CN-122001005-A - Virtual power plant optimal scheduling method and system

CN122001005ACN 122001005 ACN122001005 ACN 122001005ACN-122001005-A

Abstract

A virtual power plant optimal scheduling method and system includes the steps of establishing an operation model of a plurality of types of adjustable resource main bodies, constructing master-slave game models of an aggregator and the resource main bodies, wherein the master-slave game models comprise an upper model taking the aggregator as a leader and a lower model taking the resource main bodies as followers, solving the master-slave game models of the aggregator and the resource main bodies to obtain an optimal compensation price strategy and an optimal response power strategy, constructing an optimal power scheduling plan according to the optimal response power strategy, and issuing the optimal power scheduling plan to each adjustable resource main body for execution so as to achieve optimal scheduling of the virtual power plant. The method and the device can excite the participation enthusiasm of the resource main body, improve the user participation degree, improve the execution rate of the scheduling instruction, improve the accuracy of the model, and improve the cooperative capacity of heterogeneous resources, thereby improving the overall operation economy and stability of the virtual power plant.

Inventors

DOU ZHENLAN
ZHANG TAO
CHU LINLIN
YI YUE
CHEN YANJUN
ZHENG YURONG
ZHOU JING
MENG FANQIANG
GAO JUN
SUN ZHIPENG

Assignees

国网上海市电力公司

Dates

Publication Date: 20260508
Application Date: 20251224

Claims (11)

1. The virtual power plant optimal scheduling method is characterized by comprising the following steps of: The method comprises the steps of establishing an operation model of a multi-type adjustable resource main body, establishing a master-slave game model of an aggregator and the resource main body, comprising an upper model taking the aggregator as a leader and a lower model taking the resource main body as a follower, wherein in the upper model, the aggregator makes a decision under a preset constraint condition with the aim of minimizing the total operation cost to obtain a compensation price strategy issued to various resource main bodies, in the lower model, each resource main body makes a decision with the aim of maximizing the income of each resource main body based on the established operation model to obtain a response power strategy of each resource main body, the resource main body makes a game according to the compensation price strategy decision power response strategy, the power response strategy influences the aggregator to formulate the compensation price strategy, the resource main body and the aggregator are used for carrying out the game, the master-slave game model of the aggregator and the resource main body is solved to obtain an optimal compensation price strategy and an optimal response power strategy, and the optimal power scheduling plan is used for being issued to each adjustable resource main body to execute so as to realize the optimal scheduling of the virtual power plant.
2. The virtual power plant optimization scheduling method of claim 1, wherein: the objective function of the following aggregator is constructed with the aim of minimizing the total operating cost: Wherein, the Representing the total operating cost of the aggregator, Representing the price of electricity purchased to the upper grid, The electricity purchasing power of the upper power grid at the time t is represented, Representing the offset price that the aggregator publishes to resource principal i, The response power of the resource body i is indicated, Representing the cost incurred when the aggregate's total power deviates from the grid instructions.
3. The virtual power plant optimization scheduling method of claim 1, wherein: constructing an objective function of each resource main body with the aim of maximizing the benefit of each resource main body: Wherein, the Representing the benefit of the resource principal i, The response power of the resource main body i is represented, and comprises photovoltaic reduction amount, energy storage regulation power, air conditioner regulation power and EV regulation power, Representing the cost corresponding to resource principal i.
4. The virtual power plant optimal scheduling method according to claim 1, wherein: the master-slave game model for solving the aggregators and the resource main body comprises the following steps: initializing an upper layer particle swarm, coding a compensation price strategy of an aggregator into particle positions, and randomly initializing the particle swarm; For the price strategy represented by each particle, calling a genetic algorithm to solve, and aiming at maximizing the income of each resource main body, solving to obtain the optimal response power strategy of all the resource main bodies under the price strategy; calculating the total operation cost corresponding to the price strategy by combining the optimal response power strategies of all resource main bodies; Updating the speed and the position of the particles according to the total operation cost corresponding to the price strategy to update the price strategy; repeating the steps until the particle swarm algorithm converges to obtain the equilibrium solution of the master-slave game model.
5. The virtual power plant optimization scheduling method of claim 4, wherein: updating the inertia weight according to the difference between the total operation cost of the aggregation provider at the current moment and the total operation cost at the previous moment, and based on the updated inertia weight and the speed of new particles, specifically: Wherein, the The inertia weight at the moment of t is indicated, Representing the maximum value of the inertial weight, Representing the minimum value of the inertial weight, Representing the difference between the total operation cost of the upper layer aggregation business at the time t and the total operation cost at the previous time, Representing the total operating cost of the upper layer aggregator at the initial iteration, Representing the coefficient of the scheduling fluctuation, Represents an adjustment factor for controlling the degree to which the inertial weights vary with the total operating cost.
6. The virtual power plant optimization scheduling method of claim 4, wherein: the speed of updating particles by combining the resource response power is specifically as follows: Wherein, the The velocity of particle i at time t +1 is indicated, The velocity of the particle i at time t is indicated, The inertia weight at the moment of t is indicated, Indicating that particle i historically performed the best price strategy at time t, Indicating the best price strategy to perform throughout the population at time t, The price policy of particle i at time t is represented, The local learning factor is represented as such, Representing the global learning factor(s), 、 Are random numbers in the range of 0 to 1, Indicating that the response rate deviation correction factor is, And the difference value of the actual resource response rate and the target resource response rate is represented, and the response rate is an index for evaluating the actual response effect and is used for reflecting whether the total response rate of all resource main bodies in the virtual power plant reaches the total response rate expected by an aggregator.
7. The virtual power plant optimization scheduling method of claim 4, wherein: And calculating the response sensitivity of the resource according to the sensitivity degree of the response power of the resource main body to the compensation price change, and setting the interval between the upper iteration and the lower iteration according to the response sensitivity of the resource to perform hierarchical iteration.
8. The virtual power plant optimization scheduling method of claim 4, wherein: after the upper layer particles are updated, whether the genetic algorithm is called for solving the response power strategy or not is determined according to the change rate of the compensation price.
9. A virtual power plant optimal scheduling system implementing the virtual power plant optimal scheduling method of any one of claims 1-8, the system comprising: the resource building and managing module is used for building an operation model of the multi-type adjustable resource main body; The two-way game optimization calculation module comprises a master-slave game module and a solving module, The method comprises the steps of constructing a master-slave game module for constructing master-slave game models of an aggregator and resource main bodies, wherein the master-slave game module comprises an upper model taking the aggregator as a leader and a lower model taking the resource main bodies as followers, in the upper model, the aggregator makes a decision under a preset constraint condition with the aim of minimizing the total operation cost to obtain compensation price strategies issued to various resource main bodies, and in the lower model, each resource main body makes a decision with the aim of maximizing the benefits of each resource main body based on the established operation model to obtain response power strategies of each resource main body; The solving module is used for solving a master-slave game model of the aggregator and the resource main body to obtain an optimal compensation price strategy and an optimal response power strategy, and constructing an optimal power scheduling plan according to the optimal response power strategy, wherein the optimal power scheduling plan is used for being issued to each adjustable resource main body for execution so as to realize optimal scheduling of the virtual power plant.
10. An electronic device comprises a processor and a storage medium, and is characterized in that: The storage medium is used for storing instructions; The processor is operative according to the instructions to perform the steps of the virtual power plant optimized scheduling method according to any one of claims 1-8.
11. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the virtual power plant optimal scheduling method of any one of claims 1-8.

Description

Virtual power plant optimal scheduling method and system Technical Field The invention belongs to the technical field of operation and control of power systems, in particular relates to an optimal scheduling method and system of a virtual power plant, and particularly relates to an optimal scheduling method and system considering a bi-directional game relationship between an aggregator and a multi-type adjustable resource main body. Background As energy conversion proceeds, the permeability of distributed energy in the distribution network continues to increase. VPP (Virtual Power Plant ) has received a lot of attention as an important technical means to aggregate distributed resources and participate in grid scheduling and market trading. The core participants of the virtual power plant include aggregators and various types of adjustable resource bodies including, but not limited to, distributed photovoltaics, energy storage, flexible loads. At present, most of optimization scheduling methods of virtual power plants are centralized optimization, namely, an aggregator is used as a decision center, and a scheduling instruction is issued to a resource main body with the aim of lowest cost or maximum benefit. This approach has significant drawbacks: The resource main body is regarded as an object of passively receiving the instruction, decision rights and income appeal of the resource main body as independent benefit main bodies are ignored, so that the user participation degree is low, and the instruction execution is difficult to dispatch. And secondly, the model is disconnected from the reality, namely, under the electric power market environment, the aggregator and the resource main body are in a benefit game relationship essentially. Centralized optimization cannot characterize this inherent market mechanism, and the model is not accurate enough. The resource isomerism treatment is insufficient, namely the resources such as distributed photovoltaic, energy storage, air conditioning load and electric automobiles have huge differences in operation characteristics, constraint conditions and adjustment capacity, and the problem of collaborative optimization of the isomerism resources is difficult to effectively treat under a unified frame by the existing method. Therefore, a new virtual power plant scheduling method capable of effectively describing the interactive game relationship between the aggregators and the resource main body and comprehensively optimizing various heterogeneous resources is urgently needed. Disclosure of Invention In order to solve the defects in the prior art, the invention provides the optimal scheduling method and the optimal scheduling system for the virtual power plant, which are used for realizing the minimization of the operation cost of an aggregate on the premise of guaranteeing the reasonable benefit of a resource main body by establishing a two-way game model, thereby exciting the participation enthusiasm of the resource main body, improving the participation degree of users, improving the execution rate of scheduling instructions, improving the accuracy of the model, improving the cooperative capability of heterogeneous resources and further improving the overall operation economy and the stability of the virtual power plant. The invention adopts the following technical scheme. The first aspect of the invention provides a virtual power plant optimal scheduling method, which comprises the following steps: Establishing an operation model of a multi-type adjustable resource main body; The method comprises the steps of constructing master-slave game models of an aggregator and resource main bodies, wherein the master-slave game models comprise an upper model taking the aggregator as a leader and a lower model taking the resource main bodies as followers, in the upper model, the aggregator makes a decision under a preset constraint condition with the aim of minimizing the total operation cost to obtain a compensation price strategy issued to various resource main bodies, in the lower model, each resource main body makes a decision with the aim of maximizing the income of each resource main body based on the established operation model to obtain a response power strategy of each resource main body, the resource main body makes a power response strategy according to the compensation price strategy, the power response strategy influences the aggregator to make a compensation price strategy, and the resource main bodies and the aggregator make games; And solving a master-slave game model of the aggregator and the resource main body to obtain an optimal compensation price strategy and an optimal response power strategy, and constructing an optimal power scheduling plan according to the optimal response power strategy, wherein the optimal power scheduling plan is used for being issued to each adjustable resource main body for execution so as to realize optimal scheduling of the virtua