Virtual power plant management method and control system for new energy access, and computer equipment

Abstract

The invention provides a virtual power plant management method for new energy access, a control system and computer equipment, and belongs to the technical field of power distribution network control. The method comprises the following steps: establishing a power utilization node cost model according to the power utilization demand of a load user and an energy interaction balance principle between direct current nodes or alternating current nodes of a virtual power plant to which new energy is accessed; calculating the power consumption cost corresponding to the power consumption demand of the load user through a power consumption node cost model; and under the condition that the load user accepts the power utilization cost, energy interaction among the micro-grid, the new energy access node and the load user is regulated and controlled according to a linkage control strategy based on an energy control law. According to the method, the power utilization node cost model is established based on the energy interaction balance principle, energy interaction among the microgrid, the new energy access node and the load user is regulated and controlled according to the linkage control strategy based on the energy control law, effective consumption of new energy is realized, and impact on the power grid is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network control, and in particular to a virtual power plant management method and control system for new energy access, and a computer device. Background Art

[0002] A virtual power plant (VPP) uses information and communication technologies and energy management systems to aggregate dispersed, small-scale distributed energy resources to form a coordinated, "virtual" power system. While VPPs lack the structure of a physical power plant, they can simulate the power supply and peak-shaving functions of traditional power plants through intelligent control. VPPs typically integrate the following resources: distributed generation equipment, energy storage systems, and adjustable loads (such as charging stations). Users actively adjust their electricity usage in response to electricity prices or incentive signals.

[0003] However, with the rapid development of distributed power sources and electric vehicles, the penetration rate of distributed power sources in distribution networks is increasing, bringing the following challenges to traditional power systems: (1) A large number of connections will change the power supply structure of the distribution network, transforming the traditional unidirectional power supply mode into a bidirectional multi-power supply mode, increasing the complexity of distribution network scheduling, and accompanied by the risk of reverse flow, which will affect the safe operation of the power grid; (2) Solar power generation is a representative example. It is greatly affected by environmental factors and its output is often accompanied by uncertainty. These characteristics not only bring difficulties to the dispatching and operation of traditional distribution networks, but may also cause varying degrees of damage to grid equipment. (3) The economic dispatch of electric vehicles in multiple scenarios, how to ensure the balance of supply and demand, solve the peak load problem during peak hours, and promote the consumption of new energy power generation such as photovoltaics in the context of virtual power plants, is a weak link in current technology.

[0004] In summary, the high proportion of distributed renewable energy connected to the distribution system is strongly coupled with random loads, further increasing system power fluctuations. Given the uncertainty of renewable energy generation and load user electricity consumption, how to effectively absorb distributed renewable energy and minimize its impact on the power grid is a pressing issue. Summary of the Invention

[0005] In order to solve one of the defects of the existing technology, the present invention provides a virtual power plant management method and control system and computer equipment for new energy access to achieve distributed new energy consumption optimization.

[0006] A first aspect of the present invention provides a virtual power plant management method for renewable energy access, comprising: Based on the electricity demand of load users and the energy interaction balance principle between each DC node or AC node of the virtual power plant connected to new energy, a power node cost model is established; Calculate the electricity cost corresponding to the load user's electricity demand through the electricity node cost model; Under the condition that load users accept the electricity cost, the energy interaction between the microgrid, the new energy access node and the load users is regulated according to the linkage control strategy based on the energy control law.

[0007] In the embodiment of the present invention, the cost parameters of the electricity consumption node cost model include: the cost of new energy power generation, and the energy interaction cost between each DC node and each AC node.

[0008] In the embodiment of the present invention, the energy interaction cost between each DC node and each AC node is calculated based on a variable load scheduling model in which the load follows the source; The variable load scheduling model with load following source is a consistency benchmark scheduling model for voltage, current, frequency and power of DC system and DC and AC hybrid system established based on the connection relationship between the power generation nodes and power consumption nodes of the virtual power plant.

[0009] In the embodiment of the present invention, the expression of the variable load scheduling model in which the load follows the source is: ; Among them, Z ij Indicates the DC node i With DC node j The energy interaction balance equation between ki Represents a communication node k With DC node i The energy interaction balance equation between them; z im For the i The DC node m DC variable, z jm For z im DC variables of adjacent DC nodes, including voltage, current, and power; For the k The first communication node n AC variables include: voltage, current, frequency and power.

[0010] In an embodiment of the present invention, regulating the energy interaction between the microgrid, the new energy access node, and the load user according to the linkage control strategy based on the energy control law includes: Establish energy control laws for a single DC node and a single AC node; According to the number of nodes in the AC system and the DC system, the energy control law of the AC system and the DC system is established; Determining correction factors based on capacity changes of subnetworks of the AC and DC systems; Correction factors are used to control DC voltage, current, AC voltage and AC frequency so that the control results meet the optimal target optimization function.

[0011] In the embodiment of the present invention, the energy control law of a single DC node and a single AC node is expressed as follows: ; ; in, m ij 、 m ki The coefficient representing the energy transfer between nodes, m ij ≤1, m ki ≤1; P zi (t) indicates the i The DC power variable of a DC node at time t; P zi (t-1) represents the i The DC power variable of each DC node at time t-1; P zj (t-1) represents the j The DC power variable of each DC node at time t-1; P ak (t) indicates the k The effective power variable of each AC node at time t; P ak (t-1) represents the k The effective power variable of each AC node at time t-1.

[0012] In the embodiment of the present invention, the energy control law of the AC system and the DC system is expressed as follows: ; ; P z sum (t) represents the energy difference of the DC system at time t, P a sum (t) represents the energy difference of the AC system at time t, i represents the DC node, m is the number of DC nodes, k represents the communication node,n is the number of communication nodes.

[0013] In the embodiment of the present invention, the expression for determining the correction factor based on the capacity change of the subnets of the AC system and the DC system is: ; ; in, l z (t) represents the correction factor of the DC system at time t, l a (t) is the correction factor of the AC system at time t, P z sum (t) represents the energy difference of the DC system at time t, P z sum (t-1) represents the energy difference of the DC system at time t-1, P a sum (t) represents the energy difference of the AC system at time t, P a sum (t-1) represents the energy difference of the AC system at time t-1.

[0014] In the embodiment of the present invention, the expression for regulating the DC voltage using the correction factor is: ; Where Vz(t) represents the DC voltage at time t, Vz(t+1) represents the DC voltage at time t+1, l z is the DC correction factor, V z max 、V z min Respectively represent the maximum and minimum values ​​allowed for DC voltage, It represents the average value of DC voltage during the time period t.

[0015] In the embodiment of the present invention, the expression of the objective optimization function is: ; Among them, X 比例 is the proportion of new energy consumption, Xz is the DC output power of new energy power generation, X A is the AC output power of renewable energy power generation, X 装机 is the installed power of new energy, or 效率 For the utilization efficiency of new energy, X 装机 * or 效率 Indicates the maximum output power that new energy can provide; CAggregate charging cost under different charging modes, including: fast charging cost C 快 , slow charging cost C 慢 , Adjustable cost C 可调 ; V z Indicates DC voltage, V a Indicates AC voltage and F a represents the AC frequency, Φ represents all constraints on system operation; st is a mathematical symbol representing the constraint condition.

[0016] A second aspect of the present invention provides a virtual power plant control system for accessing new energy, the virtual power plant comprising: an AC / DC system, a distributed new energy power generation system, and a load system, the virtual power plant control system for accessing new energy comprising: a coordination system; The coordination system exchanges information with the AC / DC system, the distributed renewable energy power generation system and the load system respectively, and exchanges energy among the AC / DC system, the distributed renewable energy power generation system and the load system; The coordination system is used to: establish an electricity node cost model based on the electricity demand of load system users and the energy interaction balance principle between the nodes of the distributed renewable energy power generation system and the AC / DC system; calculate the electricity cost corresponding to the electricity demand of the load system users through the electricity node cost model; and, under the condition that the load system users accept the electricity cost, regulate the energy interaction between the nodes of the distributed renewable energy power generation system and the AC / DC system and the load system according to the linkage control strategy based on the energy control law.

[0017] In an embodiment of the present invention, the coordination system establishes a consistency benchmark scheduling model for the voltage, current, frequency and power of the DC system and the DC and AC hybrid system based on the connection relationship between the power generation nodes and the power consumption nodes of the virtual power plant, establishes a variable load scheduling model with load following source based on the consistency benchmark scheduling model, calculates the energy interaction cost between each DC node and each AC node according to the variable load scheduling model with load following source, and establishes a power consumption node cost model according to the energy interaction cost between each DC node and each AC node.

[0018] In an embodiment of the present invention, the coordination system regulates the energy interaction between the distributed renewable energy power generation system, each node of the AC / DC system, and the load system according to a linkage control strategy based on an energy control law, including: Establish energy control laws for a single DC node and a single AC node; According to the number of nodes in the AC system and the DC system, the energy control law of the AC system and the DC system is established; Determining correction factors based on capacity changes of subnetworks of the AC and DC systems; Correction factors are used to control DC voltage, current, AC voltage and AC frequency so that the control results meet the optimal target optimization function.

[0019] In the embodiment of the present invention, the energy control law of a single DC node and a single AC node is expressed as follows: ; ; in, m ij 、 m ki The coefficient representing the energy transfer between nodes, m ij ≤1, m ki ≤1; P zi (t) indicates the i The DC power variable of a DC node at time t; P zi (t-1) represents the i The DC power variable of each DC node at time t-1; P zj (t-1) represents the j The DC power variable of each DC node at time t-1; P ak (t) indicates the k The effective power variable of each AC node at time t; P ak (t-1) represents the k The effective power variable of each AC node at time t-1.

[0020] The present invention also provides a computer device, including: a memory and a processor, the memory storing a computer program, and the processor being used to execute the computer program to implement the above-mentioned virtual power plant management method for new energy access.

[0021] The above technical solution establishes an electricity node cost model based on the electricity demand of load users and the energy interaction balance principle between the nodes of the virtual power plant connected to new energy. Under the cost constraint, the energy interaction between microgrids, new energy access nodes and load users is regulated according to the linkage control strategy based on the energy control law, so as to realize the effective absorption of new energy, reduce the impact on the power grid, and improve the dynamic robustness of the power grid through the new energy subject.

[0022] Other features and advantages of the technical solution of the present invention will be described in detail in the specific implementation section below. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 This is a flow chart of a virtual power plant management method for new energy access provided by an embodiment of the present invention; Figure 2 This is a flowchart of a virtual power plant management method for new energy access provided by a specific example of the present invention; Figure 3 This is an architectural diagram of a virtual power plant control system for new energy access provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] To make the technical solutions and advantages of the embodiments of the present invention more clearly understood, exemplary embodiments of the present invention are further described in detail below with reference to the accompanying drawings. It should be noted that the embodiments described are only a portion of the embodiments of the present invention, and are not an exhaustive list of all embodiments. It should be noted that the embodiments of the present invention and the features thereof may be combined with each other unless they conflict.

[0025] The embodiments of the present invention provide a new energy access management method applicable to virtual power plants, focusing on photovoltaic power generation and charging piles. Through orderly optimization management, it can achieve effective absorption of new energy while reducing the impact on the power grid. In response to the uncertainty of power generation and consumption, a consistency benchmark scheduling model is constructed, and an adaptive method for variable loads with load following source is proposed to achieve coordination and transformation of source and load, and improve the dynamic robustness of the power grid through the main body of new energy. In terms of source-load coordination, a linkage control strategy based on key indicators is proposed, which utilizes the regulatory characteristics of variable loads and the potential of new energy power generation to increase the proportion of new energy absorption.

[0026] Figure 1 This is a flow chart of a virtual power plant management method for new energy access provided by an embodiment of the present invention. Figure 1 As shown, the virtual power plant management method for new energy access provided in this embodiment includes the following steps: S110, establishing a power node cost model based on the power demand of load users and the energy interaction balance principle between each DC node or AC node of the virtual power plant connected to the new energy; S120, calculating the electricity cost corresponding to the load user's electricity demand using an electricity node cost model; S130 , under the condition that the load user accepts the electricity cost, regulating the energy interaction between the microgrid, the new energy access node and the load user according to the linkage control strategy based on the energy control law.

[0027] In a specific embodiment, in the above step S110, according to the participating entities of the distribution microgrid and the connection relationship between the power generation and power consumption nodes, a DC system Z is established. ij and DC and AC systems A ki The voltage, current, frequency, power and other consistency benchmark scheduling models provide a control model basis for the iterative calculation of energy balance in the microgrid.

[0028] A distribution microgrid is a power generation and distribution network based on a microgrid. A microgrid, also known as a microgrid, is a small power generation and distribution system consisting of distributed power sources, energy storage devices, energy conversion devices, loads, and monitoring and protection devices.

[0029] A virtual power plant is similar to a microgrid. Resources in a virtual power plant include both AC and DC, with each node interacting locally through interconnected lines. Based on the connections between the generation and consumption nodes of the virtual power plant, a consistent benchmark scheduling model for voltage, current, frequency, and power is established for DC systems and hybrid DC / AC systems. This serves as the foundation for a variable load scheduling model that follows the source. The model is expressed as: ; Among them, Z ij Indicates the DC node i With DC node j The energy interaction balance equation between ki Represents a communication node k With DC node i The energy interaction balance equation between them; z im For the i The DC node m DC variable, z jm For z im DC variables of adjacent DC nodes, including voltage, current, and power; For the k The first communication node n AC variables include: voltage, current, frequency and power.

[0030] The above model serves as an electrical parameter model for all nodes within the virtual power plant (including both DC and AC nodes), providing the foundation for power generation and consumption scheduling among participating entities. This model represents the energy flow and power quality within the system. To achieve energy balance during scheduling, it is also necessary to ensure that electrical parameters meet relevant requirements. For example, for three-phase power supply of 10 kV and below, energy exchange is performed under the following conditions: the voltage tolerance is ±7% of the rated value, the nominal frequency is 50 Hz, the tolerance is controlled within ±0.5 Hz, and the harmonic content is within the range of 5-10%. Due to the volatile nature of renewable energy generation, which can result in voltage fluctuations, frequency deviations, and harmonic components, these indicators are incorporated into this electrical parameter model.

[0031] According to the variable load scheduling model where load follows source, the energy interaction cost between each DC node and each AC node can be calculated. Based on the above-mentioned consistency benchmark scheduling model (electrical quantity parameter model), according to the electricity demand of load users, an electricity node cost model is established to provide a basis for the controllable load in the virtual power plant system.

[0032] The cost parameters of the electricity consumption node cost model include: the cost of renewable energy power generation, the energy interaction cost between each DC node and each AC node.

[0033] Taking charging piles as an example, the electricity cost of charging users is divided into three charging modes: mode 1 is fast charging mode, mode 2 is slow charging mode, and mode 3 is adjustable mode. The electricity node cost model based on different charging modes is expressed as: ; Among them, the fast charging cost C 快 Including: New energy power generation (DC) X Z The cost of C Xz , the interaction energy Z between DC nodes ij The cost of C Zij , the cost C of parking T T ; Slow charging cost C 慢 Including: New energy power generation (AC) X A The cost of C XA , the interaction energy A between DC node and AC node ki The cost of C Aki , the cost C of parking T T ; Adjustable cost C 可调 Including: Energy A of the interaction between the charging pile and the power grid ki With Z ij The resulting profit difference and the recoil energy X provided by the charging pile to the grid 反冲 cost.

[0034] In step S120, the electricity cost corresponding to the load user's electricity demand is calculated using the electricity node cost model, and the electricity cost information is distributed to the load user. The load user determines whether the electricity cost is acceptable. If the load user does not accept the electricity cost, they can modify the charging mode (change the electricity demand) and recalculate the electricity cost using the electricity node cost model. If the load user accepts the electricity cost, subsequent linkage control is carried out.

[0035] In the above step S130, the energy interaction between the microgrid, the new energy access node and the load users is regulated according to the linkage control strategy based on the energy control law.

[0036] In a specific embodiment, considering different scenarios under virtual power plants, a key indicator linkage control strategy method consisting of power, voltage, current, frequency and cost under AC / DC hybrid working conditions is proposed to increase the proportion of new energy consumption and reduce users' electricity costs. The specific process of the key indicator linkage control strategy method is as follows: Step 1: Establish the energy control law of a single DC node and a single AC node; Step 2: Establish energy control laws for the AC system and the DC system based on the number of nodes in the AC system and the DC system; Step 3: determining a correction factor based on the capacity change of the subnets of the AC system and the DC system; Step 4: Use the correction factor to control the DC voltage, current, AC voltage and AC frequency so that the control results meet the optimal target optimization function.

[0037] In step 1 above, differentiated definitions can be performed based on the unified model. i1 =Pz i is the DC power variable of the i-th DC node, z i2 =Vz i The voltage variable of the i-th DC node, a k1 =P ak The effective power variable of the kth AC node, a k2 =V ak The voltage variable of the kth AC node, a a3 =F ak The frequency variable of the kth AC node. The energy control law of a single DC node and a single AC node is established, and its expression is: ; ; in, m ij 、 m ki The coefficient representing the energy transfer between nodes, mij ≤1, m ki ≤1; P zi (t) indicates the i The DC power variable of a DC node at time t, P zi = Z i1 ; P zi (t-1) represents the i The DC power variable of each DC node at time t-1; P zj (t-1) represents the j The DC power variable of each DC node at time t-1; P ak (t) indicates the k The effective power variable of each AC node at time t; P ak (t-1) represents the k The effective power variable of each AC node at time t-1.

[0038] In step 2 above, the energy control law of the AC and DC systems is established based on the number of nodes in the AC and DC systems. The energy control law of the AC and DC systems is expressed as: ; ; P z sum (t) represents the energy difference of the DC system at time t, P a sum (t) represents the energy difference of the AC system at time t, i represents the DC node, m is the number of DC nodes, k represents the communication node, n is the number of communication nodes.

[0039] In step 3 above, the correction factor is determined based on the capacity change of the AC and DC subnets, and its expression is: ; ; in, l z (t) represents the correction factor of the DC system at time t, la (t) is the correction factor of the AC system at time t, P z sum (t) represents the energy difference of the DC system at time t, P z sum (t-1) represents the energy difference of the DC system at time t-1, P a sum (t) represents the energy difference of the AC system at time t, P a sum (t-1) represents the energy difference of the AC system at time t-1.

[0040] In step 4 above, the correction factors are used to determine the output results of DC voltage Vz, current I, AC voltage Va, and AC frequency Fa for coordinated control. The correction factors for DC voltage Vz, current I, AC voltage Va, and AC frequency Fa are expressed as: ; ; ; ; Where Vz(t) represents the DC voltage at time t, Vz(t+1) represents the DC voltage at time t+1, l z is the DC correction factor, V z max 、V z min Respectively represent the maximum and minimum values ​​allowed for DC voltage, It represents the average value of DC voltage in the time period t; Iz(t) represents the current at time t, Iz(t+1) represents the current at time t+1, l z is the DC correction factor, I z max , I z min Respectively represent the maximum and minimum values ​​allowed for current, Represents the average current value during the time period t; Va(t) represents the AC voltage at time t, Va(t+1) represents the AC voltage at time t+1, l a is the AC correction factor, V a max 、V a min Respectively represent the maximum and minimum values ​​allowed for AC voltage, It represents the average value of AC voltage in the time period t; Fa(t) represents the AC frequency at time t, Fa(t+1) represents the AC frequency at time t+1, l a is the AC correction factor, F a max 、F a min Respectively represent the maximum and minimum values ​​allowed for AC frequency, It represents the average value of AC frequency during the time period t.

[0041] Taking into account the output characteristics of distributed photovoltaics and the adjustable nature of flexible resources, with the goal of maximizing the proportion of renewable energy consumption, the key indicators of each node of the virtual power plant under the local consumption of distributed photovoltaics are met. While also considering operational economics, the cost C of adjustable resources on the user side is considered. With the goal of minimizing user electricity costs, a target optimization function is constructed to control the output results of DC voltage, current, and AC voltage and frequency in real time.

[0042] The expression of the objective optimization function is: ; Among them, X 比例 is the proportion of new energy consumption, Xz is the DC output power of new energy power generation, X A is the AC output power of renewable energy power generation, X 装机 is the installed power of new energy, or 效率 For the utilization efficiency of new energy, X 装机 * or 效率 Indicates the maximum output power that new energy can provide; C Aggregate charging cost under different charging modes, including: fast charging cost C 快 , slow charging cost C 慢 , Adjustable cost C 可调 ; V z Indicates DC voltage, V a Indicates AC voltage and F a represents the AC frequency, Φ represents all constraints on system operation; st is a mathematical symbol representing the constraint condition.

[0043] Reference Figure 2 In a specific example, with photovoltaic power generation as the "source" and charging piles as the "load", the virtual power plant management method based on distributed photovoltaic power generation has the following specific process: (1) Sort out system nodes and build a node consistency benchmark scheduling model; (2) Using the consistency benchmark scheduling model, select the load-follow-source charging mode and output the electricity cost corresponding to the charging mode; (3) Send the electricity cost corresponding to the charging mode to the load user (charging pile user) to determine whether the load user can accept the electricity cost; if the load user does not accept the electricity cost, modify the charging mode (change the electricity demand), recalculate the electricity cost, and send it; (4) If the load user accepts the electricity cost, the linkage control strategy of key indicators is implemented; (5) Output correction factor λ; (6) Output control parameters: DC voltage Vz, AC voltage Va, AC frequency Fa; (7) Use the target optimization function to determine whether the control parameters have reached the optimal level. If so, end the process; if not, switch the linkage control strategy of the key indicators.

[0044] Figure 3 This is the architecture diagram of the virtual power plant control system for new energy access provided by the embodiment of the present invention. Figure 3 As shown, this embodiment provides a control system for a virtual power plant with renewable energy access. The virtual power plant consists of an AC / DC system (i.e., the "grid"), a distributed renewable energy generation system (i.e., the "source"), and a load system (i.e., the "load"). The virtual power plant control system includes a coordination system that exchanges information with the AC / DC system, the distributed renewable energy generation system, and the load system, and that exchanges energy among the AC / DC system, the distributed renewable energy generation system, and the load system.

[0045] The coordination system is used to establish an electricity node cost model based on the electricity demand of load system users and the energy interaction balance principle between the nodes of the distributed renewable energy power generation system and the AC / DC system; calculate the electricity cost corresponding to the electricity demand of the load system users through the electricity node cost model; and, under the condition that the load system users accept the electricity cost, regulate the energy interaction between the nodes of the distributed renewable energy power generation system and the AC / DC system and the load system according to the linkage control strategy based on the energy control law.

[0046] In a specific embodiment, the coordination system establishes a consistency benchmark scheduling model for the voltage, current, frequency and power of the DC system and the DC and AC hybrid system based on the connection relationship between the power generation nodes and the power consumption nodes of the virtual power plant, establishes a variable load scheduling model with load following source based on the consistency benchmark scheduling model, calculates the energy interaction cost between each DC node and each AC node based on the variable load scheduling model with load following source, and establishes a power consumption node cost model based on the energy interaction cost between each DC node and each AC node.

[0047] Among them, the expression of the consistency benchmark scheduling model is: ; Among them, Z ij Indicates the DC node i With DC node j The energy interaction balance equation between ki Represents a communication node k With DC node i The energy interaction balance equation between them; z im For the i The DC node m DC variable, z jm For z im DC variables of adjacent DC nodes, including voltage, current, and power; For the k The first communication node n AC variables include: voltage, current, frequency and power.

[0048] Based on the above consistency benchmark scheduling model, an electricity consumption node cost model is established according to the electricity demand of load users, providing a basis for the controllable load in the virtual power plant system. Taking charging piles as an example, the electricity consumption cost of charging users is divided into three charging modes: mode 1 is fast charging mode, mode 2 is slow charging mode, and mode 3 is adjustable mode. The electricity consumption node cost model based on different charging modes is expressed as: ; Among them, the fast charging cost C 快 Including: New energy power generation (DC) X Z The cost of C Xz , the interaction energy Z between DC nodes ij The cost of C Zij , the cost C of parking T T ; Slow charging cost C 慢 Including: New energy power generation (AC) X A The cost of C XA , the interaction energy A between DC node and AC node ki The cost of C Aki , the cost C of parking T T ; Adjustable cost C 可调 Including: Energy A of the interaction between the charging pile and the power grid ki With Z ij The resulting profit difference and the recoil energy X provided by the charging pile to the grid 反冲 cost.

[0049] In a specific embodiment, the coordination system regulates the energy interaction between the distributed renewable energy power generation system, each node of the AC / DC system, and the load system according to a linkage control strategy based on the energy control law, including: Establish energy control laws for a single DC node and a single AC node; According to the number of nodes in the AC system and the DC system, the energy control law of the AC system and the DC system is established; Determining correction factors based on capacity changes of subnetworks of the AC and DC systems; Correction factors are used to control DC voltage, current, AC voltage and AC frequency so that the control results meet the optimal target optimization function.

[0050] The energy control law for a single DC node and a single AC node is expressed as: ; ; in, m ij 、 m ki The coefficient representing the energy transfer between nodes, m ij ≤1, m ki ≤1; P zi (t) indicates the i The DC power variable of a DC node at time t; P zi (t-1) represents the i The DC power variable of each DC node at time t-1; P zj (t-1) represents the j The DC power variable of each DC node at time t-1; P ak (t) indicates the k The effective power variable of each AC node at time t; P ak (t-1) represents the k The effective power variable of each AC node at time t-1.

[0051] According to the number of nodes in the AC and DC systems, the energy control law of the AC and DC systems is established. The expressions of the energy control law of the AC and DC systems are: ; ; P z sum (t) represents the energy difference of the DC system at time t, P a sum (t) represents the energy difference of the AC system at time t, i represents the DC node, m is the number of DC nodes, k represents the communication node, n is the number of communication nodes.

[0052] The correction factor is determined based on the capacity change of the AC and DC subnets, and its expression is: ; ; in, l z (t) represents the correction factor of the DC system at time t, l a (t) is the correction factor of the AC system at time t, P z sum (t) represents the energy difference of the DC system at time t, P z sum (t-1) represents the energy difference of the DC system at time t-1, P a sum (t) represents the energy difference of the AC system at time t, P a sum (t-1) represents the energy difference of the AC system at time t-1.

[0053] The correction factors are used to determine the output results of DC voltage Vz, current I, AC voltage Va, and AC frequency Fa for coordinated control. The correction factors of DC voltage Vz, current I, AC voltage Va, and AC frequency Fa are expressed as: ; ; ; ; Where Vz(t) represents the DC voltage at time t, Vz(t+1) represents the DC voltage at time t+1, l z is the DC correction factor, V z max 、V z min Respectively represent the maximum and minimum values ​​allowed for DC voltage, It represents the average value of DC voltage in the time period t; Iz(t) represents the current at time t, Iz(t+1) represents the current at time t+1, l z is the DC correction factor, I z max , I z min Respectively represent the maximum and minimum values ​​allowed for current, Represents the average current value during the time period t; Va(t) represents the AC voltage at time t, Va(t+1) represents the AC voltage at time t+1, l a is the AC correction factor, V a max 、V a min Respectively represent the maximum and minimum values ​​allowed for AC voltage, It represents the average value of AC voltage in the time period t; Fa(t) represents the AC frequency at time t, Fa(t+1) represents the AC frequency at time t+1, l a is the AC correction factor, F a max 、F a min Respectively represent the maximum and minimum values ​​allowed for AC frequency, It represents the average value of AC frequency during the time period t.

[0054] Considering the output characteristics of distributed photovoltaics and the adjustable characteristics of flexible resources, with the goal of maximizing the proportion of new energy consumption, the key indicators of each node of the virtual power plant under the local consumption of distributed photovoltaics are met. At the same time, considering the cost C of adjustable resources on the user side, and with the goal of minimizing user electricity costs, a target optimization function is constructed to control the output results of DC voltage, current, AC voltage, and frequency in real time. The expression of the target optimization function is: ; Among them, X 比例 is the proportion of new energy consumption, Xz is the DC output power of new energy power generation, X A is the AC output power of renewable energy power generation, X 装机 is the installed power of new energy, or 效率 For the utilization efficiency of new energy, X 装机 * or 效率 Indicates the maximum output power that new energy can provide; CAggregate charging cost under different charging modes, including: fast charging cost C 快 , slow charging cost C 慢 , Adjustable cost C 可调 ; V z Indicates DC voltage, V a Indicates AC voltage and F a represents the AC frequency, Φ represents all constraints on system operation; st is a mathematical symbol representing the constraint condition.

[0055] The virtual power plant management method and system for new energy access provided in the embodiment of the present invention adopts an adaptive method of variable load with load following source to realize the role transformation and interaction between source and load, and improve the dynamic robustness of the power grid through the new energy subject. The embodiment of the present invention focuses on photovoltaic power generation and charging piles, optimizes management based on the linkage control strategy of key indicators, utilizes the regulation characteristics of variable loads and the potential of new energy power generation to increase the proportion of new energy consumption, realizes the effective consumption of new energy, and reduces the impact on the power grid. The virtual power plant control system provided in the embodiment of the present invention realizes the optimization of distributed new energy consumption and the interactive optimization of load-side resources based on the control architecture of the virtual power plant, and improves the dynamic robustness of the power grid.

[0056] An embodiment of the present invention also provides a computer device, including: a memory and a processor, the memory storing a computer program, and the processor being used to execute the computer program to implement the above-mentioned virtual power plant management method for new energy access.

[0057] An embodiment of the present invention also provides a machine-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the virtual power plant management method for new energy access is implemented.

[0058] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk drives, CD-ROMs, optical storage devices, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0059] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure one a process or multiple processes and / or boxes Figure one A device that provides the functions specified in a block or multiple blocks.

[0060] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure one a process or multiple processes and / or boxes Figure one The function specified in one or more boxes.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure one a process or multiple processes and / or boxes Figure one A step that specifies a function in one or more boxes.

[0062] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

1. A virtual power plant management method for new energy access, characterized in that: include: Based on the electricity demand of load users and the energy interaction balance principle between each DC node or AC node of the virtual power plant connected to new energy, a power node cost model is established; Calculate the electricity cost corresponding to the load user's electricity demand through the electricity node cost model; Under the condition that load users accept the electricity cost, the energy interaction between the microgrid, the new energy access node and the load users is regulated according to the linkage control strategy based on the energy control law.

2. The virtual power plant management method for new energy access according to claim 1 is characterized in that: The cost parameters of the electricity consumption node cost model include: the cost of new energy power generation, and the energy interaction cost between each DC node and each AC node.

3. The virtual power plant management method for new energy access according to claim 2 is characterized in that: The energy interaction cost between each DC node and each AC node is calculated based on the variable load scheduling model where the load follows the source; The variable load scheduling model with load following source is a consistency benchmark scheduling model for voltage, current, frequency and power of DC system and DC and AC hybrid system established based on the connection relationship between the power generation nodes and power consumption nodes of the virtual power plant.

4. The virtual power plant management method for new energy access according to claim 3 is characterized in that: The expression of the variable load dispatch model with load following source is: ； Among them, Z ij Indicates the DC node i With DC node j The energy interaction balance equation between ki Represents a communication node k With DC node i The energy interaction balance equation between them; z im For the i The DC node m DC variable, z jm For z im DC variables of adjacent DC nodes, including voltage, current, and power; For the k The first communication node n AC variables include: voltage, current, frequency and power.

5. The virtual power plant management method for new energy access according to claim 4 is characterized in that: The energy interaction between the microgrid, the new energy access node and the load user is regulated according to the linkage control strategy based on the energy control law, including: Establish energy control laws for a single DC node and a single AC node; According to the number of nodes in the AC system and the DC system, the energy control law of the AC system and the DC system is established; Determining correction factors based on capacity changes of subnetworks of the AC and DC systems; Correction factors are used to control DC voltage, current, AC voltage and AC frequency so that the control results meet the optimal target optimization function.

6. The virtual power plant management method for new energy access according to claim 5 is characterized in that: The expression of the energy control law for a single DC node and a single AC node is: ； ； in, μ ij 、 μ ki The coefficient representing the energy transfer between nodes, μ ij ≤1, μ ki ≤1; P zi (t) indicates the i The DC power variable of a DC node at time t; P zi (t-1) represents the i The DC power variable of each DC node at time t-1; P zj (t-1) represents the j The DC power variable of each DC node at time t-1; P ak (t) indicates the k The effective power variable of each AC node at time t; P ak (t-1) represents the k The effective power variable of each AC node at time t-1.

7. The virtual power plant management method for new energy access according to claim 6, characterized in that: The energy control law of AC system and DC system is expressed as: ； ； P z sum (t) represents the energy difference of the DC system at time t, P a sum (t) represents the energy difference of the AC system at time t, i represents the DC node, m is the number of DC nodes, k represents the communication node, n is the number of communication nodes.

8. The virtual power plant management method for new energy access according to claim 7 is characterized in that: The expression for determining the correction factor based on the capacity change of the subnets of the AC system and the DC system is: ； ； in, λ z (t) represents the correction factor of the DC system at time t, λ a (t) is the correction factor of the AC system at time t, P z sum (t) represents the energy difference of the DC system at time t, P z sum (t-1) represents the energy difference of the DC system at time t-1, P a sum (t) represents the energy difference of the AC system at time t, P a sum (t-1) represents the energy difference of the AC system at time t-1.

9. The virtual power plant management method for new energy access according to claim 8, characterized in that: The expression for controlling DC voltage using the correction factor is: ； Where Vz(t) represents the DC voltage at time t, Vz(t+1) represents the DC voltage at time t+1, λ z is the DC correction factor, V z max 、V z min Respectively represent the maximum and minimum values ​​allowed for DC voltage, It represents the average value of DC voltage during the time period t.

10. The virtual power plant management method for new energy access according to claim 5, characterized in that: The expression of the objective optimization function is: ； Among them, X 比例 is the proportion of new energy consumption, Xz is the DC output power of new energy power generation, X A is the AC output power of renewable energy power generation, X 装机 is the installed power of new energy, η 效率 For the utilization efficiency of new energy, X 装机 * η 效率 Indicates the maximum output power that new energy can provide; C Aggregate charging cost under different charging modes, including: fast charging cost C 快 , slow charging cost C 慢 , Adjustable cost C 可调 ; V z Indicates DC voltage, V a Indicates AC voltage and F a represents the AC frequency, Φ represents all constraints on system operation; st is a mathematical symbol representing a constraint condition.

11. A virtual power plant control system for new energy access, characterized in that: The virtual power plant includes: an AC / DC system, a distributed new energy power generation system and a load system. The virtual power plant control system connected to the new energy includes: a coordination system; The coordination system exchanges information with the AC / DC system, the distributed renewable energy power generation system and the load system respectively, and exchanges energy among the AC / DC system, the distributed renewable energy power generation system and the load system; The coordination system is used to: Based on the electricity demand of load system users and the energy interaction balance principle between the nodes of the distributed renewable energy power generation system and the AC / DC system, a power node cost model is established; Calculate the electricity cost corresponding to the electricity demand of load system users through the electricity node cost model; Under the condition that the load system users accept the electricity cost, the energy interaction between the distributed renewable energy power generation system, each node of the AC / DC system and the load system is regulated according to the linkage control strategy based on the energy control law.

12. The virtual power plant control system for new energy access according to claim 11, characterized in that: The coordination system establishes a consistency benchmark scheduling model for the voltage, current, frequency and power of the DC system and the DC and AC hybrid system based on the connection relationship between the power generation nodes and the power consumption nodes of the virtual power plant, establishes a variable load scheduling model with load following source based on the consistency benchmark scheduling model, calculates the energy interaction cost between each DC node and each AC node based on the variable load scheduling model with load following source, and establishes a power consumption node cost model based on the energy interaction cost between each DC node and each AC node.

13. The virtual power plant control system for new energy access according to claim 12, characterized in that: The coordination system regulates the energy interaction between the distributed renewable energy power generation system, each node of the AC / DC system, and the load system according to a linkage control strategy based on the energy control law, including: Establish energy control laws for a single DC node and a single AC node; According to the number of nodes in the AC system and the DC system, the energy control law of the AC system and the DC system is established; Determining correction factors based on capacity changes of subnetworks of the AC and DC systems; Correction factors are used to control DC voltage, current, AC voltage and AC frequency so that the control results meet the optimal target optimization function.

14. The virtual power plant control system for new energy access according to claim 13, characterized in that: The expression of the energy control law for a single DC node and a single AC node is: ； ； in, μ ij 、 μ ki The coefficient representing the energy transfer between nodes, μ ij ≤1, μ ki ≤1; P zi (t) indicates the i The DC power variable of a DC node at time t; P zi (t-1) represents the i The DC power variable of each DC node at time t-1; P zj (t-1) represents the j The DC power variable of each DC node at time t-1; P ak (t) indicates the k The effective power variable of each AC node at time t; P ak (t-1) represents the k The effective power variable of each AC node at time t-1.

15. A computer device, characterized in that: include: a memory storing a computer program; A processor, configured to execute the computer program to implement the virtual power plant management method for new energy access as described in any one of claims 1-10.

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