A multi-level power grid coordinated robust dispatching method and device considering flexibility resources

Abstract

The present disclosure relates to a method and device for coordinated robust scheduling of multi-level power grids taking into account flexibility resources, and belongs to the field of probabilistic optimization scheduling of power systems. The method comprises: constructing an objective function of a robust optimization scheduling model for a transmission and distribution microgrid, wherein the objective function is to minimize the cost of power generation and maximize the consumption of new energy; constructing constraint conditions for the robust optimization scheduling model for the transmission and distribution microgrid, including: transmission network constraints, distribution network constraints, microgrid constraints, and boundary coupling constraints of the transmission and distribution microgrid; converting the robust optimization scheduling model for the transmission and distribution microgrid into a deterministic model, and solving the deterministic model to obtain the scheduling optimization result of the transmission and distribution microgrid. The present disclosure can fully tap the control capability of the flexibility resources on the distribution microgrid side and promote the consumption of new energy in the entire network.

Description

Technical Field

[0001] The present disclosure belongs to the field of probabilistic optimization scheduling of power systems, and in particular relates to a method and device for coordinated robust scheduling of multi-level power grids considering flexibility resources. Background Art

[0002] As the penetration of centralized and distributed renewable energy sources in transmission and distribution microgrids continues to increase, the strong randomness and volatility of renewable energy sources pose significant challenges to grid operation and regulation. The intermittent nature of renewable energy sources is likely to lead to significant power flow fluctuations and severe voltage overshoots. Furthermore, with the integration of a large number of distributed flexible resources on the distribution network side, such as distributed renewable energy, energy storage, and controllable loads, power flows in the grid are shifting from unidirectional to bidirectional, and traditional distribution networks are gradually transforming into active distribution networks (ANDs). Furthermore, given the current development of technologies such as autonomous renewable energy clusters and virtual power plants, small-scale microgrids are also gradually emerging within distribution networks. To fully leverage the potential of distribution networks and microgrids to enhance power system flexibility and address the uncertainties of renewable energy, transmission and distribution microgrids need to be collaboratively optimized and mutually supportive of their regulation capabilities.

[0003] Currently, transmission and distribution microgrids operate relatively independently. The traditional regulation model involves separate optimization and regulation of each microgrid at a given boundary power threshold. However, with the increasing integration of renewable energy sources, the equivalent loads of the distribution network and microgrid at the upper grid gateway are difficult to accurately predict. This traditional separation of transmission and distribution microgrids fails to fully utilize the controllable resources within the microgrid, potentially leading to unnecessary increases in regulation costs and boundary power mismatches. Furthermore, due to the high volatility of renewable energy, traditional deterministic optimization scheduling is also likely to face problems such as line flow exceeding limits and insufficient system reserve. Summary of the Invention

[0004] The purpose of this disclosure is to overcome the shortcomings of existing technologies and propose a method and device for coordinated robust scheduling of multi-level power grids that considers flexibility resources. This invention implements adjustable robust intraday rolling scheduling using renewable energy forecast intervals and has high application value.

[0005] The first embodiment of the present disclosure provides a multi-level power grid coordinated robust scheduling method considering flexibility resources, including:

[0006] Constructing an objective function for a robust optimization scheduling model for a transmission and distribution microgrid; the transmission and distribution microgrid is a three-level grid consisting of a transmission grid, a distribution grid, and a microgrid, with each distribution grid coupled to a corresponding transmission grid and multiple microgrids via tie lines; the objective function is to minimize power generation costs and maximize new energy consumption;

[0007] Constructing the constraint conditions of the robust optimization scheduling model of the transmission and distribution microgrid, including: transmission network constraints, distribution network constraints, microgrid constraints and boundary coupling constraints of the transmission and distribution microgrid;

[0008] Converting the transmission and distribution microgrid robust optimization scheduling model into a deterministic model;

[0009] The deterministic model is solved to obtain a dispatch optimization result of the transmission and distribution microgrid.

[0010] In a specific embodiment of the present disclosure, the objective function expression of the transmission and distribution microgrid robust optimization scheduling model is as follows:

[0011]

[0012] Where, the total number of dispatch periods is T; superscript AG represents AGC units; superscript G represents non-AGC units; superscript DG represents new energy stations; superscript ESS represents energy storage; and are the power generation costs of the i-th non-AGC unit and the j-th AGC unit in the t-th period respectively; and are the outputs of the i-th non-AGC unit and the j-th AGC unit in the t-th period respectively; and are the output reduction penalty costs of the g-th renewable energy station in the t-th period under the worst scenarios of the transmission network, distribution network and microgrid respectively; is the upper bound of the allowed output range of the g-th renewable energy station in the t-th period; and are the charging power and discharging power of the e-th energy storage in the t-th period respectively; is the penalty cost of charging and discharging of the e-th energy storage in the t-th period; and ID and IM are the number sets of non-AGC units and AGC units in the transmission network respectively; ID and IM are the number sets of distribution network and microgrid respectively; and are the numbered sets of the new energy stations and energy storage power stations in the d-th distribution network; is the set of numbers of new energy stations in the mth microgrid.

[0013] In a specific embodiment of the present disclosure, the power generation cost expressions of the non-AGC unit and the AGC unit are respectively as follows:

[0014]

[0015]

[0016] Where a0,i,t ,a 1,i,t ,a 2,i ,t are the constant term, linear term and quadratic term coefficients of the power generation cost of the i-th unit in the t-th period respectively;

[0017] The output reduction penalty cost expression of the g-th renewable energy station in the t-th period under the worst scenario is as follows:

[0018]

[0019]

[0020]

[0021] Where, is the upper bound of the predicted output of the g-th renewable energy station in the t-th period; M g is the output reduction penalty coefficient corresponding to the g-th new energy station;

[0022] The penalty cost expression of the charge and discharge of the e-th energy storage in the t-th period is as follows:

[0023]

[0024] Where, and are the charging efficiency and discharging efficiency of the e-th energy storage respectively.

[0025] In a specific embodiment of the present disclosure, the transmission grid constraints include:

[0026] Power balance constraints;

[0027]

[0028] Where D Trans,t is the total load demand in the transmission network in the tth period; is the power delivered by the transmission network to the d-th distribution network in the t-th period; is the actual output of the g-th renewable energy station in the t-th period; The expression of is shown in formula (9):

[0029]

[0030] Where, is the base point power of the jth AGC unit in the tth period; is the base point power of the g-th new energy station in the t-th period; α j is the mismatch power allocation coefficient of the jth AGC unit, and satisfies the following formula:

[0031]

[0032] Output constraints of conventional units;

[0033]

[0034]

[0035] Where, and are the lower and upper bounds of the output of the i-th non-AGC unit in the t-th period, respectively; and are the lower and upper bounds of the output of the j-th AGC unit in the t-th period, respectively;

[0036] Climbing constraints of conventional units;

[0037]

[0038]

[0039] Where, and are the maximum downward ramp-down power and maximum upward ramp-up power of the i-th non-AGC unit in the t-th period, respectively; and are the maximum downward ramp-up power and maximum upward ramp-up power of the jth AGC unit in the tth period respectively; Δt is the optimized time interval;

[0040] Spinning reserve constraints;

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047] Where, and are the downward and upward spinning reserve contributions of the jth AGC unit in the tth period, respectively; and are the downward and upward spinning reserve capacity requirements of the transmission network in period t, respectively;

[0048] Section capacity constraints;

[0049]

[0050] Where G Trans,l,i is the power generation transfer distribution factor of unit i in the transmission network to the transmission section l; is the generation transfer distribution factor of the virtual load corresponding to the d-th distribution network in the transmission network for transmission section l; L Trans,l,t and are the lower and upper bounds of the power flow of the transmission section l of the transmission network in the tth period respectively; L Trans is the set of sections in the transmission network;

[0051] New energy output constraints;

[0052]

[0053]

[0054]

[0055] Where, and are the upper and lower bounds of the allowed output range of the g-th renewable energy station in the t-th period respectively; and are the upper and lower bounds of the predicted output of the g-th new energy station in the t-th period, respectively.

[0056] The distribution network constraints include:

[0057] Thermal load operating constraints;

[0058]

[0059] Where, is the indoor temperature of the hth user in the tth period; is the parameter of the hth user; is the parameter of the heating / cooling state of the hth user heat load. If the hth user heat load is running in the heating mode, then is positive, if the hth user heat load is running in cooling mode, then is negative; is the set of numbers of controllable thermal loads in the d-th distribution network, and the superscript Load can represent the controllable thermal load;

[0060] Energy storage operation constraints;

[0061]

[0062] Where, and are the lower and upper limits of the e-th energy storage capacity in the d-th distribution network, respectively; is the initial value of the energy storage capacity of the e-th energy storage;

[0063]

[0064]

[0065] Where, and are the upper limit of charging power and the upper limit of discharging power of the e-th energy storage respectively;

[0066]

[0067] Power balance constraints;

[0068]

[0069] Where, is the active power in line i→j in the d-th distribution network in the t-th time period; is the network loss in line i→j in the d-th distribution network during the t-th period; is the net load connected at node j in the d-th distribution network during the t-th period;

[0070] Line network loss constraints;

[0071]

[0072] Where, and are the active power base value and reactive power base value of line i→j in the d-th distribution network in the t-th time period respectively; is the voltage base value at node i in the d-th distribution network in the t-th time period; the above base value can be obtained by base state power flow calculation; is the resistance of line i→j in the d-th distribution network;

[0073] Node net load constraints;

[0074]

[0075] Where, and are the power received by the d-th distribution network from the transmission grid in the t-th period and the power sent to the m-th microgrid connected to the node j; is the total uncontrollable load demand connected to node j in the d-th distribution network during the t-th period; and are the collections of new energy power stations, energy storage, controllable thermal loads, and lower-level microgrids connected to node j in the d-th distribution network;

[0076] Line capacity constraints;

[0077]

[0078] Where, and are the lower and upper limits of the power flow of line i→j in the d-th distribution network;

[0079] New energy output constraints;

[0080]

[0081]

[0082]

[0083] The microgrid constraints include:

[0084] Thermal load operating constraints;

[0085]

[0086] Where, is the set of numbers of controllable thermal loads in the mth microgrid;

[0087] Power balance constraints;

[0088]

[0089] Where, is the power received by the mth microgrid from the dth distribution network in the tth period, is the total uncontrollable load demand of the mth microgrid in the tth period;

[0090] New energy output constraints;

[0091]

[0092]

[0093]

[0094] The boundary coupling constraints of the transmission and distribution microgrid include:

[0095]

[0096]

[0097] In a specific embodiment of the present disclosure, before converting the transmission and distribution microgrid robust optimization scheduling model into a deterministic model, the method further includes:

[0098] The robust optimization dispatch model of the transmission and distribution microgrid is converted into a compact form, including:

[0099] The robust optimization scheduling model of the transmission and distribution microgrid is converted into a compact form as follows:

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] Where, and are the decision variables of the transmission network, the d-th distribution network and the m-th microgrid respectively; and are the uncertainty variables in the transmission network, the d-th distribution network and the m-th microgrid respectively; y Trans The transmission network depends on The adjustable variables; is the power vector sum sent from the transmission network to the connected distribution network; is the power vector sent by the d-th distribution network to the connected microgrid; Q Trans >0, A Trans ,B Trans ,C Trans ,D Trans , is a constant matrix; f Trans , E Trans , is a constant vector; r Trans , is a constant value;

[0108] x Trans include

[0109] include

[0110] include

[0111] y Trans correspond The expression is as follows:

[0112]

[0113] Where, correspond α corresponds to

[0114] Substituting Equation (51) into Equation (45), the optimization problem (PA1) is equivalently transformed into the robust optimization problem (P1) shown below:

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122] In a specific embodiment of the present disclosure, converting the transmission and distribution microgrid robust optimization scheduling model into a deterministic model includes:

[0123] Equation (53) can be converted into the following equivalent equation:

[0124]

[0125] for For each term in Trans,g > 0, then use the upper bound of the uncertainty variable Alternative If the coefficient H Trans,g ≤0, then use the lower bound of the uncertainty variable Alternative Then we get the deterministic constraint equivalent to formula (53):

[0126]

[0127] Formula (60) is about Inequality constraints;

[0128] Combining equation (54) into equation (60), the expression is as follows:

[0129]

[0130] Where, The deterministic constraint expressions equivalent to Equation (55) and Equation (57) are as follows:

[0131]

[0132]

[0133] Combining Equation (56) into Equation (62) yields the following compact form:

[0134]

[0135] Where, Combining Equation (58) into Equation (63) yields the following compact form:

[0136]

[0137] Where,

[0138] The deterministic quadratic programming problem equivalent to the robust optimization problem (P1) is obtained as follows:

[0139]

[0140] In a specific embodiment of the present disclosure, solving the deterministic model to obtain a dispatch optimization result of the transmission and distribution microgrid includes:

[0141] The deterministic model is solved using the alternating direction multiplier method to obtain the generator sets and new energy sources in the transmission network. The optimal solution for renewable energy, energy storage and controllable thermal loads in distribution networks The optimal solution for renewable energy and controllable thermal loads in microgrids The optimal solution is the dispatch optimization result of the transmission and distribution microgrid.

[0142] A second embodiment of the present disclosure provides a multi-level power grid coordinated robust scheduling method considering flexibility resources, including:

[0143] Obtaining parameters of each level of the transmission and distribution microgrid, wherein the transmission and distribution microgrid is a three-level grid consisting of a transmission grid, a distribution grid, and a microgrid, wherein each distribution grid is coupled to a corresponding transmission grid and multiple microgrids via tie lines;

[0144] The parameters are input into a preset robust optimization scheduling model for a transmission and distribution microgrid, wherein the objective function of the robust optimization scheduling model for a transmission and distribution microgrid is to minimize the power generation cost and maximize the consumption of new energy. The constraints of the robust optimization scheduling model for a transmission and distribution microgrid include: transmission network constraints, distribution network constraints, microgrid constraints, and boundary coupling constraints of the transmission and distribution microgrid. The robust optimization scheduling model for a transmission and distribution microgrid is converted into a deterministic model, and the deterministic model outputs the scheduling optimization result of the transmission and distribution microgrid.

[0145] A third embodiment of the present disclosure provides a multi-level power grid coordinated robust scheduling device considering flexibility resources, including:

[0146] An objective function construction module is used to construct the objective function of a robust optimization scheduling model for a transmission and distribution microgrid; the transmission and distribution microgrid is a three-level power grid consisting of a transmission grid, a distribution grid, and a microgrid, with each distribution grid coupled to a corresponding transmission grid and multiple microgrids via tie lines; the objective function of the robust optimization scheduling model for the transmission and distribution microgrid is to minimize power generation costs and maximize new energy consumption;

[0147] A constraint condition construction module is used to construct the constraint conditions of the transmission and distribution microgrid robust optimization scheduling model, including: transmission network constraints, distribution network constraints, microgrid constraints and boundary coupling constraints of the transmission and distribution microgrid;

[0148] A deterministic model generation module, configured to convert the transmission and distribution microgrid robust optimization scheduling model into a deterministic model;

[0149] The scheduling optimization module is used to solve the deterministic model to obtain a scheduling optimization result of the transmission and distribution microgrid.

[0150] A fourth embodiment of the present disclosure provides a multi-level power grid coordinated robust scheduling device considering flexibility resources, including:

[0151] A parameter acquisition module is used to respectively acquire parameters of each level of the transmission and distribution microgrid. The transmission and distribution microgrid is a three-level grid consisting of a transmission network, a distribution network, and a microgrid. Each distribution network is coupled to the corresponding transmission network and multiple microgrids through tie lines.

[0152] A scheduling model construction module is used to input the parameters into a preset transmission and distribution microgrid robust optimization scheduling model, where the objective function of the transmission and distribution microgrid robust optimization scheduling model is to minimize the power generation cost and maximize the new energy consumption. The constraints of the transmission and distribution microgrid robust optimization scheduling model include: transmission network constraints, distribution network constraints, microgrid constraints, and boundary coupling constraints of the transmission and distribution microgrid; by converting the transmission and distribution microgrid robust optimization scheduling model into a deterministic model, the deterministic model outputs the scheduling optimization result of the transmission and distribution microgrid.

[0153] A fifth embodiment of the present disclosure provides an electronic device, including:

[0154] at least one processor; and a memory communicatively coupled to the at least one processor;

[0155] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the above-mentioned multi-level power grid coordinated robust scheduling method considering flexibility resources.

[0156] The sixth aspect embodiment of the present disclosure proposes a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned multi-level power grid coordinated robust scheduling method considering flexibility resources.

[0157] The characteristics and beneficial effects of the present disclosure are:

[0158] 1. This disclosure establishes a method for probabilistic scheduling of active power for coordinated transmission and distribution microgrids.

[0159] 2. This disclosure converts the robust model into a deterministic model, which is reliable and easy to apply.

[0160] 3. The model disclosed in this paper is accurate and effective, and can be solved using the existing alternating direction multiplier method. It shows the effect of promoting the consumption of new energy and providing system rotating reserve, and has high application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0161] Figure 1 This is an overall flow chart of a multi-level power grid coordinated robust scheduling method considering flexibility resources in an embodiment of the present disclosure.

[0162] Figure 2 It is a structural diagram of a transmission and distribution microgrid in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0163] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0164] A first aspect of the present disclosure provides a method for coordinated robust scheduling of a multi-level power grid taking flexibility resources into consideration, including:

[0165] Constructing an objective function for a robust optimization scheduling model for a transmission and distribution microgrid; the transmission and distribution microgrid is a three-level grid consisting of a transmission grid, a distribution grid, and a microgrid, with each distribution grid coupled to a corresponding transmission grid and multiple microgrids via tie lines; the objective function is to minimize power generation costs and maximize new energy consumption;

[0166] Constructing the constraint conditions of the robust optimization scheduling model of the transmission and distribution microgrid, including: transmission network constraints, distribution network constraints, microgrid constraints and boundary coupling constraints of the transmission and distribution microgrid;

[0167] Converting the transmission and distribution microgrid robust optimization scheduling model into a deterministic model;

[0168] The deterministic model is solved to obtain a dispatch optimization result of the transmission and distribution microgrid.

[0169] In a specific embodiment of the present disclosure, the multi-level grid coordinated robust scheduling method considering flexibility resources has the following overall process: Figure 1 As shown, the following steps are included:

[0170] 1) Establish a robust optimization scheduling model for transmission and distribution microgrids.

[0171] In a specific embodiment of the present disclosure, the transmission and distribution microgrid structure is as follows: Figure 2 As shown, it is a three-level interconnected architecture consisting of a transmission network, a distribution network and a microgrid. Each distribution network is coupled with the corresponding transmission network and multiple microgrids through tie lines. In this embodiment, as shown in FIG. Figure 2 As shown in the figure, the transmission network is connected to N distribution networks, each of which is connected to multiple microgrids. Among them, distribution network No. 1 to distribution network No. N are all connected to the transmission network, distribution network No. 1 is connected to its n downstream microgrids, and distribution network No. N is connected to its m downstream microgrids.

[0172] In the transmission and distribution microgrid of this embodiment, the robust optimization intraday rolling dispatch mode is established as follows:

[0173] Based on interval forecasts, renewable energy stations generate output ranges within a certain confidence level and transmit them to control centers at all levels. Based on the interval forecasts and ultra-short-term load forecasts reported by the renewable energy stations, the control centers calculate the permissible output ranges for each renewable energy station, predicated on meeting grid operation and safety constraints. For the transmission network control center, the basepoint power values ​​of AGC units and the dispatch plan values ​​of non-AGC units must also be calculated. For the distribution network and microgrid control centers, dispatch plans for energy storage and controllable loads must also be calculated separately.

[0174] When the output of the new energy station is within the allowable output range, the new energy station operates in maximum power point tracking mode; when the actual available power of the new energy station exceeds the allowable output range, the output of the new energy station is controlled at the boundary of the allowable output range.

[0175] In a specific embodiment of the present invention, the specific steps of establishing a robust optimization scheduling model for a transmission and distribution microgrid are as follows:

[0176] 1-1) Establish the objective function

[0177] Robust optimization of transmission and distribution microgrids for intraday rolling scheduling aims to minimize power generation costs and maximize renewable energy consumption:

[0178]

[0179] Where, the total number of dispatch periods is T; superscript AG represents AGC units; superscript G represents non-AGC units; superscript DG represents new energy stations; superscript ESS represents energy storage; and are the power generation costs of the i-th non-AGC unit and the j-th AGC unit in the t-th period respectively; and are the outputs of the i-th non-AGC unit and the j-th AGC unit in the t-th period respectively; and The output reduction penalty cost of the g-th renewable energy station in the t-th period under the worst scenario for the transmission network, distribution network, and microgrid, respectively. The non-zero output reduction penalty cost item does not mean that there is an actual cost for reducing the output of renewable energy. The purpose of introducing this item is to maximize the absorption of renewable energy. is the upper bound of the allowed output range of the g-th renewable energy station in the t-th period; and are the charging power and discharging power of the e-th energy storage in the t-th period respectively; is the penalty cost of charging and discharging of the e-th energy storage in the t-th period; and ID and IM are the number sets of non-AGC units and AGC units in the transmission network respectively; ID and IM are the number sets of distribution network and microgrid respectively; and are the numbered sets of the new energy stations and energy storage power stations in the d-th distribution network; is the set of numbers of new energy stations in the mth microgrid.

[0180] The power generation costs of both non-AGC units and AGC units can be described by quadratic functions:

[0181]

[0182]

[0183] Where a 0,i,t ,a 1,i,t ,a 2,i,t are the constant term, linear term and quadratic term coefficients of the power generation cost of the i-th unit in the t-th period respectively.

[0184] The expression of the penalty cost of reducing the output of new energy stations in the worst scenario is as follows:

[0185]

[0186]

[0187]

[0188] Where, is the upper bound of the predicted output of the g-th renewable energy station in the t-th period; M g is the output reduction penalty coefficient corresponding to the g-th new energy station. In this embodiment, M g =10 4 .

[0189] In order to avoid simultaneous charging and discharging of energy storage, this embodiment introduces the following penalty term into the objective function:

[0190]

[0191] Where, and are respectively the charging efficiency and discharging efficiency of the e-th energy storage. In this embodiment, both parameters are set to 0.9.

[0192] From equations (2) to (6), we can see that the incremental rates of conventional units and renewable energy stations are non-negative and non-positive, respectively. According to the equal incremental rate criterion, renewable energy stations can have a higher power generation priority than conventional units.

[0193] 1-2) Establish transmission network constraints, including:

[0194] 1-2-1) Power balance constraints;

[0195]

[0196] Where, and are the number sets of non-AGC units and AGC units in the transmission network respectively. Trans,t is the total load demand in the transmission network in the tth period; is the power delivered by the transmission network to the d-th distribution network in the t-th period; is the actual output of the g-th renewable energy station in the t-th period; The expression of is shown in formula (9); in the model of this embodiment, the influence of network loss can be ignored.

[0197]

[0198] Where, is the base point power of the jth AGC unit in the tth period; is the base point power of the g-th new energy station in the t-th period; α j is the mismatch power allocation coefficient of the jth AGC unit, which is manually specified offline by the system operator in advance and satisfies equation (10).

[0199]

[0200] 1-2-2) Output constraints of conventional units;

[0201]

[0202]

[0203] Where, and are the lower and upper bounds of the output of the i-th non-AGC unit in the t-th period, respectively; and are the lower and upper bounds of the output of the j-th AGC unit in the t-th period, respectively.

[0204] 1-2-3) Climbing constraints of conventional units;

[0205]

[0206]

[0207] Where, and are the maximum downward ramp-down power and maximum upward ramp-up power of the i-th non-AGC unit in the t-th period, respectively; and are the maximum downward climbing power and maximum upward climbing power of the jth AGC unit in the tth period respectively; Δt is the optimized time interval.

[0208] 1-2-4) Spinning reserve constraints;

[0209]

[0210]

[0211]

[0212]

[0213]

[0214]

[0215] Where, and are the downward and upward spinning reserve contributions of the jth AGC unit in the tth period, respectively; and are the downward and upward spinning reserve capacity requirements of the transmission network in the tth period, respectively.

[0216] 1-2-5) Section capacity constraints;

[0217]

[0218] Where G Trans,l,i is the power generation transfer distribution factor of unit i in the transmission network to the transmission section l; is the generation transfer distribution factor of the virtual load corresponding to the d-th distribution network in the transmission network for transmission section l; L Trans,l,t and are the lower and upper bounds of the power flow of the transmission section l of the transmission network in the tth period respectively; L Trans is the set of sections in the transmission network.

[0219] 1-2-6) Constraints on new energy output;

[0220]

[0221]

[0222]

[0223] Where, and are the upper and lower bounds of the allowed output range of the g-th renewable energy station in the t-th period respectively; and are the upper and lower bounds of the predicted output of the g-th new energy station in the t-th period, respectively.

[0224] In this embodiment, the upper limit of the allowable output range of the new energy station does not exceed the upper limit of the predicted range, and the lower limit of the allowable output range does not exceed the lower limit of the predicted range.

[0225] 1-3) Establish distribution network constraints, including:

[0226] 1-3-1) Thermal load operation constraints;

[0227]

[0228] Where, is the indoor temperature of the hth user in the tth period; is the known parameter of the hth user, which reflects the impact of outdoor temperature changes on room temperature; is the parameter of the heating / cooling state of the hth user heat load. If the hth user heat load is running in the heating mode, then is positive, if the hth user heat load is running in cooling mode, then is negative; is the set of numbers of controllable thermal loads in the d-th distribution network, and the superscript Load can represent the controllable thermal load.

[0229] 1-3-2) Energy storage operation constraints;

[0230] Considering the characteristics of energy storage, its operation should meet the following constraints:

[0231]

[0232] Where, and are the lower and upper limits of the e-th energy storage capacity in the d-th distribution network, respectively; is the initial value of the energy storage capacity of the e-th energy storage.

[0233]

[0234]

[0235] Where, and are the upper limit of charging power and the upper limit of discharging power of the e-th energy storage respectively.

[0236]

[0237] To avoid simultaneous charging and discharging, this embodiment introduces the complementary constraint (29); however, Equation (29) is essentially a bilinear constraint that is difficult to convexify and solve. Therefore, a penalty term as shown in Equation (7) is introduced into the cost function, which also has the effect of avoiding simultaneous charging and discharging.

[0238] 1-3-3) Power balance constraints;

[0239] In the distribution network, this embodiment adopts the linearized AC power flow equation:

[0240]

[0241] Where, is the active power in line i→j in the d-th distribution network in the t-th time period; is the network loss in line i→j in the d-th distribution network during the t-th period; is the net load connected at node j in the d-th distribution network in the t-th time period.

[0242] 1-3-4) Line network loss constraints;

[0243]

[0244] Where, and are the active power base value and reactive power base value of line i→j in the d-th distribution network in the t-th time period respectively; is the voltage base value at node i in the d-th distribution network in the t-th time period; the above base value can be obtained by base state power flow calculation; is the resistance of line i→j in the d-th distribution network.

[0245] 1-3-5) Node net load constraints;

[0246]

[0247] Where, and are the power received by the d-th distribution network from the transmission grid in the t-th period and the power sent to the m-th microgrid connected to the node j; is the total demand of the uncontrollable load connected to the node j in the tth period in the dth distribution network. In this embodiment, are known input parameters; and are the collections of new energy power stations, energy storage, controllable thermal loads and lower-level microgrids connected to node j in the d-th distribution network.

[0248] 1-3-6) Line capacity constraints;

[0249]

[0250] Where, and are the lower and upper limits of the power flow of line i→j in the d-th distribution network, respectively.

[0251] 1-3-7) Constraints on new energy output;

[0252]

[0253]

[0254]

[0255] 1-4) Establish microgrid constraints, including:

[0256] 1-4-1) Thermal load operation constraints;

[0257]

[0258] Where, is the set of numbers of controllable thermal loads in the mth microgrid.

[0259] 1-4-2) Power balance constraints;

[0260]

[0261] Where, is the power received by the mth microgrid from the dth distribution network in the tth period, is the total uncontrollable load demand of the mth microgrid in the tth period.

[0262] 1-4-3) Constraints on new energy output;

[0263]

[0264]

[0265]

[0266] 1-5) Boundary coupling constraints of transmission and distribution microgrids;

[0267] In this embodiment, the transmission and distribution microgrids are coupled together via a gateway. The boundary coupling of the transmission and distribution microgrids is reflected by the consistency of boundary active power. The boundary power is equivalent to a virtual load in the upper grid and a virtual generator in the lower grid. The positive direction of the boundary power is defined as the power transmitted from the upper grid to the lower grid.

[0268]

[0269]

[0270] 2) Convert the robust optimization scheduling model of the transmission and distribution microgrid established in step 1).

[0271] In this embodiment, the transmission and distribution microgrid robust optimization scheduling model in step 1) is converted into a compact form as follows:

[0272]

[0273]

[0274]

[0275]

[0276]

[0277]

[0278]

[0279] Where, and are the decision variables of the transmission network, the d-th distribution network and the m-th microgrid respectively; and are the uncertainty variables in the transmission network, the d-th distribution network and the m-th microgrid respectively; y Trans The transmission network depends on The adjustable variables; is the power vector sum sent from the transmission network to the connected distribution network; is the power vector sent by the d-th distribution network to the connected microgrid.

[0280] Q Trans >0, A Trans ,B Trans ,C Trans ,D Trans , is a constant matrix; f Trans , E Trans , is a constant vector; r Trans , Is a constant value.

[0281] Specifically, x Trans include

[0282] include

[0283] include

[0284] y Trans correspond The expression is as follows:

[0285]

[0286] Where, correspond α corresponds to

[0287] Equation (44) is the overall objective function of the robust optimization scheduling model for the transmission and distribution microgrid, which is also the compact form of Equation (1). Equation (45) is the compact form of Equations (8)-(21), describing the operation constraints of the transmission network; Equation (47) is the compact form of Equations (26)-(28), (30)-(33), describing the operation constraints of the distribution network; Equation (49) is the compact form of Equations (37)-(38), describing the operation constraints of the microgrid; Equations (46), (48), and (50) correspond to constraints (22)-(24), (34)-(36), and (39)-(41), respectively.

[0288] Substituting Equation (51) into Equation (45), the optimization problem (PA1) can be equivalently transformed into the robust optimization problem (P1) shown below:

[0289]

[0290]

[0291]

[0292]

[0293]

[0294]

[0295]

[0296] 3) Convert the robust optimization scheduling model converted in step 2) into a deterministic model.

[0297] As shown in equations (53), (55), and (57), regarding the uncertainty variable and The constraints are all linear constraints, and the coefficients are all constants. Therefore, the worst scenarios of the robust optimization scheduling model are all taken on the boundaries of the uncertainty variables. The boundaries of the uncertainty variables can be directly brought into the corresponding constraints, and then the uncertainty model can be converted into an equivalent deterministic model.

[0298] In this embodiment, equation (53) is equivalently transformed as shown below:

[0299]

[0300] for For each term in Trans,g > 0, then use the upper bound of the uncertainty variable to replace If the coefficient HTrans,g ≤0, then use the lower bound of the uncertainty variable to replace Then we can get the deterministic constraint equivalent to formula (53):

[0301]

[0302] After the constraint equivalence transformation, Equation (60) is about The inequality constraint of , then Equation (54) can be merged into Equation (60) as follows:

[0303]

[0304] Where,

[0305] Furthermore, the deterministic constraint expressions equivalent to Equation (55) and Equation (57) are as follows:

[0306]

[0307]

[0308] Combining Equation (56) into Equation (62) yields the following compact form:

[0309]

[0310] Where,

[0311] Combining Equation (58) into Equation (63) yields the following compact form:

[0312]

[0313] Where,

[0314] Then we can get the following deterministic quadratic programming problem which is equivalent to the robust optimization problem (P1):

[0315]

[0316] It should be noted that, in the traditional robust model, the upper and lower bounds of the uncertainty variables are given constants; whereas in the affine adjustable robust optimization model of this embodiment, the upper and lower bounds of the uncertainty variables are decision variables.

[0317] 4) Solve the deterministic model using the existing alternating direction multiplier method to obtain the dispatch optimization result of the transmission and distribution microgrid. In this embodiment, the proposed model can be solved using commercial optimization solvers such as Cplex and Gurobi; the model results give the generation units and new energy in the transmission network. The optimal solution for renewable energy, energy storage and controllable thermal loads in distribution networks The optimal solution for renewable energy and controllable thermal loads in microgrids The optimal solution is the dispatch optimization result of the transmission and distribution microgrid, which can effectively promote the consumption of new energy and the improvement of system spinning reserve.

[0318] To implement the above embodiment, a second embodiment of the present disclosure proposes a multi-level power grid coordinated robust scheduling method considering flexibility resources, including:

[0319] Obtaining parameters of each level of the transmission and distribution microgrid, wherein the transmission and distribution microgrid is a three-level grid consisting of a transmission grid, a distribution grid, and a microgrid, wherein each distribution grid is coupled to a corresponding transmission grid and multiple microgrids via tie lines;

[0320] The parameters are input into a preset robust optimization scheduling model for a transmission and distribution microgrid, wherein the objective function of the robust optimization scheduling model for a transmission and distribution microgrid is to minimize the power generation cost and maximize the consumption of new energy. The constraints of the robust optimization scheduling model for a transmission and distribution microgrid include: transmission network constraints, distribution network constraints, microgrid constraints, and boundary coupling constraints of the transmission and distribution microgrid. The robust optimization scheduling model for a transmission and distribution microgrid is converted into a deterministic model, and the deterministic model outputs the scheduling optimization result of the transmission and distribution microgrid.

[0321] It should be noted that the above explanation of the embodiment of a multi-level power grid coordinated robust scheduling method considering flexibility resources in the first aspect embodiment is also applicable to the multi-level power grid coordinated robust scheduling method considering flexibility resources in this embodiment, and will not be repeated here.

[0322] To implement the above embodiment, a third embodiment of the present disclosure proposes a multi-level power grid coordinated robust scheduling device considering flexibility resources, including:

[0323] An objective function construction module is used to construct the objective function of a robust optimization scheduling model for a transmission and distribution microgrid; the transmission and distribution microgrid is a three-level power grid consisting of a transmission grid, a distribution grid, and a microgrid, with each distribution grid coupled to a corresponding transmission grid and multiple microgrids via tie lines; the objective function of the robust optimization scheduling model for the transmission and distribution microgrid is to minimize power generation costs and maximize new energy consumption;

[0324] A constraint condition construction module is used to construct the constraint conditions of the transmission and distribution microgrid robust optimization scheduling model, including: transmission network constraints, distribution network constraints, microgrid constraints and boundary coupling constraints of the transmission and distribution microgrid;

[0325] A deterministic model generation module, configured to convert the transmission and distribution microgrid robust optimization scheduling model into a deterministic model;

[0326] The scheduling optimization module is used to solve the deterministic model to obtain a scheduling optimization result of the transmission and distribution microgrid.

[0327] It should be noted that the above-mentioned explanation of the embodiment of a multi-level power grid coordinated robust scheduling method considering flexibility resources in the first aspect embodiment is also applicable to a multi-level power grid coordinated robust scheduling device considering flexibility resources in this embodiment, and will not be repeated here.

[0328] According to an embodiment of the present invention, a multi-level grid coordinated robust scheduling device considering flexibility resources is proposed. By constructing the objective function of a robust optimization scheduling model for a transmission and distribution microgrid, the transmission and distribution microgrid is a three-level grid consisting of a transmission grid, a distribution grid, and a microgrid, with each distribution grid coupled to a corresponding transmission grid and multiple microgrids via tie lines. The objective function of the robust optimization scheduling model for the transmission and distribution microgrid is to minimize power generation costs and maximize renewable energy consumption. Constraints for the robust optimization scheduling model are constructed, including transmission grid constraints, distribution grid constraints, microgrid constraints, and boundary coupling constraints of the transmission and distribution microgrid. The robust optimization scheduling model for the transmission and distribution microgrid is converted into a deterministic model. The deterministic model is solved to obtain the scheduling optimization result of the transmission and distribution microgrid. This allows for adjustable robust intraday rolling scheduling using the renewable energy forecast interval, which has high application value.

[0329] To implement the above embodiment, a fourth aspect of the present disclosure provides a multi-level power grid coordinated robust scheduling device considering flexibility resources, including:

[0330] A parameter acquisition module is used to respectively acquire parameters of each level of the transmission and distribution microgrid. The transmission and distribution microgrid is a three-level grid consisting of a transmission network, a distribution network, and a microgrid. Each distribution network is coupled to the corresponding transmission network and multiple microgrids through tie lines.

[0331] A scheduling model solving module is used to input the parameters into a preset transmission and distribution microgrid robust optimization scheduling model, where the objective function of the transmission and distribution microgrid robust optimization scheduling model is to minimize the power generation cost and maximize the new energy consumption. The constraints of the transmission and distribution microgrid robust optimization scheduling model include: transmission network constraints, distribution network constraints, microgrid constraints, and boundary coupling constraints of the transmission and distribution microgrid; by converting the transmission and distribution microgrid robust optimization scheduling model into a deterministic model, the deterministic model outputs the scheduling optimization result of the transmission and distribution microgrid.

[0332] It should be noted that the above-mentioned explanation of the embodiment of a multi-level power grid coordinated robust scheduling method considering flexibility resources in the first aspect embodiment is also applicable to a multi-level power grid coordinated robust scheduling device considering flexibility resources in this embodiment, and will not be repeated here.

[0333] To implement the above embodiment, a fifth aspect of the present invention provides an electronic device, including:

[0334] at least one processor; and a memory communicatively coupled to the at least one processor;

[0335] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the above-mentioned multi-level power grid coordinated robust scheduling method considering flexibility resources.

[0336] To implement the above-mentioned embodiment, the sixth aspect of the present invention proposes a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned multi-level power grid coordinated robust scheduling method considering flexibility resources.

[0337] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0338] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to execute the method for coordinated robust scheduling of multi-level power grids that considers flexibility resources, as described in the above embodiment.

[0339] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0340] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0341] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0342] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0343] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be 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 and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0344] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0345] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0346] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0347] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for coordinated robust dispatching of multi-level power grids considering flexibility resources, characterized in that: include: Constructing an objective function for a robust optimization scheduling model for a transmission and distribution microgrid; the transmission and distribution microgrid is a three-level grid consisting of a transmission grid, a distribution grid, and a microgrid, wherein each distribution grid is coupled to a corresponding transmission grid and multiple microgrids via tie lines; The objective function is to minimize the cost of power generation and maximize the consumption of new energy; Constructing the constraint conditions of the robust optimization scheduling model of the transmission and distribution microgrid, including: transmission network constraints, distribution network constraints, microgrid constraints and boundary coupling constraints of the transmission and distribution microgrid; Converting the transmission and distribution microgrid robust optimization scheduling model into a deterministic model; Solving the deterministic model to obtain a dispatch optimization result of the transmission and distribution microgrid; The objective function expression of the robust optimization scheduling model of the transmission and distribution microgrid is as follows: Where, the total number of dispatch periods is T; superscript AG represents AGC units; superscript G represents non-AGC units; superscript DG represents new energy stations; superscript ESS represents energy storage; and are the power generation costs of the i-th non-AGC unit and the j-th AGC unit in the t-th period respectively; and are the outputs of the i-th non-AGC unit and the j-th AGC unit in the t-th period respectively; and are the output reduction penalty costs of the g-th renewable energy station in the t-th period under the worst scenarios of the transmission network, distribution network and microgrid respectively; is the upper bound of the allowed output range of the g-th renewable energy station in the t-th period; and are the charging power and discharging power of the e-th energy storage in the t-th period respectively; is the penalty cost of charging and discharging of the e-th energy storage in the t-th period; and ID and IM are the number sets of non-AGC units and AGC units in the transmission network respectively; ID and IM are the number sets of distribution network and microgrid respectively; and are the numbered sets of the new energy stations and energy storage power stations in the d-th distribution network; is the set of numbers of new energy stations in the mth microgrid.

2. The method according to claim 1, characterized in that The power generation cost expressions of the non-AGC unit and the AGC unit are respectively as follows: Where a 0,i,t ,a 1,i,t ,a 2,i,t are the constant term, linear term and quadratic term coefficients of the power generation cost of the i-th unit in the t-th period respectively; The output reduction penalty cost expression of the g-th renewable energy station in the t-th period under the worst scenario is as follows: Where, is the upper bound of the predicted output of the g-th renewable energy station in the t-th period; M g is the output reduction penalty coefficient corresponding to the g-th new energy station; The penalty cost expression of the charge and discharge of the e-th energy storage in the t-th period is as follows: Where, and are the charging efficiency and discharging efficiency of the e-th energy storage respectively.

3. The method according to claim 1, characterized in that The transmission grid constraints include: Power balance constraints; Where D Trans,t is the total load demand in the transmission network in the tth period; is the power delivered by the transmission network to the d-th distribution network in the t-th period; is the actual output of the g-th renewable energy station in the t-th period; The expression of is shown in formula (9): Where, is the base point power of the jth AGC unit in the tth period; is the base point power of the g-th new energy station in the t-th period; α j is the mismatch power allocation coefficient of the jth AGC unit, and satisfies the following formula: Output constraints of conventional units; Where, and are the lower and upper bounds of the output of the i-th non-AGC unit in the t-th period, respectively; and are the lower and upper bounds of the output of the j-th AGC unit in the t-th period, respectively; Climbing constraints of conventional units; Where, and are the maximum downward ramp-down power and maximum upward ramp-up power of the i-th non-AGC unit in the t-th period, respectively; and are the maximum downward ramp-up power and maximum upward ramp-up power of the jth AGC unit in the tth period respectively; Δt is the optimized time interval; Spinning reserve constraints; Where, and are the downward and upward spinning reserve contributions of the jth AGC unit in the tth period, respectively; and are the downward and upward spinning reserve capacity requirements of the transmission network in period t, respectively; Section capacity constraints; Where G Trans,l,i is the power generation transfer distribution factor of unit i in the transmission network to the transmission section l; G Trans,l,Bd is the generation transfer distribution factor of the virtual load corresponding to the d-th distribution network in the transmission network for transmission section l; L Trans,l,t and are the lower and upper bounds of the power flow of the transmission section l of the transmission network in the tth period respectively; L Trans is the set of sections in the transmission network; New energy output constraints; Where, and are the upper and lower bounds of the allowed output range of the g-th renewable energy station in the t-th period respectively; and are the upper and lower bounds of the predicted output of the g-th renewable energy station in the t-th period, respectively; The distribution network constraints include: Thermal load operating constraints; Where, T t in,h is the indoor temperature of the hth user in the tth period; is the parameter of the hth user; is the parameter of the heating / cooling state of the hth user heat load. If the hth user heat load is running in the heating mode, then is positive, if the hth user heat load is running in cooling mode, then is negative; is the set of numbers of controllable thermal loads in the d-th distribution network, and the superscript Load can represent the controllable thermal load; Energy storage operation constraints; Where, and are the lower and upper limits of the e-th energy storage capacity in the d-th distribution network, respectively; is the initial value of the energy storage capacity of the e-th energy storage; Where, and are the upper limit of charging power and the upper limit of discharging power of the e-th energy storage respectively; Power balance constraints; Where, is the active power in line i→j in the d-th distribution network in the t-th time period; is the network loss in line i→j in the d-th distribution network during the t-th period; is the net load connected at node j in the d-th distribution network during the t-th period; Line network loss constraints; Where, and are the active power base value and reactive power base value of line i→j in the d-th distribution network in the t-th time period respectively; is the voltage base value at node i in the d-th distribution network in the t-th time period; the above base value can be obtained by base state power flow calculation; is the resistance of line i→j in the d-th distribution network; Node net load constraints; Where, and are the power received by the d-th distribution network from the transmission grid in the t-th period and the power sent to the m-th microgrid connected to the node j; is the total uncontrollable load demand connected to node j in the d-th distribution network during the t-th period; and are the collections of new energy power stations, energy storage, controllable thermal loads, and lower-level microgrids connected to node j in the d-th distribution network; Line capacity constraints; Where, and are the lower and upper limits of the power flow of line i→j in the d-th distribution network; New energy output constraints; The microgrid constraints include: Thermal load operating constraints; Where, is the set of numbers of controllable thermal loads in the mth microgrid; Power balance constraints; Where, is the power received by the mth microgrid from the dth distribution network in the tth period, is the total uncontrollable load demand of the mth microgrid in the tth period; New energy output constraints; The boundary coupling constraints of the transmission and distribution microgrid include:

4. The method according to claim 3, characterized in that Before converting the transmission and distribution microgrid robust optimization scheduling model into a deterministic model, the method further includes: The robust optimization dispatch model of the transmission and distribution microgrid is converted into a compact form, including: The robust optimization scheduling model of the transmission and distribution microgrid is converted into a compact form as follows: Where, and are the decision variables of the transmission network, the d-th distribution network and the m-th microgrid respectively; and are the uncertainty variables in the transmission network, the d-th distribution network and the m-th microgrid respectively; y Trans The transmission network depends on The adjustable variables; is the power vector sum sent from the transmission network to the connected distribution network; is the power vector sent by the d-th distribution network to the connected microgrid; is a constant matrix; is a constant vector; is a constant value; x Trans include include include y Trans correspond The expression is as follows: Where, correspond α corresponds to Substituting Equation (51) into Equation (45), the optimization problem (PA1) is equivalently transformed into the robust optimization problem (P1) shown below:

5. The method according to claim 4, characterized in that The converting of the transmission and distribution microgrid robust optimization scheduling model into a deterministic model includes: Equation (53) can be converted into the following equivalent equation: for For each term in Trans,g > 0, then use the upper bound of the uncertainty variable Alternative If the coefficient H Trans,g ≤0, then use the lower bound of the uncertainty variable Alternative Then we get the deterministic constraint equivalent to formula (53): Formula (60) is about Inequality constraints; Combining equation (54) into equation (60), the expression is as follows: Where, The deterministic constraint expressions equivalent to Equation (55) and Equation (57) are as follows: Combining Equation (56) into Equation (62) yields the following compact form: Where, Combining Equation (58) into Equation (63) yields the following compact form: Where, The deterministic quadratic programming problem equivalent to the robust optimization problem (P1) is obtained as follows:

6. The method according to claim 5, characterized in that Solving the deterministic model to obtain a dispatch optimization result of the transmission and distribution microgrid includes: The deterministic model is solved using the alternating direction multiplier method to obtain the generator sets and new energy sources in the transmission network. The optimal solution for renewable energy, energy storage and controllable thermal loads in distribution networks The optimal solution for renewable energy and controllable thermal loads in microgrids The optimal solution is the dispatch optimization result of the transmission and distribution microgrid.

7. A method for coordinated robust dispatching of multi-level power grids considering flexibility resources, characterized in that: include: Obtaining parameters of each level of the transmission and distribution microgrid, wherein the transmission and distribution microgrid is a three-level grid consisting of a transmission grid, a distribution grid, and a microgrid, wherein each distribution grid is coupled to a corresponding transmission grid and multiple microgrids via tie lines; Inputting the parameters into a preset transmission and distribution microgrid robust optimization scheduling model, wherein the objective function of the transmission and distribution microgrid robust optimization scheduling model is to minimize power generation costs and maximize new energy consumption; The constraints of the transmission and distribution microgrid robust optimization scheduling model include: transmission network constraints, distribution network constraints, microgrid constraints, and boundary coupling constraints of the transmission and distribution microgrid; by converting the transmission and distribution microgrid robust optimization scheduling model into a deterministic model, the deterministic model outputs the scheduling optimization result of the transmission and distribution microgrid; The objective function expression of the robust optimization scheduling model of the transmission and distribution microgrid is as follows: Where, the total number of dispatch periods is T; superscript AG represents AGC units; superscript G represents non-AGC units; superscript DG represents new energy stations; superscript ESS represents energy storage; and are the power generation costs of the i-th non-AGC unit and the j-th AGC unit in the t-th period respectively; and are the outputs of the i-th non-AGC unit and the j-th AGC unit in the t-th period respectively; and are the output reduction penalty costs of the g-th renewable energy station in the t-th period under the worst scenarios of the transmission network, distribution network and microgrid respectively; is the upper bound of the allowed output range of the g-th renewable energy station in the t-th period; and are the charging power and discharging power of the e-th energy storage in the t-th period respectively; is the penalty cost of charging and discharging of the e-th energy storage in the t-th period; and ID and IM are the number sets of non-AGC units and AGC units in the transmission network respectively; ID and IM are the number sets of distribution network and microgrid respectively; and are the numbered sets of the new energy stations and energy storage power stations in the d-th distribution network; is the set of numbers of new energy stations in the mth microgrid.

8. A multi-level power grid coordinated robust dispatching device considering flexibility resources, characterized in that: include: An objective function construction module is used to construct the objective function of a robust optimization scheduling model for a transmission and distribution microgrid; the transmission and distribution microgrid is a three-level power grid consisting of a transmission grid, a distribution grid, and a microgrid, with each distribution grid coupled to a corresponding transmission grid and multiple microgrids via tie lines; the objective function of the robust optimization scheduling model for the transmission and distribution microgrid is to minimize power generation costs and maximize new energy consumption; A constraint condition construction module is used to construct the constraint conditions of the transmission and distribution microgrid robust optimization scheduling model, including: transmission network constraints, distribution network constraints, microgrid constraints and boundary coupling constraints of the transmission and distribution microgrid; A deterministic model generation module, configured to convert the transmission and distribution microgrid robust optimization scheduling model into a deterministic model; A dispatch optimization module, configured to solve the deterministic model to obtain a dispatch optimization result for the transmission and distribution microgrid; The objective function expression of the robust optimization scheduling model of the transmission and distribution microgrid is as follows: Where, the total number of dispatch periods is T; superscript AG represents AGC units; superscript G represents non-AGC units; superscript DG represents new energy stations; superscript ESS represents energy storage; and are the power generation costs of the i-th non-AGC unit and the j-th AGC unit in the t-th period respectively; and are the outputs of the i-th non-AGC unit and the j-th AGC unit in the t-th period respectively; and are the output reduction penalty costs of the g-th renewable energy station in the t-th period under the worst scenarios of the transmission network, distribution network and microgrid respectively; is the upper bound of the allowed output range of the g-th renewable energy station in the t-th period; and are the charging power and discharging power of the e-th energy storage in the t-th period respectively; is the penalty cost of charging and discharging of the e-th energy storage in the t-th period; and ID and IM are the number sets of non-AGC units and AGC units in the transmission network respectively; ID and IM are the number sets of distribution network and microgrid respectively; and are the numbered sets of the new energy stations and energy storage power stations in the d-th distribution network; is the set of numbers of new energy stations in the mth microgrid.

9. A multi-level power grid coordinated robust dispatching device considering flexibility resources, characterized in that: include: A parameter acquisition module is used to respectively acquire parameters of each level of the transmission and distribution microgrid. The transmission and distribution microgrid is a three-level grid consisting of a transmission network, a distribution network, and a microgrid. Each distribution network is coupled to the corresponding transmission network and multiple microgrids through tie lines. a scheduling model construction module, configured to input the parameters into a preset transmission and distribution microgrid robust optimization scheduling model, wherein the objective function of the transmission and distribution microgrid robust optimization scheduling model is to minimize power generation costs and maximize new energy consumption, and the constraints of the transmission and distribution microgrid robust optimization scheduling model include: transmission network constraints, distribution network constraints, microgrid constraints, and boundary coupling constraints of the transmission and distribution microgrid; and by converting the transmission and distribution microgrid robust optimization scheduling model into a deterministic model, the deterministic model outputs a scheduling optimization result of the transmission and distribution microgrid; The objective function expression of the robust optimization scheduling model of the transmission and distribution microgrid is as follows: Where, the total number of dispatch periods is T; superscript AG represents AGC units; superscript G represents non-AGC units; superscript DG represents new energy stations; superscript ESS represents energy storage; and are the power generation costs of the i-th non-AGC unit and the j-th AGC unit in the t-th period respectively; and are the outputs of the i-th non-AGC unit and the j-th AGC unit in the t-th period respectively; and are the output reduction penalty costs of the g-th renewable energy station in the t-th period under the worst scenarios of the transmission network, distribution network and microgrid respectively; is the upper bound of the allowed output range of the g-th renewable energy station in the t-th period; and are the charging power and discharging power of the e-th energy storage in the t-th period respectively; is the penalty cost of charging and discharging of the e-th energy storage in the t-th period; and ID and IM are the number sets of non-AGC units and AGC units in the transmission network respectively; ID and IM are the number sets of distribution network and microgrid respectively; and are the numbered sets of the new energy stations and energy storage power stations in the d-th distribution network; is the set of numbers of new energy stations in the mth microgrid.

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