Receiving end power grid multi-source optimization scheduling method based on voltage stability margin verification

Abstract

The invention discloses a receiving-end power grid multi-source optimization scheduling method based on voltage stability margin verification, and relates to the technical field of power system scheduling. The method comprises the following steps: establishing a receiving-end power grid multi-source collaborative optimization scheduling model, a voltage margin variable quantity calculation model and a system power adjustment model, wherein the receiving-end power grid multi-source collaborative optimization scheduling model takes minimization of operation cost as a target; solving an optimization scheduling scheme based on the multi-source collaborative optimization scheduling model, and calculating a voltage stability index under the optimization scheduling scheme; if the voltage stability index threshold value is not met, calculating a voltage stability margin variable quantity which can be accepted by the system according to the current voltage stability index, and feeding back the voltage stability margin variable quantity to a system power adjustment model to obtain a power adjustment parameter; and the power adjustment parameters are fed back to the multi-source collaborative optimization scheduling model to be solved again, and a new optimization scheduling scheme is obtained. According to the method, the power adjustment meeting the system voltage stability margin acts on the optimal scheduling model, and the voltage stability margin of the receiving-end power grid and the operation stability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a receiving-end power grid multi-source optimization dispatching method based on voltage stability margin verification. Background Art

[0002] As the installed capacity of clean energy continues to increase across the country, it poses a major challenge to the safe operation of the power grid and the absorption of new energy. At the same time, new energy and large hydropower bases are usually far away from the load center. The increase in DC transmission equipment and the input of energy through DC lines can easily cause problems such as "hollowing out" at the receiving end of the power grid, bringing voltage stability problems to the receiving end system.

[0003] Due to the complex geographical characteristics of some power transmission, the increase in the proportion of DC channels and new energy has strengthened the power electronic characteristics of the system, and the receiving load center lacks power support, making it difficult to ensure power supply. Therefore, how to improve the power grid's power receiving capacity, give play to the role of energy mutual assistance, promote the consumption of new energy, and ensure the voltage stability of the receiving power grid is an urgent problem that power dispatching needs to solve. Summary of the invention

[0004] The present invention provides a multi-source optimal dispatching method for a receiving-end power grid based on voltage stability margin verification, so as to solve the problem of insufficient voltage stability of the receiving-end power grid caused by the increase of new energy and direct current transmission equipment.

[0005] The present invention is achieved through the following technical solutions:

[0006] A first aspect of the present invention provides a multi-source optimal dispatching method for a receiving-end power grid based on voltage stability margin verification, comprising:

[0007] Taking the minimization of the operation cost of the receiving-end power grid as the optimization goal, a multi-source collaborative optimization scheduling model of the receiving-end power grid is established;

[0008] A voltage margin change calculation model is established based on voltage stability indicators;

[0009] Establishing a system power adjustment model according to the voltage margin variation;

[0010] Solving an optimal scheduling scheme based on the multi-source collaborative optimization scheduling model, and calculating a voltage stability index under the optimal scheduling scheme;

[0011] Determine whether the voltage stability index meets the voltage stability index threshold, and if so, output the optimization scheduling scheme; if not, obtain the voltage stability margin change that the system can accept according to the voltage margin change calculation model, and feed the voltage stability margin change back to the system power adjustment model to obtain the power adjustment parameter;

[0012] The power adjustment parameters are fed back to the multi-source collaborative optimization scheduling model, and the multi-source collaborative optimization scheduling model is solved again to obtain an optimized scheduling solution.

[0013] The present invention establishes an optimal dispatching model for a receiving-end power grid with the goal of minimizing the total system operation cost, considers the voltage stability margin of the receiving-end power grid optimized multi-source optimal dispatching, and applies power adjustment that satisfies the system voltage stability margin to the optimal dispatching model, thereby improving the voltage stability margin of the receiving-end power grid, alleviating the hollowing-out problem of the receiving-end power grid, and improving the operating stability of the receiving-end system.

[0014] Furthermore, the multi-source collaborative optimization scheduling model is expressed as:

[0015]

[0016] Among them, C represents the operating cost objective function of the receiving power grid, min is the minimization function, t is the time period number, e is the segment number of the coal consumption cost curve, g represents the thermal power unit, w represents the wind power unit, c ge represents the incremental coal consumption of thermal power unit g in the eth section, P get represents the power generation of thermal power unit g in the eth period during period t, They represent the fuel consumption of starting and stopping the thermal power unit g during period t, C fuel represents the unit price of fuel, represents the abandoned wind volume of wind turbine w in period t, C w represents the unit wind abandonment penalty cost, and StD represents the constraint condition of the multi-source collaborative optimization scheduling model.

[0017] Furthermore, the constraints of the multi-source collaborative optimization scheduling model include: branch flow constraints, node balance constraints, thermal power unit operation constraints, cascade hydropower unit operation constraints, wind turbine unit operation constraints, energy storage equipment constraints, DC transmission line constraints and SVC operation constraints.

[0018] Furthermore, the voltage stability index includes:

[0019]

[0020] Among them, B jt is the voltage stability index of node j in period t, For indicator B jt The real part of For indicator B jt The imaginary part of

[0021] i is the generator node number, i∈Ω G ,Ω G Represents the generator node set, j is the load node number, j∈Ω L,Ω L represents the load node set; t is the time period number, represents the voltage phasor of the generator node in period t, represents the voltage phasor of the load node in period t; F ji represents the load participation factor, which is the element of the jth row and ith column of the F matrix, F = -(B LL ) - 1 B LG , B LL is the admittance matrix Y LL The imaginary part of B LG is the admittance matrix Y LG The imaginary part of the admittance matrix Y and its block representation are Y = θ it is the voltage phase angle of node i during period t, θ jt is the voltage phase angle of node j during period t; v it is the voltage amplitude of node i at time period t, v jt is the voltage amplitude of node j during period t.

[0022] Furthermore, the voltage margin variation calculation model is expressed as:

[0023]

[0024] Where, ΔB jt For indicator B jt The rate of change at node j during period t;

[0025] ΔB jt The imaginary part of ΔB jt The real part of :

[0026]

[0027] In the formula, For indicator B jt The real part of For indicator B jt The imaginary part, v it is the voltage amplitude of node i at time period t, v jt is the voltage amplitude of node j at time period t, Δv jt is the voltage amplitude change of node i during period t, Δv jt is the voltage amplitude change of node j during period t, θ jt is the voltage phase angle of node i during period t, θ jt is the voltage phase angle of node j during period t, Δθ it is the phase angle change of node i during period t, Δθ jtis the phase angle change of node j in period t, F ji represents the load participation factor;

[0028] is the active power deviation of node j in period t, is the reactive power deviation of node j in period t, ρ j is the power factor of node j.

[0029] Furthermore, the system power adjustment model is expressed as:

[0030]

[0031] in, is the voltage stability index change allowed for node j in period t, is the active adjustment of node j to meet the voltage margin in period t, is the reactive power adjustment of node j to meet the voltage margin in period t, B thr is the voltage stability index threshold, Calculate the initial value of the voltage stability index, ΔP jt is the active power fluctuation of the set node j in period t, is the active power deviation of node j in period t, ΔQ jt is the reactive power fluctuation of the set node j in period t, is the reactive power deviation of node j in period t.

[0032] A second aspect of the present invention provides a receiving-end power grid multi-source optimization dispatching device based on voltage stability margin verification, comprising:

[0033] The first modeling module is used to establish a multi-source collaborative optimization scheduling model of the receiving-end power grid with minimizing the operating cost of the receiving-end power grid as the optimization target;

[0034] The second modeling module is used to establish a voltage margin change calculation model according to the voltage stability index;

[0035] A third modeling module is used to establish a system power adjustment model according to the voltage margin change;

[0036] A first optimization module is used to solve an optimization scheduling scheme based on the multi-source collaborative optimization scheduling model and calculate a voltage stability index under the optimization scheduling scheme;

[0037] A first determination module, used to determine whether the voltage stability index meets the voltage stability index threshold, and if so, output the optimization scheduling scheme; if not, enter the first calculation module;

[0038] A first calculation module, used for obtaining a voltage stability margin variation acceptable to the system according to the voltage margin variation calculation model, and feeding back the voltage stability margin variation to the system power adjustment model to obtain a power adjustment parameter;

[0039] The second optimization module is used to feed back the power adjustment parameter to the multi-source collaborative optimization scheduling model, solve the multi-source collaborative optimization scheduling model again, and obtain an optimized scheduling result.

[0040] According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the multi-source optimization scheduling method for a receiving-end power grid based on voltage stability margin verification as described in any one of the first aspects of the present invention is implemented.

[0041] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the multi-source optimization scheduling method for a receiving-end power grid based on voltage stability margin verification as described in any one of the first aspects of the present invention is implemented.

[0042] A fifth aspect of the present invention provides a computer program product, which, when executed on a computer, enables the computer to execute the multi-source optimal scheduling method for a receiving-end power grid based on voltage stability margin verification as described in any one of the first aspects of the invention.

[0043] Compared with the prior art, the present invention has the following advantages and beneficial effects: the present invention comprehensively considers the positive effects of thermal power units, cascade hydropower units, wind power units, energy storage equipment and DC input lines in the receiving-end power grid on the voltage stability improvement of the receiving-end power grid, and proposes a multi-source optimization scheduling method for the receiving-end power grid optimization based on voltage stability margin verification, thereby improving the voltage stability margin of the receiving-end power grid and ensuring the voltage stability of the receiving-end power grid. It has important research significance for formulating a voltage stability margin verification method for multi-source collaborative optimization of the receiving-end power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0045] Figure 1 It is a flow chart of a multi-source optimal dispatching method for a receiving-end power grid based on voltage stability margin verification according to an embodiment of the present invention;

[0046] Figure 2 is a system schematic diagram of a receiving-end power grid according to an embodiment of the present invention;

[0047] Figure 3 A predicted load and wind power output diagram of a receiving-end power grid system according to an embodiment of the present invention;

[0048] Figure 4 This is a sensitivity analysis diagram of the DC transmission adjustment times of a receiving-end power grid system according to an embodiment of the present invention;

[0049] Figure 5 is a voltage stability index diagram of a receiving-end power grid system according to an embodiment of the present invention;

[0050] Figure 6 The present invention is a diagram of the unit output and DC line transmission power of a receiving-end power grid system. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0052] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to or inherent to other steps or units of the device.

[0053] The terms used in the various embodiments of the application are only used to describe the purpose of specific embodiments and are not intended to limit the various embodiments of the application. As used herein, the singular form is intended to also include the plural form, unless the context clearly indicates otherwise. Unless otherwise limited, all terms used here (including technical terms and scientific terms) have the same meaning as the meanings commonly understood by ordinary technicians in the field of the various embodiments of the application. The terms (such as the terms defined in the dictionary generally used) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning, unless clearly defined in the various embodiments of the application.

[0054] The embodiment of the present invention provides a multi-source optimal scheduling method for a receiving-end power grid based on voltage stability margin verification, which is suitable for optimal scheduling of a receiving-end power grid, is conducive to improving the voltage margin of the receiving-end power grid, and ensures the stability of system operation.

[0055] like Figure 1The flowchart of a multi-source optimal dispatching method of a receiving-end power grid based on voltage stability margin verification according to an embodiment of the present invention includes the following steps.

[0056] S1, establishing a multi-source collaborative optimization dispatching model of the receiving-end power grid with minimizing the operating cost of the receiving-end power grid as the optimization goal, establishing a voltage margin change calculation model based on the voltage stability index, and establishing a system power adjustment model based on the voltage margin change.

[0057] S2, solves the optimal scheduling scheme based on the multi-source collaborative optimization scheduling model and calculates the voltage stability index under the optimal scheduling scheme.

[0058] S3, determine whether the voltage stability index meets the voltage stability index threshold. If so, output the optimized scheduling plan; if not, obtain the acceptable voltage stability margin change of the system according to the voltage margin change calculation model, and feed the voltage stability margin change back to the system power adjustment model to obtain the power adjustment parameters.

[0059] S4, feeding back the power adjustment parameters to the multi-source collaborative optimization scheduling model, solving the multi-source collaborative optimization scheduling model again, and obtaining a new optimization scheduling scheme.

[0060] In step S1, the method of establishing the multi-source collaborative optimization scheduling model of the receiving-end power grid with the minimization of the operating cost of the receiving-end power grid as the optimization target is to first establish an objective function based on the total cost of the receiving-end power grid, as follows:

[0061]

[0062] Among them, C represents the total cost objective function of the receiving power grid, t is the time period number, e is the segment number of the coal consumption cost curve, g represents the thermal power unit, w represents the wind power unit, c ge represents the incremental coal consumption of thermal power unit g in the eth section, P get represents the power generation of thermal power unit g in the eth period during period t, They represent the fuel consumption of starting and stopping the thermal power unit g during period t, C fuel represents the unit price of fuel, represents the abandoned wind volume of wind turbine w in period t, C w Represents the unit wind curtailment penalty cost.

[0063] Taking into account the positive effects of thermal power units, cascade hydropower units, wind turbines, energy storage equipment and DC input lines in the receiving power grid on the stable improvement of the voltage in the receiving power grid, branch flow constraints, node balance constraints, thermal power unit operation constraints, cascade hydropower unit operation constraints, wind turbine operation constraints, energy storage equipment constraints, DC transmission line constraints and SVC operation constraints are established.

[0064] (1) Branch flow constraints are expressed as:

[0065]

[0066] θ ij,t =θ it -θ jt

[0067]

[0068] Among them, P ij,t represents the active power flow on the transmission line (i, j) during period t, Q ij,t represents the reactive power flow on the transmission line (i, j) during period t, g ij represents the real part of the corresponding line (i, j) in the admittance matrix, b ij represents the imaginary part of the admittance matrix corresponding to line (i, j), v it represents the voltage amplitude of node i during period t, v jt represents the voltage amplitude of node j at time period t, θ ij,t is the phase angle difference between nodes i and j during period t, θ it represents the voltage phase angle of node i during period t, θ it represents the voltage phase angle of node j during period t, (·) max 、(·) max They represent the maximum and minimum values ​​respectively, and i and j are the node numbers.

[0069] (2) The node balance constraint is expressed as:

[0070]

[0071] Where, k is the node number of the power system, b is the SVC (static var compensation device) number, h is the hydropower unit number; c is the converter station number, Ω j is the device set of node j; They represent the active and reactive power demands of node j in period t respectively; is the active adjustment of node j to meet the voltage margin in period t, is the reactive power adjustment of node j to meet the voltage margin in period t, are respectively the active and reactive power transmitted from the AC side of converter station c to the power grid during period t; P gt is the active power output of thermal power unit g during period t, Q gt P is the reactive power output of thermal power unit g during period t; wt is the active power output of wind turbine w during period t, Q wt is the reactive power output of wind turbine w during period t; are the charging and discharging power of energy storage s in period t; Q bt is the reactive power support of SVC in period t.

[0072] (3) The operating constraints of thermal power units are expressed as:

[0073]

[0074] Among them, P g,t-1 is the output of thermal power unit g during period t-1, I gt is a 0-1 variable, indicating the start and stop status of the thermal power unit g in period t (1 means start, 0 means stop), I g,t-1 Indicates the start and stop status of the t-1 period; They are the upward and downward climbing limits of thermal power unit g respectively; are the unit start / stop costs of thermal power unit g respectively; are the unit start / stop cost limits of thermal power unit g respectively; are the minimum start / shutdown time of thermal power unit g respectively; They represent the time that the thermal power unit g is continuously running and remains in shutdown state before time period t, P get It represents the active output of thermal power unit g in segment e during time period t, It indicates the upper limit of active output of thermal power unit g section e.

[0075] (4) The operating constraints of cascade hydropower units are expressed as:

[0076] P ht =η h L ht H ht

[0077]

[0078] E ht =E h,t-1 +q ht -L ht -S ht

[0079]

[0080] E h,0 =E h,T

[0081] Among them, P ht is the active power output of hydropower unit h in period t, η h represents the hydropower conversion efficiency of cascade hydropower station h, L ht is the power generation flow of hydropower station h during period t, H ht is the generating head of the hydropower station h during period t;

[0082] a h are the initial water head and water head coefficient of the hydropower station h, which are related to the physical characteristics of the hydropower station itself, E ht is the storage capacity of hydropower station h in period t;

[0083] I ht is the operating status of the hydropower station h during period t, are the maximum and minimum power generation flows allowed for the hydropower station h;

[0084] E h,t-1 is the generating head of the hydropower station h at time t-1, q ht is the inflow of hydropower station t in period t, S ht is the water discharge of hydropower station h in period t;

[0085] u is the upstream hydropower station of hydropower station h, t is the time period number, τ u is the time it takes for the water from hydropower station u to flow directly downstream, They represent the power generation flow and abandoned water flow of the hydropower station u directly upstream of the hydropower station h at time t, considering the water flow time lag, and n ht is the amount of running water of hydropower station h in period t;

[0086] is the minimum and maximum storage capacity of hydropower station h in period t;

[0087] E h,0 、E h,T They respectively represent the initial and final storage capacities of the hydropower station h.

[0088] (5) The wind turbine operation constraints are expressed as:

[0089]

[0090] In the formula, represents the wind curtailment of wind turbine w in period t, represents the predicted output of wind turbine w in period t, Q wt represents the reactive power output of wind turbine w in period t, Represents the upper limit of reactive power of wind turbine w.

[0091] (6) The energy storage device constraint is expressed as:

[0092]

[0093] In the formula, E st is the capacity of energy storage s in period t, are the upper and lower limits of the capacity of energy storage s;

[0094] E s,t-1 is the capacity of energy storage s in period t-1, are the charging and discharging efficiency of energy storage s, are the charging power and discharging power of energy storage s respectively;

[0095] are the upper and lower limits of the energy storage s charging power, are the upper and lower limits of the energy storage s discharge power, They are 0-1 variables, representing the charge and discharge status of the energy storage s in time period t (1 represents charging, 0 represents discharging).

[0096] (7) The DC transmission line constraint is expressed as:

[0097]

[0098]

[0099] In the formula, They represent the AC active and reactive power transmitted by converter station c during period t, They represent the active and reactive power flowing from converter station c during period t, They represent the active and reactive power losses of converter station c during period t, They represent the lower and upper limits of the AC active power transmitted by converter station c, They represent the lower and upper limits of the AC reactive power transmitted by converter station c, They represent the upper and lower limits of reactive power flowing from converter station c during period t respectively;

[0100] v ct represents the voltage value of converter station c in period t, They represent the upper and lower limits of the voltage at converter station c respectively;

[0101] represents the active power flowing from converter station c during period t-1, are the maximum upward and downward adjustment limits of the DC receiving line transmission power connected to converter station c in adjacent time periods, They respectively represent the upward and downward adjustment states of the DC receiving-end line connected to the converter station c during the period t;

[0102] They respectively represent the upward and downward adjustment states of the DC receiving-end line connected to the converter station c in the period t-1;

[0103] N c Indicates the maximum number of adjustments to the DC receiving-end line connected to converter station c within the dispatching period;

[0104] The contracted transmission power of the DC receiving line connected to converter station c.

[0105] (8) The SVC (static VAR compensation device) operation constraints are expressed as:

[0106]

[0107] Among them, Q bt represents the reactive power of SVC in period t, Indicates the upper limit of SVC reactive power.

[0108] The multi-source collaborative optimization dispatching model of the receiving-end power grid is established with the minimization of the operating cost of the receiving-end power grid as the optimization target, which is expressed as:

[0109] minC

[0110] StD

[0111] Among them, C is the total operating cost objective function of the receiving power grid, and StD represents the constraints, including the above branch flow constraints, node balance constraints, thermal power unit operation constraints, cascade hydropower unit operation constraints, wind turbine unit operation constraints, energy storage equipment constraints, DC transmission line constraints and SVC operation constraints.

[0112] The voltage stability index of the power system is the basis for evaluating the voltage stability of the system. The relevant contents of voltage stability analysis include voltage stability margin calculation, safety domain boundary construction, voltage stability index design, etc. According to the relative distance of the loop amplitude-phase characteristic curve points, the relative stability margin of the system can be determined. When the stability margin is applied, the amplitude stability margin and the phase angle stability margin are usually considered. In order to solve the problem of insufficient voltage stability of the receiving-end power grid caused by the increase of new energy and DC transmission equipment, the present invention considers multi-source information and voltage stability margin verification in the optimal scheduling of the receiving-end power grid to improve the operating stability of the receiving-end network.

[0113] A voltage margin change calculation model is established based on the voltage stability index, where the voltage stability index is expressed as:

[0114]

[0115] In the formula, B jt is the voltage stability index of node j in period t, For indicator B jt The real part of For indicator B jt The imaginary part of

[0116] i is the generator node number, i∈Ω G , j is the load node number, j∈Ω L,Ω G ,Ω L Represent the generator nodes and load node sets respectively;

[0117] t is the time period number, Respectively represent the voltage phasors of the generator and load nodes in period t;

[0118] F ji represents the load participation factor, which is the element in the jth row and ith column of the F matrix;

[0119] F=-(B LL ) -1 B LG

[0120] B LL is the admittance matrix Y LL The imaginary part, B LG is the admittance matrix Y LG The imaginary part of the admittance matrix Y and its block representation are as follows;

[0121]

[0122] θ it ,θ jt is the voltage phase angle of nodes i and j during period t, v it 、v jt is the voltage amplitude of nodes i and j during period t.

[0123] The voltage margin change calculation model is expressed as:

[0124]

[0125]

[0126] in, For indicator B jt The real part of For indicator B jt The imaginary part, ΔB jt For indicator B jt The rate of change at node j during period t is, ΔB jt The imaginary part of ΔB jt The real part of v it is the voltage amplitude of node i at time period t, v jt is the voltage amplitude of node j at time period t, Δv jt is the voltage amplitude change of node i during period t, Δv jt is the voltage amplitude change of node j during period t, θ it is the voltage phase angle of node i during period t, θ jtis the voltage phase angle of node j during period t, Δθ it is the phase angle change of node i during period t, Δθ jt is the phase angle change of node j in period t; matrix T is the sensitivity matrix between the index change and the voltage phase angle and amplitude change;

[0127] F ji represents the load participation factor;

[0128] The matrix J is the Jacobian matrix of the power flow, the matrix S is the sensitivity matrix between the index change and the active and reactive load changes, ΔP jt is the active power fluctuation of node j in period t, ΔQ jt is the reactive power fluctuation of the set node j in period t;

[0129] is the active and reactive power deviation of node j in period t, ρ j is the power factor of node j.

[0130] The system power adjustment model is expressed as:

[0131]

[0132] in, is the voltage stability index change allowed for node j in period t, is the active adjustment of node j to meet the voltage margin in period t, is the reactive power adjustment of node j to meet the voltage margin in period t, B thr is the voltage stability index threshold, Calculate the initial value of the voltage stability index, ΔP jt is the active power fluctuation of the set node j in period t, is the active power deviation of node j in period t, ΔQ jt is the reactive power fluctuation of the set node j in period t, is the reactive power deviation of node j in period t.

[0133] Steps S2 to S4 are multi-source optimization scheduling methods that consider the voltage stability margin of the receiving power grid. The parameters solved in step S2 and step S4 include unit combination, DC input power and its adjustment, energy storage equipment charging and discharging strategy, and known parameters include system data and generator sets, energy storage equipment information, DC input power contract quantity, etc. Its main contents are: initialize the system-related data model, solve the optimal scheduling decision according to the multi-source collaborative optimization scheduling model of the receiving power grid established in step S1; judge whether the solved result meets the voltage stability requirements, that is, on the basis of the solved optimal scheduling decision, obtain the voltage value of each node, bring the set active power, reactive power deviation, and optimized voltage value into the calculation, obtain the voltage stability index value, and compare it with the voltage stability index threshold of the system. If the system operation meets the voltage stability margin requirements, a scheduling decision that meets the voltage stability is obtained; otherwise, on the basis of the calculated scheduling decision, the calculated voltage stability index is substituted into the voltage margin change calculation model and the system power adjustment model of step S1 to calculate the voltage margin change ΔB under the current scheduling decision. jt Deviation from system power Then get the margin change that the system can accept under the current voltage stability index threshold And the corresponding power adjustment The feedback is fed back to the multi-source optimal dispatching model of the receiving power grid, and the multi-source optimal dispatching model of the receiving power grid considering the voltage stability margin is solved again to obtain a new optimal dispatching decision.

[0134] Furthermore, the above voltage stability margin verification and feedback solution process is repeated, and finally a dispatching scheme that meets the voltage stability requirements is obtained, thereby improving the system stability of the receiving-end power grid.

[0135] The present invention is further described below with reference to an example of optimized scheduling.

[0136] like Figure 2 The figure shows a system diagram of the receiving power grid, which includes 2 thermal power units (G1, G2), 2 cascade hydropower units (H1, H2), 1 wind farm (W1), 1 energy storage power station (E1), and 2 DC transmission lines (C1, C2). In the figure, B1, B2, B3, B5, and B6 represent bus numbers, and arrows represent loads. Among them, the installed capacity of thermal power, hydropower, wind power, and energy storage is 400MW, 410MW, 400MW, and 150MW respectively. The predicted load of the system and the predicted output of wind power are shown in Figure 2. Figure 3 The parameter settings of the thermal power unit in the system are shown in Table 1.

[0137] Table 1 Parameters of thermal power units

[0138]

[0139] The present invention adopts three scenario solutions to solve the multi-source collaborative optimization model of the receiving-end power grid. The factors considered in each scenario are shown in Table 2. The total cost, operating cost, and wind curtailment cost results of the above three scenarios are shown in Table 3.

[0140] Table 2 Elements included in each plan

[0141]

[0142]

[0143] Table 3 Comparison of load loss costs of various schemes

[0144]

[0145] Comparative Example 1: Set Scheme 1 to solve the multi-source optimization dispatching model of the receiving power grid without considering DC power adjustment and voltage stability. The system cost during the dispatching period is 123,987.1 yuan, of which the operating cost is 109,961.4 yuan, the solution cost is 14,025.7 yuan, and the solution calculation time is 5.7 seconds.

[0146] Comparative Example 2: Scheme 1 is solved under the influence of the number of power adjustment of the DC transmission line, and a sensitivity analysis is performed on the number of times the DC line power can be adjusted. The results are as follows: Figure 4 shown.

[0147] As the upper limit of the number of times a single line can adjust power increases, the total system cost and operating cost both decrease, and the amount of wind abandoned also decreases. The initial decline is obvious, and the decline gradually decreases after the second time. The power adjustment of the DC line is conducive to the generator set to adjust the output more flexibly according to demand, but frequent power adjustment will reduce the service life of power electronic devices and increase the probability of system failure. Therefore, in actual operation, it is necessary to choose an adjustable number that is conducive to balancing the economy and safety of system operation.

[0148] The voltage stability index calculation is added on the basis of comparative example 2. According to the definition of voltage stability margin, in the 6-node calculation example, the voltage stability index of node B1 containing only load is calculated. The power deviation is set to 1.5MW and 0.5Mvar, and the voltage stability margin threshold is 0.055.

[0149] The method of the present invention: Based on the comparison example 2, the voltage stability margin check is considered for solution. Compared with the comparison example 2, the total cost is reduced by 1.85%, the operating cost is increased by 0.58%, and the wind abandonment cost is reduced. The improvement of voltage stability requires the system to adjust the active and reactive power, so the operating cost of the system increases, but the power demand for the improvement of voltage stability in some time periods can alleviate the pressure of wind power consumption, so the wind abandonment cost of the method of the present invention is reduced compared with the comparison example 2.

[0150] Voltage stability margin Figure 5 As shown in the figure, the output of G1 unit and DC input power under different iteration times are as follows: Figure 6 As shown. In the first iteration, 7:00-22:00 did not meet the voltage stability margin threshold requirements. By calculating the allowable power fluctuations and the power adjustment that the system needs to provide, in the second iteration, the voltage stability indicators of all time periods meet the voltage stability margin requirements, which illustrates the effectiveness of the method of the present invention in improving voltage stability. At the same time, the output of unit G1 in the second iteration from 7:00-10:00, 14:00-15:00, and 17:00-19:00 is higher than that in the first iteration, and the DC input power of the second iteration from 11:00-13:00 and 16:00 is higher than that of the first iteration, showing a certain energy complementary characteristic, reflecting the ability of multiple sources to collaboratively improve the voltage stability margin, and illustrating the effectiveness of the multi-source collaborative optimization scheduling scheme considering voltage stability.

[0151] An embodiment of the present invention further provides a receiving-end power grid multi-source optimization dispatching device based on voltage stability margin verification, which is used to execute the receiving-end power grid multi-source optimization dispatching method based on voltage stability margin verification of the above embodiment of the present invention, and the device includes:

[0152] The first modeling module is used to establish a multi-source collaborative optimization scheduling model of the receiving-end power grid with the minimization of the operating cost of the receiving-end power grid as the optimization target;

[0153] The second modeling module is used to establish a voltage margin change calculation model according to the voltage stability index;

[0154] A third modeling module is used to establish a system power adjustment model according to the voltage margin change;

[0155] The first optimization module is used to solve the optimal scheduling scheme based on the multi-source collaborative optimization scheduling model and calculate the voltage stability index under the optimal scheduling scheme;

[0156] A first determination module is used to determine whether the voltage stability index meets the voltage stability index threshold, and if so, output the optimization scheduling plan; if not, enter the first calculation module;

[0157] The first calculation module is used to obtain a voltage stability margin variation that the system can accept according to a voltage margin variation calculation model, and feed the voltage stability margin variation back to a system power adjustment model to obtain a power adjustment parameter;

[0158] The second optimization module is used to feed back the power adjustment parameters to the multi-source collaborative optimization scheduling model, and solve the multi-source collaborative optimization scheduling model to obtain an optimized scheduling solution.

[0159] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory, and the number of processors may be one or more. The memory, as a computer-readable storage medium, can be used to store software programs, computer executable programs, and modules. The processor executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory, so as to implement the multi-source optimization scheduling method of the receiving-end power grid based on voltage stability margin verification according to any of the above embodiments of the present invention.

[0160] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0161] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a multi-source optimal scheduling method for a receiving-end power grid based on voltage stability margin verification according to any embodiment of the present invention.

[0162] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: 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 invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0163] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0164] An embodiment of the present invention further provides a computer program product, which, when executed on a computer, enables the computer to execute the multi-source optimal dispatching method for a receiving-end power grid based on voltage stability margin verification according to any of the above embodiments of the present invention. The above-described specific implementation methods further elaborate on the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A multi-source optimal dispatching method for a receiving-end power grid based on voltage stability margin verification, characterized in that: include: A multi-source collaborative optimization dispatch model of the receiving-end power grid is established with the minimization of the operating cost of the receiving-end power grid as the optimization goal, a voltage margin change calculation model is established according to the voltage stability index, and a system power adjustment model is established according to the voltage margin change; Solve the optimal scheduling scheme based on the multi-source collaborative optimization scheduling model, calculate the voltage stability index under the optimal scheduling scheme, Determine whether the voltage stability index meets the voltage stability index threshold, and if so, output the optimization scheduling scheme; if not, obtain the voltage stability margin change that the system can accept according to the voltage margin change calculation model, and feed the voltage stability margin change back to the system power adjustment model to obtain the power adjustment parameter; The power adjustment parameters are fed back to the multi-source collaborative optimization scheduling model, and the multi-source collaborative optimization scheduling model is solved again to obtain an optimized scheduling solution.

2. The receiving-end power grid multi-source optimization dispatching method based on voltage stability margin verification according to claim 1 is characterized in that: The multi-source collaborative optimization scheduling model is expressed as: minC Among them, C represents the total cost objective function of the receiving power grid, min is the minimization function, t is the time period number, e is the segment number of the coal consumption cost curve, g represents the thermal power unit, w represents the wind power unit, c ge represents the incremental coal consumption of thermal power unit g in the eth section, P get represents the power generation of thermal power unit g in the eth period during period t, They represent the fuel consumption of starting and stopping the thermal power unit g during period t, C fuel represents the unit price of fuel, represents the abandoned wind volume of wind turbine w in period t, C w represents the unit wind abandonment penalty cost, and stD represents the constraint condition of the multi-source collaborative optimization scheduling model.

3. The receiving-end power grid multi-source optimization dispatching method based on voltage stability margin verification according to claim 2 is characterized in that: The constraints of the multi-source collaborative optimization scheduling model include: branch flow constraints, node balance constraints, thermal power unit operation constraints, cascade hydropower unit operation constraints, wind turbine unit operation constraints, energy storage equipment constraints, DC transmission line constraints and SVC operation constraints.

4. The receiving-end power grid multi-source optimization dispatching method based on voltage stability margin verification according to claim 1 is characterized in that: The voltage stability index includes: Among them, B jt is the voltage stability index of node j in period t, For indicator B jt The real part of For indicator B jt The imaginary part of i is the generator node number, i∈Ω G ,Ω G Represents the generator node set, j is the load node number, j∈Ω L ,Ω L represents a set of load nodes; t is the time period number, represents the voltage phasor of the generator node in period t, It represents the voltage phasor of the load node in the period t; F ji represents the load participation factor, which is the element in the jth row and ith column of the F matrix. F=-(B LL ) -1 B LG B LL is the admittance matrix Y LL The imaginary part, B LG is the admittance matrix Y LG The imaginary part of the admittance matrix Y and its block representation are: θ it is the voltage phase angle of node i during period t, θ jt is the voltage phase angle of node j during period t, v it is the voltage amplitude of node i at time period t, v jt is the voltage amplitude of node j during period t.

5. The receiving-end power grid multi-source optimization dispatching method based on voltage stability margin verification according to claim 4 is characterized in that: The voltage margin variation calculation model is expressed as: Where, ΔB jt is the index B of node j in period t jt The rate of change of = ΔB jt The imaginary part of = ΔB jt The real part of : In the formula, For indicator B jt The real part of For indicator B jt The imaginary part, v it is the voltage amplitude of node i at time period t, v jt is the voltage amplitude of node j at time period t, Δv it is the voltage amplitude change of node i during period t, Δv jt is the voltage amplitude change of node j during period t, θ it is the voltage phase angle of node i during period t, θ jt is the voltage phase angle of node j during period t, Δθ it is the phase angle change of node i during period t, Δθ jt is the phase angle change of node j in period t, F ji represents the load participation factor; is the active power deviation of node j in period t, is the reactive power deviation of node j in period t, ρ j is the power factor of node j.

6. The receiving-end power grid multi-source optimization dispatching method based on voltage stability margin verification according to claim 5 is characterized in that: The system power adjustment model is expressed as: in, is the voltage stability index change allowed for node j in period t, is the active adjustment of node j to meet the voltage margin in period t, is the reactive power adjustment of node j to meet the voltage margin in period t, B thr is the voltage stability index threshold, Calculate the initial value for the voltage stability index, ΔP jt is the active power fluctuation of the set node j in period t, is the active power deviation of node j in period t, ΔQ jr is the reactive power fluctuation of the set node j in period t, is the reactive power deviation of node j in period t.

7. A multi-source optimization dispatching device for a receiving-end power grid based on voltage stability margin verification, characterized in that: include: The first modeling module is used to establish a multi-source collaborative optimization scheduling model of the receiving-end power grid with minimizing the operating cost of the receiving-end power grid as the optimization target; The second modeling module is used to establish a voltage margin change calculation model according to the voltage stability index; A third modeling module is used to establish a system power adjustment model according to the voltage margin change; A first optimization module is used to solve an optimization scheduling scheme based on the multi-source collaborative optimization scheduling model and calculate a voltage stability index under the optimization scheduling scheme; A first determination module, used to determine whether the voltage stability index meets the voltage stability index threshold, and if so, output the optimization scheduling scheme; if not, enter the first calculation module; A first calculation module, used for obtaining a voltage stability margin variation acceptable to the system according to the voltage margin variation calculation model, and feeding back the voltage stability margin variation to the system power adjustment model to obtain a power adjustment parameter; The second optimization module is used to feed back the power adjustment parameter to the multi-source collaborative optimization scheduling model, solve the multi-source collaborative optimization scheduling model again, and obtain an optimized scheduling result.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the receiving-end power grid multi-source optimization scheduling method based on voltage stability margin verification described in any one of claims 1-6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the receiving-end power grid multi-source optimization scheduling method based on voltage stability margin verification described in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that When the computer program product is run on a computer, the computer is enabled to execute the receiving-end power grid multi-source optimization scheduling method based on voltage stability margin verification according to any one of claims 1 to 6.

Images