Online active support method and system for following network type new energy equipment under weak power grid

Abstract

The invention discloses an online active support method and system for following network type new energy equipment under a weak power grid, and relates to the technical field of power systems, and the method comprises the steps: simplifying the system at an equipment access point through employing a Thevenin equivalent circuit; identifying system parameters in real time based on a least square method; deducing a system critical short-circuit ratio and transmission capacity expression by using an equivalent circuit; and optimizing the equipment PQ control parameter value under the constraints of the system transmission capacity and the equipment capacity. According to the method, the Thevenin equivalent circuit parameters of the grid-connected nodes of the equipment are accurately identified on line by adopting the least square method, multi-type equipment parameters and specific control characteristics in the power distribution network do not need to be obtained, and a large amount of manpower and material resources are saved. The system is actively supported by reasonably distributing the capacity of the network-following type new energy equipment, and the transmission capacity of the system and the power injected into the system by the equipment are fully improved. Compared with a reactive power compensation device, the transmission capacity of the system is improved, the economic cost is lower, and equipment control is more convenient and flexible.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an online active support method and system for grid-following new energy equipment under a weak power grid. Background Art

[0002] Driven by the development of high-tech, grid-following new energy equipment is being connected to the distribution network on a large scale. With the gradual increase in the penetration rate of new energy, the operation mode of the distribution network has changed from passive to active, the flow distribution has become more complex and changeable, and the difficulty of power balance scheduling has increased. At the same time, the characteristics of diversified load types and diversified user behaviors have reduced the safety and stability of its power supply. In addition, grid-following new energy equipment often uses a phase-locked loop to track the grid voltage to achieve synchronization. If the output power of the equipment exceeds the system transmission capacity, it will cause the equipment to oscillate or become unstable. Therefore, fully calling the active support capability of grid-following new energy equipment according to the actual operation of the distribution network is of great practical significance for improving the equipment transmission power and system safety margin.

[0003] In existing research, the carrying capacity of the distribution network describes the maximum capacity of the system to accept new energy equipment under safety constraints, that is, the system transmission capacity. Its evaluation methods are divided into two categories: mathematical optimization method and random analysis method. The mathematical optimization method obtains the maximum grid-connected capacity of the distribution network by solving the nonlinear programming model taking into account various constraints. Common constraints include voltage safety constraints, short-circuit current constraints and power quality constraints. Further considering the uncertainty factors in the system, the interval power flow and affine power flow analysis methods can be used to calculate the transmission capacity of the distribution network under different prediction accuracy and confidence levels of new energy output. Although the optimization method gives a strict boundary value of the system transmission capacity, it requires a preset distribution network working scenario and the calculation results are conservative, which has certain limitations in practical applications. The random analysis method calculates the system transmission capacity after processing the random factors in the system through scene sampling or modeling. Common methods include using Monte Carlo simulation to generate the operating status of the distribution network under different new energy penetration rates, using random scenario methods to analyze the impact of photovoltaic access location and capacity combination on the carrying capacity of the distribution network, and using statistical laws to describe the distribution characteristics of the research object and derive the carrying capacity of the distribution network. However, the uncertainty of sources and loads in active distribution networks is large, and the coupling relationship between characteristic variables is complex. Although the stochastic analysis method can give reasonable reference values, it is difficult to establish a mechanistic understanding between system variables and the carrying capacity of the distribution network.

[0004] In terms of improving the transmission capacity of the distribution network, the existing technology mainly starts from the perspective of optimizing line expansion, energy storage devices, reactive compensation transposition and load demand response, while combining demand-side management and network reconstruction to establish a system to build a distribution network carrying capacity model, and finally use various improved optimization algorithms to find an implementation plan. However, the above methods focus on improving the system carrying capacity through reactive compensation equipment, which will inevitably increase the construction cost and cycle. At the same time, the connection of a large number of reactive compensation equipment will increase the complexity and uncertainty of the system, and thus face the challenge of orderly coordination between reactive equipment. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: how to establish a mechanistic understanding between system variables and distribution network carrying capacity under source-load uncertainty in an active distribution network, so as to accurately evaluate and improve the maximum grid-connected capacity of the distribution network under complex coupling relationships.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for online active support of grid-following new energy equipment under weak power grid, which comprises the following steps:

[0008] The system is simplified using the Thevenin equivalent circuit at the device access point; system parameters are identified in real time based on the least squares method; expressions for the system's critical short-circuit ratio and transmission capacity are derived using equivalent circuits; and device PQ control parameter values ​​are optimized under the constraints of system transmission capacity and device capacity.

[0009] As a preferred solution of the online active support method for the grid-following new energy equipment under the weak power grid described in the present invention, wherein: the simplified system using the Thevenin equivalent circuit includes obtaining the system state by solving the injection current balance equation, expressed as,

[0010]

[0011] Among them, where Y∈C n×n , V∈R n are the system admittance matrix and node voltage amplitude vector, θ∈R n is the angle between the device's internal dq coordinate system and the global coordinate system, e i and e k YesR n The unit vector in space, [e jθ ] is a diagonal matrix, h di (V i ) and h qi (V i ) represent the mapping relationship between the device d-axis and q-axis injected current and the grid-connected node voltage, h dk (Vk ,β) and h qk (V k ,β) represents the d-axis and q-axis currents injected into the system by the device when the control parameter β of the k-th node device in the network changes.

[0012] As a preferred solution of the online active support method for the grid-connected new energy equipment under a weak power grid described in the present invention, wherein: the simplified system using the Thevenin equivalent circuit also includes performing Thevenin equivalent on the power network outside the equipment grid-connected node, and obtaining according to Ohm's law,

[0013]

[0014] Among them, E eq ∠0 and Z eq =R eq +jX eq are the equivalent potential and reactance respectively, V∠δ is the equivalent circuit port voltage, I d +jI q is the injected current.

[0015] After expanding the real and imaginary parts, we get:

[0016] VE eq cosδ=I d R eq -I q X eq

[0017] E eq sinδ=I d X eq +I q R eq

[0018] The real-time identification of system parameters based on the least square method includes constructing the sum of squares of errors between actual measured values ​​and calculated values ​​as an objective function, expressed as:

[0019] J(X)=(AX-Y) T (AX-Y)

[0020] Where Y = AX, X = [E eq ,R eq ,X eq ] T , Y=[V,0] T , A∈R 2m×3 is the coefficient matrix.

[0021] According to the least square method, the impedance coefficient X = (A T A) -1 A T Y.

[0022] As a preferred solution of the online active support method for the grid-following new energy equipment under the weak power grid described in the present invention, wherein: the expression of the critical short-circuit ratio and the transmission capacity of the system is derived, and the system power flow equation is derived, which is expressed as:

[0023]

[0024] Among them, P re and Q re are the active power and reactive power control parameters respectively.

[0025] After the power flow equation is expanded according to sin 2 θ+cos 2 θ=1 for merging, expressed as,

[0026]

[0027] When it is greater than or equal to 0, the solution is V with respect to P re and Q re The analytical expression relationship is expressed as,

[0028]

[0029] Analyze the relationship between the existence of system voltage value and short-circuit ratio, expressed as,

[0030]

[0031] Among them, the complex modulus composed of λ and μ is,

[0032]

[0033] Among them, Z eq =R eq +jX eq , is the short-circuit capacity of the system.

[0034] The relationship between the modulus of the complex number λ+jμ and the short-circuit ratio is,

[0035]

[0036]

[0037]

[0038]

[0039] As a preferred solution of the online active support method for the grid-following new energy equipment under the weak power grid described in the present invention, wherein: the expression of the derived system critical short-circuit ratio and transmission capacity also includes that when the system reaches the system critical point, the system limit transmission power is 0, which is expressed as,

[0040] 1+4(λ-μ 2 )=0

[0041] The critical short-circuit ratio is solved and expressed as,

[0042]

[0043] Substituting the power factor of new energy equipment into the critical short-circuit ratio, it is expressed as,

[0044]

[0045] Based on the critical short-circuit ratio, the system transmission limit active power is obtained, which is expressed as:

[0046]

[0047] As a preferred solution of the online active support method for the grid-following new energy equipment under the weak power grid described in the present invention, wherein: the PQ control parameter value of the optimized equipment includes taking the maximum active power output of the new energy equipment as the control target, optimizing the active and reactive reference values ​​of the equipment, expressed as,

[0048] maxP out

[0049]

[0050] (1+ε)P out ≤P lim

[0051] Among them, S lim represents the capacity of new energy equipment, ε represents the system stability margin, P out Indicates the maximum active power output of new energy equipment, P ref Indicates the active reference value of the equipment, Q ref Indicates the reactive power reference value of the equipment;

[0052] When the steady-state P out =P ref When, you can get

[0053] maxP out

[0054]

[0055]

[0056] As a preferred solution of the online active support method for the grid-following new energy equipment under the weak power grid described in the present invention, wherein: the optimized equipment PQ control parameter value also includes, when the equipment capacity exceeds the system transmission power, when the equipment increases reactive power, the maximum transmission power of the system is obtained, which is expressed as,

[0057]

[0058]

[0059] Another object of the present invention is to provide an online active support system for grid-connected new energy equipment under weak power grid conditions, which can automatically adjust the active and reactive outputs of the equipment through real-time monitoring and rapid response to changes in the state of the distribution network, thereby solving the problems in the prior art of poor grid-connected stability of new energy equipment under weak power grid conditions, weak system anti-interference ability and limited transmission capacity.

[0060] To solve the above technical problems, the present invention provides the following technical solutions: an online active support system for grid-following new energy equipment under weak power grid, comprising a model parameter identification module, a system flow analysis module, an active support control strategy module and an optimization algorithm module.

[0061] The model parameter identification module is responsible for fitting the Thevenin equivalent circuit linearization model parameters using the least squares method.

[0062] The system power flow analysis module is responsible for calculating the system limit transmission power and critical short circuit ratio.

[0063] The active support control strategy module is responsible for solving the maximum transmission power and determining the active and reactive reference values ​​of the equipment.

[0064] The optimization algorithm module is responsible for quickly solving optimization problems.

[0065] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned method for online active support of grid-following new energy equipment under a weak power grid are implemented.

[0066] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for online active support of a grid-following new energy device under a weak power grid.

[0067] Beneficial effects of the present invention: The present invention uses the least squares method to accurately identify the Thevenin equivalent circuit parameters of the equipment grid-connected nodes online, without the need to obtain parameters and specific control characteristics of multiple types of equipment in the distribution network, thus saving a lot of manpower and material resources. The present invention realizes active support for the system by rationally allocating the capacity of the grid-connected new energy equipment itself, fully improving the system transmission capacity and the power injected into the system by the equipment. Compared with improving the system transmission capacity through reactive power compensation devices, the present invention has lower economic costs, more convenient and flexible equipment control, and effectively reduces the difficulty of operation and maintenance of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0069] Figure 1 An overall flow chart of an online active support method for grid-following type new energy equipment under a weak power grid provided in the first embodiment of the present invention.

[0070] Figure 2 A schematic diagram of the Thevenin equivalent circuit in a method for online active support of grid-following new energy equipment under a weak power grid provided in the first embodiment of the present invention.

[0071] Figure 3 This is an overall framework diagram of an online active support system for grid-following new energy equipment in a weak power grid provided by the second embodiment of the present invention.

[0072] Figure 4 A schematic diagram of an IEEE-33 node distribution network in an online active support method for grid-following type new energy equipment under a weak power grid provided in the third embodiment of the present invention.

[0073] Figure 5 The third embodiment of the present invention provides a curve of active power and node voltage change in an online active support method for a grid-following type new energy device under a weak power grid.

[0074] Figure 6 The third embodiment of the present invention provides a voltage and power angle curve at 3.5MW in an online active support method for a grid-following type new energy equipment under a weak power grid.

[0075] Figure 7 The third embodiment of the present invention provides a voltage and power angle curve at 4.5MW in an online active support method for a grid-following type new energy equipment under a weak power grid.

[0076] Figure 8A third embodiment of the present invention provides a voltage and power angle curve under an active support strategy in an online active support method for a grid-following new energy device under a weak power grid.

[0077] Fig. 9 A power curve under the active support strategy in an online active support method for a grid-following new energy device under a weak power grid is provided in the third embodiment of the present invention.

[0078] Fig.10 A graph showing the variation of node voltage with injected current in an online active support method for grid-following type new energy equipment under a weak power grid provided in the third embodiment of the present invention.

[0079] Fig.11 A histogram of error estimation using different equivalent value methods in an online active support method for grid-following new energy equipment under a weak power grid provided by the third embodiment of the present invention.

[0080] Fig.12 The third embodiment of the present invention provides a method for online active support of grid-following new energy equipment under a weak power grid, in which the critical short-circuit ratio varies with the impedance ratio and the power factor.

[0081] Fig.13 The third embodiment of the present invention provides a method for online active support of grid-following new energy equipment under different network conditions. DETAILED DESCRIPTION

[0082] 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.

[0083] Example 1, reference Figure 1-2 , is an embodiment of the present invention, and provides an online active support method for grid-following new energy equipment under a weak power grid, characterized in that:

[0084] S1: Simplify the system using Thevenin equivalent circuits at the device access point.

[0085] The operating characteristics of a high proportion of power electronic new energy equipment in the active distribution network are closely related to the voltage at the grid connection point. The state change of the operating equipment in the system will cause nonlinear interactions between the equipment and between the equipment and the network, thus causing the system to operate to a new state. Without loss of generality, assuming that the distribution network has n nodes, when the state of the kth node device changes, the system state can be obtained by solving the injection current balance equation.

[0086]

[0087] in are the system admittance matrix and node voltage amplitude vector, respectively. is the angle between the device's internal dq coordinate system and the global coordinate system, e i and e k yes The unit vector in space, [e jθ ] is a diagonal matrix, h di (V i ) and h qi (V i ) represent the mapping relationship between the device d-axis and q-axis injected current and the grid-connected node voltage, reflecting the voltage-sensitive characteristics of power electronic new energy equipment, h dk (V k ,β) and h qk (V k ,β) describes the d-axis and q-axis currents injected into the system by the device when the control parameter β of the k-th node device in the network changes. When the device control parameter β changes, if equation (1) has a solution, it means that the system will run to a new equilibrium point, otherwise it will cause the device at node k to become unstable.

[0088] Since equation (1) is a highly nonlinear system of equations, the calculation is complex and time-consuming. In addition, some parameters of the equipment in the system are unknown, so it is difficult to perform accurate online calculation of equation (1). In order to facilitate online analysis of the operation of the grid-connected equipment after the control parameters change, the power network outside the equipment grid-connected node is subjected to the Thevenin equivalent, so that the multi-node complex system is transformed into Figure 2 A two-node system is shown.

[0089] Where E eq ∠0 and Z eq =R eq +jX eq are equivalent potential and reactance respectively. At the same time, let the voltage at the equivalent circuit port be V∠δ and the injected current be I d +jI q , then according to Ohm's law,

[0090]

[0091] Simplifying equation (2) and expanding it according to the real and imaginary parts, we get:

[0092] VE eq cosδ=I d R eq -I q X eq (3a)

[0093] E eq sinδ=I d X eq +I q R eq (3b)

[0094] S2: Real-time identification of system parameters based on the least squares method.

[0095] Considering that the actual system has a large difference in the node voltage changes caused by different directions under the same disturbance current amplitude, in order to reduce the parameter identification error, this paper injects disturbance current into the system from m directions and observes the node voltage changes, and then uses the least squares method to fit the Thevenin equivalent circuit linearization model parameters.

[0096] First, rewrite equation (4) into a matrix form Y=AX, where X=[E eq ,R eq ,X eq ] T , Y=[V,0] T , is the coefficient matrix. Then, the sum of squares of the errors between the actual measured values ​​and the calculated values ​​is constructed as the objective function J.

[0097] J(X)=(AX-Y) T (AX-Y)(4)

[0098] According to the principle of least square method, the impedance coefficient X = (A T A) -1 A T Y. The least squares method can track the Thevenin equivalent circuit parameters in real time, providing a reliable basis for subsequent system limit transmission power calculation and control parameter optimization.

[0099] S3: Use equivalent circuits to derive expressions for the system's critical short-circuit ratio and transmission capacity.

[0100] In such Figure 2 The Thevenin equivalent circuit port access capacity shown is a new energy device, and its active power and reactive power control parameters are P re and Q re , the system power flow equation at this time is,

[0101]

[0102] After the power flow equation is expanded according to sin 2 θ+cos 2 θ=1 for merging,

[0103]

[0104] When the discriminant of equation (6) is greater than or equal to 0, the solution is V with respect to P re and Q re The analytical expression relationship is:

[0105]

[0106] In order to analyze the relationship between the existence of system voltage value and short-circuit ratio, let

[0107]

[0108] At this time, the complex modulus composed of λ and μ is,

[0109]

[0110] Where Z eq =R eq +jX eq , Notice is the system short-circuit capacity. At the same time, the short-circuit ratio (SCR) is usually defined as the ratio of the system short-circuit capacity to the rated capacity of the connected equipment. It can be seen that the relationship between the modulus value of the complex number λ+jμ and the SCR is,

[0111]

[0112] Combining (8) and (10) we can obtain the expressions of λ and μ with respect to SCR,

[0113]

[0114] Then substitute equation (11) into equation (7) and divide both sides by After getting,

[0115]

[0116] From equation (12), we can see that when the voltage reaches the critical point of the system, continuing to increase the system transmission power will result in the equation having no solution. Therefore, the system limit transmission power corresponds to the discriminant being 0.

[0117] 1+4(λ-μ 2 )=0(13)

[0118] Substituting equation (11) into equation (13), the SCR corresponding to the system limit transmission power can be solved and defined as the critical short circuit ratio (CSCR),

[0119]

[0120] The critical short-circuit ratio provides the minimum short-circuit ratio required for safe and stable operation of the system. Further assuming that the power factor of the new energy equipment is cosα, substitute it into equation (14) to simplify it:

[0121]

[0122] Formula (15) shows that the system critical short-circuit ratio is closely related to the power factor and impedance ratio of the new energy equipment. Finally, the CSCR definition is substituted into formula (15) to obtain the system transmission limit active power:

[0123]

[0124] From formula (16), it can be seen that the system's ultimate transmission capacity is inversely proportional to the system's critical short-circuit ratio. Reducing the critical short-circuit ratio is conducive to increasing the system's ultimate transmission power. Specifically, when the system impedance ratio remains unchanged, the system's transmission capacity can be improved by actively injecting more reactive power into the system by reducing the power factor of new energy equipment.

[0125] S4: Optimize the device PQ control parameter values ​​under the constraints of system transmission capacity and device capacity.

[0126] Insufficient reactive power in the system is one of the main reasons for the limited transmission capacity of the system. Fully tapping the reactive capacity of new energy equipment can effectively improve the transmission capacity of the system. lim , under the system transmission capacity limitation and system stability margin ε, the maximum active power P output by new energy equipment out To control the target, optimize the active and reactive reference values ​​P of the equipment ref With Q ref , to achieve active support of the system steady-state voltage. The mathematical form of the entire optimization problem is,

[0127] maxP out

[0128]

[0129] (1+ε)P out ≤P lim (17)

[0130] Steady state P out =P ref , and then simplify formula (16) into formula (17) to obtain:

[0131] maxP out

[0132]

[0133]

[0134] Combined with the actual operation characteristics of the system, it can be seen that when the equipment capacity exceeds the system transmission power, the equipment can increase the reactive power to improve the system active transmission limit, but limited by the equipment overcurrent capacity, the equipment's active and reactive power cannot exceed the maximum capacity limit. Therefore, in order to transmit as much active power as possible while meeting the equipment capacity and system transmission capacity constraints, both inequality constraints in equation (18) will take effect, so that the maximum transmission power of the system can be obtained by solving equation group (19).

[0135]

[0136]

[0137] To quickly solve equation (19), we first derive from equation (19a) Then substitute into equation (19b) and construct a fixed point iteration format to solve P out Finally, the active and reactive reference values ​​of the new energy equipment are obtained. The present invention does not need to use gradient information and has a faster solution speed in practical applications.

[0138] Example 2, reference Figure 3 , which is an embodiment of the present invention, provides a system for an online active support method for grid-following new energy equipment under a weak power grid, characterized in that it includes a model parameter identification module 100, a system flow analysis module 200, an active support control strategy module 300 and an optimization algorithm module 400.

[0139] The model parameter identification module 100 is responsible for fitting the Thevenin equivalent circuit linearization model parameters using the least square method.

[0140] The system power flow analysis module 200 is responsible for calculating the system limit transmission power and critical short circuit ratio.

[0141] The active support control strategy module 300 is responsible for solving the maximum transmission power and determining the active and reactive reference values ​​of the equipment.

[0142] The optimization algorithm module 400 is responsible for quickly solving the optimization problem.

[0143] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0144] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0145] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0146] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0147] Example 3, reference Figure 4-Figure 13 In this embodiment, in order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments. This embodiment experiments the existing traditional method and the method of this embodiment respectively.

[0148] The present invention adopts an IEEE-33 node active distribution network construction example including new energy equipment, energy storage and multi-type loads, uses Matlab to solve the Thevenin equivalent circuit, and uses Simulink to simulate the system state variable adjustment process, so as to analyze the influence mechanism of PQ control parameters on the stable operation of new energy equipment and verify the effectiveness of the PQ control parameter optimization method under different critical short-circuit ratios and system transmission capacities. The new energy equipment built in Simulink includes a PQ power control loop, a phase-locked loop and a current limiting link. Since the time scale of the current inner loop is much smaller than that of other control links, it is ignored here.

[0149] This section Figure 4 The middle node 10 is used as the device access node. A small current disturbance is injected into it in all directions and the voltage is observed. Then, the least squares method is used to identify the Thevenin equivalent circuit potential to be 0.9534 and the impedance to be 0.1117+j0.2062. Assuming that the device capacity limitation is ignored and the PQ control parameters of the access device are continuously changed, the device output active power and node voltage change curves are obtained as follows: Figure 5 shown.

[0150] from Figure 5 It is found that under constant power factor, as the output active power of the equipment increases, the system voltage will gradually approach the critical value of static voltage stability due to insufficient reactive power. If the output active power of the equipment continues to increase at this time, there will be no voltage balance point in the system. In addition, under the same output active power of the equipment, reducing the power factor will cause the equipment to provide more reactive power to the system, increase the system transmission capacity, and thus help improve the system voltage stability. Therefore, by changing the PQ control parameters of the equipment, the system transmission capacity and voltage stability margin can be effectively adjusted.

[0151] Based on the above understanding of the impact of PQ control parameters on the system, Figure 4 The PQ control parameters of the connected new energy equipment are constantly adjusted to observe whether the equipment can operate stably. Assume that the capacity of the new energy equipment is 4.5MW, the current output power is 2.5MW, and the power factor is 1. Combined with the Thevenin equivalent circuit, the node voltage information is calculated, and the initial values ​​of each state variable in the equipment control link are derived. Then, the output power of the equipment is increased by 1MW and 2MW respectively, and the equipment phase-locking angle and node voltage amplitude are obtained as follows Figure 6 and Figure 7 shown.

[0152] from Figure 6 It was found that after a period of adjustment, the phase-locked angle and node voltage amplitude of the device entered a new stable operating state. This is because the output power of the device is less than the system transmission power capacity of 3.7MW, and the system provides a stable voltage balance point for the phase-locked loop, so that the system resumes stable operation under the joint action of the phase-locked loop and the power outer loop. In addition, Figure 6 It also shows that the equipment will increase the power angle to output more power, but due to the lack of sufficient reactive power support, the voltage will drop significantly.

[0153] Figure 7 It shows that when the output power of the device exceeds the transmission capacity of the system, the phase-locked loop and port voltage will oscillate. Observing the system voltage change curve, it can be seen that under the integral action of the power outer loop, the active current output by the device continues to increase until it reaches the system transmission limit at 0.12s, and the system voltage is at the critical point. Since the output power of the device is still less than the active control reference value, the power outer loop will further increase the output current, and at this time there is no voltage balance point in the system, so the phase-locked loop is unstable. The sharp change in the power angle of the phase-locked loop causes the output current of the power loop to increase rapidly and enter the limiting link. Because the injected current is too large and exceeds the critical value of the normal operation of the system, it causes abnormal oscillation of the phase-locked loop and the port voltage. Therefore, the existence of a voltage balance point in the system is crucial for new energy equipment with power electronics as the grid-connected interface, otherwise it will cause the new energy equipment to enter an abnormal operating state.

[0154] The above analysis shows that the current system cannot support the working scenario where new energy equipment generates full active power. If the equipment is to deliver as much active power to the system as possible, the PQ control parameters of the equipment should be optimized online to actively provide reactive power support to the system. Therefore, the system Thevenin equivalent circuit parameter value and the system safety margin ε = 0.05 are substituted into equation (20), and the active and reactive control reference values ​​are obtained by the fixed point iteration method to be 4.46MW and 0.63MW respectively. After being substituted into the simulation model, it is observed that the equipment phase-locking angle and node voltage amplitude are as follows Figure 8 As shown, the power changes as Fig. 9 shown. Figure 8The state variable reaches the steady-state value after 0.5s, and at this time the device output active power increases by 20% compared with the maximum stable output power of the device when the unit factor is 1, indicating the effectiveness of the online optimization of PQ control parameters.

[0155] With the large-scale access of new energy devices to the distribution network, the voltage-current relationship at the device access point becomes nonlinear when the interaction of network components is taken into account. In this section, the d-axis and q-axis currents injected into the system by the device are changed by continuously modifying the control parameters in equation (1), and then the node voltage is solved by equation (1) to obtain the relationship between the node voltage and the injected current as follows: Fig.10 shown.

[0156] from Fig.10 It can be seen from the figure that as the injected current increases, the node voltage will also increase accordingly, but the degree of increase will gradually decrease. Fig.10 It is found that under the same current amplitude, the node voltage changes caused by different injection current directions are significantly different. In addition, the q-axis injection current is more likely to cause node voltage amplitude changes than the d-axis injection current, indicating that the reactance value in the system Thevenin equivalent circuit observed from node 10 is greater than the resistance value, which is consistent with the above-mentioned circuit online parameter identification results.

[0157] To further analyze the impact of network nonlinearity on the Thevenin equivalent parameters, the corresponding parameters were identified under 5 different injection current directions, and the equivalent parameters were identified using the least squares method based on 5 test results. Then, the node voltage changes with the injection current in a specific direction in each case were obtained and named scenarios 1 to 5. Finally, they were compared with Fig.10 Compare the node voltage and current relationship shown in the figure, and get the error result as follows Fig.11 shown.

[0158] from Fig.11 It is found that the least squares method based on information in all directions has the smallest overall error in identifying the Thevenin equivalent parameters. The other five cases show a small error in the same direction of the disturbance current, but a large error in other directions of the injected current, and the error increases with the increase of the angle between the two. Therefore, the identification of Thevenin equivalent parameters based on the least squares method can make full use of more network information, making the identification results more accurate.

[0159] During the long-term operation of the distribution network, equipment is constantly withdrawn or connected, which causes the node Thevenin equivalent parameters to change accordingly, thereby affecting the critical short-circuit ratio of the system. Fig.12 The impedance ratio X of the Thevenin circuit at different power factors is plotted in eq / R eq The impact of changes on the critical short-circuit ratio. Fig.12It can be seen that with the increase of impedance ratio, the critical short-circuit ratio continues to increase, that is, the system transmission capacity decreases; the growth trend of the critical short-circuit ratio is fast at first and then slow. When the critical short-circuit ratio is greater than 20, increasing the impedance ratio has little effect on its change. Fig.12 It also shows that reducing the power factor will reduce the critical short-circuit ratio, so reducing the system impedance ratio and equipment power factor are both effective means to increase the system transmission capacity.

[0160] In order to cope with the ever-changing critical short-circuit ratio of the distribution network and ensure the safety of equipment power transmission, it is necessary to optimize the PQ control parameter value of the equipment in real time. Figure 4 In the topology shown, the device capacity is 4.5MW, the output power reference value is set to 8MW, and then it goes through four links in sequence: node 8 exits the device, node 13 connects to the device, node 9 connects to the device, and node 22 exits the device. The Thevenin equivalent parameters, critical short-circuit ratio and system transmission capacity of each link are shown in Table 1. The PQ reference value of the new energy device is obtained through the simulink simulation model as shown in Fig.13 shown.

[0161] It can be seen from Table 1 that when the device operates in scenarios 1, 2, and 3, the device capacity is greater than the system transmission capacity, so the device cannot work at a unity factor. At this time, it is necessary to optimize the PQ control parameters to increase the system transmission capacity. Fig.13 It shows that under the action of the PQ control parameter optimization algorithm, the equipment can safely and stably deliver as much active power as possible to the system. According to the active support strategy of the equipment, the active reference value of the equipment in scenario 1 is set to 4.46MW; in scenario 2, due to the decrease in system transmission capacity, the reference value of the previous state cannot be met. After the optimization algorithm, the active reference value is changed to 4.45MW; in scenario 3, the system transmission capacity increases but does not exceed the equipment capacity. At this time, the active control value of the equipment is set to 4.48MW. In scenario 4, after the equipment is connected, the system transmission capacity is greater than the equipment capacity, and the equipment works at a unit power factor; in scenario 5, the equipment is exited but the system transmission capacity is still greater than 4.5MW, so the active reference value of the equipment remains unchanged. Therefore, the experimental results of multiple processes show that the PQ control algorithm proposed in this paper can effectively adapt to various scenarios, adjust the equipment PQ control parameters online according to the system operation status, and ensure the safe and stable operation of the equipment and the system.

[0162] Table 1 Thevenin equivalent circuit parameters under different network conditions

[0163]

[0164] 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 online active support of grid-following new energy equipment under weak power grid, characterized in that: include: Simplify the system by using Thevenin equivalent circuits at the device access point; Real-time identification of system parameters based on least squares method; Using equivalent circuits, the expressions of system critical short-circuit ratio and transmission capacity are derived; Optimize the equipment PQ control parameter values ​​under the constraints of system transmission capacity and equipment capacity.

2. The method for online active support of a new energy device in a weak power grid as claimed in claim 1, characterized in that: The method of simplifying the system using the Thevenin equivalent circuit includes obtaining the system state by solving the injection current balance equation, which is expressed as: Among them, where Y∈C n×n , V∈R n are the system admittance matrix and node voltage amplitude vector, θ∈R n is the angle between the device's internal dq coordinate system and the global coordinate system, e i and e k YesR n The unit vector in space, [e jθ ] is a diagonal matrix, h di (V i ) and h qi (V i ) represent the mapping relationship between the device d-axis and q-axis injected current and the grid-connected node voltage, h dk (V k ,β) and h qk (V k ,β) represents the d-axis and q-axis currents injected into the system by the device when the control parameter β of the k-th node device in the network changes.

3. The method for online active support of a new energy device in a weak power grid as claimed in claim 2, characterized in that: The method of simplifying the system by using the Thevenin equivalent circuit also includes performing Thevenin equivalent on the power network outside the device grid-connected node, and obtaining, according to Ohm's law, Among them, E eq ∠0 and Z eq =R eq +jX eq are the equivalent potential and reactance respectively, V∠δ is the equivalent circuit port voltage, I d +jI q is the injected current; After expanding the real and imaginary parts, we get: V-E eq cosδ=I d R eq -I q X eq E eq sinδ=I d X eq +I q R eq The real-time identification of system parameters based on the least square method includes constructing the sum of squares of errors between actual measured values ​​and calculated values ​​as an objective function, expressed as: J(X)=(AX-Y) T (AX-Y) Where Y = AX, X = [E eq ,R eq ,X eq ] T , Y=[V,0] T , A∈R 2m×3 is the coefficient matrix; According to the least square method, the impedance coefficient X = (A T A) -1 A T Y.

4. The method for online active support of a new energy device in a weak power grid as claimed in claim 3, characterized in that: The derivation of the system critical short-circuit ratio and transmission capacity expression includes deriving the system power flow equation, which is expressed as: Among them, P re and Q re are active power and reactive power control parameters respectively; After the power flow equation is expanded according to sin 2 θ+cos 2 θ=1 for merging, expressed as, When it is greater than or equal to 0, the solution is V with respect to P re and Q re The analytical expression relationship is expressed as, Analyze the relationship between the existence of system voltage value and short-circuit ratio, expressed as, Among them, the complex modulus composed of λ and μ is, Among them, Z eq =R eq +jX eq , is the system short-circuit capacity; The relationship between the modulus of the complex number λ+jμ and the short-circuit ratio is, 5. The method for online active support of a new energy device in a weak power grid as claimed in claim 4, characterized in that: The derivation of the system critical short-circuit ratio and transmission capacity expression also includes that when the system reaches the system critical point, the system limit transmission power is 0, which is expressed as: 1+4(λ-μ) 2 )=0 The critical short-circuit ratio is solved and expressed as, Substituting the power factor of new energy equipment into the critical short-circuit ratio, it is expressed as, Based on the critical short-circuit ratio, the system transmission limit active power is obtained, which is expressed as:

6. The method for online active support of a new energy device in a weak power grid as claimed in claim 5, characterized in that: The optimized device PQ control parameter value includes taking the maximum active power output of the new energy device as the control target and optimizing the active and reactive reference values ​​of the device, expressed as: max P out (1+ε)P out ≤P lim Among them, S lim represents the capacity of new energy equipment, ε represents the system stability margin, P out Indicates the maximum active power output of new energy equipment, P ref Indicates the active reference value of the equipment, Q ref Indicates the reactive power reference value of the equipment; When the steady-state P out =P ref When, you can get max P out 7. The method for online active support of a new energy device in a weak power grid as claimed in claim 6, characterized in that: The optimized device PQ control parameter value also includes that when the device capacity exceeds the system transmission power, the device increases reactive power to obtain the maximum system transmission power, which is expressed as:

8. A system using the online active support method for grid-following type new energy equipment under weak power grid as claimed in any one of claims 1 to 7, characterized in that: It comprises a model parameter identification module (100), a system power flow analysis module (200), an active support control strategy module (300) and an optimization algorithm module (400); The model parameter identification module (100) is responsible for fitting the Thevenin equivalent circuit linearization model parameters using the least squares method; The system power flow analysis module (200) is responsible for calculating the system limit transmission power and critical short circuit ratio; The active support control strategy module (300) is responsible for solving the maximum transmission power and determining the active and reactive reference values ​​of the equipment; The optimization algorithm module (400) is responsible for quickly solving the optimization problem.

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 a method for online active support of grid-following new energy equipment under a weak power grid as 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 a method for online active support of grid-following new energy equipment under a weak power grid as described in any one of claims 1 to 7 are implemented.

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