Multi-virtual power plant energy carbon transaction price determination method and system based on master-slave game

Abstract

The invention discloses a multi-virtual power plant energy carbon transaction price determination method and system based on a master-slave game. The method comprises the following steps: determining a master-slave game framework between a power distribution system operator and a plurality of virtual power plants; based on a master-slave game framework, a double-layer optimization model of energy and carbon sharing of the multiple virtual power plants is established, the upper layer of the double-layer optimization model is a dynamic pricing model with the purpose of maximizing the income of a power distribution system operator, and the lower layer of the double-layer optimization model is an energy management model with the purpose of minimizing the operation cost of the virtual power plants; and establishing a double-layer Kriging meta-model taking the transaction electricity price and the carbon price as inputs and the transaction electricity quantity and the carbon quota quantity as outputs, fitting and replacing the energy management model of the double-layer optimization model by adopting the double-layer Kriging meta-model, and solving to obtain a global optimal solution. According to the method, sharing and circulation of energy and carbon quotas among virtual power plants are effectively promoted, the economic and environmental benefits are improved, and the privacy security of each subject can be protected.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a method and system for determining the price and quantity of energy-carbon trading of multiple virtual power plants based on master-slave game. Background Art

[0002] Under the severe situation of global energy shortage and increasing environmental pollution, the popularization of clean energy has become the key to energy transformation. However, its large-scale grid connection poses a challenge to the stability of the power grid, which has prompted the rapid development of virtual power plant technology. Virtual power plants integrate distributed energy resources to form a virtual whole to participate in market operations and power grid dispatching, promoting the construction of smart grids. However, due to resource constraints, small virtual power plants are difficult to cope with complex challenges independently. Therefore, multi-virtual power plant alliances came into being, realizing resource complementarity and coordination through energy transactions among members, and enhancing overall operating efficiency.

[0003] However, while pursuing economic benefits, research on the low-carbon coordinated operation of multiple virtual power plant systems is still weak, especially in the context of the increasingly mature carbon trading market. Multiple virtual power plants need to balance economic benefits and low-carbon goals, and it is crucial to formulate an effective carbon quota trading strategy. This can not only optimize the use of carbon quotas and reduce system carbon emissions, but also achieve a win-win situation for the economy and the environment.

[0004] To achieve this goal, it is necessary to accurately characterize the interactions among stakeholders, and game theory has become an important tool for analyzing the equilibrium of interests among multiple entities. Currently, the methods for solving game equilibrium solutions mainly include numerical optimization based on Karush-Kuhn-Tucker (KKT) conditions and heuristic intelligent algorithms. Although the former can simplify the model, it is limited by convex programming requirements and privacy protection issues; although the latter can protect privacy, it faces the challenges of complex calculations and low efficiency. Summary of the invention

[0005] The first purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to provide a method for determining the price and quantity of energy and carbon trading of multiple virtual power plants based on master-slave game, which can significantly improve the efficiency of solving the game model while protecting the privacy of the virtual power plant.

[0006] The second object of the present invention is to provide a multi-virtual power plant energy carbon trading price and quantity determination system based on master-slave game.

[0007] The first object of the present invention is achieved by the following technical solution: a method for determining the price and quantity of energy and carbon trading of multiple virtual power plants based on master-slave game, comprising the following steps:

[0008] S1: Establish a master-slave game framework between distribution system operators and multiple virtual power plants;

[0009] S2: Based on the master-slave game framework, a two-layer optimization model for energy and carbon sharing of multiple virtual power plants is established. The upper layer of the two-layer optimization model is a dynamic pricing model, which aims to maximize the benefits of distribution system operators, and the lower layer is an energy management model, which aims to minimize the operating costs of virtual power plants.

[0010] S3: Establish a two-layer Kriging metamodel with trading electricity price and carbon price as input and trading electricity volume and carbon quota as output. Use this two-layer Kriging metamodel to fit the energy management model that replaces the two-layer optimization model and solve it to obtain the global optimal solution, that is, the optimal solution for energy and carbon quota trading between distribution system operators and multiple virtual power plants.

[0011] Furthermore, the master-slave game framework is specifically as follows: the distribution system operator DSO plays the role of a leader, and maximizes its operating benefits by setting trading electricity prices and carbon prices based on the fluctuations in electricity and carbon prices in the power grid and carbon market, as well as the electricity and carbon quota purchase and sales data reported by each virtual power plant VPP; each virtual power plant acts as a follower, and according to the trading electricity and carbon prices set by the DSO, it reasonably plans the output scheduling of its internal equipment, formulates the electricity and carbon quotas traded with the DSO, and minimizes its own operating costs.

[0012] Further, the details of the dynamic pricing model are as follows:

[0013] With the goal of maximizing the profit of distribution system operators, the objective function of DSO is determined as:

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] Where U DSO is the profit of DSO, T is the maximum time of the scheduling cycle, I is the number of VPPs, and are the on-grid electricity price and grid electricity price at time t, and are the selling price and purchasing price of carbon in the carbon market at time t, and are the electricity purchase price and electricity sales price set by DSO at time t, and are the carbon purchase price and carbon selling price set by DSO at time t, P t DSO,s and P t DSO,b are the power sold and purchased by DSO in the power market at time t, and are the carbon quota sales and purchases of DSO in the carbon market at time t, and are the power purchase and power sales of the ith VPP from the DSO at time t, and are the carbon quota purchased and sold by the ith VPP from the DSO at time t, P t DSO is the total electric power traded between DSO and the power market at time t, is the total carbon quota traded between DSO and the carbon market at time t;

[0022] The electricity and carbon prices set by DSOs should meet the following constraints:

[0023]

[0024]

[0025] Furthermore, the specific situation of the energy management model is as follows:

[0026] With the goal of minimizing the operating cost of the virtual power plant, the objective function of the VPP is determined as:

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] In the formula, is the operating cost of the ith VPP, is the transaction cost between the ith VPP and the power grid and carbon market, is the operating cost of the micro gas turbine of the i-th VPP, is the energy storage operation cost of the i-th VPP, is the interruptible load cost of the i-th VPP, is the transferable load cost of the ith VPP, a i is the quadratic cost coefficient of the i-th VPP micro gas turbine, b i is the first-order cost coefficient of the micro gas turbine of the i-th VPP, c i is the constant cost coefficient of the micro gas turbine of the i-th VPP, is the output power of the micro gas turbine of the i-th VPP at time t, is the energy storage dispatch cost coefficient of the i-th VPP, is the charging power of the energy storage of the i-th VPP at time t, is the discharge power of the energy storage of the i-th VPP at time t, is the interruption compensation price of the interruptible load of the i-th VPP, is the compensation price of the shiftable load of the i-th VPP, is the interruption power of the interruptible load of the i-th VPP at time t, is the translation power of the translatable load of the i-th VPP at time t, is the planned power of the shiftable load of the i-th VPP at time t;

[0034] Imposing VPP internal operating constraints, including power balance constraints, power purchase and sales constraints, micro gas turbine operation constraints, energy storage operation constraints, interruptible load constraints, shiftable load constraints, renewable energy generation constraints and carbon trading constraints;

[0035] The power balance constraint is:

[0036]

[0037] In the formula, is the output power of the wind turbine of the i-th VPP at time t, is the output power of the photovoltaic system of the i-th VPP at time t, is the load forecast value of the i-th VPP at time t;

[0038] The power purchase and sale constraints are:

[0039]

[0040]

[0041] In the formula, is the upper limit of the electric power traded between the ith VPP and the DSO;

[0042] The micro gas turbine operation constraints are:

[0043]

[0044]

[0045] In the formula, is the upper limit of the micro-turbine output power of the i-th VPP, is the output power of the micro gas turbine of the i-th VPP at time t-1, is the lower limit of the micro gas turbine ramp rate of the i-th VPP, is the upper limit of the ramp rate of the micro gas turbine of the i-th VPP;

[0046] The energy storage operation constraints are:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052] In the formula, is the upper limit of the charging and discharging power of the energy storage of the i-th VPP, u i,t is the binary variable of the energy storage of the ith VPP at time t, and are the charging and discharging efficiencies of the energy storage of the i-th VPP, is the state of charge of the energy storage of the i-th VPP at time t, is the state of charge of the energy storage of the i-th VPP at time t-1, E i is the energy storage capacity of the i-th VPP, is the lower limit of the energy storage charge state of the i-th VPP, is the upper limit of the energy storage charge state of the i-th VPP, is the initial state of charge of the energy storage of the i-th VPP, is the state of charge of the energy storage of the i-th VPP at time T;

[0053] The interruptible load constraint is:

[0054]

[0055] In the formula, is the upper limit of the interruptible load of the i-th VPP;

[0056] The translational load constraint is:

[0057]

[0058]

[0059] In the formula, is the upper limit of the translatable load of the ith VPP, is the total demand of the shiftable load of the ith VPP in the scheduling time domain;

[0060] The renewable energy generation constraint is:

[0061]

[0062]

[0063] In the formula, is the upper limit of the output power of the wind turbine of the i-th VPP at time t, is the upper limit of the output power of the PV system of the i-th VPP at time t;

[0064] The carbon trading constraints are:

[0065]

[0066]

[0067]

[0068] In the formula, is the carbon emissions of the ith VPP at time t, is the carbon quota allocated to the ith VPP at time t, χ MT is the carbon quota coefficient per unit power supply of the micro gas turbine, χ g is the carbon quota coefficient per unit power supply of the grid, δ MT is the carbon emission coefficient per unit power supply of the micro gas turbine, δ g It is the carbon emission coefficient per unit power supply of the power grid.

[0069] Furthermore, the established two-layer Kriging metamodel is as follows:

[0070]

[0071] Where P i kri , Q i kriare the transaction power and carbon quota of the ith VPP and DSO calculated by the double-layer Kriging meta-model, and F i Indicates the implicit mapping relationship between transaction price and transaction volume;

[0072] The energy management model of the two-level optimization model is replaced by the two-level Kriging metamodel fitting, and the two-level optimization model is transformed into:

[0073]

[0074] In the formula, Ω DSO It is the feasible domain space for trading electricity price and carbon price.

[0075] Furthermore, the two-layer optimization model is solved, and the process is as follows:

[0076] S31: Use Latin hypercube sampling method LHS to generate initial price sample points of low confidence sample pool and high confidence sample pool, substitute the initial price sample points into the energy management model of each VPP to obtain the traded electricity and carbon quota, and form the initial sample data set with electricity price, carbon price, traded electricity and carbon quota;

[0077] S32: construct a two-layer Kriging meta-model of each VPP based on the current initial sample data set;

[0078] S33: Calculate the objective function value corresponding to each sample point, divide N key areas according to the objective function value, and calculate the optimal value of each area;

[0079] S34: In each key area, a genetic algorithm is used to solve equation (35) to obtain the local optimal electricity price and carbon price, and the low confidence sample pool is updated;

[0080] S35: calling the energy management model, calculating the actual transaction electricity and carbon quota, and updating the high confidence sample pool; calculating the objective function value of the dynamic pricing model, and if the objective function value is greater than the current optimal value, updating the optimal value to the objective function value;

[0081] S36: Compare the optimal values ​​of all key areas and take the maximum value as the global optimal solution of the current iteration;

[0082] S37: Determine whether the global optimal solution of the two iterations meets the convergence condition. If so, stop the iteration; otherwise, return to step S32.

[0083] The second object of the present invention is achieved through the following technical solution: a system for determining the price and quantity of energy and carbon trading of multiple virtual power plants based on master-slave game, which is used to implement the above-mentioned method for determining the price and quantity of energy and carbon trading of multiple virtual power plants based on master-slave game, comprising:

[0084] A master-slave game framework design module is used to establish a master-slave game framework between distribution system operators and multiple virtual power plants;

[0085] The model building module establishes a two-layer optimization model for energy and carbon sharing of multiple virtual power plants based on the master-slave game framework. The upper layer of the two-layer optimization model is a dynamic pricing model, which aims to maximize the benefits of distribution system operators, and the lower layer is an energy management model, which aims to minimize the operating costs of virtual power plants.

[0086] The solution module is used to establish a two-layer Kriging metamodel with trading electricity price and carbon price as input and trading electricity volume and carbon quota as output. The two-layer Kriging metamodel is used to fit the energy management model that replaces the two-layer optimization model and solve it to obtain the global optimal solution, that is, the optimal solution for energy and carbon quota trading between the distribution system operator and multiple virtual power plants.

[0087] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0088] 1. Based on the concept of electricity-carbon coupling, the present invention establishes a master-slave game framework for energy and carbon quota trading between distribution system operators (DSO) and multiple virtual power plants (VPPs), effectively promoting resource (including energy and carbon quota) sharing among virtual power plants and improving economic and environmental benefits.

[0089] 2. In the solution process, the present invention innovatively introduces a two-layer Kriging metamodel to replace the energy management model of the virtual power plant. In each key area, a genetic algorithm is used to solve the problem, obtain the local optimal electricity price and carbon price, and update the low-confidence sample pool, effectively avoiding the tedious direct calculation of the underlying game model (i.e., the energy management model), which not only protects the privacy information of the virtual power plant VPP, but also greatly improves the efficiency of the optimization solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 The figure is a flow chart of the method of the present invention.

[0091] Figure 2 This is a master-slave game framework diagram between the distribution system operator and multiple virtual power plants of the present invention.

[0092] Figure 3 Flowchart for solving the two-level optimization model.

[0093] Figure 4 Schematic diagram of the system of the present invention. DETAILED DESCRIPTION

[0094] The present invention is further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0095] Example 1

[0096] like Figure 1 As shown, this embodiment discloses a method for determining the price and quantity of energy and carbon trading of multiple virtual power plants based on master-slave game, and the specific situation is as follows:

[0097] S1: Establish a master-slave game framework between distribution system operators and multiple virtual power plants, such as Figure 2 As shown in the figure, specifically: the distribution system operator (DSO) plays the role of a leader, and maximizes its operating benefits by setting trading electricity prices and carbon prices based on the fluctuations in electricity and carbon prices in the power grid and carbon market, as well as the electricity and carbon quota purchase and sales data reported by each virtual power plant (VPP); each virtual power plant acts as a follower, and according to the trading electricity and carbon prices set by the DSO, it reasonably plans the output dispatch of its internal equipment, formulates the electricity and carbon quotas traded with the DSO, and minimizes its own operating costs.

[0098] S2: Based on the master-slave game framework, a two-layer optimization model for energy and carbon sharing of multiple virtual power plants is established. The upper layer of the two-layer optimization model is a dynamic pricing model, which aims to maximize the benefits of distribution system operators, and the lower layer is an energy management model, which aims to minimize the operating costs of virtual power plants.

[0099] The details of the dynamic pricing model are as follows:

[0100] With the goal of maximizing the profit of distribution system operators, the objective function of DSO is determined as:

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] Where U DSOis the profit of DSO, T is the maximum time of the scheduling cycle, I is the number of VPPs, and are the on-grid electricity price and grid electricity price at time t, and are the selling price and purchasing price of carbon in the carbon market at time t, and are the electricity purchase price and electricity sales price set by DSO at time t, and are the carbon purchase price and carbon selling price set by DSO at time t, P t DSO,s and P t DSO,b are the power sold and purchased by DSO in the power market at time t, and are the carbon quota sales and purchases of DSO in the carbon market at time t, and are the power purchase and power sales of the ith VPP from the DSO at time t, and are the carbon quota purchased and sold by the i-th VPP from the DSO at time t, is the total electric power traded between DSO and the power market at time t, is the total carbon quota traded between DSO and the carbon market at time t;

[0109] The electricity and carbon prices set by DSOs should meet the following constraints:

[0110]

[0111]

[0112] The details of the energy management model are as follows:

[0113] With the goal of minimizing the operating cost of the virtual power plant, the objective function of the VPP is determined as:

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] In the formula, is the operating cost of the ith VPP, is the transaction cost between the ith VPP and the power grid and carbon market, is the operating cost of the micro gas turbine of the i-th VPP, is the energy storage operation cost of the i-th VPP, is the interruptible load cost of the i-th VPP, is the transferable load cost of the ith VPP, a i is the quadratic cost coefficient of the i-th VPP micro gas turbine, b i is the first-order cost coefficient of the micro gas turbine of the i-th VPP, c i is the constant cost coefficient of the micro gas turbine of the i-th VPP, is the output power of the micro gas turbine of the i-th VPP at time t, is the energy storage dispatch cost coefficient of the i-th VPP, is the charging power of the energy storage of the i-th VPP at time t, is the discharge power of the energy storage of the i-th VPP at time t, is the interruption compensation price of the interruptible load of the i-th VPP, is the compensation price of the shiftable load of the i-th VPP, is the interruption power of the interruptible load of the i-th VPP at time t, is the translation power of the translatable load of the i-th VPP at time t, is the planned power of the shiftable load of the i-th VPP at time t;

[0121] Imposing VPP internal operating constraints, including power balance constraints, power purchase and sales constraints, micro gas turbine operation constraints, energy storage operation constraints, interruptible load constraints, shiftable load constraints, renewable energy generation constraints and carbon trading constraints;

[0122] The power balance constraint is:

[0123]

[0124] In the formula, is the output power of the wind turbine of the i-th VPP at time t, is the output power of the photovoltaic system of the i-th VPP at time t, is the load forecast value of the i-th VPP at time t;

[0125] The power purchase and sale constraints are:

[0126]

[0127]

[0128] In the formula, is the upper limit of the electric power traded between the ith VPP and the DSO;

[0129] The micro gas turbine operation constraints are:

[0130]

[0131]

[0132] In the formula, is the upper limit of the micro-turbine output power of the i-th VPP, is the output power of the micro gas turbine of the i-th VPP at time t-1, is the lower limit of the micro gas turbine ramp rate of the i-th VPP, is the upper limit of the ramp rate of the micro gas turbine of the i-th VPP;

[0133] The energy storage operation constraints are:

[0134]

[0135]

[0136]

[0137]

[0138]

[0139] In the formula, is the upper limit of the charging and discharging power of the energy storage of the i-th VPP, u i,t is the binary variable of the energy storage of the ith VPP at time t, and are the charging and discharging efficiencies of the energy storage of the i-th VPP, is the state of charge of the energy storage of the i-th VPP at time t, is the state of charge of the energy storage of the i-th VPP at time t-1, E i is the energy storage capacity of the i-th VPP, is the lower limit of the energy storage charge state of the i-th VPP, is the upper limit of the energy storage charge state of the i-th VPP, is the initial state of charge of the energy storage of the i-th VPP, is the state of charge of the energy storage of the i-th VPP at time T;

[0140] The interruptible load constraint is:

[0141]

[0142] In the formula, is the upper limit of the interruptible load of the i-th VPP;

[0143] The translational load constraint is:

[0144]

[0145]

[0146] In the formula, is the upper limit of the translatable load of the ith VPP, is the total demand of the shiftable load of the ith VPP in the scheduling time domain;

[0147] The renewable energy generation constraint is:

[0148]

[0149]

[0150] In the formula, is the upper limit of the output power of the wind turbine of the i-th VPP at time t, is the upper limit of the output power of the PV system of the i-th VPP at time t;

[0151] The carbon trading constraints are:

[0152]

[0153]

[0154]

[0155] In the formula, is the carbon emissions of the ith VPP at time t, is the carbon quota allocated to the ith VPP at time t, χ MT is the carbon quota coefficient per unit power supply of the micro gas turbine, χ g is the carbon quota coefficient per unit power supply of the grid, δ MT is the carbon emission coefficient per unit power supply of the micro gas turbine, δ g It is the carbon emission coefficient per unit power supply of the power grid.

[0156] S3: Establish a two-layer Kriging metamodel with trading electricity price and carbon price as input and trading electricity volume and carbon quota as output. Use this two-layer Kriging metamodel to fit the energy management model that replaces the two-layer optimization model and solve it to obtain the global optimal solution, that is, the optimal solution for energy and carbon quota trading between distribution system operators and multiple virtual power plants.

[0157] The established two-layer Kriging metamodel is as follows:

[0158]

[0159] Where P i kri , are the transaction power and carbon quota of the ith VPP and DSO calculated by the double-layer Kriging meta-model, and F i Indicates the implicit mapping relationship between transaction price and transaction volume;

[0160] The energy management model of the two-level optimization model is replaced by the two-level Kriging metamodel fitting, and the two-level optimization model is transformed into:

[0161]

[0162] In the formula, Ω DSO It is the feasible domain space for trading electricity price and carbon price.

[0163] like Figure 3 As shown, the two-layer optimization model is solved, and the process is as follows:

[0164] S31: Use Latin Hypercube Sampling (LHS) to generate initial price sample points for the low-confidence sample pool and the high-confidence sample pool, substitute the initial price sample points into the energy management model of each VPP to obtain the traded electricity and carbon quota, and use the electricity price, carbon price, traded electricity, and carbon quota to form the initial sample data set;

[0165] S32: construct a two-layer Kriging meta-model of each VPP based on the current initial sample data set;

[0166] S33: Calculate the objective function value corresponding to each sample point, divide N key areas according to the objective function value, and calculate the optimal value of each area;

[0167] S34: In each key area, a genetic algorithm is used to solve equation (35) to obtain the local optimal electricity price and carbon price, and the low confidence sample pool is updated;

[0168] S35: calling the energy management model, calculating the actual transaction electricity and carbon quota, and updating the high confidence sample pool; calculating the objective function value of the dynamic pricing model, and if the objective function value is greater than the current optimal value, updating the optimal value to the objective function value;

[0169] S36: Compare the optimal values ​​of all key areas and take the maximum value as the global optimal solution of the current iteration;

[0170] S37: Determine whether the global optimal solution of the two iterations meets the convergence condition. If so, stop the iteration; otherwise, return to step S32.

[0171] In summary, the present invention establishes a master-slave game framework for energy and carbon quota transactions between distribution system operators and multiple virtual power plants based on the concept of electricity-carbon coupling, and introduces a two-layer Kriging metamodel to achieve efficient solution. The master-slave game framework is led by the distribution system operator, which effectively promotes the sharing and circulation of energy and carbon quotas between virtual power plants, and improves both economic and environmental benefits. The complex virtual power plant energy management model is replaced by a two-layer Kriging metamodel, and a genetic algorithm is used to solve each key area to obtain the local optimal electricity price and carbon price, and update the low-confidence sample pool to avoid the complicated calculation of the lower-level game model (i.e., the energy management model), which greatly improves the optimization efficiency while protecting the privacy of the virtual power plant.

[0172] Example 2

[0173] This embodiment discloses a multi-virtual power plant energy carbon transaction price determination system based on master-slave game, which is used to implement the multi-virtual power plant energy carbon transaction price determination method based on master-slave game described in Example 1. Figure 4 As shown, including:

[0174] A master-slave game framework design module is used to establish a master-slave game framework between distribution system operators and multiple virtual power plants;

[0175] The model building module establishes a two-layer optimization model for energy and carbon sharing of multiple virtual power plants based on the master-slave game framework. The upper layer of the two-layer optimization model is a dynamic pricing model, which aims to maximize the benefits of distribution system operators, and the lower layer is an energy management model, which aims to minimize the operating costs of virtual power plants.

[0176] The solution module is used to establish a two-layer Kriging metamodel with trading electricity price and carbon price as input and trading electricity volume and carbon quota as output. The two-layer Kriging metamodel is used to fit the energy management model that replaces the two-layer optimization model and solve it to obtain the global optimal solution, that is, the optimal solution for energy and carbon quota trading between the distribution system operator and multiple virtual power plants.

[0177] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A method for determining the price and quantity of energy and carbon trading of multiple virtual power plants based on master-slave game, characterized in that: The following steps are involved: S1: Establish a master-slave game framework between distribution system operators and multiple virtual power plants; S2: Based on the master-slave game framework, a two-layer optimization model for energy and carbon sharing of multiple virtual power plants is established. The upper layer of the two-layer optimization model is a dynamic pricing model, which aims to maximize the benefits of distribution system operators, and the lower layer is an energy management model, which aims to minimize the operating costs of virtual power plants. S3: Establish a two-layer Kriging metamodel with trading electricity price and carbon price as input and trading electricity volume and carbon quota as output. Use this two-layer Kriging metamodel to fit the energy management model that replaces the two-layer optimization model and solve it to obtain the global optimal solution, that is, the optimal solution for energy and carbon quota trading between distribution system operators and multiple virtual power plants.

2. The method for determining the price and quantity of energy and carbon trading of multiple virtual power plants based on master-slave game according to claim 1 is characterized in that: The master-slave game framework is specifically as follows: the distribution system operator DSO plays the role of a leader, and maximizes its operating benefits by setting trading electricity prices and carbon prices based on the fluctuations in electricity and carbon prices in the power grid and carbon market, as well as the electricity and carbon quota purchase and sales data reported by each virtual power plant VPP; each virtual power plant acts as a follower, and according to the trading electricity and carbon prices set by the DSO, it reasonably plans the output scheduling of its internal equipment, formulates the electricity and carbon quotas traded with the DSO, and minimizes its own operating costs.

3. The method for determining the price and quantity of energy and carbon trading of multiple virtual power plants based on master-slave game according to claim 2 is characterized in that: The details of the dynamic pricing model are as follows: With the goal of maximizing the profit of distribution system operators, the objective function of DSO is determined as: Where U DSO is the profit of DSO, T is the maximum time of the scheduling cycle, I is the number of VPPs, and are the on-grid electricity price and grid electricity price at time t, and are the selling price and purchasing price of carbon in the carbon market at time t, and are the electricity purchase price and electricity sales price set by DSO at time t, and are the carbon purchase price and carbon selling price set by DSO at time t, P t DSO,s and P t DSO,b are the power sold and purchased by DSO in the power market at time t, and are the carbon quota sales and purchases of DSO in the carbon market at time t, and are the power purchase and power sales of the ith VPP from the DSO at time t, and are the carbon quota purchased and sold by the i-th VPP from the DSO at time t, P t DSO is the total electric power traded between DSO and the power market at time t, is the total carbon quota traded between DSO and the carbon market at time t; The electricity and carbon prices set by DSOs should meet the following constraints:

4. The method for determining the price and quantity of energy and carbon trading of multiple virtual power plants based on master-slave game according to claim 3 is characterized in that: The details of the energy management model are as follows: With the goal of minimizing the operating cost of the virtual power plant, the objective function of the VPP is determined as: In the formula, C i VPP is the operating cost of the ith VPP, is the transaction cost between the ith VPP and the power grid and carbon market, is the operating cost of the micro gas turbine of the i-th VPP, is the energy storage operation cost of the i-th VPP, is the interruptible load cost of the i-th VPP, is the transferable load cost of the ith VPP, a i is the quadratic cost coefficient of the i-th VPP micro gas turbine, b i is the first-order cost coefficient of the micro gas turbine of the i-th VPP, c i is the constant cost coefficient of the micro gas turbine of the i-th VPP, is the output power of the micro gas turbine of the i-th VPP at time t, is the energy storage dispatch cost coefficient of the i-th VPP, is the charging power of the energy storage of the i-th VPP at time t, is the discharge power of the energy storage of the i-th VPP at time t, is the interruption compensation price of the interruptible load of the i-th VPP, is the compensation price of the shiftable load of the i-th VPP, is the interruption power of the interruptible load of the i-th VPP at time t, is the translation power of the translatable load of the i-th VPP at time t, is the planned power of the shiftable load of the i-th VPP at time t; Imposing VPP internal operating constraints, including power balance constraints, power purchase and sales constraints, micro gas turbine operation constraints, energy storage operation constraints, interruptible load constraints, shiftable load constraints, renewable energy generation constraints and carbon trading constraints; The power balance constraint is: In the formula, is the output power of the wind turbine of the i-th VPP at time t, is the output power of the photovoltaic system of the i-th VPP at time t, is the load forecast value of the i-th VPP at time t; The power purchase and sale constraints are: In the formula, is the upper limit of the electric power traded between the ith VPP and the DSO; The micro gas turbine operation constraints are: In the formula, is the upper limit of the micro-turbine output power of the i-th VPP, is the output power of the micro gas turbine of the i-th VPP at time t-1, is the lower limit of the micro gas turbine ramp rate of the i-th VPP, is the upper limit of the ramp rate of the micro gas turbine of the i-th VPP; The energy storage operation constraints are: In the formula, is the upper limit of the charging and discharging power of the energy storage of the i-th VPP, u i,t is the binary variable of the energy storage of the ith VPP at time t, and are the charging and discharging efficiencies of the energy storage of the i-th VPP, is the state of charge of the energy storage of the i-th VPP at time t, is the state of charge of the energy storage of the i-th VPP at time t-1, E i is the energy storage capacity of the i-th VPP, is the lower limit of the energy storage charge state of the i-th VPP, is the upper limit of the energy storage charge state of the i-th VPP, is the initial state of charge of the energy storage of the i-th VPP, is the state of charge of the energy storage of the i-th VPP at time T; The interruptible load constraint is: In the formula, is the upper limit of the interruptible load of the i-th VPP; The translational load constraint is: In the formula, is the upper limit of the translatable load of the ith VPP, is the total demand of the shiftable load of the ith VPP in the scheduling time domain; The renewable energy generation constraint is: In the formula, is the upper limit of the output power of the wind turbine of the i-th VPP at time t, is the upper limit of the output power of the PV system of the i-th VPP at time t; The carbon trading constraints are: In the formula, is the carbon emissions of the ith VPP at time t, is the carbon quota allocated to the i-th VPP at time t, X MT is the carbon quota coefficient per unit power supply of the micro gas turbine, χ g is the carbon quota coefficient per unit power supply of the grid, δ MT is the carbon emission coefficient per unit power supply of the micro gas turbine, δ g It is the carbon emission coefficient per unit power supply of the power grid.

5. The method for determining the price and quantity of energy and carbon trading of multiple virtual power plants based on master-slave game according to claim 4 is characterized in that: The established two-layer Kriging metamodel is as follows: Where P i kri , are the transaction power and carbon quota of the ith VPP and DSO calculated by the double-layer Kriging meta-model, and F i Indicates the implicit mapping relationship between transaction price and transaction volume; The energy management model of the two-level optimization model is replaced by the two-level Kriging metamodel fitting, and the two-level optimization model is transformed into: In the formula, Ω DSO It is the feasible domain space for trading electricity price and carbon price.

6. The method for determining the price and quantity of energy and carbon trading of multiple virtual power plants based on master-slave game according to claim 5 is characterized in that: The process of solving the two-level optimization model is as follows: S31: Use Latin hypercube sampling method LHS to generate initial price sample points of low confidence sample pool and high confidence sample pool, substitute the initial price sample points into the energy management model of each VPP to obtain the traded electricity and carbon quota, and form the initial sample data set with electricity price, carbon price, traded electricity and carbon quota; S32: construct a two-layer Kriging meta-model of each VPP based on the current initial sample data set; S33: Calculate the objective function value corresponding to each sample point, divide N key areas according to the objective function value, and calculate the optimal value of each area; S34: In each key area, a genetic algorithm is used to solve equation (35) to obtain the local optimal electricity price and carbon price, and the low confidence sample pool is updated; S35: calling the energy management model, calculating the actual transaction electricity and carbon quota, and updating the high confidence sample pool; calculating the objective function value of the dynamic pricing model, and if the objective function value is greater than the current optimal value, updating the optimal value to the objective function value; S36: Compare the optimal values ​​of all key areas and take the maximum value as the global optimal solution of the current iteration; S37: Determine whether the global optimal solution of the two iterations meets the convergence condition. If so, stop the iteration; otherwise, return to step S32.

7. A multi-virtual power plant energy carbon trading price and quantity determination system based on master-slave game, characterized in that: The method for determining the price and quantity of energy and carbon trading of multiple virtual power plants based on master-slave game for realizing any one of claims 1 to 6 comprises: A master-slave game framework design module is used to establish a master-slave game framework between distribution system operators and multiple virtual power plants; The model building module establishes a two-layer optimization model for energy and carbon sharing of multiple virtual power plants based on the master-slave game framework. The upper layer of the two-layer optimization model is a dynamic pricing model, which aims to maximize the benefits of distribution system operators, and the lower layer is an energy management model, which aims to minimize the operating costs of virtual power plants. The solution module is used to establish a two-layer Kriging metamodel with trading electricity price and carbon price as input and trading electricity volume and carbon quota as output. The two-layer Kriging metamodel is used to fit the energy management model that replaces the two-layer optimization model and solve it to obtain the global optimal solution, that is, the optimal solution for energy and carbon quota trading between the distribution system operator and multiple virtual power plants.

Images