A distributed photovoltaic bearing capacity evaluation method, device, equipment and medium

Abstract

The application discloses a kind of distributed photovoltaic carrying capacity evaluation method, device, equipment and medium, method includes constructing microgrid control model and distribution network scheduling model;Based on the upper limit of intertie interactive power cyclic adjustment method, microgrid control model and distribution network scheduling model are trained simultaneously microgrid control model and distribution network scheduling model;The maximum installed capacity of distributed power supply in power grid is calculated;According to the maximum installed capacity of distributed power supply, microgrid control model and distribution network scheduling model, construct comprehensive distributed photovoltaic carrying capacity evaluation model.The application comprehensively considers the characteristics of photovoltaic power generation and energy storage system, uses reducing system energy cost as its scheduling objective function, to avoid the occurrence of microgrid simultaneously buying and selling electricity in scheduling result;Based on the operating state of distribution network and microgrid, the rapid evaluation of new energy carrying capacity is realized by comprehensively considering various constraint conditions.

Description

Technical Field

[0001] This invention relates to the field of power grid assessment technology, and in particular to a method, apparatus, equipment and medium for assessing the carrying capacity of distributed photovoltaic power generation. Background Technology

[0002] With the rapid growth of energy demand, distributed renewable energy has become the focus of renewable energy development due to its advantages such as low installation cost and low local consumption loss.

[0003] While renewable energy units promote cleaner energy sources, their output is accompanied by significant uncertainty. Furthermore, with the continuous increase in the grid-connected capacity of distributed power sources, bidirectional power flows are emerging in the distribution network, even leading to substantial backflow of electricity to the upper-level grid. Therefore, the uncertainty of output from high-proportion renewable energy units connected to the distribution network may cause irreversible damage to the system, while cutting off their output would affect the economic benefits for their investors.

[0004] Therefore, how to assess the carrying capacity of distributed photovoltaic power and thus rationally control the output of distributed energy has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and medium for assessing the carrying capacity of distributed photovoltaic systems, so as to realize the assessment of the carrying capacity of distributed photovoltaic systems.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide a method for assessing the carrying capacity of distributed photovoltaic power, comprising:

[0007] Based on the microgrid power supply and operation data, a microgrid control model is constructed with minimizing energy consumption as the primary objective function.

[0008] Based on the power supply operation data of the distribution network, a distribution network scheduling model is constructed with the preset operation and control target as the second objective function.

[0009] Based on the method of cyclically adjusting the upper limit of the tie-line interactive power, the microgrid control model and the distribution network scheduling model are combined, and the microgrid control model and the distribution network scheduling model are trained according to the first objective function and the second objective function.

[0010] The maximum installed capacity of distributed power sources in the power grid is calculated based on the microgrid power supply operation data and the distribution network power supply operation data after training is completed.

[0011] A comprehensive distributed photovoltaic carrying capacity assessment model is constructed based on the maximum installed capacity of the distributed power source, the microgrid control model, and the distribution network dispatch model. The comprehensive distributed photovoltaic carrying capacity assessment model is then run to assess the carrying capacity of distributed photovoltaics.

[0012] Furthermore, the first objective function F of the microgrid control model MG for:

[0013]

[0014] Among them, C1 MG For the interaction cost between the microgrid and the upper-level smart distribution network, C2 MG For the cost of calling up energy storage within the microgrid, C3 MG The cost of the tie-line power loss between the microgrid and the upper-level smart distribution network is given by T, where T is a scheduling cycle, Δt is a simulation step size, and C is the cost of the tie-line power loss between the microgrid and the upper-level smart distribution network. t,buy and C t,sell These represent the purchase and sale prices of electricity in the microgrid at time t, respectively. and These represent the electricity purchased and sold by the microgrid at node i at time t, respectively. This represents the cost of charging / discharging 1 kWh of energy storage within a microgrid. and Let u represent the discharge and charging power of the energy stored inside the microgrid at node i at time t, respectively. i,t This represents the voltage amplitude at node i at time t.

[0015] Furthermore, the second objective function of the distribution network scheduling model is:

[0016]

[0017] in, For cost-effectiveness, For distribution network operation losses, For demand response with transferable load, The energy storage dispatch cost is represented by ΔU, the voltage deviation index is represented by α, and β represents the weighting factors for economic efficiency and voltage quality, respectively.

[0018] Furthermore, the training process for the microgrid control model and the distribution network dispatching model is as follows:

[0019] The microgrid control model and the distribution network scheduling model are iteratively trained using a method based on the cyclic adjustment of the tie-line interactive power limit until they meet the convergence criteria. The convergence criteria are as follows:

[0020]

[0021] Where, N mg The total number of nodes. and The power sold or purchased by the distribution network to the microgrid at node i at time t; and These represent the power purchased / sold by the microgrid at node i at time t, respectively. and Let represent the power that virtual trader r buys from and sells to the distribution network at time t, respectively. and Let represent the power of virtual trader r in buying from and selling to other virtual traders at time t, respectively; ε is the convergence residual.

[0022] Furthermore, the maximum installed capacity of the distributed power source is:

[0023]

[0024] Wherein, ΔP I i Let be the maximum installed capacity of the distributed power source at node i under thermal stability safety constraints. Let be the maximum installed capacity of distributed power sources at node i under voltage safety constraints.

[0025] Furthermore, the step of constructing a comprehensive distributed photovoltaic carrying capacity assessment model based on the maximum installed capacity of the distributed power source, the microgrid control model, and the distribution network dispatch model includes:

[0026] The upper limit of grid energy storage capacity is calculated using the equal-annual value method based on the maximum installed capacity of the distributed power source.

[0027] Based on the microgrid control model and the distribution network dispatch model, and with the upper limit of the total amount of distributed power sources and the range of installed capacity of distributed power sources as constraints, a comprehensive distributed photovoltaic carrying capacity assessment model is constructed.

[0028] Furthermore, the upper limit constraint on the total amount of distributed power sources is as follows:

[0029]

[0030] in, This represents the upper limit of the distributed power generation quota allowed to be connected to the distribution network. Let i be the i-th distributed power source.

[0031] The installed capacity range of the distributed power source is constrained as follows:

[0032]

[0033] in, and These represent the minimum and maximum installed capacities of distributed power sources on node i in the planning scheme, respectively, α ES The proportion of installed capacity for energy storage at node i to the total installed capacity of distributed power generation. The installed capacity for energy storage at node i.

[0034] Another embodiment of the present invention provides a distributed photovoltaic carrying capacity assessment device, comprising:

[0035] The microgrid model building module is used to construct a microgrid control model based on microgrid power supply and operation data, with minimizing energy consumption as the primary objective function.

[0036] The distribution network model construction module is used to construct a distribution network scheduling model based on the power supply operation data of the distribution network and with the preset operation and control target as the second objective function.

[0037] The collaborative training module is used to combine the microgrid control model and the distribution network scheduling model based on the tie-line interactive power upper limit cyclic adjustment method, and to train the microgrid control model and the distribution network scheduling model according to the first objective function and the second objective function.

[0038] The capacity calculation module is used to calculate the maximum installed capacity of distributed power sources in the power grid based on the microgrid power supply operation data and the distribution network power supply operation data after training.

[0039] The integrated module construction module is used to construct an integrated distributed photovoltaic carrying capacity assessment model based on the maximum installed capacity of the distributed power source, the microgrid control model, and the distribution network dispatch model, and to run the integrated distributed photovoltaic carrying capacity assessment model to assess the carrying capacity of distributed photovoltaic.

[0040] Another embodiment of the present invention provides a computer device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the distributed photovoltaic carrying capacity assessment method as described above.

[0041] In another embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, the distributed photovoltaic carrying capacity assessment method described above is implemented.

[0042] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0043] (1) Taking into account the characteristics of photovoltaic power generation and energy storage systems, the goal function of scheduling is to reduce the energy cost of the system, so as to avoid the situation in which the microgrid simultaneously buys and sells electricity in the scheduling results.

[0044] (2) Based on the operating status of the distribution network and microgrid, and taking into account various constraints, the high-dimensional photovoltaic multi-node grid connection optimization problem is reduced to one-dimensional photovoltaic total access capacity optimization by utilizing the distribution network operation margin information, thereby realizing a rapid assessment of the new energy carrying capacity. Attached Figure Description

[0045] Figure 1 A flowchart illustrating the steps of the distributed photovoltaic carrying capacity assessment method provided in this embodiment of the invention;

[0046] Figure 2 This is a structural block diagram of the distributed photovoltaic carrying capacity assessment device provided in an embodiment of the present invention;

[0047] Figure 3 A structural diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0049] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0050] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0051] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0052] One embodiment of the present invention provides a method for assessing the carrying capacity of distributed photovoltaic power generation. For details, please refer to [link / reference]. Figure 1 , Figure 1 The flowchart shown is a step-by-step diagram of a distributed photovoltaic carrying capacity assessment method according to one embodiment of the present invention, including steps S11 to S15:

[0053] Step S11: Based on the microgrid power supply operation data, construct a microgrid control model with minimizing energy consumption as the first objective function.

[0054] The optimal control model for photovoltaic-storage microgrids has diverse objectives. When performing dispatch control, an appropriate objective function should be selected based on the system scale, operating status, and actual operational needs. Minimizing energy consumption is a commonly used objective function for microgrid dispatch, which can achieve benefits such as saving energy costs, improving energy utilization efficiency, and reducing environmental impact. Therefore, this paper adopts minimizing system energy consumption as the first objective function for microgrid dispatch, denoted as FMG, as shown in the following equation:

[0055]

[0056] Among them, C1 MG For the interaction cost between the microgrid and the upper-level smart distribution network, C2 MG For the cost of calling up energy storage within the microgrid, C3 MGThe cost of the tie-line power loss between the microgrid and the upper-level smart distribution network is given by T, which is a dispatch cycle, typically 24 hours, and Δt, which is a simulation step size, typically 1 hour. t,buy and C t,sell These represent the purchase and sale prices of electricity in the microgrid at time t, respectively. and These represent the electricity purchased and sold by the microgrid at node i at time t, respectively. This represents the cost of charging / discharging 1 kWh of energy storage within a microgrid. and Let u represent the discharge and charging power of the energy stored inside the microgrid at node i at time t, respectively. i,t The voltage amplitude at node i at time t is represented. In this embodiment, it is approximately assumed that the microgrid has sufficient reactive power to maintain the voltage amplitude at the grid-connected node at 1.00 pu.

[0057] Considering the output of distributed power sources and energy storage in photovoltaic-storage microgrids, as well as the power purchase and sale to the upper-level grid, the following power balance constraints should be met on the interconnection line between the microgrid and the distribution network:

[0058]

[0059] in, and These represent the power purchased / sold by the microgrid at node i at time t, respectively. and Let represent the discharge / charge power of the energy stored inside the microgrid at node i at time t, respectively; This represents the microgrid load demand at node i at time t; This indicates the output of distributed power sources.

[0060] In addition, when a photovoltaic-storage microgrid purchases or sells electricity to the distribution network through a tie line, it must meet the upper limit constraint of the tie line capacity. The specific constraints are as follows:

[0061]

[0062] Where α1 and α2 represent 0-1 indicator variables of the microgrid's power purchase and sale status, respectively; and These represent the power purchased / sold by the microgrid at node i at time t, respectively. This indicates the maximum available capacity of the tie line. During peak output times of some distributed generation sources, the distribution network, due to safety operation requirements, cannot fully meet the power purchase / sale demand of the microgrid and will issue a power purchase / sale cap to the microgrid. Therefore, the tie line power should also meet the following constraints:

[0063]

[0064] in, and These represent the upper limits of the power that the smart distribution network can purchase / sell from the microgrid at node i. During the initial optimization of the microgrid, and These two values ​​can be set to infinity. Due to the upper limit constraint on tie-line capacity, the tie-line power constraint has no effect during the initial optimization of the microgrid. However, during the second or subsequent optimizations of the microgrid... and These two values ​​are provided by the superior smart distribution network based on the network's operating conditions. These two variables are direct coupling variables between the smart distribution network and the microgrid, and are also the key to iterative solution in the method presented in this paper. With the help of these two variables, the smart distribution network and the microgrid can complete the collaborative distributed optimization scheduling between different stakeholders through the interaction of tie-line power information, thereby achieving the goal of privacy protection.

[0065] To avoid situations where microgrids simultaneously purchase and sell electricity in the dispatch results, the Big M method is used to set the following linear constraints:

[0066]

[0067] Where α1 and α2 represent 0-1 indicator variables of the microgrid's power purchase and sale status, respectively; and These represent the power purchased / sold by the microgrid at node i at time t, respectively; M is a very large positive number, which can be selected as 999999. When setting this value, just make sure that the value is much larger than the upper limit of the tie line capacity.

[0068] Distributed power sources typically have a maximum output limit, i.e., the maximum power they can provide. This is determined by the capacity and technical characteristics of the distributed power source. This maximum output limit needs to be considered during scheduling to ensure the safe operation of the distributed power source. Simultaneously, the output response speed of the distributed power source must also meet control requirements, and the following linear constraints are set:

[0069]

[0070] in, Indicates the output of distributed power sources; This represents the predicted maximum output of the distributed generation units of the microgrid at node i at time t. A microgrid with distributed generation and energy storage can achieve reactive power balance by controlling its reactive power.

[0071] Operating constraints for energy storage devices generally include limits on charge / discharge efficiency, charge / discharge rate, and energy storage capacity. These constraints must be considered during scheduling to ensure the safe, stable, and reliable operation of the energy storage devices. The following linear constraints are set:

[0072]

[0073] in, η1 represents the remaining energy stored in the microgrid at time t; η2 represents the charging / discharging efficiency, respectively. The rated energy storage capacity of the microgrid. and These represent the percentage of the minimum / maximum remaining energy of microgrid energy storage relative to the installed capacity, respectively. and These represent the minimum and maximum charging power of energy storage, respectively. and These represent the minimum and maximum discharge power of the stored energy, respectively. and These represent the remaining power of the energy storage device at the initial and final moments of the scheduling process, respectively. The fact that the remaining power of the energy storage device is equal at the initial and final moments of the scheduling process helps to achieve energy balance and improve scheduling feasibility.

[0074] Step S12: Based on the power supply operation data of the distribution network, construct a distribution network scheduling model with the preset operation and control target as the second objective function.

[0075] At the distribution network level, this embodiment prioritizes meeting its own load demand and sells excess electricity to the upper-level power grid. The distribution system operator (DSO) needs to formulate various flexible resource scheduling schemes and send boundary interaction information to each microgrid while meeting safety constraints.

[0076] The second objective function of the distribution network dispatching model is:

[0077]

[0078] in, For cost-effectiveness, For distribution network operation losses, For demand response with transferable load, The energy storage dispatch cost is represented by ΔU, the voltage deviation index is represented by α, and β represents the weighting factors for economic efficiency and voltage quality, respectively.

[0079] The distribution network is actually used to verify whether the solution results of the microgrid meet the following security constraints. Therefore, it should be ensured that the voltage of each node must be within the safe range:

[0080]

[0081] Among them, V min The minimum allowable voltage (typically 0.95 pu), V max The maximum allowable voltage is 1.05 pu, and nodes is the collection of all nodes. Ensuring that the voltage does not exceed the limit is the primary condition for passing the safety verification when writing the program.

[0082] Power flow constraints require that the power flow of each branch must be less than its rated capacity.

[0083]

[0084] Among them, P ij Q is the power of branch ij. ij Let P be the charge of branch ij. max For maximum power, Q max Let be the maximum charge, and branches be the set of all branches.

[0085] Equipment capacity constraints must also be met: the load on equipment such as transformers and switches cannot exceed their rated capacity, specifically:

[0086] S j.min (n)≤S j (t,n)≤S j.max (n)

[0087] Among them, S j.min (n) represents the minimum output power of unit j in state n, S j (t,n) represents the output power of unit j at time t and state n, S j.max (n) represents the maximum output power of unit j in state n.

[0088] Considering the need for subsequent collaboration with the microgrid control model, when formulating boundary interaction information with the microgrid, the microgrid's electricity purchase demand should be met as much as possible, and an upper limit for the distribution network's electricity purchase should be set. The mathematical constraints are as follows:

[0089]

[0090] in, and These represent the power purchased / sold by the microgrid at node i at time t, respectively. and The power sold or purchased by the distribution network to the microgrid at node i at time t.

[0091] Similarly, when a distribution network purchases electricity from or sells electricity to a higher-level grid, it must also meet power constraints, as shown in the following formula:

[0092]

[0093] Wherein, β1 and β2 are 0-1 indicator variables representing the state of the smart distribution network purchasing electricity from the upper-level grid / selling electricity to the upper-level grid. This indicates the rated power of the interconnection line between the smart distribution network and the upper-level power grid. Let t be the power purchased at time t. Let t be the electricity sales power at time t.

[0094] Energy storage devices experience some losses during charging and discharging, subject to the following constraints:

[0095]

[0096] in, This represents the limit of reactive power absorbed / generated by the centralized energy storage at node i. Let be the reactive power of node i at time t.

[0097] The remaining power constraint and power constraint of the energy storage device are as follows:

[0098] W t+1 =W t +P t C *η*Δt-P t B *Δt / η

[0099]

[0100] Among them, W t This represents the stored energy quantity at time t. The maximum power at time t, Let be the minimum power at time t, η represent the charge / discharge efficiency of the energy storage device, and Δt represent the time step. i B This represents the remaining battery power during the i-th time period. This indicates the minimum allowable power capacity of the energy storage device. This indicates the maximum allowable power capacity of the energy storage device.

[0101] Initial constraints for energy storage:

[0102] W begin =W end (W1=W 24 )

[0103] Among them, W beginThe initial power of the energy storage device, W end The final charge of the energy storage device, W1 = W 24 This indicates that the amount of electricity consumed in the 24th hour is assumed to be equal to the amount consumed in the 25th hour

[0104] Step S13: Based on the method of cyclic adjustment of tie-line interactive power upper limit, the microgrid control model and the distribution network scheduling model are combined, and the microgrid control model and the distribution network scheduling model are trained according to the first objective function and the second objective function.

[0105] The aforementioned microgrid and distribution network models are both optimized and scheduled by their respective control centers. While considering the optimization and control objectives of both, this embodiment proposes a method based on the cyclic adjustment of the upper limit of the tie-line interactive power to handle the strong coupling relationship between the various microgrids and the smart distribution network. Based on simultaneously considering the objective functions of both, the microgrid control model and the distribution network scheduling model are collaboratively trained, as detailed below:

[0106] During the day-ahead dispatch phase, each microgrid first collects historical power generation and consumption data through its EMS, formulates a controllable and flexible resource production and consumption plan based on the day-ahead time-of-use electricity price, and then formulates a time-series power purchase and sale scheme and reports it to the DSO.

[0107] Secondly, the DSO performs smart distribution network scheduling based on the power purchase and sale curves of each microgrid, and distributes the power purchase and sale optimization results (power purchase and sale upper limit) of each microgrid interconnection line to the energy management system (EMS) of each microgrid.

[0108] Then, each microgrid EMS will conduct day-ahead dispatching again based on the power purchase and sale ceiling set by the distribution network, and submit a new power purchase and sale plan.

[0109] Finally, the microgrid control model and distribution network dispatching model are iterated repeatedly until the following convergence criterion is met. The result serves as the final decision-making basis for day-ahead dispatching. The iterative convergence criterion is shown in the following formula:

[0110]

[0111] Where, N mg The total number of nodes. and The power sold or purchased by the distribution network to the microgrid at node i at time t; and These represent the power purchased / sold by the microgrid at node i at time t, respectively. and Let represent the power that virtual trader r buys from and sells to the distribution network at time t, respectively. and Let represent the power of virtual trader r to buy from and sell to other virtual traders at time t, respectively; ε represents the convergence residual, which is a small positive number, typically 0.1.

[0112] Step S14: Calculate the maximum installed capacity of distributed power sources in the power grid based on the microgrid power supply operation data and the distribution network power supply operation data after training is completed.

[0113] Under the condition that wind and solar power curtailment is not allowed, the maximum installed capacity of distributed generation is calculated using the hyperplane expression of the distribution network security domain boundary. Constrained by the voltage upper limit of node i, the maximum installed capacity of distributed generation at node i is ΔP1. i,M .

[0114] Constrained by the upper and lower voltage limits of N nodes in the distribution network, the maximum installed capacity of distributed generation with 2N nodes i can be obtained: ΔP1 i,M ΔP1 i,m , …… Therefore, under voltage safety constraints, the maximum installed capacity of the distributed power supply at node i is:

[0115]

[0116] Similarly, subject to the thermal stability safety constraints of the lines in the distribution network, the maximum installed capacity ΔP of the distributed generation at node i can be obtained. I i Therefore, the maximum installed capacity of the distributed power supply at node i is:

[0117]

[0118] Then, the grid connection locations of distributed power sources are selected, and quota capacity is allocated to these grid-connected nodes. Calculate the maximum installed capacity of the distributed power supply for each node to obtain the set. Based on constraints such as the installation sites of N nodes in the distribution network and the number of grid-connected nodes of distributed power sources in the distribution network, according to ΔP i PV Nint grid-connected nodes are selected in descending order to meet the project requirements. Based on the principle of marginal utility, the system achieves optimal absorption of distributed power sources when the curtailment rates of wind and solar power at these Nint grid-connected nodes are equal. For a selected distributed power grid-connected node x, the initial installed capacity of the distributed power source is shown in the following formula:

[0119]

[0120] The main scheduling objective of the supporting energy storage for distributed power during the normal operation of the distribution network is to cut peaks and fill valleys. Therefore, the following optimization model is used to mathematically quantify the energy storage's suppression of the volatility of new energy output:

[0121]

[0122] Among them, is the rated power of node i, represents the overall power output of the photovoltaic-storage microgrid at node i at time t; P i avg-pv represents the average value of the distributed photovoltaic output of node i; represents the remaining power of the microgrid energy storage at time t; η1 and η2 represent the charge / discharge efficiency respectively; and represent the percentages of the minimum / maximum remaining power of the microgrid energy storage to the installed capacity respectively; and represent the minimum / maximum charging power of the energy storage respectively; and represent the minimum / maximum discharging power of the energy storage respectively; and represent the remaining power of the energy storage device at the initial and end times of scheduling respectively. At this time, the energy storage system plays the role of cutting peaks and filling valleys, and the objective function is to smooth the volatility of the photovoltaic output curve. Then, considering the participation of the energy storage device, the maximum installed capacity ΔP i PV ' of the distributed power at node i can be calculated by the following formula:

[0123]

[0124] Among them, represents the maximum per-unit coefficient of the predicted photovoltaic output of node i within a scheduling period.

[0125] Step S15, construct a comprehensive distributed photovoltaic carrying capacity evaluation model according to the maximum installed capacity of the distributed power, the microgrid control model, and the distribution network scheduling model, and run the comprehensive distributed photovoltaic carrying capacity evaluation model to evaluate the carrying capacity of distributed photovoltaics.

[0126] Calculate the upper limit of the grid energy storage capacity by the equal annual value method according to the maximum installed capacity of the distributed power;[[ID=​​​​​

[0129]

[0130] in, This represents the upper limit of the distributed power generation quota allowed to be connected to the distribution network. This is the i-th distributed power source;

[0131] The installed capacity range constraints for distributed power sources are as follows:

[0132]

[0133] in, and These represent the minimum and maximum installed capacities of distributed power sources on node i in the planning scheme, respectively, α ES The proportion of installed capacity for energy storage at node i to the total installed capacity of distributed power generation. The installed capacity for energy storage at node i.

[0134] The distributed photovoltaic (PV) carrying capacity assessment method of this invention comprehensively considers the characteristics of PV power generation and energy storage systems. The model uses reducing system energy costs as its scheduling objective function. To avoid situations where microgrids simultaneously purchase and sell electricity in the scheduling results, based on the operating status of the distribution network and microgrids, and comprehensively considering various constraints, it utilizes distribution network operating margin information to reduce the high-dimensional PV multi-node grid connection optimization problem to a one-dimensional PV total access capacity optimization problem, thereby achieving rapid assessment of the new energy carrying capacity.

[0135] This invention also provides a distributed photovoltaic carrying capacity assessment device for performing the distributed photovoltaic carrying capacity assessment method described above. Figure 2 This is a structural block diagram of a distributed photovoltaic carrying capacity assessment device according to an embodiment of the present invention. The device includes:

[0136] The microgrid model construction module 21 is used to construct a microgrid control model based on the microgrid power supply operation data, with minimizing energy consumption as the first objective function.

[0137] The distribution network model construction module 22 is used to construct a distribution network scheduling model based on the power supply operation data of the distribution network and with the preset operation and control target as the second objective function.

[0138] The collaborative training module 23 is used to combine the microgrid control model and the distribution network scheduling model based on the tie-line interactive power upper limit cyclic adjustment method, and to train the microgrid control model and the distribution network scheduling model according to the first objective function and the second objective function.

[0139] The capacity calculation module 24 is used to calculate the maximum installed capacity of distributed power sources in the power grid based on the microgrid power supply operation data and the distribution network power supply operation data after training.

[0140] The integrated module construction module 25 is used to construct an integrated distributed photovoltaic carrying capacity assessment model based on the maximum installed capacity of the distributed power source, the microgrid control model, and the distribution network dispatch model, and to run the integrated distributed photovoltaic carrying capacity assessment model to assess the carrying capacity of distributed photovoltaic.

[0141] The technical features and effects of the device proposed in the embodiments of the present invention are the same as those of the method proposed in the embodiments of the present invention, and will not be repeated here. Each module in the above-described device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0142] This invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the distributed photovoltaic carrying capacity assessment method as described above.

[0143] This invention also provides a computer device. Figure 3 This is a structural block diagram of a preferred embodiment of a computer device provided by the present invention. The computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the distributed photovoltaic carrying capacity assessment method as described above.

[0144] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, ...), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0145] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor. The processor is the control center of the computer device, connecting various parts of the computer device through various interfaces and lines.

[0146] The memory mainly includes a program storage area and a data storage area. The program storage area can store the operating system, applications required for at least one function, etc., while the data storage area can store related data, etc. Furthermore, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard drive, a SmartMedia Card (SMC), a Secure Digital (SD) card, and a Flash Card, or other volatile solid-state storage devices.

[0147] It should be noted that the aforementioned computer equipment may include, but is not limited to, processors and memory, as will be understood by those skilled in the art. Figure 3 The block diagram is merely an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than shown, or combine certain components, or different components.

[0148] In summary, the distributed photovoltaic carrying capacity assessment method, apparatus, equipment, and medium provided by the embodiments of the present invention have the following beneficial effects compared with the prior art:

[0149] (1) Taking into account the characteristics of photovoltaic power generation and energy storage systems, this model adopts reducing the system's energy cost as its scheduling objective function. To avoid the occurrence of microgrids simultaneously purchasing and selling electricity in the scheduling results;

[0150] (2) Based on the operating status of the distribution network and microgrid, and taking into account various constraints, the high-dimensional photovoltaic multi-node grid connection optimization problem is reduced to one-dimensional photovoltaic total access capacity optimization by utilizing the distribution network operation margin information, thereby realizing a rapid assessment of the new energy carrying capacity.

[0151] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for assessing the carrying capacity of distributed photovoltaic power, characterized in that, include: Based on the microgrid power supply operation data, a microgrid control model is constructed with minimizing energy consumption as the primary objective function; Based on the power supply operation data of the distribution network, a distribution network scheduling model is constructed with the preset operation and control target as the second objective function; Based on the method of cyclically adjusting the upper limit of the tie-line interactive power, the microgrid control model and the distribution network scheduling model are combined, and the microgrid control model and the distribution network scheduling model are trained according to the first objective function and the second objective function; The maximum installed capacity of distributed power sources in the power grid is calculated based on the microgrid power supply operation data and the distribution network power supply operation data after training is completed. A comprehensive distributed photovoltaic carrying capacity assessment model is constructed based on the maximum installed capacity of the distributed power source, the microgrid control model, and the distribution network dispatch model. The comprehensive distributed photovoltaic carrying capacity assessment model is then run to assess the carrying capacity of distributed photovoltaics.

2. The method for assessing the carrying capacity of distributed photovoltaic power as described in claim 1, characterized in that, The first objective function F of the microgrid control model MG for: Among them, C1 MG For the interaction cost between the microgrid and the upper-level smart distribution network, C2 MG For the cost of calling up energy storage within the microgrid, C3 MG The cost of the tie-line power loss between the microgrid and the upper-level smart distribution network is given by T, where T is a scheduling cycle, Δt is a simulation step size, and C is the cost of the tie-line power loss between the microgrid and the upper-level smart distribution network. t,buy and C t,sell These represent the purchase and sale prices of electricity in the microgrid at time t, respectively. and These represent the electricity purchased and sold by the microgrid at node i at time t, respectively. This represents the cost of charging / discharging 1 kWh of energy storage within a microgrid. and Let u represent the discharge and charging power of the energy stored inside the microgrid at node i at time t, respectively. i,t This represents the voltage amplitude at node i at time t.

3. The method for assessing the carrying capacity of distributed photovoltaic power as described in claim 1, characterized in that, The second objective function of the distribution network scheduling model is: in, For cost-effectiveness, For distribution network operation losses, For demand response with transferable load, The energy storage dispatch cost is represented by ΔU, the voltage deviation index is represented by α, and β represents the weighting factors for economic efficiency and voltage quality, respectively.

4. The method for assessing the carrying capacity of distributed photovoltaic power as described in claim 1, characterized in that, The training process for the microgrid control model and the distribution network dispatching model is as follows: The microgrid control model and the distribution network scheduling model are iteratively trained using a method based on the cyclic adjustment of the tie-line interactive power limit until they meet the convergence criteria. The convergence criteria are as follows: Where, N mg The total number of nodes. and The power sold or purchased by the distribution network to the microgrid at node i at time t; and These represent the power purchased / sold by the microgrid at node i at time t, respectively. and These represent the power that virtual trader r purchases from and sells to the distribution network at time t, respectively. and Let represent the power of virtual trader r in buying from and selling to other virtual traders at time t, respectively; ε is the convergence residual.

5. The method for assessing the carrying capacity of distributed photovoltaic power as described in claim 1, characterized in that, The maximum installed capacity of the distributed power source is: in, Let be the maximum installed capacity of the distributed power source at node i under thermal stability safety constraints. Let be the maximum installed capacity of distributed power sources at node i under voltage safety constraints.

6. The method for assessing the carrying capacity of distributed photovoltaic power as described in claim 1, characterized in that, The step of constructing a comprehensive distributed photovoltaic carrying capacity assessment model based on the maximum installed capacity of the distributed power source, the microgrid control model, and the distribution network dispatch model includes: Based on the maximum installed capacity of the distributed power source, the upper limit of the grid energy storage capacity is calculated using the equal annual value method. Based on the microgrid control model and the distribution network dispatch model, and with the upper limit of the total amount of distributed power sources and the range of installed capacity of distributed power sources as constraints, a comprehensive distributed photovoltaic carrying capacity assessment model is constructed.

7. The method for assessing the carrying capacity of distributed photovoltaic power as described in claim 6, characterized in that, The upper limit constraint on the total amount of distributed power sources is: in, This represents the upper limit of the distributed power generation quota allowed to be connected to the distribution network. This is the i-th distributed power source; The installed capacity range of the distributed power source is constrained as follows: in, and These represent the minimum and maximum installed capacities of distributed power sources on node i in the planning scheme, respectively, α ES The proportion of installed capacity for energy storage at node i to the total installed capacity of distributed power generation. The installed capacity for energy storage at node i.

8. A distributed photovoltaic carrying capacity assessment device, characterized in that, include: The microgrid model building module is used to build a microgrid control model based on microgrid power supply and operation data, with minimizing energy consumption as the primary objective function. The distribution network model construction module is used to construct a distribution network scheduling model based on the power supply operation data of the distribution network and with the preset operation and control target as the second objective function. The collaborative training module is used to combine the microgrid control model and the distribution network scheduling model based on the tie-line interactive power limit cyclic adjustment method, and to train the microgrid control model and the distribution network scheduling model according to the first objective function and the second objective function; The capacity calculation module is used to calculate the maximum installed capacity of distributed power sources in the power grid based on the microgrid power supply operation data and the distribution network power supply operation data after training. The integrated module construction module is used to construct an integrated distributed photovoltaic carrying capacity assessment model based on the maximum installed capacity of the distributed power source, the microgrid control model, and the distribution network dispatch model, and to run the integrated distributed photovoltaic carrying capacity assessment model to assess the carrying capacity of distributed photovoltaic.

9. A computer device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the distributed photovoltaic carrying capacity assessment method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the distributed photovoltaic carrying capacity assessment method as described in any one of claims 1 to 7.

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