CN-121984118-A - Virtual power plant energy scheduling method and system based on distributed optimization

CN121984118ACN 121984118 ACN121984118 ACN 121984118ACN-121984118-A

Abstract

The invention relates to the technical field of virtual power plants, in particular to a virtual power plant energy scheduling method and system based on distributed optimization, wherein the method comprises the steps of constructing a distributed energy node state sensing network, collecting running state data and load prediction data of each distributed energy unit in real time, and transmitting the running state data and load prediction data to a distributed optimal scheduling center; the method comprises the steps of establishing a multi-target energy scheduling optimization model based on an improved distributed model predictive control algorithm, decomposing a global optimization problem into local sub-problems of each node, realizing parallel iterative solution of each sub-problem through a distributed collaborative solution mechanism, dynamically correcting a scheduling scheme, self-adaptively adjusting a scheduling strategy based on real-time operation deviation and external environment change, and issuing the scheduling strategy to each energy unit for execution. According to the invention, the distributed energy resource is integrated through the distributed optimization framework, so that the efficient collaborative scheduling of the energy of the virtual power plant is realized, the energy utilization rate is improved, the running cost is reduced, and the running stability and the flexibility of the system are enhanced.

Inventors

XIAO BO
WANG PENG
LI CHENGLIANG
QI YINGYI
LU HEHE
LI YELIN
Lv huan
WANG FEI

Assignees

江苏林洋智维技术有限公司

Dates

Publication Date: 20260505
Application Date: 20260106

Claims (10)

1. The virtual power plant energy scheduling method based on distributed optimization is characterized by comprising the following steps of: Step S10, constructing a distributed energy node state sensing network, acquiring running state data, load prediction data and external environment data of the distributed energy units in real time at each distributed energy unit and each load side distribution sensing terminal, and transmitting the running state data, the load prediction data and the external environment data to a distributed optimal scheduling center; Step S20, based on an improved distributed model predictive control algorithm, a multi-objective energy scheduling optimization model containing multiple constraint conditions is established, and a virtual power plant global energy scheduling optimization problem is decomposed into local sub-problems of each energy node; Step S30, carrying out parallel iteration solution on local sub-problems by each energy node through a distributed collaborative solution mechanism, carrying out global optimal solution convergence through limited information interaction among nodes, and dynamically correcting a preliminary scheduling scheme; and S40, monitoring running deviation and external environment dynamic change in the execution process of the scheduling scheme in real time, generating a final scheduling instruction according to the self-adaptive adjustment scheduling strategy, and transmitting the final scheduling instruction to each distributed energy unit for execution.
2. The method for power dispatching in a virtual power plant based on distributed optimization according to claim 1, wherein the distributed energy units in step S10 include distributed photovoltaic power stations, distributed wind farms, energy storage devices, micro gas turbines and controllable load units, the operation state data include output power, residual capacity, operation efficiency, start-stop state and fault information, the load prediction data include short-term load prediction values, load types and load elastic coefficients, and the external environment data include illumination intensity, wind speed, temperature, electricity price and grid constraint parameters.
3. The method for virtual power plant energy scheduling based on distributed optimization according to claim 1, wherein the step of constructing a distributed energy node state aware network in step S10 includes: The sensing terminal deployment, namely installing a voltage sensor, a current sensor, a power sensor, an environment sensor and a load monitoring terminal at key positions of each distributed energy unit, and acquiring multidimensional data; the data preprocessing, namely denoising, outlier rejection, data complement and standardization processing are carried out on the collected original data, a Kalman filtering algorithm is adopted to remove sensor noise, the outlier data is identified and rejected through a3 sigma criterion, and the missing data is complemented based on a linear interpolation method; And the data transmission and synchronization are realized by adopting a 5G edge computing technology to construct a low-delay data transmission network, and the time-space calibration of the data of each node is realized by a time stamp synchronization mechanism, so that the data consistency is ensured, and the data security is ensured by adopting an encryption protocol in the transmission process.
4. The method for virtual power plant energy scheduling based on distributed optimization according to claim 1, wherein the step of establishing a multi-objective energy scheduling optimization model including multiple constraints based on the improved distributed model predictive control algorithm in the step S20 includes: The method comprises the steps of constructing an objective function, namely converting multiple objectives into single objective optimization problems by using a weighted summation method with minimum total running cost, maximum new energy consumption rate and minimum carbon emission intensity of a virtual power plant, wherein the expression of the objective function is minF=ω 1 C 1 +ω 2 (1-η)+ω 3 C e , the expression of the objective function is ω 1 、ω 2 and ω 3 respectively represent weight coefficients of the total running cost, the new energy consumption rate and the carbon emission intensity of the virtual power plant, ω 1 +ω 2 +ω 3 =1,C 1 is the total running cost, η is the new energy consumption rate, and C e is the carbon emission cost; The constraint condition setting comprises system constraint, equipment constraint and safety constraint, wherein the system constraint comprises power balance constraint and tie line power constraint, the equipment constraint comprises upper and lower limit constraint of output power of a distributed energy unit, charge and discharge power constraint of an energy storage device, depth constraint of charge and discharge and controllable load adjustment range constraint, and the safety constraint comprises voltage constraint, frequency constraint and power flow constraint; and decomposing the global problem into local sub-problems of each energy node based on an ADMM alternating direction multiplier method, and carrying out local optimization calculation on each node based on local data and adjacent node interaction information.
5. The method for virtual power plant energy scheduling based on distributed optimization according to claim 1, wherein the step S20 of improving the distributed model predictive control algorithm includes introducing a rolling time domain optimization mechanism, dividing the scheduling period into a plurality of predictive time domains and control time domains, updating an optimization model according to the latest observed data in each control time domain, and optimizing the control law of the model predictive control by using a fusion particle swarm optimization algorithm.
6. The virtual power plant energy scheduling method based on distributed optimization according to claim 1, wherein the distributed collaborative solving mechanism in step S30 includes: initializing parameter setting, namely initializing a local optimization variable, a Lagrangian multiplier and a penalty factor by each energy node; solving a local sub-problem, namely solving the local sub-problem by each node based on the local data and the previous round of interaction information to obtain a local optimal solution; The information interaction and updating are that each node transmits a local optimal solution to adjacent nodes and a distributed optimal scheduling center, and the scheduling center feeds back correction parameters after summarizing the information of each node; And (3) convergence judgment, namely calculating a deviation value of the global optimization objective function, judging that the global optimal solution is achieved when the deviation value is smaller than a preset threshold value, outputting a scheduling scheme, otherwise, updating Lagrange multipliers and penalty factors, and returning to the local sub-problem solving step to continue iteration.
7. The method for virtual power plant energy scheduling based on distributed optimization according to claim 1, wherein the step of generating the final scheduling instruction to be issued to each distributed energy unit according to the adaptive adjustment scheduling policy in step S40 comprises: the operation deviation monitoring, namely comparing the scheduling instruction value with the actual operation value of the energy unit in real time, and calculating the power deviation, the voltage deviation and the frequency deviation; environmental change identification, namely analyzing the change trend of external environmental data by a sliding window method, and identifying illumination mutation, wind speed fluctuation and load mutation conditions; the strategy adjustment rule is that when the operation deviation is smaller than a preset threshold value and no environment change exists, an original scheduling strategy is maintained, and when the operation deviation exceeds the threshold value or the environment change occurs, the output power distribution coefficient of each energy unit is adjusted based on a proportional integral control algorithm; And the instruction issuing execution, namely the adjusted scheduling instruction is issued to each energy unit in real time through a distributed communication network, and the energy storage device, the controllable load and other units are quickly responded and adjusted according to the instruction.
8. The virtual power plant energy scheduling system based on distributed optimization as claimed in claim 1, wherein the virtual power plant energy scheduling method based on distributed optimization is performed, and comprises: the distributed state sensing module is used for constructing a distributed energy node state sensing network, acquiring running state data, load prediction data and external environment data of the distributed energy units in real time at each distributed energy unit and each load side sensing terminal, and transmitting the running state data, the load prediction data and the external environment data to the distributed optimal scheduling center; The optimization model construction and decomposition module is used for building a multi-objective energy scheduling optimization model containing multi-constraint conditions based on an improved distributed model predictive control algorithm and decomposing a global energy scheduling optimization problem of a virtual power plant into local sub-problems of each energy node; The distributed collaborative solving module is used for carrying out parallel iteration solving on the local sub-problems through a distributed collaborative solving mechanism, carrying out global optimal solution convergence through limited information interaction among nodes, and dynamically correcting the preliminary scheduling scheme; The self-adaptive scheduling execution module is used for monitoring running deviation and external environment dynamic change in the execution process of the scheduling scheme in real time, generating a final scheduling instruction according to the self-adaptive adjustment scheduling strategy and transmitting the final scheduling instruction to each distributed energy unit for execution.
9. A virtual power plant energy scheduling apparatus based on distributed optimization, comprising: A memory, a processor, and a distributed optimization-based virtual power plant energy scheduler stored on the memory and operable on the processor, which when executed by the processor implements the distributed optimization-based virtual power plant energy scheduling method of any one of claims 1 to 7.
10. A computer program product comprising a distributed optimization based virtual power plant energy scheduler, which when executed by a processor implements the distributed optimization based virtual power plant energy scheduling method of any of claims 1 to 7.

Description

Virtual power plant energy scheduling method and system based on distributed optimization Technical Field The invention relates to the technical field of virtual power plants, in particular to a virtual power plant energy scheduling method and system based on distributed optimization. Background With the rapid development of distributed energy sources, such as photovoltaics, wind power and the like, the virtual power plant is used as an effective carrier for integrating the distributed energy sources, and the effect in the energy Internet is increasingly remarkable. However, there are still a number of issues to be addressed in the current virtual power plant energy scheduling technology: The traditional virtual power plant mostly adopts a centralized dispatching architecture, and all data are required to be transmitted to a central dispatching center for unified calculation and optimization. The distributed energy unit number is increased, the calculation load of the central dispatching center is increased sharply, the dispatching command is delayed, the dynamic change of energy output and load is difficult to adapt, meanwhile, the requirement of the centralized architecture on the bandwidth and reliability of the communication network is extremely high, and once the communication link is interrupted, the whole dispatching system is paralyzed, and the robustness is insufficient. The existing scheduling algorithm mostly adopts single-objective optimization, so that the requirements of multiple aspects such as running cost, new energy consumption rate, system stability and the like are difficult to be considered, and the problems that the new energy consumption rate is too high or the system safety and stability are influenced due to excessive consumption of the new energy due to the fact that the one-sided pursuit cost is lowest are easily caused. In addition, when the traditional optimization algorithm is used for solving the complex optimization problem under the multi-constraint condition, the convergence speed is low, the solution is easy to fall into a local optimal solution, and global optimal scheduling cannot be realized. The distributed energy units have the characteristics of large fluctuation of output force and strong uncertainty, and the existing scheduling method has insufficient adaptability to external environment changes, such as illumination mutation and load fluctuation, and lacks an effective dynamic adjustment mechanism, so that the deviation between a scheduling scheme and an actual running state is larger, and the scheduling effect is influenced. Therefore, a technical scheme for virtual power plant energy scheduling capable of integrating distributed energy resources to achieve multi-objective collaborative optimization, quick response and strong robustness is needed, so as to solve the defects in the prior art. Disclosure of Invention Aiming at the defects of the prior art, the invention provides a virtual power plant energy scheduling method and system based on distributed optimization, which are used for solving the problems of large computing pressure, response delay, multi-objective optimization unbalance, low convergence speed and poor adaptability to dynamic change of a centralized architecture in the existing virtual power plant energy scheduling technology. The invention is realized by the following technical scheme: there is provided a virtual power plant energy scheduling method based on distributed optimization, the method comprising the steps of: step S10, constructing a distributed energy node state sensing network, collecting running state data, load prediction data and external environment data of the distributed energy units in real time in each distributed energy unit and each load side distribution sensing terminal, preprocessing and synchronizing the running state data, the load prediction data and the external environment data, and transmitting the data to a distributed optimal scheduling center; Step S20, based on an improved distributed model predictive control algorithm, a multi-objective energy scheduling optimization model containing multiple constraint conditions is established, and a virtual power plant global energy scheduling optimization problem is decomposed into local sub-problems of all energy nodes by adopting an alternate direction multiplication method; Step S30, through a distributed collaborative solving mechanism, each energy node carries out parallel iterative solving on a local sub-problem, and through limited information interaction among nodes, the convergence of a global optimal solution is realized, and a preliminary scheduling scheme is dynamically corrected; And S40, monitoring running deviation and external environment dynamic change in the execution process of the scheduling scheme in real time, adaptively adjusting the scheduling strategy based on a preset adjusting rule, generating a final scheduling instruction and transmitting the