Power grid energy storage planning method, electronic equipment, storage medium and program product

Abstract

The invention provides a power grid energy storage planning method, electronic equipment, a storage medium and a program product, and relates to the field of power planning. The method comprises the following steps: acquiring characteristics of a flexible controllable load in a power distribution network; according to the characteristics, constructing a virtual energy storage operation model corresponding to the flexible controllable load; based on the virtual energy storage operation model, establishing a corresponding power distribution network optimization planning model; performing fuzzification operation on the power distribution network optimization planning model to obtain a fuzzification planning model; and solving the fuzzy programming model to obtain a virtual energy storage configuration result of the power distribution network. According to the invention, the construction and application cost of the corresponding energy storage system is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power planning, and particularly relates to a power grid energy storage planning method, an electronic device, a storage medium and a program product. BACKGROUND

[0002] By the end of 2024, the cumulative installed capacity of distributed photovoltaic power generation in China has reached 370 million kilowatts, accounting for 42% of the total installed capacity of photovoltaic power generation and 11% of the total installed capacity of power generation in China. Although new energy power generation has obvious advantages such as renewable, clean and environmental protection, its large-scale grid-connected operation has brought many challenges to the power system. In order to cope with these challenges, it has become an inevitable trend for the industry to use distributed energy storage technology to improve the regulation capability of distribution networks and promote new energy consumption.

[0003] In existing distributed energy storage technology, the energy storage converter, as a core component, mainly operates in power regulation mode. In this mode, the energy storage converter mainly achieves grid synchronization by relying on a phase-locked loop, and optimizes scheduling according to the instructions of the distribution network dispatching center. However, this approach has obvious limitations: the construction and operation cost of the corresponding energy storage system is high. SUMMARY

[0004] The present application provides a power grid energy storage planning method, an electronic device, a storage medium and a program product to reduce the construction and operation cost of the corresponding energy storage system.

[0005] In a first aspect, the present application provides a power grid energy storage planning method, comprising:

[0006] Obtaining the characteristics of the flexible controllable load in the distribution network;

[0007] According to the characteristics, a virtual energy storage operation model corresponding to the flexible controllable load is constructed;

[0008] Based on the virtual energy storage operation model, a corresponding distribution network optimization planning model is established;

[0009] Performing a fuzzification operation on the distribution network optimization planning model to obtain a fuzzification planning model;

[0010] Solving the fuzzification planning model to obtain the virtual energy storage configuration result of the distribution network.

[0011] In one possible implementation, the virtual energy storage operation model includes an active power synchronization model, a reactive power synchronization model and a voltage and current control model, and according to the characteristics, the virtual energy storage operation model corresponding to the flexible controllable load is constructed, comprising:

[0012] According to the characteristics, the active regulation coefficient of the flexible controllable load is determined;

[0013] Determine input power corresponding to the flexible controllable load according to the active regulation coefficient and the voltage deviation of the flexible controllable load;

[0014] According to the input power, construct an active power synchronization model;

[0015] According to the reactive power set value of the flexible controllable load, construct a reactive power synchronization model;

[0016] According to the active power synchronization model and the reactive power synchronization model, determine the voltage reference value of the different axes of the converter output voltage of the flexible controllable load;

[0017] Based on the voltage reference value, construct a voltage current control model.

[0018] In a possible implementation, based on the virtual energy storage operation model, an optimal planning model of the power distribution network is established, including:

[0019] According to the operation cost, network loss and investment cost of the entity energy storage device of the power distribution network, a first objective function of the power distribution network is determined;

[0020] According to the new energy carrying capacity in the power distribution network, a second objective function of the power distribution network is determined;

[0021] According to the branch capacity balance degree of the power distribution network, a third objective function of the power distribution network is determined;

[0022] According to the voltage deviation rate of the power distribution network, a fourth objective function of the power distribution network is determined;

[0023] Based on the first objective function, the second objective function, the third objective function and the fourth objective function, a total objective function is determined;

[0024] With the minimization of the total objective function as the goal, based on the virtual energy storage operation model, an optimal planning model of the power distribution network is constructed under the constraint conditions of the power distribution network, wherein the constraint conditions include the entity energy storage power constraint, the branch power flow constraint, the alternating current node constraint and the new energy output constraint.

[0025] In a possible implementation, a fuzzification operation is performed on the optimal planning model of the power distribution network to obtain a fuzzification planning model, including:

[0026] The constraint conditions and the total objective function in the optimal planning model of the power distribution network are respectively selected to obtain corresponding tolerances;

[0027] Based on the tolerances, corresponding membership functions are constructed;

[0028] According to the membership functions, a corresponding fuzzification model is determined;

[0029] The fuzzification model is determined as the fuzzification planning model.

[0030] In a possible implementation, the corresponding fuzzification model is determined according to the membership function, including:

[0031] If the output value of the membership function is greater than or equal to a preset satisfaction threshold, a constraint condition of the fuzzification model is generated;

[0032] An optimization objective of the fuzzification model is determined by maximizing the satisfaction of the output value of the membership function.

[0033] In a possible implementation, the fuzzification planning model is solved to obtain a virtual energy storage configuration result of the power distribution network, including:

[0034] A linearization and cone relaxation operation is performed on the fuzzification planning model to convert the fuzzification planning model into a second-order cone programming model;

[0035] The second-order cone programming model is solved to obtain the virtual energy storage configuration result of the power distribution network.

[0036] In a second aspect, the present application provides a power grid energy storage planning device, including:

[0037] An acquisition module is configured to acquire characteristics of a flexible controllable load in a power distribution network;

[0038] A construction module is configured to construct a virtual energy storage operation model corresponding to the flexible controllable load according to the characteristics, and establish a corresponding power distribution network optimization planning model based on the virtual energy storage operation model;

[0039] A fuzzification module is configured to perform a fuzzification operation on the power distribution network optimization planning model to obtain a fuzzification planning model;

[0040] A solving module is configured to solve the fuzzification planning model to obtain a virtual energy storage configuration result of the power distribution network.

[0041] In a possible implementation, the virtual energy storage operation model includes an active power synchronization model, a reactive power synchronization model, and a voltage and current control model, and the construction module is specifically configured to:

[0042] An active regulation coefficient of the flexible controllable load is determined according to the characteristics;

[0043] An input power corresponding to the flexible controllable load is determined according to the active regulation coefficient and a voltage deviation of the flexible controllable load;

[0044] The active power synchronization model is constructed according to the input power;

[0045] The reactive power synchronization model is constructed according to a reactive power set value of the flexible controllable load;

[0046] According to the active power synchronization model and the reactive power synchronization model, different-axis voltage reference values of the converter output voltage of the flexible controllable load are determined;

[0047] Based on the voltage reference values, a voltage current control model is constructed.

[0048] In a possible implementation, the construction module is specifically configured to:

[0049] According to the operation cost, network loss and entity energy storage device investment cost of the power distribution network, a first target function of the power distribution network is determined;

[0050] According to the new energy carrying capacity in the power distribution network, a second target function of the power distribution network is determined;

[0051] According to the branch capacity balance degree of the power distribution network, a third target function of the power distribution network is determined;

[0052] According to the voltage deviation rate of the power distribution network, a fourth target function of the power distribution network is determined;

[0053] Based on the first target function, the second target function, the third target function and the fourth target function, a total target function is determined;

[0054] With the minimization of the total target function as the goal, under the constraint conditions of the power distribution network, a corresponding power distribution network optimization planning model is constructed based on the virtual energy storage operation model, wherein the constraint conditions include entity energy storage power constraints, branch power flow constraints, alternating current node constraints and new energy output constraints.

[0055] In a possible implementation, the fuzzification module is specifically configured to:

[0056] The constraint conditions and the total target function in the power distribution network optimization planning model are respectively subjected to tolerance selection to obtain corresponding tolerances;

[0057] Based on the tolerances, corresponding membership functions are constructed;

[0058] According to the membership functions, corresponding fuzzification models are determined;

[0059] The fuzzification model is determined as a fuzzification planning model.

[0060] In a possible implementation, the power grid energy storage planning device further includes a determination module, and the determination module is specifically configured to:

[0061] If the output value of the membership function is greater than or equal to a preset satisfaction threshold, a constraint condition of the fuzzification model is generated;

[0062] By maximizing the satisfaction degree of the output value of the membership function, an optimization target of the fuzzification model is determined.

[0063] In a possible implementation, the solving module is specifically configured to:

[0064] performing linearization and cone relaxation operation on the fuzzified planning model to convert the fuzzified planning model into a second-order cone programming model;

[0065] solving the second-order cone programming model to obtain the virtual energy storage configuration result of the power distribution network.

[0066] In a third aspect, the present application provides an electronic device, comprising: a memory, a processor;

[0067] The memory stores computer-executable instructions.

[0068] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the first aspect and / or various possible implementation manners of the first aspect.

[0069] In a fourth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed to implement the first aspect and / or various possible implementation manners of the first aspect.

[0070] In a fifth aspect, the present application provides a computer program product, comprising a computer program, and the computer program is executed to implement the first aspect and / or various possible implementation manners of the first aspect.

[0071] The power grid energy storage planning method, the electronic device, the storage medium and the program product provided by the application relate to the field of power planning. The method comprises the following steps: obtaining the characteristics of flexible controllable loads in a power distribution network; constructing a virtual energy storage operation model corresponding to the flexible controllable loads according to the characteristics; establishing a corresponding power distribution network optimization planning model based on the virtual energy storage operation model; performing a fuzzification operation on the power distribution network optimization planning model to obtain a fuzzified planning model; and solving the fuzzified planning model to obtain a virtual energy storage configuration result of the power distribution network. By obtaining the characteristics of the flexible controllable loads in the power distribution network and constructing a virtual energy storage operation model corresponding to the flexible controllable loads according to the characteristics, the flexible controllable loads can be converted into a predictable and schedulable virtual energy storage operation model, which can greatly improve the flexibility and stability of the power distribution network operation, realize accurate peak shaving and valley filling, relieve local line congestion, and thus suppress the fluctuation of the power distribution network. According to the determined virtual energy storage operation model, a corresponding power distribution network optimization planning model is established, which helps to optimize the power distribution network resource configuration and significantly reduce the construction cost and operation cost, realizes the dual improvement of power system operation efficiency and economic benefit by reducing the demand for the capacity of the physical energy storage device, performs a fuzzification operation on the power distribution network optimization planning model to obtain a fuzzified planning model, and makes the obtained fuzzified planning model effectively cope with fuzzy or inaccurate input data, so as to avoid the limitations of the traditional accurate model in the face of fuzzy or inaccurate input data, and make the output of the fuzzified planning model more in line with the actual situation. The fuzzified planning model is solved to obtain a virtual energy storage configuration result of the power distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0072] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and serve to explain the principles of the application together with the specification.

[0073] Figure 1 An existing energy storage system optimization configuration scheme corresponding to an energy storage converter provided by the application;

[0074] Figure 2 A flowchart of a power grid energy storage planning method provided by an embodiment of the application Figure 1 ;

[0075] Figure 3 A schematic diagram of a virtual energy storage operation model provided by an embodiment of the application;

[0076] Figure 4 A flowchart of a power grid energy storage planning method provided by an embodiment of the application Figure 1 ;

[0077] Figure 5A structural schematic diagram of a power grid energy storage planning device provided by an embodiment of the present application is shown in the figure.

[0078] Figure 6 A structural schematic diagram of an electronic device provided by an embodiment of the present application is shown in the figure.

[0079] The specific embodiments of the present application have been shown in the above figures, and will be described in more detail hereinafter. These figures and the textual description are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0080] The exemplary embodiments will be described in detail herein with reference to the attached drawings. In the following description, the same numbers are used to indicate the same or similar components. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0081] Most existing energy storage converters operate in power regulation mode, achieve grid synchronization through a phase-locked loop, and optimize scheduling according to power distribution network scheduling instructions, thereby changing power distribution network power flow distribution, improving power distribution network new energy consumption capacity, and reducing network loss. The existing energy storage system corresponding to the energy storage converter has the problem of high construction cost, which affects the speed of popularization and application.

[0082] Further, the application of the existing energy storage system corresponding to the energy storage converter in the power distribution network is often concentrated in power quality, power supply reliability, new energy consumption capacity, etc., and uses its characteristics of rapid regulation and storage of electric energy to plan and configure in weak areas of the grid end network, such as Figure 1 . Figure 1 An optimal configuration scheme of the existing energy storage system corresponding to the energy storage converter provided by the present application is shown in the figure. From Figure 1 It can be known that the corresponding energy storage system includes photovoltaic (PV), battery (BA), and wind turbine (WT).

[0083] The planning configuration of the scheme is generally based on a double-layer optimization configuration method of the energy storage system: firstly, one or all of the indexes of power distribution network operation economy, new energy consumption capacity, power supply capacity, power supply reliability, and power quality are taken as the objective function, and the system power constraint, node voltage constraint, and device capacity constraint are taken as the constraint condition to construct a double-layer optimization configuration model of the traditional energy storage planning problem; then, a double-layer model solving method is proposed for the characteristics of the model, that is, a double-layer solving method combining a commercial mixed integer solver and an artificial intelligence algorithm, which can balance the solving accuracy and solving speed.

[0084] However, the traditional energy storage system described above has a high cost itself, and more economic returns need to be considered during configuration. In some cases, the traditional energy storage can be used for a single purpose to achieve considerable returns; in other cases, the energy storage needs to be used for two or more purposes to make the income greater than the cost. If you want to solve this problem, you need to find a terminal load with production and consumption as virtual energy storage to improve the performance-price ratio of energy storage.

[0085] With the widespread application of multi-element loads, a large number of user-side heavy loads have both power generation and power consumption attributes, and the terminal load characteristics have changed from traditional rigid and pure consumption type to flexible and production and consumption type. By aggregating the terminal load with flexible and production and consumption type, the above flexible load can be transformed into virtual energy storage.

[0086] Among them, virtual energy storage is an innovative energy management technology that simulates the function of traditional energy storage system through digital means. It does not rely on physical energy storage devices, but uses intelligent algorithms to coordinate distributed energy, such as photovoltaic, wind power, flexible controllable load and power distribution network demand, to realize time shifting and space allocation of electric energy, and accept superior dispatching, thereby reducing the number of nodes configured with distributed energy storage or reducing the capacity and time of energy storage configuration, reducing the scale and cost of traditional energy storage required to improve the regulation capacity of power distribution network and new energy consumption capacity. Further, virtual energy storage has the following advantages: it can reduce hardware investment cost; it can improve the utilization rate of existing power equipment; and it can support high proportion of renewable energy grid connection.

[0087] In view of the above, the application provides a power grid energy storage planning method, which comprises the following steps: constructing a virtual energy storage operation model corresponding to the flexible controllable load according to the characteristics of the flexible controllable load in the power distribution network; establishing a corresponding power distribution network optimization planning model based on the virtual energy storage operation model; performing a fuzzification operation on the power distribution network optimization planning model to obtain a fuzzification planning model; and solving the fuzzification planning model to obtain a virtual energy storage configuration result of the power distribution network.

[0088] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0089] Figure 2 A flowchart illustrating the grid energy storage planning method provided in this application embodiment. Figure 1 ,like Figure 2 As shown, the method includes:

[0090] S201. Obtain the characteristics of flexible and controllable loads in the distribution network.

[0091] Flexible and controllable loads refer to user-side loads in a distribution network whose electricity consumption behavior can be actively adjusted and optimized based on the network's operational needs or market signals. Furthermore, electricity consumption behavior includes consumption time, electricity consumption, or power consumption.

[0092] S202. Based on the characteristics, construct a virtual energy storage operation model corresponding to the flexible and controllable load.

[0093] After obtaining the characteristics of the flexible and controllable load through S201, a virtual energy storage operation model corresponding to the flexible and controllable load is constructed based on the obtained characteristics.

[0094] The virtual energy storage operation model includes an active power synchronization model, a reactive power synchronization model, and a voltage and current control model.

[0095] Furthermore, the active power synchronization model is used to control the real-time energy balance in the distribution network. By adjusting the output frequency and phase of generators or inverters, it ensures that the active power of generation matches that of the load, thereby maintaining the frequency stability of the distribution network. The reactive power synchronization model is responsible for managing the voltage stability of the distribution network. It supports the voltage level by adjusting reactive power. The voltage and current control model is used to dynamically adjust the output voltage or current through closed-loop control, so that it can quickly track the reference value.

[0096] For example, based on the characteristics, a virtual energy storage operation model corresponding to the flexible and controllable load is constructed, including: determining the active power regulation coefficient of the flexible and controllable load based on the characteristics; determining the input power corresponding to the flexible and controllable load based on the active power regulation coefficient and the voltage deviation of the flexible and controllable load; constructing an active power synchronization model based on the input power; constructing a reactive power synchronization model based on the reactive power setpoint of the flexible and controllable load; determining the voltage reference values ​​of different axes of the converter output voltage of the flexible and controllable load based on the active power synchronization model and the reactive power synchronization model; and constructing a voltage and current control model based on the voltage reference values.

[0097] In this example, it can be understood that the construction of the active power synchronization model is based on the characteristics of the flexible controllable load. Specifically, first, the active regulation coefficient of the flexible controllable load is determined according to the characteristics of the flexible controllable load; then the input power corresponding to the flexible controllable load is determined according to the active regulation coefficient of the flexible controllable load coefficient and the voltage deviation of the flexible controllable load; and then the input power corresponding to the flexible controllable load is used to construct the active power synchronization model by simulating the rotor motion equation of the synchronous generator.

[0098] The constructed active power synchronization model can simulate the inertia response and damping characteristics of the synchronous generator, so that the flexible controllable load can actively participate in the frequency stability of the distribution network, rather than passively responding to the dispatching instruction. When the frequency of the distribution network decreases, the active power synchronization model will instruct the flexible controllable load to reduce active power absorption or release active power, thereby providing active support to the distribution network and slowing down the frequency decline speed.

[0099] The construction of the reactive power synchronization model is based on the reactive power set value of the flexible controllable load, and is realized by simulating the reactive characteristics of the synchronous generator. The constructed reactive power synchronization model can give the flexible controllable load a voltage regulation capability similar to that of the synchronous generator. The above-mentioned reactive power synchronization model can convert these dispersed flexible controllable load resources from pure power injectors / absorbers into active participants and stabilizers of the distribution network voltage, which plays an irreplaceable role in improving the voltage stability, power quality, new energy consumption capacity and overall operation efficiency of the distribution network.

[0100] After the active power synchronization model and the reactive power synchronization model are determined, different-axis voltage reference values of the converter output voltage of the flexible controllable load are determined according to the active power synchronization model and the reactive power synchronization model, for example, dq-axis voltage reference values of the converter output voltage of the flexible controllable load are determined according to the active power synchronization model and the reactive power synchronization model. The dq-axis is two orthogonal axes defined in the synchronous rotating coordinate system, the d-axis represents the direct axis, and the q-axis represents the quadrature axis. Finally, the voltage and current control model is constructed by a closed-loop control strategy according to the determined voltage reference values.

[0101] The voltage and current control model constructed above realizes precise regulation of the output voltage and current of the flexible controllable load converter, thereby ensuring stable and efficient interaction with the power distribution network. The core value of the voltage and current control model lies in that it can upgrade the traditionally passive and dispersed flexible controllable load resource into an electric energy router and an electric energy quality guarantee unit with precise regulation capability. Such precise control can not only significantly improve the overall electric energy quality of the power distribution network through active harmonic suppression and reactive power compensation, but also enable the converter to quickly respond to the instructions of the power distribution network to realize flexible power throughput, greatly accelerating the response speed of the power distribution network and optimizing its operation efficiency.

[0102] Figure 3 A schematic diagram of the virtual energy storage operation model provided by the embodiments of the present application is shown in FIG. 1. Figure 3 As can be seen from FIG. 1, the active power synchronization model can be expressed by the following formula:

[0103]

[0104] wherein J is the moment of inertia of the virtual energy storage converter, the moment of inertia makes the active-frequency response of the virtual energy storage show inertia; D is a damping coefficient; θ is the virtual internal potential phase angle, ω, ω g and ω n are the virtual angular frequency, the grid angular frequency and the rated angular frequency, respectively; P m and P e are the input power and the output power of the virtual energy storage, respectively.

[0105] In order to enable the virtual energy storage to accept the dispatching of the power distribution network and to adaptively adjust the load according to the voltage frequency, the input power P m of the virtual energy storage is obtained by the following formula: P m = P ref + K p (U ref -U). Wherein P ref is the rated power of the virtual energy storage, K p is the active regulation coefficient, U ref and U are the rated voltage and the actual voltage of the grid connection point of the virtual energy storage, respectively.

[0106] It is worth noting that the sign of the rated power setting value P ref of the load virtual energy storage control strategy is negative, and the greater the absolute value of P m is, the greater the load is, that is, the greater the charging power of the virtual energy storage is.

[0107] In summary, the load virtual synchronous machine control strategy generates a virtual internal potential of the converter to simulate the internal potential of the synchronous generator through the power synchronization ring, so as to simulate the output characteristics of the synchronous machine of the device converter.

[0108] Since the reactive power-voltage link and the voltage and current control link in the load virtual synchronous machine control of the virtual energy storage in the embodiments of the present application are the same as the traditional control, details are not described herein again, and can be referred to in the prior art Figure 3 . Figure 3 Q ref is a reactive power given value, Q e is an actual reactive output, K q is a voltage regulation coefficient. ω* and E* are a virtual angular frequency and an internal potential reference value of the converter, respectively, U d * and U q * are dq-axis reference values of the converter output voltage, respectively, i Ld_ref and i Lq_ref are dq-axis reference values of the converter inductance current, respectively, u d and u q and i Ld and i Lq are dq-axis components corresponding to actual values, respectively.

[0109] S203, based on the virtual energy storage operation model, an optimal planning model of the power distribution network corresponding to the virtual energy storage operation model is established.

[0110] In this step, it can be understood that after the virtual energy storage operation model is determined through S202, an optimal planning model of the power distribution network corresponding to the virtual energy storage operation model needs to be established.

[0111] Further, the core idea of establishing the optimal planning model of the power distribution network is to break through the barrier of independent solution of the traditional operation model and the optimal planning model, and effectively map the dynamic operation rule into the macro and static planning decision. The above fusion can significantly improve the accuracy and feasibility of the planning decision, and determine that the planned scheme can not only be actually economic and efficient, but also physically realized, thereby avoiding resource waste or planning scheme failure caused by insufficient consideration of operation details.

[0112] S204, performing a fuzzification operation on the optimal planning model of the power distribution network to obtain a fuzzy planning model.

[0113] In this step, the optimal planning model of the power distribution network constructed in S203 is subjected to a fuzzification operation, so as to obtain a fuzzy planning model corresponding to the optimal planning model of the power distribution network. The fuzzification operation is a core link in the fuzzy logic system, and its essence is to convert the precise and real value input data into the membership degree of one or more fuzzy sets.

[0114] Further, by performing a fuzzification operation on the power distribution network optimization planning model, the obtained fuzzy planning model can effectively deal with fuzzy or inaccurate input data, thereby avoiding the limitations of traditional precise models in the face of fuzzy or inaccurate input data, and making the output of the fuzzy planning model more in line with the actual situation.

[0115] S205, solving the fuzzy planning model to obtain the virtual energy storage configuration result of the power distribution network.

[0116] After the fuzzy planning model is determined, the fuzzy planning model is solved to obtain the virtual energy storage configuration result of the power distribution network.

[0117] For example, solving the fuzzy planning model to obtain the virtual energy storage configuration result of the power distribution network includes: performing linearization and cone relaxation operations on the fuzzy planning model to convert the fuzzy planning model into a second-order cone programming model; and solving the second-order cone programming model to obtain the virtual energy storage configuration result of the power distribution network.

[0118] In this example, it can be known that when solving the fuzzy planning model, linearization and cone relaxation operations need to be performed on the fuzzy planning model to convert the fuzzy planning model into a second-order cone programming model that is easy to solve. Linearization refers to the process of converting a nonlinear problem into a linear problem; cone relaxation is a method of relaxing a non-convex problem into a convex cone optimization problem.

[0119] In an implementation, the fuzzy planning model is segmented and linearly approximated to complete the linearization operation on the fuzzy planning model; and the fuzzy planning model after the linearization operation is completed is subjected to a second-order heap operation to convert the fuzzy planning model into a second-order cone programming model that is easy to solve.

[0120] Then, the second-order cone programming model is solved to obtain the virtual energy storage configuration result of the power distribution network, for example, by using a solver to solve the second-order cone programming model to obtain the virtual energy storage configuration result of the power distribution network.

[0121] Further, the above example can not only retain the essential characteristics of the original problem, but also significantly improve the solvability and solving efficiency of the problem by converting the fuzzy planning model into a second-order cone programming model that is easy to solve, thereby providing an effective solution for dealing with complex uncertain optimization problems.

[0122] The embodiment of the application can obtain the characteristics of the flexible controllable load in the power distribution network, and construct a virtual energy storage operation model corresponding to the flexible controllable load according to the characteristics, so that the dispersed flexible controllable load can be converted into a predictable and schedulable virtual energy storage operation model. This conversion can greatly improve the flexibility and stability of the power distribution network operation, and can realize precise peak shaving and valley filling and relieve local line congestion, so as to suppress the fluctuation of the power distribution network. According to the determined virtual energy storage operation model, a corresponding power distribution network optimization planning model is established, which helps to optimize the power distribution network resource allocation and significantly reduce the construction cost and operation cost, and realizes the dual improvement of power system operation efficiency and economic benefit by reducing the demand for entity energy storage device capacity. The fuzzy operation is performed on the power distribution network optimization planning model to obtain a fuzzy planning model. By performing the fuzzy operation on the power distribution network optimization planning model, the obtained fuzzy planning model can effectively deal with fuzzy or inaccurate input data, thereby avoiding the limitations of traditional precise models in the face of fuzzy or inaccurate input data, and making the output of the fuzzy planning model more consistent with the actual situation. The fuzzy planning model is solved to obtain the virtual energy storage configuration result of the power distribution network.

[0123] On the basis of the above embodiment, the power distribution network optimization planning model corresponding to the virtual energy storage operation model is established based on the virtual energy storage operation model, including: determining a first objective function of the power distribution network according to the operation cost, network loss and entity energy storage device investment cost of the power distribution network; determining a second objective function of the power distribution network according to the new energy carrying capacity of the power distribution network; determining a third objective function of the power distribution network according to the branch capacity balance degree of the power distribution network; determining a fourth objective function of the power distribution network according to the voltage deviation rate of the power distribution network; determining a total objective function based on the first objective function, the second objective function, the third objective function and the fourth objective function; and constructing a corresponding power distribution network optimization planning model based on the virtual energy storage operation model under the constraint conditions of the power distribution network, with the goal of minimizing the total objective function, wherein the constraint conditions include entity energy storage power constraint, branch power flow constraint, alternating current node constraint and new energy output constraint.

[0124] In this embodiment, it can be understood that when the corresponding power distribution network optimization planning model is established, the total objective function of the power distribution network optimization planning model and the constraint conditions need to be determined. The total objective function includes the first objective function, the second objective function, the third objective function and the fourth objective function, and the constraint conditions include the entity energy storage power constraint, the branch power flow constraint, the alternating current node constraint and the new energy output constraint.

[0125] (1) Total objective function of power distribution network optimization planning model

[0126] Objective function 1, economic index

[0127] The main economic indicators of a power distribution network include operating costs, network losses, and investment costs for energy storage equipment, as shown in the following formula:

[0128]

[0129] Among them, C G (t) represents the electricity purchase cost of the distribution network during time period t; C S (t) represents the electricity sales revenue of the distribution network during time period t; C LOSS (t) represents the cost of network losses within the distribution network during time period t; c S This refers to the cost per unit capacity over the entire lifecycle of a physical energy storage device, calculated per unit time. BA denoted as the total installed capacity of energy storage within the distribution network, and T represents the entire optimization period.

[0130] Objective function 2: New energy carrying capacity index

[0131] The maximum carrying capacity of renewable energy in a distribution network can be obtained by summing the distributed renewable energy access capacity of all nodes in the distribution network, as shown in the following formula:

[0132]

[0133] Where N is the number of nodes in the energy storage system, and Si DG The new energy capacity connected to node i.

[0134] Objective function 3, branch capacity balance index

[0135] The branch capacity balance index is used to measure the adjustment flexibility of the energy storage system, as shown in the following formula:

[0136]

[0137] Among them, I ij,max I is the upper limit value of the current in branch ij. ij,t Let be the effective value of the current in branch ij at time t. A L This is the total set of branch paths.

[0138] Since branch capacity balance is the average of the ratios between the load margin of all branches in a distribution network and the maximum transmission capacity of each branch, a higher balance indicates that the branch capacity is sufficient to meet the actual load demand and has a larger adjustment margin. Conversely, a lower balance may lead to branch overload.

[0139] Objective function 4, voltage deviation rate index

[0140] Voltage deviation rate is an important indicator for measuring the power quality of a distribution network. It can be represented by the average voltage deviation rate of the distribution network over a period of time. The smaller the voltage deviation rate, the smaller the voltage fluctuation, and the better the power quality of the distribution network. As shown in the following formula:

[0141]

[0142] wherein, U i is the voltage effective value at node i at time t, U ref is the rated voltage of the distribution network node.

[0143] Further, the total objective function: min F = min (σ1F1+σ2F2+σ3F3+σ4F4). Wherein, F is the total objective function, σ1+σ2+σ3+σ4=1, the rest of the parameters are the same as above.

[0144] (2) Constraint conditions

[0145] Constraint condition 1, physical energy storage power constraint

[0146] Since the purchase cost of physical energy storage is large, its configuration capacity and location need to be concerned, so the state of charge SOC of the energy storage needs to meet the following constraints:

[0147]

[0148] wherein, ESS i,t , ESS i,t+Δt represent the remaining capacity value of the energy storage at node i at time t and t+Δt, α min , α max are the minimum and maximum values of SOC respectively; EBA i represents the configured energy storage capacity at node i, P i,t bess-in , P i,t bess-out are the charging power and discharging power of the energy storage at node i at time t, k bi , k bo are the charging and discharging efficiency of the energy storage; EBA max represents the maximum capacity of the energy storage configured to run at node i; β i represents the access state, which is a 0-1 variable.

[0149] In addition, P i,t bess-in and P i,t bess-out must meet the following constraints:

[0150]

[0151] wherein, P i,ch max and P i,d ismax are the upper limits of the maximum charging and discharging power of the energy storage respectively; U i,ch,t and Ui,dis,t respectively, are the state of charge of the energy storage at time t, which are also 0-1 variables; the other parameters are the same as above.

[0152] Constraint 2, branch power flow constraint

[0153] The embodiment of the present application takes a radial distribution network as the research object, selects the running state of a branch at time t to establish a branch power flow model, as shown in the following formula (2), the branch power flow should satisfy the following constraint conditions: Figure 1

[0154]

[0155]

[0156] wherein, U i,t and U j,t respectively represent the voltage of nodes i and j at time t; P ij,t and Q ij,t respectively represent the active and reactive power flowing through the first end of the branch ij at time t; I ij,t represents the current flowing through the branch ij at time t; R ij and X ij are the resistance and reactance values of the branch respectively.

[0157] Constraint 3, AC node constraint

[0158] Node power balance constraint

[0159]

[0160] wherein, B κ，i and B χ,i are the sets of nodes corresponding to the outgoing / incoming lines of AC node i; P i,t inj , Q i,t inj are the injected active and reactive power of node i. P i,t DG , Q i,t DG are the active and reactive power of the distributed new energy at node i at time t; P i,t load and Q i,t load are the active and reactive power of the load at node i at time t; K p is the active regulation coefficient of the virtual energy storage, A N is the set of nodes of the distribution network.

[0161] Node voltage constraint

[0162] ​

[0163] wherein, U ui , U li is the voltage limit of AC node i.

[0164] Branch capacity constraint

[0165]

[0166] wherein, I lmax is the maximum current on branch ij, A L is the set of AC branches of the distribution network.

[0167] Constraint 4, distributed new energy output constraint

[0168]

[0169] wherein, P i,t DG is the actual output of new energy at node i in the distribution network, P i,t DG,pre is the predicted value thereof. represents an uncertain quantity, that is, a fuzzy processing under a certain tolerance needs to be performed.

[0170] Further, under the background of existing high-precision new energy power prediction, the actual new energy output is necessarily fluctuated around the new energy predicted value, and directly using the actual output fluctuated around the new energy predicted output percentage in the optimization process is too random without considering the probability of occurrence of each fluctuation, thereby possibly leading to an overly conservative optimization result, and the construction and operation cost of the energy storage is relatively high, so it is necessary to obtain a more accurate optimization result in view of the new energy uncertainty.

[0171] Therefore, on the basis of the above, a fuzzy operation is performed on the distribution network optimization planning model to obtain a fuzzy planning model, including: respectively selecting a tolerance for the constraint condition and the total target function in the distribution network optimization planning model to obtain a corresponding tolerance; constructing a corresponding membership function based on the tolerance; determining a corresponding fuzzy model according to the membership function; and determining the fuzzy model as the fuzzy planning model.

[0172] In one implementation, assuming that the new energy output fluctuates within the predicted value η% boundary, first, the distributed new energy output constraint is fuzzy processed, that is, a tolerance is selected for the distributed new energy distribution constraint, and then a membership function of the new energy output is established according to the distributed new energy output tolerance as shown in the following formula:

[0173]

[0174] Wherein, μ1(x) is the membership function corresponding to the new energy output constraint; x is the error between the actual value and the predicted value.

[0175] After the membership function is determined, the corresponding fuzzy model needs to be determined according to the membership function. Specifically, the corresponding fuzzy model is determined according to the membership function, including: if the output value of the membership function is greater than or equal to a preset satisfaction threshold, generating a constraint condition of the fuzzy model; by maximizing the satisfaction of the output value of the membership function, an optimization objective of the fuzzy model is determined.

[0176] If the output value of the membership function is greater than or equal to the preset satisfaction threshold, the constraint condition of the fuzzy model is generated, because the membership function reflects the membership, i.e. satisfaction, of the fuzzy constraint within the original threshold to the acceptable tolerance. In this constraint, the more the error exceeds the threshold, the lower the membership of the membership function, and the worse the satisfaction. Therefore, the output value of the membership function needs to be greater than or equal to the preset satisfaction threshold, wherein the satisfaction threshold can be set according to actual needs, for example, the satisfaction threshold is set to α. At this time, it can be considered that when the output value of the membership function is greater than or equal to α, the constraint condition of the fuzzy model is generated, i.e. when μ1(x)≥α, the constraint condition of the fuzzy model is generated.

[0177] Further, the inequality μ1(x)≥α is brought into the above membership function to obtain the fuzzy expression of the membership function:

[0178]

[0179] Similarly, the variation range of the total objective function is calculated, as shown in the following two formulas:

[0180]

[0181] It should be noted that the remaining deterministic constraints remain unchanged and will not be repeated below. The variation range of the total objective function can be obtained by the above two formulas, i.e. the interval range of the total objective function value obtained by the above two formulas, denoted as F∈[F″, F′], F′ and F″ are the larger and smaller ones of the total objective function values obtained by the above two formulas, respectively.

[0182] The membership function of the total objective function is established:

[0183]

[0184] Similarly, the membership of the total objective function also needs to satisfy the minimum satisfaction β, i.e. μ F (x)≥β. The μ F(x) ≥ β, this inequality is brought into the membership function of the total objective function, and the fuzzy constraint expression of the total objective function is obtained: F ≤ F" + (F'-F") (1-β).

[0185] In summary, the virtual hybrid energy storage planning model of the power distribution network is converted into a fuzzy planning model, and the optimal planning scheme can be obtained by optimizing the maximum satisfaction, and the fuzzy planning model of the virtual hybrid energy storage of the power distribution network is as follows:

[0186]

[0187] The above fuzzy planning model of the virtual hybrid energy storage of the power distribution network only lists the constraints of the distributed new energy after being fuzzed, and the upper and lower bound constraints of the total objective function after being fuzzed, and the remaining traditional constraints also need to be included in the above model, which will not be listed one by one in the embodiments of the application.

[0188] The embodiments of the application perform the fuzzy operation on the optimal planning model of the power distribution network, so that the obtained fuzzy planning model can effectively deal with fuzzy or inaccurate input data, thereby avoiding the limitations of the traditional accurate model in the face of fuzzy or inaccurate input data, and making the output of the fuzzy planning model more consistent with the actual situation.

[0189] The typical application scenarios of the power grid energy storage planning method provided by the embodiments of the application include demand side response, microgrid operation and auxiliary service market, etc. Next, taking electric vehicles, air conditioning loads and the like as examples, how to use the power grid energy storage planning method provided by the embodiments of the application will be described. Figure 4 Flowchart of the power grid energy storage planning method provided by the embodiments of the application Figure 1 As shown in Figure 4 , the method comprises the following steps:

[0190] Step one, constructing a virtual energy storage operation model based on load virtual synchronous machine control.

[0191] In order to realize the participation of flexible controllable devices such as electric vehicles and variable frequency air conditioners in the optimal operation of the power distribution network, the power of the flexible controllable devices in the network needs to be self-adapted according to the grid voltage frequency. When the grid voltage frequency rises, the flexible controllable device must increase the load, and when the voltage frequency drops, the flexible controllable device must reduce the load, realizing the function of virtual energy storage, thereby supporting the operation of the power distribution network. In order to realize this function, the converter of the flexible controllable device such as the electric vehicle and the variable frequency air conditioner is first modified, and the original converter control method is modified to a load virtual synchronous machine control strategy, that is, the converter control strategy of the flexible controllable device is modified to a load virtual synchronous machine control strategy, and a virtual energy storage operation model is constructed through the load virtual synchronous machine control strategy, wherein the virtual energy storage operation model comprises an active power synchronization model, a reactive power synchronization model and a voltage and current control model.

[0192] Step two, constructing a power distribution network optimization planning model containing virtual hybrid energy storage through a virtual energy storage operation model.

[0193] Step three, performing a fuzzy operation on the power distribution network optimization planning model to obtain a fuzzy power distribution network optimization planning model.

[0194] Step four, solving the fuzzy power distribution network optimization planning model.

[0195] Currently, when solving the above power distribution network planning and configuration problem containing virtual hybrid energy storage, existing commercial solvers often have difficulty in effectively processing nonlinear constraints in the power flow model. Although traditional random scenario simulation methods and heuristic algorithms can be used as an alternative solution, they will increase the difficulty of solving and prolong the model calculation time.

[0196] Therefore, the embodiments of the present application use linearization and cone relaxation means to convert the original fuzzy nonlinear model into a second-order cone programming model that is easy to solve, and then solve it through a solver.

[0197] It should be noted that the details of step one, step two and step three have been described in detail in the previous embodiments, so the embodiments of the present application will not be repeated here.

[0198] In summary, the embodiments of the present application take electric vehicles, air conditioning loads and the like as examples, perform virtual energy storage transformation, and propose a power distribution network virtual hybrid energy storage planning method based on fuzzy theory, to provide models and methods for improving the flexibility of the power distribution network and the utilization rate of renewable energy, and reducing the planning and construction cost.

[0199] In other words, the embodiments of the present application transform flexible controllable loads such as electric vehicles and variable frequency air conditioners into virtual energy storage, so that these flexible devices participate in the optimal operation of the power distribution network, reduce the configuration capacity of the physical energy storage, and improve the economy. Further, the embodiments of the present application also use a power distribution network virtual hybrid energy storage planning model based on fuzzification, which can effectively fuzz the output of new energy such as photovoltaic power, and by selecting a certain tolerance, it can be more consistent with the actual situation of uncertain output of new energy, so that the planning result is more realistic.

[0200] Further, it can be understood that the embodiments of the present application aim to solve the problem of inaccurate optimization configuration caused by the uncertainty of distributed new energy output in the process of optimizing the configuration of multi-element hybrid energy storage in the power distribution network, including modeling the randomness of distributed new energy output and the high investment cost of energy storage. In particular, the purpose of the embodiments of the present application is to use a certain prediction tolerance and membership degree to reflect the fluctuation of new energy output in the real situation, and further obtain the site selection, capacity determination and optimal operation result of hybrid energy storage and other devices in the power distribution network.

[0201] The following is an embodiment of the device of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0202] Figure 5 The structure diagram of the power grid energy storage planning device provided in the embodiment of the present application is shown in FIG. 5, and the embodiment provided by the present application includes: Figure 5

[0203] The acquisition module 501 is configured to acquire the characteristics of the flexible controllable load in the power distribution network.

[0204] The construction module 502 is configured to construct a virtual energy storage operation model corresponding to the flexible controllable load according to the characteristics, and establish a corresponding power distribution network optimization planning model based on the virtual energy storage operation model.

[0205] The fuzzification module 503 is configured to perform a fuzzification operation on the power distribution network optimization planning model to obtain a fuzzification planning model.

[0206] The solving module 504 is configured to solve the fuzzification planning model to obtain a virtual energy storage configuration result of the power distribution network.

[0207] In a possible implementation, the virtual energy storage operation model includes an active power synchronization model, a reactive power synchronization model and a voltage and current control model, and the construction module 502 is specifically configured to:

[0208] determine an active regulation coefficient of the flexible controllable load according to the characteristics;

[0209] determine an input power corresponding to the flexible controllable load according to the active regulation coefficient and a voltage deviation of the flexible controllable load;

[0210] construct the active power synchronization model according to the input power;

[0211] construct the reactive power synchronization model according to a reactive power set value of the flexible controllable load;

[0212] determine a voltage reference value of different axes of a converter output voltage of the flexible controllable load according to the active power synchronization model and the reactive power synchronization model;

[0213] construct the voltage and current control model based on the voltage reference value.

[0214] In a possible implementation, the construction module 502 is specifically configured to:

[0215] determine a first objective function of the power distribution network according to an operation cost, a network loss and an investment cost of an entity energy storage device of the power distribution network;

[0216] ​determine a second objective function of the power distribution network according to a new energy carrying capacity in the power distribution network;

[0217] determine a third objective function of the power distribution network according to a branch capacity balance degree of the power distribution network;

[0218] determine a fourth objective function of the power distribution network according to a voltage deviation rate of the power distribution network;

[0219] determine a total objective function based on the first objective function, the second objective function, the third objective function, and the fourth objective function;

[0220] construct a corresponding power distribution network optimization planning model based on the virtual energy storage operation model under the constraint conditions of the power distribution network, with the goal of minimizing the total objective function, wherein the constraint conditions include entity energy storage power constraints, branch power flow constraints, alternating current node constraints, and new energy output constraints.

[0221] In a possible implementation, the fuzzification module 503 is specifically configured to:

[0222] select tolerances for the constraint conditions and the total objective function in the power distribution network optimization planning model respectively to obtain corresponding tolerances;

[0223] construct a corresponding membership function based on the tolerances;

[0224] determine a corresponding fuzzification model according to the membership function;

[0225] determine the fuzzification model as a fuzzification planning model.

[0226] In a possible implementation, the power grid energy storage planning apparatus further includes a determination module (not shown), which is specifically configured to:

[0227] if the output value of the membership function is greater than or equal to a preset satisfaction threshold, generate a constraint condition of the fuzzification model;

[0228] determine an optimization target of the fuzzification model by maximizing the satisfaction degree of the output value of the membership function.

[0229] In a possible implementation, the solving module 504 is specifically configured to:

[0230] perform linearization and cone relaxation operations on the fuzzification planning model to convert the fuzzification planning model into a second-order cone programming model;

[0231] solve the second-order cone programming model to obtain a virtual energy storage configuration result of the power distribution network.

[0232] The power grid energy storage planning apparatus provided in this embodiment can perform the method provided in the method embodiments described above, and has similar implementation principles and technical effects. Therefore, no further description is given here.

[0233] It should be noted that the division of each module of the above apparatus is only a logical functional division, and all or part of the modules can be integrated into one physical entity or physically separated in actual implementation. The modules can all be implemented in the form of software invoked by a processing element; all be implemented in the form of hardware; or part of the modules be implemented in the form of software invoked by a processing element and part of the modules be implemented in the form of hardware. For example, the processing module can be a separately established processing element, or can be integrated in a chip of the above apparatus, and in addition, the processing module can be in the form of program code stored in a memory of the above apparatus and invoked and executed by a processing element of the above apparatus to implement the functions of the above processing module. The implementation of other modules is similar. In addition, all or part of the modules can be integrated together or independently implemented. The processing element herein can be an integrated circuit having a signal processing capability. In the implementation process, each step of the above method or each of the above modules can be completed by an integrated logic circuit of hardware in the processing element or an instruction in the form of software.

[0234] For example, the above modules can be one or more integrated circuits configured to implement the above method, such as one or more application specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), etc. For another example, when a certain module above is implemented in the form of program code invoked by a processing element, the processing element can be a general-purpose processor such as a central processing unit (CPU) or other processor capable of invoking program code. For another example, the modules can be integrated together to implement in the form of a system on a chip (SOC).

[0235] Figure 6 A structural schematic diagram of an electronic device provided by an embodiment of the present application is shown in FIG. 6. As shown in FIG. 6, the electronic device 600 provided by the embodiment of the present application can include a processor 601 and a memory 602 in communication connection with the processor, wherein: Figure 6 The memory stores computer execution instructions;

[0236] The memory stores computer execution instructions;

[0237] The processor executes the computer execution instructions stored in the memory to implement the method described in the foregoing method embodiment.

[0238] It should be understood that the processor 601 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor. The memory 602 can include a high-speed random access memory (RAM), and can also include a non-volatile memory NVM (non-volatile memory), such as at least one disk memory, and can also be a U disk, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0239] Optionally, the electronic device 600 can further include a communication interface 603. In a specific implementation, if the communication interface 603, the memory 602 and the processor 601 are independently implemented, the communication interface 603, the memory 602 and the processor 601 can be connected with each other through a bus and complete communication between each other. The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc., but it does not mean that there is only one bus or one type of bus.

[0240] Optionally, in a specific implementation, if the communication interface 603, the memory 602 and the processor 601 are integrated on a chip, the communication interface 603, the memory 602 and the processor 601 can complete communication through an internal interface.

[0241] The embodiment of the application further provides a computer readable storage medium, the computer readable storage medium stores computer execution instructions, and the computer execution instructions are used to implement the method described in any of the foregoing embodiments when executed.

[0242] It is understood that the computer-readable storage medium can be realized by any type of volatile or nonvolatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0243] An exemplary computer-readable storage medium is coupled to the processor such that the processor can read information from the computer-readable storage medium and can write information to the computer-readable storage medium. Of course, the computer-readable storage medium can be a part of the processor. The processor and the computer-readable storage medium can be located in an ASIC. Of course, the processor and the computer-readable storage medium can exist as discrete components.

[0244] The integrated modules in the form of software function modules described above can be stored in a computer-readable storage medium. The software function modules described above stored in a computer-readable storage medium include a plurality of instructions for causing an electronic device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute part of the steps of the methods described in various embodiments of the present application.

[0245] The embodiments of the present application also provide a computer program product, which includes a computer program that is executed to implement the method described in any of the preceding embodiments.

[0246] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the order of the described actions, because according to the present application, certain steps can be performed in other orders or at the same time. Secondly, those skilled in the art should know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0247] It should be further understood that, although the steps of the methods herein can be shown as a sequential process, various steps can be conducted concurrently, in different orders, or omitted entirely. Unless otherwise specified, the steps of the methods herein are not necessarily performed in the order shown, and the steps of the methods herein can be performed in any order. Furthermore, at least some of the steps of the methods herein can include multiple sub-steps or stages, which can not necessarily be performed at the same time, and which can be performed in any order, concurrently or alternately with other steps or sub-steps or stages of other steps.

[0248] In the above embodiments, the description of each embodiment focuses on different aspects, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments. Each technical feature of the above embodiments can be combined arbitrarily, and in order to make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the disclosure.

[0249] Other embodiments of this application will be readily apparent to those skilled in the art upon considering the specification and practice of the application disclosed herein. The application is intended to cover any adaptations or variations of the application followed in the general principles of the application and including such modifications as would be readily apparent to those skilled in the art. The specification and examples are to be considered exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0250] It should be understood that the application is not limited to the precise construction that has been described and illustrated herein and that various modifications and changes can be made therein without departing from the scope of the application. The scope of the application is limited only by the claims that follow.

Claims

1. A power grid energy storage planning method, characterized in that, The method comprises the following steps: obtaining the characteristics of the flexible controllable load in the power distribution network; constructing a virtual energy storage operation model corresponding to the flexible controllable load according to the characteristics; establishing a corresponding power distribution network optimization planning model based on the virtual energy storage operation model; performing a fuzzification operation on the power distribution network optimization planning model to obtain a fuzzified planning model; solving the fuzzified planning model to obtain the virtual energy storage configuration result of the power distribution network.

2. The method of claim 1, wherein, The virtual energy storage operation model comprises an active power synchronization model, a reactive power synchronization model and a voltage and current control model, and the construction of the virtual energy storage operation model corresponding to the flexible controllable load according to the characteristics comprises the following steps: determining the active regulation coefficient of the flexible controllable load according to the characteristics; determining the input power corresponding to the flexible controllable load according to the active regulation coefficient and the voltage deviation of the flexible controllable load; constructing the active power synchronization model according to the input power; constructing the reactive power synchronization model according to the reactive power set value of the flexible controllable load; determining the voltage reference value of the different axes of the converter output voltage of the flexible controllable load according to the active power synchronization model and the reactive power synchronization model; constructing the voltage and current control model based on the voltage reference value.

3. The method according to claim 1 or 2, characterized in that, The establishment of the corresponding power distribution network optimization planning model based on the virtual energy storage operation model comprises the following steps: determining the first objective function of the power distribution network according to the operation cost, network loss and investment cost of the entity energy storage device of the power distribution network; determining the second objective function of the power distribution network according to the new energy carrying capacity in the power distribution network; determining the third objective function of the power distribution network according to the branch capacity balance degree of the power distribution network; determining the fourth objective function of the power distribution network according to the voltage deviation rate of the power distribution network; determining the total objective function based on the first objective function, the second objective function, the third objective function and the fourth objective function; constructing the corresponding power distribution network optimization planning model based on the virtual energy storage operation model under the constraint conditions of the power distribution network, with the minimization of the total objective function as the target, wherein the constraint conditions comprise the entity energy storage power constraint, the branch power flow constraint, the alternating current node constraint and the new energy output constraint.

4. The method of claim 3, wherein, The fuzzification operation on the power distribution network optimization planning model to obtain the fuzzified planning model comprises the following steps: selecting the tolerance of the constraint conditions and the total objective function in the power distribution network optimization planning model respectively to obtain the corresponding tolerance; constructing the corresponding membership function based on the tolerance; determining the corresponding fuzzified model according to the membership function; determining the fuzzified model as the fuzzified planning model.

5. The method of claim 4, wherein, The determination of the corresponding fuzzified model according to the membership function comprises the following steps: if the output value of the membership function is greater than or equal to the preset satisfaction threshold, generating the constraint condition of the fuzzified model; determining the optimization target of the fuzzified model by maximizing the satisfaction degree of the output value of the membership function.

6. The method of claim 1 or 2, wherein, The solving of the fuzzification planning model obtains the virtual energy storage configuration result of the power distribution network, including: Performing linearization and cone relaxation operation on the fuzzification planning model to convert the fuzzification planning model into a second-order cone programming model; Solving the second-order cone programming model to obtain the virtual energy storage configuration result of the power distribution network.

7. A power grid energy storage planning apparatus, characterized by, Comprising: An acquisition module is configured to acquire the characteristics of the flexible controllable load in the power distribution network; A construction module is configured to construct a virtual energy storage operation model corresponding to the flexible controllable load according to the characteristics; And, based on the virtual energy storage operation model, a corresponding power distribution network optimization planning model is established; A fuzzification module is configured to perform a fuzzification operation on the power distribution network optimization planning model to obtain a fuzzification planning model; A solving module is configured to solve the fuzzification planning model to obtain the virtual energy storage configuration result of the power distribution network.

8. An electronic device, comprising: Comprising: A memory, a processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory, so that the processor executes the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed to implement the method of any one of claims 1-6.

10. A computer program product, characterised in that, A computer program is executed to implement the method of any one of claims 1-6.

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