A power resource aggregation method based on load reduction user regulation willingness

Abstract

The present invention provides a method for aggregating electric power resources based on the adjustment willingness of users of curtailable loads. The method comprises: establishing a curtailable load access model taking into account the probability of user participation based on user participation and the adjustable potential of the load; constructing resource models in the distribution network system, and establishing an active-reactive optimization model of the distribution network system based on flow constraints; establishing a curtailable load access model under the condition of full participation of users of curtailable loads and setting constraints, and obtaining an aggregated feasible domain of the distribution network system at a common connection point by solving the curtailable load access model under the condition of full user participation; and dynamically revising the aggregated feasible domain based on the probability of participation of users of curtailable loads. The method of the present invention can solve the problem of flexibility resource aggregation taking into account user participation, and obtain a probability-based distribution network feasible domain aggregate. The constructed distribution network feasible domain can more accurately describe the adjustable potential of flexibility resources.

Description

Technical Field

[0001] The present invention relates to the technical field of power resource aggregation and regulation, and in particular to a power resource aggregation method based on load reduction user regulation intentions. Background Art

[0002] Compared to traditional power sources, the output power of renewable energy sources exhibits randomness due to the influence of natural conditions, and the balance characteristics of the power system are transitioning from "source follows load" to "source and load interact." It is difficult to achieve power balance in new power systems through source-load matching alone.

[0003] To support the safe and stable operation of the power system with a high proportion of renewable energy access, it is necessary to deeply tap into the system's flexibility resources. There are three types of flexible resources in the system: flexible thermal power units, energy storage, and load-side adjustable resources. Currently, the flexibility transformation of thermal power is still ongoing, and the minimum stable power remains high. To compensate for the power gap caused by the randomness of the source and load, a large number of flexible thermal power plants are required to support it. This significantly reduces the economic efficiency of system operation and makes it difficult to achieve a high proportion of clean energy. Energy storage resources are widely distributed and have small single-point regulation capacity. The storage capacity on the renewable energy side lags far behind the construction rate of renewable energy. Single electrochemical energy storage cannot meet the system's balance requirements. The load-side adjustable resources are abundant and have great regulation potential, which can support the power balance of the new power system.

[0004] Load-side flexibility resources are widely distributed and diverse, and research has been conducted on the modeling of various resource types, such as air conditioners and P2X (power-to-X, including power-to-gas, power-to-heat, power-to-cooling, and power-to-vehicle). Load-side resources can participate in power dispatch through the aggregation of flexible resources. The control rights of some resources belong to power system operators and can be directly called upon when needed. However, the participation of curtailable load resources, such as air conditioners, in the market depends on the user and needs to be coordinated through demand response policies. Therefore, the probability of user participation affects the aggregation of flexible resources in the distribution network.

[0005] At present, a microgrid distributed economic dispatch method based on a consistency algorithm in the existing technology includes: based on the consistency principle, the incremental cost of the generator set in the microgrid system is used as the consistency variable, and the minimum power generation cost of the distributed power source in the system is used as the optimization objective function. Through information interaction between adjacent units, after several data exchanges, the microgrid distributed economic dispatch is realized.

[0006] A distributed dynamic economic dispatch method and system for multiple virtual power plants in the prior art includes: analyzing the internal operating conditions of the virtual power plant based on the line impedance, network structure, adjustable equipment access location and parameters within the virtual power plant, and then establishing a virtual power plant flow model that takes into account the three-phase asymmetry characteristics; combining the virtual power plant flow model that takes into account the three-phase asymmetry characteristics and the distributed robust optimization theory to establish an uncertain variable distribution set that takes into account high-order information; establishing a single virtual power plant distributed robust economic dispatch model for each virtual power plant; establishing a distributed dynamic economic dispatch model for multiple virtual power plants that takes into account high-order uncertainty, and solving to obtain multiple virtual power plant dynamic economic dispatch decisions with global optimality.

[0007] The disadvantages of the above-mentioned prior art power resource aggregation methods include:

[0008] (1) When modeling load, the existing power resource aggregation method does not consider the probability of users participating in load reduction, resulting in the load model being unable to accurately reflect the actual situation.

[0009] (2) The existing feasible domain of power resource aggregation only considers the inherent characteristics of each resource and does not take into account the probability of user participation, resulting in a larger feasible domain. Summary of the Invention

[0010] The embodiments of the present invention provide a method for aggregating power resources based on the load-reducing user's adjustment willingness, so as to more accurately describe the reliable working state of the distribution network system and accurately provide the subsystem operation reference range for the new power system's day-ahead and intra-day scheduling.

[0011] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions.

[0012] A method for aggregating power resources based on the adjustment willingness of users with curtailable loads, comprising:

[0013] Based on user participation and load adjustment potential, a load reduction model considering user participation probability is established;

[0014] Construct resource models for the distribution network system and establish an active-reactive power optimization model for the distribution network system based on power flow constraints;

[0015] Establishing an updated curtailable load access model under the condition of full participation of curtailable load users, setting constraints for the updated curtailable load access model based on the active-reactive power optimization model of the distribution network system, and obtaining an aggregated feasible region of the distribution network system at the common connection point by solving the updated curtailable load access model;

[0016] The aggregated feasible region of the distribution network system at the common connection point is dynamically modified based on the participation probability of users who can reduce loads.

[0017] Preferably, the load reduction model based on user participation and load adjustment potential is established, which takes into account the user participation probability and includes:

[0018] The load curtailment model for the distribution network system is established as follows:

[0019] P Dload,i,t =P Eload,i,t -q i C P,i (1)

[0020] Q Dload,i,t =Q Eload,i,t -q i C Q,i (2)

[0021] C Q,i =C P,i tanγ i (3)

[0022] 0≤q i ≤1 (4)

[0023] Where, P Dload,i,t is the actual active load size of the i-th node when it can reduce load and participate in regulation at time t; P Eload,i,t is the active load size of the i-th node at time t when the load can be reduced without participating in the regulation; q i is the probability that the user with load reduction at the i-th node participates in the market regulation, indicating the user's participation; C P,i is the maximum active regulation capability of the load reduction user at the i-th node; Q Dload,i,t is the actual reactive load size of the i-th node when the load can be reduced and participate in the regulation at time t; Q Eload,i,t is the reactive load size of the i-th node at time t when the load is not regulated; C Q,i is the maximum reactive power regulation capability of the load user that can be reduced by the i-th node; γ i is the power factor angle at which the load can be reduced at node i.

[0024] Preferably, the resource models of the distribution network system are constructed, and the active-reactive power optimization model of the distribution network system is established based on the power flow constraint, including:

[0025] Establishing an energy storage optimization model for the distribution network system:

[0026] 0≤SOC i,t ≤1 (5)

[0027] 0≤P dis,i,t ≤P e ·U bat,t (6)

[0028] 0≤P ch,i,t ≤P E ·(1-U bat,t ) (7)

[0029] E e ·SOC i,t =E e ·SOC i,t-1 -P dis,i,t / η+P ch,i,t ·η (8)

[0030] Where, SOC i,t is the state of charge of the i-th energy storage device at time t; P dis,i,t is the discharge power of the i-th energy storage device at time t; P e is the rated charge and discharge power of the energy storage device; U bat,t is the charging and discharging working state of the energy storage device at time t; P ch,i,t is the charging power of the i-th energy storage device at time t; E e is the rated capacity of the energy storage device; η is the charge and discharge efficiency of the energy storage device, equations (5) to (7) are the power constraints of the energy storage device, and equation (8) is the state of charge (SOC) change constraint of the energy storage device on a time scale;

[0031] Establish a distributed energy model for the distribution network system:

[0032] 0≤P DER,i,t ≤P DERmax (9)

[0033] -P DER,i,t tanα≤Q DER,i,t ≤P DER,i,t tanα (10)

[0034] Where, P DER,i,t is the active power output of the ith distributed energy at time t; P DERmax The upper limit of the active power output of distributed energy; Q DER,i,t is the reactive output of the ith distributed energy at time t; α is the power factor angle of the distributed energy output, and equations (9) and (10) are the output constraints of the distributed energy;

[0035] Set the continuous reactive power compensation device SVC constraints as follows:

[0036] Q SVCmin ≤Q SVC,i,t ≤Q SVCmax(11)

[0037] Where Q SVCmin The lower limit of reactive power compensation output of SVC; Q SVC,i,t is the actual reactive power compensation output of the SVC on the i-th node at time t; Q SVCmax The upper limit of reactive power compensation output of SVC;

[0038] Set the generator constraints as follows:

[0039] P gmin ≤P g,i,t ≤P gmax (12)

[0040] Q gmin ≤Q g,i,t ≤Q gmax (13)

[0041] Where, P gmin is the minimum active output of the generator; P g,i,t is the actual active power output of the generator at the i-th node at time t; P gmax is the maximum active output of the generator; Q gmin is the minimum reactive power output of the generator; Q g,i,t is the actual reactive power output of the generator at the i-th node at time t; Q gmax is the maximum reactive power output of the generator;

[0042] Set the network constraints of the distribution network as follows:

[0043] P in,i,t +P g,i,t +P dis,i,t +P DER,i,t -P load,i,t -P Dload,i,t -P ch,i,t =0 (14)

[0044] Q in,i,t +Q g,i,t +Q DER,i,t +Q SVC,i,t -Q load,i,t -Q Dload,i,t =0 (15)

[0045]

[0046]

[0047]

[0048] U i min ≤U i,t ≤U i max(19)

[0049] I ij min ≤I ij,t ≤I ij max (20)

[0050] Where, P in,i,t is the active power injected by node i from other lines at time t; Q in,i,t is the reactive power injected by node i from other lines at time t; P ij,t is the active power transmitted by line ij at time t; Q ij,t is the reactive power transmitted by line ij at time t; S ij is the maximum transmission capacity of line ij; U i,t is the node voltage of node i at time t; g ij is the conductance of circuit ij; b ij is the susceptance of line ij; θ i,t is the phase angle of node i at time t; U imax and U imin are the upper and lower limits of the voltage at node i; I ij max and I ijmin are the upper and lower limits of the voltage at node i respectively;

[0051] The above expressions (1)-(20) represent the constraints of the active-reactive optimization model in the distribution network system considering the adjustment willingness of users who can reduce loads.

[0052] Preferably, the step of establishing an updated curtailable load access model under the condition of full participation of curtailable load users, setting constraints of the updated curtailable load access model according to the active-reactive power optimization model of the distribution network system, and obtaining an aggregated feasible region of the distribution network system at the point of common connection by solving the updated curtailable load access model comprises:

[0053] When the users of curtailable load fully participate in the regulation, the q in the curtailable load access model shown in formula (1) and formula (2) is i Taking the value as 1, the load reduction model changes from equation (1) to equation (2) to:

[0054] P Dload,i,t =P Eload,i,t -C P,i (twenty one)

[0055] Q Dload,i,t =Q Eload,i,t -C Q,i (twenty two)

[0056] Traversing the power factor angle δ on the common connection point, the active-reactive power transmitted on the PCC works in different states, and maximizing the objective function in each state, forming a convex hull feasible domain projection on the active-reactive two-dimensional plane;

[0057] The objective function of the vertex enumeration method is:

[0058]

[0059] Where μ h =(cosδ, sinδ) is the direction vector for searching the optimal solution in the vertex enumeration method, z h =(P PCC ,

[0060] Q PCC ) T It is the projection of the system feasible region space onto the operating point on the active-reactive plane of the common connection point;

[0061] The constraints of the updated load reduction model include:

[0062] Common connection point constraint:

[0063] Q PCC,t =P PCC,t tanβ (24)

[0064]

[0065]

[0066]

[0067] Where Q PCC,t is the reactive power of the common connection point node; P PCC,t is the active power of the common connection point node; β is the power factor angle of the common connection point node; Ω PCC is the set of lines connected to the common connection point node;

[0068] Get the optimized model:

[0069] The optimization objective of the optimization model is: Formula (23);

[0070] The constraints of the optimization model are: Equations (4)-(22), (24)-(27);

[0071] The optimization model adopts linear relaxation simplification to relax the constraints into convex constraints. By solving the optimization model, the adjustable range of each node in the power grid topology is aggregated at the coupling node, and the aggregated feasible domain of the distribution network system at the PCC node is obtained when the curtailable load fully participates in the regulation.

[0072] Preferably, the dynamically revising the aggregated feasible region of the distribution network system at the common connection point based on the participation probability of users who can reduce loads includes:

[0073] From equations (1)-(4), we can see that the participation probability q of load users can be reduced i Compared with the actual active load P Dload,i,t , reactive load Q Dload,i,t There is a linear relationship. When the probability of participation of the i-th user is q i When the coupled nodes are coupled, the feasible domain space is represented by the linear translation of the feasible domain boundary composed of the constraints (1)-(4). The feasible domain space after aggregation considering the participation probability of all users is y = f(q1,q2,…,q i ,…), when one of the variables q i When the user participation probability q i When it decreases, the load that can be reduced on the Internet increases, the transmission power on the PCC increases, and the boundary of the feasible region expands outward.

[0074] It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the method of the present invention establishes a model for load access that can be reduced taking into account the probability of user participation, and characterizes the actual regulation potential of load that can be reduced by means of participation probability and maximum adjustable capacity; secondly, constructs each resource model in the system, and establishes a system active-reactive optimization model based on flow constraints; then, uses the vertex enumeration method to construct a distribution network feasible domain aggregate based on full user participation; finally, combines the user participation probability to dynamically characterize the feasible domain range of the distribution network. The present invention aims to solve the problem of flexibility resource aggregation taking into account user participation, and obtains a probability-based distribution network feasible domain aggregate. The constructed distribution network feasible domain can more accurately describe the adjustable potential of flexibility resources.

[0075] Additional aspects and advantages of the present invention will be set forth in part in the following description, will become apparent from the following description, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0077] Figure 1A processing flow chart of a method for modeling flexible resource aggregation based on load-reducing user adjustment willingness provided by an embodiment of the present invention;

[0078] Figure 2 An embodiment of the present invention provides an aggregated feasible region of a distribution network system at a PCC (Point of Common Coupling) node;

[0079] Figure 3 A schematic diagram of the impact of load reduction user participation on the PCC feasible region provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0080] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention.

[0081] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the description of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or couplings. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0082] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention pertains. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as such herein, will not be interpreted in an idealized or overly formal sense.

[0083] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0084] The embodiment of the present invention discloses a flexibility resource aggregation modeling method based on the adjustment willingness of users of curtailable loads. The method first establishes an online model of curtailable loads considering the probability of user participation, and characterizes the actual adjustment potential of curtailable loads by participation probability and maximum adjustable capacity; secondly, constructs each resource model in the system, and establishes a system active-reactive optimization model based on flow constraints; then, uses the vertex enumeration method to construct a distribution network feasible domain aggregate based on full user participation; finally, combines the user participation probability to dynamically characterize the feasible domain range of the distribution network. The constructed distribution network feasible domain can more accurately describe the adjustable potential of flexibility resources. The present invention proposes a resource aggregation method considering the probability of user participation, which realizes the accurate characterization of the feasible domain of flexibility resource aggregation.

[0085] The embodiment of the present invention provides a processing flow of a flexible resource aggregation modeling method based on load reduction user adjustment willingness. Figure 1 As shown, the processing steps include the following:

[0086] Step S10: Based on the user participation and load adjustment potential, a load reduction Internet access model that takes into account the user participation probability is established.

[0087] Compared with rigid load, curtailable load can participate in the electricity market based on load regulation capability and user participation probability, and has higher flexible regulation performance. The curtailable load access model of the distribution network system is established as follows:

[0088] P Dload,i,t =P Eload,i,t -q i C P,i (1)

[0089] Q Dload,i,t =Q Eload,i,t -q i C Q,i (2)

[0090] C Q,i =C P,i tanγ i (3)

[0091] 0≤q i ≤1 (4)

[0092] Where, P Dload,i,t is the actual active load size of the i-th node when it can reduce load and participate in regulation at time t; P Eload,i,t is the active load size of the i-th node at time t when the load can be reduced without participating in the regulation; q i is the probability that the user with load reduction at the i-th node participates in the market regulation, indicating the user's participation; C P,iis the maximum active regulation capability of the load reduction user at the i-th node; Q Dload,i,t is the actual reactive load size of the i-th node when the load can be reduced and participate in the regulation at time t; Q Eload,i,t is the reactive load size of the i-th node at time t when the load is not regulated; C Q,i is the maximum reactive power regulation capability of the load user that can be reduced by the i-th node; γ i is the power factor angle at which the load can be reduced at node i.

[0093] Step S20: construct resource models of the distribution network system, and establish an active-reactive power optimization model of the distribution network system based on power flow constraints.

[0094] The simulated power system adopts the IEEE33-node network optimization model.

[0095] Establishing an energy storage optimization model for the distribution network system:

[0096] 0≤SOC i,t ≤1 (5)

[0097] 0≤P dis,i,t ≤P e ·U bat,t (6)

[0098] 0≤P ch,i,t ≤P E ·(1-U bat,t ) (7)

[0099] E e ·SOC i,t =E e ·SOC i,t-1 -P dis,i,t / η+P ch,i,t ·η (8)

[0100] Where, SOC i,t is the state of charge of the i-th energy storage device at time t; P dis,i,t is the discharge power of the i-th energy storage device at time t; P e is the rated charge and discharge power of the energy storage device; U bat,t is the charging and discharging working state of the energy storage device at time t; P ch,i,t is the charging power of the i-th energy storage device at time t; E e is the rated capacity of the energy storage device; η is the charge and discharge efficiency of the energy storage device. Equations (5) to (7) are the power constraints of the energy storage device, and Equation (8) is the SOC (State of Charge) change constraint of the energy storage device on a time scale.

[0101] Establish a distributed energy model for the distribution network system:

[0102] 0≤P DER,i,t ≤P DERmax (9)

[0103] -P DER,i,t tanα≤Q DER,i,t ≤P DER,i,t tanα(10)

[0104] Where, P DER,i,t is the active power output of the ith distributed energy at time t; P DERmax The upper limit of the active power output of distributed energy; Q DER,i,t is the reactive power output of the ith distributed energy resource at time t; α is the power factor angle of the distributed energy resource output. Equations (9) and (10) are the output constraints of distributed energy resources.

[0105] Set the SVC (Static Var Compensator) constraints as follows:

[0106] Q SVCmin ≤Q SVC,i,t ≤Q SVCmax (11)

[0107] Where Q SVCmin The lower limit of reactive power compensation output of SVC; Q SVC,i,t is the actual reactive power compensation output of the SVC on the i-th node at time t; Q SVCmax It is the upper limit of reactive power compensation output of SVC.

[0108] Set the generator constraints as follows:

[0109] P gmin ≤P g,i,t ≤P gmax (12)

[0110] Q gmin ≤Q g,i,t ≤Q gmax (13)

[0111] Where, P gmin is the minimum active output of the generator; P g,i,t is the actual active power output of the generator at the i-th node at time t; P gmax is the maximum active output of the generator; Q gmin is the minimum reactive power output of the generator; Q g,i,t is the actual reactive power output of the generator at the i-th node at time t; Q gmax It is the maximum reactive power output of the generator.

[0112] Set the network constraints of the distribution network as follows:

[0113] P in,i,t +P g,i,t +P dis,i,t +P DER,i,t -P load,i,t -P Dload,i,t -P ch,i,t =0 (14)

[0114] Q in,i,t +Q g,i,t +Q DER,i,t +Q SVC,i,t -Q load,i,t -Q Dload,i,t =0 (15)

[0115]

[0116]

[0117]

[0118] U imin ≤U i,t ≤U imax (19)

[0119] I ijmin ≤I ij,t ≤I ijmax (20)

[0120] Where, P in,i,t is the active power injected by node i from other lines at time t; Q in,i,t is the reactive power injected by node i from other lines at time t; P ij,t is the active power transmitted by line ij at time t; Q ij,t is the reactive power transmitted by line ij at time t; S ij is the maximum transmission capacity of line ij; U i,t is the node voltage of node i at time t; g ij is the conductance of circuit ij; b ij is the susceptance of line ij; θ i,t is the phase angle of node i at time t; U imax and U imin are the upper and lower limits of the voltage at node i; I ijmax and I ijmin are the upper and lower limits of the voltage at node i respectively;

[0121] The above expressions (1)-(20) represent the constraints of the active-reactive optimization model in the distribution network system considering the adjustment willingness of users who can reduce loads.

[0122] Step S30: Establish a curtailable load access model under the condition of full participation of curtailable load users, set constraints for the curtailable load access model based on the active-reactive power optimization model of the distribution network system, and use the vertex enumeration method to solve the curtailable load access model under the condition of full participation of curtailable load users to obtain an aggregated feasible region of the distribution network system at the PCC (Point of Common Coupling).

[0123] When the users of curtailable load fully participate in the regulation, the q in the curtailable load access model shown in formula (1) and formula (2) is i The value is 1. At this time, the load reduction model changes from equation (1) to equation (2) to:

[0124] P Dload,i,t =P Eload,i,t -C P,i (twenty one)

[0125] Q Dload,i,t =Q Eload,i,t -C Q,i (twenty two)

[0126] To reflect the active-reactive power regulation range on the PCC, a vertex enumeration method is used to project the feasible domain space of each flexible resource in the distribution network onto the PCC, forming a two-dimensional feasible domain space for active-reactive power on the PCC. Specifically, the power factor angle δ on the PCC is traversed to ensure that the active-reactive power transmitted on the PCC operates in different states. The objective function is maximized in each state, forming a convex hull feasible domain projection on the two-dimensional active-reactive plane.

[0127] The objective function of the vertex enumeration method is:

[0128]

[0129] Where μ h =(cosδ, sinδ) is the direction vector for searching the optimal solution in the vertex enumeration method, z h =(P PCC , Q PCC ) T It is the projection of the system feasible domain space onto the operating point on the PCC active-reactive plane.

[0130] PCC Constraints:

[0131] Q PCC,t =P PCC,t tanβ (24)

[0132]

[0133]

[0134]

[0135] Where Q PCC,t is the reactive power of the PCC node; P PCC,t is the active power of the PCC node; β is the power factor angle of the PCC node; Ω PCC is the set of lines connected to the PCC node.

[0136] Therefore, the optimized model can be obtained:

[0137] The optimization objective of the optimization model is: Formula (23);

[0138] The constraints of the optimization model are: equations (4)-(22), (24)-(27).

[0139] By solving the optimization problem M0, we can obtain the following equations when the curtailable load fully participates in the regulation: Figure 2 The aggregated feasible region of the distribution network system at the PCC node is shown.

[0140] The above optimization model uses linear relaxation to simplify the constraints, reducing them to convex ones. Solving this optimization model aggregates the adjustable ranges of each node in the grid topology at the coupled nodes, forming a large adjustable range. This allows for rapid solution of the feasible region for the coupled nodes, achieving resource aggregation in the distribution network system.

[0141] Step S40: dynamically modify the aggregated feasible region of the distribution network system on the PCC based on the probability of participation of users who can reduce loads, and dynamically characterize the feasible region range of the distribution network.

[0142] From equations (1)-(4), we can see that the participation probability q of load users can be reduced i Compared with the actual active load P Dload,i,t , reactive load Q Dload,i,t Therefore, when the probability of participation of the i-th user is q i When , the feasible region space of the coupling node is represented by the linear translation of the feasible region boundary formed by the constraints (1)-(4). Here q i The feasible domain space aggregated by the method proposed in this invention takes into account the participation probability of all users. The feasible domain can be expressed as y = f(q1,q2,…,q i ,…), when one of the variables q i When the user participation probability q iWhen it decreases, the load that can be reduced on the Internet increases, the transmission power on the PCC increases, and the boundary of the feasible region expands outward.

[0143] For the user participation probability q i The acquisition of can be obtained through survey questionnaires or extracted from historical data. i Related to electricity market policies and environmental factors.

[0144] According to the feasible domain range of the distribution network, the subsequent operations and uses of the feasible domain can be carried out:

[0145] (a) Distribution network operators can provide the feasible region to the transmission network for optimizing the transmission network dispatch.

[0146] (b) The feasible region can be combined with the real-time operating point of the line to give the current adjustable capacity of the distribution network, providing the system with real-time adjustment capabilities for the prediction error of new energy.

[0147] Figure 3 This is a schematic diagram of the impact of load reduction user participation on the PCC feasible region provided by an embodiment of the present invention. Figure 3 As shown, when the user participation probability q i When it decreases, the load that can be reduced on the Internet increases, the transmission power on the PCC increases, and the boundary of the feasible region expands outward.

[0148] In summary, compared to existing load modeling methods, the embodiments of the present invention take into account the willingness and regulatory capabilities of users of curtailable loads to participate in market regulation, enabling a more accurate description of the actual market participation of users of curtailable loads. Compared to existing feasible domain aggregation methods, the dynamic performance of the feasible domain, in addition to its temporal variation, also includes a dynamic description of load users' market participation. This allows for a more accurate description of the system's reliable operating state, providing a precise reference range for subsystem operation in the day-ahead and intraday scheduling of new power systems.

[0149] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0150] From the above description of the embodiments, it can be seen that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus the necessary general-purpose hardware platform. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present invention or certain parts of the embodiments.

[0151] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, refer to the partial description of the method embodiments. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. A person of ordinary skill in the art can understand and implement it without making any creative efforts.

[0152] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for aggregating power resources based on the adjustment willingness of users who can reduce loads, characterized in that: include: Based on user participation and load adjustment potential, a load reduction model considering user participation probability is established; Construct resource models for the distribution network system and establish an active-reactive power optimization model for the distribution network system based on power flow constraints; Establishing an updated curtailable load access model under the condition of full participation of curtailable load users, setting constraints for the updated curtailable load access model based on the active-reactive power optimization model of the distribution network system, and obtaining an aggregated feasible region of the distribution network system at the common connection point by solving the updated curtailable load access model; The aggregated feasible region of the distribution network system at the common connection point is dynamically modified based on the participation probability of users who can reduce loads.

2. The method according to claim 1, characterized in that The load reduction model based on user participation and load adjustment potential is established by taking into account the probability of user participation, including: The load curtailment model for the distribution network system is established as follows: P Dload,i,t =P Eload,i,t -q i C P,i (1) Q Dload,i,t =Q Eload,i,t -q i C Q,i (2) C Q,i =C P,i ·tanγ i (3) 0≤q i ≤1 (4) Where, P Dload,i,t is the actual active load size of the i-th node when it can reduce load and participate in regulation at time t; P Eload,i,t is the active load size of the i-th node at time t when the load is not regulated; q i is the probability that the user with load reduction at the i-th node participates in the market regulation, indicating the user's participation; C P,i is the maximum active regulation capability of the load reduction user at the i-th node; Q Dload,i,t is the actual reactive load size of the i-th node when the load can be reduced and participate in the regulation at time t; Q Eload,i,t is the reactive load size of the i-th node at time t when the load is not regulated; C Q,i is the maximum reactive power regulation capability of the load user that can be reduced by the i-th node; γ i is the power factor angle at which the load can be reduced at node i.

3. The method according to claim 2, characterized in that The resource models of the distribution network system are constructed, and the active-reactive power optimization model of the distribution network system is established based on the power flow constraints, including: Establishing an energy storage optimization model for the distribution network system: 0≤SOC i,t ≤1 (5) 0≤P dis,i,t ≤P e ·IN bat,t (6) 0≤P ch,i,t ≤P e ·(1-U bat,t ) (7) IN e ·SOC i,t =E e ·SOC i,t-1 -P dis,i,t / η+P ch,i,t ·η (8) Where, SOC i,t is the state of charge of the i-th energy storage device at time t; P dis,i,t is the discharge power of the i-th energy storage device at time t; P e is the rated charge and discharge power of the energy storage device; U bat,t is the charging and discharging working state of the energy storage device at time t; P ch,i,t is the charging power of the i-th energy storage device at time t; E e is the rated capacity of the energy storage device; η is the charge and discharge efficiency of the energy storage device, equations (5) to (7) are the power constraints of the energy storage device, and equation (8) is the state of charge (SOC) change constraint of the energy storage device on a time scale; Establish a distributed energy model for the distribution network system: 0≤P DER,i,t ≤P DERmax (9) -P DER,i,t ·tanα≤Q DER,i,t ≤P DER,i,t ·tanα(10) Where, P DER,i,t is the active power output of the ith distributed energy at time t; P DERmax The upper limit of the active power output of distributed energy; Q DER,i,t is the reactive output of the ith distributed energy at time t; α is the power factor angle of the distributed energy output, and equations (9) and (10) are the output constraints of the distributed energy; Set the continuous reactive power compensation device SVC constraints as follows: Q SVCmin ≤Q SVC,i,t ≤Q SVCmax (11) Where Q SVCmin The lower limit of reactive power compensation output of SVC; Q SVC,i,t is the actual reactive power compensation output of the SVC on the i-th node at time t; Q SVCmax The upper limit of reactive power compensation output of SVC; Set the generator constraints as follows: P gmin ≤P g,i,t ≤P gmax (12) Q gmin ≤Q g,i,t ≤Q gmax (13) Where, P gmin is the minimum active output of the generator; P g,i,t is the actual active power output of the generator at the i-th node at time t; P gmax is the maximum active output of the generator; Q gmin is the minimum reactive power output of the generator; Q g,i,t is the actual reactive power output of the generator at the i-th node at time t; Q gmax is the maximum reactive power output of the generator; Set the network constraints of the distribution network as follows: P in,i,t +P g,i,t +P dis,i,t +P DER,i,t -P load,i,t -P Dload,i,t -P ch,i,t =0 (14) Q in,i,t +Q g,i,t +Q DER,i,t +Q SVC,i,t -Q load,i,t -Q Dload,i,t =0 (15) IN imin ≤U i,t ≤U imax (19) I ijmin ≤I ij,t ≤I ijmax (20) Where, P in,i,t is the active power injected by node i from other lines at time t; Q in,i,t is the reactive power injected by node i from other lines at time t; P ij,t is the active power transmitted by line ij at time t; Q ij,t is the reactive power transmitted by line ij at time t; S ij is the maximum transmission capacity of line ij; U i,t is the node voltage of node i at time t; g ij is the conductance of circuit ij; b ij is the susceptance of line ij; θ i,t is the phase angle of node i at time t; U imax and U imin are the upper and lower limits of the voltage at node i; I ijmax and I ijmin are the upper and lower limits of the voltage at node i respectively; The above expressions (1)-(20) represent the constraints of the active-reactive optimization model in the distribution network system considering the adjustment willingness of users who can reduce loads.

4. The method according to claim 3, characterized in that The method of establishing an updated curtailable load access model under the condition of full participation of curtailable load users, setting constraints of the updated curtailable load access model based on the active-reactive power optimization model of the distribution network system, and obtaining an aggregated feasible region of the distribution network system at the point of common connection by solving the updated curtailable load access model includes: When the users of curtailable load fully participate in the regulation, the q in the curtailable load access model shown in Equation (1) and Equation (2) is i Taking the value as 1, the load reduction model changes from equation (1) to equation (2) to: P Dload,i,t =P Eload,i,t -C P,i (21) Q Dload,i,t =Q Eload,i,t -C Q,i (22) Traversing the power factor angle δ on the common connection point, the active-reactive power transmitted on the PCC works in different states, and maximizing the objective function in each state, forming a convex hull feasible domain projection on the active-reactive two-dimensional plane; The objective function of the vertex enumeration method is: Where μ h =(cosδ, sinδ) is the direction vector for searching the optimal solution in the vertex enumeration method, z h =(P PCC , Q PCC ) T It is the operating point of the system feasible domain space projected onto the active-reactive plane of the common connection point; The constraints of the updated load reduction model include: Common connection point constraint: Q PCC,t =P PCC,t ·tanβ (24) Where Q PCC,t is the reactive power of the common connection point node; P PCC,t is the active power of the common connection point node; β is the power factor angle of the common connection point node; Ω PCC is the set of lines connected to the common connection point node; Get the optimized model: The optimization objective of the optimization model is: Formula (23); The constraints of the optimization model are: Equations (4)-(22), (24)-(27); The optimization model adopts linear relaxation simplification to relax the constraints into convex constraints. By solving the optimization model, the adjustable range of each node in the power grid topology is aggregated at the coupling node, and the aggregated feasible domain of the distribution network system at the PCC node is obtained when the curtailable load fully participates in the regulation.

5. The method according to claim 4, characterized in that The dynamically revising the aggregated feasible region of the distribution network system at the common connection point based on the probability of participation of users who can reduce loads includes: From equations (1)-(4), we can see that the participation probability q of load users can be reduced i The actual active load P Dload,i,t , reactive load Q Dload,i,t There is a linear relationship. When the participation probability of the i-th user is q i When the coupled nodes are coupled, the feasible domain space is represented by the linear translation of the feasible domain boundary composed of the constraints (1)-(4). The feasible domain space after aggregation considering the participation probability of all users is y = f(q1,q2,…,q i ,…), when one of the variables q i When the user participation probability q i When it decreases, the load that can be reduced on the Internet increases, the transmission power on the PCC increases, and the boundary of the feasible region expands outward.

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