A quasi-linear demand response method for realizing power transmission and distribution coordination

Abstract

The application discloses a kind of quasi-linear demand response methods for realizing power transmission and distribution cooperation, comprising the following steps: step one, solving the system level load quasi-line of power transmission network;Step two, solving the node load quasi-line of power transmission network;Step three, solving the node load quasi-line of distribution network;In the step one, the principle of "adjustable" balance "unadjustable" is that: the power fluctuation caused by unadjustable resources can be quantified by the superposition of the predicted maximum output of renewable energy generation and rigid load, and based on this, the system level ideal load curve shape of DR load for relieving power fluctuation is formulated;The application defines the respective guide targets for the active distribution network of different positions, flexible load participating in large-scale demand response, realizes demand response under the cooperation of transmission and distribution, and is beneficial to improving the renewable energy consumption capacity of transmission and distribution network while alleviating congestion based on the method of carrying out demand response, and forms a win-win situation of transmission and distribution network.

Description

Technical Field

[0001] This invention relates to the field of power transmission and distribution technology, specifically to a quasi-linear demand response method for achieving coordinated power transmission and distribution. Background Technology

[0002] Demand response (DR) is crucial for enhancing the flexibility of new power systems and promoting the integration of renewable energy. Quasi-linear DR, a novel mechanism proposed by domestic scholars in recent years, can support large-scale DR. It effectively overcomes the problems faced by price-based DR, such as lack of direct guiding targets, over-adjustment, and difficulty in accurately reflecting system regulation needs, as well as the problems faced by incentive-based DR, such as heavy computational burden, controversial baselines, and lack of data for routine implementation. Quasi-linear DR uses the load baseline published by the DR implementer as the target for guiding user participation in response and as the benchmark for post-implementation evaluation of user subsidy benefits.

[0003] In the existing technology, Reference 1 "Fan Shuai, Jia Kunqi, Wang Fen, Wang Zhihua, Yang Lin, He Guangyu. Large-scale demand response based on load profile [J]. Automation of Electric Power Systems, 2020, 44(15):19-27." and Patent CN109754335B disclose a high-proportion renewable energy consumption method based on load profile, which proposes a quadratic convex optimization model of load profile with the objective of minimizing the sum of adjustable unit operating cost and renewable energy curtailment cost. Based on this, Patent CN115441459A proposes a power system security constraint unit combination decision method based on load profile, and Patent CN115688976A proposes a comprehensive energy system operation decision method including user profile demand response. Reference 2, "Shuai Fan, Zuyi Li, Lin Yang, Guangyu He. Customer directrix load-based large-scale demand response for integrating renewable energy sources. Electric Power Systems Research, Volume 181, 2020, 106-175," proposes a method of using the curve inversion of rigid load minus renewable energy output as the load guideline. This greatly simplifies the calculation process of the load guideline and lays the foundation for the lightweight development of guideline-type DR. References 3, “Xu Boqiang, Zhang Peichao, He Guangyu, Zhao Jianli. Load metric control method for water heater clusters based on master-slave game theory [J]. Proceedings of the CSEE, 2022, 42(21): 7785-7797” and 4, “Xu Boqiang, Zhao Jianli, Zhang Peichao, Fan Shuai, He Guangyu. High proportion of renewable energy consumption method based on load metric and Nash negotiation [J / OL]. Automation of Electric Power Systems: 1-14 [2023-06-26]”, propose an incentive price method based on load metric to promote the consumption of renewable energy.

[0004] However, the above-mentioned demand response methods based on load profiles all ignore network constraints. The load profiles formulated are uniform across the entire system. In reality, the scale and output differences of renewable energy distributed in different locations of the system result in spatial differences in the demand for renewable energy consumption. A uniform load profile is difficult to accurately guide users in different locations to participate in DR in order to achieve local and nearby consumption of renewable energy. To this end, the nodal load profile method considering the uncertainty of demand response disclosed in reference 5 "[1] Meng Yan, Xiao Jucheng, Hong Juhua et al. Node load profile considering the uncertainty of demand response: concept and model [J]. Automation of Electric Power Systems, 2023, 47(13):28-39." and patent CN115186952A propose a nodal load profile method considering the uncertainty of demand response, which proposes a load profile demand response method considering network constraints. A load profile is formulated for each node in the system to guide users to participate in the response. The shortcomings are twofold: First, the aforementioned coupling of load-based demand response and economic dispatch of conventional adjustable units presents certain barriers in practical application. In fact, the demand-side flexible resources tapped from large-scale load-based demand response (DR) are abundant and inexpensive, and the formulation of load profiles and user responses usually take precedence over the dispatch of conventional adjustable units. Second, existing methods for formulating load profiles do not consider the division of responsibilities between the transmission and distribution networks to which resource endowments belong, and lack consideration for the transmission and distribution boundaries, making it difficult to achieve a win-win situation of simultaneously improving the renewable energy absorption capacity of both the transmission and distribution networks. Summary of the Invention

[0005] The purpose of this invention is to provide a quasi-linear demand response method for achieving coordinated power transmission and distribution, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a quasi-linear demand response method for realizing coordinated power transmission and distribution, comprising the following steps: Step 1, solving the system-level load quasi-linearity of the transmission network; Step 2, solving the node load quasi-linearity of the transmission network; Step 3, solving the node load quasi-linearity of the distribution network;

[0007] In step one above, based on the principle of balancing "adjustable" and "non-adjustable" that needs to be considered when formulating LCDL, power balance is established, and the system-level load guideline of the transmission network is solved.

[0008] In step two above, energy transfer is carried out based on the system-level load baseline of the transmission network obtained in step one to form a T-LCDL that satisfies network constraints. Based on the fairness principle that needs to be considered when formulating the LCDL, the total transferred energy is minimized, thereby establishing and solving a convex optimization model for the T-LCDL.

[0009] In step three above, after receiving the T-LCDL published by the TSO, the DSO formulates the D-LCDL for the DR load in the distribution network. On the one hand, it should try to minimize the difference between the total power consumption curve of the distribution network and the T-LCDL, so as to actively respond to the guidance of the T-LCDL and promote the consumption of new energy in the transmission network. On the other hand, considering the principle of fairness, the difference between D-LCDLs should be minimized. Based on the above requirements, a convex optimization model for D-LCDL is formulated and solved.

[0010] Preferably, in step one, the ideal load curve shape of DR loads located at different locations is defined as the locational customer directrix load (LCDL), including the transmission-level locational customer directrix load (T-LCDL) and the distribution-level locational customer directrix load (D-LCDL). At the transmission-level, the T-LCDL is formulated and published by the transmission system operator (TSO) to the distribution system operator (D-LCDL) of the corresponding node. The Distribution Operator (DSO) guides the active distribution network to participate in the Distribution Response (DR) to promote the absorption of renewable energy in the transmission network. When the consumption curve of the active distribution network perfectly matches the Transmission-Distributed Load Line (T-LCDL), the power fluctuations caused by renewable energy and rigid loads in the transmission network are effectively smoothed. The unsmoothed portion will be smoothed by conventional thermal power units. In practice, when conventional units reach their regulation capacity limit, further measures such as renewable energy curtailment and load shedding will be taken. At the distribution network level, the DSO formulates the D-LCDL and publishes it to users registered to participate in the DR at different nodes. This guides users to effectively shape their load curves to approximate the D-LCDL. As a result, on the one hand, the net load curve of the active distribution network approaches the corresponding T-LCDL, which is conducive to the economical and green operation of the transmission network. On the other hand, it effectively alleviates local congestion in the distribution network and promotes the absorption of local renewable energy in the distribution network.

[0011] Preferably, in step one, the principle of "adjustable" balancing "unadjustable" means that power fluctuations caused by unadjustable resources can be quantified by superimposing the predicted maximum output of renewable energy generation with rigid loads, and based on this, the ideal load curve shape of the DR load to mitigate power fluctuations is formulated.

[0012] Preferably, in step one, specifically: consider the power balance that is "adjustable" while satisfying "non-adjustable" as shown in equation (1):

[0013]

[0014] Where t, g, i, r, j are indices for time period, conventional generating unit, active distribution network, and renewable energy generating unit, respectively, corresponding to the set R, Considering the duration of DR implementation is t1 to This is the predicted total power output of conventional generating units, which can be obtained based on historical data. It is the predicted total power consumption of the distribution network. It is the system-level regulation responsibility curve of a conventional unit. It is the load baseline at the transmission network system level. and These are the maximum output of renewable energy and the power of rigid load, respectively. and Satisfying Relationship:

[0015]

[0016] Substituting equation (2) into equation (1) yields...

[0017]

[0018] Thus, the load baseline at the transmission network system level is obtained.

[0019] Preferably, in step two, the principle of fairness is that there is no single way to decompose the system-level ideal load curve to each node. Considering that LCDL determines the revenue of DR users, it is necessary to ensure the fairness and objectivity of LCDL formulation in order to avoid user disputes and ensure that the results of LCDL are not affected by users' strategic manipulation.

[0020] Preferably, step two specifically involves: under actual network constraints, subject to spatial distribution differences of unadjustable resources, It is difficult to accurately characterize the regulation needs at different locations, thus hindering the precise guidance of distribution networks at different locations to participate in DR (Renewable Energy Transfer) to improve the absorption capacity of renewable energy. Therefore, energy transfer needs to be carried out based on the system-level node load baseline to form T-LCDLs that satisfy network constraints. Considering the principle of fairness, the total transferred energy should be minimized, that is, the difference between T-LCDLs should be minimized. Similarly, consider the following optimization problem:

[0021]

[0022] Constraints:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] in, The T-LCDL of the distribution network is compared to offset ( This indicates that the T-LCDL of the distribution network rises relative to the system-level load baseline within a time period t, and vice versa. Similarly; it should be noted that the actual energy shift is due to Multiply Thus, constraints (4b) and (4c) ensure that conventional generating units and the distribution network participate in regulation, resulting only in the temporal transfer of electricity, without increasing or decreasing the amount of electricity. Constraint (4d) represents the generation attribute of conventional generating units, and constraint (4e) is the tie-line constraint between the transmission network and the distribution network. Let l be the upper limit of the transmission power of the tie line. Equation (4f) is the spatial coupling constraint that ensures power balance in each time period. The network constraint of the transmission network is shown in Equation (4g), where l is the index of the line and the corresponding set is... These are the lower and upper limits of the power flow along the line, respectively. Let represent the power transmission allocation factors of conventional generating units, renewable energy generating units, distribution network, and rigid loads to the line, respectively. Since the DR scheme only considers active power, a linear DC power flow model that ignores network losses is introduced to describe the network constraints of the system. After solving the above convex optimization model, the T-LCDL of each node in the transmission network can be obtained:

[0030]

[0031] Preferably, in step three, the D-LCDL model is specifically defined as follows:

[0032]

[0033]

[0034] Constraints:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] In the objective function (6a), k is the index of the DR load in the distribution network, and the corresponding set is... The D-LCDL value of DR load in time period t. The average value of D-LCDLs is shown in equation (6b), vector The total net power consumption curve profile of the distribution network is represented by a penalty constant to ensure that the distribution network responds to the guidance of T-LCDL to a certain extent. Equations (6c)-(6d) are D-LCDL related constraints, equation (6e) is the profile constraint of the total net power consumption curve of the distribution network, equation (6f) is the tie-line power constraint, and equation (6g) is the power balance constraint. For the predicted maximum output of renewable energy in the distribution network, the corresponding set The power of rigid loads in the distribution network, corresponding to the set Equation (6h) represents the network constraints of the distribution network, where P l line,min and P l line,max These are the lower and upper limits of the power flow, respectively. These are respectively renewable energy generating units, tie line nodes, DR loads, and rigid load pairs on line l (corresponding sets). The power transmission allocation factor; the established distribution network node load guideline model (6a)-(6h) is a convex optimization problem, which can be solved directly by the Gurobi commercial solver.

[0042] Compared with the prior art, the beneficial effects of the present invention are: the present invention clarifies the respective guiding objectives for active distribution networks and flexible loads in different locations to participate in large-scale demand response, realizes demand response under transmission and distribution coordination, and carries out demand response based on this method, which is conducive to improving the renewable energy absorption capacity of transmission and distribution networks while alleviating congestion, thus forming a win-win situation for transmission and distribution networks. Attached Figure Description

[0043] Figure 1 A schematic diagram of the load profile of a node for transmission and distribution coordination;

[0044] Figure 2 For testing system topology diagram;

[0045] Figure 3 Output curves for new energy sources and rigid loads in the power transmission network;

[0046] Figure 4 As the system-level load baseline for the power transmission network;

[0047] Figure 5 For the load baseline of the transmission network nodes;

[0048] Figure 6 (a) is the power output curve of new energy sources in active distribution network 1;

[0049] Figure 6 (b) is the rigid load curve of active distribution network 1;

[0050] Figure 7 For the node load baseline of active distribution network 1;

[0051] Figure 8 (a) is the power output curve of the second new energy source in the active distribution network;

[0052] Figure 8 (b) is the rigid load curve of active distribution network 2;

[0053] Figure 9 For the node load baseline of active distribution network 2;

[0054] Figure 10 (a) The photovoltaic energy consumption status of the power transmission network;

[0055] Figure 10 (b) The wind power energy consumption situation of the power transmission network;

[0056] Figure 11 (a) The photovoltaic energy consumption status of distribution network 1;

[0057] Figure 11 (b) shows the wind power energy consumption of distribution network 1;

[0058] Figure 12 (a) The photovoltaic energy consumption status of distribution network 2;

[0059] Figure 12 (b) shows the wind power energy consumption of distribution network 2;

[0060] Figure 13 This is a flowchart of the method of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Please see Figure 1-13 The present invention provides an embodiment of a quasilinear demand response method for achieving coordinated power transmission and distribution, comprising the following steps: Step 1, solving the system-level load quasilinearity of the transmission network; Step 2, solving the node load quasilinearity of the transmission network; Step 3, solving the node load quasilinearity of the distribution network.

[0063] In step one above, based on the principle of "adjustable" balancing "non-adjustable" that needs to be considered when formulating LCDL, a power balance is established, and the system-level load guideline of the transmission network is solved. The principle of "adjustable" balancing "non-adjustable" means that the power fluctuation caused by non-adjustable resources can be quantified by superimposing the predicted maximum output of renewable energy generation with the rigid load. Based on this, the ideal system-level load curve shape of the DR load that mitigates power fluctuation is formulated, specifically: considering the power balance that "adjustable" satisfies "non-adjustable" as shown in equation (1):

[0064]

[0065] Where t, g, i, r, j are indices for time period, conventional generating unit, active distribution network, and renewable energy generating unit, respectively, corresponding to the set R, Considering the duration of DR implementation is t1 to This is the predicted total power output of conventional generating units, which can be obtained based on historical data. It is the predicted total power consumption of the distribution network. It is the system-level regulation responsibility curve of a conventional unit. It is the load baseline at the transmission network system level. and These are the maximum output of renewable energy and the power of rigid load, respectively. and Satisfying Relationship:

[0066]

[0067] Substituting equation (2) into equation (1) yields...

[0068]

[0069] Thus, the load baseline at the transmission network system level is obtained.

[0070] In step two above, energy transfer is performed based on the system-level load profile of the transmission network obtained in step one, forming a T-LCDL that satisfies network constraints. Based on the fairness principle considered when formulating the LCDL, the total transferred energy is minimized, thereby establishing and solving a convex optimization model for the T-LCDL. The fairness principle states that there is no unique way to decompose the system-level ideal load curve to each node. Considering that the LCDL determines the revenue of DR users, it is necessary to ensure the fairness and objectivity of the LCDL formulation to avoid user disputes and ensure that the LCDL results are not influenced by user strategic manipulation. Specifically, under actual network constraints, due to the spatial distribution differences of unadjustable resources, M... t D It is difficult to accurately characterize the regulation needs at different locations, thus hindering the precise guidance of distribution networks at different locations to participate in DR (Renewable Energy Transfer) to improve the absorption capacity of renewable energy. Therefore, energy transfer needs to be carried out based on the system-level node load baseline to form T-LCDLs that satisfy network constraints. Considering the principle of fairness, the total transferred energy should be minimized, that is, the difference between T-LCDLs should be minimized. For M t G Similarly, consider the following optimization problem:

[0071]

[0072] Constraints:

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079] in, The T-LCDL of the distribution network is compared to offset ( This indicates that the T-LCDL of the distribution network rises relative to the system-level load baseline within a time period t, and vice versa. Similarly; it should be noted that the actual energy shift is due to Multiply Constraints (4b) and (4c) ensure that conventional generating units and distribution networks participate in regulation, resulting only in the temporal transfer of electricity without increasing or decreasing the amount of electricity. Constraint (4d) represents the generation attribute of conventional generating units, and constraint (4e) is the tie-line constraint between the transmission and distribution networks. i tie,max Let l be the upper limit of the transmission power of the tie line. Equation (4f) is the spatial coupling constraint that ensures power balance in each time period. The network constraint of the transmission network is shown in Equation (4g), where l is the index of the line and the corresponding set is... P l line,min P l line,max These are the lower and upper limits of the power flow along the line, respectively. Let represent the power transmission allocation factors of conventional generating units, renewable energy generating units, distribution network, and rigid loads to the line, respectively. Since the DR scheme only considers active power, a linear DC power flow model that ignores network losses is introduced to describe the network constraints of the system. After solving the above convex optimization model, the T-LCDL of each node in the transmission network can be obtained:

[0080]

[0081] In step three above, after receiving the T-LCDL published by the TSO, the DSO formulates the D-LCDL for the DR loads in the distribution network. On the one hand, it should minimize the difference between the total power consumption curve of the distribution network and the T-LCDL, thereby actively responding to the guidance of the T-LCDL and promoting the consumption of new energy in the transmission network. On the other hand, considering the principle of fairness, the differences between D-LCDLs should be minimized. Based on the above requirements, a convex optimization model for D-LCDL is formulated and solved. Specifically, the D-LCDL formulation model is as follows:

[0082]

[0083]

[0084] Constraints:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] In the objective function (6a), k is the index of the DR load in the distribution network, and the corresponding set is... The D-LCDL value of DR load in time period t. The average value of D-LCDLs is shown in equation (6b), vector The total net power consumption curve profile of the distribution network is represented by a penalty constant to ensure that the distribution network responds to the guidance of T-LCDL to a certain extent. Equations (6c)-(6d) are D-LCDL related constraints, equation (6e) is the profile constraint of the total net power consumption curve of the distribution network, equation (6f) is the tie-line power constraint, and equation (6g) is the power balance constraint. For the predicted maximum output of renewable energy in the distribution network, the corresponding set The power of rigid loads in the distribution network, corresponding to the set Equation (6h) represents the network constraints of the distribution network, where and These are the lower and upper limits of the power flow, respectively. These are respectively renewable energy generating units, tie line nodes, DR loads, and rigid load pairs on line l (corresponding sets). The power transmission allocation factor; the established distribution network node load guideline model (6a)-(6h) is a convex optimization problem, which can be solved directly by the Gurobi commercial solver.

[0092] Using the method provided in the above embodiments, this paper illustrates the calculation results of the load baseline of the transmission and distribution coordination nodes, taking a system with a 6-node transmission network and two active distribution networks as an example. The example system is as follows: Figure 2 As shown, the power output curves of new energy sources and the rigid load curves of the power transmission network are as follows: Figure 3 As shown, the system-level load baseline of the transmission network can be obtained according to expression (3), such as Figure 4 As shown, after solving models (4a)-(4g), the load baseline of the transmission network node can be obtained according to equation (5), such as Figure 5 As shown, the renewable energy output curve and rigid load curve of distribution network 1 are as follows: Figure 6 As shown, solving model (6a)-(6h) yields the nodal load profile of distribution network 1 as follows: Figure 7 As shown, the new energy output curve and rigid load curve of distribution network 2 are as follows: Figure 8 As shown, solving model (6a)-(6h) yields the nodal load profile of distribution network 2 as follows: Figure 9 As shown, Figure 10 , 11 Tables 1 and 12 respectively demonstrate the renewable energy consumption of the transmission network, distribution network 1, and distribution network 2.

[0093] Based on the above, the advantages of this invention are that, when used, by establishing load guidelines for transmission network nodes and distribution network nodes under network constraints, users can participate in demand response under the guidance of the node load guidelines, which can simultaneously promote the consumption of renewable energy in the transmission and distribution networks, realize the coordinated operation of the transmission and distribution networks, and form a win-win situation for the transmission and distribution networks.

[0094] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A quasi-linear demand response method for realizing power transmission and distribution coordination, comprising the following steps: Step 1: Solve for the system-level load profile of the transmission network; Step 2: Solve for the node load profile of the transmission network; Step 3: Solve for the node load profile of the distribution network; The key feature is: In step one above, based on the principle of "adjustable" balancing "non-adjustable" that needs to be considered when formulating LCDL, power balance is established, and the system-level load guideline of the transmission network is solved. In step two above, energy transfer is carried out based on the system-level load baseline of the transmission network obtained in step one to form a T-LCDL that satisfies network constraints. Based on the fairness principle that needs to be considered when formulating the LCDL, the total transferred energy is minimized, thereby establishing and solving a convex optimization model for the T-LCDL. In step three above, after receiving the T-LCDL issued by the TSO, the DSO formulates the D-LCDL for the DR load in the distribution network. On the one hand, it should minimize the difference between the total power consumption curve of the distribution network and the T-LCDL, so as to actively respond to the guidance of the T-LCDL and promote the consumption of new energy in the transmission network. On the other hand, considering the principle of fairness, the difference between D-LCDLs should be minimized. Based on the above requirements, a convex optimization model for D-LCDL is formulated and solved. In step three, specifically: D-LCDL formulates the model as follows: (6b)； Constraints: (6d)； In the objective function (6a), k is the index of the DR load in the distribution network, and the corresponding set is... , The D-LCDL value of DR load in time period t. The average value of D-LCDLs is shown in equation (6b), vector This represents the profile of the total net power consumption curve of the distribution network. As a penalty constant, to ensure that the distribution network responds to the guidance of T-LCDL to a certain extent, equations (6c)-(6d) are D-LCDL related constraints, equation (6e) is the contour constraint of the total net power consumption curve of the distribution network, equation (6f) is the tie-line power constraint, and equation (6g) is the power balance constraint, where Let R be the predicted maximum output of renewable energy in the distribution network; The rigid load power of the distribution network corresponds to set C, and equation (6h) represents the network constraints of the distribution network, where... and These are the lower and upper limits of the power flow, respectively. , The distribution factors are the power transmission allocation factors of renewable energy units, tie line nodes, DR loads, and rigid loads to line l, respectively. The established distribution network node load guideline model (6a)-(6h) is a convex optimization problem, which can be solved directly by the Gurobi commercial solver.

2. The quasi-linear demand response method for achieving coordinated power transmission and distribution as described in claim 1, characterized in that: In step one, the ideal load curve shape of DR loads located at different locations is defined as the node load guideline LCDL, including the node load guideline T-LCDL at the transmission network level and the node load guideline D-LCDL at the distribution network level. At the transmission network level, the T-LCDL is formulated and published by the transmission system operator (TSO) to the corresponding node distribution system operator (DSO) to guide the active distribution network to participate in DR in order to promote the consumption of renewable energy in the transmission network. When the consumption curve of the active distribution network perfectly matches the T-LCDL, the power fluctuations caused by renewable energy and rigid loads in the transmission network are effectively controlled. The load will be effectively smoothed out, while the unsmoothed portion will be smoothed out by conventional thermal power units. In practice, when conventional units reach their regulation capacity limit, further measures such as renewable energy curtailment and load shedding will be taken. At the distribution network level, the DSO formulates the D-LCDL and publishes it to users registered to participate in DR at different nodes. This is used to guide users to effectively shape their load curves to approach the D-LCDL, so that on the one hand, the net load curve of the active distribution network approaches the corresponding T-LCDL, which is conducive to the economical and green operation of the transmission network. On the other hand, it effectively alleviates local congestion in the distribution network and promotes the consumption of local renewable energy in the distribution network.

3. The quasi-linear demand response method for achieving coordinated power transmission and distribution as described in claim 1, characterized in that: In step one, the principle of "adjustable" balancing "unadjustable" means that power fluctuations caused by unadjustable resources are quantified by superimposing the predicted maximum output of renewable energy generation with rigid loads, and based on this, the ideal load curve shape of the DR load to mitigate power fluctuations is formulated.

4. The quasi-linear demand response method for achieving coordinated power transmission and distribution as described in claim 1, characterized in that: In step one, specifically: consider the power balance that is "adjustable" while satisfying "non-adjustable" as shown in equation (1): （1）； in, , , , , These are indices for time periods, conventional generating units, active distribution networks, and renewable energy generating units, corresponding to the following sets. G, D, R, C, considering the duration of DR implementation is arrive , This is the predicted total power output of conventional generating units, which can be obtained based on historical data. It is the predicted total power consumption of the distribution network. It is the system-level regulation responsibility curve of a conventional unit. It is the load baseline at the transmission network system level. and These are the maximum output of renewable energy and the power of rigid load, respectively. and Satisfying Relationship: (2)； Substituting equation (2) into equation (1) yields : Thus, the load baseline at the transmission network system level is obtained. .

5. The quasi-linear demand response method for achieving coordinated power transmission and distribution as described in claim 1, characterized in that: In step two, the principle of fairness is that there is no single way to decompose the ideal load curve at the system level to each node. Considering that LCDL determines the revenue of DR users, it is necessary to ensure the fairness and objectivity of LCDL formulation in order to avoid user disputes and ensure that the results of LCDL are not affected by users' strategic manipulation.

6. The quasi-linear demand response method for achieving coordinated power transmission and distribution according to claim 1, characterized in that: In step two, specifically, it involves: under actual network constraints, the spatial distribution differences of unadjustable resources... It is difficult to accurately characterize the regulation needs at different locations, thus hindering the precise guidance of distribution networks at different locations to participate in DR (Renewable Energy Transfer) to improve the absorption capacity of renewable energy. Therefore, energy transfer needs to be carried out based on the system-level node load baseline to form T-LCDLs that satisfy network constraints. Considering the principle of fairness, the total transferred energy should be minimized, that is, the difference between T-LCDLs should be minimized. Similarly, consider the following optimization problem: Constraints: (4g)； in, The T-LCDL of the distribution network is compared to The offset, This indicates that the T-LCDL of the distribution network rises relative to the system-level load baseline within a time period t, and vice versa. Similarly; it should be noted that the actual energy shift is due to Multiply Thus, constraints (4b) and (4c) ensure that conventional generating units and the distribution network participate in regulation, resulting only in the temporal transfer of electricity, without increasing or decreasing the amount of electricity. Constraint (4d) represents the generation attribute of conventional generating units, and constraint (4e) is the tie-line constraint between the transmission network and the distribution network. The upper limit of the transmission power of the tie line is given by equation (4f), which is the spatial coupling constraint to ensure power balance in each time period. The network constraints of the transmission network are shown in equation (4g). The index of the line, the corresponding set is , and These are the lower and upper limits of the power flow, respectively. , Let represent the power transmission allocation factors of conventional generating units, renewable energy generating units, distribution network, and rigid loads to the line, respectively. Since the DR scheme only considers active power, a linear DC power flow model that ignores network losses is introduced to describe the network constraints of the system. After solving the above convex optimization model, the T-LCDL of each node in the transmission network can be obtained: (5)。

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