Source network load storage system energy storage capacity configuration method and system based on probability sequence theory

Abstract

The invention discloses a source network load storage system energy storage capacity configuration method and system based on a probability sequence theory. The method comprises the following steps: obtaining a probability distribution model of wind power, photovoltaic power and load; discretizing the probability density functions of the wind power, the photovoltaic power and the load to obtain discretization probability sequences of the wind power, the photovoltaic power and the load; obtaining the net load of energy storage power supply, and obtaining a net load probability sequence according to the discretization probability sequence of wind power, photovoltaic and load; establishing an opportunity constraint based on the net load and the net load probability sequence, and obtaining the minimum energy storage power under a specific confidence coefficient based on the opportunity constraint; according to the required off-grid power protection duration, calculating the net load electric quantity within the power protection duration after off-grid occurs at 24 moments of the whole day in a rolling manner, and obtaining the required minimum energy storage electric quantity; based on the minimum energy storage power and the minimum energy storage electric quantity, selecting the multiplying power of a source network load storage system, and carrying out energy storage capacity configuration; according to the method, the opportunity constraint can be established based on the net load and the net load probability sequence on the basis of considering the uncertainty of the source and the load, so that the subsequent energy storage capacity configuration is closer to the reality.

Description

Technical Field

[0001] The present invention relates to a method and system for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory, and belongs to the technical field of energy storage planning. Background Art

[0002] With the rapid development of social economy, electricity consumption scenarios are constantly expanding. The clean and green characteristics of renewable energy have greatly reduced the power supply pressure of traditional thermal power units, but it also has the characteristics of strong randomness and difficulty in control, which brings new challenges to the power system. In order to improve the local consumption capacity of new energy and enhance the high-quality and reliable power supply capacity of regional power grids, the source-grid-load-storage system has developed rapidly.

[0003] The source-grid-load-storage system is a power generation, transmission and distribution system that is composed of new energy, primary grid, controllable load, energy storage, and dispatching, control, and protection devices. It is an autonomous system that can achieve self-control, protection, and management. The system presents the characteristics of a high proportion of new energy and a high proportion of power electronics. The optimal configuration method of energy storage capacity in the source-grid-load-storage system refers to the reasonable configuration of energy storage capacity by comprehensively considering new energy conditions, load characteristics, and grid operation mode. At present, in the research on the optimal configuration of energy storage capacity in the source-grid-load-storage system, there are the following problems to be solved: 1) When there are only daily data for wind power, photovoltaics, and load data, but no annual data, how to fully consider the uncertainty of the source and load in the system when configuring the energy storage capacity; 2) When setting the energy storage capacity, the usual method is to directly select the average load multiplied by the power protection time as the energy storage capacity, without considering the situation of power outages at different times. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method and system for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory. The method and system fully consider the output uncertainty of wind power, photovoltaic power and load (source and load) at each moment by establishing a probability distribution model. On the basis of considering the uncertainty of source and load, opportunity constraints are established based on net load and net load probability sequence, and the net load power demand after power outage at each moment is calculated in a rolling manner, so that the subsequent energy storage capacity configuration is closer to reality.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] On the one hand, the present invention provides a method for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory, comprising:

[0007] Obtain probability distribution models of wind power, photovoltaic power and load;

[0008] Based on the probability distribution models of wind power, photovoltaic power and load, the probability density functions of wind power, photovoltaic power and load are discretized respectively to obtain the discretized probability sequences of wind power, photovoltaic power and load;

[0009] Obtain the net load of energy storage power supply, and obtain the net load probability sequence based on the discretized probability sequence of wind power, photovoltaic power and load;

[0010] Establishing a chance constraint based on the net load and the net load probability sequence, and obtaining a minimum energy storage power under a specific confidence level based on the chance constraint;

[0011] According to the required off-grid power-keeping duration, the net load power within the power-keeping duration after off-grid is calculated on a rolling basis at 24 times a day to obtain the minimum required energy storage power;

[0012] Based on the minimum energy storage power and the minimum energy storage capacity, the multiple of the source-grid-load-storage system is selected to configure the energy storage capacity.

[0013] Optionally, the probability distribution model of wind power, photovoltaic power and load is obtained by the following method:

[0014] (1) Wind power probability distribution model

[0015] The wind power prediction error has a mean of 0 and a variance of The normal distribution characteristics of are expressed as follows:

[0016]

[0017] ε wt =P Wt -P wt

[0018] σ wt =0.2P wt +0.02P z

[0019] Where: P Wt is the actual output of the wind turbine at time t, that is, the actual wind power output at time t; P wt is the predicted output of the wind turbine at time t, that is, the predicted wind power output at time t; ε wt is the prediction error of the wind turbine at time t, that is, the wind power prediction error at time t; P z is the installed capacity of the wind turbine; σ wt is the standard deviation of wind power prediction error;

[0020] Based on the normal distribution characteristics of wind power prediction error, the normal distribution characteristics of wind power actual output are obtained, which are expressed as follows:

[0021]

[0022] The probability density function of the actual wind power output, that is, the probability distribution model of wind power, is expressed as follows:

[0023]

[0024] Where: f(P Wt ) represents the probability density function of the actual wind power output;

[0025] (2) Photovoltaic probability distribution model

[0026] The actual photovoltaic output follows the Beta distribution, and its probability density function, that is, the photovoltaic probability distribution model, is expressed as follows:

[0027]

[0028] Among them, f(P St ) is the probability density function of the actual photovoltaic output; P St is the actual output of the photovoltaic unit at time t, that is, the actual photovoltaic output at time t; P smax is the maximum output power of a single photovoltaic power generation unit; Γ(·) is the gamma function; α, β are the shape parameters of the gamma function; σ 2 , μ are the variance and expectation of Beta distribution respectively;

[0029] (3) Load probability distribution model

[0030] The load in the source-grid-load-storage system obeys the normal distribution, and its probability density function, that is, the load probability distribution model, is expressed as follows:

[0031]

[0032] Among them, f(P Lt ) is the probability density function of the actual load value; A is the load prediction accuracy; ε t is the load forecast error at time t; T is the total number of hours in a day, i.e. 24 hours; P Lt is the actual value of the load at time t; P lt is the predicted value of the load at time t.

[0033] Optionally, based on the probability distribution models of wind power, photovoltaic power and load, the probability density functions of wind power, photovoltaic power and load are discretized respectively to obtain the discretized probability sequences of wind power, photovoltaic power and load, including:

[0034] Substitute the probability density functions of the actual wind power output, the actual photovoltaic output and the actual load value into the discretized formula to obtain the probability sequence S of wind power, photovoltaic power and load. w , Ss and S l ; The discretization formula is expressed as follows:

[0035]

[0036] Among them, f(x) is the continuous probability density function to be discretized, which can be f(P Lt )、f(P W t), f(P St ); x is a random variable, that is, P Wt , P St , P Lt ; F(i) is a discretized probability sequence, where i is the sequence number of the discretized probability sequence; is the discrete step length; x max is the maximum value of the random variable; N step is the length of the probability sequence F(i).

[0037] Optionally, a net load probability sequence is obtained according to the discretized probability sequences of wind power, photovoltaic power and load, including:

[0038] The net load probability sequence is calculated using the volume sum and volume difference operations. The formula is as follows:

[0039]

[0040] Among them, S dt is the net load probability sequence; For volume and operation; It is the roll difference operation.

[0041] Optionally, establishing the opportunity constraint based on the net load and the net load probability sequence includes:

[0042] Get the set confidence α;

[0043] Based on the set confidence α, a chance constraint is established to ensure that the probability of the event "the energy storage discharge power can supply power to all net loads at time t" is not less than the confidence α. The chance constraint is expressed as follows:

[0044] P r {P Dt ≤P BESS}≥α

[0045] P BESS ≥0

[0046] Among them, P BESS is the energy storage discharge power; α is the set confidence level, 0≤α≤1; P Dt is the net load at time t, P Dt =P Lt-P Wt -P Vt ;P r (·) indicates probability operation.

[0047] Optionally, based on the chance constraint, obtaining a minimum energy storage power under a specific confidence level includes:

[0048] According to the probability sequence theory, the chance constraint is converted into an inequality constraint, which is expressed as follows:

[0049]

[0050] Among them, I αt Indicates that in the net load probability sequence S dt The sequence number when the cumulative sum of each element is equal to α is I αt satisfy:

[0051] Based on the inequality constraints and considering the power conditions at each moment in 24 hours, the energy storage discharge power should satisfy the following constraints:

[0052]

[0053] According to the constraints that the above energy storage discharge power should meet, the minimum energy storage power under a specific confidence level is obtained.

[0054] Optionally, the net load power within the power-saving time after the off-grid occurs at 24 moments throughout the day is calculated on a rolling basis according to the required off-grid power-saving time to obtain the required minimum energy storage power, including:

[0055] Obtain the required off-grid power-saving duration, and calculate the net load power within the power-saving duration after off-grid at 24 hours a day. The formula is as follows:

[0056]

[0057] Where h is the required off-grid power-saving duration, E(j) represents the net load power when a power outage occurs at the jth moment and lasts for h hours; P Dt,α represents the net load power at time t under the confidence level α;

[0058] Energy storage battery capacity E BESS The following constraints should be met:

[0059] E BESS ≥max(E(1),...,E(24))

[0060] Based on the energy storage battery capacity E BESSThe constraints that should be met and the net load power within the power protection period after being off-grid at any time throughout the day can be used to obtain the minimum required energy storage power.

[0061] On the other hand, the present invention provides a system for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory, comprising:

[0062] Probability distribution acquisition module, used to obtain the probability distribution model of wind power, photovoltaic power and load;

[0063] A discretization module is used to discretize the probability density functions of wind power, photovoltaic power and load respectively based on the probability distribution models of wind power, photovoltaic power and load, so as to obtain the discretized probability sequences of wind power, photovoltaic power and load;

[0064] The net load module is used to obtain the net load of energy storage power supply and obtain the net load probability sequence based on the discretized probability sequence of wind power, photovoltaic power and load;

[0065] A first constraint module is used to establish a chance constraint based on the net load and the net load probability sequence, and obtain a minimum energy storage power under a specific confidence level based on the chance constraint;

[0066] The second constraint module is used to calculate the net load power within the power-saving time after the grid is disconnected at 24 moments throughout the day according to the required off-grid power-saving time, and obtain the required minimum energy storage power;

[0067] The capacity configuration module is used to select the multiple of the source-grid-load-storage system based on the minimum energy storage power and the minimum energy storage capacity to configure the energy storage capacity.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] The energy storage capacity configuration method of the source-grid-load-storage system based on probability sequence theory proposed in the present invention can fully consider the output uncertainty of wind power, photovoltaic power and load (source and load) at each moment by establishing a probability distribution model based on wind power signals, and establish opportunity constraints based on net load and net load probability sequence on the basis of considering the uncertainty of source and load, so that the subsequent energy storage capacity configuration is closer to reality.

[0070] The energy storage capacity configuration method of the source-grid-load-storage system based on probability sequence theory proposed in the present invention takes into account the net load demand after a power outage at various times during a day, so that the energy storage capacity configuration can more comprehensively meet the possible power supply protection needs; at the same time, the confidence parameter is introduced to provide a more intuitive basis for adjusting the energy storage configuration scale according to specific needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative labor, among which:

[0072] Figure 1 The flowchart of the method for configuring the energy storage capacity of the source-grid-load-storage system in an embodiment of the present invention is shown;

[0073] Figure 2 The 24-hour wind power and photovoltaic output curve diagram of a certain place in the embodiment of the present invention is shown;

[0074] Figure 3 The 24-hour load curve diagram of a certain place in the embodiment of the present invention is shown;

[0075] Figure 4 The discretized probability sequence of a 24-hour wind power output curve of a certain place in the embodiment of the present invention is shown;

[0076] Figure 5 The discretized probability sequence of the 24-hour photovoltaic output curve of a certain place in the embodiment of the present invention is shown;

[0077] Figure 6 The discretized probability sequence of a 24-hour load curve of a certain place in an embodiment of the present invention is shown;

[0078] Figure 7 FIG. 1 shows a net load probability sequence of a certain location in an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0080] The application principle of the present invention is described in detail below in conjunction with the accompanying drawings.

[0081] Example 1

[0082] like Figure 1 As shown, an embodiment of the present invention provides a method for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory, comprising the following steps:

[0083] S01: Obtain the probability distribution model of wind power, photovoltaic power and load;

[0084] S02: Based on the probability distribution models of wind power, photovoltaic power and load, the probability density functions of wind power, photovoltaic power and load are discretized respectively to obtain the discretized probability sequences of wind power, photovoltaic power and load;

[0085] S03: Obtain the net load of energy storage power supply, and obtain the net load probability sequence according to the discretized probability sequence of wind power, photovoltaic power and load;

[0086] S04: establishing a chance constraint based on the net load and the net load probability sequence, and obtaining a minimum energy storage power under a specific confidence level based on the chance constraint;

[0087] S05: According to the required off-grid power-keeping duration, the net load power within the power-keeping duration after off-grid operation is calculated on a rolling basis at 24 times a day to obtain the required minimum energy storage power;

[0088] S06: Based on the minimum energy storage power and the minimum energy storage capacity, select the multiple of the source-grid-load-storage system to configure the energy storage capacity.

[0089] Specifically, in an embodiment of the present invention, for a power grid that includes wind power and photovoltaic power during grid-connected operation, when its upper power grid fails, if it is to maintain power for a certain period of time, a certain capacity of grid-connected energy storage is required as a voltage source, and operate off-grid together with wind power and photovoltaic power to supply power to the load.

[0090] The energy storage capacity configuration method of the source-grid-load-storage system based on probability sequence theory proposed in the present invention can fully consider the output uncertainty of wind power, photovoltaic power and load (source and load) at each moment by establishing a probability distribution model based on wind power signals, and establish opportunity constraints based on net load and net load probability sequence on the basis of considering the uncertainty of source and load, so that the subsequent energy storage capacity configuration is closer to reality.

[0091] In the embodiment of the present invention, by analyzing the volatility of wind power, photovoltaic power and load, probability distribution models are established for them respectively to reflect their uncertainties. Due to the continuity of their random variables, it can also be said that probability density functions are established.

[0092] Specifically, wind power and photovoltaics, as renewable energy sources, have the advantages of being clean and environmentally friendly. However, because their actual output levels are strongly related to natural conditions, wind and solar outputs are highly random and volatile and difficult to predict. At the same time, power load is affected by users' electricity usage habits, weather, etc., and there is also a certain degree of uncertainty.

[0093] The probability density function is used to describe the possibility of a continuous random variable being near a certain value point. Establishing separate probability density functions for wind power, photovoltaic power and load can reflect the possibility that their output values ​​at each moment fall near the predicted value.

[0094] Specifically, the probability distribution model of wind power, photovoltaic power and load in step S01 is obtained by the following method:

[0095] (1) Wind power probability distribution model

[0096] By collecting funds, we can obtain the 24-hour wind power output data of a 700MW wind farm in a certain place, such as Figure 2 As shown, denoted as P wt , is the predicted output of the wind turbine at time t.

[0097] Assume that the wind power prediction error has a mean of 0 and a variance of Modeling the normal distribution characteristics of:

[0098] ε wt =P Wt -P wt

[0099]

[0100] σ wt =0.2P wt +0.02P z

[0101] Where: P Wt is the actual output of the wind turbine at time t; wt is the prediction error of the wind turbine at time t; P z is the installed capacity of the wind turbine; σ wt is the standard deviation of wind power prediction error;

[0102] According to the basic theory of probability, if the wind power prediction error obeys the normal distribution, then the actual wind power output also obeys the normal distribution, with the same variance and mean P wt ; The normal distribution characteristics of actual wind power output are expressed as follows:

[0103]

[0104] The probability density function of the actual wind power output is expressed as follows:

[0105]

[0106] Where: f(P Wt ) represents the probability density function of the actual wind power output;

[0107] (2) Photovoltaic probability distribution model

[0108] By collecting funds, we can obtain the 24-hour photovoltaic output power data of a 200MW photovoltaic power station in a certain place, such as Figure 2 shown.

[0109] The actual photovoltaic output follows the Beta distribution, and its probability density function is expressed as follows:

[0110]

[0111] Among them, f(P St ) is the probability density function of the actual photovoltaic output; P St is the actual output of the photovoltaic unit at time t, that is, the actual photovoltaic output at time t; P smax is the maximum output power of a single photovoltaic power generation unit, which is 200MW in this embodiment; Γ(·) is the gamma function; α, β are the shape parameters of the gamma function; σ 2 , μ are the variance and expectation of Beta distribution respectively;

[0112] (3) Load probability distribution model

[0113] Obtain 24-hour load data of a certain place by collecting funds, such as Figure 3 As shown, denoted as P lt , is the predicted value of the load at time t.

[0114] The load in the source-grid-load-storage system obeys the normal distribution, and its probability density function is expressed as follows:

[0115]

[0116] Among them, f(P Lt ) is the probability density function of the actual load value; A is the load prediction accuracy; ε t is the load forecast error at time t; T is the total number of hours in a day, i.e. 24 hours; P Lt is the actual value of the load at time t.

[0117] Specifically, as a key component of the power system, power load has been widely studied and its power forecasting is relatively mature. The uncertainty faced by short-term load forecasting is usually lower than that of wind and solar power generation, and its change trend has a certain degree of predictability. The forecast error is larger at peak and trough loads, and smaller near the average load level. In load forecasting research, accuracy is the core criterion for measuring forecast accuracy.

[0118] In this embodiment, in step S02, based on the probability distribution models of wind power, photovoltaic power and load, the probability density functions of wind power, photovoltaic power and load are discretized respectively to obtain the discretized probability sequences of wind power, photovoltaic power and load, which specifically include:

[0119] Since the probability density functions of the actual wind power output, the actual photovoltaic output and the actual load value obtained in step S01 are all continuous, in order to facilitate the subsequent mathematical operations on wind power, photovoltaic power and load, they need to be serialized according to a certain step length to obtain discrete probability sequences of wind power, photovoltaic power and load;

[0120] Assume a length of N a There are N discrete sequences a(i) a +1 value if it meets the following conditions:

[0121] a(i)≥0,i=0,1,...,N a

[0122]

[0123] Then the sequence a(i) is called a probabilistic sequence; where i represents the sequence number or element subscript of the probabilistic sequence;

[0124] From the above definition, we can know that the length of the probability sequence is 1 less than the number of its digits, and the sum of each point in the probability sequence is 1. According to the basic theory of probability, the value at each point of the probability sequence can be understood as the probability that a discrete random variable is equal to that point. For example, if the random variable is X, then its probability at point i can be expressed as:

[0125] P(X=i)=a(i),i=0,1,2,...,N a

[0126] Therefore, the probability density functions of the actual wind power output, the actual photovoltaic output and the actual load value are substituted into the discretized formula to obtain the probability sequence S of wind power, photovoltaic power and load. w , S s and S l ; The discretization formula is expressed as follows:

[0127]

[0128] Among them, f(x) is the continuous probability density function to be discretized, which can be f(P Lt )、f(P W t), f(P St ); x is a random variable, that is, P Wt , P St , P Lt ; F(i) is a discretized probability sequence, where i is the sequence number of the discretized probability sequence; is the discrete step length; x max is the maximum value of the random variable; N step is the length of the probability sequence F(i).

[0129] Specifically, use the above formula to Figure 2 The wind power and photovoltaic curves shown in Figure 3 The load curve shown in the figure is discretized to obtain Figure 4 , 5 ,The wind power, photovoltaic,load probability sequences shown in Figure 6.

[0130] In this embodiment, the net load of energy storage power supply is obtained in step S03, and a net load probability sequence is obtained according to the discretized probability sequences of wind power, photovoltaic power and load, which specifically includes:

[0131] Step S031, obtaining the net load of energy storage power supply;

[0132] Specifically, to determine the capacity of energy storage, it is necessary to analyze the load left for energy storage power supply, that is, the load minus the total output of wind power and photovoltaic power, also known as the net load, and the formula is as follows:

[0133] P Dt =P Lt -P Wt -P Vt

[0134] Among them, P Dt is the net load at time t;

[0135] Step S032, obtaining a net load probability sequence according to the discretized probability sequences of wind power, photovoltaic power and load;

[0136] Specifically, for the discretized probability sequences of load, wind power, and photovoltaic power, the roll sum and roll difference operations can be used to obtain the probability sequence of net load;

[0137] The definitions of volume sum and volume difference operations are as follows:

[0138] Convolution operation: Assume that the probability sequences of random variables A and B are a(i a ),b(i b ), with lengths of N a1 ,N b1 , then the probability sequence x(i) of the random variable X = A + B can be obtained by the convolution operation as shown below:

[0139]

[0140] N x =N a1 +N b1

[0141] The above formula can be written as Where N x is the length of the sequence x(i).

[0142] Roll difference operation: Assume that the probability sequences of random variables C and D are c(i c ),d(i d ), with lengths of N c ,N d , then the probability sequence y(i) of the random variable Y=CD can be obtained by the roll difference operation as shown below:

[0143]

[0144] N y =N c

[0145] The above formula can be written as Where N y is the length of the sequence y(i).

[0146] The net load probability sequence is calculated using the above volume sum and volume difference operations. The formula is as follows:

[0147]

[0148] Among them, S dt is the net load probability sequence; For volume and operation; It is the roll difference operation.

[0149] Specifically, the above method can be used to calculate the net load probability sequence, such as Figure 7 shown.

[0150] In this embodiment, in step S04, a chance constraint is established based on the net load and the net load probability sequence, and based on the chance constraint, a minimum energy storage power under a specific confidence level is obtained, which specifically includes:

[0151] Step S041: establishing an opportunity constraint based on the net load and the net load probability sequence;

[0152] Get the set confidence α;

[0153] Based on the set confidence α, a chance constraint is established to ensure that the probability of the event "the energy storage discharge power can supply power to all net loads at time t" is not less than the confidence α. The chance constraint is expressed as follows:

[0154] P r {P Dt ≤P BESS}≥α

[0155] P BESS ≥0

[0156] Among them, P BESSis the energy storage discharge power; α is the set confidence level, 0≤α≤1; P Dt is the net load at time t, P Dt =P Lt -P Wt -P Vt ;P r (·) indicates probability operation.

[0157] Specifically, for optimization problems with random variables, chance constraints are used to restrict boundary conditions. Chance constraints allow a certain degree of constraint non-satisfaction, but guarantee that the constraint conditions are satisfied at a given confidence level.

[0158] Step S042: Based on the probability sequence theory, the chance constraint is converted into an inequality constraint, which is expressed as follows:

[0159]

[0160] Among them, I αt Indicates that in the net load probability sequence S dt The sequence number when the cumulative sum of each element is equal to α;

[0161] Specifically, the net load P Dt It can be regarded as a continuous random variable. After discretizing its probability density function, the probability sequence is S dt Therefore, according to the probability theory, if the desired event "net load power P Dt Less than the energy storage discharge power P BESS " is greater than α, we should first find the critical power value corresponding to the probability of the event equal to α, and let P BESS It only needs to be greater than the critical power value.

[0162] Specifically, starting from the 0th element, the net load probability sequence S dt The elements of S are accumulated and summed until the sum is equal to α, and then S is recorded. dt The element subscript is denoted by I αt , that is:

[0163]

[0164] Specifically, since the step size is taken when discretizing the probability density function Therefore, the power value corresponding to the sequence number of each probability sequence should be the sum of the sequence number i and the step length The product of is:

[0165]

[0166] Therefore, according to the probability sequence theory, the above chance constraints can be transformed into inequality constraints.

[0167] Step S043: Calculate the minimum energy storage power under a specific confidence level;

[0168] Based on the inequality constraints and considering the power conditions at each moment in 24 hours, the energy storage discharge power should satisfy the following constraints:

[0169]

[0170] According to the constraints that the above energy storage discharge power should meet, the minimum energy storage power under a specific confidence level is obtained.

[0171] In this embodiment, in step S05, according to the required off-grid power conservation time, the net load power within the power conservation time after off-grid occurs at 24 moments throughout the day is calculated in a rolling manner to obtain the required minimum energy storage power, specifically including:

[0172] Based on the constraints that the energy storage discharge power should meet, the energy storage battery capacity E is obtained. BESS The constraints that should be satisfied are expressed as follows:

[0173] E BESS ≥max(E(1),...,E(24))

[0174] Obtain the required off-grid power-saving duration, and calculate the net load power within the power-saving duration after off-grid at 24 hours a day. The formula is as follows:

[0175]

[0176] Where h is the required off-grid power-saving duration, E(j) represents the net load power when a power outage occurs at the jth moment and lasts for h hours; P Dt,α represents the net load power at time t under the confidence level α.

[0177] Based on the energy storage battery capacity E BESS The constraints that should be met and the net load power within the power protection period after being off-grid at any time throughout the day can be used to obtain the minimum required energy storage power.

[0178] In this embodiment, in step S06, based on the minimum energy storage power and the minimum energy storage capacity, the ratio of the energy storage system is selected to configure the energy storage capacity, wherein the ratio of the energy storage system refers to the ratio of the power that the energy storage system can output per unit time to its battery capacity, usually expressed in units of "C". For example, the ratio of a 5MW / 10MWh energy storage system is 0.5C. Currently, common ratios include 0.5C, 1C, 0.25C, etc.

[0179] Specifically, the energy storage capacity configuration includes: determining the rated power of the energy storage system based on the minimum energy storage power, and determining the total battery capacity of the energy storage system based on the common energy storage system multiples, the minimum energy storage capacity and the energy storage efficiency of the energy storage system.

[0180] Example 2

[0181] The embodiment of the present invention provides a system for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory, including:

[0182] Probability distribution acquisition module, used to obtain the probability distribution model of wind power, photovoltaic power and load;

[0183] A discretization module is used to discretize the probability density functions of wind power, photovoltaic power and load respectively based on the probability distribution models of wind power, photovoltaic power and load, so as to obtain the discretized probability sequences of wind power, photovoltaic power and load;

[0184] The net load module is used to obtain the net load of energy storage power supply and obtain the net load probability sequence based on the discretized probability sequence of wind power, photovoltaic power and load;

[0185] A first constraint module is used to establish a chance constraint based on the net load and the net load probability sequence, and obtain a minimum energy storage power under a specific confidence level based on the chance constraint;

[0186] The second constraint module is used to calculate the net load power within the power-saving time after the grid is disconnected at 24 moments throughout the day according to the required off-grid power-saving time, and obtain the required minimum energy storage power;

[0187] The capacity configuration module is used to select the multiple of the source-grid-load-storage system based on the minimum energy storage power and the minimum energy storage capacity to configure the energy storage capacity.

[0188] Example 3

[0189] In this embodiment, the present invention further introduces a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in Embodiment 1 are implemented.

[0190] Example 4

[0191] In this embodiment, the present invention further introduces a computer device, including:

[0192] A memory for storing instructions;

[0193] The processor is used to execute the instruction so that the device performs the operation of implementing the image preprocessing method as described in Example 1.

[0194] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0195] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0196] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory, characterized in that: include: Obtain probability distribution models of wind power, photovoltaic power and load; Based on the probability distribution models of wind power, photovoltaic power and load, the probability density functions of wind power, photovoltaic power and load are discretized respectively to obtain the discretized probability sequences of wind power, photovoltaic power and load; Obtain the net load of energy storage power supply, and obtain the net load probability sequence based on the discretized probability sequence of wind power, photovoltaic power and load; Establishing a chance constraint based on the net load and the net load probability sequence, and obtaining a minimum energy storage power under a specific confidence level based on the chance constraint; According to the required off-grid power-keeping duration, the net load power within the power-keeping duration after off-grid is calculated on a rolling basis at 24 times a day to obtain the minimum required energy storage power; Based on the minimum energy storage power and the minimum energy storage capacity, the multiple of the source-grid-load-storage system is selected to configure the energy storage capacity.

2. The method for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory according to claim 1 is characterized in that: The probability distribution model of wind power, photovoltaic power and load is obtained by the following method: (1) Wind power probability distribution model The wind power prediction error has a mean of 0 and a variance of The normal distribution characteristics of are expressed as follows: e wt =P Wt -P wt s wt =0.2P wt +0.02P z Where: P Wt is the actual output of the wind turbine at time t, that is, the actual wind power output at time t; P wt is the predicted output of the wind turbine at time t, that is, the predicted wind power output at time t; ε wt is the prediction error of the wind turbine at time t, that is, the wind power prediction error at time t; P z is the installed capacity of the wind turbine; σ wt is the standard deviation of wind power prediction error; Based on the normal distribution characteristics of wind power prediction error, the normal distribution characteristics of wind power actual output are obtained, which are expressed as follows: The probability density function of the actual wind power output, that is, the probability distribution model of wind power, is expressed as follows: Where: f(P Wt ) represents the probability density function of the actual wind power output; (2) Photovoltaic probability distribution model The actual photovoltaic output follows the Beta distribution, and its probability density function, that is, the photovoltaic probability distribution model, is expressed as follows: Among them, f(P St ) is the probability density function of the actual photovoltaic output; P S t is the actual output of the photovoltaic unit at time t, that is, the actual photovoltaic output at time t; P smax is the maximum output power of a single photovoltaic power generation unit; Γ(·) is the gamma function; α, β are the shape parameters of the gamma function; σ 2 , μ are the variance and expectation of Beta distribution respectively; (3) Load probability distribution model The load in the source-grid-load-storage system obeys the normal distribution, and its probability density function, that is, the load probability distribution model, is expressed as follows: Among them, f(P Lt ) is the probability density function of the actual load value; A is the load prediction accuracy; ε t is the load forecast error at time t; T is the total number of hours in a day, i.e. 24 hours; P Lt is the actual value of the load at time t; P lt is the predicted value of the load at time t.

3. The method for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory according to claim 1 is characterized in that: Based on the probability distribution models of wind power, photovoltaic power and load, the probability density functions of wind power, photovoltaic power and load are discretized respectively to obtain the discretized probability sequences of wind power, photovoltaic power and load, including: Substitute the probability density functions of the actual wind power output, the actual photovoltaic output and the actual load value into the discretized formula to obtain the probability sequence S of wind power, photovoltaic power and load. w , S s and S l ; The discretization formula is expressed as follows: Among them, f(x) is the continuous probability density function to be discretized, which can be f(P Lt )、f(P W t), f(P St ); x is a random variable, that is, P Wt , P St , P Lt ; F(i) is the discretized probability sequence, i is the sequence number of the discretized probability sequence; is the discrete step length; x max is the maximum value of the random variable; N step is the length of the probability sequence F(i).

4. The method for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory according to claim 3 is characterized in that: According to the discretized probability sequences of wind power, photovoltaic power and load, the net load probability sequence is obtained, including: The net load probability sequence is calculated using the volume sum and volume difference operations. The formula is as follows: Among them, S dt is the net load probability sequence; For volume and operation; It is the roll difference operation.

5. The method for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory according to claim 4 is characterized in that: The establishing of the opportunity constraint based on the net load and the net load probability sequence comprises: Get the set confidence α; Based on the set confidence level α, a chance constraint is established to ensure that the probability of the event "the energy storage discharge power can supply power to all net loads at time t" is not less than the confidence level α. The chance constraint is expressed as follows: P r {P Dt ≤P BESS }≥α P BESS ≥0 Among them, P BESS is the energy storage discharge power; α is the set confidence level, 0≤α≤1; P Dt is the net load at time t, P Dt =P Lt -P Wt -P Vt ;P r (·) indicates probability operation.

6. The method for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory according to claim 5 is characterized in that: Based on the opportunity constraint, the minimum energy storage power under a specific confidence level is obtained, including: According to the probability sequence theory, the chance constraint is converted into an inequality constraint, which is expressed as follows: Among them, I αt Indicates that in the net load probability sequence S dt The sequence number when the cumulative sum of each element is equal to α is I αt satisfy: Based on the inequality constraints and considering the power conditions at each moment in 24 hours, the energy storage discharge power should satisfy the following constraints: According to the constraints that the above energy storage discharge power should meet, the minimum energy storage power under a specific confidence level is obtained.

7. The method for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory according to claim 6 is characterized in that: According to the required off-grid power-saving duration, the net load power within the power-saving duration after off-grid occurs at 24 moments throughout the day is calculated on a rolling basis to obtain the required minimum energy storage power, including: Obtain the required off-grid power-saving duration, and calculate the net load power within the power-saving duration after off-grid at 24 hours a day. The formula is as follows: Where h is the required off-grid power-saving duration, E(j) represents the net load power when a power outage occurs at the jth moment and lasts for h hours; P Dt,α represents the net load power at time t under the confidence level α; Energy storage battery capacity E BESS The following constraints should be met: AND BESS ≥max(E(1),...,E(24)) Based on the energy storage battery capacity E BESS The constraints that should be met and the net load power within the power protection period after being off-grid at any time throughout the day can be used to obtain the minimum required energy storage power.

8. A system for configuring energy storage capacity of a source-grid-load-storage system based on probability sequence theory, characterized in that: include: Probability distribution acquisition module, used to obtain the probability distribution model of wind power, photovoltaic power and load; A discretization module is used to discretize the probability density functions of wind power, photovoltaic power and load respectively based on the probability distribution models of wind power, photovoltaic power and load, so as to obtain the discretized probability sequences of wind power, photovoltaic power and load; The net load module is used to obtain the net load of energy storage power supply and obtain the net load probability sequence based on the discretized probability sequence of wind power, photovoltaic power and load; A first constraint module is used to establish a chance constraint based on the net load and the net load probability sequence, and obtain a minimum energy storage power under a specific confidence level based on the chance constraint; The second constraint module is used to calculate the net load power within the power-saving time after the grid is disconnected at 24 moments throughout the day according to the required off-grid power-saving time, and obtain the required minimum energy storage power; The capacity configuration module is used to select the multiple of the source-grid-load-storage system based on the minimum energy storage power and the minimum energy storage capacity to configure the energy storage capacity.

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