Renewable energy power generation-based source grid load storage transformer substation operation management method and system

Abstract

The invention discloses a renewable energy power generation-based source network load storage substation operation management method and system. The method comprises the steps of constructing a basic mathematical model of a source network load storage substation; based on the basic mathematical model, predicting the photovoltaic output power, the wind power output power and the transformer substation load by adopting an autoregressive integral moving average model; based on a prediction result, establishing a target function with the purpose of minimizing the system operation cost; constructing constraint conditions including operation management of the source network load storage transformer substation; and solving based on the objective function and the constraint condition, and realizing operation management of the source network load storage transformer substation based on a solving result. Compared with the prior art, the power system stability can be improved, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to a source-grid-load-storage substation operation management method and system, and in particular to a source-grid-load-storage substation operation management method and system based on renewable energy power generation, belonging to the field of source-grid-load-storage substation operation management technology. Background Art

[0002] With the increasing demand for energy and the improvement of environmental protection requirements in modern society, the power system is undergoing a rapid transformation. This transformation is mainly manifested in the following aspects: Access to renewable energy: The access to renewable energy such as wind power and solar energy has brought new challenges and requirements to the traditional power system; Development of smart grid: The development of smart grid technology enables the power system to operate, monitor and manage more intelligently; Growth in power demand: The increase in electricity demand makes the load management of the power system more complicated.

[0003] The rapid development of Internet of Things (IoT) technology has provided new solutions for power systems. By combining various sensors, control devices and communication technologies, IoT can achieve real-time monitoring and management of power systems. Substations, as key facilities in power systems, are responsible for voltage conversion, power distribution and power quality control. With the complexity of power systems and the increase in the proportion of renewable energy, higher requirements are placed on the operation and management of substations.

[0004] Traditional substation operation and management methods often rely on manual operation and regular inspections, which are inefficient and difficult to cope with complex changes in the power system. Renewable energy generation can achieve real-time monitoring and data collection of substation equipment, thereby improving operation efficiency and accuracy. Failure of substation equipment may lead to power supply interruption, which has a serious impact on society and the economy. Renewable energy generation can achieve real-time monitoring and early warning of equipment status, detect potential problems in advance, and thus reduce the probability of failure. Renewable energy generation can conduct detailed analysis of substation energy consumption, optimize energy use and distribution, and reduce overall energy consumption and operating costs. This is of great significance to the sustainable development of the power system. With the large-scale access of renewable energy and the fluctuation of power demand, load management of the power system has become more complicated. Summary of the invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a source-grid-load-storage substation operation management method and system based on renewable energy power generation, which can improve the stability of the power system and reduce operating costs.

[0006] Technical solution: The present invention provides a method for operating and managing a source-grid-load-storage substation based on renewable energy power generation, comprising:

[0007] S1: Construct the basic mathematical model of the substation with source, grid, load and storage;

[0008] S2: Based on the basic mathematical model, the autoregressive integral moving average model is used to predict the photovoltaic output power, wind power output power and substation load;

[0009] S3: Based on the prediction results, an objective function is established to minimize the system operation cost;

[0010] S4: Construct constraints for substation operation management including source, grid, load and storage;

[0011] S5: Solve the problem based on the objective function and constraints, and implement the operation management of the source-grid-load-storage substation based on the solution results.

[0012] Furthermore, the step S1 comprises:

[0013] The power P of renewable energy g (t) is expressed as:

[0014] P g (t) = f g (t) (1)

[0015] Among them, f g (t) is the time series function of the power of the generating source;

[0016] The load power P l (t) is expressed as:

[0017] P l (t) = f l (t) (2)

[0018] Among them, f l (t) is the time series function of load demand;

[0019] The energy storage capacity E s (t) and energy storage power P s (t) is represented by the following formula:

[0020]

[0021] Among them, η charge is the charging efficiency, η discharge is the discharge efficiency, P s,charge (t) is the charging power of the energy storage system, P s,discharge is the discharge power of the energy storage system, Δt is the time step;

[0022] Let the power of the transmission grid be P sub (t)Satisfy power balance:

[0023] Pg (t)+P s,discharge (t)-P s,charge (t) = P l (t)+P sub (t) (4).

[0024] Furthermore, the prediction of photovoltaic output power in step S2 includes:

[0025] Construct a photovoltaic output power model and convert the theoretical photovoltaic output power P pv,th (t) is expressed as:

[0026] P pv,th (t) = η pv ·A pv ·G(t) (5)

[0027] Among them, η pv is the conversion efficiency of the photovoltaic module; A pv is the effective area of ​​the photovoltaic panel; G(t) is the solar radiation intensity at time t;

[0028] Substituting the temperature effect, the actual photovoltaic output power P pv (t) is expressed as:

[0029] P pv (t) = P pv,th (t)·[1-β pv ·(T(t)-T ref )] (6)

[0030] Among them, β pv is the temperature coefficient of the photovoltaic module; T(t) is the ambient temperature at time t; T ref is the reference temperature;

[0031] The autoregressive integrated moving average model is used to predict the photovoltaic output power:

[0032] P pv (t+1)=φ1P pv (t)+φ2P pv (t-1)+…+φ p P pv (tp)+∈ t +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q (7)

[0033] Where: φ i is the autoregressive coefficient; θ i is the sliding mean coefficient; ∈ tis white noise; p represents the order of the autoregressive term, and q represents the order of the moving average term.

[0034] Furthermore, the prediction of wind power output power in step S2 includes:

[0035] Construct a wind power output model and convert the theoretical wind power output power P wind,th (t) is expressed as:

[0036]

[0037] Where ρ is the air density; A wind is the swept area of ​​the wind wheel; C p (V(t)) is the power coefficient, which depends on the wind speed V(t) and is determined by the power curve provided by the wind rotor manufacturer;

[0038] For the actual wind power output P wind (t), when the wind speed is lower than the starting wind speed V cut-in When the wind speed is higher than the rated wind speed V rated When the fan runs at rated power P rated ; When the wind speed is higher than the cut-out wind speed V cut-out When the fan stops running to prevent damage;

[0039] The actual wind power output power P is adjusted by the following formula: wind (t) is expressed as:

[0040]

[0041] The autoregressive integrated moving average model is used to predict wind power output.

[0042] P wind (t+1)=φ1P wind (t)+φ2P wind (t-1)+…+φ p P wind (tp)+∈ t +θ t-1 +θ2∈ t-2 +…+θ q ∈ t-q (10)

[0043] Among them, φ i is the autoregressive coefficient; θ i is the sliding mean coefficient; ∈ t It's white noise.

[0044] Furthermore, the prediction of the substation load in step S2 is specifically as follows:

[0045] Assume that the power time series data of photovoltaic and wind power where Y t represents the power time series data at time t;

[0046] First, determine the model order, including the autoregressive part and the moving average part;

[0047] The determining of the autoregressive part is specifically to use the partial autocorrelation function PACF to determine the autoregressive order p:

[0048] PACF(a)=Cov(Y t ,Y t-1 )a=1,2,...,p (11)

[0049] Among them, the autoregressive order p is determined by observing the truncation of the PACF plot;

[0050] The moving average determination part is specifically: using the autocorrelation function ACF to determine the sliding average term order q:

[0051] ACF(b)=Cov(Y t ,Y t-1 )b=1,2,...,q (12)

[0052] Among them, the order q of the sliding average term is determined by observing the truncation of the ACF graph;

[0053] Next, the first difference is expressed as:

[0054] ΔY t =Y t -Y t-1 (13)

[0055] The autoregressive integrated moving average model ARIMA (p, d, q) model is expressed as:

[0056] Φ(B)Δ d Y t =Θ(B)∈ t (14)

[0057] Among them, the number of differences d is calculated to make the sequence stable; B is the lag operator (B k Y t =Y t-k ), Φ(B)=1-φ1B-φ2B 2 -…-φ p B p is an autoregressive polynomial, θ(B)=1+θ1B+θ2B 2 +…+θ q B q is a moving average polynomial, ∈ t is a white noise sequence

[0058] Use maximum likelihood estimation (MLE) to estimate the coefficient φ i and θ i , with maximizing the likelihood function as the optimization goal:

[0059]

[0060] Among them, f(∈ t |φ,θ) is the conditional probability density function of the residual;

[0061] Generate load forecasts using the established ARIMA model:

[0062]

[0063] in, It is the predicted value at time t+h, where h is the time in the future.

[0064] Furthermore, the autocorrelation function ACF is used to measure the correlation of the time series at different lag periods, and the autocorrelation coefficient ρ of the lag period k is k Represents the time series Y t The correlation between time t and tk,

[0065] First, calculate the mean of the time series It is expressed as:

[0066]

[0067] Calculate the variance σ of the time series 2 It is expressed as,

[0068]

[0069] Calculate the covariance Cov(Y t ,Y t-h ):

[0070]

[0071] Calculate the autocorrelation coefficient ρ k :

[0072]

[0073] Furthermore, the partial autocorrelation function PACF is used to measure the autocorrelation of the kth lag period of the time series under the condition of given other lag periods. The partial autocorrelation function PACF is implemented as follows:

[0074] Build an autoregressive model:

[0075] Yt =φ1Y t-1 +φ2Y t-2 +L+φ p Y t-p +∈ t (twenty one)

[0076] Estimate the parameters of the autoregressive model using the Yule-Walker equation:

[0077] Rφ=r (22)

[0078] Where R is the autocorrelation matrix:

[0079]

[0080] r is the autocorrelation vector:

[0081]

[0082] φ is the vector of autoregressive coefficients:

[0083]

[0084] Solve the above system of equations to estimate φ i ;

[0085] The partial autocorrelation coefficient φ of lag period k kk , which represents the autocorrelation of lag k in the time series after controlling the influence of the first k-1 lags, is calculated by the recursive formula:

[0086]

[0087] Among them, ρ k is the autocorrelation coefficient of lag period k, φ i is the autoregressive coefficient.

[0088] Furthermore, the objective function Minimize C in step S3 is specifically:

[0089]

[0090] Among them, C g is the unit cost of electricity generation; C s is the cost of using the energy storage system; C sub It is the operating cost of the power grid.

[0091] Furthermore, the constraints in step S4 include: power balance constraints, energy storage system power constraints, energy storage system power constraints, power generation and load constraints, and grid power constraints;

[0092] The power balance constraint is expressed as:

[0093] P g (t)+P s,discharge (t)-P s,charge (t) = P l (t)+P sub (t) (28)

[0094] The energy storage system power constraint is expressed as:

[0095] E s,min ≤E s (t)≤E s,max (29)

[0096] Among them, E s (t) represents the amount of energy storage system at time t; E s,min and E s,max Respectively represent the minimum and maximum power of the energy storage system;

[0097] The energy storage system power constraint is expressed as:

[0098] 0≤P s,charge (t)≤P s,max,charge (30)

[0099] 0≤P s,discharge (t)≤P s,max,discharge (31) Among them, P s,max,charge and P s,max,discharge is the maximum charging and discharging power of the energy storage system;

[0100] The generation and load constraints are expressed as,

[0101] 0≤P g (t)≤P g,max (32)

[0102] 0≤P l (t)≤P l,max (33)

[0103] Among them, P l,max and P g,max Represent the maximum values ​​of load power and power generation respectively;

[0104] The grid power constraint is expressed as,

[0105] 0≤P sub (t)≤P sub,max (34)

[0106] Among them, P sub,max It is the maximum power carrying capacity of the transmission network.

[0107] Based on the same inventive concept, the present invention also provides a source-grid-load-storage substation operation management system based on renewable energy power generation, including:

[0108] Basic model building module, building the basic mathematical model of the substation with source, grid, load and storage;

[0109] A prediction module is used to predict photovoltaic output power, wind power output power and substation load based on a basic mathematical model and an autoregressive integral moving average model;

[0110] An objective function establishment module is used to establish an objective function with the goal of minimizing the system operation cost based on the predicted results;

[0111] The constraint condition establishment module is used to construct the constraint conditions for the operation and management of the substation including the source, grid, load and storage;

[0112] The operation management module is used to solve based on the objective function and constraints, and realize the operation management of the source-grid-load-storage substation based on the solution results.

[0113] Beneficial effects: Compared with the prior art, the present invention optimizes the operation and management of substations by combining the dynamic information of each link of source-grid-load-storage power generation, transmission, load and energy storage, and utilizing advanced monitoring and control technology; the present invention constructs an intelligent substation model that integrates renewable energy power generation, energy storage system and load management; through real-time data collection and analysis, the model can accurately predict changes in power generation and load demand, and timely adjust the charging and discharging strategy of the energy storage system to achieve an effective balance of renewable energy fluctuations, which is used to optimize the distribution of electricity and the operation of equipment, and ensure that the substation operates in an efficient and safe state; the effectiveness and superiority of this method in improving the stability of the power system, reducing operating costs, and improving resource utilization will help promote the intelligent and green development of the power system and achieve the goal of sustainable energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0115] Figure 2 Schematic diagram of power and load changes in an embodiment of the present invention. DETAILED DESCRIPTION

[0116] In order to enable those skilled in the art to better understand the present application scheme, the principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention. The present invention is described in more detail by way of example with reference to the accompanying drawings in the following paragraphs. The advantages and features of the present invention will become clearer according to the following description and claims. It should be noted that the drawings are all in a very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0117] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0118] As attached Figure 1 As shown, the source-grid-load-storage substation operation management method based on renewable energy power generation in this embodiment includes:

[0119] S1: Construct a basic mathematical model of the substation including source, grid, load and storage;

[0120] S2: Use autoregressive integrated moving average model to predict photovoltaic and wind power output power;

[0121] S3: Establish an objective function based on the prediction results to minimize the system operation cost;

[0122] S4: Construct constraints for substation operation management including source, grid, load and storage;

[0123] S5: solving the established model;

[0124] Furthermore, the following steps are included to realize the construction of a substation mathematical model including a source, a grid, a load and a storage:

[0125] Construct a mathematical model of a substation that includes source, grid, load and storage, including: power source modeling, load modeling, energy storage system modeling and transmission network modeling;

[0126] Power source P g (t) varies with time and can be expressed by the function f g (t) means:

[0127] P g (t) = f g (t) (1)

[0128] Among them, f g (t) is a time series function of the power of the power generation source, which depends on the characteristics of the power generation source and environmental factors. The power generation source in this patent refers to photovoltaic and wind power renewable energy.

[0129] Load power P l (t) varies with time and can be expressed by the function f l (t) means:

[0130] P l (t) = f l (t) (2)

[0131] Among them, f l (t) is the time series function of load demand.

[0132] Energy storage capacity E s (t) and power P s (t) can be described by the following dynamic equation:

[0133]

[0134] Among them, η charge is the charging efficiency, η discharge is the discharge efficiency, P s,charge (t) is the charging power of the energy storage system, P s,discharge (t) is the discharge power of the energy storage system, Δt is the time step;

[0135] Power of the transmission grid P sub (t)Power balance needs to be met:

[0136] P g (t)+P s,discharge (t)-P s,charge (t) = P l (t)+P sub (t) (4)

[0137] Further, the following steps are included to realize the construction of a photovoltaic power generation source model in a substation mathematical model including a source, a grid, a load and a storage:

[0138] Photovoltaic power generation P pv (t) is mainly affected by solar radiation G(t) and ambient temperature T(t). The power of photovoltaic power generation can be modeled by the following steps: Theoretical photovoltaic output power P pv,th (t) is based on the light intensity G(t) and the efficiency η of the photovoltaic module pv Calculated:

[0139] P pv,th (t) = η pv ·A pv ·G(t) (5)

[0140] Where: η pv is the conversion efficiency of the photovoltaic module; A pvis the effective area of ​​the photovoltaic panel (square meters); G(t) is the solar radiation intensity at time t (W / m 2 ).

[0141] Actual photovoltaic output power P pv (t) will be affected by the temperature effect. The temperature coefficient of the photovoltaic module β pv Used to adjust power changes due to temperature changes:

[0142] P pv (t) = P pv,th (t)·[1-β pv ·(T(t)-T ref )] (6)

[0143] Where: β pv is the temperature coefficient of the photovoltaic module; T(t) is the ambient temperature at time t (℃); T ref is the reference temperature (usually 25°C).

[0144] According to the obtained photovoltaic power generation power sequence, the autoregressive integral moving average model is used to predict the photovoltaic output power.

[0145] P pv (t+1)=φ1P pv (t)+φ2P pv (t-1)+…+φ p P pv (tp)+∈ t +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q (7)

[0146] Where: P pv (t+1) is the photovoltaic output power value at time t+1; φ i is the autoregressive coefficient; θ i is the sliding mean coefficient; ∈ t is white noise; p represents the order of the autoregressive term, that is, the number of lagged output values ​​used in the model. Specifically, P pv The values ​​of the past p moments of (t) will be used to predict the photovoltaic output power at the current moment; q represents the order of the sliding average term, which indicates the number of lag error terms used in the model. In other words, the errors of the past q moments (i.e., the difference between the predicted value and the actual value) will be used to correct the current predicted value.

[0147] Further, the following steps are included to realize the construction of a wind power generation source model in a substation mathematical model including a source, a grid, a load and a storage:

[0148] Wind power P wind (t) depends on the wind speed V(t) and the characteristics of the wind turbine. The power of wind power generation can be modeled by the following steps. The theoretical wind power P wind,th (t) is usually estimated based on the wind speed V(t) using the wind speed power curve:

[0149]

[0150] Where: ρ is the air density (about 1.225kg / m 3 );A wind is the swept area of ​​the wind wheel (square meters); C p (V(t)) is the power coefficient, which depends on the wind speed V(t) and is usually determined from the power curves provided by the rotor manufacturer.

[0151] The actual wind power output is also limited by the starting wind speed, rated wind speed and cut-out wind speed of the wind turbine: 1) Starting wind speed V eut-in : When the wind speed is lower than this value, the wind turbine does not generate electricity; 2) Rated wind speed V rated : When the wind speed is higher than this value, the fan runs at rated power 3) Cut out the wind speed V eut-out : When the wind speed is higher than this value, the fan stops running to prevent damage;

[0152] Actual wind power P wind (t) can be adjusted using the following formula:

[0153]

[0154] Among them, P rated is the rated power of the fan.

[0155] According to the obtained wind power generation power sequence, the autoregressive integral moving average model is used to predict the wind power output power.

[0156] P wind (t+1)=φ1P wind (t)+φ2P wind (t-1)+…+φ p P wind (tp)+∈ t +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q (10)

[0157] Where: P wind (t+1) is the wind power output value at time t+1; φ i is the autoregressive coefficient; θ i is the sliding mean coefficient; ∈t It is white noise;

[0158] Furthermore, the following steps are included to predict the photovoltaic and wind power output power using an autoregressive integrated moving average model:

[0159] Power time series data for photovoltaic and wind power where Y t Represents the power time series data at time t; First, it is necessary to determine the model order, including the autoregressive (AR) part and the moving average (MA) part;

[0160] 1) Autoregressive part: Determine the order p using the partial autocorrelation function (PACF):

[0161] PACF(a)=Cov(Y t ,Y t-1 ) for a=1,2,...,p (11)

[0162] Determine p by observing the truncation of the PACF plot;

[0163] 2) Moving average part: Determine the order q using the autocorrelation function (ACF):

[0164] ACF(b)=Cov(Y t ,Y t-1 ) for b = 1, 2, ..., q (12)

[0165] Determine q by observing the truncation of the ACF plot;

[0166] Furthermore, we need to determine the number of differences (d). If the data is not stationary, we can make it stationary by difference. The first difference is expressed as:

[0167] ΔY t =Y t -Y t-1 (13)

[0168] The form of the autoregressive integrated moving average model (ARIMA) is ARIMA(p, d, q), where: p is the autoregressive order, d is the number of differences, and q is the moving average order;

[0169] The ARIMA (p, d, q) model can be expressed as:

[0170] Φ(B)Δ d Y t =Θ(B)∈ t (14)

[0171] Where: The number of times d to calculate the difference makes the series stable; B is the lag operator (B k Yt =Y t-k ), Φ(B)=1-φ1B-φ2B 2 -…-φ p B p is an autoregressive polynomial, θ(B)=1+θ1B+θ2B 2 +…+θ q B q is a moving average polynomial, ∈ t is a white noise sequence;

[0172] Estimate the parameter φ using maximum likelihood estimation (MLE) i and θ i The optimization goal is to maximize the likelihood function:

[0173]

[0174] Among them, f(∈ t |φ,θ) is the conditional probability density function of the residual.

[0175] Generate load forecasts using the established ARIMA model:

[0176]

[0177] in, is the predicted value at time t+h.

[0178] Further, the following steps are included to realize the calculation of autocorrelation function (ACF):

[0179] The autocorrelation function (ACF) is used to measure the correlation of time series at different lag periods. The autocorrelation coefficient ρ of lag period k is k Represents the time series Y t The correlation between time t and tk,

[0180] First, calculate the mean and variance of the time series. It is expressed as:

[0181]

[0182] Calculate the variance σ of the time series 2 It is expressed as,

[0183]

[0184] Calculate the covariance Cov(Y t ,Y t-h ):

[0185]

[0186] Calculate the autocorrelation coefficient ρ k :

[0187]

[0188] As a further improvement of the technical solution, the following steps are included to realize the calculation of the partial autocorrelation function (PACF):

[0189] The partial autocorrelation function (PACF) is used to measure the autocorrelation of the kth lag period of a time series given other lag periods. PACF can help determine the order of the AR model.

[0190] In order to calculate the partial autocorrelation coefficient, it is necessary to establish an autoregressive model (AR model) to control the influence of other lag periods. The formula of the autoregressive model is:

[0191] Y t =φ1Y t-1 +φ2Y t-2 +L+φ p Y t-p +∈ t (twenty one)

[0192] Among them, φ i is the coefficient in the autoregressive model, ∈ t It's white noise.

[0193] Use the Yule-Walker equation to estimate the parameters of the autoregressive model:

[0194] Rφ=r (22)

[0195] Where R is the autocorrelation matrix:

[0196]

[0197] Where r is the autocorrelation vector:

[0198]

[0199] where φ is the autoregressive parameter vector:

[0200]

[0201] Solve the above system of equations to estimate φ i ;

[0202] The partial autocorrelation coefficient φ kkis the coefficient of lag k in the autoregressive model, which represents the autocorrelation of lag k in the time series after controlling the influence of the first k-1 lags.

[0203] The partial autocorrelation coefficient φ of lag period k kk It can be calculated by the recursive formula:

[0204]

[0205] Among them, ρ k is the autocorrelation coefficient of lag period k, φ i are the coefficients of the autoregressive model.

[0206] Further, the following steps are included to realize the establishment of an objective function with the goal of minimizing the system operation cost;

[0207] The optimization goal is to minimize the system operating costs, including power generation costs, energy storage costs, and grid operation costs. The objective function C can be expressed as:

[0208]

[0209] Where: C g is the unit cost of electricity generation; C s is the cost of using the energy storage system (charging and discharging); C sub is the operating cost of the grid;

[0210] Furthermore, the following steps are included to realize the construction of constraints for operation management of the substation including source, grid, load and storage:

[0211] Constraints include: power balance constraints, energy storage system power constraints, energy storage system power constraints, power generation and load constraints, and grid power constraints;

[0212] The power balance constraint can be expressed as:

[0213] P g (t)+P s,discharge (t)-P s,charge (t) = P l (t)+P sub (t) (28)

[0214] The energy storage system power constraint is expressed as:

[0215] E s,min ≤E s (t)≤E s,max (29)

[0216] The power constraint of the energy storage system can be expressed as:

[0217] 0≤P s,charge(t)≤P s,max,charge (30)

[0218] 0≤P s,discharge (t)≤P s,max,discharge (31) Among them, P s,max,charge and P s,max,discharge is the maximum charging and discharging power of the energy storage system.

[0219] The generation and load constraints can be expressed as,

[0220] 0≤P g (t)≤P g,max (32)

[0221] 0≤P l (t)≤P l,max (33)

[0222] The grid power constraint can be expressed as,

[0223] 0≤P sub (t)≤P sub,max (34) Among them. sub,max It is the maximum power carrying capacity of the transmission network.

[0224] Furthermore, simulation was performed based on Matlab to analyze the system performance, including economic benefits, utilization efficiency of the energy storage system, and load satisfaction.

[0225] As attached Figure 2 As shown in the figure, the changes of wind power generation, solar power generation and load demand over time are shown. The specific analysis is as follows: The power of wind power generation (blue line) varies during the day and night, but it shows periodic fluctuations overall. The power of wind power generation is higher at noon (when the wind speed is higher) and lower in the morning and evening. This shows that wind power generation is affected by wind speed and may provide higher power generation capacity during certain periods. The power generation of solar energy (red dashed line) shows a clear peak during the day and zero at night. Solar power generation reaches its peak at noon, which is usually proportional to the intensity of solar radiation. The power generation at night is zero, indicating that the system relies entirely on other power generation sources or the grid at night. The load demand (black dotted line) gradually increases and then decreases during the day, showing a typical daily load curve. The peak of load demand occurs in the evening, usually because household and commercial activities increase during this period. For example, the peak period of household electricity consumption is in the evening, and commercial activities also increase electricity demand after dinner time.

[0226] In summary, wind and solar power generation increases during the day, and load demand also increases during this period. The system needs to comprehensively consider the power generation capacity of these two renewable energy sources to meet the load demand.

[0227] Renewable energy has certain complementarity: there are differences in the power generation periods of wind and solar energy, and the combination of these two energy sources can increase the power generation reliability of the system. For example, wind power generation may be more stable at night, while solar power generation provides peak power during the day. The peak of load demand occurs at night, while solar power generation is zero at this time, so the load at night is mainly provided by wind power generation and the power grid.

[0228] Furthermore, the battery charging power appears when the system's power generation exceeds the load demand, and is displayed as a negative value. When the wind and solar power generation is higher than the load demand, the battery system will charge and store the excess power. This helps balance future load demand and insufficient power generation. The battery discharging power appears when the load demand exceeds the power generation, and is displayed as a positive value. When renewable energy generation is insufficient to meet the load demand, the battery will release the stored power to supplement the insufficient power. Battery discharge ensures that the load demand is met and avoids over-reliance on the power grid.

[0229] The charging of the battery mainly occurs during the peak period of wind and solar power generation. The system effectively uses excess power generation capacity for charging, thereby improving the efficiency of renewable energy use. The discharge of the battery is mainly carried out during the peak load demand period to ensure the reliability of power supply. When the load demand is high, the battery discharge reduces the burden on the power grid. The battery charge and discharge status should take into account the battery capacity and efficiency to maximize the overall economy and stability of the system. An effective charging and discharging strategy can reduce the grid's electricity purchase cost while optimizing the use of renewable energy.

[0230] Based on the above analysis, we can know that renewable energy generation can monitor and analyze load changes in real time, help substations achieve load balance and dynamic adjustment, and improve the stability and reliability of the system. Smart grid requires intelligent management of all aspects of the power system. Renewable energy generation provides technical support for the intelligence of substations, enabling them to better integrate into the smart grid system and improve the intelligence level of the overall power system.

[0231] The operation and management method of the source-grid-load-storage substation based on renewable energy generation can not only improve the operation efficiency and equipment reliability of the power system, but also optimize energy consumption management, cope with complex load changes, support the construction of smart grids, and meet policy requirements. With the continuous development and maturity of renewable energy generation, this management method will become an important direction for the future development of the power system.

[0232] Based on the same inventive concept, this embodiment also provides a source-grid-load-storage substation operation management system based on renewable energy power generation, including:

[0233] Basic model building module, building the basic mathematical model of the substation with source, grid, load and storage;

[0234] A prediction module is used to predict photovoltaic output power, wind power output power and substation load based on a basic mathematical model and an autoregressive integral moving average model;

[0235] An objective function establishment module is used to establish an objective function with the goal of minimizing the system operation cost based on the predicted results;

[0236] The constraint condition establishment module is used to construct the constraint conditions for the operation and management of the substation including the source, grid, load and storage;

[0237] The operation management module is used to solve based on the objective function and constraints, and realize the operation management of the source-grid-load-storage substation based on the solution results.

[0238] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the drawings and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A method for operating and managing a source-grid-load-storage substation based on renewable energy power generation, characterized in that: include: S1: Construct the basic mathematical model of the substation with source, grid, load and storage; S2: Based on the basic mathematical model, the autoregressive integral moving average model is used to predict the photovoltaic output power, wind power output power and substation load; S3: Based on the prediction results, an objective function is established to minimize the system operation cost; S4: Construct constraints for substation operation management including source, grid, load and storage; S5: Solve the problem based on the objective function and constraints, and implement the operation management of the source-grid-load-storage substation based on the solution results.

2. The operation management method of the source-grid-load-storage substation based on renewable energy power generation according to claim 1 is characterized in that: The step S1 comprises: The power P of renewable energy g (t) is expressed as: P g (t)=f g (t) (1) Among them, f g (t) is the time series function of the power of the generating source; The load power P l (t) is expressed as: P l (t)=f l (t) (2) Among them, f l (t) is the time series function of load demand; The energy storage capacity E s (t) and energy storage power P s (t) is represented by the following formula: Among them, η charge is the charging efficiency, η discharge is the discharge efficiency, P s,charge (t) is the charging power of the energy storage system, P s,discharge is the discharge power of the energy storage system, Δt is the time step; Let the power of the transmission grid P sub (t)Satisfy power balance: P g (t)+P s,discharge (t)-P s,charge (t)=P l (t)+P sub (t) (4)。 3. The operation management method of the source-grid-load-storage substation based on renewable energy power generation according to claim 2 is characterized in that: The prediction of photovoltaic output power in step S2 includes: Construct a photovoltaic output power model and convert the theoretical photovoltaic output power P pv,th (t) is expressed as: P pv,th (t)=η pv ·A pv ·G(t) (5) Among them, η pv is the conversion efficiency of the photovoltaic module; A pv is the effective area of ​​the photovoltaic panel; G(t) is the solar radiation intensity at time t; Substituting the temperature effect, the actual photovoltaic output power P pv (t) is expressed as: P pv (t)=P pv,th (t)·[1-β pv ·(T(t)-T ref )] (6) Among them, β pv is the temperature coefficient of the photovoltaic module; T(t) is the ambient temperature at time t; T ref is the reference temperature; The autoregressive integrated moving average model is used to predict the photovoltaic output power: P pv (t+1)=φ1P pv (t)+φ2P pv (t-1)+…+φ p P pv (t-p)+∈ t +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q (7) Where: φ i is the autoregressive coefficient; θ i is the sliding mean coefficient; ∈ t is white noise; p represents the order of the autoregressive term, and q represents the order of the moving average term.

4. The operation management method of the source-grid-load-storage substation based on renewable energy power generation according to claim 3 is characterized in that: The prediction of wind power output power in step S2 includes: Construct a wind power output model and convert the theoretical wind power output power P wind,th (t) is expressed as: Where ρ is the air density; A wind is the swept area of ​​the wind wheel; C p (V(t)) is the power coefficient, which depends on the wind speed V(t) and is determined by the power curve provided by the wind rotor manufacturer; For the actual wind power output P wind (t), when the wind speed is lower than the starting wind speed V cut-in When the wind speed is higher than the rated wind speed V rated When the fan runs at rated power P rated ; When the wind speed is higher than the cut-out wind speed V cut-out When the fan stops running to prevent damage; The actual wind power output power P is adjusted by the following formula: wind (t) is expressed as: The autoregressive integrated moving average model is used to predict wind power output. P wind (t+1)=φ1P wind (t)+φ2P wind (t-1)+…+φ p P wind (t-p)+∈ t +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q (10) Among them, φ i is the autoregressive coefficient; θ i is the sliding mean coefficient; ∈ t It's white noise.

5. The operation management method of the source-grid-load-storage substation based on renewable energy power generation according to claim 4 is characterized in that: The specific method of predicting the substation load in step S2 is as follows: Assume that the power time series data of photovoltaic and wind power where Y t represents the power time series data at time t; First, determine the model order, including the autoregressive part and the moving average part; The determining of the autoregressive part is specifically to use the partial autocorrelation function PACF to determine the autoregressive order p: PACF(a)=Cov(Y t ,Y t-1 )a=1,2,...,p (11) Among them, the autoregressive order p is determined by observing the truncation of the PACF plot; The moving average determination part is specifically: using the autocorrelation function ACF to determine the sliding average term order q: ACF(b)=Cov(Y t ,Y t-1 )b=1,2,...,q (12) Among them, the order q of the sliding average term is determined by observing the truncation of the ACF graph; Next, the first difference is expressed as: ΔY t =Y t -Y t-1 (13) The autoregressive integrated moving average model ARIMA (p, d, q) model is expressed as: F(B)D d Y t =Θ(B)∈ t (14) Among them, the number of differences d is calculated to make the sequence stable; B is the lag operator (B k Y t =Y t-k ), Φ(B)=1-φ1B-φ2B 2 -…-φ p B p is an autoregressive polynomial, θ(B)=1+θ1B+θ2B 2 +…+θ q B q is a moving average polynomial, ∈ t is a white noise sequence using the maximum likelihood estimation MLE to estimate the coefficient φ i and θ i , with maximizing the likelihood function as the optimization goal: Among them, f(∈ t |φ,θ) is the conditional probability density function of the residual; Generate load forecasts using the established ARIMA model: in, It is the predicted value at time t+h, where h is the time in the future.

6. The method for operating and managing a source-grid-load-storage substation based on renewable energy power generation according to claim 5 is characterized in that: The autocorrelation function ACF is used to measure the correlation of the time series at different lag periods, and the autocorrelation coefficient ρ of the lag period k k Represents the time series Y t The correlation between time t and tk, First, calculate the mean of the time series It is expressed as: Calculate the variance σ of the time series 2 It is expressed as, Calculate the covariance Cov(Y t ,Y t-h ): Calculate the autocorrelation coefficient ρ k :

7. The operation management method of the source-grid-load-storage substation based on renewable energy power generation according to claim 5 is characterized in that: The partial autocorrelation function PACF is used to measure the autocorrelation of the kth lag period of the time series under the condition of given other lag periods. The partial autocorrelation function PACF is implemented as follows: Build an autoregressive model: Y t =φ1Y t-1 +φ2Y t-2 +L+φ p Y t-p +∈ t (21) Estimate the parameters of the autoregressive model using the Yule-Walker equation: Rφ=r (22) Where R is the autocorrelation matrix: r is the autocorrelation vector: φ is the vector of autoregressive coefficients: Solve the above system of equations to estimate φ i ; The partial autocorrelation coefficient φ of lag period k kk , which represents the autocorrelation of lag k in the time series after controlling the influence of the first k-1 lags, is calculated by the recursive formula: Among them, ρ k is the autocorrelation coefficient of lag period k, φ i is the autoregressive coefficient.

8. The method for operating and managing a source-grid-load-storage substation based on renewable energy power generation according to claim 5, characterized in that: The objective function Minimize C in step S3 is specifically: Among them, C g is the unit cost of electricity generation; C s is the cost of using the energy storage system; C sub It is the operating cost of the power grid.

9. The method for operating and managing a source-grid-load-storage substation based on renewable energy power generation according to claim 8, characterized in that: The constraints in step S4 include: power balance constraints, energy storage system power constraints, energy storage system power constraints, power generation and load constraints, and grid power constraints; The power balance constraint is expressed as: P g (t)+P s,discharge (t)-P s,charge (t)=P l (t)+P sub (t) (28) The energy storage system power constraint is expressed as: E s,min ≤E s (t)≤E s,max (29) Among them, E s (t) represents the amount of energy storage system at time t; E s,min and E s,max Respectively represent the minimum and maximum power of the energy storage system; The energy storage system power constraint is expressed as: 0≤P s,charge (t)≤P s,max,charge (30) 0≤P s,discharge (t)≤P s,max,discharge (31) Among them, P s,max,charge and P s,max,discharge is the maximum charging and discharging power of the energy storage system; The generation and load constraints are expressed as, 0≤P g (t)≤P g,max (32) 0≤P l (t)≤P l,max (33) Among them, P l,max and P g,max Represent the maximum values ​​of load power and power generation respectively; The grid power constraint is expressed as, 0≤P sub (t)≤P sub,max (34) Among them, P sub,max It is the maximum power carrying capacity of the transmission network.

10. A source-grid-load-storage substation operation management system based on renewable energy power generation, characterized in that: include: Basic model building module, building the basic mathematical model of the substation with source, grid, load and storage; A prediction module is used to predict photovoltaic output power, wind power output power and substation load based on a basic mathematical model and an autoregressive integral moving average model; An objective function establishment module is used to establish an objective function with the goal of minimizing the system operation cost based on the predicted results; The constraint condition establishment module is used to construct the constraint conditions for the operation and management of the substation including the source, grid, load and storage; The operation management module is used to solve based on the objective function and constraints, and realize the operation management of the source-grid-load-storage substation based on the solution results.

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