Virtual power plant deviation balance cost distribution method and system based on cooperative game
By employing a cooperative game theory approach and the Aumann-Shapley value method, the unfairness in the cost allocation of deviation balancing in virtual power plants was resolved, achieving accurate cost allocation and fair incentives, and improving the operational flexibility and market transaction efficiency of virtual power plants.
Patent Information
- Application Number
- CN202511870891.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-12
- Publication Date
- 2026-03-20
AI Technical Summary
Existing virtual power plant deviation balancing cost allocation methods fail to fully consider the impact of power grid topology and distribution network losses, resulting in unfair cost allocation. They also fail to accurately quantify the actual transmission and loss costs of resources at different node locations when providing output or causing deviations, affecting the fairness of market participants and their investment decisions.
A cooperative game theory approach is adopted, which uses the Aumann-Shapley value method combined with a high-precision loss model and piecewise cost function to accurately quantify network loss costs, and calculates the deviation balance cost allocation for each virtual power plant unit through cooperative game theory.
It achieves a fair allocation of deviation balancing costs in virtual power plants, improves the accuracy and reliability of allocation, ensures fairness for market participants and the effectiveness of incentive mechanisms, and promotes optimal resource allocation.
Smart Images
Figure CN121710243A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of virtual power plant technology, and in particular to a method and system for allocating deviation balance costs in a virtual power plant based on cooperative game theory. Background Technology
[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.
[0003] With the rapid development of distributed energy resources (DER) and demand response, new user-side entities, represented by new energy power generation, electric vehicles, distributed energy storage, and controllable loads, are gradually integrating into active distribution networks. Compared with traditional inelastic users, these entities exhibit a bidirectional power interaction mode with the grid, and the forms are more diverse.
[0004] Virtual power plants (VPPs) can aggregate various distributed resources to participate in the electricity market as a whole. Thanks to the complementary nature of their internal resources, VPPs can effectively mitigate the impact of fluctuations in renewable energy output on system operation, thereby enhancing the system's operational flexibility. This allows aggregated VPPs to serve as a more competitive entity in providing energy bidding and ancillary services, thus increasingly becoming an important vehicle for improving the operational flexibility of the power system and supporting the effective conduct of market transactions.
[0005] After participating in the electricity market, VPPs (Vacuum Power Providers) obtain revenue or bear costs according to the principle of "whoever generates, is responsible; whoever benefits, bears." In the electricity spot market, their total revenue is affected by both the day-ahead market and the real-time market. However, due to the inherent deviation between the intraday forecast and actual output of renewable energy, and the ambiguity of the actual output boundary caused by insufficient aggregation accuracy of VPPs (which makes it risky that the awarded power cannot be accurately allocated to each internal device), there is often a significant deviation between the actual output of VPPs and the day-ahead plan, leading to power imbalance. To maintain the safe and stable operation of the system, VPPs must pay additional deviation balancing costs in the real-time market to balance supply and demand. Deviation balancing costs mainly include two parts: one is the cost incurred in buying and selling electricity in the market to balance the deviation; the other is the assessment penalty borne due to insufficient actual output. Due to the inherent volatility and uncertainty of distributed renewable energy, coupled with the inherent uncertainty in the VPP dispatch instruction decomposition process, the deviation balancing costs borne by VPPs are not only difficult to avoid, but also exhibit highly unstable characteristics.
[0006] This cost of offsetting deviations needs to be reasonably allocated among the units of the VPP (Virtual Power Plant) to guide multiple VPP units to actively support the safety and stability of the system. However, in the context of virtual power plants aggregating massive distributed resources to participate in the electricity market, many existing cost-sharing schemes usually assume that all entities are connected to the same bus of the grid. This simplification ignores the actual topology of the power network and therefore cannot reflect the differences in network losses caused by different geographical locations of distributed resources. This leads to distorted cost accounting and an inability to accurately quantify the real transmission and loss costs incurred by resources at different node locations when providing output or causing deviations, ultimately resulting in unreliable cost allocation. In addition, not considering losses means that the value of location cannot be reflected, and it is impossible to fairly distinguish the actual output costs of VPP units at different access points and the real cost differences in the impact of their deviations on system balance. Finally, this cost-sharing mechanism that does not consider physical constraints cannot provide market participants with the correct economic signals reflecting the impact of their actual behavior. This not only undermines fairness but may also mislead the investment decisions and operating strategies of VPPs, hindering the optimal allocation of resources and weakening the incentive effect of the mechanism design to guide VPPs to actively support the safety and stability of the system. Summary of the Invention
[0007] To address the aforementioned issues, this invention proposes a method and system for allocating virtual power plant deviation balancing costs based on cooperative game theory. It fully considers the impact of power grid topology and distribution network losses, constructing a high-precision loss model to accurately quantify network loss costs. Simultaneously, a piecewise cost function is introduced into the real-time market to precisely characterize the nonlinear relationship between the total deviation balancing cost and the total output deviation of the distribution network operator. Finally, the Shapley allocation rule in cooperative game theory is improved, employing the Aumann-Shapley value method for cost allocation, thus achieving a fair allocation of virtual power plant deviation balancing costs.
[0008] In some implementations, the following technical solutions are adopted: A method for allocating deviation balancing costs in a virtual power plant based on cooperative game theory includes: The day-ahead planned output of the distribution network operator is obtained based on the day-ahead output plan of the virtual power plant units and the day-ahead network loss calculation; the real-time output of the distribution network operator is obtained based on the real-time output of the VPP units and the network loss calculation in the real-time market. The output deviation of the distribution network operator is obtained based on the difference between the real-time output and the day-ahead planned output. Based on the output deviation, the Aumann-Shapley value method based on cooperative game theory is used to calculate the average marginal contribution of each virtual power plant unit to the total cost function during the overall expansion process, thereby obtaining the deviation balance cost allocated to each virtual power plant unit.
[0009] As a further solution, the day-ahead network loss is calculated based on the active and reactive power flowing through each branch of the distribution network connected to the virtual power plant, the branch resistance, and the node voltage.
[0010] As a further solution, the aforementioned day-ahead network loss specifically refers to: ; in, branch road j Recently, network damage occurred. , They are respectively j The active and reactive power flowing through the branch line, branch road j resistance, branch road j Voltage amplitude at the end node.
[0011] As a further solution, the network loss in the real-time market is obtained by fitting a quadratic expression, which is: ; in, branch road j Network loss in real-time markets , These represent the active and reactive power flowing through each branch of the distribution network to which the virtual power plant is connected. For branch resistance, This refers to the voltage amplitude at the distribution network node. For virtual power plant units i The deviation between real-time output and planned output; H ij A value of 0 or 1 indicates a branch. j Is it related to virtual power plant units? i Connected; M represents the number of virtual power plant units.
[0012] As a further step, the specific output deviation of the distribution network operator is obtained as follows:
[0013] in, For branch resistance, This refers to the voltage amplitude at the distribution network node. For virtual power plant units i The deviation between real-time output and planned output; H ij A value of 0 or 1 indicates a branch. j Is it related to virtual power plant units? i Connected; M is the number of virtual power plant units, and N is the number of branches.
[0014] As a further approach, the Aumann-Shapley value method based on cooperative game theory is used to calculate the average marginal contribution of each virtual power plant unit to the total cost function during the overall expansion process. Specifically: ; in, This represents the output deviation of the i-th virtual power plant unit. , For the deviation balancing cost of the first i Partial derivatives of the power deviation of each virtual power plant unit Let C be the output deviation vector of the virtual power plant unit, and let C represent the piecewise linear cost function of the output deviation.
[0015] As a further solution, the piecewise linear cost function for output deviation is as follows: ; in, , These refer to the prices for purchasing and selling electricity on the market, respectively.
[0016] As a further option, the deviation balancing cost allocated to each virtual power plant unit Specifically: ; in, , representing the network loss factor that characterizes the cost caused by network loss; For the first i The deviation between the real-time output of a virtual power plant unit and its planned output for the day; This indicates the distribution network to which the virtual power plant is connected. j The active power flowing through each branch is used as A value of 0 or 1 indicates a branch. j Is it related to virtual power plant units? i Connected; For the first j The resistance of each branch, branch road j End node voltage amplitude, , These refer to the prices for purchasing and selling electricity on the market, respectively.
[0017] In other embodiments, the following technical solutions are adopted: A virtual power plant deviation balancing cost allocation system based on cooperative game theory includes: The output calculation module is configured to obtain the day-ahead planned output of the distribution network operator based on the day-ahead output plan of the virtual power plant units and the day-ahead network loss calculation; and to obtain the real-time output of the distribution network operator based on the real-time output of the VPP units and the network loss calculation in the real-time market. The output deviation calculation module is configured to obtain the output deviation of the distribution network operator based on the difference between the real-time output and the day-ahead planned output. The deviation balance cost allocation module is configured to calculate the average marginal contribution of each virtual power plant unit to the total cost function during the overall expansion process based on the output deviation using the Aumann-Shapley value method based on cooperative game theory, thereby obtaining the deviation balance cost allocated to each virtual power plant unit.
[0018] As a further solution, the network loss in the real-time market is obtained by fitting a quadratic expression, which is: ; in, branch road j Network loss in real-time markets , These represent the active and reactive power flowing through each branch of the distribution network to which the virtual power plant is connected. For branch resistance, This refers to the voltage amplitude at the distribution network node. For virtual power plant units i The deviation between real-time output and planned output; H ij A value of 0 or 1 indicates a branch. j Is it related to virtual power plant units? i Connected; M represents the number of virtual power plant units.
[0019] In other embodiments, the following technical solutions are adopted: A terminal device includes a processor and a memory, the processor being used to implement instructions; the memory being used to store multiple instructions, the instructions being adapted to be loaded and executed by the processor to perform the above-described method for allocating deviation balancing costs in a virtual power plant based on cooperative game theory.
[0020] In other embodiments, the following technical solutions are adopted: A computer-readable storage medium storing a plurality of instructions adapted for loading and execution by a processor of a terminal device of the above-described method for allocating deviation balancing costs in a virtual power plant based on cooperative game theory.
[0021] Compared with the prior art, the beneficial effects of the present invention are: (1) The method of the present invention fully considers the influence of the power grid topology and distribution network loss, and accurately quantifies the network loss by constructing a high-precision distribution network operator output deviation model; it can fairly distinguish the actual output cost of VPP units at different access points and the real cost difference of the impact of their deviation on system balance, thereby achieving the fairness and reliability of cost allocation.
[0022] (2) This invention selects the Aumann-Shapley value method based on cooperative game theory as the core allocation rule. This method is applicable to cooperative game scenarios with a continuous or indivisible number of participants. It extends the concept of the traditional Shapley value, extending it from discrete participants to continuous space. It is particularly suitable for handling resource allocation problems with complex and nonlinear cost structures, such as power systems. This method naturally has axiomatic fairness guarantees, such as efficiency, symmetry, and additivity. It can bypass direct high-dimensional integration, effectively solving the problem of exponential explosion in computation, and ensuring that the method is still computationally feasible and fair in scenarios with massive virtual power plants.
[0023] Other features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description
[0024] Figure 1 This is a schematic diagram of the power distribution network topology in an embodiment of the present invention; Figure 2 This is a flowchart of the virtual power plant deviation balance cost allocation method based on cooperative game theory in an embodiment of the present invention; Figure 3 This is a schematic diagram of a virtual power plant deviation balancing cost allocation system based on cooperative game theory in an embodiment of the present invention. Detailed Implementation
[0025] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0026] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0027] Terminology Explanation: Deviation balancing costs: The uncertainty of new energy output and the low aggregation accuracy of virtual power plants often lead to significant deviations between the actual output of virtual power plant units and the day-ahead plan, resulting in power imbalance. To maintain the safe and stable operation of the system, virtual power plants must incur additional deviation balancing costs in the real-time market to balance supply and demand. These costs mainly consist of two parts: first, the expenses incurred in buying and selling electricity in the market to balance the deviation; and second, the performance penalties incurred due to insufficient actual output.
[0028] Example 1 In one or more embodiments, a virtual power plant deviation balance cost allocation method based on cooperative game theory is disclosed, combining... Figure 2 Specifically, it includes the following process: S101: The day-ahead planned output of the distribution network operator is obtained based on the day-ahead output plan of the virtual power plant units and the day-ahead network loss calculation; the real-time output of the distribution network operator is obtained based on the real-time output of the VPP units and the network loss calculation in the real-time market.
[0029] Specifically, assume that the virtual power plant (hereinafter referred to as VPP) is connected to the distribution network at node 0. The distribution network has a total of N +1 node and N A side road. A side road j The terminal voltage amplitude is Side road j The resistance and reactance are respectively and Side road j The active power and reactive power flowing upstream are respectively , The distribution network has M VPP unit. Definition N × M Order coefficient matrix H ,use H ij 0 or 1 indicates a branch j Is it related to VPP units? i Connected. The distribution network topology is shown below. Figure 1 As shown.
[0030] VPP units submit their output boundaries to the distribution network operator, who then acts as their agent in the day-ahead and real-time electricity markets. The distribution network operator settles the overall deviation balancing cost in the electricity market. C Then, the cost is allocated to each unit, thus obtaining... A i .
[0031] For the day-ahead market, VPP units need to declare their output boundaries to distribution network operators, who then participate in the clearing process to obtain their planned day-ahead output. This allows for the breakdown of power outputs to the VPP units and the resulting day-ahead output plan. With the recent network loss Specifically: (1) Among them, the recent network loss The calculation is based on the active and reactive power flowing through each branch of the distribution network connected to the virtual power plant, the branch resistance, and the node voltage. The specific calculation formula is as follows: (2) For the real-time market, assume the real-time output of the VPP unit is... The deviation between real-time output and planned output is ,but: (3) Meanwhile, the real-time output of the distribution network operator Real-time output from VPP unit and real-time network loss The composition is as follows: (4) Since the actual power expression is nonlinear, this embodiment constructs a quadratic expression to fit the network loss in the actual market. To balance efficiency and accuracy in the calculation process, the quadratic expression is as follows: (5) in, This indicates the impact of output deviation on network loss, assuming that voltage phase angle and reactive power changes are ignored.
[0032] S103: Based on the difference between the real-time output and the day-ahead planned output of the distribution network operator, the output deviation of the distribution network operator is obtained.
[0033] Specifically, the deviation between the real-time power output and the planned output of the distribution network operator. Determined in the real-time market, specifically as follows: (6) Finally, based on formulas (1)-(6), the total output deviation can be obtained. The parsing expression: (7) This embodiment fully considers network losses caused by the distribution network topology when establishing the total output deviation of the distribution network operator. This allows for the accurate quantification of the actual transmission and loss costs incurred by resources at different node locations when providing output or causing deviations. It also accurately distinguishes the actual output costs of VPP units at different access points and the real cost differences in the impact of their deviations on system balance, thereby improving the accuracy and reliability of virtual power plant deviation balance cost allocation.
[0034] S103: Based on the output deviation, the Aumann-Shapley value method based on cooperative game theory is used to calculate the average marginal contribution of each virtual power plant unit to the total cost function during the overall expansion process, thereby obtaining the deviation balance cost allocated to each virtual power plant unit.
[0035] VPP aggregation involves a massive number of distributed resources with highly heterogeneous output characteristics, and conflicts of interest exist between different VPP units. Considering the complex physical network details, traditional methods such as marginal cost allocation or simple proportional allocation face an exponential increase in complexity due to high-dimensional computation, making it difficult to efficiently and fairly decompose the nonlinear, strongly coupled total cost across a vast number of entities. Existing methods often sacrifice fairness for computational feasibility in complex scenarios.
[0036] To address the challenges of high-dimensional problem-solving and the need for fair allocation arising from massive distributed entities, this embodiment innovatively applies the Aumann-Shapley value method as the core allocation rule. This method is applicable to cooperative game scenarios with a continuous or indivisible number of participants, extending the concept of the traditional Shapley value by extending it from discrete participants to continuous spaces. It is particularly suitable for handling resource allocation problems with complex, nonlinear cost structures, such as those in power systems. This rule inherently possesses axiomatic fairness guarantees, such as efficiency, symmetry, and additivity, and bypasses direct high-dimensional integration, effectively solving the problem of exponential computational explosion. This ensures that the method remains computationally feasible and fair even in scenarios with massive VPPs.
[0037] The core of the Aumann-Shapley method lies in calculating the average marginal contribution of each member in each cooperative alliance. This method ignores the impact of the order in which members join the alliance on the allocation result, possessing economic consistency and equality, and achieving fair allocation.
[0038] Since the virtual power plant is relatively small in scale, this embodiment assumes that the virtual power plant and distribution network operator are price takers, and therefore do not affect the actual clearing price. It is assumed that the market purchase and sale prices of electricity are respectively... and Then C represents the piecewise linear cost function of the deviation, specifically: (8) (9) (10) Formula (10) specifies that the net cost is greater than zero to avoid VPP arbitrage.
[0039] , These represent the costs when the power deviation is positive and when the power deviation is negative, respectively.
[0040] This embodiment introduces a piecewise cost function in the real-time market, fully considering the cost differences caused by power redundancy and power deficit, in order to accurately characterize the nonlinear relationship between the total deviation balancing cost and the total output deviation of the distribution network operator.
[0041] To represent the output deviation of a VPP unit, a row vector is defined. q : (11) Since power deviation is a continuous variable, allocating scalar costs, which are jointly determined by multiple participants, is a non-atomic cost allocation problem. Therefore, this embodiment proposes the Aumann-Shapley method based on cooperative game theory to allocate the total deviation balance cost among VPPs.
[0042] Formula (12) means that when the power deviation of all VPP units increases proportionally and synchronously, each VPP unit contributes its average marginal contribution to the total cost function during the overall expansion process.
[0043] (12) In the formula, This represents the average marginal contribution of each VPP unit to the total cost function during the overall expansion process. It is the deviation balance cost for the first i Partial derivatives of the power deviation of each VPP unit; For the first i The output deviation of each VPP unit, and equal.
[0044] The partial derivative of cost is calculated as follows: (13) (14) When equation (10) is multiplied by a positive number, its sign remains unchanged. hour Solve as shown in equation (15), for The calculation method is similar.
[0045] (15) Finally, formula (12) can be written in the following form: (16) (17) In the formula, The network loss factor is used to characterize the costs incurred due to network loss.
[0046] The final result is the deviation balancing cost allocated to each virtual power plant unit. .
[0047] This embodiment innovatively applies the Aumann-Shapley value as the core allocation rule, effectively solving the problem of exponential explosion in computation, ensuring that the method remains computationally feasible and fair in massive VPP scenarios, and effectively guaranteeing the incentive effect of the cost-sharing mechanism on the safety and stability of the VPP unit active support system.
[0048] The following verifies whether the allocation method proposed in this embodiment complies with the principles of fairness and consistency.
[0049] (1) Balance of income and expenditure principle: that is, the sum of the deviation balance costs allocated to each VPP should be equal to the total deviation balance cost.
[0050] Now, by summing the deviation balancing costs of each VPP unit, equation (18) can be derived from equations (7) and (16): (18) As can be seen from equation (18), the allocation method proposed in this embodiment satisfies the principle of income and expenditure balance.
[0051] (2) Fairness principle: If two VPP units a and b are connected to the same bus and have the same output deviation, they are allocated the same cost.
[0052] As can be seen from equation (17), if two VPP units are connected to the same bus, their correlation matrix... Similarly, the network loss factor The corresponding columns are all the same. Furthermore, since they have the same output deviation, the cost of balancing the deviation to be allocated is also equal; the specific formula is: (19) (20) (3) Monotonicity principle: If two VPP units a and b are located at the same node and their power deviations have the same sign, then the VPP unit with the larger absolute value of the power deviation should bear a larger absolute value of the deviation balancing cost, that is: (twenty one) (twenty two) Since both are located at the same node, their network loss factors are the same. Therefore, the ratio of their respective offset balancing costs is equal to the ratio of their power offsets; thus, this principle holds true.
[0053] (4) Independence principle: The deviation balancing cost allocated to any subset of resources within a virtual power plant shall not be greater than the cost of that subset participating in the real-time market as a new virtual power plant.
[0054] Assume that any subset S of the resources within the virtual power plant has a power deviation in the real-time market. ,but: (twenty three) If this subset participates independently in the real-time market, the deviation balancing cost is: (twenty four) In the formula, This represents the deviation balancing cost of the subset.
[0055] If this subset still participates in the market using the original aggregation method, its deviation balancing cost is: (25) According to equation (9), we can deduce that: (26) (27) That is, the principle of independence is proven.
[0056] (5) Causal principle: When VPP i Power deviation Deviation from total power When the signs are the same, the power deviation of the VPP unit will increase the total deviation balancing cost; when the VPP i Power deviation Deviation from total power When the signs are reversed, the power deviation of the VPP unit will reduce the total deviation balancing cost. That is, VPP units with increased power deviation will be penalized, while VPP units with decreased power deviation will be rewarded.
[0057] (28) (29) The ratio of allocated costs to total costs is as follows: (30) Due to network loss factor Much less than 1, therefore and They have the same symbols; that is, the proposed allocation method satisfies the causal principle.
[0058] Based on the above proof, it can be concluded that the deviation balancing cost allocation method proposed in this embodiment is a stable method based on cost causality. The principle of cost balancing ensures that the total cost is allocated among VPP units without surplus or shortage. The principles of fairness, monotonicity, and causality indicate that the allocated deviation cost is determined by the contribution of the total cost of each individual VPP unit. The independence principle property guarantees the stability of aggregation, because exiting aggregation does not reduce the cost allocation of any VPP unit.
[0059] Example 2 In one or more embodiments, a virtual power plant deviation balancing cost allocation system based on cooperative game theory is disclosed, combining... Figure 3 Specifically, it includes: The output calculation module is configured to obtain the day-ahead planned output of the distribution network operator based on the day-ahead output plan of the virtual power plant units and the day-ahead network loss calculation; and to obtain the real-time output of the distribution network operator based on the real-time output of the VPP units and the network loss calculation in the real-time market. The output deviation calculation module is configured to obtain the output deviation of the distribution network operator based on the difference between the real-time output and the day-ahead planned output. The deviation balance cost allocation module is configured to calculate the average marginal contribution of each virtual power plant unit to the total cost function during the overall expansion process based on the output deviation using the Aumann-Shapley value method based on cooperative game theory, thereby obtaining the deviation balance cost allocated to each virtual power plant unit.
[0060] The network loss in the real-time market is obtained by fitting a quadratic expression, which is: ; in, This refers to the network loss of the distribution network where the virtual power plant units are located in the real-time market. , These represent the active and reactive power flowing through each branch of the distribution network to which the virtual power plant is connected. For branch resistance, This refers to the voltage amplitude at the end node of the line. The deviation between the real-time output and the planned output of the virtual power plant units; using H ij A value of 0 or 1 indicates a branch. j Is it related to virtual power plant units? i Connected; M represents the number of virtual power plant units.
[0061] The specific implementation methods of the above modules are exactly the same as those in Example 1, and will not be described in detail again.
[0062] Example 3 In one or more embodiments, a terminal device is disclosed, comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions adapted to be loaded by the processor and executed by the processor to perform the virtual power plant deviation balancing cost allocation method based on cooperative game theory as described in Embodiment 1.
[0063] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.
[0064] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.
[0065] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions.
[0066] Example 4 In one or more embodiments, a computer-readable storage medium is disclosed, wherein a plurality of instructions are stored, the instructions being adapted to be loaded by a processor of a terminal device and executed by the virtual power plant deviation balancing cost allocation method based on cooperative game theory described in Embodiment 1.
[0067] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.
Claims
1. A method for allocating deviation balance costs in a virtual power plant based on cooperative game theory, characterized in that, include: The day-ahead planned output of the distribution network operator is obtained based on the day-ahead output plan of the virtual power plant units and the day-ahead network loss calculation; The real-time output of the distribution network operator is obtained based on the real-time output of the VPP unit and the network loss calculation in the real-time market. The output deviation of the distribution network operator is obtained based on the difference between the real-time output and the day-ahead planned output. Based on the output deviation, the Aumann-Shapley value method based on cooperative game theory is used to calculate the average marginal contribution of each virtual power plant unit to the total cost function during the overall expansion process, thereby obtaining the deviation balance cost allocated to each virtual power plant unit.
2. The method for allocating deviation balance costs in a virtual power plant based on cooperative game theory as described in claim 1, characterized in that, The day-ahead network loss is calculated based on the active and reactive power flowing through each branch of the distribution network connected to the virtual power plant, as well as the branch resistance and node voltage.
3. The method for allocating deviation balance costs in a virtual power plant based on cooperative game theory as described in claim 2, characterized in that, The aforementioned network loss specifically refers to: ； in, branch road j Recently, network damage occurred. , They are respectively j The active and reactive power flowing through the branch line, branch road j resistance, branch road j Voltage amplitude at the end node.
4. The method for allocating deviation balance costs in a virtual power plant based on cooperative game theory as described in claim 1, characterized in that, The network loss in the real-time market is obtained by fitting a quadratic expression, which is: ； in, branch road j Network loss in real-time markets , These represent the active and reactive power flowing through each branch of the distribution network to which the virtual power plant is connected. For branch resistance, This refers to the voltage amplitude at the distribution network node. For virtual power plant units i The deviation between real-time output and planned output; H ij A value of 0 or 1 indicates a branch. j Is it related to virtual power plant units? i Connected; M represents the number of virtual power plant units.
5. The method for allocating deviation balance costs in a virtual power plant based on cooperative game theory as described in claim 1, characterized in that, The specific output deviation of the distribution network operator is as follows: ； in, For branch resistance, This refers to the voltage amplitude at the distribution network node. The active power flowing through each branch of the distribution network to which the virtual power plant is connected; For virtual power plant units i The deviation between real-time output and planned output; H ij A value of 0 or 1 indicates a branch. j Is it related to virtual power plant units? i Connected; M is the number of virtual power plant units, and N is the number of branches.
6. The method for allocating deviation balance costs in a virtual power plant based on cooperative game theory as described in claim 1, characterized in that, Using the Aumann-Shapley value method based on cooperative game theory, the average marginal contribution of each virtual power plant unit to the total cost function during the overall expansion process is calculated. Specifically: ； in, This represents the output deviation of the i-th virtual power plant unit. , For the deviation balancing cost of the first i Partial derivatives of the power deviation of each virtual power plant unit Let C be the output deviation vector of the virtual power plant unit, and let C represent the piecewise linear cost function of the output deviation.
7. The method for allocating deviation balance costs in a virtual power plant based on cooperative game theory as described in claim 6, characterized in that, The piecewise linear cost function for output deviation is as follows: ； in, , These refer to the prices for purchasing and selling electricity on the market, respectively.
8. The method for allocating deviation balance costs in a virtual power plant based on cooperative game theory as described in claim 1, characterized in that, Deviation balancing cost allocated to each virtual power plant unit Specifically: ； in, , representing the network loss factor that characterizes the cost caused by network loss; For the first i The deviation between the real-time output of a virtual power plant unit and its planned output for the day; This indicates the distribution network to which the virtual power plant is connected. j The active power flowing through each branch is used as A value of 0 or 1 indicates a branch. j Is it related to virtual power plant units? i Connected; For the first j The resistance of each branch, branch road j End node voltage amplitude, , These refer to the prices for purchasing and selling electricity on the market, respectively.
9. A virtual power plant deviation balancing cost allocation system based on cooperative game theory, characterized in that, include: The output calculation module is configured to obtain the day-ahead planned output of the distribution network operator based on the day-ahead output plan of the virtual power plant units and the day-ahead network loss calculation; The real-time output of the distribution network operator is obtained based on the real-time output of the VPP unit and the network loss calculation in the real-time market. The output deviation calculation module is configured to obtain the output deviation of the distribution network operator based on the difference between the real-time output and the day-ahead planned output. The deviation balance cost allocation module is configured to calculate the average marginal contribution of each virtual power plant unit to the total cost function during the overall expansion process based on the output deviation using the Aumann-Shapley value method based on cooperative game theory, thereby obtaining the deviation balance cost allocated to each virtual power plant unit.
10. A virtual power plant deviation balancing cost allocation system based on cooperative game theory as described in claim 9, characterized in that, The network loss in the real-time market is obtained by fitting a quadratic expression, which is: ； in, branch road j Network loss in real-time markets , These represent the active and reactive power flowing through each branch of the distribution network to which the virtual power plant is connected. For branch resistance, This refers to the voltage amplitude at the distribution network node. For virtual power plant units i The deviation between real-time output and planned output; H ij A value of 0 or 1 indicates a branch. j Is it related to virtual power plant units? i Connected; M represents the number of virtual power plant units.
11. A terminal device comprising a processor and a memory, the processor for implementing instructions; the memory for storing multiple instructions, characterized in that, The instructions are adapted to be loaded by a processor and executed by the virtual power plant deviation balancing cost allocation method based on cooperative game theory as described in any one of claims 1-8.
12. A computer-readable storage medium storing a plurality of instructions, characterized in that, The instructions are adapted to be loaded by the processor of the terminal device and executed by the virtual power plant deviation balancing cost allocation method based on cooperative game theory as described in any one of claims 1-8.