Smart power grid resource allocation method and system based on matching game

Abstract

The invention relates to a power grid resource allocation technology, in particular to an intelligent power grid resource allocation method and system based on a matching game, and the method comprises the steps: carrying out the initial matching with the minimization of the power supply cost and the electric energy loss as a target under the condition that the power demand of a user is satisfied in the power supply capability range of a power supplier, and forming a matching pair; based on the power supply cost, the electric energy loss and the power supply stability, respectively establishing preference lists of the user side and the power supply side; each user searches whether an exchange blocking pair exists or not according to the preference table, if yes, exchange matching is conducted between the exchange blocking pairs, the preference table of the user is updated after exchange matching is completed, the step is repeated till no blocking pair exists, and matching is completed. According to the invention, appropriate power supply equipment can be effectively matched for users, resource configuration is optimized, power supply cost is reduced, electric energy loss is reduced, and stability of a power grid is improved.

Description

Technical Field

[0001] This invention relates to power grid resource allocation technology, and in particular to a smart grid resource allocation method and system based on matching game theory. Background Technology

[0002] To improve the efficiency and reliability of power supply, and to promote the widespread integration and efficient utilization of new energy sources, power plants need to undergo digital transformation, leading to the emergence of smart grids. Due to the diverse electricity demands, power supply costs, and usage patterns of different users, distributed energy systems integrating distributed generation and energy storage can meet these power supply needs.

[0003] The development of advanced technologies such as big data, cloud computing, and the Internet of Things provides technical support for power plants to optimize resource allocation and achieve precise matching with different users. Through these technologies, distributed generation has developed. Distributed generation facilities are often dispersed near load centers, typically in the form of small or micro-generation devices, directly meeting the electricity needs of nearby users, reducing reliance on centralized power plants, and enhancing grid flexibility. The combination of distributed generation and energy storage systems allows these systems to absorb excess electricity during peak generation periods and release it during peak demand periods, effectively smoothing out peak flows. This approach enables real-time matching of electricity supply and demand, alleviating the pressure caused by seasonal and diurnal load differences, thereby reducing reliance on centralized power plants at different times. For example, for special users with extremely high requirements for power supply stability, such as hospitals and data centers, smart grids can match them with optimal micro-power sources or energy storage devices to ensure a continuous and stable power supply to their critical loads.

[0004] Traditional power supply relies on centralized large-scale power plants to generate electricity, which is then transmitted to users over long distances and through multiple stages of the power system. This method is costly to build and suffers from significant energy losses. Smart microgrids, on the other hand, contain numerous distributed generation and energy storage systems. The generated and stored energy is consumed locally, effectively reducing dependence on centralized large-scale power plants and the losses associated with long-distance transmission and multi-stage distribution. Therefore, it is necessary to find a suitable way to match users with the optimal power plants, distributed generation, or energy storage devices, taking into account factors such as the user's geographical location, power supply costs, and electricity consumption characteristics. Summary of the Invention

[0005] To address the problems existing in the prior art, and in order to effectively match users with suitable distributed generation devices, energy storage equipment, and power plants, optimize resource allocation, reduce power supply costs, reduce power loss, and improve grid stability, this invention proposes a smart grid resource allocation method based on matching game theory, specifically including the following steps:

[0006] Under the premise of meeting the user's electricity demand within the power supply capacity of the power supplier, initial matching is carried out with the goal of minimizing power supply costs and power loss, and matching pairs are formed.

[0007] Based on power supply cost, power loss and power supply stability, preference lists are established for both the user side and the power supplier side.

[0008] Each user checks their preference table to see if there are any swap blocking pairs. If so, the swap blocking pairs are matched. After the swap matching is completed, the user's preference table is updated. This process is repeated until there are no more blocking pairs, and the matching is complete.

[0009] This invention also proposes a smart grid resource allocation system based on matching game theory, used to implement a smart grid resource allocation method based on matching game theory. The system includes:

[0010] The initial matching module is used to perform initial matching to form matching pairs, under the premise of meeting the user's electricity demand within the power supply capacity of the power supplier and with the goal of minimizing power supply costs and power loss.

[0011] The user-side preference list generation module is used to construct a user-side preference list based on power supply cost, power loss, and power supply stability.

[0012] The electricity supplier preference list generation module is used to build a preference list for the electricity supplier based on the user's electricity consumption, electricity consumption time, and geographical location.

[0013] The blocking pair determination module checks the preference table to see if there are any swapped blocking pairs. If they exist, the blocking pairs are swapped and matched. After the swapping and matching is completed, the user's preference table is updated. When no blocking pairs exist, the current resource allocation strategy is output.

[0014] The present invention also proposes a computer device, including a memory and a processor, wherein the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement a smart grid resource allocation method based on matching game theory.

[0015] The present invention also proposes a computer storage medium storing computer execution instructions, which, when executed by a processor, are used to implement a smart grid resource allocation method based on matching game theory.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] 1. Apply matching game theory to smart grids to solve the matching problem between users and power suppliers, optimize power supply costs, reduce power loss and improve power supply stability.

[0018] 2. By using a many-to-many matching model in matching games, complex matching problems are addressed, and a swap-stable matching algorithm is used to effectively handle externality problems in matching games. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the many-to-many matching game model between power grid equipment and users in this invention;

[0020] Figure 2 This is a schematic diagram illustrating the exchange matching between exchange blocking pairs in this invention;

[0021] Figure 3 This is a flowchart of a smart grid resource allocation method based on matching game theory according to the present invention.

[0022] Figure 4 This is a schematic diagram of a smart grid resource allocation system based on matching game theory according to the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] This invention proposes a smart grid resource allocation method based on matching game theory, such as... Figure 3 Specifically, it includes the following steps:

[0025] Under the premise of meeting the user's electricity demand within the power supply capacity of the power supplier, initial matching is carried out with the goal of minimizing power supply costs and power loss, and matching pairs are formed.

[0026] Based on power supply cost, power loss and power supply stability, preference lists are established for both the user side and the power supplier side.

[0027] Each user checks their preference table to see if there are any swap blocking pairs. If so, the swap blocking pairs are matched. After the swap matching is completed, the user's preference table is updated. This process is repeated until there are no more blocking pairs, and the matching is complete.

[0028] In this embodiment, users and power suppliers are modeled as two disjoint sets, where the user set consists of N user terminals, denoted as U = {u1, u2, ..., u...}. N}, u nLet S represent the nth user, where n∈{1,2,…,N}. In this embodiment, the user is the electricity demander, and each user terminal has different electricity demand, geographical location, and electricity consumption characteristics. The electricity supplier set consists of M electricity suppliers, represented as S={s1,s2,…,s…}. M Electricity suppliers can be distributed generation devices, energy storage devices, and power plants, each with different power supply capabilities, costs, and geographical locations.

[0029] The resource allocation problem proposed in this invention aims to match users and power suppliers, with the goal of minimizing power supply costs, reducing energy loss, and improving power supply stability. Based on this, the objective of this invention is to find a matching solution that meets user electricity demand within the power supplier's capacity, with the goal of minimizing power supply costs and energy loss. The optimization problem can be expressed as:

[0030] Objective function:

[0031] Constraints:

[0032]

[0033] Among them, C ij Indicates user u i From the power supplier j The cost of obtaining electricity, where N is the number of users and M is the number of suppliers; L ij For user u i With power supplier s j The energy loss between them; α is the weighting coefficient for energy loss; x ij Indicates whether a user is matched with a power supplier. i With power supplier s j Match x ij =1, otherwise 0; P j For power suppliers j Power supply capacity; D j For user u i The electricity demand.

[0034] The optimal solution to the optimization problem established in this invention is the user set matched by each power supplier, i.e., the power allocation matrix X. The element values ​​in matrix X can be obtained through x. ij Obtained. The constraint (1) of this invention represents the user demand constraint, whereby the electricity demand of each user must be met, where P... j It is the power supplier. j Power supply capacity, D i User u iThe power demand; constraint (2) means that the power supply capacity of each power supplier cannot exceed its maximum power supply capacity; constraint (3) means that the matching variable can only take the value of 0 or 1; constraint (4) means that in order to ensure the stability of power supply, each user needs to be matched with at least one power supplier.

[0035] In the objective function, to minimize power supply costs and reduce energy loss, each power supply device should select a suitable set of users for power supply. For an individual user, the selection of power equipment needs to be based on their own power demand and consumption characteristics; for an individual power equipment, it needs to supply power to different users based on user preferences. Therefore, this optimization problem can be modeled as a many-to-many matching game model, where both users and power suppliers are players in the matching game, and both sides are equal, such as... Figure 1 This is a many-to-many matching game model between users and power suppliers. Power plants can supply electricity to multiple users, such as factories and hospitals, while residential and commercial areas can obtain electricity through different power supply equipment. Similarly, other power generation equipment, such as energy storage devices and distributed generation devices, can also supply electricity to multiple users, and different users can be matched with different power supply equipment to obtain electricity. Therefore, there is no situation where a user has no power supplier, nor is there a situation where a power supply device is not used by any user, thereby improving resource utilization.

[0036] In this invention, the many-to-many matching μ is a mapping from set U∪S to set U∪S, i.e., μ: U∪S→U∪S. s j For ∈S, the following conditions should be met:

[0037] (1) and

[0038] (2)

[0039] (3) if and only if u i ∈μ(s j ), s j ∈μ(u i ).

[0040] In the above conditions, condition (1) represents user u i or power equipment j The matching result must be a subset of sets U and S; condition (2) indicates that user u i The total power consumption must not exceed the device's buffer capacity P. j Condition (3) implies that the matching is mutual, that is, if user u i With power equipment j If matched, then the power equipment s j Matched user ui ,vice versa.

[0041] After the initial matching state is established, a preference list is constructed based on the user's preferences and the power supplier's preferences. The user's preferences are based on power supply cost, power loss, and power supply stability; the lower the power supply cost, the higher the preference value; the lower the power loss, the higher the preference value; and the higher the power supply stability, the higher the preference value. As an optional implementation, the user's preference value for the power supplier can be represented by the following utility functions in ascending order:

[0042]

[0043] Among them, w1, w2, and w3 are weighting factors used to balance the importance of the parameters; C max The maximum cost of electricity supply between the user and the power supplier; L max Energy loss between the user and the power supplier; SS represents user u i From the power supplier j Obtain a comprehensive stability score for the power supply, which measures indicators including the probability of load shedding (LOLP) and the root mean square deviation of power fluctuations. The probability of load loss is expressed as LOLP = T outage / T total T outage T represents the time during which the supplier is unable to supply power due to malfunction or insufficient power. total The total uptime of the power supply network is represented by LOLP. A smaller LOLP indicates a more stable power supply; the root mean square deviation of power fluctuation is expressed as... P i P represents the power supplied at the k-th sampling point during the actual power supply period. avg The average power supply during the actual power supply period is K, where K is the number of sampling points during the actual power supply period, and the variance is... The smaller the value, the more stable the power supply. In summary, user u i From the power supplier j The overall stability score for power acquisition is expressed as follows: s1 and s2 are the weighting factors corresponding to the load failure probability and the root mean square error of power fluctuation, which can be adjusted by those skilled in the art based on experience in different application scenarios.

[0044] Similarly, the electricity supplier's preferences are constructed based on the user's electricity consumption, electricity usage time, and geographical location, where the higher the user's electricity consumption and the more stable the user's electricity usage time, the higher the preference value. As an optional implementation, the electricity supplier's preference values ​​for users can be represented by the following utility functions in ascending order:

[0045]

[0046] Among them, w4, w5, and w6 are weighting factors used to balance the importance of the parameters; D max SA represents the user's maximum power consumption; SA represents the user's maximum power consumption. i The stability of electricity consumption over time is expressed as SA = T u / T supply T u For user u i Required electricity usage time, T supply For the actual power supply time provided by the power supplier, the closer SA is to 1, the more stable the power supply time for the user; G ij Indicates user u i With power supplier s j The geographical distance between them, G max This indicates the maximum geographical distance between the user and the power supplier.

[0047] Users and power equipment can calculate preference indices using the methods described above, but manual tagging can also be used to set priorities based on the relationship between users and power equipment; the higher the priority, the higher the preference value. Alternatively, users and power equipment connected to smart meters can be automatically matched based on demand relationships. For example, under the premise of meeting power supply cost and energy loss constraints, users and equipment whose supply and demand are closer will have higher preference values.

[0048] In the preference value calculation formula provided in this embodiment, the preference value of power equipment for users is not independent; it is affected not only by its own electricity consumption but also by that of other power equipment. Therefore, many-to-many matching has externalities. In many-to-many matching with externalities, using a delayed acceptance algorithm may result in the non-existence of stable matching. Therefore, the concept of stability cannot be directly defined. This invention uses exchange matching to achieve its stability. The definition of exchange matching is explained below.

[0049] In the matching μ, the exchange matching is defined as:

[0050]

[0051] in, Indicates two matching pairs (u i ,s j ) and (u i' ,s j' Perform a swap match, and the new matching pair after the swap match is represented as (u i ,s j' ) and (u i' ,s j In this embodiment, i represents the user's serial number, and μ(i) represents the user u. iThe matching result, i.e., the power equipment paired with the user, where j represents the serial number of the power equipment, and (i, μ(i)) represents the user u. i The matching pair formed by the index i and its matching result μ(i); μ\{(i,μ(i)),(i',μ(i'))} means removing {(i,μ(i)),(i',μ(i'))} from the set μ.

[0052] A match swap is achieved by exchanging players with an existing match without altering other matches. However, this swap operation requires both players to participate. The match swap process is as follows: Figure 2 As shown.

[0053] The swapping matching process takes place between swapping blocking pairs. Therefore, the definition of a swapping blocking pair is given below. When two matching pairs (u i ,s j ) and (u i' ,s j' There is a blockage, that is, the user is blocking (u) i ,u i' If a blocking pair is to be swapped, then the following condition must be met:

[0054] In the user's preference list, those that satisfy U ij' ≥U ij And U i'j ≥U i'j' And at least one strict inequality holds, and the strict inequality holds if U is satisfied. ij' >U ij or U i'j >U i'j' ;

[0055] In the preference list of the power supplier, those that satisfy V j'i ≥V ji And V ji' ≥V j'i' And at least one strict inequality holds, and the strict inequality holds if V is satisfied. j'i >V ji or V ji' >V j'i' ;

[0056] Among them, U ij' Indicates user u i In the preference list for power suppliers j' Preference value, U ij Indicates user u i In the preference list for power suppliers j Preference value, U i'j Indicates user u i' In the preference list for power suppliersj Preference value, U i'j' Indicates user u i' In the preference list for power suppliers j' Preference value; V j'i Indicates the power supplier s j' In the preference list for user u i Preference value, V ji Indicates the power supplier s j In the preference list for user u i Preference value, V ji' Indicates the power supplier s j In the preference list for user u i' Preference value, V j'i' Indicates the power supplier s j' In the preference list for user u i' The preference value; the above condition (1) indicates that the exchange blocking pair (u i ,u i' After the exchange operation is performed, the utility of the users participating in the exchange is improved, that is, the preference value cannot be reduced and at least one of the preference values ​​will be improved after the exchange is matched; condition (2) indicates the improvement of the utility of the power supplier. When there are no exchange blocking pairs in the match, the match is bilaterally stable.

[0057] The process of exchanging and matching is repeated, checking for the existence of exchange blocking pairs. When no exchange blocking pairs are found within a certain number of consecutive iterations, the process ends. That is, after establishing the preference list in this invention, an initial iteration count is set, and then the exchange and matching process begins. Each user searches for other users based on the preference list, forming exchange blocking pairs. When a blocking pair forms a blocking pair with a matched electronic device, the matching state needs to be exchanged and updated, and the iteration count is reset. Then, the process returns to each user searching for blocking pairs. If no exchange blocking pair is formed with other users, the iteration count is incremented by 1. This search for blocking pairs is repeated until the iteration count exceeds a maximum value, at which point the algorithm ends. The resulting match is a stable match. In this invention, only the matching state needs to be updated. After the first instance of no blocking pair being found, the search for blocking pairs with other users continues. Furthermore, the preference list is not updated after its establishment, but users can actively configure it. Thus, the solution to the optimization problem is obtained: the stable match μ* between the user and the power supplier, and the power allocation matrix X.

[0058] This invention also proposes a smart grid resource allocation system based on matching game theory, used to implement a smart grid resource allocation method based on matching game theory. The system includes:

[0059] The initial matching module is used to perform initial matching to form matching pairs, under the premise of meeting the user's electricity demand within the power supply capacity of the power supplier and with the goal of minimizing power supply costs and power loss.

[0060] The user-side preference list generation module is used to construct a user-side preference list based on power supply cost, power loss, and power supply stability.

[0061] The electricity supplier preference list generation module is used to build a preference list for the electricity supplier based on the user's electricity consumption, electricity consumption time, and geographical location.

[0062] The blocking pair determination module checks the preference table to see if there are any swapped blocking pairs. If they exist, the blocking pairs are swapped and matched. After the swapping and matching is completed, the user's preference table is updated. When no blocking pairs exist, the current resource allocation strategy is output.

[0063] As an optional implementation method, such as Figure 4 The initial matching module and the blocking pair judgment module can be deployed on the server. The initial matching result obtained by the initial matching module is transmitted through the communication network. The user end and the power supplier end update their local preference list based on the matching result and upload the preference list to the server. The server determines whether there is an exchange blocking pair based on the uploaded preference list. If there is, the exchange matching is performed until there is no blocking pair. Finally, the final matching strategy is sent to the user end and the power supplier end.

[0064] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart grid resource allocation method based on matching game theory, wherein, under the premise of meeting user electricity demand within the power supply capacity of the power supplier, initial matching is performed with the objective of minimizing power supply costs and energy losses to form matching pairs, characterized in that, It also includes the following steps: Based on power supply cost, power loss and power supply stability, preference lists are established for both the user side and the power supplier side. Each user checks their preference table to see if there are any swap blocking pairs. If they exist, the swap blocking pairs are matched. After the swap matching is completed, the user's preference table is updated. This process is repeated until there are no more blocking pairs, and the matching is complete.

2. The smart grid resource allocation method based on matching game theory according to claim 1, characterized in that, When two matching pairs (u i ,s j ) and (u i' ,s j' There is a blockage, that is, the user is blocking (u) i ,u i' If a blocking pair is to be swapped, then the following condition must be met: In the user's preference list, those that satisfy U ij' ≥U ij And U i'j ≥U i'j' And at least one strict inequality holds; In the preference list of the power supplier, those that satisfy V j'i ≥V ji And V ji' ≥V j'i' And at least one strict inequality holds; Among them, U ij' Indicates user u i In the preference list for power suppliers j' Preference value, U ij Indicates user u i In the preference list for power suppliers j Preference value, U i'j Indicates user u i' In the preference list for power suppliers j Preference value, U i'j' Indicates user u i' In the preference list for power suppliers j' Preference value; V j'i Indicates the power supplier s j' In the preference list for user u i Preference value, V ji Indicates the power supplier s j In the preference list for user u i Preference value, V ji' Indicates the power supplier s j In the preference list for user u i' Preference value, V j'i' Indicates the power supplier s j' In the preference list for user u i' Preference value.

3. A smart grid resource allocation method based on matching game theory according to claim 1 or 2, characterized in that, A user preference list is constructed based on power supply cost, power loss, and power supply stability. When other parameters are fixed, the lower the power supply cost, the higher the preference value; when other parameters are fixed, the smaller the power loss, the higher the preference value; when other parameters are fixed, the higher the power supply stability, the higher the preference value.

4. The smart grid resource allocation method based on matching game theory according to claim 3, characterized in that, User u i In the preference list for power suppliers j Preference value U ij Represented as: Where w1, w2, and w3 are weighting factors; C ij For user u i With power supplier s j The power supply cost between C max The maximum cost of electricity supply between the user and the power supplier; L ij For user u i With power supplier s j The power loss between them, L max Energy loss between the user and the power supplier; SS represents the user u i From the power supplier j The overall stability score for power acquisition is expressed as: LOLP is the probability of load loss, expressed as LOLP = T outage / T total T outage For the supplier s j The time during which power is unavailable due to fault or insufficient power, T total Total operating time of the power supply network; This indicates that for user u i The variance of power supply during the power supply period is calculated by comparing the power supply at each moment with the average power supply during the power supply period; s1 and s2 are weighting factors.

5. A smart grid resource allocation method based on matching game theory according to claim 1 or 2, characterized in that, A preference list for the power supplier is constructed based on the user's electricity consumption, electricity consumption time, and geographical location. When other parameters are fixed, the higher the user's electricity consumption, the higher the preference value; when other parameters are fixed, the more stable the user's electricity consumption time, the higher the preference value; when other parameters are fixed, the closer the user is geographically to the power supplier, the higher the preference value.

6. The smart grid resource allocation method based on matching game theory according to claim 5, characterized in that, Power supplier v j In the preference list for user u i' Preference value V ji Represented as: Among them, w4, w5, and w6 are weighting factors; D i For user u i Electricity consumption, D max SA represents the user's maximum power consumption; SA represents the user's maximum power consumption. i The stability of electricity consumption over time is expressed as SA = T u / T supply T u For user u i Request power supply, T supply For actual users u i Power supply time; G ij Indicates user u i With power supplier s j The geographical distance between them, G max This indicates the maximum geographical distance between the user and the power supplier.

7. The smart grid resource allocation method based on matching game theory according to claim 1, characterized in that, The process of initial matching with the goal of minimizing power supply costs and energy losses is represented as follows: Objective function: Constraints: (1) (2) (3) (4) Among them, C ij Indicates user u i From the power supplier j The cost of obtaining electricity, where N is the number of users and M is the number of suppliers; L ij For user u i With power supplier s j The energy loss between them; α is the weighting coefficient for energy loss; x ij Indicates whether a user is matched with a power supplier. When user u i With power supplier s j Match x ij =1, otherwise 0; P j For power suppliers j Power supply capacity; D j For user u i The electricity demand.

8. A smart grid resource allocation system based on matching game theory, characterized in that, For implementing the smart grid resource allocation method based on matching game theory as described in any one of claims 1 to 7, the system comprises: The initial matching module is used to perform initial matching to form matching pairs, under the premise of meeting the user's electricity demand within the power supply capacity of the power supplier and with the goal of minimizing power supply costs and power loss. The user-side preference list generation module is used to construct a user-side preference list based on power supply cost, power loss, and power supply stability. The electricity supplier preference list generation module is used to build a preference list for the electricity supplier based on the user's electricity consumption, electricity consumption time, and geographical location. The blocking pair determination module checks whether there are any swapped blocking pairs in the preference table. If they exist, the swapped blocking pairs are matched and then the user's preference table is updated. If no blocking pairs exist, the current resource allocation strategy is output.

9. A computer device, comprising a memory and a processor, wherein the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the smart grid resource allocation method based on matching game theory as described in any one of claims 1 to 7.

10. A computer storage medium storing computer execution instructions, wherein the computer execution instructions, when executed by a processor, are used to implement the smart grid resource allocation method based on matching game theory as described in any one of claims 1 to 7.

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