Stability regulation and control method and system for energy storage of power distribution network of power system

Abstract

The invention discloses a stability regulation and control method and system for energy storage of a power distribution network of a power system. The system comprises an energy storage module, a load regulation and control module, an active power distribution module and a data interaction module, the energy storage module collects and analyzes parameter data based on power system requirements and resources, and reasonably stores the parameter data in a database; the load regulation and control module monitors the energy storage condition in real time and predicts and preferentially controls the load; the active power distribution module monitors power flow distribution and optimizes a power distribution scheme; the data interaction module is responsible for issuing a power distribution scheme to related equipment and adjusting and optimizing the scheme according to feedback; an energy storage objective function is established by comprehensively considering various factors, a dynamic optimization algorithm is used for load prediction, real-time monitoring and optimization of a power distribution scheme, a regulation and control scheme is continuously adjusted and optimized through a data interaction and closed-loop feedback mechanism, and meanwhile, an abnormal trigger limit value is set to guarantee stable operation of the system. Therefore, the expandability and maintainability of the system are improved.

Description

Technical Field

[0001] The present invention relates to a power grid energy storage control method, and in particular to a stability control method system for power distribution network energy storage. Background Art

[0002] The introduction of energy storage technology in the distribution network of the power system has undoubtedly brought revolutionary changes to improve the stability, flexibility and efficiency of the power grid. However, despite the huge potential of energy storage technology, the current energy storage regulation method of the power system distribution network still has several key problems, which limit the full utilization of the advantages of energy storage technology and affect the overall performance and efficiency of the power system.

[0003] The problems existing in the existing technologies specifically include: (1) Traditional energy storage control methods generally lack comprehensive consideration of the actual needs of the power system and available resources. These methods often only focus on the technical parameters of the energy storage equipment itself, while ignoring the overall operating conditions of the power system, load characteristics, and the impact of external environmental factors (such as weather, seasonal changes, etc.) on the energy storage charging and discharging strategy. Therefore, the charging and discharging strategies of energy storage equipment are often not optimized enough to fully utilize the capacity and charging and discharging speed of the energy storage equipment, resulting in the power system being unable to cope with load fluctuations and emergencies, and the overall operating efficiency is affected.

[0004] (2) With the development of smart grid and Internet of Things technologies, the load characteristics of power systems are becoming increasingly complex and changeable. Traditional load control methods can no longer meet the needs of modern power systems. When power load fluctuates greatly, traditional methods often fail to respond quickly and effectively balance the supply and demand relationship, which may lead to problems such as grid frequency fluctuations and voltage instability, thus affecting the stable operation of the grid.

[0005] (3) Traditional power distribution methods are usually designed based on fixed grid topology and line parameters, lacking real-time monitoring and dynamic optimization of power system flow distribution. With the access of new loads such as distributed power sources and electric vehicles and the continuous changes in the grid structure, traditional power distribution solutions are not flexible and efficient enough in dealing with grid changes. This may not only lead to increased grid losses and reduced operating efficiency, but also affect the stability and economy of the power system.

[0006] (4) Currently, many power systems have problems such as delays and losses in data interaction, which makes it impossible for the control center to obtain accurate and comprehensive grid operation data in a timely manner. At the same time, due to the lack of an effective feedback mechanism, problems that arise during the implementation of the control plan are difficult to be discovered and corrected in a timely manner. These problems seriously affect the accuracy and effectiveness of the control plan, and thus affect the overall operation of the power system.

[0007] Therefore, there is an urgent need for a stability control method system for power system distribution network energy storage to solve the technical problems existing in the above-mentioned prior art. Summary of the invention

[0008] The present invention overcomes the deficiencies of the prior art and provides a stability control method system for energy storage in a power distribution network.

[0009] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a stability control method system for energy storage in a power distribution network, comprising: an energy storage module, a load control module, an active power distribution module and a data interaction module;

[0010] The energy storage module collects parameter data of various states of the power system and performs statistical analysis and display according to the actual needs and available resources of the power system, combined with the energy storage capacity, charging and discharging speed, cost, life and the impact of environmental factors on the power system, and then performs rationality check and limit value alarm on the collected parameter data, and stores the collected parameter data in a database, wherein the storage method is hierarchical storage according to the data granularity;

[0011] The load control module monitors the energy storage of the power system in real time, evaluates the controllability of various load demands in the power system according to the load demand of the power system, determines the load type to be controlled first, and predicts the load of the power system using historical data and dynamic optimization algorithms;

[0012] The active power distribution module monitors the power flow distribution of the power system in real time, and optimizes the power distribution scheme based on the topological structure and line parameters of the power system;

[0013] The data interaction module sends the formulated optimized power distribution plan to relevant equipment in the power system for execution, monitors the execution effect in real time, and adjusts and optimizes the optimized power distribution plan again according to the feedback information.

[0014] In a preferred embodiment of the present invention, the energy storage module comprehensively considers the actual needs of the power system, available resources and influencing factor characteristics, and establishes an energy storage objective function by using a scenario analysis method with the goal of maximizing the amount of electricity stored in the power system:

[0015]

[0016] In the formula, maxW is the maximum storage capacity of the power system; i is the time period number; T i is the total number of time periods; Δt is the duration of each time period; K is the number of influencing factors of power system operation; s is the serial number of the influencing factor; π s is the environmental condition corresponding to the sth influencing factor; N is the total number of power systems; W is the stored power; is the planned output of power system j at time t; It is the planned output of power system j at time t after comprehensive consideration.

[0017] In a preferred embodiment of the present invention, the energy storage module constrains the operating state of the energy storage module according to different actual needs, available resources and influencing factor characteristics, and the constraints include the maximum charge and discharge power constraint of the power system, the energy storage state of charge constraint and the energy storage charge and discharge balance constraint; wherein the function expression of the maximum charge and discharge power constraint of the energy storage is:

[0018]

[0019] In the formula, represents the output of power system j in period t; when When the power system is in charging mode, When P E,jmin is the maximum charging power of the power system, and its value is less than 0; P E,jmax is the maximum discharge power of the power system;

[0020] The functional expression of the energy storage charge state constraint is:

[0021] SOC min ≤SOC(t)≤SOC max

[0022] In the formula, SOC(t) represents the state of charge of the power system in the period t; SOC min and SOC max Indicates the upper and lower limits of the energy storage charge state;

[0023] The functional expression of the energy storage charge and discharge balance constraint is:

[0024]

[0025] In the formula, It represents the output of power system j in time period t.

[0026] In a preferred embodiment of the present invention, the load prediction function expression of the power system load predicted by the load control module based on historical data and dynamic optimization algorithm is:

[0027] LP(t)=σ(ω1*L(t)+ω2*W(t)+ω3*S(t)+ω4*P(t)+b*H)

[0028] Where LP(t) is the prediction result; L(t) is the load demand at time t; W(t) is the weather condition at time t; S(t) is the seasonal factor at time t; P(t) is the price signal at time t; H is the historical data; b is the bias term; σ is the activation function; ω1, ω2, ω3, ω4 are the weights of the neural network.

[0029] In a preferred embodiment of the present invention, the active power distribution module establishes a branch flow model with branch current and power as modeling objects based on the power system flow equation established by Ohm's law and Kirchhoff's law. The function expression of the branch DC model is:

[0030]

[0031]

[0032]

[0033] In the formula, is the inflow power at node k of power system j; is the outflow power at node k of power system j; U i and U j are the square values ​​of the voltage amplitudes at nodes a and b in the topological structure respectively; P ab is the power difference between nodes a and b in power system j; P k is the power flowing into node a of power system j; Q ab is the outflow power difference between nodes a and b in power system j; q k is the outflow power of node a in power system j; R ab is the resistance of the topological structure; X ab is the reactance of the topological structure.

[0034] In a preferred embodiment of the present invention, the active power distribution module adjusts the parameters of the power system based on the LinDistFlow AC approximation model to achieve a power flow distribution that minimizes the objective function while satisfying all specified constraints; the function expression of the LinDistFlow AC approximation model is:

[0035]

[0036]

[0037] U j =U i -2(R ab P ab +X ab Q ab )

[0038] In the formula, is the engineering limit of active power of k nodes in the power system; is the voltage inflow difference between nodes a and b in the power system; is the engineering limit of reactive power of node k in the power system; q k d is the voltage difference between nodes a and b in the power system; the other variables are the same as above.

[0039] In a preferred embodiment of the present invention, the data interaction module, based on the optimized power distribution scheme provided by the active power distribution module, realizes information interaction by introducing auxiliary variables, performs alternating iterative solutions to the optimized power distribution scheme, and obtains the best control scheme; wherein the process of alternating iterative solutions to the optimized power distribution scheme is as follows:

[0040]

[0041] In the formula, Maximize and optimize the storage capacity of the power system; is the iterative inflow power value at node k of power system j; equal is the inflow power at node k of power system j; is the iterative inflow power value at node k of power system j; equal is the inflow power at node k of power system j; and U i and U j The iteration value of and P ab and Q ab The remaining variables are the same as above.

[0042] In a preferred embodiment of the present invention, a method for regulating and controlling the stability of energy storage in a power distribution network is provided, which is applied to the above-mentioned stability regulation and control system for energy storage in a power distribution network, and specifically includes: starting the system, loading each module and initializing relevant parameters, in the energy storage module, setting the influencing parameters of energy storage capacity, charging and discharging speed, cost, and life according to the actual needs and available resources of the power system, and considering the influence of environmental factors on the power system, the energy storage module collects parameter data of each state of the power system in real time, performs statistical analysis and display on the collected parameter data, performs rationality check on the collected parameter data, stores the collected parameter data in a database, and performs hierarchical storage according to the data granularity;

[0043] The load control module monitors the energy storage of the power system in real time, evaluates the controllability of various load demands in the power system according to the load demand of the power system, determines the load type to be controlled first, predicts the load of the power system using historical data and dynamic optimization algorithms, and generates a load prediction function;

[0044] The active power distribution module monitors the power flow distribution of the power system in real time, establishes a branch flow model based on the topological structure and line parameters of the power system to optimize the power distribution plan, and uses the LinDistFlow AC approximate model to adjust the parameters of the power system to achieve the power flow distribution that minimizes the objective function;

[0045] The data interaction module sends the formulated optimized power distribution plan to the relevant equipment in the power system for execution. The data interaction module monitors the execution effect of the optimized power distribution plan in real time, and adjusts and optimizes the optimized power distribution plan again based on the feedback information. By introducing auxiliary variables to realize information interaction, the optimized power distribution plan is solved alternately and iteratively to obtain the best control plan.

[0046] In a preferred embodiment of the present invention, an abnormal trigger limit value is set in the energy storage module. When the collected parameter data is checked for rationality, if the parameter data exceeds the abnormal trigger limit value, an alarm is triggered and sent to an administrator.

[0047] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0048] (1) This technical solution improves the stability of energy storage in the power distribution network through comprehensive energy storage management, intelligent load control, active distribution optimization, data interaction and feedback optimization, exception handling and alarm mechanism, modular design and algorithm. Specifically, it establishes an energy storage objective function by comprehensively considering multiple factors, uses a dynamic optimization algorithm to predict the load, monitors and optimizes the distribution plan in real time, continuously adjusts and optimizes the control plan through data interaction and closed-loop feedback mechanism, and sets an exception trigger limit value to ensure the stable operation of the system, thereby improving the scalability and maintainability of the system.

[0049] (2) The present invention uses a load control module and a dynamic optimization algorithm to predict the load of the energy storage function of the power system, and the active distribution module optimizes the flow distribution of the energy storage of the power system based on Ohm's law, Kirchhoff's law and LinDistFlow AC approximation model, thereby further improving the accuracy and efficiency of regulation.

[0050] (3) The present invention realizes flexible adjustment and optimization of the power distribution scheme through real-time monitoring of the power grid topology and line parameters, and dynamic optimization of the power flow distribution, thereby improving the transmission efficiency and stability of the power grid. In addition, the present technical solution can also adaptively adjust the power distribution scheme to ensure the stability and economy of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 It is a system flow chart of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0055] like Figure 1 As shown, a stability control method system for energy storage in a power distribution network of a power system comprises: an energy storage module, a load control module, an active power distribution module and a data interaction module;

[0056] The energy storage module collects parameter data of each state of the power system and performs statistical analysis and display according to the actual needs and available resources of the power system, combined with the energy storage capacity, charging and discharging speed, cost, life, and the impact of environmental factors on the power system. It then performs rationality checks and limit value alarms on the collected parameter data and stores the collected parameter data in the database. The storage method is hierarchical storage according to the data granularity.

[0057] The energy storage module comprehensively considers the actual needs of the power system, available resources, and the characteristics of influencing factors, and establishes the energy storage objective function through scenario analysis with the goal of maximizing the amount of power stored in the power system:

[0058]

[0059] In the formula, maxW is the maximum storage capacity of the power system; i is the time period number; T i is the total number of time periods; Δt is the duration of each time period; K is the number of influencing factors of power system operation; s is the serial number of the influencing factor; π s is the environmental condition corresponding to the sth influencing factor; N is the total number of power systems; W is the stored power; is the planned output of power system j at time t; It is the planned output of power system j at time t after comprehensive consideration.

[0060] The energy storage module constrains the operating state of the energy storage module according to different actual needs, available resources and influencing factor characteristics. The constraints include the maximum charge and discharge power constraint of the power system, the energy storage charge state constraint and the energy storage charge and discharge balance constraint. The function expression of the maximum charge and discharge power constraint of the energy storage is:

[0061]

[0062] In the formula, represents the output of power system j in period t; when When the power system is in charging mode, When P E,jmin is the maximum charging power of the power system, and its value is less than 0; P E,jmax is the maximum discharge power of the power system;

[0063] The functional expression of energy storage charge state constraint is:

[0064] SOC min ≤SOC(t)≤SOC max

[0065] In the formula, SOC(t) represents the state of charge of the power system in the period t; SOC min and SOC max Indicates the upper and lower limits of the energy storage charge state;

[0066] The functional expression of energy storage charge and discharge balance constraint is:

[0067]

[0068] In the formula, It represents the output of power system j in time period t.

[0069] The introduction of energy storage technology can effectively alleviate the pressure on the power system during peak loads. During peak loads, energy storage equipment can release stored electrical energy to meet the needs of the power system, reduce dependence on traditional power sources, and reduce power supply pressure. In addition, energy storage technology can also charge during load valleys through reverse operation, making full use of the surplus energy of the power grid and improving the utilization rate of the power system.

[0070] The load control module monitors the energy storage status of the power system in real time, evaluates the controllability of various load demands in the power system according to the load demand of the power system, determines the load type to be controlled first, and predicts the load of the power system using historical data and dynamic optimization algorithms;

[0071] When choosing energy storage technology, it is necessary to comprehensively consider factors such as capacity, charging and discharging speed, cost, life, safety, environmental impact, etc. For example, battery energy storage has high energy density and charging and discharging efficiency, but it is expensive and has safety hazards; pumped storage has low cost and long life, but it is restricted by geographical conditions and has a long construction period.

[0072] The most appropriate energy storage technology or combination of technologies should be selected based on the actual needs of the power grid and available resources. For example, in areas with abundant renewable energy, battery energy storage can be combined with pumped storage to balance the advantages and disadvantages of different energy storage technologies.

[0073] The selection of load controllable technology should focus on its monitoring accuracy, control response time, reliability and user acceptance. Smart meters and load control switches are common load controllable devices that can realize real-time monitoring and control of power grid loads.

[0074] Combining load controllable technology with energy storage technology, flexible load management can be achieved through intelligent scheduling algorithms. For example, during peak hours, unnecessary power consumption can be reduced through load control switches, and excess power can be stored in the energy storage system; during off-peak hours, power can be released from the energy storage system to meet load demand.

[0075] The active power distribution module monitors the power flow distribution of the power system in real time and optimizes the power distribution plan based on the topological structure and line parameters of the power system;

[0076] The load forecasting function expression of the power system predicted by the load control module based on historical data and dynamic optimization algorithm is:

[0077] LP(t)=σ(ω1*L(t)+ω2*W(t)+ω3*S(t)+ω4*P(t)+b*H)

[0078] Where LP(t) is the prediction result; L(t) is the load demand at time t; W(t) is the weather condition at time t; S(t) is the seasonal factor at time t; P(t) is the price signal at time t; H is the historical data; b is the bias term; σ is the activation function; ω1, ω2, ω3, ω4 are the weights of the neural network.

[0079] The active power distribution module establishes the power system flow equation based on Ohm's law and Kirchhoff's law, and establishes a branch flow model with branch current and power as the modeling objects. The function expression of the DC model is:

[0080]

[0081]

[0082]

[0083] In the formula, is the inflow power at node k of power system j; is the outflow power at node k of power system j; U i and U j are the square values ​​of the voltage amplitudes at nodes a and b in the topological structure respectively; P ab is the power difference between nodes a and b in power system j; P k is the power flowing into node a of power system j; Q ab is the outflow power difference between nodes a and b in power system j; q k is the outflow power of node a in power system j; R ab is the resistance of the topological structure; X ab is the reactance of the topological structure.

[0084] The active power distribution module adjusts the parameters of the power system based on the LinDistFlow AC approximate model to achieve the power flow distribution that minimizes the objective function while satisfying all specified constraints; the function expression of the LinDistFlow AC approximate model is:

[0085]

[0086]

[0087] U j =U i -2(R ab P ab +X ab Q ab )

[0088] In the formula, is the engineering limit of active power of k nodes in the power system; is the voltage inflow difference between nodes a and b in the power system; is the engineering limit of reactive power of node k in the power system; q k d is the voltage difference between nodes a and b in the power system; the other variables are the same as above.

[0089] The active power distribution module adjusts the parameters of the power system based on the LinDistFlow AC approximate model to achieve the power flow distribution that minimizes the objective function while satisfying all specified constraints; the function expression of the LinDistFlow AC approximate model is:

[0090]

[0091]

[0092] U j =U i -2(R ab P ab +X ab Q ab )

[0093] In the formula, is the engineering limit of active power of k nodes in the power system; is the voltage inflow difference between nodes a and b in the power system; is the engineering limit of reactive power of k nodes in the power system; is the voltage difference between nodes a and b in the power system; the other variables are the same as above.

[0094] The data interaction module sends the formulated optimized power distribution plan to the relevant equipment in the power system for execution, monitors the execution effect in real time, and adjusts and optimizes the optimized power distribution plan again based on the feedback information.

[0095] The data interaction module is based on the optimized power distribution plan provided by the active power distribution module. By introducing auxiliary variables to achieve information interaction, the optimized power distribution plan is solved alternately and iteratively to obtain the best control plan. The process of alternately and iteratively solving the optimized power distribution plan is as follows:

[0096]

[0097] In the formula, Maximize and optimize the storage capacity of the power system; is the iterative inflow power value at node k of power system j; equal is the inflow power at node k of power system j; is the iterative inflow power value at node k of power system j; equal is the inflow power at node k of power system j; and U i and U j The iteration value of and P ab and Q ab The remaining variables are the same as above.

[0098] A stability control method for energy storage in a power distribution network of an electric power system is applied to the above-mentioned stability control system for energy storage in a power distribution network of an electric power system, and specifically comprises: starting the system, loading each module and initializing relevant parameters, in the energy storage module, setting the influencing parameters of energy storage capacity, charging and discharging speed, cost, and life according to the actual needs and available resources of the power system, and considering the influence of environmental factors on the power system, the energy storage module collects parameter data of each state of the power system in real time, performs statistical analysis and display on the collected parameter data, performs rationality check on the collected parameter data, stores the collected parameter data in a database, and performs hierarchical storage according to the data granularity;

[0099] The energy storage module is set with an abnormal trigger limit value. When the collected parameter data is checked for rationality, if the parameter data exceeds the abnormal trigger limit value, an alarm is triggered and sent to the administrator;

[0100] The load control module monitors the energy storage of the power system in real time, evaluates the controllability of various load demands in the power system according to the load demand of the power system, determines the load type to be controlled first, predicts the load of the power system using historical data and dynamic optimization algorithms, and generates a load prediction function;

[0101] The active power distribution module monitors the power flow distribution of the power system in real time, establishes a branch flow model based on the topological structure and line parameters of the power system to optimize the power distribution plan, and uses the LinDistFlow AC approximate model to adjust the parameters of the power system to achieve the power flow distribution that minimizes the objective function;

[0102] Furthermore, active power distribution technology is a general term for a series of advanced technologies that aim to improve the intelligence level of distribution networks and enhance the flexibility and reliability of power grids. These technologies achieve active scheduling and optimal configuration of power grid resources by integrating information communication, control strategies, power electronics and other technical means. In this technical solution, one or more of the following active power distribution technologies can be used:

[0103] 1. Distributed power access and control technology

[0104] Technical content: Supporting efficient access and flexible control of distributed power sources (such as solar energy, wind energy and other renewable energy generation). This includes active power and reactive voltage control of distributed power sources, as well as coordinated operation strategies with the power grid. Improve the utilization rate of renewable energy and enhance the power supply capacity and reliability of the power grid.

[0105] 2. Energy storage technology

[0106] Technical content: Use battery energy storage, pumped storage and other energy storage technologies to achieve grid load stabilization and regulation. This includes the charge and discharge control strategy of the energy storage system, the interaction mechanism with the grid, etc. Improve the flexibility of the grid, stabilize load fluctuations, and improve power supply quality and reliability.

[0107] 3. Load control technology

[0108] Technical content: Through smart meters, load control switches and other equipment, real-time monitoring and control of power grid loads can be achieved. This includes load forecasting, scheduling and management. Improve the load management capabilities of the power grid, reduce peak loads, and optimize power grid operations.

[0109] 4. Advanced measurement system

[0110] Technical content: Through the advanced measurement system composed of smart meters, communication networks, measurement data management systems and user indoor networks, comprehensive monitoring and data analysis of the power grid operation status can be achieved. Data support is provided for the intelligent management of the power grid, and the operation efficiency and reliability of the power grid are improved.

[0111] 5. Power distribution automation and control system

[0112] Technical content: including distribution automation (DA), substation automation (SA), etc., to realize remote monitoring, fault isolation and recovery of distribution network, etc., to improve the automation level of power grid, shorten fault recovery time and improve power supply reliability.

[0113] 6. Active power distribution management system

[0114] Technical content: Use advanced algorithms and models to optimize the configuration and dispatch of power grid resources. This includes load forecasting, energy storage charging and discharging strategies, distributed power supply dispatching, etc. Improve the intelligence level of the power grid, achieve optimal utilization of power grid resources, and reduce operating costs.

[0115] 7. Multi-microgrid coordinated dispatching technology

[0116] Technical content: Support the coordinated operation and dispatching of multiple microgrids, realize the sharing and optimal configuration of grid resources, improve the flexibility and reliability of the grid, and promote the efficient use of distributed energy.

[0117] 8. Big data analysis and cloud computing technology

[0118] Technical content: Use big data analysis and cloud computing technology to monitor and analyze power grid operation data in real time, provide decision support for intelligent management of power grids, improve power grid operation efficiency and reliability, and optimize power grid resource allocation.

[0119] 9. Flexible power distribution technology

[0120] Technical content: including power quality related technologies of distribution system (such as net value reactive power compensator, static synchronous compensator, etc.) and flexible power flow control technology (such as unified power flow controller, power flow path switch, etc.). Improve the power quality and power supply reliability of the power grid and promote friendly access to distributed power sources.

[0121] 10. Energy Internet Technology

[0122] Technical content: Through advanced power electronics technology, information technology and intelligent energy management technology, distributed energy, energy storage systems and loads are connected to achieve two-way flow and sharing of energy and information. Promote the development of power grids towards intelligence, networking and interaction, and improve energy efficiency and sustainability.

[0123] The data interaction module sends the formulated optimized power distribution plan to the relevant equipment in the power system for execution. The data interaction module monitors the execution effect of the optimized power distribution plan in real time, and adjusts and optimizes the optimized power distribution plan again based on the feedback information. By introducing auxiliary variables to realize information interaction, the optimized power distribution plan is solved alternately and iteratively to obtain the best control plan.

[0124] Embodiment 1

[0125] Verify the effectiveness and performance of the proposed stability control method for power system distribution network energy storage system, and ensure its stability and optimization effect under various operating conditions.

[0126] The IEEE 33-node, IEEE 69-node and IEEE 123-node test systems are used to test power systems of different sizes;

[0127] The system has three generators and three loads. Generator G1 is a balancing node, and the other two are PV nodes. All of them adopt the sixth-order model, and the rated capacity of the system is 100MVA.

[0128] The power flow data are: P1=71.64MW; Q1=27.046Mvar; P2=163MW; Q2=6.654Mvar; P3=85MW; Q3=-10.86Mvar.

[0129] Load data: P L1 =125MW; Q L1 =50Mvar; P L2 =90MW; Q L2 =30Mvar; P L3 =100MW; Q L3 =35Mvar.

[0130] The regulation parameters of the conventional power system are: minimum technical output 50%, maximum technical output 100%; the regulation parameters of the conventional power system (with regulation) are: minimum technical output 0%, maximum technical output 100%; the regulation parameters of the conventional power system (without regulation) are: minimum technical output 100%, maximum technical output 100%.

[0131] The energy storage parameters of the power system are: rated power 120MW, rated capacity 480MW, SOC min 0.2; SOC max is 0.8.

[0132] Purpose

[0133] This experiment aims to verify the effectiveness and performance of the proposed power system distribution network energy storage stability control method system under a specific power system configuration containing 3 generators and 3 loads. By introducing specific power flow data and load data, the stability and optimization effect of the system under actual operating conditions are ensured.

[0134] In the power system simulation software, according to the given generator configuration and system rated capacity, a power system model including 3 generators and 3 loads is built, and then the flow data and load data are input into the model.

[0135] Initialize the relevant parameters of the energy storage module, load control module, active power distribution module and data interaction module to ensure that each module is in standby status.

[0136] Start the energy storage module to collect key status parameters of the power system such as voltage, current, power, etc. in real time, then clean, verify and format the collected data to ensure the accuracy and consistency of the data, and store the pre-processed data in the database for subsequent analysis and processing.

[0137] Start the load control module to monitor the energy storage situation and load demand of the power system in real time, predict the load demand for a period of time in the future based on historical load data and dynamic optimization algorithms, compare the predicted results with the actual load data, and evaluate the accuracy and reliability of the prediction algorithm.

[0138] Start the active power distribution module, monitor the power flow distribution of the power system in real time, combine the topological structure and line parameters of the power system, use the power flow data and load forecast results to optimize the power distribution plan, reduce power loss and improve voltage stability, send the optimized power distribution plan to the relevant equipment in the power system for execution, and monitor its execution effect in real time, start the data interaction module, and realize real-time data interaction and information sharing between modules.

[0139] According to the execution effect of the power distribution plan and the feedback information of the data interaction module, the power distribution plan is dynamically adjusted and optimized, and the optimal power distribution control plan is obtained by introducing auxiliary variables and alternating iterative solution methods.

[0140] Set abnormal trigger conditions in the energy storage module, simulate the abnormal situation where the parameter data exceeds the limit value, check whether the system can trigger the alarm in time and send it to the administrator, and verify the effectiveness and reliability of the abnormal handling and alarm mechanism.

[0141] The results of optimized power system distribution network energy storage are shown in Table 1:

[0142] index Total power reduction Total translation power Total network loss Shift Time Before optimization 1836.41 / kW 864.37 / kW 763.13 / kW 31 After optimization 2956.75 / kW 3167.24 / kW 349.51 / kW 67

[0143] Based on the comparison of data indicators, we can conduct an in-depth analysis of the stability control effect of the power system distribution network energy storage before and after optimization:

[0144] The total power reduction after optimization has been significantly improved compared to before optimization, increasing by about 1120.34kW. This means that in the process of stability regulation of power distribution network energy storage, the system can more effectively reduce unnecessary power consumption and improve energy utilization efficiency.

[0145] The total shift power after optimization increased significantly by about 2302.87kW compared to before optimization. The increase in shift power may mean that the system adjusted the power load more flexibly during the optimization process to balance the supply and demand of the power grid, thereby improving the stability and reliability of the power system.

[0146] The total network loss after optimization has been significantly reduced by about 413.62kW compared to before optimization. The reduction in network loss indicates that the power system has reduced energy loss in the process of transmitting and distributing electric energy, which helps to improve the overall efficiency and economic benefits of the power system.

[0147] Although the shift time increases after optimization, it does not directly mean a decrease in the regulation effect. The increase in the shift time may reflect the more complex scheduling and adjustment of the system to balance the grid load during the optimization process. In practical applications, the rationality of the shift time needs to be evaluated based on the specific conditions and needs of the power system.

[0148] The increase in total power reduction and total shift power after optimization indicates that the system can use energy more efficiently and reduce unnecessary waste during the regulation process. The significant reduction in total network losses means that the power grid is more stable in the process of transmitting and distributing electric energy, reducing the risk of power grid fluctuations and failures caused by energy losses. Although the shift time has increased, this may reflect the system's higher flexibility in dealing with complex power load changes. Through reasonable scheduling and adjustment, the system can better balance the supply and demand of the power grid.

[0149] In summary, the optimized power system distribution network energy storage stability control method has achieved remarkable results in improving energy utilization efficiency, enhancing grid stability and improving dispatching flexibility. These optimization results are of great significance to improving the overall operation efficiency and economic benefits of the power system.

[0150] Experimental Conclusion

[0151] Based on the given power system configuration, flow data and load data, the following conclusions can be drawn through the verification of this experiment:

[0152] The proposed power system distribution network energy storage stability control method system can effectively improve the stability and optimization effect of the power system.

[0153] Under specific data conditions, the load control module can accurately predict load demand, and the active power distribution module can optimize the power distribution plan, reduce power loss and improve voltage stability.

[0154] The implementation of data interaction modules and feedback optimization mechanisms enhances the flexibility and adaptability of the system and ensures the effective implementation of optimization plans.

[0155] The effectiveness of the exception handling and alarm mechanism has been verified, providing a strong guarantee for the stable operation of the system.

[0156] The above is based on the ideal embodiment of the present invention. Through the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the content in the specification, and the technical scope must be determined according to the scope of the claims.

Claims

1. A stability control system for energy storage in a power distribution network, characterized in that: include: Energy storage module, load control module, active power distribution module and data interaction module; The energy storage module collects parameter data of various states of the power system and performs statistical analysis and display according to the actual needs and available resources of the power system, combined with the energy storage capacity, charging and discharging speed, cost, life and the impact of environmental factors on the power system, and then performs rationality check and limit value alarm on the collected parameter data, and stores the collected parameter data in a database, wherein the storage method is hierarchical storage according to the data granularity; The load control module monitors the energy storage of the power system in real time, evaluates the controllability of various load demands in the power system according to the load demand of the power system, determines the load type to be controlled first, and predicts the load of the power system using historical data and dynamic optimization algorithms; The active power distribution module monitors the power flow distribution of the power system in real time, and optimizes the power distribution scheme based on the topological structure and line parameters of the power system; The data interaction module sends the formulated optimized power distribution plan to relevant equipment in the power system for execution, monitors the execution effect in real time, and adjusts and optimizes the optimized power distribution plan again according to the feedback information.

2. A stability control system for power distribution network energy storage according to claim 1, characterized in that: The energy storage module comprehensively considers the actual needs of the power system, available resources and influencing factors, and establishes an energy storage objective function by maximizing the power storage capacity of the power system through scenario analysis: In the formula, maxW is the maximum storage capacity of the power system; i is the time period number; T i is the total number of time periods; Δt is the duration of each time period; K is the number of influencing factors of power system operation; s is the serial number of the influencing factor; π s is the environmental condition corresponding to the sth influencing factor; N is the total number of power systems; W is the stored power; is the planned output of power system j at time t; It is the planned output of power system j at time t after comprehensive consideration.

3. A stability control system for power distribution network energy storage according to claim 1 or 2, characterized in that: The energy storage module constrains the operating state of the energy storage module according to different actual needs, available resources and influencing factor characteristics. The constraints include the maximum charge and discharge power constraint of the power system, the energy storage state of charge constraint and the energy storage charge and discharge balance constraint; wherein the function expression of the maximum charge and discharge power constraint of the energy storage is: In the formula, represents the output of power system j in period t; when When the power system is in charging mode, When P E,jmin is the maximum charging power of the power system, and its value is less than 0; P E,jmax is the maximum discharge power of the power system; The functional expression of the energy storage charge state constraint is: SOC min ≤SOC(t)≤SOC max In the formula, SOC(t) represents the state of charge of the power system in the period t; SOC min and SOC max Indicates the upper and lower limits of the energy storage charge state; The functional expression of the energy storage charge and discharge balance constraint is: In the formula, It represents the output of power system j in time period t.

4. The stability control system for energy storage in a power distribution network of a power system according to claim 1, characterized in that: The load prediction function expression of the power system load predicted by the load control module based on historical data and dynamic optimization algorithm is: LP(t)=σ(ω1*L(t)+ω2*W(t)+ω3*S(t)+ω4*P(t)+b*H) Where LP(t) is the prediction result; L(t) is the load demand at time t; W(t) is the weather condition at time t; S(t) is the seasonal factor at time t; P(t) is the price signal at time t; H is the historical data; b is the bias term; σ is the activation function; ω1, ω2, ω3, ω4 are the weights of the neural network.

5. The stability control system for energy storage in a power distribution network according to claim 1, characterized in that: The active power distribution module establishes a branch flow model with branch current and power as modeling objects based on the power system flow equation established by Ohm's law and Kirchhoff's law. The function expression of the branch DC model is: In the formula, is the inflow power at node k of power system j; is the outflow power at node k of power system j; U i and U j are the square values ​​of the voltage amplitudes at nodes a and b in the topological structure respectively; P ab is the power difference between nodes a and b in power system j; P k is the power flowing into node a of power system j; Q ab is the outflow power difference between nodes a and b in power system j; q k is the outflow power of node a in power system j; R ab is the resistance of the topological structure; X ab is the reactance of the topological structure.

6. A stability control system for power distribution network energy storage according to claim 1 or 5, characterized in that: The active power distribution module adjusts the parameters of the power system based on the LinDistFlow AC approximate model to achieve the power flow distribution that minimizes the objective function while satisfying all specified constraints; the function expression of the LinDistFlow AC approximate model is: U j =U i -2(R ab P ab +X ab Q ab ) In the formula, is the engineering limit of active power of k nodes in the power system; is the voltage inflow difference between nodes a and b in the power system; is the engineering limit of reactive power of k nodes in the power system; is the voltage difference between nodes a and b in the power system; the other variables are the same as above.

7. The stability control system for energy storage in a power distribution network of claim 1, characterized in that: The data interaction module, based on the optimized power distribution scheme provided by the active power distribution module, realizes information interaction by introducing auxiliary variables, performs alternating iterative solutions to the optimized power distribution scheme, and obtains the best control scheme; wherein the process of alternating iterative solutions to the optimized power distribution scheme is as follows: In the formula, Maximize and optimize the storage capacity of the power system; is the iterative inflow power value at node k of power system j; equal is the inflow power at node k of power system j; is the iterative inflow power value at node k of power system j; equal is the inflow power at node k of power system j; and U i and U j The iteration value of and P ab and Q ab The remaining variables are the same as above.

8. A method for regulating the stability of energy storage in a power distribution network, applied to a system for regulating the stability of energy storage in a power distribution network as claimed in any one of claims 1 to 7, characterized in that: Start the system, load each module and initialize relevant parameters. In the energy storage module, set the influencing parameters of energy storage capacity, charging and discharging speed, cost and life according to the actual needs and available resources of the power system, and consider the impact of environmental factors on the power system. The energy storage module collects parameter data of each state of the power system in real time, performs statistical analysis and display on the collected parameter data, performs rationality check on the collected parameter data, stores the collected parameter data in the database, and performs hierarchical storage according to the data granularity; The load control module monitors the energy storage of the power system in real time, evaluates the controllability of various load demands in the power system according to the load demand of the power system, determines the load type to be controlled first, predicts the load of the power system using historical data and dynamic optimization algorithms, and generates a load prediction function; The active power distribution module monitors the power flow distribution of the power system in real time, establishes a branch flow model based on the topological structure and line parameters of the power system to optimize the power distribution plan, and uses the LinDistFlow AC approximate model to adjust the parameters of the power system to achieve the power flow distribution that minimizes the objective function; The data interaction module sends the formulated optimized power distribution plan to the relevant equipment in the power system for execution. The data interaction module monitors the execution effect of the optimized power distribution plan in real time, and adjusts and optimizes the optimized power distribution plan again based on the feedback information. By introducing auxiliary variables to realize information interaction, the optimized power distribution plan is solved alternately and iteratively to obtain the best control plan.

9. A method for regulating the stability of energy storage in a power distribution network according to claim 8, characterized in that: The energy storage module is provided with an abnormal trigger limit value. When the collected parameter data is checked for rationality, if the parameter data exceeds the abnormal trigger limit value, an alarm is triggered and sent to an administrator.

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