Dynamic power distribution optimized energy storage confluence direct-current transformation segmented conversion method

Abstract

The invention discloses a dynamic power distribution optimized energy storage confluence direct current transformation segmented conversion method, and relates to the technical field of power grid energy storage segmented conversion, and the method comprises the steps: obtaining the operation data of an energy storage module, and carrying out the state estimation through a Kalman filter, and obtaining the state vector of a current energy storage system; the energy storage system receives an external frequency modulation instruction, predicts a future power demand, dynamically adjusts a weight matrix in model prediction control through a fuzzy logic controller in combination with a current system state, and adaptively optimizes the frequency modulation performance; and based on the optimized weight matrix, optimizing and solving an optimal control sequence, generating a control instruction, sending an adjusting command to the high-voltage converter device, and realizing real-time power adjustment and system response. According to the invention, the dynamic power distribution optimization algorithm is combined with the fuzzy logic controller, so that adaptive power adjustment is realized, the future power demand can be accurately predicted, the control strategy can be optimized in real time, and the power dispatching efficiency and the response speed of the system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of power grid energy storage segmented conversion, and in particular to an energy storage converging direct current transformer segmented conversion method with dynamic power distribution optimization. Background Art

[0002] In modern power systems, thermal power units are one of the main sources of power supply. However, during operation, thermal power units are limited by their inherent physical characteristics and their frequency regulation capabilities are usually limited. This means that when faced with rapidly changing load demands and grid frequency fluctuations, thermal power units are difficult to respond quickly, thus affecting the stability and reliability of the grid.

[0003] At present, the typical design of the power supply system of large thermal power units is to tap the step-down transformer at the generator outlet, which can be divided into two-stage transformers. The first stage is to step down the 20kV at the generator outlet to 6kV, and the second stage is to step down the 6kV to 400V. Both use industrial frequency transformers and involve AC power. With the continuous increase in the capacity of the unit, the capacity of the power supply system is also increasing. The capacity of the high-voltage power supply transformer is getting larger, and the short-circuit current of the power supply system is very large, which puts higher requirements on the short-circuit breaking capacity of the high-voltage switch. In order to meet the requirements of short-circuit thermal stability, the high-voltage cable of the power supply system needs to choose thicker cables, which increases the investment cost. Traditional high-voltage power supply transformers use industrial frequency transformers, which are huge in size, and in order to obtain a larger capacity of the power supply system, they are often remodeled for capacity expansion. The cost of the transformation is huge, the construction period is long, and the normal power generation of the power plant is delayed.

[0004] The use of DC networking in the power supply system can effectively solve some bottleneck problems in the development of traditional AC power supply systems. Compared with the traditional AC power supply system, DC networking has many advantages: DC networking has a larger power supply capacity; DC networking does not need to consider phase angle and frequency, and can realize asynchronous system interconnection. The power supply load is connected to the DC power supply system through the rectifier, which can improve the efficiency of power conversion and reduce equipment loss, realize load variable frequency drive, simplify the internal circuit of the load, and reduce the failure rate and equipment cost. Summary of the invention

[0005] In view of the problems existing in the existing energy storage converging DC transformer segmented conversion method with dynamic power distribution optimization, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is that it is difficult for thermal power generation units to respond quickly to rapidly changing load demands and grid frequency fluctuations, thereby affecting the stability and reliability of the grid.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, an embodiment of the present invention provides a method for dynamically optimizing energy storage and DC conversion with dynamic power distribution, which includes obtaining operating data of the energy storage module and using a Kalman filter to perform state estimation to obtain a state vector of the current energy storage system; the energy storage system receives an external frequency modulation instruction, predicts future power demand, and dynamically adjusts the weight matrix in the model predictive control through a fuzzy logic controller in combination with the current system state to adaptively optimize the frequency modulation performance; based on the optimized weight matrix, the optimal control sequence is optimized and solved, a control instruction is generated, and an adjustment command is sent to the high-voltage converter to achieve real-time power regulation and system response.

[0009] As a preferred solution of the energy storage converging DC transformer segmented conversion method with dynamic power distribution optimization described in the present invention, the operating data of the energy storage module includes the state of charge SOC, output power, unit operating parameters and grid status data of the energy storage system.

[0010] As a preferred solution of the energy storage converging DC transformer segmented conversion method for dynamic power distribution optimization described in the present invention, the state estimation is to process the collected operation data through a Kalman filter to estimate the current system state, which is expressed as:

[0011] x(k)=[SOC1(k),SOC2(k),P1(k),P2(k)] T

[0012] u(k)=[ΔP1(k),ΔP2(k)] T

[0013] y(k)=[Pout1(k),Pout2(k)] T

[0014] Among them, x(k) represents the system state vector, u(k) represents the control input vector, y(k) represents the output vector, SOC1(k) and SOC2(k) are the charge states of the two energy storage systems, P1(k) and P2(k) are the current output powers of the two energy storage systems, ΔP1(k) and ΔP2(k) are the power adjustment values, and Pout1(k) and Pout2(k) are the actual output powers of the two energy storage systems.

[0015] As a preferred solution of the energy storage converging DC transformer segmented conversion method with dynamic power allocation optimization described in the present invention, the external frequency modulation instruction includes a frequency modulation demand, that is, the power change trajectory that the system needs to provide in a period of time in the future is defined as a target power trajectory r(k), which is expressed as:

[0016] r(k)=[r(1),r(2),...,r(N)]T

[0017] Where r(k) represents the target output power at the kth moment in the prediction period, and N is the prediction step size.

[0018] As a preferred solution of the energy storage converging DC transformer segmented conversion method for dynamic power allocation optimization described in the present invention, based on the current system state x(k) and the external frequency modulation instruction r(k), the system predicts the power demand at the next N moments, which is expressed as:

[0019] x(k+1)=Ax(k)+Bu(k)+Ew(k)

[0020] y(k)=Cx(k)+v(k)

[0021] Among them, A is the state transfer matrix, B is the input matrix, C is the output matrix, E is the interference matrix, w(k) is the system noise, υ(k) is the measurement noise, y(k) is the actual output power of the system, describing the actual output of the energy storage system at the current moment; determine the constraints, including SOC constraints, power constraints and ramp rate constraints; the SOC constraint is that the state of charge of the energy storage system needs to be maintained within a preset safety range, expressed as:

[0022] SOCmin≤SOCi(k)≤SOCmax

[0023] Among them, SOCmin is the lowest allowed state of charge, SOCmax is the highest allowed state of charge; the power constraint is that the output power of the energy storage system is limited to the rated power range of the equipment, expressed as:

[0024] Pmin≤Pi(k)≤Pmax

[0025] Among them, Pmin is the minimum output power of the energy storage system, and Pmax is the maximum output power of the energy storage system; the ramp rate constraint is to prevent instability caused by rapid changes in power output, and the rate of change of power regulation is limited, which is expressed as:

[0026] |ΔPi(k)|≤ΔPmax

[0027] Among them, ΔPmax is the maximum allowed power change rate.

[0028] As a preferred solution of the energy storage converging DC transformer segmented conversion method for dynamic power allocation optimization described in the present invention, a fuzzy logic controller is defined to dynamically adjust the weight matrices Q, R, and S in the MPC, so that the system can adaptively adjust power according to the current state and optimize the objective function, which is expressed as:

[0029]

[0030] Among them, r(k) is the external frequency modulation instruction, which represents the desired power output, Q is the weight matrix of the output error, R is the weight matrix of the control input, S is the weight matrix of the control input change rate, and N is the prediction step size; by solving the objective function J, the optimal control sequence u*(k) is found and the frequency modulation performance of the system is optimized.

[0031] As a preferred solution of the energy storage converging DC transformer segmented conversion method for dynamic power distribution optimization described in the present invention, a series of optimal control sequences u*(k) are obtained based on the optimization solution, including the optimal control input values ​​from the current moment k to the future N steps. Since only the first control instruction in the sequence, i.e. u*(1), needs to be executed at the current moment, the first control instruction u*(1) is extracted as the optimal control input, and the extracted optimal control input u*(1)=[ΔP1(1), ΔP2(1)] T It is sent to the high-voltage converter to transmit the power size and direction that needs to be adjusted at the current moment. The high-voltage converter adjusts the power output according to the received adjustment instructions. After the converter executes the control instructions, it detects the response of the energy storage system in real time through sensors and monitoring modules, and feeds back to the controller in real time for further optimization and adjustment in subsequent cycles.

[0032] In a second aspect, an embodiment of the present invention provides an energy storage bus DC transformer segmented conversion system, which includes: a thermal power grid-connected energy storage T-type commutation access unit 1, and an energy storage bus DC access unit 2; wherein the thermal power grid-connected energy storage T-type commutation access unit 1 includes a #1 generator and a #2 generator, the #1 generator and the #2 generator are connected to the 330kV power grid through a #1 main transformer and a #2 main transformer, the #1 energy storage high-voltage commutation device and the #2 energy storage high-voltage commutation device are respectively connected to the outlets of the 1 generator and the #2 generator, and the DC sides of the #1 energy storage high-voltage commutation device and the #2 energy storage high-voltage commutation device are uniformly connected to the DC 31kV busbar; the energy storage module in the energy storage bus DC access unit 2 is connected to the 9.76kV energy storage DC busbar through a DC-DC converter, and a DC transformer is used for DC boosting to boost the energy storage system voltage from DC 9.76kV to DC 31kV, and connected to the DC 31kV busbar in the thermal power grid-connected energy storage T-type commutation access unit 1.

[0033] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned energy storage converging DC transformer segmented conversion method with dynamic power distribution optimization is implemented.

[0034] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned energy storage converging DC transformer segmented conversion method with dynamic power distribution optimization is implemented.

[0035] The beneficial effect of the present invention is that the present invention realizes adaptive power regulation by combining the dynamic power allocation optimization algorithm with the fuzzy logic controller, and can flexibly switch between single-unit and dual-unit modes to ensure efficient utilization of energy storage resources. Based on the state estimation of model predictive control and Kalman filter, the system can accurately predict future power demand and optimize the control strategy in real time, thereby improving the efficiency of power dispatching and the response speed of the system, effectively meeting the frequency regulation requirements of the power grid, and improving overall stability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0037] Figure 1 This is a diagram of the thermal power energy storage bus direct-hung segmented transformation system for the energy storage bus DC transformation segmented transformation method.

[0038] Figure 2 This is a graph showing the change rate of virtual inductance in different energy storage converters. DETAILED DESCRIPTION

[0039] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0040] 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, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0041] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0042] Example 1

[0043] Reference Figure 1 , which is the first embodiment of the present invention, and provides a method for sectional conversion of energy storage current DC transformer with dynamic power allocation optimization, comprising:

[0044] S1: Obtain the operating data of the energy storage module and use the Kalman filter to perform state estimation to obtain the state vector of the current energy storage system.

[0045] The operating data collected from the energy storage module includes the state of charge (SOC), output power, unit operating parameters and grid status data of the energy storage system.

[0046] The output power includes: real-time output power, historical power change trend, power limit and power adjustment amount.

[0047] The grid status data includes: grid frequency data, grid voltage data, grid power demand data and grid fault information.

[0048] State estimation is to process the collected operating data through the Kalman filter to estimate the current system state, which is expressed as:

[0049] x(k)=[SOC1(k),SOC2(k),P1(k),P2(k)] T

[0050] u(k)=[ΔP1(k),ΔP2(k)] T

[0051] y(k)=[Pout1(k),Pout2(k)] T

[0052] Among them, x(k) represents the system state vector, u(k) represents the control input vector, y(k) represents the output vector, SOC1(k) and SOC2(k) are the charge states of the two energy storage systems, P1(k) and P2(k) are the current output powers of the two energy storage systems, ΔP1(k) and ΔP2(k) are the power adjustment values, and Pout1(k) and Pout2(k) are the actual output powers of the two energy storage systems.

[0053] S2: The energy storage system receives external frequency modulation instructions, predicts future power demand, and combines the current system status to dynamically adjust the weight matrix in the model predictive control through the fuzzy logic controller to adaptively optimize the frequency modulation performance.

[0054] The energy storage system receives frequency modulation instructions from the outside, which contain frequency modulation requirements, that is, the power change trajectory that the system needs to provide in the future. The frequency modulation instruction is usually given in the form of target power output, called the target power trajectory r(k), which is used to guide the actual output Pout(k) of the system to follow this trajectory.

[0055] r(k)=[r(1),r(2),...,r(N)]T

[0056] Where r(k) represents the target output power at the kth moment in the prediction period, and N is the prediction step size, indicating that the system will generate a frequency modulation control strategy for the next N moments.

[0057] The target power trajectory represents the power change demand that the system should provide and serves as a reference signal in the subsequent control strategy.

[0058] Based on the current system state x(k) and the external frequency modulation command r(k), the system predicts the power demand at N moments in the future. The future power demand prediction model is constructed using the current state of charge (SOC) of the energy storage system, the current output power, and the state information of the external power grid. The predicted output power demand is expressed as:

[0059] x(k+1)=Ax(k)+Bu(k)+Ew(k)

[0060] y(k)=Cx(k)+v(k)

[0061] Among them, A is the state transfer matrix, B is the input matrix, C is the output matrix, E is the interference matrix, w(k) is the system noise, v(k) is the measurement noise, and y(k) is the actual output power of the system, which describes the actual output of the energy storage system at the current moment;

[0062] In order to ensure the stability of the system and the safety of the equipment, the following constraints need to be considered, including: SOC constraint, power constraint and ramp rate constraint.

[0063] The SOC constraint is that the state of charge of the energy storage system needs to be kept within a preset safety range, which can be expressed as:

[0064] SOCmin≤SOCi(k)≤SOCmax

[0065] Among them, SOCmin is the lowest allowed state of charge, and SOCmax is the highest allowed state of charge. The power constraint is that the output power of the energy storage system is limited to the rated power range of the equipment, expressed as:

[0066] Pmin≤Pi(k)≤Pmax

[0067] Among them, Pmin is the minimum output power of the energy storage system, and Pmax is the maximum output power of the energy storage system.

[0068] The ramp rate constraint is to prevent instability caused by rapid changes in power output. The rate of change of power regulation is limited, which is expressed as:

[0069] |ΔPi(k)|≤ΔPmax

[0070] Among them, ΔPmax is the maximum allowed power change rate.

[0071] A fuzzy logic controller is defined to dynamically adjust the weight matrices Q, R, and S in the MPC so that the system can adaptively adjust power according to the current state.

[0072] The input variables include:

[0073] eSOC: State of charge deviation, which indicates the deviation between the current SOC and the target value.

[0074] eP: Power deviation, indicating the deviation between the current output power and the frequency modulation instruction.

[0075] δSOC: SOC change rate, reflecting the speed of change of the charge state of the energy storage system.

[0076] Output variables include:

[0077] αQ: Factor used to adjust the weight matrix Q, affecting the penalty for frequency modulation error.

[0078] αR: Factor used to adjust the weight matrix R, affecting the penalty for power regulation.

[0079] αS: A factor used to adjust the weight matrix S, affecting the penalty for the power change rate.

[0080] Specifically, when eSOC is high and eP is low, the weight factor α of Q is increased to indicate the importance attached to the power error.

[0081] When δSOC changes quickly, the weight factor αS of S is increased to limit the power change rate.

[0082] The fuzzy logic controller outputs the adjusted weight factors αQ, αR, αS according to the current state of the system. These factors are applied to the MPC optimization problem to optimize the objective function, which is expressed as:

[0083]

[0084] Among them, r(k) is the external frequency modulation command, which represents the desired power output, Q is the weight matrix of the output error, R is the weight matrix of the control input, which limits the power adjustment amplitude, S is the weight matrix of the control input change rate, and N is the prediction step size.

[0085] By solving the objective function J, the optimal control sequence u*(k) is found to ensure that the power regulation matches the frequency modulation instruction and optimize the frequency modulation performance of the system.

[0086] Furthermore, it is determined whether the current frequency regulation requirement is for a single unit or a dual unit. If frequency regulation is required for a single unit, the system optimizes resource usage to improve efficiency and reduce energy consumption.

[0087] All power is allocated to the currently working segment to ensure that the unit can maximize its output, and the non-working segment is placed in a low-power standby state to reduce unnecessary energy consumption and loss.

[0088] If both units need to regulate frequency at the same time, the system will rationally allocate energy storage resources to meet higher frequency regulation requirements.

[0089] The power distribution is initialized according to the proportion of the frequency regulation instructions to ensure the load balance of the two units. A dynamic power distribution optimization algorithm is used to adjust the output of the energy storage system in real time during operation to adapt to the changing grid demand.

[0090] S3: Based on the optimized weight matrix, the optimal control sequence is optimized and solved, control instructions are generated, and adjustment commands are sent to the high-voltage converter to achieve real-time power regulation and system response.

[0091] Through the optimization process, a series of optimal control sequences u*(k) are obtained. These sequences contain the optimal control input values ​​from the current time k to the next N steps. Since the control system runs in real time, only the first control instruction in the sequence, i.e. u*(1), needs to be executed at the current time.

[0092] The optimization result is a set of control input sequences u*(k)=[u*(1), u*(2), …, u*(N)], where each u*(k) represents the value of the regulated power.

[0093] Extract the first control instruction u*(1). This value contains the power regulation amount that the energy storage system needs to adjust at the current moment, expressed as:

[0094]

[0095] Among them, ΔP1(1) is the power regulation of energy storage system 1 at the current moment, and ΔP2(1) is the power regulation of energy storage system 2 at the current moment. It means that the system needs to make corresponding adjustments to the power output of energy storage system 1 and energy storage system 2 at the current moment to meet the frequency regulation requirements of the power grid.

[0096] Furthermore, the extracted optimal control input u*(1)=[ΔP1(1), ΔP2(1)] T The high-voltage converter is sent to the high-voltage converter to transmit the power size and direction that needs to be adjusted at the current moment. The high-voltage converter adjusts the power output according to the received adjustment instructions, including: controlling the voltage amplitude and phase angle to ensure that the power injection or extraction is consistent with the needs of the power grid, and dynamically adjusting the DC power of the energy storage system to convert it into AC power, or adjusting the distribution of DC power among different energy storage units.

[0097] After the commutation device executes the control command, the system will detect the response of the energy storage system in real time through sensors and monitoring modules. The parameters monitored at this time include:

[0098] The actual output power Pout1(k), Pout2(k) and the current energy storage system status are fed back to the controller in real time for further optimization and adjustment in subsequent cycles to ensure the system regulation accuracy.

[0099] In summary, the present invention realizes adaptive power regulation by combining a dynamic power allocation optimization algorithm with a fuzzy logic controller, and can flexibly switch between single-unit and dual-unit modes to ensure efficient utilization of energy storage resources. Based on model predictive control and state estimation of the Kalman filter, the system can accurately predict future power demand and optimize the control strategy in real time, thereby improving the efficiency of power dispatch and the response speed of the system, effectively meeting the frequency regulation needs of the power grid, and improving overall stability and reliability.

[0100] Example 2

[0101] Reference Figure 1 Based on the first embodiment, this embodiment further provides an energy storage converging DC transformer segmented converter system, including:

[0102] Thermal power grid-connected energy storage T-type commutation access unit 1, energy storage converging DC access unit 2;

[0103] The thermal power grid-connected energy storage T-type commutation access unit 1 includes a #1 generator and a #2 generator.

[0104] The #1 generator and #2 generator are connected to the 330kV power grid through the #1 main transformer and the #2 main transformer, the #1 energy storage high-voltage converter and the #2 energy storage high-voltage converter are connected to the outlets of the #1 generator and the #2 generator respectively, and the DC sides of the #1 energy storage high-voltage converter and the #2 energy storage high-voltage converter are uniformly connected to the DC 31kV busbar;

[0105] The energy storage module in the energy storage busbar DC access unit 2 is connected to the 9.76kV energy storage DC busbar through a DC-DC converter, and a DC transformer is used to boost the DC voltage, thereby boosting the energy storage system voltage from DC 9.76kV to DC 31kV, and connecting it to the DC 31kV busbar in the thermal power grid-connected energy storage T-type commutation access unit 1.

[0106] This embodiment also provides a computer device, which is suitable for the case of a dynamic power distribution optimization energy storage converging DC transformer segmented conversion method, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the dynamic power distribution optimization energy storage converging DC transformer segmented conversion method proposed in the above embodiment.

[0107] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0108] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the energy storage converging DC transformer segmented conversion method for realizing dynamic power allocation optimization as proposed in the above embodiment is implemented.

[0109] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0110] Example 3

[0111] Reference Figure 2 , which is the third embodiment of the present invention, is based on the first two embodiments and is scientifically demonstrated through simulation experiments in order to verify the beneficial effects of the present invention.

[0112] The present invention adopts droop control in the commutation unit. According to the relationship between the output active power and frequency and the reactive power and voltage of the energy storage device, the energy storage converter adjusts its own active and reactive output to achieve frequency synchronization and voltage stability, thus forming the active-frequency and reactive-voltage droop control of the microgrid. The specific expression is:

[0113]

[0114] Among them, P0 and Q0 are the rated active power and rated reactive power respectively; P and Q are the active power and reactive power output by the energy storage converter respectively; ω0 and ω* are the rated frequency and reference frequency of the energy storage converter respectively; E0 and E * are the rated voltage and reference voltage of the energy storage converter respectively; k p , k q They are the active power droop coefficient and the reactive power droop coefficient respectively.

[0115] Under the condition of equal feeder impedance, the energy storage converter adjusts its frequency and voltage amplitude according to the active-frequency and reactive-voltage characteristic curves, and finally ensures that the energy storage converter runs to a stable operating point. If the capacity of the energy storage converters is equal and the same droop coefficient is selected, the output power of the energy storage converters will be equal; if the capacity of the energy storage converters is unequal, the output power of the energy storage converters can be reasonably distributed according to their capacity ratio by selecting a suitable droop coefficient inversely proportional to their capacity. When the active power and reactive power of the load increase or decrease, the active-frequency and reactive-voltage droop control based on the droop control characteristics can autonomously adjust the frequency and voltage reference values ​​of the energy storage converter, thereby realizing the reasonable distribution of active power and reactive power. However, the above adjustment process is only based on the ideal condition of equal feeder impedance of the energy storage converter, and it can only be realized when both frequency and voltage can be used as global variables of the system. In actual engineering practice, since the voltage is greatly affected by the feeder impedance and can no longer be used as a global variable of the system, it is difficult to achieve a reasonable distribution of reactive power of parallel-operated energy storage converters using traditional reactive-voltage droop control.

[0116] The present invention draws on the global variable concept of P-ω droop control and constructs a virtual inductance change rate L in reactive power droop control. v 'This global variable is proposed based on QL v The adaptive virtual inductor power sharing strategy based on droop is used to achieve the reactive power sharing of the energy storage converter in steady state. By introducing the global variable virtual inductor change rate L in the reactive droop loop, the reactive power of the energy storage converter is evenly distributed. v', in steady state, the inconsistency of voltage drops on the lines of different energy storage converters can be eliminated, and the reasonable distribution of reactive power between energy storage converters can be achieved.

[0117] Figure 2 In the example, set the virtual inductance change rate L v 'As global variables, construct reactive power Q and virtual inductance change rate L v The droop control between ' can be expressed as:

[0118]

[0119] In steady state, L v 'It needs to be restored to 0 to ensure that the virtual inductance does not change, and at the same time ensure that the deviation of the reactive power Q of the energy storage converter is consistent, so as to achieve a reasonable distribution of reactive power between energy storage converters. The recovery mechanism can be expressed as:

[0120]

[0121] Among them, k res For L v 'Restitution coefficient; Q R is the rated reactive power capacity of the energy storage converter; ΔQ0 is the rated reactive compensation amount. To achieve effective compensation, L v 'The response speed of recovery to 0 is much slower than QL v 'Sag control process.

[0122] In L v 'During the recovery process, Q 01 (t2) gradually increases to Q1(t2), Q 02 (t2) gradually increases to Q2(t2), at t3 Q 01 (t3) = Q1(t3) = Q1(t2), Q 02 (t3) = Q2(t3) = Q2(t2), L v1 '=L v2 '=0,Complete L v 'Restore to 0 process to achieve reasonable distribution of reactive power.

[0123] L v '(s) and L v The time constants τ of the recovery process are:

[0124]

[0125] In QL v 'Drop control, L v '(s) is affected by the DGs output reactive power Q(s) and the initial reactive power value Q0(0) of the energy storage converter, and further deduces that L vThe expression of (s) is:

[0126]

[0127] in:

[0128] In order to make L v 'The time constant of the recovery process is much greater than QL v 'The time constant of droop control needs to be reasonably designed k res The value of k res Need to meet:

[0129]

[0130] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for sectional conversion of energy storage and DC current with dynamic power distribution optimization, characterized in that: include, Obtain the operating data of the energy storage module and use the Kalman filter to perform state estimation to obtain the state vector of the current energy storage system; The energy storage system receives external frequency modulation instructions, predicts future power demand, and dynamically adjusts the weight matrix in the model predictive control through the fuzzy logic controller based on the current system status to adaptively optimize the frequency modulation performance. Based on the optimized weight matrix, the optimal control sequence is optimized and solved, control instructions are generated, and adjustment commands are sent to the high-voltage converter to achieve real-time power regulation and system response.

2. The method for energy storage converging DC transformer segmented conversion with dynamic power distribution optimization according to claim 1, characterized in that: The operating data of the energy storage module includes the state of charge SOC, output power, unit operating parameters and grid status data of the energy storage system.

3. The method for energy storage converging DC transformer segmented conversion with dynamic power distribution optimization according to claim 2 is characterized in that: The state estimation is to process the collected operation data through the Kalman filter to estimate the current system state, which is expressed as: x(k)=[SOC1(k),SOC2(k),P1(k),P2(k)] T u(k)=[ΔP1(k),ΔAP2(k)] T y(k)=[Pout1(k),Pout2(k)] T Among them, x(k) represents the system state vector, u(k) represents the control input vector, y(k) represents the output vector, SOC1(k) and SOC2(k) are the charge states of the two energy storage systems, P1(k) and P2(k) are the current output powers of the two energy storage systems, ΔP1(k) and ΔP2(k) are the power adjustment values, and Pout1(k) and Pout2(k) are the actual output powers of the two energy storage systems.

4. The method for energy storage converging DC transformer segmented conversion with dynamic power distribution optimization as claimed in claim 3 is characterized in that: The external frequency modulation instruction includes the frequency modulation demand, that is, the power change trajectory that the system needs to provide in the future, which is defined as the target power trajectory r(k) and is expressed as: r(k)=[r(1),r(2),..,r(N)]T Where r(k) represents the target output power at the kth moment in the prediction period, and N is the prediction step size.

5. The method for energy storage converging direct current transformer segmented conversion with dynamic power distribution optimization as claimed in claim 4, characterized in that: Based on the current system state x(k) and the external frequency modulation command r(k), the system predicts the power demand in the next N moments, which is expressed as: x(k+1)=Ax(k)+Bu(k)+Ew(k) y(k)=Cx(k)+v(k) Among them, A is the state transfer matrix, B is the input matrix, C is the output matrix, E is the interference matrix, w(k) is the system noise, v(k) is the measurement noise, and y(k) is the actual output power of the system, which describes the actual output of the energy storage system at the current moment; Determine constraints, including SOC constraints, power constraints, and ramp rate constraints; The SOC constraint is that the state of charge of the energy storage system needs to be kept within a preset safety range, which is expressed as: SOCmin≤SOCi(k)≤SOCmax Among them, SOCmin is the lowest allowed state of charge, and SOCmax is the highest allowed state of charge; The power constraint is that the output power of the energy storage system is limited to the rated power range of the equipment, which is expressed as: Pmin≤Pi(k)≤Pmax Among them, Pmin is the minimum output power of the energy storage system, and Pmax is the maximum output power of the energy storage system; The ramp rate constraint is to prevent instability caused by rapid changes in power output. The rate of change of power regulation is limited, which is expressed as: |ΔPi(k)|≤ΔPmax Among them, ΔPmax is the maximum allowed power change rate.

6. The method for energy storage converging direct current transformer segmented conversion with dynamic power distribution optimization according to claim 5, characterized in that: The fuzzy logic controller is defined to dynamically adjust the weight matrices Q, R, and S in the MPC, so that the system can adaptively adjust power according to the current state and optimize the objective function, which is expressed as: Among them, r(k) is the external frequency modulation command, which represents the desired power output, Q is the weight matrix of the output error, R is the weight matrix of the control input, S is the weight matrix of the control input change rate, and N is the prediction step size; By solving the objective function J, we can find the optimal control sequence u * (k) and optimize the frequency modulation performance of the system.

7. The method for energy storage converging direct current transformation and segmented conversion with dynamic power distribution optimization according to claim 6 is characterized in that: Based on the optimization solution, a series of optimal control sequences u are obtained * (k), contains the optimal control input value from the current time k to the next N steps. Since only the first control instruction in the sequence needs to be executed at the current time, that is, u * (1), then extract the first control instruction u * (1) As the optimal control input, the extracted optimal control input u * (1) = [ΔP1(1), ΔP2(1)] T It is sent to the high-voltage converter to transmit the power size and direction that needs to be adjusted at the current moment. The high-voltage converter adjusts the power output according to the received adjustment instructions. After the converter executes the control instructions, it detects the response of the energy storage system in real time through sensors and monitoring modules, and feeds back to the controller in real time for further optimization and adjustment in subsequent cycles.

8. An energy storage bus DC transformer segmented conversion system, based on the energy storage bus DC transformer segmented conversion method with dynamic power allocation optimization according to any one of claims 1 to 7, characterized in that: include, Thermal power grid-connected energy storage T-type commutation access unit 1, energy storage converging DC access unit 2; The thermal power grid-connected energy storage T-type commutation access unit 1 includes a #1 generator and a #2 generator. The #1 generator and #2 generator are connected to the 330kV power grid through the #1 main transformer and the #2 main transformer, the #1 energy storage high-voltage converter and the #2 energy storage high-voltage converter are connected to the outlets of the #1 generator and the #2 generator respectively, and the DC sides of the #1 energy storage high-voltage converter and the #2 energy storage high-voltage converter are uniformly connected to the DC 31kV busbar; The energy storage module in the energy storage busbar DC access unit 2 is connected to the 9.76kV energy storage DC busbar through a DC-DC converter, and a DC transformer is used to boost the DC voltage, thereby boosting the energy storage system voltage from DC 9.76kV to DC 31kV, and connecting it to the DC 31kV busbar in the thermal power grid-connected energy storage T-type commutation access unit 1.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the energy storage converging DC transformer segmented conversion method with dynamic power distribution optimization described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the energy storage converging DC transformer segmented conversion method with dynamic power distribution optimization described in any one of claims 1 to 7 are implemented.

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