A distributed energy storage power station output optimization method, medium and system

Abstract

The present invention provides a method, medium and system for optimizing the output of a distributed energy storage power station, belonging to the technical field of distributed energy storage power stations, comprising: establishing a regional power grid topology map, continuously obtaining the load of each load node in each time period and the output of each energy storage node in each time period; calculating the frequency and voltage stability influence index caused by each energy storage node on the regional power grid when outputting electric energy; obtaining energy storage loss and transmission loss of each edge; establishing an optimization objective function of the regional power grid topology map; optimizing the regional power grid topology map by using an optimization algorithm to minimize the value of the optimization objective function and obtain an optimized topology map for each time period; sorting all optimized topology maps according to the frequency and voltage stability influence index, and selecting the optimized topology map with the best stability influence index as the optimal topology map; calculating the output of each energy storage node in each time period, and summarizing them into the total output of the distributed energy storage power station.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed energy storage power stations, and in particular, relates to a distributed energy storage power station output optimization method, medium and system. Background Art

[0002] In recent years, with the rapid development of renewable energy, distributed energy storage power stations have been increasingly used in power systems. Distributed energy storage power stations are usually composed of a large number of small energy storage devices, which are dispersed in different geographical locations and connected to the grid through grid connection. Compared with traditional centralized large-scale battery energy storage, distributed energy storage power stations have the advantages of low investment cost, flexible construction, and proximity to load centers.

[0003] Distributed energy storage power stations mainly play a role in the following aspects:

[0004] 1. Peak shaving and valley filling: Distributed energy storage power stations can store electricity when the grid load is low and discharge it when it is high, thus realizing the peak shaving and valley filling function of the grid load curve. This helps to reduce the peak load regulation pressure of the grid, reduce the frequent start and stop of thermal power units, improve power generation efficiency, and reduce emissions.

[0005] 2. Voltage boosting: When the reactive power regulation of the power grid is insufficient, the active power output of distributed energy storage can boost the voltage of the power grid by affecting the line voltage drop. At the same time, the electrochemical characteristics of the energy storage device can also provide reactive power support for the power grid.

[0006] 3. Regulate renewable energy: Energy storage devices can store intermittent renewable energy such as photovoltaic and wind power, realize time shift of energy, thereby improving the grid’s ability to absorb renewable energy and increasing the proportion of clean energy in the grid.

[0007] 4. Improve power quality: Energy storage devices have the ability to respond quickly, can track power fluctuations in real time, suppress abnormal fluctuations in voltage and frequency, eliminate harmonics and flicker, and thus improve the power quality level of the power grid.

[0008] 5. Provide backup capacity: Distributed energy storage power stations can also serve as backup power sources, providing emergency power in the event of grid failure or emergency, thereby improving the reliability and safety of the grid.

[0009] 6. Power trading: In the power ancillary service market, distributed energy storage power stations can participate in power trading businesses such as reactive power compensation and rotating reserve to increase revenue.

[0010] However, due to the cluster characteristics of distributed energy storage power stations and their close coupling with distribution networks, the existing technology only considers the operating status of the energy storage power station itself, and fails to fully consider the matching degree between the output of the distributed energy storage power station and the regional power grid topology structure and the impact on grid frequency, voltage and other indicators, resulting in technical problems such as unstable grid operation. Summary of the invention

[0011] In view of this, the present invention provides a distributed energy storage power station output optimization method, medium and system, which can solve the technical problems existing in the prior art that the matching degree between the output of the distributed energy storage power station and the regional power grid topology structure and the impact on the power grid frequency, voltage and other indicators are not fully considered, resulting in unstable power grid operation.

[0012] The present invention is achieved in that:

[0013] A first aspect of the present invention provides a method for optimizing the output of a distributed energy storage power station, which includes the following steps:

[0014] S10. Establish a regional power grid topology map according to the structure of the regional power grid, wherein the nodes of the regional power grid topology map include load nodes and energy storage nodes, and the edges of the regional power grid topology map are paths between nodes. The load nodes represent loads in the regional power grid, the energy storage nodes represent sub-energy storage power stations of distributed energy storage power stations in the regional power grid, and the edges represent transmission lines between the nodes.

[0015] S20, continuously obtaining the load of each load node in each time period and the output of each energy storage node in each time period;

[0016] S30, calculating the frequency and voltage stability impact index of each energy storage node on the regional power grid when outputting electric energy according to the monitored regional power grid frequency and voltage;

[0017] S40, obtaining the energy storage loss of each energy storage node and the power transmission loss of each edge in each time period;

[0018] S50, establishing an optimization objective function of the regional power grid topology map, and taking the sum of energy storage loss and transmission loss as the optimization objective;

[0019] S60, optimizing the regional power grid topology map by using an optimization algorithm to minimize the value of the optimization objective function, and obtaining an optimized topology map for each time period;

[0020] S70, sorting all optimized topology graphs according to frequency and voltage stability impact index, and selecting the optimized topology graph with the best stability impact index as the optimal topology graph;

[0021] S80. Calculate the output of each energy storage node in each time period according to the optimal topology diagram, and summarize them into the total output of the distributed energy storage power station.

[0022] On the basis of the above technical solution, the output optimization method of a distributed energy storage power station of the present invention can also be improved as follows:

[0023] The time period may be any time period between 15 and 30 minutes.

[0024] Furthermore, the optimization objective function of establishing the regional power grid topology diagram is specifically: designing the optimization objective function as the minimum value of the sum of energy storage loss and transmission loss, and converting the power demand satisfaction, the total energy storage output being greater than or equal to the total load, the power balance, the frequency and voltage deviation being controlled within a safe range, and the line power not exceeding the rated value as constraints into equations or inequality constraints on the optimization variables, and incorporating them into the optimization objective function.

[0025] Furthermore, the energy storage loss is obtained by data mining based on the loss estimation value and the working state of the energy storage battery, and the transmission loss is calculated based on a π-type equivalent circuit model.

[0026] Among them, the step of optimizing the regional power grid topology map by using an optimization algorithm is specifically: encoding the regional power grid topology map, randomly generating an initial population, calculating individual objective function values ​​and constraint function values, performing genetic operations such as selection, crossover, and mutation according to the fitness size to generate a new generation of population, repeating the iteration until the termination condition is met, and outputting the solution corresponding to the optimal individual, which is the optimized topology structure.

[0027] Furthermore, the termination condition is that the number of iteration steps reaches 2000 or the similarity of ten consecutive generations is greater than 0.8.

[0028] Among them, the step of calculating the output of each energy storage node in each time period according to the optimal topology diagram and summing up the total output of the distributed energy storage power station is specifically: according to the optimal topology diagram, extracting the output value corresponding to each energy storage node, multiplying the output value by the length of the corresponding output power time period to obtain the output power, and summing the output power of all energy storage nodes in the time period to obtain the total output power of the energy storage power station.

[0029] Among them, the calculation method of the stability impact index is: obtain real-time data on regional power grid frequency and voltage, use machine learning algorithms such as generalized regression neural networks to train the mathematical model between the output of energy storage nodes and the power grid frequency and voltage, substitute the planned output of each energy storage node into the model for simulation, calculate the impact value on the power grid frequency and voltage, compare the impact value with the upper and lower limits of the stable operation range, and obtain two deviation values. Substitute the two deviation values ​​into nonlinear functions for processing, and obtain the frequency stability impact index and the voltage stability impact index. The weighted sum of the two indexes is used as the comprehensive stability impact index.

[0030] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed, they are used to execute the above-mentioned method for optimizing the output of a distributed energy storage power station.

[0031] A third aspect of the present invention provides a distributed energy storage power station output optimization system, which includes the above-mentioned computer-readable storage medium.

[0032] Compared with the prior art, the distributed energy storage power station output optimization method, medium and system provided by the present invention have the following beneficial effects:

[0033] 1. The coordinated optimization of the regional power grid and distributed energy storage power stations in terms of topology and output scheme has been achieved, giving full play to the advantages of the flexible adjustment of distributed energy storage clusters.

[0034] 2. A stability impact index associated with the frequency and voltage of the power grid is established, and the safe operation of the power grid is included as a core constraint in the optimization target to ensure the safety and reliability of the energy storage power station output plan. Fluctuations in the frequency and voltage of the power grid will directly affect the operational stability of the power grid. However, most existing optimization schemes ignore this important factor, which can easily cause the energy storage output to impact the power grid and cause power grid failures. The present invention quantifies these two indicators into a stability impact index, and limits them to a safe range through constrained optimization, fundamentally avoiding potential safety hazards in the power grid.

[0035] In summary, the solution of the present invention solves the technical problem that the prior art fails to fully consider the matching degree between the output of the distributed energy storage power station and the regional power grid topology structure and the impact on the power grid frequency, voltage and other indicators, resulting in unstable power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0037] Figure 1 is a flow chart of the method of the present invention; DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0039] like Figure 1 As shown, it is a flow chart of a method for optimizing the output of a distributed energy storage power station provided by the present invention. The method comprises the following steps:

[0040] S10. Establish a regional power grid topology map according to the structure of the regional power grid, wherein the nodes of the regional power grid topology map include load nodes and energy storage nodes, the edges of the regional power grid topology map are paths between nodes, the load nodes represent loads in the regional power grid, the energy storage nodes represent sub-energy storage power stations of distributed energy storage power stations in the regional power grid, and the edges represent transmission lines between nodes;

[0041] S20, continuously obtaining the load of each load node in each time period and the output of each energy storage node in each time period;

[0042] S30, calculating the frequency and voltage stability impact index of each energy storage node on the regional power grid when outputting electric energy according to the monitored regional power grid frequency and voltage;

[0043] S40, obtaining the energy storage loss of each energy storage node and the power transmission loss of each edge in each time period;

[0044] S50, establishing an optimization objective function of the regional power grid topology map, and taking the sum of energy storage loss and transmission loss as the optimization objective;

[0045] S60, optimizing the regional power grid topology map by using an optimization algorithm to minimize the value of the optimization objective function, and obtaining an optimized topology map for each time period;

[0046] S70, sorting all optimized topology graphs according to frequency and voltage stability impact index, and selecting the optimized topology graph with the best stability impact index as the optimal topology graph;

[0047] S80. Calculate the output of each energy storage node in each time period according to the optimal topology diagram, and summarize them into the total output of the distributed energy storage power station.

[0048] The specific implementation methods of the above steps are described in detail below:

[0049] The specific process of implementing step S10 is as follows:

[0050] The establishment of a regional power grid topology is the basis of the entire method. First, the scope of the regional power grid needs to be clarified. This is usually a power grid structure within a certain radius centered on a certain substation. Then, all information about the regional power grid needs to be collected, including the location of the substation, the location of the load center, the location of the sub-storage power station of the distributed energy storage power station, the direction of the transmission line, etc. This information can be obtained through field surveys, consulting power grid planning drawings, or directly obtaining relevant data from power companies.

[0051] Next, you need to build nodes. There are two types of nodes: load nodes and energy storage nodes. Load nodes represent areas with concentrated electricity loads in regional power grids, such as residential areas, industrial parks, etc. Energy storage nodes represent sub-energy storage power stations in distributed energy storage power stations. Each sub-energy storage power station corresponds to an energy storage node. The location of the node needs to be accurate to longitude and latitude coordinates, which can be obtained through GIS or other geographic information systems.

[0052] The next step is to build edges. Edges represent transmission lines between nodes, including high-voltage and low-voltage lines. You need to obtain data such as the starting and ending point coordinates, voltage level, line length, cable material, etc. of each line. If there are multiple lines connecting the same pair of nodes, then multiple edges need to be built.

[0053] Finally, the above data are integrated into a data structure to obtain the regional power grid topology map. This data structure can be a classic graph theory data structure representation such as an adjacency matrix or an adjacency list. The selection of a specific data structure needs to be considered comprehensively based on factors such as the size of the topology map and the performance requirements of the algorithm.

[0054] In summary, building a regional power grid topology map requires a lot of power grid data collection and processing, involving GIS technology, graph theory knowledge and other fields. This is the basic work of the entire optimization method, and it is crucial to do this step well.

[0055] The specific process of implementing step S20 is as follows:

[0056] The purpose of continuously acquiring the load of each load node in each time period and the output of each energy storage node in each time period is to grasp the instantaneous operation status of the regional power grid. The length of the time period is usually set to any length between 15 and 30 minutes. This is a reasonable time window that can better reflect the dynamic changes of the power grid without causing excessive data volume and calculation.

[0057] The method to obtain the load amount of load nodes is to install load monitoring equipment at each load node, which can monitor the current load value in real time and transmit the data to the central server. The monitoring equipment can be a special instrument such as a smart meter, a load recorder, or an embedded computer system. It is important that these monitoring devices have accurate time synchronization to ensure that the data of all nodes are obtained in the same time period.

[0058] The method to obtain the output of energy storage nodes is to install a monitoring system in each energy storage power station to collect the output data of each sub-energy storage power station in real time. After these data are transmitted to the central server, the output value of each energy storage node in each time period is formed.

[0059] After the central server receives the monitoring data from all nodes, it needs to perform data cleaning and preprocessing to remove outliers, fill in missing values, etc., to ensure the integrity and quality of the data.

[0060] It is worth noting that the entire network data needs to be updated every 15 to 30 minutes, and the amount of data may be very large, so an efficient data transmission and storage solution must be designed. For example, distributed storage, streaming computing and other technologies can be used to disperse the computing pressure to multiple nodes to improve real-time performance and scalability.

[0061] The specific process of implementing step S30 is as follows:

[0062] The output of energy storage power stations will affect the frequency and voltage of the regional power grid. In order to evaluate this impact, it is necessary to calculate the stability impact index of the frequency and voltage of the regional power grid caused by the output of each energy storage node. This index reflects the impact of energy storage output on the stability of the power grid.

[0063] First, it is necessary to monitor the frequency and voltage of the regional power grid in real time. Monitoring devices can be deployed at major nodes to obtain real-time data of frequency and voltage. Frequency monitoring can use phasor measurement units (PMUs), and voltage monitoring can use power quality monitoring devices.

[0064] Next, it is necessary to establish a mathematical model between the output of the energy storage node and the frequency and voltage of the power grid. This can be done by using machine learning algorithms such as generalized regression neural networks (GRNN) to train an accurate model using a large amount of historical data. The model needs to consider multiple influencing factors such as the output size, location, and transmission line impedance of the energy storage node.

[0065] With the above model, the planned output of each energy storage node can be simulated to calculate its impact on the grid frequency and voltage. By comparing these two impact values ​​with the upper and lower limits of the stable operation range, two deviation values ​​can be obtained.

[0066] The magnitude of these two deviation values ​​represents the degree to which the frequency and voltage deviate from the stable range. They can be processed by nonlinear functions to obtain the frequency stability impact index Fi and the voltage stability impact index Vi. The larger the values ​​of these two indexes, the more serious the adverse impact of energy storage output on stability.

[0067] The calculation formulas for Fi and Vi can be designed as follows:

[0068]

[0069]

[0070] Among them, a and b are adjustment parameters, which can be set to values ​​of 5 to 10; Δf is the deviation of the grid frequency; Δfmax is the maximum allowable frequency deviation; ΔV is the voltage deviation; ΔVmax is the maximum allowable voltage deviation. When the deviation is 0, the minimum index is 0; as the deviation increases, the index gradually increases, and when the deviation approaches the maximum allowable value, the index approaches 1.

[0071] Finally, Fi and Vi can be weighted and summed to obtain the comprehensive stability impact index W:

[0072] W=w1*Fi+w2*Vi

[0073] Among them, w1 and w2 are weight coefficients, which are determined according to actual needs. If the frequency influence is emphasized, w1>w2 can be made.

[0074] The smaller the stability impact index W is, the smaller the impact of the energy storage node output plan on the grid stability is. Therefore, when optimizing the energy storage output, it is necessary to minimize the value of W.

[0075] It should be noted that the above method involves some threshold parameters, such as the maximum allowable frequency deviation Δfmax, the maximum allowable voltage deviation ΔVmax, etc. The setting of these parameters needs to comply with the national grid safety operation standards, such as the frequency fluctuation range of ±0.2Hz, the voltage fluctuation range of +7% to -10%, etc. At the same time, it is also necessary to make appropriate adjustments based on the specific conditions of the local power grid and the actual characteristics of the energy storage power station.

[0076] In general, the calculation of the stability impact index integrates multiple technologies such as power system theory, optimization algorithm, and machine learning. It is a key technical point of this optimization method and an important guarantee for ensuring the safe and efficient operation of energy storage power stations.

[0077] The specific process of implementing step S40 is as follows:

[0078] The energy storage loss of each energy storage node and the transmission loss of each edge in each time period are calculated to evaluate the energy loss of the entire energy storage power station system during operation. The purpose of this work is to take energy loss into account in the subsequent optimization process, thereby improving the overall efficiency of the energy storage power station.

[0079] The energy storage loss of energy storage nodes mainly comes from the chemical reaction energy loss during the battery charging and discharging process. Different types of energy storage batteries, such as lead-acid batteries and lithium-ion batteries, have different internal reaction mechanisms and losses. Therefore, when calculating energy storage losses, it is necessary to classify and process different types of energy storage batteries.

[0080] Taking lithium-ion batteries as an example, the electrochemical kinetic model can be used to calculate its energy loss during the charging and discharging process. This model mainly involves a series of differential equations such as voltage equation, heat equation, concentration equation, etc., which are used to describe the electrochemical processes of multiple parts such as electrolyte, positive and negative electrodes, and diaphragms. These equations need to be solved based on multiple variables such as battery structural parameters, temperature, current, etc. to obtain an estimate of the energy loss. Numerical calculation methods such as finite element analysis can be used to solve the problem.

[0081] In addition to the above internal chemical losses, the thermal loss of the battery also needs to be considered. It can be estimated using a battery thermal model, which usually consists of an energy balance equation, a heat conduction equation, etc.

[0082] There are many practical parameters in the battery management system (BMS) that can directly reflect the energy loss of the battery, such as internal resistance, thermal runaway risk, power factor, etc. These parameters can also be included in the calculation formula.

[0083] Transmission loss mainly refers to the energy loss on the transmission line. It is related to factors such as the material, length, and voltage level of the line. The transmission energy loss per unit time can be obtained by using the π-type equivalent circuit model of the transmission line, considering parameters such as resistance, reactance, and conductance, and applying the corresponding circuit power transmission calculation formula.

[0084] When solving the above energy loss estimation formula, the instantaneous operation data of the power grid is required as an input parameter. Therefore, the energy loss calculation process must be combined with the real-time power grid data obtained in the previous steps to give an accurate calculation result.

[0085] In order to improve the calculation efficiency, the working modes of energy storage batteries, transmission lines and other equipment can be classified, the energy loss values ​​under various working conditions can be calculated in advance, and a lookup table can be established. By simply looking up the table based on the real-time operation data of the power grid, the corresponding loss estimation value can be quickly obtained, thus greatly saving the calculation time.

[0086] In short, energy loss estimation requires theoretical knowledge from multiple fields such as electrochemistry, thermodynamics, and circuit principles, and requires coupling mathematical models from multiple professional fields. In the specific calculation process, the model can be simplified and adjusted according to the actual application scenario to balance the calculation accuracy and efficiency.

[0087] The specific process of implementing step S50 is as follows:

[0088] The purpose of establishing the optimization objective function of the regional power grid topology map is to formally define the target to be optimized and the corresponding constraints. The design of the optimization objective function directly determines the solution direction and final result of the optimization algorithm.

[0089] In the method of the present invention, the sum of energy storage loss and transmission loss is taken as the optimization target. This is because reducing the energy loss of the energy storage power station system can maximize the use of the output power of the energy storage power station and improve the overall operation efficiency of the power grid. At the same time, energy storage loss and transmission loss also reflect the degree of wear of batteries and line equipment. Including both in the optimization target can also extend the service life of the equipment and reduce operation and maintenance costs.

[0090] Therefore, the optimization objective function can be designed as:

[0091] min f(x)=∑(Ei*Li(x))+∑(Pj*Lj(x))

[0092] in:

[0093] x is the variable to be optimized, representing the regional power grid topology

[0094] Ei is the output of the i-th energy storage node

[0095] Li(x) is the energy storage loss function of the i-th energy storage node under the current topology x

[0096] Pj is the power flow of transmission line j

[0097] Lj(x) is the transmission loss function of transmission line j under the current topology x.

[0098] The energy storage loss function Li(x) can be obtained by fitting through data mining methods such as regression analysis based on the loss value estimated in the previous step and factors such as the working status of the energy storage battery. The transmission loss function Lj(x) can be solved by substituting line parameters, power factors, etc. based on the π-type equivalent circuit model.

[0099] When establishing the optimization objective function, some constraints need to be considered, such as:

[0100] 1. The power demand of each load node must be met;

[0101] 2. The sum of the outputs of each energy storage node must be greater than or equal to the sum of all loads;

[0102] 3. The power injection of each node must satisfy the power balance equation;

[0103] 4. The deviation of grid frequency and voltage must be controlled within a safe range;

[0104] 5. The power flow of the transmission line shall not exceed its rated value;

[0105] These constraints need to be transformed into equality or inequality constraints on the optimization variable x and incorporated into the description of the optimization objective function. Satisfying all constraints is the prerequisite for the optimization algorithm to obtain a feasible solution.

[0106] The constraints involve professional knowledge of the power system, such as frequency and voltage, and require a detailed formula derivation process based on specific application scenarios. In addition, the constraints also need to be combined with the actual operation procedures of the power grid, and refer to relevant national standards and industry specifications to set a reasonable value range for the safety threshold.

[0107] In general, the establishment of the optimization objective function integrates multiple theoretical methods such as operations research, power system analysis, and data analysis, which is the core part of the method of the present invention and is crucial to ensuring the optimization effect. Reasonable design of the objective function and constraints directly affects the scientificity and feasibility of the final optimization result.

[0108] The specific process of implementing step S60 is as follows:

[0109] The optimization algorithm is used to optimize the regional power grid topology in order to solve the optimization objective function established in the previous step, so as to find the optimal topology with the minimum energy loss. The selection of the optimization algorithm is crucial to the efficiency and accuracy of the solution.

[0110] According to the characteristics of the optimization problem, biologically inspired intelligent optimization algorithms such as genetic algorithm (GA), ant colony algorithm (ACO), particle swarm algorithm (PSO) can be selected as solvers. These algorithms have the advantages of strong global optimization ability and not easy to fall into local optimum, and are suitable for dealing with complex nonlinear and non-convex optimization problems.

[0111] Taking genetic algorithm as an example, the specific implementation steps are as follows:

[0112] 1. First, encode the topological structure of the regional power grid and convert it into a chromosome representation suitable for genetic algorithm operation. Various representation forms such as tree encoding and sequence encoding can be used.

[0113] 2. Randomly generate a certain number of initial individuals to form the initial population.

[0114] 3. For each individual, calculate its objective function value and constraint function value, and calculate its fitness based on the objective function value.

[0115] 4. Based on the fitness of the individuals, genetic operations such as selection, crossover, and mutation are used to generate a new generation of populations. Among them, a certain retention rate can be set to ensure that the best individuals can be retained.

[0116] 5. Repeat steps 3 and 4 until the termination conditions are met, such as the number of iterations reaches the upper limit, the optimal solution remains unchanged in a certain generation, etc.

[0117] 6. Output the solution corresponding to the best individual in the current population, which is the optimal topological structure.

[0118] In actual operation, the dynamic changes of the regional power grid can be introduced into the genetic algorithm solution process. That is to say, after each time period, the genetic algorithm is iterated again based on the newly acquired power grid data and the optimal solution of the previous period is used as the initial population. In this way, the algorithm results can continuously track the changing trend of the power grid and maintain the optimal state at all times.

[0119] In addition, some improvements can be made to the genetic algorithm to improve the convergence speed and solution accuracy, such as introducing elite retention strategy, adaptive mutation rate, parallel computing and other technologies.

[0120] In addition to genetic algorithms, other intelligent optimization algorithms can also be tried, such as particle swarm optimization, species gene algorithm, etc. For power grid optimization problems of different scales, the most suitable algorithm can be selected through comparative experiments.

[0121] In short, the search space of power grid topology optimization problem is huge and the constraints are complex. It requires efficient optimization algorithm to give a satisfactory solution in a limited time. As an important solution, bio-inspired algorithm plays a vital role in the method of the present invention.

[0122] The specific process of implementing step S70 is as follows:

[0123] All optimized topologies are sorted according to the frequency and voltage stability impact index, and the optimized topology with the best stability impact index is selected as the optimal topology. The purpose of this step is to select the solution with the least impact on power grid stability from multiple feasible solutions.

[0124] First, we need to review the calculation formula of the stability impact index mentioned above:

[0125] Stability influence index W = w1*Fi+w2*Vi

[0126] Among them, Fi is the frequency stability impact index, Vi is the voltage stability impact index; w1 and w2 are weight coefficients.

[0127] For each optimized topology, it is necessary to calculate its corresponding stability impact index W. The specific method is to substitute the energy storage node output plan of the topology into the previously established mathematical model, simulate and calculate the corresponding frequency deviation Δf and voltage deviation ΔV, and then substitute these deviation values ​​into the index calculation formula to obtain Fi, Vi and the comprehensive index W.

[0128] After obtaining the W values ​​of all topological diagrams, sort them from small to large, and select the topological diagram with the smallest W value as the final optimization result. This ensures that the selected topological solution has the least impact on the grid frequency and voltage, and the grid operation is the most stable and reliable.

[0129] In specific implementation, the screening process can be further optimized to improve the computational efficiency:

[0130] 1. For each time period, only the first N optimal topological graphs are retained, and those with larger W values ​​are eliminated to reduce the amount of subsequent calculations. N can be set according to actual conditions, such as retaining 50.

[0131] 2. Track the changes in the W value of each topology in different time periods. If the W value of a topology is ranked at the top in K consecutive time periods, it can be directly determined as the optimal topology to avoid repeated calculations in the remaining time periods. K can be set to 5 to 10.

[0132] 3. Establish a rapid estimation model for W value, such as using artificial neural network to perform regression fitting on W value, thereby greatly reducing complex mathematical model calculations.

[0133] 4. For each optimized topology, calculate its comprehensive W value, frequency index Fi and voltage index Vi at the same time to form a three-dimensional vector. Then perform hierarchical analysis on these three-dimensional vectors, that is, first sort by Fi value, then sort the top N by Vi value, and finally determine the optimal solution by W value. This hierarchical sorting method can reduce computational complexity.

[0134] In addition, when selecting the optimal topology, other auxiliary information, such as battery aging, line failure rate, etc., can be combined to ensure the long-term reliability of the solution.

[0135] In short, through reasonable algorithm optimization, the computing performance of this step can be greatly improved, ensuring that a satisfactory solution is obtained efficiently under limited time and resources. At the same time, the selection process of the optimal solution also follows the safe, reliable and economical operation principles of the power grid, ensuring the feasibility and pertinence of the optimization results.

[0136] The specific process of implementing step S80 is as follows:

[0137] According to the optimal topology diagram, the output of each energy storage node in each time period is calculated and summarized as the total output of the distributed energy storage power station. This is the ultimate goal and important output result of the method of the present invention.

[0138] First, the data structure of the optimal topology needs to be parsed to extract the output value of each energy storage node under the topology. This output value may not be completely equivalent to the real-time output data obtained previously, because it may be adjusted during the optimization process.

[0139] Next, it is necessary to check the rationality of these output values. Specifically, the optimized output of each energy storage node is substituted into the mathematical model mentioned above, and the stability impact of the energy storage node on the power grid is simulated and calculated to verify whether it meets expectations. If not, it needs to be optimized and adjusted again to meet the constraints. In addition, it is necessary to check whether the total output is greater than the total load to ensure power balance, etc.

[0140] For the output value that passes the rationality check, it needs to be multiplied by the length of the corresponding time period to obtain the total output power of the time period. The total output power of the entire energy storage power station in the time period can be obtained by summing up the output power of all energy storage nodes in the time period.

[0141] The following is a specific embodiment of the method of the present invention:

[0142] Step S10: Establishment of regional power grid topology map

[0143] First, it is necessary to clarify the scope of the regional power grid, which is usually centered on a substation and the power grid structure within a radius of R, where R is an appropriate radius value.

[0144] Then you need to collect the following information:

[0145] Substation location coordinate set S = (x s ,y s )|s∈S

[0146] Load center position coordinate set L = (x l ,y l )|l∈L

[0147] Distributed energy storage power station sub-energy storage power station location coordinate set E = (x e ,y e )|e∈E

[0148] Transmission line start and end point coordinates and parameter information collection Where V t is the line voltage level, l t is the line length, m t Line cable material.

[0149] The above coordinate and parameter information can be obtained through field survey, reference to planning drawings or from power companies.

[0150] Next, we construct the node set V and edge set E of the regional power grid topology graph G = (V, E):

[0151] Load node set V L =v l |(x l ,y l )∈L

[0152] Energy storage node set V E =v e |(x e ,y e )∈E

[0153] The edge set E = (v i , v j )|((x i ,y i ), (x j ,y j ))∈T

[0154] By integrating the above information into data structures such as adjacency matrix and adjacency list, the regional power grid topology graph G = (V, E) can be obtained.

[0155] Step S20: Real-time acquisition of load and energy storage output data

[0156] At each load node v l ∈V L Install load monitoring equipment to obtain the current node load p in real time l (t), where t is time. All monitoring equipment is required to have accurate time synchronization function.

[0157] A monitoring system is installed in each energy storage power station to collect the output data of each sub-energy storage power station in the power station in real time. e (t), where e∈E is the energy storage node number where the energy storage power station is located.

[0158] The collected data p l (t), p e (t) The data is transmitted to the central server, which pre-processes the data as follows:

[0159] Remove outliers:

[0160] Where f is the outlier detection function, such as Gaussian distribution fitting based on the 3σ principle; θ is the parameter of the function

[0161] Fill missing values:

[0162] Where g is the missing value filling function, such as the interpolation method based on time series; φ is the parameter of the function

[0163] To ensure that the data obtained by the server Complete and reliable.

[0164] Step S30: Calculation of stability impact index of energy storage node output

[0165] Monitoring devices are arranged at the main nodes to obtain real-time data of regional power grid frequency f(t) and average voltage V(t).

[0166] Using machine learning algorithms such as generalized regression neural network (GRNN), through training samples (p e (t), f(t), V(t)) to fit the mathematical model between the energy storage node output and the grid frequency and voltage:

[0167] f(t)=h f (p e (t); β f )

[0168] V(t)=h V (p e (t); β V )

[0169] where h f ,hV is the model function obtained by fitting; β f , β V is the model parameter. According to the actual situation, other influencing factors such as temperature and transmission line parameters can be included in the model.

[0170] 3) For each energy storage node v e ∈V E Planning output Substitute into the above model and calculate the corresponding frequency impact value and voltage impact value

[0171] 4) Compare the impact value with the upper and lower limits of the stable operating range to obtain the deviation value:

[0172]

[0173]

[0174] Where f0, V0 are the rated values ​​of frequency and voltage; Δf max , ΔY max is the maximum allowable deviation.

[0175] 5) Substitute the deviation value into the nonlinear function to obtain the stability influence index of frequency and voltage:

[0176]

[0177]

[0178] Among them, a and b are adjustment parameters, and their value range is 5 to 10. When the deviation is 0, the minimum index is 0; as the deviation increases, the index gradually increases, and when the deviation approaches the maximum allowable value, the index approaches 1.

[0179] 6) Take the weighted sum of the above two indices and get the energy storage node v e Comprehensive stability impact index:

[0180] W e (t) = w f ·F e (t)+w V ·V e (t)

[0181] where w f , w V is the weight coefficient, which is determined according to the requirements. If the frequency effect is important, w can be taken f >w V .

[0182] The above process gives the stability impact index W of each energy storage node at each moment: e (t), the smaller the index value, the smaller the impact of the output scheme of the energy storage node on the stability of the power grid.

[0183] It is worth noting that some reference values ​​of parameters need to be given in the specific implementation, such as:

[0184] - Maximum permissible frequency deviation Δf max Refer to national standard ±0.2Hz

[0185] -Maximum allowable voltage deviation ΔV max Reference ±7% symmetrical fluctuation

[0186] -Weight coefficient w f , w V Can be 0.6, 0.4 respectively

[0187] Step S40: Calculation of energy storage loss and transmission loss

[0188] 1) For energy storage batteries, such as lithium-ion batteries, the electrochemical kinetic model can be used to estimate its energy loss. The model mainly includes:

[0189] - Voltage equation: φ s =U s -i s R s -η c

[0190] where φ s is the potential relative to the reference electrode, which determines the battery terminal voltage; U s is the open circuit voltage; R s is the internal resistance; η c is the concentration polarization potential; i s For current.

[0191] -Concentration polarization equation:

[0192] where c s is the solid concentration, x is the particle coordinate, D s is the expansion coefficient, F is the Faraday constant, t + is the cation mobility.

[0193] -Thermal evolution equation:

[0194] Where ρ is the density, C p is the specific heat capacity at constant pressure, T is the temperature, k is the thermal conductivity, Q rev is the heat source term.

[0195] 2) In terms of transmission loss, the π-type equivalent circuit model of the transmission line can be used, and the energy balance at both ends can be performed to obtain the transmission power loss formula:

[0196]

[0197] Among them I line , P line , Q line , V line are line current, active power, reactive power and line voltage respectively; R, X are line equivalent resistance and reactance respectively.

[0198] 3) The above energy storage loss and transmission loss change with the working status of the energy storage battery and the line. In order to improve the calculation efficiency, a lookup table can be constructed:

[0199] - Discretize the state parameters of the energy storage battery, such as SOC, current, and temperature, and pre-calculate the battery loss rate η under all state combinations by solving the electrochemical model batt (SOC, I, T)

[0200] - Discretize the line's operating parameters such as current, voltage, and power factor, and use the line loss formula to pre-calculate the line loss rate η under all working conditions line (I, V, cosφ)

[0201] In the actual calculation process, you only need to find the corresponding loss rate according to the real-time working status and multiply it by the time period length Δt to obtain the energy loss value:

[0202] E batt\_loss (t) = η batt (SOC, I, T)·P batt (t)·Δt

[0203] E line\_loss (t) = η line (I, V, cosφ)·P line (t)·Δt

[0204] Where P batt , P line are the current power of the energy storage battery and the power of the line respectively.

[0205] This method reduces the computational complexity by using a lookup table and can achieve higher computational efficiency.

[0206] Step S50: Establishment of optimization objective function

[0207] The optimization goal is to minimize the sum of energy storage loss and transmission loss:

[0208] min f(x,p E)=∑ e∈E E batt_loss (x e , p e )+∑ (i，j)∈E E line_loss (x ij , p ij );

[0209] in,

[0210] ∑ e∈E p e (t)≥∑ l∈L P l (t);

[0211]

[0212]

[0213]

[0214]

[0215]

[0216] in:

[0217] -x is the regional power grid topology to be optimized

[0218] -p E is the energy storage node output vector p E =(p e ) e∈E

[0219] -E batt_loss , E line_loss are the energy storage loss and line loss calculated in the previous step respectively

[0220] - Respectively represent the set of inbound and outbound edges connected to the load node l

[0221] -P l (t) is the power demand of load node l at time t

[0222] - Respectively represent the set of inbound and outbound edges connected to any node i

[0223] - is the upper limit of the rated transmission power of line (i, j)

[0224] - and The feasible solution spaces of topological structure and energy storage output are

[0225] The constraints of the above objective function are expressed as follows:

[0226] -The power demand of each load node must be met

[0227] -The total output of the energy storage nodes must not be less than the total load demand

[0228] -The power injection at each node satisfies the balance equation

[0229] - Frequency and voltage deviations must be controlled within the allowable range

[0230] - Line power flow must not exceed the rated value

[0231] These constraints need to be transformed into optimization variables x, p E Satisfying all constraints is a prerequisite for the optimization algorithm to obtain a feasible solution.

[0232] Step S60: Application of intelligent optimization algorithm

[0233] 1) Encode the regional power grid topology x by using tree coding, sequence coding, etc. to obtain the corresponding chromosome representation c.

[0234] 2) Randomly generate an initial population of N individuals

[0235] 3) For each individual c (i) , decoding to obtain the corresponding topological structure x (i) And output plan Calculate the objective function value and constraint function value

[0236] 4) Calculate the fitness of each individual based on the objective function value and constraint violation degree:

[0237]

[0238] Where ρ is the penalty coefficient.

[0239] 5) According to the fitness value, genetic operations such as selection, crossover, and mutation are used to select k Generate a new generation of population P k+1 , iteratively.

[0240] 6) When the termination condition is met (such as the number of iterations reaches the upper limit, convergence, etc.), output the individual c with the highest fitness in the current population * , decoding to obtain the optimal topological structure x * And output plan

[0241] 7) Introduce the dynamic changes of the power grid into the optimization process. After each time period, the optimization result x of the previous period is used to * , is the initial population, and the optimization is iterated again.

[0242] The optimization performance of the algorithm can be further improved through strategies such as parallel computing and adaptive genetic operations.

[0243] Step S70: Selecting the optimal topology

[0244] A series of optimized topological graphs {x * (t)}, the one with the least impact on grid stability is selected as the final optimal topology x ** The specific steps are as follows:

[0245] 1) For each optimized topology graph x * (t), according to its output plan Calculate its stability impact index vector:

[0246]

[0247] 2) Calculate the comprehensive stability index of each topology:

[0248]

[0249] where w e The weight coefficient for each energy storage node can be set according to its importance.

[0250] 3) For {W total (x * (t))} Sort from small to large and remove the last few topological graphs with larger values.

[0251] 4) For the remaining first M topological graphs, construct a three-dimensional vector:

[0252]

[0253] in is the total frequency impact of the topology, is the total voltage effect.

[0254] 5) For {q (i)} Perform hierarchical analysis:

[0255] - Press F first total Value from small to large {q (i)} Rough sort

[0256] -In the first K vectors after rough sorting, press V totalSort values ​​from small to large

[0257] -Finally, select W total The topological graph corresponding to the vector with the smallest value is taken as x **

[0258] This step considers the effects of frequency and voltage on stability separately, avoiding the deviation that may be caused by single-factor optimization, and fully utilizing the advantages of hierarchical analysis to ensure the rationality of the selection results.

[0259] Step S80: Calculation of total output of energy storage power station

[0260] 1) For the optimal topology graph x ** Analyze and extract the optimal output corresponding to each energy storage node e∈E

[0261] 2) Substitute the mathematical model established previously to simulate and calculate the frequency deviation corresponding to the node and voltage deviation

[0262] 3) Check whether these deviation values ​​meet the constraints:

[0263]

[0264]

[0265] If any node is not satisfied, the output value of the node needs to be adjusted again, and steps 2) and 3) are repeated until all constraints are met.

[0266] 4) For the optimal output solution that meets the constraints Calculate the total output power in the time period [t, t+Δt]:

[0267]

[0268] Where Δt is the length of the time period, such as 30 minutes.

[0269] The above steps give the total output of the distributed energy storage power station in the time period [t, t+Δt] By repeating this process, the total output of the energy storage power station at any time can be obtained.

[0270] It should be noted that in actual applications, due to the uncertainty of power grid data, the results obtained by the above calculations may not be the global optimal solution, but through the iteration of the intelligent optimization algorithm, it can gradually approach the optimal state, ensuring the feasibility and effectiveness of the solution. At the same time, the real-time response to the dynamic changes of the power grid also ensures the timeliness of the solution.

[0271] It should be noted that, in the above embodiments, the specific variables used are explained as shown in the following table:

[0272] Table 1. Variable explanation table

[0273]

[0274]

[0275]

[0276] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed, they are used to execute the above-mentioned method for optimizing the output of a distributed energy storage power station.

[0277] A third aspect of the present invention provides a distributed energy storage power station output optimization system, which includes the above-mentioned computer-readable storage medium.

[0278] Specifically, the principle of the present invention is to establish a mathematical model of energy storage output regulation, grid topology, and grid operation status, and solve the model through an intelligent optimization algorithm to obtain the optimal output solution that takes into account both energy storage efficiency and grid security. Specifically, it mainly includes the following key technical links:

[0279] 1. Establish regional power grid topology

[0280] In this step, it is necessary to collect all the structural information of the regional power grid, such as the geographical location of substations, transmission lines, load centers and energy storage devices, and convert them into the form of nodes and edges in graph theory, that is, the regional power grid topology map. This lays the foundation for subsequent optimization modeling.

[0281] 2. Real-time monitoring of load and energy storage output

[0282] By deploying monitoring equipment, the power consumption of each load node in the regional power grid and the real-time output data of the energy storage device are continuously obtained to reflect the dynamic operation status of the power grid. Due to the huge amount of data, technologies such as distributed computing can be used to achieve efficient computing and storage.

[0283] 3. Calculate the stability impact of energy storage output

[0284] The mathematical model trained by the machine learning algorithm can simulate and calculate the impact of the energy storage cluster output on the grid frequency and voltage. By converting the impact value into a stability impact index, the impact of the energy storage output on the stable operation of the grid can be quantified. The higher the index value, the greater the impact.

[0285] 4. Establish a mathematical model of energy storage output and grid efficiency

[0286] The model takes the minimization of energy storage battery loss and transmission line loss as the optimization goal. Battery loss can be modeled by thermal and electrochemical equations; line loss is obtained by line equivalent circuit calculation. These models integrate theoretical knowledge from multiple disciplines and describe the essence of power grid operation efficiency.

[0287] 5. Integrate grid stability and efficiency into the optimization framework

[0288] Combining the above models, a unified mathematical programming model can be established, with the overall output of the energy storage power station as the decision variable, the efficiency and loss considered in the objective function, and the constraints including grid stability, load satisfaction, line capacity and other factors.

[0289] 6. Apply intelligent optimization algorithms to solve optimization models

[0290] In view of the characteristics of this optimization problem, biological heuristic algorithms such as genetic algorithms and ant colony algorithms can be used as solvers to find the global optimal solution. These algorithms have the advantages of not easily falling into local extreme values ​​and strong optimization ability, which are suitable for dealing with complex engineering problems.

[0291] 7. Post-process and apply the optimization results

[0292] The optimal output plan of each energy storage power station is extracted from the optimized topology structure obtained by solution. The output plan is comprehensively analyzed and stability checked to generate the final output plan of the distributed energy storage power station. The plan can be updated in real time to closely track the dynamic changes of the power grid.

[0293] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing the output of a distributed energy storage power station, characterized in that: The following steps are involved: S10. Establish a regional power grid topology map according to the structure of the regional power grid, wherein the nodes of the regional power grid topology map include load nodes and energy storage nodes, and the edges of the regional power grid topology map are paths between nodes. The load nodes represent loads in the regional power grid, the energy storage nodes represent sub-energy storage power stations of distributed energy storage power stations in the regional power grid, and the edges represent transmission lines between the nodes. S20, continuously obtaining the load of each load node in each time period and the output of each energy storage node in each time period; S30. According to the monitored regional power grid frequency and voltage, calculate the stability impact index of the frequency and voltage of the regional power grid caused by each energy storage node when outputting electric energy. The calculation method of the stability impact index is as follows: obtain real-time data of the regional power grid frequency and voltage, use machine learning algorithms such as generalized regression neural networks to train a mathematical model between the output of the energy storage node and the frequency and voltage of the power grid, substitute the planned output of each energy storage node into the model for simulation, calculate the impact value on the power grid frequency and voltage, compare the impact value with the upper and lower limits of the stable operation range, and obtain two deviation values. Substitute the two deviation values ​​into nonlinear functions for processing, obtain a frequency stability impact index and a voltage stability impact index, and take the weighted sum of the two indexes as a comprehensive stability impact index. S40, obtaining the energy storage loss of each energy storage node and the power transmission loss of each edge in each time period; S50, establishing an optimization objective function of the regional power grid topology map, wherein the optimization objective function of establishing the regional power grid topology map is: using the sum of the energy storage loss and the transmission loss obtained in step S40 to design an optimization objective function; the energy storage loss is obtained by data mining based on the loss estimation value combined with the working state of the energy storage battery, and the transmission loss is calculated based on a π-type equivalent circuit model; S60, optimizing the regional power grid topology map by using an optimization algorithm to minimize the value of the optimization objective function, and obtaining an optimized topology map for each time period; S70, sorting all optimized topology graphs according to frequency and voltage stability impact index, and selecting the optimized topology graph with the best stability impact index as the optimal topology graph; S80. Calculate the output of each energy storage node in each time period according to the optimal topology diagram, and summarize them into the total output of the distributed energy storage power station.

2. A method for optimizing the output of a distributed energy storage power station according to claim 1, characterized in that: The time period may be any length between 15 and 30 minutes.

3. A method for optimizing the output of a distributed energy storage power station according to claim 1, characterized in that: The optimization of the regional power grid topology map adopts a genetic algorithm.

4. A method for optimizing the output of a distributed energy storage power station according to claim 3, characterized in that: The termination condition is that the number of iterations reaches 2000 or the similarity of ten consecutive generations is greater than 0.

8.

5. A distributed energy storage power station output optimization method according to claim 1, characterized in that: The step of calculating the output of each energy storage node in each time period according to the optimal topology diagram and summing up the total output of the distributed energy storage power station is specifically: according to the optimal topology diagram, extracting the output value corresponding to each energy storage node, multiplying the output value by the length of the corresponding output power time period to obtain the output power, and summing the output power of all energy storage nodes in the time period to obtain the total output power of the energy storage power station.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are executed, they are used to execute the method for optimizing the output of a distributed energy storage power station according to any one of claims 1 to 5.

7. A distributed energy storage power station output optimization system, characterized in that: Contains the computer-readable storage medium of claim 6.

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