CN-121395397-B - Energy management control method for source network load storage integrated virtual power plant

CN121395397BCN 121395397 BCN121395397 BCN 121395397BCN-121395397-B

Abstract

The invention provides an energy management control method for a source network load and storage integrated virtual power plant, and belongs to the technical field of virtual power plant control. The method comprises the steps of obtaining real-time, historical operation data and future meteorological data of each region of a power grid, calculating electricity consumption prediction data of each region through an electricity consumption prediction model based on the historical data and the meteorological data, calculating load pressure indexes according to the real-time operation data and the electricity consumption prediction data, identifying virtual power plants to be reconstructed and reconstruction types, calculating energy control values of each virtual power plant to be reconstructed through an optimization model, matching virtual power plants of different reconstruction types for a plurality of times based on the energy control values, determining reconstruction modes of unpaired virtual power plants, and generating reconstruction instructions to control the virtual power plants to complete framework reconstruction and energy control. The invention realizes dynamic balance and self-adaptive elastic regulation and control of the cross-regional resources of the virtual power plant, and remarkably improves the new energy consumption capability and the running economy of the power grid.

Inventors

LI XIAOBO
XU WUWEI
WANG XIANG
LU HAILIN
LIU YIBO
YANG LIANGLIANG
ZHU CHANGMIN
YUAN JIANFENG
FANG QIANWEN
JIANG XUEDONG
WANG LILIANG
WANG XIAOJING
LV QINGMIN
YANG HUIJING
WANG CHAOQUN
CHEN YI
DING HAIBO

Assignees

浙江浙达能源科技有限公司

Dates

Publication Date: 20260508
Application Date: 20251224

Claims (8)

1. The energy management control method for the source network load storage integrated virtual power plant is characterized by comprising the following steps of: Step S1, acquiring real-time operation data and historical operation data of each region in a power grid and weather data in a future preset time, wherein the real-time operation data comprise load power; step S2, calculating electricity consumption prediction data of each region through a preset electricity consumption prediction model based on the historical operation data and the meteorological data; Step S3, calculating load pressure indexes of each region according to load power in the real-time operation data, the corresponding regional power grid capacity and the electricity consumption prediction data, and identifying a virtual power plant needing to be reconstructed and a corresponding reconstruction type by combining a safe load region, wherein the safe load region is a preset load pressure range for guaranteeing stable operation of the regional power grid, the reconstruction type comprises a split type and a combined type, and the split type is identified when the load pressure index is smaller than the safe load region, and the combined type is identified when the load pressure index is greater than the safe load region; Step S4, after the virtual power plants needing to be reconstructed are identified, calculating an energy control value according to a preset optimization model for each virtual power plant to be reconstructed, wherein the energy control value is the split quantity of various types of resources which can be split for the split virtual power plant, and the energy control value is the acceptance quantity of various types of resources which need to be accepted for the combined virtual power plant; The method comprises the steps of S5, matching virtual power plants of different reconstruction types through multiple rounds of matching according to the calculated energy management control values, wherein the multiple rounds of matching comprise the steps of establishing a feature matrix according to the reconstruction types, the energy management control values and regional power grid topological characteristics of all the virtual power plants to be reconstructed, wherein the feature matrix comprises split vectors of split virtual power plants and demand vectors of combined virtual power plants, calculating the coincidence degree by comparing the split vectors with the corresponding resource types in the demand vectors, taking a pair of split vectors and demand vectors with the coincidence degree larger than a preset threshold value as candidate matching groups, calculating the matching degree score of the split vectors and the demand vectors by adopting an Euclidean distance algorithm according to each candidate matching group and combining the transmission distance of a line, locking the virtual power plants corresponding to the candidate matching group with the highest matching degree, marking the virtual power plants as matched, repeating the matching process until no effective candidate group exists, determining the reconstruction mode of the unpaired virtual power plants, and generating a reconstruction instruction of each virtual power plant to be reconstructed, and controlling the virtual power plant to complete reconstruction and energy.
2. The energy management control method of claim 1, wherein the historical operating data comprises historical load power and historical meteorological data, the electricity consumption prediction model is constructed based on a long-short-term memory neural network, and the electricity consumption prediction model outputs the electricity consumption prediction data by analyzing the historical load power and the historical meteorological data and combining future meteorological data.
3. The energy management control method according to claim 1, characterized in that in the step S4, for the split-type virtual power plant, the calculation of the split amount includes: calculating a first difference value between the current load pressure index of the virtual power plant and the lower limit of the safe load interval; determining a detachable maximum resource threshold based on the first difference value and the electricity consumption prediction data; based on the maximum resource threshold and the basic proportion of the various types of resources, outputting the disassembly amount of the various types of resources through a preset optimization model.
4. The energy management control method according to claim 1, characterized in that in the step S4, the calculation of the acceptance amount for the merged virtual power plant includes: Calculating a second difference value between the current load pressure index of the virtual power plant and the upper limit of the safe load interval; based on the second difference, determining the total amount of energy gaps to be supplemented in combination with the load increase rate in the power consumption prediction data; and outputting the acceptance amount of each type of resource through an optimization model based on the total energy gap.
5. The energy management control method of claim 1, wherein the reconfiguration method of the unpaired virtual power plant includes resource sharing, resource calling and new creation, and the determination process of the reconfiguration method includes: for unpaired split type virtual power plants, determining a reconstruction mode of the unpaired split type virtual power plants as resource sharing; extracting the acceptable quantity of each type of resource in the energy management and control value of the unpaired combined virtual power plant; Updating the inventory of a preset shared resource pool based on the sum of energy control values of all unpaired split type virtual power plants, and comparing the acceptance amount; If the inventory meets the acceptance amount, determining a reconstruction mode of the virtual power plant as resource calling; And if the stock does not meet the acceptance, determining that the reconstruction mode of the virtual power plant is new, and determining the specification of the new virtual power plant based on the notch and the electricity consumption prediction data.
6. The energy management control method of claim 1, wherein the pairing result includes paired and unpaired virtual power plants and resource exchange types and energy management control values of each virtual power plant, and the generation process of the reconfiguration instruction includes: Aiming at the paired virtual power plants, analyzing the resource exchange type and the energy control value in the pairing result to generate a basic instruction; Generating constraint parameters for basic instructions by combining real-time operation parameters of the regional power grid; and integrating the basic instruction and the constraint parameters into a standardized reconstruction instruction according to a preset communication protocol.
7. The energy management control method of claim 6, wherein the constraint parameters include a transmission power constraint, a voltage stability constraint, a frequency response constraint, and a timing constraint, and wherein the generation of the constraint parameters includes: Determining a single resource transmission power range as a transmission power constraint based on the line transmission capacity in the real-time operating parameters; according to the node voltage monitoring value in the real-time operation parameter, determining a voltage fluctuation threshold value during resource exchange to serve as a voltage stability constraint; Determining a frequency deviation amplitude range caused by resource exchange as a frequency response constraint according to the real-time frequency data in the real-time operation parameters; and determining a time window of resource transmission as a time sequence constraint according to the power grid load change curve.
8. The energy management control method of claim 5, wherein the generating of the reconfiguration instruction further comprises: Generating a resource sharing instruction for a virtual power plant needing resource sharing based on splitting amounts of various types of resources in the energy management control value of the virtual power plant, and storing the split resources into a shared resource pool according to types; generating a corresponding resource calling instruction to call the resources stored in the shared resource pool for the virtual power plant needing resource calling according to the energy control value of the virtual power plant; and generating a new regulation instruction for a scene needing to be newly built based on the specification of the newly built virtual power plant.

Description

Energy management control method for source network load storage integrated virtual power plant Technical Field The invention relates to the technical field of virtual power plant control, in particular to an energy management control method for a source network load storage integrated virtual power plant. Background The permeability of distributed renewable energy sources represented by wind power and photovoltaic in an electric power system is continuously improved. The randomness and fluctuation of the output of the power grid provide great challenges for safe and stable operation of the power grid. The virtual power plant is used as an advanced technology and management mode, and resources such as a distributed power supply, an energy storage system, a controllable load and the like which are widely distributed are aggregated through an advanced information communication technology and a software system, and the virtual power plant is used as a special power plant to participate in power grid operation and power market transaction, so that the virtual power plant is one of key means for improving the distributed energy consumption capability of the system and enhancing the toughness of the power grid. However, the existing virtual power plant energy management method focuses on optimizing and scheduling the internal aggregate resources of the virtual power plant energy management method so as to achieve the goals of lowest running cost or maximum benefit and the like. In terms of organization morphology, virtual power plants are typically pre-statically built based on geographical or administrative boundaries, with their aggregate resource scope and regulatory capability relatively fixed. The static architecture is difficult to adapt to dynamic changes of power flow of a power grid, wherein in the peak load period, virtual power plants in a local area can not effectively relieve power grid blockage due to insufficient regulation and control capability, and in the low load and renewable energy large-generation period, virtual power plants in another area can waste due to excessive regulation and control resources. The prior art lacks a mechanism capable of dynamically adjusting the self organization scale and structure of the virtual power plant according to the real-time and future situation of the power grid, so that the cross-regional resource optimization configuration capability is insufficient, and the overall operation efficiency is required to be improved. Disclosure of Invention The invention provides an energy management control method for a source network load and storage integrated virtual power plant, which is used for solving the problems that in the prior art, static solidification of a virtual power plant organization architecture is difficult to adapt to dynamic fluctuation of a power grid, and partial load pressure cannot be effectively untangling due to lack of coordination of cross-regional resource regulation and control. In order to achieve the above objective, the embodiment of the invention provides an energy management control method for a source network load storage integrated virtual power plant, which comprises the steps of S1, acquiring real-time operation data and historical operation data of each area in a power grid and weather data in a future preset time, S2, calculating electricity utilization prediction data of each area through a preset electricity utilization prediction model based on the historical operation data and the weather data, S3, calculating load pressure indexes of each area according to the real-time operation data and the electricity utilization prediction data, and identifying a virtual power plant needing to be reconstructed and a corresponding reconstruction type according to a safe load zone, S4, calculating an energy management control value through a preset optimization model for each virtual power plant to be reconstructed according to the corresponding load pressure index, and S5, matching the virtual power plants of different reconstruction types through matching multiple times according to the calculated energy management control value, determining a reconstruction mode of unpaired virtual power plants according to a matching result, and generating a virtual power plant needing to be reconstructed so as to complete the reconstruction of the virtual power plant needing to be reconstructed and the virtual power plant needing to be reconstructed. Optionally, the historical operation data comprises historical load power and historical meteorological data, the electricity consumption prediction model is constructed based on a long-short-term memory neural network, and the electricity consumption prediction model outputs the electricity consumption prediction data by analyzing the historical load power and the historical meteorological data and combining future meteorological data. Optionally, the real-time operation data comprises load power, the po