Power demand side resource operation evaluation method and system based on improved Shapley value method

Abstract

The invention provides a power demand side resource operation evaluation method and system based on an improved Shapley value method, and the method comprises the steps: aggregating power demand side resources of the same type into a class of aggregates, and constructing an operation value function based on the power demand side resource aggregates. By introducing an uncertainty factor, a power demand side resource aggregation operation value distribution model considering uncertainty and based on an improved shapley value method is constructed. According to the method, a power demand side resource operation value index system is constructed, an operation value contribution initial matrix is established for a power demand side resource aggregate according to an index weight, and then different contribution indexes of different subjects in the power demand side resource aggregate are corrected according to a unit electricity price / income coefficient; and calculating to obtain the final operation value of each main body in the power demand side resource polymer. According to the method, the defect of high calculation complexity of the Shapley value method caused by numerous participants is overcome, and the difficulty of power demand side resource operation value evaluation is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of load demand response management of power systems, and in particular to a method, device and storage medium for evaluating power demand-side resource operation based on an improved Shapley value method. Background Art

[0002] Introducing demand response into the power industry and promoting a certain effect on the demand side based on price or incentives can improve the reliability of the system and the efficiency of power transmission, and promote the development of the power industry. In the key business links of the power system, demand response plays an increasingly important role, and the economic and social benefits it brings are becoming more and more significant.

[0003] However, as more and more demand-side resources are tapped, the lack of theories and technologies for evaluating the value of demand-side resources makes it difficult to reasonably evaluate and reflect the value of demand response. It is particularly necessary and important to clarify the benefits of multiple participants in demand response projects, build a benefit evaluation model system, enhance the initiative and spontaneity of all participants in demand response, and ensure its sustainable development in order to promote the application of demand response resources.

[0004] The joint operation of demand-side resources brings cooperative incremental benefits (gains) to the system. Formulating a fair cooperative gain allocation strategy can promote the stable formation of a complementary operating system among multiple subjects. Cooperative game theory has been widely used in fields such as energy management and gain allocation. The Shapley value method is a traditional cooperative game allocation method. Some studies have proposed a gain allocation model based on the improved Shapley value method based on the Shapley value method, combined with the influencing factors of optimal scheduling, and introduced a comprehensive correction coefficient. Such improvements can more accurately measure the contribution value of individuals. However, in reality, the Shapley value method has the following problems: 1) High computational complexity: The Shapley value method considers all possible cooperation schemes and arranges these schemes in the order in which the participants join, so as to calculate the contribution value of each participant. This method requires calculating all possible cooperation combinations, resulting in a complex and time-consuming calculation process. 2) It is necessary to know the profitability of all cooperation: Using the Shapley value calculation requires knowing the profitability of all cooperation methods, which is difficult to do in reality because it is often difficult to obtain all possible cooperation profit data. 3) Ignoring the interaction between participants: The allocation plan of the Shapley value method is affected by the revenue situation, and does not take into account the differences in input factors, risk factors, effort factors, customer factors, etc., and ignores the interaction and influence between participants. At different times, different types of demand-side resources operate together, and the benefits generated are different. How to evaluate the contribution between them cannot only consider the power consumption, but also the impact of the complementary characteristics of different members on the demand response effect. In summary, although the Shapley value method is an effective contribution evaluation method in cooperative games, its shortcomings need to be considered and weighed in practical applications, and the response should be improved according to the specific situation. Summary of the invention

[0005] The technical problem to be solved by the present invention is how to overcome the shortcoming of high calculation complexity of the Shapley value method caused by a large number of participating entities.

[0006] The present invention solves the above technical problems through the following technical means:

[0007] The power demand side resource operation evaluation method based on the improved Shapley value method includes the following steps:

[0008] Step 1: Construct a value allocation model for power demand-side resource aggregation operation based on the improved Shapley value method:

[0009]

[0010] In the formula, ψ Σ The total value of all DRA collaboration operations, is the operation value allocation result of the power demand side resource aggregate A under the improved Shapley value method, ψ A is the operating value of the power demand side resource aggregate A, m is the number of DRA categories, and the uncertainty of DRA is {τ1,τ2,...,τ m}, τ k is the kth uncertainty factor;

[0011] Step 2: Construct an operation value allocation model for the same type of power demand side resources based on asymmetric Nash negotiation, and then calculate the final operation value of each subject in the power demand side resource aggregate; Combined with the initial allocation result of the Shapley value of the power demand side resource aggregate A, construct a multi-subject asymmetric Nash negotiation model based on cooperative operation value contribution factors, as shown in the following formula:

[0012]

[0013] In the formula, is the asymmetric Nash negotiation allocation result of subject i in the power demand side resource aggregate A; U0 is the value of the subject in the power demand side resource aggregate A when operating independently; ω A,i is the cooperative operation value contribution factor of subject i in the power demand side resource aggregate A, i = 1, 2, …, k′, and k′ is the number of subjects in the power demand side resource aggregate A;

[0014] The final allocation of the operating value of subject i in the power demand side resource aggregation A for:

[0015]

[0016] Where: F({i}) is the row value generated by the individual operation of subject i in the power demand side resource aggregate A.

[0017] Furthermore, the step 1 specifically includes the following steps:

[0018] 1a) Construct a value allocation model for resource aggregation operation on the power demand side;

[0019] Assuming that there are m types of power demand side resources, the aggregated power demand side resource aggregates participating in the cooperative game are denoted as M, where M = {DRA1, DRA2, …, DRA m};

[0020] According to cooperative game theory, m types of DRA exist in 2 m -1 seed alliance, the sub-alliance generated by DRA is The operation value function based on the power demand side resource aggregate A is constructed as follows:

[0021]

[0022] Where: A is the operating value of resource aggregate A on the power demand side, Indicates different sub-alliances composed of different DRAs, is a subset of M; |S| represents a sub-alliance The size of the sub-alliance The number of DRAs in Indicates sub-alliance The operating value of P A Indicates a sub-alliance that does not include a certain power demand side resource aggregation A The probability of occurrence, i.e. the weight of the sub-alliance; represents all possible sub-alliances excluding the power demand side resource aggregation A; Represents the power demand side resource aggregation A to the sub-alliance The marginal operating value of

[0023] 1b) Construct a value allocation model for power demand side resource aggregation operation based on the improved Shapley value method;

[0024] DRA will face many uncertain factors in the actual operation process. By introducing uncertainty factors, we can get the DRA operation value improvement allocation model taking into account the uncertainty. Assuming that the uncertainty of different DRAs is {τ1,τ2,...,τ m},

[0025]

[0026] Where: P for,m,x is the power prediction value of the m-th type of subject in the x-th period; P ture,m,x is the real value of power of the mth type of subject in the xth period; z is the number of statistical periods;

[0027] Then the power demand side resource aggregation operation value allocation model of the improved Shapley value method is proposed, in which the operation value of the power demand side resource aggregate A is expressed as:

[0028]

[0029] In the formula, ψ Σ The total value of all DRA collaboration operations, This is the operating value allocation result of resource aggregate A on the power demand side under the improved Shapley value method.

[0030] Furthermore, the multi-agent asymmetric Nash negotiation model based on the cooperative operation value contribution factor in step 2 specifically includes the following steps: establishing a detailed power demand side resource operation value index, assuming that the power demand side resource operation value index is α y , where y is the number of power demand side resource operation value indicators. According to the cooperative operation value contribution indicator, combined with the operation value allocation result of the power demand side resource aggregate A obtained in step 1 under the improved Shapley value method, The initial operation value contribution matrix is ​​established for each power demand side resource aggregate, as shown in the following formula:

[0031]

[0032] Where: k′ is the number of entities in the power demand side resource aggregation A, x=1,2,...k′; is the yth operation value contribution index of subject x in the power demand side resource aggregate A, and the specific expression is as follows:

[0033]

[0034] In the above formula: P N,x is the rated capacity of entity x in the power demand side resource aggregation A;

[0035] According to the unit electricity price / revenue coefficient, the different contribution indicators of different entities in the power demand side resource aggregate A are corrected as shown in the following formula:

[0036]

[0037] in: is the modified cooperative operation value contribution matrix of the power demand side resource aggregate A; θ j is the correction factor of the jth cooperative operation value contribution index; j The unit electricity price / benefit coefficient corresponding to the quantitative result of the j-th cooperative operation value contribution indicator; r ij express The element in row i and column j;

[0038] Normalize the matrix elements and calculate the comprehensive contribution value H of the i-th, i=1, 2, …, k′th subject in the power demand side resource aggregation A A,i , as shown below:

[0039]

[0040] Calculate the cooperative operation value contribution factor ω of subject i, i = 1, 2, ..., k′ in the power demand side resource aggregation A A,i , as shown below:

[0041]

[0042] Furthermore, the power demand side resource operation value index is α y It includes the following 9 indicators:

[0043] Indicator 1: Reduced new installation costs α 1

[0044]

[0045] Where, ΔC n Represents the unit investment cost of new installed capacity; ΔR f Indicates the peak load capacity reduced after the power demand side resources participate in the grid regulation; ΔP f represents the avoidable peak power; i f It indicates that the peak load capacity factor can be avoided;

[0046] Indicator 2: Reducible fuel costs α 2

[0047] α 2 =ΔL×ΔAC

[0048] In the formula, ΔL represents the reduction in electricity consumption after the power demand side resources participate in grid regulation; ΔAC represents the marginal fuel cost per unit of electricity;

[0049] Indicator 3: Cost of avoiding unit startup and shutdown α 3

[0050] α 3 =c q ×N

[0051] In the formula, c q represents the cost of starting and stopping a generator set once; N represents the number of times the generator set is started and stopped;

[0052] Indicator 4: Benefit of delayed construction of power generation units α 4

[0053]

[0054] In the formula, μ is the unit investment cost of the generator set; i is the benchmark interest rate; t is the delay period of the generator set;

[0055] Indicator 5: Reducible grid investment cost α 5

[0056]

[0057] In the formula, C Z Represents the total investment cost of the power grid; RZ Indicates the total capacity of the power grid;

[0058] Indicator 6: Reducible system operation and maintenance costs α 6

[0059]

[0060] Where M m Represents the total operation and maintenance cost of the power grid;

[0061] Indicator 7: Improve power supply reliability α 7 ;

[0062] Indicator 8: Improve customer satisfaction α 8 ;

[0063] Indicator 9: Reduce the load peak-to-valley difference α 9 ;

[0064]

[0065] Wherein, L2 represents the peak value of the load before the participation of power demand side resources; L2′ represents the peak value of the load after the participation of power demand side resources; L1 represents the trough value of the load before the participation of power demand side resources; L1′ represents the trough value of the load after the participation of power demand side resources.

[0066] The present invention also provides a power demand side resource operation evaluation system based on an improved Shapley value method, comprising:

[0067] The first model building module: Constructing a value allocation model for power demand-side resource aggregation operation based on the improved Shapley value method:

[0068]

[0069] In the formula, ψ Σ The total value of all DRA collaboration operations, is the operation value allocation result of the power demand side resource aggregate A under the improved Shapley value method, ψ A is the operating value of the power demand side resource aggregate A, m is the number of DRA categories, and the uncertainty of DRA is {τ1,τ2,...,τ m}, τ k is the kth uncertainty factor;

[0070] The second model building module: construct an operation value allocation model for the same type of power demand side resources based on asymmetric Nash negotiation, and then calculate the final operation value of each subject in the power demand side resource aggregate; combined with the initial allocation result of the Shapley value of the power demand side resource aggregate A, construct a multi-agent asymmetric Nash negotiation model based on cooperative operation value contribution factors, as shown in the following formula:

[0071]

[0072] In the formula, is the asymmetric Nash negotiation allocation result of subject i in the power demand side resource aggregate A; U0 is the value of the subject in the power demand side resource aggregate A when operating independently; ω A,i is the cooperative operation value contribution factor of subject i in the power demand side resource aggregate A, i = 1, 2, …, k′, and k′ is the number of subjects in the power demand side resource aggregate A;

[0073] The final allocation of the operating value of subject i in the power demand side resource aggregation A for:

[0074]

[0075] Where: F({i}) is the row value generated by the individual operation of subject i in the power demand side resource aggregate A.

[0076] Furthermore, the first model building module specifically includes the following steps:

[0077] 1a) Construct a value allocation model for resource aggregation operation on the power demand side;

[0078] Assuming that there are m types of power demand side resources, the aggregated power demand side resource aggregates participating in the cooperative game are denoted as M, where M = {DRA1, DRA2, …, DRA m};

[0079] According to cooperative game theory, m types of DRA exist in 2 m -1 seed alliance, the sub-alliance generated by DRA is The operation value function based on the power demand side resource aggregate A is constructed as follows:

[0080]

[0081] Where: A is the operating value of resource aggregate A on the power demand side, Indicates different sub-alliances composed of different DRAs, is a subset of M; |S| represents a sub-alliance The size of the sub-alliance The number of DRAs in Indicates sub-alliance The operating value of P A Indicates a sub-alliance that does not include a certain power demand side resource aggregation A The probability of occurrence, i.e. the weight of the sub-alliance; represents all possible sub-alliances excluding the power demand side resource aggregation A; Represents the power demand side resource aggregation A to the sub-alliance The marginal operating value of

[0082] 1b) Construct a value allocation model for power demand side resource aggregation operation based on the improved Shapley value method;

[0083] DRA will face many uncertain factors in the actual operation process. By introducing uncertainty factors, we can get the DRA operation value improvement allocation model taking into account the uncertainty. Assuming that the uncertainty of different DRAs is {τ1,τ2,...,τ m},

[0084]

[0085] Where: P for,m,x is the power prediction value of the m-th type of subject in the x-th period; P ture,m,x is the real value of power of the mth type of subject in the xth period; z is the number of statistical periods;

[0086] Then the power demand side resource aggregation operation value allocation model of the improved Shapley value method is proposed, in which the operation value of the power demand side resource aggregate A is expressed as:

[0087]

[0088] In the formula, ψ Σ The total value of all DRA collaboration operations, This is the operating value allocation result of resource aggregate A on the power demand side under the improved Shapley value method.

[0089] Furthermore, the multi-agent asymmetric Nash negotiation model based on the cooperative operation value contribution factor in the second model building module specifically includes the following steps: establishing a detailed power demand side resource operation value index, assuming that the power demand side resource operation value index is α y , where y is the number of power demand side resource operation value indicators. According to the cooperative operation value contribution indicator, combined with the operation value allocation result of the power demand side resource aggregate A obtained in step 1 under the improved Shapley value method, The initial operation value contribution matrix is ​​established for each power demand side resource aggregate, as shown in the following formula:

[0090]

[0091] Where: k′ is the number of entities in the power demand side resource aggregation A, x=1,2,...k′; is the yth operation value contribution index of subject x in the power demand side resource aggregate A, and the specific expression is as follows:

[0092]

[0093] In the above formula: P N,x is the rated capacity of entity x in the power demand side resource aggregation A;

[0094] According to the unit electricity price / revenue coefficient, the different contribution indicators of different entities in the power demand side resource aggregate A are corrected as shown in the following formula:

[0095]

[0096] in: is the modified cooperative operation value contribution matrix of the power demand side resource aggregate A; θ j is the correction factor of the jth cooperative operation value contribution index; j The unit electricity price / benefit coefficient corresponding to the quantitative result of the j-th cooperative operation value contribution indicator; r ij express The element in row i and column j;

[0097] Normalize the matrix elements and calculate the comprehensive contribution value H of the i-th, i=1, 2, …, k′th subject in the power demand side resource aggregation A A,i , as shown below:

[0098]

[0099] Calculate the cooperative operation value contribution factor ω of subject i, i = 1, 2, ..., k′ in the power demand side resource aggregation A A,i , as shown below:

[0100]

[0101] Furthermore, the power demand side resource operation value index is α y It includes the following 9 indicators:

[0102] Indicator 1: Reduced new installation costs α 1

[0103]

[0104] Where, ΔC n Represents the unit investment cost of new installed capacity; ΔR f Indicates the peak load capacity reduced after the power demand side resources participate in the grid regulation; ΔP f represents the avoidable peak power; i f It indicates that the peak load capacity factor can be avoided;

[0105] Indicator 2: Reducible fuel costs α 2

[0106] α 2 =ΔL×ΔAC

[0107] In the formula, ΔL represents the reduction in electricity consumption after the power demand side resources participate in grid regulation; ΔAC represents the marginal fuel cost per unit of electricity;

[0108] Indicator 3: Cost of avoiding unit startup and shutdown α 3

[0109] α 3 =c q ×N

[0110] In the formula, c q represents the cost of starting and stopping a generator set once; N represents the number of times the generator set is started and stopped;

[0111] Indicator 4: Benefit of delayed construction of power generation units α 4

[0112]

[0113] In the formula, μ is the unit investment cost of the generator set; i is the benchmark interest rate; t is the delay period of the generator set;

[0114] Indicator 5: Reducible grid investment cost α 5

[0115]

[0116] In the formula, C Z Represents the total investment cost of the power grid; R Z Indicates the total capacity of the power grid;

[0117] Indicator 6: Reducible system operation and maintenance costs α 6

[0118]

[0119] Where M m Represents the total operation and maintenance cost of the power grid;

[0120] Indicator 7: Improve power supply reliability α 7 ;

[0121] Indicator 8: Improve customer satisfaction α 8 ;

[0122] Indicator 9: Reduce the load peak-to-valley difference α 9 ;

[0123]

[0124] Wherein, L2 represents the peak value of the load before the participation of power demand side resources; L2′ represents the peak value of the load after the participation of power demand side resources; L1 represents the trough value of the load before the participation of power demand side resources; L1′ represents the trough value of the load after the participation of power demand side resources.

[0125] The present invention also provides a processing device, comprising at least one processor and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor can execute the above method by calling the program instructions.

[0126] The present invention also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0127] The advantages of the present invention are:

[0128] The present invention overcomes the shortcoming of high calculation complexity of the Shapley value method due to the large number of participating entities, comprehensively considers that various entities will face many uncertain factors in the actual operation process, introduces risk factors, and takes the uncertainty of resources on the power demand side into account in the operation value allocation model, thereby obtaining improved operation value allocation quotas for each entity taking risk into account, and significantly reducing the difficulty of evaluating the operation value of resources on the power demand side. BRIEF DESCRIPTION OF THE DRAWINGS

[0129] Figure 1 This is a flowchart of an embodiment of the present invention;

[0130] Figure 2 A framework diagram of the demand-side resource operation evaluation strategy of the present invention. DETAILED DESCRIPTION

[0131] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0132] At present, when the Shapley value method is used to distribute gains, the calculation unit is still in the form of an alliance, which fails to go deep into the individual members, resulting in the failure to reflect the operation value of individual demand-side resources well, violating the principle of fairness and justice, and seriously dampening the enthusiasm of resources to participate in demand-side response. In order to clarify the benefits of multiple participants in the demand response project, build a benefit evaluation model system, enhance the initiative and spontaneity of each participant in the demand response, and ensure its sustainable development, the embodiment of the present invention adopts the following technical solutions: A method for evaluating the operation value of power demand-side resources based on the improved Shapley value method, such as Figure 1 As shown, the method comprises the following steps:

[0133] Step 1:

[0134] The improved Shapley value method is used to construct a resource aggregation operation value allocation model for the power demand side based on demand response resource aggregation and response uncertainty. This method reduces the modeling dimension and significantly improves the accuracy of the resource aggregation operation value allocation model for the power demand side.

[0135] Step 2:

[0136] Based on the first step, the operation value distribution results of each power demand side resource aggregate are obtained, the value of different demand response varieties is quantified, and an asymmetric Nash negotiation model of multiple agents within the aggregate is constructed taking into account the cooperative operation value contribution factors.

[0137] The following describes the above two steps in detail:

[0138] The step 1 specifically comprises the following steps:

[0139] Step 1a) Constructing a value allocation model for power demand side resource aggregation operation

[0140] In the joint optimization scheduling of the cooperative alliance, it is considered that the same type of stakeholders often have similar behavioral characteristics in the same period. For example, the photovoltaic stakeholders have the largest output during the noon period. If their on-grid electricity price is low, in order to smooth out the fluctuation of on-grid output and maximize the system operation benefits, all photovoltaic stakeholders in the alliance will store a large amount of energy and connect to the grid in a small amount. Therefore, firstly, the same type of stakeholders in the cooperative alliance are aggregated and bundled as one stakeholder to reduce the size of the alliance, and all sub-alliances composed of aggregated stakeholders are optimized and scheduled;

[0141] The same type of interest subjects, regardless of their size, can be aggregated into a type of interest subject, denoted as aggregate Ai (i = 1, 2, ..., m), where m is the number of types of aggregates. According to cooperative game theory, these m types of aggregated interest subjects coexist (2 m -1) Seed alliance combination, the sub-alliance generated by the aggregated interest entity is recorded as The operation value function based on resource aggregation is constructed as follows:

[0142]

[0143] in:

[0144] For sub-alliance Incremental benefits from collaboration;

[0145] For sub-alliance the total benefits generated by the collaboration;

[0146] F({A}) is the sub-alliance The revenue generated when each aggregate A runs alone.

[0147] The step (2) specifically comprises the following steps:

[0148] (2) Construct a resource aggregation operation value allocation model on the power demand side based on the Shapley value method.

[0149] According to the scheduling solution, the Shapley value method is used for initial allocation to obtain the initial allocation result of the cooperative operation value of the aggregated interest entities of the same type;

[0150] Operation value allocation model based on Shapley value method Operation value allocation based on Shapley value method takes the marginal contribution value of a certain interest subject to the alliance as the measurement standard for operation value allocation. According to the resource aggregation operation value function (1), the marginal operation value contribution of each aggregated interest subject is quantified, and the operation value allocation model based on Shapley value method is constructed as follows:

[0151]

[0152] Where:

[0153] ψ A is the operating value of resource aggregate A on the power demand side;

[0154] m represents the number of stakeholders participating in the cooperative game;

[0155] M represents the set of stakeholders participating in the cooperative game, M = {A1, A2, ..., A m};

[0156] Indicates different sub-alliances composed of different stakeholders. is a subset of M;

[0157] |S| represents a sub-alliance The size of the sub-alliance The number of interested parties in the

[0158] Indicates sub-alliance of income;

[0159] P A Indicates a sub-alliance that does not include stakeholder A The probability of occurrence, i.e. the weight of the sub-alliance;

[0160] It represents the sum of all possible sub-coalitions that do not include stakeholder A;

[0161] Indicates that stakeholder A has a stake in the sub-alliance marginal contribution;

[0162] (3) Improved Shapley value allocation for multi-agent operation based on risk factors

[0163] The classic Shapley value method regards each subject as having exactly the same allocation result, which is not reasonable. Each subject will face many uncertain factors in the actual operation process. The present invention introduces risk factors, takes the uncertainty of power demand-side resources into account in the operation value allocation model, and obtains the improved operation value allocation quota of each subject taking into account the risk.

[0164] Assume that subject k∈K, then the risk of different subjects is {τ1,τ2,...,τ k},

[0165]

[0166] Where: Pfor,k,x is the power prediction value of the k-th subject in the x-th period; P ture,k,x is the true value of power of the k-th subject in the x-th period; z is the number of statistical periods.

[0167] Influenced by the risk level and correction factor, the multi-agent operation value allocation of the improved Shapley value can be expressed as:

[0168]

[0169] In the formula, ψ Σ The total operating value quota for all entities’ cooperation, is the operating value distribution result of entity A under the improved Shapley value method. The step (3) specifically includes the following steps:

[0170] (3) Constructing a value allocation model for the same type of power demand-side resources based on asymmetric Nash negotiation

[0171] Within the aggregated stakeholder, the operating value of each sub-subject in the large alliance M is quantified, and the cooperative operation value contribution index is constructed; for the same type of stakeholder, several cooperative operation value contribution indexes are constructed to comprehensively quantify the individual contribution. The sub-stakeholders within the aggregated stakeholder use their respective contribution factors as negotiation criteria to determine the detailed allocation results, and ultimately achieve fair sharing of the alliance cooperative operation value by all stakeholders. The specific allocation steps are as follows.

[0172] 3a) Sort out the operational value of power demand resources from the power generation side and the power grid side, and establish a detailed operational value of power demand resources. The specific indicators are as follows:

[0173] 1) Indicator 1: Reduced new installation cost α 1

[0174] The cost of newly installed capacity that can be reduced refers to the fact that after the power demand side resources participate in the grid regulation, the peak load of the grid is reduced by taking off-peak power consumption behavior during the peak power consumption period, replacing the corresponding installed capacity and reducing the cost of new units. The capacity provided by the power demand side resources can be regarded as the reduced newly installed capacity. It can be expressed by the following formula:

[0175]

[0176] Where, ΔC n Represents the unit investment cost of new installed capacity; ΔR f Indicates the peak load capacity reduced after the power demand side resources participate in the grid regulation; ΔP f represents the avoidable peak power; i f Indicates that peak load capacity factor can be avoided.

[0177] 2) Index 2: Reducible fuel costs α 2

[0178] The reducible fuel cost refers to the fuel cost saved by reducing electricity consumption after the power demand side resources participate in the grid regulation, which is expressed by the following formula:

[0179] α 2 =ΔL×ΔAC (5)

[0180] Where ΔL represents the reduction in electricity consumption after the power demand side resources participate in grid regulation; ΔAC represents the marginal fuel cost per unit of electricity.

[0181] 3) Indicator 3: Cost of avoiding unit startup and shutdown α 3

[0182] When power demand-side resources are not involved in grid regulation, in order to meet the requirements of load changes, generators often track the load. When the load changes dramatically, the generators need to be started and stopped multiple times, which will lead to extra energy consumption and waste of time, and easily cause damage to the equipment. When demand-side resources participate in grid regulation, it can suppress the sudden change of load to a certain extent, thereby reducing the cost of starting and stopping the unit due to the sudden change of load. The cost of starting and stopping the unit can be avoided 3 It is expressed by the following formula:

[0183] α 3 =c q ×N (6)

[0184] In the formula, c q Represents the cost of a single start and stop of a generator set; N represents the number of starts and stops of the generator set.

[0185] 4) Indicator 4: Delayed construction benefit of power generation units α 4

[0186] After the power demand side resources participate in the grid regulation, the power system can avoid or delay the investment in new generator sets, which can bring certain benefits to power generation companies. 4 It can be expressed by the following formula:

[0187]

[0188] In the formula, μ represents the unit investment cost of the generator set; i represents the benchmark interest rate; and t represents the construction delay period of the generator set.

[0189] 5) Indicator 5: Reducible grid investment cost α 5

[0190] After the demand-side resources participate in grid regulation, the grid capacity investment cost reduced due to load transfer can be expressed as follows:

[0191]

[0192] In the formula, C Z Represents the total investment cost of the power grid; R Z Indicates the total capacity of the power grid.

[0193] 6) Indicator 6: Reducible system operation and maintenance costs α 6

[0194] After the demand-side resources participate in grid regulation, the system operation and maintenance costs reduced due to load transfer can be expressed as follows:

[0195]

[0196] Where M m Represents the total operation and maintenance cost of the power grid.

[0197] 7) Indicator 7: Improve power supply reliability α 7

[0198] The participation of power demand-side resources in grid regulation is conducive to shortening the power outage time, which will correspondingly improve power supply reliability. However, since this indicator cannot be calculated using a mathematical model at present, it is usually obtained using the expert method in actual work.

[0199] 8) Indicator 8: Improve customer satisfaction α 8

[0200] The improvement of power supply reliability will indirectly lead to the improvement of customer satisfaction, but there is currently no scientific and reasonable formula to express the relationship between the two, so the expert method is also used in actual work.

[0201] 9) Index 9: Reduce the load peak-to-valley difference α 9

[0202] After the power demand side resources participate in the grid regulation, users are encouraged to use electricity at off-peak times during the peak period, thereby reducing the peak-to-valley difference in load. The reduction of the peak-to-valley difference in load is conducive to a more economical and safe operation of the grid. It can be expressed by the following formula:

[0203]

[0204] Wherein, L2 represents the peak value of the load before the participation of power demand side resources; L2′ represents the peak value of the load after the participation of power demand side resources; L1 represents the trough value of the load before the participation of power demand side resources; L1′ represents the trough value of the load after the participation of power demand side resources.

[0205] 3b) According to the cooperative operation value contribution index, an initial operation value contribution matrix is ​​established for each aggregated stakeholder, as shown in the following formula:

[0206]

[0207] Where: is the yth operating value contribution index of subject x within the aggregated interest subject A; k′ is the number of subjects within a certain type of aggregate A.

[0208] 3) According to the unit electricity price / revenue coefficient, the impact degree of each contribution indicator is corrected, as shown in the following formula.

[0209]

[0210] in: is the modified cooperative operation value contribution matrix of aggregate A; θ j is the correction factor of the jth cooperative operation value contribution index; j The unit electricity price / benefit coefficient corresponding to the quantitative result of the j-th cooperative operation value contribution indicator; r ij express The element in row i and column j.

[0211] 4) Normalize the matrix elements and calculate the comprehensive contribution value H of the i-th (i=1, 2, ..., k′) subject A,i , as shown below:

[0212]

[0213] 5) Calculate the cooperative operation value contribution factor ω of each subject in each aggregate A,i , as shown below:

[0214]

[0215] 6) Combined with the initial allocation results of Shapley values ​​of various aggregates, an asymmetric Nash negotiation model of multi-agent complementary power generation system based on cooperative operation value contribution factors is constructed, as shown in the following formula:

[0216]

[0217] In the formula, is the asymmetric Nash negotiation allocation result of interest subject i; U0 is the profit when running independently, that is, the negotiation breakdown point.

[0218] The final distribution of cooperation benefits among stakeholders for:

[0219]

[0220] Where: F({i}) is the profit generated by the independent operation of interest subject i.

[0221] Take 20 individual complementary power generation systems of air conditioners, energy storage, electric vehicles and photovoltaics in a certain area as an example, and the number of individuals is 5. The 5 air conditioner individuals are counted as AC1, AC2, AC3, AC4 and AC5, and their regulation capacities are assumed to be 0.5MW, 1MW, 1.5MW, 2MW and 2.5MW respectively. The power demand side resource aggregate composed of the above 5 air conditioner individuals is counted as Ω AC ; The five energy storage units are counted as BES1, BES2, BES3, BES4 and BES5, and their capacities are assumed to be 0.5MW, 1MW, 1.5MW, 2MW and 2.5MW respectively. The power demand side resource aggregate composed of the above five energy storage units is counted as Ω BES ; The five electric vehicle individuals are counted as EV1, EV2, EV3, EV4 and EV5, and their regulation capacities are assumed to be 0.1MW, 0.1MW, 0.2MW, 0.2MW and 0.2MW respectively. The power demand side resource aggregate composed of the above five electric vehicle individuals is counted as Ω EV ; The five photovoltaic individuals are counted as PV1, PV2, PV3, PV4 and PV5, and their power generation capacities are assumed to be 1MW, 3MW, 3MW, 5MW and 5MW respectively. The power demand side resource aggregate composed of the above five photovoltaic individuals is counted as Ω PV .

[0222] According to the local air conditioning, energy storage, electric vehicles and photovoltaic output characteristic curves on typical days in 2023, a joint optimization dispatch model of 20 main complementary power generation systems in the above four categories is established. The benefits of each power demand side resource aggregate under the traditional Shapley value method without considering uncertainty and the improved Shapley value method considering uncertainty are obtained, as shown in the following table:

[0223] Among them, the risks of the above four categories of different entities involved in the improved Shapley value method considering uncertainty are {τ1,τ2,...,τ4}. According to the formula and historical data, we can get:

[0224]

[0225] Where: P for,m,x is the power forecast value of the m-th type of subject in the x-th period (obtained based on historical data); P ture,m,x is the actual power value of the mth type of subject in the xth period (obtained based on historical data); z is the number of statistical periods. Based on the historical data between the predicted and actual values ​​of air conditioning, energy storage, electric vehicles and photovoltaic output in a certain place, the statistics are obtained.

[0226] τ 空调 =0.28, τ 储能 =0.05, τ电动汽车 = 0.17 and τ 光伏 =0.12

[0227] Table 1 Comparison of the benefits of power demand side resource aggregation under different operation modes Unit: Yuan

[0228]

[0229] Obviously, the overall revenue under cooperative operation of power demand side resource aggregators is 12.6% higher than that under non-cooperative operation, and the operating revenue of various types of power demand side resource aggregators is also significantly improved.

[0230] Taking the distribution of electric vehicle benefits as an example, the five electric vehicles mentioned in the case have different charging curve characteristics, inconsistent charging start and end times, and charging durations, which leads to different operating values ​​for each entity. The energy costs of each electric vehicle during the charging period of the day and after the cooperative operation of the power demand side resource aggregator have decreased significantly, as shown in Table 2.

[0231] Table 2 Comparison of the benefits of various entities within the power demand side resource aggregation under different operation modes

[0232] (Take electric vehicles as an example)

[0233] Unit: Yuan

[0234]

[0235] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0236] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0237] In another embodiment provided in the present application, a computer program product comprising instructions is also provided, which, when executed on a computer, enables the computer to execute any of the power demand side resource operation evaluation methods based on the improved Shapley value method in the above embodiments.

[0238] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above methods.

[0239] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0240] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power demand side resource operation evaluation method based on the improved Shapley value method, characterized in that: The following steps are included: Step 1: Construct a value allocation model for power demand-side resource aggregation operation based on the improved Shapley value method: In the formula, ψ Σ The total value of all DRA collaboration operations, is the operation value allocation result of the power demand side resource aggregate A under the improved Shapley value method, ψ A is the operating value of the power demand side resource aggregate A, m is the number of DRA categories, and the uncertainty of DRA is {τ1,τ2,...,τ m }, τ k is the kth uncertainty factor; Step 2: Construct an operation value allocation model for the same type of power demand side resources based on asymmetric Nash negotiation, and then calculate the final operation value of each subject in the power demand side resource aggregate; Combined with the initial allocation results of the Shapley value of the resource aggregate A on the power demand side, a multi-agent asymmetric Nash negotiation model based on the cooperative operation value contribution factor is constructed, as shown in the following formula: In the formula, is the asymmetric Nash negotiation allocation result of subject i in the power demand side resource aggregate A; U0 is the value of the subject in the power demand side resource aggregate A when operating independently; ω A,i is the cooperative operation value contribution factor of subject i in the power demand side resource aggregate A, i = 1, 2, …, k′, and k′ is the number of subjects in the power demand side resource aggregate A; The final allocation of the operating value of subject i in the power demand side resource aggregation A for: Where: F({i}) is the row value generated by the individual operation of subject i in the power demand side resource aggregate A.

2. The power demand side resource operation evaluation method based on the improved Shapley value method according to claim 1 is characterized in that: The step 1 specifically comprises the following steps: 1a) Construct a value allocation model for resource aggregation operation on the power demand side; Assuming that there are m types of power demand side resources, the aggregated power demand side resource aggregates participating in the cooperative game are denoted as M, where M = {DRA1, DRA2, …, DRA m }; According to cooperative game theory, m types of DRA exist in 2 m -1 seed alliance, the sub-alliance generated by DRA is The operation value function based on the power demand side resource aggregate A is constructed as follows: Where: A is the operating value of resource aggregate A on the power demand side, Indicates different sub-alliances composed of different DRAs, is a subset of M; |S| represents a sub-alliance The size of the sub-alliance The number of DRAs in Indicates sub-alliance The operating value of P A Indicates a sub-alliance that does not include a certain power demand side resource aggregation A The probability of occurrence, i.e. the weight of the sub-alliance; represents all possible sub-alliances excluding the power demand side resource aggregation A; Represents the power demand side resource aggregation A to the sub-alliance The marginal operating value of 1b) Construct a value allocation model for power demand side resource aggregation operation based on the improved Shapley value method; DRA will face many uncertain factors in the actual operation process. By introducing uncertainty factors, we can get the DRA operation value improvement allocation model taking into account the uncertainty. Assuming that the uncertainty of different DRAs is {τ1,τ2,...,τ m }, Where: P for,m,x is the power prediction value of the m-th type of subject in the x-th period; P ture,m,x is the real value of power of the mth type of subject in the xth period; z is the number of statistical periods; Then the power demand side resource aggregation operation value allocation model of the improved Shapley value method is proposed, in which the operation value of the power demand side resource aggregate A is expressed as: In the formula, ψ Σ The total value of all DRA collaboration operations, This is the operating value allocation result of resource aggregate A on the power demand side under the improved Shapley value method.

3. The method for evaluating the operation of power demand side resources based on the improved Shapley value method according to claim 1 is characterized in that: The multi-agent asymmetric Nash negotiation model based on the cooperative operation value contribution factor in step 2 specifically includes the following steps: establishing a detailed power demand side resource operation value index, assuming that the power demand side resource operation value index is α y , where y is the number of power demand side resource operation value indicators. According to the cooperative operation value contribution indicator, combined with the operation value allocation result of the power demand side resource aggregate A obtained in step 1 under the improved Shapley value method, The initial operation value contribution matrix is ​​established for each power demand side resource aggregate, as shown in the following formula: Where: k′ is the number of entities in the power demand side resource aggregation A, x=1,2,...k′; is the yth operation value contribution index of subject x in the power demand side resource aggregate A, and the specific expression is as follows: In the above formula: P N,x is the rated capacity of entity x in the power demand side resource aggregation A; According to the unit electricity price / revenue coefficient, the different contribution indicators of different entities in the power demand side resource aggregate A are corrected as shown in the following formula: in: is the modified cooperative operation value contribution matrix of the power demand side resource aggregate A; θ j is the correction factor of the jth cooperative operation value contribution index; j The unit electricity price / benefit coefficient corresponding to the quantitative result of the j-th cooperative operation value contribution indicator; r ij express The element in row i and column j; Normalize the matrix elements and calculate the comprehensive contribution value H of the i-th, i=1, 2, …, k′th subject in the power demand side resource aggregation A A,i , as shown below: Calculate the cooperative operation value contribution factor ω of subject i, i = 1, 2, ..., k′ in the power demand side resource aggregation A A,i , as shown below:

4. The method for evaluating the operation of power demand side resources based on the improved Shapley value method according to claim 3 is characterized in that: The power demand side resource operation value index is α y It includes the following 9 indicators: Indicator 1: Reduced new installation costs α 1 Where, ΔC n Represents the unit investment cost of new installed capacity; ΔR f Indicates the peak load capacity reduced after the power demand side resources participate in the grid regulation; ΔP f represents the avoidable peak power; i f It indicates that the peak load capacity factor can be avoided; Indicator 2: Reducible fuel costs α 2 a 2 =ΔL×ΔAC In the formula, ΔL represents the reduction in electricity consumption after the power demand side resources participate in grid regulation; ΔAC represents the marginal fuel cost per unit of electricity; Indicator 3: Cost of avoiding unit startup and shutdown α 3 In the formula, c q represents the cost of starting and stopping a generator set once; N represents the number of times the generator set is started and stopped; Indicator 4: Benefit of delayed construction of power generation units α 4 In the formula, μ is the unit investment cost of the generator set; i is the benchmark interest rate; t is the delay period of the generator set; Indicator 5: Reducible grid investment cost α 5 In the formula, C Z Represents the total investment cost of the power grid; R Z Indicates the total capacity of the power grid; Indicator 6: Reducible system operation and maintenance costs α 6 Where M m Represents the total operation and maintenance cost of the power grid; Indicator 7: Improve power supply reliability α 7 ; Indicator 8: Improve customer satisfaction α 8 ; Indicator 9: Reduce the load peak-to-valley difference α 9 ; Wherein, L2 represents the peak value of the load before the participation of power demand side resources; L2′ represents the peak value of the load after the participation of power demand side resources; L1 represents the trough value of the load before the participation of power demand side resources; L1′ represents the trough value of the load after the participation of power demand side resources.

5. The power demand side resource operation evaluation system based on the improved Shapley value method is characterized by comprising: The first model building module: Constructing a value allocation model for power demand-side resource aggregation operation based on the improved Shapley value method: In the formula, ψ Σ The total value of all DRA collaboration operations, is the operation value allocation result of the power demand side resource aggregate A under the improved Shapley value method, ψ A is the operating value of the power demand side resource aggregate A, m is the number of DRA categories, and the uncertainty of DRA is {τ1,τ2,...,τ m }, τ k is the kth uncertainty factor; The second model building module: construct an operation value allocation model for the same type of power demand side resources based on asymmetric Nash negotiation, and then calculate the final operation value of each subject in the power demand side resource aggregate; combined with the initial allocation result of the Shapley value of the power demand side resource aggregate A, construct a multi-agent asymmetric Nash negotiation model based on cooperative operation value contribution factors, as shown in the following formula: In the formula, is the asymmetric Nash negotiation allocation result of subject i in the power demand side resource aggregate A; U0 is the value of the subject in the power demand side resource aggregate A when operating independently; ω A,i is the cooperative operation value contribution factor of subject i in the power demand side resource aggregate A, i = 1, 2, …, k′, and k′ is the number of subjects in the power demand side resource aggregate A; The final allocation of the operating value of subject i in the power demand side resource aggregation A for: Where: F({i}) is the row value generated by the individual operation of subject i in the power demand side resource aggregate A.

6. The power demand side resource operation evaluation system based on the improved Shapley value method according to claim 5 is characterized by: The first model building module specifically includes the following steps: 1a) Construct a value allocation model for resource aggregation operation on the power demand side; Assuming that there are m types of power demand side resources, the aggregated power demand side resource aggregates participating in the cooperative game are denoted as M, where M = {DRA1, DRA2, …, DRA m }; According to cooperative game theory, m types of DRA exist in 2 m -1 seed alliance, the sub-alliance generated by DRA is The operation value function based on the power demand side resource aggregate A is constructed as follows: Where: A is the operating value of resource aggregate A on the power demand side, Indicates different sub-alliances composed of different DRAs, is a subset of M; |S| represents a sub-alliance The size of the sub-alliance The number of DRAs in Indicates sub-alliance The operating value of P A Indicates a sub-alliance that does not include a certain power demand side resource aggregation A The probability of occurrence, i.e. the weight of the sub-alliance; represents all possible sub-alliances excluding the power demand side resource aggregation A; Represents the power demand side resource aggregation A to the sub-alliance The marginal operating value of 1b) Construct a value allocation model for power demand side resource aggregation operation based on the improved Shapley value method; DRA will face many uncertain factors in the actual operation process. By introducing uncertainty factors, we can get the DRA operation value improvement allocation model taking into account the uncertainty. Assuming that the uncertainty of different DRAs is {τ1,τ2,...,τ m }, Where: P for,m,x is the power prediction value of the m-th type of subject in the x-th period; P ture,m,x is the real value of power of the mth type of subject in the xth period; z is the number of statistical periods; Then the power demand side resource aggregation operation value allocation model of the improved Shapley value method is proposed, in which the operation value of the power demand side resource aggregate A is expressed as: In the formula, ψ Σ The total value of all DRA collaboration operations, This is the operating value allocation result of resource aggregate A on the power demand side under the improved Shapley value method.

7. The power demand side resource operation evaluation system based on the improved Shapley value method according to claim 5 is characterized by: The multi-agent asymmetric Nash negotiation model based on the cooperative operation value contribution factor in the second model building module specifically includes the following steps: establishing a detailed power demand side resource operation value index, assuming that the power demand side resource operation value index is α y , where y is the number of power demand side resource operation value indicators. According to the cooperative operation value contribution indicator, combined with the operation value allocation result of the power demand side resource aggregate A obtained in step 1 under the improved Shapley value method, The initial operation value contribution matrix is ​​established for each power demand side resource aggregate, as shown in the following formula: Where: k′ is the number of entities in the power demand side resource aggregation A, x=1,2,...k′; is the yth operation value contribution index of subject x in the power demand side resource aggregate A, and the specific expression is as follows: In the above formula: P N,x is the rated capacity of entity x in the power demand side resource aggregation A; According to the unit electricity price / revenue coefficient, the different contribution indicators of different entities in the power demand side resource aggregate A are corrected as shown in the following formula: in: is the modified cooperative operation value contribution matrix of the power demand side resource aggregate A; θ j is the correction factor of the jth cooperative operation value contribution index; j The unit electricity price / benefit coefficient corresponding to the quantitative result of the j-th cooperative operation value contribution indicator; r ij express The element in row i and column j; Normalize the matrix elements and calculate the comprehensive contribution value H of the i-th, i=1, 2, …, k′th subject in the power demand side resource aggregation A A,i , as shown below: Calculate the cooperative operation value contribution factor ω of subject i, i = 1, 2, ..., k′ in the power demand side resource aggregation A A,i , as shown below:

8. The power demand side resource operation evaluation system based on the improved Shapley value method according to claim 7 is characterized in that: The power demand side resource operation value index is α y It includes the following 9 indicators: Indicator 1: Reduced new installation costs α 1 Where, ΔC n Represents the unit investment cost of new installed capacity; ΔR f Indicates the peak load capacity reduced after the power demand side resources participate in the grid regulation; ΔP f represents the avoidable peak power; i f It indicates that the peak load capacity factor can be avoided; Indicator 2: Reducible fuel costs α 2 a 2 =ΔL×ΔAC In the formula, ΔL represents the reduction in electricity consumption after the power demand side resources participate in grid regulation; ΔAC represents the marginal fuel cost per unit of electricity; Indicator 3: Cost of avoiding unit startup and shutdown α 3 a 3 =c q ×N In the formula, c q represents the cost of starting and stopping a generator set once; N represents the number of times the generator set is started and stopped; Indicator 4: Benefit of delayed construction of power generation units α 4 In the formula, μ is the unit investment cost of the generator set; i is the benchmark interest rate; t is the delay period of the generator set; Indicator 5: Reducible grid investment cost α 5 In the formula, C Z Represents the total investment cost of the power grid; R Z Indicates the total capacity of the power grid; Indicator 6: Reducible system operation and maintenance costs α 6 Where M m Represents the total operation and maintenance cost of the power grid; Indicator 7: Improve power supply reliability α 7 ; Indicator 8: Improve customer satisfaction α 8 ; Indicator 9: Reduce the load peak-to-valley difference α 9 ; Wherein, L2 represents the peak value of the load before the participation of power demand side resources; L2′ represents the peak value of the load after the participation of power demand side resources; L1 represents the trough value of the load before the participation of power demand side resources; L1′ represents the trough value of the load after the participation of power demand side resources.

9. A processing device, characterized in that: It comprises at least one processor and at least one memory in communication with the processor, wherein the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute any method according to claims 1 to 4.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 4.

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