Flexible resource equivalent adjustable potential assessment method and system considering network constraint

Abstract

The invention discloses a flexibility resource equivalent adjustable potential assessment method and system considering network constraints, and relates to the technical field of power systems, and the method comprises the steps: constructing an adjustment model of a single adjustable resource; aggregating the adjustment characteristics of the mass controllable load resources to obtain adjustment external characteristics; based on a Newton-Raphson method and sensitivity analysis, obtaining a relation between an adjustable resource adjustment amount and line transmission power and voltage sensitivity, and obtaining network constraints; the method comprises the following steps: constructing an optimization problem taking the maximum adjustment amount of adjustable resources in the power distribution network as a target, considering network constraints and adjustment upper and lower amount constraints, obtaining the maximum adjustment value of the adjustable resources of each power distribution network, and obtaining a multi-voltage-level source load storage adjustment potential model of each power distribution network considering the network constraints. According to the network constraint-considered flexible resource equivalent adjustable potential evaluation method provided by the invention, an optimization problem taking the maximum adjustment amount as an optimization target is constructed, and a network constraint-considered flexible resource equivalent adjustable potential evaluation result is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for evaluating equivalent adjustable potential of flexibility resources considering network constraints. Background Art

[0002] With the access of volatile, random and intermittent renewable energy to the power system, the contradiction between power grid supply and demand has become increasingly prominent. Against the background of increasingly scarce adjustable resources on the power supply side, it is necessary to fully tap the flexible and adjustable resource adjustment capabilities of the low-voltage side of the distribution network to achieve large-scale resource optimization allocation of the power grid. However, the country currently adopts a hierarchical and graded management system for multi-voltage power systems. High-voltage transmission systems and low-voltage distribution systems are usually managed by different control centers. In order to better regulate the flexible resources of low-voltage distribution networks below 110KV, the transmission network side needs to evaluate and obtain the regulation capabilities of the distribution network.

[0003] A lot of research has been carried out to address the problem that there are a large number of controllable resources distributed on the low-voltage distribution network side, they are dispersed in space, and their single capacity is small, so their adjustable potential cannot be obtained. Common methods mainly include mechanism model-driven methods and data model-driven methods. The mechanism-driven method usually establishes an adjustment capacity evaluation model from the perspective of the operating mechanism and adjustment characteristics of flexible resources. For example, the aggregation response potential and distribution characteristics of temperature control loads were evaluated; the heating, ventilation, air-conditioning systems and battery loads in commercial buildings were modeled, and a commercial building demand response potential prediction model based on a decision tree model was established. The mechanism-driven method mostly evaluates one resource, and the modeling process is complex and has low universality. The data-driven method does not rely on the detailed mechanism model of flexible resources, and uses machine learning and other technologies to analyze the adjustment rules in its historical data; the mechanism and data hybrid driven method is used to approximate and quantify the flexibility boundary of industrial energy systems under different operating conditions. In view of the lack of historical electricity consumption data of users by load aggregators, a user potential evaluation method based on deep sub-domain adaptation is proposed. The data-driven approach is easier to model and faster to implement, but it requires a large amount of data and is difficult to process multi-source data. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: based on the current research work, in the evaluation of the adjustable capacity of source, grid, load and storage, most methods are to evaluate a certain type of controllable resources, and the universality is low. In the face of a wide variety of source, grid, load and storage resources with different characteristics, there is still a lack of research on how to conduct potential evaluation. At the same time, the R / X ratio in the distribution network is high, and the influence of network constraints cannot be ignored. The influence of network topology and line parameters should be fully considered in the evaluation process.

[0006] In order to solve the above technical problems, the present invention provides the following technical solution: a method for evaluating the equivalent adjustable potential of flexible resources considering network constraints, comprising:

[0007] Construct a regulation model for a single adjustable resource and propose regulation characteristic indicators;

[0008] Using the superposition method, the regulation characteristics of massive controllable load resources are aggregated to obtain the external regulation characteristics;

[0009] Combined with the grid structure, the relationship between the adjustable resource adjustment amount and the line transmission power and voltage sensitivity is obtained based on the Newton-Raphson method and sensitivity analysis, and the network constraints are obtained;

[0010] An optimization problem with the goal of maximizing the adjustable amount of adjustable resources in the distribution network is constructed. The network constraints and the upper and lower constraints of the regulation are taken into consideration to obtain the maximum value of the adjustable resource regulation of each distribution network, and a multi-voltage level source-load-storage regulation potential model considering network constraints is obtained for each distribution network.

[0011] As a preferred solution of the method for evaluating equivalent adjustable potential of flexible resources considering network constraints described in the present invention, the corresponding controllable resource adjustment characteristics are obtained, which are divided into upward adjustment characteristics and downward adjustment characteristics.

[0012] As a preferred solution of the method for evaluating the equivalent adjustable potential of flexible resources considering network constraints described in the present invention, wherein: the upward adjustment characteristic is expressed as,

[0013]

[0014] T i =Tdelay i +Tadjust i +Tduration i

[0015] The downward regulation characteristic is expressed as,

[0016]

[0017] T i =Tdelay i +Tadjust i +Tduration i

[0018] Where t is time; Padjust i is the response adjustment of controllable resource i; Tduration i is the continuous adjustment time of controllable resource i; Tdelay iis the delay time of controllable resource i; Tadjust i is the response adjustment time of controllable resource i; RampUp i is the upward climbing rate of controllable resource i; Uplimit i is the upward adjustment limit of controllable resource i, RampDown i is the downward climbing rate of controllable resource i; Downlimit i is the downward adjustment limit of controllable resource i.

[0019] As a preferred solution of the method for evaluating equivalent adjustable potential of flexible resources considering network constraints described in the present invention, wherein: the network constraints are obtained including ensuring that the line transmission power and the node voltage do not exceed their limit values ​​when considering the network constraints;

[0020] The sensitivity analysis method is used to construct the sensitivity relationship matrix between adjustable resources and line transmission power and voltage, and the constraints on line transmission power and node voltage are converted into constraints on adjustable resources.

[0021] As a preferred solution of the method for evaluating the equivalent adjustable potential of flexibility resources considering network constraints described in the present invention, before performing sensitivity analysis, the initial value point of the flow is obtained, the SCADA system is used to collect the power grid operation data at time t for flow calculation, and the Newton-Raphson method is used for flow calculation.

[0022] As a preferred solution of the method for evaluating equivalent adjustable potential of flexible resources considering network constraints described in the present invention, wherein: the power flow calculation includes combining a power flow calculation program to calculate a correction equation;

[0023] Invert the Jacobian matrix;

[0024] Ignoring the reactive power changes, the relationship between voltage and the adjustable resource regulation amount is obtained.

[0025] As a preferred solution of the method for evaluating the equivalent adjustable potential of flexible resources considering network constraints described in the present invention, wherein: the multi-voltage level source-load-storage regulation potential model includes constructing an optimization problem with the maximum regulation amount as the goal according to the obtained network constraints, and obtaining the maximum value of the adjustable resource regulation amount of the equivalent model in each distribution network;

[0026] Combining the external characteristics of the aggregation curve and the maximum value of the node regulation considering network constraints obtained from the optimization problem, the source-grid-load-storage regulation characteristic curve taking into account the influence of the line network is obtained.

[0027] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for evaluating the equivalent adjustable potential of flexible resources considering network constraints are implemented.

[0028] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for evaluating the equivalent adjustable potential of flexible resources considering network constraints as described above.

[0029] Beneficial effects of the present invention: The method for evaluating the equivalent adjustable potential of flexible resources considering network constraints provided by the present invention obtains the aggregation characteristics of adjustable load resources without considering network constraints through the superposition method, and at the same time obtains the sensitivity relationship between adjustable resources and line transmission power and voltage based on the Newton-Raphson and sensitivity analysis methods to construct network constraints, and constructs an optimization problem with the maximum adjustment amount as the optimization goal, thereby obtaining the evaluation result of the equivalent adjustable potential of flexible resources considering network constraints.

[0030] The method of the present invention is verified by using the T30-DF6 standard example to effectively construct an equivalent model of adjustable resources, which is beneficial for the dispatching department to call on controllable resources, maintain the balance of the power system, and provide a new control method for the economic, safe and stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0032] Figure 1 An overall flow chart of a method for evaluating equivalent adjustable potential of flexible resources considering network constraints provided by an embodiment of the present invention.

[0033] Figure 2 A controllable resource upward adjustment response process diagram of a flexible resource equivalent adjustable potential assessment method considering network constraints provided by an embodiment of the present invention.

[0034] Figure 3 A controllable resource downward adjustment response process diagram of a flexible resource equivalent adjustable potential assessment method considering network constraints provided by an embodiment of the present invention.

[0035] Figure 4 A T30-DF6 example grid structure diagram of a method for evaluating equivalent adjustable potential of flexible resources considering network constraints provided by an embodiment of the present invention.

[0036] Figure 5 A DF1 diagram of an equivalent adjustable potential of flexible resources considering network constraints is provided as a method for evaluating the equivalent adjustable potential of flexible resources considering network constraints according to an embodiment of the present invention.

[0037] Figure 6 A flexibility resource equivalent adjustable potential evaluation method considering network constraints provided by an embodiment of the present invention is provided in the form of DF1, a flexibility resource equivalent adjustable potential diagram not considering network constraints.

[0038] Figure 7 A DF4 diagram of an equivalent adjustable potential of flexible resources considering network constraints is provided as a method for evaluating the equivalent adjustable potential of flexible resources considering network constraints according to an embodiment of the present invention.

[0039] Figure 8 A DF4 diagram of the equivalent adjustable potential of flexible resources without considering network constraints is provided as a method for evaluating the equivalent adjustable potential of flexible resources considering network constraints according to an embodiment of the present invention.

[0040] Fig. 9 A DF6 diagram of the equivalent adjustable potential of flexible resources considering network constraints is provided as a method for evaluating the equivalent adjustable potential of flexible resources considering network constraints according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0043] Example 1

[0044] Reference Figure 1-Figure 3 , which is an embodiment of the present invention, provides a method for evaluating the equivalent adjustable potential of flexible resources considering network constraints, including:

[0045] Step 1: Construct a regulation model for a single adjustable resource and propose regulation characteristic indicators.

[0046] Step 2: Use the superposition method to aggregate the regulation characteristics of massive controllable load resources to obtain the external regulation characteristics.

[0047] Step 3: Combined with the grid structure, the relationship between the adjustable resource adjustment amount and the line transmission power and voltage sensitivity is obtained based on the Newton-Raphson method and sensitivity analysis to obtain the network constraints.

[0048] Step 4: Construct an optimization problem with the goal of maximizing the adjustable amount of adjustable resources in the distribution network, consider network constraints and upper and lower regulation constraints, obtain the maximum value of the adjustable resource regulation of each distribution network, and obtain a multi-voltage level source-load-storage regulation potential model for each distribution network considering network constraints.

[0049] In step 1 of this embodiment: single controllable resource regulation characteristic modeling

[0050] The controllable resources involved in the regulation report their own regulation characteristics, including the delay time Tdelay i , that is, the time from when the controllable resource receives the adjustment signal to when it starts to adjust; the response adjustment time Tadjust i , which is the time when the controllable resources start to adjust to the upper limit of the adjustment; the upward climbing rate RampUp i , Ramp Down rate i , Upward adjustment limit Uplimit i , downward adjustment limit Downlimit i Form a controllable resource regulation characteristic curve.

[0051] The corresponding controllable resource adjustment characteristics are obtained, which are divided into upward adjustment characteristics and downward adjustment characteristics. The upward adjustment characteristics are as follows:

[0052]

[0053] T i =Tdelay i +Tadjust i +Tduration i (3)

[0054] t is time; Padjust i is the response adjustment of controllable resource i; Tduration i is the continuous adjustment time of controllable resource i; Tdelay i is the delay time of controllable resource i; Tadjust i is the response adjustment time of controllable resource i; RampUp i is the upward climbing rate of controllable resource i; Uplimit i is the upward adjustment limit of controllable resource i.

[0055] The downward regulation characteristic is as follows:

[0056]

[0057] T i =Tdelay i +Tadjust i +Tduration i (6)

[0058] RampDown i is the downward climbing rate of controllable resource i; Downlimit i is the downward adjustment limit of controllable resource i.

[0059] The controllable resource upward adjustment response process and the controllable resource downward adjustment response process are shown in the figure Figure 2 and Figure 3 shown.

[0060] In step 2 of this embodiment: controllable resource regulation characteristic aggregation modeling

[0061] Since there are many types of controllable resources under the source-grid-load-storage controllable resources, and the regulation characteristics of each type of controllable resources are obtained by the general model, the superposition method is used to aggregate them to obtain the external characteristics.

[0062]

[0063] Pjuhe t is the adjustable resource adjustment amount after aggregation at time t; N is the number of adjustable resources.

[0064] After aggregation, its upward adjustment characteristics are shown in the following formula. For N controllable resources, the upward adjustment characteristics can be expressed as:

[0065]

[0066] For N controllable resources, the external characteristics are

[0067]

[0068] In step 3 of this embodiment: network constraint acquisition based on Newton-Raphson method and sensitivity analysis method

[0069] In order to maintain safe operation, the line transmission power and node voltage should not exceed their limits when considering network constraints. The sensitivity relationship matrix between adjustable resources and line transmission power and voltage is constructed using the sensitivity analysis method, and the constraints on line transmission power and node voltage are converted into constraints on adjustable resources.

[0070] Before conducting sensitivity analysis, it is necessary to obtain the initial value point of the power flow and use the SCADA system to collect the power grid operation data at time t for power flow calculation. Because there is no r much smaller than x in the distribution network, the influence of line resistance r cannot be ignored. Therefore, the Newton-Raphson method is used for power flow calculation.

[0071] Combined with the power flow calculation program, the correction equation is calculated:

[0072]

[0073] Where ΔP and ΔQ are the active and reactive power injected into the node, Δθ and ΔU are the phase angle and voltage of the node; H, N, M, and L are the block matrices of the Jacobian matrix, with orders of (n-1)×(n-1), (n-1)×m, m×(n-1), and (m×m), respectively, where m is the number of PQ nodes.

[0074] The elements of each block matrix in the Jacobian matrix are:

[0075]

[0076] By inverting the Jacobian matrix from formula (10), we can get:

[0077]

[0078] Where A, B, C, and D are the block matrices of the inverse matrix of the Jacobian matrix.

[0079] Ignoring the reactive power change, the relationship between voltage and adjustable resource regulation can be obtained as follows:

[0080] ΔU=C·ΔP (13)

[0081]

[0082] The horizontal axis is the voltage on the system's heavy PQ node, and the vertical axis is the adjustable resource adjustment amount on each node except the balancing node.

[0083] Then for a single node, we have:

[0084]

[0085] According to the voltage constraints of the nodes:

[0086] V min ≤V i,0 +ΔU≤V max (16)

[0087] V min -V i,0 ≤ΔU≤V max -V i,0(17)

[0088] The constraints of the adjustable quantity can be obtained as follows:

[0089]

[0090] Among them, V i,0 is the voltage of node i at the current moment, which is calculated by the power flow before adjustment; ΔU is the adjustment amount of the apparent power of the controllable resources on the line; V max is the maximum voltage allowed on the node; V min is the minimum voltage allowed on the node.

[0091] The initial value of line transmission can also be used to process the branch power flow equation according to the above method to analyze the relationship between the power of each branch and the node injection power.

[0092]

[0093] Where: P ij , Q ij are the active and reactive power transmitted from node i to node j respectively; t ij is the transformer ratio on branch ij. If the branch is only a conductor, it is 0. If it contains a transformer, it is the ratio of the reference voltage at the beginning and end of the busbar. B ij It is half of the capacity of branch ij.

[0094] Taylor expansion is performed on equation (19), ignoring the higher-order terms, to obtain:

[0095]

[0096] Where: ΔP l , ΔQ l are the active and reactive power transmitted by each branch respectively; M is the sensitivity coefficient matrix between the power of each branch and the phase angle voltage of each node; R, S, L, T are the sensitivity relationships between the active and reactive power of each branch and the active and reactive power injected by each node respectively.

[0097] Among them, M is composed of E, F, G, and K blocks, i is the line, and j is each node.

[0098]

[0099] From formula (20), we can get:

[0100] ΔP l =R·ΔP (22)

[0101] ΔQ l =L·ΔP (23)

[0102]

[0103]

[0104] Since the limit in the line constraint is the limit on the apparent power, the transmission power constraint in the network is:

[0105]

[0106] Among them, S line,0 is the power transfer on line line at the current moment, calculated from the power flow before regulation; ΔS line is the adjustment amount of the apparent power of the controllable resources on the line, S max It is the maximum apparent power allowed to be transmitted on the line.

[0107] Ignoring the reactive power change, the relationship between the line transmission power and the adjustable resource adjustment amount can be obtained as follows:

[0108]

[0109] In step 4 of this embodiment: Considering the flexibility of network constraints and the adjustable potential of resources

[0110] According to the obtained network constraints, an optimization problem with the goal of maximizing the regulation amount is constructed to obtain the maximum value of the regulation amount of the adjustable resources in the equivalent model in each distribution network.

[0111] Objective function:

[0112]

[0113] Network constraints: Equation (18) and Equation (29).

[0114] Flexible and adjustable resource adjustment constraints:

[0115]

[0116] Where NDF is the number of nodes in the distribution network.

[0117] Set ΔP>0 to obtain the upper limit of the adjustable resource on the given node; set ΔP<0 to obtain the lower limit of the adjustable resource on the given node.

[0118] Combining the external characteristics of the aggregation curve and the maximum value of the node regulation considering network constraints obtained from the optimization problem, the source-grid-load-storage regulation characteristic curve taking into account the influence of the line network is obtained.

[0119] Example 2

[0120] Reference Figure 2 , which is an embodiment of the present invention, provides a flexible resource equivalent adjustable potential evaluation system considering network constraints, including:

[0121] The indicator adjustment module is used to build an adjustment model for a single adjustable resource and propose adjustment characteristic indicators;

[0122] The characteristic aggregation module is used to aggregate the regulation characteristics of massive controllable load resources by using the superposition method to obtain the external regulation characteristics;

[0123] The network constraint module is used to combine the power grid structure, obtain the relationship between the adjustable resource adjustment amount and the line transmission power and voltage sensitivity based on the Newton-Raphson method and sensitivity analysis, and obtain the network constraint;

[0124] The potential assessment module is used to construct an optimization problem with the goal of maximizing the adjustable amount of adjustable resources in the distribution network, taking into account network constraints and upper and lower regulation constraints, obtaining the maximum value of the adjustable resource regulation of each distribution network, and obtaining a multi-voltage level source-load-storage regulation potential model for each distribution network considering network constraints.

[0125] Example 3

[0126] An embodiment of the present invention is different from the first two embodiments in that:

[0127] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0128] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0129] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0130] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0131] Example 4

[0132] Reference Figure 4-Figure 9 , which is an embodiment of the present invention, provides a method for evaluating the equivalent adjustable potential of flexible resources considering network constraints. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0133] This paper uses the T30-DF6 example for example testing, which includes 30 nodes of the transmission network and 6 distribution network systems connected to each other, and each distribution network has a generator. The grid structure of the system is as follows Figure 2 The data of the transmission network and distribution network are shown in Table 1 and Table 2. At the same time, 1000 adjustable resources are connected to the system, and their specific types, parameters and distribution are shown in Table 3.

[0134] Table 1 T30 transmission network system data

[0135]

[0136] Table 2DF6 distribution network system data

[0137]

[0138] Table 3 Adjustable resource data table

[0139]

[0140] Table 4 The nodes and line connection relationships between the distribution networks

[0141]

[0142] By using the T30-DF6 system to perform case analysis, we can obtain the upper and lower limits of each node. The results are shown in Table 5:

[0143] Table 5 The upper limit of equivalent adjustable resources of each distribution network

[0144]

[0145] Combining the regulation upper limits and adjustable resource curves of each distribution network in Table 5, the evaluation results of the equivalent adjustable potential of flexibility resources considering network constraints can be obtained:

[0146] Figure 4 This is the structure diagram of the T30-DF6 system, which consists of 30 nodes of the transmission network and 6 distribution network systems interconnected. The transmission network voltage level is 135kV, and the distribution network system includes 44 nodes with a voltage level of 44kV.

[0147] Figure 5 and Figure 6 They are the adjustment curves of DF1 before and after considering network constraints. In DF1, because the upper and lower limits of the adjustment amount of the adjustable resources aggregated in the original distribution network are greater than the upper and lower limits of the adjustable potential adjustment calculated after considering network constraints, the maximum value in the adjustable potential curve is the adjustable potential limit value, and the remaining curves are the adjustable resource aggregation curves in the original distribution network. If network constraints are not considered, the adjustable potential evaluation curve is the result of superposition and aggregation of massive adjustable resources.

[0148] Figure 7 and Figure 8 They are the adjustment curves of DF4 before and after considering network constraints. Because the upper limit of the upward adjustment curve of the adjustable resource aggregation connected to DF4 is smaller, which is less than the upper limit of the adjustable potential adjustment calculated after considering network constraints, the upward adjustment curve in DF4 is the aggregation curve of the original adjustable resources, and the curves before and after considering network constraints are consistent. In DF4, because the lower limit of the adjustment amount of the adjustable resource aggregation in the original distribution network is greater than the lower limit of the adjustable potential adjustment calculated after considering network constraints, the maximum value in the downward adjustment potential curve is the adjustable potential limit, and the remaining curves are the aggregation curves of the adjustable resources in the original distribution network.

[0149] like Fig. 9As shown, the upper and lower limits of the adjustable resource aggregation upward adjustment curve connected in DF6 are relatively small, which are smaller than the upper and lower limits of the adjustable potential adjustment calculated after considering the network constraints. Therefore, the upward adjustment curve in DF6 is the aggregation curve of the original adjustable resources, and the curves are consistent before and after considering the network constraints.

[0150] The adjustable potential curve obtained by evaluating each distribution network is related to the type and quantity of adjustable resources connected to the distribution network, the distribution network structure and network parameters. Different distribution networks present different adjustment characteristics. The upper limit of adjustable resources is mainly constrained by voltage and flow, and the upper limit of adjustable resources is mainly constrained by voltage.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the equivalent adjustable potential of flexible resources considering network constraints, characterized in that: include: Construct a regulation model for a single adjustable resource and propose regulation characteristic indicators; Using the superposition method, the regulation characteristics of massive controllable load resources are aggregated to obtain the external regulation characteristics; Combined with the grid structure, the relationship between the adjustable resource adjustment amount and the line transmission power and voltage sensitivity is obtained based on the Newton-Raphson method and sensitivity analysis, and the network constraints are obtained; An optimization problem with the goal of maximizing the adjustable amount of adjustable resources in the distribution network is constructed. The network constraints and the upper and lower constraints of the regulation are taken into consideration to obtain the maximum value of the adjustable resource regulation of each distribution network, and a multi-voltage level source-load-storage regulation potential model considering network constraints is obtained for each distribution network.

2. The method for evaluating equivalent adjustable potential of flexible resources considering network constraints according to claim 1, characterized in that: The corresponding controllable resource adjustment characteristics are obtained, which are divided into upward adjustment characteristics and downward adjustment characteristics.

3. The method for evaluating the equivalent adjustable potential of flexible resources considering network constraints according to claim 2, characterized in that: The upward regulation characteristic is expressed as, T i =Tdelay i +Tadjust i +Tduration i The downward regulation characteristic is expressed as, T i =Tdelay i +Tadjust i +Tduration i Where t is time; Padjust i is the response adjustment of controllable resource i; Tduration i is the continuous adjustment time of controllable resource i; Tdelay i is the delay time of controllable resource i; Tadjust i is the response adjustment time of controllable resource i; RampUp i is the upward climbing rate of controllable resource i; Uplimit i is the upward adjustment limit of controllable resource i, RampDown i is the downward climbing rate of controllable resource i; Downlimit i is the downward adjustment limit of controllable resource i.

4. The method for evaluating equivalent adjustable potential of flexible resources considering network constraints according to claim 3, characterized in that: The network constraints include ensuring that the line transmission power and the node voltage do not exceed their limits when considering the network constraints; The sensitivity analysis method is used to construct the sensitivity relationship matrix between adjustable resources and line transmission power and voltage, and the constraints on line transmission power and node voltage are converted into constraints on adjustable resources.

5. The method for evaluating equivalent adjustable potential of flexible resources considering network constraints according to claim 4, characterized in that: Before conducting sensitivity analysis, the initial value point of the power flow is obtained, and the power grid operation data at time t is collected by the SCADA system for power flow calculation, and the Newton-Raphson method is used for power flow calculation.

6. The method for evaluating equivalent adjustable potential of flexible resources considering network constraints according to claim 5, characterized in that: The power flow calculation includes combining the power flow calculation program to calculate the correction equation; Invert the Jacobian matrix; Ignoring the reactive power changes, the relationship between voltage and the adjustable resource regulation amount is obtained.

7. The method for evaluating equivalent adjustable potential of flexible resources considering network constraints according to claim 6, characterized in that: The multi-voltage level source-load-storage regulation potential model includes constructing an optimization problem with the maximum regulation amount as the goal according to the obtained network constraints, and obtaining the maximum value of the adjustable resource regulation amount of the equivalent model in each distribution network; Combining the external characteristics of the aggregation curve and the maximum value of the node regulation considering network constraints obtained from the optimization problem, the source-grid-load-storage regulation characteristic curve taking into account the influence of the line network is obtained.

8. A system using the method for evaluating the equivalent adjustable potential of flexible resources considering network constraints as claimed in any one of claims 1 to 7, characterized in that: include: The indicator adjustment module is used to build an adjustment model for a single adjustable resource and propose adjustment characteristic indicators; The characteristic aggregation module is used to aggregate the regulation characteristics of massive controllable load resources by using the superposition method to obtain the external regulation characteristics; The network constraint module is used to combine the power grid structure, obtain the relationship between the adjustable resource adjustment amount and the line transmission power and voltage sensitivity based on the Newton-Raphson method and sensitivity analysis, and obtain the network constraint; The potential assessment module is used to construct an optimization problem with the goal of maximizing the adjustable amount of adjustable resources in the distribution network, taking into account network constraints and upper and lower regulation constraints, to obtain the maximum value of the adjustable resource regulation of each distribution network, and to obtain a multi-voltage level source-load-storage regulation potential model for each distribution network considering network constraints.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for evaluating the equivalent adjustable potential of flexible resources considering network constraints described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for evaluating the equivalent adjustable potential of flexible resources considering network constraints according to any one of claims 1 to 7 are implemented.

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