Virtual power plant optimal scheduling method and system considering supply and demand-time integrating degree, medium and processor

Abstract

The invention provides a virtual power plant optimal scheduling method considering supply and demand-time integrating degree. The method comprises the following steps: taking a transaction group composed of supply and demand parties as a transaction alliance N; the transaction alliance comprises a plurality of alliance combinations; each alliance combination comprises a supplier and a demander which can reach a transaction; the method specifically comprises the steps of calculating the total number of alliance combinations in a transaction alliance N; constructing a time integrating degree model of the transaction alliance; constructing an electricity purchasing and selling curve supply and demand integrating degree model of the transaction alliance; constructing a revenue function model of the transaction alliance; building a cooperative game model of the virtual power plant and the users based on the models; the cooperative game model comprises an objective function and constraint conditions; solving the models to enable the target function to reach the maximum value, and further solving an optimal electricity purchasing and selling combination; and on the basis of obtaining the optimal electricity purchasing and selling combination, the cooperation income of the combination is distributed by adopting a kernel solution method so as to realize optimal scheduling of the virtual power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of cross-regional consumption of clean energy, and in particular to a virtual power plant optimization scheduling method, system, medium and processor that consider supply, demand and time fit. Background Art

[0002] my country's clean energy has long been unable to be transported and consumed on a large scale. For a long time, my country's electricity has been distributed by province, and the main form is self-generation and self-use. my country has deficiencies in policies, systems and technical means for cross-regional consumption of clean energy, including the lack of a wider range of carbon emissions trading, green certificate trading and other measures. my country should establish an energy trading system suitable for the market environment of the socialist system with Chinese characteristics based on national conditions.

[0003] Based on the comprehensive consideration of the economy and environment of each local province, a unified national electricity market should be established. In the early stage, large-scale clean energy can be absorbed through medium- and long-term transactions, and then gradually transitioned to a market framework where medium- and long-term and real-time spot markets coexist, so as to ensure the interests of all transaction participants.

[0004] Since electricity trading is a special commodity transaction, the most important feature is to ensure the real-time balance between load power and power generation power in order to achieve better consumption of clean energy. Currently, existing technologies are still insufficient to ensure cross-regional matching of clean energy and load supply and demand.

[0005] In view of this, there is a need for a virtual power plant optimization scheduling method, system, medium and processor that considers supply and demand-time fit. Summary of the invention

[0006] In view of the problem that the existing technology is not enough to ensure the matching of cross-regional clean energy and load supply and demand, the present invention provides a virtual power plant optimization scheduling method, system, medium and processor that considers the supply and demand-time fit, which can ensure the real-time load power and power generation balance according to the power supply situation of provinces with a high proportion of clean energy and the load situation of provinces with high electricity consumption, so as to achieve better consumption of clean energy. The specific technical solution is as follows:

[0007] A virtual power plant optimization scheduling method considering supply-demand-time fit, comprising a trading group consisting of both supply and demand parties as a trading alliance N; the trading alliance includes a plurality of alliance combinations; each alliance combination includes both supply and demand parties that can reach a transaction, so as to ensure that all trading entities in the combination can trade; specifically, the optimization scheduling steps are as follows:

[0008] Calculate the total number of alliance combinations in trading alliance N;

[0009] The time fit of building a trading alliance η(wi )Model;

[0010] Construct the supply and demand fit S(w i )Model;

[0011] Construct the profit function U(w i )Model;

[0012] Based on the above models, a cooperative game model between the virtual power plant and the user is constructed; the cooperative game model includes an objective function and constraints; the above models are solved so that the objective function reaches the maximum value, thereby obtaining the best power purchase and sales combination;

[0013] On the basis of obtaining the best power purchase and sales combination, the core solution method is used to distribute the cooperative benefits of the combination to achieve the optimal scheduling of the virtual power plant.

[0014] Furthermore, the total number of alliance combinations is calculated as follows:

[0015]

[0016] In the above formula, r is the number of electricity buyers participating in the transaction in the trading alliance; s is the number of virtual power plants participating in the transaction in the trading alliance; It represents the number of combinations corresponding to the way of selecting i combinations from the total number r; r / 2 and s / 2 are both integers greater than 0.

[0017] Furthermore, the time fit η(w i ) The model calculation formula is as follows:

[0018]

[0019] In the above formula, ρ(G i ,L i )∈[-1,1] is the correlation coefficient between the power supply and user load in the virtual power plant of the i-th alliance combination within a certain period of time, and m is the set correlation coefficient value, that is, the correlation coefficient ρ(G i ,L i ) must reach the value m before matching transactions can be carried out.

[0020] Furthermore, the correlation coefficient ρ(G i ,L i ) is calculated as follows:

[0021]

[0022] In the formula, cov(G i ,L i ) is the power G of the i-th alliance combination during a period of time iWith load L i The covariance of G is the standard deviation of the power generation of the virtual power plant within a certain period of time; σ L is the standard deviation of load power within a certain period of time; G ik Calculate the power output for each time period k in the time period for the i-th alliance combination; Calculate the average power output of the i-th alliance combination within the time period; L ik Calculate the load power for each time period k in the time period for the i-th alliance combination; The average value of load power within the calculation time period for the i-th alliance combination; h is the number of time periods in each calculation cycle.

[0023] Furthermore, the supply-demand matching degree S(w i ) The model calculation formula is as follows:

[0024]

[0025] In the above formula, n is the number of matching pairs in the i-th alliance combination, and each matching pair contains 1 user and 1 virtual power plant; λ 1 is the weight of slope fit to total fit; 2 is the weight of variance fit to total fit; s is the scaling factor; G * ik (t) represents the fitted power output curve function of the kth virtual power plant in the ith alliance combination; L * ik (t) represents the fitted electricity load curve function of k users in the alliance combination.

[0026] Furthermore, the profit function U(w i ) The calculation formula of the model is as follows:

[0027]

[0028] In the above formula, Indicates that at t i Time period user A and virtual power plant B j Considering the transmission channel, is time t i The electricity purchase price of the electricity purchasing node k; r a,b is the equivalent reduced electricity price of the transmission path (a, b); is time t i The electricity price of the virtual power plant node l; Indicates that at t i Time period user A and virtual power plant B j The transaction volume of Q is the set of paths q; B is Bj a and b are the two end nodes of a transmission path; T is a calculation period.

[0029] Furthermore, the model formula of the nucleolar solution is as follows:

[0030] min e(w,x);

[0031] e(w,x)=V(w)-z(w)

[0032]

[0033] z o -V(N)≤0;

[0034] In the above formula, e(w,x) is the dissatisfaction value, and the objective function is to minimize the dissatisfaction value; V(w) is the expected benefit of the cooperative game of the optimal power purchase and sales combination; z(w) is the actual sum of the benefits shared by the trading entities participating in the optimal power purchase and sales combination; V(N) is the total benefit of the alliance N; w is the optimal power purchase and sales combination; z o is the profit of the oth participant in the optimal power purchase and sales combination; x is the profit distribution method.

[0035] A virtual power plant optimization scheduling system considering supply and demand-time compatibility is applied to the above-mentioned virtual power plant optimization scheduling method considering supply and demand-time compatibility, comprising:

[0036] A calculation module, which is used to calculate the total number of alliance combinations in the trading alliance N;

[0037] The first building block is used to build the time fit of the trading alliance η(w i )Model;

[0038] The second building block is used to build the supply and demand fit S(w i )Model;

[0039] The third building block is used to construct the profit function U(w i )Model;

[0040] A solution module is used to construct a cooperative game model between a virtual power plant and a user based on the above models; the cooperative game model includes an objective function and constraints; the above models are solved so that the objective function reaches the maximum value, thereby obtaining the best power purchase and sales combination;

[0041] The allocation module is used to allocate the cooperative benefits of the combination based on the best power purchase and sales combination using the nucleus solution method to achieve optimal scheduling of the virtual power plant.

[0042] A computer-readable storage medium, comprising a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned virtual power plant optimization scheduling method considering the supply-demand-time fit.

[0043] A processor is used to run a program, wherein the program, when running, executes the virtual power plant optimization scheduling method considering the supply-demand-time fit mentioned above.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. Aiming at the gap between the overall output curve to be traded and the overall load curve caused by the number of virtual power plants, the volatility of output, the number of users in the virtual power plants, and the uncertainty of load, the present invention proposes a cooperative game method based on supply-demand-time fit. By calculating the values ​​of the fit of various combinations and pairings, the optimal combination between virtual power plants and users is finally determined, and then transactions are carried out. Ultimately, according to the power supply conditions in provinces with a high proportion of clean energy and the load conditions in provinces with high electricity consumption, the real-time load power and power generation power balance can be guaranteed, so as to achieve better absorption of clean energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0047] Figure 1 The figure is a flow chart of a virtual power plant optimization scheduling method considering supply-demand-time fit. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in 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.

[0049] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0050] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0051] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0052] Embodiment 1

[0053] like Figure 1 The figure shows a flow chart of a virtual power plant optimization scheduling method considering the supply-demand-time fit, wherein a trading group consisting of both supply and demand parties is used as a trading alliance N; the trading alliance includes several alliance combinations; each alliance combination includes both supply and demand parties that can reach a transaction, so as to ensure that all trading entities in the combination can trade; specifically, the optimization scheduling steps are as follows:

[0054] S1: Calculate the total number of alliance combinations in trading alliance N.

[0055] Furthermore, the total number of alliance combinations is calculated as follows:

[0056]

[0057] In the above formula, r is the number of electricity buyers participating in the transaction in the trading alliance; s is the number of virtual power plants participating in the transaction in the trading alliance; It represents the number of combinations corresponding to the way of selecting i combinations from the total number r; r / 2 and s / 2 are both integers greater than 0.

[0058] For example, if trading alliance N has 3 power suppliers and 2 demanders, the calculation method for all possible combinations of power suppliers and demanders is: the combination of selecting 1, 2, or 3 from the 3 power suppliers and the combination of selecting 1 or 2 from the 2 demanders can be calculated separately. The calculation formula for the number of combinations is:

[0059]

[0060] Where N is the total number of power suppliers or demanders (users), and k is the number of choices. Specific examples are as follows:

[0061] (1) Methods for selecting power suppliers

[0062] Choose 1 supplier from 3 suppliers:

[0063]

[0064] Choose 2 suppliers from 3 suppliers:

[0065]

[0066] Choose 3 suppliers from 3 suppliers:

[0067]

[0068] (2) Methods for selecting the demand side

[0069] Choose 1 demander from 2 demanders:

[0070]

[0071] Choose 1 supplier from 2 suppliers:

[0072]

[0073] The total number of alliance combinations I is calculated:

[0074]

[0075] The number of matching pairs refers to the number of pairing relationships formed between power suppliers and demand suppliers in one of the alliance combinations. Assuming that there are 2 power suppliers (A, B) and 1 demand supplier (X) in a combination, the possible matching pairs are:

[0076] A and X pair

[0077] B is paired with X

[0078] In this example, there are 2 matching pairs in total. If we consider different combinations of suppliers and demanders, the number of matching pairs will change depending on the number of suppliers and demanders involved.

[0079] S2: Time fit for building a trading alliance η(w i )Model.

[0080] Furthermore, the time fit η(w i ) The model calculation formula is as follows:

[0081]

[0082] In the above formula, ρ(G i ,L i )∈[-1,1] is the correlation coefficient between the power supply and user load in the virtual power plant of the i-th alliance combination within a certain period of time, and m is the set correlation coefficient value, that is, the correlation coefficient ρ(G i ,L i) must reach the value m before matching transactions can be carried out.

[0083] Furthermore, the correlation coefficient ρ(G i ,L i ) is calculated as follows:

[0084]

[0085] In the formula, cov(G i ,L i ) is the power G of the i-th alliance combination during a period of time i With load L i The covariance of G is the standard deviation of the power generation of the virtual power plant within a certain period of time; σ L is the standard deviation of load power within a certain period of time; G ik Calculate the power output for each time period k in the time period for the i-th alliance combination; Calculate the average power output of the i-th alliance combination within the time period; L ik Calculate the load power for each time period k in the time period for the i-th alliance combination; The average value of load power within the calculation time period for the i-th alliance combination; h is the number of time periods in each calculation cycle.

[0086] S3: Constructing the supply and demand fit of the electricity purchase and sales curve of the trading alliance S(w i )Model.

[0087] Furthermore, the supply-demand matching degree S(w i ) The model calculation formula is as follows:

[0088]

[0089] In the above formula, n is the number of matching pairs in the i-th alliance combination, and each matching pair contains 1 user and 1 virtual power plant; λ 1 is the weight of slope fit to total fit; 2 is the weight of variance fit to total fit; s is the scaling factor; G * ik (t) represents the fitted power output curve function of the kth virtual power plant in the ith alliance combination; L * ik (t) represents the fitted electricity load curve function of k users in the alliance combination.

[0090] S4: Construct the profit function U(w i )Model.

[0091] Furthermore, the profit function U(wi ) The calculation formula of the model is as follows:

[0092]

[0093] In the above formula, Indicates that at t i Time period user A and virtual power plant B j Considering the transmission channel, is time t i The electricity purchase price of the electricity purchasing node k; r a,b is the equivalent reduced electricity price of the transmission path (a, b); is time t i The electricity price of the virtual power plant node l; Indicates that at t i Time period user A and virtual power plant B j The transaction volume of Q is the set of paths q; B is B j a and b are the two end nodes of a transmission path; T is a calculation period.

[0094] S5: Construct a cooperative game model between the virtual power plant and the user based on the above models; the cooperative game model includes an objective function and constraints; solve the above models to maximize the objective function, and then obtain the optimal power purchase and sales combination.

[0095] The objective function of the cooperative game model is as follows:

[0096] maxF=η(w i )·S(w i )·U(w i ), i∈I;

[0097] In the above formula, F is the objective function, which represents the value of the supply-demand-time fit function, and pursues the maximum time-supply-demand fit; w represents a sub-alliance combination in the total alliance; η(w i ) represents the time fit between power supply and load of the ith alliance combination in the calculation cycle; S(w i ) represents the degree of fit between the supply and demand of the electricity purchase and sales curve of the ith alliance combination; U(w i ) represents the profit function of the ith alliance combination.

[0098] The constraints include the following:

[0099] a. Constraints on virtual power plant output and user load power:

[0100] Represents the upper and lower limit constraints of the virtual power plant power output;

[0101] Indicates the upper and lower limit constraints of user load;

[0102] b. Constraints on the transaction volume between the electricity buyer and seller:

[0103] It means that the amount of electricity traded between market trading subject A and the corresponding trading object cannot be less than its own maximum demand (or pre-sale) electricity;

[0104] Indicates market transaction subject B j The trading volume with market trading entity A cannot be greater than that with B j The maximum pre-sale (or demand) amount of electricity;

[0105] In the above formula, Indicates that at t i The power demand of user A in the virtual power plant during the period, Indicates that at t i Time period virtual power plant B j The amount of electricity that can be generated,.

[0106] c. Constraints on the quotations and quotation differences between the electricity buyers and sellers:

[0107] Indicates the upper and lower limit constraints of the virtual power plant quotation;

[0108] Indicates the upper and lower limits of the price declared by the user;

[0109] It means that the price of the virtual power plant user among the two parties involved in the transaction must be greater than the sum of the virtual power plant's quotation and the transmission line network loss discount, that is, the price difference is greater than zero.

[0110] d. Available capacity constraints of transmission channel paths:

[0111] 0≤x t,q,a,b ≤X t,q,a,b , represents the available capacity constraint of the transmission channel, where x t,q,a,b represents the amount of electricity passing through the (a, b) line segment in the q path; X t,q,a,b represents the upper limit of the capacity available in the (a, b) line segment in the q path;

[0112] X t,q,a,b =τ t,a,b X t,q,a,b , represents the line maintenance plan constraint, τ t,a,b It indicates the maintenance factor. When the value is 1, it means that the line is not under maintenance. When the value is 0, it means that the line is under maintenance.

[0113] Since the method considered in this model is mainly to optimize the transaction path problem, the DC network flow method is used here to model the problem, without considering the voltage in the network nodes. The transaction path refers to the physical transmission path for electricity from the virtual power plant (power supplier) to the user (power user). This path includes multiple line segments in the power grid, and the electricity is transmitted on these lines until it finally reaches the user. Each line segment in the transaction path will have a corresponding transmission capacity upper limit, which is the "available capacity constraint of the transmission channel path" mentioned in the model.

[0114] In summary, by calculating each feasible pairing method in multiple combinations, the alliance benefit U(w i ), power generation and consumption curve fit S(w i ) and time fit η(w i ) is optimal, that is, the function F takes the maximum value, and the corresponding optimal combination of power purchase and sale can be obtained, and combined with the transmission path constraint problem in actual power transmission, the transaction problem between the virtual power plant and the users in the virtual power plant is solved. After the virtual power plant and the power user form a combination, the profit distribution needs to be carried out. This is a two-stage optimization. The solution to the transaction problem in the first stage refers to the optimal combination method, corresponding to the objective function F; the second stage is the optimal profit distribution, and the corresponding objective function is the following formula e(w,x).

[0115] S6: On the basis of obtaining the best power purchase and sales combination, the core solution method is used to distribute the cooperative benefits of the combination to achieve the optimal scheduling of the virtual power plant.

[0116] Solving the above model can obtain the optimal pairing combination, that is, while considering the optimal social benefit, it can also ensure the best adaptation of the power generation and purchase curves. In this model, the core solution method is used to distribute the cooperative benefits. The idea of ​​the core solution is to establish a solution that minimizes the dissatisfaction among the members of the alliance. At this time, the benefit distribution method x * This is the core solution of this cooperative game. Based on the above, the model formula of the core solution is as follows:

[0117] min e(w,x);

[0118] e(w,x)=V(w)-z(w)

[0119]

[0120]

[0121] z o -V(N)≤0;

[0122] In the above formula, e(w,x) is the dissatisfaction value, and the objective function is to minimize the dissatisfaction value; V(w) is the expected benefit of the cooperative game of the optimal power purchase and sales combination; z(w) is the actual sum of the benefits shared by the trading entities participating in the optimal power purchase and sales combination; V(N) is the total benefit of the alliance N; w is the optimal power purchase and sales combination; z o is the profit of the oth participant in the optimal power purchase and sales combination; x is the profit distribution method. The nucleolus solution can be used as the best method to solve the cooperative profit.

[0123] Maximizing the supply-demand fit and optimizing the distribution of benefits are two relatively independent but interrelated processes. The mutual influence and relationship between the two can be understood from the entire optimization and decision-making process.

[0124] (1) Mutual influence

[0125] The impact of supply-demand fit on revenue distribution: The output of fit optimization is the input of revenue distribution: First, the best combination of electricity purchase and sales is determined by optimizing the supply-demand-time fit. This process will generate a certain amount of economic benefits (i.e., the value of the power generation and consumption revenue function). This benefit is the basis of the revenue distribution model and determines how much revenue needs to be distributed among the parties. The fit result affects satisfaction: If the supply-demand fit is high, the generated economic benefits will also be higher, so that there will be a larger "cake" to be distributed during the distribution, which can theoretically improve the satisfaction of all participants.

[0126] Feedback of benefit distribution on supply-demand fit: The fairness of benefit distribution may affect future supply-demand fit: if benefit distribution is seen as fair, then parties are more likely to maintain or expand cooperation in the future, thereby maintaining or improving supply-demand fit in the long run.

[0127] Management of dissatisfaction: By minimizing dissatisfaction (the core solution method of benefit distribution), it can be ensured that all participants are allocated benefits within an acceptable range, which helps maintain the stability and efficiency of the entire system.

[0128] (2) Independence and connection of calculation formulas

[0129] Although there may be no parameter transfer between direct calculation formulas, that is, the output of maximizing fit (such as the optimal supply and demand combination and the corresponding total revenue) is not directly converted into the parameters of the revenue distribution model in the mathematical model, in fact, the overall optimization process is continuous. The optimization of supply and demand fit determines the scale of total revenue available for distribution, and the revenue distribution model is further optimized on this basis to ensure that the revenue is distributed fairly and reasonably to all parties.

[0130] Operationally, the optimization results of supply-demand fit (such as total revenue) need to be passed to the revenue distribution model as a key input to ensure that the calculation of revenue distribution is based on the actual distributable economic benefits.

[0131] Therefore, although the calculation formulas seem independent, in the framework of the entire virtual power plant optimization scheduling, supply and demand fit and profit distribution are two complementary and interdependent parts that work together to maximize system efficiency and economic benefits.

[0132] Embodiment 2

[0133] A virtual power plant optimization scheduling system considering supply and demand-time compatibility is applied to the above-mentioned virtual power plant optimization scheduling method considering supply and demand-time compatibility, comprising:

[0134] A calculation module, which is used to calculate the total number of alliance combinations in the trading alliance N;

[0135] The first building block is used to build the time fit of the trading alliance η(w i )Model;

[0136] The second building block is used to build the supply and demand fit S(w i )Model;

[0137] The third building block is used to construct the profit function U(w i )Model;

[0138] A solution module is used to construct a cooperative game model between a virtual power plant and a user based on the above models; the cooperative game model includes an objective function and constraints; the above models are solved so that the objective function reaches the maximum value, thereby obtaining the best power purchase and sales combination;

[0139] The allocation module is used to allocate the cooperative benefits of the combination based on the best power purchase and sales combination using the nucleus solution method to achieve optimal scheduling of the virtual power plant.

[0140] Embodiment 3

[0141] A computer-readable storage medium, comprising a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned virtual power plant optimization scheduling method considering the supply-demand-time fit.

[0142] Embodiment 4

[0143] A processor is used to run a program, wherein the program, when running, executes the virtual power plant optimization scheduling method considering the supply-demand-time fit mentioned above.

[0144] The present application provides a virtual power plant optimization scheduling method considering the supply-demand-time fit, wherein a trading group consisting of both supply and demand parties is used as a trading alliance N; the trading alliance includes a plurality of alliance combinations; each alliance combination includes both supply and demand parties that can reach a transaction, so as to ensure that all trading entities in the combination can trade; specifically, the following optimization scheduling steps are included: calculating the total number of alliance combinations in the trading alliance N; constructing the time fit η(w i ) model; construct the supply and demand fit S(w i ) model; construct the profit function U(w i ) model; based on the above models, a cooperative game model between the virtual power plant and the user is constructed; the cooperative game model contains objective functions and constraints; the above models are solved to maximize the objective function, and then the optimal purchase and sale combination of electricity is obtained; on the basis of obtaining the optimal purchase and sale combination of electricity, the core solution method is used to distribute the cooperative benefits of the combination to achieve the optimal scheduling of the virtual power plant.

[0145] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0146] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0147] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0148] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A virtual power plant optimization scheduling method considering supply-demand-time fit, characterized in that: The trading group composed of both supply and demand parties is called trading alliance N; the trading alliance includes several alliance combinations; each alliance combination contains both supply and demand parties that can reach the transaction, so as to ensure that all trading entities in the combination can trade; specifically, the following optimization scheduling steps are included: Calculate the total number of alliance combinations in trading alliance N; The time fit of building a trading alliance η(w i )Model; Construct the supply and demand fit S(w i )Model; Construct the profit function U(w i )Model; Based on the above models, a cooperative game model between the virtual power plant and the user is constructed; the cooperative game model includes an objective function and constraints; the above models are solved so that the objective function reaches the maximum value, thereby obtaining the best power purchase and sales combination; On the basis of obtaining the best power purchase and sales combination, the core solution method is used to distribute the cooperative benefits of the combination to achieve the optimal scheduling of the virtual power plant.

2. The virtual power plant optimization scheduling method considering supply-demand-time fit according to claim 1 is characterized in that: The total number of alliance combinations is calculated as follows: In the above formula, r is the number of electricity buyers participating in the transaction in the trading alliance; s is the number of virtual power plants participating in the transaction in the trading alliance; It represents the number of combinations corresponding to the way of selecting i combinations from the total number r; r / 2 and s / 2 are both integers greater than 0.

3. The virtual power plant optimization scheduling method considering supply-demand-time fit according to claim 1 is characterized in that: The time fit η(w i ) The model calculation formula is as follows: In the above formula, ρ(G i ,L i )∈[-1,1] is the power source G in the virtual power plant of the i-th alliance combination within a certain period of time i With user load L i The correlation coefficient of G is ρ(G i ,L i ) must reach the value m before matching transactions can be carried out.

4. The virtual power plant optimization scheduling method considering supply-demand-time fit according to claim 3 is characterized in that: The correlation coefficient ρ(G i ,L i ) is calculated as follows: In the formula, cov(G i ,L i ) is the power G of the i-th alliance combination during a period of time i With load L i The covariance of G is the standard deviation of the power generated by the virtual power plant over a period of time; σ L is the standard deviation of load power within a certain period of time; G ik Calculate the power output for each time period k in the time period for the i-th alliance combination; Calculate the average power output of the i-th alliance combination within the time period; L ik Calculate the load power for each time period k in the time period for the i-th alliance combination; The average value of load power within the calculation time period for the i-th alliance combination; h is the number of time periods in each calculation cycle.

5. The virtual power plant optimization scheduling method considering supply-demand-time fit according to claim 1 is characterized in that: The supply and demand matching degree S(w i ) The model calculation formula is as follows: In the above formula, n is the number of matching pairs in the i-th alliance combination, and each matching pair contains 1 user and 1 virtual power plant; λ1 is the weight of the slope fit to the total fit; λ2 is the weight of variance fit to total fit; s is the scaling factor; G * ik (t) represents the fitted power output curve function of the kth virtual power plant in the ith alliance combination; L * ik (t) represents the fitted electricity load curve function of k users in the alliance combination.

6. The virtual power plant optimization scheduling method considering supply-demand-time fit according to claim 1 is characterized in that: The profit function U(w i ) The calculation formula of the model is as follows: In the above formula, Indicates that at t i Time period user A and virtual power plant B j The price difference; Considering the transmission channel, is time t i The electricity purchase price of the electricity purchasing node k; r a,b is the equivalent reduced electricity price of the transmission path (a, b); is time t i The electricity price of the virtual power plant node l; Indicates that at t i Time period user A and virtual power plant B j The transaction volume of Q is the set of paths q; B is B j a and b are the two end nodes of a transmission path; T is a calculation period.

7. The virtual power plant optimization scheduling method considering supply-demand-time fit according to claim 1 is characterized in that: The model formula of the nucleolar solution is as follows: min e(w,x); e(w,x)=V(w)-z(w) with o -V(N)≤0; In the above formula, e(w,x) is the dissatisfaction value, and the objective function is to minimize the dissatisfaction value; V(w) is the expected benefit of the cooperative game of the optimal power purchase and sales combination; z(w) is the actual sum of the benefits shared by the trading entities participating in the optimal power purchase and sales combination; V(N) is the total benefit of the alliance N; w is the optimal power purchase and sales combination; z o is the profit of the oth participant in the optimal power purchase and sales combination; x is the profit distribution method.

8. A virtual power plant optimization scheduling system considering supply and demand-time fit, characterized in that: The virtual power plant optimization scheduling method considering supply-demand-time fit as described in any one of claims 1 to 7 comprises: A calculation module, which is used to calculate the total number of alliance combinations in the trading alliance N; The first building block is used to build the time fit of the trading alliance η(w i )Model; The second building block is used to build the supply and demand fit S(w i )Model; The third building block is used to construct the profit function U(w i )Model; A solution module is used to construct a cooperative game model between a virtual power plant and a user based on the above models; the cooperative game model includes an objective function and constraints; the above models are solved so that the objective function reaches the maximum value, thereby obtaining the best power purchase and sales combination; The allocation module is used to allocate the cooperative benefits of the combination based on the best power purchase and sales combination using the nucleus solution method to achieve optimal scheduling of the virtual power plant.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the virtual power plant optimization scheduling method considering the supply-demand-time fit as described in any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein when the program is running, the virtual power plant optimization scheduling method considering the supply-demand-time fit as described in any one of claims 1 to 7 is executed.

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