Source network storage reactive power coordination distribution robust optimization method and system and medium

Abstract

The invention discloses a source network storage reactive power coordination distribution robust optimization method and system and a medium, and the method comprises the steps: building a DC feed converter station equivalent model based on the coupling effect of a DC feed factor on the power receiving capability; constructing a first objective function comprising source, network, storage and reactive compensation equipment; the first objective function is a deterministic source network storage reactive power coordinated planning objective function; based on a preset constraint condition, optimizing the first target function to obtain an optimized second target function; the second objective function is a deterministic source network storage reactive power coordinated planning objective function considering improvement of the power receiving capability; constructing a wind and light output fuzzy uncertainty set by using 1-norm and infinity-norm constraints, and reconstructing a second objective function to obtain a two-stage distribution robust optimization model; a column and constraint generation algorithm is used to decompose the model into a main problem and a sub-problem for iterative solution. According to the method, the power receiving capability and the operation economy of the receiving-end power grid can be effectively improved while the system safety is ensured.

Description

Technical Field

[0001] This invention relates to the field of high-voltage direct current transmission technology, specifically to a method, system, and medium for optimizing reactive power coordination between power sources, grids, and storage using a distributed broom rod. Background Technology

[0002] High-voltage direct current (HVDC) transmission boasts advantages such as large transmission capacity, long transmission distance, and high economic efficiency. By the end of 2023, my country had completed 20 ultra-high-voltage direct current (UHVDC) transmission projects, playing a crucial role in the large-scale optimization of China's energy supply. However, the large-capacity DC inflow presents new challenges to the security of the receiving-end power grid, leading to issues such as insufficient voltage support and complex operation modes. Reactive power support is a vital component in improving the receiving capacity of the receiving-end power grid. Currently, research on reactive power optimization mainly focuses on establishing reactive power compensation configuration optimization models based on risk indicators and configuration economics, primarily addressing the static or transient voltage stability issues arising from centralized renewable energy grid integration, such as large-scale wind farms. Further research is needed on reactive power optimization considering the impact of DC inflow. On the other hand, existing reactive power planning research mainly focuses on site selection and capacity determination for one or more reactive power compensation methods. While research exists that considers the coordinated planning of reactive power compensation and electrochemical energy storage, few studies consider multiple extended planning objects such as generators and grid structures. Further consideration is needed regarding the interaction between reactive power compensation facilities and planning objects such as generators and grid structures.

[0003] On the other hand, with the large-scale integration of new energy sources bringing uncertainties, the existing power system planning method of debulking and optimizing wind and solar power does not take into account the uncertainties of wind and solar power, which has problems such as not being able to guarantee the safety of the power system, low power receiving capacity of the receiving-end grid, and high operating costs.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] The technical problem this invention aims to solve is that existing distributed bar optimization methods in power system planning do not consider the combined effects of wind and solar uncertainties and power receiving capacity, resulting in issues such as inadequate power system security, low power receiving capacity of the receiving-end grid, and high operating costs. This invention aims to provide a distributed bar optimization method, system, and medium for source-grid-storage reactive power coordination. For planning DC-fed receiving-end grids, it uses a DC-fed power contract selection method to improve the power receiving capacity of the receiving-end grid under the planning target. Based on this, considering the uncertainties of wind and solar power generation in the power system, it constructs a fuzzy uncertainty set for wind and solar output using the 1-norm and ∞-norm, and proposes a two-stage distributed bar optimization model. This invention can effectively improve the power receiving capacity and operational economy of the receiving-end grid while ensuring system security.

[0006] This invention is achieved through the following technical solution:

[0007] In a first aspect, the present invention provides a source-grid-storage reactive power coordination and distribution bar optimization method, the method comprising:

[0008] Based on the coupling effect of DC feed factors on power receiving capacity, an equivalent model of DC feed converter station is constructed.

[0009] Based on the equivalent model of the DC-fed converter station, a first objective function is constructed, which includes the source, grid, storage, and reactive power compensation equipment; the first objective function is a deterministic source-grid-storage reactive power coordination planning objective function.

[0010] Based on preset constraints, the first objective function is optimized to obtain the optimized second objective function; the second objective function is a deterministic source-grid-storage reactive power coordination planning objective function that considers the improvement of power receiving capacity.

[0011] A fuzzy uncertainty set for wind and solar power output is constructed using 1-norm and ∞-norm constraints; based on the fuzzy uncertainty set for wind and solar power output, the second objective function is reconstructed to obtain a two-stage sub-Bruker optimization model.

[0012] The two-stage bibliometric optimization model is decomposed into a main problem and sub-problems using a column and constraint generation algorithm, and the planning scheme is obtained by iterative solution.

[0013] Furthermore, the equivalent model of a DC-feed converter station converts the DC feed into AC power through the converter station. The converter station installed at a certain node is equivalent to an equivalent impedance and ideal voltage source converter.

[0014] Furthermore, the first objective function is to minimize the system planning cost. System operating costs With penalty fees sum;

[0015] The system planning includes the construction of gas turbine units, transmission lines, pumped storage power stations, static var compensators, and capacitor banks.

[0016] Furthermore, the preset constraints include:

[0017] The first constraint includes: construction cost constraint and contract selection constraint. The construction cost constraint takes into account the construction cost of DC feed-in contract selection, and the contract selection constraint takes into account the DC feed-in contract selection constraint.

[0018] The second set of constraints includes: construction constraints, node balance constraints, linear AC power flow constraints, pumped storage operation constraints, wind and solar power output and curtailment constraints, and generator operation constraints.

[0019] The second set of constraints includes: static var compensator operation constraints and capacitor bank operation constraints.

[0020] Furthermore, the operating constraints of the static var compensator are: In the formula: and These represent the lower and upper limits of the output power of the K-type static var compensator, respectively. The reactive power output of the static var compensator connected to node j;

[0021] The operating constraints of the capacitor bank are: In the formula: Let be the maximum capacity of a single capacitor that can be built on node j. The reactive power output of the capacitor bank connected to node j.

[0022] Furthermore, the expression for the second objective function is:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028] In the formula: Choose the cost for the contract; , , , and These are the candidate sets for gas turbines, static var compensators, pumped storage power stations, transmission lines, and capacitor banks, respectively, where k is the equipment model index. , and These are the annualized construction costs for the K-type gas turbine, transmission lines, and static var compensators, respectively. , and These are binary variables representing the construction status of the gas turbine, static var compensator, and transmission line, respectively. When the value is 1, it means that construction has been selected. , These are the cost coefficients for constructing pumped storage power stations. and These are the maximum power of the pumped storage power station and the maximum reservoir capacity, respectively. This is a binary variable representing whether a capacitor bank is installed at node j. A value of 1 indicates that a capacitor bank is installed. Let J be the number of capacitors installed at node j. The fixed cost of installing the capacitor bank at node j, The value represents the investment cost of a single capacitor bank; r is the index for a typical day, h is the index for the intraday scheduling period, and GT represents the gas turbine unit. This represents the number of days contained in a typical day (r). and Representing the unit price of natural gas and coal, and These are the fuel consumption figures for gas-fired units and coal-fired units, respectively. and The cost of starting and shutting down a single generating unit within a pumped storage power station. and The number of pumped storage power stations connected to node j during the h-hour period of a typical day r; , and Here are the penalty factors for wind curtailment, solar curtailment, and load shedding at node j. , and For wind curtailment, solar curtailment, and load shedding; v is the DC feed-in contract index. The binary variable representing the DC feed contract selection, when it is 1, indicates that contract v is selected; This represents the cost of the annual DC feed-in contract (v).

[0029] Furthermore, the two-stage sub-Bruker optimization model includes:

[0030] Phase 1: Determining the system equipment deployment and configuration decisions;

[0031] Phase 2: Optimize the system operation strategy under the worst probability distribution to minimize the expected value of system operation cost and over-limit penalty cost.

[0032] Secondly, this invention provides a source-grid-storage reactive power coordinated distribution bar optimization system, which includes:

[0033] The equivalent model construction unit is used to construct an equivalent model of a DC-feed converter station based on the coupling effect of DC feed factors on power receiving capacity.

[0034] The first objective function construction unit is used to construct a first objective function including power sources, grids, storage, and reactive power compensation equipment based on the equivalent model of the DC-feed converter station; the first objective function is a deterministic power source-grid-storage reactive power coordination planning objective function;

[0035] The second objective function optimization unit is used to optimize the first objective function based on preset constraints to obtain the optimized second objective function; the second objective function is a deterministic source-grid-storage reactive power coordination planning objective function that considers the improvement of power receiving capacity;

[0036] The objective function reconstruction unit is used to construct the fuzzy uncertainty set of wind and solar power output using the 1-norm and ∞-norm constraints; based on the fuzzy uncertainty set of wind and solar power output, the second objective function is reconstructed to obtain a two-stage sub-Bruker optimization model;

[0037] The model solving unit is used to decompose the two-stage bibar optimization model into a main problem and sub-problems using a column and constraint generation algorithm, and then iteratively solve them to obtain the planning scheme.

[0038] Furthermore, the preset constraints include:

[0039] The first constraint includes: construction cost constraint and contract selection constraint. The construction cost constraint takes into account the construction cost of DC feed-in contract selection, and the contract selection constraint takes into account the contract selection of DC feed-in.

[0040] The second set of constraints includes: construction constraints, node balance constraints, linear AC power flow constraints, pumped storage operation constraints, wind and solar power output and curtailment constraints, and generator operation constraints.

[0041] The second set of constraints includes: static var compensator operation constraints and capacitor bank operation constraints.

[0042] Thirdly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-mentioned source-grid-storage reactive power coordination and distribution bar optimization method.

[0043] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0044] 1. This invention relates to a source-grid-storage reactive power coordination sub-Bluer bar optimization method, system, and medium. Based on the transmission network expansion planning problem, this invention establishes a source-grid-storage coordinated sub-Bluer bar planning model that considers the improvement of the power system's power receiving capacity. Through coordinated planning of source-grid-storage and reactive power compensation equipment, the power system's power receiving capacity is improved, and operating costs are reduced. Considering the uncertainty of wind and solar power output in the transmission network, a sub-Bluer bar model is established using 1-norm and ∞-norm constraints to describe the fluctuations in new energy sources. Finally, numerical examples verify the correctness and effectiveness of the model.

[0045] 2. This invention relates to a distributed blue bar optimization method, system, and medium for reactive power coordination between power generation, grid, and storage. Simulation analysis first verifies the role of pumped-storage power stations in absorbing new energy sources, effectively reducing wind and solar curtailment penalties, lowering operating costs, and demonstrating greater economic efficiency. Considering DC feedin, coordinated planning for reactive power compensation can effectively increase DC feedin power, enhance the power receiving capacity of power system nodes, and further reduce costs. However, using a distributed blue bar model to account for the uncertainties in wind and solar power output requires investment in larger-capacity energy storage and reactive power compensation to achieve absorption and safe, stable operation of the power system, resulting in increased planning and operating costs. Attached Figure Description

[0046] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0047] Figure 1 This is a flowchart of the reactive power coordination and distribution optimization method of the power source, grid, and storage system of the present invention.

[0048] Figure 2 This is a typical intraday load and renewable energy output diagram;

[0049] Figure 3 It is an IEEE 24-node standard system;

[0050] Figure 4 The output curve of the static var compensator in Scheme 3;

[0051] Figure 5 This is a comparison chart of the power generation costs of schemes 1-3;

[0052] Figure 6 The diagram shows the power output of new energy sources corresponding to scenarios 1-5;

[0053] Figure 7 Sensitivity analysis chart for the total cost of the power system;

[0054] Figure 8 This is a block diagram of the reactive power coordination and distribution system optimization of the power grid and energy storage in this invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are only for explaining this invention and are not intended to limit this invention.

[0056] The existing power system planning debulking optimization method does not take into account the combined effects of the uncertainties of wind and solar power and the power receiving capacity. This results in problems such as the inability to guarantee the safety of the power system, low power receiving capacity of the receiving-end grid, and high operating costs.

[0057] This invention considers the uncertainties brought about by the large-scale integration of new energy sources, and recognizing the crucial role of wind and solar uncertainties in power system planning. The sub-Browser bar optimization method, capable of making decisions based on the most unfavorable distribution by characterizing the distribution set of uncertain variables when the probability distribution is unknown but some statistical properties are available, balances economic efficiency and robustness, and has significant theoretical and practical value in power system planning problems. The method of constructing fuzzy sets by utilizing known historical data scenarios of random variables and comprehensively considering the probabilities of 1-norm and ∞-norm constraints has numerous applications due to its lack of complex dual transformations. Given the ever-increasing installed capacity of new energy sources in power systems, it is necessary to further explore the application of the sub-Browser bar method in power system planning.

[0058] Example 1

[0059] like Figure 1 As shown, the present invention provides a source-grid-storage reactive power coordination and distribution bar optimization method, which includes:

[0060] Step 1: Considering the AC power flow and its linearization model of the power system, construct an equivalent model of the DC-feed converter station based on the coupling effect of DC feed-in factors on the power receiving capacity.

[0061] In this embodiment, the equivalent model of the DC-feed converter station is to convert the DC feed into AC power through the converter station. The converter station installed at a certain node is equivalent to an equivalent impedance and an ideal voltage source converter. There are also power flow constraints (1)-(3) inside it. At the same time, the converter station needs to meet the restrictions on the AC power flow and the upper and lower limits of the node voltage (4)-(5). The calculation method of power loss of the power flow inside the converter station is shown in equations (6) and (7).

[0062] (1)

[0063] (2)

[0064] (3)

[0065] (4)

[0066] (5)

[0067] (6)

[0068] (7)

[0069] In the formula: r is the typical daily index, and h is the intraday scheduling period index. and For internal nodes of the converter station Injected active and reactive power, and For the active and reactive power losses inside the converter station, and For the conductance and susceptance of the equivalent line of the converter station, This refers to the active power input to the DC side of the converter station. The active power fed into converter station c. For each scheduling period in the DC feed contract v, the DC feed power is... and These are the upper and lower limits of AC side power. This refers to the AC side voltage amplitude of the converter station.

[0070] Step 2: Based on the equivalent model of the DC-fed converter station, construct a first objective function that includes the power source, grid, storage, and reactive power compensation equipment; the first objective function is a deterministic power source-grid-storage reactive power coordination planning objective function.

[0071] In this embodiment, when modeling the AC grid expansion planning problem considering DC feed, the first objective function is to minimize the system planning cost. System operating costs With penalty fees The sum; the specific calculation formulas are shown in equations (8)-(10). Among them, the objects of system planning include the construction of gas turbine units, the construction of transmission lines, the site selection and capacity determination of pumped storage power stations, the construction of static var compensators (SVC) and capacitor banks (CB). The operating cost of the system includes the power generation cost of gas turbine units and coal-fired generators and the start-up and shutdown cost of pumped storage power stations. The penalty costs include the load shedding penalty and the wind and solar curtailment penalty.

[0072] The expression for the first objective function is: min The details are as follows:

[0073] (8)

[0074] (9)

[0075] (10)

[0076] (11)

[0077] In the formula: , , , and These are the candidate sets for gas turbines, static var compensators, pumped storage power stations, transmission lines, and capacitor banks, respectively, where k is the equipment model index. , and These are the annualized construction costs for the K-type gas turbine, transmission lines, and static var compensators, respectively. , and These are binary variables representing the construction status of the gas turbine, static var compensator, and transmission line, respectively. When the value is 1, it means that construction has been selected. , These are the cost coefficients for constructing pumped storage power stations. and These are the maximum power of the pumped storage power station and the maximum reservoir capacity, respectively. This is a binary variable representing whether a capacitor bank is installed at node j. A value of 1 indicates that a capacitor bank is installed. Let J be the number of capacitors installed at node j. The fixed cost of installing the capacitor bank at node j, The value represents the investment cost of a single capacitor bank; r is the index for a typical day, h is the index for the intraday scheduling period, and GT represents the gas turbine unit. This represents the number of days contained in a typical day (r). and Representing the unit price of natural gas and coal, and These are the fuel consumptions for gas-fired units and coal-fired units, respectively. The specific calculation is shown in equation (11), where... and These are the consumption coefficients of the generator set. and Heat consumption during start-up and shutdown For natural gas with high calorific value, take ; and The cost of starting and shutting down a single generating unit within a pumped storage power station. and The number of pumped storage power stations connected to node j during the h-hour period of a typical day r; , and Here are the penalty factors for wind curtailment, solar curtailment, and load shedding at node j. , and This refers to power curtailment, solar power curtailment, and power loss due to load shedding.

[0078] Step 3: Based on preset constraints, optimize the first objective function to obtain the optimized second objective function; the second objective function is a deterministic source-grid-storage reactive power coordination planning objective function that considers the improvement of power receiving capacity.

[0079] In this embodiment, the preset constraints include:

[0080] The first constraint includes: construction cost constraint and contract selection constraint. The construction cost constraint takes into account the construction cost of DC feed-in contract selection, and the contract selection constraint takes into account the DC feed-in contract selection constraint.

[0081] The second set of constraints includes: construction constraints, node balance constraints, linear AC power flow constraints, pumped storage operation constraints, wind and solar power output and curtailment constraints, and generator operation constraints.

[0082] The second set of constraints includes: static var compensator operation constraints and capacitor bank operation constraints.

[0083] Specifically, the constraints are as follows:

[0084] (1) Considering the construction cost constraints of DC feed-in contract selection

[0085] Considering the need to add contract selection cost to the objective function after considering DC feed-in contract selection, the first objective function (8)-(10) is modified to obtain the second objective function as follows:

[0086] The expression for the second objective function is:

[0087] (12)

[0088] (13)

[0089] In the formula, Select cost for the contract; v is the DC feed contract index. The binary variable representing the DC feed contract selection, when it is 1, indicates that contract v is selected; This represents the cost of the annual DC feed-in contract (v).

[0090] (2) Considering the selection constraints of DC feed-in contracts

[0091] (14)

[0092] (15)

[0093] (3) Investment and construction constraints

[0094] There is a maximum limit on the number of capacitors connected to the same node during system planning, i.e., Equation (16).

[0095] (16)

[0096] In the formula: Let j be the maximum number of capacitors that can be connected to node j.

[0097] (4) Node balance constraints

[0098] Considering the connection of wind power and photovoltaic power to the power grid, the system active power and reactive power balance constraints for node j are shown in equations (17) and (18). Equation (19) is the limit on the load shedding ratio of the node, and equation (20) is the limit on the load shedding ratio.

[0099] (17)

[0100] (18)

[0101] (19)

[0102] (20)

[0103] In the formula: Let j be the set of devices connected to node j. Let j be the set of sending nodes when j is the receiving node. Let j be the set of receiving nodes when j is the sending node. and Contribute to wind and solar power. To provide power to the generator set, For the line Active power on This refers to the active power loss on the line; This refers to the power generation of the pumped storage power station. Let be the active load of node j. and Contributing reactive power to wind and solar power, For the reactive power output of the generator set, For the line reactive power on This refers to reactive power loss on the line. To feed reactive power into converter station C, and These represent the reactive power compensation power of the capacitor bank and the static var compensator, respectively. For reactive loads of the power system, The proportional coefficient for shedding load at the same node j is calculated in (19).

[0104] (5) Linear AC power flow constraints

[0105] This invention uses the AC power flow method to model the power flow of the transmission network. Combining the construction status and selection results of the lines, equations (21) and (22) are obtained to calculate the active and reactive power flow on the lines. Equations (23) and (24) constrain the upper limit of the active and reactive power flow on the lines. Equation (25) limits the safe upper and lower limits of the voltage amplitude.

[0106] (twenty one)

[0107] (twenty two)

[0108] (twenty three)

[0109] (twenty four)

[0110] (25)

[0111] In the formula: For a relatively large number, Let be the voltage amplitude of node j at time h on a typical day r. and Type K line The conductivity and susceptance, For the line The phase angle difference between the two endpoints, and This represents the maximum permissible active and reactive power transmission capacity for the K-type line. and This represents the maximum permissible active and reactive power transmission capacity of the existing transmission lines in the system. and These represent the lower and upper limits allowed for the node voltage amplitude.

[0112] The nonlinear characteristics of the AC power flow models (21)-(25) increase the difficulty of calculation, requiring linearization of the AC power flow models. In actual operation of the power grid, due to the phase angle difference between nodes... Very small and the node voltage amplitude is close to the rated voltage. and Define variables ,Will As independent variables, the linear AC power flow model is obtained as shown in equations (26)-(29).

[0113] (26)

[0114] (27)

[0115] (28)

[0116] (29)

[0117] In the formula, Let (i,j) be the phase angle difference of the line, serving as the reference point for the first-order Taylor series expansion in the power flow linearization process. Let be the voltage amplitude of node i during time period h, and be the reference point for the first-order Taylor series expansion.

[0118] (6) Operational constraints of pumped storage

[0119] This invention models a closed variable speed pumped storage power station. The operating parameters of the pumped storage power station are first restricted by the construction status (30), the number of units in the pumped storage power station should be continuous (31)-(32), there is an upper limit to the number of start-ups and shutdowns per day (33), and only pumping or power generation can be selected during a scheduling period (34). There are also constraints on the upper and lower limits of the power of pumping or power generation (35)-(36), as well as the continuity constraints between the pumping power and the upper and lower reservoir capacity (37)-(38), the maximum capacity constraints of the upper and lower reservoirs (39), the initial and final capacity constraints of the upper and lower reservoirs (40), and the calculation of the power generation of the pumped storage power station (41).

[0120] (30)

[0121] (31)

[0122] (32)

[0123] (33)

[0124] (34)

[0125] (35)

[0126] (36)

[0127] (37)

[0128] (38)

[0129] (39)

[0130] (40)

[0131] (41)

[0132] In the formula: p is the unit index in the pumped storage power station at node j. Let be the total number of pumped storage units in node j. This represents the maximum number of generating units that can be built within a pumped storage power station. and Let p be the maximum reservoir capacity and power corresponding to a single pumped storage unit in node j. and This represents the number of generating units that were in pumping and generating mode during time period h. and This represents the number of generating units that started or stopped during the time period h. The number of start-ups and shutdowns allowed per unit's scheduling cycle; / This is a binary variable representing the state of the pumped storage power station. When the value is 1, it means that the pumped storage power station is pumping water / generating electricity during the time period h. , and , The minimum and maximum pumping and power generation capacities of a single unit. and The total pumping and power generation capacity of the pumped storage power station connected to node j; and For the capacity of the upper and lower reservoirs, Harmony The conversion coefficient of water volume to electricity volume for pumped storage unit power generation and pumping; and To regulate the reservoir capacity at the beginning and end of the operation, and The reservoir capacity at the beginning and end of the scheduling process.

[0133] (7) Static Var Compensator Operation Constraints

[0134] The reactive power output of a static var compensator is related to its installed capacity, as shown in equation (42).

[0135] (42)

[0136] In the formula: and These represent the lower and upper limits of the output power of the K-type static var compensator, respectively. Let be the reactive power output of the static var compensator connected to node j.

[0137] (8) Capacitor bank operating constraints

[0138] The reactive power compensation of the capacitor is limited by the capacity of a single capacitor and the number of capacitors installed at node j (43).

[0139] (43)

[0140] In the formula: Let be the maximum capacity of a single capacitor that can be built on node j. The reactive power output of the capacitor bank connected to node j.

[0141] (9) Wind and solar power output and curtailment constraints

[0142] The sum of the dispatched output of the wind turbine and the curtailed power is equal to the predicted output of the wind turbine (44). The reactive power output of the wind turbine is constrained (45), and the curtailed power is a positive number less than the predicted value (46). The output constraints of the photovoltaic unit are similar to those of wind power, namely equations (47)-(49).

[0143] (44)

[0144] (45)

[0145] (46)

[0146] (47)

[0147] (48)

[0148] (49)

[0149] In the formula: This is the predicted value for wind turbine units. The power factor angle of the wind turbine; Forecasting power output for photovoltaic units Let be the power factor angle of the photovoltaic unit at node j.

[0150] (10) Generator operating constraints

[0151] The normal operation of existing generator units in the system needs to meet the following constraints: ramp rate constraint (50), start-stop fuel consumption constraint (51), and active and reactive power output upper and lower limits constraint (52). For the GT units to be put into operation, considering the parameter differences corresponding to different unit models, the operating constraints of the units to be put into operation include ramp rate constraint (53), start-stop fuel consumption constraint (54), and active and reactive power output constraint (55).

[0152] (50)

[0153] (51)

[0154] (52)

[0155] (53)

[0156] (54)

[0157] (55)

[0158] In the formula: and The ramp-up and ramp-down rates of the units in the system. and The uphill and downhill ramp rates for the type K-type tractor unit awaiting commissioning. and These represent the amount of fuel consumed by each unit in the system during a single start-up and shutdown, respectively. and These represent the fuel consumption of a single start-up and shutdown of a type K generating unit awaiting commissioning. This is a binary variable representing the generator's start / stop status; a value of 1 indicates that the generator is on. / and / These represent the maximum and minimum active power output of the conventional / K-type alternative units, respectively. / and / The values ​​for the maximum and minimum reactive power output of the conventional / K-type alternative units are provided.

[0159] In this embodiment, the optimized second objective function can be written in matrix form (56)-(59), where and For binary variables (57), These represent planning decision variables and contract selection decision variables. Represents the remaining binary variables; The representative model includes all continuous variables related to wind and solar curtailment. Equation (56) is the objective function, i.e., (8)-(13), and Equation (58) represents the relationship between... The relevant constraints are (14)-(16), and equation (59) represents the operational constraints of the model, namely equations (1)-(7) and (17)-(55).

[0160] (56)

[0161] (57)

[0162] (58)

[0163] (59)

[0164] In the formula: , , , , , , , and These are the coefficient vectors and matrices corresponding to the abstract objective function and constraints.

[0165] Step 4: Construct a fuzzy uncertainty set for wind and solar power output using 1-norm and ∞-norm constraints; reconstruct the second objective function based on the fuzzy uncertainty set for wind and solar power output to obtain a two-stage sub-Bruker optimization model;

[0166] In this embodiment, the uncertain fuzzy set of new energy output is as follows:

[0167] This invention employs a split-Bruker bar model to characterize the prediction uncertainty of renewable energy output. First, renewable energy output scenarios are randomly generated using Latin hypercube sampling. Then, a synchronous back-substitution method is used to reduce the number of scenarios to S, along with the initial probability for each scenario. By using the 1-norm and ∞-norm as constraints, fuzzy sets are obtained. .

[0168] (60)

[0169] In the formula, s is the index of the new energy power output scenario, and S is the total number of scenarios. For scenario probabilities, The initial scene probability. and These are the upper limits of probability deviation under 1-norm and ∞-norm constraints, respectively.

[0170] Since both the 1-norm and the ∞-norm contain absolute value terms, for After linearization, the linear 1-norm and ∞-norm constraints are obtained as shown in equation (61), and the linear uncertainty set is shown in equation (62).

[0171] (61)

[0172] (62)

[0173] In this embodiment, the two-stage sub-Bruker optimization model is as follows:

[0174] The model of this invention is divided into two stages. First, in the first stage, decisions are made regarding equipment deployment and the selection of DC feed-in power contracts. Then, in the second stage, operating costs are calculated based on the obtained new energy output scenarios, the system operation is verified, and the expected operating costs for the worst-case probability distribution scenario are obtained. The specific sub-Bruker programming model expressed in matrix form is shown in equation (63). Represents the planning cost of the system. The operating cost of scenario s.

[0175] (63)

[0176] Step 5: Use the column and constraint generation algorithm to decompose the two-stage bibliometric optimization model into the main problem and sub-problems for iterative solution to obtain the planning scheme.

[0177] The two-stage sub-Brussels optimization model (63) proposed in this invention is a three-level optimization problem in the form of min-max-min. The model can be decomposed into a master problem (MP) and a sub-problem (SP) by the column and constraint generation algorithm (CCG) for iterative solution. The master problem model is shown in equation (64), and the sub-problem model is shown in equation (65).

[0178] (64)

[0179] (65)

[0180] The upper-level subproblem variables in the two-stage sub-Bruker optimization model. This does not affect the feasible region of the lower-level subproblems, and the runtime variables of each scenario... and They are independent of each other, so the lower-level subproblem (SP1) and the upper-level subproblem (SP2) can be decoupled.

[0181] (66)

[0182] (67)

[0183] When solving subproblems, first obtain the solution from the main problem. Calculate the operating costs for each new energy power output scenario, and then solve for the worst-case probability distribution that maximizes the expected operating cost within the uncertainty set. The specific iterative solution steps using the CCG algorithm are as follows:

[0184] Step 1: Set the lower bound Upper Realm Number of iterations Solution accuracy ;

[0185] Step 2: On the main problem Solving the problem yields the following results: and and update the lower bound. ;

[0186] Step 3: Solve in Operating costs of various new energy operation scenarios under the decision The worst probability distribution is then obtained by solving the problem. ;

[0187] Step 4: Update the upper bound based on the probability distribution obtained in Step 3. ;

[0188] Step 5: Determine if the iteration has converged. If the accuracy requirement is met, then... If the accuracy requirement is not met, the iteration stops; if not, the probability distribution obtained from the subproblem is passed to the main problem. and update Go back to step 2.

[0189] In practical implementation, this invention utilizes the IEEE 24-node computational system to verify the established planning model. Typical intraday load and wind / solar power forecast output are shown below. Figure 2 For the specific topology, see Figure 3 The DC feed point is busbar 20, and there are 7 existing traditional thermal power units, 1 photovoltaic power station, and 1 wind power station. The wind curtailment penalty and solar curtailment penalty are 10,000 yuan / MWh. The maximum number of units that can be built within a pumped storage power station is... The value is 5. The minimum active power of a single generator in a pumped storage power station is 4MW, and the maximum is 20MW. The water conversion coefficient is 5. 0.15Hm 3 / MW, energy conversion factor 0.1Hm 3 / MW; Static Var Compensator selection data is shown in Table 4, and DC feed-in contract price is shown in Table 5.

[0190] To verify the correctness of the proposed model and to compare and analyze the impact of factors such as pumped storage power stations, DC feed-in, reactive power compensation, and uncertainties on the planning results, four different planning schemes were set up (see Table 1). The construction and operating costs of schemes 1-4 were obtained using Gurobi algorithms (see Table 2), and the planning results are shown in Table 3. The penalty costs in Table 2 only include wind curtailment penalties in schemes 1-4. However, since scheme 5 is based on a distributed bar model considering uncertainties, its operating cost calculation method differs from the deterministic schemes 1-4, representing the expected value under the worst-case distribution. The symbols in Table 3 have the following meanings: L represents the transmission line, with the superscript indicating the maximum capacity of the line under construction and the subscript indicating the specific node to which it is connected; G represents the gas turbine, with the superscript indicating the selection result and the subscript indicating the location result; S represents the pumped storage power station, with the superscript indicating the number of generators in the power station and the subscript indicating the location result; D represents the selection of the DC feed-in contract, with the superscript indicating the contract number; V represents the static var compensator, with the superscript indicating the selection result and the subscript indicating the location result; C represents the capacitor bank, with the subscript indicating the node and the superscript indicating the number of capacitor banks under construction. Under the data settings of this invention, schemes 1-4 all select the more economical static var compensator for reactive power compensation and do not construct capacitor banks.

[0191] Table 1 Specific settings for schemes 1-5

[0192]

[0193] Table 2 Costs of Options 1-5

[0194]

[0195] *: This item represents the expected operating cost of the new energy processing scenario under the worst-case probability distribution.

[0196] Table 3 Planning Decisions for Schemes 1-4

[0197]

[0198] Table 4 Operating parameters of static var compensator

[0199]

[0200] Table 5 DC Feed Injection Contract Data

[0201]

[0202] 1) Analysis of the impact of DC feed-in and reactive power compensation

[0203] Option 3 adds the option of a DC feed-in contract compared to Option 1. Since DC feed-in costs are lower than traditional thermal power units or gas turbine power generation, and considering the economic operation of the power system, the system tends to select contracts with the largest possible DC feed-in capacity. Therefore, Option 3 selects a DC feed-in contract. To reduce the total system cost, as shown in Table 2. The DC feed-in contract selected in Scheme 2 is... However, from an economic perspective, the system should select a feed-in contract with a DC feed-in power that is as large as possible. or In Scheme 2, the power grid is limited by safety constraints such as voltage amplitude limits, thus not fully realizing the economic benefits of DC feedin. In contrast, Scheme 3, considering the role of reactive power compensation, chooses to install static var compensators (SVCs). This enabled the implementation of feed-in contracts. The choice. Figure 4 The static var compensator in Scheme 3 is given. The reactive power output curves for each scheduling period during a typical day and the total reactive power output of generators in the system. Figure 4 As can be seen, the reactive power output of the generator in Scheme 3 is significantly lower than that in Scheme 2. The static var compensator operates at its maximum power of 150 Mvar from 3:00 to 7:00 and from 12:00 to 16:00. If the DC feed-in increases further, a larger capacity static var compensator will be needed for compensation.

[0204] The joint planning of reactive power compensation and DC feed-in enables the improvement of DC feed-in power under the security constraints of the transmission network, thereby increasing the power receiving capacity of the example system. Figure 5 The power generation costs of the systems in Schemes 1-3 were compared, including the fuel costs of gas turbines, traditional thermal power units, and DC feed-in costs. Figure 5 It can be observed that the total power generation cost shows a downward trend, and the power generation cost of gas turbines also decreases. Combined with the construction results in Table 3, this is because, with the selection of DC feed-in contracts, the decision to construct gas turbines decreased from 5 units in Scheme 1 to 4 units in Scheme 2. Finally, only 2 gas turbines were constructed in Scheme 3, which verifies the economic efficiency of the DC feed-in and reactive power compensation coordinated planning method.

[0205] 2) Impact analysis of the split-bar model

[0206] To address the uncertainty in wind and solar power output, random wind and solar scenarios are generated, and the covariance matrix parameters of the Latin hypercube sampling function are set. Then, the synchronous back-substitution method was used to reduce the number of scenes to five representative ones to simulate the uncertainty of the scenery. The initial probability distribution of the random scenery scenes is shown in Table 6. The specific scenery output of the five scenes is shown in Table 6. Figure 6Set the upper limit of the probability deviation under the 1-norm and ∞-norm constraints respectively. =0.3、 =0.1. Table 6 shows the probability distribution of the initialization steps and the last iteration during the solution process of Scheme 4, as well as the running costs for each scenario.

[0207] Table 6. Probability distribution and operating costs of scenarios 1-5

[0208]

[0209] Based on the calculation results in Tables 2 and 3, and considering uncertainties, both the construction cost and operating cost have increased compared to Scheme 3: In terms of construction decisions, the selection of the static var compensator has changed, with the more expensive Model 3 being chosen. Its reactive power compensation output limit is 200 Mvar, an increase of 50 Mvar compared to the reactive power compensation power limit of Scheme 2. Simultaneously, the number of generating units within the pumped storage power station has been adjusted accordingly, increasing the power and capacity limits to enhance the system's ability to regulate the fluctuations in renewable energy. At the operational level, due to scenarios where renewable energy output is lower than the typical daily forecast, power generation costs have increased. Therefore, the expected operating cost corresponding to the worst-case scenario has also increased compared to Scheme 3.

[0210] The results of the initialization steps shown in Table 6 reveal the operating costs and penalty fees for each scenario corresponding to the Scheme 4 construction decision. Due to the excessive renewable energy output in Schemes 1, 4, and 5, the pumped-storage power stations selected in Scheme 4 cannot absorb it, resulting in high penalty fees for these three scenarios. However, after the iteration, by adding units to the pumped-storage power stations, the wind curtailment penalty for these scenarios was reduced, enabling the absorption of excess renewable energy in Schemes 1, 4, and 5, reducing the penalty fees to zero. Simultaneously, due to the increased equipment, all scenarios experienced a reduction in operating costs, validating the model's correctness.

[0211] To further analyze the upper limit of probability deviation under the constraints of 1-norm and ∞-norm and The impact on power system planning decisions is determined by setting different... , Calculate the values ​​to obtain the total cost and different and See relationship Figure 7 . Figure 7 As can be seen, the upper limit of the allowable probability deviation increases under the constraints of 1-norm and ∞-norm. and With the growth of [unclear], the total cost of power system planning is showing an upward trend. or The increase in the worst probability distribution means that the deviation from the initial distribution is allowed to increase, which increases the probability of scenarios with high operating costs and worsens the worst probability distribution scenarios, thus leading to an increase in total cost. Figure 7 You can also see when When the value is 0.05 or 0.1, the following occurs: The phenomenon that the system cost is equal under different values ​​is because the worst probability distribution is now constrained by the ∞-norm constraint. Changing the upper limit of the allowable deviation of the 1-norm constraint does not affect the result of the worst probability distribution.

[0212] The parameters of the covariance matrix set in the Latin hypercube sampling during the scene generation step above are... It will also affect the calculation results, settings Scene generation and reduction were performed using values ​​of 0.05 and 0.2 respectively, and then selected... =0.3、 =0.1 Recalculate the corresponding different values Sensitivity analysis was performed on system planning decisions and costs within a specific scenario set. The calculated planning results and total costs are shown in Table 7. The table shows that due to… The increase in wind and solar power has led to greater uncertainty, making investment decisions more conservative. To ensure that wind and solar power can be absorbed as much as possible, the system has invested in additional energy storage and reactive power compensation resources, which has also led to an increase in total costs.

[0213] Table 7. Differences in the random scene generation steps Corresponding planning decisions and total costs

[0214]

[0215] This invention addresses the planning problem of DC-DC feed-in receiving-end power grids by proposing a distributed bar planning method for reactive power coordination between power sources, grids, and storage. This method comprehensively considers the impacts of DC feed-in power, uncertainties in wind and solar power output, pumped storage regulation capacity, and reactive power compensation equipment configuration on system operation. Through numerical examples, the following conclusions are drawn:

[0216] (1) The integrated configuration of pumped storage and reactive power compensation equipment can effectively improve the power receiving capacity of the receiving-end power grid under the condition of high proportion of new energy DC feed-in;

[0217] (2) The proposed contract selection method can improve the power receiving capacity boundary under the optimization model and ensure the power flow security of the system;

[0218] (3) Based on the sub-Bruker optimization method, it can effectively resist the operational risks caused by the uncertainty of wind and solar power generation, and reduce the system operating costs and penalty fees;

[0219] (4) The proposed method has achieved a synergistic improvement in system security, economy and new energy absorption capacity, and verified the practical value of source-grid-storage reactive power coordination and uncertainty modeling in receiving-end power grid planning.

[0220] Example 2

[0221] like Figure 8 As shown, the difference between this embodiment and Embodiment 1 is that this embodiment provides a source-grid-storage reactive power coordination Blower optimization system, which corresponds one-to-one with the source-grid-storage reactive power coordination Blower optimization method in Embodiment 1; the system includes:

[0222] The equivalent model construction unit is used to construct an equivalent model of a DC-feed converter station based on the coupling effect of DC feed factors on power receiving capacity.

[0223] The first objective function construction unit is used to construct a first objective function including power sources, grids, storage, and reactive power compensation equipment based on the equivalent model of the DC-feed converter station; the first objective function is a deterministic power source-grid-storage reactive power coordination planning objective function;

[0224] The second objective function optimization unit is used to optimize the first objective function based on preset constraints to obtain the optimized second objective function; the second objective function is a deterministic source-grid-storage reactive power coordination planning objective function that considers the improvement of power receiving capacity;

[0225] The objective function reconstruction unit is used to construct the fuzzy uncertainty set of wind and solar power output using the 1-norm and ∞-norm constraints; based on the fuzzy uncertainty set of wind and solar power output, the second objective function is reconstructed to obtain a two-stage sub-Bruker optimization model;

[0226] The model solving unit is used to decompose the two-stage bibar optimization model into a main problem and sub-problems using a column and constraint generation algorithm, and then iteratively solve them to obtain the planning scheme.

[0227] As a further implementation, the preset constraints include:

[0228] The first constraint includes: construction cost constraint and contract selection constraint. The construction cost constraint takes into account the construction cost of DC feed-in contract selection, and the contract selection constraint takes into account the contract selection of DC feed-in.

[0229] The second set of constraints includes: construction constraints, node balance constraints, linear AC power flow constraints, pumped storage operation constraints, wind and solar power output and curtailment constraints, and generator operation constraints.

[0230] The second set of constraints includes: static var compensator operation constraints and capacitor bank operation constraints.

[0231] The execution process of each unit can be carried out according to the process flow of the source-grid-storage reactive power coordination and distribution bar optimization method in Example 1, and will not be described in detail in this example.

[0232] Meanwhile, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned source-grid-storage reactive power coordination and distribution bar optimization method.

[0233] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0234] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0235] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0236] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0237] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment 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 should be included within the scope of protection of the present invention.

Claims

1. A robust optimization method for source network storage reactive power coordination distribution, characterized in that, The method comprises: An equivalent model of a DC feeding converter station is constructed based on the coupling of the DC feeding factor to the power receiving capacity; A first target function including sources, a network, storage, and reactive compensation devices is constructed according to the equivalent model of the DC feeding converter station; the first target function is a deterministic source-network-storage-reactive coordination planning target function; The first target function is optimized based on preset constraint conditions to obtain an optimized second target function; the second target function is a deterministic source-network-storage-reactive coordination planning target function considering the improvement of the power receiving capacity; A fuzzy uncertain set of wind and solar power output is constructed using 1-norm and ∞-norm constraints; the second target function is reconstructed according to the fuzzy uncertain set of wind and solar power output to obtain a two-stage distribution robust optimization model; The two-stage distribution robust optimization model is decomposed into a main problem and a sub-problem for iterative solving by using a column and constraint generation algorithm to obtain a planning scheme. 2.The source network and energy storage reactive power coordination distribution robust optimization method according to claim 1, characterized in that, The equivalent model of the DC feeding converter station is to convert DC feeding through the converter station into AC power, and the converter station installed at a certain node is equivalent to an equivalent impedance and an ideal voltage source converter. 3.The source network and energy storage reactive power coordination distribution robust optimization method according to claim 1, characterized in that, the first objective function is to minimize a sum of system planning cost , system operation cost and penalty cost . The object of the system planning includes the construction of gas turbine units, the construction of transmission lines, the site selection and capacity determination of pumped storage power stations, the construction of static reactive compensation, and the construction of capacitor banks.

4. The source network storage and reactive power coordination distribution robust optimization method according to claim 1, characterized in that, The preset constraint conditions include: A first constraint condition includes a construction cost constraint and a contract selection constraint; the construction cost constraint is a construction cost constraint considering the selection of a DC feeding contract; and the contract selection constraint is a constraint considering the selection of a DC feeding contract; A second constraint condition includes a construction constraint, a node balance constraint, a linear AC power flow constraint, a pumped storage operation constraint, a wind and solar power output and curtailment constraint, and a generator operation constraint; A second constraint condition includes a static reactive compensation device operation constraint and a capacitor bank operation constraint.

5. The source network storage and reactive power coordination distribution robust optimization method according to claim 4, characterized in that, The static reactive power compensator operation constraint is: wherein: and are the lower and upper output limits of the kth static reactive power compensator, respectively, is the reactive power output of the static reactive power compensator connected to node j. The capacitor bank operation constraints are: where: is the maximum capacity of a single capacitor that can be built at node j, is the reactive power output of the capacitor bank connected to node j.

6. The source network storage and reactive power coordination distribution robust optimization method according to claim 3, characterized in that, An expression of the second target function is: wherein: is the contract selection cost; , , , and are the alternative sets of gas turbines, static var compensators, pumped storage power stations, transmission lines and capacitor banks, respectively, and k is the equipment type index; , and are the annualized capital and installation costs of k-type gas turbines, transmission lines and static var compensators, respectively, , and are binary variables representing the installation status of gas turbines, static var compensators and transmission lines, respectively, and take the value 1 if installation is selected; , are the cost coefficients for installing pumped storage power stations, and are the maximum power of pumped storage power stations and the maximum reservoir capacity, respectively; is a binary variable representing whether capacitor banks are installed at node j, and takes the value 1 if installation is selected; is the number of capacitor banks installed at node j, is the fixed cost of installing capacitor banks at node j, is the investment cost of a single capacitor bank; r is the index of a typical day, h is the index of the scheduling period within a day, and GT represents a gas turbine, represents the number of days included in the typical day r, and represent the unit prices of natural gas and coal, and are the fuel consumptions of gas turbine units and coal-fired units, respectively; and are the unit startup and shutdown costs of units in pumped storage power stations, and are the numbers of unit startups and shutdowns of pumped storage power stations connected to node j in the h period of the typical day r; , and are the wind curtailment, light curtailment and load loss penalty coefficients at node j, , and are the wind curtailment, light curtailment and load loss powers. v is the index of the direct current feed-in contract, is a binary variable indicating the selection of a direct current feed-in contract, which takes the value 1 if contract v is selected; is the annual cost of direct current feed-in contract v.

7. The source network storage and reactive power coordination distribution robust optimization method according to claim 1, characterized in that, The two-stage distribution robust optimization model includes: A first stage: determining system device construction and configuration decisions; A second stage: optimizing system operation strategies under the worst probability distribution to minimize the expected value of system operation cost and overrun penalty.

8. A source network storage reactive power coordinated distribution robust optimization system, characterized in that, The system comprises: An equivalent model construction unit configured to construct an equivalent model of a DC feeding converter station based on the coupling of the DC feeding factor to the power receiving capacity; A first target function construction unit configured to construct a first target function including sources, a network, storage, and reactive compensation devices according to the equivalent model of the DC feeding converter station; the first target function is a deterministic source-network-storage-reactive coordination planning target function; A second target function optimization unit configured to optimize the first target function based on preset constraint conditions to obtain an optimized second target function; the second target function is a deterministic source-network-storage-reactive coordination planning target function considering the improvement of the power receiving capacity; A target function reconstruction unit configured to construct a fuzzy uncertain set of wind and solar power output using 1-norm and ∞-norm constraints; and reconstruct the second target function according to the fuzzy uncertain set of wind and solar power output to obtain a two-stage distribution robust optimization model. A model solving unit is configured to decompose the two-stage distribution robust optimization model into a master problem and a sub-problem for iterative solving by using a column-and-constraint generation algorithm to obtain a planning scheme.

9. The source network storage and reactive power coordination distribution robust optimization system of claim 8, wherein, The preset constraint conditions include: The first constraint condition includes a construction cost constraint and a contract selection constraint, the construction cost constraint being a construction cost constraint considering a direct current feeding contract selection, and the contract selection constraint being a contract selection constraint considering a direct current feeding; The second constraint condition includes a construction constraint, a node balance constraint, a linear alternating current flow constraint, a pumped storage operation constraint, a wind power and photovoltaic output and abandoned wind and light constraint, and a generator operation constraint; The second constraint condition includes a static reactive power compensator operation constraint and a capacitor bank operation constraint.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1-9. The computer program, when executed by a processor, implements the source network storage reactive power coordination distribution robust optimization method according to any one of claims 1 to 7.

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