Distributed energy storage ramp rate control method

Abstract

The invention discloses a distributed energy storage ramp rate control method, and belongs to the technical field of power systems. In order to promote the application and popularization of the distributed energy storage in the fields of power grid dispatching, power load regulation, renewable energy consumption and the like, the invention provides the optimal ramp rate control method for the distributed energy storage. Firstly, the typical structure of an energy storage system and the climbing characteristics of distributed energy storage are analyzed, then the SOC characteristics of energy storage are researched, the climbing speed of energy storage is adjusted in real time according to the interval where the SOC of an energy storage battery is located, and the SOC of energy storage is prevented from being out of limit. According to the method, distributed energy storage is firstly controlled in a grouped mode, then based on a model predictive control (MPC) method, a specific control method for the optimal ramp rate based on the distributed energy storage is provided, the specific control method mainly comprises a ramp rate prediction model, online rolling optimization and online feedback correction, and control over the optimal ramp rate of the distributed energy storage is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for controlling a ramp rate of distributed energy storage. Background Art

[0002] The power generation characteristics of renewable energy sources such as wind and solar energy are highly uncertain and volatile. When connected to the power grid, it may cause an imbalance between supply and demand when the grid changes instantaneously, which brings great challenges to the modern power system. In order to meet this challenge, the distributed energy storage system is applied to the power system, which can not only balance the volatility of renewable energy, but also improve the flexibility and stability of the power grid and provide a stable power supply. At present, a variety of energy storage technologies such as lithium-ion batteries, lead-acid batteries, and sodium-sulfur batteries have been introduced, which greatly improves the energy density and charging and discharging efficiency of energy storage systems. In addition, emerging technologies such as flywheel energy storage and compressed air energy storage are also developing. The advancement of these energy storage technologies enables energy storage systems to have higher charging and discharging efficiency and rapid response capabilities, and can achieve rapid power regulation in a shorter time, thereby meeting the power requirements of the power grid.

[0003] Distributed energy storage systems can be flexibly adjusted according to demand to adapt to different load demands and power generation conditions. They are usually distributed on the user side, reducing the loss of long-distance power transmission and improving the efficiency of the overall system. At the same time, combined with renewable energy, it can achieve efficient use of green energy and promote sustainable development. Modern distributed energy storage systems are usually equipped with intelligent control systems that can monitor and adjust in real time to improve operating efficiency. With technological advances and cost reductions, the economic efficiency of distributed energy storage has gradually increased, and it can provide users with more competitive electricity prices. Through the above analysis, it can be seen that distributed energy storage has broad application prospects and significant advantages in distribution networks, and can effectively improve the stability of the power grid and the utilization rate of renewable energy.

[0004] Therefore, based on the development background of distributed energy storage technology in the above distribution network, the effective ramp control technology for distributed energy storage has become a hot topic of current research. Energy storage ramp control aims to limit the power change rate of the energy storage system during the charging and discharging process to avoid impact on the power grid. Through reasonable ramp rate control, the energy storage system can quickly respond to changes in power grid demand in a short time, improve the flexibility and reliability of the power system, and has become an important means to ensure the safe and stable operation of the energy storage system. Common control algorithms for ramp rate control include PID control, fuzzy control and predictive control, which can dynamically adjust the charging and discharging strategy of the energy storage system according to real-time data to achieve effective control of the power change rate. In practical applications, the state of the energy storage device is obtained in real time through sensors and monitoring systems to provide a basis for control decisions. This state monitoring and feedback mechanism enables the energy storage system to interact with the power grid, obtain power grid status information, and ensure that the energy storage system always remains within a safe range during the charging and discharging process to avoid overcharging or over-discharging.

[0005] In summary, through in-depth research on the ramp control strategy of distributed energy storage, we can provide important theoretical and technical support for the design, operation and management of distributed energy storage, promote the application and promotion of distributed energy storage in the fields of grid dispatching, power load regulation, renewable energy consumption, etc., and enable it to play an increasingly important role in modern power systems. Summary of the invention

[0006] The purpose of the present invention is to provide a method for controlling the ramp rate of distributed energy storage in order to solve the above-mentioned problem.

[0007] To achieve the above object, the technical solution adopted by the present invention is: comprising the following steps:

[0008] S1. Characteristic analysis of distributed energy storage ramping and distributed energy storage SOC based on the typical structure of distributed energy storage system;

[0009] S2. Formulate distributed energy storage ramp rate control strategy

[0010] S2.1 Grouping control strategy of energy storage units

[0011] Divide multiple subsystems of distributed energy storage into two groups, which work in discharge state and charge state respectively, to reduce unnecessary state switching and improve the operating conditions of energy storage units. The power of the system is positive when discharging and negative when charging.

[0012] 2.2. Optimal ramp rate model predictive control strategy for energy storage subsystem

[0013] The model predictive control (MPC) is transformed into modeling each subsystem and solving their own MPC problems, which is divided into three parts: ramp rate prediction model, online rolling optimization and real-time feedback correction.

[0014] The overall control steps are as follows:

[0015] Step 1: First, sample the voltage and current of the distributed energy storage, perform power prediction of the distributed energy storage subsystem, and then perform ramp rate prediction based on this;

[0016] Step 2: Use group ramp control for distributed energy storage;

[0017] Step 3: Set the distributed energy storage subsystem ramp rate control objective function including SOC characteristics. Distributed settings is inf, then enter the loop statement to predict the climbing rate prediction value of the next sampling time;

[0018] Step 4: Finally, by solving the objective function, when The control effect is best when the value is minimum. The switch state at this time acts on the switch tube, thereby completing the entire climbing rate control process.

[0019] Furthermore, the events in the distributed energy storage ramp characteristic analysis mainly include the following characteristic quantities:

[0020] (1) Power change , that is, the change value of power during the observation period;

[0021] (2) Climbing duration , i.e., the duration of the large power fluctuation;

[0022] (3) Climbing rate , that is, the rate of power change during the climbing period;

[0023] (4) Initial time , which is the starting time of the climbing event, can also be defined as the middle time of the climbing event;

[0024] (5) Climbing direction: up-climbing and down-climbing. When the system's upward climbing capacity is insufficient, there is a risk of power shortage due to insufficient supply; when the system's downward climbing capacity is insufficient, there is a risk of power abandonment due to excessive supply;

[0025] The energy storage ramping event is defined as an event in which the power change rate is greater than a threshold value within a certain period of time, as shown in formula (1). The power change rate during this period of time is also called the ramping rate of distributed energy storage:

[0026] (1)

[0027] In the formula, is the ramp rate of distributed energy storage, and They are Moment and The power of the moment, The value is the given threshold.

[0028] Furthermore, the distributed energy storage SOC characteristic analysis is to partition the battery SOC state according to the SOC state, determine the output target of each interval and formulate a corresponding coordinated control strategy to complete the distributed energy storage ramp rate control, distribute the control instructions to each energy storage subsystem, and each energy storage subsystem charges and discharges according to the received instructions;

[0029] The energy storage SOC restrictions are as follows:

[0030] (2)

[0031] Where: is the battery SOC, is the battery SOC lower limit; The upper limit of battery SOC;

[0032] According to the energy storage charging and discharging efficiency and regulation capability, SOC is divided into , , , , These 5 intervals, including They are the lower and upper limits of SOC when the energy storage is working normally. When SOC exceeds these two limits, that is, SOC is Interval and interval, the energy storage power will be limited; when SOC is In the range, the energy storage charging regulation ability is strong but the discharge ability is insufficient. If the energy storage is discharged, the discharge power should be reduced to avoid a rapid decrease in SOC. When the SOC is In the range, the energy storage discharge regulation ability is strong but the charging ability is insufficient. If the energy storage is charged, the charging power should be reduced to avoid a rapid increase in SOC. In the interval, the energy storage charging and discharging regulation capabilities are very strong, which is an ideal working area for distributed energy storage;

[0033] In order to control the energy storage to work in the ideal range, SOC can be used as the feedback for climbing rate adjustment, and the energy storage climbing rate can be adjusted in real time according to the range where SOC is located to prevent SOC from exceeding the limit.

[0034] Introducing SOC constraint adjustment coefficient , the charge state SOC constraint adjustment coefficient expression is: , the SOC constraint adjustment coefficient expression under discharge state is: , as shown in formula (3) and formula (4) respectively:

[0035] (3)

[0036] (4)

[0037] in

[0038] (5)

[0039] (6)

[0040] Therefore, the charging and discharging power of distributed energy storage considering SOC constraints can be obtained as shown in formula (7):

[0041] (7)

[0042] Where: is the target power of the energy storage power station, is the corrected energy storage target power considering only the SOC constraint, It is the energy storage charging flag. It is the energy storage discharge flag, and its value is determined by the following formula:

[0043] (8)

[0044] Furthermore, the climbing rate prediction model is as follows:

[0045] First, establish the power prediction model of the distributed energy storage subsystem:

[0046] (10)

[0047] Known The predicted values ​​of the grid-connected active power and reactive power of the distributed energy storage subsystem at the moment are According to formula (10), we can get The predicted values ​​of active power and reactive power of distributed energy storage grid-connected at the moment are ;

[0048] Based on the active power prediction model of distributed energy storage subsystem The energy storage system climbing rate is calculated at the moment. The predicted value of the system ramp rate at the moment is , we can conclude that The expression of the climbing rate of the distributed energy storage subsystem at the moment is as follows:

[0049] (11)

[0050] According to The predicted value of the energy storage subsystem ramp rate at the moment is , to predict the ramp rate of the energy storage subsystem, the first The predicted value of the subsystem ramp rate at time , the expression is as follows:

[0051] (12)

[0052] Furthermore, the specific contents of online rolling optimization are as follows: In distributed predictive control, the controller optimization solution is distributed in each subsystem. According to the ultra-short-term power prediction information given by the power prediction module and the identified climbing power segment, the output is optimized for the corresponding climbing power segment, the corresponding reference power is given, and the reference climbing rate under the reference power is obtained; the optimization goal of the online optimization module is to minimize the deviation between the climbing rate after climbing control and the given reference climbing rate. By adopting the quadratic programming algorithm, the solution is quickly obtained to obtain the distributed energy storage output curve under predictive control;

[0053] 1 Objective function: The optimization goal is to minimize the deviation between the climbing rate of the energy storage subsystem after climbing control and the given reference climbing rate. The objective function expression is as follows:

[0054] (13)

[0055] Where: For the Reference ramp rate of the distributed energy storage subsystem at each moment;

[0056] 2 Constraints:

[0057] In order to ensure that the energy storage subsystem operates within a safe range, system constraints include SOC constraints, battery compartment charging and discharging power constraints, and climbing constraints;

[0058] a The SOC constraints of the energy storage battery are as follows:

[0059] (14)

[0060] Where: is the lower limit of SOC of the energy storage subsystem; The SOC upper limit of the energy storage subsystem;

[0061] b The charging and discharging power constraints of the distributed energy storage subsystem are as follows:

[0062] (15)

[0063] (16)

[0064] In the formula, is the charging power of the energy storage subsystem, is the lower limit of charging power, is the upper limit of charging power, is the discharge power of the energy storage subsystem, is the lower limit of discharge power, is the upper limit of discharge power;

[0065] c The ramp rate constraints of the distributed energy storage subsystem are as follows:

[0066] (17)

[0067] In the formula, are the climbing rates of the distributed energy storage subsystem, is the lower limit of the climbing rate, The upper limit of the climbing rate.

[0068] Furthermore, the feedback correction module aims to compensate for the prediction error and accurately track the power reference instruction according to the distributed energy storage subsystem ramp rate control method, so as to ensure that the output of the energy storage subsystem can accurately track the reference power set by the online optimization module in real time. The predicted value of the energy storage subsystem ramp rate at the moment is , the actual output power of the energy storage subsystem is , then the active power and reactive power prediction errors are defined as follows:

[0069] (18)

[0070] The energy storage subsystem ramp rate prediction error is used as the ramp rate adjustment feedback to correct The reference power input at the moment is:

[0071] (19)

[0072] The climbing rate optimization control can be achieved by substituting the corrected reference climbing rate input into the objective function and resolving it.

[0073] Furthermore, in the actual operation of S2.1, the energy storage power required When the power exceeds the rated power of the system, the actual energy storage power command issued is for:

[0074] (9)

[0075] Among them, sign(⋅) is the sign function;

[0076] Assume that the distributed energy storage system consists of 2n energy storage subsystems. According to the different SOCs of each subsystem, n units are selected to form a discharge group, and the other n units are selected to form a charging group. The rated power of a single subsystem is , then the rated power of the entire system is The rated power of the discharge group and the charging group are .

[0077] Compared with the prior art, the present invention has the following beneficial effects:

[0078] The technical solution of the present invention first analyzes the typical structure and climbing characteristics of the distributed energy storage system, and then studies the state of charge characteristics of the distributed energy storage. The climbing rate of the distributed energy storage is adjusted in real time according to the state of charge interval of the energy storage battery, which can prevent the energy storage state of charge from exceeding the limit.

[0079] The distributed energy storage is first controlled in groups, and then based on the distributed model predictive control method, the optimal ramp rate control method of distributed energy storage is proposed, which mainly includes the ramp rate prediction model, online rolling optimization and online feedback correction, and the optimal ramp rate control of distributed energy storage is realized. Through this distributed energy storage optimal ramp rate control strategy, this method can effectively improve the rapid response capability of the distributed energy storage system and can adapt to the requirements of modern power systems for the flexibility and stability of energy storage participating in the operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a typical structure of distributed energy storage of the present invention;

[0081] Figure 2 It is the energy storage climbing curve of the present invention;

[0082] Figure 3 It is a state partition diagram of the SOC of the present invention;

[0083] Figure 4 The distributed model predictive control principle of the present invention;

[0084] Figure 5 This is a block diagram of the distributed energy storage optimal ramp rate control of the present invention;

[0085] Figure 6 This is a flow chart of optimal climbing rate control of the distributed energy storage subsystem of the present invention. DETAILED DESCRIPTION

[0086] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0087] In order to promote the application and promotion of distributed energy storage in the fields of power grid dispatching, power load regulation, and renewable energy consumption, the present invention proposes an optimal climbing rate control method for distributed energy storage. First, the typical structure of the energy storage system and the climbing characteristics of the distributed energy storage are analyzed. Next, the state of charge (SOC) characteristics of the energy storage are studied. The climbing speed of the energy storage is adjusted in real time according to the range of the energy storage battery state of charge to prevent the energy storage state of charge from exceeding the limit. The distributed energy storage is first grouped and controlled, and then based on the model predictive control (MPC) method, a specific control method for the optimal climbing rate based on distributed energy storage is proposed, which mainly includes a climbing rate prediction model, online rolling optimization, and online feedback correction, thereby realizing the optimal climbing rate control of distributed energy storage. Through this distributed energy storage optimal climbing rate control strategy, the rapid response capability of distributed energy storage can be greatly enhanced, and the reliability and stability of the power grid can be improved.

[0088] The specific contents are as follows:

[0089] 1. Typical structure of distributed energy storage system

[0090] The typical structure of a distributed energy storage system is as follows: Figure 1 As shown in Figure 1. The distributed energy storage system consists of multiple dispersed battery energy storage units connected in parallel, which together form a megawatt-level energy storage system. In order to achieve coordinated and optimized operation of each battery unit, the units are interconnected through a distributed communication network to complete information exchange between adjacent nodes.

[0091] The distributed energy storage system calculates the total power instruction for the distributed energy storage system in real time according to the grid-connected power target value. The control center sends the total power instruction to each energy storage unit in the energy storage system. Each energy storage unit uses a distributed communication network and a distributed algorithm to decompose the power instruction and calculates the power instruction of each energy storage unit in real time.

[0092] Analysis of distributed energy storage ramping characteristics

[0093] As an important new energy access method, distributed energy storage is flexible and adjustable, and can provide fast response and regulation capabilities for the power grid. The ramp characteristics of distributed energy storage are one of the important indicators for evaluating whether it can increase power output in a short period of time. As an important indicator, the ramp characteristics analysis can help energy storage operators determine the response speed and stability of distributed energy storage when the instantaneous load increases.

[0094] Energy storage ramping events refer to events in which energy storage output changes significantly within a short time scale. This event is a special case of distributed energy storage fluctuations. Its scale is mainly measured by exceeding a certain proportion of installed capacity or reaching a certain power threshold. Distributed energy storage ramping events mainly include the following characteristics:

[0095] (1) Power change , that is, the change value of power during the observation period;

[0096] (2) Climbing duration , i.e., the duration of the large power fluctuation;

[0097] (3) Climbing rate , that is, the rate of power change during the climbing period;

[0098] (4) Initial time , which is the starting time of the climbing event, can also be defined as the middle time of the climbing event.

[0099] (5) The climbing direction is divided into upward climbing (the terminal power is greater than the first-stage power during the observation period) and downward climbing (the terminal power is less than the first-stage power during the observation period). When the system's upward climbing capacity is insufficient, there is a risk of power shortage due to insufficient supply; when the system's downward climbing capacity is insufficient, there is a risk of power abandonment due to excessive supply.

[0100] This patent defines an energy storage ramping event as an event in which the power change rate is greater than a threshold value within a certain period of time, as shown in formula (1). The power change rate during this period of time is also called the ramping rate of distributed energy storage:

[0101] (1)

[0102] In the formula, is the ramp rate of distributed energy storage, and They are Moment and The power of the moment, The value is the given threshold.

[0103] Analysis of SOC characteristics of distributed energy storage

[0104] Due to the heterogeneity of distributed energy storage subsystems, if power is directly allocated according to the requirements of the dispatching agency, the SOC of some batteries may exceed the limit, which is not conducive to the power regulation and economic use of distributed energy storage. In order to ensure that the energy storage battery group operates within a safe range, the SOC of the energy storage battery needs to be limited. Therefore, a distributed energy storage ramp rate control strategy considering the SOC characteristics is proposed. According to the dispatching instructions, the information of each distributed energy storage is stored through the blockchain. The regional dispatching layer comprehensively considers the energy storage charge state management on the basis of clarifying the power demand of the power grid. The battery SOC is partitioned according to the SOC status, the output target of each interval is determined, and the corresponding coordinated control strategy is formulated to complete the distributed energy storage ramp rate control, and the control instructions are distributed to each energy storage subsystem. Each energy storage subsystem charges and discharges according to the received instructions. The SOC state partition diagram is shown in the figure. Figure 2 As shown:

[0105] The energy storage SOC restrictions are as follows:

[0106] (2)

[0107] Where: is the battery SOC lower limit; It is the upper limit of battery SOC.

[0108] According to the energy storage charging and discharging efficiency and regulation capability, SOC is divided into , , , , These 5 intervals, including They are the lower and upper limits of SOC when the energy storage is working normally. When SOC exceeds these two limits, that is, SOC is Interval and The energy storage power will be limited when SOC is In the range, the energy storage charging regulation ability is strong but the discharge ability is insufficient. If the energy storage is discharged, the discharge power should be reduced to avoid a rapid decrease in SOC. When the SOC is In the range, the energy storage discharge regulation ability is strong but the charging ability is insufficient. If the energy storage is charged, the charging power should be reduced to avoid a rapid increase in SOC. In the interval, the energy storage charging and discharging regulation capabilities are very strong, and it is an ideal working area for distributed energy storage.

[0109] In order to control the energy storage to work in the ideal range, SOC can be used as the feedback for climbing rate adjustment, and the energy storage climbing rate can be adjusted in real time according to the range where SOC is located to prevent SOC from exceeding the limit.

[0110] Introducing SOC constraint adjustment coefficient , charging state and discharging state The expressions of are shown in formula (3) and formula (4) respectively:

[0111] (3)

[0112] (4)

[0113] in

[0114] (5)

[0115] (6)

[0116] Therefore, the charging and discharging power of distributed energy storage considering SOC constraints can be obtained as shown in formula (7):

[0117] (7)

[0118] Where: is the corrected energy storage target power considering only the SOC constraint It is the energy storage charge and discharge flag, and its value is determined by the following formula:

[0119] (8)

[0120] 2. Distributed energy storage ramp rate control strategy

[0121] 2.1. Grouping control strategy of energy storage units

[0122] Since distributed energy storage needs to frequently switch between charging and discharging states to ensure that the system tracks the grid-connected power target value, this accelerates the aging of the energy storage subsystem to a certain extent, and is also not conducive to the full utilization of the system capacity. Figure 1 Based on the structure shown in the figure, this paper divides the distributed energy storage subsystems into two groups, which work in the discharge state and the charging state respectively, to reduce unnecessary state switching and improve the operating conditions of the energy storage unit. It is stipulated that the power of the system is positive when discharging and negative when charging.

[0123] Energy storage power required in actual operation The power may be greater than the rated power of the system. Therefore, in order to ensure the safe operation of the system, the actual energy storage power command issued for:

[0124] (9)

[0125] Among them, sign(⋅) is the sign function.

[0126] Assume that the distributed energy storage system consists of 2n energy storage subsystems. According to the different SOCs of each subsystem, n units are selected to form a discharge group, and the other n units are selected to form a charging group. The rated power of a single subsystem is , then the rated power of the entire system is The rated power of the discharge group and the charging group are When the rated power of a subsystem is insufficient to meet the power demand, a short-term battery state switching strategy is adopted to ensure the overall system throughput. Taking charging as an example, if the charging group cannot provide the required power alone, the battery cells in the discharge group will switch states for a short time to jointly achieve the charging power. During operation, when the SOC of a subsystem in any group reaches the preset upper / lower threshold, the state transition of the charge / discharge group is triggered, and the corresponding subsystem completes a charge or discharge process.

[0127] 2.2. Optimal ramp rate model predictive control strategy for energy storage subsystem

[0128] Model predictive control (MPC) is a special type of control method. Its current control action is obtained by solving a finite-time open-loop optimal control problem at each sampling moment. The current state is used as the initial state of the optimal control problem, and the optimal control sequence obtained only implements the first control action. In essence, model predictive control solves an open-loop optimal control problem. The essence of distributed model predictive control is the mutual coupling of multiple MPCs. When modeling a large and complex system as a whole, the problem of huge computational complexity often arises when using MPC problems for solving. Therefore, the overall complex system can be regarded as consisting of multiple relatively simple subsystems, which are often coupled through the communication of related parameters. Therefore, the solution to the MPC problem of the entire system can be transformed into modeling each subsystem separately and solving their own MPC problems, which can greatly reduce the amount of computation and improve the efficiency of the solution. Its core ideas are as follows: Figure 3 shown.

[0129] The SOC state of the energy storage battery is taken into account in the design of the ramp rate control strategy of distributed energy storage. MPC can consider the constraints in the current moment and the prediction time domain, and can make full use of the information provided by the reference trajectory.

[0130] The process of optimal climbing rate control of distributed energy storage can be roughly divided into three parts: climbing rate prediction model, online rolling optimization and real-time feedback correction. First, the SOC of the distributed energy storage system is monitored based on the prediction model, and the climbing rate optimal control problem is constructed by calculating the climbing rate reference value under the SOC constraint. The continuous form of the optimal control problem is discretized using a discrete method, and then the optimal control switch signal of the energy storage inverter is solved online. The obtained optimal control switch signal is initially applied to the inverter to control the discharge or charging process of the energy storage, so that the energy storage subsystem can absorb or provide power at the optimal rate. This cycle is repeated to achieve online rolling optimization and achieve the optimal climbing rate control of distributed energy storage. The block diagram of the optimal climbing rate control of the distributed energy storage subsystem proposed in this paper is shown in the figure. Figure 4 As shown:

[0131] (1) Climbing rate prediction model

[0132] First, establish the power prediction model of the distributed energy storage subsystem:

[0133] (10)

[0134] Known The predicted values ​​of the grid-connected active power and reactive power of the distributed energy storage subsystem at the moment are According to formula (10), we can get The predicted values ​​of active power and reactive power of distributed energy storage grid-connected at the moment are .

[0135] Based on the active power prediction model of distributed energy storage subsystem The energy storage system climbing rate is calculated at the moment. The predicted value of the system ramp rate at the moment is , we can conclude that The expression of the climbing rate of the distributed energy storage subsystem at the moment is as follows:

[0136] (11)

[0137] According to The predicted value of the energy storage subsystem ramp rate at the moment is , to predict the ramp rate of the energy storage subsystem and obtain the The predicted value of the subsystem ramp rate at time , the expression is as follows:

[0138] (12)

[0139] (2) Online rolling optimization

[0140] In distributed predictive control, the controller optimization solution is distributed in each subsystem. According to the ultra-short-term power prediction information given by the power prediction module and the identified climbing power segment, the output is optimized for the corresponding climbing power segment, the corresponding reference power is given, and the reference climbing rate under the reference power is obtained. The optimization goal of the online optimization module is to minimize the deviation between the climbing rate after climbing control and the given reference climbing rate. By using the quadratic programming algorithm, a quick solution is obtained to obtain the distributed energy storage output curve under predictive control.

[0141] 1 Objective function: The optimization goal is to minimize the deviation between the climbing rate of the energy storage subsystem after climbing control and the given reference climbing rate. The objective function expression is as follows:

[0142] (13)

[0143] Where: For the The reference ramp rate of the distributed energy storage subsystem at each moment.

[0144] 2 Constraints:

[0145] In order to ensure that the energy storage subsystem operates within a safe range, system constraints include SOC constraints, battery compartment charging and discharging power constraints, and climbing constraints.

[0146] a The SOC constraints of the energy storage battery are as follows:

[0147] (14)

[0148] Where: is the lower limit of SOC of the energy storage subsystem; It is the upper limit of SOC of energy storage subsystem.

[0149] b The charging and discharging power constraints of the distributed energy storage subsystem are as follows:

[0150] (15)

[0151] (16)

[0152] In the formula, is the charging power of the energy storage subsystem, is the lower limit of charging power, is the upper limit of charging power, is the discharge power of the energy storage subsystem, is the lower limit of discharge power, is the upper limit of discharge power;

[0153] c The ramp rate constraints of the distributed energy storage subsystem are as follows:

[0154] (17)

[0155] In the formula, are the climbing rates of the distributed energy storage subsystem, is the lower limit of the climbing rate, The upper limit of the climbing rate.

[0156] (3) Feedback correction control

[0157] The feedback correction module is based on the distributed energy storage subsystem climbing rate control method, which aims to compensate for the prediction error while achieving accurate tracking of the power reference instruction, ensuring that the output of the energy storage subsystem can accurately track the reference power set by the online optimization module in real time. The predicted value of the energy storage subsystem ramp rate at the moment is , the actual output power of the energy storage subsystem is , then the active power and reactive power prediction errors are defined as follows:

[0158] (18)

[0159] The energy storage subsystem ramp rate prediction error is used as the ramp rate adjustment feedback to correct The reference power input at the moment is:

[0160] (19)

[0161] The climbing rate optimization control can be achieved by substituting the corrected reference climbing rate input into the objective function and resolving it.

[0162] The distributed energy storage subsystem ramp rate model predictive control process is as follows: Figure 6 As shown, the overall control steps are as follows:

[0163] Step 1: First, sample the voltage and current of the distributed energy storage, perform power prediction of the distributed energy storage subsystem, and then perform ramp rate prediction based on this;

[0164] Step 2: Use group ramp control for distributed energy storage;

[0165] Step 3: Set the distributed energy storage subsystem ramp rate control objective function including SOC characteristics. Distributed settings is inf, then enter the loop statement to predict the climbing rate prediction value of the next sampling time;

[0166] Step 4: Finally, by solving the objective function, when The control effect is best when the value is minimum. The switch state at this time acts on the switch tube, thereby completing the entire climbing rate control process.

[0167] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0168] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for controlling the ramp rate of distributed energy storage, characterized in that: The steps include: S1. Characteristic analysis of distributed energy storage ramping and distributed energy storage SOC based on the typical structure of distributed energy storage system; S2. Formulate distributed energy storage ramp rate control strategy S2.1 Grouping control strategy of energy storage units Divide multiple subsystems of distributed energy storage into two groups, which work in discharge state and charge state respectively, to reduce unnecessary state switching and improve the operating conditions of energy storage units. The power of the system is positive when discharging and negative when charging. S2.

2. Optimal ramp rate model predictive control strategy for energy storage subsystem The model predictive control (MPC) is transformed into modeling each subsystem and solving their own MPC problems, which is divided into three parts: ramp rate prediction model, online rolling optimization and real-time feedback correction. The overall control steps are as follows: Step 1: First, sample the voltage and current of the distributed energy storage, perform power prediction of the distributed energy storage subsystem, and then perform ramp rate prediction based on this; Step 2: Use group ramp control for distributed energy storage; Step 3: Set the distributed energy storage subsystem ramp rate control objective function including SOC characteristics. Distributed settings is inf, then enter the loop statement to predict the climbing rate prediction value of the next sampling time; Step 4: Finally, by solving the objective function, when The control effect is best when the value is minimum. The switch state at this time acts on the switch tube, thereby completing the entire climbing rate control process.

2. A distributed energy storage ramp rate control method according to claim 1, characterized in that: The events in the distributed energy storage ramping characteristic analysis mainly include the following characteristic quantities: (1) Power change , that is, the change value of power during the observation period; (2) Climbing duration , i.e., the duration of the large power fluctuation; (3) Climbing rate , that is, the rate of power change during the climbing period; (4) Initial time , which is the starting time of the climbing event, can also be defined as the middle time of the climbing event; (5) Climbing direction: up-climbing and down-climbing. When the system's upward climbing capacity is insufficient, there is a risk of power shortage due to insufficient supply; when the system's downward climbing capacity is insufficient, there is a risk of power abandonment due to excessive supply; The energy storage ramping event is defined as an event in which the power change rate is greater than a threshold value within a certain period of time, as shown in formula (1). The power change rate during this period of time is also called the ramping rate of distributed energy storage: (1) In the formula, is the ramp rate of distributed energy storage, and They are Moment and The power of the moment, The value is the given threshold.

3. A distributed energy storage ramp rate control method according to claim 1, characterized in that: The distributed energy storage SOC characteristic analysis is to partition the battery SOC state according to the SOC state, determine the output target of each interval and formulate a corresponding coordinated control strategy to complete the distributed energy storage ramp rate control, distribute the control instructions to each energy storage subsystem, and each energy storage subsystem charges and discharges according to the received instructions; The energy storage SOC restrictions are as follows: (2) Where: is the battery SOC, is the battery SOC lower limit; The upper limit of battery SOC; According to the energy storage charging and discharging efficiency and regulation capability, SOC is divided into , , , , These 5 intervals, including They are the lower and upper limits of SOC when the energy storage is working normally. When SOC exceeds these two limits, that is, SOC is Interval and interval, the energy storage power will be limited; when SOC is In the range, the energy storage charging regulation ability is strong but the discharge ability is insufficient. If the energy storage is discharged, the discharge power should be reduced to avoid a rapid decrease in SOC. When the SOC is In the range, the energy storage discharge regulation ability is strong but the charging ability is insufficient. If the energy storage is charged, the charging power should be reduced to avoid a rapid increase in SOC. In the interval, the energy storage charging and discharging regulation capabilities are very strong, which is an ideal working area for distributed energy storage; In order to control the energy storage to work in the ideal range, SOC can be used as the feedback for climbing rate adjustment, and the energy storage climbing rate can be adjusted in real time according to the range where SOC is located to prevent SOC from exceeding the limit. Introducing SOC constraint adjustment coefficient , the charge state SOC constraint adjustment coefficient expression is: , the SOC constraint adjustment coefficient expression under discharge state is: , as shown in formula (3) and formula (4) respectively: (3) (4) in (5) (6) Therefore, the charging and discharging power of distributed energy storage considering SOC constraints can be obtained as shown in formula (7): (7) Where: is the target power of the energy storage power station, is the corrected energy storage target power considering only the SOC constraint, It is the energy storage charging flag. It is the energy storage discharge flag, and its value is determined by the following formula: (8)。 4. A distributed energy storage ramp rate control method according to claim 1, characterized in that: The climbing rate prediction model is as follows: First, establish the power prediction model of the distributed energy storage subsystem: (10) Known The predicted values ​​of the grid-connected active power and reactive power of the distributed energy storage subsystem at the moment are According to formula (10), we can get The predicted values ​​of active power and reactive power of distributed energy storage grid-connected at the moment are ; Based on the active power prediction model of distributed energy storage subsystem The energy storage system climbing rate is calculated at the moment. The predicted value of the system ramp rate at the moment is , and the The expression of the climbing rate of the distributed energy storage subsystem at the moment is as follows: (11) According to The predicted value of the energy storage subsystem ramp rate at the moment is , to predict the ramp rate of the energy storage subsystem and obtain the The predicted value of the subsystem ramp rate at time , the expression is as follows: (12)。 5. The method for controlling the ramp rate of distributed energy storage according to claim 1, characterized in that: The specific contents of online rolling optimization are as follows: In distributed predictive control, the controller optimization solution is distributed in each subsystem. According to the ultra-short-term power prediction information given by the power prediction module and the identified climbing power segment, the output is optimized for the corresponding climbing power segment, the corresponding reference power is given, and the reference climbing rate under the reference power is obtained; the optimization goal of the online optimization module is to minimize the deviation between the climbing rate after climbing control and the given reference climbing rate. By adopting the quadratic programming algorithm, the distributed energy storage output curve under predictive control is obtained quickly; ① Objective function: The optimization goal is to minimize the deviation between the climbing rate of the energy storage subsystem after climbing control and the given reference climbing rate. The objective function expression is as follows: (13) Where: For the Reference ramp rate of the distributed energy storage subsystem at each moment; ② Constraints: In order to ensure that the energy storage subsystem operates within a safe range, system constraints include SOC constraints, battery compartment charging and discharging power constraints, and climbing constraints; a The SOC constraints of the energy storage battery are as follows: (14) Where: is the lower limit of SOC of the energy storage subsystem; The upper limit of SOC of the energy storage subsystem; b The charging and discharging power constraints of the distributed energy storage subsystem are as follows: (15) (16) In the formula, is the charging power of the energy storage subsystem, is the lower limit of charging power, is the upper limit of charging power, is the discharge power of the energy storage subsystem, is the lower limit of discharge power, is the upper limit of discharge power; c The ramp rate constraints of the distributed energy storage subsystem are as follows: (17) In the formula, are the climbing rates of the distributed energy storage subsystem, is the lower limit of the climbing rate, The upper limit of the climbing rate.

6. A distributed energy storage ramp rate control method according to claim 1, characterized in that: The feedback correction module is based on the distributed energy storage subsystem climbing rate control method, which aims to compensate for the prediction error while achieving accurate tracking of the power reference instruction, ensuring that the output of the energy storage subsystem can accurately track the reference power set by the online optimization module in real time. The predicted value of the energy storage subsystem ramp rate at the moment is , the actual output power of the energy storage subsystem is , then the active power and reactive power prediction errors are defined as follows: (18) The energy storage subsystem ramp rate prediction error is used as the ramp rate adjustment feedback to correct The reference power input at the moment is: (19) The climbing rate optimization control can be achieved by substituting the corrected reference climbing rate input into the objective function and resolving it.

7. A distributed energy storage ramp rate control method according to claim 1, characterized in that: Energy storage power demand in actual operation of S2.1 When the power exceeds the rated power of the system, the actual energy storage power command issued is for: (9) Among them, sign(⋅) is the sign function; Assume that the distributed energy storage system consists of 2n energy storage subsystems. According to the different SOCs of each subsystem, n units are selected to form a discharge group, and the other n units are selected to form a charging group. The rated power of a single subsystem is , then the rated power of the entire system is The rated power of the discharge group and the charging group are .

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