Micro-grid dispatching monitoring method and system for industrial park

Abstract

The invention discloses a micro-grid dispatching monitoring method and system for an industrial park, and relates to the field of micro-grid dispatching. The method comprises the following steps: constructing a micro-grid topological structure model of a to-be-monitored industrial park, sampling operation state parameters of each node in the topological structure in a micro-grid scheduling strategy execution process in real time at a fixed sampling frequency, and obtaining an operation state parameter set of the ith node at the current t moment, extracting any element in the time sequence data sequence of the jth running state parameter of the ith node within the execution time domain length L of the scheduling strategy, and re-expressing by using a Kalman filtering equation to obtain the time sequence data sequence of the jth running state parameter of the ith node after Kalman filtering reconstruction, according to the sampling value # imgabs0 # of the jth running state parameter of the ith node at the current t moment and the reconstruction value subjected to Kalman filtering, an abnormal data judgment function is constructed, and whether the sampling value is abnormal data or not is judged according to the value of the function.

Description

Technical Field

[0001] The present invention relates to the field of microgrid dispatching, and in particular to a microgrid dispatching monitoring method and system for industrial parks. Background Art

[0002] The microgrid in an industrial park usually includes a variety of distributed power generation devices (such as wind turbine power generation, solar power generation, hydropower generation, and internal combustion engine power generation), energy storage devices, various loads, and corresponding monitoring and protection devices. It can be regarded as a small power generation and distribution system that can achieve self-control and management, and can effectively optimize the energy within the grid. Compared with large power grids, microgrids have flexible operation modes and can switch freely between grid-connected operation and off-grid island operation. This reduces the energy supply cost and network loss of industrial parks.

[0003] Energy optimization scheduling is an important means and core technical issue to achieve optimized management of industrial park microgrids. Different from the energy scheduling strategy of traditional power systems, the power generation form in microgrids is easily affected by the environment, resulting in inaccuracy in predicting power output. Through microgrid energy optimization scheduling, the distributed power sources, energy storage units, loads, and the current operating status and historical data of the power grid are analyzed to make scientific evaluations and predictions; and according to the priority scheduling rights classification, load classification, and different choices of the main grid system electricity price type enjoyed by various types of distributed power sources in the microgrid system, different energy scheduling strategies, the corresponding optimization scheduling model is determined, and an effective algorithm is used to determine the optimal operation plan of the park microgrid in the future scheduling cycle.

[0004] Energy optimization scheduling aims to formulate the optimal operation plan strategy for microgrids. As the complexity of the microgrid structure in the industrial park increases, the dimensions of the relevant operation data in the scheduling process also increase. At present, the existing technology cannot analyze the operation status of the industrial park microgrid by multi-dimensionally integrating the relevant operation data in the scheduling process, and thus cannot evaluate and monitor the operation risks of the microgrid under the formulated microgrid optimal operation plan strategy. There is a lack of effective supervision means for the energy optimization scheduling process of the industrial park microgrid system, which is not conducive to improving the safety management level of microgrid operation and promoting the stable operation level of microgrids. To this end, we propose a microgrid scheduling monitoring method and system for industrial parks. Summary of the invention

[0005] The main purpose of the present invention is to provide a microgrid dispatching monitoring method and system for industrial parks, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] The microgrid dispatching monitoring method for industrial parks includes:

[0008] Step 1: Construct a microgrid topology model of the industrial park to be monitored, and perform real-time sampling of the operating state parameters of each node in the topology structure during the execution of the microgrid dispatch strategy at a fixed sampling frequency to obtain the operating state parameter set of the i-th node at the current time t.

[0009] Step 2: Extract the time series data sequence of the jth running state parameter of the i-th node within the execution time domain length L of the scheduling strategy

[0010] Step 3: Convert the time series data Any element in is re-expressed using the Kalman filter formula to obtain the time series data sequence of the jth operating state parameter of the i-th node after Kalman filter reconstruction

[0011] The specific process includes the following steps:

[0012] Step S31: define the jth operating state parameter of the i-th node at time k as Where k = 1, 2, ..., t; when k = 1, take

[0013] Step S32: Calculate the state prediction value of the jth running state parameter of the i-th node at time k The calculation formula is:

[0014]

[0015] In the formula, F(k) represents the prediction factor at time k; v(k-1) represents the sampling error at time k-1; where F(k)∈(0,1);

[0016] Step S33: Calculate the mean square error prediction value P(kk-1) of the j-th running state parameter sampling value of the i-th node at time k, and the calculation formula is:

[0017] P(kk-1)=F(k)P(k-1)F T (k)+Qv(k)

[0018] Where, P(k-1) is the mean square error of the sampling value of the operating state parameter at time k-1; Qv(k) is the error covariance of the sampling value of the operating state parameter at time k; F T (k) is the transposed matrix of F(k);

[0019] Step S34: Calculate the sampling error v(k) at time k, the calculation formula is:

[0020]

[0021] Where H(k) represents the measurement matrix of the sampling values ​​before time k;

[0022] Step S35: Calculate the sampling error estimate Qv(k) of the sampled value at time k, using the following formula:

[0023] Qv(k)=Qv(k-1)(1-λ k )+λ k [v(k)v T (k)-v(k)P(kk-1)v T (k)]

[0024] In the formula, λ k It is expressed as the weighted coefficient at time k, and the calculation formula is: Where λ is a constant coefficient and λ∈(0,1); v T (k) is represented as the transposed matrix of the sampling error v(k);

[0025] Step S36: Calculate the filter gain K(k) at time k, the calculation formula is:

[0026]

[0027] Step S37: Calculate the state estimate of the sampled value at time k The calculation formula is:

[0028]

[0029] Step S38: Calculate the sampling value at time k The reconstruction value of The calculation formula is:

[0030]

[0031] Step 4: Based on the sampling value of the jth running state parameter of the i-th node at the current time t and the reconstructed value after Kalman filtering Construct an abnormal data judgment function f(t), where f(t) = 1 or f(t) = 0, and perform an analysis on the sampled value according to the value of the function f(t). Whether it is abnormal data is determined based on the following principles:

[0032] If f(t) = 1, then the sample value is normal data;

[0033] If f(t) = 0, then the sample value Abnormal data.

[0034] The expression of abnormal data judgment function f(t) is:

[0035]

[0036] In the formula, σ is the time series data sequence The standard deviation of all data points in is calculated as: in, It is represented by the jth running state parameter of the i-th node at time k, k = 1, 2, ..., t; N is the total amount of data; is represented as the mean of all data points, where

[0037] Step 5: Based on the time series data Reconstruct the running status parameter set Get the reconstructed running status parameter set

[0038] Step 6: Calculate the operating status parameter set And get the reconstructed running status parameter set The distance value D of each element in i (t), the calculated distance value is used as the scheduling risk characterization value of the i-th node at the current time t, wherein the calculation formula of the distance value is: Where α is the state parameter, α=0 or α=1;

[0039] The principle for selecting the value of the state parameter α is:

[0040] When the jth operating state parameter is not generated during the execution of the microgrid dispatch strategy, α = 0;

[0041] When the jth operating state parameter is generated during the execution of the microgrid dispatching strategy, α=1.

[0042] Step 7: Set the risk threshold of the i-th node in the microgrid topology and According to the set risk demarcation threshold, the scheduling risk level of the i-th node at the current time t is classified into three categories: no risk, slight risk, and severe risk. The classification principle is:

[0043] when When , the scheduling risk level is risk-free;

[0044] when When , the scheduling risk level is mild risk;

[0045] when , the scheduling risk level is severe.

[0046] Microgrid dispatching and monitoring system for industrial parks, including:

[0047] A grid modeling module for building a microgrid topology model for the industrial park to be monitored;

[0048] A data sampling module for real-time sampling of operating state parameters of each node in the topology structure during the execution of the microgrid dispatch strategy;

[0049] Used to extract the time series data sequence of the jth running status parameter of the i-th node within the execution time domain length L of the scheduling strategy Data stream acquisition module;

[0050] For time series data Perform Kalman filtering on any element in to obtain the time series data sequence of the jth running state parameter of the i-th node reconstructed by Kalman filtering. Data processing module;

[0051] It is used to construct the abnormal data judgment function f(t) and to adjust the sampling value according to the value of the function f(t). An abnormal data determination module for determining whether the data is abnormal;

[0052] A risk characterization module for obtaining a scheduling risk characterization value of a node;

[0053] A risk level classification module is used to classify the scheduling risk level of nodes.

[0054] The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0055] The present invention has the following beneficial effects:

[0056] Compared with the prior art, by constructing a microgrid topology model of the industrial park to be monitored, the operating state parameters of each node in the topology structure during the execution of the microgrid scheduling strategy are sampled in real time at a fixed sampling frequency, and the operating state parameter set of the i-th node at the current time t is obtained. The arbitrary element in the time series data sequence of the j-th operating state parameter of the i-th node within the execution time domain length L of the scheduling strategy is extracted and re-expressed using the Kalman filter formula to obtain the time series data sequence of the j-th operating state parameter of the i-th node after Kalman filtering reconstruction. According to the sampling value of the j-th operating state parameter of the i-th node at the current time t An abnormal data judgment function is constructed with the reconstructed value obtained by Kalman filtering, and whether the sampled value is abnormal data is judged according to the value of the function, and the distance value between the operating state parameter set and each element in the reconstructed operating state parameter set is calculated as the scheduling risk characterization value of the i-th node at the current time t, and the risk demarcation threshold of the i-th node in the microgrid topology structure is set, and the scheduling risk degree of the i-th node at the current time t is classified according to the scheduling risk characterization value and the set risk demarcation threshold, and the Kalman filter is used to perform multi-dimensional integration of the relevant operating data in the scheduling process, so as to realize the quantitative analysis and evaluation of the operating status and operating risks of the industrial park microgrid, so as to effectively supervise the energy optimization scheduling process of the industrial park microgrid system, which is conducive to improving the safety management level of microgrid operation and promoting the stable operation level of microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of the microgrid dispatching and monitoring method for industrial parks of the present invention;

[0058] Figure 2 This is a structural diagram of a microgrid dispatching and monitoring system for an industrial park according to the present invention. DETAILED DESCRIPTION

[0059] The present invention will be further described below in conjunction with specific implementation methods, wherein the accompanying drawings are only used for exemplary descriptions and represent only schematic diagrams rather than actual drawings, and should not be understood as limiting the present invention. In order to better illustrate the specific implementation methods of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0060] The specific implementation process of the technical solution of the present invention includes the following steps:

[0061] Step 1: Construct a microgrid topology model of the industrial park to be monitored

[0062] The microgrid in an industrial park usually includes a variety of distributed power generation devices, energy storage devices, various loads, and corresponding monitoring and protection devices. Distributed power generation devices include wind turbine power generation, solar power generation, hydropower generation, and internal combustion engine power generation. According to the type and quantity of distributed power generation devices in the park, they can be classified into several power generation device units. The connectors between the devices of different units are called terminals, and the points where two terminals are connected are called connection points. The equipment connection relationship plus the functional location information determines a normal topological network, and the switch information of the distribution network automation and dispatching platform forms a real-time topological structure network.

[0063] Step 2: Perform real-time sampling of the operating state parameters of each node in the topology structure during the execution of the microgrid dispatch strategy at a fixed sampling frequency to obtain the operating state parameter set of the i-th node at the current time t.

[0064] Generally, the operating status parameters of different nodes are also different. For example, the operating status parameters of photovoltaic power generation mainly include: power generation power, real-time voltage, real-time current, conversion efficiency, irradiance, operating time, etc.; the operating status parameters of wind power generation mainly include: hub height, impeller diameter or swept area, rated power, rated wind speed, cut-in wind speed, cut-out wind speed, wind speed, pitch angle, etc.; the operating status parameters of the inverter mainly include: input voltage, output voltage, output frequency, maximum output power, efficiency, etc.; the specific operating status parameter type needs to be determined according to the node type.

[0065] Step 3: Extract the time series data sequence of the jth operating status parameter of the i-th node within the scheduling strategy execution time domain length L The time series data Any element in is re-expressed using the Kalman filter formula to obtain the time series data sequence of the jth operating state parameter of the i-th node after Kalman filter reconstruction.

[0066] The specific process includes the following steps:

[0067] Step S31: define the jth running state parameter of the i-th node at time k as Where k = 1, 2, ..., t; when k = 1, take

[0068] Step S32: Calculate the state prediction value of the jth running state parameter of the i-th node at time k The calculation formula is:

[0069]

[0070] In the formula, F(k) represents the prediction factor at time k; v(k-1) represents the sampling error at time k-1; where F(k)∈(0,1);

[0071] Step S33: Calculate the mean square error prediction value P(kk-1) of the j-th running state parameter sampling value of the i-th node at time k, and the calculation formula is:

[0072] P(kk-1)=F(k)P(k-1)F T (k)+Qv(k)

[0073] Where, P(k-1) is the mean square error of the sampling value of the operating state parameter at time k-1; Qv(k) is the error covariance of the sampling value of the operating state parameter at time k; F T (k) is the transposed matrix of F(k);

[0074] Step S34: Calculate the sampling error v(k) at time k, the calculation formula is:

[0075]

[0076] Where H(k) represents the measurement matrix of the sampling values ​​before time k;

[0077] Step S35: Calculate the sampling error estimate Qv(k) of the sampled value at time k, using the following formula:

[0078] Qv(k)=Qv(k-1)(1-λ k )+λ k [v(k)v T (k)-v(k)P(kk-1)v T (k)]

[0079] In the formula, λ k It is expressed as the weighted coefficient at time k, and the calculation formula is: Where λ is a constant coefficient and λ∈(0,1); v T (k) is represented as the transposed matrix of the sampling error v(k);

[0080] Step S36: Calculate the filter gain K(k) at time k, the calculation formula is:

[0081]

[0082] Step S37: Calculate the state estimate of the sampled value at time k The calculation formula is:

[0083]

[0084] Step S38: Calculate the sampling value at time k The reconstruction value of The calculation formula is:

[0085]

[0086] Step 4: Based on the sampling value of the jth running state parameter of the i-th node at the current time t and the reconstructed value after Kalman filtering Construct the abnormal data judgment function f(t), the expression is:

[0087]

[0088] In the formula, σ is the time series data sequence The standard deviation of all data points in is calculated as: in, It is represented as the jth running state parameter of the i-th node at time k, k = 1, 2, ..., t; N is the total amount of data; is represented as the mean of all data points, where

[0089] Step 5: Sample values ​​according to the value of function f(t) Whether it is abnormal data is determined based on the following principles:

[0090] If f(t) = 1, then the sample value is normal data;

[0091] If f(t) = 0, then the sample value Abnormal data.

[0092] Abnormal data judgment function f(t)

[0093] Step 6: Based on the time series data Reconstruct the running status parameter set Get the reconstructed running status parameter set

[0094] Step 7: Calculate the operating status parameter set And get the reconstructed running status parameter set The distance value D of each element in i (t), the calculation formula is: Where α is the state parameter, α=0 or α=1;

[0095] The principle for selecting the value of the state parameter α is:

[0096] When the jth operating state parameter is not generated during the execution of the microgrid dispatch strategy, α = 0;

[0097] When the jth operating state parameter is generated during the execution of the microgrid dispatching strategy, α=1.

[0098] Step 8: Use the calculated distance value as the dispatch risk characterization value of the i-th node at the current time t, and set the risk boundary threshold of the i-th node in the microgrid topology structure. and

[0099] Step 9: According to the obtained dispatch risk characterization value and the set risk demarcation threshold, the dispatch risk level of the i-th node at the current time t is classified. The dispatch risk level includes three categories: no risk, slight risk, and severe risk. The specific classification principles are as follows:

[0100] when When , the scheduling risk level is risk-free;

[0101] when When , the scheduling risk level is mild risk;

[0102] when , the scheduling risk level is severe.

[0103] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A microgrid dispatching and monitoring method for an industrial park, characterized in that: include: Step 1: Construct a microgrid topology model of the industrial park to be monitored, and perform real-time sampling of the operating state parameters of each node in the topology structure during the execution of the microgrid dispatch strategy at a fixed sampling frequency to obtain the operating state parameter set of the i-th node at the current time t. Step 2: Extract the time series data sequence of the jth running state parameter of the i-th node within the execution time domain length L of the scheduling strategy Step 3: Convert the time series data Any element in is re-expressed using the Kalman filter formula to obtain the time series data sequence of the jth operating state parameter of the i-th node after Kalman filter reconstruction Step 4: Based on the sampling value of the jth running state parameter of the i-th node at the current time t and the reconstructed value after Kalman filtering Construct an abnormal data judgment function f(t), where f(t) = 1 or f(t) = 0, and perform an analysis on the sampled value according to the value of the function f(t). Whether it is abnormal data is determined based on the following principles: If f(t) = 1, then the sample value is normal data; If f(t) = 0, then the sample value Abnormal data.

2. The microgrid dispatching and monitoring method for an industrial park according to claim 1, characterized in that: The method further comprises: Step 5: Based on the time series data Reconstruct the running status parameter set Get the reconstructed running status parameter set Step 6: Calculate the operating status parameter set And get the reconstructed running status parameter set The distance value D of each element in i (t), the calculated distance value is used as the scheduling risk characterization value of the i-th node at the current time t, wherein the calculation formula of the distance value is: Where α is the state parameter, α=0 or α=1; Step 7: Set the risk threshold of the i-th node in the microgrid topology and According to the set risk demarcation threshold, the scheduling risk level of the i-th node at the current time t is classified into three categories: no risk, slight risk, and severe risk. The classification principle is: when When , the scheduling risk level is risk-free; when When , the scheduling risk level is mild risk; when , the scheduling risk level is severe.

3. The microgrid dispatching and monitoring method for an industrial park according to claim 1, characterized in that: The expression of abnormal data judgment function f(t) is: In the formula, σ is the time series data sequence The standard deviation of all data points in is calculated as: in, It is represented by the jth running state parameter of the i-th node at time k, k = 1, 2, ..., t; N is the total amount of data; is represented as the mean of all data points, where 4. The microgrid dispatching and monitoring method for an industrial park according to claim 1, characterized in that: In step 3, the Kalman filter formula is used to filter the time series data The calculation process of re-expressing the elements in is as follows: Step S31: define the jth operating state parameter of the i-th node at time k as Where k = 1, 2, ..., t; when k = 1, take Step S32: Calculate the state prediction value of the jth running state parameter of the i-th node at time k The calculation formula is: In the formula, F(k) represents the prediction factor at time k; v(k-1) represents the sampling error at time k-1; where F(k)∈(0,1); Step S33: Calculate the mean square error prediction value P(kk-1) of the j-th running state parameter sampling value of the i-th node at time k, and the calculation formula is: P(kk-1)=F(k)P(k-1)F T (k)+Qv(k) Where, P(k-1) is the mean square error of the sampling value of the operating state parameter at time k-1; Qv(k) is the error covariance of the sampling value of the operating state parameter at time k; F T (k) is the transposed matrix of F(k); Step S34: Calculate the sampling error v(k) at time k, the calculation formula is: Where H(k) represents the measurement matrix of the sampling values ​​before time k; Step S35: Calculate the sampling error estimate Qv(k) of the sampled value at time k, using the following formula: Qv(k)=Qv(k-1)(1-λ k )+λ k [v(k)v T (k)-v(k)P(kk-1)v T (k)] In the formula, λ k Expressed as the weighting coefficient at time k; v T (k) is represented as the transposed matrix of the sampling error v(k); Step S36: Calculate the filter gain K(k) at time k, the calculation formula is: Step S37: Calculate the state estimate of the sampled value at time k The calculation formula is: Step S38: Calculate the sampling value at time k The reconstruction value of The calculation formula is:

5. The microgrid dispatching and monitoring method for industrial parks according to claim 2, characterized in that: The principle for selecting the value of the state parameter α is: When the jth operating state parameter is not generated during the execution of the microgrid dispatch strategy, α = 0; When the jth operating state parameter is generated during the execution of the microgrid dispatching strategy, α=1.

6. The microgrid dispatching and monitoring method for industrial parks according to claim 4, characterized in that: The weighting coefficient λ at time k k The calculation formula is: Where λ is a constant coefficient and λ∈(0,1).

7. Microgrid dispatching and monitoring system for industrial parks, characterized in that: The system is used to implement the steps of the microgrid dispatching monitoring method for an industrial park according to any one of claims 1 to 6, including: A grid modeling module for building a microgrid topology model for the industrial park to be monitored; A data sampling module for real-time sampling of operating state parameters of each node in the topology structure during the execution of the microgrid dispatch strategy; Used to extract the time series data sequence of the jth running status parameter of the i-th node within the execution time domain length L of the scheduling strategy Data stream acquisition module; For time series data Perform Kalman filtering on any element in to obtain the time series data sequence of the jth running state parameter of the i-th node reconstructed by Kalman filtering. Data processing module; It is used to construct the abnormal data judgment function f(t) and to adjust the sampling value according to the value of the function f(t). An abnormal data determination module for determining whether the data is abnormal; A risk characterization module for obtaining a scheduling risk characterization value of a node; A risk level classification module is used to classify the scheduling risk level of nodes.

8. The microgrid dispatching and monitoring system for industrial parks according to claim 7, characterized in that: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can implement the steps of the microgrid dispatching and monitoring method for an industrial park as described in any one of claims 1 to 6 when executing the program.

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