CN-122026367-A - Market clearing method and system based on virtual power plant climbing capacity constraint

CN122026367ACN 122026367 ACN122026367 ACN 122026367ACN-122026367-A

Abstract

A market clearing method and system based on virtual power plant climbing capacity constraint capable of improving feasibility and new energy consumption level of clearing results comprises the steps of collecting real-time operation data of a load side, a power side and an energy storage side of a virtual power plant in a current dispatching period, generating load prediction and new energy output prediction of a next dispatching period by using a prediction model, calculating maximum ascending rate and maximum descending rate according to various adjustable resource operation parameters, determining ascending capacity and descending capacity, polymerizing to form a virtual power plant basic climbing capacity curve, combining an output plan of the current dispatching period to obtain effective climbing capacity and effective descending climbing capacity between adjacent dispatching periods, constructing a virtual power plant climbing constraint, establishing a market clearing optimization model comprising electric power balance, upper and lower limits of output, power grid safety and climbing constraint, and solving by taking the minimum running cost of the system and/or the maximum overall market benefit as targets to obtain clear electric quantity of each market main body and a virtual power plant output plan.

Inventors

SUN MINGYI
ZHANG CHEN
NING JIAXIN
LIU ZHONGKANG
ZHU GUANGYU
FAN HENGJIAN
LIU WENXUAN
LI CHENGCHENG
YANG XIAOMING
ZHAN KEMING
YANG LIN
CAI ZHUANG
ZHANG MEISHAN
XU XIAO
ZHAO XIN
ZHOU HANG
YE PENGPENG
JIANG FENG
WANG LIANG
HAN QIU
XIAO NAN
ZHANG JINHUI
LIU JIAN
PANG HANWEN

Assignees

国网辽宁省电力有限公司锦州供电公司
国家电网有限公司

Dates

Publication Date: 20260512
Application Date: 20251226

Claims (10)

1. A market clearing method based on virtual power plant climbing capacity constraints, comprising: collecting real-time operation data of a load side, a power supply side and an energy storage side in a virtual power plant in a current dispatching period, and generating a load predication result and a new energy output predication result of a next dispatching period based on a predication model; Acquiring operation parameters of various adjustable resources, calculating the maximum ascending rate and the maximum descending rate of the various adjustable resources in the next scheduling period according to the operation parameters of the various adjustable resources, determining the ascending capacity and the descending capacity of the various adjustable resources according to the maximum ascending rate and the maximum descending rate, and aggregating the ascending capacity and the descending capacity of the various adjustable resources to form a basic climbing capacity curve of the virtual power plant in the next scheduling period; obtaining a power plan of various adjustable resources in the virtual power plant in a current scheduling period, obtaining effective ascending and descending climbing capacities of the virtual power plant between adjacent scheduling periods according to the power plan of various adjustable resources in the virtual power plant in the current scheduling period and a basic climbing capacity curve of a next scheduling period, and constructing climbing constraints of the virtual power plant between the adjacent scheduling periods based on the effective ascending and descending climbing capacities; Based on the load prediction result, the new energy output prediction result and the climbing constraint of the virtual power plant between adjacent scheduling periods, taking electricity price and electricity quantity quotation submitted by each market main body as input variables, constructing a market clearing optimization model containing the climbing capacity constraint of the virtual power plant, wherein constraint conditions of the market clearing optimization model at least comprise electric power balance constraint, unit output upper and lower limit constraint, power grid safety constraint and climbing constraint of the virtual power plant between adjacent scheduling periods; And solving the market clearing optimization model with the aim of minimum system running cost and/or maximum market overall benefit to obtain the clearing electric quantity of each market subject in the next scheduling period and the output plan of the virtual power plant.
2. The market clearing method based on the climbing capacity constraint of the virtual power plant according to claim 1, wherein after the clearing capacity of each market subject in the next scheduling period and the output plan of the virtual power plant are obtained, in the next scheduling period, the up-regulating capacity and the down-regulating capacity of various adjustable resources in the virtual power plant are decomposed and distributed according to the output plan of the virtual power plant; And acquiring actual execution output of the virtual power plant in the next dispatching cycle, taking the actual execution output and real-time operation data of a load side, a power side and an energy storage side as new input of a prediction model, and rolling and updating a load prediction result, a new energy output prediction result and basic climbing capacity of the virtual power plant in the subsequent dispatching cycle.
3. The method for market clearing based on virtual power plant climbing capacity constraints of claim 1, wherein determining the up-regulation capacity and the down-regulation capacity of each type of adjustable resource in a next scheduling period comprises: Multiplying the maximum rising rate of various adjustable resources by the scheduling period duration to obtain theoretical calling force increment; the theoretical downward regulating output force decrement is obtained by multiplying the maximum descending rate of various adjustable resources and the scheduling period duration; acquiring an output upper limit and an output lower limit of various adjustable resources in a current scheduling period; taking the difference between the current output of the adjustable resource and the upper limit of the output as an up-regulating margin, and determining the smaller value of the theoretical out-regulating force increment and the up-regulating margin as the up-regulating capacity of the adjustable resource in the next dispatching period; and taking the difference value between the current output force and the lower output force limit of various adjustable resources as a down-regulating margin, and determining the smaller value of the theoretical down-regulating output force decrement and the down-regulating margin as the down-regulating capability of the adjustable resources in the next dispatching cycle.
4. The virtual power plant climbing capacity constraint-based market clearing method of claim 1, wherein forming a virtual power plant base climbing capacity curve for a next scheduling period comprises: Performing time sequence superposition on the up-regulation capacity and the down-regulation capacity of various adjustable resources in the next scheduling period according to a preset time period to obtain the total up-regulation capacity and the total down-regulation capacity of the virtual power plant in each time period; and constructing the basic climbing capacity curve by the total up-regulating capacity and the total down-regulating capacity of the virtual power plant in each time period.
5. The method for market clearing based on virtual power plant climbing capacity constraint of claim 1, wherein obtaining effective uphill climbing capacity and effective downhill climbing capacity of the virtual power plant between adjacent scheduling periods according to a power plan of each type of adjustable resource in the virtual power plant in a current scheduling period and a basic climbing capacity curve of a next scheduling period comprises: Determining a baseline output of the virtual power plant at the end time of the current dispatching cycle and a planned output of the virtual power plant at the initial time of the next dispatching cycle according to the output plans of various adjustable resources in the current dispatching cycle, and obtaining the output variation between the baseline output and the planned output; Under the constraint of the basic climbing capacity curve, distributing the output variable quantity between adjacent scheduling periods according to preset time segments to obtain up-regulation power and down-regulation power occupied by each time segment for meeting the output plan; on each time segment, subtracting the up-regulating power occupied by the output plan from the total up-regulating capacity in the basic climbing capacity curve, and subtracting the down-regulating power occupied by the output plan from the total down-regulating capacity in the basic climbing capacity curve to obtain the residual up-regulating spare capacity and the residual down-regulating spare capacity; And according to a preset safety margin coefficient, the remaining up-regulation reserve capacity and the remaining down-regulation reserve capacity are reduced, the reduced up-regulation reserve capacity is used as the effective uphill climbing capacity, and the reduced down-regulation reserve capacity is used as the effective downhill climbing capacity.
6. The virtual power plant climbing capacity constraint-based market clearing method of claim 1, wherein constructing a virtual power plant climbing constraint between adjacent scheduling periods based on the effective up-hill climbing capacity and effective down-hill climbing capacity comprises: determining uplink constraint conditions and downlink constraint conditions of the virtual power plant between adjacent scheduling periods; in the uplink constraint condition, limiting the output increment between the virtual power plant planned output of the latter scheduling period and the virtual power plant planned output of the former scheduling period to be not more than the effective uphill capacity of the corresponding time period; In the downlink constraint condition, limiting the output decrement between the virtual power plant planned output of the previous scheduling period and the virtual power plant planned output of the subsequent scheduling period to be not more than the effective downhill climbing capability of the corresponding time period; and taking the uplink constraint condition and the downlink constraint condition as climbing constraint of the virtual power plant between adjacent scheduling periods to be incorporated into the market clearing optimization model.
7. The virtual power plant climbing capacity constraint-based market clearing method according to claim 1, wherein the prediction module construction process is: Acquiring historical operation data of a load side, a power supply side and an energy storage side of a virtual power plant, and acquiring meteorological data, electricity price data and holiday information corresponding to the historical operation data; Aligning and cleaning the historical operation data with meteorological data, electricity price data and holiday information to construct a training sample set containing multidimensional features; dividing the training sample set into a training set and a verification set, and performing iterative training on the training set based on a preset deep learning model to obtain model parameters of a load predictor model and a new energy output predictor model; And evaluating the prediction precision of the load predictor model and the new energy output predictor model on a verification set, adjusting a model structure or training super parameters according to an evaluation result until the prediction precision meets a preset threshold value, wherein the preset threshold value is set according to the precision requirement of the model, and fusing the load predictor model and the new energy output predictor model meeting the requirement into the prediction model.
8. The method for market clearing based on virtual power plant climbing capacity constraints according to claim 1, wherein solving the market clearing optimization model with the goal of minimum system running cost and/or maximum overall market benefit comprises: constructing an objective function comprising a system operation cost objective item and a market overall benefit objective item, wherein the system operation cost objective item is used for representing the comprehensive operation cost of a unit, an energy storage device, an adjustable load and new energy output, and the market overall benefit objective item is used for representing the comprehensive benefit of the electric energy supply and demand matching degree, the electricity price level and the new energy consumption level; according to a preset weight coefficient, weighting and synthesizing a system running cost target item and a market overall benefit target item to obtain a comprehensive target function; And solving the comprehensive objective function under the conditions of meeting the power balance constraint, the upper and lower limit constraints of the unit output, the safety constraint of the power grid and the climbing constraint of the virtual power plant between adjacent dispatching cycles to obtain the clear electric quantity of each market main body in the next dispatching cycle and the output plan of the virtual power plant in each time period.
9. The virtual power plant climbing capacity constraint based market clearing method of claim 1, wherein the adjustable resources comprise one or more of a conventional generator set, an adjustable load, an energy storage device, and a distributed power source; the operating parameters of the adjustable resource at least comprise rated output, maximum current output, start-stop state, maximum rising rate, maximum falling rate, capacity of the energy storage device and charge state.
10. A market clearing system based on virtual power plant climbing capacity constraints, comprising: a processor and a memory; the processor is connected with the memory through a communication bus: The processor is used for calling and executing the program stored in the memory; the memory for storing a program for performing at least a market clearing method based on virtual power plant climbing capacity constraints according to any one of claims 1-9.

Description

Market clearing method and system based on virtual power plant climbing capacity constraint Technical Field The application relates to the technical field of virtual power plant market clearing, in particular to a market clearing method and system based on virtual power plant climbing capacity constraint. Background With the continuous improvement of the access proportion of new energy sources such as wind power, photovoltaic and the like, the fluctuation and uncertainty of the output of the power system are obviously increased. In order to improve the aggregate utilization efficiency of resources such as distributed power sources, energy storage devices and adjustable loads, virtual power plants participate in the power market clearing and scheduling execution in a unified main body mode through aggregating the various adjustable resources such as the distributed power sources, the energy storage devices and the adjustable loads, and become an important means for improving the new energy consumption capability and enhancing the power grid regulation capability. The existing market clearing method generally builds an optimization model based on load prediction results and electricity price and electricity quantity quotation submitted by each market main body, considers power balance constraint, unit output upper and lower limit constraint and power grid safety constraint in constraint conditions, and carries out clearing calculation with the minimum system running cost or the maximum market benefit as a target. However, in the virtual power plant scenario, the existing method mostly equates the virtual power plant to a single power generation unit for processing, or introduces a simple climbing rate constraint only at the unit level, and lacks a mechanism for uniformly modeling and dynamically aggregating the up-regulation capability and the down-regulation capability of multiple types of adjustable resources in the virtual power plant. Meanwhile, when the market of the prior art is clear in a multi-period, the power plan of the current dispatching cycle and the available climbing capacity of the next dispatching cycle are not fully combined, the power change between the adjacent dispatching cycles is lack of fine climbing constraint description, and effective climbing capacity and effective descending climbing capacity of the virtual power plant between the continuous dispatching cycles are difficult to be reflected in time. The problems that the power climbing exceeds the physical capacity, the new energy output is required to be frequently rescheduled or reduced in pressure and the like are easily caused in the actual implementation of the market clearing result, and the feasibility of a scheduling scheme is not facilitated, and the capacity of the virtual power plant for absorbing the new energy fluctuation is improved. Disclosure of Invention The invention provides a market clearing method based on constraint of the climbing capacity of a virtual power plant, aiming at solving the problems that the whole climbing capacity of the virtual power plant cannot be finely described and constrained in the market clearing process in the background art, the feasibility of a dispatching result is insufficient and the new energy consumption efficiency is low. The technical scheme of the invention is as follows: a market clearing method based on virtual power plant climbing capacity constraints, comprising: collecting real-time operation data of a load side, a power supply side and an energy storage side in a virtual power plant in a current dispatching period, and generating a load predication result and a new energy output predication result of a next dispatching period based on a predication model; Acquiring operation parameters of various adjustable resources, calculating the maximum ascending rate and the maximum descending rate of the various adjustable resources in the next scheduling period according to the operation parameters of the various adjustable resources, determining the ascending capacity and the descending capacity of the various adjustable resources according to the maximum ascending rate and the maximum descending rate, and aggregating the ascending capacity and the descending capacity of the various adjustable resources to form a basic climbing capacity curve of the virtual power plant in the next scheduling period; obtaining a power plan of various adjustable resources in the virtual power plant in a current scheduling period, obtaining effective ascending and descending climbing capacities of the virtual power plant between adjacent scheduling periods according to the power plan of various adjustable resources in the virtual power plant in the current scheduling period and a basic climbing capacity curve of a next scheduling period, and constructing climbing constraints of the virtual power plant between the adjacent scheduling periods based on the effective ascending and descending climbing