Novel electric power balance risk prevention and control method for electric power system

Abstract

The invention discloses an electric power balance risk prevention and control method of a novel electric power system. The probability distribution of new energy power generation is calculated according to historical statistical data of new energy power generation; introducing new energy output as a random variable, and performing balance constraint according to balance risk management and control requirements; determining balance boundary parameters, and calculating a key balance point to obtain a novel power balance equation; on the basis of a novel power balance equation, the starting capacity is calculated; if the obtained power-on strategy meets the maximum power-on constraint, new energy abandoned and limited electric quantity data can be calculated based on a novel electric power balance equation, and a balance scheme is formed; and if not, applying for emergency call tie line standby power or taking controllable load power utilization measures to form a new balance scheme. According to the invention, the balance risk management and control model directly suitable for large power grid power balance practice of the novel power system is constructed, the applicability of the novel power system risk prevention and control method is improved, and the ability of the system to cope with the power balance risk is improved.

Description

Technical Field

[0001] The present invention relates to a method for preventing and controlling power balance risks, and in particular to a method for preventing and controlling power balance risks of a novel power system, and belongs to the field of power systems. Background Art

[0002] In recent years, the penetration rate of new energy has continued to increase. While the high proportion of new energy access has greatly promoted the clean and low-carbon transformation of new energy, its "unreliable" characteristics such as randomness and volatility have made the power and safety of large power grids face huge challenges. It is necessary to find a new balance model to prevent the balance risks of large power grids. How to achieve power risk prevention and control of new power systems has become a key issue in ensuring the safe and stable operation of the power grid.

[0003] At present, in response to the power balance problem of new power systems, relevant research focuses on the balance risk prevention and control of new power systems, and conducts research from the perspectives of problems and impacts, methods and strategies, or conducts joint analysis of the balance risk problems that arise when conducting a series of studies on new power systems. However, it is difficult to achieve an organic combination of basic balance principles and balance risk management mechanisms, and the combination with engineering practice scenarios is not close enough. There is a lack of balance risk management models that are directly applicable to the power balance practice of large power grids in new power systems. Summary of the invention

[0004] In order to solve the deficiencies existing in the above-mentioned technologies, the present invention provides a new method for preventing and controlling power balance risks in a power system.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a new power balance risk prevention and control method for power system, including the following control strategies:

[0006] Calculate the probability distribution of renewable energy generation based on historical statistical data of renewable energy generation;

[0007] After introducing the output of new energy as a random variable, balance constraints are applied according to the requirements of balance risk management;

[0008] Determine the equilibrium boundary parameters and calculate the key equilibrium point t A , and then obtain the new power balance equation;

[0009] Based on the new power balance equation, the startup capacity R is calculated;

[0010] If the startup strategy obtained meets the maximum startup constraint, the data on renewable energy curtailment and power restrictions can be calculated based on the new power balance equation, thereby forming a balance plan.

[0011] If the maximum startup constraints are not met, apply for emergency use of the interconnection line backup power or take controllable load power consumption measures to form a new balance plan.

[0012] As a preferred option, the probability distribution of renewable energy generation is divided into medium- and long-term and day-ahead power balance dimensions;

[0013] The output of new energy is determined by the probability distribution function of the output of new energy. The formula is:

[0014]

[0015] Where N° is the probability distribution, n and ξ are the output of new energy, and f η is the probability density function of the new energy simultaneous rate, F n is the probability distribution function of the new energy output.

[0016] As a preferred option, there is no specific prediction capability for new energy in the medium and long term, and new energy installed capacity is used as the benchmark for participating in power balance;

[0017] The output / installed capacity of new energy is recorded as the new energy simultaneous rate. The historical data of the new energy simultaneous rate is statistically analyzed to obtain the sample space of the new energy simultaneous rate. In the sample space of the new energy simultaneous rate, the probability density of the random variable new energy simultaneous rate η is obtained by statistical methods as follows:

[0018] η°=f η (η) (1)

[0019] In the formula, η is the specific new energy simultaneous rate, η° is the probability of the corresponding new energy simultaneous rate, and f η is the probability density function of the new energy simultaneous rate;

[0020] According to formula (1) and the definition of the new energy simultaneous rate, the probability density of new energy output in the medium and long-term balance dimension can be obtained as follows:

[0021]

[0022] In formula (2), n is the specific new energy output, which is the new energy output in the medium and long-term balance dimension; n° is the probability of the corresponding new energy output, f n is the probability density function of renewable energy output, C N Installed capacity for new energy sources;

[0023] The probability distribution function of the new energy output can be further deduced from formula (2).

[0024] As a preferred method, the new energy has a specific predicted output in the day-ahead dimension, and the predicted output of new energy is used as the benchmark for participating in power balance;

[0025] By statistically analyzing the historical data of the new energy forecast deviation rate, we can obtain the sample space of the new energy forecast deviation rate. The mathematical expression of the new energy forecast deviation rate is:

[0026]

[0027] In formula (4), ρ is the new energy prediction deviation rate, n is the specific new energy output, Contribute to new energy forecasting;

[0028] In the sample space of the new energy prediction deviation rate, the probability density of the random variable new energy prediction deviation rate ρ can be obtained by statistical methods as follows:

[0029] ρ°=f ρ (ρ) (5)

[0030] In formula (5), ρ is the prediction deviation rate, ρ° is the probability of occurrence of the corresponding prediction deviation rate, and f ρ is the probability density function of the new energy prediction deviation rate;

[0031] From equations (4) and (5), we can get the probability density of renewable energy output in the day-ahead balance dimension as follows:

[0032]

[0033] In formula (6), n is the specific new energy output, here it is the new energy output in the day-ahead balance dimension; n° is the probability of the corresponding new energy output appearing, f n is the probability density function of the output of new energy;

[0034] The probability distribution function of the new energy output can be further deduced from formula (6).

[0035] Preferably, the balance constraint includes the probabilistic balance margin and the probabilistic balance constraint;

[0036] With f n (n) Unified representation of the probability density of new energy output in the medium- and long-term and day-ahead dimensions, with F n (n) Unified representation of the probability distribution of new energy output in the medium- to long-term and day-ahead dimensions;

[0037] The probabilistic process of the balance margin is:

[0038] The power balance margin of any power grid at any time is expressed as:

[0039] δ=R+T+n-υl+x (8)

[0040] In formula (8), δ is the balance margin, R is the maximum power generation capacity of conventional power sources, T is the planned power of the interconnection line, n is the output of new energy, l is the load, υ is the reserve rate coefficient, and x is the controllable load;

[0041] When the new energy output n in equation (8) is a random variable, the balance margin δ also becomes a random variable, and the probability density function of δ is:

[0042] δ°=f δ (δ) = f n (δ-R-T+υl-x) (9)

[0043] In formula (9), δ° is the probability of occurrence of the corresponding balance margin, f δ is the probability density function of δ;

[0044] The probability distribution of δ is further obtained as:

[0045]

[0046] In formula (10), Δ° is the probability distribution of the equilibrium margin δ when it is a random variable, and F δ is the probability distribution function; Formula (10) is the probabilistic power balance margin;

[0047] The probabilistic process of equilibrium constraints is:

[0048] Before the introduction of random variables, the equilibrium constraint of the traditional equilibrium model is described as:

[0049] δ=R+T+n-υl+x≥δ0 (11)

[0050] In formula (11), δ0 is the balance margin threshold; after introducing the new energy output n as a random variable, the balance constraint is converted to:

[0051] F δ (δ0)≤λ (12)

[0052] In formula (12), λ is the allowed probability threshold, which is called “balance risk factor”. Formula (12) is the probabilistic balance constraint.

[0053] As a preferred embodiment, based on the balance margin threshold δ0 and the balance risk factor λ, the parameters of the probabilistic balance equation are further decoupled from the key controllable quantities, and the key controllable quantities include the power supply startup capacity R, that is, the maximum power generation capacity of the conventional power supply, the planned power of the tie line T, and the controllable load x;

[0054] The decoupling process is: Substituting equations (9) and (10) into equation (12), we can get:

[0055]

[0056] Substituting the probability distribution function of renewable energy output into equation (13), it can be converted into:

[0057]

[0058] In formula (14), is the inverse function of the probability distribution function of renewable energy output; the controllable quantities include the power supply startup capacity R, i.e. the maximum power generation capacity of conventional power supply, the planned power of the interconnection line T and the controllable load x, which is the controllable quantity in the balancing arrangement process; the standby load υl, the balance margin threshold δ0 and the participation of renewable energy in balancing power They are all uncontrollable quantities in the balancing arrangement process, that is, the balancing objects.

[0059] Preferably, in the process of constructing the new power balance equation, the single-point power balance of the new power system is first determined, and the process includes:

[0060] The single-point power balance equation of the new power system is constructed by formula (14) and the basic principle of single-point power balance:

[0061]

[0062] In formula (18), y is the restricted power of renewable energy; α is the peak load coefficient of conventional power supply; t is the number of balance points in a day, with 15 minutes as one balance point, that is, 96 balance points in a day.

[0063] As a preferred method, formula (18) analyzes the single-point power balance principle of the new power system at the basic theoretical level, analyzes the correlation between 96 balance points in combination with engineering practice, and further constructs the daily power balance equation for engineering. The process is:

[0064] The maximum power generation capacity of conventional power sources within a day is basically stable, and the balance risk control requirements of each balance point are the same, namely:

[0065]

[0066] It can be deduced from formula (18) and (19):

[0067]

[0068] In formula (20), Y is the power limit of renewable energy; there must be a time point t A , so that:

[0069]

[0070] Then formula (20) can be converted to:

[0071]

[0072] Formula (22) is the daily power balance equation of the new power system, and is called A The key balance point.

[0073] As a preferred embodiment, on the basis of formula (22), a multi-balance risk constraint is introduced, which is to expand the single balance risk constraint (δ0 λ) into multiple groups of constraints:

[0074] (δ0 λ)→[(δ1 λ1),…,(δ i λ i )…,(δ K λ K )] (25)

[0075] Substituting formula (25) into formula (20) expands to:

[0076]

[0077] Formula (26) is a new equilibrium equation with multiple equilibrium risk constraints.

[0078] As a preferred embodiment, for formula (14), in the balancing practice of the new power system, the standby load υl, the balancing margin threshold δ0 and the tie line planned power T are boundary quantities, and the controllable load measures are not considered, that is, x=0. From (14), it can be further deduced that:

[0079]

[0080] In formula (15), R min is the minimum power generation capacity of conventional power supply, which is approximately considered as the minimum startup capacity; it can be seen that R min and Related, is an increasing function of λ, then R min Negatively correlated with λ;

[0081] In order to quantitatively analyze the impact of differences in the characteristics of renewable energy generation in different power grids and eliminate the impact of renewable energy participation in the balance benchmark, a standard conversion function g(λ) is defined:

[0082]

[0083] In formula (16), U is the benchmark amount of new energy participation balance, and the medium- and long-term balance dimension is U = C N , day-ahead balance dimension

[0084] For formula (22), in the balancing practice of the new power system, l(t) and n(t) are uncontrollable balancing objects, T(t) is the boundary condition, and the peak load parameter α of the conventional power source is relatively fixed. From the second sub-formula of formula (22), it can be seen that the power limit Y of the new energy source is affected by the maximum power generation capacity R of the conventional power source, and the key balancing point t A , balance margin threshold δ0(t A ), controllable load x(t A) New energy participates in balancing electricity The impact of

[0085] Given the critical balance point t A , controllable load x(t A ) and the standard conversion function g(λ) are objective constant parameters, and the balance is optimal when the power supply startup capacity R takes the minimum value of the first formula in formula (22); formula (22) can be simplified as:

[0086] Y=f Y [δ0(t A ),λ] (23)

[0087] That is, the power limit of renewable energy is a function of the balance margin threshold of the key balance point and its balance risk;

[0088] In actual operation, the balance margin threshold δ0(t A )=0, formula (23) can be further simplified as:

[0089]

[0090] That is, when the balance margin threshold is zero, the renewable energy power limit is a function of only a single variable of balance risk.

[0091] The present invention discloses a power balance risk prevention and control method suitable for a new power system, so as to improve the shortcomings of the existing power balance risk prevention and control method, clarify the basic principles of the new power system balance risk prevention and control, construct a balance risk management model directly applicable to the power balance practice of the new power system large power grid, improve the applicability of the new power system risk prevention and control method, and improve the system's ability to cope with power balance risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0093] Figure 2 It is the basic data sample space used for analysis in the application example of the present invention.

[0094] Figure 3 This is a graph showing a typical daily load of a power grid and predicted output of new energy sources in an application example of the present invention.

[0095] Figure 4 It is the probability distribution curve of the new energy output in the application example of the present invention.

[0096] Figure 5 It is the standard conversion function used by the present invention for the risk of new energy participating in the balancing process in the example.

[0097] Figure 6This is an example of the power balance curve of new energy participating in different balance risks in the application example of the present invention.

[0098] Figure 7 This is a time-based standard risk power conversion ratio curve for daily power balance in an application example of the present invention.

[0099] Figure 8 It is the power balance curve and the minimum startup baseline of new energy participating in each period in the application example of the present invention.

[0100] Fig. 9 It is the new energy abandonment rate curve under different balance risk control boundaries of the application example of the present invention. DETAILED DESCRIPTION

[0101] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0102] The present invention relates to a method for preventing and controlling power balance risks of a new type of power system, and constructs a balance risk control model that is directly applicable to the realization of power balance of a large power grid in a new type of power system. Based on the practice of power balance in large power grids, the unified law of renewable energy power generation is combined with the principle of power balance in large power grids, and a systematic study is conducted on the basic principles of power balance risk prevention and control in new power systems. A "standard conversion function" is constructed to quantify the balance risk and then introduce it into the balance constraint equation, and a new balance equation containing balance risk control requirements is derived, and the basic principles of balance risk prevention and control in new power systems are explained accordingly. The new balance equation is substituted into the engineering practice scenario, and the negative correlation between balance risk prevention and control and the "growth and decline" of new energy consumption space is discussed, and then a working method for power balance risk prevention and control in new power systems is proposed.

[0103] Furthermore, the idea of ​​constructing the power balance risk prevention and control method of the present invention is as follows:

[0104] 1. Principles of risk prevention and control of new power system balance

[0105] 1.1 Probabilistic Output of New Energy

[0106] Based on the statistics of historical power generation data of new energy, a mathematical model of new energy participating in power balance is established through mathematical statistics and probability theory methods. Due to the different benchmarks of new energy participating in the balance in the medium- and long-term and day-ahead power balance dimensions, different processing methods are used in mathematical modeling.

[0107] 1.1.1 Medium and long term

[0108] There is no specific prediction capability for new energy in the medium and long term, and it uses new energy installed capacity as the benchmark for participating in power balance. In order to reflect the relationship between new energy output and installed capacity, the historical data of new energy output / installed capacity (referred to as "new energy simultaneous rate" in this patent) is statistically analyzed to obtain the sample space of new energy simultaneous rate. In the sample space of new energy simultaneous rate, the probability density of the random variable new energy simultaneous rate η can be obtained by statistical methods as follows:

[0109] η°=f η (η) (1)

[0110] In formula (1), η is the specific new energy simultaneous rate, η° is the probability of the corresponding new energy simultaneous rate, and f η is the probability density function of the new energy simultaneous rate.

[0111] According to formula (1) and the definition of the new energy simultaneous rate, the probability density of new energy output in the medium and long-term balance dimension can be obtained as follows:

[0112]

[0113] In formula (2), n is the specific new energy output, which is the new energy output in the medium and long-term balance dimension; n° is the probability of the corresponding new energy output, f n is the probability density function of renewable energy output, C N Installed capacity for new energy.

[0114] The probability distribution function of the new energy output can be further deduced from formula (2):

[0115]

[0116] In formula (3), N° is the probability distribution, n and ξ are the output of new energy, and F n is the probability distribution function of the new energy output.

[0117] 1.1.2 and before

[0118] Recently, Dimension New Energy has a specific forecast output, which is used as the benchmark for participating in power balance. In order to reflect the relationship between the actual output and the forecast output of new energy, the historical data of the new energy forecast deviation rate is statistically analyzed to obtain the sample space of the new energy forecast deviation rate. The mathematical expression of the new energy forecast deviation rate is:

[0119]

[0120] In formula (4), ρ is the prediction deviation rate of new energy, n is the specific new energy output, that is, the actual output of new energy, Contribute to new energy forecasting.

[0121] In the sample space of the new energy prediction deviation rate, the probability density of the random variable new energy prediction deviation rate ρ can be obtained by statistical methods as follows:

[0122] ρ°=f ρ (ρ) (5)

[0123] In formula (5), ρ is the prediction deviation rate, ρ° is the probability of occurrence of the corresponding prediction deviation rate, and f ρ is the probability density function of the new energy prediction deviation rate.

[0124] From equations (4) and (5), we can get the probability density of renewable energy output in the day-ahead balance dimension as follows:

[0125]

[0126] In formula (6), n is the specific new energy output, here it is the new energy output in the day-ahead balance dimension; n° is the probability of the corresponding new energy output appearing, f n is the probability density function of renewable energy output.

[0127] Referring to formula (3), formula (6) can further deduce the probability distribution function of renewable energy output in the day-ahead dimension as follows:

[0128]

[0129] Since the basic balance principle of the medium- and long-term and the day-ahead dimension is the same, for the convenience of analysis, f is used below. n (n) The probability density of new energy output in the medium and long term and day-ahead dimension is expressed uniformly as F n (n) The probability distribution of new energy output in the medium- to long-term and day-ahead dimensions is uniformly expressed and no further details are given.

[0130] 1.2 Probabilistic Balance Margin

[0131] The power balance margin of any power grid at any time can be expressed as:

[0132] δ=R+T+n-υl+x (8)

[0133] In formula (8), δ is the balance margin, R is the power supply startup capacity, where the power supply startup capacity represents the maximum power generation capacity of the conventional power supply, which is proportional to and approximately equal to the startup capacity. Theoretically, it should be multiplied by a conversion coefficient. To simplify the expression, the present invention treats the two as equivalent; T is the planned power of the interconnection line, n is the output of new energy, l is the load, υ is the reserve rate coefficient, and x is the controllable load.

[0134] When the new energy output n in formula (8) is a random variable, the balance margin δ also becomes a random variable accordingly. The probability density function of the balance margin δ when it is a random variable can be obtained from formula (2)(8) or formula (6)(8):

[0135] δ°=f δ (δ) = f n (δ-R-T+υl-x) (9)

[0136] In formula (9), δ° is the probability of occurrence of the corresponding balance margin, f δ is the probability density function of δ. From formula (7), we can further get the probability distribution of δ:

[0137]

[0138] In formula (10), Δ° is the probability distribution of the equilibrium margin δ when it is a random variable, and F δ is the probability distribution function. Formula (10) is the probabilistic power balance margin.

[0139] 1.3 Probabilistic Equilibrium Constraints

[0140] Before the introduction of random variables, the balance constraint of the traditional balance model can be described as:

[0141] δ=R+T+n-υl+x≥δ0 (11)

[0142] In formula (11), δ0 is the balance margin threshold. After introducing the new energy output in 1.1 as a random variable, the balance constraint is converted to:

[0143] F δ (δ0)≤λ (12)

[0144] In formula (12), λ is the allowed probability threshold. The physical meaning of formula (12) is: the probability that the balance margin is less than δ0 is not greater than λ. The smaller λ is, the smaller the probability that the actual balance margin is less than the balance margin threshold δ0 is. Based on this, this patent calls λ a "balance risk factor".

[0145] Formula (12) is the probabilistic balance constraint. Directly applying this formula to guide power balance practice has the following shortcomings:

[0146] (a) The left side of formula (12) is a probability distribution function, and its function value is the result of probability calculation and cannot be directly used to carry out power balance;

[0147] (b) The key controllable quantities in power balance practice, such as the power supply startup capacity R, i.e. the maximum power generation capacity of conventional power supply, the planned power of the interconnection line T, and the controllable load x, are parameters of formula (12) rather than variables, i.e. the function structure F of formula (12)δ The function parameters are intertwined with the controllable quantity, which makes it impossible to directly apply the above function to carry out analytical settlement with the goal of balancing the controllable quantity optimization adjustment.

[0148] 1.4 Balance constraint equations of new power systems

[0149] Based on the above formula (12), the power balance practice is insufficient. It is necessary to further decouple the parameters of the probabilistic balance equation from the key controllable quantities in the balance practice. Substituting formula (9) (10) into formula (12), we can get:

[0150]

[0151] Substituting (3) or (7) into equation (13), equation (13) can be converted to:

[0152]

[0153] In formula (14), is the inverse function of the probability distribution function of renewable energy output, and F n The derivation procedure shows that The parameters are only related to the characteristics of the new energy itself, and have nothing to do with the controllable quantities of power balance R, T, x, etc., that is, The function structure is objective and fixed. The left side of formula (14) is the conventional power generation capacity, the planned power of the interconnection line and the controllable load, which are the controllable quantities in the balancing arrangement process. The right side has three quantities: load (including reserve), margin requirement, and the new energy output inferred from the balancing risk constraint (that is, the participation of new energy in balancing power). ), are all uncontrollable quantities in the balancing arrangement process, namely, the balancing objects.

[0154] Therefore, compared with the probabilistic equilibrium equation (12), equation (14) has the following advantages:

[0155] (a) The decoupling of controllable quantities and function parameters is achieved, and the function structure is solidified, which lays a mathematical foundation for analytical calculations aimed at optimizing the balance of controllable quantities.

[0156] (b) Equation (14) is consistent with the traditional power balance constraint equation model and has the same variables, which can directly guide the power balance analysis.

[0157] (c), where After being determined by statistical methods, formula (14) is an explicit equation for key balance factors such as R, T, x, l, δ0, λ, etc., which creates conditions for conducting theoretical analysis of the correlation between key balance factors based on this equation.

[0158] Comparing formula (14) with the traditional balance equation (11), the two have different processing modes for the participation of new energy in the balance. Formula (14) has the following advantages:

[0159] (a) In the traditional balance equation, the output of renewable energy participates in the balance with a fixed value n. The balance equation cannot reflect the balance risk brought by the volatility and randomness of renewable energy. As the penetration rate of renewable energy increases, the balance conclusion will become increasingly unreliable. In other words, the traditional balance equation cannot adapt to the balance characteristics of the new power system.

[0160] (b) Formula (14) introduces a balance risk factor λ compared to the traditional balance equation. The output of new energy is a function of the balance risk The mode participates in the balance, It integrates the balancing factors such as the fluctuation characteristics of new energy (the statistical characteristics of simultaneity rate in the medium and long term, and the statistical characteristics of forecast deviation rate in the day ahead), the benchmark quantity of new energy (installed capacity in the medium and long term, and forecast output in the day ahead), and the balance risk (the probability that the balance margin is less than the margin threshold), and realizes the quantitative control of the balance risk.

[0161] (c) The basic principle of power balance and the principle of risk control of new energy participating in power balance are organically combined through formula (14). That is, formula (14) not only represents the basic constraints of power balance, but also represents the balance risk brought by new energy, and incorporates the balance risk into the basic balance equation in the mode of "participating in balancing power".

[0162] Based on this, formula (14) is the "new power system balance constraint equation" (referred to as "new power balance equation") in the theoretical sense. It is called "new energy participation in balancing electricity". It is called the conversion function from “new energy participation in balancing risk” to “new energy participation in balancing electricity”.

[0163] 1.5 Characteristic analysis of the new equilibrium constraint equation

[0164] In the practice of power balance of new power system, load (υl) is an objective quantity, margin threshold (δ0), tie line planned power (T) and other boundary quantities are used, and controllable load measures are not considered for the time being (i.e. x = 0). Then the key conversion of power balance is based on the characteristics of new energy. The conventional power supply startup capacity R can be reasonably determined by balancing the risk prevention and control requirements (λ). Formula (14) can be further deduced as follows:

[0165]

[0166] In formula (15), R min is the minimum power generation capacity of conventional power supply (approximately considered as the minimum startup capacity); it can be seen that R min and Related.

[0167] Furthermore, by conducting a specific characteristic analysis in two directions, namely, the requirements for balancing risk management and control and the characteristics of natural power generation from renewable energy, the following aspects are included:

[0168] 1.5.1 Balancing the impact of risk management requirements

[0169] According to the general principles of probability theory and equations (3) and (7) about F n The definition of F n is an increasing function of λ, then is also an increasing function of λ. Combining formula (15), we can see that R min Negatively correlated with λ, that is, the smaller λ is, the higher R min The bigger.

[0170] Its physical meaning is: the lower the risk of new energy participating in power balance, the greater the requirement for starting up conventional power sources; conversely, the greater the risk of allowing new energy to participate in power balance, the lower the requirement for starting up conventional power sources.

[0171] 1.5.2 Impact of natural power generation characteristics of new energy

[0172] When λ is the same, the conversion functions of different power grids are Different, then R min Correspondingly different. It is related to the statistical law of the natural power generation characteristics of new energy and the benchmark amount of new energy participating in the balance (installed capacity in the medium and long term, predicted output in the day before). In order to quantitatively analyze the impact of the differences in the power generation characteristics of new energy in different power grids and eliminate the impact of the benchmark amount of new energy participating in the balance, a standard conversion function g(λ) is defined:

[0173]

[0174] In formula (16), U is the benchmark amount of new energy participation balance, and the medium- and long-term balance dimension is U = C N , day-ahead balance dimension When the new energy participation balance benchmark quantity U and the balance risk control requirement λ are the same, the larger g(λ) is, the smaller the requirement for starting up the conventional power supply; conversely, the greater the requirement for starting up the conventional power supply.

[0175] This article calls g(λ) the standard risk power conversion ratio, and its physical meaning is: g(λ) reflects the reliability and stability of renewable energy power generation. The larger g(λ) is, the more stable the corresponding renewable energy power generation characteristics are, the smaller the volatility of the medium- and long-term balance dimension, that is, renewable energy, and the higher the accuracy of the day-ahead balance dimension, that is, renewable energy prediction. The more stable and reliable the renewable energy power generation characteristics are, the lower the cost of preventing and controlling renewable energy balance risks is, the greater the output of renewable energy that can participate in power balance, and the looser the constraints on balance are (R min the smaller).

[0176] 2. Power balance principle of new power system

[0177] The basic principles of risk control of the new power system balance explained in this article are applicable to the risk control of power balance in any power grid at any time. In engineering practice, the actual object of power balance is the daily balance curve composed of multiple continuous points (weekly, monthly and other longer-term power balances are based on daily balances). It is necessary to combine the principles of risk control of the new power system balance with power balance engineering practice. The specific process is:

[0178] 2.1 Basic principles of single-point power balancing

[0179] Before introducing the new power balance constraint equation, it is known that the basic principle of single balance point power balance is:

[0180]

[0181] In formula (17), y is the restricted power of renewable energy; α is the peak load coefficient of conventional power supply, which is generally considered to be a relatively stable constant; t is the number of balance points in a day. This patent takes 15 minutes as a balance point, that is, 96 balance points throughout the day.

[0182] Substituting formula (14) into formula (17), we can obtain:

[0183]

[0184] Formula (18) is the basic equation for single-point power balance. Among them, the first sub-formula is the balance constraint equation, and its physical meaning is: the upper limit of the controllable balance capacity must be higher than the uncontrollable balance object to ensure sufficient safety reserve margin and prevent low-frequency risks caused by power shortages; the second sub-formula is the consumption equation, and its physical meaning is: when the lower limit of the controllable balance capacity is higher than the uncontrollable balance object, equal new energy control measures must be taken to ensure the balance between power generation and consumption and prevent high-frequency risks caused by excess power.

[0185] 2.2 Principle of daily power balance

[0186] 2.2.1 Balance equation

[0187] Formula (18) explains the single-point power balance principle of the new power system at the basic theoretical level. However, there are 96 balance points in a day. It is necessary to analyze the correlation between the 96 balance points in combination with engineering practice and further construct the daily power balance equation for engineering use.

[0188] In engineering practice, the maximum power generation capacity of conventional power sources within a day is basically stable, and the balance risk control requirements of each balance point are the same, namely:

[0189]

[0190] It can be deduced from formula (18) and (19):

[0191]

[0192] In formula (20), Y is the power limit of new energy. There must be a time point t A , so that:

[0193]

[0194] Then formula (20) can be converted to:

[0195]

[0196] Formula (22) is the daily power balance equation of the new power system, and is called A The key balance point.

[0197] 2.2.2 Characteristic Analysis of the Daily Power Balance Equation of the Power System

[0198] In practice, l(t) and n(t) are uncontrollable balancing objects, T(t) is the boundary condition, and the peak-shaving parameter α of the conventional power source is relatively fixed. From the second sub-formula of equation (22), it can be seen that the renewable energy power limit Y mainly depends on the maximum power generation capacity (start-up capacity) R of the conventional power source, and is positively correlated with R.

[0199] Based on this, further analysis of the first sub-equation in equation (22) shows that the new energy power limit Y is mainly affected by the following factors:

[0200] (a) Critical balance point t A Among the 96 balance points in a day, there is only one key point that determines the startup of conventional power sources and the power limit of new energy sources; by controlling the balance risk of the key balance point, the balance risk control of the other 95 balance points is achieved simultaneously. The balance risk control constraint is optimized from the 96 points in formula (18) to a single point through formula (22), which greatly simplifies the difficulty of power balance analysis.

[0201] (b) Balance margin threshold δ0(t A). The smaller the balance margin threshold of the key point, the smaller the startup capacity, and the corresponding reduction in the amount of power restriction for new energy. Therefore, reasonably retaining the balance margin has a positive significance for promoting the consumption of new energy.

[0202] (c) Controllable load x(t A ). The larger the controllable load at the key point, the smaller the startup capacity, and the smaller the power limit of new energy. In other words, implementing controllable load measures at the key point of power balance has positive significance for promoting the consumption of new energy.

[0203] (d) New energy participates in balancing electricity The power limit of new energy is negatively correlated with the size of new energy participating in balancing power at key points, that is, increasing the participation of new energy at key points in balancing power has a positive significance for promoting the consumption of new energy. From the analysis of the characteristics of the new balance constraint equation in 1.5, it can be seen that in order to increase the participation of new energy at key points in balancing power, it is necessary to relax the balance risk control requirements (increase λ) or optimize the standard conversion function g(λ).

[0204] Given the critical balance point t A , controllable load x(t A ), standard conversion function g(λ), etc. are generally objective and invariant parameters; further considering the basic rules of the power market and the actual power balance, the balance is optimal when the conventional power supply startup capacity R takes the minimum value of the first formula in formula (22). Combined with the above analysis, formula (22) can be simplified as:

[0205] Y=f Y [δ0(t A ),λ] (23)

[0206] That is, the power limit of renewable energy is a function of the balance margin threshold of the key balance point and its balance risk. In actual operation, the balance margin greater than zero is generally used as the balance target, that is, the balance margin threshold δ0(t A )=0, formula (23) can be further simplified as:

[0207]

[0208] That is, when the balance margin threshold is zero, the power limit of renewable energy is a function of a single variable, balance risk. Since the key point balance margin threshold, balance risk, etc. are the core indicators of balance risk management, equations (23) and (24) reveal the mathematical relationship between the power limit of renewable energy and the balance risk management standard. Further, from the detailed analysis of the daily power balance equation in 2.2.2, it is known that the higher the risk control standard (δ0(t A The larger the value of )) and the smaller the value of λ), the greater the power limit of new energy; conversely, the lower the risk control standard, the less impact on new energy consumption. In other words, the impact on new energy consumption is the objective physical cost of balancing risk control.

[0209] 2.2.3 Multi-risk Constraint Balance Equation

[0210] The above analysis adopts a single balance risk constraint (δ0 λ), that is, the probability of the unique balance margin δ0 is required to be less than λ. In actual operation scenarios, the granularity of a single balance risk constraint is relatively coarse, and it may be impossible to fully characterize the balance situation. For example, there may be several situations that meet the single balance risk constraint (δ0 λ), but the actual balance boundary and balance situation of each situation are quite different. In the tense balance periods such as winter and summer, important power supply periods, and natural disaster-prone periods, it is necessary to further strengthen the balance risk management and control on the basis of a single balance constraint. At this time, it is necessary to introduce multiple balance risk constraints.

[0211] Multi-balance risk constraints are to expand a single balance risk constraint (δ0 λ) into multiple sets of constraints:

[0212] (δ0 λ)→[(δ1 λ1),…,(δ i λ i )…,(δ K λ K )] (25)

[0213] Substituting formula (25) into formula (20) expands to:

[0214]

[0215] Formula (26) is a new type of equilibrium equation with multiple equilibrium risk constraints. In practice, according to the equilibrium situation, several equilibrium risk constraints can be scientifically set for key equilibrium points to achieve differentiated and refined equilibrium risk management and control.

[0216] So far, based on the above theoretical analysis and combined with the practice of power balance engineering, the power balance risk prevention and control method of the new power system of the present invention can be summarized as follows: Figure 1 The process shown:

[0217] First, the probability distribution of renewable energy power generation is calculated based on the historical statistical data of renewable energy power generation, as shown in equations (3) and (7). After introducing renewable energy output as a random variable, the balance constraint can be expressed as equation (12) according to the balance risk management requirements. Then, the balance boundary parameters are determined and the key balance point t is calculated. A , and then a new power balance equation is obtained, as shown in equations (22) and (26). Based on the new power balance equation, the startup capacity R can be calculated. If the startup strategy obtained meets the maximum startup constraint, the new energy abandonment data can be calculated based on the new power balance equation to form a balance plan. If the maximum startup constraint is not met, an application should be made for emergency call-up of the interconnection line standby power or controllable load power consumption measures should be taken to form a new balance plan.

[0218] Compared with the traditional power balance workflow, the improvements of the power balance risk prevention and control method of the novel power system of the present invention are as follows:

[0219] (1) Calculate the probability distribution function of renewable energy generation based on historical statistical data of renewable energy generation in advance The probability distribution function of renewable energy power generation is an important characteristic parameter for renewable energy to participate in power balance.

[0220] (2) Critical balance point (t A ) needs to consider more influencing factors comprehensively.

[0221] (3) The core of the balance calculation is to construct a "new type of power balance equation".

[0222] Compared with the prior art, the present invention has the following technical advantages:

[0223] A systematic theoretical study and case analysis were conducted on the new power system balance risk prevention and control methods, and a differentiated balance power statistical model was established in the medium- and long-term and day-ahead balance dimensions. Through the constructed "standard conversion function", the "new energy participation balance risk" was mapped into "new energy participation balance power", realizing the organic combination of the basic balance principle and the balance risk control mechanism, making it possible to analyze and solve the power balance risk prevention and control model of the new power system. The negative correlation between balance risk prevention and control and the "growth and decline" of the new energy consumption space was discussed, and the decoupling of the controllable quantity and the main parameter quantity was achieved. It was closely combined with the engineering practice scenario, and a balance risk control model directly applicable to the power balance practice of the new power system large power grid was constructed. It has important significance and reference value for the research on power balance risk prevention and control of the new power system, and has engineering application value.

[0224] Application Examples

[0225] Based on the actual operation data of a certain power grid, the analysis is carried out, and the statistical data of renewable energy power generation in 2023 of this power grid is used as the basic data sample space for obtaining the probability distribution function, risk conversion function, standard conversion function, etc. of renewable energy participating in the balanced power. For example, Figure 2 As shown; Figure 2 (a) is the time domain collection of the simultaneous rate of renewable energy generation in a certain power grid in 2023, with a resolution of 15 minutes per sampling point; Figure 2 (b) is the time domain set of the predicted output deviation rate of renewable energy of a certain power grid in 2023, with a resolution of 15 minutes per sampling point.

[0226] The actual load of a typical day of this power grid and the predicted output of new energy are used as the balance boundary of daily power balance analysis, such as Figure 3shown.

[0227] 1. Calculation example of the new balanced risk prevention and control principle

[0228] 1.1 Probabilistic Output of New Energy

[0229] According to the statistical method, the simultaneous rate of renewable energy generation and the forecast deviation rate of renewable energy in the application examples are statistically analyzed. The results are as follows: Figure 4 shown. Figure 4 The middle dotted line is the probability density of the new energy output simultaneity rate and the prediction deviation rate, that is, the line connecting the probability of occurrence of any level of simultaneity rate and prediction deviation rate; it corresponds to η° in formula (1) and ρ° in formula (5), respectively. Figure 4 The solid line in the middle is the probability distribution of the simultaneous rate and prediction deviation rate of new energy output obtained by continuous integration based on the probability density in the figure, that is, the probability line of the simultaneous rate and prediction deviation rate being less than any level. Figure 4 It can be seen that, unlike conventional power sources, the simultaneous output rate and forecast deviation rate of renewable energy in the medium- and long-term and day-ahead power balance dimensions are no longer unique quantities, but functions of probability; their specific numerical distribution range is relatively large, and different levels correspond to different probabilities. Figure 4 In (a), the new energy simultaneous rate varies between 1% and 45%, with the maximum probability density occurring near 10%; Figure 4 In (b), the new energy prediction deviation rate varies between -100% and 80%, and the maximum probability density appears near 0%.

[0230] Figure 4 It reflects the statistical characteristics of the volatility of new energy output in the medium and long term and day-ahead dimensions. The corresponding functional relationships (simultaneity rate-probability, prediction deviation rate-probability) are the basic parameters for constructing a mathematical model of new energy participating in power balance under a new power system environment. As a result, new energy is transformed from a deterministic quantity to a random quantity in power balance risk analysis, and the new power system balance risk prevention and control is transformed from a deterministic mathematical problem to a random mathematical problem.

[0231] 1.2 Standard conversion functions

[0232] based on Figure 4 The probability distribution of the medium- and long-term new energy simultaneous rate and the forecast deviation rate is calculated according to the processing method of the probability distribution of new energy participating in the balanced output in formula (2-3) (6-7) and the definition of the standard conversion function in formula (16). The standard conversion function of the medium- and long-term new energy participating in the balanced risk and the day-ahead dimension is calculated respectively. The standard conversion function of the medium- and long-term dimension is as follows: Figure 5 As shown in (a), the standard conversion function of the day-ahead dimension is Figure 5 (b) as shown.

[0233] Figure 5 The horizontal axis is the probability λ, which is different from Figure 4The physical meaning corresponding to the medium probability (the possibility of new energy simultaneous rate and prediction deviation rate) has changed, representing the possibility of equilibrium risk. Figure 5 The middle vertical axis is the standard risk power conversion ratio g(λ), which is the standardized mapping of the participation of new energy in balancing power. It represents the size of the participation of new energy in balancing power. Its value multiplied by the benchmark amount (installed capacity in the medium and long term, predicted output in the day before) is the participation of new energy in balancing power.

[0234] Depend on Figure 5 It can be seen that, whether in the medium or long term or in the day ahead, the standard conversion function is a monotonically increasing function of the balance risk λ, that is, any balance risk corresponds to a unique conversion ratio, and the greater the balance risk, the greater the conversion ratio. Figure 5 (b) Taking the standard conversion function of the day before as an example, different risk levels λ are selected, and the corresponding standard risk power conversion ratio g(λ) can be obtained by checking the curve. The results are shown in Table 1.

[0235] Table 1 Standard risk power conversion ratio corresponding to different balance risks

[0236]

[0237] 1.3 New energy participates in balancing electricity

[0238] It can be seen from Table 1 that if the risk prevention and control target is set at 5%, the corresponding standard risk power conversion ratio is 72%. Taking into account that the benchmark amount of new energy participating in the balance in the day-ahead dimension is the predicted output, that is, the output of new energy participating in the power balance is 72% of the predicted output of new energy.

[0239] Based on Table 1, taking the actual predicted output curve of renewable energy in a certain period of time of a certain power grid as an example, the output curve of renewable energy participating in the balance corresponding to different balance risks in this period of time can be calculated. The results are as follows: Figure 6 shown.

[0240] Depend on Figure 6 It can be seen that during the day-ahead power balance process:

[0241] (1) Considering the risk control requirements, the power balance of new energy sources should be less than the predicted output;

[0242] (2) The participation of new energy in balancing electricity is related to the predicted output of new energy and changes in proportion to the predicted output of new energy;

[0243] (3) The participation of new energy in balancing electricity is positively correlated with the balancing risk. The greater the risk, the greater the participation of new energy in balancing electricity. Moreover, the participation of new energy in balancing electricity gradually approaches the predicted output as the risk increases.

[0244] This reveals a practical principle for risk prevention and control of new power system balance:

[0245] Practice Principle 1: Balancing risk is positively correlated with the amount of new energy involved in balancing electricity. To reduce balancing risk, the participation of new energy in balancing electricity should be appropriately reduced.

[0246] 1.4 Minimum startup capacity

[0247] After the participation of new energy in balancing power is determined, the minimum startup capacity of conventional power can be calculated according to formula (15). The balance boundary is set as shown in Table 2, and the minimum startup capacity of conventional power under different balance risks λ and balance margin requirements δ0 is shown in Table 3.

[0248] Table 2. Equilibrium boundary conditions of the example (unit: pu)

[0249]

[0250] Table 3. Minimum startup capacity of conventional power supply under different working conditions (unit: pu)

[0251]

[0252] It can be seen from Table 3 that the minimum startup capacity of conventional power supply is negatively correlated with the balance risk and positively correlated with the balance margin requirement. That is, the smaller the balance risk (the higher the risk control requirement) and the higher the balance margin requirement, the larger the startup capacity of conventional power supply.

[0253] In summary, through the standard conversion function, the balance risk prevention and control problem can be converted into a conventional power balance problem, thereby realizing the decoupling of balance risk prevention and control from power balance calculation, and effectively solving the problem of mutual coupling and mutual influence between balance risk prevention and control and power balance calculation in the new power system environment.

[0254] 2. Calculation example of new power balance principle

[0255] Based on the example of risk prevention and control principle (taking day-ahead power balance as an example), the new power balance principle example introduces the risk prevention and control principle into the actual day-ahead power balance scenario. Figure 1 The process shown carries out unified balance risk prevention and control and power balance analysis under the new power system environment.

[0256] 2.1 Segment analysis

[0257] First, a daily balancing cycle is divided into 12 balancing periods. Historical data statistical analysis is carried out for each balancing period. The standard conversion function of the balancing period is obtained according to the definition of formula (16). Then, the risk probability is uniformly selected and substituted into the standard conversion function of each balancing period to obtain the standard risk power conversion ratio of each period under different risk probabilities and connect them into a line. The results are shown in Figure 7 shown.

[0258] Depend on Figure 7 The following characteristics can be seen:

[0259] (1) Under the same risk probability, the standard risk power conversion ratio g(λ) in each balancing period varies greatly. For example, when the balancing risk is 5%, the maximum value of g(λ) can reach 95% and the minimum value is only 60%.

[0260] (2) The standard risk power conversion ratio g(λ) during the noon period is significantly higher than that during other periods. The main reason for this is that the proportion of solar power generation during the noon period is high, while the solar power forecast deviation rate is generally much smaller than that of wind power.

[0261] (3) The greater the balancing risk probability, the greater the standard risk power conversion ratio in each period. For example, in the eleventh balancing period, g(λ) with a 10% risk probability reaches 75%, while g(λ) with a 2% risk probability is only 38%.

[0262] (4) In different balancing periods, the impact of the balancing risk probability on the standard risk power conversion ratio is different. For example, in the eleventh balancing period, the 10% risk probability g(λ) is 37 percentage points higher than the 2% risk probability, while in the seventh balancing period, the 10% risk probability g(λ) is only 8 percentage points higher than the 2% risk probability.

[0263] Since g(λ) directly determines the extent of new energy participation in power balance, the difference in g(λ) in different balancing periods reflects the different characteristics of new energy participation in power balance in different balancing periods. Therefore, it is scientifically necessary to conduct statistical analysis of the daily power balance cycle in different time periods.

[0264] 2.2 Unified Balance

[0265] use Figure 7 The standard risk power conversion ratio curve with λ=5% is used to obtain the power balance curve of new energy participating in each period according to the actual predicted output of new energy in each period of the day; then, according to the load forecast curve of the day, the power balance curve of new energy participating in each period, etc., the minimum startup baseline of conventional power supply in each period is obtained by formula (15); according to the principle of formula (19)(21), the highest point of the minimum startup baseline of conventional power supply is the key balance point. The results are as follows Figure 8 shown.

[0266] Figure 8 In the figure, the black solid line is the calculated output curve of renewable energy participating in power balance. On the one hand, it is closely related to the predicted output of renewable energy in general, and on the other hand, it has different differences with the predicted output of renewable energy in different balance periods. A During the corresponding balancing period, the predicted output of new energy is 0.24pu, and the participating balancing output is 0.15pu.

[0267] Figure 8 In the figure, the red solid line is the calculated minimum startup baseline, and its highest point is the key balance point. As can be seen from the figure, the key point of the day appears at 20:30, and the corresponding amplitude is 0.89pu. When the conventional power supply is scheduled to start on that day, the maximum power generation capacity of the conventional power supply should be greater than 0.89pu. In addition, it can be seen from the figure that the minimum startup baseline is significantly different from the traditional load curve, showing a typical "canyon curve" feature. Its trough point appears at noon, which is only 0.29pu. The peak-to-valley difference rate throughout the day is 67.4%. The greater the peak-to-valley difference rate, the higher the system regulation requirements, which is also an important feature of the power balance of the new power system.

[0268] 2.3 Comprehensive impact

[0269] based on Figure 2 (b) Figure 3 Example data, set different balance margins and balance risks to represent different risk control boundaries, obtain key balance points and calculate the minimum conventional power supply startup, and then calculate the new energy abandonment power and abandonment rate according to formula (22), and obtain the new energy abandonment rate curve under different risk control boundaries as follows Fig. 9 shown.

[0270] Fig. 9 The horizontal axis in the middle is the balance risk, and the vertical axis is the new energy abandonment rate. Different curves correspond to different balance margins. The following rules can be seen from the figure:

[0271] (1) The renewable energy power abandonment rate is negatively correlated with the balance risk and positively correlated with the balance margin. That is, the greater the balance risk and the smaller the balance margin, the lower the renewable energy power abandonment rate.

[0272] Since both the balance risk and the balance margin characterize the reliability of power balance, the smaller the risk and the larger the balance margin, the higher the reliability; and the new energy curtailment rate determines the level of new energy utilization, the lower the new energy curtailment rate, the higher the utilization level. Therefore, the practical principles for the prevention and control of balance risks in new power systems (practical principles 1) can be further expanded to:

[0273] Practice Principle 2: Under the new power system environment, balance reliability is negatively correlated with the utilization rate of new energy. To improve balance reliability, the utilization rate of new energy must be appropriately reduced.

[0274] (2) In different risk segments, the rate of change of the new energy curtailment rate with the balance risk is different. When the balance risk is in the [0, 5%] segment, the new energy curtailment rate is sensitive to the change of the balance risk. For example, the corresponding curve of margin = 0, the new energy curtailment rate corresponding to the 5% balance risk is 15 percentage points lower than the new energy curtailment rate corresponding to the 0% balance risk; when the balance risk is in the [10%, ∞) segment, the new energy curtailment rate tends to be "passive" in its response to the change of the balance risk. For example, the corresponding curve of margin = 0, the new energy curtailment rate corresponding to the 20% balance risk is only 2 percentage points lower than the new energy curtailment rate corresponding to the 10% balance risk.

[0275] Taking into account the general requirements of power balance risk management (requiring high reliability and being unable to accept higher risks), we can summarize another practical principle for the risk prevention and control of new power system balance:

[0276] Practice Principle 3: The coordinated management and control of power balance risks and the consumption of new energy should be carried out in the low-risk period. In principle, the balance risk should not exceed 5%.

[0277] In summary, the power balance risk control method and practice principles proposed in this patent can solve the power grid security problems brought about by the power balance risks of new power systems. The present invention proposes a balance risk control model that is directly applicable to the power balance practice of large power grids in new power systems, which can realize the organic combination of basic balance principles and balance risk control mechanisms, making it possible to analyze and solve the power balance risk prevention and control model of new power systems. It also discusses the negative correlation between balance risk prevention and control and the "growth and decline" of new energy consumption space, and then proposes a new power system power balance risk prevention and control working method. The proposed balance risk control model and method realize the decoupling of controllable quantities and main parameter quantities, are closely integrated with engineering practice scenarios, are practical and effective, and have obvious advantages over many other power balance risk control methods.

[0278] The above implementation modes are not limitations of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the scope of the technical solution of the present invention also belong to the protection scope of the present invention.

Claims

1. A new method for preventing and controlling power balance risks in a power system, characterized by: The following control strategies are included: Calculate the probability distribution of renewable energy generation based on historical statistical data of renewable energy generation; After introducing the output of new energy as a random variable, balance constraints are applied according to the requirements of balance risk management; Determine the equilibrium boundary parameters and calculate the key equilibrium point t A , and then obtain the new power balance equation; Based on the new power balance equation, the power supply startup capacity R is calculated; If the startup strategy obtained meets the maximum startup constraint, the data on renewable energy power abandonment and power limit can be calculated based on the new power balance equation, thus forming a balance plan; If the maximum startup constraints are not met, apply for emergency use of the interconnection line backup power or take controllable load power consumption measures to form a new balance plan.

2. The method for preventing and controlling power balance risks of a new power system according to claim 1 is characterized in that: The probability distribution of renewable energy generation is divided into medium- and long-term and day-ahead power balance dimensions; The output of new energy is determined by the probability distribution function of the output of new energy. The formula is: Where N° is the probability distribution, n and ξ are the output of new energy, and f η is the probability density function of the new energy simultaneous rate, F n is the probability distribution function of the new energy output.

3. The method for preventing and controlling power balance risks of a new power system according to claim 2 is characterized in that: There is no specific prediction capability for new energy in the medium and long term, and new energy installed capacity is used as the benchmark for participating in power balance; The output / installed capacity of new energy is recorded as the new energy simultaneous rate. The historical data of the new energy simultaneous rate is statistically analyzed to obtain the sample space of the new energy simultaneous rate. In the sample space of the new energy simultaneous rate, the probability density of the random variable new energy simultaneous rate η is obtained by statistical methods as follows: η°=f η (h) (1) In the formula, η is the specific new energy simultaneous rate, η° is the probability of the corresponding new energy simultaneous rate, and f η is the probability density function of the new energy simultaneous rate; According to formula (1) and the definition of the new energy simultaneous rate, the probability density of new energy output in the medium and long-term balance dimension can be obtained as follows: In formula (2), n is the specific new energy output, which is the new energy output in the medium and long-term balance dimension; n° is the probability of the corresponding new energy output, f n is the probability density function of renewable energy output, C N Installed capacity for new energy sources; The probability distribution function of the new energy output can be further deduced from formula (2).

4. The method for preventing and controlling power balance risks of a new power system according to claim 2 is characterized in that: There is a specific forecast output of new energy in the day-to-day dimension, and the forecast output of new energy is used as the benchmark for participating in power balance; By statistically analyzing the historical data of the new energy forecast deviation rate, we can obtain the sample space of the new energy forecast deviation rate. The mathematical expression of the new energy forecast deviation rate is: In formula (4), ρ is the new energy prediction deviation rate, n is the specific new energy output, Contribute to new energy forecasting; In the sample space of the new energy prediction deviation rate, the probability density of the random variable new energy prediction deviation rate ρ can be obtained by statistical methods as follows: p°=f ρ (p) (5) In formula (5), ρ is the prediction deviation rate, ρ° is the probability of occurrence of the corresponding prediction deviation rate, and f ρ is the probability density function of the new energy prediction deviation rate; From equations (4) and (5), we can get the probability density of renewable energy output in the day-ahead balance dimension as follows: In formula (6), n is the specific new energy output, here it is the new energy output in the day-ahead balance dimension; n° is the probability of the corresponding new energy output appearing, f n is the probability density function of the output of renewable energy; The probability distribution function of the new energy output can be further deduced from formula (6).

5. The method for preventing and controlling power balance risks of a new power system according to claim 3 or 4, characterized in that: Balance constraints include balance margin probabilization and balance constraint probabilization; With f n (n) Unified representation of the probability density of new energy output in the medium- and long-term and day-ahead dimensions, with F n (n) Unified representation of the probability distribution of new energy output in the medium- to long-term and day-ahead dimensions; The probabilistic process of the balance margin is: The power balance margin of any power grid at any time is expressed as: δ=R+T+n-υl+x (8) In formula (8), δ is the balance margin, R is the power supply startup capacity, where the power supply startup capacity represents the maximum power generation capacity of the conventional power supply, T is the planned power of the interconnection line, n is the specific new energy output, l is the load, υ is the reserve rate coefficient, and x is the controllable load; When the new energy output n in equation (8) is a random variable, the balance margin δ also becomes a random variable, and the probability density function of δ is: δ°=f δ (d)=f n (δ-R-T+υl-x) (9) In formula (9), δ° is the probability of occurrence of the corresponding balance margin, f δ is the probability density function of δ; The probability distribution of δ is further obtained as: In formula (10), Δ° is the probability distribution of the equilibrium margin δ when it is a random variable, and F δ is the probability distribution function; Formula (10) is the probabilistic power balance margin; The probabilistic process of equilibrium constraints is: Before the introduction of random variables, the equilibrium constraint of the traditional equilibrium model is described as: δ=R+T+n-υl+x≥δ0 (11) In formula (11), δ0 is the balance margin threshold; after introducing the new energy output n as a random variable, the balance constraint is converted to: F δ (δ0)≤λ (12) In formula (12), λ is the allowed probability threshold, which is called "balance risk factor". Formula (12) is the probabilistic balance constraint.

6. The method for preventing and controlling power balance risks of a new power system according to claim 5 is characterized in that: Based on the balance margin threshold δ0 and the balance risk factor λ, the parameters of the probabilistic balance equation are further decoupled from the key controllable quantities, which include the maximum power generation capacity of conventional power sources, i.e., the power supply startup capacity R, the planned power of the tie line T, and the controllable load x. The decoupling process is: Substituting equations (9) and (10) into equation (12), we can get: Substituting the probability distribution function of renewable energy output into equation (13), it can be converted into: In formula (14), It is the inverse function of the probability distribution function of renewable energy output; Conventional power generation capacity R, interconnection line planned power T and controllable load x are controllable quantities in the balancing arrangement process; including standby load υl, balancing margin threshold δ0 and new energy participating in balancing power They are all uncontrollable quantities in the balancing arrangement process, that is, the balancing objects.

7. The method for preventing and controlling power balance risks of a new power system according to claim 6 is characterized in that: In the process of constructing the new power balance equation, the single-point power balance of the new power system is first determined. The process is as follows: The single-point power balance equation of the new power system is constructed by formula (14) and the basic principle of single-point power balance: In formula (18), y is the restricted power of renewable energy; α is the peak load coefficient of conventional power supply; t is the number of balance points in a day, with 15 minutes as one balance point, that is, 96 balance points in a day.

8. The method for preventing and controlling power balance risks of a new power system according to claim 7 is characterized in that: Formula (18) analyzes the single-point power balance principle of the new power system at the basic theoretical level, analyzes the correlation between 96 balance points in combination with engineering practice, and further constructs the daily power balance equation for engineering. The process is: The maximum power generation capacity of conventional power sources within a day is basically stable, and the balance risk control requirements of each balance point are the same, namely: It can be deduced from formula (18) and (19): In formula (20), Y is the power limit of renewable energy; there must be a time point t A , so that: Then formula (20) can be converted to: Formula (22) is the daily power balance equation of the new power system, and is called A The key balance point.

9. The method for preventing and controlling power balance risks of a new power system according to claim 7 is characterized in that: On the basis of formula (22), multiple equilibrium risk constraints are introduced. Multiple equilibrium risk constraints are to expand the single equilibrium risk constraint (δ0λ) into multiple groups of constraints: (δ0λ)→[(δ1λ1),…,(δ i l i )…,(d K l K )] (25) Substituting formula (25) into formula (20) expands to: Formula (26) is a new equilibrium equation with multiple equilibrium risk constraints.

10. The novel power balance risk prevention and control method of the power system according to claim 6 is characterized by: For formula (14), in the balancing practice of the new power system, the standby load υl, the balancing margin threshold δ0 and the planned power T of the tie line are boundary quantities, and the controllable load measures are not considered, that is, x = 0. From (14), it can be further deduced that: In formula (15), R min is the minimum power generation capacity of conventional power supply, which is approximately considered as the minimum startup capacity; it can be seen that R min and Related, is an increasing function of λ, then R min Negatively correlated with λ; In order to quantitatively analyze the impact of differences in the characteristics of renewable energy generation in different power grids and eliminate the impact of renewable energy participation in the balance benchmark, a standard conversion function g(λ) is defined: In formula (16), U is the benchmark amount of new energy participation balance, and the medium- and long-term balance dimension is U = C N , day-ahead balance dimension For formula (22), in the balancing practice of the new power system, l(t) and n(t) are uncontrollable balancing objects, T(t) is the boundary condition, and the peak load parameter α of the conventional power supply is relatively fixed. From the second sub-formula of formula (22), it can be seen that the power limit Y of the new energy source is affected by the power supply startup capacity R, and the key balancing point t A , balance margin threshold δ0(t A ), controllable load x(t A ) New energy participates in balancing electricity The impact of Given the critical balance point t A , controllable load x(t A ) and the standard conversion function g(λ) are objective constant parameters, and the balance is optimal when the power supply startup capacity R takes the minimum value of the first formula in formula (22); formula (22) can be simplified as: Y=f Y [δ0(t A ),λ] (23) That is, the power limit of renewable energy is a function of the balance margin threshold of the key balance point and its balance risk; In actual operation, the balance margin threshold δ0(t A )=0, formula (23) can be further simplified as: That is, when the balance margin threshold is zero, the renewable energy power limit is a function of only a single variable of balance risk.

Images