increases with the increase in capacity, the marginal benefit declines significantly. In particular, when the configuration ratio reaches 40%, operators need to bear great investment risks.

[0250] In summary, under the cooperation-signal game mechanism, when the shared energy storage capacity is configured at about 30% of the installed capacity of the new energy power station cluster, the synergistic optimal benefits of flexible resource entities on the multi-type user side, operator benefits, and overall system economy can be achieved.

[0251] As illustrated by the above examples, the method proposed in this application addresses the problem of conflicting interests arising from information asymmetry between various types of flexible resources on the user side and shared energy storage operators, and the difficulty of effectively coordinating private information exchange using traditional game theory methods. It coordinates multi-party interaction through a dynamic game framework, allowing resource entities to choose leased capacity and charging / discharging strategies based on their own volatility types. Shared energy storage operators, in turn, update their beliefs and dynamically adjust lease pricing by observing behavior, achieving dynamic identification of the true type of resource entities. The introduction of penalty contracts for misreporting effectively suppresses the motivation for information manipulation and ensures the incentive compatibility of the mechanism.

[0252] Based on the same inventive concept, this application also provides a shared energy storage operation system for implementing the above-mentioned shared energy storage operation method for multiple types of user-side flexibility resources.

[0253] The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the shared energy storage operation system for multiple types of user-side flexibility resources provided below can be found in the limitations of the shared energy storage operation method for multiple types of user-side flexibility resources described above, and will not be repeated here.

[0254] like Figure 15 As shown, this application embodiment provides a shared energy storage operation system for flexible resources on multiple user sides, including:

[0255] The application information acquisition module 100 is used to acquire the leased capacity applied for by each resource entity to the shared energy storage operator;

[0256] The alliance screening module 200 is used to obtain all candidate alliances in which each resource entity participates, evaluate the benefits of each candidate alliance, and, under the constraint of the rental capacity of each resource entity, screen out the optimal alliance with the highest benefit and determine the actual rental capacity of each resource entity under the optimal alliance.

[0257] The energy scheduling optimization module 300 is used to optimize the energy scheduling of each resource entity in the optimal alliance under the constraint of the actual leased capacity of each resource entity, with the goal of maximizing the total revenue of the shared energy storage operator, and obtain the energy scheduling scheme of each resource entity.

[0258] In some embodiments, the alliance screening module 200 is configured to:

[0259] Enumerate all non-empty subsets in the set of all resource entities participating in the shared energy storage system, and treat each non-empty subset as a candidate alliance.

[0260] For each candidate alliance, the characteristic function value of the candidate alliance and the actual rental capacity of each resource entity under the candidate alliance are determined according to the rental capacity of each resource entity; wherein, the characteristic function value is the maximum total revenue value under the preset alliance operation constraints, which maximizes the total revenue of the candidate alliance.

[0261] Compare the characteristic function values ​​of each candidate alliance, select the candidate alliance with the largest characteristic function value as the optimal alliance, and determine the actual rental capacity of each resource entity in the optimal alliance.

[0262] In some embodiments, the energy scheduling optimization module 300 is used for:

[0263] Obtain revenue and cost information from shared energy storage operators, and construct an objective function to maximize the total revenue of shared energy storage operators based on the revenue and cost information.

[0264] The operational constraints of the shared energy storage operator are determined. Based on the operational constraints of the shared energy storage operator and the constraints of the actual leased capacity of each resource entity, the objective function of maximizing total revenue is optimized and solved. The energy dispatch scheme of each resource entity is obtained based on the optimal solution.

[0265] In some embodiments, the system further includes a lease attribute determination module, used for:

[0266] Obtain the volatility preference types declared by each resource entity to the shared energy storage operator; among them, volatility preference types include high volatility type and low volatility type;

[0267] Leasing attribute values ​​are generated based on volatility preference types; these values ​​characterize the cost for resource entities to access shared energy storage systems; and the leasing attribute values ​​for high volatility types are greater than those for low volatility types.

[0268] After executing the energy dispatch plans of each resource entity, obtain the call data of each resource entity to the shared energy storage system;

[0269] Based on the data accessed, the true fluctuation preference type of each resource subject is inferred using Bayesian methods;

[0270] For each resource entity whose actual volatility preference type is high volatility, determine whether the volatility preference type declared by the resource entity is consistent with the actual volatility preference type;

[0271] If there is a discrepancy, the resource subject will be marked as a false resource subject, and the rental attribute value of the false resource subject will be re-evaluated according to the true fluctuation preference type. The re-evaluated rental attribute value will be adjusted upward to obtain the punitive rental attribute value of the false resource subject.

[0272] In some embodiments, the system further includes: a fluctuation type determination module, used for:

[0273] Acquire historical and predicted operational trajectory data for each resource entity;

[0274] Based on historical and predicted operational trajectory data, the normalized volatility is determined; whereby the normalized volatility is the ratio of the deviation between the historical and predicted operational trajectory data of the resource entity to the predicted operational trajectory data.

[0275] Determine whether the normalized volatility is greater than a preset deviation threshold;

[0276] If the normalized volatility is determined to be greater than the preset deviation threshold, the resource entity is determined to be of the high volatility type.

[0277] If the normalized volatility is determined to be no greater than the preset deviation threshold, the resource entity is determined to be of the low volatility type.

[0278] In some embodiments, the lease attribute determination module is configured to:

[0279] Obtain the prior probability distribution of each resource subject in the actual fluctuation preference type;

[0280] Based on the call data and prior probability distribution, a likelihood function of the resource subject under different real fluctuation preference types is constructed.

[0281] By iteratively updating the prior probability of the likelihood function using Bayes' rule, the posterior probability of each resource subject having different real fluctuation preference types is obtained.

[0282] The true fluctuation preference type of each resource subject is determined based on the maximum posterior probability.

[0283] like Figure 16 As shown, this application embodiment provides an electronic device. The electronic device 10 includes a memory 20 and a processor 30. The memory 20 stores a computer program. When the computer program is executed by the processor 30, the processor 30 performs the following steps:

[0284] Obtain the leased capacity declared by each resource entity to the shared energy storage operator;

[0285] Obtain all candidate alliances in which each resource entity participates, evaluate the benefits of each candidate alliance, and under the constraint of the rental capacity of each resource entity, select the optimal alliance with the highest benefit, and determine the actual rental capacity of each resource entity under the optimal alliance.

[0286] Under the constraint of the actual leased capacity of each resource entity in the optimal alliance, with the goal of maximizing the total revenue of the shared energy storage operator, energy scheduling optimization is carried out on each resource entity in the optimal alliance to obtain the energy scheduling scheme of each resource entity.

[0287] In some embodiments, when the computer program is executed by the processor 30, the processor 30 also performs the following steps:

[0288] Enumerate all non-empty subsets in the set of all resource entities participating in the shared energy storage system, and treat each non-empty subset as a candidate alliance.

[0289] For each candidate alliance, the characteristic function value of the candidate alliance and the actual rental capacity of each resource entity under the candidate alliance are determined according to the rental capacity of each resource entity; wherein, the characteristic function value is the maximum total revenue value under the preset alliance operation constraints, which maximizes the total revenue of the candidate alliance.

[0290] Compare the characteristic function values ​​of each candidate alliance, select the candidate alliance with the largest characteristic function value as the optimal alliance, and determine the actual rental capacity of each resource entity in the optimal alliance.

[0291] In some embodiments, when the computer program is executed by the processor 30, the processor 30 also performs the following steps:

[0292] Obtain revenue and cost information from shared energy storage operators, and construct an objective function to maximize the total revenue of shared energy storage operators based on the revenue and cost information.

[0293] The operational constraints of the shared energy storage operator are determined. Based on the operational constraints of the shared energy storage operator and the constraints of the actual leased capacity of each resource entity, the objective function of maximizing total revenue is optimized and solved. The energy dispatch scheme of each resource entity is obtained based on the optimal solution.

[0294] In some embodiments, when the computer program is executed by the processor 30, the processor 30 also performs the following steps:

[0295] Obtain the volatility preference types declared by each resource entity to the shared energy storage operator; among them, volatility preference types include high volatility type and low volatility type;

[0296] Leasing attribute values ​​are generated based on volatility preference types; these values ​​characterize the cost for resource entities to access shared energy storage systems; and the leasing attribute values ​​for high volatility types are greater than those for low volatility types.

[0297] After executing the energy dispatch plans of each resource entity, obtain the call data of each resource entity to the shared energy storage system;

[0298] Based on the data accessed, the true fluctuation preference type of each resource subject is inferred using Bayesian methods;

[0299] For each resource entity whose actual volatility preference type is high volatility, determine whether the volatility preference type declared by the resource entity is consistent with the actual volatility preference type;

[0300] If there is a discrepancy, the resource subject will be marked as a false resource subject, and the rental attribute value of the false resource subject will be re-evaluated according to the true fluctuation preference type. The re-evaluated rental attribute value will be adjusted upward to obtain the punitive rental attribute value of the false resource subject.

[0301] In some embodiments, when the computer program is executed by the processor 30, the processor 30 also performs the following steps:

[0302] Acquire historical and predicted operational trajectory data for each resource entity;

[0303] Based on historical and predicted operational trajectory data, the normalized volatility is determined; whereby the normalized volatility is the ratio of the deviation between the historical and predicted operational trajectory data of the resource entity to the predicted operational trajectory data.

[0304] Determine whether the normalized volatility is greater than a preset deviation threshold;

[0305] If the normalized volatility is determined to be greater than the preset deviation threshold, the resource entity is determined to be of the high volatility type.

[0306] If the normalized volatility is determined to be no greater than the preset deviation threshold, the resource entity is determined to be of the low volatility type.

[0307] In some embodiments, when the computer program is executed by the processor 30, the processor 30 also performs the following steps:

[0308] Obtain the prior probability distribution of each resource subject in the actual fluctuation preference type;

[0309] Based on the call data and prior probability distribution, a likelihood function of the resource subject under different real fluctuation preference types is constructed.

[0310] By iteratively updating the prior probability of the likelihood function using Bayes' rule, the posterior probability of each resource subject having different real fluctuation preference types is obtained.

[0311] The true fluctuation preference type of each resource subject is determined based on the maximum posterior probability.

[0312] This application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed, it performs the following steps:

[0313] Obtain the leased capacity declared by each resource entity to the shared energy storage operator;

[0314] Obtain all candidate alliances in which each resource entity participates, evaluate the benefits of each candidate alliance, and under the constraint of the rental capacity of each resource entity, select the optimal alliance with the highest benefit, and determine the actual rental capacity of each resource entity under the optimal alliance.

[0315] Under the constraint of the actual leased capacity of each resource entity in the optimal alliance, with the goal of maximizing the total revenue of the shared energy storage operator, energy scheduling optimization is carried out on each resource entity in the optimal alliance to obtain the energy scheduling scheme of each resource entity.

[0316] In some embodiments, when a computer program is executed, it also performs the following steps:

[0317] Enumerate all non-empty subsets in the set of all resource entities participating in the shared energy storage system, and treat each non-empty subset as a candidate alliance.

[0318] For each candidate alliance, the characteristic function value of the candidate alliance and the actual rental capacity of each resource entity under the candidate alliance are determined according to the rental capacity of each resource entity; wherein, the characteristic function value is the maximum total revenue value under the preset alliance operation constraints, which maximizes the total revenue of the candidate alliance.

[0319] Compare the characteristic function values ​​of each candidate alliance, select the candidate alliance with the largest characteristic function value as the optimal alliance, and determine the actual rental capacity of each resource entity in the optimal alliance.

[0320] In some embodiments, when a computer program is executed, it also performs the following steps:

[0321] Obtain revenue and cost information from shared energy storage operators, and construct an objective function to maximize the total revenue of shared energy storage operators based on the revenue and cost information.

[0322] The operational constraints of the shared energy storage operator are determined. Based on the operational constraints of the shared energy storage operator and the constraints of the actual leased capacity of each resource entity, the objective function of maximizing total revenue is optimized and solved. The energy dispatch scheme of each resource entity is obtained based on the optimal solution.

[0323] In some embodiments, when a computer program is executed, it also performs the following steps:

[0324] Obtain the volatility preference types declared by each resource entity to the shared energy storage operator; among them, volatility preference types include high volatility type and low volatility type;

[0325] Leasing attribute values ​​are generated based on volatility preference types; these values ​​characterize the cost for resource entities to access shared energy storage systems; and the leasing attribute values ​​for high volatility types are greater than those for low volatility types.

[0326] After executing the energy dispatch plans of each resource entity, obtain the call data of each resource entity to the shared energy storage system;

[0327] Based on the data accessed, the true fluctuation preference type of each resource subject is inferred using Bayesian methods;

[0328] For each resource entity whose actual volatility preference type is high volatility, determine whether the volatility preference type declared by the resource entity is consistent with the actual volatility preference type;

[0329] If there is a discrepancy, the resource subject will be marked as a false resource subject, and the rental attribute value of the false resource subject will be re-evaluated according to the true fluctuation preference type. The re-evaluated rental attribute value will be adjusted upward to obtain the punitive rental attribute value of the false resource subject.

[0330] In some embodiments, when a computer program is executed, it also performs the following steps:

[0331] Acquire historical and predicted operational trajectory data for each resource entity;

[0332] Based on historical and predicted operational trajectory data, the normalized volatility is determined; whereby the normalized volatility is the ratio of the deviation between the historical and predicted operational trajectory data of the resource entity to the predicted operational trajectory data.

[0333] Determine whether the normalized volatility is greater than a preset deviation threshold;

[0334] If the normalized volatility is determined to be greater than the preset deviation threshold, the resource entity is determined to be of the high volatility type.

[0335] If the normalized volatility is determined to be no greater than the preset deviation threshold, the resource entity is determined to be of the low volatility type.

[0336] In some embodiments, when a computer program is executed, it also performs the following steps:

[0337] Obtain the prior probability distribution of each resource subject in the actual fluctuation preference type;

[0338] Based on the call data and prior probability distribution, a likelihood function of the resource subject under different real fluctuation preference types is constructed.

[0339] By iteratively updating the prior probability of the likelihood function using Bayes' rule, the posterior probability of each resource subject having different real fluctuation preference types is obtained.

[0340] The true fluctuation preference type of each resource subject is determined based on the maximum posterior probability.

[0341] This application provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the following steps:

[0342] Obtain the leased capacity declared by each resource entity to the shared energy storage operator;

[0343] Obtain all candidate alliances in which each resource entity participates, evaluate the benefits of each candidate alliance, and under the constraint of the rental capacity of each resource entity, select the optimal alliance with the highest benefit, and determine the actual rental capacity of each resource entity under the optimal alliance.

[0344] Under the constraint of the actual leased capacity of each resource entity in the optimal alliance, with the goal of maximizing the total revenue of the shared energy storage operator, energy scheduling optimization is carried out on each resource entity in the optimal alliance to obtain the energy scheduling scheme of each resource entity.

[0345] In some embodiments, when program instructions are executed by a computer, the computer also performs the following steps:

[0346] Enumerate all non-empty subsets in the set of all resource entities participating in the shared energy storage system, and treat each non-empty subset as a candidate alliance.

[0347] For each candidate alliance, the characteristic function value of the candidate alliance and the actual rental capacity of each resource entity under the candidate alliance are determined according to the rental capacity of each resource entity; wherein, the characteristic function value is the maximum total revenue value under the preset alliance operation constraints, which maximizes the total revenue of the candidate alliance.

[0348] Compare the characteristic function values ​​of each candidate alliance, select the candidate alliance with the largest characteristic function value as the optimal alliance, and determine the actual rental capacity of each resource entity in the optimal alliance.

[0349] In some embodiments, when program instructions are executed by a computer, the computer also performs the following steps:

[0350] Obtain revenue and cost information from shared energy storage operators, and construct an objective function to maximize the total revenue of shared energy storage operators based on the revenue and cost information.

[0351] The operational constraints of the shared energy storage operator are determined. Based on the operational constraints of the shared energy storage operator and the constraints of the actual leased capacity of each resource entity, the objective function of maximizing total revenue is optimized and solved. The energy dispatch scheme of each resource entity is obtained based on the optimal solution.

[0352] In some embodiments, when program instructions are executed by a computer, the computer also performs the following steps:

[0353] Obtain the volatility preference types declared by each resource entity to the shared energy storage operator; among them, volatility preference types include high volatility type and low volatility type;

[0354] Leasing attribute values ​​are generated based on volatility preference types; these values ​​characterize the cost for resource entities to access shared energy storage systems; and the leasing attribute values ​​for high volatility types are greater than those for low volatility types.

[0355] After executing the energy dispatch plans of each resource entity, obtain the call data of each resource entity to the shared energy storage system;

[0356] Based on the data accessed, the true fluctuation preference type of each resource subject is inferred using Bayesian methods;

[0357] For each resource entity whose actual volatility preference type is high volatility, determine whether the volatility preference type declared by the resource entity is consistent with the actual volatility preference type;

[0358] If there is a discrepancy, the resource subject will be marked as a false resource subject, and the rental attribute value of the false resource subject will be re-evaluated according to the true fluctuation preference type. The re-evaluated rental attribute value will be adjusted upward to obtain the punitive rental attribute value of the false resource subject.

[0359] In some embodiments, when program instructions are executed by a computer, the computer also performs the following steps:

[0360] Acquire historical and predicted operational trajectory data for each resource entity;

[0361] Based on historical and predicted operational trajectory data, the normalized volatility is determined; whereby the normalized volatility is the ratio of the deviation between the historical and predicted operational trajectory data of the resource entity to the predicted operational trajectory data.

[0362] Determine whether the normalized volatility is greater than a preset deviation threshold;

[0363] If the normalized volatility is determined to be greater than the preset deviation threshold, the resource entity is determined to be of the high volatility type.

[0364] If the normalized volatility is determined to be no greater than the preset deviation threshold, the resource entity is determined to be of the low volatility type.

[0365] In some embodiments, when program instructions are executed by a computer, the computer also performs the following steps:

[0366] Obtain the prior probability distribution of each resource subject in the actual fluctuation preference type;

[0367] Based on the call data and prior probability distribution, a likelihood function of the resource subject under different real fluctuation preference types is constructed.

[0368] By iteratively updating the prior probability of the likelihood function using Bayes' rule, the posterior probability of each resource subject having different real fluctuation preference types is obtained.

[0369] The true fluctuation preference type of each resource subject is determined based on the maximum posterior probability.

[0370] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, electronic devices, computer storage media, and computer program products described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0371] It should be noted that the user information (including but not limited to user images, user portrait information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations.

[0372] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products or devices.

[0373] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0374] In the several embodiments provided by this invention, it should be understood that the disclosed systems, electronic devices, computer storage media, computer program products, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0375] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0376] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0377] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of the present invention through a computer device (which may be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0378] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A shared energy storage operation method for flexible resources on multiple user sides, characterized in that, include: Obtain the leased capacity declared by each resource entity to the shared energy storage operator; Obtain all candidate alliances in which each resource entity participates, evaluate the revenue of each candidate alliance, and under the constraint of the rental capacity of each resource entity, select the optimal alliance with the highest revenue, and determine the actual rental capacity of each resource entity under the optimal alliance. Under the constraint of the actual leased capacity of each resource entity in the optimal alliance, and with the goal of maximizing the total revenue of the shared energy storage operator, energy scheduling optimization is performed on each resource entity in the optimal alliance to obtain the energy scheduling scheme of each resource entity.

2. The shared energy storage operation method for flexible resources on multiple user sides according to claim 1, characterized in that, The process of acquiring all candidate alliances in which each resource entity participates, evaluating the profitability of each candidate alliance, selecting the optimal alliance with the highest profitability under the constraint of the rental capacity of each resource entity, and determining the actual rental capacity of each resource entity under the optimal alliance includes: Enumerate all non-empty subsets in the set of all resource entities participating in the shared energy storage system, and use each non-empty subset as the candidate alliance; For each candidate alliance, the characteristic function value of the candidate alliance and the actual rental capacity of each resource entity under the candidate alliance are determined according to the rental capacity of each resource entity under the candidate alliance; wherein, the characteristic function value is the maximum total revenue value under the preset alliance operation constraints, which maximizes the total revenue of the candidate alliance. The characteristic function values ​​of each candidate alliance are compared, and the candidate alliance corresponding to the largest characteristic function value is selected as the optimal alliance. The actual rental capacity of each resource entity in the optimal alliance is then determined.

3. The shared energy storage operation method for flexible resources on multiple user sides according to claim 1, characterized in that, Under the constraint of the actual leased capacity of each resource entity in the optimal alliance, and with the goal of maximizing the total operating revenue of the shared energy storage operator, energy scheduling optimization is performed on each resource entity in the optimal alliance to obtain an energy scheduling scheme for each resource entity, including: Obtain the revenue and cost information of the shared energy storage operator, and construct an objective function to maximize the total revenue of the shared energy storage operator based on the revenue and cost information. The operational constraints of the shared energy storage operator are determined. Based on the operational constraints of the shared energy storage operator and the constraints of the actual leased capacity of each resource entity, the objective function of maximizing total revenue is optimized and solved. The energy scheduling scheme of each resource entity is obtained based on the optimal solution.

4. The shared energy storage operation method for flexible resources on multiple user sides according to claim 1, characterized in that, Also includes: Obtain the volatility preference type declared by each of the resource entities to the shared energy storage operator; wherein, the volatility preference type includes high volatility type and low volatility type; A leasing attribute value is generated based on the volatility preference type; wherein the leasing attribute value is used to characterize the cost of the resource entity calling the shared energy storage system; wherein the leasing attribute value for the high volatility type is greater than the leasing attribute value for the low volatility type; After executing the energy scheduling schemes of each of the resource entities, the call data of each of the resource entities to the shared energy storage system is obtained; Based on the call data, the true fluctuation preference type of each resource subject is inferred using Bayesian method; For each resource subject whose actual volatility preference type is high volatility, determine whether the volatility preference type declared by the resource subject is consistent with the actual volatility preference type; If there is a discrepancy, the resource subject is marked as a false resource subject, and the rental attribute value of the false resource subject is re-evaluated according to the true fluctuation preference type. The re-evaluated rental attribute value is then increased to obtain the punitive rental attribute value of the false resource subject.

5. The shared energy storage operation method for flexible resources on multiple user sides according to claim 4, characterized in that, Also includes: Obtain historical and predicted operational trajectory data for each of the resource entities; Based on the historical operating trajectory data and the predicted operating trajectory data, a normalized volatility is determined; wherein, the normalized volatility is the ratio of the deviation between the historical operating trajectory data and the predicted operating trajectory data of the resource subject to the predicted operating trajectory data; Determine whether the normalized volatility is greater than a preset deviation threshold; If the normalized volatility is determined to be greater than the preset deviation threshold, then the resource entity is determined to be of a high volatility type. If the normalized volatility is determined to be no greater than the preset deviation threshold, then the resource entity is determined to be of the low volatility type.

6. The shared energy storage operation method for flexible resources on multiple user sides according to claim 4, characterized in that, The step of inferring the true fluctuation preference type of each resource subject based on the invoked data using Bayesian methods includes: Obtain the prior probability distribution of each resource subject in the true fluctuation preference type; Based on the call data and the prior probability distribution, a likelihood function of the resource subject under different real fluctuation preference types is constructed; The prior probability of the likelihood function is iteratively updated using Bayes' rule to obtain the posterior probability that each resource subject has a different real fluctuation preference type. The true fluctuation preference type of each resource subject is determined based on the maximum value of the posterior probability.

7. A shared energy storage operation system for flexible resources on multiple user sides, characterized in that, include: The application information acquisition module is used to obtain the leased capacity applied for by each resource entity to the shared energy storage operator; The alliance screening module is used to obtain all candidate alliances in which each resource entity participates, evaluate the benefits of each candidate alliance, screen out the optimal alliance with the highest benefit under the constraint of the rental capacity of each resource entity, and determine the actual rental capacity of each resource entity under the optimal alliance. The energy scheduling optimization module is used to optimize the energy scheduling of each resource entity in the optimal alliance under the constraint of the actual leased capacity of each resource entity in the optimal alliance, with the goal of maximizing the total revenue of the shared energy storage operator, so as to obtain the energy scheduling scheme of each resource entity.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the shared energy storage operation method for flexible resources for multiple types of users as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the steps of the shared energy storage operation method for multiple types of user-side flexibility resources as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the shared energy storage operation method for multi-type user-side flexibility resources as described in any one of claims 1-6.

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