Distributed photovoltaic bearing capacity assessment method, device, equipment and medium

Abstract

The invention discloses a distributed photovoltaic bearing capacity assessment method, device and equipment and a medium. The method comprises the following steps: constructing a micro-grid control model and a power distribution network dispatching model; based on a tie line interaction power upper limit cyclic adjustment method, a micro-grid control model and a power distribution network scheduling model are combined, and the micro-grid control model and the power distribution network scheduling model are trained; calculating the maximum installed capacity of the distributed power supply in the power grid; and according to the maximum installed capacity of the distributed power supply, the micro-grid control model and the power distribution network dispatching model, constructing a comprehensive distributed photovoltaic bearing capacity evaluation model. The method comprehensively considers the characteristics of the photovoltaic power generation and energy storage system, reduces the energy consumption cost of the system as a scheduling objective function, and avoids the situation that the micro-grid simultaneously purchases and sells electricity in a scheduling result; based on the operation states of the power distribution network and the micro-grid, various constraint conditions are comprehensively considered to realize rapid evaluation of the new energy bearing capacity.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid assessment, and in particular to a distributed photovoltaic carrying capacity assessment method, device, equipment and medium. Background Art

[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 the cleanness of energy sources, their output is accompanied by strong uncertainty. With the continuous increase in the capacity of distributed power sources connected to the grid, two-way power flows appear in the distribution network, and even a large amount of power is reversed to the upper grid. Therefore, the connection of a high proportion of new energy units to the distribution network may cause irreversible damage to the system due to the uncertainty of their output, and cutting off the output of new energy units will affect the economic benefits of their investors.

[0004] It can be seen that how to evaluate the carrying capacity of distributed photovoltaics and thus reasonably control the output of distributed energy has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0005] The present invention provides a distributed photovoltaic carrying capacity evaluation method, device, equipment and medium to achieve the evaluation of the distributed photovoltaic carrying capacity.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a distributed photovoltaic carrying capacity evaluation method, including:

[0007] According to the microgrid power supply operation data, a microgrid control model is constructed with minimizing energy consumption as the first objective function.

[0008] According to the power supply operation data of the distribution network, a distribution network dispatching model is constructed with the preset operation control target as the second objective function.

[0009] Based on the method of cyclic adjustment of the interactive power upper limit of the tie line, the microgrid control model and the distribution network scheduling model are jointly established, 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 the distributed power source in the power grid is calculated based on the trained microgrid power supply operation data and the distribution network power supply operation data.

[0011] A comprehensive distributed photovoltaic carrying capacity evaluation model is constructed according to the maximum installed capacity of the distributed power source, the microgrid control model and the distribution network scheduling model, and the comprehensive distributed photovoltaic carrying capacity evaluation model is run to evaluate the carrying capacity of distributed photovoltaics.

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

[0013]

[0014] Among them, C1 MG is the interaction cost between the microgrid and the upper-level smart distribution network, C2 MG C3 is the cost of calling the energy storage inside the microgrid. MG is the power loss cost of the interconnection line between the microgrid and the upper-level smart distribution network, T is a scheduling cycle, Δt is a simulation step, C t,buy and C t,sell They represent the unit price of electricity purchase and electricity sales of the microgrid at time t, and They represent the power purchased and sold by the microgrid at node i at time t, represents the cost of charging / discharging 1 kWh of energy storage within the microgrid, and They represent the discharge and charging power of the energy storage inside the microgrid at node i at time t, respectively, and u i,t Represents the node voltage amplitude at node i at time t.

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

[0016]

[0017] in, For cost-effective operation, The network loss of the distribution network. For the transferable load demand response, is the energy storage call cost, ΔU is the voltage deviation index, and α and β represent the weight factors of economy and voltage quality, respectively.

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

[0019] The microgrid control model and the distribution network dispatching model are iteratively trained based on the method of cyclic adjustment of the upper limit of the interactive power of the tie line until the microgrid control model and the distribution network dispatching model meet the convergence basis, and the convergence basis is:

[0020]

[0021] Among them, N mg is 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 They represent the power purchased / sold by the microgrid at node i at time t respectively; and They represent the power purchased from and sold to the distribution network by the virtual trader r at time t. and They represent the power purchased by virtual trader r from and sold 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] Among them, ΔP I i is the maximum installed capacity of distributed generation at node i under thermal stability and safety constraints, is the maximum installed capacity of distributed generation at node i under voltage safety constraints.

[0025] Furthermore, the comprehensive distributed photovoltaic carrying capacity evaluation model is constructed according to the maximum installed capacity of the distributed power source, the microgrid control model and the distribution network dispatching model, including:

[0026] According to the maximum installed capacity of the distributed generation, the upper limit of the grid energy storage capacity is calculated using the equal annual value method.

[0027] According to the microgrid control model and the distribution network dispatching model, a comprehensive distributed photovoltaic carrying capacity evaluation model is constructed with the total upper limit constraint of distributed power sources and the installed capacity range constraint of distributed power sources as constraint conditions.

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

[0029]

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

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

[0032]

[0033] in, and are the minimum and maximum installed capacities of distributed generation at node i in the planning scheme, α ES is the proportion of the installed capacity of energy storage at node i to the installed capacity of distributed generation, is the installed capacity of energy storage at node i.

[0034] Another embodiment of the present invention provides a distributed photovoltaic load-bearing capacity evaluation device, comprising:

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

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

[0037] A collaborative training module is used to jointly integrate the microgrid control model and the distribution network scheduling model based on the method of cyclic adjustment of the interactive power upper limit of the tie line, and 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 the distributed power source in the power grid according to the trained microgrid power supply operation data and the distribution network power supply operation data.

[0039] The comprehensive module construction module is used to construct a comprehensive distributed photovoltaic carrying capacity evaluation model according to the maximum installed capacity of the distributed power source, the microgrid control model and the distribution network scheduling model, run the comprehensive distributed photovoltaic carrying capacity evaluation model, and evaluate the carrying capacity of distributed photovoltaics.

[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 implements the distributed photovoltaic carrying capacity assessment method as described above when executing the computer program.

[0041] Yet another embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the distributed photovoltaic carrying capacity assessment method as described above is implemented.

[0042] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0043] (1) Comprehensively consider the characteristics of photovoltaic power generation and energy storage systems, and adopt reducing the system energy cost as its scheduling objective function to avoid the situation in which the microgrid purchases and sells electricity at the same time in the scheduling results;

[0044] (2) Based on the operating status of the distribution network and microgrid, various constraints are comprehensively considered and the distribution network operating margin information is used to reduce the high-dimensional photovoltaic multi-node grid-connected optimization problem to a one-dimensional photovoltaic total access capacity optimization, thereby achieving a rapid assessment of the new energy carrying capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flow chart of the steps of a distributed photovoltaic load-bearing capacity evaluation method provided by an embodiment of the present invention;

[0046] Figure 2 A structural block diagram of a distributed photovoltaic load-bearing capacity assessment device provided by 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 DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the 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 ordinary technicians in this field without creative work 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 are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0050] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

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

[0052] An embodiment of the present invention provides a method for evaluating the carrying capacity of distributed photovoltaic power generation. Figure 1 , Figure 1 The flowchart of the distributed photovoltaic load capacity evaluation method in one embodiment of the present invention includes steps S11 to S15:

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

[0054] The objectives of the optimal control model of the photovoltaic storage microgrid are diverse. When performing dispatch control, the appropriate objective function should be selected according to the system scale, operating status and actual operating requirements. Minimizing energy consumption is a common 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 system minimization of energy consumption as the first objective function of microgrid dispatch, denoted as FMG, as shown in the following formula:

[0055]

[0056] Among them, C1 MG is the interaction cost between the microgrid and the upper-level smart distribution network, C2 MG C3 is the cost of calling the energy storage inside the microgrid. MGis the power loss cost of the interconnection line between the microgrid and the upper-level smart distribution network, T is a scheduling cycle, generally 24 hours, Δt is a simulation step, generally 1 hour, C t,buy and C t,sell They represent the unit price of electricity purchase and electricity sales of the microgrid at time t, and They represent the power purchased and sold by the microgrid at node i at time t, represents the cost of charging / discharging 1 kWh of energy storage within the microgrid, and They represent the discharge and charging power of the energy storage inside the microgrid at node i at time t, respectively, and u i,t It represents the voltage amplitude of the node at the node i at time t. In this embodiment, it is approximately assumed that the microgrid has sufficient reactive power to maintain the voltage amplitude of the grid-connected node at 1.00 pu.

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

[0058]

[0059] in, and They represent the power purchased / sold by the microgrid at node i at time t respectively; and They represent the discharge / charge power of the energy storage inside the microgrid at node i at time t respectively; represents the microgrid load demand at node i at time t; Indicates the output of distributed power generation.

[0060] In addition, when the photovoltaic storage microgrid purchases electricity from the distribution network or sells electricity to the distribution network through the interconnection line, the upper limit of the interconnection line capacity should be met. The specific constraints are as follows:

[0061]

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

[0063]

[0064] in, and They represent the upper limit of the power that the smart distribution network can purchase / sell from the microgrid at node i. When the microgrid is optimized for the first time, and These two values ​​can be set to infinity. Due to the existence of the upper limit constraint of the tie line capacity, the tie line power constraint formula does not work when the microgrid is optimized for the first time. and These two values ​​are given by the superior smart distribution network according to the operating conditions of the network. These two variables are the direct coupling variables of the smart distribution network-microgrid, and are also the key to the iterative solution in the method of this paper. With the help of these two variables, the smart distribution network and the microgrid can complete the coordinated distributed optimization scheduling between different stakeholders only through the interaction of the interconnection line power information, thereby achieving the purpose of privacy protection.

[0065] In order to avoid the situation in which the microgrid simultaneously purchases and sells electricity in the dispatch results, the following linear constraints are set using the Big M method:

[0066]

[0067] Among them, α1 and α2 represent the 0-1 indicator variables of the microgrid's electricity purchase and sales status; and They respectively represent the power purchased / sold by the microgrid at node i at time t; M is a very large positive number, which can be 999999. When setting this value, you only need to pay attention to making the value much larger than the upper limit of the interconnection line capacity.

[0068] Distributed power sources usually have a maximum output limit, that is, 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 taken into account during scheduling to ensure the safe operation of the distributed power source. At the same time, the output response speed of the default distributed power source must also meet the control requirements, and the following linear constraints are set:

[0069]

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

[0071] The operating constraints of energy storage equipment generally include charging and discharging efficiency limits, charging and discharging rate limits, and energy storage capacity limits. These constraints need to be considered during scheduling to ensure the safe, stable, and reliable operation of energy storage equipment. The following linear constraints are set:

[0072]

[0073] in, represents the remaining power of the microgrid energy storage at time t; η1 and η2 represent the charging / discharging efficiency respectively; is the rated capacity of microgrid energy storage, and They represent the percentage of the minimum / maximum remaining power of the microgrid energy storage to the installed capacity respectively; and Respectively represent the minimum / maximum charging power of energy storage; and Respectively represent the minimum / maximum discharge power of energy storage; and They respectively represent the remaining power of the energy storage device at the initial and end times of scheduling. The remaining power of the energy storage device is equal at the initial and end times of scheduling, which helps to achieve energy balance and improve scheduling feasibility.

[0074] Step S12: constructing a distribution network dispatching model based on the power supply operation data of the distribution network and taking the preset operation control target as the second objective function.

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

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

[0077]

[0078] in, For cost-effective operation, The network loss of the distribution network. For the transferable load demand response, is the energy storage call cost, ΔU is the voltage deviation index, and α and β represent the weight factors of economy and voltage quality, respectively.

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

[0080]

[0081] Among them, V min is the minimum allowable voltage (usually 0.95pu), V max is the maximum allowable voltage (generally 1.05pu), nodes is the collection of all nodes. When writing a program, ensuring that the voltage does not exceed the limit is the primary condition for passing the safety check.

[0082] The power flow constraint requires that the power flow of each branch must be less than its rated capacity:

[0083]

[0084] Among them, P ij is the power of branch ij, Q ij is the charge of branch ij, P max is the maximum power, Q max is the maximum charge, and branches is the collection 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 to work with the microgrid control model in the future, when formulating the boundary interaction information with the microgrid, the power purchase demand of the microgrid should be met as much as possible, and the power purchase upper limit of the distribution network should be set. The mathematical constraints are as follows:

[0089]

[0090] in, and They 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, the distribution network also needs to meet power constraints when purchasing electricity from the upper grid or selling electricity to the upper grid, as shown in the following formula:

[0092]

[0093] Among them, β1 and β2 are 0-1 indicator variables indicating that the smart distribution network is in the state of purchasing electricity from the upper power grid / selling electricity to the upper power grid. Indicates the rated power of the interconnection line between the smart distribution network and the upper power grid. is the purchased power at time t, is the electricity sales power at time t.

[0094] Energy storage equipment will have certain losses during the charging and discharging process, and the constraints are as follows:

[0095]

[0096] in, represents the reactive power limit absorbed / generated by the centralized energy storage at node i, is 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 represents the energy storage capacity at time t, is the maximum power at time t, is the minimum power at time t, η represents the charging and discharging efficiency of the energy storage device, and Δt represents the time step. i B Indicates the remaining battery power in the i-th time period, Indicates the minimum allowable power of the energy storage device. Indicates the maximum allowable power of the energy storage device.

[0101] Energy storage initial value constraints:

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

[0103] Among them, W beginInitial capacity of the energy storage device, W end The final power of the energy storage device, W1 = W 24 It indicates that it is assumed to be equal to the electricity in the 24th hour.

[0104] Step S13, based on the method of cyclic adjustment of the interactive power upper limit of the tie line, the microgrid control model and the distribution network scheduling model are jointly established, 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 above-mentioned microgrid and distribution network models are optimized and dispatched by the control centers of their respective entities. Under the condition of taking into account the optimization control objectives of both parties, this embodiment proposes a method based on the cyclic adjustment of the upper limit of the interactive power of the tie line to deal with the strong coupling relationship between each microgrid and the smart distribution network. The microgrid control model and the distribution network dispatching model are collaboratively trained on the basis of taking into account the objective functions of both. The specific description is as follows:

[0106] In the day-ahead dispatching stage, first, each microgrid collects historical power generation and consumption data through its EMS, and formulates a controllable flexible resource production and consumption plan based on the day-ahead time-of-use electricity price, thereby formulating a time-series power purchase and sales plan and reporting it to the DSO.

[0107] Secondly, DSO dispatches the smart distribution network according to the power purchase and sales power curves of each microgrid, and delegates the power purchase and sales optimization results (power purchase and sales 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 dispatch again according to the upper limit of power purchase and sale set by the distribution network, and report the new power purchase and sale plan.

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

[0110]

[0111] Among them, N mg is 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 They represent the power purchased / sold by the microgrid at node i at time t respectively; and They represent the power purchased from and sold to the distribution network by the virtual trader r at time t. and They represent the power that the virtual trader r buys from and sells to other virtual traders at time t respectively; ε represents the convergence residual, which is a small positive number, generally 0.1.

[0112] Step S14, calculating the maximum installed capacity of the distributed power source in the power grid according to the trained microgrid power supply operation data and the distribution network power supply operation data.

[0113] Under the condition that wind and solar power are not allowed to be abandoned, the maximum installed capacity of distributed generation is calculated using the hyperplane expression of the distribution network safety 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] Subject to the voltage upper and lower limits of N nodes in the distribution network, the maximum installed capacity of distributed generation at 2N nodes i can be obtained: ΔP1 i,M , ΔP1 i,m , ……、 Therefore, under the voltage safety constraint, the maximum installed capacity of distributed generation at node i is:

[0115]

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

[0117]

[0118] Then, the grid-connected locations of distributed power sources are screened and quota capacity is allocated at these grid-connected nodes. Calculate the maximum installed capacity of distributed power generation at each node and get the set According to the installation sites of N nodes in the distribution network, the number of grid-connected nodes of distributed generation in the distribution network and other constraints, according to ΔP i PV Select Nint grid-connected nodes that meet the engineering requirements in descending order. According to the marginal effect principle, when the wind and solar power abandonment ratios of Nint grid-connected nodes are equal, the system achieves the best absorption of distributed power sources. For a selected distributed power grid-connected node x, the installed capacity of distributed power sources in the initial plan is as shown in the following formula:

[0119]

[0120] The main dispatching goal of the supporting energy storage of distributed power generation during the normal operation of the distribution network is to reduce peak loads and fill valleys. Therefore, there is an optimization model to mathematically quantify the effect of energy storage on smoothing the output volatility of new energy sources:

[0121]

[0122] in, is the rated power of node i, represents the power output of the photovoltaic-storage microgrid as a whole at time t; P i avg-pv represents the average value of distributed photovoltaic output at node i; represents the remaining power of the microgrid energy storage at time t; η1 and η2 represent the charging / discharging efficiency respectively; and They represent the percentage of the minimum / maximum remaining power of the microgrid energy storage to the installed capacity respectively; and Respectively represent the minimum / maximum charging power of energy storage; and Respectively represent the minimum / maximum discharge power of energy storage; and They represent the remaining power of the energy storage device at the initial and end time of the scheduling respectively. At this time, the energy storage system plays the role of peak shaving and valley filling, and the objective function is to smooth the volatility of the photovoltaic output curve. Then, considering the participation of energy storage equipment, the maximum installed capacity of the distributed power generation at node i, ΔP i PV 'It can be calculated by the following formula:

[0123]

[0124] in, It represents the maximum per unit coefficient of the predicted photovoltaic output of node i within a scheduling cycle.

[0125] Step S15, constructing a comprehensive distributed photovoltaic carrying capacity evaluation model according to the maximum installed capacity of the distributed power source, the microgrid control model and the distribution network dispatching model, running the comprehensive distributed photovoltaic carrying capacity evaluation model, and evaluating the carrying capacity of distributed photovoltaics.

[0126] According to the maximum installed capacity of distributed generation, the upper limit of grid energy storage capacity is calculated using the equal annual value method;

[0127] According to the microgrid control model and distribution network dispatching model, a comprehensive distributed photovoltaic carrying capacity evaluation model is constructed with the upper limit constraint of the total amount of distributed power sources and the installed capacity range constraint of distributed power sources as constraints.

[0128] Among them, the upper limit constraint of the total amount of distributed power generation is:

[0129]

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

[0131] The installed capacity range of distributed generation is constrained as follows:

[0132]

[0133] in, and are the minimum and maximum installed capacities of distributed generation at node i in the planning scheme, α ES is the proportion of the installed capacity of energy storage at node i to the installed capacity of distributed generation, is the installed capacity of energy storage at node i.

[0134] The distributed photovoltaic carrying capacity evaluation method of the present invention comprehensively considers the characteristics of photovoltaic power generation and energy storage systems. The model adopts reducing the system energy cost as its scheduling objective function to avoid the situation in which the microgrid simultaneously purchases and sells electricity in the scheduling results; based on the operating status of the distribution network and the microgrid, various constraints are comprehensively considered, and the distribution network operation margin information is used to reduce the high-dimensional photovoltaic multi-node grid-connected optimization problem to a one-dimensional photovoltaic total access capacity optimization, thereby realizing a rapid evaluation of the new energy carrying capacity.

[0135] The embodiment of the present invention further provides a distributed photovoltaic carrying capacity evaluation device, which is used to execute the distributed photovoltaic carrying capacity evaluation method as described above. Figure 2 This is a structural block diagram of a distributed photovoltaic load-bearing capacity assessment device according to an embodiment of the present invention, and the device comprises:

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

[0137] The distribution network model building module 22 is used to build a distribution network dispatching model according to the power supply operation data of the distribution network and with a preset operation control target as the second objective function.

[0138] The collaborative training module 23 is used to jointly integrate the microgrid control model and the distribution network scheduling model based on the method of cyclic adjustment of the interactive power upper limit of the tie line, and 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 the distributed power source in the power grid according to the trained microgrid power supply operation data and the distribution network power supply operation data.

[0140] The comprehensive module construction module 25 is used to construct a comprehensive distributed photovoltaic carrying capacity evaluation model according to the maximum installed capacity of the distributed power source, the microgrid control model and the distribution network scheduling model, run the comprehensive distributed photovoltaic carrying capacity evaluation model, and evaluate the carrying capacity of distributed photovoltaics.

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

[0142] An embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the distributed photovoltaic carrying capacity assessment method as described above.

[0143] The embodiment of the present invention further provides a computer device, Figure 3 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, and the processor implements the distributed photovoltaic carrying capacity assessment method as described above when executing the computer program.

[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 that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0145] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor. The processor is the control center of the computer device, and various parts of the computer device are connected using various interfaces and lines.

[0146] The memory mainly includes a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function, etc., and the data storage area can store related data, etc. In addition, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, and a flash card (Flash Card), etc., or the memory can also be other volatile solid-state storage devices.

[0147] It should be noted that the above-mentioned computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that Figure 3 The structural block diagram is merely an example of a computer device and does not constitute a limitation of the computer device, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components.

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

[0149] (1) Comprehensively consider the characteristics of photovoltaic power generation and energy storage systems. This model uses reducing the system energy cost as its scheduling objective function to avoid the situation in which the microgrid purchases and sells electricity at the same time in the scheduling results;

[0150] (2) Based on the operating status of the distribution network and microgrid, various constraints are comprehensively considered and the distribution network operating margin information is used to reduce the high-dimensional photovoltaic multi-node grid-connected optimization problem to a one-dimensional photovoltaic total access capacity optimization, thereby achieving a rapid assessment of the new energy carrying capacity.

[0151] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A distributed photovoltaic carrying capacity assessment method, characterized in that: include: According to the microgrid power supply operation data, a microgrid control model is constructed with minimizing energy consumption as the first objective function; According to the power supply operation data of the distribution network, a distribution network dispatching model is constructed with the preset operation control target as the second objective function; Based on the method for cyclic adjustment of the upper limit of interactive power of tie lines, the microgrid control model and the distribution network dispatching model are jointly established, and the microgrid control model and the distribution network dispatching model are trained according to the first objective function and the second objective function; Calculate the maximum installed capacity of the distributed power source in the power grid according to the trained microgrid power supply operation data and the distribution network power supply operation data; A comprehensive distributed photovoltaic carrying capacity evaluation model is constructed according to the maximum installed capacity of the distributed power source, the microgrid control model and the distribution network scheduling model, and the comprehensive distributed photovoltaic carrying capacity evaluation model is run to evaluate the carrying capacity of distributed photovoltaics.

2. The distributed photovoltaic carrying capacity evaluation method according to claim 1, characterized in that: The first objective function F of the microgrid control model MG for: Among them, C1 MG is the interaction cost between the microgrid and the upper-level smart distribution network, C2 MG C3 is the cost of calling the energy storage inside the microgrid. MG is the power loss cost of the interconnection line between the microgrid and the upper-level smart distribution network, T is a scheduling cycle, Δt is a simulation step, C t,buy and C t,sell They represent the unit price of electricity purchase and electricity sales of the microgrid at time t, and They represent the power purchased and sold by the microgrid at node i at time t, represents the cost of charging / discharging 1 kWh of energy storage within the microgrid, and They represent the discharge and charging power of the energy storage inside the microgrid at node i at time t, respectively, and u i,t Represents the node voltage amplitude at node i at time t.

3. The distributed photovoltaic carrying capacity evaluation method according to claim 1, characterized in that: The second objective function of the distribution network dispatching model is: in, For cost-effective operation, The network loss of the distribution network. For the transferable load demand response, is the energy storage call cost, ΔU is the voltage deviation index, and α and β represent the weight factors of economy and voltage quality, respectively.

4. The distributed photovoltaic carrying capacity evaluation method according to claim 1, characterized in that: The training process of the microgrid control model and the distribution network dispatching model is: The microgrid control model and the distribution network dispatching model are iteratively trained based on the method of cyclic adjustment of the upper limit of the interactive power of the tie line until the microgrid control model and the distribution network dispatching model meet the convergence basis, and the convergence basis is: Among them, N mg is 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 They represent the power purchased / sold by the microgrid at node i at time t respectively; and They represent the power purchased from and sold to the distribution network by the virtual trader r at time t; and They represent the power purchased by virtual trader r from and sold to other virtual traders at time t respectively; ε is the convergence residual.

5. The distributed photovoltaic carrying capacity evaluation method according to claim 1, characterized in that: The maximum installed capacity of the distributed power source is: in, is the maximum installed capacity of distributed generation at node i under thermal stability and safety constraints, is the maximum installed capacity of distributed generation at node i under voltage safety constraints.

6. The distributed photovoltaic carrying capacity evaluation method according to claim 1, characterized in that: The comprehensive distributed photovoltaic carrying capacity evaluation model is constructed according to the maximum installed capacity of the distributed power source, the microgrid control model and the distribution network dispatching model, including: According to 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; According to the microgrid control model and the distribution network dispatching model, a comprehensive distributed photovoltaic carrying capacity evaluation model is constructed with the total upper limit constraint of distributed power sources and the installed capacity range constraint of distributed power sources as constraint conditions.

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

8. A distributed photovoltaic carrying capacity assessment device, characterized in that: include: A microgrid model building module is used to build a microgrid control model based on the microgrid power supply operation data with minimizing energy consumption as the first objective function; A distribution network model building module is used to build a distribution network dispatching model based on the power supply operation data of the distribution network and taking the preset operation control target as the second objective function; A collaborative training module, for jointly integrating the microgrid control model and the distribution network dispatching model based on a method for cyclic adjustment of the upper limit of interactive power of tie lines, and training the microgrid control model and the distribution network dispatching model according to the first objective function and the second objective function; A capacity calculation module, used to calculate the maximum installed capacity of the distributed power source in the power grid according to the trained microgrid power supply operation data and the distribution network power supply operation data; The comprehensive module construction module is used to construct a comprehensive distributed photovoltaic carrying capacity evaluation model according to the maximum installed capacity of the distributed power source, the microgrid control model and the distribution network scheduling model, run the comprehensive distributed photovoltaic carrying capacity evaluation model, and evaluate the carrying capacity of distributed photovoltaics.

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

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

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