CN116024747A

CN116024747A - Dynamic balance autonomous method, device and equipment of power grid and storage medium

Info

Publication number: CN116024747A
Application number: CN202310039258.1A
Authority: CN
Inventors: 陈凤超; 叶暖平; 刘文豪; 张鑫; 周立德; 郑惠芳; 饶欢; 邱泽坚; 胡润锋; 鲁承波; 徐睿烽
Original assignee: Guangdong Power Grid Co Ltd; Dongguan Power Supply Bureau of Guangdong Power Grid Co Ltd
Current assignee: Guangdong Power Grid Co Ltd; Dongguan Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date: 2023-01-12
Filing date: 2023-01-12
Publication date: 2023-04-28

Abstract

The invention discloses a dynamic balance autonomous method, a device, equipment and a storage medium for a power grid, wherein the method comprises the following steps: predicting future power generation data of the power supply equipment in the next time period; predicting future load data of the electric equipment in the next time period; optimizing future power generation data and future load data by taking the operation cost of the edge cluster as an optimization target; if the optimization is completed, correcting errors between the future power generation data and the future load data so as to balance the future power generation data and the future load data. The distributed energy is managed through the prediction and the monitoring of the future load data and the future power generation data so as to achieve the autonomy of the edge clusters and fully exert the flexibility of various distributed energy. According to the predicted future load data of the electric equipment in the power grid and the future power generation data of the power supply equipment, the operation of the power grid is coordinated, the use efficiency of energy sources is improved, and the distributed energy sources are efficiently managed and controlled.

Description

Dynamic balance autonomous method, device and equipment of power grid and storage medium

Technical Field

The present invention relates to the field of power grid technologies, and in particular, to a dynamic balance autonomous method, apparatus, device, and storage medium for a power grid.

Background

The power grid is connected into low-voltage distributed energy in a large scale, the low-voltage distributed energy supports energy independence, sustainability is promoted, cost can be saved for consumers, and in some areas, more energy is directly transmitted from a distribution layer than from a transmission layer. However, the low-voltage distributed energy monomer has small capacity and low access voltage level, and most of the low-voltage distributed energy monomer is in an 'invisible, undetectable and uncontrollable' state.

In order to fully exploit the flexibility of various distributed energy sources, improve energy efficiency, coordinate the operation between the supply and use of various energy sources and the power grid, model predictive control uses a display model to predict the future response of the system in order to adjust the control strategy over time. And different prediction methods, such as artificial neural networks, time series models, gray models, and kalman filter algorithms, can be used to predict the uncertain renewable energy generation and load predictions.

The time series model and the neural network model train a model for predicting renewable energy power generation and load demand through a large amount of historical data, and time cost is increased in real-time application. The lack of consideration of factors of load and photovoltaic power fluctuation, the lack of consideration of prediction errors and real-time feedback correction for renewable energy power generation and load demand in real-time applications, as compensation.

Disclosure of Invention

The invention provides a dynamic balance autonomous method, a dynamic balance autonomous device, dynamic balance autonomous equipment and a storage medium of a power grid, which aim to solve the problem of prediction errors of compensating renewable energy power generation and load demands.

According to an aspect of the present invention, there is provided a dynamic balancing autonomous method of a power grid, the method comprising: determining an edge cluster in a power grid, wherein the edge cluster comprises a plurality of power supply devices and electric equipment;

predicting future power generation data of the power supply equipment in the next time period;

predicting future load data of the electric equipment in the next time period;

optimizing the future power generation data and the future load data by taking the running cost of the edge cluster as an optimization target;

if optimization is completed, an error between the future power generation data and the future load data is corrected to balance the future power generation data with the future load data.

According to another aspect of the present invention there is provided a dynamically balanced autonomous device of an electrical network, the device comprising: the power generation data prediction module is used for predicting future power generation data of the power supply equipment in the next time period;

The load data prediction module is used for predicting future load data of the electric equipment in the next time period;

the data optimization module is used for optimizing the future power generation data and the future load data by taking the running cost of the edge cluster as an optimization target;

and the error correction module is used for correcting errors between the future power generation data and the future load data if the optimization is completed, so that the future power generation data and the future load data are kept balanced.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a dynamic balancing autonomous method of a power grid according to any of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing a computer program for enabling a processor to implement a dynamic balancing autonomous method of an electrical grid according to any of the embodiments of the present invention when executed.

In the present embodiment, future power generation data of the power supply apparatus in the next time period is predicted; predicting future load data of the electric equipment in the next time period; optimizing future power generation data and future load data by taking the operation cost of the edge cluster as an optimization target; if the optimization is completed, correcting errors between the future power generation data and the future load data so as to balance the future power generation data and the future load data. The distributed energy is managed through the prediction and the monitoring of the future load data and the future power generation data so as to achieve the autonomy of the edge clusters and fully exert the flexibility of various distributed energy. According to the future load data of the electric equipment in the predicted power grid and the future power generation data of the power supply equipment, the operation of the power grid is coordinated, the use efficiency of energy sources is improved, distributed energy sources are efficiently managed and controlled, the prediction precision of the future load data and the future power generation data in the prediction range is improved, the real-time operation of the edge clusters is supported, and therefore real-time mismatch between supply and demand is reduced to the greatest extent, and balanced autonomy between power generation and load in the power grid is achieved.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method of dynamically balancing autonomy of a power grid according to a first embodiment of the present invention;

fig. 2 is a schematic structural diagram of a dynamic balance autonomous device of a power grid according to a second embodiment of the present invention;

fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The time sequence method is a statistical analysis method, and is a method for predicting the future development trend according to a historical power generation data sequence and a historical load data sequence for a certain time, namely a time sequence trend extrapolation method, and is suitable for practical prediction in a continuous process.

The Kalman filtering algorithm is an algorithm for optimally estimating the state to be measured by inputting or outputting historical power generation and historical power utilization data by utilizing a linear system state equation, and the optimal estimation can be regarded as a filtering process because the observed data comprises the influence of noise and interference. The data filtering is a data processing method for removing noise and restoring real data, and the Kalman filtering can estimate the state of the dynamic edge cluster station from a series of historical power generation data and historical load data with measurement noise under the condition that the measurement variance is known.

In the case of a large distributed resource distribution, it is assumed that the resource output of each cluster is the same, while the resource yield of different clusters varies greatly. The clusters are respectively a wind generating set, a photovoltaic set, a cogeneration set, a generating set, a battery pack and the like. Aiming at a power grid, a functional and energy consumption site is established to participate in the edge cluster aggregation of the power grid regulation scene.

In a power grid, future power generation data of power supply equipment and future load data of electric equipment are predicted, and firstly, an edge cluster is determined in the power grid, wherein the edge cluster comprises a plurality of power supply equipment and electric equipment.

The energy dynamic balance flow in the edge cluster is divided into a prediction control part and a feedback correction part. In the predictive control section, future power generation data of the power supply apparatus, future load data of the power consumer are predicted at each time interval, and the information obtained by the prediction is used to minimize the running cost of the edge cluster in a given time range. In the predictive control section, the time range is shifted forward to the next time interval by applying the result of the first time interval. And a feedback correction section that applies feedback control with error correction a plurality of times in each time interval predicted in the prediction control section to adjust the energy output of the edge cluster so as to balance the mismatch between the predicted future power generation data, the future load data, and the actual power generation data, the actual load data in the time range. The output of all energy of the edge clusters is adjusted by a feedback correction part according to the monitoring data to maintain optimal control, and meanwhile uncertainty of future power generation data and future load data is allowed.

Example 1

Fig. 1 is a flowchart of a dynamic balance autonomous method of a power grid, where the method may be performed by a dynamic balance autonomous device of the power grid, the dynamic balance autonomous device of the power grid may be implemented in a form of hardware and/or software, and the dynamic balance autonomous device of the power grid may be configured in an electronic device. As shown in fig. 1, the method includes:

step 101, determining an edge cluster in a power grid, wherein the edge cluster comprises a plurality of power supply equipment and electric equipment.

In the power grid, different prediction methods exist for predicting future power generation data of solar photovoltaic power generation equipment and future load data of electric equipment, such as an artificial neural network, a time sequence model, a gray model and a Kalman filtering algorithm, so as to determine the future power generation data and the future load data. In addition, different prediction methods have different data requirements and usability for future power generation data and future load data. Wherein, the time series and neural network model trains the prediction model through a large amount of historical power generation data and historical load data.

However, the Kalman filtering algorithm and the gray model can establish a prediction model through a small amount of historical power generation data and historical load data, and are more suitable for prediction application of future power generation data and future load data in a power grid. In one example of the invention, a hybrid algorithm based on a time series method and a kalman filter algorithm is used for predicting future power generation data of power supply equipment, future load data of electric equipment and renewable energy output quantity in a power grid.

Step 102, predicting future power generation data of the power supply equipment in the next time period.

And in each time interval, predicting future power generation data of the power supply equipment and future load data of the electric equipment in a future time range by using the updated historical power generation data and the historical load data.

Further, in the edge cluster, power supply equipment is selected, and prediction of future power generation data of the next time period is performed on the corresponding power supply equipment.

In one embodiment of the present invention, step 102 may include the steps of:

step 1021, generating a definition index for the solar photovoltaic power generation equipment.

Based on the updated historical electricity consumption data and the historical load data, the obtained prediction information is used in a plan for executing optimization.

The power supply device comprises solar photovoltaic power generation equipment, and future power generation data comprise photovoltaic power generation power.

In predicting future power generation data of a solar photovoltaic power generation device, a sharpness index is first generated for the solar photovoltaic power generation device. The photovoltaic power generation power is determined according to a definition index, and the definition index is calculated by solar irradiance.

Among the solar irradiance uncertainty causes are: continuously changing cloudy weather conditions.

Step 1022, calculating solar irradiance absorbed by the solar photovoltaic power generation device during each time period based on the sharpness index.

Solar irradiance absorbed by the solar photovoltaic power generation device is calculated according to the out-of-ground irradiance and the definition index.

The photovoltaic power generation power is determined according to a definition index, the definition index is calculated by solar irradiance, and solar irradiance absorbed by solar photovoltaic power generation equipment in each time period is calculated based on the definition index. The extraterrestrial solar irradiance can be derived from geographic information of the site and earth orbit, and the sharpness index can be generated using a convex hull algorithm.

Solar irradiance was calculated by the following formula:

h _pv,t ＝s _t h _ex,t

wherein ,h_pv,t Representing the solar irradiance, s, absorbed by the solar photovoltaic power generation device at time t _t Represents the sharpness index, h _ex,t Represents the extraterrestrial solar irradiance at time t.

Step 1023, taking solar irradiance absorbed by the solar photovoltaic power generation equipment as a time sequence, and performing Kalman filtering processing on the solar irradiance to obtain solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period.

Since the data in solar irradiance is one-dimensional, a time series can be determined.

The time series is determined by the following equation:

h(k)＝a ₁ h(k-1)+a ₂ h(k-2)+……+a _m h(k-m)+a _m+1 h(k-m-1)+β _k

wherein h (k) represents solar irradiance of the kth time interval, h (k-1) represents solar irradiance of the kth-1 time interval, h (k-2) represents solar irradiance of the kth-2 time interval, h (k-m) represents solar irradiance of the kth-m time interval, a ₁ 、a ₂ ……a _m 、a _m+1 Representing the coefficient, beta _k Representing the residual error;

the following equations are combined to rewrite the time series:

h ₁ (k)＝h(k),h ₂ (k)＝h(k-1),……,h _m+1 (k)＝h(k-m)

h(k+1)＝a ₁ h(k)+a ₂ h(k-1)+……+a _m+1 h(k-m)+β _k+1

the rewritten time sequence is determined by the following formula:

h ₁ (k+1)＝a ₁ h ₁ (k)+a ₂ h ₂ (k)+……+a _m+1 h _m+1 (k)+β _k+1

wherein ,h₁ (k) Solar irradiance, h, representing the kth time interval ₂ (k) Represents solar irradiance, h, of the kth-1 time interval _m+1 (k) Represents solar irradiance of the kth-m time interval, h (k) represents solar irradiance of the kth time interval, h (k-1) represents solar irradiance of the kth-1 time interval, h (k-m) represents solar irradiance of the kth-m time interval, beta _k+1 Representing residual error, h ₁ (k+1) represents solar irradiance for the (k+1) th time interval;

let h ₂ (k)＝h(k+1)，……，h _m+1 (k) =h (k+m), the state equation is determined by the following formula:

wherein ,ω_k Representing the system noise vector in the Kalman filter, h ₁ (k+1) represents solar irradiance of the first period of the (k+1) th period, h ₂ (k+1) represents solar irradiance of the second period of the (k+1) th period, h _m (k+1) represents solar irradiance of the mth period of the kth+1 period, h _m+1 (k+1) represents solar irradiance of the (m+1) th period of the (k+1) th period, h ₂ (k) Represents solar irradiance, h, of the kth-1 time interval ₁ (k) Solar irradiance, h, representing the kth time interval _m+1 (k) Solar irradiance representing the k-m time interval;

the state equation is rewritten by the following equation:

wherein H (k) is the kth time periodA state vector of solar irradiance within an interval, H (k+1) represents a state vector of solar irradiance within a k+1th time interval,

a state transmission matrix representing a state equation, Γ (k+1, k) representing an excitation transfer matrix in the kalman filter, ω (k) representing a system noise vector in the kalman filter;

listing the observation equation by the following equation and performing Kalman filtering processing to obtain solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period:

Wherein Z (k+1) represents an observation vector,

representing the prediction output transition matrix, H (k+1) representing the observation vector in the kalman filter, and n (k+1) representing the measurement noise in the kalman filter.

Wherein the data of solar irradiance is one-dimensional, so that a time series can be determined. Based on the time series, the observation equation of the kalman filter is obtained. And performing Kalman filtering processing on the solar irradiance to obtain the solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period.

Step 1024, predicting the photovoltaic power generation power of the solar photovoltaic power generation device in the next time period based on the solar irradiance absorbed by the solar photovoltaic power generation device in the next time period.

The solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period is predicted by an observation equation of Kalman filtering, and the photovoltaic power generation power of the solar photovoltaic power generation equipment in the next time period is predicted based on the solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period.

Further, the power generated by solar irradiance absorbed by the solar photovoltaic power generation device may be calculated based on solar irradiance, rated capacity, rated solar irradiance.

The power generated by the solar photovoltaic power generation equipment is calculated by the following formula:

wherein ,P_pv,r Indicating rated capacity of solar photovoltaic power generation equipment, h _r Represents rated solar irradiance, h _pv,t Representing the power generated by solar irradiance absorbed by the solar photovoltaic power generation device at time t, P _pv,t Representing the power of solar irradiance absorbed by the solar photovoltaic power generation device.

Step 103, predicting future load data of the electric equipment in the next time period.

In each time interval, future load data and photovoltaic power generation power of the solar photovoltaic power generation device are predicted. And predicting future load data of the electric equipment in the next time period after predicting solar photovoltaic power generation power based on solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period.

During each time interval of the prediction, feedback with error correction is applied to adjust the energy output of the remote clusters to balance the mismatch between the predicted and actual values. The output of all energy of the edge clusters is adjusted according to the predicted future load data and the photovoltaic power generation power to maintain optimal control while allowing uncertainty in future power generation data and future load data requirements.

Step 104, optimizing future power generation data and future load data by taking the operation cost of the edge cluster as an optimization target.

The optimization is performed by taking predictions over a certain limited time frame, the edge clusters comprising distributed photovoltaic power generation, load and battery energy storage stations, connected to the distribution stations and operating through a communication network. There is a bi-directional energy flow in the station and the optimization procedure is expressed as a mixed integer linear programming problem.

Future power generation data and future load data are predicted and optimized for each time interval. The goal of this optimization is to minimize the running cost over a fixed future time frame.

Illustratively, future power generation data and future load data are predicted and optimized within each time interval k. Starting at time interval k, time interval k+1 is preceded by a prediction of future power generation data and future load data over a future time range (from time interval k+1 to time interval k+m), but only time interval k+1 in the time schedule of time intervals has been implemented. M represents the length of the prediction horizon and k represents the time interval. Also, in the next time interval, the time range is shifted forward by one time interval and again predicted and optimized again based on the latest message. The optimization process takes into account the future time intervals, so that the control implementation remains optimal.

Wherein future power generation data is predicted from historical data of the power supply device.

The power supply equipment comprises solar photovoltaic power generation equipment, the edge cluster further comprises at least one of a load and a battery energy storage station.

The running cost of the edge cluster is determined by the following formula:

wherein i represents an ith bus bar, 1 represents a lower limit of the range, N represents an upper limit of the range, J _c (t) represents the running cost in edge clusters, J _p Representing potential benefits of charging or discharging the battery energy storage station, J representing operating costs, t representing a time range from k+1 to k+m;

the running cost and potential benefit of the edge cluster are determined by the following formula:

J _c (t)＝π _g (t)P _g,i (t)-π _d (t)P _d,i (t)T _s

J _p ＝(S _b,i (k+M)-S _b,i (k))π _g,avg (k)

wherein ,J_c (t) represents the running cost in the edge cluster at time t, J _p Indicating potential benefits of charging or discharging a battery energy storage station, pi _g (t) represents the energy price at time t, P _g,i (t) represents the load demand of the ith bus at time t, pi _d (T) represents the electricity price corresponding to the load demand of the ith bus at time T, T _s Represents the sampling time S _b,i (k+M) represents the charge state of the i-th bus battery energy storage station at the k+M time, pi _g,avg (k) Mean value of load demand price in kth time S _b,i (k) Indicating the charge state of the battery energy storage station of the ith bus at the kth moment, P _d,i And (t) represents the ith bus load demand at the t moment.

Under the constraint of at least one of the conditions of the power generation condition of the solar photovoltaic power generation equipment, the maximum power condition applied by the inverter, the charging state condition of the battery energy storage station, the discharging state condition of the battery energy storage station, the charging power of the battery energy storage station, the discharging power condition, the charging speed of the battery energy storage station, the discharging speed condition, the power exchange condition of the power grid, the line flow condition and the power balance condition, the operation cost of the edge cluster is minimum, and future power generation data and future load data are optimized.

Wherein, the power generated by the solar photovoltaic power generation equipment is determined as the power generation condition of the solar photovoltaic power generation equipment by the following formula:

P _pv,T,i (t)＝P _pv,i (t)+P _pv,c,i (t)

wherein ,P_pv,T,i (t) represents the generated power of the solar photovoltaic power generation facility at time t, P _pv,i (t) represents the photovoltaic power used on the ith bus at time t, P _pv,c,i And (t) represents the photovoltaic reduction power on the ith bus at the moment t.

The maximum power applied by the inverter is determined as the maximum power condition applied by the inverter by the following formula:

wherein ,P_pv,T,i (t) represents the generated power of the solar photovoltaic power generation device at time t, μ _pv,i (t) represents a binary decision quantity of the operating state of the solar photovoltaic power generation apparatus at the ith bus bar at the time t,

the maximum power limit imposed by the inverter is tabulated.

The state of charge of the battery energy storage station is determined as a state of charge condition of the battery energy storage station by linking the following equation with the inequality:

S _bi (t+(M-1))＝S _bi,ini

wherein ,S_b,i (t+1) represents the state of charge of the i-th bus bar battery energy storage station at time t+1, S _bi (t+ (M-1)) represents the state of charge of the i-th bus bar battery energy storage station at time t+ (M-1), S _b,i (t) represents the state of charge, σ, of the ith bus bar battery energy storage station at time t _bi Representing self-discharge energy loss of battery energy storage station, T _s Representing the sampling time, eta of the battery energy storage station _ch,i Representing the charging efficiency of the battery energy storage station on the ith bus, P _ch,i (t) represents the charging power of the battery energy storage station at the time t, P _dch,i (t) represents the discharge power, eta of the battery energy storage station at the time t _dch,i The representation represents the discharge efficiency of the battery energy storage station on the ith bus,

indicating the minimum state of charge of the i-th bus battery energy storage station,/->

Represent the firstMaximum state of charge of i bus battery energy storage stations S _bi,ini Representing an initial state of charge of the battery energy storage station, S _b,i And (t+1) represents the charging state of the ith bus bar battery energy storage station at the time t+1.

Determining the charging power limit and the discharging power limit of the battery energy storage station by the following formula, wherein the charging power limit and the discharging power limit serve as the charging power and the discharging power condition of the battery energy storage station:

μ _ch,i (t)+μ _dcg,i (t)≤1

wherein ,P_ch,i (t) represents the charging power of the battery energy storage station at the time t, mu _ch,i (t) a binary decision variable, μ, representing the ith bus charge decision at time t _dcg,i (t) a binary decision variable representing the discharge decision of the ith bus at time t, P _dch,i (t) represents the discharge power of the battery energy storage station at time t,

indicating maximum discharge power of the battery energy storage station, < >>

Indicating the maximum charge power of the battery storage station.

The charging speed limit and the discharging speed limit of the battery energy storage station are determined through the following formulas and are used as the conditions of the charging speed and the discharging speed of the battery energy storage station:

-T _S R _ch,i ≤P _ch,i (t)-P _ch,i (t-1)≤T _s R _ch,i

-T _S R _dch,i ≤P _dch,i (t)-P _dch,i (t-1)≤T _s R _dch,i

wherein ,T_S Representing the sampling time of the battery energy storage station, R _ch,i Indicating the charging speed of the battery energy storage station on the ith bus at time t, R _dch,i Indicating the discharge rate of the battery energy storage station on the ith bus at time t, P _ch,i (t) represents the charging power of the battery energy storage station at the time t, P _dch,i (t) represents the discharge power of the battery energy storage station at time t, P _ch,i (t-1) represents the charging power of the battery energy storage station at the time t-1, P _dch,i (t-1) represents the discharge power of the battery energy storage station at time t-1.

The power balance of the edge cluster is determined as a power balance condition by the following formula:

the power flow of a set of transmission lines connected to buses i and j is calculated as a line flow condition by the following equation:

l _i,j (t)＝B _i,j (θ _i (t)-θ _j (t))

wherein ,

indicating that the j-th bus belongs to the set of all buses i, indicating the lower limit of the range, N indicating the upper limit of the range, l _i,j (t) represents the DC power flow of the transmission line connecting bus i and bus j at time t, B _i,j Representing susceptance, θ, of the line between buses i and j _i (t) represents the voltage phase angle of the bus i at time t, θ _j (t) represents the voltage phase angle of the bus j at time t, P _ch,i (t) represents the charging power of the battery energy storage station at the time t, P _d,i (t) represents the load demand of bus i in the battery energy storage station, P _pv,c,i (t) represents the photovoltaic reduction power, P, on bus i at time t _dch,i (t) represents the discharge power of the battery energy storage station at time t, P _pv,T,i (t) represents the generated power of the solar photovoltaic power generation facility at time t, P _g,i (t) represents the exchange of the ith bus and the power grid at the moment tPower.

The power bearing capacity range of the power flow on each line in the corresponding line is determined as a power exchange condition of the power grid through the following inequality:

-l _i,j,min ≤l _i,j (t)≤l _i,j,max

the power exchange between the edge cluster and the grid is limited by the connection capacity determined by the following inequality:

P _g,i,min ≤P _g,i (t)≤P _g,i,max

The ramp rate limit imposed by the power exchange of the grid at bus i is determined by the inequality:

-T _s R _g,i ≤P _g,i (t)-P _g,i (t-1)≤T _s R _g,i

wherein ,T_s Represents the sampling time, P _g,i (t) represents the exchange power between the ith bus and the power grid at the moment t, P _g,i (t-1) represents the exchange power between the ith bus and the power grid at the moment t-1, R _g,i Indicating a limit of ramp speed, l _i,j,min Representing the minimum capacity limit of buses i and j, l _i,j,max Representing the maximum capacity limit of buses i and j, l _i,j (t) represents the DC power flow of the transmission line connecting bus i and bus j at time t, P _g,i,min Representing minimum power exchange limit of the grid at bus i, P _g,i,max Representing maximum power exchange limit of the grid at bus i, P _g,i And (t) represents the exchange power of the ith bus and the power grid at the moment t.

And 105, if the optimization is completed, correcting errors between the future power generation data and the future load data so as to balance the future power generation data and the future load data.

And if the optimization is finished, correcting errors between the future power generation data and the future load data based on the static model so as to keep the future power generation data and the future load data balanced.

The feedback control with error correction does not interrupt the scheduling, but adjusts the output of the solar photovoltaic power plant in accordance with the error between the future power generation data and the future load data. The feedback control is a static model that adjusts the output of the solar power plant only at the next time interval k+1. The static model is represented as a linear programming problem. The goal of the static model is to maximize the flexibility and resiliency of the edge clusters.

When correcting an error between future power generation data and future load data, the error between the future power generation data and the future load data is first calculated.

Calculating an error between future power generation data and future load data by the following formula:

wherein F represents the total adjustment of the edge prediction plan setpoint, ω ₁ 、ω ₂ Representing the weight coefficient, P _g,i,max Representing the maximum power exchange limit of the grid at bus i,

indicating maximum charging power of the battery energy storage station, < >>

Indicating maximum discharge power of battery energy storage station, l _i,j,max Represents the maximum capacity limit of bus i and j, |ΔP _g,i I represents the correction of the error by the maximum power exchange limit of the grid at busbar i, |Δp _ch,i I represents the correction amount of the charging power of the battery energy storage station to the error, |Δp _dch,i I represents the correction amount of the discharge power of the battery energy storage station to the error, |Δl _i,j I represents the correction of the error by the dc power flow of the transmission lines of the bus i and bus j of the battery energy storage station.

Further, with the aim of minimizing errors, photovoltaic power generation power in the edge cluster, charging data of the battery energy storage stations, photovoltaic power reduction amount on the buses, direct current power flow of transmission lines between buses, charging data, discharging power and exchange power between the buses and the power grid are adjusted so as to keep balance between future power generation data and future load data.

And under the constraint of at least one of the edge cluster correction condition, the battery energy storage station adjustment condition, the power balance adjustment condition, the power grid output and/or input power adjustment condition, the error is targeted, and the photovoltaic power generation power, the charging data of the battery energy storage station, the photovoltaic power reduction amount on the bus, the direct current power flow of the transmission line between buses, the charging data, the discharging power and the exchange power between the bus and the power grid in the edge cluster are optimized.

Wherein, based on the adjustment of error to the photovoltaic power generation power and the limit power, confirm the constraint condition of photovoltaic power generation power and limit power of the complaint through the following equation and inequality of the linkage, as the correction condition of the edge cluster:

wherein ,P_pv,T,i (t) represents the generated power of the solar photovoltaic power generation facility at time t, P _pv,i (t) represents the photovoltaic power used on the ith bus at time t, P _pv,c,i (t) represents the photovoltaic reduction power, ΔP, on the ith bus at time t _pv,c,i This evidence of photovoltaic reduced power versus error on the ith bus at time t is shown,

representation error, mu _pv,i (t) represents a binary decision quantity of the operating state of the solar photovoltaic power generation device at the ith bus bar at time t,/->

The maximum power limit imposed by the inverter is tabulated.

Based on the adjustment of the error to the battery energy storage station, determining a constraint condition to the battery energy storage station as a battery energy storage station adjustment condition by combining the following equation with the inequality:

-T' _s R _ch,i ≤P _ch,i (t)+ΔP _ch,i -P _ch,i (t'-1)≤T' _s R _ch,i

-T' _s R _dch,i ≤P _dch,i (t)+ΔP _dch,i -P _dch,i (t'-1)≤T' _s R _dch,i

wherein ,

Representing the maximum state of charge, sigma, of an i-th busbar battery energy storage station _bi Representing the loss of self-discharging energy of the battery energy storage station, S _b,i (t '+1) represents the state of charge of the i-th bus battery energy storage station at time t' +1, S _b,i (T ') represents the charging state of the i-th bus battery energy storage station at the time T ', T ' _s Representing the sampling time of the battery energy storage station, P _ch,i (t) represents the charging power of the battery energy storage station at the time t, mu _ch,i (t) binary decision representing the ith bus charging decision at the t-th moment of the battery energy storage stationStrategy variable, mu _dcg,i (t) binary decision variable representing the ith bus discharge decision at the t-th moment of the battery energy storage station, P _ch,i (t '-1) represents the charging power of the battery energy storage station at time t' -1, ΔP _ch,i Correction amount P representing charging power of ith bus of battery energy storage station to error _dch,i (t) represents the discharge power of the battery energy storage station at time t, ΔP _dch,i Correction quantity indicating charging power of ith bus bar of battery energy storage station to error, +.>

Indicating maximum charging power of the battery energy storage station, < > >

Representing the maximum discharge power, T ', of the battery energy storage station' _s Representing the sampling time.

If P _ch,i (t')＝P _ch,i +ΔP _ch,i And based on the adjustment of the power balance of the future power generation data and the future load data by the error, determining the power balance constraint condition of the future power generation data and the future load data by the following formula as a power balance adjustment condition:

wherein ,P_d,i (t) represents the load demand of bus i at time t, σ _bi Representing self-discharge energy loss of battery energy storage station, P _pv,c,i (t) represents the photovoltaic reduction power, deltaP, on the ith bus at time t _pv,c,i Representing the correction of the photovoltaic reduced power on the ith bus to the error, P _ch,i (t) represents the charging power of the battery energy storage station at time t, ΔP _ch,i Correction amount P representing charging power of ith bus of battery energy storage station to error _dch,i (t) represents the discharge power of the ith bus bar of the battery energy storage station at the moment t, and DeltaP _dch,i Correction amount P representing charging power of ith bus of battery energy storage station to error _g,i (t) represents the exchange power of the ith bus bar and the power grid at the moment t,ΔP _g,i correction quantity representing error of exchange power of ith bus bar and power grid, P _g,i (t) represents the exchange power between the ith bus and the power grid at the moment t, P _pv,T,i (t) represents the power generated by the solar photovoltaic power generation facility at time t, l _i,j (t) represents the DC power flow, deltal, of the transmission line connecting bus i and bus j at time t _i,j The correction of the error by the dc power flow of the transmission line connecting bus i and bus j is shown,

load error correction amount->

Representing the error.

The power flow constraint condition is determined by the following formula, and is determined as the power balance adjustment condition by the following formula:

-l _i,j,max ≤l _i,j (t)+Δl _i,j ≤l _i,j,max

wherein ,l_i,j,min Representing the minimum capacity limit of buses i and j, l _i,j,max Representing the maximum capacity limit of buses i and j, l _i,j (t) represents the DC power flow, deltal, of the transmission line connecting bus i and bus j at time t _i,j Correction of errors for the direct current power flow of the transmission line connecting bus i and bus j.

Determining a power constraint condition for the power grid output power and/or the input power adjustment based on the error as a power grid output power and/or input power adjustment condition by the following formula:

P _g,i,min ≤P _g,i (t)+ΔP _g,i ≤P _g,i,max

-T _s R _g,i ≤P _g,i (t)+ΔP _g,i (t)-P _g,i (t'-1)≤T _s R _g,i

wherein ,P_g,i,min Representing minimum power exchange limit of the grid at bus i, P _g,i,max Representing maximum power exchange limit of the grid at bus i, P _g,i (t) represents t timeThe exchange power of the ith bus and the power grid is carved, delta P _g,i (T) represents a correction amount of the error of the exchange power of the ith bus bar and the power grid, T _S Represents the sampling time, P _g,i (t '-1) represents the exchange power between the ith bus and the power grid at the moment t' -1, R _g,i Indicating a ramp rate limit.

Example two

Fig. 2 is a schematic structural diagram of a dynamic balance autonomous device of a power grid according to a third embodiment of the present invention. As shown in fig. 2, the apparatus includes:

An edge cluster determining module 201, configured to determine an edge cluster in a power grid, where the edge cluster includes a plurality of power supply devices and electric devices;

a power generation data prediction module 202, configured to predict future power generation data of the power supply device in a next time period;

the load data prediction module 203 is configured to predict future load data of the electric device in a next time period;

a data optimization module 204, configured to optimize the future power generation data and the future load data with an operation cost of the edge cluster as an optimization target;

and the error correction module 205 is configured to correct an error between the future power generation data and the future load data to balance the future power generation data and the future load data if the optimization is completed.

In one embodiment of the invention, the power supply device comprises a solar photovoltaic power generation device, and the future power generation data comprises photovoltaic power generation power;

the power generation data prediction module 202 includes:

the definition index generation module is used for generating a definition index for the solar photovoltaic power generation equipment;

a solar irradiance calculating module for calculating solar irradiance absorbed by the solar photovoltaic power generation device in each time period based on the sharpness index;

The Kalman filtering processing module is used for taking solar irradiance absorbed by the solar photovoltaic power generation equipment as a time sequence, and performing Kalman filtering processing on the solar irradiance to obtain solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period;

and the photovoltaic power generation power prediction module is used for predicting the photovoltaic power generation power of the solar photovoltaic power generation equipment in the next time period based on the solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period.

In one embodiment of the present invention, the solar irradiance calculating module includes:

the irradiance calculating module is used for calculating the solar irradiance through the following formula:

h _pv,t ＝s _t h _ex,t

wherein ,h_pv,t Representing the solar irradiance absorbed by the solar photovoltaic power plant at time t, s _t Represents the sharpness index, h _ex,t Representing the solar irradiance outside of time t;

the predicting the photovoltaic power generation power of the solar photovoltaic power generation device in the next time period based on the solar irradiance absorbed by the solar photovoltaic power generation device in the next time period comprises:

the photovoltaic power generation power calculation module is used for calculating solar irradiance absorbed in the next time period and predicting photovoltaic power generation power of the solar photovoltaic power generation equipment in the next time period according to the following formula:

wherein ,P_pv,r And h represents the rated capacity of the solar photovoltaic power generation equipment _r Represents the rated solar irradiance, h _pv,t Representing the power generated by the solar irradiance absorbed by the solar photovoltaic power generation device at time t, P _pv,t Representing the power of solar irradiance absorbed by the solar photovoltaic power generation device.

In one embodiment of the present invention, the kalman filter processing module includes:

a time sequence obtaining module for determining a time sequence by the following equation:

h(k)＝a ₁ h(k-1)+a ₂ h(k-2)+……+a _m h(k-m)+a _m+1 h(k-m-1)+β _k

wherein h (k) represents the solar irradiance of the kth time interval, h (k-1) represents the solar irradiance of the kth-1 time interval, h (k-2) represents the solar irradiance of the kth-2 time interval, h (k-m) represents the solar irradiance of the kth-m time interval, a ₁ 、a ₂ ……a _m 、a _m+1 Representing the coefficient, beta _k Representing the residual error;

a time sequence rewriting module, configured to rewrite the time sequence in conjunction with the following equation:

h ₁ (k)＝h(k),h ₂ (k)＝h(k-1),……,h _m+1 (k)＝h(k-m)

h(k+1)＝a ₁ h(k)+a ₂ h(k-1)+……+a _m+1 h(k-m)+β _k+1

a time sequence determining module, configured to determine the rewritten time sequence according to the following formula:

h ₁ (k+1)＝a ₁ h ₁ (k)+a ₂ h ₂ (k)+……+a _m+1 h _m+1 (k)+β _k+1

wherein ,h₁ (k) Representing the solar irradiance, h, of the kth time interval ₂ (k) Representing the solar irradiance, h, of the kth-1 time interval _m+1 (k) Representing the solar irradiance of the kth-m time interval, h (k) representing the solar irradiance of the kth time interval, h (k-1) representing the solar irradiance of the kth-1 time interval, h (k-m) representing the solar irradiance of the kth-m time interval, beta _k+1 Representing residual error, h ₁ (k+1) represents the solar irradiance of the kth+1th time interval;

wherein ,ω_k Representing a systematic noise vector, h, in the Kalman filter ₁ (k+1) represents solar irradiance of the first period of the (k+1) th period, h ₂ (k+1) represents solar irradiance of the second period of the (k+1) th period, h _m (k+1) represents solar irradiance of the mth period of the kth+1 period, h _m+1 (k+1) represents solar irradiance of the (m+1) th period of the (k+1) th period, h ₂ (k) Representing the solar irradiance, h, of the kth-1 time interval ₁ (k) Representing the solar irradiance, h, of the kth time interval _m+1 (k) Representing the solar irradiance of a kth-m time interval;

a state equation rewriting module, configured to rewrite the state equation by the following equation:

wherein H9 k) is the state vector of the solar irradiance during the kth time interval, H (k+1) is the state vector of the solar irradiance during the kth+1 time interval,

a state transmission matrix representing the state equation, Γ (k+1, k) representing an excitation transfer matrix in the kalman filter, ω (k) representing a system noise vector in the kalman filter;

The observation equation determining module is used for listing the observation equation through the following equation and executing Kalman filtering processing to obtain solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period:

wherein Z (k+1) represents an observation vector,

In one embodiment of the invention, the power supply device comprises a solar photovoltaic power generation device, and the edge cluster further comprises at least one of a load and a battery energy storage station;

the data optimization module 204 includes:

the operation cost determining module is used for determining the operation cost of the edge cluster through the following formula:

wherein i represents the ith bus bar, 1 represents the rangeLimit, N represents the upper limit, J _c (t) represents the running cost in the edge cluster, J _p Representing potential benefits of charging or discharging the battery energy storage station, J representing operating costs, t representing a time range from k+1 to k+m;

a potential benefit determining module, configured to determine an operation cost of the edge cluster and the potential benefit by the following formula:

J _c (t)＝π _g (t)P _g,i (t)-π _d (t)P _d,i (t)T _s

J _p ＝(S _b,i (k+M)-S _b,i (k))π _g,avg (k)

wherein ,J_c (t) represents the running cost in the edge cluster at time t, J _p Indicating potential benefits of charging or discharging the battery energy storage station, pi _g (t) represents the energy price at time t, P _g,i (t) represents the load demand of the ith bus at time t, pi _d (T) represents the electricity price corresponding to the load demand of the ith bus at time T, T _s Represents the sampling time S _b,i (k+M) represents the state of charge, pi, of the battery energy storage station of the ith bus at the k+M time _g,avg (k) Mean value of load demand price in kth time S _b,i (k) Representing the state of charge of the battery energy storage station of the ith bus at the kth time, P _d,i (t) represents the ith bus load demand at time t;

the data optimization module is used for optimizing the future power generation data and the future load data by taking the minimum running cost of the edge cluster as a target under the constraint of at least one condition of a power generation condition of the solar photovoltaic power generation equipment, a maximum power condition applied by an inverter, a charging state condition of the battery energy storage station, a discharging state condition of the battery energy storage station, charging power of the battery energy storage station, a discharging power condition, a charging speed of the battery energy storage station, a discharging speed condition, a power grid power exchange condition, a line flow condition and a power balance condition;

Wherein the power generated by the solar photovoltaic power generation device is determined as the power generation condition of the solar photovoltaic power generation device by the following formula:

P _pv,T,i (t)＝P _pv,i (t)+P _pv,c,i (t)

wherein ,P_pv,T,i (t) represents the generated power of the solar photovoltaic power generation device at time t, P _pv,i (t) represents the photovoltaic power used on the ith bus at time t, P _pv,c,i (t) represents the photovoltaic reduction power on the ith bus at time t;

a maximum power condition determining module, configured to determine a maximum power applied by the inverter as a maximum power condition applied by the inverter by the following formula:

wherein ,P_pv,T,i (t) represents the generated power of the solar photovoltaic power generation device at time t, μ _pv,i (t) represents a binary decision quantity of the operating state of the solar photovoltaic power generation device at the ith bus at time t,

-tabulating a maximum power limit imposed by the inverter;

a state of charge condition determining module configured to determine a state of charge of the battery energy storage station as a state of charge condition of the battery energy storage station by linking the following equation with the inequality:

S _bi (t+(M-1))＝S _bi,ini

wherein ,S_b,i (t+1) represents the state of charge of the battery energy storage station of the ith bus at time t+1, S _bi (t+ (M-1)) represents the state of charge of the battery energy storage station of the ith bus at time t+ (M-1), S _b,i (t) represents the state of charge of the battery energy storage station at the ith bus at time t，σ _bi Representing the self-discharge energy loss of the battery energy storage station, T _s Representing the sampling time, eta, of the battery energy storage station _ch,i Representing the charging efficiency, P, of the battery energy storage station on the ith bus _ch,i (t) represents the charging power of the battery energy storage station at the time t, P _dch,i (t) represents the discharge power, eta of the battery energy storage station at the moment t _dch,i The representation represents the discharge efficiency of the battery energy storage station on the ith bus,

representing the minimum state of charge of the battery storage station of the ith busbar, < >>

Representing the maximum charge state of the battery energy storage station of the ith bus, S _bi,ini Representing an initial state of charge of the battery energy storage station, S _b,i (t+1) represents the state of charge of the battery energy storage station at time t+1 for the ith bus bar;

the power condition determining module is used for determining the charging power limit and the discharging power limit of the battery energy storage station through the following formulas, wherein the charging power limit and the discharging power limit are used as the charging power and the discharging power condition of the battery energy storage station:

μ _ch,i (t)+μ _dcg,i (t)≤1

wherein ,P_ch,i (t) represents the charging power of the battery energy storage station at the time t, mu _ch,i (t) a binary decision variable, μ, representing the ith bus charge decision at time t _dcg,i (t) a binary decision variable representing the discharge decision of the ith bus at time t, P _dch,i (t) represents a discharge of the battery energy storage station at time tThe power of the electric motor is calculated,

indicating the maximum discharge power of said battery energy storage station,/->

Representing the maximum charging power of the battery energy storage station;

the speed condition determining module is used for determining the charging speed limit and the discharging speed limit of the battery energy storage station through the following formulas, and the charging speed limit and the discharging speed limit are used as the charging speed and the discharging speed conditions of the battery energy storage station:

-T _S R _ch,i ≤P _ch,i (t)-P _ch,i (t-1)≤T _s R _ch,i

-T _S R _dch,i ≤P _dch,i (t)-P _dch,i (t-1)≤T _s R _dch,i

wherein ,T_S Representing the sampling time of the battery energy storage station, R _ch,i Indicating the charging speed of the battery energy storage station on the ith bus at time t, R _dch,i Indicating the discharge rate of the battery energy storage station on the ith bus at time t, P _ch,i (t) represents the charging power of the battery energy storage station at the time t, P _dch,i (t) represents the discharge power of the battery energy storage station at the time t, P _ch,i (t-1) represents the charging power of the battery energy storage station at the time t-1, P _dch,i (t-1) represents a discharge power representing the battery energy storage station at time t-1;

the power balance condition determining module is configured to determine a power balance of the edge cluster as the power balance condition by using the following formula:

a line flow condition determining module for calculating a set of power flows of transmission lines connected to buses i and j as line flow conditions by the following formula:

l _i,j (t)＝B _i,j (θ _i (t)-θ _j (t))

wherein ,

indicating that the j-th bus belongs to the set of all buses i, indicating the lower limit of the range, N indicating the upper limit of the range, l _i,j (t) represents the DC power flow of the transmission line connecting bus i and bus j at time t, B _i,j Representing susceptance, θ, of the line between buses i and j _i (t) represents the voltage phase angle of the bus i at time t, θ _j (t) represents the voltage phase angle of the bus j at time t, P _ch,i (t) represents the charging power of the battery energy storage station at the time t, P _d,i (t) represents the load demand of bus i in the battery energy storage station, P _pv,c,i (t) represents the photovoltaic reduction power, P, on bus i at time t _dch,i (t) represents the discharge power of the battery energy storage station at the time t, P _pv,T,i (t) represents the generated power of the solar photovoltaic power generation facility at time t, P _g,i (t) represents the exchange power of the ith bus and the power grid at the moment t;

the power grid power exchange condition determining module is used for determining the power bearing capacity range of the power flow on each line in the corresponding line through the following inequality as a power grid power exchange condition:

-l _i,j,min ≤l _i,j (t)≤l _i,j,max

a limitation range determining module for determining that the power exchange between the edge cluster and the grid is limited by the connection capacity by the following inequality:

P _g,i,min ≤P _g,i (t)≤P _g,i,max

a ramp rate limit determination module for determining that the power exchange of the grid at bus i imposes a ramp rate limit by the following inequality:

-T _s R _g,i ≤P _g,i (t)-P _g,i (t-1)≤T _s R _g,i

In one embodiment of the present invention, the error correction module 205 includes:

an error calculation module for calculating an error between the future power generation data and the future load data;

and the power adjustment module is used for adjusting photovoltaic power generation power in the edge cluster, charging data of battery energy storage stations, photovoltaic power reduction amount on buses, direct current power flow of transmission lines between buses, charging data, discharging power and exchange power between buses and the power grid with the aim of minimizing the error so as to keep balance between the future power generation data and the future load data.

In one embodiment of the present invention, the error calculation module includes:

a data error calculation module for calculating an error between the future power generation data and the future load data by the following formula:

indicating the maximum charging power of the battery energy storage station, < >>

Indicating the maximum discharge power of the battery energy storage station, l _i,j,max Represents the maximum capacity limit of bus i and j, |ΔP _g,i I represents the correction of the error by the maximum power exchange limit of the grid at busbar i, |Δp _ch,i I represents the correction amount of the charging power of the battery energy storage station to the error, |Δp _dch,i I represents a correction amount of the error by the discharge power of the battery energy storage station, |Δl _i,j I represents the correction of the error by the direct current power flow of the transmission lines of bus i and bus j of the battery energy storage station;

the power adjustment module includes:

the edge cluster optimization module is used for optimizing photovoltaic power generation power, charging data of the battery energy storage station, photovoltaic power reduction amount on the bus, direct current power flow of a transmission line between buses, charging data, discharging power and exchange power between the bus and the power grid in the edge cluster by taking the error as a target under the constraint of at least one of an edge cluster correction condition, a battery energy storage station adjustment condition, a power balance adjustment condition, a power flow constraint condition and the power grid output and/or input power adjustment condition;

The edge cluster correction condition determining module is used for determining the photovoltaic power generation power and the limited power constraint condition as an edge cluster correction condition by combining the following equation and the inequality based on the adjustment of the error on the photovoltaic power generation power and the limited power:

wherein ,P_pv,T,i (t) represents the generated power of the solar photovoltaic power generation device at time t, P _pv,i (t) represents the photovoltaic power used on the ith bus at time t, P _pv,c,i (t) represents the photovoltaic reduction power, ΔP, on the ith bus at time t _pv,c,i Representing the evidence of the photovoltaic reduction power on the ith bus at time t for the error,

representing the error, mu _pv,i (t) represents a binary decision quantity of the operating state of the solar photovoltaic power generation device at the ith bus at time t,/->

-tabulating a maximum power limit imposed by the inverter;

a battery energy storage station adjustment condition determining module, configured to determine, based on the adjustment of the error to the battery energy storage station, a constraint condition on the battery energy storage station as the battery energy storage station adjustment condition by combining the following equation with the inequality:

-T' _s R _ch,i ≤P _ch,i (t)+ΔP _ch,i -P _ch,i (t'-1)≤T' _s R _ch,i

-T' _s R _dch,i ≤P _dch,i (t)+ΔP _dch,i -P _dch,i (t'-1)≤T' _s R _dch,i

wherein ,

Representing the maximum state of charge, sigma, of the battery energy storage station of the ith bus _bi Representing the loss of self-discharging energy of the battery energy storage station, S _b,i (t '+1) represents the state of charge of the battery energy storage station of the ith bus at time t' +1, S _b,i (T ') represents the state of charge, T ', of the battery energy storage station of the ith bus at time T ' _s Representing the sampling time of the battery energy storage station, P _ch,i (t) represents the charging power of the battery energy storage station at time t, mu _ch,i (t) a binary decision variable, μ, representing the ith bus charging decision at the time t of the battery energy storage station _dcg,i (t) a binary decision variable representing the ith bus discharge decision at the t-th moment of the battery energy storage station, P _ch,i (t '-1) represents the charging power of the battery station at time t' -1, ΔP _ch,i Representing the correction amount of the charging power of the ith bus of the battery energy storage station to the error, P _dch,i (t) represents the discharge power of the battery energy storage station at time t, ΔP _dch,i Correction amount of charging power of ith bus bar of battery energy storage station to the error>

Representing the maximum discharge power, T 'of the battery energy storage station' _s Representing a sampling time;

a power balance adjustment condition determining module for determining if P _ch,i (t')＝P _ch,i +ΔP _ch,i Based on the adjustment of the power balance of the future power generation data and the future load data by the error, determining the power balance constraint condition of the future power generation data and the future load data by the following formula, and doing For the power balance adjustment conditions:

wherein ,P_d,i (t) represents the load demand of bus i at time t, σ _bi Representing the self-discharge energy loss of the battery energy storage station, P _pv,c,i (t) represents the photovoltaic reduction power, deltaP, on the ith bus at time t _pv,c,i Representing the correction of the photovoltaic reduction power on the ith bus to the error, P _ch,i (t) represents the charging power of the battery energy storage station at time t, ΔP _ch,i Representing the correction amount of the charging power of the ith bus of the battery energy storage station to the error, P _dch,i (t) represents the discharge power of the ith bus bar of the battery energy storage station at the moment t, and delta P _dch,i Representing the correction amount of the charging power of the ith bus of the battery energy storage station to the error, P _g,i (t) represents the exchange power between the ith bus and the power grid at the moment t, and delta P _g,i Representing the correction of the error by the exchange power of the ith bus and the power grid, P _g,i (t) represents the exchange power between the ith bus and the power grid at the moment t, P _pv,T,i (t) represents the power generated by the photovoltaic power generation facility at time t, l _i,j (t) represents the DC power flow, deltal, of the transmission line connecting bus i and bus j at time t _i,j Representing the correction of said error by the dc power flow of the transmission line connecting bus i and bus j,

load error correction amount- >

Representing the error;

the power flow constraint condition determining module is used for determining a power flow constraint condition through the following formula, and determining the power flow constraint condition as the power flow constraint condition through the following formula:

-l _i,j,max ≤l _i,j (t)+Δl _i,j ≤l _i,j,max

wherein ,l_i,j,min Representing the minimum capacity limit of buses i and j, l _i,j,max Representing the maximum capacity limit of buses i and j, l _i,j (t) represents the DC power flow, deltal, of the transmission line connecting bus i and bus j at time t _i,j Correction of the error for the direct current power flow of the transmission line connecting bus i and bus j;

the power adjustment condition determining module is configured to determine the power constraint condition as the power grid output power and/or input power adjustment condition based on the error and the power grid output power and/or input power adjustment by using the following formula:

P _g,i,min ≤P _g,i (t)+ΔP _g,i ≤P _g,i,max

-T _s R _g,i ≤P _g,i (t)+ΔP _g,i (t)-P _g,i (t'-1)≤T _s R _g,i

wherein ,P_g,i,min Representing minimum power exchange limit of the grid at bus i, P _g,i,max Representing maximum power exchange limit of the grid at bus i, P _g,i (t) represents the exchange power between the ith bus and the power grid at the moment t, and delta P _g,i (T) represents a correction amount of the error by the exchange power of the ith bus bar and the power grid, T _S Represents the sampling time, P _g,i (t '-1) represents the exchange power between the ith bus and the power grid at the moment t' -1, R _g,i Indicating a ramp rate limit.

The dynamic balance autonomous device of the power grid provided by the embodiment of the invention can execute the dynamic balance autonomous method of the power grid provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the dynamic balance autonomous method of the power grid.

Example III

Fig. 3 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 3, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the dynamic balancing autonomous method of the power grid.

In some embodiments, the dynamic balancing autonomy method of the power grid may be implemented as a computer program, tangibly embodied on a computer readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the dynamic balancing autonomous method of the power grid described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform a dynamic balancing autonomous method of the power grid by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

Example IV

Embodiments of the present invention also provide a computer program product comprising a computer program which, when executed by a processor, implements a dynamic balancing autonomous method of a power grid as provided by any of the embodiments of the present invention.

Computer program product in the implementation, the computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A method of dynamic balancing autonomy of an electrical grid, comprising:

determining an edge cluster in a power grid, wherein the edge cluster comprises a plurality of power supply devices and electric equipment;

predicting future load data of the electric equipment in the next time period;

2. The method of claim 1, wherein the power supply device comprises a solar photovoltaic power generation device and the future generation data comprises photovoltaic power generation;

the predicting future power generation data of the power supply device in a next time period includes:

generating a definition index for solar photovoltaic power generation equipment;

calculating solar irradiance absorbed by the solar photovoltaic power generation device during each time period based on the sharpness index;

taking solar irradiance absorbed by the solar photovoltaic power generation equipment as a time sequence, and performing Kalman filtering processing on the solar irradiance to obtain solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period;

and predicting the photovoltaic power generation power of the solar photovoltaic power generation equipment in the next time period based on the solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period.

3. The method of claim 2, wherein said calculating solar irradiance absorbed by the solar photovoltaic power plant during each time period based on the sharpness index comprises:

The solar irradiance is calculated by the following formula:

h _pv,t ＝s _t h _ex,t

calculating solar irradiance absorbed in the next time period to predict photovoltaic power generation power of the solar photovoltaic power generation equipment in the next time period according to the following formula:

4. The method according to claim 2, wherein the performing a kalman filter process on the solar irradiance as a time sequence to obtain solar irradiance absorbed by the solar photovoltaic power generation device in a next time period includes:

The time series is determined by the following equation:

h(k)＝a ₁ h(k-1)+a ₂ h(k-2)+……+a _m h(k-m)+a _m+1 h(k-m-1)+β _k

the following equations are used in parallel to rewrite the time series:

h ₁ (k)＝h(k),h ₂ (k)＝h(k-1),……,h _m+1 (k)＝h(k-m)

h(k+1)＝a ₁ h(k)+a ₂ h(k-1)+……+a _m+1 h(k-m)+β _k+1

determining the time sequence after overwriting by the following formula:

h ₁ (k+1)＝a ₁ h ₁ (k)+a ₂ h ₂ (k)+……+a _m+1 h _m+1 (k)+β _k+1

wherein ,h₁ (k) The said ether representing the kth time intervalIrradiance of yang, h ₂ (k) Representing the solar irradiance, h, of the kth-1 time interval _m+1 (k) Representing the solar irradiance of the kth-m time interval, h (k) representing the solar irradiance of the kth time interval, h (k-1) representing the solar irradiance of the kth-1 time interval, h (k-m) representing the solar irradiance of the kth-m time interval, beta _k+1 Representing residual error, h ₁ (k+1) represents the solar irradiance of the kth+1th time interval;

the state equation is rewritten by the following equation:

wherein H (k) represents the state vector of the solar irradiance in the kth time interval, H (k+1) represents the state vector of the solar irradiance in the kth+1 time interval,

representing the saidA state transmission matrix of the state equation, Γ (k+1, k) represents an excitation transfer matrix in the Kalman filter, ω (k) represents a system noise vector in the Kalman filter;

listing an observation equation by the following equation and performing Kalman filtering processing to obtain solar irradiance absorbed by the solar photovoltaic power generation device in the next time period:

wherein Z (k+1) represents an observation vector,

5. The method of claim 1, wherein the power supply device comprises a solar photovoltaic power generation device, the edge cluster further comprising at least one of a load, a battery storage station;

the optimizing the future power generation data and the future load data with the operation cost of the edge cluster as an optimization target includes:

determining the running cost of the edge cluster by the following formula:

wherein i represents an ith bus bar, 1 represents a lower limit of the range, N represents an upper limit of the range, J _c (t) represents the running cost in the edge cluster, J _p Representing potential benefits of charging or discharging the battery energy storage station, J representing operating costs, t representing a time range from k+1 to k+m;

determining the running cost of the edge cluster and the potential benefit by the following formula:

J _c (t)＝π _g (t)P _g,i (t)-π _d (t)P _d,i (t)T _s

J _p ＝(S _b,i (k+M)-S _b,i (k))π _g,avg (k)

under the constraint of at least one of the conditions of the generated power of the solar photovoltaic power generation equipment, the maximum power condition applied by an inverter, the charging state condition of the battery energy storage station, the discharging state condition of the battery energy storage station, the charging power of the battery energy storage station, the discharging power condition, the charging speed of the battery energy storage station, the discharging speed condition, the power exchange condition of a power grid, the line flow condition and the power balance condition, the operation cost of the edge cluster is minimum, and the future power generation data and the future load data are optimized;

P _pv,T,i (t)＝P _pv,i (t)+P _pv,c,i (t)

wherein ,P_pv,T,i (t) represents the generated power of the solar photovoltaic power generation device at time t, P _pv,i (t) represents the photovoltaic power used on the ith bus at time t, P _pv,c,i (t) representsPhotovoltaic power reduction is carried out on the ith bus at the moment t;

Determining the maximum power applied by the inverter as a maximum power condition applied by the inverter by the following formula:

-tabulating a maximum power limit imposed by the inverter;

determining a state of charge of the battery energy storage station as a state of charge condition of the battery energy storage station by linking the following equation with the inequality:

/>

S _bi (t+(M-1))＝S _bi,ini

wherein ,S_b,i (t+1) represents the state of charge of the battery energy storage station of the ith bus at time t+1, S _bi (t+ (M-1)) represents the state of charge of the battery energy storage station of the ith bus at time t+ (M-1), S _b,i (t) represents the state of charge, σ, of the battery station at the ith bus at time t _bi Representing the self-discharge energy loss of the battery energy storage station, T _s Representing the sampling time, eta, of the battery energy storage station _ch,i Representing the charging efficiency, P, of the battery energy storage station on the ith bus _ch,i (t) represents the batteryCharging power of energy storage station at t moment, P _dch,i (t) represents the discharge power, eta of the battery energy storage station at the moment t _dch,i The representation represents the discharge efficiency of the battery energy storage station on the ith bus,

μ _ch,i (t)+μ _dcg,i (t)≤1

wherein ,P_ch,i (t) represents the charging power of the battery energy storage station at the time t, mu _ch,i (t) a binary decision variable, μ, representing the ith bus charge decision at time t _dcg,i (t) a binary decision variable representing the discharge decision of the ith bus at time t, P _dch,i t represents the discharge power of the battery energy storage station at the time t,

Representing the maximum charging power of the battery energy storage station;

and determining the charging speed limit and the discharging speed limit of the battery energy storage station by the following formulas, wherein the charging speed limit and the discharging speed limit are used as the conditions of the charging speed and the discharging speed of the battery energy storage station:

-T _S R _ch,i ≤P _ch,i (t)-P _ch,i (t-1)≤T _s R _ch,i

-T _S R _dch,i ≤P _dch,i (t)-P _dch,i (t-1)≤T _s R _dch,i

determining the power balance of the edge cluster as the power balance condition by the following formula:

l _i,j (t)＝B _i,j (θ _i (t)-θ _j (t))

wherein ,

indicating that the j-th bus belongs toIn the set of all buses i, the lower limit of the range is represented, N represents the upper limit of the range, l _i,j (t) represents the DC power flow of the transmission line connecting bus i and bus j at time t, B _i,j Representing susceptance, θ, of the line between buses i and j _i (t) represents the voltage phase angle of the bus i at time t, θ _j (t) represents the voltage phase angle of the bus j at time t, P _ch,i (t) represents the charging power of the battery energy storage station at the time t, P _d,i (t) represents the load demand of bus i in the battery energy storage station, P _pv,c,i (t) represents the photovoltaic reduction power, P, on bus i at time t _dch,i (t) represents the discharge power of the battery energy storage station at the time t, P _pv,T,i (t) represents the generated power of the solar photovoltaic power generation facility at time t, P _g,i (t) represents the exchange power of the ith bus and the power grid at the moment t;

determining the power carrying capacity range of the power flow on each line in the corresponding line as a power exchange condition of the power grid through the following inequality:

-l _i,j,min ≤l _i,j (t)≤l _i,j,max

P _g,i,min ≤P _g,i (t)≤P _g,i,max

-T _s R _g,i ≤P _g,i (t)-P _g,i (t-1)≤T _s R _g,i

wherein ,T_s Represents the sampling time, P _g,i (t) represents the exchange power between the ith bus and the power grid at the moment t, P _g,i (t-1) represents the exchange power between the ith bus and the power grid at the moment t-1, R _g,i Indicating a limit of ramp speed, l _i,j,min Representing the minimum capacity limit of buses i and j, l _i,j,max Representing the maximum capacity limit of buses i and j, l _i,j (t) represents the DC power flow of the transmission line connecting bus i and bus j at time t, P _g,i,min Representing the grid at bus iMinimum power exchange limit, P _g,i,max Representing maximum power exchange limit of the grid at bus i, P _g,i And (t) represents the exchange power of the ith bus and the power grid at the moment t.

6. The method of any of claims 1-5, wherein correcting the error between the future power generation data and the future load data to balance the future power generation data with the future load data comprises:

Calculating an error between the future power generation data and the future load data;

and aiming at minimizing the error, adjusting photovoltaic power generation power in the edge cluster, charging data of a battery energy storage station, photovoltaic power reduction amount on a bus, direct current power flow of a transmission line between buses, charging data, discharging power and exchange power between the bus and the power grid so as to keep balance between the future power generation data and the future load data.

7. The method of claim 6, wherein said calculating an error between said future power generation data and said future load data comprises:

calculating an error between the future power generation data and the future load data by the following formula:

the adjusting, with the objective of minimizing the error, the photovoltaic power generation power in the edge cluster, the charging data of the battery energy storage station, the photovoltaic power reduction amount on the bus, the direct current power flow of the transmission line between buses, the charging data, the discharging power, and the exchange power between the bus and the power grid, so as to keep the future power generation data balanced with the future load data, includes:

under the constraint of at least one of an edge cluster correction condition, a battery energy storage station adjustment condition, a power balance adjustment condition, a power flow constraint condition and a power grid output and/or input power adjustment condition, taking the error as a target, optimizing photovoltaic power generation power, charging data of the battery energy storage station, photovoltaic power reduction amount on a bus, direct current power flow of a transmission line between buses, charging data, discharging power and exchange power between the bus and the power grid in the edge cluster;

Wherein, based on the adjustment of the error to the photovoltaic power generation power and the limited power, the photovoltaic power generation power and the limited power constraint condition are determined by combining the following equation and the inequality as an edge cluster correction condition:

-tabulating a maximum power limit imposed by the inverter;

based on the adjustment of the error to the battery energy storage station, determining a constraint condition to the battery energy storage station as the battery energy storage station adjustment condition by combining the following equation with the inequality:

-T' _s R _ch,i ≤P _ch,i (t)+ΔP _ch,i -P _ch,i (t'-1)≤T' _s R _ch,i

-T' _s R _dch,i ≤P _dch,i (t)+ΔP _dch,i -P _dch,i (t'-1)≤T' _s R _dch,i

wherein ,

if P _ch,i (t')＝P _ch,i +ΔP _ch,i And based on the adjustment of the power balance of the future power generation data and the future load data by the error, determining a power balance constraint condition of the future power generation data and the future load data by the following formula as a power balance adjustment condition:

load error correction amount->

Representing the error;

determining a power flow constraint condition by the following formula, and determining the power flow constraint condition by the following formula as the power flow constraint condition:

-l _i,j,max ≤l _i,j (t)+Δl _i,j ≤l _i,j,max

determining the power constraint condition as the grid output power and/or input power adjustment condition based on the error on the grid output power and/or input power adjustment by the following formula:

P _g,i,min ≤P _g,i (t)+ΔP _g,i ≤P _g,i,max

-T _s R _g,i ≤P _g,i (t)+ΔP _g,i (t)-P _g,i (t'-1)≤T _s R _g,i

8. A dynamically balanced autonomous device of an electrical network, comprising:

the edge cluster determining module is used for determining an edge cluster in the power grid, and the edge cluster comprises a plurality of power supply devices and electric equipment;

the power generation data prediction module is used for predicting future power generation data of the power supply equipment in the next time period;

9. An electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the dynamic balancing autonomous method of the electrical grid of any of claims 1-7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for causing a processor to implement a dynamically balanced autonomous method of an electrical network according to any of claims 1-7 when executed.