Power grid planning capacity analysis method considering dispatchable flexible resource operation characteristics

Abstract

The present application relates to a kind of power grid planning capacity analysis method considering the operation characteristics of schedulable flexible resources, comprising: analyzing the operation characteristics of schedulable flexible resources;Identify key schedulable flexible resources;According to the guiding mechanism of various schedulable flexible resources for power grid planning;According to the guiding mechanism of various schedulable flexible resources for power grid planning, schedulable flexible resources with different capacity are accessed to distribution network, and the collaborative optimization of schedulable flexible resources and power grid planning layout is carried out, finally analyze the impact of capacity on power grid planning problem.The present application establishes the guiding mechanism of schedulable flexible resources, explores the collaborative optimization technology of schedulable flexible resources and power grid planning layout, realizes effective promotion schedulable flexible resources participate in power grid operation and planning, gives full play to the value of schedulable flexible load resources, reduces the capacity demand of power system power grid planning and improves the utilization efficiency of power grid planning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of demand analysis of power grid planning, and particularly relates to a power grid planning capacity analysis method considering operation characteristics of dispatchable flexible resources. BACKGROUND

[0002] Under the background of global energy resource shortage and environmental pollution, developing green low-carbon and sustainable new energy power generation and clean emission electric vehicles has become the consensus of the world, and a large number of relevant supporting policies have been introduced. With the explosive development of distributed new energy generation and the development of energy storage technology, the proportion of energy storage systems with "source-load" dual attribute characteristics in the power grid load is rising. On the other hand, the application of demand response means such as time-of-use electricity price enables part of the traditional load to adjust its electricity demand according to the incentive or electricity price information, thereby having the ability to interact with the power grid and be adjustable, i.e. flexible load.

[0003] Distributed renewable energy represented by photovoltaic power generation and wind power generation, electric vehicles and energy storage systems with "source-load" dual attribute, and new flexible load with self-regulating ability can be collectively referred to as dispatchable flexible resources in the power grid. Different dispatchable flexible resources have different characteristics, such as distributed new energy generation, which has the problems of fluctuation, intermittency and low controllability. This causes the increase of power system planning layout capacity and the reduction of power grid equipment utilization efficiency, thereby greatly reducing the operation economy of the power grid. Therefore, the analysis of the operation characteristics and working principles of different dispatchable flexible resources is an important basis for further research and full utilization of dispatchable flexible resources.

[0004] In recent years, the peak load of power and the continuous growth of new energy have posed a major challenge to the regulation capacity of the power system; at the same time, under the support of relevant policies, the development of dispatchable flexible resources represented by demand side management, electric vehicles, energy storage, etc. has been booming. The access of various types of dispatchable flexible resources has increased the difficulty of power grid planning and reduced the utilization efficiency of equipment. Under the new form, the safe and economic operation of the power grid and the planning of the power grid have higher requirements and challenges. Abroad, many enterprises have long been engaged in the research and application of flexible resource project scheduling problems, and the data accumulation is relatively sufficient, but most of the research is limited to the scheduling of flexible resources under the power market itself, and the connection with the planning layout of the power grid needs to be strengthened, and the load characteristics of flexible resources have not been uniformly analyzed and classified, and the relevance of different flexible resources and power grid development planning has not been specifically analyzed. The development of the domestic power market has just started, and the power spot market is not perfect, so how to link the scheduling of flexible resources and the development planning of the power grid is a problem that we need to solve urgently. Therefore, it is necessary to carry out research on power grid planning under the participation of flexible load, and to carry out research on the operation characteristics of dispatchable flexible resources, the guiding mechanism, the relevance and collaborative optimization technology of the power grid planning. SUMMARY

[0005] In order to solve the defects of the increase of power system planning layout capacity and the decrease of power grid equipment utilization efficiency caused by the access of various types of dispatchable flexible resources into the power grid, the purpose of the present application is to provide a power grid planning capacity analysis method considering the operation characteristics of dispatchable flexible resources, which can realize the cooperation of various types of flexible resources and power grid planning in time and space, can make full use of dispatchable flexible resources to the greatest extent, and can improve the reliability and economy of power grid operation.

[0006] In order to achieve the above-mentioned purpose, the following technical scheme is adopted in the present application: a power grid planning capacity analysis method considering the operation characteristics of dispatchable flexible resources, which comprises the following sequential steps:

[0007] (1) analyzing the operation characteristics of dispatchable flexible resources: dividing the dispatchable flexible resources into energy one-way interactive flexible resources and energy two-way interactive flexible resources, wherein the energy one-way interactive flexible resources include flexible load and dispatchable new energy, and the energy two-way interactive flexible resources include energy storage devices, electric vehicles and microgrids;

[0008] (2) identifying dispatchable flexible resources: according to the operation characteristics of each dispatchable flexible resource, establishing a two-stage robust optimization model of power grid planning considering the uncertainty of new energy output and a two-stage robust optimization model of power grid planning considering the uncertainty of electric vehicle output, analyzing the correlation degree of the two models, and then identifying the key dispatchable flexible resources;

[0009] (3) According to the operation characteristics of each schedulable flexible resource and the key schedulable flexible resource, a guiding mechanism for each schedulable flexible resource oriented to power grid planning is proposed;

[0010] (4) Analysis of the impact on power grid planning: according to the guiding mechanism for each schedulable flexible resource oriented to power grid planning, schedulable flexible resources with different capacities are connected to the distribution network, and the schedulable flexible resources and the layout of power grid planning are optimized, and finally the impact of capacity on power grid planning is analyzed.

[0011] The step (1) specifically comprises the following steps:

[0012] (1a) Analysis of the operation characteristics of the energy storage device: due to its own bidirectional energy flow characteristics, the energy storage device can smooth the fluctuation of new energy output, has millisecond-level fast, stable and accurate charging and discharging power regulation characteristics, and can improve the instantaneous, short-time and period balancing capability of the power system;

[0013] (1b) Analysis of the operation characteristics of schedulable new energy: distributed installation can effectively improve the node voltage value and improve the power flow distribution, reduce the load shedding operation, and improve the power supply reliability; there is a problem of overvoltage at the new energy access point or overload of the adjacent line, which affects the operation stability of the distribution network, thereby reducing the power supply reliability; the operation has output uncertainty, and the start-stop and unstable power output of the new energy itself will cause voltage fluctuation and impact on the power supply voltage of the distribution network users;

[0014] (1c) Analysis of the operation characteristics of electric vehicles: electric vehicles have time and space uncertainty, obvious load growth, expand the peak-valley difference of load, increase the difficulty of power grid control, expand the capacity of distribution transformers, and increase the cost of power grid; the energy can be bidirectionally transmitted, and can be orderly charged and discharged to reduce the network loss of the distribution network, participate in scheduling to improve the node voltage waveform of the distribution network, affect the power flow of the line, and further affect the line loss;

[0015] (1d) Analysis of the operation characteristics of flexible load: flexible load has energy storage characteristics, can reduce the load during the peak electricity consumption period, and orderly participate in scheduling to reduce the line loss of the power grid; flexible load can reduce the load, reduce the maximum reactive load, and increase the critical voltage; orderly start and stop, reduce the line loss of the power grid, and reduce the peak-valley difference;

[0016] (1e) Analysis of the operation characteristics of microgrid: microgrid contains various schedulable flexible resources and has "source and load" dual attributes; by introducing energy storage devices, the power shortage during the peak electricity consumption period can be alleviated; high penetration of new energy causes impact on the normal operation of the main grid, and supplies power to "island" areas, which is a powerful supplement to the traditional power supply form.

[0017] The step (2) specifically comprises the following steps:

[0018] (2a) introducing a robustness index Γ wt and Γ pv characterize the uncertainty of new energy output as a key operating characteristic, taking the initial investment cost and operation and maintenance cost of the power grid as the target, and establish a two-stage robust optimization model of power grid planning considering the uncertainty of new energy output;

[0019] Uncertainty scenario set of wind turbine:

[0020]

[0021] Uncertainty scenario set of photovoltaic power station:

[0022]

[0023] In the formula, is the allowed range of wind power and photovoltaic output; is the number of wind turbine and photovoltaic units; T is the total number of time periods; and are the predicted output values of wind turbine and distributed photovoltaic power station at t time period respectively; and are the actual output values of wind turbine and distributed photovoltaic power station at t time period respectively; and are the fluctuation amounts of wind turbine and distributed photovoltaic power station at t time period; and are the maximum fluctuation amounts of wind turbine and distributed photovoltaic power station at t time period;

[0024] (2b) taking the space-time uncertainty of electric vehicles as a key operating characteristic, and using a normal distribution model to characterize the characteristic, and establishing a two-stage robust optimization model of power grid planning considering the uncertainty of electric vehicle output, which includes two charging modes of regular charging and fast charging:

[0025] Regular charging: formula (3) and formula (4) are charging and discharging charge models, formula (5) is a normal distribution model of charging start time, and formula (6) is a normal distribution model of daily driving distance:

[0026]

[0027]

[0028]

[0029]

[0030] where E CCS (t), E CCS (t-1) are the state of charge of the electric vehicle at time t and t-1; t c is the actual charging duration, t d is the actual discharging duration; t max_c is the maximum charging duration, t max_d is the maximum discharging duration; E max is the maximum state of charge of the electric vehicle when fully charged; μ x and σ x are the mean and standard deviation of the charging start time , respectively, μ x = 17.6, σ x = 3.4; μ L and σ L are the mean and variance of the log of the daily driving distance L, lnL, respectively, μ L = 3.2, σ L = 0.88; is the normal distribution model expression of the charging start time; is the normal distribution model expression of the daily driving distance;

[0031] Fast charging: Equations (7), (8) and (9) are the M / G / k model of the fast charging users, and Equations (10) and (11) are the charging model of the fast charging station:

[0032]

[0033]

[0034]

[0035]

[0036]

[0037] where t BCS is the charging time of the fast charging user; is the rated capacity of the battery of the fast charging user; B i,BCS is the remaining state of charge of the i-th fast charging user when arriving at the integrated station; is the rated charging power of the fast charging device; E T is theBCS the expectation of the remaining power of the total fast charging users when arriving at the integrated station; D T the variance of the remaining power of the total fast charging users when arriving at the integrated station BCS the variance of the remaining power of the total fast charging users when arriving at the integrated station; P BCS the total charging power of the fast charging station at the t-th time period; P i,BCS the charging power of the i-th fast charging user at the t-th time period; η BCS the charging power of the fast charging device of the fast charging station; ω i,BCS (t) is a binary variable, representing the charging state of the i-th fast charging user at the t-th time period, being 1 if charging, otherwise 0; E(B i,BCS ) the expectation of the remaining power of the total fast charging users when arriving at the integrated station; D i,BCS the variance of the remaining power of the total fast charging users when arriving at the integrated station

[0038] (2c) Through the two-stage robust optimization model of power grid planning considering the uncertainty of new energy output and the two-stage robust optimization model of power grid planning considering the uncertainty of electric vehicle output, a data set under multiple scenarios of power grid planning is obtained, and a correlation degree is calculated by using a data mining algorithm: the correlation degree between each cost X and Y under n scenarios is comprehensively analyzed by using the grey correlation theory, the correlation degree between each cost and the total cost is obtained, the correlation degree between the new energy output fluctuation and each cost under n scenarios is comprehensively analyzed by using the entropy weight method, and finally the correlation degree between the new energy output fluctuation and the total cost is obtained by comprehensive weighting, and it is identified whether the flexible resource is a key adjustable flexible resource.

[0039] The step (3) specifically comprises the following steps:

[0040] (3a) analyzing the diversified operation modes of the adjustable flexible resources;

[0041] (3b) based on the analysis of the operation modes, response characteristics and flexible resource optimization scheduling model of the adjustable flexible resources, five kinds of typical adjustable flexible resource guiding mechanisms are proposed, including a flexible load guiding mechanism for power grid planning, a price type guiding mechanism based on user side energy storage price optimization, an electric vehicle charging load guiding mechanism based on space-time price optimization, a price type guiding mechanism based on micro-grid system response characteristics, and a reward and punishment mechanism for the participation of adjustable new energy in power grid dispatching;

[0042] (3c) analyzing the research on the flexible load guiding mechanism for power grid planning: by constructing a peak, flat and valley period division model based on improved boundary moving technology, adding constraint conditions, and taking the Deng index as the objective function to solve the optimal period division, a peak, flat and valley period division model based on improved boundary moving technology is constructed, after implementing peak-valley time-of-use electricity price in the peak, flat and valley periods of the load curve, the user adjusts his own power consumption mode, and the power consumption in each period is described as:

[0043]

[0044]

[0045]

[0046] wherein, , , respectively represent the electricity consumption before the implementation of time-of-use electricity price; Q p0 , Q f0 , Q v0 respectively represent the electricity consumption in each time period after the implementation of time-of-use electricity price; ΔQ p , ΔQ f , ΔQ v respectively represent the change in electricity consumption before and after the implementation of time-of-use electricity price, E is the electricity price elasticity matrix, λ pp , λ pf , λ pv respectively are the self-elasticity coefficients of each time period, λ fp , λ ff , λ fv respectively are the cross-elasticity coefficients of any two time periods; λ vp , λ vf , λ vv are the electricity consumptions in peak, flat and valley time periods;

[0047] The constraint conditions of the peak, flat and valley time period division model based on the improved boundary moving technique are:

[0048] (3c1) User benefit constraint: the electricity bill of the user does not increase before and after the implementation of time-of-use electricity price;

[0049] (3c2) Peak, flat and valley time period electricity price constraint: the peak time period electricity price is greater than the flat time period electricity price, and the flat time period electricity price is greater than the valley time period electricity price after the implementation of time-of-use electricity price;

[0050] (3c3) Power grid company benefit constraint: the implementation of time-of-use electricity price can reduce the investment cost of the power supplier, and will not reduce the overall benefit of the power supplier;

[0051] (3c4) Marginal electricity price constraint: the valley time period electricity price should not be lower than the marginal electricity price;

[0052] The objective function of the peak, flat and valley time period division model based on the improved boundary moving technique is:

[0053]

[0054]

[0055]

[0056] where p represents the decision variable; α and β are the weight coefficients of the objective function; and H is the penalty function item.

[0057] The step (4) specifically comprises the following steps:

[0058] (4a) The region to be planned is divided into three regions, and three schemes are first set: scheme one considers that all the region to be planned is ordinary load; scheme two accesses the dispatchable new energy and energy storage device with a capacity of 10 MW in the three regions; scheme three accesses the dispatchable new energy and energy storage device with a capacity of 15 MW in the three regions, and considers that 10% of the ordinary load is flexible load and electric vehicle charging pile; since the substation capacity is directly related to the cost, the dispatchable flexible resource access substation fixed capacity matching index based on the cost is proposed to measure the influence of the access capacity on the substation planning, and the dispatchable flexible resource access substation fixed capacity matching index is as follows:

[0059]

[0060] wherein, represents the fixed capacity matching index; C t is the substation planning cost considering the dispatchable flexible resource access; c t is the conventional substation planning cost without considering the dispatchable flexible resource access;

[0061] (4b) The equivalent capacity of the dispatchable flexible resource is calculated, and the substation capacity-load ratio formula is updated according to the equivalent capacity;

[0062] Objective function:

[0063]

[0064] wherein C is the total cost, including the distribution network planning cost and the operation cost, N T represents the number of substations; represents the number of network frame lines; C Tp (Si) is the substation investment cost, r is the discount rate, m is the substation depreciation period, C j is the cost of newly built one-way power supply line, C rT (Si) is the substation operation and maintenance cost, n year is the simulation period; x i is a binary decision variable, the value of 1 indicates that the ith line is selected, and the value of 0 indicates that the ith line is not selected; x jis a binary decision variable, and its value is 1 if the jth line is selected, and 0 if the jth line is not selected; c loss is the unit network loss cost; ΔP i is the active power loss of the ith line;

[0065] The capacity-load ratio formula is:

[0066]

[0067] In the formula, R s represents the capacity-load ratio; S i represents the capacity of the ith transformer; P max represents the peak load value of the region to be planned; m s is the number of substations; n is the number of transformers used in each substation; λ is the load coincidence rate; P mgeq is the equivalent capacity of the microgrid, including the output of the distributed power source P dg and the output of the flexible resource Fl mg of the microgrid;

[0068] (4c) Coordinated optimization of dispatchable flexible resources and grid planning layout: a distribution network bi-level planning model considering the access of flexible resources is established, with the total investment and operation cost as the objective function. The upper half of formula (21) is the upper model used for planning the distribution network, and the lower half of formula (21) is the lower model used for calculating the operation cost of the distribution network:

[0069]

[0070] In the formula, f0 and f1 are the objective functions of the upper model and the lower model, respectively, wherein the upper model is an investment decision model, and the lower model is an operation scheduling model; x ov , x iv are the decision variables of the upper model and the lower model, respectively; x iv,s is the decision variable of the lower model under the s scenario; H(·) and G(·) correspond to the constraint conditions of the upper model and the lower model, respectively, including equality constraints and inequality constraints;

[0071] Objective function:

[0072]

[0073]

[0074]

[0075] where C is the total cost, including planning investment cost C p and operation cost C r ; the first term of investment planning cost is substation investment cost, and the second term is grid construction planning cost, where C Tp (S i ) is substation investment cost, m is substation depreciation life, represents line network loss cost;

[0076] The line flexibility constraint condition is added to ensure the adequacy of grid transmission capacity during the operation of flexible resources:

[0077]

[0078] Equation (25) is the line flexibility constraint condition, where FL l is the used flexibility adjustment capacity of the lth line, i.e., the used transmission capacity of the line; β is the margin coefficient, which is in the range of [0, 1]; FL max is the maximum flexibility adjustment capacity of the line;

[0079] Because the grid planning part of the distribution network planning model has a nonlinear mathematical model and the scale of the solution is large, a genetic algorithm is used for solving. In the process of using the genetic algorithm to solve the grid planning of the distribution network planning, the output grid structure may have a cross phenomenon, which is not allowed in the actual planning operation. The fitness function after the line cross judgment is considered as follows:

[0080]

[0081] In the formula, F is the cost after considering the line cross judgment, J is the number of conventional constraint conditions; K j is the penalty function of the jth conventional constraint condition in the planning process; a linecross is the line cross judgment binary variable; K' is the penalty function corresponding to the line cross.

[0082] In step (2a), the power grid planning two-stage robust optimization model considering the uncertainty of new energy output is specifically a two-stage robust optimization model established with the lowest total cost of the microgrid. The first-stage objective function is to minimize the initial investment cost in the microgrid, and the second-stage objective function is to minimize the dispatching and operation cost in the microgrid.

[0083] The initial investment cost in the first-stage objective function is the equipment investment cost of the wind turbine, distributed photovoltaic power station, energy storage device and micro gas turbine unit, as shown below:

[0084] (27)

[0085] (28)

[0086] wherein, is the maximum battery capacity of the energy storage device; C bat is the investment cost per unit power of the energy storage device; and are the maximum technical output and the investment cost per unit power of the i-th power equipment, respectively; F CRE (ri, Yi) is the annual capital recovery rate; r i and Y i are the discount rate and the discount year of the i-th power equipment, respectively, wherein the discount year of the energy storage device is the float life;

[0087] Scheduling operation cost in the second stage objective function is the operation cost of the micro gas turbine, the microgrid electricity purchase and sale cost, and the equipment maintenance cost, as follows:

[0088] (29)

[0089] (30)

[0090] (31)

[0091] (32)

[0092] wherein, , C grid , C op are the operation cost of the micro gas turbine, the microgrid electricity purchase and sale cost, and the equipment maintenance cost, respectively; is the fuel cost at time period t; P G,t is the real-time output of the micro gas turbine at time period t; k n,t and c n,t are the n-th pollutant emission amount and the treatment unit price of the micro gas turbine at time period t; c buy,t and c sell,t represent the electricity purchase and sale unit price at time period t; and are the power purchased and sold by the microgrid to the power grid at time period t; is the maintenance cost unit price of the energy storage device, the wind turbine generator, the distributed photovoltaic power station, and the micro gas turbine; P i,t represents the output of the energy storage device, the wind turbine generator, the distributed photovoltaic power station, and the micro gas turbine at time period t;

[0093] The constraints include the following constraints:

[0094] The power balance constraint is:

[0095] (33)

[0096] The micro-turbine constraint is:

[0097] (34)

[0098] The energy storage device constraint is:

[0099] (35)

[0100] (36)

[0101] The state of charge constraint is:

[0102] (37)

[0103] (38)

[0104] The exchange power constraint is:

[0105] (39)

[0106] (40)

[0107] In the formula, P wt,t , P pv,t , and P G,t are the outputs of the wind turbine, the distributed photovoltaic power station, the energy storage device and the micro-turbine at time period t, is the load at time period t; is the charging power of the energy storage device at time period t, ε ch and ε dis are the ratios of the maximum charging and discharging power of the energy storage to the maximum capacity of the battery; and are the minimum and maximum outputs of the micro-turbine at time period t; is the state of charge of the energy storage, SOC min and SOC max are the lower and upper limits of the state of charge of the battery, and the state of charge is not less than 20% to prevent the discharge depth from being too large and the battery life from being too fast; beg and SOC end indicate that the state of charge of the battery is the same at the beginning and end of the dispatching period T, and respectively represent the upper limit of the power purchased / sold by the micro-grid to the distribution network; is an integer variable with value 1 indicating that the micro-grid purchases power from the distribution network at time period t; represents the maximum capacity of the energy storage device.

[0108] In step (2b), the two-stage robust optimization model of power grid planning considering the uncertainty of electric vehicle output is specifically as follows: the charging station to which the electric vehicle goes is determined according to the selection cost of the electric vehicle to each charging station, and the charging load is calculated to the distribution network; the electric vehicle is guided to go to the charging station close to the new energy by changing the charging price, and the specific form is as follows:

[0109] A three-layer robust optimization model is established, and the objective function is as follows:

[0110] (41)

[0111] In the formula, D is a set of new energy output scenarios, and P(ξ) is the probability of the output of each scenario; represents the square of the voltage amplitude of node i at time t; is the square of the current amplitude from node i to node j at time t; represents the active power from node i to node j at time t; represents the active power from node i to node j at time t; represents the feasible region of the decision variable;

[0112] The constraint conditions are as follows:

[0113] Charging station selection constraint:

[0114] (42)

[0115] (43)

[0116] In the formula, is the comprehensive cost of the i th electric vehicle to the j th charging station at time t; is the charging price of the j th charging station at time t; is the expected waiting time of the j th charging station at time t; ω1, ω2 and ω3 are weight coefficients; is the lowest comprehensive cost; n is the number of charging stations, and when =0, it represents that the vehicle i does not select the charging station j; when =1, it represents that the vehicle i selects the charging station j; M is an arbitrarily large positive number; is the equivalent distance from the current position of the i th vehicle to the j th charging station at time t;

[0117] Electric vehicle charging state constraint:

[0118] (44)

[0119] (45)

[0120] (46)

[0121] In the formula, V i T represents the speed of the i-th vehicle; i Indicates the time it takes for the car owner to express their intention to charge; Ts. i For the initial charging time of electric vehicles; T ch,i Charging time for electric vehicles; γ i =1 indicates that the electric vehicle is in a charging state at time t0, γ i =0 indicates that it is not in a charging state; Let t be the travel time of the i-th vehicle; Let be the estimated waiting time at the i-th charging station at time t;

[0122] Charging station price constraints:

[0123] (47)

[0124] Current balance constraints:

[0125] (48)

[0126] Power balance constraints for charging stations:

[0127] (49)

[0128] (50)

[0129] In the formula, k3 and k4 are proportionality coefficients. Let be the electricity price at the power station at time t. Let Ω be the charging price of the i-th charging station at time t; a Let Ω be the set of nodes between the k-th electric vehicle and the i-th charging station. b Let be the set of all nodes in the distribution network, Rij and Xij be the resistance and reactance values ​​of branch ij, respectively, Pt ij and Qt ij be the active and reactive power flow of branch ij, respectively, Pt iD and Qt iD be the active and reactive load of node i at time t, respectively, and Pt i.en and Qt i.en be the active and reactive power injection of the power source at node i at time t. and respectively represent the active and reactive power injection of charging station i at time t; Ptcs.j represents the active power output of charging station j at time t, p c is the charging power of a single charging pile, N is the total number of charging vehicles at all times in a day, represents the selection of electric vehicle i to charging station j, represents the charging state of electric vehicle i at time t, and λ is the power factor of the charging pile.

[0130] In step (3b), the flexible load guidance mechanism for power grid planning refers to guiding the dispatchable flexible resources using a price-based guidance mechanism. The basis is to scientifically and reasonably divide the peak, flat and valley periods according to the user load characteristics and formulate the corresponding time-of-use price to guide the users. Combined with the control of the load, the purpose of peak clipping and valley filling is achieved.

[0131] The price-based guidance mechanism based on user-side energy storage price optimization refers to the response characteristics of the user-side energy storage power station, which is that the energy storage device will try to "store low and release high". Guiding the user-side energy storage using time-of-use price can reduce the user's own electricity cost, also can reduce the peak-valley difference of the power system load, improve the utilization rate of power transmission and distribution equipment, and delay equipment investment, so as to achieve a win-win situation.

[0132] The electric vehicle charging load guidance mechanism based on space-time price optimization refers to that the electric vehicle charging load not only has certain randomness in time, but also shows great flow characteristics in space. The grid-connected electric vehicle is also used as a kind of mobile energy storage device to realize the reverse feeding of electric energy to the system. Based on the space-time response characteristics of electric vehicles, time-of-use price can be used to guide electric vehicles to participate in time and space peak clipping and valley filling, reduce network loss, and improve the economy and reliability of the power grid.

[0133] The price-based guidance mechanism based on the response characteristics of the micro-grid system refers to that the micro-grid system interacts with the main grid through time-of-use price. When the micro-grid energy output is insufficient, the micro-grid system purchases electricity from the main grid at a low price. When the micro-grid energy output meets the load demand and has excess, the excess electricity is sold to the main grid at a high price to obtain benefits and reduce the economic cost of the micro-grid system.

[0134] The reward and punishment mechanism for dispatchable new energy participating in power grid dispatching refers to guiding the enthusiasm of dispatchable new energy participating in peak shaving through reward and punishment policies, which indirectly improves the consumption capacity of new energy by the power system, effectively guarantees the stable operation of the power system, and takes into account the reduction of the peak shaving pressure of the power system and the interests of new energy power suppliers. Guiding new energy suppliers to reasonably match storage according to their actual situation can reduce the peak shaving pressure of the power grid.

[0135] From the above technical scheme, the beneficial effects of the present application are: first, the present application studies the influence characteristics and correlation of the dispatchable flexible load and the power grid planning, establishes and perfects the dispatchable flexible resource guiding mechanism, and explores the collaborative optimization technology of the dispatchable flexible resource and the power grid planning layout, realizes the effective promotion of the dispatchable flexible resource participating in the power grid operation and planning, fully plays the value of the dispatchable flexible load resource, reduces the capacity demand of the power system power grid planning, and improves the utilization efficiency of the power grid planning; second, the present application changes part of the load from "rigidity" to "flexibility" through time-of-use price and other control means, the electricity consumption changes in the specified period or shifts between different periods according to the power grid demand, effectively utilizes the power grid interaction of the flexible load to realize optimization and control, the large-scale access of electric vehicles, flexible load, energy storage system and other power grids can realize peak clipping and valley filling through reasonable guidance of the dispatchable flexible resource, and reduce the peak-valley difference of the power grid; third, the present application accesses the energy storage system and electric vehicles with "source-load" double attributes as power supply to the power grid, discards the traditional power grid planning method based on artificial experience, proposes a new type of power grid planning model, reasonably improves the planning result, improves the utilization rate of equipment, reasonably utilizes the flexible resource to coordinate and absorb the new energy generation represented by photovoltaic power generation and wind power generation, effectively alleviates the influence of the randomness and volatility of the new energy generation, and improves the safety and stability of the power grid operation. BRIEF DESCRIPTION OF DRAWINGS

[0136] Figure 1 It is the influence mechanism diagram of the dispatchable flexible resource obtained by the present application on the power grid planning;

[0137] Figure 2 It is the schematic diagram of the influence of scheme one on the substation planning cost obtained by the present application;

[0138] Figure 3 It is the schematic diagram of the influence of scheme two on the substation planning cost obtained by the present application;

[0139] Figure 4 It is the schematic diagram of the influence of scheme three on the substation planning cost obtained by the present application;

[0140] Figure 5 It is the influence diagram of the penetration rate of the dispatchable flexible resource on the network frame planning obtained by the present application;

[0141] Figure 6 It is the influence diagram of the penetration rate of the dispatchable flexible resource on the substation planning obtained by the present application;

[0142] Figure 7 It is the load level change schematic diagram before and after the time-of-use price obtained by the present application;

[0143] Figure 8 It is the load level change schematic diagram after the flexible load proportion is improved by 10% obtained by the present application. DETAILED DESCRIPTION

[0144] A power grid planning capacity analysis method considering the operation characteristics of schedulable flexible resources, the method comprising the following sequential steps:

[0145] (1) analyzing the operation characteristics of schedulable flexible resources: schedulable flexible resources are divided into energy one-way interactive flexible resources and energy two-way interactive flexible resources, wherein the energy one-way interactive flexible resources include flexible loads and schedulable new energy, and the energy two-way interactive flexible resources include energy storage devices, electric vehicles and microgrids;

[0146] (2) identifying schedulable flexible resources: according to the operation characteristics of each schedulable flexible resource, a power grid planning two-stage robust optimization model considering the uncertainty of new energy output and a power grid planning two-stage robust optimization model considering the uncertainty of electric vehicle output are established, the correlation of the two models is analyzed, and then the key schedulable flexible resources are identified; by analyzing the operation characteristics of each schedulable flexible resource, the influence mechanism of schedulable flexible resources on power grid planning is obtained, as shown in FIG. Figure 1

[0147] (3) according to the operation characteristics of each schedulable flexible resource and the key schedulable flexible resources, a guiding mechanism for each type of schedulable flexible resource for power grid planning is proposed;

[0148] (4) analyzing the influence on power grid planning: according to the guiding mechanism for each type of schedulable flexible resource for power grid planning, schedulable flexible resources with different capacities are connected to the distribution network, and the schedulable flexible resources and the layout of power grid planning are optimized, and finally the influence of capacity on power grid planning is analyzed.

[0149] The step (1) specifically comprises the following steps:

[0150] (1a) analyzing the operation characteristics of energy storage devices: due to its own energy two-way flow characteristics, energy storage devices can smooth the fluctuation of new energy output, have millisecond-level fast, stable and accurate charge and discharge power regulation characteristics, and can improve the instantaneous, short-time and period balancing ability of the power system;

[0151] (1b) analyzing the operation characteristics of schedulable new energy: distributed installation can effectively improve the node voltage value and improve the power flow distribution, reduce the load shedding operation and improve the power supply reliability; there is a problem of voltage being too high at the place where new energy is connected or the adjacent line being overloaded, which affects the operation stability of the distribution network and reduces the power supply reliability; the operation has output uncertainty, and the start-stop and unstable power output of new energy itself will cause voltage fluctuation and impact on the power supply voltage of the distribution network users;

[0152] ​(1c) Analysis of the operating characteristics of electric vehicles: electric vehicles have space-time uncertainty, load growth is obvious, the peak-valley difference of the expanded load increases the difficulty of power grid control, expands the capacity of distribution transformers, and increases the cost of the power grid; its energy can be bidirectional, and can be orderly charged and discharged to reduce the network loss of the distribution network, participate in scheduling to improve the node voltage waveform of the distribution network, affect the power flow of the line, and then affect the line loss;

[0153] (1d) Analysis of the operating characteristics of flexible load: flexible load has energy storage characteristics, can reduce the load during the peak period of electricity consumption, reduce the peak-valley difference of the power grid, and orderly participate in scheduling to reduce the line loss of the power grid; flexible load can reduce the characteristics of load increase, maximum reactive load reduction, and critical voltage rise, orderly start and stop, reduce the line loss of the power grid, and reduce the peak-valley difference;

[0154] (1e) Analysis of the operating characteristics of microgrid: microgrid contains various dispatchable flexible resources and has "source and load" dual attributes; by introducing energy storage devices, the power shortage during the peak period of electricity consumption can be alleviated; high penetration of new energy causes the impact of microgrid on the normal operation of the main network, and the power supply to "island" areas is a powerful supplement to traditional power supply forms.

[0155] The step (2) specifically comprises the following steps:

[0156] (2a) Introducing the robustness index Γ wt and Γ pv characterize the uncertainty of new energy output, which is a key operating characteristic, and a two-stage robust optimization model of power grid planning considering the uncertainty of new energy output is established with the initial investment cost and operation and maintenance cost of the power grid as the target;

[0157] Uncertainty scenario set of wind turbine:

[0158]

[0159] Uncertainty scenario set of photovoltaic power station:

[0160]

[0161] In the formula, is the allowed range of wind power and photovoltaic output; is the number of wind turbines and photovoltaic units; T is the total number of time periods; and are the predicted output values of the wind turbine and the distributed photovoltaic power station at t time period, respectively; and are the actual output values of the wind turbine and the distributed photovoltaic power station at t time period, respectively; and The fluctuation amount of the wind turbine generator and the distributed photovoltaic power station in the t period; and The maximum fluctuation amount of the wind turbine generator and the distributed photovoltaic power station in the t period;

[0162] (2b) Taking the space-time uncertainty of the electric vehicle as a key operation characteristic, and adopting a normal distribution model to represent the characteristic, a two-stage robust optimization model of power grid planning considering the output uncertainty of the electric vehicle is established, the model including two charging modes of regular charging and fast charging:

[0163] Regular charging: formula (3) and formula (4) are charging and discharging charge amount models, formula (5) is a normal distribution model of charging start time, and formula (6) is a normal distribution model of daily driving distance:

[0164]

[0165]

[0166]

[0167]

[0168] In the formula, E CCS (t) and E CCS (t-1) are the charge amounts of the electric vehicle at the t moment and the t-1 moment; t c is the actual charging time, t d is the actual discharging time; t max_c is the maximum charging time, t max_d is the maximum discharging time; E max is the maximum charge amount of the electric vehicle when fully charged; μ x and σ x respectively represent the expectation and standard deviation of the charging start time , μ x =17.6, σ x =3.4; μ L and σ L are respectively the expectation and variance of the logarithm lnL of the driving distance L, μ L =3.2, σ L =0.88; is a normal distribution model expression of the charging start time; is a normal distribution model expression of the daily driving distance;

[0169] Fast charging: formula (7), formula (8) and formula (9) are the M / G / k model of fast charging users in line, formula (10) and formula (11) are the charging model of fast charging station:

[0170]

[0171]

[0172]

[0173]

[0174]

[0175] In the formula, t BCS is the charging time of fast charging user; is the rated capacity of fast charging user battery; B i,BCS is the remaining power of the i th fast charging user when arriving at the integrated station; is the rated charging power of fast charging device; E T is the expectation of t BCS ; D T is the variance of t BCS ; P BCS (t) is the total charging power of fast charging station at the t th period; P i,BCS (t) is the charging power of the i th fast charging user at the t th period; η BCS is the charging power of fast charging device of fast charging station; ω i,BCS (t) is a binary variable, indicating the charging state of the i th fast charging user at the t th period, being 1 if charging and 0 otherwise; E(B i,BCS ) is the expectation of the remaining power of total fast charging user when arriving at the integrated station; D(B i,BCS ) is the variance of the remaining power of total fast charging user when arriving at the integrated station;

[0176] (2c) through the two-stage robust optimization model of power grid planning considering the uncertainty of new energy output and the two-stage robust optimization model of power grid planning considering the uncertainty of electric vehicle output, obtain the data set of power grid planning under multiple scenarios, and use data mining algorithm to calculate the correlation degree: use grey correlation theory to comprehensively analyze the correlation degree between each cost X and Y under n scenarios, obtain the correlation degree between each cost and total cost, use entropy weight method to comprehensively analyze the correlation degree between new energy output fluctuation and each cost under n scenarios, finally comprehensively weight the correlation degree between new energy output fluctuation and total cost, and identify whether the flexible resource is a key adjustable flexible resource.

[0177] The step (3) specifically comprises the following steps:

[0178] (3a) analyzing the operation mode of the schedulable flexible resource;

[0179] (3b) based on the analysis of the operation mode of the schedulable flexible resource, the response characteristic and the flexible resource optimization scheduling model, proposing five typical schedulable flexible resource guiding mechanisms, including a flexible load guiding mechanism for power grid planning, a price type guiding mechanism based on user side energy storage price optimization, an electric vehicle charging load guiding mechanism based on space-time price optimization, a price type guiding mechanism based on micro-grid system response characteristics and a reward and punishment mechanism for schedulable new energy participating in power grid dispatching;

[0180] (3c) analyzing the flexible load guiding mechanism for power grid planning: by constructing a peak, flat and valley period division model based on an improved boundary moving technology, adding constraint conditions, solving the optimal period division with a Deng index as a target function, constructing a peak, flat and valley period division model based on an improved boundary moving technology, after implementing peak and valley time-of-use electricity price in the peak, flat and valley periods of the load curve, the user adjusts the power consumption mode, and the power consumption in each period is described as:

[0181]

[0182]

[0183]

[0184] In the formula, , , respectively represent the power consumption before implementing time-of-use electricity price in the peak, flat and valley periods; Q p0 , Q f0 , Q v0 respectively represent the power consumption in each period after implementing time-of-use electricity price; ΔQ p , ΔQ f , ΔQ v respectively represent the change amount of power consumption before and after implementing time-of-use electricity price, E is an electricity price elasticity matrix, λ pp , λ pf , λ pv respectively are self-elasticity coefficients of each period, λ fp , λ ff , λ fv respectively are cross-elasticity coefficients of any two periods; λ vp , λ vf , λ vvThe electricity consumption in the peak-valley period;

[0185] The constraint condition of the peak-valley period division model based on the improved boundary moving technique is:

[0186] (3c1) User benefit constraint: the electricity charge of the user does not increase before and after the implementation of the time-of-use electricity price;

[0187] (3c2) Peak, flat and valley period electricity price constraint: after the implementation of the time-of-use electricity price, the peak period electricity price is greater than the flat period electricity price, and the flat period electricity price is greater than the valley period electricity price;

[0188] (3c3) Power grid company benefit constraint: the implementation of the time-of-use electricity price can reduce the investment cost of the power supplier, and will not reduce the overall benefit of the power supplier;

[0189] (3c4) Marginal electricity price constraint: the valley period electricity price should not be lower than the marginal electricity price;

[0190] The objective function of the peak-valley period division model based on the improved boundary moving technique is:

[0191]

[0192]

[0193]

[0194] In the formula, p represents a decision variable; α and β are weight coefficients of the objective function; and H is a penalty function item.

[0195] The particle swarm optimization algorithm is used to optimize the time-of-use electricity price of the flexible load with different proportions, and the optimization result is as shown in Figure 7 From Figure 8 it can be seen that under the joint participation of the flexible load and the time-of-use electricity price, the accuracy and efficiency of the period division can be effectively improved, and the proposed flexible load optimal guiding mechanism based on the direct load control and the time-of-use electricity price can effectively reduce the peak-valley difference, so as to achieve the effect of smoothing the load curve and peak regulation.

[0196] The step (4) specifically comprises the following steps:

[0197] (4a) The to-be-planned area is divided into three areas, and three schemes are first set: scheme one considers that all the to-be-planned area is ordinary load; scheme two accesses the dispatchable new energy and energy storage device with a capacity of 10 MW in the three areas; scheme three accesses the dispatchable new energy and energy storage device with a capacity of 15 MW in the three areas, and considers that 10% of the ordinary load is flexible load and electric vehicle charging pile; since the substation capacity is directly related to the cost, the substation fixed capacity matching index of dispatchable flexible resource access based on cost is proposed to measure the influence of the access capacity on the substation planning, and the substation fixed capacity matching index of dispatchable flexible resource access is as follows:

[0198]

[0199] In the formula, C represents the fixed capacity matching index; C t represents the substation planning cost considering the dispatchable flexible resource access; c t represents the conventional substation planning cost without considering the dispatchable flexible resource access;

[0200] (4b) The equivalent capacity of the dispatchable flexible resource is calculated, and the substation capacity-load ratio formula is updated according to the equivalent capacity;

[0201] Objective function:

[0202]

[0203] In the formula, C is the total cost, including the distribution network planning cost and the operation cost, N T represents the number of substations; represents the number of network lines; C Tp (Si) is the substation investment cost, r is the discount rate, m is the substation depreciation period, C j is the cost of newly building a power supply line, C rT (Si) is the substation operation and maintenance cost, n year is the simulation period; x i is a binary decision variable, and the value is 1 when the i-th line is selected, and is 0 when the i-th line is not selected; x j is a binary decision variable, and the value is 1 when the j-th line is selected, and is 0 when the j-th line is not selected; c loss is the unit network loss cost; ΔP i is the active power loss of the i-th line;

[0204] Capacity-load ratio formula:

[0205]

[0206] where R s represents the load ratio; S i represents the capacity of the ith transformer; P max represents the peak load value of the area to be planned; m s is the number of substations; n is the number of transformers used by each substation; λ is the load coincidence rate; P mgeq is the equivalent capacity of the microgrid, including the output of distributed power sources P dg and the output of flexible resources of the microgrid Fl mg ;

[0207] (4c) Coordinated optimization of dispatchable flexible resources and grid planning layout: A double-layer planning model of distribution network considering the access of flexible resources is established, with the total investment and operation cost as the objective function. The upper model in the upper half of formula (21) is used to plan the distribution network, and the lower model in the lower half of formula (21) is used to calculate the operation cost of the distribution network:

[0208]

[0209] where f0 and f1 are the objective functions of the upper model and the lower model, respectively, wherein the upper model is an investment decision model, and the lower model is an operation scheduling model; x ov and x iv are the decision variables of the upper model and the lower model, respectively; x iv,s is the decision variable of the lower model under scenario s; H(·) and G(·) correspond to the constraint conditions of the upper model and the lower model, respectively, including equality constraints and inequality constraints;

[0210] Objective function:

[0211]

[0212]

[0213]

[0214] where C is the total cost, including the planning investment cost C p and the operation cost C r ; the first term in the investment planning cost is the substation investment cost, and the second term is the network construction planning cost, wherein C Tp (S i ) is the substation investment cost, m is the depreciation period of the substation, represents the line loss cost;

[0215] The line flexibility constraint condition is added to ensure the adequacy of the transmission capacity of the network during the operation of the flexible resource:

[0216]

[0217] Formula (25) is the line flexibility constraint condition, wherein FL l is the used flexibility adjustment capacity of the lth line, i.e., the transmission capacity used by the line; β is a margin coefficient, which has a value range of [0, 1]; FL max is the maximum flexibility adjustment capacity of the line;

[0218] Since the network planning part of the distribution network planning model has a nonlinear mathematical model and the scale of the solution is large, a genetic algorithm is used for solving. In the process of using the genetic algorithm to solve the network planning of the distribution network planning, the output network structure may have a cross phenomenon, which is not allowed in the actual planning operation. The fitness function after the line cross judgment is considered as follows:

[0219]

[0220] In the formula, F is the cost after the line cross judgment, J is the number of conventional constraint conditions; K j is the penalty function of the jth conventional constraint condition in the planning process; a linecross is a line cross judgment binary variable; K' is the penalty function corresponding to the line cross.

[0221] In step (2a), the power grid planning two-stage robust optimization model considering the uncertainty of new energy output specifically refers to: a two-stage robust optimization model is established with the lowest total cost of the microgrid, the first-stage objective function is the lowest initial investment cost in the microgrid, and the second-stage objective function is the lowest scheduling and operation cost in the microgrid;

[0222] The initial investment cost in the first-stage objective function is the equipment investment cost of the wind turbine, the distributed photovoltaic power station, the energy storage device and the micro gas turbine unit, as follows:

[0223] (27)

[0224] (28)

[0225] In the formula, is the maximum battery capacity of the energy storage device; C bat is the investment cost per unit power of the energy storage device; and respectively the maximum technical output and the investment cost per unit power of the i-th power equipment; F CRE (ri, Yi) is the annual capital recovery rate; r i and Y i respectively the discount rate and the discount year of the i-th power equipment, wherein the discount year of the energy storage device is the float charging life;

[0226] The dispatching operation cost in the second stage objective function is the operation cost of the micro gas turbine, the electricity purchase and sale cost of the micro-grid, and the maintenance cost of the equipment, as shown below:

[0227] (29)

[0228] (30)

[0229] (31)

[0230] (32)

[0231] In the formula, , C grid , C op is the operation cost of the micro gas turbine, the electricity purchase and sale cost of the micro-grid, and the maintenance cost of the equipment; is the fuel cost of the t period; P G,t is the real-time output of the micro gas turbine in the t period; k n,t and c n,t respectively the n-th pollutant emission amount and the treatment unit price of the micro gas turbine in the t period; c buy,t and c sell,t respectively represent the electricity purchase and sale unit price in the t period; and respectively the power purchased and sold by the micro-grid to the power grid in the t period; is the maintenance cost unit price of the energy storage device, the wind turbine generator, the distributed photovoltaic power station, and the micro gas turbine; P i,t indicates the output of the energy storage device, the wind turbine generator, the distributed photovoltaic power station, and the micro gas turbine in the t period;

[0232] The constraint conditions include the following constraints:

[0233] Among them, the power balance constraint is:

[0234] (33)

[0235] The micro gas turbine constraint is:

[0236] (34)

[0237] The energy storage device constraint is:

[0238] (35)

[0239] (36)

[0240] The state of charge constraint is:

[0241] (37)

[0242] (38)

[0243] The exchange power constraint is:

[0244] (39)

[0245] (40)

[0246] In the formula, P wt,t , P pv,t , and P G,t are the outputs of the wind turbine, the distributed photovoltaic power station, the energy storage device and the micro gas turbine unit at the t period, is the load at the t period; is the charging power of the energy storage device at the t period, ε ch and ε dis are the ratios of the maximum charging and discharging power of the energy storage to the maximum capacity of the battery; and respectively represent the minimum and maximum values of the output of the micro gas turbine unit at the t period; represents the charging and discharging state of the energy storage, SOC min and SOC max are the lower and upper limits of the state of charge of the battery, and the state of charge is not less than 20% to prevent the discharge depth from being too large and the battery life from being too fast; beg and SOC end represent that the state of charge of the battery is the same at the beginning and end of the dispatching period T, and respectively represent the upper limits of the power purchased and sold by the micro grid to the power grid; is an integer variable between 0 and 1, and its value is 1, which means that the micro grid purchases power from the power grid at the t period; represents the maximum capacity of the energy storage device.

[0247] In step (2b), the two-stage robust optimization model of power grid planning considering the uncertainty of electric vehicle output refers to: according to the selection cost of electric vehicles to each charging station, the charging station to which the electric vehicle goes is determined, and then the charging load is calculated to the distribution network; by changing the charging price, the electric vehicle is guided to go to the charging station close to the new energy source for charging, and the specific form is as follows:

[0248] A three-layer robust optimization model is established, and the objective function is as follows:

[0249] (41)

[0250] In the formula, D is a set of new energy output scenarios, and P(ξ) is the probability of the output of each scenario; represents the square of the voltage amplitude of node i at time t; is the square of the current amplitude from node i to node j at time t; represents the active power from node i to node j at time t; represents the active power from node i to node j at time t; represents the feasible region of the decision variable;

[0251] The constraint conditions are as follows:

[0252] Charging station selection constraint:

[0253] (42)

[0254] (43)

[0255] In the formula, is the comprehensive cost of the i th electric vehicle to the j th charging station at time t; is the charging price of the j th charging station at time t; is the expected waiting time of the j th charging station at time t; ω1, ω2 and ω3 are weight coefficients; is the lowest comprehensive cost; n is the number of charging stations, when =0, it represents that the vehicle i does not select the charging station j; when =1, it represents that the vehicle i selects the charging station j; M is an arbitrary large positive number; is the equivalent distance from the current position of the i th vehicle to the j th charging station at time t;

[0256] Electric vehicle charging state constraint:

[0257] (44)

[0258] (45)

[0259] (46)

[0260] wherein V i is the driving speed of the ith vehicle; T i represents the time when the owner generates the charging intention; Ts. i is the starting charging time of the electric vehicle; T ch,i is the charging duration of the electric vehicle; γ i = 1 indicates that the electric vehicle is in the charging state at t0, γ i = 0 indicates that it is not in the charging state; is the driving time of the ith vehicle at t; is the expected waiting time of the ith charging station at t;

[0261] Charging station charging price constraint:

[0262] (47)

[0263] Power flow balance constraint:

[0264] (48)

[0265] Charging station power balance constraint:

[0266] (49)

[0267] (50)

[0268] wherein k3and k4are proportional coefficients, is the electricity price of the charging station at t, is the charging price of the ith charging station at t; Ω a is the node set between the kth electric vehicle and the ith charging station, Ω b is the set of all nodes in the distribution network, Rijand Xijare the resistance and reactance values of branch ij, Pt ijand Qt ijrepresent the active and reactive power flows of branch ij, Pt i.Dand Qt i.Drepresent the active and reactive load sizes of node i at t, Pt i.enand Qt i.enrepresent the active and reactive power injections of the power source at node i at t; and represent the active and reactive power injections of the charging station at node i at t; Ptcs.jrepresents the active power output of charging station j at t, p c is the charging power of a single charging pile, N is the total number of charging vehicles in a day, represents the selection of electric vehicle i to charging station j, represents the charging state of electric vehicle i at t, and λ is the power factor of the charging pile.

[0269] In step (3b), the grid planning-oriented flexible load guiding mechanism refers to guiding the dispatchable flexible resources by using the price-based guiding mechanism, which is based on scientific and reasonable division of peak, flat and valley periods of the user load characteristics and formulating corresponding time-of-use electricity price to guide the user, combined with the load control, to achieve the purpose of peak clipping and valley filling.

[0270] The price-based guiding mechanism based on user-side energy storage price optimization refers to that the response characteristics of the user-side energy storage power station are embodied in that the energy storage device will try to "store less and release more". Guiding the user-side energy storage by using the time-of-use electricity price can reduce the user's own electricity cost, also can reduce the load peak-valley difference of the power system, improve the utilization rate of power transmission and distribution equipment, delay equipment investment, so as to realize win-win;

[0271] The electric vehicle charging load guiding mechanism based on space-time price optimization refers to that the electric vehicle charging load not only has certain randomness in time, but also shows great flow characteristics in space. The grid-connected electric vehicle is also used as a kind of mobile energy storage device to realize the reverse feeding of electric energy to the system. Based on the space-time response characteristics of the electric vehicle, the time-of-use electricity price can be used to guide the electric vehicle to participate in the peak clipping and valley filling in time and space, reduce the network loss, and improve the economy and reliability of the power grid;

[0272] The price-based guiding mechanism based on the response characteristics of the micro-grid system refers to that the micro-grid system and the main grid interact with each other through the time-of-use electricity price. When the micro-grid energy output is insufficient, the main grid is purchased at a low price. When the micro-grid energy output meets the load demand and has excess, the excess electricity is sold to the main grid at a high price to obtain benefits and reduce the economic cost of the micro-grid system.

[0273] The reward and punishment mechanism of the dispatchable new energy participating in the power grid dispatching refers to that the reward and punishment policy is used to guide the enthusiasm of the dispatchable new energy participating in peak shaving, which indirectly improves the consumption capacity of the power system to the new energy, effectively guarantees the stable operation of the power system, and takes into account the reduction of the peak shaving pressure of the power system and the interests of the new energy generator; the new energy manufacturer is guided to reasonably match the storage according to the actual situation, so as to reduce the peak shaving pressure of the power grid.

[0274] Firstly, compare the substation investment costs in Figure 2 and Figure 3 It can be seen that whether the dispatchable flexible resources are connected or not has no effect on the load partitioning and substation site selection and capacity determination. Since the total capacity of the micro-grid in the to-be-planned area is small, the change of substation cost is in a stepwise relationship with the system capacity, so the substation operation costs of the two schemes are the same. Compare Figure 3As can be seen from the line investment costs and network loss costs, the existence of microgrids leads to reverse power flow in the distribution network. Reverse power flow constraints are necessary during planning, increasing the total line length and investment costs. Line investment costs are positively correlated with microgrid capacity. However, when the reverse power flow generated by the microgrid does not exceed limits at any time, some loads can be directly powered by the microgrid, thus reducing the total system line length and lowering line investment costs. (Comparison) Figure 4 As can be seen from the line investment costs and network loss costs, increasing the capacity of schedulable flexible resources in the planned area has little impact on the network planning cost. This is because the original planning model has already considered the transmission margin of flexible resource lines, so the cost only increases slightly. After the schedulable flexible resource capacity is increased, the network loss cost is further reduced. Therefore, the access of schedulable flexible resources can effectively reduce the system operating cost.

[0275] Through the Figure 5 and Figure 6 Based on the above analysis, the integration of dispatchable flexible resources can reduce line network losses, lower system operating costs, and improve economic efficiency. However, attention should be paid to the impact of reverse power flow on investment costs. This impact mainly depends on the microgrid's output power and upstream load power. Therefore, when planning nodes containing microgrids, they should be placed as upstream as possible in the topology, or their upstream load capacity should be increased to prevent reverse power flow from exceeding limits. The type and capacity of dispatchable flexible resources have a stepwise impact on substation planning costs, requiring a certain cumulative effect to reduce planning costs. It is recommended that substation planning should be re-evaluated if the proportion of dispatchable flexible resources exceeds 20%.

[0276] In summary, the present application studies the influence characteristics and correlation of dispatchable flexible load and power grid planning, establishes and perfects the dispatchable flexible resource guiding mechanism, and explores the collaborative optimization technology of dispatchable flexible resource and power grid planning layout, so as to effectively promote the dispatchable flexible resource to participate in power grid operation and planning, fully play the value of dispatchable flexible load resource, reduce the capacity demand of power system power grid planning, and improve the utilization efficiency of power grid planning; the present application changes part of the load from 'rigidity' to 'flexibility' through time-of-use price and other control means, and the electricity consumption changes in the specified period or shifts between different periods according to the power grid demand, so as to effectively utilize the power grid interaction of flexible load to realize optimization and control; the large-scale access of electric vehicles, flexible load, energy storage system and the like to the power grid can realize peak clipping and valley filling through reasonable guiding of dispatchable flexible resource, and reduce the peak-valley difference of the power grid; the present application accesses the energy storage system and electric vehicle with'source-load' double attributes to the power grid as a power supply, discards the traditional power grid planning method based on artificial experience, proposes a new type of power grid planning model, reasonably improves the planning result, improves the utilization rate of equipment, reasonably utilizes the flexible resource to coordinate and accommodate the new energy generation represented by photovoltaic power generation and wind power generation, effectively alleviates the influence of new energy generation randomness and volatility, and improves the safety and stability of power grid operation.

Claims

1. A power grid planning capacity analysis method considering operation characteristics of dispatchable flexible resources, characterized in that: The method comprises the following steps in sequence: (1) analyzing the operation characteristics of the schedulable flexible resources: the schedulable flexible resources are divided into energy one-way interactive flexible resources and energy two-way interactive flexible resources, wherein the energy one-way interactive flexible resources include schedulable new energy and flexible load, and the energy two-way interactive flexible resources include energy storage devices, electric vehicles and microgrids; (2) identifying the schedulable flexible resources: according to the operation characteristics of each schedulable flexible resource, a two-stage robust optimization model of power grid planning considering the uncertainty of new energy output and a two-stage robust optimization model of power grid planning considering the uncertainty of electric vehicle output are established, the correlation degrees of the two models are analyzed, and then the key schedulable flexible resources are identified; (3) proposing the guiding mechanism for each type of schedulable flexible resource for power grid planning according to the operation characteristics of each schedulable flexible resource and the key schedulable flexible resources; (4) analyzing the influence on the power grid planning: according to the guiding mechanism for each type of schedulable flexible resource for power grid planning, schedulable flexible resources with different capacities are connected to the distribution network, and the schedulable flexible resources and the layout of the power grid planning are optimized in coordination, and finally the influence of the capacity on the power grid planning problem is analyzed. 2.The power grid planning capacity analysis method considering dispatchable flexible resource operating characteristics according to claim 1, characterized in that: The step (1) specifically comprises the following steps: (1a) analyzing the operation characteristics of the energy storage devices: due to the energy two-way flow characteristics of the energy storage devices, the energy storage devices can suppress the fluctuation of the new energy output, have millisecond-level fast, stable and accurate charging and discharging power regulation characteristics, and can improve the instantaneous, short-time and period balancing capability of the power system; (1b) analyzing the operation characteristics of the schedulable new energy: distributed installation can effectively improve the node voltage value and improve the power flow distribution, reduce the load shedding operation and improve the power supply reliability; there is a problem of overhigh voltage at the new energy access point or overload of the adjacent line, which affects the operation stability of the distribution network and reduces the power supply reliability; the operation has output uncertainty, and the start-stop and unstable power output of the new energy itself will cause voltage fluctuation and impact on the power supply voltage of the distribution network users; (1c) analyzing the operation characteristics of the electric vehicles: the electric vehicles have time and space uncertainty, the load growth is obvious, the peak-valley difference of the load is expanded, the difficulty of power grid control is increased, the capacity of the distribution transformer is expanded, and the cost of the power grid is increased; the energy of the electric vehicles can be two-way transferred, can be orderly charged and discharged to reduce the network loss of the distribution network, can participate in dispatching to improve the node voltage waveform of the distribution network, affect the power flow of the line, and further affect the line loss; (1d) analyzing the operation characteristics of the flexible load: the flexible load has energy storage characteristics, can reduce the load in the power consumption peak period, reduce the peak-valley difference of the power grid load, and orderly participate in dispatching to reduce the line loss of the power grid; the flexible load can reduce the load, reduce the maximum reactive load, and increase the critical voltage; orderly start and stop can reduce the line loss of the power grid and reduce the peak-valley difference. (1e) Analyzing the operation characteristics of the micro-grid: the micro-grid contains various schedulable flexible resources, and has the dual attributes of "source and load"; by introducing energy storage devices, the power tension during the peak electricity consumption period can be alleviated; the high penetration of new energy causes the impact of the micro-grid on the normal operation of the main grid, and the power supply to the "island" area is a powerful supplement to the traditional power supply form.

3. The method of claim 1, wherein the method further comprises: determining a capacity of the power grid based on the dispatchable flexible resource operating characteristics. The step (2) specifically comprises the following steps: (2a) Introducing the robustness indicator Γ wt and Γ pv The uncertainty of new energy output is characterized as its key operating characteristics. The two-stage robust optimization model of power grid planning considering the uncertainty of new energy output is established, aiming at the initial investment cost and operation and maintenance cost of power grid. The uncertainty scenario set of the wind turbine generator: ， The uncertainty scenario set of the photovoltaic power station: ， In the formula, is the allowable range of wind power and photovoltaic power output; is the number of wind turbine and photovoltaic turbine units; T is the total number of time periods; and are respectively the predicted output values of the wind turbine and the distributed photovoltaic power station in the t time period; and are respectively the actual output values of the wind turbine and the distributed photovoltaic power station in the t time period; and are respectively the fluctuation amounts of the wind turbine and the distributed photovoltaic power station in the t time period; and are respectively the maximum fluctuation amounts of the wind turbine and the distributed photovoltaic power station in the t time period; (2b) Taking the space-time uncertainty of the electric vehicle as a key operation characteristic, and using a normal distribution model to represent the characteristic, a two-stage robust optimization model of the power grid planning considering the uncertainty of the electric vehicle output is established, the model includes two charging modes of conventional charging and fast charging: Conventional charging: formula (3) and formula (4) are the charging and discharging charge models, formula (5) is a normal distribution model of the charging start time, and formula (6) is a normal distribution model of the daily driving distance: ， ， ， ， where E CCS (t) is the state of charge of the electric vehicle at time t; t CCS (t-1) is the state of charge of the electric vehicle at time t and t-1; t c is the actual charging duration, t d is the actual discharging duration; t max_c is the maximum charging duration, t max_d is the maximum discharging duration; E max is the maximum state of charge of the electric vehicle when fully charged; μ x and σ x are the mean and standard deviation of the charging start time , respectively, μ x = 17.6, σ x = 3.4; μ L and σ L are the mean and variance of the log of the daily driving distance L, lnL, respectively, μ L = 3.2, σ L = 0.88; is the normal distribution model expression of the charging start time; is the normal distribution model expression of the daily driving distance. Fast charging: formula (7), formula (8) and formula (9) are the M / G / k model of the fast charging user queue, and formula (10) and formula (11) are the charging model of the fast charging station: ， ， ， ， ， where t BCS is the charging time for fast charging users; is the rated capacity of fast charging users' batteries; B i,BCS is the remaining battery level of the ith fast charging user when arriving at the integrated station; is the rated charging power of fast charging devices; E T is the expectation of t BCS ; D T is the variance of t BCS ; P BCS (t) is the total charging power of the fast charging station at the tth time period; P i,BCS (t) is the charging power of the ith fast charging user at the tth time period; η BCS is the charging power of the fast charging devices of the fast charging station; ω i,BCS (t) is a binary variable indicating the charging status of the ith fast charging user at the tth time period, 1 if charging, otherwise 0; E(B i,BCS ) is the expectation of the remaining battery level of all fast charging users when arriving at the integrated station; D(B i,BCS ) is the variance of the remaining battery level of all fast charging users when arriving at the integrated station; (2c) Through the two-stage robust optimization model of the power grid planning considering the uncertainty of the new energy output and the two-stage robust optimization model of the power grid planning considering the uncertainty of the electric vehicle output, the data set of the power grid planning under multiple scenarios is obtained, and the correlation degree is calculated by using a data mining algorithm: the correlation degree between each cost X and Y under n scenarios is comprehensively analyzed by using the grey correlation theory, the correlation degree between each cost and the total cost is obtained, the correlation degree between the new energy output fluctuation and each cost under n scenarios is comprehensively analyzed by using the entropy weight method, and finally the correlation degree between the new energy output fluctuation and the total cost is obtained by comprehensive weighting, and it is identified whether the flexible resource is a key schedulable flexible resource.

4. The method of claim 1, wherein the method further comprises: determining a capacity of the power grid based on the dispatchable flexible resource operating characteristics. The step (3) specifically comprises the following steps: (3a) analyzing the diversified operation mode of the schedulable flexible resource; (3b) according to the analysis of the operation mode, response characteristic and flexible resource optimization scheduling model of the schedulable flexible resource, five typical schedulable flexible resource guiding mechanisms are proposed, including the flexible load guiding mechanism for power grid planning, the price type guiding mechanism based on user side energy storage price optimization, the electric vehicle charging load guiding mechanism based on space-time price optimization, the price type guiding mechanism based on the response characteristic of the micro-grid system, and the reward and punishment mechanism of the schedulable new energy participating in the power grid dispatching; (3c) analyzing the flexible load guiding mechanism for power grid planning: by constructing a peak, flat and valley period division model based on an improved boundary moving technology, adding constraint conditions, and taking the Deng index as the objective function to solve the optimal period division, a peak, flat and valley period division model based on an improved boundary moving technology is constructed, after the implementation of peak and valley time-of-use electricity price in the peak, flat and valley periods of the load curve, the user adjusts his own power consumption mode, and the power consumption in each period is described as: ， ， ， In the formula, , , respectively represent the electricity consumption before the time-of-use electricity price is implemented in the peak, flat, and valley periods; Q p0 , Q f0 , Q v0 respectively represent the electricity consumption in each period after the time-of-use electricity price is implemented; ΔQ p , ΔQ f , ΔQ v respectively represent the change in electricity consumption before and after the time-of-use electricity price is implemented, E is an electricity price elasticity matrix, λ pp , λ pf , λ pv respectively are self-elasticity coefficients of each period, λ fp , λ ff , λ fv respectively are cross-elasticity coefficients of any two periods; λ vp , λ vf , λ vv are the electricity consumptions in the peak, flat, and valley periods; The constraint conditions of the peak, flat and valley period division model based on the improved boundary moving technology are: (3c1) user benefit constraint: the user's electricity bill does not increase before and after the implementation of time-of-use electricity price; (3c2) Peak, flat, and valley period electricity price constraints: after the implementation of time-of-use electricity price, the peak period electricity price is greater than the flat period electricity price, and the flat period electricity price is greater than the valley period electricity price; (3c3) Power grid company benefit constraint: the implementation of time-of-use electricity price can reduce the investment cost of the power supply company, and will not reduce the overall benefit of the power supply company; (3c4) Marginal electricity price constraint: the valley period electricity price should not be lower than the marginal electricity price; The objective function of the peak, flat, and valley period division model based on the improved boundary movement technology is as follows: ， ， ， In the formula, p represents a decision variable; and a and β are weight coefficients of the objective function; and H is a penalty function item.

5. The method of claim 1, wherein the method further comprises: determining a capacity of the power grid based on the dispatchable flexible resource operating characteristics. The step (4) specifically comprises the following steps: (4a) The to-be-planned region is divided into three regions, three schemes are first set: scheme one considers that all the to-be-planned region is ordinary load; scheme two accesses the dispatchable new energy and energy storage device with a capacity of 10 MW in the three regions; scheme three accesses the dispatchable new energy and energy storage device with a capacity of 15 MW in the three regions, and considers that 10% of the ordinary load is flexible load and electric vehicle charging pile; since the substation capacity is directly related to the cost, a dispatchable flexible resource access substation fixed capacity matching index based on the cost is proposed to measure the influence of the access capacity of the dispatchable flexible resource on the substation planning, and the dispatchable flexible resource access substation fixed capacity matching index is as follows: ， In the formula, represents the constant volume matching index; C t is the substation planning cost considering the dispatchable flexible resource access; c t is the conventional substation planning cost without considering the dispatchable flexible resource access; (4b) The equivalent capacity of the dispatchable flexible resource is calculated, and the substation capacity-load ratio formula is updated according to the equivalent capacity; Objective function: ， where C is the total cost, including the planning cost and operation cost of distribution network, N T represents the number of substations; represents the number of network lines; C Tp (Si) is the substation investment cost, r is the discount rate, m is the substation depreciation period, C j is the cost of building a new power supply line, C rT (Si) is the substation operation and maintenance cost, n year is the simulation period; x i is a binary decision variable, with a value of 1 indicating that the ith line is selected, and 0 indicating that the ith line is not selected; x j is a binary decision variable, with a value of 1 indicating that the jth line is selected, and 0 indicating that the jth line is not selected; c loss is the unit network loss cost; ΔP i is the active power loss of the ith line; Capacity-load ratio formula: ， where R s represents the load ratio; S i represents the capacity of the i-th transformer; P max represents the peak load value of the area to be planned; m s is the number of substations; n is the number of transformers used by each substation; λ is the load coincidence rate; P mgeq is the equivalent capacity of the microgrid, including the distributed power output P dg and the microgrid flexible resource output Fl mg ; (4c) Dispatchable flexible resource and power grid planning layout collaborative optimization: a distribution network double-layer planning model considering flexible resource access is established, and the total investment and operation cost is taken as the objective function, the upper half of formula (21) is used for the upper layer model to plan the distribution network, and the lower half of formula (21) is used for the lower layer model to calculate the operation cost of the distribution network: ， In the formula, f0, f1 are objective functions of the upper model and the lower model respectively, wherein the upper model is an investment decision model, and the lower model is an operation scheduling model; x ov , x iv are decision variables of the upper model and the lower model respectively; x iv,s is a decision variable of the lower model under the s scenario; H(·) and G(·) correspond to constraint conditions of the upper model and the lower model respectively, including equality constraints and inequality constraints; Objective function: ， ， ， where C is the total cost, including planning investment cost C p and operation cost C r ; the first term of the investment planning cost is the substation investment cost, the second term is the grid construction planning cost, where C Tp (S i ) is the substation investment cost, m is the substation depreciation period, represents the line network loss cost; A line flexibility constraint condition is added to ensure the adequacy of the transmission capacity of the grid in the operation process of the flexible resource: ， Equation (25) represents the line flexibility constraint, where FL l FL represents the used flexibility adjustment capability of the l-th line, i.e., the transmission capacity already used by the line; β is the margin coefficient, whose value ranges from [0,1]; max To maximize the line's flexibility and adjustability; Since there is a nonlinear mathematical model in the grid planning part of the distribution network planning model, and the scale of the solution is large, a genetic algorithm is used for solution; in the process of using the genetic algorithm to solve the grid planning, the output grid structure may have a cross phenomenon, which is not allowed in the actual planning and operation process, and the fitness function after the line cross judgment is as follows: ， In the formula, F is the cost after considering the line crossing judgment, J is the number of regular constraint conditions; K j is the penalty function of the jth regular constraint condition in the planning process; a linecross is the line crossing judgment binary variable; K' is the penalty function corresponding to the line crossing.

6. The method of claim 3, wherein the method further comprises: In step (2a), the power grid planning two-stage robust optimization model considering the uncertainty of new energy output specifically refers to: a two-stage robust optimization model is established with the lowest total cost of the microgrid, the first stage objective function is the lowest initial investment cost in the microgrid, and the second stage objective function is the lowest dispatching and operation cost in the microgrid; Initial investment cost in the first stage objective function The equipment investment cost for the wind turbine, the distributed photovoltaic power station, the energy storage device and the micro gas turbine unit is as follows: （27） （28） wherein Cmaxis the maximum battery capacity of the energy storage device; C bat Cp is the investment cost per unit power of the energy storage device; and Fmaxand Fprespectively are the maximum technical output and the investment cost per unit power of the i-th power plant; F CRE (ri, Yi) is the annual capital recovery rate; r i and Y i are respectively the discount rate and the discount year of the i-th power plant, wherein the discount year of the energy storage device is the float life; Scheduling operating cost in the second stage objective function The operating cost of the micro gas turbine, the cost of buying and selling electricity of the micro-grid, the maintenance cost of the equipment, as follows: （29） （30） （31） （32） In the formula, , C grid , C op respectively are the operation cost of the micro gas turbine, the electricity purchase and sale cost of the micro-grid, and the maintenance cost of the equipment; is the fuel cost of the t period; P G,t is the real-time output of the micro gas turbine in the t period; k n,t and c n,t respectively are the discharge amount of the nth pollutant of the micro gas turbine in the t period and the treatment unit price; c buy,t and c sell,t respectively represent the electricity purchase and sale unit price in the t period; and respectively are the power purchased and sold by the micro-grid to the power grid in the t period; is the maintenance cost unit price of the energy storage device, the wind turbine generator, the distributed photovoltaic power station, and the micro gas turbine set; P i,t Presents the output of energy storage device, wind turbine generator, distributed photovoltaic power station and micro gas turbine generator in t period; The constraint conditions include the following constraints: The power balance constraint is as follows: （33） The micro gas turbine unit constraint is as follows: （34） The energy storage device constraint is as follows: （35） （36） The state of charge constraint is as follows: （37） （38） The exchange power constraint is as follows: （39） （40） In the formula, P wt,t P pv,t , and P G,t These represent the power outputs of wind turbines, distributed photovoltaic power stations, energy storage devices, and micro gas turbines during time period t. The load during time period t; ε represents the charging power of the energy storage device during time period t. ch and ε dis These are the ratios of the maximum charging and discharging power of the energy storage to the maximum capacity of the battery. and These represent the minimum and maximum output values ​​of the micro gas turbine unit during time period t, respectively. State of Charge (SOC) indicates the charge / discharge state of energy storage. min and SOC max These are the lower and upper limits of the battery's state of charge (SOC), respectively. To prevent excessive depth of discharge from prematurely shortening battery life, the SOC is specified to be no less than 20%. beg and SOC end This indicates that the state of charge of the battery remains the same throughout the scheduling period T. and These represent the upper limits of the power that the microgrid can purchase or sell to the distribution network; It is an integer variable from 0 to 1, where a value of 1 indicates that the microgrid purchases electricity from the distribution network during time period t; This indicates the maximum capacity of the energy storage device.

7. The method of claim 3, wherein the method further comprises: In step (2b), the two-stage robust optimization model for power grid planning that takes into account the uncertainty of electric vehicle output specifically refers to: determining the power station to which the electric vehicle should go based on the selection cost of each charging station, and then allocating the charging load to the distribution network; guiding electric vehicles to charge at power stations closer to renewable energy sources by changing the charging price, as follows: A three-layer robust optimization model is established, with the objective function as follows: （41） In the formula, D is a new energy output scene set, and P(ξ) is the probability of output of each scene. represents the square of the voltage amplitude of node i at time t; represents the square of the current amplitude from node i to j at time t; represents the active power from node i to j at time t; represents the active power from node i to j at time t; represents the feasible region of the decision variable; The constraints are as follows: Charging station selection constraints: （42） （43） wherein, is the comprehensive cost of the ith electric vehicle to the jth charging station at time t; is the charging price of the jth charging station at time t; is the expected waiting time of the jth charging station at time t; ω1, ω2 and ω3 are weight coefficients; is the lowest comprehensive cost; n is the number of charging stations, when = 0 represents that the vehicle i does not select the charging station j, when = 1 represents that the vehicle i selects the charging station j; M is an arbitrarily large positive number; is the equivalent distance of the ith vehicle from the current position to the jth charging station at time t; Electric vehicle charging state constraints: （44） （45） （46） In the formula, V i is the driving speed of the ith vehicle; T i represents the time when the owner generates the charging intention; Ts. i Tstart ch,i Tcharge γ i = 1 indicates that the electric vehicle is in the charging state at the time t0, γ i = 0 indicates that it is not in the charging state; is the travel time of the ith vehicle at the time t; is the expected waiting time of the ith charging station at the time t; Charging station price constraints: （47） Current balance constraints: （48） Power balance constraints for charging stations: （49） （50） where k3 and k4 are proportional coefficients, is the electricity price at time t for power plants, is the charging price of the i-th charging station at time t; Ω a is the set of nodes between the k-th number of electric vehicles and the i-th charging station, Ω b is the set of all nodes in the distribution network, Rij and Xij are the resistance and reactance values of branch ij, respectively, Ptij and Qtij represent the active and reactive power flow of branch ij, respectively, Pt i.D and Qt i.D represent the active and reactive load sizes of node i at time t, respectively, Pt i.en and Qt i.en represent the active and reactive power injection of the power source at node i at time t, respectively; and represent the active and reactive power injection of the charging station at node i at time t, respectively; Ptcs.j represents the active power output of charging station j at time t, p c is the charging power of a single charging pile, N is the total number of charging vehicles at all times of the day, represents the selection of charging station j by electric vehicle i, represents the charging state of electric vehicle i at time t, and λ is the power factor of the charging pile.

8. The method of claim 4, wherein the method further comprises: In step (3b), the flexible load guidance mechanism for power grid planning refers to: using a price-based guidance mechanism to guide dispatchable flexible resources. The basis is to scientifically and reasonably divide peak, flat and valley periods according to the user load characteristics and formulate corresponding time-of-use electricity prices to guide users. Combined with load control, the purpose of peak shaving and valley filling is achieved. The electricity price-based incentive mechanism based on user-side energy storage price optimization refers to the following: the response characteristics of user-side energy storage power stations are reflected in the energy storage devices' tendency to "store at low cost and generate at high cost"; using time-of-use pricing to guide user-side energy storage can reduce users' own electricity costs, reduce the peak-valley load difference of the power system, improve the utilization rate of power generation and transmission equipment, and delay equipment investment, thereby achieving a win-win situation; The electric vehicle charging load guidance mechanism based on time-of-use pricing optimization refers to the fact that electric vehicle charging load not only has a certain degree of randomness in time, but also exhibits significant flow characteristics in space; grid-connected electric vehicles are also used as mobile energy storage devices to feed back energy to the system; based on the time-of-use response characteristics of electric vehicles, time-of-use pricing can guide electric vehicles to participate in peak shaving and valley filling in time and space, while reducing grid losses and improving the economy and reliability of the power grid. The electricity price guidance mechanism based on the response characteristics of microgrid systems refers to guiding the energy interaction between the microgrid system and the large grid through time-of-use pricing. When the energy output of the microgrid is insufficient, it purchases electricity from the large grid when the electricity price is low. When the energy output of the microgrid meets the load demand and there is a surplus, the excess electricity is sold to the large grid. At this time, the electricity price is higher, which can generate revenue and reduce the economic cost of the microgrid system. The aforementioned reward and punishment mechanism for dispatchable renewable energy to participate in grid dispatch refers to: guiding the enthusiasm of dispatchable renewable energy to participate in peak shaving through reward and punishment policies, thereby indirectly improving the power system's ability to absorb renewable energy, effectively ensuring the stable operation of the power system, and balancing the reduction of power system peak shaving pressure with the interests of renewable energy generators; guiding renewable energy manufacturers to allocate storage reasonably according to their own actual situation, thereby reducing grid peak shaving pressure.

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