Island power distribution network scheduling strategy considering flexible transfer of energy of new energy network car-hailing group

Abstract

The invention belongs to the technical field of power systems, and particularly relates to an island power distribution network short-time scheduling strategy considering flexible transfer of energy of a new energy network car-hailing group. Comprising the following steps: 1, considering the contradiction between the contract cost of new energy online car hailing and the support value of a power grid, and proposing a new energy online car hailing group incentive calling model with flexible and adjustable participation period and quantity; 2, carrying out the quantitative analysis of the traffic migration and charging and discharging characteristics of the new energy online car-hailing, and constructing a new energy online car-hailing group energy idling power supply model; 3, on the basis, taking the minimum system cost as a target, proposing an island new energy power distribution network short-time scheduling model considering the power flow constraint; and step 4, verifying the proposed model based on the improved IEEE 33-node power distribution system. The strategy provided by the invention can reduce the operation cost of the power distribution network by 7.05%. According to the invention, a new perspective is provided for distributed new energy consumption and new energy vehicle operation optimization.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and particularly relates to a short-term scheduling strategy for island distribution networks considering flexible energy transfer of new energy online car-hailing groups. Background Art

[0002] Driven by the dual goals of "carbon peak and carbon neutrality" and the construction of a new power system, the large-scale access of distributed new energy has brought challenges to the operation of distribution networks, such as intensified power fluctuations, local voltage over-limit, and insufficient scheduling flexibility. Electric vehicles, with their flexible charging and vehicle-to-grid (V2G) capabilities, provide a new solution to this problem. As mobile energy storage units, electric vehicles can charge and store energy during low-load periods and discharge energy during high-load periods, which can not only suppress new energy fluctuations, improve system operation efficiency, but also alleviate the problem of "peak on peak", and promote friendly interaction between vehicles and the grid.

[0003] Currently, the V2G mode has received extensive attention in the field of power systems and has achieved relatively rich research results. For example, existing literature has proposed a day-ahead and real-time optimal scheduling strategy for transmission and distribution coordination with V2G active support, and established a distributed optimal scheduling framework based on the alternating direction method of multipliers. Existing literature has proposed an electric vehicle charging and discharging capacity prediction framework based on the random forest algorithm and Monte Carlo simulation, which can effectively distinguish scheduling behaviors and predict load distribution by combining user willingness and vehicle behavior characteristics. Existing literature has constructed a V2G power trading model combining blockchain and reverse auction to achieve complete decentralization. Existing literature has established a coordination framework including a charging priority function, a discharge profit distribution mechanism, and a battery life compensation model, and proposed a multi-objective optimization strategy based on a dynamic pricing mechanism and vehicle state constraints. Existing literature has established a multi-objective optimization model considering the coordinated configuration of administrative and residential area charging stations through a hybrid method of multi-objective particle swarm optimization and Monte Carlo simulation. Existing literature has constructed a two-layer scheduling model aiming at minimizing the peak-valley difference and charging cost to address the problem of grid load pressure caused by the disordered charging and discharging of a large number of electric vehicles in the V2G mode. Existing literature has coordinated the charging load of electric vehicles and the output of distributed energy, improving the economy of distribution network operation and the renewable energy consumption capacity. The above-mentioned literature mainly discusses the orderly charging and discharging methods of electric vehicles in the station, and their supporting effect on the grid is easily affected by node constraints, and the actual supporting value is limited.

[0004] Large-scale electric vehicle clusters achieve the spatio-temporal migration of electric energy through cross-regional scheduling. Its technical feasibility and system value as distributed mobile energy storage units participating in energy allocation have been verified in multiple scenarios. Existing literature realizes "one driving multiple" flexible loading and "carrier sharing" collaborative transportation by optimizing the transfer path and module combination of vehicles in space. Existing literature proposes a multi-objective configuration optimization method that integrates the spatial dynamic allocation ability of mobile energy storage devices and the transferable load regulation strategy. Existing literature realizes the coordinated optimization of dynamic site switching and charging and discharging strategies in the active distribution system by combining model predictive control and particle swarm optimization algorithm. Existing literature proposes a coordinated planning method for charging infrastructure and distribution network considering mobile energy storage vehicles, and establishes a two-layer coordinated planning model based on the coupling interaction mechanism of vehicle-station-network. Existing literature forms distributed energy storage nodes by spatially scheduling electric vehicles to V2G charging stations before disasters, and optimizes the layout of mobile energy storage warehouses to achieve the spatio-temporal coordinated scheduling of V2G reverse power supply and mobile energy storage after disasters. In addition, the above research mostly adopts static strategies with preset vehicle numbers and fixed scheduling periods, ignoring the dynamic demand for spatio-temporal migration of electric energy between stations, and it is difficult to achieve precise matching of source-load resources.

[0005] Therefore, the present invention proposes a multi-modal spatio-temporal coordinated optimization scheduling strategy to solve the above defects. Summary of the Invention

[0006] The purpose of the present invention is to provide a scheduling strategy for island distribution networks considering the flexible energy transfer of new energy online car-hailing groups. The present invention introduces an optimization algorithm to dynamically adjust the system to ensure the maximum utilization of photovoltaic power and reduce the system operation cost.

[0007] To solve the above technical problems, the present invention provides a short-term scheduling strategy for island distribution networks considering the flexible energy transfer of new energy online car-hailing groups, including the following steps:

[0008] Step 1: Considering the contradiction between the signing cost of new energy online car-hailing and the power grid support value, propose an incentive summoning model for new energy online car-hailing groups with flexible adjustable participation cycles and quantities;

[0009] Step 2: Quantitatively analyze the traffic migration and charging and discharging characteristics of new energy online car-hailing, and construct an energy spatio-temporal transfer model for new energy online car-hailing groups;

[0010] Step 3: On this basis, with the goal of minimizing the system cost, propose a short-term scheduling model for island new energy distribution networks considering power flow constraints;

[0011] Step 4: Verify the proposed model based on the improved IEEE 33-node distribution system.

[0012] Preferably, the scheduling strategy specifically includes:

[0013] First, the distribution network control center predicts the photovoltaic power output, wind power output and local load demand for the next day, and determines the new energy power that can be directly utilized in combination with the capacity limit of the transformers at the photovoltaic sites.

[0014] Secondly, based on the prediction results, an optimization model is constructed with the goal of minimizing the comprehensive operating cost to calculate the optimal scheduling strategies for new energy online car-hailing vehicles in different time periods. The optimal scheduling strategies include the charging and discharging times of the vehicles, route selection and station stopping plans.

[0015] Then, based on the vehicle call constraints, the scheduling of online car-hailing vehicles is optimized so that they charge during the peak of new energy output and discharge during the peak of load, realizing the spatio-temporal transfer of energy.

[0016] Finally, in combination with the optimized charging and discharging scheduling plan, the output of the gas turbine is adjusted, and the power interaction of the distribution network is coordinated to ensure the overall power balance and optimal economy of the system.

[0017] Preferably, the specific steps of step two include: by dynamically coupling the grid node status and vehicle operation behavior, the model accurately depicts the differential state response mechanisms of new energy online car-hailing vehicles in three types of functional areas: power generation source areas, load centers and charging and discharging hubs, which are specifically expressed as follows:

[0018] The sum of fully loaded and empty new energy online car-hailing vehicles should be equal to the total number of online car-hailing vehicles put into use, as shown in the following formula (6); fully loaded or empty online car-hailing vehicles include fully loaded or empty online car-hailing vehicles transporting and staying in each area, as shown in the following formula (7):

[0019]

[0020] In the formula: is the number of fully loaded online car-hailing vehicles at time t; is the number of empty online car-hailing vehicles at time t; and are the numbers of fully loaded and empty online car-hailing vehicles in transit from area i to area j at time t, respectively; and are the numbers of fully loaded and empty online car-hailing vehicles available in area i at time t, respectively;

[0021] The total number of available fully loaded or empty online car-hailing vehicles in area i should be equal to the difference between the number of charging or discharging online car-hailing vehicles and the number of discharging or charging online car-hailing vehicles in this area, plus the number of fully loaded or empty online car-hailing vehicles that have just arrived in the area, minus the number of fully loaded or empty online car-hailing vehicles that have just left the area, as shown in the following formula (8):

[0022]

[0023] In the formula: and The number of online car-hailing vehicles charging and discharging in area i at time t, respectively; and The number of fully-loaded and empty online car-hailing vehicles leaving area i and heading to area j at time t, respectively; and The number of fully-loaded and empty online car-hailing vehicles arriving at area j from area i at time t, respectively; is the set of all areas in the network; is the time interval;

[0024] The change in the number of fully-loaded or empty online car-hailing vehicles from area i to area j should be equal to the number of fully-loaded or empty online car-hailing vehicles that have just left area i minus the number of fully-loaded or empty online car-hailing vehicles that have just arrived at area j, as shown in the following formula (9):

[0025]

[0026] The number of fully-loaded or empty online car-hailing vehicles in transit from area i to area j at time t should be greater than the number of fully-loaded or empty online car-hailing vehicles that departed from area i during the period from t - Δ i→j + 1 to t as shown in the following formula (10):

[0027]

[0028] where: Δ i→j is the travel time delay from area i to area j;

[0029] The number of fully-loaded or empty online car-hailing vehicles in transit from area i to area j at time t should be greater than the number of fully-loaded or empty online car-hailing vehicles that arrived at area j from area i during the period from t + 1 to t + Δ i→j as shown in the following formula (11):

[0030]

[0031] Thus, the construction of the constraint system of the above-mentioned new energy online car-hailing dispatching model for islands is completed.

[0032] Preferably, it further includes:

[0033] A constraint mechanism based on Boolean variables to achieve precise control of vehicle calling behavior through logical state combinations. The following are the core constraint expressions:

[0034] μ t represents the state of whether the vehicle is running, and it is constrained by taking the difference between two increasing Boolean variables, as shown in the following formulas (12) to (15); the fluctuation range of the number of online car-hailing vehicles is associated with whether the vehicle is in a running state. By introducing a large number M, it is possible to determine according to μ tThe dynamically adjustable range of the value 0 or 1 is as shown in the following formulas (16) to (17);

[0035] B s,t-1 ≤B s,t (12)

[0036] B e,t-1 ≤B e,t (13)

[0037] B e,t ≤B s,t (14)

[0038] μ t =B s,t -B e,t (15)

[0039] V - M + μ t ·M ≤ V t ≤ V + M - μ t ·M (16)

[0040] 0 ≤ V t ≤ μ t ·M (17)

[0041] In the formulas: B s,t 、B e,t and μ t are all Boolean variables; B s,t and B e,t are used to mark the start and end of the scheduling; M is a large number; where when μ t equals 0, the vehicle does not participate; when μ t equals 1, the vehicle participates.

[0042] Preferably, it further includes:

[0043] At the start of the scheduling, the number of full and empty vehicles at the stations and between stations is the same as the initial state; at the end of the scheduling, the number of full and empty vehicles at the stations and between stations is the same as the end state; the number of full and empty vehicles during the scheduling process is managed through the following formulas (18) to (25) to ensure the state consistency at the start and end of the scheduling and maintain the coherence of the state between time periods;

[0044]

[0045]

[0046] In the formulas: and are respectively the number of fully loaded and empty vehicles at the station at the start of the scheduling; and are the number of fully-loaded and empty vehicles at the station when the scheduling ends, respectively; and are the number of fully-loaded and empty vehicles between stations when the scheduling starts, respectively; and are the number of fully-loaded and empty vehicles between stations when the scheduling ends, respectively.

[0047] Preferably, it further includes:

[0048] If the vehicle is still participating when the scheduling ends, it is required that the energy at the end of the scheduling is equal to the energy when the vehicle exits, ensuring that the state of the vehicle when exiting the system meets the expectations and avoiding resource waste and system instability; as shown in the following formulas (26) to (29):

[0049]

[0050] The consistency of the vehicle numbers at the start and end of the scheduling is the basic guarantee for system stability. Through this requirement, the allocation conflicts caused by the mismatch of vehicle numbers can be avoided, as shown in the following formulas (30) to (33):

[0051]

[0052] The following formulas (34) and (35) reflect the direct relationship between the charging and discharging power and the number of vehicles at the station, ensuring the reasonable allocation and scheduling of the charging power, enabling the charging station to respond in a timely manner when the system load demand fluctuates;

[0053]

[0054] In the formula: is the charging power of the charging and discharging station at node i; is the discharging power of the charging and discharging station at node i; P m is the rated charging and discharging power of the vehicle.

[0055] Preferably, in the third step, the goal of the system is to minimize the total operating cost, and the specific objective function is as follows:

[0056]

[0057] In the formula: F is the comprehensive cost; is the operating cost of the gas turbine; is the charging and discharging compensation cost of the online car-hailing; is the on-road cost of the online car-hailing; is the one-time compensation cost of the online car-hailing;

[0058] Among them, the operating cost of the gas turbine is as follows:

[0059]

[0060] Where: i is the number of gas turbine units; a i is the fuel cost coefficient of gas unit i; is the power generation of gas turbine unit i at time t;

[0061] The charging and discharging compensation cost of online car-hailing is as follows:

[0062]

[0063] Where: k c and k d are the subsidy cost coefficients for the charging and discharging power per unit of online car-hailing respectively; and are the charging and discharging powers of online car-hailing at time t respectively;

[0064] The on-road cost of online car-hailing is as follows:

[0065]

[0066] Where: is the transport path; Δ i→j is the passing time of online car-hailing at the station; c o is the unit operation cost of online car-hailing (yuan / hour); is the number of fully-loaded online car-hailing from i to j at time t; is the number of empty online car-hailing from i to j at time t;

[0067] The one-time compensation cost is as follows:

[0068]

[0069] Where: p ev,neg is the compensation cost per vehicle; V t is the total number of online car-hailing.

[0070] Preferably, in the third step, the power flow constraint includes:

[0071] The sum of the conventional load, gas turbine unit, charging and discharging station, and renewable energy output connected to grid node i determines the injected active power P i (t) and reactive power Q i (t) at this point, as shown in the following formula:

[0072]

[0073] Where: is the conventional active power load of node i; is the active power generation of the gas turbine at node i; is the photovoltaic power generation at node i;

[0074] The active and reactive power balance constraints of the branch are:

[0075]

[0076] In the formula: P j,t is the active power injection at node j; Q j,t is the reactive power injection at node j; P ij,t is the active power injected from the i side of the node; Q ij,t is the reactive power injected from the i side of the node; r ij is the equivalent resistance of branch j; x ij is the reactance of branch j; is the sum of the active power injected by node j into all branches (except branch ij); is the sum of the reactive power injected by node j into all branches (except branch ij).

[0077] The relationship between the voltages of adjacent nodes based on Ohm's law is shown in the following formula:

[0078]

[0079] The apparent power and node voltage are used to calculate the current as shown in the following formula:

[0080]

[0081] The two-norm form of the voltage and branch current in standard form is shown in the following formula:

[0082]

[0083] v j,t = V j,t 2 (43)

[0084] The branch power loss is shown in the following formula:

[0085]

[0086] For each node i, the voltage amplitude of each node in the distribution system should be within the specified range to ensure the power supply quality and the normal operation of the equipment, as shown in the following formula:

[0087]

[0088] In the formula: are the lower and upper limits of the node voltage operation respectively;

[0089] For each line ij, the line transmission power should be within the thermal stability and safe operation range of the line, as shown in the following formula:

[0090]

[0091] In the formula: is the rated capacity (MVA) of line j.

[0092] Preferably, it further includes:

[0093] Photovoltaic power constraint:

[0094]

[0095] In the formula: is the maximum active power output of the photovoltaic system under given lighting conditions; is the maximum active power output of the photovoltaic system under given lighting conditions; θ is the power angle of the photovoltaic system;

[0096] Gas turbine operation constraint:

[0097]

[0098] In the formula: are the minimum and maximum output powers of the i-th gas turbine unit, respectively.

[0099] The present invention also provides an island distribution network short-term scheduling system considering the flexible energy transfer of new energy online car-hailing groups, which executes a short-term scheduling strategy for an island distribution network considering the flexible energy transfer of new energy online car-hailing groups as described above.

[0100] Compared with the prior art, the present invention has the following beneficial effects:

[0101] Aiming at the problems of large-scale abandonment of electricity and equipment overload in the distribution network caused by large-scale distributed new energy access, the present invention proposes a short-term scheduling strategy for an island distribution network considering the flexible energy transfer of new energy online car-hailing groups. First, considering the contradiction between the signing cost of new energy online car-hailing and the grid support value, an incentive calling mechanism for new energy online car-hailing groups with flexible adjustable participation cycle and quantity is proposed. Then, the traffic migration and charging and discharging characteristics of new energy online car-hailing are quantitatively analyzed, and an energy spatio-temporal transfer model of new energy online car-hailing groups is constructed. On this basis, with the goal of minimizing the system cost, a short-term scheduling method for an island new energy distribution network considering power flow constraints is proposed. Finally, the proposed method is verified based on the improved IEEE 33-node distribution system. The simulation results show that the strategy proposed by the present invention can reduce the operation cost of the distribution network by 7.05%. The present invention provides a new perspective for the consumption of distributed new energy and the operation optimization of new energy vehicles. Description of the Drawings

[0102] Figure 1 It is the structure diagram of the power distribution network for new energy online car-hailing included in the present invention.

[0103] Figure 2 It is the scenario diagram of the scheduling model for the light-storage-vehicle mode of the present invention.

[0104] Figure 3 It is the diagram of the number of online car-hailing at the sites of the present invention.

[0105] Figure 4 It is the spatio-temporal distribution diagram of online car-hailing during scheduling of the present invention.

[0106] Figure 5 It is the charging and discharging power diagram of the sites of the present invention.

[0107] Figure 6 It is the power balance diagram when online car-hailing participates in the present invention.

[0108] Figure 7 It is the power balance diagram when online car-hailing does not participate in the present invention.

[0109] Figure 8 It is the flowchart of a short-term scheduling strategy for an island power distribution network considering the flexible transfer of energy of a new energy online car-hailing group of the present invention. Specific embodiments

[0110] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the accompanying drawings are all in very simplified forms and use non-precise scales, only for the purpose of facilitating and clearly assisting in explaining the objectives of the embodiments of the present invention.

[0111] The embodiment of the present invention provides a light-storage-vehicle multi-modal spatio-temporal collaborative optimization scheduling strategy. First, based on the flexibility of online car-hailing as a mobile energy storage unit, an adaptive optimization model is constructed to dynamically determine the scheduling quantity and time of online car-hailing. Further, considering the path planning and charging and discharging behaviors of vehicles, vehicle summons constraints are proposed to achieve precise control of the spatio-temporal distribution of online car-hailing and improve the efficiency of electric energy transportation. In addition, to solve the influence of the uncertainty of new energy output on scheduling, an optimization algorithm is introduced in this paper to dynamically adjust the system to ensure the maximization of photovoltaic power consumption and reduce the system operation cost. The simulation results verify the effectiveness of this strategy. The main innovation points of the present invention are as follows: 1) Considering the contradiction between the signing cost of new energy online car-hailing and the power grid support value, an incentive summons model for a new energy online car-hailing group with a flexible adjustable participation period and quantity is established. 2) The traffic migration and charging and discharging characteristics of new energy online car-hailing are evaluated, and an energy spatio-temporal transfer model for a new energy online car-hailing group is constructed.

[0112] The embodiments of the present invention specifically include the following:

[0113] I. Structure of the power distribution network with new - energy online car - hailing vehicles:

[0114] The structure of the power distribution network with new - energy online car - hailing vehicles is as Figure 1 shown, including photovoltaic units, wind turbine units, gas turbines, loads, and new - energy online car - hailing vehicles with flexible charging and discharging capabilities, etc.

[0115] The photovoltaic power station converts renewable energy into electric energy. A part of it is directly supplied to local loads, and the other part may not be directly consumed due to the capacity limit of the transformer at the photovoltaic site. The gas turbine serves as a regulating power source and jointly meets the load demand with renewable energy. As a mobile energy storage unit, the new - energy online car - hailing vehicle charges at the station during the peak of photovoltaic power generation and discharges at the target station during the peak load, realizing the cross - regional transfer of electric energy. Through optimized scheduling, the number and path of the online car - hailing vehicles are dynamically determined to enable them to flexibly participate in energy interaction at different time periods.

[0116] II. Optimization scheduling strategy of the power distribution network with new - energy online car - hailing vehicles:

[0117] Firstly, the power distribution network control center predicts the photovoltaic power output, wind power output, and local load demand for the next day, and determines the available new - energy electricity that can be directly utilized in combination with the capacity limit of the transformer at the photovoltaic site. Secondly, based on the prediction results, an optimization model is constructed with the goal of minimizing the comprehensive operation cost, and the optimal scheduling strategy of new - energy online car - hailing vehicles in different time periods is calculated, including the charging and discharging time of the vehicles, path selection, and station docking plan. Then, based on the vehicle call constraint, the scheduling of the online car - hailing vehicles is optimized to enable them to charge during the peak of new - energy output and discharge during the peak load, realizing the spatio - temporal transfer of energy. Finally, in combination with the optimized charging and discharging scheduling plan, the output of the gas turbine is adjusted, and the power interaction of the power distribution network is coordinated to ensure the overall power balance and optimal economy of the system.

[0118] III. "Photovoltaic - energy storage - vehicle" collaborative optimization scheduling model:

[0119] (1) Objective function:

[0120] By optimizing the power dispatching, the operation cost of the entire system is minimized. This cost mainly includes the following parts: Firstly, the operation cost of the gas turbine, which is directly related to fuel consumption and power generation; Secondly, the charging and discharging compensation cost of new - energy online car - hailing vehicles, covering the compensation fees when the online car - hailing vehicles charge or discharge in the power grid; Then, the driving cost when the online car - hailing vehicles perform the electric energy transportation task, which depends on the driving distance and energy consumption; Finally, it also includes the one - time compensation cost of the online car - hailing vehicles, which is the start - up cost or incentive cost for the online car - hailing vehicles to participate in power dispatching. Based on these cost factors, the goal of the system is to minimize the total operation cost.

[0121] The specific objective function is as follows.

[0122]

[0123] Where: F is the comprehensive cost; is the operating cost of the gas turbine; is the charging and discharging compensation cost of the online car-hailing service; is the on-road cost of the online car-hailing service; is the one-time compensation fee for the online car-hailing service.

[0124] The specific expressions for each part are as follows:

[0125] 1) Operating cost of the gas turbine:

[0126]

[0127] Where: i is the number of gas turbine units; a i is the fuel cost coefficient of gas turbine unit i; is the power generation power of gas turbine unit i at time t.

[0128] 2) Charging and discharging compensation cost of the online car-hailing service:

[0129]

[0130] Where: k c and k d are the subsidy cost coefficients for the charging and discharging power per unit of the online car-hailing service respectively; and are the charging and discharging powers of the online car-hailing service at time t respectively.

[0131] 3) On-road cost of the online car-hailing service:

[0132]

[0133] Where: is the transportation route; Δ i→j is the passing time of the online car-hailing service at the station; c o is the unit operating cost of the online car-hailing service (yuan / hour); is the number of fully-loaded online car-hailing services arriving from i to j at time t; is the number of empty online car-hailing services arriving from i to j at time t.

[0134] 4) One-time compensation fee:

[0135]

[0136] Where: p ev,neg is the compensation fee per vehicle; V t is the total number of online car-hailing services.

[0137] (2) Energy transfer and supply constraints for new energy online car-hailing:

[0138] In view of the characteristics of uneven spatio-temporal distribution of energy supply and demand in island scenarios, the present invention proposes a flexible vehicle scheduling model based on multi-state partitioning. By dynamically coupling the state of power grid nodes with vehicle operation behaviors, this model accurately depicts the differential state response mechanisms of new energy online car-hailing in three types of functional areas: power generation source areas, load centers, and charging and discharging hubs, which are specifically expressed as follows:

[0139] The sum of fully-loaded and empty new energy online car-hailing vehicles should be equal to the total number of online car-hailing vehicles put into use, as shown in formula (6). Fully-loaded (empty) online car-hailing vehicles include fully-loaded (empty) online car-hailing vehicles transporting and staying in each area, as shown in formula (7):

[0140]

[0141]

[0142] In the formula: is the number of fully-loaded online car-hailing vehicles at time t; is the number of empty online car-hailing vehicles at time t. and are the numbers of fully-loaded and empty online car-hailing vehicles in transit from area i to area j at time t, respectively; and are the numbers of fully-loaded and empty online car-hailing vehicles available in area i at time t, respectively.

[0143] The total number of available fully-loaded (or empty) online car-hailing vehicles in area i should be equal to the difference between the number of online car-hailing vehicles charging (or discharging) in this area and the number of online car-hailing vehicles discharging (or charging), plus the number of fully-loaded (or empty) online car-hailing vehicles that have just arrived in area j, minus the number of fully-loaded (or empty) online car-hailing vehicles that have just left area i:

[0144]

[0145] In the formula: and are the numbers of online car-hailing vehicles charging and discharging in area i at time t, respectively; and are the numbers of fully-loaded and empty online car-hailing vehicles leaving area i and heading for area j at time t, respectively; and are the numbers of fully-loaded and empty online car-hailing vehicles arriving in area j from area i at time t, respectively; is the set of all areas in the network; is the time interval.

[0146] The change in the number of fully-loaded (empty) online car-hailing vehicles from area i to area j should be equal to the number of fully-loaded (empty) online car-hailing vehicles that have just left area i minus the number of fully-loaded (empty) vehicles that have just arrived in area j:

[0147]

[0148] The number of fully loaded (or empty) online car-hailing vehicles in transit from area i to area j at time t should be greater than that from t - Δ i→j +1 to t for the number of fully loaded (or empty) online car-hailing vehicles departing from area i, as follows:

[0149]

[0150] Where: Δ i→j is the driving time delay from area i to area j.

[0151] The number of fully loaded (or empty) online car-hailing vehicles in transit from area i to area j at time t should be greater than that of fully loaded (or empty) online car-hailing vehicles arriving at area j from area i during t + 1 to t + Δ i→j as follows:

[0152]

[0153] After completing the construction of the constraint system of the new energy online car-hailing dispatching model for islands, it is necessary to focus on solving the operational feasibility problem of vehicles during the specified dispatching period. The constraint mechanism based on Boolean variables realizes precise control of vehicle calling behavior through logical state combinations. The following are the core constraint expressions:

[0154] μ t represents the state of whether the vehicle is operating. It is constrained by taking the difference between two increasing Boolean variables, as shown in formulas (12)-(15). The fluctuation range of the number of online car-hailing vehicles is associated with whether the vehicle is in an operating state. By introducing a large number M, the range of V t can be dynamically adjusted according to the value of μ t (0 or 1), as shown in formulas (16)-(17). This design realizes flexible control of the number of vehicles through the combination of logical variables and the large number M, ensuring the rationality and feasibility of the model.

[0155] B s,t-1 ≤B s,t (12)

[0156] B e,t-1 ≤B e,t (13)

[0157] B e,t ≤B s,t (14)

[0158] μ t =B s,t -B e,t (15)

[0159] V - M + μ t ·M ≤ V t ≤ V + M - μ t ·M (16)

[0160] 0 ≤ V t ≤ μ t ·M (17)

[0161] Where: B s,t 、B e,t and μ t are all Boolean variables; B s,t and B e,t are used to mark the start and end of scheduling; M is a large number. Among them, when μ t equals 0, the vehicle does not participate; when μ t equals 1, the vehicle participates.

[0162] It is required that at the start of scheduling, the number of full and empty vehicles at the stations and between stations is the same as the initial state; at the end of scheduling, the number of full and empty vehicles at the stations and between stations is the same as the end state. Formulas (18)-(25) are used to manage the number of full and empty vehicles during the scheduling process, ensuring the state consistency at the start and end of scheduling and maintaining the coherence of the state between time periods.

[0163]

[0164] Where: and are the number of full and empty vehicles at the station at the start of scheduling respectively; and are the number of full and empty vehicles at the station at the end of scheduling respectively; and are the number of full and empty vehicles between stations at the start of scheduling respectively; and are the number of full and empty vehicles between stations at the end of scheduling respectively.

[0165] If the vehicle is still participating at the end of scheduling, then it is required that the energy at the end of scheduling is equal to the vehicle's exit energy, ensuring that the state of the vehicle when exiting the system meets the expectations and avoiding resource waste and system instability. As shown in Formulas (26)-(29):

[0166]

[0167]

[0168] The consistency of the number of vehicles at the start and end of scheduling is the basic guarantee for system stability. Through this requirement, allocation conflicts caused by mismatched vehicle numbers can also be avoided. As shown in Formulas (30)-(33):

[0169]

[0170] Equations (34) and (35) reflect the direct relationship between the charging and discharging power and the number of vehicles at the station, ensuring the reasonable distribution and scheduling of the charging power, enabling the charging station to respond in a timely manner to fluctuations in the system load demand.

[0171]

[0172] In the formula: is the charging power of the charging and discharging station at node i; is the discharging power of the charging and discharging station at node i; P m is the rated charging and discharging power of the vehicle.

[0173] (3) Power flow constraint

[0174] The sum of the conventional load, gas turbine unit, charging and discharging station, and renewable energy output connected to grid node i determines the active power P i (t) and reactive power Q i (t) injected at that point, as follows:

[0175]

[0176] In the formula: is the conventional active power load of node i; is the active power generation of the gas turbine at node i; is the photovoltaic power generation at node i.

[0177] The active and reactive power balance constraints of the branch are:

[0178]

[0179] In the formula: P j,t is the active power injection of node j; Q j,t is the reactive power injection of node j; P ij,t is the active power injected from the side of node i; Q ij,t is the reactive power injected from the side of node i; r ij is the equivalent resistance of branch j; x ij is the reactance of branch j; is the sum of the active power injected by node j into all branches (excluding branch ij); is the sum of the reactive power injected by node j into all branches (excluding branch ij).

[0180] The relationship between the voltages of adjacent nodes based on Ohm's law:

[0181]

[0182] Apparent power and node voltage to calculate current:

[0183]

[0184] Two - norm form of voltage and branch current in standard form:

[0185]

[0186] v j,t = V j,t 2 (43)

[0187] Branch network loss:

[0188]

[0189] The voltage magnitudes of each node in the distribution system should be within the specified range to ensure power supply quality and normal operation of equipment. For each node i:

[0190]

[0191] Where: Are the lower and upper limits of the node voltage operation respectively.

[0192] The line transmission power should be within the thermal stability and safe operation range of the line. For each line ij:

[0193]

[0194] Where: Is the rated capacity (MVA) of line j.

[0195] (4) Other constraints:

[0196] 1) Photovoltaic power constraint

[0197]

[0198] Where: Is the maximum active power output of the photovoltaic system under given light conditions; Is the maximum active power output of the photovoltaic system under given light conditions; θ is the power angle of the photovoltaic system.

[0199] 2) Gas turbine operation constraint

[0200]

[0201] Where: Are respectively the iThe minimum and maximum output powers of a gas turbine unit.

[0202] IV. Example Verification

[0203] (1) Basic data of the example

[0204] The present invention uses an adjusted IEEE 33-node distribution network system for simulation analysis.

[0205] Figure 2 It is an improved distribution network system. This distribution network system has 33 nodes, 32 branches, a base voltage of 12.66 KV, and a base capacity of 10 MVA. Gas turbines are installed at nodes 6, 8, and 15, photovoltaic stations are set at nodes 21 and 29. Additionally, the photovoltaic stations are equipped with transformers with a capacity of 1.5 MVA, wind turbines are at node 11, and charging and discharging piles are set at nodes 18, 21, and 29. The allowable range of node voltage is 0.93 - 1.07 p.u., the compensation for charging and discharging one degree of electricity by new energy online car-hailing is 0.45 yuan, the cost of gas turbines is 1500 yuan / MW, the driving cost of online car-hailing is 15 yuan / h / vehicle, and the net income of each vehicle is 15 yuan / h. The rated charging and discharging power of online car-hailing is 0.07 MW, the scheduling period is 12 h, and the passing time of online car-hailing at the station is set to 1 h. Simulation analysis shows that after optimization, the system is configured with 23 new energy online car-hailing vehicles, and the optimal scheduling time period is determined to be from 11:00 to 16:00.

[0206] The photovoltaic accommodation and new energy online car-hailing collaborative optimization scheduling model constructed by the present invention is transformed into a mixed-integer second-order cone programming problem. It is programmed in the Yalmip environment of MATLAB on a computer configured with an Intel(R) Core(TM) i5-9300H CPU@2.40GHz and a working environment of Windows 10 system, and the Gurobi solver is called for solution.

[0207] (2) Analysis of simulation results

[0208] To verify the superiority of the strategy proposed by the present invention, the present invention sets two schemes for comparison:

[0209] Scheme 1: Adopt the strategy proposed by the present invention.

[0210] Scheme 2: Adopt the traditional scheduling method, that is, new energy vehicles do not participate in the distribution network scheduling.

[0211] Figure 3 Shows the number of fully charged online car-hailing vehicles and the number of online car-hailing vehicles to be charged at the station in Scheme 1. Above the abscissa represents the fully charged online car-hailing vehicles; below the abscissa represents the number of online car-hailing vehicles to be charged.

[0212] During different time periods, the dynamic changes in the number of online car-hailing vehicles at each station reflect the vehicle dispatching situation between different stations. The number of fully charged online car-hailing vehicles changes over time and is closely related to the PV output and load demand of the system, while the number of not fully charged online car-hailing vehicles is affected by vehicle charging behavior and dispatching strategies. This shows that online car-hailing vehicles, as mobile energy storage units, play an active role in the balance and dispatching of electric energy in the spatio-temporal dimension.

[0213] Combined with Figure 4 (Scenario 1) The dispatching results of new energy online car-hailing vehicles after system optimization show that the electric energy transportation of vehicles mainly concentrates in the period from 11:00 to 16:00, which is the high-output period of load photovoltaic power generation. From 11:00 to 12:00, mainly fully charged online car-hailing vehicles transport electric energy from PV stations (Stations 2 and 3) to Station 1, and at the same time, some vehicles continue to charge at Stations 2 and 3; from 12:00 to 14:00, in addition to the fully charged online car-hailing vehicles continuing to transport to Station 1, there are also many online car-hailing vehicles waiting to be charged going from Station 1 to PV stations for charging; from 14:00 to 16:00, the online car-hailing vehicles waiting to be charged continue to move from Station 1 to PV stations, and the fully charged online car-hailing vehicles transport electric energy to Station 1, but the overall transportation frequency gradually decreases, indicating that the PV output decreases and the system tends to be balanced.

[0214] The movement trajectories of online car-hailing vehicles show a two-way flow mode of transmitting electricity from PV stations to Station 1 and transporting vehicles waiting to be charged from Station 1 to PV stations, which optimizes the system energy distribution and matches new energy consumption with load demand.

[0215] Figure 5 Shows the charging and discharging conditions of online car-hailing vehicles at 3 stations in Scenario 1. When the output power of the online car-hailing vehicle is positive, it means the vehicle is discharging; otherwise, it means the vehicle is charging.

[0216] 1) The charging peak appears from 11:00 to 15:00, which is consistent with the high-output time of PV. The dispatching strategy reasonably utilizes the charging and discharging station to consume some surplus electric energy through charging, and the discharging power fluctuates synchronously with the charging power to adjust the power between different stations. Stations 2 and 3 undertake most of the charging tasks, making full use of the local power generation resources of PV stations and reducing PV curtailment.

[0217] 2) The discharging peak of vehicles at Station 1 appears from 12:00 to 15:00, indicating that this station is the main load supply point, and the online car-hailing vehicles transport electric energy here after charging. After 16:00, the discharging amount decreases significantly, indicating that the PV output decreases, the electric energy transportation task of online car-hailing vehicles gradually reduces, and the system enters a balanced state.

[0218] Overall, the role of the charging and discharging station in the system is mainly reflected in balancing the PV output and optimizing the matching of electricity consumption demand.

[0219] Figure 6 and Figure 7 respectively show the power comparison of each device in System 1 and System 2. It can be seen that the gas turbine undertakes most of the load supply with a relatively high output. Especially during the period when the photovoltaic output is low, the participation of new energy online car-hailing plays a certain role in peak shaving and valley filling, reducing the dependence on the gas turbine. On the one hand, it also enhances the scheduling flexibility of the system, enabling the effective allocation of electric energy in different time and space. From Figure 6 it can be seen that each device can cooperate with each other to achieve power balance and meet the load demand. During the period from 12:00 to 16:00, due to the relatively high new energy output, the output of the gas turbine decreases. The online car-hailing gives priority to charging and discharging during this period to make full use of new energy power. During the period from 16:00 to 18:00, as the photovoltaic output decreases, the output of the gas turbine gradually increases, and at the same time, the online car-hailing releases some of the stored electric energy to reduce the burden on the gas turbine.

[0220] As a mobile energy storage unit, the charging and discharging scheduling of the online car-hailing is not only affected by the power supply and demand relationship between stations, but also jointly affected by the photovoltaic output, load demand and traffic flow constraints. The present invention analyzes the flow of the online car-hailing between different time periods and different stations, and explores its spatio-temporal distribution characteristics and the impact on system scheduling.

[0221] The simulation results show that the online car-hailing mainly concentrates on charging at the photovoltaic charging stations during the period with high photovoltaic output, while it preferentially transfers to high-load stations for discharging during the peak load period, realizing the spatio-temporal migration of electric energy. In the morning (7:00), the online car-hailing mainly concentrates on charging at each station. At noon (12:00 - 16:00), charging and discharging are carried out simultaneously to balance the power supply and demand. In the evening (17:00 - 18:00), the discharging decreases, and some vehicles carry out a small amount of charging to meet the subsequent scheduling requirements.

[0222] In addition, the spatio-temporal distribution characteristics of the online car-hailing are affected by the traffic travel time and the charging and discharging demand. Due to insufficient photovoltaic output or limited traffic flow at some stations, the online car-hailing may be detained, reducing the overall system scheduling efficiency. Therefore, reasonably planning the path selection and scheduling sequence of the online car-hailing can further improve the flexibility of electric energy scheduling, increase the photovoltaic accommodation rate, and reduce the output demand of the gas turbine.

[0223] Combined with the spatio-temporal distribution characteristics of the online car-hailing, the station layout and scheduling strategy can be further optimized to ensure the efficient flow of electric energy in the system under different load scenarios, and improve the economy and stability of the distribution network.

[0224] (3) Economic Analysis

[0225] Table 1 shows the economic benefits of the distribution network under two scenarios. The operating cost of the gas turbine under Scenario 1 is 61,538.87 yuan, the cost of the online car-hailing service is 9,015.50 yuan, and the total cost is 70,554.37 yuan. The advantage of vehicles participating in power transportation is that it can effectively reduce the operating time of the gas turbine, reduce the system's dependence on traditional power generation, and thus reduce the overall power supply cost. In contrast, the operating cost of the gas turbine under Scenario 2 is 75,905.80 yuan, which is significantly higher than the total cost when vehicles participate. Through comparison, it can be found that when vehicles participate, although the cost of the online car-hailing service increases by 9,015.50 yuan, due to the significant reduction in the operating cost of the gas turbine (14,366.93 yuan), the overall total cost is reduced (7.05%), showing the economic benefits brought by vehicles participating in power transportation. This result indicates that the participation of online car-hailing services as flexible loads can not only balance power demand but also effectively reduce the dependence on high-cost power sources (such as gas turbines), improving the overall economy of the system. Therefore, increasing the participation of vehicles in power transportation, especially during periods when new energy generation is sufficient and load fluctuations are large, can significantly improve the economy of the power system, achieving cost savings and optimizing resource utilization.

[0226] Table 1

[0227]

[0228] In summary, for the power dispatching problem in the island scenario, the present invention proposes an optimized dispatching strategy that introduces new energy online car-hailing services to maximize the utilization rate of renewable energy and reduce the system operating cost.

[0229] 1) Compared with the traditional dispatching strategy, the "photovoltaic-storage-vehicle" collaborative system optimized dispatching strategy proposed by the present invention improves the economy by 7.05%.

[0230] 2) As a mobile energy storage device, the online car-hailing service stores excess electric energy during the peak period of photovoltaic power generation and discharges during the peak demand period, thus suppressing the power fluctuations of the power grid and improving the consumption capacity of new energy.

[0231] 3) The power transmission of the traditional power grid is limited by fixed lines, while the online car-hailing service as a mobile energy storage unit can flexibly adjust the charging and discharging positions and times, enhancing the flexible dispatching ability of the power grid.

[0232] The above description is only a description of the preferred embodiments of the present invention and does not limit the scope of the present invention in any way. Any changes and modifications made by those of ordinary skill in the art of the present invention based on the above disclosure shall fall within the protection scope of the claims.

Claims

1. A short-term scheduling strategy for an island distribution network considering flexible energy transfer of new energy online car-hailing groups, characterized in that, It includes the following steps: Step 1: Considering the contradiction between the signing cost of new energy online car-hailing and the grid support value, propose an incentive call model for a group of new energy online car-hailing with flexible adjustable participation cycle and quantity; Step 2: Quantitatively analyze the traffic migration and charging and discharging characteristics of new energy online car-hailing, and construct an energy spatio-temporal transfer model for a group of new energy online car-hailing; Step 3: On this basis, with the goal of minimizing the system cost, propose a short-term scheduling model for an island new energy distribution network considering power flow constraints; Step 4: Verify the proposed model based on the improved IEEE 33-node distribution system.

2. The short-term scheduling strategy for an island distribution network considering the flexible transfer of energy among new energy online car-hailing groups as described in claim 1, wherein The specific scheduling strategy includes: First, the distribution network control center predicts the next day's photovoltaic output, wind power output and local load demand, and determines the directly available new energy power considering the capacity limit of the photovoltaic site transformer; Secondly, according to the prediction results, construct an optimization model, with the goal of minimizing the comprehensive operation cost, and calculate the optimal scheduling strategy for new energy online car-hailing in different time periods. The optimal scheduling strategy includes the charging and discharging time, path selection and site docking plan of the vehicle; Then, based on the vehicle call constraint, optimize the scheduling of online car-hailing, so that it charges during the peak of new energy output and discharges during the peak of load, realizing the spatio-temporal transfer of energy; Finally, combined with the optimized charging and discharging scheduling plan, adjust the output of the gas turbine and coordinate the power interaction of the distribution network to ensure the overall power balance and economic optimality of the system.

3. The short-term scheduling strategy for an island distribution network considering flexible energy transfer of new energy online car-hailing groups according to claim 1, wherein, The specific content of Step 2 includes: This model accurately depicts the differential state response mechanism of new energy online car-hailing in three functional areas: power generation source area, load center and charging and discharging hub by dynamically coupling the grid node state and vehicle operation behavior, which is specifically expressed as follows: The sum of fully loaded and empty new energy online car-hailing should be equal to the total number of online car-hailing put into use, as shown in the following formula (6); fully loaded or empty online car-hailing includes fully loaded or empty online car-hailing transporting and staying in each area, as shown in the following formula (7): In the formula: is the number of fully-loaded online car-hailing vehicles at time t; is the number of empty online car-hailing vehicles at time t; and are the numbers of fully-loaded and empty online car-hailing vehicles in transit from area i to area j at time t, respectively; and are the numbers of available fully-loaded and empty online car-hailing vehicles in area i at time t, respectively. The total number of available fully loaded or empty online car-hailing in area i should be equal to the difference between the number of charging or discharging online car-hailing and the number of discharging or charging online car-hailing in this area, plus the number of fully loaded or empty online car-hailing that has just arrived in the area, minus the number of fully loaded or empty online car-hailing that has just left the area, as shown in the following formula (8): Wherein: and are respectively t the number of online car-hailing vehicles charging and discharging in the i area at time and are respectively t the number of full and empty online car-hailing vehicles leaving the i area and driving to the j area at time and are respectively t the number of full and empty online car-hailing vehicles arriving at the i area from the j area at time is the set of areas in the network; is the time interval; From i area to j the change in the number of fully-loaded or empty online car-hailing vehicles in the area should be equal to the number of fully-loaded or empty online car-hailing vehicles that have just left i area minus the number of fully-loaded or empty online car-hailing vehicles that have just arrived at j area, as shown in the following formula (9): The number of fully-loaded or empty online car-hailing vehicles in transit from area i to area j at time t should be greater than that from t - Δ i→j +1 to the number of fully-loaded or empty online car-hailing vehicles departing from area i during the period from t, as shown in the following formula (10): Where: Δ i→j is the driving time delay from area i to area j; The number of fully or partially occupied online car-hailing vehicles in transit from area i to area j at time t should be greater than the number of fully or partially occupied online car-hailing vehicles arriving from area i to area j during the period from t + 1 to t + Δ i→j as shown in the following formula (11): Thus, the construction of the constraint system of the above-mentioned new energy online car-hailing scheduling model for islands is completed.

4. The short-term scheduling strategy for an island distribution network considering flexible energy transfer of a new energy online car-hailing group as claimed in claim 3, wherein It also includes: Based on the constraint mechanism of Boolean variables, accurately control the vehicle call behavior through logical state combination. The following are the core constraint expressions: μ t represents the state of whether the vehicle is in operation. It is constrained by taking the difference between two increasing Boolean variables, as shown in the following formulas (12) to (15); the fluctuation range of the number of online car-hailing vehicles is associated with whether the vehicle is in an operating state. By introducing a large number M, it is possible to dynamically adjust the range according to the value of μ t which is 0 or 1, as shown in the following formulas (16) to (17); B s,t-1 ≤B s,t (12) B e,t-1 ≤B e,t (13) B e,t ≤B s,t (14) μ t = B s,t - B e,t (15) V - M + μ t ·M ≤ V t ≤ V + M - μ t ·M (16) 0 ≤ V t ≤ μ t ·M(17) Where: B s,t , B e,t and μ t are all Boolean variables; B s,t and B e,t are used to mark the start and end of scheduling; M is a large number; where when μ t equals 0, the vehicle does not participate; when μ t equals 1, the vehicle participates.

5. The short-term scheduling strategy for an island distribution network considering the flexible energy transfer of a new energy online car-hailing group according to claim 4, characterized in that, It also includes: At the beginning of the scheduling, the number of full and empty cars at the site and between stations is the same as the initial state; at the end of the scheduling, the number of full and empty cars at the site and between stations is the same as the end state; manage the number of full and empty cars during the scheduling process through the following formulas (18) - (25) to ensure the state consistency at the beginning and end of the scheduling and maintain the state coherence between time periods; Wherein: and are respectively the numbers of fully-loaded and empty vehicles at the station at the start of the scheduling; and are respectively the numbers of fully-loaded and empty vehicles at the station at the end of the scheduling; and are respectively the numbers of fully-loaded and empty vehicles between stations at the start of the scheduling; and are respectively the numbers of fully-loaded and empty vehicles between stations at the end of the scheduling.

6. The short-term scheduling strategy for an island distribution network considering flexible energy transfer of new energy online car-hailing groups as described in claim 5, characterized in that, It also includes: If the vehicle is still participating at the end of the scheduling, it is required that the energy at the end of the scheduling is equal to the vehicle's exit energy to ensure that the vehicle's state when exiting the system meets the expectations and avoid resource waste and system instability; as shown in the following formulas (26) to (29): By ensuring the consistency of the number of vehicles at the start and end of the scheduling, it is possible to avoid allocation conflicts caused by mismatched vehicle numbers, as shown in the following formulas (30) to (33): The following formulas (34) and (35) reflect the direct relationship between the charging and discharging power and the number of vehicles at the site, ensuring the reasonable allocation and scheduling of the charging power, enabling the charging station to respond in a timely manner when the system load demand fluctuates; In the formula: is the charging power of the charging and discharging station at node i; is the discharging power of the charging and discharging station at node i; P m is the rated charging and discharging power of the vehicle.

7. A short-term scheduling strategy for an island distribution network considering flexible energy transfer of a new energy online car-hailing group as described in claim 1, characterized in that, In the third step, the goal of the system is to minimize the total operating cost, and the specific objective function is as follows: Where: F is the comprehensive cost; is the operating cost of the gas turbine; is the charging and discharging compensation cost of the online car-hailing; is the on-road cost of the online car-hailing; is the one-time compensation cost of the online car-hailing; Among them, the operating cost of the gas turbine is as follows: Where: i is the number of gas turbine units; a i is the fuel cost coefficient of gas turbine unit i; is the power generation of gas turbine unit i at time t; The charging and discharging compensation cost of the online car-hailing is as follows: where: k c and k d are respectively the subsidy cost coefficients for the charging and discharging power per unit of the online car-hailing service; and are respectively the charging and discharging powers at the moment of the online car-hailing service; The on-road cost of the online car-hailing is as follows: In the formula: is the transportation path; Δ i→j is the passing time of the online car-hailing at the station; c o is the unit operation cost of the online car-hailing; is the number of fully-loaded online car-hailings from i to j at time t; is the number of empty online car-hailings from i to j at time t; The one-time compensation cost is as follows: Where: p ev,neg is the compensation cost for each vehicle; V t is the total number of online car-hailing vehicles.

8. The short-term scheduling strategy for an island distribution network considering flexible energy transfer of new energy online car-hailing groups as claimed in claim 1, wherein In the third step, the power flow constraints include: The sum of the conventional load, gas turbine unit, charging and discharging station, and renewable energy output connected to the grid node i determines the active power P i (t) and reactive power Q i (t) as shown in the following formula: In the formula: is the regular active power load of node i; is the active power generation of the gas turbine at node i; is the photovoltaic power generation at node i; The active and reactive power balance constraints of the branch are: Where: P j,t is the active injection power of node j; Q j,t is the reactive injection power of node j; P ij,t is the active power injected from the side of node i; Q ij,t is the reactive power injected from the side of node i; r ij is the equivalent resistance of branch j; x ij is the reactance of branch j; is the sum of the active power injected by node j into all branches; is the sum of the reactive power injected by node j into all branches; The relationship between the voltages of adjacent nodes based on Ohm's law is shown in the following formula: The calculation of the current from the apparent power and the node voltage is shown in the following formula: The two-norm form of the voltage and the branch current in the standard form is shown in the following formula: v j,t = V j,t 2 (43) The branch power loss is shown in the following formula: For each node i, the voltage amplitude of each node in the distribution system should be within the specified range to ensure the power supply quality and the normal operation of the equipment, as shown in the following formula: where: are the lower and upper limits of the node voltage operation respectively; For each line ij , the line transmission power should be within the thermal stability and safe operation range of the line, as shown in the following formula: In the formula: is the line j rated capacity.

9. The short-term scheduling strategy for an island distribution network considering the flexible energy transfer of a new energy online car-hailing group as described in claim 8, wherein It also includes: Photovoltaic power constraint: Where: is the maximum active output power of the PV system under given light conditions; is the maximum active output power of the PV system under given light conditions; θ is the power angle of the PV system; Gas turbine operation constraint: Wherein: are respectively the i minimum and maximum output powers of the th gas turbine unit.

10. An island distribution network short-term scheduling system considering flexible energy transfer of new energy online car-hailing groups, characterized in that, Implement a short-term scheduling strategy for an island distribution network considering the flexible energy transfer of a new energy online car-hailing group as described in any one of claims 1 to 9.

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