CN-121984133-A - Virtual power plant optimal scheduling method, system, equipment and medium

CN121984133ACN 121984133 ACN121984133 ACN 121984133ACN-121984133-A

Abstract

The invention belongs to the technical field of operation control of power systems, and discloses a virtual power plant optimal scheduling method, a system, equipment and a medium, wherein the method comprises the steps of generating a wind-light combined output sequence based on a wind-light historical output sequence by utilizing a Copula function and a Monte Carlo simulation method; dividing the internal load of the virtual power plant into a fixed load and a demand response load, constructing a demand response model based on a response mechanism of the demand response load and calculating a demand response compensation cost, calculating a carbon transaction cost by using a stepped carbon price, constructing an optimized scheduling model comprising a purchase energy cost, a carbon transaction cost, a new energy waste wind punishment cost and the demand response compensation cost by taking the total running cost of the virtual power plant as a target, and outputting a virtual power plant electricity-carbon joint scheduling strategy based on a wind-light joint output sequence by the optimized scheduling model. The model can reveal the conduction path of carbon price change to the economic-environmental benefit of the virtual power plant, and balance the energy scheduling cost and the carbon emission cost to finally output the scheduling strategy.

Inventors

WANG HUIDONG
CHENG YING
LU PENGFEI
HUANG RONGGUO
LU CHUNGUANG
YU JIALI
JIANG CHI
NI LINNA
SUN GANG
WU YUEBO
WEN FUSHUAN
LI ZIHAO
WANG JIAYING
ZHANG JINGCHEN
CHEN YUHAO

Assignees

国网浙江省电力有限公司营销服务中心
国网浙江省电力有限公司

Dates

Publication Date: 20260505
Application Date: 20260408

Claims (8)

1. The virtual power plant optimal scheduling method is characterized by comprising the following steps of: Based on a historical output sequence of wind power and photovoltaic, generating a wind-light combined output sequence considering wind-light output uncertainty and correlation by utilizing a Copula function and a Monte Carlo simulation method; Dividing the internal load of the virtual power plant into a fixed load and a demand response load, constructing a demand response model based on a response mechanism of the demand response load, and calculating a demand response compensation cost based on the demand response model, wherein the demand response load comprises a price type demand response load and a substitution type demand response load, the price type demand response load is used for adjusting electricity consumption behavior based on electricity price, heat price and gas price, and the substitution type demand response load is used for adjusting consumption structures of the electricity load, the heat load and the gas load based on electricity price, heat price and gas price; Calculating the carbon transaction amount of the virtual power plant, and calculating the carbon transaction cost by adopting the stepped carbon price; And constructing an optimized scheduling model comprising energy purchasing cost, carbon transaction cost, new energy waste wind punishment cost and demand response compensation cost by taking the total running cost of the virtual power plant as a target, wherein the optimized scheduling model outputs an electric-carbon combined scheduling strategy of the virtual power plant based on a wind-solar combined output sequence, and constraint conditions of the optimized scheduling model comprise energy storage module charging and discharging energy constraint, equipment output upper and lower limit constraint, equipment climbing constraint and electric-heat-gas multi-energy flow balance constraint.
2. The method of claim 1, wherein the generating a wind-solar combined output sequence taking into account wind-solar output uncertainty and correlation using a Copula function and a monte carlo simulation method based on the historical output sequence of wind power and photovoltaic comprises: calculating the skewness and kurtosis of the historical wind power output sequence, and carrying out the normalization test on the historical wind power output sequence; based on a normalization test result, calculating an empirical distribution function value and a nuclear distribution estimated value of a wind power historical output sequence, and calculating the empirical distribution function value and the nuclear distribution estimated value of the photovoltaic historical output sequence to obtain an empirical Copula function; Based on the wind power historical output sequence and the photovoltaic historical output sequence, calculating the spearman rank correlation coefficient and the kendel rank correlation coefficient of various Copula functions, and carrying out parameter estimation on the various Copula functions to obtain theoretical Copula functions; calculating Euclidean distance between the empirical Copula function and each theoretical Copula function to determine a best fit Copula function; based on the best fit Copula function, the Monte Carlo inverse sampling is adopted to generate a wind-solar combined output sequence.
3. The method of claim 1, wherein the price type demand responsive loads include a load reducible that reduces power usage during a high price period in response to a change in electricity price and a load transferable that transfers power demand from a high price period to a low price period in response to a change in electricity price.
4. The method of claim 1, wherein the upper and lower limits of the demand response conversion amounts of the electric load, the thermal load, and the gas load are constrained when constructing the alternative demand response model.
5. The method of claim 1, wherein calculating the virtual power plant carbon trade volume comprises: Calculating an initial carbon quota of the virtual power plant based on the gas load carbon quota, the gas turbine carbon quota, the gas boiler carbon quota and the upper-level unit electricity purchasing carbon quota; calculating the actual carbon emission of the virtual power plant based on the gas load carbon emission, the gas turbine carbon emission, the gas boiler carbon emission, the upper-level unit electricity purchasing carbon emission and the carbon emission absorption by the electricity conversion equipment; the carbon trade amount of the virtual power plant is the difference between the actual carbon emission amount of the virtual power plant and the initial carbon allowance of the virtual power plant.
6. A virtual power plant optimization scheduling system for implementing the method of any one of claims 1-5, comprising: The wind-light combined output sequence calculation module is configured to generate a wind-light combined output sequence considering wind-light output uncertainty and correlation by utilizing a Copula function and a Monte Carlo simulation method based on a historical output sequence of wind power and photovoltaic; The demand response compensation cost calculation module is configured to divide the internal load of the virtual power plant into a fixed load and a demand response load, construct a demand response model based on a response mechanism of the demand response load, and calculate demand response compensation cost based on the demand response model; The carbon transaction cost calculation module is configured to calculate the carbon transaction amount of the virtual power plant and calculate the carbon transaction cost by adopting the stepped carbon price; The optimization scheduling module is configured to construct an optimization scheduling model comprising energy purchasing cost, carbon transaction cost, new energy wind abandon punishment cost and demand response compensation cost with the aim of minimizing the total running cost of the virtual power plant, and the optimization scheduling model outputs a virtual power plant electricity-carbon joint scheduling strategy based on the wind-light joint output sequence.
7. A computer device comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program which, when executed by the processing unit, causes the processing unit to perform the method of any of claims 1-5.
8. A computer readable storage medium, characterized in that it stores a computer program executable by an electronic device, which when run on the electronic device causes the electronic device to perform the method according to any one of claims 1-5.

Description

Virtual power plant optimal scheduling method, system, equipment and medium Technical Field The invention belongs to the technical field of operation control of power systems, and particularly relates to a virtual power plant optimal scheduling method, a system, equipment and a medium. Background Carbon emission rights trade is a marketized emission reduction mechanism, aiming at realizing greenhouse gas emission control through quota trade. The system requires that the key emission enterprises finish the carbon emission control target within the specified performance period, the initial quota allocation can be obtained according to the industry standard or the historical emission level, the quota holding enterprises can trade through the secondary market, and the market supply and demand relationship forms a dynamic carbon price signal. The virtual power plant is used as an energy aggregate integrating distributed new energy (mainly wind power and photovoltaic), multi-energy coupling equipment, an energy storage module and multiple loads, is an important carrier for improving new energy absorption capacity and guaranteeing stable operation of a power system, and is a core main body participating in carbon transaction and achieving the aim of low-carbon emission reduction. Currently, some progress has been made in the research related to the optimization of the scheduling of virtual power plants, but the following problems still remain. On one hand, most of the existing researches focus on uncertainty analysis of a single dimension of new energy output, and neglect the coupling correlation of wind power and photovoltaic output in a geographic space. The correlation of wind and light output directly influences the stability and predictability of new energy power supply, if the scheduling model does not fully consider the characteristics, deviation between a scheduling strategy and an actual operation scene is easy to occur, and the system operation risk is increased. On the other hand, the existing optimization scheme focuses on decision optimization of participation of the virtual power plant in the electric power market, does not deeply integrate a carbon transaction mechanism and energy scheduling, lacks accurate depiction of a carbon price signal transmission path, and is difficult to balance economic benefits and low-carbon emission reduction responsibilities of the virtual power plant. And the traditional carbon transaction mechanism mostly adopts a fixed carbon price calculation mechanism, so that the constraint on high carbon emission is insufficient, and the active emission reduction of the virtual power plant cannot be effectively guided. Disclosure of Invention Aiming at the defects existing in the prior art, the invention provides a virtual power plant optimal scheduling method, a system, equipment and a medium, wherein the method introduces Copula function and Monte Carlo simulation to capture the coupling characteristic and correlation of wind power and photovoltaic output in time and space; the method comprises the steps of selecting a demand response model, mining load regulation potential through the demand response model, improving new energy consumption capability, incorporating carbon transaction cost into an optimized scheduling model, realizing coupling optimization of carbon transaction cost and energy scheduling cost, and enabling the finally output scheduling strategy to give consideration to economic benefit and low-carbon emission reduction requirement of the virtual power plant. The invention provides the following technical scheme: The first object of the present invention is to provide a virtual power plant optimization scheduling method, comprising: Based on a historical output sequence of wind power and photovoltaic, generating a wind-light combined output sequence considering wind-light output uncertainty and correlation by utilizing a Copula function and a Monte Carlo simulation method; Dividing the internal load of the virtual power plant into a fixed load and a demand response load, constructing a demand response model based on a response mechanism of the demand response load, and calculating demand response compensation cost based on the demand response model; Calculating the carbon transaction amount of the virtual power plant, and calculating the carbon transaction cost by adopting the stepped carbon price; And (3) constructing an optimized scheduling model comprising energy purchasing cost, carbon transaction cost, new energy waste wind punishment cost and demand response compensation cost by taking the total operation cost of the virtual power plant as a target, wherein the optimized scheduling model outputs an electric-carbon combined scheduling strategy of the virtual power plant based on the wind-solar combined output sequence. The Copula function and the Monte Carlo simulation are introduced, so that the coupling characteristic and the correlation of wind power an