Power demand response collaborative optimization method and device based on flexibility sharing, equipment and medium

Abstract

The invention discloses a power demand response collaborative optimization method and device based on flexibility sharing, equipment and a medium, and relates to the technical field of power system demand response, and the method comprises the steps: obtaining load flexibility demands and load flexibility supply cost information reported by all target users participating in power demand response collaborative operation; the load flexibility demand is a power vector difference between an actual load curve and a standard load curve after each target user independently adjusts the load, and the load flexibility demand and the load flexibility supply cost information are input into a preset power demand response collaborative optimization model; determining a corresponding power demand response collaborative scheme through a preset power demand response collaborative optimization model; and executing the power demand response coordination scheme to perform flexible settlement and flexible transaction surplus distribution according to the execution condition of the target user. The execution of a demand response cooperation scheme based on flexibility sharing is realized, and the economical efficiency and reliability of load regulation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system demand response, and in particular to a method, device, equipment and medium for collaborative optimization of power demand response based on flexibility sharing. Background Art

[0002] Electricity demand response (DR) can tap into the demand-side flexible load regulation potential and alleviate the grid's regulation burden. It is divided into price-based and incentive-based models. The price-based model has problems reflecting the system's dynamic demand and is prone to rebound. The incentive-based model has a heavy baseline calculation burden in large-scale scenarios, lacks historical data in normalized scenarios, and its formulation is prone to controversy. Load-based DR uses the ideal load curve as its guiding target, has the characteristics of large-scale promotion and normalized implementation, and can support the construction of new power systems. However, existing research focuses on independent user participation in response. In addition, some existing inventions, such as collaborative optimization methods for load elasticity control, P2P market mechanisms considering demand response, and multi-blockchain node energy sharing alliance flexibility trading methods, have shortcomings in terms of flexibility trading, participant restrictions, and computational complexity.

[0003] In summary, the current bottlenecks faced by users' independent participation in DR are high response costs, low economy, large uncertainty in response results, low reliability, insufficient exploration of user flexibility, and low adequacy. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, apparatus, device, and medium for collaborative optimization of power demand response based on flexibility sharing, which can provide a method for defining and quantifying the supply and demand of user flexibility in DR, and enable collaborative user participation in DR. The specific solution is as follows: In a first aspect, the present application discloses a collaborative optimization method for power demand response based on flexibility sharing, which is applied to power demand response organizers, comprising: Obtaining load flexibility demand and load flexibility supply cost information reported by each target user participating in the power demand response collaborative operation; the load flexibility demand is the power vector difference between the actual load curve and the standard load curve after each target user independently adjusts the load; Inputting the load flexibility demand and the load flexibility supply cost information into a preset power demand response collaborative optimization model, so as to determine a corresponding power demand response collaborative solution through the preset power demand response collaborative optimization model; executing the electricity demand response coordination plan to perform flexibility settlement and flexibility trading surplus distribution according to the performance of the target users; Among them, the preset power demand response collaborative optimization model is a collaborative optimization model constructed based on the objective function of minimizing the total cost of load supply, the load supply and demand balance constraint, the first energy conservation constraint, the single target user load supply and demand direction constraint, and the single target user load supply and demand feasible domain constraint.

[0005] Optionally, the obtaining of load flexibility demand and load flexibility supply cost information reported by each target user participating in the power demand response collaborative operation includes: Sending a standard load curve and load flexibility price limit information to each target user participating in the power demand response collaborative operation, so that each target user can determine whether each target user has a load flexibility demand based on the standard load curve, the load flexibility price limit information, the independent adjustment load cost information and the actual load curve; If the load flexibility demand exists, the load flexibility demand and load flexibility supply cost information are reported to the power demand response organizer through the target user.

[0006] Optionally, determining whether each target user has a load flexibility demand based on the standard load curve, the load flexibility price limit information, the independent load adjustment cost information, and the actual load curve includes: Constructing a load flexibility demand forecasting model that includes a cost minimization function, a power constraint for tracking a standard load pair curve, a second energy conservation constraint, a load demand constraint, and a user's individual operating feasible region constraint; wherein the cost minimization function includes minimizing a utility loss caused by power adjustment of an actual load curve according to the standard load pair curve and a cost function that a target user is expected to pay for purchasing the load demand; The standard load curve, the load flexibility price limit information, the independent adjustment load cost information and the actual load curve of each target user are input into the load flexibility demand prediction model so that the load flexibility demand prediction model predicts whether each target user has a load flexibility demand.

[0007] Optionally, the load supply and demand balance constraint is that the total load flexibility demand of each coupled target user is equal to the total load supply vector of all target users; the first energy conservation constraint is that the total energy regulation amount of each target user is zero; the load supply and demand direction constraint of a single target user is that the load supply vector of a single target user and the total load demand vector have opposite signs in any same time period; the feasible domain constraint of the load supply and demand of a single target user includes a power boundary constraint and a climbing rate constraint, the power boundary constraint is that the load supply amount of a single target user does not exceed the upper and lower limits of the physical regulation capability, and the climbing rate constraint is that the load supply regulation amount between adjacent time periods meets the maximum climbing limit.

[0008] Optionally, determining a corresponding power demand response collaborative solution by using the preset power demand response collaborative optimization model includes: The preset power demand response collaborative optimization model is used to determine a power demand response collaborative scheme including a load flexibility sharing price and a load supply standard quantity.

[0009] Optionally, determining a target load supplier and a target load demander from each target user, and notifying the target load supplier and the target load demander to execute the power demand response coordination scheme includes: Determining a target load supplier and a target load demander from each of the target users according to the power demand response coordination plan; Notify the target load supplier to charge the target load demander corresponding target coordination fees based on the load supply winning quantity and the load flexibility sharing price to complete the flexibility settlement.

[0010] Optionally, the flexible transaction surplus distribution according to the execution status of the target user includes: If the directions of the load flexibility demands reported by the target users are opposite, the difference between the absolute values ​​of the load flexibility demands is taken as the load demand complementation amount; The flexibility trading surplus is distributed according to the flexibility trading surplus corresponding to the load demand complementary amount and the contribution ratio of the target load demand party to the load demand complementary amount.

[0011] In a second aspect, the present application discloses a power demand response collaborative optimization device based on flexibility sharing, which is applied to power demand response organizers, including: An information acquisition module is configured to acquire load flexibility demand and load flexibility supply cost information reported by each target user participating in the power demand response collaborative operation; the load flexibility demand is the power vector difference between the actual load curve and the standard load curve after each target user independently adjusts the load; a response coordination module, configured to input the load flexibility demand and the load flexibility supply cost information into a preset power demand response collaborative optimization model, so as to determine a corresponding power demand response collaborative solution through the preset power demand response collaborative optimization model; a scheme execution module, configured to execute the power demand response coordination scheme to perform flexibility settlement and flexibility trading surplus distribution according to the execution status of the target user; Among them, the preset power demand response collaborative optimization model is a collaborative optimization model constructed based on the objective function of minimizing the total cost of load supply, the load supply and demand balance constraint, the first energy conservation constraint, the single target user load supply and demand direction constraint, and the single target user load supply and demand feasible domain constraint.

[0012] In a third aspect, the present application discloses an electronic device, comprising: Memory, used to store computer programs; A processor is used to execute the computer program to implement the steps of the aforementioned disclosed method for collaborative optimization of power demand response based on flexibility sharing.

[0013] In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed method for collaborative optimization of power demand response based on flexibility sharing.

[0014] It can be seen that the present application discloses a collaborative optimization method for power demand response based on flexibility sharing, which is applied to power demand response organizers, including: obtaining load flexibility demand and load flexibility supply cost information reported by each target user participating in the power demand response collaborative operation; the load flexibility demand is the power vector difference between the actual load curve and the standard load curve after each target user independently adjusts the load; the load flexibility demand and the load flexibility supply cost information are input into a preset power demand response collaborative optimization model, so as to determine the corresponding power demand response collaborative plan through the preset power demand response collaborative optimization model; the power demand response collaborative plan is executed to perform flexibility settlement and flexibility transaction surplus distribution according to the execution status of the target user; wherein, the preset power demand response collaborative optimization model is a collaborative optimization model constructed based on the objective function of minimizing the total cost of load supply, the load supply and demand balance constraint, the first energy conservation constraint, the load supply and demand direction constraint of a single target user, and the feasible domain constraint of the load supply and demand of a single target user. This demonstrates that by obtaining user-reported information on load flexibility demands and flexibility supply costs, users can make their own decisions and report their flexibility demands. By constructing an optimization model with the goal of minimizing total load supply costs, users with low regulation costs can be prioritized as suppliers, reducing overall transaction costs. Demanders can participate in flexibility sharing by purchasing flexibility that is cheaper than their own regulation costs to track the target curve. This reduces the cost of tracking the target curve for power demand response, achieving a win-win situation for all parties. Furthermore, the pre-defined power demand response collaborative optimization model incorporates load supply and demand balance constraints, energy conservation constraints, and supply and demand direction constraints, ensuring that all users' actual load curves strictly track the standard load curve. Through shared and collaborative participation in power demand response, all users fully track the target load curve, reducing or even eliminating uncertainty in user response results, ensuring the effectiveness of power demand response implementation, and enabling power demand response to provide reliable regulation services for the power grid while eliminating uncertainty in response coordination outcomes. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0016] Figure 1 This is a flow chart of a collaborative optimization method for power demand response based on flexibility sharing disclosed in this application; Figure 2 A schematic diagram of user flexibility requirements disclosed in this application; Figure 3 A user flexibility demand decision diagram disclosed in this application; Figure 4 A schematic diagram of user flexibility provision disclosed in this application; Figure 5 A schematic diagram of a flexible shared transaction architecture disclosed in this application; Figure 6 This is a user's original electricity purchase curve disclosed in this application (without DR); Figure 7 A schematic diagram of user flexibility requirements disclosed in this application; Figure 8 A schematic diagram of a flexibility supply disclosed in this application; Figure 9 A diagram of the flexibility sharing clearing price and the marginal price of user flexibility supply disclosed in this application; Figure 10 An average marginal contribution graph of user's complementary amount of flexibility demand disclosed in this application; Figure 11 A profit distribution result diagram disclosed in this application; Figure 12 This is a schematic diagram of the structure of a power demand response collaborative optimization device based on flexibility sharing disclosed in this application; Figure 13 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] Electricity demand response (DR) can tap into the demand-side flexible load regulation potential and alleviate the grid regulation burden. It is divided into price-based and incentive-based types. The price-based type has the problem of difficulty in reflecting the dynamic demand of the system and is prone to rebound; the incentive-based type has a heavy baseline calculation burden in large-scale scenarios, lacks historical data in normalized scenarios, and is prone to controversy. DR based on the load criterion takes the ideal load curve as its guiding goal, has the characteristics of large-scale promotion and normalized implementation, and can support the construction of new power systems, but existing research focuses more on users' independent participation in response. In addition, some existing inventions, such as collaborative optimization methods for load elasticity control, P2P market mechanisms considering demand response, and multi-blockchain node energy sharing alliance flexibility trading methods, have shortcomings in flexibility trading, participant restrictions, and computational complexity.

[0019] In summary, the current bottlenecks faced by users' independent participation in DR are high response costs, low economy, large uncertainty in response results, low reliability, insufficient exploration of user flexibility, and low adequacy.

[0020] To this end, the present invention provides a collaborative optimization scheme for power demand response, which can provide a supply and demand definition and quantification method for user flexibility in DR, and realize user collaborative participation in DR.

[0021] Reference Figure 1 As shown, an embodiment of the present invention discloses a power demand response collaborative optimization method based on flexibility sharing, which is applied to a power demand response organizer, including: Step S11: Obtain the load flexibility demand and load flexibility supply cost information reported by each target user participating in the power demand response collaborative operation; the load flexibility demand is the power vector difference between the actual load curve and the standard load curve after each target user independently adjusts the load.

[0022] In this embodiment, the standard load curve and load flexibility price limit information are sent to each target user participating in the power demand response collaborative operation, so that each target user can determine whether each target user has a load flexibility demand based on the standard load curve, the load flexibility price limit information, the independent adjustment load cost information and the actual load curve; if the load flexibility demand exists, the load flexibility demand and load flexibility supply cost information are reported to the power demand response organizer through the target user. It can be understood that after the target user participating in DR receives the standard load curve, i.e., the load directrix (CDL) and the flexibility sharing price limit (load flexibility price limit information) issued by the power demand response organizer, the target user independently decides on his or her own flexibility demand (load flexibility demand). See the diagram of user flexibility demand. Figure 2 As shown, according to Figure 2 It can be seen that the target user considers its own adjustment ability and independently adjusts the load cost information to adjust the electricity consumption to obtain the actual load curve, and then determines the difference power vector between the actual load curve and CDL as the load flexibility demand. The original load curve (without DR) is The standard load curve (ideal target curve) released by the power demand response organizer is , the power vector autonomously adjusted by the target user is ,user The actual load curve after self-regulation is: , then flexibility requirements The mathematical expression is: ; The flexibility requirements are determined independently by the target users based on their adjustment cost assessment, risk tolerance for price caps, and individual constraints. This design has the following advantages: (1) Enhanced decision-making autonomy: Users respond to the uncertainty of the flexibility sharing clearing price based on their own risk preferences. Respecting user decisions helps promote their continued participation in flexibility sharing and maintain their enthusiasm.

[0023] (2) Reducing the computational burden on organizers: Differences in users’ flexibility resources and individual needs lead to complex decision-making models. Allowing users to independently determine their flexibility needs not only protects their privacy but also significantly reduces the computational burden on electricity demand response organizers.

[0024] (3) Improving user flexibility: Under the guidance of the target CDL and price cap, users can accurately assess their flexibility and identify relevant influencing factors, thereby making targeted adjustments to improve individual flexibility.

[0025] In this embodiment, a load flexibility demand forecasting model is constructed, which includes a cost minimization function, a power constraint for tracking the standard load pair curve, a second energy conservation constraint, a load demand constraint, and a user's individual operation feasible domain constraint; wherein, the cost minimization function is a minimization function including the utility loss caused by power adjustment of the actual load curve according to the standard load pair curve, and the cost function expected to be paid by the target user for purchasing the load demand; the standard load curve, the load flexibility price limit information, the independent adjustment load cost information and the actual load curve of each target user are input into the load flexibility demand forecasting model, so that the load flexibility demand forecasting model predicts whether each target user has a load flexibility demand. It can be understood that, if Figure 3 The following is a schematic diagram of the load flexibility demand forecasting model for users to make their own flexibility decisions, which is convenient for subsequent analysis and explanation. In practice, users can flexibly modify the load flexibility demand forecasting model based on their own risk preferences and specific constraints. The cost function to minimize the flexibility requirement for autonomous decision-making is: ; in, Indicates target users Compared with the original load curve Perform power regulation The utility loss or cost caused by increasing or decreasing the same power is assumed to be the same. Without loss of generality, the utility loss is represented by a quadratic function of the absolute value of the power regulation amount. , is the corresponding utility loss coefficient. For users Cost function of expected payment for purchasing flexibility; For users The flexibility demand vector of Indicates The power vector at time t, Represents a vector 1-norm of ; The maximum price set by the electricity demand response organizer.

[0026] The constraints include the power constraint for tracking the ideal target curve CDL (tracking line constraint), the second energy regulation constraint (energy regulation constraint), and the load demand constraint (flexibility demand constraint). Considering that the target user can achieve the shaping of the electricity consumption curve by shifting energy in the time dimension while keeping the total energy constant, the user's self-energy regulation and purchase flexibility requirements must both meet the constraint of constant total energy. The power constraint for tracking the ideal target curve CDL is: ; The second energy regulation constraint is as follows: ; The load demand constraints are as follows: ; In addition, user energy regulation should be performed within the feasible domain of individual operation. The constraints of the feasible domain of individual user operation are as follows: ; in, Indicates target users The individual operation feasible domain includes constraints such as the purchased power is not less than 0, the upper and lower limits of the adjustment power, and the adjustment power ramp. , For users Minimum and maximum adjustable power; , For users Adjust the power for maximum downward and upward climbing; .

[0027] make It means that the objective function is to minimize the cost function and optimize the optimal solution of the objective function in combination with the above constraints. , then the user Does not generate flexible purchasing needs; if , indicating that the user The demand for purchasing flexibility is generated, which is defined in this invention as the target load demander (purchaser) for flexibility sharing, and the set of target load demanders is recorded as , the corresponding cardinality is .

[0028] Thus, the total demand for flexibility sharing for: .

[0029] After the electricity demand response organizer collects flexibility demand information, the target user needs to report the cost curve of providing flexibility (load flexibility supply cost information). The electricity demand response organizer will conduct a clearing to determine the flexibility supply of each user. The flexibility supply of the target user is defined as the power vector that the winning bidder needs to further adjust to provide flexibility based on the user's self-regulation of electricity consumption to fully track the ideal target curve CDL. See the schematic diagram Figure 4 Let the set of flexibility supply users be , the cardinality is ;user The final actual load curve after further adjustment is ,user Flexibility of tendering The mathematical expression is: ; The flexibility suppliers are divided into two categories as follows: Users who do not require flexibility, i.e. users : These users, due to their high flexibility or low regulation costs, can independently track the ideal target curve through their own energy regulation. Therefore, these users can act as flexibility suppliers, selling excess flexibility to help users with lower flexibility.

[0030] Users with flexibility needs, i.e. users These users can also participate in bidding for flexibility supply, ensuring fairness in flexibility sharing. When a user both demands flexibility and successfully bids for flexibility supply during the same time period, it demonstrates a lower adjustment cost and therefore wins the bid. Furthermore, allowing demanders to bid allows them to provide effective and cost-effective flexibility during other periods, even though they may demand flexibility during certain periods. This helps improve flexibility utilization. This is particularly applicable to users with significant time-varying flexibility, such as the aluminum smelter industry and data centers.

[0031] Step S12: Input the load flexibility demand and the load flexibility supply cost information into the preset power demand response collaborative optimization model, so as to determine the corresponding power demand response collaborative scheme through the preset power demand response collaborative optimization model; wherein, the preset power demand response collaborative optimization model is a collaborative optimization model constructed based on the objective function of minimizing the total cost of load supply, the load supply and demand balance constraint, the first energy conservation constraint, the single target user load supply and demand direction constraint, and the single target user load supply and demand feasible domain constraint. In this embodiment, the power demand response organizer performs flexibility sharing clearing based on the flexibility supply cost curve reported by the user. The objective function of the preset power demand response collaborative optimization model is to minimize the total cost of flexibility supply: ; in, For users flexibility supply vector; and is a user exist The adjustment cost coefficient of the time period is reported by the user independently; Represents the power vector of the user compared to the self-adjusted Further adjust the power The cost of regulation incurred to provide flexibility. It should be noted that since the reference power benchmarks for power regulation are different when determining flexibility demand and supply, the cost coefficients in the preset power demand response collaborative optimization model and the cost coefficients in the flexibility demand decision model are different.

[0032] like Figure 5 As shown, an architecture for flexibility sharing transactions is disclosed, wherein the constraints of the preset power demand response collaborative optimization model are as follows: the load supply and demand balance constraint is that the total load flexibility demand of each coupled target user is equal to the total load supply vector of all target users; the first energy conservation constraint is that the total energy regulation of each target user is zero; the load supply and demand direction constraint of a single target user is that the load supply vector of a single target user and the total load demand vector have opposite signs in any same time period; the feasible domain constraints of the load supply and demand of a single target user include power boundary constraints and climbing rate constraints, the power boundary constraint is that the load supply of a single target user does not exceed the upper and lower limits of the physical regulation capability, and the climbing rate constraint is that the load supply regulation between adjacent time periods meets the maximum climbing limit. It can be understood that the load supply and demand balance constraint couples the flexibility supply vectors of multiple users; the first energy conservation constraint ensures that the total energy of each flexibility supply user remains unchanged; the load supply and demand direction constraint of a single target user requires that the flexibility supply and total flexibility demand of a single user in each time period are in opposite directions, wherein represents the Hadamard product, and this constraint indicates that the user Flexible supply No more flexibility requirements for a certain period of time: ,user To satisfy The demand of the time period has contributed to ,user To satisfy The demand during the time period does not contribute; the feasible domain constraints of load supply and demand for a single target user include power purchase constraints, flexibility supply constraints and its ramp rate constraints. , For users The maximum downward and upward slope of the regulated power is the same as the original load curve without DR. , autonomously regulated power vector The load supply and demand balance constraint is: ; The first energy conservation constraint is: ; The load supply and demand direction constraints for a single target user are: ; The feasible region constraints of load supply and demand for a single target user are: ; Depend on It can be deduced that and Expression , where , For users The actual maximum downward and upward slope of purchased power.

[0033] The preset electricity demand response collaborative optimization model for flexibility sharing transactions is constructed by combining the objective function based on minimizing the total cost of load supply, the load supply and demand balance constraint, the first energy conservation constraint, the load supply and demand direction constraint for a single target user, and the load supply and demand feasible domain constraint for a single target user. The preset electricity demand response collaborative optimization model is a minimization optimization problem containing the absolute value of the optimization variable. Considering that the signs before the absolute value of the optimization variable in the objective function are all positive and the constraints are all linear constraints, the constructed model can be equivalently converted into a quadratic convex optimization problem for precise solution. In addition, due to the particularity of the constructed model, the following method can also be used to transform the preset electricity demand response collaborative optimization model. Let The symbol vector is , as shown below, ; This optimizes the variables It can be expressed as , Substituting this formula into the above-mentioned preset power demand response collaborative optimization model can transform the optimization problem containing absolute values ​​into a quadratic convex optimization problem.

[0034] In this embodiment, the power demand response collaborative optimization model is used to determine the power demand response collaborative solution including the load flexibility sharing price and the load supply winning bid amount. It can be understood that according to the marginal cost pricing method, the flexibility sharing price (load flexibility sharing price) is defined as the highest marginal cost of the winning flexibility supply. : ; in, The flexibility supply vector for clearing. Flexibility sharing price is a T×1 dimensional vector, Reflects the unit flexibility supply The value of time slots. Due to the flexibility provided to users The cost of flexibility provision varies at different times, so the flexibility sharing price may be different at different times. Flexibility suppliers (target load suppliers) Revenue and demand side (target load demand side) The expenditures are expressed as follows: ; .

[0035] Step S13: executing the electricity demand response coordination plan to perform flexibility settlement and flexibility transaction surplus distribution according to the execution status of the target user.

[0036] In this embodiment, the target load supplier and the target load demander are determined from each of the target users according to the power demand response coordination scheme; the target load supplier is notified to charge the target load demander the corresponding target coordination fee based on the load supply winning amount and the load flexibility sharing price to complete the flexibility settlement. If the directions of the load flexibility demands reported by each target user are opposite, the difference between the absolute values ​​of the load flexibility demands is taken as the load demand complementary amount; the flexibility transaction surplus is distributed according to the contribution ratio of the target load demander to the load demand complementary amount based on the flexibility transaction surplus corresponding to the load demand complementary amount. It can be understood that the proposed flexibility transaction surplus is non-negative, so no additional imbalance fee is required to maintain the implementation of flexibility sharing. The flexibility transaction surplus is as follows: ; The proof process of the above-mentioned flexibility transaction surplus is as follows: ; After adding up the individual flexibility demands, the total demand is less than the algebraic sum of their absolute values. The reduction in total demand resulting from the addition of the individual demands is defined as the complementary flexibility demand. Clearly, the flexibility trading surplus comes from complementary flexibility demands, as the directions of the individual flexibility supply vectors are aligned, and the total flexibility supply equals the total flexibility demand. Since electricity demand response organizers serve as non-profit platforms aimed at facilitating flexibility-sharing transactions, the flexibility trading surplus should be fairly distributed among participating members.

[0037] According to the principle of cost causality, the demand side triggers the complementary amount of flexibility demand, which in turn leads to transaction surplus, and the demand side should bear the corresponding surplus. Therefore, the surplus should be fairly distributed to the demand side according to the degree of its contribution to the complementary amount of flexibility demand. The Shapley value in cooperative game theory can be used to describe the average marginal contribution of each member of the system to a certain indicator of the alliance set to which it belongs. Member Pair Collection The contribution of flexibility demand complementarity can be expressed as , the contribution is expressed as follows: ; The first bracket represents a set The second bracket indicates the complementary amount of flexibility demand. Remove members The new set obtained Furthermore, considering the contribution of users in different subsets, the average marginal contribution of users to the total flexibility demand complementarity is Calculated by the following formula: ; It should be noted that the average marginal contribution here represents the average incremental effect on the complementary amount of flexibility demand brought about by a flexibility-demand user when joining different user combinations (i.e., different subsets of the demand side set). Obviously, , Indicates the number of elements in the set. According to the properties of Shapley value, all users Average marginal contribution to the complementary amount of flexibility demand The sum of the two is equal to the complementary amount of flexibility requirements, as shown in the following formula: ; It can be seen that the flexibility trading surplus can be distributed to the corresponding users in a fair way: according to their average marginal contribution to the complementary flexibility demand, as shown in the following formula: ; Based on the above flexibility sharing transaction architecture, the flexibility sharing transaction process is as follows: 1. The DR organizer publishes the ideal target curve and the maximum price for flexibility sharing; 2. Users make their own decisions and report flexibility needs; 3. User-reported flexibility supply cost curve; 4. The organizer conducts clearing based on the constructed flexibility sharing clearing model and publishes the flexibility sharing clearing price and the winning flexibility supply quantity to users; 5. Users make corresponding load adjustments based on the clearing results; 6. The DR organizer conducts flexibility sharing settlement based on the user's response results, including paying flexibility supply fees, collecting flexibility demand fees, and allocating flexibility transaction surpluses.

[0038] It can be seen that the demand-side flexibility sharing transaction method of this application enables users to collaboratively participate in DR, significantly improving the implementation effect of DR. The specific advantages are reflected in:

[0039] (1) Improve DR economic efficiency. Through flexibility sharing, flexibility suppliers can generate profits by selling excess flexibility after tracking the target curve, while flexibility demanders can track the target curve by purchasing flexibility that is cheaper than their own adjustment costs. By participating in flexibility sharing, users can reduce the cost of tracking the DR target curve, achieving a win-win situation for all parties.

[0040] (2) Improve DR reliability. Through flexibility sharing and collaborative participation in DR, all users can fully track the target load curve, reducing or even eliminating the uncertainty of user response results, ensuring the implementation effect of DR, and enabling DR to provide reliable regulation services for the power grid.

[0041] (3) Improving DR sufficiency. Based on the proposed method, high-flexibility users further sell flexibility after self-adjusting their load to track the target curve, helping low-flexibility users participate in DR. This realizes the mining and release of demand-side flexibility and can improve the utilization rate of DR on demand-side flexibility. In addition, this method does not restrict the types of users participating in sharing, and has a wider participation rate, which can introduce more users to participate in sharing transactions.

[0042] It can be seen that the present application discloses a collaborative optimization method for power demand response based on flexibility sharing, which is applied to power demand response organizers, including: obtaining load flexibility demand and load flexibility supply cost information reported by each target user participating in the power demand response collaborative operation; the load flexibility demand is the power vector difference between the actual load curve and the standard load curve after each target user independently adjusts the load; the load flexibility demand and the load flexibility supply cost information are input into a preset power demand response collaborative optimization model, so as to determine the corresponding power demand response collaborative plan through the preset power demand response collaborative optimization model; the power demand response collaborative plan is executed to perform flexibility settlement and flexibility transaction surplus distribution according to the execution status of the target user; wherein, the preset power demand response collaborative optimization model is a collaborative optimization model constructed based on the objective function of minimizing the total cost of load supply, the load supply and demand balance constraint, the first energy conservation constraint, the load supply and demand direction constraint of a single target user, and the feasible domain constraint of the load supply and demand of a single target user. This demonstrates that by acquiring user-reported load flexibility demand and supply cost information, users can make their own decisions and report their flexibility demands. By constructing an optimization model with the goal of minimizing the total cost of load supply, users with low regulation costs can be prioritized as suppliers, reducing overall transaction costs. Demanders can participate in flexibility sharing by purchasing flexibility that is cheaper than their own regulation costs to track the target curve. This reduces the cost of tracking the target curve for power demand response, achieving a win-win situation for all parties. Furthermore, the pre-defined power demand response collaborative optimization model incorporates load supply and demand balance constraints, energy conservation constraints, and supply and demand direction constraints, ensuring that all users' actual load curves strictly track the standard load curve. Through shared and collaborative participation in power demand response, all users fully track the target load curve, reducing or even eliminating uncertainty in user response results, ensuring the effectiveness of power demand response implementation, and enabling power demand response to provide reliable regulation services for the power grid while eliminating uncertainty in response coordination outcomes.

[0043] This paper uses a system analysis with four DR users to illustrate the effectiveness of the proposed flexibility sharing method. The power purchase curves obtained by the four users in the absence of DR according to their own electricity consumption habits or economic benefit maximization are as follows: Figure 6 As shown, Figure 6 The different colored lines (pink, dark blue, light blue, and orange) correspond to the electricity purchase curves for users 1 through 4. The flexibility supply cost coefficients for these four users are detailed in Tables 1 and 2. The load adjustment constraints for each user per period are: User 1 must not exceed the original power consumption; User 2 and User 3 are each limited to within ±10% of the average load; and User 4 is limited to within ±50% of the average load. The maximum price for the flexibility sharing mechanism is set at 15 yuan / kWh.

[0044] Table 1 Quadratic cost coefficients of flexible supply ( ) Table 2. Primary cost coefficients of flexible supply ( ) Based on the described scheme, the user's flexibility requirements can be calculated, e.g. Figure 7 As shown in Figure 1. The load curve after user 1 adjusts itself completely tracks the load standard line. This is because it is more cost-effective to purchase flexibility, such as Figure 7 As shown in (a), user 1 can follow the guideline at a lower adjustment cost, so the flexibility demand generated is 0 and there is no need to purchase flexibility. Figure 7 As shown in (b) and (c), the curves of users 2 and 3 after self-adjustment do not completely track the guideline, and they need to purchase flexibility: user 2's load from 1 to 7 hours is lower than the guideline, and the corresponding flexibility demand is positive, while the load from 8 to 19 hours is higher than the guideline, and the resulting flexibility demand is negative; on the other hand, user 3's self-adjusted load from 11 to 16 hours is still lower than the guideline, and the corresponding flexibility demand is positive. Figure 7 As shown in (d), user 4 has a positive flexibility demand from 7 to 16 hours, and a large negative flexibility demand from 18 to 20 hours. However, from 21 to 24 hours, due to the high flexibility and low adjustment cost, the user can fully track the alignment through self-adjustment without purchasing flexibility.

[0045] Based on the described scheme, the user's flexibility provision can be calculated, e.g. Figure 8 As shown. Figure 8 As shown in (a), user 1 won the bid for a higher flexibility supply due to the availability of more cheap flexibility, and the amplitude trend of the supply in different time periods is consistent with the amplitude trend of the total demand; in the 22-23 period, there is no need to provide flexibility because the flexibility demand is 0; the reason why user 4 did not win the bid at 21h is that the marginal price of user 4 is smaller when the flexibility supply is smaller, so user 4 is given priority to win the bid. Figure 8 As shown in (b), User 2 has a flexibility supply close to amplitude 2 at 5h and 18h. The reason is that its first-order coefficient of regulation cost at 5h and 18h, 7.7 yuan / kWh and 6.6 yuan / kWh, is lower than the first-order coefficient of 8 yuan / kWh corresponding to User 1. When the flexibility supply is small, the corresponding marginal price of the former is lower than that of the latter. Therefore, the former has priority to win the bid for a small part of the flexibility supply. Figure 8 As shown in (c), user 3 has the highest adjustment cost and the corresponding bid is also the highest, and the flexibility supply obtained by clearing is 0. Figure 8As shown in (d), user 4 is prioritized for flexibility bids of 1 and 2 at 21 hours and 24 hours, respectively, due to its lower adjustment costs. Furthermore, if the four users clear flexibility supply during the same period, their directions are consistent, indicating that they all contribute to meeting flexibility demand, thus satisfying the flexibility supply and demand directional constraints.

[0046] Based on the scheme, the flexibility sharing clearing price can be calculated, such as Figure 9 As shown in the figure, users 1 and 2 won the bid in the 5th hour, but the latter's marginal price was higher. According to the marginal clearing principle, the clearing price is user 2's marginal price. Users 1 and 4 won the bid in the 9th, 12th, 17th, 18th, and 24th hours. User 4's marginal price was higher in the first four periods, becoming the clearing price, while user 1's marginal price became the clearing price in the fifth period. Three users won the bid in the 18th and 20th hours, with user 4 having the highest marginal price, becoming the clearing price. In the remaining periods, only the user with the lowest cost won the bid, and its marginal price became the clearing price. Because there is no flexibility sharing in the 22nd and 23rd hours, the clearing price is 0.

[0047] Based on the scheme, the surplus distribution result can be calculated, where the average marginal contribution of users to the complementary amount of flexibility demand is as follows: Figure 10 As shown in the figure, the profit distribution results based on the average marginal contribution are as follows: Figure 11 As shown. The sum of the average marginal contributions is 60.53 kW. Since user 1 does not generate flexibility demand, it does not participate in the surplus distribution, and the surplus it receives is 0. Users 2 and 4 receive relatively high surpluses, while user 3 receives a lower surplus. Users 2 and 4 generate high flexibility demand from 8 to 16 hours, and in opposite directions, indicating that they contribute significantly to the complementary amount of flexibility demand. Although user 3's flexibility demand from 13 to 16 hours can also offset that of user 1, its demand amplitude is smaller, so user 3's contribution to the complementary amount of flexibility demand is relatively low.

[0048] Based on the scheme, the flexibility sharing benefit of each user can be calculated, as shown in Table 3.

[0049] Table 3 Comparison of DR user cost-effectiveness with and without flexibility sharing (unit: yuan) Among them, the definitions in Table 3 are as follows: Net cost of flexibility sharing = adjustment cost + flexibility sharing expenditure - flexibility sharing income - surplus return; adjustment cost refers to the utility loss caused by users adjusting the load curve compared to the one without DR, and this cost includes the loss caused by users providing flexibility supply; Flexibility sharing benefit = - adjustment cost without flexibility sharing - net cost of flexibility sharing; adjustment cost without flexibility sharing refers to the utility loss caused by users adjusting the load curve compared to the one without DR to fully track the guideline.

[0050] The results in Table 3 show that participating in flexibility sharing brings significant benefits to all users. Specifically, compared to the regulation costs incurred by users to track the node load benchmark without flexibility sharing, the net cost of tracking the load benchmark is significantly reduced with flexibility sharing. After participating in flexibility sharing, the tracking costs of the four users decreased by 18.7%, 52.5%, 44.5%, and 56.2%, respectively, and the total cost for all users decreased by 47.9%. User 1, as the primary supplier of flexibility, incurred a higher regulation cost (3115.45 - 1355.43 = 1760.02 yuan) than without sharing. However, by using relatively inexpensive flexibility, User 1 gained 2013.35 yuan, reducing their net tracking costs by 253.33 yuan. This translates to a 253.33 yuan benefit from participating in flexibility sharing. User 3 reduced load regulation, lowering his regulation costs from 1,603.77 yuan (without sharing) to 315.07 yuan. As a buyer, he only spent 575.6 yuan (639.81-64.21 yuan) to purchase the flexibility required to track the alignment from other users, resulting in a net cost of 890.67 yuan. Therefore, participating in flexibility sharing saved him 713.1 yuan. Users 2 and 4 acted as both buyers and sellers, primarily purchasing flexibility at a lower cost than their own regulation costs to track the alignment. Their benefits from participating in flexibility sharing were 1,435.95 yuan and 2,188 yuan, respectively.

[0051] This demonstrates that the proposed flexibility-sharing scheme enables flexible energy regulation transactions between users. Users with low regulation costs assist those with high regulation costs, reducing their own tracking costs and achieving a win-win situation for all parties. This demonstrates that the proposed mechanism effectively motivates users to participate in flexibility sharing, encouraging more users to participate, thereby supporting the large-scale and regular implementation of DR and providing reliable and economical regulation services for the power grid.

[0052] Reference Figure 12 As shown, the present invention also discloses a power demand response collaborative optimization device based on flexibility sharing, which is applied to the power demand response organizer, including: An information acquisition module 11 is configured to acquire load flexibility requirements and load flexibility supply cost information reported by each target user participating in the power demand response collaborative operation; the load flexibility requirements are the power vector difference between the actual load curve and the standard load curve after each target user independently adjusts the load; a response coordination module 12, configured to input the load flexibility demand and the load flexibility supply cost information into a preset power demand response collaborative optimization model, so as to determine a corresponding power demand response collaborative solution through the preset power demand response collaborative optimization model; A scheme execution module 13 is used to execute the power demand response coordination scheme to perform flexibility settlement and flexibility transaction surplus distribution according to the execution status of the target user; Among them, the preset power demand response collaborative optimization model is a collaborative optimization model constructed based on the objective function of minimizing the total cost of load supply, the load supply and demand balance constraint, the first energy conservation constraint, the single target user load supply and demand direction constraint, and the single target user load supply and demand feasible domain constraint.

[0053] It can be seen that the present application discloses the acquisition of load flexibility demand and load flexibility supply cost information reported by each target user participating in the power demand response collaborative operation; the load flexibility demand is the power vector difference between the actual load curve and the standard load curve after each target user independently adjusts the load; the load flexibility demand and the load flexibility supply cost information are input into the preset power demand response collaborative optimization model so as to determine the corresponding power demand response collaborative scheme through the preset power demand response collaborative optimization model; the power demand response collaborative scheme is executed to perform flexibility settlement and flexibility transaction surplus distribution according to the execution status of the target user; wherein, the preset power demand response collaborative optimization model is a collaborative optimization model constructed based on the objective function of minimizing the total cost of load supply, the load supply and demand balance constraint, the first energy conservation constraint, the load supply and demand direction constraint of a single target user, and the feasible domain constraint of the load supply and demand of a single target user. It can be seen that by obtaining the load flexibility demand and supply cost information reported by the user, the user can make independent decisions and report flexibility demands, and the optimization model is constructed with the goal of minimizing the total cost of load supply. Users with low adjustment costs can be given priority as suppliers to reduce the overall transaction cost. Demanders participate in flexibility sharing by purchasing flexibility that is cheaper than their own regulation costs to track the target curve. This reduces the cost of tracking the target curve for power demand response, achieving a win-win situation for all parties. Furthermore, the pre-set power demand response collaborative optimization model incorporates load supply and demand balance constraints, energy conservation constraints, and supply and demand direction constraints, ensuring that all users' actual load curves strictly track the standard load curve. By sharing and collaboratively participating in power demand response, all users fully track the target load curve, reducing or even eliminating the uncertainty of user response results, ensuring the effectiveness of power demand response implementation, and enabling power demand response to provide reliable regulation services for the power grid and eliminate uncertainty in response coordination results.

[0054] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 13 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram should not be considered as any limitation to the scope of application of the present application.

[0055] Figure 13 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the power demand response collaborative optimization method based on flexibility sharing disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0056] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0057] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0058] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0059] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, so as to enable the processor 21 to calculate and process the massive amount of data 223 in the memory 22. The operating system 221 can be Windows Server, Netware, Unix, Linux, etc. In addition to including computer programs capable of implementing the flexible sharing-based power demand response collaborative optimization method performed by the electronic device 20 as disclosed in any of the aforementioned embodiments, the computer program 222 can further include computer programs capable of completing other specific tasks. In addition to data received by the electronic device and transmitted from an external device, the data 223 can also include data collected by its own input and output interface 25.

[0060] Furthermore, this application discloses a computer-readable storage medium for storing a computer program. When executed by a processor, the computer program implements the aforementioned method for collaborative optimization of power demand response based on flexibility sharing. The specific steps of this method can be found in the corresponding content disclosed in the aforementioned embodiments and will not be further elaborated here.

[0061] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0062] Professionals may further appreciate that the units and algorithmic steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. The steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in a random access memory RAM (Random Access Memory), memory, read-only memory ROM (Read Only Memory), electrically programmable EPROM (Electrically Programmable Read Only Memory), electrically erasable programmable EEPROM (Electric Erasable Programmable Read Only Memory), registers, hard disk, removable disk, CD-ROM (Compact Disc-Read Only Memory), or any other form of storage medium known in the technical field.

[0063] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0064] The above is a detailed introduction to the solution provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A collaborative optimization method for power demand response based on flexibility sharing, characterized in that: Applicable to electricity demand response organizers, including: Obtaining load flexibility demand and load flexibility supply cost information reported by each target user participating in the power demand response collaborative operation; the load flexibility demand is the power vector difference between the actual load curve and the standard load curve after each target user independently adjusts the load; Inputting the load flexibility demand and the load flexibility supply cost information into a preset power demand response collaborative optimization model, so as to determine a corresponding power demand response collaborative solution through the preset power demand response collaborative optimization model; executing the electricity demand response coordination plan to perform flexibility settlement and flexibility trading surplus distribution according to the performance of the target users; Among them, the preset power demand response collaborative optimization model is a collaborative optimization model constructed based on the objective function of minimizing the total cost of load supply, the load supply and demand balance constraint, the first energy conservation constraint, the single target user load supply and demand direction constraint, and the single target user load supply and demand feasible domain constraint.

2. The power demand response collaborative optimization method based on flexibility sharing according to claim 1 is characterized in that: The obtaining of load flexibility demand and load flexibility supply cost information reported by each target user participating in the power demand response collaborative operation includes: Sending a standard load curve and load flexibility price limit information to each target user participating in the power demand response collaborative operation, so that each target user can determine whether each target user has a load flexibility demand based on the standard load curve, the load flexibility price limit information, the independent adjustment load cost information and the actual load curve; If the load flexibility demand exists, the load flexibility demand and load flexibility supply cost information are reported to the power demand response organizer through the target user.

3. The power demand response collaborative optimization method based on flexibility sharing according to claim 2 is characterized in that: The determining whether each target user has a load flexibility demand according to the standard load curve, the load flexibility price limit information, the independent load adjustment cost information, and the actual load curve includes: Constructing a load flexibility demand forecasting model that includes a cost minimization function, a power constraint for tracking a standard load pair curve, a second energy conservation constraint, a load demand constraint, and a user's individual operating feasible region constraint; wherein the cost minimization function includes minimizing a utility loss caused by power adjustment of an actual load curve according to the standard load pair curve and a cost function that a target user is expected to pay for purchasing the load demand; The standard load curve, the load flexibility price limit information, the independent adjustment load cost information and the actual load curve of each target user are input into the load flexibility demand prediction model so that the load flexibility demand prediction model predicts whether each target user has a load flexibility demand.

4. The power demand response collaborative optimization method based on flexibility sharing according to claim 1 is characterized in that: The load supply and demand balance constraint is that the total load flexibility demand of each coupled target user is equal to the total load supply vector of all target users; the first energy conservation constraint is that the total energy regulation amount of each target user is zero; the load supply and demand direction constraint of a single target user is that the load supply vector of a single target user and the total load demand vector have opposite signs in any same time period; the feasible domain constraint of the load supply and demand of a single target user includes a power boundary constraint and a climbing rate constraint. The power boundary constraint is that the load supply amount of a single target user does not exceed the upper and lower limits of the physical regulation capacity, and the climbing rate constraint is that the load supply regulation amount between adjacent time periods meets the maximum climbing limit.

5. The power demand response collaborative optimization method based on flexibility sharing according to claim 1 is characterized in that: The determining of the corresponding power demand response collaborative solution by using the preset power demand response collaborative optimization model includes: The preset power demand response collaborative optimization model is used to determine a power demand response collaborative scheme including a load flexibility sharing price and a load supply standard quantity.

6. The power demand response collaborative optimization method based on flexibility sharing according to claim 5 is characterized in that: The executing the electricity demand response coordination plan to perform flexible settlement according to the execution status of the target user includes: Determining a target load supplier and a target load demander from each of the target users according to the power demand response coordination plan; Notify the target load supplier to charge the target load demander corresponding target coordination fees based on the load supply winning quantity and the load flexibility sharing price to complete the flexibility settlement.

7. The power demand response collaborative optimization method based on flexibility sharing according to claim 1 is characterized in that: Flexible trading surplus distribution is carried out according to the execution performance of the target users, including: If the directions of the load flexibility demands reported by the target users are opposite, the difference between the absolute values ​​of the load flexibility demands is taken as the load demand complementation amount; The flexibility trading surplus is distributed according to the flexibility trading surplus corresponding to the load demand complementary amount and the contribution ratio of the target load demand party to the load demand complementary amount.

8. A power demand response collaborative optimization device based on flexibility sharing, characterized in that: Applicable to electricity demand response organizers, including: An information acquisition module is configured to acquire load flexibility demand and load flexibility supply cost information reported by each target user participating in the power demand response collaborative operation; the load flexibility demand is the power vector difference between the actual load curve and the standard load curve after each target user independently adjusts the load; a response coordination module, configured to input the load flexibility demand and the load flexibility supply cost information into a preset power demand response collaborative optimization model, so as to determine a corresponding power demand response collaborative solution through the preset power demand response collaborative optimization model; a scheme execution module, configured to execute the power demand response coordination scheme to perform flexibility settlement and flexibility trading surplus distribution according to the execution status of the target user; Among them, the preset power demand response collaborative optimization model is a collaborative optimization model constructed based on the objective function of minimizing the total cost of load supply, the load supply and demand balance constraint, the first energy conservation constraint, the single target user load supply and demand direction constraint, and the single target user load supply and demand feasible domain constraint.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor is used to execute the computer program to implement the steps of the power demand response collaborative optimization method based on flexibility sharing as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Used to store computer programs; wherein, when the computer program is executed by a processor, the steps of the power demand response collaborative optimization method based on flexibility sharing as described in any one of claims 1 to 7 are implemented.

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