Centralized-distributed control method and system for distributed resource access power distribution network

Abstract

The invention discloses a centralized-distributed control method and system oriented to a distributed resource access power distribution network, and belongs to the field of power distribution network operation control. The method comprises the steps that power distribution network composition units are determined and comprise a photovoltaic power generation unit, an energy storage unit, a grid-connected inverter unit and a load unit, all the units are connected through a direct-current bus, and a power distribution network model under distributed resource access is constructed; analyzing a distributed photovoltaic grid-connected mechanism, and constructing a centralized optimization control model of the power distribution network by taking minimum network loss of the power distribution network, minimum node voltage fluctuation and maximum consumption of the distributed power supply as targets; constructing a distributed optimization control model of the power distribution network by taking the maximum source-load-storage matching degree as a target in consideration of distributed energy storage grid-connected characteristics; for a high-proportion photovoltaic and energy storage access scene, a power distribution network centralized-distributed double-layer control model is constructed. The operation control of the power distribution network can be effectively realized.

Description

Technical Field

[0001] The present invention relates to a centralized - distributed control method and system for a distribution network with distributed resource access, belonging to the field of distribution network operation. Background Art

[0002] Vigorously developing renewable energy and increasing the proportion of electric energy in terminal consumption are important means for a new power system to promote the realization of the "dual - carbon" goal. Distributed generation has many advantages such as clean environmental protection and flexible operation mode, and has become one of the important forms of renewable energy generation.

[0003] With the continuous increase in the grid - connection ratio of distributed generation (DG), many new challenges are faced in the operation and regulation issues such as power supply quality, stability, and security of large - scale distributed resources accessing the distribution network. The power generation side and load side of a high - proportion distributed AC - DC hybrid distribution network show strong spatio - temporal uncertainty. Compared with traditional AC distribution networks, there are obvious differences in their operation modes, power balance, etc. The traditional source - following - load method is no longer applicable to the new power system. In addition, a large number of controllable resources in the new power system exacerbate the complexity of the operating conditions of high - proportion distributed AC - DC hybrid distribution networks, and the coupling effect and coordinated operation of a large number of flexible devices such as power electronic components make the operation and regulation of the distribution network more difficult, and establishing a distribution network regulation model is also more complex, severely restricting the efficient access of large - scale distributed resources to the distribution network, the improvement of operation flexibility, and the power supply capacity of the grid framework. Therefore, in order to fully tap its regulation potential, it is necessary to clearly depict the full - life - cycle operating conditions of multi - level high - proportion distributed AC - DC hybrid distribution networks under different scenarios, realize the flexible interaction and coordinated regulation of source - network - load - storage, and ensure the deep balance between supply and demand. The present invention studies the centralized - distributed control strategy for large - scale distributed resources accessing the distribution network, taking the operation and regulation of the distribution network as one of the core technologies for studying the distribution network, which has important theoretical significance and practical value. Summary of the Invention

[0004] The present invention provides a centralized - distributed control method and system for a distribution network with large - scale distributed resource access to achieve the operation control of the distribution network.

[0005] The technical solution of the present invention is as follows:

[0006] According to the first aspect of the present invention, a centralized - distributed control method for a distribution network with large - scale distributed resource access is provided, including: determining the constituent units of the distribution network, including a photovoltaic power generation unit, an energy storage unit, a grid - connected inverter unit, and a load unit, and connecting each unit through a DC bus to construct a distribution network model under distributed resource access;

[0007] Analyze the mechanism of distributed photovoltaic grid connection, and construct a centralized optimal control model for the distribution network with the goals of minimizing the network loss of the distribution network, minimizing the node voltage fluctuation, and maximizing the consumption of distributed power sources;

[0008] Considering the grid connection characteristics of distributed energy storage, construct a distributed optimal control model for the distribution network with the goal of maximizing the source-load-storage matching degree;

[0009] For the scenario of high proportion of photovoltaic and energy storage access, construct a centralized-distributed two-layer control model for the distribution network.

[0010] The specific components of the distribution network include:

[0011] (1) Photovoltaic power generation unit

[0012] The output characteristic equation of the photovoltaic cell is:

[0013]

[0014] In the formula, R s is the series equivalent resistance of the photovoltaic cell; R sp is the parallel equivalent resistance; I, I ph , I o1 , I o2 are the output current, photocurrent, and reverse saturation currents of diodes 1 and 2 of the photovoltaic cell respectively; q is the charge of an electron; U pv is the output voltage of the photovoltaic cell; T is the absolute temperature of the battery; K is the Boltzmann constant; A 1 , A 2 are constant factors;

[0015] The power-voltage output characteristic equation of the photovoltaic cell is:

[0016]

[0017] In the formula, P pv is the output power of the photovoltaic cell, U oc is the open-circuit voltage, I sc is the short-circuit current; R sh is the internal parallel resistance of the photovoltaic cell, U oc is the open-circuit voltage.

[0018] The photovoltaic side DC / DC converter adopts a bilateral active bridge DC / DC converter;

[0019] (2) Energy storage unit

[0020] The storage battery adopts a typical internal resistance equivalent model;

[0021] (3) Grid-connected inverter unit

[0022] Adopt a voltage source PWM converter.

[0023] The comprehensive objective function of the distribution network distributed optimization control model is as follows:

[0024]

[0025] In the formula, P N.loss is the reference value of network loss, P loss is the network loss, V D is the node voltage deviation, V N is the reference value of node voltage deviation, P DG is the grid-connected power of distributed power sources, P N.DG is the reference value of the grid-connected power of distributed power sources, w 1 ~w 3 are the weights of each sub-objective, w 1 +w 2 +w 3 = 1;

[0026] The constraint conditions include system power flow constraints, node voltage constraints, and distributed photovoltaic grid-connected output constraints.

[0027] The distribution network distributed optimization control model takes the maximum source-load matching degree of the low-voltage layer of the distribution network as the objective function:

[0028]

[0029] In the formula, θ is the source-load matching degree, δ M is the correlation coefficient between the source, load, and storage, δ N is the variance between the source, load, and storage, α 1 、α 2 are the weight coefficients;

[0030] The constraint conditions include three types of constraints: the charge-discharge state, power, and energy of the energy storage element.

[0031] Constructing the distribution network centralized-distributed two-layer control model specifically includes:

[0032] Through real-time data acquisition, control the source-load matching degree of distributed power sources in the distribution network. According to the distribution network voltage level, adopt centralized and distributed control strategies for the medium-voltage layer and the low-voltage layer respectively, and then design the distribution network hierarchical control architecture; design the matching principle of different hierarchical time scales, and construct the distribution network hierarchical coordinated control model for large-scale distributed resource access.

[0033] According to a second aspect of the present invention, there is provided a centralized-distributed control system for a distribution network facing large-scale distributed resource access, including: a first construction module for constructing a distribution network model under distributed resource access; a second construction module for constructing a centralized optimization model of the distribution network; a third construction module for constructing a distributed optimization control model of the distribution network; a fourth construction module for constructing a centralized-distributed two-layer control model of the distribution network for a scenario with high-proportion photovoltaic and energy storage access; and a solving module for solving the centralized-distributed two-layer control model of the distribution network based on an optimization algorithm.

[0034] According to a third aspect of the present invention, there is provided a processor for running a program, wherein when the program runs, it executes the centralized-distributed control method for a distribution network facing large-scale distributed resource access described in any one of the above.

[0035] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium, which includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the centralized-distributed control method for a distribution network facing large-scale distributed resource access described in any one of the above.

[0036] The beneficial effects of the present invention are as follows:

[0037] The present invention establishes a centralized-distributed control model for a distribution network facing large-scale distributed resource access. The distribution network is divided into a medium-voltage layer and a low-voltage layer according to the voltage level. Considering the characteristics of uncertainty and randomness of distributed resources, a hierarchical control architecture of the distribution network is designed. At the same time, combining three dimensions of different hierarchical control mechanisms, methods, and time matching scales, the coordinated control of the medium and low voltage layers of the distribution network is realized, and a centralized-distributed control model for a distribution network facing large-scale distributed resource access is constructed, which can effectively realize the operation control of the distribution network. Description of the Drawings

[0038] Figure 1 is a block diagram of the control method of the present invention;

[0039] Figure 2 Flow chart for solving the centralized-distributed two-layer control model of the distribution network;

[0040] Figure 3 is a block diagram of the system of the present invention. Detailed Embodiments

[0041] The following will further illustrate the present invention in conjunction with the drawings and embodiments, but the content of the present invention is not limited to the described scope.

[0042] Embodiment 1: As Figures 1-3As shown, according to the first aspect of the embodiments of the present invention, a centralized-distributed control method for a distribution network facing large-scale distributed resource access is provided, including: determining the constituent units of the distribution network, including photovoltaic power generation units, energy storage units, grid-connected inverter units, and load units, with each unit connected by a DC bus, and constructing a distribution network model under distributed resource access;

[0043] Analyze the mechanism of distributed photovoltaic grid connection, and construct a centralized optimization control model for the distribution network with the goals of minimizing the network loss of the distribution network, minimizing the node voltage fluctuation, and maximizing the consumption of distributed power sources;

[0044] Considering the grid connection characteristics of distributed energy storage, construct a distributed optimization control model for the distribution network with the goal of maximizing the source-load-storage matching degree;

[0045] For the scenario of high proportion of photovoltaic and energy storage access, construct a centralized-distributed two-layer control model for the distribution network.

[0046] To make the technical means, creative features, achieved goals and effects of the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments:

[0047] I. Construct a distribution network model under distributed resource access

[0048] When photovoltaic power sources are connected in parallel to the distribution network for operation, the uncertainty of their output power poses a huge challenge to the operation and control of the power grid. To deeply analyze the control technology of DC distribution networks, a distribution network model containing distributed resources is required as the research basis. The topology of the photovoltaic access distribution network system adopted in the present invention mainly includes three parts: photovoltaic power generation units, energy storage power generation units, and grid-connected inverter units. The working principles and mathematical models of each unit are as follows:

[0049] (1) Photovoltaic power generation unit

[0050] The output characteristic equation of the photovoltaic cell is:

[0051]

[0052] In the formula, R s is the series equivalent resistance of the photovoltaic cell (less than 1Ω); R sp is the parallel equivalent resistance (order of magnitude of kΩ); I, I ph 、I o1 、I o2 are the output current of the photovoltaic cell, the photocurrent, and the reverse saturation currents of diodes 1 and 2 respectively; q is the charge of an electron (1.6×10 -19 C); U is the output voltage of the photovoltaic cell; T is the absolute temperature of the battery (K); K is the Boltzmann constant (1.38e -23 J / K); A 1, A 2 is a constant factor (about 2.8 when T = 300K).

[0053] The power - voltage output characteristic equation of the photovoltaic cell is:

[0054]

[0055] In the formula, P pv is the output power of the photovoltaic cell, U oc is the open - circuit voltage (V), I sc is the short - circuit current (A).

[0056] Photovoltaic - side DC / DC converter:

[0057] The present invention adopts a bilateral active - bridge DC / DC converter. By controlling the switching networks on the primary side and the secondary side, an alternating - square - wave voltage V ab and V cd can be obtained respectively, and their amplitudes are V 1 and V 2 respectively. The control mode of the converter is the phase - shift control mode. The mid - point voltages V ab and V cd of the primary - side and secondary - side bridge arms of the converter are both set to 1. The state variables of the photovoltaic - side converter are selected as the output current I L , and the output voltage U o . When the turns ratio of the transformer T is 1:1, the state - space equations are listed as follows:

[0058]

[0059] In the formula, sgn(t) is the sign function. When t ∈ [DT, 0.5T + DT], sgn(t)=1; when t ∈ [0.5T + DT, T + DT], sgn(t)= - 1. D is the duty cycle between the fundamental components of V ab and V cd . I L is the output current of the photovoltaic - side converter, U o is the output voltage of the photovoltaic - side converter, L S is the internal inductor of the converter, C o is the output capacitor, R o is the load resistance, R c is the equivalent series resistance of the inductor, and f is the switching frequency.

[0060] From the above formulas, the expression of the average power input to the primary side during power transmission can be derived as:

[0061] D ∈ [-0.5, 0.5]

[0062] In the formula, I1_ave is the average value of the primary side current of the converter, and T S is the switching period.

[0063] (2) Energy storage unit

[0064] The battery of the present invention adopts a typical internal resistance equivalent model, and its mathematical model is expressed as:

[0065]

[0066] In the formula, E 0 , Eb, and Vb are the constant voltage, no-load voltage, and terminal voltage of the battery respectively; R is the internal resistance of the battery; Q is the capacity of the battery; i is the battery current; A is the amplitude of the exponential region (V); B is the reciprocal of the time constant of the exponential region (A·h -1 ); k is the polarization voltage (V).

[0067] (3) Grid-connected inverter unit

[0068] The present invention adopts a voltage-source PWM converter G-VSC. Taking the d-axis as the direction of the grid voltage, the voltage equation and power equation of the grid-connected converter in the dq0 coordinate system are respectively:

[0069]

[0070] In the formula, u gd , u gq are the grid-side voltage components of G-VSC in the d-q coordinate axes, i gd , i gq are the current components of G-VSC in the d-q coordinate axes, R g , L g are the resistance and inductance of the filter inductor, and ω is the system angular frequency.

[0071] The control mode of the converter adopts a double closed-loop controller structure. The outer loop controls the reference value of the inner loop for the given current. The inner loop uses a PI controller to make the actual current feedback values i gd and i gq track the given reference currents i gd_ref and i gq_ref to eliminate the steady-state error of current tracking.

[0072] II. Centralized optimization model of the distribution network

[0073] (1) Grid loss of the distribution network with distributed PV access

[0074] The distribution network generally consists of a power source, transmission lines, and loads. The power flow direction is from the power source side to the load side. Ignoring the line loss during the normal operation of the power grid system, it is assumed that the system voltage is constant.

[0075] After grid connection of photovoltaic power, the line network loss is jointly composed of the active power losses generated by the load side and grid-connected photovoltaic power. After grid connection of photovoltaic power, the first part of the network loss is:

[0076]

[0077] In the formula, I S represents the current flowing from the power source into the system, I D represents the current flowing to the load side, I A represents the current flowing into the system after grid connection of distributed photovoltaic power, L s represents the distance from the power source to the distributed photovoltaic access point, L represents the distance from the distributed photovoltaic access point to the load, and the active power is P L ; the reactive power is Q L , U is the line voltage, and R is the resistance per unit length of the line.

[0078] For the second part of the network loss after grid connection of photovoltaic power, because I D remains unchanged, so this part has not changed, that is

[0079]

[0080] By the superposition theorem, the total line network loss is:

[0081]

[0082] In the formula, P PV is the active power of the photovoltaic power, Q PV is the reactive power of the photovoltaic power.

[0083] (2) Influence of distributed photovoltaic power on the node voltage of the distribution network

[0084] In the traditional distribution network, the highest point of the system voltage is generally at the busbar, and the lowest point is at the load side. After large-scale grid connection of distributed photovoltaic power, it will affect the line structure and cause the change of the lowest voltage point. Assume that the distributed photovoltaic access point is point x, the grid connection voltage is U o , and the voltage at any point in the system is U m The grid connection capacity is P PV +Q PV .

[0085] When 0 < m < x, the voltage drop at any point m on the line is:

[0086]

[0087] When x ≤ m ≤ L, the voltage drop at m is:

[0088]

[0089] The node voltage of the distribution network is affected by the grid-connected capacity and grid-connected location of distributed photovoltaic power generation. Moreover, for the grid-connected points of distributed photovoltaic power generation that are relatively forward, the line voltage drop of the distribution network is more significant than when the location is relatively backward.

[0090] (3) Construct a centralized optimal control model for the distribution network

[0091] The model constructed in the present invention takes the minimum network loss of the distribution network, the minimum node voltage fluctuation, and the maximum absorption of distributed power sources as the optimization objectives, and their mathematical functions are respectively:

[0092]

[0093] In the formula: P loss is the network loss, P Gg.i is the grid-connected power of the photovoltaic power station at node i, V i and V j respectively represent the effective values of the corresponding node voltages, n is the number of grid-connected photovoltaic power stations, i and j are node numbers, N is the number of network nodes, θ ij is the voltage phase difference between nodes i and j, g ij represents the conductance between nodes i and j, V max and V min respectively represent the maximum and minimum values of the node voltage.

[0094] The comprehensive objective function is:

[0095]

[0096] In the formula, P N.loss is the network loss reference value, P loss is the network loss, V D is the node voltage deviation, V N is the node voltage deviation reference value; P DG is the grid-connected power of the distributed power source, P N.NG is the grid-connected power reference value of the distributed power source, w 1 ~w 3 are the weights of each sub-objective, and it is required that w 1 +w 2 +w 3 =1.

[0097] Constraint conditions: including system power flow constraints, node voltage constraints, and grid-connected output constraints of distributed photovoltaics.

[0098] Power flow balance constraint:

[0099]

[0100] In the formula, i and j represent node numbers, G ij and B ijDenote the conductance and susceptance values of the line between nodes i and j, P i and Q i Denote the active power load and reactive power load of the node, U i and U j Denote the node voltage, θ ij is the voltage phase difference between nodes i and j.

[0101] The node voltage constraint is:

[0102] V min ≤V i ≤V max

[0103] The PV output constraint is:

[0104] P DG,imin ≤P DG,i ≤P DG,imax

[0105] In the formula, P DG.i is the output of the PV power station connected to node i, P DG.imin and P DG.imax are the minimum and maximum values of the output of the PV power station at this node respectively.

[0106] III. Construct a distributed control optimization model for the distribution network

[0107] (1) Network loss of the distribution network after energy storage is connected to the grid

[0108] Introducing distributed energy storage technology in the low-voltage level of the distribution network can significantly improve the system's power balance by virtue of the fast charge and discharge capabilities of energy storage elements.

[0109] Regarding the active power charge and discharge of energy storage, its technology is mainly applied to peak shaving and frequency modulation, suppressing fluctuations, and tracking planned output. The peak shaving and valley filling characteristics of energy storage elements can effectively optimize the operation power flow and spatio-temporal distribution of power in the power grid, thereby achieving goals such as reducing line network losses.

[0110] In terms of the reactive power charge and discharge of energy storage, this technology is mainly applied to the regulation of system voltage and providing reactive power support. The reactive power compensation and voltage regulation characteristics of energy storage elements can significantly improve the network loss distribution in the system, thus realizing the optimization goal of reducing network losses.

[0111] (2) Influence of energy storage on the node voltage of the distribution network

[0112] Currently, energy storage in China is mainly used to undertake frequency modulation and peak shaving tasks and enhance the operation stability of the system. During peak load periods, energy storage injects electric energy into the grid through discharging, similar to the role of a power source; while during off-peak load periods, energy storage stores part of the electric energy, playing a role in peak shaving and valley filling, and its function is similar to that of a capacitor.

[0113] When the distributed energy storage is incorporated into the power grid and the imaginary component of the node voltage is ignored, the longitudinal voltage drop of the node can be expressed as:

[0114]

[0115] After the energy storage system is connected to node j in the system, if the discharging power to the distribution network is P, the active power of the power grid line will decrease, and the voltage difference between the two points at this time is:

[0116]

[0117] (3) Distribution network distributed optimization control model

[0118] Objective function:

[0119] The present invention takes the maximum source-load matching degree of the low-voltage layer of the distribution network as the objective function:

[0120]

[0121] In the formula, θ is the source-load matching degree, δ M is the correlation coefficient between the source, load, and energy storage, and δ N is the variance between the source, load, and energy storage;

[0122] The correlation coefficient and variance function between the source and load can be written as:

[0123]

[0124] In the formula, P ψ.1 represents the starting power of each time window, and P ψ.2 represents the end power of each time window; P η.av represents the average value of the starting and ending powers, where the subscript L represents the load, PV represents the photovoltaic, and ess represents the energy storage; α 1 and α 2 are the weight coefficients.

[0125] Constraint conditions:

[0126] The distribution network distributed optimization control model constructed by the present invention has constraint conditions including three types of constraint conditions: the charge and discharge state, power, and energy of the energy storage element.

[0127] Energy storage charge and discharge state constraint:

[0128]

[0129] In the formula, and respectively represent the discharge or charge state of the energy storage unit k. Since the energy storage cannot be in the charging and discharging states simultaneously at any time, the sum of the two is less than or equal to 1.

[0130] Power constraint of energy storage unit:

[0131] -P ess.dischmax ≤P ess.k ≤P ess.chmax

[0132] In the formula, P ess.k represents the instantaneous power of energy storage unit k, P ess.dischmax is the maximum discharge power of the energy storage unit, and P ess.chmax is the maximum charging power of the energy storage unit.

[0133] Energy constraint of energy storage unit:

[0134]

[0135] In the formula, E ess.t is the energy stored in the energy storage unit at any time, E ess.max is the maximum energy of the energy storage, and E ess.min is the minimum energy of the energy storage.

[0136] IV. For the scenario of high proportion of photovoltaic and energy storage access, a centralized-distributed two-layer control model of the distribution network is constructed

[0137] (1) Medium- and low-voltage two-layer control strategy of the distribution network

[0138] Figure 2 shows the two-layer regulation and control architecture of the distribution network designed by the present invention. Based on the acquisition of real-time data, the core goal is to adjust the low-voltage supply-demand balance problem brought by distributed photovoltaics and integrated energy storage in the distribution network.

[0139] For the medium-voltage and low-voltage levels with different voltage levels, centralized and distributed regulation and control strategies are respectively adopted. The communication mechanism between the levels ensures that the medium-voltage layer can effectively convey power dispatch commands to each node and receive the response feedback from the low-voltage layer, thereby realizing the coordinated regulation between the medium- and low-voltage levels of the distribution network.

[0140] In the medium-voltage level, first, the low-voltage distribution network containing many household photovoltaics is regarded as a controllable resource unit of the medium-voltage network, and then a centralized optimization regulation and control model of the medium-voltage network is constructed and solved to obtain the regulation and control response commands of the low-voltage network accessing distributed photovoltaics and energy storage. These commands are then transmitted to each node of the medium-voltage layer and the point of common coupling (PCC).

[0141] In the low-voltage level, starting from the distributed energy storage on the low-voltage side, the power regulation potential of the low-voltage network containing a large number of household photovoltaics is evaluated, and a distributed optimization regulation and control model is constructed and solved. The regulated power of the low-voltage network obtained by the solution is then fed back to the medium-voltage network.

[0142] The power command transfer and response between the medium-voltage and low-voltage levels need to meet specific time-scale matching requirements. Specifically, the regulation period of the medium-voltage network is generally set to 15 minutes, and during this period, power scheduling commands are generated and sent to each node; the low-voltage network receives these regulation commands through the PCC node and performs dynamic matching based on its own power response range, and its response time is approximately 10 seconds.

[0143] (2) Centralized-Distributed Double-Layer Control Model of Distribution Network

[0144] The double-layer control model of the distribution network proposed by the present invention adopts a centralized-distributed architecture, and its core components are a centralized control module and a distributed control module.

[0145] Centralized Control Module of Distribution Network

[0146] This link mainly includes four steps, specifically as follows:

[0147] Initialization setting: Set the starting time point of the centralized control of the medium-voltage distribution network as t = t k , and clarify the regulation time interval as T.

[0148] Data collection: Comprehensively obtain the parameter information of each node in the medium-voltage distribution network.

[0149] Model construction: Establish a centralized optimal control model for the medium-voltage distribution network.

[0150] Power response constraint of controllable low-voltage areas:

[0151]

[0152] In the formula, and are respectively the minimum and maximum values of the power response of the controllable low-voltage areas.

[0153] Model solution: Optimally solve the medium-voltage layer model to obtain the control strategy.

[0154] Distributed Control Link of Distribution Network

[0155] The distributed control link of the low-voltage distribution network mainly includes four steps, specifically as follows:

[0156] Time interval setting: Determine the regulation time interval of the low-voltage distribution network.

[0157] Determine the regulation time interval of the low-voltage distribution network, set its starting time point as t = t ko , and clarify the regulation time interval as τ. There is a specific relationship between this time interval τ and the regulation time interval T of the medium-voltage distribution network:

[0158] T = n × τ

[0159] Data collection and analysis: Collect the parameter information of each node in the low-voltage distribution network, including the predicted output of user-side photovoltaics, the load prediction curve, and key data such as the status of distributed energy storage within the low-voltage substation area.

[0160] Model construction: Based on the collected data, establish a distributed optimization control model for the low-voltage distribution network.

[0161] Model solution: Optimize and solve the low-voltage layer model to formulate corresponding control strategies.

[0162] According to the second aspect of the embodiments of the present invention, a centralized-distributed control system for a distribution network with distributed resource access is provided, as Figure 3 shown, including: a first construction module for constructing a distribution network model under distributed resource access; a second construction module for constructing a centralized optimization model of the distribution network; a third construction module for constructing a distributed optimization control model of the distribution network; a fourth construction module for constructing a centralized-distributed two-layer control model of the distribution network for scenarios with high-proportion photovoltaic and energy storage access. A solution module for solving the centralized-distributed two-layer control model of the distribution network based on an optimization algorithm. For the parts not detailed for each module above, reference can be made to the relevant descriptions in the embodiments. It should be noted that the above-mentioned each module can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above-mentioned each module can be located in the same processor; and / or, the above-mentioned each module is located in different processors in any combination.

[0163] According to the third aspect of the embodiments of the present invention, a processor is provided, and the processor is used to run a program, wherein when the program runs, it executes the centralized-distributed control strategy for a distribution network with distributed resource access described in any one of the above.

[0164] According to the fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, characterized in that the computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the centralized-distributed control strategy for a distribution network with distributed resource access described in any one of the above.

[0165] The specific embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those of ordinary skill in the art, various changes can be made without departing from the purpose of the present invention.

Claims

1. A centralized-distributed control method for distributed resources accessing a distribution network, characterized in that: include: Determine the components of the distribution network, including photovoltaic power generation units, energy storage units, grid-connected inverter units and load units. Each unit is connected through a DC bus to build a distribution network model under distributed resource access; Analyze the distributed photovoltaic grid-connected mechanism, and build a centralized optimization control model for the distribution network with the goal of minimizing the network loss of the distribution network, minimizing the node voltage fluctuation, and maximizing the consumption of distributed power sources; Considering the grid-connected characteristics of distributed energy storage, a distributed optimization control model for distribution network is constructed with the goal of maximizing the matching degree of source, load and storage. For scenarios with high proportion of photovoltaic and energy storage access, a centralized-distributed two-layer control model of the distribution network is constructed.

2. The centralized-distributed control method for distributed resource access to a distribution network according to claim 1, characterized in that: The distribution network components include: (1) Photovoltaic power generation unit The output characteristic equation of the photovoltaic cell is: In the formula, R s is the series equivalent resistance of the photovoltaic cell; R sp is the parallel equivalent resistance; I, I ph ,I o1 ,I o2 are the photovoltaic cell output current, photocurrent and reverse saturation current of diodes 1 and 2 respectively; q is the charge of the electron; U pv is the output voltage of the photovoltaic cell; T is the absolute temperature of the cell; k is the Boltzmann constant; A1 and A2 are constant factors; The power-voltage output characteristic equation of photovoltaic cells is: Where P pv is the output power of the photovoltaic cell, U oc is the open circuit voltage, I sc is the short-circuit current; R sh is the internal parallel resistance of the photovoltaic cell, U oc is the open circuit voltage; The photovoltaic side DC / DC converter uses a double-sided active bridge DC / DC converter; (2) Energy storage unit The battery adopts a typical internal resistance equivalent model; (3) Grid-connected inverter unit Adopt voltage type PWM converter.

3. The centralized-distributed control method for distributed resource access to a distribution network according to claim 1, characterized in that: The comprehensive objective function of the distributed optimization control model of the distribution network is: Where P N.loss is the network loss reference value, P loss is the network loss, V D is the node voltage deviation, V N is the node voltage deviation reference value, P DG is the grid-connected power of distributed generation, P N.DG is the grid-connected power benchmark value of distributed generation, w1~w3 are the weights of each sub-goal, w1+w2+w3=1; The constraints include system power flow constraints, node voltage constraints, and distributed photovoltaic grid-connected output constraints.

4. The centralized-distributed control method for distributed resource access to a distribution network according to claim 1, characterized in that: The distributed optimization control model of the distribution network takes the maximum matching degree of the source and load in the low-voltage layer of the distribution network as the objective function: Where θ is the source-charge matching degree, δ M is the correlation coefficient between source, charge and storage, δ N is the variance between source, charge and storage, α1 and α2 are weight coefficients; The constraints include three types of constraints: charging and discharging state of energy storage elements, power, and energy.

5. The centralized-distributed control method for distributed resource access to a distribution network according to claim 1, characterized in that: The construction of a centralized-distributed two-layer control model for distribution networks specifically includes: Through real-time data collection, the source-load matching degree of distributed power sources in the distribution network is controlled. According to the voltage level of the distribution network, centralized and distributed control strategies are adopted for the medium voltage layer and the low voltage layer respectively, and then the hierarchical control architecture of the distribution network is designed; the time scale matching principles of different levels are designed, and a hierarchical coordinated control model of the distribution network under large-scale distributed resource access is constructed.

6. A centralized-distributed control system for distributed resources accessing a distribution network, characterized in that: include: The first building module is used to build a distribution network model under distributed resource access; The second building module is used to build a centralized optimization control model for the distribution network; The third building module is used to build a distributed optimization control model for the distribution network; The fourth building block is to build a centralized-distributed two-layer control model for the distribution network for scenarios with high proportion of photovoltaic and energy storage access. The solution module is used to solve the centralized-distributed two-layer control model of the distribution network based on the optimization algorithm.

7. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the centralized-distributed control method for access of distributed resources to a distribution network as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the centralized-distributed control method for accessing a distribution network to distributed resources as described in any one of claims 1-5.

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