A method, device, equipment and storage medium for autonomous dynamic balance of power grid

Abstract

The present invention discloses a dynamic balance autonomous method, device, equipment and storage medium for a power grid, the method comprising: predicting the future power generation data of the power supply equipment in the next time period; predicting the future load data of the power consumption equipment in the next time period; optimizing the future power generation data and the future load data with the operation cost of the edge cluster as the optimization goal; if the optimization is completed, correcting the error between the future power generation data and the future load data so that the future power generation data and the future load data are balanced. By predicting and monitoring the future load data and the future power generation data, the distributed energy is managed to achieve the autonomy of the edge cluster and give full play to the flexibility of various distributed energy sources. According to the prediction of the future load data of the power consumption equipment and the future power generation data of the power supply equipment in the power grid, the operation of the power grid is coordinated to improve the efficiency of energy use, and the distributed energy is efficiently managed and controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of power grids, and in particular to a dynamic balance autonomous method, device, equipment and storage medium for a power grid. Background Art

[0002] Low-voltage distributed energy is connected to the power grid on a large scale. Low-voltage distributed energy supports energy independence, promotes sustainability, and can save costs for consumers. In some areas, the energy directly transmitted from the distribution layer is now more than that from the transmission layer. However, the single capacity of low-voltage distributed energy is small, and the access voltage level is low. Most of them are in a state of "invisible, unmeasurable, and uncontrollable".

[0003] In order to give full play to the flexibility of various distributed energies, improve energy efficiency, and coordinate the operation between the supply and use of multiple energies and the power grid, model predictive control uses a display model to predict the future response of the system, so as to adjust the control strategy over time. And different prediction methods can be used, such as artificial neural networks, time series models, grey models, and Kalman filter algorithms, to predict the uncertain renewable energy power generation and load forecasting.

[0004] Time series models and neural network models train models for predicting renewable energy power generation and load demand through a large amount of historical data, which increases the cost of time in real-time applications. They lack consideration of factors such as load and photovoltaic power fluctuations, and lack consideration of prediction errors and real-time feedback correction for renewable energy power generation and load demand in real-time applications as compensation. Summary of the Invention

[0005] The present invention provides a dynamic balance autonomous method, device, equipment and storage medium for a power grid to solve the prediction error compensation for renewable energy power generation and load demand.

[0006] According to an aspect of the present invention, a dynamic balance autonomous method for a power grid is provided. The method includes: determining an edge cluster in the power grid, where the edge cluster includes a plurality of power supply devices and power consumption devices;

[0007] Predicting the future power generation data of the power supply devices in the next time period;

[0008] Predicting the future load data of the power consumption devices in the next time period;

[0009] Taking the operating cost of the edge cluster as the optimization goal, optimizing the future power generation data and the future load data;

[0010] If the optimization is completed, correcting the error between the future power generation data and the future load data to make the future power generation data and the future load data balanced.

[0011] According to another aspect of the present invention, there is provided a dynamic balance autonomous device for a power grid, the device comprising: a power generation data prediction module for predicting future power generation data of the power supply device in the next time period;

[0012] a load data prediction module for predicting future load data of the power consumption device in the next time period;

[0013] a data optimization module for optimizing the future power generation data and the future load data with the operating cost of the edge cluster as the optimization target;

[0014] an error correction module for correcting the error between the future power generation data and the future load data if the optimization is completed, so as to keep the future power generation data and the future load data balanced.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the dynamic balance autonomous method of the power grid according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, there is provided a computer-readable storage medium storing a computer program for causing a processor to implement the dynamic balance autonomous method of the power grid according to any embodiment of the present invention when executed.

[0020] In this embodiment, future power generation data of a power supply device in the next time period is predicted; future load data of an electrical device in the next time period is predicted; the future power generation data and the future load data are optimized with the operating cost of an edge cluster as the optimization objective; if the optimization is completed, the error between the future power generation data and the future load data is corrected so that the future power generation data and the future load data are balanced. By predicting and monitoring the future load data and the future power generation data, distributed energy is managed to achieve edge cluster autonomy and give full play to the flexibility of various distributed energy sources. According to the predicted future load data of electrical devices in the power grid and the future power generation data of power supply devices, the operation with the power grid is coordinated, the energy utilization efficiency is improved, distributed energy is efficiently managed and controlled, the prediction accuracy of the future load data and the future power generation data within the prediction range is improved, and real-time operation of the edge cluster is supported, so as to minimize the real-time mismatch between supply and demand and achieve balanced autonomy between power generation and load in the power grid.

[0021] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 is a flowchart of a method for dynamic balance autonomy of a power grid according to Embodiment 1 of the present invention;

[0024] Figure 2 is a schematic structural diagram of a device for dynamic balance autonomy of a power grid according to Embodiment 2 of the present invention;

[0025] Figure 3 is a schematic structural diagram of an electronic device for implementing Embodiment 3 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] The time series method is a statistical analysis method for predicting future development trends based on historical power generation data series and historical load data series over a certain period of time. It is also known as the time series trend extrapolation method and is applicable to the prediction of practical affairs in a continuous process.

[0029] The Kalman filter algorithm is an algorithm that uses a linear system state equation to optimally estimate the state to be measured by inputting or outputting historical power generation and historical power consumption data. Since the observed data includes the influence of noise and interference, the optimal estimate can also be regarded as a filtering process. Data filtering is a data processing method for removing noise and restoring real data. The Kalman filter can estimate the state of a dynamic edge cluster station from a series of historical power generation data and historical load data with measurement noise when the measurement variance is known.

[0030] In the case of a large distribution of distributed resources, it is assumed that the resource output of each cluster is the same, while the resource outputs of different clusters vary greatly. The clusters are respectively wind turbine generators, photovoltaic units, combined heat and power units, generator sets, battery packs, etc. For the power grid, an edge cluster aggregation participating in the power grid regulation scenario is established based on functions and energy-consuming sites.

[0031] In the power grid, the future power generation data of power supply equipment and the future load data of power consumption equipment are predicted. First, edge clusters are determined in the power grid, and the edge clusters include multiple power supply equipment and power consumption equipment.

[0032] The energy dynamic balance process in the edge cluster is divided into a predictive control part and a feedback correction part. In the predictive control part, at each time interval, the future power generation data of the power supply equipment and the future load data of the power consumption equipment are predicted, and the information obtained by the prediction is used to minimize the operating cost of the edge cluster within a given time range. In the predictive control part, the result of the first time interval is applied, and the time range is moved forward to the next time interval. In the feedback correction part, within each time interval predicted in the predictive control part, feedback control with error correction is applied multiple times to adjust the energy output of the edge cluster to balance the mismatch between the predicted future power generation data, future load data and the actual power generation data, actual load data within the time range. The output of all the energy of the edge cluster is adjusted by the feedback correction part according to the monitoring data to maintain optimal control while allowing for the uncertainty of future power generation data and future load data.

[0033] Embodiment 1

[0034] Figure 1 The flowchart of a dynamic balance autonomous method for a power grid provided in Embodiment 1 of the present invention. This embodiment is applicable to predicting the future power generation data of power supply equipment and the future load data of power consumption equipment in a power grid to achieve the situation of dynamic balance autonomy of the power grid energy. This method can be executed by a dynamic balance autonomous device of the power grid. The dynamic balance autonomous device of the power grid can be implemented in the form of hardware and / or software, and the dynamic balance autonomous device of the power grid can be configured in an electronic device. As Figure 1 shown, the method includes:

[0035] Step 101, determine an edge cluster in the power grid. The edge cluster includes multiple power supply equipment and power consumption equipment.

[0036] In the power grid, for the prediction of the future power generation data of solar photovoltaic power generation equipment and the future load data of power consumption equipment, there are different prediction methods, such as artificial neural networks, time series models, grey models and Kalman filter algorithms to determine the future power generation data and future load data. In addition, different prediction methods have different data requirements and usability for future power generation data and future load data. Among them, time series and neural network models train prediction models through a large amount of historical power generation data and historical load data.

[0037] However, the Kalman filter algorithm and the grey model can establish prediction models through a small amount of historical power generation data and historical load data, and are more suitable for the prediction application of future power generation data and future load data in the power grid. In an example of the present invention, a hybrid algorithm based on the time series method and the Kalman filter algorithm is used to predict the future power generation data of power supply equipment, the future load data of power consumption equipment and the output of renewable energy in the power grid.

[0038] Step 102: Predict the future power generation data of the power supply equipment in the next time period.

[0039] Within each time interval, use the updated historical power generation data and historical load data to predict the future power generation data of the power supply equipment and the future load data of the power consumption equipment within the future time range.

[0040] Furthermore, in the edge cluster, select the power supply equipment and predict the future power generation data of the corresponding power supply equipment for the next time period.

[0041] In an embodiment of the present invention, Step 102 may include the following steps:

[0042] Step 1021: Generate a clarity index for the solar photovoltaic power generation equipment.

[0043] Based on the updated historical power consumption data and historical load data, use the obtained prediction information in the optimized plan.

[0044] The power supply equipment includes solar photovoltaic power generation equipment, and the future power generation data includes photovoltaic power generation power.

[0045] When predicting the future power generation data of the solar photovoltaic power generation equipment, first generate a clarity index for the solar photovoltaic power generation equipment. The photovoltaic power generation power is determined according to the clarity index, and the clarity index is calculated from the solar irradiance.

[0046] Among them, the reasons for the uncertainty of solar irradiance are: changing cloudy weather conditions.

[0047] Step 1022: Calculate the solar irradiance absorbed by the solar photovoltaic power generation equipment in each time period based on the clarity index.

[0048] The solar irradiance absorbed by the solar photovoltaic power generation equipment is calculated according to the extraterrestrial irradiance and the clarity index.

[0049] The photovoltaic power generation power is determined according to the clarity index, the clarity index is calculated from the solar irradiance, and the solar irradiance absorbed by the solar photovoltaic power generation equipment in each time period is calculated based on the clarity index. The extraterrestrial solar irradiance can be derived according to the geographical information of the site and the Earth's orbit, and the clarity index can be generated using the convex hull algorithm.

[0050] Calculate the solar irradiance through the following formula:

[0051] h pv,t =s t h ex,t

[0052] Among them, h pv,tRepresents the solar irradiance absorbed by the solar photovoltaic power generation equipment at time t, s t represents the clarity index, h ex,t represents the extraterrestrial solar irradiance at time t.

[0053] Step 1023: Take the solar irradiance absorbed by the solar photovoltaic power generation equipment as a time series, perform Kalman filtering on the solar irradiance, and obtain the solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period.

[0054] Since the data in solar irradiance is one-dimensional, a time series can be determined.

[0055] The time series is determined by the following equation:

[0056] h(k)=a 1 h(k-1)+a 2 h(k-2)+……+a m h(km)+a m+1 h(km-1)+β k

[0057] Wherein, h(k) represents the solar irradiance at the kth time interval, h(k-1) represents the solar irradiance at the k-1th time interval, h(k-2) represents the solar irradiance at the k-2th time interval, h(km) represents the solar irradiance at the kmth time interval, and a 1 、a 2 ……a m 、a m+1 represents the coefficient, β k represents residual;

[0058] Rewrite the time series by combining the following equations:

[0059] h 1 (k)=h(k),h 2 (k)=h(k-1),……,h m+1 (k)=h(km)

[0060] h(k+1)=a 1 h(k)+a 2 h(k-1)+……+a m+1 h(km)+β k+1

[0061] The rewritten time series is determined by the following formula:

[0062] h 1 (k+1)=a 1 h 1 (k)+a 2 h​​2 (k) + …… + a m+1 h m+1 (k) + β k+1

[0063] where h 1 (k) represents the solar irradiance at the k-th time interval, h 2 (k) represents the solar irradiance at the (k - 1)-th time interval, h m+1 (k) represents the solar irradiance at the (k - m)-th time interval, h(k) represents the solar irradiance at the k-th time interval, h(k - 1) represents the solar irradiance at the (k - 1)-th time interval, h(k - m) represents the solar irradiance at the (k - m)-th time interval, β k+1 represents the residual, h 1 (k + 1) represents the solar irradiance at the (k + 1)-th time interval;

[0064] Let h 2 (k) = h(k + 1), ……, h m+1 (k) = h(k + m), and determine the state equation through the following formula:

[0065]

[0066] where ω k represents the system noise vector in the Kalman filter, h 1 (k + 1) represents the solar irradiance at the first period in the (k + 1)-th period, h 2 (k + 1) represents the solar irradiance at the second period in the (k + 1)-th period, h m (k + 1) represents the solar irradiance at the m-th period in the (k + 1)-th period, h m+1 (k + 1) represents the solar irradiance at the (m + 1)-th period in the (k + 1)-th period, h 2 (k) represents the solar irradiance at the (k - 1)-th time interval, h 1 (k) represents the solar irradiance at the k-th time interval, h m+1 (k) represents the solar irradiance at the (k - m)-th time interval;

[0067] Rewrite the state equation through the following equation:

[0068]

[0069] where H(k) is the state vector of the solar irradiance within the k-th time interval, H(k + 1) represents the state vector of the solar irradiance within the (k + 1)-th time interval, The state transfer matrix representing the state equation, Γ(k + 1, k) represents the excitation transfer matrix in the Kalman filter, and ω(k) represents the system noise vector in the Kalman filter;

[0070] List the observation equation through the following equation and perform Kalman filtering processing to obtain the solar irradiance absorbed by the solar photovoltaic power generation device in the next time period:

[0071]

[0072] Among them, Z(k + 1) represents the observation vector, represents the predicted output transfer matrix, H(k + 1) represents the observation vector in the Kalman filter, and n(k + 1) represents the measurement noise in the Kalman filter.

[0073] Since the data of the solar irradiance is one-dimensional, the time series can be determined. Based on the time series, the observation equation of the Kalman filter is obtained. Perform Kalman filtering processing on the solar irradiance to obtain the solar irradiance absorbed by the solar photovoltaic power generation device in the next time period.

[0074] Step 1024: Predict 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.

[0075] The solar irradiance absorbed by the solar photovoltaic power generation device in the next time period is determined by the observation equation of the Kalman filter. Predict 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.

[0076] Furthermore, the power generated by the solar irradiance absorbed by the solar photovoltaic power generation device can be calculated based on the solar irradiance, the rated capacity, and the rated solar irradiance.

[0077] Calculate the power generated by the solar photovoltaic power generation device through the following formula:

[0078]

[0079] Among them, P pv,r represents the rated capacity of the solar photovoltaic power generation device, h r represents the rated solar irradiance, h pv,t represents the power generated by the solar irradiance absorbed by the solar photovoltaic power generation device at time t, P pv,t represents the power of the solar irradiance absorbed by the solar photovoltaic power generation device.

[0080] Step 103: Predict the future load data of the electrical equipment in the next time period.

[0081] In each time interval, the future load data and the photovoltaic power generation of the solar photovoltaic power generation device are predicted. After predicting the solar photovoltaic power generation based on the solar irradiance absorbed by the solar photovoltaic power generation device in the next time period, the future load data of the electrical equipment in the next time period is predicted.

[0082] Within each predicted time interval, feedback with error correction is applied to adjust the energy output of the remote cluster to balance the mismatch between the predicted value and the actual value. The output of all the energy of the edge cluster is adjusted according to the predicted future load data and photovoltaic power generation to maintain optimal control while allowing for the uncertainty of future generation data and future load data requirements.

[0083] Step 104: Optimize the future generation data and future load data with the operating cost of the edge cluster as the optimization objective.

[0084] When a prediction is obtained within a specific finite time range, optimization is performed. The edge cluster includes distributed photovoltaic power generation, loads, and battery energy storage stations, is connected to a distribution substation and operates through a communication network. There is a two-way energy flow in the substation, and the optimization program is represented as a mixed-integer linear programming problem.

[0085] Predict and optimize the future generation data and future load data within each time interval. The objective of this optimization is to minimize the operating cost within a fixed future time range.

[0086] Exemplarily, predict and optimize the future generation data and future load data within each time interval k. Starting from time interval k and before time interval k + 1, according to the prediction of the future generation data and future load data within the future time range (from time interval k + 1 to time interval k + M), but only time interval k + 1 in the time schedule of the time intervals has been implemented. M represents the length of the prediction range, and k represents the time interval. Similarly, in the next time interval, the time range moves forward by one time interval, and re-prediction and optimization are performed again based on the latest information. The optimization process takes into account future time intervals, so the control implementation remains optimal.

[0087] Among them, the future generation data is predicted based on the historical data of the power supply equipment.

[0088] The power supply equipment includes at least one of a solar photovoltaic power generation device, a load, and a battery energy storage station in the edge cluster.

[0089] The operating cost of the edge cluster is determined by the following formula:

[0090]

[0091] Among them, i represents the i-th bus, 1 represents the lower limit of the range, N represents the upper limit of the range, and J c (t) represents the operating cost in the edge cluster, and J p represents the potential benefit of charging or discharging the battery energy storage station. J represents the operating cost, and t represents the time range from k + 1 to k + M;

[0092] The operating cost and potential benefit of the edge cluster are determined by the following formula:

[0093] J c (t) = π g (t)P g,i (t) - π d (t)P d,i (t)T s

[0094] J p = (S b,i (k + M) - S b,i (k))π g,avg (k)

[0095] Among them, J c (t) represents the operating cost in the edge cluster at time t, and J p represents the potential benefit of charging or discharging the battery energy storage station, π g (t) represents the energy price at time t, P g,i (t) represents the load demand of the i-th bus at time t, π d (t) represents the electricity price corresponding to the load demand of the i-th bus at time t, T s represents the sampling time, S b,i (k + M) represents the charging state of the battery energy storage station of the i-th bus at the (k + M)-th moment, π g,avg (k) represents the average value of the load demand price within the k-th moment, S b,i (k) represents the charging state of the battery energy storage station of the i-th bus at the k-th moment, P d,i (t) represents the load demand of the i-th bus at the t-th moment.

[0096] Under the constraints of at least one of the conditions of the power generation power condition of the solar photovoltaic power generation device, 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 and discharging power conditions of the battery energy storage station, the charging speed and discharging speed conditions of the battery energy storage station, the grid power exchange condition, the line flow condition, and the power balance condition, with the goal of minimizing the operating cost of the edge cluster, the future power generation data and future load data are optimized.

[0097] Among them, the power generated by the solar photovoltaic power generation device is determined by the following formula as the power generation power condition of the solar photovoltaic power generation device:

[0098] P pv,T,i (t) = P pv,i (t) + P pv,c,i (t)

[0099] Among them, P pv,T,i (t) represents the power generation power of the solar photovoltaic power generation device at time t, P pv,i (t) represents the photovoltaic power used on the i-th bus at time t, P pv,c,i (t) represents the photovoltaic reduction power on the i-th bus at time t.

[0100] The maximum power applied by the inverter is determined by the following formula as the maximum power application condition of the inverter:

[0101]

[0102] Among them, P pv,T,i (t) represents the power generation power of the solar photovoltaic power generation device at time t, μ pv,i (t) represents the binary decision variable of the operating state of the solar photovoltaic power generation device at the i-th bus at time t, Table represents the maximum power limit applied by the inverter.

[0103] The charging state of the battery energy storage station is determined by connecting the following equations and inequalities as the charging state condition of the battery energy storage station:

[0104]

[0105]

[0106] S bi (t + (M - 1)) = S bi,ini

[0107] Among them, S b,i (t + 1) represents the charging state of the battery energy storage station on the i-th bus at time t + 1, S bi (t + (M - 1)) represents the charging state of the battery energy storage station on the i-th bus at time t + (M - 1), S b,i (t) represents the charging state of the battery energy storage station on the i-th bus at time t, σ bi represents the self-discharge energy loss of the battery energy storage station, T s represents the sampling time of the battery energy storage station, η ch,i represents the charging efficiency of the battery energy storage station on the i-th bus, P ch,i (t) represents the charging power of the battery energy storage station at time t, Pdch,i (t) represents the discharge power of the battery energy storage station at time t, η dch,i represents the discharge efficiency of the battery energy storage station on the i-th bus, represents the minimum charging state of the battery energy storage station on the i-th bus, represents the maximum charging state of the battery energy storage station on the i-th bus, S bi,ini represents the initial charging state of the battery energy storage station, S b,i (t + 1) represents the charging state of the battery energy storage station on the i-th bus at time t + 1.

[0108] The charging power limit and discharge power limit of the battery energy storage station are determined by the following formula, serving as the charging power and discharge power conditions of the battery energy storage station:

[0109]

[0110]

[0111] μ ch,i (t) + μ dcg,i (t) ≤ 1

[0112] Among them, P ch,i (t) represents the charging power of the battery energy storage station at time t, μ ch,i (t) represents the binary decision variable for the charging decision of the i-th bus at time t, μ dcg,i (t) represents the binary decision variable for the discharging decision of the i-th bus at time t, P dch,i (t) represents the discharge power of the battery energy storage station at time t, represents the maximum discharge power of the battery energy storage station, represents the maximum charging power of the battery energy storage station.

[0113] The charging speed limit and discharge speed limit of the battery energy storage station are determined by the following formula, serving as the charging speed and discharge speed conditions of the battery energy storage:

[0114] -T S R ch,i ≤ P ch,i (t) - P ch,i (t - 1) ≤ T s R ch,i

[0115] -T S R dch,i ≤ P dch,i (t) - P dch,i (t - 1) ≤ T s R dch,i

[0116] Among them, T S represents the sampling time of the battery energy storage station, and R ch,i represents the charging rate of the battery energy storage station on the i-th bus at time t, and R dch,i represents the discharging rate of the battery energy storage station on the i-th bus at time t, and P ch,i (t) represents the charging power of the battery energy storage station at time t, and P dch,i (t) represents the discharging power of the battery energy storage station at time t, and P ch,i (t - 1) represents the charging power of the battery energy storage station at time t - 1, and P dch,i (t - 1) represents the discharging power of the battery energy storage station at time t - 1.

[0117] The power balance of the edge cluster is determined by the following formula as the power balance condition:

[0118]

[0119] The power flow of a group of transmission lines connected to buses i and j is calculated by the following formula as the line flow condition:

[0120] l i,j (t) = B i,j (θ i (t) - θ j (t))

[0121] Among them, represents the lower limit of the range in the set of all buses i to which the j-th bus belongs, N represents the upper limit of the range, and l i,j (t) represents the DC power flow of the transmission line connecting bus i and bus j at time t, and B i,j represents the susceptance of the line between buses i and j, and θ i (t) represents the voltage phasor angle of bus i at time t, and θ j (t) represents the voltage phasor angle of bus j at time t, and P ch,i (t) represents the charging power of the battery energy storage station at time t, and P d,i (t) represents the load demand of bus i in the battery energy storage station, and P pv,c,i (t) represents the PV curtailment power on bus i at time t, and P dch,i (t) represents the discharging power of the battery energy storage station at time t, and P pv,T,i (t) represents the power generation of the solar photovoltaic power generation device at time t, and P g,i (t) represents the exchanged power between the i-th bus and the power grid at time t.

[0122] The power flow on each line is determined within the power carrying capacity range of the corresponding line by the following inequality as the power grid power exchange condition:

[0123] -l i,j,min ≤l i,j (t)≤l i,j,max

[0124] The power exchange between the edge cluster and the power grid is determined by the following inequality to be limited by the connection capacity:

[0125] P g,i,min ≤P g,i (t)≤P g,i,max

[0126] The power exchange of the power grid at bus i is determined by the following inequality to be subject to a ramp rate limit:

[0127] -T s R g,i ≤P g,i (t)-P g,i (t - 1)≤T s R g,i

[0128] where T s represents the sampling time, P g,i (t) represents the power exchange of the i-th bus with the power grid at time t, P g,i (t - 1) represents the power exchange of the i-th bus with the power grid at time t - 1, R g,i represents the limit of the ramp rate, l i,j,min represents the minimum capacity limit of buses i and j, l i,j,max represents 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 represents the minimum power exchange limit of the power grid at bus i, P g,i,max represents the maximum power exchange limit of the power grid at bus i, P g,i (t) represents the power exchange of the i-th bus with the power grid at time t.

[0129] Step 105: If the optimization is completed, correct the error between the future generation data and the future load data so that the future generation data and the future load data are balanced.

[0130] If the optimization is completed, correct the error between the future generation data and the future load data based on the static model so that the future generation data and the future load data are balanced.

[0131] Feedback control with error correction does not interrupt scheduling, but adjusts the output of the solar photovoltaic power generation equipment according to the error between future power generation data and future load data. This feedback control is a static model that only adjusts the output of the solar power generation equipment 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 resilience of the edge cluster.

[0132] When correcting the error between future power generation data and future load data, first calculate the error between future power generation data and future load data.

[0133] The error between future power generation data and future load data is calculated by the following formula:

[0134]

[0135] where F represents the total adjustment of the edge prediction plan fixed point, ω 1 、ω 2 represent weight coefficients, P g,i,max represents the maximum power exchange limit of the power grid at bus i, represents the maximum charging power of the battery energy storage station, represents the maximum discharge power of the battery energy storage station, l i,j,max represents the maximum capacity limit of buses i and j, |ΔP g,i | represents the correction amount of the error for the maximum power exchange limit of the power grid at bus i, |ΔP ch,i | represents the correction amount of the error for the charging power of the battery energy storage station, |ΔP dch,i | represents the correction amount of the error for the discharge power of the battery energy storage station, |Δl i,j | represents the correction amount of the error for the DC power flow of the transmission line between bus i and bus j of the battery energy storage station.

[0136] Furthermore, with the goal of minimizing the error, the photovoltaic power generation in the edge cluster, the charging data of the battery energy storage station, the reduction amount of photovoltaic power on the bus, the DC power flow of the transmission line between buses, the charging data, the discharge power, and the exchange power between the bus and the power grid are adjusted to balance the future power generation data and future load data.

[0137] Under the constraints of at least one of the edge cluster correction conditions, the battery energy storage station adjustment conditions, the power balance adjustment conditions, the grid output and / or input power adjustment conditions, with the error as the goal, the photovoltaic power generation in the edge cluster, the charging data of the battery energy storage station, the reduction amount of photovoltaic power on the bus, the DC power flow of the transmission line between buses, the charging data, the discharge power, and the exchange power between the bus and the power grid are optimized.

[0138] Among them, the photovoltaic power generation power and power limit are adjusted based on the error, and the photovoltaic power generation power and power limit constraints are determined by combining the following equations and inequalities as the edge cluster correction conditions:

[0139]

[0140]

[0141] Among them, P pv,T,i (t) represents the power generation of the solar photovoltaic power generation equipment at time t, P pv,i (t) represents the photovoltaic power used on the i-th bus at time t, P pv,c,i (t) represents the photovoltaic power reduction on the i-th bus at time t, ΔP pv,c,i represents the error of photovoltaic power reduction on the ith bus at time t, indicates error, μ pv,i (t) represents the binary decision quantity of the operating state of the solar photovoltaic power generation equipment at the ith bus at time t, The maximum power limit imposed by the table inverter.

[0142] Based on the error, the battery energy storage station is adjusted. By combining the following equations and inequalities, the constraints on the battery energy storage station are determined as the adjustment conditions of the battery energy storage station:

[0143]

[0144]

[0145]

[0146]

[0147] -T' s R ch,i ≤P ch,i (t)+ΔP ch,i -P ch,i (t'-1)≤T' s R ch,i

[0148] -T' s R dch,i ≤P dch,i (t)+ΔP dch,i -P dch,i (t'-1)≤T' s R dch,i

[0149] Among them, represents the minimum charging state of the ith busbar battery energy storage station,​ Denotes the maximum charging state of the battery energy storage station on the \(i\)-th bus, \(\sigma\) bi Denotes the loss of self-discharge energy of the battery energy storage station, \(S\) b,i (\(t'+1\)) Denotes the charging state of the battery energy storage station on the \(i\)-th bus at time \(t'+1\), \(S\) b,i (\(t'\)) Denotes the charging state of the battery energy storage station on the \(i\)-th bus at time \(t'\), \(T'\) s Denotes the sampling time of the battery energy storage station, \(P\) ch,i (\(t\)) Denotes the charging power of the battery energy storage station at time \(t\), \(\mu\) ch,i (\(t\)) Denotes the binary decision variable of the charging decision of the \(i\)-th bus of the battery energy storage station at the \(t\)-th moment, \(\mu\) dcg,i (\(t\)) Denotes the binary decision variable of the discharging decision of the \(i\)-th bus of the battery energy storage station at the \(t\)-th moment, \(P\) ch,i (\(t'-1\)) Denotes the charging power of the battery energy storage station at time \(t'-1\), \(\Delta P\) ch,i Denotes the correction amount of the charging power error of the \(i\)-th bus of the battery energy storage station, \(P\) dch,i (\(t\)) Denotes the discharging power of the battery energy storage station at time \(t\), \(\Delta P\) dch,i Denotes the correction amount of the charging power error of the \(i\)-th bus of the battery energy storage station Denotes the maximum charging power of the battery energy storage station Denotes the maximum discharging power of the battery energy storage station, \(T'\) s Denotes the sampling time

[0150] If \(P\) ch,i (\(t'\)) = \(P\) ch,i +\(\Delta P\) ch,i , Based on the error, the adjustment of the power balance between future generation data and future load data is determined by the following formula to obtain the power balance constraint condition between future generation data and future load data as the power balance adjustment condition:

[0151]

[0152] Where, \(P\) d,i (\(t\)) Denotes the load demand of bus \(i\) at the \(t\)-th moment, \(\sigma\) bi Denotes the self-discharge energy loss of the battery energy storage station, \(P\) pv,c,i (\(t\)) Denotes the reduced power of photovoltaic on the \(i\)-th bus at time \(t\), \(\Delta P\) pv,c,i Denotes the correction amount of the reduced power of photovoltaic on the \(i\)-th bus to the error, \(P\) ch,i (\(t\)) Denotes the charging power of the battery energy storage station at time \(t\), \(\Delta P\) ch,i Denotes the correction amount of the charging power of the \(i\)-th bus of the battery energy storage station to the error, \(P\) dch,i (\(t\)) Denotes the discharging power of the \(i\)-th bus of the battery energy storage station at time \(t\), \(\Delta P\) dch,iDenotes the correction amount of the charging power of the i-th bus of the battery energy storage station to the error, P g,i (t) Denotes the exchanged power between the i-th bus and the power grid at time t, ΔP g,i Denotes the correction amount of the exchanged power between the i-th bus and the power grid to the error, P g,i (t) Denotes the exchanged power between the i-th bus and the power grid at time t, P pv,T,i (t) Denotes the power generation power of the solar photovoltaic power generation equipment at time t, l i,j (t) Denotes the DC power flow of the transmission line connecting bus i and bus j at time t, Δl i,j Denotes the correction amount of the DC power flow of the transmission line connecting bus i and bus j to the error, Load error correction amount, Denotes the error.

[0153] Determine the power flow constraint conditions through the following formula, determine the power flow constraint conditions through the following formula, and use it as the power balance adjustment condition:

[0154] -l i,j,max ≤l i,j (t)+Δl i,j ≤l i,j,max

[0155] Where, l i,j,min Denotes the minimum capacity limit of buses i and j, l i,j,max Denotes the maximum capacity limit of buses i and j, l i,j (t) Denotes the DC power flow of the transmission line connecting bus i and bus j at time t, Δl i,j Is the correction amount of the DC power flow of the transmission line connecting bus i and bus j to the error.

[0156] Determine the power constraint conditions based on the error for the grid output power and / or input power adjustment through the following formula, and use it as the grid output power and / or input power adjustment condition:

[0157] P g,i,min ≤P g,i (t)+ΔP g,i ≤P g,i,max

[0158] -T s R g,i ≤P g,i (t)+ΔP g,i (t)-P g,i (t'-1)≤T s R g,i

[0159] Where, P g,i,minrepresents the minimum power exchange limit of the power grid at bus i, P g,i,max represents the maximum power exchange limit of the power grid at bus i, P g,i (t) represents the exchanged power between the i-th bus and the power grid at time t, ΔP g,i (t) represents the correction amount of the error of the exchanged power between the i-th bus and the power grid, T S represents the sampling time, P g,i (t'-1) represents the exchanged power between the i-th bus and the power grid at time t'-1, R g,i represents the ramp rate limit.

[0160] In this embodiment, the future power generation data of the power supply equipment in the next time period is predicted; the future load data of the power consumption equipment in the next time period is predicted; the future power generation data and the future load data are optimized with the operating cost of the edge cluster as the optimization target; if the optimization is completed, the error between the future power generation data and the future load data is corrected to balance the future power generation data and the future load data. By predicting and monitoring the future load data and the future power generation data, the distributed energy is managed to achieve edge cluster autonomy and give full play to the flexibility of various distributed energy sources. According to the predicted future load data of the power consumption equipment and the future power generation data of the power supply equipment in the power grid, the operation with the power grid is coordinated, the energy use efficiency is improved, the distributed energy is efficiently managed and controlled, the prediction accuracy of the future load data and the future power generation data within the prediction range is improved, and the real-time operation of the edge cluster is supported, so as to minimize the real-time mismatch between supply and demand and achieve the balance autonomy between power generation and load in the power grid.

[0161] Embodiment 2

[0162] Figure 2 is a schematic structural diagram of a dynamic balance autonomous device for a power grid provided in Embodiment 3 of the present invention. As Figure 2 shown, the device includes:

[0163] An edge cluster determination module 201, configured to determine an edge cluster in the power grid, where the edge cluster includes a plurality of power supply devices and power consumption devices;

[0164] A power generation data prediction module 202, configured to predict the future power generation data of the power supply device in the next time period;

[0165] A load data prediction module 203, configured to predict the future load data of the power consumption device in the next time period;

[0166] A data optimization module 204, configured to optimize the future power generation data and the future load data with the operating cost of the edge cluster as the optimization target;

[0167] An error correction module 205, which is used to correct the error between the future power generation data and the future load data if the optimization is completed, so as to balance the future power generation data and the future load data.

[0168] In an embodiment of the present invention, the power supply device includes a solar photovoltaic power generation device, and the future power generation data includes photovoltaic power generation power;

[0169] The power generation data prediction module 202 includes:

[0170] A clarity index generation module, which is used to generate a clarity index for the solar photovoltaic power generation device;

[0171] A solar irradiance calculation module, which is used to calculate the solar irradiance absorbed by the solar photovoltaic power generation device in each time period based on the clarity index;

[0172] A Kalman filter processing module, which is used to take the solar irradiance absorbed by the solar photovoltaic power generation device as a time series, perform Kalman filter processing on the solar irradiance, and obtain the solar irradiance absorbed by the solar photovoltaic power generation device in the next time period;

[0173] A photovoltaic power generation power prediction module, which is used to predict 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.

[0174] In an embodiment of the present invention, it is characterized in that the solar irradiance calculation module includes:

[0175] An irradiance calculation module, which is used to calculate the solar irradiance through the following formula:

[0176] h pv,t =s t h ex,t

[0177] Where h pv,t represents the solar irradiance absorbed by the solar photovoltaic power generation device at time t, s t represents the clarity index, and h ex,t represents the extra-terrestrial solar irradiance at time t;

[0178] 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 includes:

[0179] The photovoltaic power generation calculation module is used to calculate the solar irradiance absorbed in the next time period through the following formula to predict the photovoltaic power generation power of the solar photovoltaic power generation equipment in the next time period:

[0180]

[0181] Among them, P pv,r represents the rated capacity of the solar photovoltaic power generation equipment, h r Represents the rated solar irradiance, h pv,t represents the power generated by the solar irradiance absorbed by the solar photovoltaic power generation equipment at time t, P pv,t Indicates the power of solar irradiance absorbed by the solar photovoltaic power generation equipment.

[0182] In one embodiment of the present invention, the Kalman filter processing module includes:

[0183] The time series acquisition module is used to determine the time series through the following equation:

[0184] h(k)=a 1 h(k-1)+a 2 h(k-2)+……+a m h(km)+a m+1 h(km-1)+β k

[0185] Wherein, h(k) represents the solar irradiance at the kth time interval, h(k-1) represents the solar irradiance at the k-1th time interval, h(k-2) represents the solar irradiance at the k-2th time interval, h(km) represents the solar irradiance at the kmth time interval, a 1 、a 2 ……a m 、a m+1 represents the coefficient, β k represents residual;

[0186] The time series rewriting module is used to rewrite the time series by combining the following equations:

[0187] h 1 (k)=h(k),h 2 (k)=h(k-1),……,h m+1 (k)=h(km)

[0188] h(k+1)=a 1 h(k)+a 2 h(k-1)+……+a m+1 h(km)+β k+1 ​

[0189] A time series determination module for determining the rewritten time series through the following formula:

[0190] h 1 (k + 1)= a 1 h 1 (k)+ a 2 h 2 (k)+……+ a m+1 h m+1 (k)+ β k+1

[0191] Wherein, h 1 (k) represents the solar irradiance of the k-th time interval, h 2 (k) represents the solar irradiance of the (k - 1)-th time interval, h m+1 (k) represents the solar irradiance of the (k - m)-th time interval, h(k) represents the solar irradiance of the k-th time interval, h(k - 1) represents the solar irradiance of the (k - 1)-th time interval, h(k - m) represents the solar irradiance of the (k - m)-th time interval, β k+1 represents the residual, h 1 (k + 1) represents the solar irradiance of the (k + 1)-th time interval;

[0192] Let h 2 (k)= h(k + 1),……, h m+1 (k)= h(k + m), and determine the state equation through the following formula:

[0193]

[0194] Wherein, ω k represents the system noise vector in the Kalman filter, h 1 (k + 1) represents the solar irradiance of the first time period in the (k + 1)-th time period, h 2 (k + 1) represents the solar irradiance of the second time period in the (k + 1)-th time period, h m (k + 1) represents the solar irradiance of the m-th time period in the (k + 1)-th time period, h m+1 (k + 1) represents the solar irradiance of the (m + 1)-th time period in the (k + 1)-th time period, h 2 (k) represents the solar irradiance of the (k - 1)-th time interval, h 1 (k) represents the solar irradiance of the k-th time interval, h m+1 (k) represents the solar irradiance of the (k - m)-th time interval;

[0195] A state equation rewriting module for rewriting the state equation through the following equation:

[0196]

[0197] Among them, \(H(k)\) is the state vector of the solar irradiance in the \(k\)th time interval, and \(H(k + 1)\) represents the state vector of the solar irradiance in the \((k + 1)\)th time interval. represents the state transfer matrix of the state equation, \(\Gamma(k + 1, k)\) represents the excitation transfer matrix in the Kalman filter, and \(\omega(k)\) represents the system noise vector in the Kalman filter.

[0198] The observation equation determination module is used to list the observation equation through the following equation and perform Kalman filtering processing to obtain the solar irradiance absorbed by the solar photovoltaic power generation device in the next time period:

[0199]

[0200] Among them, \(Z(k + 1)\) represents the observation vector. represents the prediction output transfer matrix, \(H(k + 1)\) represents the observation vector in the Kalman filter, and \(n(k + 1)\) represents the measurement noise in the Kalman filter.

[0201] In an embodiment of the present invention, the power supply device includes at least one of a solar photovoltaic power generation device, and the edge cluster further includes a load and a battery energy storage station.

[0202] The data optimization module 204 includes:

[0203] The operating cost determination module is used to determine the operating cost of the edge cluster through the following formula:

[0204]

[0205] Among them, \(i\) represents the \(i\)th bus, \(1\) represents the lower limit of the range, \(N\) represents the upper limit of the range, \(J\) c (t) represents the operating cost in the edge cluster, \(J\) p represents the potential benefit of charging or discharging the battery energy storage station, \(J\) represents the operating cost, and \(t\) represents the time range from \(k + 1\) to \(k+M\).

[0206] The potential benefit determination module is used to determine the operating cost and the potential benefit of the edge cluster through the following formula:

[0207] \(J\) c (t)=\(\pi\) g (t) \(P\) g,i (t)-\(\pi\) d (t) \(P\) d,i (t) \(T\) s

[0208] J p = (S b,i (k + M) - S b,i (k))π g,avg (k)

[0209] Wherein, J c (t) represents the operating cost in the edge cluster at time t, and J p represents the potential benefit of charging or discharging the battery energy storage station, and π g (t) represents the energy price at time t, and P g,i (t) represents the load demand of the i-th bus at time t, and π d (t) represents the electricity price corresponding to the load demand of the i-th bus at time t, and T s represents the sampling time, and S b,i (k + M) represents the charging state of the battery energy storage station on the i-th bus at the (k + M)-th moment, and π g,avg (k) represents the average value of the load demand price within the k-th moment, and S b,i (k) represents the charging state of the battery energy storage station on the i-th bus at the k-th moment, and P d,i (t) represents the load demand of the i-th bus at the t-th moment;

[0210] A data optimization module, configured to optimize the future power generation data and the future load data with the goal of minimizing the operating cost of the edge cluster under the constraint of at least one of the following conditions: the power generation power condition of the solar photovoltaic power generation device, 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 and discharging power conditions of the battery energy storage station, the charging speed and discharging speed conditions of the battery energy storage station, the grid power exchange condition, the line flow condition, and the power balance condition;

[0211] Wherein, the power generated by the solar photovoltaic power generation device is determined by the following formula as the power generation power condition of the solar photovoltaic power generation device:

[0212] P pv,T,i (t) = P pv,i (t) + P pv,c,i (t)

[0213] Wherein, P pv,T,i (t) represents the power generation power of the solar photovoltaic power generation device at time t, and P pv,i (t) represents the photovoltaic power used on the i-th bus at time t, and P pv,c,i (t) represents the photovoltaic reduction power on the i-th bus at time t;

[0214] The maximum power condition determination module is used to determine the maximum power applied by the inverter through the following formula as the maximum power condition applied by the inverter:

[0215]

[0216] where P pv,T,i (t) represents the power generation power of the solar photovoltaic power generation device at time t, and μ pv,i (t) represents the binary decision variable of the operating state of the solar photovoltaic power generation device at the i-th bus at time t, represents the maximum power limit applied by the inverter;

[0217] The charge state condition determination module is used to determine the charge state of the battery energy storage station by relating the following equations and inequalities as the charge state condition of the battery energy storage station:

[0218]

[0219] S bi (t+(M - 1)) = S bi,ini

[0220] where S b,i (t + 1) represents the charge state of the battery energy storage station at the i-th bus at time t + 1, S bi (t+(M - 1)) represents the charge state of the battery energy storage station at the i-th bus at time t+(M - 1), S b,i (t) represents the charge state of the battery energy storage station at the i-th bus at time t, σ bi represents the self-discharge energy loss of the battery energy storage station, T s represents the sampling time of the battery energy storage station, η ch,i represents the charging efficiency of the battery energy storage station on the i-th bus, P ch,i (t) represents the charging power of the battery energy storage station at time t, P dch,i (t) represents the discharging power of the battery energy storage station at time t, η dch,i represents the discharging efficiency of the battery energy storage station on the i-th bus, represents the minimum charge state of the battery energy storage station on the i-th bus, represents the maximum charge state of the battery energy storage station on the i-th bus, S bi,ini represents the starting charge state of the battery energy storage station, S b,i (t + 1) represents the charge state of the battery energy storage station at the i-th bus at time t + 1;

[0221] A power condition determination module, which is used to determine the charging power limit and discharging power limit of the battery energy storage station through the following formula as the charging power and discharging power conditions of the battery energy storage station:

[0222]

[0223]

[0224] μ ch,i (t)+μ dcg,i (t)≤1

[0225] Wherein, P ch,i (t) represents the charging power of the battery energy storage station at time t, μ ch,i (t) represents the binary decision variable of the charging decision of the i-th bus at the t-th moment, μ dcg,i (t) represents the binary decision variable of the discharging decision of the i-th bus at the t-th moment, P dch,i (t) represents the discharging power of the battery energy storage station at time t, represents the maximum discharging power of the battery energy storage station, represents the maximum charging power of the battery energy storage station;

[0226] A speed condition determination module, which is used to determine the charging speed limit and discharging speed limit of the battery energy storage station through the following formula as the charging speed and discharging speed conditions of the battery energy storage:

[0227] -T S R ch,i ≤P ch,i (t)-P ch,i (t - 1)≤T s R ch,i

[0228] -T S R dch,i ≤P dch,i (t)-P dch,i (t - 1)≤T s R dch,i

[0229] Wherein, T S represents the sampling time of the battery energy storage station, R ch,i represents the charging speed of the battery energy storage station on the i-th bus at time t, R dch,i represents the discharging speed of the battery energy storage station on the i-th bus at time t, P ch,i (t) represents the charging power of the battery energy storage station at time t, P dch,i (t) represents the discharging power of the battery energy storage station at time t, Pch,i (t - 1) represents the charging power of the battery energy storage station at time t - 1, P dch,i (t - 1) represents the discharging power of the battery energy storage station at time t - 1;

[0230] The power balance condition determination module is used to determine the power balance of the edge cluster through the following formula as the power balance condition:

[0231]

[0232] The line flow condition determination module is used to calculate the power flow of a group of transmission lines connecting buses i and j through the following formula as the line flow condition:

[0233] l i,j (t) = B i,j (θ i (t) - θ j (t))

[0234] Among them, represents the lower limit of the range in the set of all buses i to which the j - th bus belongs, 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 represents the susceptance of the line between buses i and j, θ i (t) represents the voltage phasor angle of bus i at time t, θ j (t) represents the voltage phasor angle of bus j at time t, P ch,i (t) represents the charging power of the battery energy storage station at 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 PV power reduction on bus i at time t, P dch,i (t) represents the discharging power of the battery energy storage station at time t, P pv,T,i (t) represents the power generation of the solar photovoltaic power generation device at time t, P g,i (t) represents the power exchange between the i - th bus and the power grid at time t;

[0235] The grid power exchange condition determination module is used to determine that the power flow on each line is within the power - carrying capacity range corresponding to the line through the following inequality as the grid power exchange condition:

[0236] -l i,j,min ≤l i,j (t) ≤ l i,j,max

[0237] A limit range determination module, configured to determine that the power exchange between the edge cluster and the power grid is limited by the connection capacity through the following inequality:

[0238] P g,i,min ≤P g,i (t)≤P g,i,max

[0239] A ramp rate limit determination module, configured to determine that a ramp rate limit is imposed on the power exchange of the power grid at bus i through the following inequality:

[0240] -T s R g,i ≤P g,i (t)-P g,i (t - 1)≤T s R g,i

[0241] Wherein, T s represents the sampling time, P g,i (t) represents the power exchange between the i-th bus and the power grid at time t, P g,i (t - 1) represents the power exchange between the i-th bus and the power grid at time t - 1, R g,i represents the limit of the ramp rate, l i,j,min represents the minimum capacity limit of buses i and j, l i,j,max represents 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 represents the minimum power exchange limit of the power grid at bus i, P g,i,max represents the maximum power exchange limit of the power grid at bus i, P g,i (t) represents the power exchange between the i-th bus and the power grid at time t.

[0242] In an embodiment of the present invention, the error correction module 205 includes:

[0243] An error calculation module, configured to calculate the error between the future power generation data and the future load data;

[0244] A power adjustment module, configured to adjust the photovoltaic power generation in the edge cluster, the charging data of the battery energy storage station, the reduction amount of the photovoltaic power on the bus, the DC power flow of the transmission line between the buses, the charging data, the discharge power, and the power exchange between the bus and the power grid with the goal of minimizing the error, so as to balance the future power generation data and the future load data.

[0245] In an embodiment of the present invention, the error calculation module includes:

[0246] The data error calculation module is used to calculate the error between the future power generation data and the future load data by the following formula:

[0247]

[0248] Where F represents the total adjustment of the edge prediction plan fixed point, ω 1 、ω 2 represents the weight coefficient, P g,i,max represents the maximum power exchange limit of the power grid at bus i, represents the maximum charging power of the battery energy storage station, represents the maximum discharge power of the battery energy storage station, l i,j,max represents the maximum capacity limit of buses i and j, |ΔP g,i | represents the correction amount of the maximum power exchange limit of the power grid at bus i to the error, |ΔP ch,i | represents the correction amount of the charging power of the battery energy storage station to the error, |ΔP dch,i | represents the correction amount of the discharge power of the battery energy storage station to the error, |Δl i,j | represents the correction amount of the error by the DC power flow of the transmission lines of bus i and bus j of the battery energy storage station;

[0249] The power adjustment module includes:

[0250] An edge cluster optimization module is used to optimize the photovoltaic power generation power in the edge cluster, the charging data of the battery energy storage station, the photovoltaic power reduction on the bus, the DC power flow of the transmission line between the buses, the charging data, the discharge power, and the exchange power between the bus and the grid, with the error as the target, under the constraints of at least one of the edge cluster correction conditions, the battery energy storage station adjustment conditions, the power balance adjustment conditions, the power flow constraint conditions, and the output and / or input power adjustment conditions of the grid;

[0251] The edge cluster correction condition determination module is used to adjust the photovoltaic power generation power and power limit based on the error, and determine the photovoltaic power generation power and power limit constraint conditions by combining the following equations and inequalities as edge cluster correction conditions:

[0252]

[0253]

[0254] Among them, P pv,T,i (t) represents the power generation of the solar photovoltaic power generation equipment at time t, P pv,i (t) represents the photovoltaic power used on the i-th bus at time t, P pv,c,i ​(t) represents the PV power reduction on the i-th bus at time t, ΔP pv,c,i represents the sensitivity of the PV power reduction on the i-th bus at time t to the error, represents the error, μ pv,i (t) represents the binary decision variable of the operating state of the solar PV power generation device at the i-th bus at time t, represents the maximum power limit applied by the inverter;

[0255] The battery energy storage station adjustment condition determination module is used to determine the constraint conditions for the battery energy storage station based on the error by solving the following equations and inequalities, and use them as the adjustment conditions for the battery energy storage station:

[0256]

[0257]

[0258]

[0259] -T' s R ch,i ≤P ch,i (t)+ΔP ch,i -P ch,i (t'-1)≤T' s R ch,i

[0260] -T' s R dch,i ≤P dch,i (t)+ΔP dch,i -P dch,i (t'-1)≤T' s R dch,i

[0261] Among them, represents the minimum charge state of the battery energy storage station on the i-th bus, represents the maximum charge state of the battery energy storage station on the i-th bus, σ bi represents the loss of self-discharge energy of the battery energy storage station, S b,i (t'+1) represents the charge state of the battery energy storage station on the i-th bus at time t'+1, S b,i (t') represents the charge state of the battery energy storage station on the i-th bus at time t', T' s represents 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, μ ch,i (t) represents the binary decision variable of the charging decision of the battery energy storage station on the i-th bus at time t, μdcg,i (t) represents the binary decision variable of the discharge decision of the i-th bus 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 the moment of t'-1, ΔP ch,i represents the correction amount of the charging power of the i-th 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 the moment of t, ΔP dch,i represents the correction amount of the charging power of the i-th bus of the battery energy storage station to the error, represents the maximum charging power of the battery energy storage station, represents the maximum discharge power of the battery energy storage station, T' s represents the sampling time;

[0262] The power balance adjustment condition determination module is used to, if P ch,i (t') = P ch,i + ΔP ch,i , based on the error, adjust the power balance between the future power generation data and the future load data, and determine the power balance constraint condition between the future power generation data and the future load data through the following formula as the power balance adjustment condition:

[0263]

[0264] Among them, P d,i (t) represents the load demand of bus i at the t-th moment, σ bi represents the self-discharge energy loss of the battery energy storage station, P pv,c,i (t) represents the reduced power of photovoltaic on the i-th bus at the moment of t, ΔP pv,c,i represents the correction amount of the reduced power of photovoltaic on the i-th bus to the error, P ch,i (t) represents the charging power of the battery energy storage station at the moment of t, ΔP ch,i represents the correction amount of the charging power of the i-th bus of the battery energy storage station to the error, P dch,i (t) represents the discharge power of the i-th bus of the battery energy storage station at the moment of t, ΔP dch,i represents the correction amount of the charging power of the i-th bus of the battery energy storage station to the error, P g,i (t) represents the exchange power between the i-th bus and the power grid at the moment of t, ΔP g,i represents the correction amount of the exchange power between the i-th bus and the power grid to the error, P g,i (t) represents the exchange power between the i-th bus and the power grid at the moment of t, P pv,T,i (t) represents the power generation power of the solar photovoltaic power generation device at the moment of t, l i,j(t) represents the DC power flow of the transmission line connecting bus i and bus j at time t, Δl i,j represents the correction amount of the DC power flow of the transmission line connecting bus i and bus j to the error, load error correction amount, represents the error;

[0265] The power flow constraint condition determination module is used to determine the power flow constraint condition through the following formula, and determine the power flow constraint condition through the following formula as the power flow constraint condition:

[0266] -l i,j,max ≤l i,j (t)+Δl i,j ≤l i,j,max

[0267] where, l i,j,min represents the minimum capacity limit of buses i and j, l i,j,max represents 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, Δl i,j is the correction amount of the DC power flow of the transmission line connecting bus i and bus j to the error;

[0268] The power adjustment condition determination module is used to determine the power constraint condition based on the error for the grid output power and / or input power adjustment through the following formula as the grid output power and / or input power adjustment condition:

[0269] P g,i,min ≤P g,i (t)+ΔP g,i ≤P g,i,max

[0270] -T s R g,i ≤P g,i (t)+ΔP g,i (t)-P g,i (t'-1)≤T s R g,i

[0271] where, P g,i,min represents the minimum power exchange limit of the grid at bus i, P g,i,max represents the maximum power exchange limit of the grid at bus i, P g,i (t) represents the power exchange between the i-th bus and the grid at time t, ΔP g,i (t) represents the correction amount of the power exchange between the i-th bus and the grid to the error, T S represents the sampling time, P g,i(t'-1) represents the power exchanged between the i-th busbar and the power grid at the moment of t'-1, and R g,i represents the ramp rate limit.

[0272] The dynamic balance autonomous device of the power grid provided by the embodiments of the present invention can execute the dynamic balance autonomous method of the power grid provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the dynamic balance autonomous method of the power grid.

[0273] Embodiment III

[0274] Figure 3 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described herein and / or claimed.

[0275] As Figure 3 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 11 can 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 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0276] Multiple 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, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0277] The processor 11 may be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the autonomous method for dynamic balancing of the power grid.

[0278] In some embodiments, the autonomous method for dynamic balancing of the power grid may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the 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 the RAM 13 and executed by the processor 11, one or more steps of the autonomous method for dynamic balancing of the power grid described above may be executed. Alternatively, in other embodiments, the processor 11 may be configured to execute the autonomous method for dynamic balancing of the power grid by any other suitable means (e.g., by means of firmware).

[0279] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that 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 can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0280] The computer program for implementing the method 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, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0281] In the context of the present invention, a computer-readable storage medium can 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 can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, 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.

[0282] In order to provide interaction with a user, the systems and techniques described herein 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 the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0283] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of the communication network include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0284] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0285] Embodiment 4

[0286] The embodiment of the present invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the dynamic balance autonomous method of the power grid provided in any embodiment of the present invention.

[0287] In the process of implementing the computer program product, computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0288] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and this is not limited herein.

[0289] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for autonomous dynamic balance of a power grid, characterized in that: include: Determine an edge cluster in the power grid, wherein the edge cluster includes a plurality of power supply devices and power consumption devices; Predicting future power generation data of the power supply equipment in the next time period; Predicting future load data of the electrical equipment in the next time period; Taking the operation cost of the edge cluster as an optimization target, optimizing the future power generation data and the future load data; If the optimization is completed, the error between the future power generation data and the future load data is corrected so that the future power generation data and the future load data are balanced; Wherein, the power supply equipment includes solar photovoltaic power generation equipment, and the future power generation data includes photovoltaic power generation power; The predicting of future power generation data of the power supply equipment in the next time period includes: Generate a clarity index for solar photovoltaic power generation equipment; Calculate the solar irradiance absorbed by the solar photovoltaic power generation device in each time period based on the clarity index; Taking the solar irradiance absorbed by the solar photovoltaic power generation equipment as a time series, performing Kalman filtering on the solar irradiance to obtain the solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period; 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.

2. The method according to claim 1, characterized in that The step of calculating the solar irradiance absorbed by the solar photovoltaic power generation device in each time period based on the clarity index comprises: The solar irradiance is calculated by the following formula: h pv,t =s t h ex,t Among them, h pv,t represents the solar irradiance absorbed by the solar photovoltaic power generation equipment at time t, s t Represents the clarity index, h ex,t represents the solar irradiance outside the Earth at time t; The method of 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 comprises: The solar irradiance absorbed in the next time period is calculated by the following formula to predict the photovoltaic power generation power of the solar photovoltaic power generation equipment in the next time period: Among them, P pv,r Represents the rated capacity of the solar photovoltaic power generation equipment, h r Indicates the rated solar irradiance, h pv,t represents the power generated by the solar irradiance absorbed by the solar photovoltaic power generation equipment at time t, P pv,t It represents the power of solar irradiance absorbed by the solar photovoltaic power generation device.

3. The method according to claim 1, characterized in that The method of taking the solar irradiance absorbed by the solar photovoltaic power generation equipment as a time series and performing Kalman filtering on the solar irradiance to obtain the solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period includes: The time series is determined by the following equation: h(k)=a1h(k-1)+a2h(k-2)+……+a m h(k-m)+a m+1 h(k-m-1)+β k Wherein, h(k) represents the solar irradiance at the kth time interval, h(k-1) represents the solar irradiance at the k-1th time interval, h(k-2) represents the solar irradiance at the k-2th time interval, and h(km) represents the solar irradiance at the kmth time interval. a1, a2, ... a m 、a m+1 represents the coefficient, β k represents the residual; Rewrite the time series by combining the following equations: h1(k)=h(k),h2(k)=h(k-1),……,h m+1 (k)=h(k-m) h(k+1)=a1h(k)+a2h(k-1)+……+a m+1 h(k-m)+β k+1 The rewritten time series is determined by the following formula: h1(k+1)=a1h1(k)+a2h2(k)+……+a m+1 h m+1 (k)+β k+1 Wherein, h1(k) represents the solar irradiance at the kth time interval, h2(k) represents the solar irradiance at the k-1th time interval, and h m+1 (k) represents the solar irradiance at the km-th time interval, h(k) represents the solar irradiance at the k-th time interval, h(k-1) represents the solar irradiance at the k-1-th time interval, h(km) represents the solar irradiance at the km-th time interval, β k+1 represents the residual, h1(k+1) represents the solar irradiance at the k+1th time interval; Let h2(k)=h(k+1), ..., h m+1 (k) = h(k + m), and the state equation is determined by the following formula: Among them, ω k represents the system noise vector in the Kalman filter, h1(k+1) represents the solar irradiance in the first period of the k+1th period, h2(k+1) represents the solar irradiance in the second period of the k+1th period, and h m (k+1) represents the solar irradiance of the mth period in the k+1th period, h m+1 h(k+1) represents the solar irradiance of the m+1th time period in the k+1th time period, h2(k) represents the solar irradiance of the k-1th time interval, h1(k) represents the solar irradiance of the kth time interval, and h m+1 (k) represents the solar irradiance at the km-th time interval; The state equation is rewritten as follows: 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 k+1th time interval, represents the state transfer matrix of the state equation, Γ(k+1,k) represents the excitation transfer matrix in the Kalman filter, and ω(k) represents the system noise vector in the Kalman filter; The observation equation is listed as follows and Kalman filtering is performed to obtain the solar irradiance absorbed by the solar photovoltaic power generation device in the next time period: Among them, Z(k+1) represents the observation vector, represents the predicted output transfer matrix, H(k+1) represents the observation vector in the Kalman filter, and n(k+1) represents the measurement noise in the Kalman filter.

4. The method according to claim 1, characterized in that The edge cluster further includes at least one of a load and a battery energy storage station; The optimizing the future power generation data and the future load data by taking the operating cost of the edge cluster as the optimization target includes: The operating cost of the edge cluster is determined by the following formula: Where i represents the i-th busbar, 1 represents the lower limit of the range, N represents the upper limit of the range, and J c (t) represents the operating cost in the edge cluster, J p represents the potential revenue of charging or discharging the battery energy storage station, J represents the operating cost, and t represents the time range from k+1 to k+M; The operating cost of the edge cluster and the potential benefits 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) Among them, J c (t) represents the operating cost in the edge cluster at time t, J p represents the potential revenue of charging or discharging the battery energy storage station, π g (t) represents the energy price at time t, P g,i (t) represents the load demand of the ith bus at time t, π 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 charging state of the battery energy storage station at the i-th bus at the k+Mth time, π g,avg (k) represents the average value of the load demand price at the kth moment, S b,i (k) represents the charging state of the battery energy storage station of the ith bus at the kth time, P d,i (t) represents the load demand of the i-th bus at the t-th moment; Under the constraints of at least one of the power generation condition of the solar photovoltaic power generation equipment, the maximum power condition imposed 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 and discharging power condition of the battery energy storage station, the charging speed and discharging speed condition of the battery energy storage station, the power exchange condition of the power grid, the line flow condition, and the power balance condition, the future power generation data and the future load data are optimized with the goal of minimizing the operating cost of the edge cluster; The power generated by the solar photovoltaic power generation equipment is determined by the following formula as the power generation condition of the solar photovoltaic power generation equipment: P pv,T,i (t)=P pv,i (t)+P pv,c,i (t) Among them, P pv,T,i (t) represents the power generation power of the solar photovoltaic power generation equipment at time t, P pv,i (t) represents the photovoltaic power used on the i-th bus at time t, P pv,c,i (t) represents the photovoltaic power reduction on the i-th bus at time t; The maximum power applied by the inverter is determined by the following formula as the maximum power condition applied by the inverter: Among them, P pv,T,i (t) represents the power generation of the solar photovoltaic power generation equipment at time t, μ pv,i (t) represents the binary decision value of the operating state of the solar photovoltaic power generation equipment at the i-th bus at time t, The maximum power limit imposed by the inverter described in the table; The charging state of the battery energy storage station is determined by linking the following equations and inequalities as the charging state condition of the battery energy storage station: S bi (t+(M-1))=S bi,ini Among them, S b,i (t+1) represents the charging state of the battery energy storage station of the ith bus at time t+1, S bi (t+(M-1)) represents the charging state of the battery energy storage station of the ith bus at time t+(M-1), S b,i (t) represents the charging state of the battery energy storage station of the ith bus at time t, σ bi represents the self-discharge energy loss of the battery energy storage station, T s represents the sampling time of the battery energy storage station, η ch,i represents the charging efficiency of the battery energy storage station on the i-th bus, P ch,i (t) represents the charging power of the battery energy storage station at time t, P dch,i (t) represents the discharge power of the battery energy storage station at time t, η dch,i represents the discharge efficiency of the battery energy storage station on the i-th bus, represents the minimum charging state of the battery energy storage station described by the i-th bus, represents the maximum charging state of the battery energy storage station described by the i-th bus, S bi,ini Indicates the initial charging state of the battery energy storage station, S b,i (t+1) represents the charging state of the battery energy storage station of the i-th bus at time t+1; The charging power limit and discharging power limit of the battery energy storage station are determined by the following formula as the charging power and discharging power conditions of the battery energy storage station: m ch,i (t)+μ dcg,i (t)≤1 Among them, P ch,i (t) represents the charging power of the battery energy storage station at time t, μ ch,i (t) represents the binary decision variable for the charging decision of the ith bus at time t, μ dcg,i (t) represents the binary decision variable for 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, represents the maximum discharge power of the battery energy storage station, Indicates the maximum charging power of the battery energy storage station; The charging speed limit and discharging speed limit of the battery energy storage station are determined by the following formula as the charging speed and discharging speed conditions of the battery energy storage: -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 Among them, T S represents the sampling time of the battery energy storage station, R ch,i represents the charging speed of the battery energy storage station on the i-th bus at time t, R dch,i represents the discharge speed of the battery energy storage station on the i-th bus at time t, P ch,i (t) represents the charging power of the battery energy storage station at 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 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 by the following formula as the power balance condition: The power flow of a set of transmission lines connected to buses i and j is calculated as line flow conditions by the following formula: l i,j (t)=B i,j (θ i (t)-θ j (t)) in, Indicates that the jth bus belongs to the set of all buses i, represents the lower limit of the range, 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 represents the susceptance of the line between buses i and j, θ i (t) represents the voltage phasor angle of bus i at time t, θ j (t) represents the voltage phasor angle of bus j at time t, P ch,i (t) represents the charging power of the battery energy storage station at 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 power reduction on bus i at time t, P dch,i (t) represents the discharge power of the battery energy storage station at time t, P pv,T,i (t) represents the power generation power of the solar photovoltaic power generation equipment at time t, P g,i (t) represents the exchange power between the ith bus and the grid at time t; The power flow on each line is determined by the following inequality within the power carrying capacity range of the corresponding line as the power exchange condition of the power grid: -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 as determined by the following inequality: P g,i,min ≤P g,i (t)≤P g,i,max The power exchange imposed by the grid at bus i is determined by the following inequality: -T s R g,i ≤P g,i (t)-P g,i (t-1)≤T s R g,i Among them, T s represents the sampling time, P g,i (t) represents the exchange power between the ith bus and the grid at time t, P g,i (t-1) represents the exchange power between the ith bus and the grid at time t-1, R g,i Indicates the limit of ramp speed, l i,j,min represents the minimum capacity limit of buses i and j, l i,j,max represents 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 represents the minimum power exchange limit of the power grid at bus i, P g,i,max represents the maximum power exchange limit of the power grid at bus i, P g,i (t) represents the exchange power between the ith bus and the grid at time t.

5. The method according to any one of claims 1 to 4, characterized in that The step of correcting the error between the future power generation data and the future load data so as to keep the future power generation data and the future load data balanced includes: calculating an error between the future power generation data and the future load data; With the goal 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 on the bus, the DC power flow of the transmission line between the buses, the charging data, the discharge power, and the exchange power between the bus and the power grid are adjusted to balance the future power generation data and the future load data.

6. The method according to claim 5, characterized in that The calculating the error between the future power generation data and the future load data comprises: The error between the future power generation data and the future load data is calculated by the following formula: Among them, F represents the total adjustment of the edge prediction plan fixed point, ω1 and ω2 represent weight coefficients, and P g,i,max represents the maximum power exchange limit of the power grid at bus i, represents the maximum charging power of the battery energy storage station, represents the maximum discharge power of the battery energy storage station, l i,j,max represents the maximum capacity limit of buses i and j, |ΔP g,i | represents the correction amount of the maximum power exchange limit of the power grid at bus i to the error, |ΔP ch,i | represents the correction amount of the charging power of the battery energy storage station to the error, |ΔP dch,i | represents the correction amount of the discharge power of the battery energy storage station to the error, |Δl i,j | represents the correction amount of the error to the DC power flow of the transmission lines of bus i and bus j of the battery energy storage station; The method aims to minimize the error, and adjusts the photovoltaic power generation in the edge cluster, the charging data of the battery energy storage station, the photovoltaic power reduction on the bus, the DC power flow of the transmission line between the buses, the charging data, the discharge power, and the exchange power between the bus and the power grid, so that the future power generation data and the future load data are balanced, including: Under the constraints of at least one of the edge cluster correction condition, the battery energy storage station adjustment condition, the power balance adjustment condition, the power flow constraint condition, and the power grid output and / or input power adjustment condition, taking the error as the target, the photovoltaic power generation power in the edge cluster, the charging data of the battery energy storage station, the photovoltaic power reduction on the bus, the DC power flow of the transmission line between the buses, the charging data, the discharge power, and the exchange power between the bus and the power grid are optimized; The photovoltaic power generation power and power limit are adjusted based on the error, and the photovoltaic power generation power and power limit constraint conditions are determined by combining the following equations and inequalities as edge cluster correction conditions: Among them, P pv,T,i (t) represents the power generation power of the solar photovoltaic power generation equipment at time t, P pv,i (t) represents the photovoltaic power used on the i-th bus at time t, P pv,c,i (t) represents the photovoltaic power reduction on the i-th bus at time t, ΔP pv,c,i It represents the amount of the error caused by the reduction of photovoltaic power on the ith bus at time t, Denotes the error, μ pv,i (t) represents the binary decision value of the operating state of the solar photovoltaic power generation equipment at the i-th bus at time t, The maximum power limit imposed by the table inverter; Based on the adjustment of the battery energy storage station to the battery energy storage station by the error, the constraint conditions for the battery energy storage station are determined by combining the following equations and inequalities as the adjustment conditions for the battery energy storage station: -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 in, represents the minimum charging state of the battery energy storage station described by the i-th bus, represents the maximum state of charge of the battery energy storage station described by the i-th bus, σ bi represents the self-discharge energy loss of the battery energy storage station, S b,i (t′+1) represents the charging state of the battery energy storage station of the ith bus at time t′+1, S b,i (t′) represents the charging state of the battery energy storage station of the ith bus at time t′, T′ s represents 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, μ ch,i (t) represents the binary decision variable for the charging decision of the ith bus of the battery energy storage station at time t, μ dcg,i (t) represents the binary decision variable for the discharge decision of the ith bus of the battery energy storage station at the tth time, P ch,i (t′-1) represents the charging power of the battery energy storage station at time t′-1, ΔP ch,i represents the correction amount of the error by the charging power of the i-th busbar of the battery energy storage station, P dch,i (t) represents the discharge power of the battery energy storage station at time t, ΔP dch,i represents the correction amount of the charging power of the i-th bus of the battery energy storage station to the error, represents the maximum charging power of the battery energy storage station, represents the maximum discharge power of the battery energy storage station, T′ s Indicates sampling time; If P ch,i (t′)=P ch,i +ΔP ch,i , based on the error, the power balance between the future power generation data and the future load data is adjusted, and the power balance constraint condition between the future power generation data and the future load data is determined by the following formula as the power balance adjustment condition: Among them, P d,i (t) represents the load demand of bus i at time t, σ bi represents the self-discharge energy loss of the battery energy storage station, P pv,c,i (t) represents the photovoltaic power reduction on the i-th bus at time t, ΔP pv,c,i represents the correction amount of the photovoltaic power reduction on the i-th bus to the error, P ch,i (t) represents the charging power of the battery energy storage station at time t, ΔP ch,i represents the correction amount of the error by the charging power of the i-th busbar of the battery energy storage station, P dch,i (t) represents the discharge power of the i-th bus of the battery energy storage station at time t, ΔP dch,i represents the correction amount of the error by the charging power of the i-th busbar of the battery energy storage station, P g,i (t) represents the exchange power between the ith bus and the grid at time t, ΔP g,i represents the correction amount of the exchange power between the ith bus and the grid to the error, P g,i (t) represents the exchange power between the ith bus and the grid at time t, P pv,T,i (t) represents the power generation power of the solar photovoltaic power generation equipment at time t, l i,j (t) represents the DC power flow of the transmission line connecting bus i and bus j at time t, Δl i,j represents the correction amount of the DC power flow of the transmission line connecting bus i and bus j to the error, Load error correction, represents the error; The power flow constraint condition is determined by the following formula, and the power flow constraint condition is determined by the following formula as the power flow constraint condition: -l i,j,max ≤l i,i (t)+Δl i,j ≤l i,j,max Among them, l i,j,min represents the minimum capacity limit of buses i and j, l i,j,max represents 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, Δl i,j is the correction amount of the error caused by the DC power flow of the transmission line connecting bus i and bus j; The power constraint condition is determined by adjusting the power output power and / or input power of the power grid based on the error through the following formula, which serves as the power output power and / or input power adjustment condition of the power grid: 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 Among them, P g,i,min represents the minimum power exchange limit of the power grid at bus i, P g,i,max represents the maximum power exchange limit of the power grid at bus i, P g,i (t) represents the exchange power between the ith bus and the grid at time t, ΔP g,i (t) represents the correction amount of the exchange power between the ith bus and the grid to the error, T S represents the sampling time, P g,i (t′-1) represents the exchange power between the ith bus and the grid at time t′-1, R g,i Indicates the ramp rate limit.

7. A method for autonomous dynamic balance of a power grid, characterized in that: include: Determine an edge cluster in the power grid, wherein the edge cluster includes a plurality of power supply devices and power consumption devices; Predicting future power generation data of the power supply equipment in the next time period; Predicting future load data of the electrical equipment in the next time period; Taking the operation cost of the edge cluster as an optimization target, optimizing the future power generation data and the future load data; If the optimization is completed, the error between the future power generation data and the future load data is corrected so that the future power generation data and the future load data are balanced; The step of correcting the error between the future power generation data and the future load data so as to keep the future power generation data and the future load data balanced includes: calculating an error between the future power generation data and the future load data; With the goal 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 on the bus, the DC power flow of the transmission line between the buses, the charging data, the discharge power, and the exchange power between the bus and the power grid are adjusted to balance the future power generation data and the future load data.

8. A dynamic balancing autonomous device for a power grid, characterized in that: include: An edge cluster determination module, used to determine an edge cluster in a power grid, wherein the edge cluster includes a plurality of power supply devices and power consumption devices; A power generation data prediction module, used to predict future power generation data of the power supply equipment in the next time period; A load data prediction module, used to predict future load data of the electrical equipment in the next time period; A data optimization module, configured to optimize the future power generation data and the future load data by taking the operation cost of the edge cluster as an optimization target; an error correction module, for correcting the error between the future power generation data and the future load data if the optimization is completed, so as to keep the future power generation data and the future load data balanced; Wherein, the power supply equipment includes solar photovoltaic power generation equipment, and the future power generation data includes photovoltaic power generation power; The power generation data prediction module includes: A clarity index generating module, used for generating a clarity index for a solar photovoltaic power generation device; A solar irradiance calculation module, used for calculating the solar irradiance absorbed by the solar photovoltaic power generation equipment in each time period based on the clarity index; A Kalman filter processing module is used to take the solar irradiance absorbed by the solar photovoltaic power generation equipment as a time series, perform Kalman filter processing on the solar irradiance, and obtain the solar irradiance absorbed by the solar photovoltaic power generation equipment in the next time period; The photovoltaic power generation prediction module is used to predict 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.

9. A dynamic balancing autonomous device for a power grid, characterized in that: include: An edge cluster determination module, used to determine an edge cluster in a power grid, wherein the edge cluster includes a plurality of power supply devices and power consumption devices; A power generation data prediction module, used to predict future power generation data of the power supply equipment in the next time period; A load data prediction module, used to predict future load data of the electrical equipment in the next time period; A data optimization module, configured to optimize the future power generation data and the future load data by taking the operation cost of the edge cluster as an optimization target; an error correction module, for correcting the error between the future power generation data and the future load data if the optimization is completed, so as to keep the future power generation data and the future load data balanced; Wherein, the error correction module comprises: An error calculation module, used for calculating the error between the future power generation data and the future load data; A power adjustment module is used to adjust the photovoltaic power generation power in the edge cluster, the charging data of the battery energy storage station, the photovoltaic power reduction on the bus, the DC power flow of the transmission line between the buses, the charging data, the discharge power, and the exchange power between the bus and the power grid with the goal of minimizing the error, so as to balance the future power generation data and the future load data.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the dynamic balancing autonomous method for a power grid as described in any one of claims 1 to 6, or the dynamic balancing autonomous method for a power grid as described in claim 7.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is used to enable a processor to implement the dynamic balancing autonomous method for a power grid as described in any one of claims 1 to 6, or the dynamic balancing autonomous method for a power grid as described in claim 7 when executed.

Images