Virtual power plant load optimization method based on source load uncertainty

Abstract

The invention discloses a virtual power plant load optimization method based on source load uncertainty, and the method comprises the steps: introducing discrete random variables based on the source load uncertainty, including the source load uncertainty based on normal distribution and the output and load of a photovoltaic power plant based on probability distribution; the probability distribution condition of the output and the load of the photovoltaic power station can be better described, and a foundation is laid for the construction of a photovoltaic output and load model; a virtual power plant load optimization scheduling model is established by taking maximization of energy consumption and minimization of operation cost as targets, virtual power plant load optimization scheduling is realized, high-efficiency operation of the virtual power plant can be ensured, a good peak clipping and valley filling effect on an electricity price peak-valley trend can be achieved, economic benefits are relatively considerable, and the method has high reliability and is suitable for popularization and application. Scientific guidance is provided for power demand side management, and the problem that an existing virtual power plant load optimization scheduling method is extremely prone to causing standby insufficiency under the condition that the source load is uncertain due to the fact that standby demands are not considered is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power demand side management, and in particular to a virtual power plant load optimization method based on source-load uncertainty. Background Art

[0002] Virtual power plants are generally considered to be power coordination management systems that use advanced information and communication technologies and software systems to achieve the aggregation and coordinated optimization of DERs such as DG energy storage systems, controllable loads, and electric vehicles, so as to participate in the power market and power grid operation as a special power plant. Virtual power plants have a powerful integrated processing function for dispersed regions, multiple energy sources, and user loads, and can also effectively improve power supply reliability. However, load levels are always changing, and optimized scheduling is needed to fundamentally achieve sustainable development in the power industry.

[0003] At present, there have been extensive studies on load scheduling. Shahkoomahalli A et al. explored the impact of downside risk constraints on the participation of virtual power plants in the reserve and energy markets on total profits based on risk-based energy management. However, this method is less practical and has large energy losses. For power load regulation, the uncertainty of source load will directly threaten its safety and stability. Since the above method does not take into account the reserve demand, it is very easy to cause insufficient reserve when the source load is uncertain. Summary of the invention

[0004] The present invention is mainly to solve the problem that the existing virtual power plant load optimization scheduling method does not take into account the backup demand, which is very likely to cause insufficient backup when the source and load are uncertain. A virtual power plant load optimization method based on source and load uncertainty is provided, and discrete random variables are introduced based on source and load uncertainty, which is helpful to better describe the probability distribution of photovoltaic power station output and load, and provide an effective reference basis for the optimal scheduling of virtual power plant load; with the goal of maximizing energy consumption and minimizing operating costs, a virtual power plant load optimization scheduling model is established, which can ensure the efficient operation of the virtual power plant while playing a good peak-to-valley effect on the peak-to-valley trend of electricity prices. The economic benefits are relatively considerable and the reliability is high, which solves the problem that the existing virtual power plant load optimization scheduling method does not take into account the backup demand, which is very likely to cause insufficient backup when the source and load are uncertain.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions.

[0006] A virtual power plant load optimization method based on source-load uncertainty comprises the following steps: Step S1: Introducing discrete random variables based on source load uncertainty; Step S2: Establishing a virtual power plant load optimization scheduling model with the goal of maximizing energy consumption and minimizing operating costs; Step S3: Obtaining the active dispatch output, unit switch status and active output of the wind farm and photovoltaic power station; Step S4: Substitute the data obtained in step S3 into the virtual power plant load optimization scheduling model constructed in step S2 to obtain the virtual power plant load optimization scheduling plan.

[0007] The present invention provides a virtual power plant load optimization method based on source-load uncertainty. Firstly, discrete random variables are introduced based on source-load uncertainty, including source-load uncertainty based on normal distribution, photovoltaic power station output based on probability distribution and load based on probability distribution, which is helpful to better describe the probability distribution of photovoltaic power station output and load, and lay a foundation for the construction of photovoltaic output and load model; then, with the goal of maximizing energy consumption and minimizing operating cost, a virtual power plant load optimization scheduling model is established, including an energy consumption model and an operating cost model; then, data is collected to obtain the active scheduling output, unit switch status and active output of wind farms and photovoltaic power stations; finally, the active scheduling output of wind farms and photovoltaic power stations is substituted into a pre-constructed energy consumption model to obtain the maximum energy consumption; the unit switch status and active output are substituted into a pre-constructed operating cost model to obtain the minimum operating cost. The present invention can ensure the efficient operation of the virtual power plant while playing a good role in shaving the peak and filling the valley of the peak-valley trend of electricity prices. The economic benefits are relatively considerable, and the reliability is high. It provides scientific guidance for the management of the power demand side and solves the problem that the existing virtual power plant load optimization scheduling method does not take the backup demand into consideration, which easily leads to insufficient backup when the source and load are uncertain.

[0008] Preferably, in step S1, the uncertainty attribute of the source load is described by normal distribution to obtain the uncertainty of the source load based on normal distribution; the output and load of the photovoltaic power station are described by the probability distribution of discrete random variables to obtain the output of the photovoltaic power station based on probability distribution and the load based on probability distribution. Based on the uncertainty of the source load, discrete random variables are introduced, which helps to better describe the probability distribution of the output and load of the photovoltaic power station, and lays the foundation for the construction of the photovoltaic output and load model.

[0009] As a preference, the source load uncertainty based on normal distribution, if the distribution of the random variable X is normally distributed, that is, it satisfies X~N(η,ξ 2 ), η and ξ in the brackets are the mean and variance respectively, thus establishing the following set of equations: Where: g(x) represents the density function, G(x) represents the integral of the density function, the variable x is any constant in the real number set, and e () represents Euler's formula; If the normal distribution is in standard form, that is, X~N(0,1), the corresponding density function and its integral equation system are as follows: Based on the probability density function characteristics of the normal distribution, the integral relationship of the density function of the opposite number of the variable is derived as follows: G′(-x)=1-G′(x); Because X~N(η,ξ 2 ), when the variable When Y~N(0,1) is satisfied, the density function integrals of the two variables X and Y are equal, that is, G(x)=G′(Y), from which the distribution law shown in the following set of equations is formulated, as shown in the following formula:

[0010] As a preferred embodiment, the photovoltaic power station output based on probability distribution, let X be a discrete random variable, use mean n to represent the meteorological type, I η Indicates the probability of occurrence of weather conditions. Represents photovoltaic output, then the photovoltaic power station output model based on probability distribution is as follows, in order to accurately obtain the probability of occurrence of different meteorological conditions and the corresponding photovoltaic power station output data information: When it is sunny: the probability of the weather occurring is I1, and the photovoltaic output is When it rains: The probability of the weather occurring is I2, and the photovoltaic output is When it is cloudy: the probability of the weather occurring is I3, and the photovoltaic output is

[0011] Preferably, the load based on probability distribution has a large change in the daily load of the virtual power plant. For the same meteorological type in the same season, the load in the same period is relatively similar. Therefore, the load model based on source load uncertainty can be defined by the following normal distribution probability model to make it time-series, as shown below: Where: The active load forecast deviation of node i at time t′ is Its normal distribution variance in the T′ time period is The expected value is η T′ .

[0012] Preferably, in step S2, the load optimization scheduling target based on source-load uncertainty is to maximize energy consumption and minimize operating costs. Therefore, the objective function expression of the virtual power plant load optimization scheduling model is as follows: min C GL =Cgen +C lL Where: max E WP represents the objective function of maximizing energy consumption, min C GL represents the objective function of minimizing the funds required for operation; the scheduling phase is divided into T time periods, and the number of wind farms and photovoltaic power stations contained in the virtual power plant is N W , the active dispatch output of wind farm i and photovoltaic power station j in time period t is respectively represents, ΔT represents the uninterrupted time; C gen represents the power supply cost of ordinary power supply, that is, the operating cost of power supply, C IL It represents the backup power supply investment cost of interruptible load. Among them, the maximum energy consumption model is constructed based on the active dispatch output of wind farms and photovoltaic power stations.

[0013] As a preference, the power supply cost of a common power source is C gen The calculation formula is: Where: for N G There are two common power supply units. The switch state of unit X in time period t is The active contribution is The operating cost parameters of the unit are a x 、b x 、c x ; The backup power supply investment cost C of the interruptible load IL The calculation formula is: Where: Based on N H The electricity price conversion rate of the interruptible load user k in time period t is The interruptible capacity is The monetary expression of unit interruption load is The interruption load is

[0014] Preferably, the load optimization scheduling target based on source-load uncertainty is to maximize energy consumption and minimize operating costs. Therefore, the definition of the virtual power plant load optimization scheduling model can be expressed by the following expression:

[0015] Preferably, the constraints of the virtual power plant load optimization scheduling model include constraints on energy operation, constraints on power supply operation, constraints on interruptible load, and constraints on power balance and spinning reserve capacity.

[0016] As a preferred method, in step S4, the active power dispatching output of the wind farm and the photovoltaic power station is Substitute the pre-built energy consumption model to obtain the virtual power plant with N in the scheduling stage T. W The number of wind farms and N P The maximum energy consumption of the number of photovoltaic power stations in time period t; the switch state of the unit And active output Substitute into the pre-built operating cost model to obtain the minimum operating cost.

[0017] Therefore, the advantages of the present invention are: (1) Introducing discrete random variables based on source-load uncertainty helps to better describe the probability distribution of PV power station output and load, and provides an effective reference for the optimal scheduling of virtual power plant loads; (2) With the goal of maximizing energy consumption and minimizing operating costs, a virtual power plant load optimization scheduling model is established. This can ensure the efficient operation of the virtual power plant while playing a good role in shaving the peak and filling the valley of electricity prices. The economic benefits are relatively considerable and the reliability is high. It solves the problem that the existing virtual power plant load optimization scheduling method does not take into account the backup demand and is prone to insufficient backup when the source and load are uncertain. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a virtual power plant load optimization method based on source-load uncertainty in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0020] A virtual power plant load optimization method based on source-load uncertainty, such as Figure 1 As shown, the following steps are included: Step S1: Introducing discrete random variables based on source load uncertainty; Step S2: Establishing a virtual power plant load optimization scheduling model with the goal of maximizing energy consumption and minimizing operating costs; Step S3: Obtaining the active dispatch output, unit switch status and active output of the wind farm and photovoltaic power station; Step S4: Substitute the data obtained in step S3 into the virtual power plant load optimization scheduling model constructed in step S2 to obtain the virtual power plant load optimization scheduling plan.

[0021] This embodiment provides a virtual power plant load optimization method based on source-load uncertainty. First, discrete random variables are introduced based on source-load uncertainty, including source-load uncertainty based on normal distribution, photovoltaic power station output based on probability distribution, and load based on probability distribution, which helps to better describe the probability distribution of photovoltaic power station output and load, and lays the foundation for the construction of photovoltaic output and load models; then, with the goal of maximizing energy consumption and minimizing operating costs, a virtual power plant load optimization scheduling model is established, including an energy consumption model and an operating cost model; then, data is collected to obtain the active scheduling output, unit switching status and active output of wind farms and photovoltaic power stations; finally, the active scheduling output of wind farms and photovoltaic power stations is substituted into the pre-constructed energy consumption model to obtain the maximum energy consumption; the unit switching status and active output are substituted into the pre-constructed operating cost model to obtain the minimized operating cost.

[0022] In step S1, the uncertainty attribute of source and load is described by normal distribution, and the uncertainty of source and load based on normal distribution is obtained; the output and load of photovoltaic power station are described by probability distribution of discrete random variables, and the output of photovoltaic power station based on probability distribution and the load based on probability distribution are obtained. Based on the uncertainty of source and load, discrete random variables are introduced, which helps to better describe the probability distribution of photovoltaic power station output and load, and lays the foundation for the construction of photovoltaic output and load model.

[0023] Based on the uncertainty of the source load of the normal distribution, if the distribution of the random variable X is normally distributed, that is, it satisfies X~N(η,ξ 2 ), η and ξ in the brackets are the mean and variance respectively, thus establishing the following set of equations: Where: g(x) represents the density function, G(x) represents the integral of the density function, the variable x is any constant in the real number set, and e () represents Euler's formula; If the normal distribution is in standard form, that is, X~N(0,1), the corresponding density function and its integral equation system are as follows: Based on the probability density function characteristics of the normal distribution, the integral relationship of the density function of the opposite number of the variable is derived as follows: G′(-x)=1-G′(x); Because X~N(η,ξ 2 ), when the variable When Y~N(0,1) is satisfied, the density function integrals of the two variables X and Y are equal, that is, G(x)=G′(Y), from which the distribution law shown in the following set of equations is formulated, as shown in the following formula:

[0024] Based on the probability distribution of photovoltaic power station output, let X be a discrete random variable, use mean n to represent the meteorological type, I η Indicates the probability of occurrence of weather conditions. Represents photovoltaic output, then the photovoltaic power station output model based on probability distribution is as follows, in order to accurately obtain the probability of occurrence of different meteorological conditions and the corresponding photovoltaic power station output data information: When it is sunny: the probability of the weather occurring is I1, and the photovoltaic output is When it rains: The probability of the weather occurring is I2, and the photovoltaic output is When it is cloudy: the probability of the weather occurring is I3, and the photovoltaic output is

[0025] Based on the probability distribution of load, since the daily load of the virtual power plant varies greatly, the load in the same period of time under the same meteorological type in the same season is relatively similar. Therefore, the load model based on source load uncertainty can be defined by the following normal distribution probability model to make it time-series, as shown below: ' Where: The active load forecast deviation of node i at time t is Its normal distribution variance in the T′ time period is The expected value is η T′ .

[0026] In step S2, the load optimization scheduling target based on source-load uncertainty is to maximize energy consumption and minimize operating costs. Therefore, the objective function expression of the virtual power plant load optimization scheduling model is as follows: min C GL =C gen +C lL Where: max E WP represents the objective function of maximizing energy consumption, min C GL represents the objective function of minimizing the funds required for operation; the scheduling phase is divided into T time periods, and the number of wind farms and photovoltaic power stations contained in the virtual power plant is N W , the active dispatch output of wind farm i and photovoltaic power station j in time period t is respectively represents, ΔT represents the uninterrupted time; C gen represents the power supply cost of ordinary power supply, that is, the operating cost of power supply, C ILIt represents the backup power supply investment cost of interruptible load. Among them, the maximum energy consumption model is constructed based on the active dispatch output of wind farms and photovoltaic power stations.

[0027] Electricity supply cost of ordinary power supply C gen The calculation formula is: Where: for N G There are two common power supply units. The switch state of unit X in time period t is The active contribution is The operating cost parameters of the unit are a x 、b x 、c x ; Backup power supply investment cost for interruptible loads C IL The calculation formula is: Where: Based on N H The electricity price conversion rate of the interruptible load user k in time period t is The interruptible capacity is The monetary expression of unit interruption load is The interruption load is

[0028] The load optimization scheduling target based on source-load uncertainty is to maximize energy consumption and minimize operating costs. Therefore, the definition of the load optimization scheduling model of the virtual power plant can be expressed by the following expression:

[0029] The constraints of the virtual power plant load optimization scheduling model include energy operation constraints, power supply operation constraints, interruptible load constraints, and power balance and spinning reserve capacity constraints, as follows: 1) Constraints of energy operation: divided into wind farm power generation conditions and photovoltaic power station power generation conditions. The constraint equations formed by the two are as follows: In the formula, the predicted active power output of the i-th wind farm in the t-th period is The predicted active output of the jth photovoltaic power station in the same period is 2) Power supply operation constraints: Consisting of output power limit, minimum switching time and ramp speed, the inequality conditions of each constraint are shown as follows: In the formula, the power limits of the xth unit are P Gx min , P Gx.max ; The start time of the unit in period t is Closing time is The corresponding minimum values ​​are t-1 represents the period before the tth period, and the output increase and decrease values ​​of the same unit are P Gx up , P Gx down ; 3) Interruptible load constraints, as shown below: Where: are the upper and lower limits of input capacity, is the number of switched capacitor groups, The input capacity of the compensation capacitor; 4) Power balancing and spinning reserve capacity constraints, as shown in the following formula: Where: and Respectively represent the power with and without total interruption load, and It represents the processing increase or decrease value of the xth unit in the tth period, is the positive spinning reserve capacity, is the negative spinning reserve capacity, They are respectively the positive and negative spinning reserves of the wind farm, They are respectively the positive and negative spinning reserve capacities of the PV power station.

[0030] In step S4, the active power output of the wind farm and the photovoltaic power station is dispatched Substitute the pre-built energy consumption model to obtain the virtual power plant with N in the scheduling stage T. W The number of wind farms and N P The maximum energy consumption of the number of photovoltaic power stations in time period t; the switch state of the unit And active output Substitute into the pre-built operating cost model to obtain the minimum operating cost.

[0031] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A virtual power plant load optimization method based on source-load uncertainty, characterized in that: The following steps are involved: Step S1: Introducing discrete random variables based on source load uncertainty; Step S2: Establishing a virtual power plant load optimization scheduling model with the goal of maximizing energy consumption and minimizing operating costs; Step S3: Obtaining the active dispatch output, unit switch status and active output of the wind farm and photovoltaic power station; Step S4: Substitute the data obtained in step S3 into the virtual power plant load optimization scheduling model constructed in step S2 to obtain the virtual power plant load optimization scheduling plan.

2. A virtual power plant load optimization method based on source-load uncertainty according to claim 1, characterized in that: In step S1, the uncertainty attribute of the source load is described by normal distribution to obtain the uncertainty of the source load based on normal distribution; the output and load of the photovoltaic power station are described by the probability distribution of discrete random variables to obtain the output of the photovoltaic power station based on probability distribution and the load based on probability distribution.

3. A virtual power plant load optimization method based on source-load uncertainty according to claim 2, characterized in that: The source load uncertainty based on normal distribution, if the distribution of random variable X is normal distribution, that is, it satisfies X~N(η,ξ 2 ), η and ξ in the brackets are the mean and variance respectively, thus establishing the following set of equations: Where: g(x) represents the density function, G(x) represents the integral of the density function, the variable x is any constant in the real number set, and e () represents Euler's formula; Because X~N(η,ξ 2 ), when the variable When Y~N(0,1) is satisfied, the density function integrals of the two variables X and Y are equal, that is, G(x)=G′(Y), from which the distribution law shown in the following set of equations is formulated, as shown in the following formula:

4. A virtual power plant load optimization method based on source-load uncertainty according to claim 2, characterized in that: The output of the photovoltaic power station based on probability distribution, let X be a discrete random variable, use mean n to represent the meteorological type, I η Indicates the probability of occurrence of weather conditions. represents the photovoltaic output, then the photovoltaic power station output model based on probability distribution is as follows: When it is sunny: the probability of the weather occurring is I1, and the photovoltaic output is When it rains: The probability of the weather occurring is I2, and the photovoltaic output is When it is cloudy: the probability of the weather occurring is I3, and the photovoltaic output is 5. A virtual power plant load optimization method based on source-load uncertainty according to claim 2, 3 or 4, characterized in that: The load based on probability distribution can be defined by the following normal distribution probability model to make it time-series, as shown below: Where: The active load forecast deviation of node i at time t′ is Its normal distribution variance in the T′ time period is The expected value is η T′ .

6. The method for optimizing virtual power plant load based on source-load uncertainty according to claim 1, characterized in that: In step S2, the objective function expression of the virtual power plant load optimization scheduling model is as follows: my C GL =C gen +C lL Where: max E WP represents the objective function of maximizing energy consumption, min C GL represents the objective function of minimizing the funds required for operation; the scheduling phase is divided into T time periods, and the number of wind farms and photovoltaic power stations contained in the virtual power plant is N W , the active dispatch output of wind farm i and photovoltaic power station j in time period t is respectively represents, ΔT represents the uninterrupted time; C gen represents the power supply cost of ordinary power supply, that is, the operating cost of power supply, C IL Represents the investment cost of backup power supply for interruptible loads.

7. A virtual power plant load optimization method based on source-load uncertainty according to claim 6, characterized in that: Electricity supply cost of ordinary power supply C gen The calculation formula is: Where: for N G There are two common power supply units. The switch state of unit X in time period t is The active contribution is The operating cost parameters of the unit are a x , b x 、c x ; The backup power supply investment cost C of the interruptible load IL The calculation formula is: Where: Based on N H The electricity price conversion rate of the interruptible load user k in time period t is The interruptible capacity is The monetary expression of unit interruption load is The interruption load is 8. A virtual power plant load optimization method based on source-load uncertainty according to claim 6, characterized in that: The definition of the virtual power plant load optimization scheduling model can be expressed by the following expression:

9. A virtual power plant load optimization method based on source-load uncertainty according to claim 6, characterized in that: The constraints of the virtual power plant load optimization scheduling model include constraints on energy operation, power supply operation and interruptible load constraints; and also include constraints on power balance and spinning reserve capacity.

10. A virtual power plant load optimization method based on source-load uncertainty according to any one of claims 6 to 9, characterized in that: In step S4, the active power output of the wind farm and the photovoltaic power station is dispatched Substitute the pre-built energy consumption model to obtain the virtual power plant with N in the scheduling stage T. W The number of wind farms and N P The maximum energy consumption of the number of photovoltaic power stations in time period t; the switch state of the unit And active output Substitute into the pre-built operating cost model to obtain the minimum operating cost.

Images