Robust low-carbon scheduling method, medium and system for hybrid power distribution network considering V2G

Abstract

The application discloses a kind of hybrid power distribution network robust low-carbon scheduling method, medium and system considering V2G, comprising: step S1: construct the objective function considering the overall operation cost of AC-DC hybrid power distribution network;Step S2: determine the constraint condition of the objective function;Wherein, the constraint condition includes: AC-DC hybrid power distribution network basic operation model and electric vehicle low-carbon scheduling model in AC-DC hybrid power distribution network;Step S3: based on the objective function and the constraint condition, considering the uncertainty of wind, light, AC load and DC load, construct two-stage robust low-carbon scheduling model;Step S4: determine the main problem and subproblem of the two-stage robust low-carbon scheduling model solution;Step S5: solve the main problem and subproblem of the two-stage robust low-carbon scheduling model, obtain power distribution network economic dispatch optimization result, for power distribution network scheduling.The application can improve the safety and low-carbon of power distribution network.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching and operation technology, and in particular to a robust low-carbon dispatching method, medium, and system for hybrid distribution networks that considers V2G. Background Technology

[0002] With the large-scale development of modern industry, modern society's demand for fossil fuels is increasing, but the problem of energy shortage is becoming increasingly prominent. At the same time, the consumption of traditional primary energy sources is often accompanied by serious environmental pollution, such as air pollution, leading to global warming, smog, and other phenomena that have a significant negative impact on the Earth's ecological environment. Currently, thanks to the joint efforts of countries around the world, the installed capacity of wind turbines (WT) and photovoltaics (PV) is increasing year by year, and fully utilizing wind and solar resources can reduce the consumption of fossil fuels. Electric vehicles (EVs), as a means of transportation actively responding to the national low-carbon development strategy, are experiencing continuous expansion in scale and have a very close connection with the power system. Based on the operating characteristics and spatiotemporal distribution patterns of EVs, they can be distributed and connected to charging stations in various areas of the power distribution network. Combined with vehicle-to-grid (V2G) technology, bidirectional interconnection and interaction between vehicles and the grid can be achieved. EVs can serve as a load regulation resource to alleviate pressure during peak electricity demand periods and fully utilize surplus electricity during off-peak electricity price periods. Therefore, integrating distributed renewable energy units and electric vehicles into the power distribution system is a key step in building a new type of power distribution system for the future, and has an important impact on the sustainable development of the entire society.

[0003] To enable EV charging and discharging to participate in peak shaving and valley filling, and renewable energy consumption in the distribution network, EVs can be regulated from the perspective of economic benefits in the optimization objective function. For example, time-of-use pricing can be set to encourage EVs to choose the most economically optimal charging and discharging time, thereby achieving optimized scheduling between the power grid and EV users.

[0004] Hybrid AC / DC distribution networks are gradually becoming an important operational structure for distribution networks, enabling more flexible network dispatching. Especially for electric vehicles, AC slow-charging or DC fast-charging stations can be easily connected to hybrid AC / DC distribution networks, reducing AC / DC conversions in the grid. Similarly, it can fully utilize various distributed power sources, achieving flexible utilization of various resources.

[0005] Current research on electric vehicle (EV) dispatching that can participate in V2G is mostly based on the microgrid level or AC distribution network. However, EV operation and control are also compatible with hybrid AC / DC distribution networks and can flexibly connect to various loads and distributed power sources, which can improve the utilization efficiency of new energy sources. While considering the flexible grid connection of EVs and new energy sources, it is also necessary to analyze the low-carbon characteristics of the entire hybrid AC / DC distribution network. Summary of the Invention

[0006] This invention provides a robust low-carbon dispatching method, medium, and system for hybrid distribution networks that considers V2G, in order to solve the problem that existing technologies, while considering the flexible grid access of EVs and new energy sources, do not take into account the low-carbon characteristics of the entire hybrid AC / DC distribution network, resulting in poor safety and low-carbon performance.

[0007] Firstly, a robust low-carbon dispatching method for hybrid distribution networks considering V2G is provided, including:

[0008] Step S1: Construct an objective function that considers the overall operating cost of the AC / DC hybrid distribution network;

[0009] Step S2: Determine the constraints of the objective function; wherein the constraints include: the basic operation model of the AC / DC hybrid distribution network and the low-carbon dispatching model of electric vehicles in the AC / DC hybrid distribution network;

[0010] Step S3: Based on the objective function and the constraints, and taking into account the uncertainties of wind, solar, AC load and DC load, construct a two-stage robust low-carbon scheduling model;

[0011] Step S4: Determine the main problem and sub-problems for solving the two-stage robust low-carbon scheduling model;

[0012] Step S5: Solve the main problem and sub-problems of the two-stage robust low-carbon scheduling model to obtain the economic scheduling optimization results of the distribution network for use in distribution network scheduling.

[0013] In a second aspect, a computer-readable storage medium is provided, on which computer program instructions are stored; when executed by a processor, the computer program instructions implement the robust low-carbon dispatching method for hybrid distribution networks considering V2G as described in the first aspect embodiment above.

[0014] Thirdly, a robust low-carbon dispatching system for hybrid distribution networks considering V2G is provided, comprising: a computer-readable storage medium as described in the second aspect embodiment above.

[0015] Thus, this embodiment of the invention, based on an AC / DC hybrid distribution network, flexibly integrates distributed power sources and EVs, and proposes a robust low-carbon dispatch model for AC / DC hybrid distribution networks that considers the V2G response of electric vehicles. It takes into account the economy and low-carbon nature of EV dispatch strategies, and more reasonably considers the uncertainty of renewable energy and power load during the dispatch operation of AC / DC hybrid distribution networks, thereby improving the safety and low-carbon nature of the distribution network. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a robust low-carbon dispatching method for hybrid distribution networks considering V2G, according to an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of the solution framework for a robust low-carbon dispatching method for hybrid distribution networks considering V2G, according to an embodiment of the present invention.

[0019] Figure 3 It is the equivalent model of the VSC converter station;

[0020] Figure 4 This is a diagram of an IEEE 33 AC / DC hybrid distribution network implementation example;

[0021] Figure 5 These are the total load and base load of example 2-4;

[0022] Figure 6 It is the charging and discharging power of the EV AC / DC charging pile in Example 4. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] This invention discloses a robust low-carbon dispatching method for hybrid distribution networks that considers V2G.

[0025] like Figure 1 and 2 As shown, the method includes the following steps:

[0026] Step S1: Construct an objective function that considers the overall operating cost of the AC / DC hybrid distribution network.

[0027] The objective function mainly includes the following components: the daily operating costs of the distribution network, namely, the cost of purchasing electricity from the main grid, the cost of EV discharge loss compensation, the cost of wind and solar curtailment penalties, the carbon emission-related costs in the distribution network, and the cost of distribution network loss penalties.

[0028] Specifically, the objective function is as follows:

[0029] min C m +C ev +C r +C c +C line (1)

[0030] Each specific expense / cost is calculated using the following formula:

[0031]

[0032]

[0033]

[0034]

[0035]

[0036] Among them, C m For the daily operating costs of the distribution network, C ev To compensate for EV discharge losses, C r The cost of penalties for curtailing wind and solar power, C c For carbon emission-related costs in the power distribution network, C line For the cost of network loss penalties in the distribution network; λ ele The price at which the distribution network purchases electricity from the main grid; λ ev The price for compensating for the additional battery loss caused by EVs discharging into the grid when participating in V2G services; λ wt The unit price for wind curtailment penalty; λ pv Price per unit of abandoned light penalty; λ c The price of a unit of CO2 emission rights in carbon trading; λ line The penalty price per unit of electricity for line network loss; The active power exchanged between the distribution network and the main grid; T is the set of scheduling times in the distribution network system; M is the total number of dispatchable EVs in the distribution network. Let be the discharge power of the m-th EV during time period t; Let be the active power of the wind turbine at node j; Let be the active power of the photovoltaic system at node j; For tradable carbon allowances in the distribution network; For EV carbon allowances; I ij,t R is the current in branch (i,j) during time period t; ij The resistance of the power line (i,j) is Ω. T A collection of substations in a power distribution network; Ω W Ω represents the set of nodes for the wind turbine. P For photovoltaic nodes; Ω L This is a collection of power distribution lines.

[0037] Step S2: Determine the constraints of the objective function.

[0038] The constraints include: the basic operation model of the AC / DC hybrid distribution network and the low-carbon dispatching model of electric vehicles in the AC / DC hybrid distribution network.

[0039] Specifically, the basic operation model of the AC / DC hybrid distribution network includes: AC distribution network power flow constraints, DC distribution network power flow constraints, system security constraints, VSC converter station steady-state operation constraints, DG operation constraints, power exchange constraints between the distribution network and the main grid, capacitor bank (CB) operation constraints applicable to both AC and DC distribution networks, and carbon trading constraints in the distribution network. Among these, the DistFlow power flow equation is used to represent the power flow constraints of the AC / DC hybrid distribution network, and it is applicable to both AC and DC distribution networks.

[0040] The specific constraints mentioned above are as follows:

[0041] (1) Power flow constraints in AC distribution networks include:

[0042] The active power flow constraints and reactive power flow constraints of the DistFlow branch in the AC distribution network are represented by equations (7)-(8), respectively; the voltage drop constraint of the AC distribution network node is represented by equation (9); and the AC branch current constraint is represented by equation (10), which is transformed into a second-order cone relaxation expression.

[0043]

[0044]

[0045]

[0046]

[0047] Where b(j) is the set of all child nodes starting from node j; P represents the reactive power exchanged between the distribution network and the main grid. ij,t and Q ij,tThese represent the active power and reactive power of the power line (i,j) at time t, respectively. and These represent the predicted active and reactive loads at node j, respectively. The charging and discharging power of the electric vehicle charging pile at node j; This refers to the reactive power of the wind turbine; The reactive power output of capacitor CB; These represent the active and reactive power input from the AC distribution network to the VSC converter; V j,t Vj is the voltage at node j; V0 is the reference node voltage; Xj is the voltage at node j. ij Ω represents the reactance of the power line (i,j). J It is a set of distribution network nodes.

[0048] (2) Power flow constraints in DC distribution networks include:

[0049]

[0050]

[0051]

[0052] in, This refers to the active power output to the DC distribution network after passing through the VSC converter.

[0053] (3) System security constraints include:

[0054] The upper and lower limits of power flow are represented by equations (14)-(15), the upper and lower limits of node voltage are represented by equation (16), and the upper and lower limits of branch current are represented by equation (17).

[0055]

[0056]

[0057]

[0058]

[0059] In this context, the superscripts max and min correspond to the maximum and minimum values ​​of the variable, respectively.

[0060] (4) Operational constraints of VSC converter stations include:

[0061] In the model, the VSC converter station is equivalent to the internal equivalent impedance and the ideal VSC, such as Figure 3As shown. During steady-state operation, the AC / DC power conversion on both sides of the VSC branch is as shown in equations (18)-(19). During steady-state operation, the voltage on both sides of the VSC should meet the constraints shown in equation (20). Equation (21) represents the constraint on the power capacity of the VSC. Equation (22) represents the upper and lower limits constraint on the reactive power of the VSC.

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] in, These are the equivalent resistance and reactance inside the VSC converter station, respectively. This refers to the current in the VSC branch; This refers to the reactive power output from the VSC converter station to the AC distribution network. and These represent the AC and DC side voltages inside the converter, respectively; μ is a constant related to the modulation method, here SPWM modulation is selected, and μ is taken as... M l For the adjustment mechanism, and 0≤M l ≤1; This refers to the power capacity of the converter station.

[0068] (5) DG operating constraints include:

[0069] The output power of wind turbines and photovoltaics does not exceed the predicted power for each time period. The power constraints of wind turbines differ depending on the control method used. In this model, the wind turbines are controlled using a constant power factor. The relevant constraints are shown in equations (23)-(25).

[0070]

[0071]

[0072]

[0073] in, and The predicted active power outputs are for wind turbines and solar power, respectively. This is the constant power factor angle of the fan.

[0074] (6) Power exchange constraints between the distribution network and the main network include:

[0075] Equations (26) and (27) respectively describe the upper and lower limits of active and reactive power exchange between the distribution network and the main network.

[0076]

[0077]

[0078] (7) Capacitor CB operating constraints include:

[0079] The reactive power compensation of capacitor CB satisfies equation (28); and the number of static CBs on a single node satisfies equation (29); the number of times each CB can be adjusted within each scheduling cycle satisfies the constraint shown in equation (30).

[0080]

[0081]

[0082]

[0083] Among them, Q CB This refers to the reactive power compensation power of a single CB group; Y represents the number of CBs accessed at node j during time period t; CB,max The maximum number of CB connections a single node can access; δ CB,max This is the maximum number of times CB can be adjusted.

[0084] (8) Carbon emission constraints include:

[0085] Since the electricity in the distribution network is mainly purchased from the main grid, the carbon emissions of the distribution network can be equivalent to those from the thermal power units in the main grid. The carbon emissions associated with the electricity purchased by the distribution network can be calculated using equation (31).

[0086]

[0087] Where, ε mg This is equivalent to the main grid carbon emission intensity; The total carbon emissions generated in the main grid corresponding to the purchase of electricity for the distribution network.

[0088] A baseline method is used to allocate free carbon emission allowances to power generating units. The equivalent carbon emission allowance constraints are as follows:

[0089]

[0090] In the formula, e mg For equivalent carbon emission quotas for thermal power units; For free carbon emissions.

[0091] The difference between actual carbon emissions and free carbon allowances can be used to calculate tradable carbon allowances.

[0092] Specifically, the low-carbon dispatch model for electric vehicles in AC / DC hybrid distribution networks includes: probabilistic constraints on EV travel characteristics, AC / DC charging and discharging constraints on EVs, and carbon quota calculation constraints for EVs.

[0093] The specific constraints mentioned above are as follows:

[0094] (1) Probabilistic constraints on EV travel characteristics:

[0095] The starting time of electric vehicle driving follows a normal distribution, and the probability distribution of the starting time of electric vehicle driving is shown in equation (34).

[0096]

[0097] Where x is time; μ s,j σ represents the average travel time for electric vehicles; s,j This represents the standard deviation of electric vehicle travel times.

[0098] The time when the electric vehicle finishes driving follows a normal distribution, and the probability distribution of the time when the electric vehicle finishes driving is shown in Equation (35).

[0099]

[0100] Where, μ e,j σ is the mean value at the end of the electric vehicle phase; s,j Let be the standard deviation of the time at which the electric vehicle mode ends.

[0101] Based on (34)-(35), the charging and discharging time interval [t] can be determined. s ,t e ].

[0102] The daily mileage of electric vehicles follows a log-normal distribution, and the probability distribution of the daily mileage of electric vehicles is shown in equation (36).

[0103]

[0104] Where, μ l,j Let σ be the average daily mileage of the electric vehicle. l,j This represents the standard deviation of the daily mileage of electric vehicles.

[0105] By considering the travel characteristics of electric vehicle users, the duration of disordered charging for electric vehicles can be represented. As shown in equation (37).

[0106]

[0107] in, and Let SOC and SOC be the initial SOC and expected SOC of electric vehicle m, respectively. For the battery capacity of electric vehicles; For electric vehicle charging efficiency; Δt represents the charging power of the electric vehicle; Δt represents the unit scheduling time.

[0108] (2) EV AC / DC charging and discharging constraints

[0109] Electric vehicle charging piles have been built in the AC / DC hybrid distribution network. Considering the difference between DC charging and discharging and AC charging and discharging, an AC / DC charging and discharging model for electric vehicles was constructed. Equation (38) is the charging and discharging mode selection for electric vehicles, that is, EV must choose either DC or AC charging and discharging mode; Equation (39) represents the charging mutual exclusion constraint of EV, that is, charging and discharging cannot occur simultaneously; Equation (40) is the EV battery state of charge balance constraint, which should be met when the EV battery is charging and discharging at any time; Charging and discharging within a certain range of EV battery SOC will help delay battery aging, and Equation (41) can be used to describe the EV battery SOC safety constraint; Equation (42) describes that each EV is charged within its schedulable time and needs to reach the expected value of EV battery capacity when leaving the charging station; Equation (43) represents the EV battery charging and discharging power constraint, the charging and discharging power in both DC and AC modes; Equation (44) represents the upper and lower limits of electric vehicle charging and discharging power constraint; Equation (45) represents the active power of EV charging pile charging and discharging per unit time.

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] in, and These represent the AC charging / discharging and DC charging / discharging modes for the m-th EV. A value of 1 indicates that the EV selects the corresponding AC and DC charging / discharging modes. and These are binary variables, representing the charging and discharging states of the m-th EV during time period t. A value of 1 indicates charging, and a value of 0 indicates discharging. Let m be the state of charge of the m-th EV during time period t; Let m be the discharge power of the mth EV during time period t; and These represent the charging power of electric vehicles in AC and DC modes, respectively. and These represent the discharge power of the electric vehicle in AC and DC modes, respectively. K represents the discharge efficiency of the m-th EV; m,j Let be the spatial state distribution matrix of EVs associated with node j of the distribution network, used to represent the set of EVs at the charging piles at node j.

[0119] (3.3) Carbon quota constraints for EVs

[0120] The proportion of clean energy sourced from electric vehicles (EVs) is quantified. Assuming all loads on the power grid absorb both clean energy and electricity generated by conventional generators, the carbon emissions of EVs can be further calculated by determining the proportion of electricity generated by conventional generators in the distribution network. The constraints for calculating the proportion of electricity generated by conventional generators are shown in Equation (46). The equivalent carbon emissions of EVs in the distribution network are obtained by calculating the charging amount per unit distance traveled by the EV, as shown in Equation (47). The carbon emission intensity of gasoline vehicles is obtained based on the energy consumption per unit distance traveled by gasoline vehicles, as shown in Equation (48). The difference in carbon emissions between the two over time periods can be expressed as the carbon allowance for the EV in time period t, as shown in Equation (49).

[0121]

[0122]

[0123]

[0124]

[0125] Where, β t The proportion of non-new energy generating units in the distribution network during time period t can be equivalent to the proportion of conventional generating units. Carbon emissions from the charging process of EVs; This refers to the carbon emissions of a gasoline-powered vehicle traveling the same distance; L ev The driving range of an electric vehicle per 1 kWh of electricity; ε fv The carbon emission coefficient for a gasoline-powered vehicle traveling 1km. Carbon emission allowances obtained for electric vehicles.

[0126] Step S3: Based on the objective function and constraints, and taking into account the uncertainties of wind, solar, AC load and DC load, construct a two-stage robust low-carbon scheduling model.

[0127] This step, by considering the uncertainties of wind, solar, AC, and DC loads, constructs a two-stage robust low-carbon dispatch model, transforming the nonlinear AC / DC hybrid distribution network dispatch model characterized by the aforementioned objective function and constraints into a mixed-integer second-order cone programming problem. Specifically, this step includes the following processes:

[0128] (1) Construct the uncertainty set of wind and solar power output and DC and AC loads, the maximum safety violation value, and the AC and DC power balance constraints.

[0129] Uncertainty Modeling: Wind, solar power output, and DC and AC loads in the model involve handling uncertain variables. Generally, the model selects a set of closed intervals with clear upper and lower boundaries as the set of uncertain parameters, as shown in Equation (50). The values ​​of the uncertain parameters are within the closed intervals.

[0130]

[0131] Where U is the uncertain set of wind and solar power output, as well as DC and AC loads; For the uncertain variable u j,t The predicted value for time period t. This represents the maximum allowable deviation value for an uncertain variable.

[0132] Introducing uncertainty budget Δ m According to equation (50), the uncertainty set of wind and solar power output and DC and AC loads can be described by equation (51).

[0133]

[0134] in, The maximum allowable deviation value for the uncertain variable; N is the number of time periods in the uncertainty set; M is the amount of wind and solar power output or load in the uncertainty set; Δ m The value range is [0, N]; For binary variables in the set of uncertainties.

[0135] To ensure the safety of the power distribution system, a two-level max-min problem (52) is used to represent the maximum safety violation scenario of the power grid under uncertain operation. It is assumed that this maximum safety violation value ΔD must not exceed the pre-set system-allowed safety violation threshold ε. Furthermore, by introducing a slack variable v... 1t ,v 2t ,v 3t ,v 4tThe adjusted system still satisfies the AC / DC power balance constraint, as shown in equations (53)-(54).

[0136]

[0137]

[0138]

[0139] v 1t ,v 2t ,v 3t ,v 4t ≥0 (55)

[0140] in,(·) u v represents the adjusted variable corresponding to real-time changes in wind and solar power output and electricity load; 1,t and v 2,t These are the slack variables for the power balance constraints of the AC distribution network; v 3,t and v 4,t These are the slack variables for the power balance constraint of the DC distribution network.

[0141] (2) The objective function and constraints considering the overall operating cost of the AC / DC hybrid distribution network, the uncertainty set of wind and solar power output and DC and AC loads, the maximum safety violation value, and the AC / DC power balance constraints are simplified to obtain a two-stage robust low-carbon dispatch model.

[0142] The proposed optimization problem can be represented as a simplified two-stage robust low-carbon scheduling model, as shown in (56)-(58). The first stage is to solve the basic scenario scheduling problem. The objective function (1)-(6) is represented in a simplified form, as shown in equation (57); and the operating constraints (7)-(49) under the basic scenario are simplified to the form shown in equation (57). The second stage ensures the safety of the system within the uncertainty set, that is, the load loss and wind and solar curtailment under the worst scenario that causes system insecurity are not greater than the pre-set threshold ε, which is represented in the model as (58), corresponding to the simplified form of equations (51)-(56).

[0143] Specifically, the two-stage robust low-carbon scheduling model includes:

[0144]

[0145] Ax + By ≤ b (57)

[0146]

[0147] Where x represents the start-up and shutdown status of the gas turbine unit; y and z represent the unit output in the basic scenario and the scenario adjusted according to uncertain system parameters, respectively; u represents variables related to uncertainty; A, B, C, D, E, F, G, H r For the abstract matrices corresponding to the constraints; b, f, h, This is the coefficient vector.

[0148] Step S4: Determine the main problem and sub-problems for solving the two-stage robust low-carbon scheduling model.

[0149] Specifically, the main problem of the two-stage robust low-carbon scheduling model is to solve the basic scenario scheduling problem. The main problem of the constraint generation (CCG) algorithm can be expressed as shown in equation (59), with the objective function being to minimize the distribution network operating cost of the basic scenario. Specifically, this means continuously optimizing the wind and solar power output under the worst operating scenario identified from the sub-problems of the constraint generation (CCG) algorithm. Add it to the main problem in equation (59) and solve the main problem.

[0150] Main question:

[0151]

[0152] Specifically, the subproblem of the two-stage robust low-carbon scheduling model is to solve for the worst-case operating result. The subproblem of the constraint generation CCG algorithm, after obtaining the optimization results x* and y* of the main problem, is to solve for the worst-case operating result, which can be represented by equation (60):

[0153] Sub-problems:

[0154]

[0155] Step S5: Solve the main problem and sub-problems of the two-stage robust low-carbon dispatch model to obtain the economic dispatch optimization results of the distribution network for use in distribution network dispatch.

[0156] Specifically, this step includes the following process:

[0157] Step S51: Initialize the number of iterations w = 0, and set the system's allowed security violation threshold ε.

[0158] Step S52: Solve the master problem of the two-stage robust low-carbon scheduling model.

[0159] Step S53: If a solution exists, update the system unit start / stop status x and unit output arrangement y.

[0160] It should be understood that if there is no solution, the iteration should stop.

[0161] Step S54: Based on the system unit start-up and shutdown status x and unit output arrangement y obtained in step S53, solve the subproblems of the two-stage robust low-carbon dispatch model to obtain the wind and solar power output under the worst-case scenario that leads to the maximum possible violation of safety regulations.

[0162] Step S55: Determine whether the safety threshold ε is met in the w-th iteration.

[0163] That is, whether the maximum security violation value obtained in the w-th iteration is less than ε.

[0164] Step S56: If satisfied, stop the iteration and output the system unit start-stop status x and unit output arrangement y obtained in step S53 as the distribution network economic dispatch optimization result.

[0165] If the maximum security violation value obtained in the w-th iteration is less than ε, then the iteration stops.

[0166] Step S57: If not satisfied, then use the wind and solar power output under the worst-case scenario obtained in step S54. And, f T v w ≤ε and Substitute the main problem of the two-stage robust low-carbon scheduling model into the equation, and set w = w + 1. Return to step S52 and continue iteratively solving the problem.

[0167] It should be understood that the above solution process can be performed in a commercial solver, such as the commercial solver Gurobi. In application, the parameters involved in the aforementioned steps, including the objective function, constraints, etc., such as AC / DC hybrid distribution network system data, operating parameters, new energy forecast data, electric vehicle equipment parameters, and operating simulation data, are input into the commercial solver, and the solution for this step is performed within the commercial solver.

[0168] The final solution for the system unit start-up and shutdown status x and unit output arrangement y can be used to guide the dispatching of AC / DC hybrid distribution networks.

[0169] This invention also discloses a computer-readable storage medium storing computer program instructions; when executed by a processor, the computer program instructions implement the robust low-carbon dispatching method for hybrid distribution networks considering V2G as described in the above embodiments.

[0170] This invention also discloses a robust low-carbon dispatching system for hybrid distribution networks that considers V2G, comprising: a computer-readable storage medium as described in the above embodiments.

[0171] The technical solutions of the embodiments of the present invention will be further explained below through specific application examples.

[0172] (1) Example introduction.

[0173] The example uses a modified AC / DC hybrid IEEE 33-bus system, such as... Figure 4 As shown in the diagram. The example uses a 24-hour scheduling cycle, with a 1-hour scheduling time span. The gas turbine units are configured on node 6 of the distribution network. Clean energy units are configured in the distribution network, including two wind turbines and two photovoltaic units. The wind turbines are connected to AC distribution network nodes 21 and 25, while the photovoltaic units are connected to the DC distribution network at nodes 14 and 32. Two VSC converter stations are connected to the AC distribution network, forming two DC sub-distribution networks.

[0174] For the private cars considered in this example, it is assumed that all electric vehicles are of the same type, each with a battery capacity of 40 kWh and a power consumption of 18 kWh per 100 kilometers. The initial battery capacity SOC of the EVs follows a normal distribution N(0.55, 0.03), and the expected charging SOC of the EV battery is 0.9. When connected to the AC distribution network, the EVs use the traditional AC slow charging mode, with a maximum charging power of 7 kW and a maximum discharging power of 4 kW; when connected to the DC distribution network, the EVs use the DC fast charging mode, with a maximum charging power of 20 kW and a maximum discharging power of 10 kW. The ratio of EVs using AC slow charging to DC fast charging in the distribution network is 7:3.

[0175] (2) Scenario description of the embodiment.

[0176] To verify the impact of the proposed invention, which considers carbon emissions and the operation and scheduling of electric vehicles, on the AC / DC hybrid distribution network, this example selects the following four scenarios for calculation and analysis.

[0177] Example 1: Consider the disordered charging mode of electric vehicles.

[0178] Example 2: Consider the orderly charging mode of electric vehicles.

[0179] Example 3: Based on Example 2, consider that 60% of electric vehicles participate in the V2G response.

[0180] Example 4: Based on Example 3, carbon emissions are considered.

[0181] (3) Analysis of the results of the examples.

[0182] When using an ordered charging mode, EVs can be scheduled according to the objective function. This allows control strategies to prevent EV users from charging excessively during peak load periods and instead shift charging to off-peak periods, thus reducing the peak-to-valley load difference. In the examples, Example 2-4 uses ordered charging. A line graph showing the daily load variation of the distribution network, including EV loads, is then plotted. Figure 5 As shown, the load results in Examples 2-4 differ under different modes. When electric vehicles adopt an ordered charging mode, or when electric vehicles can respond to V2G, the peak-valley difference in the distribution network load can be greatly reduced. Calculations show that the maximum peak-valley difference in the basic electrical load is 2.08MW. Figure 6 As can be seen, in Example 1, disordered EV charging amplified the peak-valley load difference in the distribution network, with a maximum difference reaching 2.30MW. However, using the ordered charging mode significantly reduced the peak-valley difference; the maximum peak-valley difference in Example 2 was 1.87MW, and in Example 3 it was 1.21MW. However, due to the low penetration rate of new energy sources in Examples 1-4, the impact of carbon trading on the overall system cost was not significant enough, resulting in a relatively smaller change in Example 4, with a maximum peak-valley difference of 1.17MW, which is also an improvement over Example 3. Therefore, it can be concluded that the AC / DC ordered charging model for electric vehicles considering carbon emissions proposed in this invention has a certain effect on regulating the peak-valley load difference.

[0183] Based on the calculation results in Example 4, the calculation results for charging and discharging of EV charging piles in AC / DC distribution networks are as follows: Figure 6 As shown, the charging and discharging patterns of EVs at various nodes in a hybrid AC / DC distribution network are generally consistent. Based on the intraday distribution pattern of EVs, the period from 6 PM to 7 AM the following day is when most EVs connect to charging stations and participate in charging and discharging. During this period, EVs respond to different charging and discharging modes depending on the time-of-use electricity price, thus clearly distinguishing the charging and discharging times. EVs participating in orderly charging shift their charging load to the 0-7 AM time period, which will greatly promote the distribution network's absorption and utilization of electricity during the early morning off-peak period and actively supplement the distribution network with additional electricity during peak load periods.

[0184] To verify the effectiveness of the proposed robust optimization model, based on the distribution network operation analysis example 4 under the aforementioned deterministic scenario, example 6 is set up to consider robust dispatching with uncertainties in wind and solar power output and AC / DC load. The uncertainty ranges for power load and wind and solar power output are considered to be 10% and 20% of their average values, respectively. The uncertainty budget for both is set to 24, and the violation safety threshold ε is set to 0.001 MWh, ensuring that the distribution network can operate safely and stably under any uncertainty scenario considered in the example. Based on example 6, example 7 is set up, considering that the prediction error range of power load remains unchanged, the maximum uncertainty deviation of wind and solar power output prediction values ​​is 40% of their average values, and the uncertainty budget for both is set to 24. The operating costs and some costs of examples 4 and 6-7 are shown in Table 1.

[0185] Table 1 shows the operating costs of Examples 4 and 6-7.

[0186] Cost / yuan Total cost Electricity purchase cost Gas turbine unit operation Distribution network carbon trading EV carbon trading EV discharge compensation Calculation example 4 36206 28334 0 6062.8 -362 180.4 Calculation example 6 36251 26584 2004.1 5986.4 -362.1 180.4 Calculation example 7 36320 26584 2073.2 5986.3 -361.7 180

[0187] Comparing the deterministic optimization results with the robust optimization results, the operating cost of the gas turbine units is increased, while the main grid's electricity purchase cost is reduced. As can be seen from the results of Examples 4 and 6-7, to meet the security requirements of the distribution network operation, with the increase in uncertainty, the distribution network will utilize gas turbine units for power generation more, thus increasing the output of the gas turbine units and reducing the electricity purchased by the main grid. To meet higher security requirements, some economic efficiency is sacrificed. Therefore, the robust optimization scheduling results are more conservative; under this method, the total operating cost of Example 6-7 will be higher than the total cost of Example 4 under the deterministic scenario.

[0188] Regarding low-carbon emissions, since the carbon emission intensity per unit of gas turbine units is lower than that of thermal power units in the main grid, the carbon trading cost will be reduced after the output of gas turbine units replaces part of the electricity purchased by the main grid in the robust optimization examples. As shown in Table 1, the cost is reduced by 76.4 yuan from example 4 to example 6. Comparing examples 6 and 7, the unit operating cost increased in example 7, but the carbon trading cost remained almost unchanged. This is because, in robust optimization, to meet safety requirements, gas turbine units need to remain operational during certain periods, but considering the economics of real-time electricity prices, they do not generate any output. The unit operating period in example 6 is 17-22, and in example 7 it is 15-22. In the different examples in Table 1, EV discharge compensation and EV carbon trading remain almost unchanged. This shows that the overall arrangement of power exchange between the distribution network and EVs does not change significantly in these scenarios and does not affect the safety of the distribution network operation. It is difficult to simultaneously meet the requirements of economy, low carbon emissions, and safety for system operation at all times. However, safety, low carbon emissions, and economy can be achieved in a given scenario by adjusting the set uncertainty-related parameters.

[0189] In summary, the embodiments of the present invention, when simultaneously considering factors such as electric vehicle carbon quota trading and EV-responsive V2G services to the distribution network, can better reduce the peak-valley difference of the total load of the distribution network, achieving "peak shaving and valley filling." Due to limitations imposed by factors such as time-of-use pricing, line losses, VSC power capacity, and renewable energy installed capacity in the distribution network, the total system cost does not always remain economically optimal with the increase in the number of EVs. The source-grid-load distribution can be improved to expand the EV scale. The charging and discharging process of electric vehicles can be coordinated and interact with the scheduling of renewable energy units, which has a positive impact on energy conservation and emission reduction in the energy sector. Simultaneously, carbon trading prices also have a certain impact on the charging and discharging scheduling of EVs. Robust optimization scheduling considering uncertain operating factors improves the safety of system operation and reduces the low-carbon operating costs of the system.

[0190] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A robust low-carbon dispatching method for hybrid distribution networks considering V2G, characterized in that, include: Step S1: Construct an objective function that considers the overall operating cost of the AC / DC hybrid distribution network; Step S2: Determine the constraints of the objective function; wherein the constraints include: the basic operation model of the AC / DC hybrid distribution network and the low-carbon dispatching model of electric vehicles in the AC / DC hybrid distribution network; Step S3: Based on the objective function and the constraints, and taking into account the uncertainties of wind, solar, AC load and DC load, construct a two-stage robust low-carbon scheduling model; Step S4: Determine the main problem and sub-problems for solving the two-stage robust low-carbon scheduling model; Step S5: Solve the main problem and sub-problems of the two-stage robust low-carbon dispatch model to obtain the economic dispatch optimization results of the distribution network for use in distribution network dispatch. The objective function considering the overall operating cost of the AC / DC hybrid distribution network includes: ； in, For the daily operating costs of the distribution network, To compensate for EV discharge losses and costs The cost of penalties for curtailing wind and solar power, Costs related to carbon emissions in the power distribution network Penalty costs for distribution network losses; The basic operation model of the AC / DC hybrid distribution network includes: Carbon emission constraints include: ； ； ； in, This is equivalent to the main grid carbon emission intensity; The total carbon emissions generated in the main grid corresponding to the purchase of electricity for the distribution network; Equivalent carbon emission allowances; For equivalent carbon emission quotas for thermal power units; These are tradable carbon allowances; This refers to the active power exchanged between the distribution network and the main grid. The low-carbon dispatch model for electric vehicles in the AC / DC hybrid distribution network includes: Carbon quota constraints for EVs include: ； ； ； ； in, For non-new energy units in the distribution network t The proportion of total electricity consumption during a specific time period; Carbon emissions from the charging process of EVs; This represents the carbon emissions of a gasoline-powered vehicle over the same driving distance. The driving range of an electric vehicle is defined as the range of its electric vehicle based on 1 kWh of electricity. The carbon emission coefficient for a gasoline-powered vehicle traveling 1km. Carbon emission allowances obtained for electric vehicles; For nodes j The active power of the fan; For nodes j The active power of the photovoltaic system; The scheduling time is per unit. A collection of substations in a power distribution network; For the set of nodes of the wind turbine; This is the set of nodes for photovoltaics.

2. The robust low-carbon dispatching method for hybrid distribution networks considering V2G as described in claim 1, characterized in that, ； ； ； ； ； in, The price at which the distribution network purchases electricity from the main grid; The price for compensating for the additional battery loss caused by EVs discharging into the grid when participating in V2G services; The price for wind curtailment penalties per unit; Price of penalty for abandoning light per unit; The price of a unit of CO2 emission rights in carbon trading; The penalty price per unit of electricity for line network loss; T This refers to the set of scheduling times in the distribution network system. M This represents the total number of dispatchable EVs in the distribution network. For the first m EVs in t Discharge power over a given period of time; For tradable carbon allowances in the distribution network; Carbon allowances for EVs; For branch roads ( i , j On t Current during a given period; For power lines ( i , j The resistance of ) This is a collection of power distribution lines.

3. The robust low-carbon dispatching method for hybrid distribution networks considering V2G as described in claim 2, characterized in that, The basic operating model of the AC / DC hybrid distribution network includes: (1) Power flow constraints in AC distribution networks include: ； ； ； ； in, b ( j ) as a node j The set of all child nodes of the starting node; This refers to the reactive power exchanged between the distribution network and the main grid. and They are power lines ( i , j )exist t Active power and reactive power at any given time; and They are nodes j Predicted active and reactive loads at the location; The charging and discharging power of the electric vehicle charging pile at node j; This refers to the reactive power of the wind turbine; The reactive power output of capacitor CB; , These are the active and reactive power input from the AC distribution network to the VSC converter, respectively. For nodes j Voltage at point; Reference node voltage; For power lines ( i , j The reactance of ) For distribution network nodes; (2) Power flow constraints in DC distribution networks include: ； ； ； in, The active power output to the DC distribution network after passing through the VSC converter; (3) System security constraints include: ； ； ； ； Where the superscripts max and min correspond to the maximum and minimum values ​​of the variable, respectively; (4) The steady-state operation constraints of the VSC converter station include: ； ； ； ； ； in, , These are the equivalent resistance and reactance inside the VSC converter station, respectively. This refers to the current in the VSC branch; The reactive power output from the VSC converter station to the AC distribution network; and These are the AC side voltage and DC side voltage inside the converter, respectively. It is a constant; In order to adjust the system, and ; (5) DG operating constraints include: ； ； ； in, and The predicted active power outputs are for wind turbines and solar power, respectively. The constant power factor angle of the fan; (6) Power exchange constraints between the distribution network and the main network include: ； ； (7) Capacitor CB operating constraints include: ； ； ； in, This refers to the reactive power compensation power of a single CB group; In order to be in j node t The number of CBs accessed during a given time period; This represents the maximum number of CBs that can be connected to a single node. This is the maximum number of times CB can be adjusted.

4. The robust low-carbon dispatching method for hybrid distribution networks considering V2G according to claim 3, characterized in that, Low-carbon dispatching models for electric vehicles in AC / DC hybrid distribution networks include: (1) The probabilistic constraints on EV travel characteristics include: ① The probability distribution of the start time of driving an electric vehicle: ； in, x For time; This represents the average travel time for electric vehicles. The standard deviation of electric vehicle travel times; ② The probability distribution of the end time of the electric vehicle's journey: ； in, This represents the average value at the end of the electric vehicle's journey. The standard deviation of the time when the electric vehicle finishes driving; ③ Probability distribution of daily mileage of electric vehicles: ； in, This represents the average daily mileage of electric vehicles. This represents the standard deviation of the daily mileage of electric vehicles. ④ Disorderly charging time of electric vehicles : ； in, and Electric vehicles m The initial SOC and the expected SOC; For the battery capacity of electric vehicles; For electric vehicle charging efficiency; The charging power for electric vehicles; (2) EV AC / DC charging and discharging constraints include: ； ； ； ； ； ； ； ； in, and The first m The AC charging and discharging and DC charging and discharging modes of the EV are specified. A value of 1 indicates that the EV selects the corresponding AC and DC charging and discharging modes. and These are binary variables, representing the first... m EVs in t The charging and discharging status during a period of time; a value of 1 indicates charging, and a value of 0 indicates discharging. For the first m EVs in t State of charge over a period of time; For the first m EVs during the time period t The discharge power; and These represent the charging power of electric vehicles in AC and DC modes, respectively. and These represent the discharge power of the electric vehicle in AC and DC modes, respectively. For the first m The discharge efficiency of an EV; To connect with distribution network nodes j The associated EV space state distribution matrix is ​​used to represent the state distribution of nodes. j A collection of EVs at a charging station.

5. The robust low-carbon dispatching method for hybrid distribution networks considering V2G according to claim 4, characterized in that, The steps for constructing the two-stage robust low-carbon scheduling model include: Construct the uncertainty set of wind and solar power output, DC and AC loads, the maximum safety violation value, and the AC / DC power balance constraints; The objective function and constraints considering the overall operating cost of the AC / DC hybrid distribution network, the uncertainty set of wind and solar power output and DC and AC loads, the maximum safety violation value, and the AC / DC power balance constraints are simplified to obtain the two-stage robust low-carbon dispatch model. Among them, the uncertainty set of wind and solar power output and DC and AC loads. U include: ； in, Uncertain variables exist t Forecast values ​​for the time period, This represents the maximum allowable deviation value for the uncertain variable. ; N The number of time periods in the uncertainty aggregate; M The quantity of wind and solar power output or load in the uncertain aggregate; Δ m Budgeting for uncertainty; Δ m The value range is [0, N]; , , , For binary variables in the set of uncertainties; Wherein, the maximum safety violation value Δ D : ； in, and These are the relaxation variables of the power balance constraints in the AC distribution network; and These are the relaxation variables of the power balance constraint in the DC distribution network; ε The pre-defined system's allowed security violation threshold; The AC / DC power balance constraint includes: ； ； ； in, This refers to the adjusted variables corresponding to real-time changes in wind and solar power output and electricity load.

6. The robust low-carbon dispatching method for hybrid distribution networks considering V2G according to claim 5, characterized in that, The two-stage robust low-carbon scheduling model includes: ； in, x This refers to the start-up and shutdown status of gas turbine-related units. y , z These are the basic scenario and the unit output adjusted based on uncertain system parameters, respectively. u For variables related to uncertainty; A , B , C , D , E , F , G , H r The abstract matrix corresponding to the constraint; b , f , h , This is the coefficient vector.

7. The robust low-carbon dispatching method for hybrid distribution networks considering V2G according to claim 6, characterized in that, The main problem of the two-stage robust low-carbon scheduling model is to solve the basic scenario scheduling problem, including: ； The sub-problem of the two-stage robust low-carbon scheduling model is to solve for the execution result of the worst-case operating scenario, including: 。 8. The robust low-carbon dispatching method for hybrid distribution networks considering V2G according to claim 7, characterized in that, The steps for solving the main problem and subproblems of the two-stage robust low-carbon scheduling model include: Step S51: Initialize the number of iterations w =0, sets the system's allowed security violation threshold; Step S52: Solve the main problem of the two-stage robust low-carbon scheduling model; Step S53: If a solution is found, update the system unit start / stop status. x And unit output arrangement y ; Step S54: Based on the system unit start-up and shutdown status obtained in step S53 x And unit output arrangement y Solving the subproblems of the two-stage robust low-carbon scheduling model yields the wind and solar power output under the worst-case scenario that leads to the maximum possible violation of safety regulations thresholds. ; Step S55: Determine the first w Violation of the safety threshold in the next iteration Does it meet the requirements? Step S56: If satisfied, stop the iteration and output the system unit start / stop status obtained in step S53. x And unit output arrangement y As the result of the economic dispatch optimization of the aforementioned distribution network; Step S57: If not satisfied, then use the wind and solar power output under the worst-case scenario obtained in step S54. ,as well as, and Substitute this into the main problem of the two-stage robust low-carbon scheduling model, and make... Return to step S52 and continue iteratively solving.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by a processor, they implement the robust low-carbon dispatching method for hybrid distribution networks considering V2G as described in any one of claims 1 to 8.

10. A robust low-carbon dispatching system for hybrid distribution networks considering V2G, characterized in that, include: The computer-readable storage medium as described in claim 9.

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