CN116014797A - Evaluation method for improving new energy acceptance capacity of distribution network - Google Patents

Abstract

The invention relates to an evaluation method for improving new energy admittance capacity of a distribution network, which comprises the following steps: step 1: constructing a mathematical model of the receiving capacity of the distributed power supply, adopting a lifting strategy, and planning and operating a power distribution network containing a high-proportion distributed power supply; step 2: constructing an intermittent energy power generation admission capacity evaluation model and a new energy admission capacity day-ahead evaluation model; step 3: constructing a new energy acceptance capacity assessment model based on multiple areas, and effectively assessing the installation scale of new energy acceptable under the requirement of a certain limit proportion; step 4: constructing a new energy maximum admitting capacity evaluation model based on regional power balance, and providing references for the installation scale and the construction time sequence of new energy in each region; step 5: a new energy scheduling method based on admittance is adopted to provide reference for the power grid to absorb and schedule new energy; the invention has the advantages of reducing the wind and light discarding phenomenon, accurately evaluating the receiving level, avoiding repeated iterative optimization and realizing new energy and operation control.

Description

Evaluation method for improving new energy acceptance capacity of distribution network

Technical Field

The invention belongs to the technical field of new energy, and particularly relates to an evaluation method for improving the new energy admitting capability of a distribution network.

Background

The current increasingly serious environmental pollution and energy crisis continuously promoted the strong development and utilization of distributed power Sources (DGs) such as photovoltaic, wind power and the like, which brings higher requirements to the full consumption and efficient utilization of the power distribution network DG, in addition, the new energy power generation such as wind power, photovoltaic and the like has the characteristics of uncertainty, fluctuation, opposite peak regulation and the like, challenges to the standby, peak regulation, power balance and the like of the system, is limited by the peak regulation, transportation and standby capacity of the power grid, and has serious wind and light discarding phenomena in partial areas, so that the new energy consumption becomes the urgent problem of the power grid scheduling operation; at present, wind power and photovoltaic are respectively calculated when new energy acceptance capacity is evaluated, and when the output of a traditional power supply is processed, the output of a single thermal power unit or a hydroelectric generating set is always used as a variable, so that the variables are more, the calculation is inconvenient, and the obtained result is only the total acceptance of a certain area; in addition, the grid-connected operation of the large-scale wind power provides challenges for the dispatching operation management of the power grid, and the dispatching and operation control of new energy needs to be enhanced in order to ensure the safe and stable operation of the power grid after the large-scale wind power is connected; therefore, it is very necessary to provide an evaluation method for improving the new energy acceptance capability of a distribution network, which reduces the wind and light rejection phenomenon, accurately evaluates the acceptance level, does not need repeated iterative optimization, and realizes new energy and operation control.

Disclosure of Invention

The invention aims to overcome the defects of the prior art, and provides an evaluation method for improving the new energy admittance capability of a distribution network, which is used for reducing the wind and light abandoning phenomenon, accurately evaluating the admittance level, avoiding repeated iterative optimization and realizing new energy and operation control.

The purpose of the invention is realized in the following way: an evaluation method for improving new energy receiving capability of a distribution network, comprising the following steps:

step 1: constructing a mathematical model of the receiving capacity of the distributed power supply, adopting a lifting strategy, and planning and operating a power distribution network containing a high-proportion distributed power supply;

step 2: constructing an intermittent energy power generation admitting capacity evaluation model and a new energy admitting capacity day-ahead evaluation model, and solving the new energy admitting fairness problem;

step 3: constructing a new energy acceptance capacity assessment model based on multiple areas, and effectively assessing the installation scale of new energy acceptable under the requirement of a certain limit proportion;

step 4: constructing a new energy maximum admitting capacity evaluation model based on regional power balance, and providing references for the installation scale and the construction time sequence of new energy in each region;

step 5: and a new energy scheduling method based on admission capacity is adopted to provide reference for the power grid to absorb and schedule new energy.

The mathematical model of the distributed power supply receiving capacity in the step 1 is specifically as follows: the mathematical model of the distributed power supply receiving capacity is as follows:

wherein x is a distributed power supply, i.e. DG access capacity decision vector, and x= [ x ] ₁ x ₂ ...x _n ] ^T The method comprises the steps of carrying out a first treatment on the surface of the n is the total node number of the system; e is a unit vector, e= [ 11..1.)] ^T The method comprises the steps of carrying out a first treatment on the surface of the X is a viable set of X; y is the node voltage and line current system state vector; A. b is the coefficient matrix of x, y separately; k is a system parameter vector; on the basis, an improved model considering the on-load tap adjustment, reactive compensation and inverter power factor control DG receiving capacity improvement technology is as follows: />

Where a is a coefficient vector of x, a= [ a ] ₁ a ₂ ...a _n ] ^T The method comprises the steps of carrying out a first treatment on the surface of the z is a state vector corresponding to the transformer transformation ratio, reactive compensation quantity and inverter power factor lifting technology; z is a viable set of Z; c is a coefficient matrix of z; when the uncertainty of DG and load is considered, the opportunity planning model is built as: />

Wherein P {.cndot. } is a probability vector; beta is an opportunity constraint vector and can reflect the qualification rate requirements of various operation indexes of the power distribution network.

The intermittent energy power generation acceptance assessment model in the step 2 specifically comprises the following steps: two optimization targets for intermittent energy power generation acceptance assessment are provided, namely, the intermittent energy power generation acceptance is maximum, the maximum power generation capacity of intermittent energy power generation is set to be the predicted power generation capacity, the minimum technical output is set to be 0, and accordingly, an objective function can be expressed as follows:

Wherein NT is the number of time periods in the evaluation period; NR is the number of intermittent energy generator sets; p (P) _i,t Generating a power generation plan for the unit i in a t period; secondly, setting the intermittent energy source power generation cost to be a value lower than 0 by taking the lowest total power generation cost of the system as a target, and representing intermittent energy source power generationThe environmental protection benefit, its objective function is:

wherein: NI is the number of units participating in scheduling in the system; c (C) _i,t Generating cost of the unit i in the period t; ST (ST) _i,t The starting cost of the unit i at the time t is set; when the maximum intermittent energy power generation acceptance is adopted as an evaluation target, the conventional energy power generation cost is not considered under the condition of meeting various constraint conditions; by adopting the system with the lowest overall power generation cost as a daily standard, the cost balance in the process of receiving intermittent energy power generation can be fully considered in evaluation, and the auxiliary service compensation mechanism with reasonable design is facilitated to mobilize enthusiasm of all parties, so that the intermittent energy receiving capacity is improved.

The new energy acceptance daily evaluation model in the step 2 specifically comprises the following steps:

step 2.1: determining a new energy admission capacity assessment area: according to the topological structure of the power grid, the new energy access area and the related new energy electric field information to be evaluated are automatically determined, and the main process is as follows: (1) reading new energy unit information from the power grid model; (2) according to the topological structure of the power grid, determining a station and a line from a new energy unit to a power grid load center or an outgoing tie line gateway; (3) recording the relation between the new energy unit and the plant station and the line; (4) repeating the step (1), and determining the relation between all new energy units of the power grid and the plant stations and the line; (5) determining a new energy unit and a relevant new energy unit according to the relationship between the new energy unit and the plant stations, the line relationship and the power grid partition information;

Step 2.2: partition day-ahead new energy acceptance assessment model: the method based on safety constraint economic dispatch, namely SCED model, is adopted to evaluate the day-ahead new energy admitting capability of the wind power and photovoltaic new energy access region, namely, on the basis of meeting the running conditions of a power grid and a conventional unit, the conventional unit cooperates with the new energy unit output adjustment of the new energy access region, and the safety limit of the evaluated new energy access region for admitting new energy is analyzed; the method comprises the following steps: 1) Evaluation target: for wind power and photovoltaic new energy access areas or systems, the aim of the daily new energy admission capacity assessment is to meet the requirement of a power gridOn the basis of load, power grid safety and unit operation constraint conditions, the assessment area or system receives the maximum power generation amount of new energy in the future day, and the objective function is as follows:

wherein G is _w A new energy unit collection platform in the evaluation area; g is a set of all new energy units in the system; n (N) _T For an evaluation period; p is p _w (g, t) is the maximum power output of the new energy unit g in the t period; 2) Constraint conditions: 2.1 New energy output constraint): in order to obtain the limit capability of safely receiving new energy in the evaluation area, the new energy unit output in the evaluation area should meet the maximum technical output constraint, namely: / >

In the method, in the process of the invention,

maximum technical output of the new energy unit g at the time t; for the new energy unit outside the evaluation area, the output force should meet the constraint of the new energy power predicted value, namely: />

In (1) the->

The new energy power predicted value of the new energy unit g at the time t is obtained; 2.2 System load balancing constraints): />

Wherein I is a conventional unit set except a new energy unit set; the system power generation caliber payload when L (t) is t; 2.3 Rotational reserve constraint:

in (1) the->

Is conventionalThe unit i rotates for standby at the upper part provided by the period t; />

A spare requirement for the system to rotate on the t period;r(i, t) rotating for standby under the supply of the conventional unit i in the period t;R(t) is the system's rotational standby demand at time t; 2.4 Conventional unit output constraint): p is p ^min (i,t)·U(i,t)≤p(i,t)≤p ^max (i, t). U (i, t) (10) wherein p ^min (i,t)、p ^max (i, t) are respectively the minimum and maximum technical output of the conventional unit i at the time t; u (i, t) is a running sign of the conventional unit i at the time t; 2.5 Conventional unit adjustment rate constraints: -delta _i ≤p(i,t)-p(i,t-1)≤Δ _i (11) Wherein, delta _i Maximum value of climbing rate per period for the conventional sail group i; 2.6 Branch and section flow constraints):

in (1) the->

The upper and lower limit values of the tidal current power of the branch or the section l are respectively; p is p _f (l, t) is l planning power flow at time t; 2.7 Partition backup constraint): / >

Wherein Ar is a spare partition; _Ar R、 _Ar R′the upper spinning reserve and the lower spinning reserve of the region are respectively provided.

The construction in the step 3 is based on a multi-region new energy admission capacity evaluation model, and specifically comprises the following steps:

step 3.1: objective function: the model takes the optimal system operation economy as a target, and the objective function comprises the fuel cost I of the thermal power unit, the start-stop cost H of the thermal power unit, the variable operation cost V of various units and circuits, the electricity-lack cost N and the pollutant emission cost E, namely: minZ=I+H+V+N+E (14), wherein,

wherein j is a thermal power generating unit; a, a _j Rated fuel consumption rate for the thermal power unit j; c _j The unit price of the fuel for the thermal power unit j; p (P) _i,j,k,t The continuous decision variable represents the output of the region i type j unit at the moment t of the typical scene k; />

Wherein d _j The method is the single start-stop cost of the thermal power generating unit j; u (U) _i,j,k,t As an integer Ce variable, representing the start-stop state of the power supply of the region i type j at the moment t of the typical scene k; />

In the formula, v _j 、vl _m The method comprises the steps of (1) changing operation cost coefficients of a unit j and a line m, wherein the type j comprises thermal power, hydroelectric power, pumped storage, energy storage, wind power and solar power generation, and when j is the thermal power unit, 0 represents unit shutdown and 1 represents unit startup; j is pumping energy storage and energy storage unit, 0 represents pumping/charging working condition, 1 represents charging/generating 1 working condition; l (L) _m,k,t The continuous decision variable represents the power flow of the mth power line at the moment t of the typical scene k; />

Wherein n is _i The unit electricity-shortage cost of the area i; y is Y _i,j,t As a continuous decision variable, representing the power shortage of the region i in the typical scene k; />

In the formula e _j Pollutant emission coefficients for unit power generation of unit j; z _j Cost per unit pollutant emission;

step 3.2: constraint conditions: 3.21 Resource constraint): the new energy acceptance capacity of each region is not more than the constraint of the available wind and light resources in the region:

wherein C is _wind,i Is a continuous decision variable, representingWind power receiving capability of region i; c (C) _solar,i Representing the solar power generation acceptance of region i; c (C) _max,wind,i 、C _max,solar,i The maximum loadable installed scale of the wind and light resources of the region i is respectively; 3.22 Power balance constraint): to reduce the calculation scale, the power level is updated according to the typical scene of the horizontal year, and from time of the typical scene k to time of t in the area i, the following are:

wherein P is _i,j,k,t The output of each unit in the region i is generated; l (L) _m,k,t Line output for a drop point or a start point located in region i; y is Y _i,j,t The electricity shortage condition of the area i is shown; 3.23 Upper and lower limit constraints of unit and line output: 3.231 For thermal power generating units): u (U) _i,j,k,t P _i,j,min ≤P _i,j,k,t ≤U _i,j,k,t P _i,j,max (22) Wherein j is a thermal power unit; p (P) _i,j,min 、P _i,j,max The lower limit and the upper limit of the output of the thermal power unit j are respectively; 3.232 For energy storage and pumped storage):

Wherein j is a pumped storage and energy storage unit; p (P) _i,j,genmin 、P _i,j,genmax The lower limit and the upper limit of the output of the power generation/discharge working condition of the unit j are respectively set; p (P) _i,j,pumpmin 、P _i,j,pumpmax The lower limit and the upper limit of the output of the pumping/charging working condition of the unit j are respectively set; 3.233 Wind power and solar power generation): for wind power and solar power generation, the maximum output at each moment is the product of the predicted available output per unit value at the moment and the scale of the nano-installation, namely: p is more than or equal to 0 _i,j,k,t ≤C _i P _{i,j,k,t,available} (24) Wherein j is a wind power and solar generator set; p (P) _{i,j,k,t,available} The available output per unit value at time t; c (C) _i Acceptable installation scale for region i; 3.234 Line(s): p (P) _lineminm ≤L _m,k,t ≤P _linemaxm (25) Wherein P is _lineminm 、P _linemaxm The upper and lower limits of the line output are respectively; 3.24 Continuous start-stop constraint of thermal power generating unit): for thermal power generating units, the requirements of the thermal power generating units are the mostSmall continuous on and minimum continuous off time constraints, namely: />

Wherein T is _j,on 、T _j,off The continuous opening time and the minimum continuous closing time of the dust pan of the thermal power generating unit j are respectively; 3.25 Unit and line climbing constraints): for various units and lines, climbing constraint needs to be met, namely, the power change cannot exceed the climbing rate in the normal running state, and the climbing rate limit can be broken through when the machine is started and stopped: RD (RD) _j ≤P _i,j,k,t -P _i,j,k,t-1 ≤RU _j (27) In the formula, RD _j 、RU _j The maximum climbing speed of the unit or the line j is respectively the maximum climbing speed of the unit or the line j; 3.26 Pumped storage and energy storage unit related constraints): when the pumped storage unit is operated, the water level of the upper and lower reservoirs needs to be maintained between the highest water level and the dead water level: / >

In which W is _u 、W _d Representing the water quantity of the upper reservoir and the lower reservoir respectively; w (W) _u,max 、W _u,min The maximum allowable water quantity and the minimum allowable water quantity of the upper reservoir are respectively; w (W) _d,max 、W _d,min The maximum allowable water quantity and the minimum allowable water quantity of the lower reservoir are respectively; at different moments, the water volumes of the upper reservoir and the lower reservoir have a dynamic coupling relationship, namely: />

Wherein lambda is _g 、λ _p Respectively generating and pumping conversion coefficients; for a daily/weekly cycle type unit, assuming that the day/week is divided into s time periods, the upper reservoir levels of the initial time period and the final time period should be kept consistent, i.e. +.>

In (1) the->

The initial and final time period water levels; 3.27 Hydroelectric generating set related constraints: the hydroelectric generating set can be divided into radial flow type and radial flow typeThe reservoir is adjustable, the radial water output is determined by natural water supply, the reservoir is not adjustable, and the modeling mode is similar to that of new energy; for reservoir-contained adjustable hydroelectric power, it can be equivalent to a combination of radial hydroelectric power and a pump-storage unit with reservoir on model, in which the equivalent radial hydroelectric power is determined by natural water supply, and P is used _i,j,k,t,hyo1 The equivalent pumped storage output is determined by the system requirement and the current water level of the reservoir, and is determined by P _i,j,k,t,hyo2 Expressed, the overall force of the adjustable hydropower can be expressed as: p (P) _i,j,k,t,hyo ＝P _i,j,k,t,hyo1 +P _i,j,k,t,hyo2 (31) There are the following operational constraints: p is more than or equal to 0 _i,j,k,t,hyo1 ≤P _i,j,k,t,exp (32) Wherein P is _i,j,k,t,exp Is a natural water supply condition; formula (32) represents that the equivalent radial flow hydroelectric power fraction needs to be less than the maximum expected power of natural running water; p is more than or equal to 0 _i,j,k,t,hyo1 +P _i,j,k,t,hyo2 ≤P _i,j,max (33) Formula (33) represents that the overall output force of the hydropower is greater than zero and less than the maximum rated output force of the unit; the equivalent pumped storage output part can be positive or negative, a positive value represents that water discharged from a reservoir participates in regulation, a negative value represents that natural water can be stored in the reservoir when the natural water needs to be discarded due to peak regulation, and the absolute value of the output of the part cannot exceed the rated capacity of the unit: -P _i,j,max ≤P _i,j,k,t,hyo2 ≤P _i,j,max (34) Meanwhile, the water level of the reservoir meets dynamic balance constraint, and cannot exceed the maximum and minimum water levels, which are the same as the formulas (28) to (30); 3.28 System standby constraints): rotational redundancy generally includes rotational redundancy required for the load and rotational redundancy to account for wind and solar renewable energy uncertainty: />

Wherein R is _i,j,k,t Spare capacity available at time t for each unit; beta _load Rotating the standby proportion for the load; beta _vg Rotating the standby proportion for new energy; 3.29 New energy electricity limit ratio constraint): the ratio of the wind and the light must be smaller than a given value:

wherein j is a wind-light unit; alpha is the maximum limit electric proportion.

The new energy maximum admitting ability evaluation model constructed in the step 4 based on regional power balance specifically comprises the following steps:

Step 4.1: modeling by a new energy maximum admitting capacity assessment method: the built model aims at the maximum value of new energy acceptable by the system, the whole area is divided into 13 areas, the wind power, photovoltaic, thermal power and hydropower installed capacity of each area are comprehensively considered by taking the single area as a unit, various types of power output are taken as a variable to participate in the power balance of the area, meanwhile, the transmission power of the connecting lines of each ground box and surrounding areas is taken as a variable to participate in the power balance of each area, so that each area is connected together through the connecting lines between the areas, the power balance of the whole area is realized, and the objective function of the model is as follows:

wherein p is _{i_pv} 、p _{i_wind} Photovoltaic and wind power output values of the region i are respectively; n is the number of divided areas;

step 4.2: model solving based on particle swarm optimization algorithm: and solving a model by adopting a particle swarm algorithm and combining a parallelizable adjustment mechanism, and obtaining an acceptable new energy maximum value through iterative optimization for a certain number of times.

The new energy maximum admittance capacity assessment method in the step 4.1 is used for modeling: constraints considered by the model in the calculation process include: (1) power balance constraints for each region:

Wherein N is the number of divided regions; p is p _{i_fire} 、p _{i_water} 、p _{i_pv} 、p _{i_wind} The thermal power, hydroelectric power, photovoltaic power and wind power output values of the region i are respectively; p is p _{i_load} Is the load of region i; />

For the work of region i and surrounding regionsRate exchange values; k (k) _i The number of transmission lines related to the region i and surrounding regions; (2) rotating spare capacity constraints: the system spare capacity comprises load spare, maintenance spare and accident spare, in addition, because of the uncertainty of new energy output, after the new energy output is added into the system power balance, the requirement on the system spare capacity is necessarily increased, namely: s is S _i ≥p _{imax_load} ×(l％+s％)+p _{i_pre} ×ω％+p _r (39) Wherein S is _i The spare capacity of the system needed by the region i; p is p _{imax_load} Predicting a maximum load for region i; l% is the load standby proportion; s% is the accident standby proportion; p is p _{i_pre} Predicting power for new energy; omega% is the requirement of predicted new energy output errors on various scenes; p is p _r The spare capacity is used for maintenance; (3) power supply output constraint in each region: the power output comprises thermal power, wind power, photovoltaic and hydroelectric power output of each region, wherein the thermal power output is output constraint after rotation standby is considered, and the hydroelectric power output is output according to a certain proportion of the installed capacity of the hydroelectric power of each region according to a calculation period: p is p _{i_down} ≤p _i ≤p _{i_up} (40) Wherein p is _i ＝[p _{i_fire} p _{i_water} p _{i_pv} p _{i_wind} ] ^T Power output vector for the ith area, p _{i_fire} 、p _{i_water} 、p _{i_pv} 、p _{i_wind} Respectively obtaining thermal power, hydroelectric power, photovoltaic power and wind power mountain power values of the region; p is p _{i_down} ＝[p _{id_fire} p _{id_water} p _{id_pv} p _{id_wind} ] ^T Lower limit of power supply output, p _{id_fire} 、p _{id_water} 、p _{id_pv} 、p _{id_wind} Respectively lower limit values of thermal power, hydroelectric power, optical power and wind power output; p is p _{i_up} ＝[p _{iu_fire} p _{iu_water} p _{iu_pv} p _{iu_wind} ] ^T Is the peak power upper limit vector, p _{iu_fire} 、p _{iu_water} 、p _{iu_pv} 、p _{iu_wind} The upper limit values of thermal power, hydroelectric power, photovoltaic power and wind power output are respectively set; (4) transmission line capacity constraints between regions: the transmission line comprises 38 lines between l3 ground matching areas, and the transmission capacity of all the lines is as followsThe following constraints: p is p _{id_line} ≤p _{i_line} ≤p _{iu_line} (41) Wherein->

Transmission line power delivery value vector, k, for region i and surrounding related regions _i The number of transmission lines related to the region i and the surrounding regions is i=n; p is p _{id_line} 、≤p _{iu_line} The lower limit and the upper limit vector of the transmission line capacity related to the region i and the surrounding region are respectively, and the total transmission line number is +.>

The new energy scheduling method based on the admission capacity in the step 5 specifically comprises the following steps:

step 5.1: medium-long term scheduling: 5.11 Power generation capacity prediction: a statistical analysis method is adopted, namely, a correlation is established according to actual generated energy, abandoned wind/photoelectric quantity and installed capacity of different new energy electric fields/stations in different histories, namely, the correlation is as follows: y is _t ＝ax _t +b (36) where y _t Generating power for a new energy electric field; x is x _t The electric field installed capacity is the new energy; a. b is an expression constant term; 5.12 Power generation schedule planning): according to the prediction result of the electric field generating capacity of the new energy, comprehensively considering the absorbing capacity of the power grid, if the receiving capacity of the power grid is insufficient, if the power grid is too serious, the conventional power supply is required to be rearranged to receive more new energy power, and the receiving capacity of the power grid is re-estimated according to a formula (46), and if the power grid is unavoidable, the receiving capacity of the power grid is distributed among the new energy electric fields according to a certain power limiting mode; 5.13 Electricity limit distribution mode): the new energy output limit is distributed in the following mode: pulling a loading capacity proportion equal ratio principle, an average allocation limit capacity principle, a limit principle from high to low according to electricity price, a limit principle from high to low according to power forecast accuracy, and a limit principle from high to low according to a test edge node;

step 5.2: short-term scheduling: 5.21 Power generation capacity prediction: the short-term power generation capacity is derived from short-term new energy power prediction, and a common power prediction method comprises a prediction based on digital weather prediction, a statistical prediction method represented by a time sequence method and a learning prediction technology mainly based on a neural network; 5.22 Grid acceptance capability estimation: the conventional power supply power generation plan is extracted, the tie line plan and the power limit are comprehensively considered, and the acceptance of the power grid can be estimated and obtained; 5.23 Short-term day schedule planning): analyzing and comparing the total network new energy admittance capacity according to the current power prediction result of the new energy electric field, carrying out new energy electricity limiting analysis, and obtaining a scheduling plan of each new energy electric field according to a certain electricity limiting distribution mode;

Step 5.3: ultra-short term scheduling: the scheduling flow and the calculation method are the same as short-term scheduling, and a certain electricity limiting distribution mode is adopted according to the real-time maximum admittance curve of the power grid and the power prediction result of the new energy source of the power grid, so that the ultra-short-term power generation scheduling control curve of the new energy source is adjusted in real time.

The invention has the beneficial effects that: in use, the invention considers various constraint and differentiation scenes in the actual operation of the distribution network, adopts the combination form of different lifting means to conduct targeted optimization control, and optimally configures and schedules flexible resources such as flexible adjustment power supply, energy storage and the like in the distribution network based on flexibility indexes, thereby effectively making up weak links of the system and maximally improving DG receiving capacity; the invention has the advantages of reducing the wind and light discarding phenomenon, accurately evaluating the receiving level, avoiding repeated iterative optimization and realizing new energy and operation control.

Drawings

FIG. 1 is a flow chart of the present invention.

FIG. 2 is a flow chart of intermittent energy source generating capacity assessment of the present invention.

FIG. 3 is a flow chart of the partition day-ahead new energy acceptance assessment method.

Fig. 4 is a schematic diagram of the solar day-ahead receiving capacity of new energy in Ningdong region of the present invention.

Fig. 5 is a schematic diagram of the capacity of the Wu Zhong area for receiving new energy before date according to the present invention.

Fig. 6 is a schematic diagram of the capacity of new energy day-ahead in the midrange of the invention.

Fig. 7 is a schematic diagram of the new energy day-ahead receiving capacity of the Ningxia region according to the present invention.

Fig. 8 is a diagram of the structure of the inter-regional link network frame of the present invention.

FIG. 9 is a flow chart of a particle swarm optimization algorithm according to the present invention.

FIG. 10 is a schematic view of an optimization curve of the present invention.

Fig. 11 is a flowchart of the admission capacity estimation of the present invention.

Fig. 12 is a new energy scheduling flow chart of the present invention.

Detailed Description

The invention is further described below with reference to the accompanying drawings.

Example 1

As shown in fig. 1-12, a method for evaluating the improvement of new energy receiving capability of a distribution network, the method comprises the following steps:

step 1: constructing a mathematical model of the receiving capacity of the distributed power supply, adopting a lifting strategy, and planning and operating a power distribution network containing a high-proportion distributed power supply;

step 2: constructing an intermittent energy power generation admitting capacity evaluation model and a new energy admitting capacity day-ahead evaluation model, and solving the new energy admitting fairness problem;

step 3: constructing a new energy acceptance capacity assessment model based on multiple areas, and effectively assessing the installation scale of new energy acceptable under the requirement of a certain limit proportion;

Step 4: constructing a new energy maximum admitting capacity evaluation model based on regional power balance, and providing references for the installation scale and the construction time sequence of new energy in each region;

step 5: and a new energy scheduling method based on admission capacity is adopted to provide reference for the power grid to absorb and schedule new energy.

The mathematical model of the distributed power supply receiving capacity in the step 1 is specifically as follows: the mathematical model of the distributed power supply receiving capacity is as follows:

in the method, in the process of the invention,x is the decision vector of distributed power supply, namely DG access capacity, and x= [ x ] ₁ x ₂ ...x _n ] ^T The method comprises the steps of carrying out a first treatment on the surface of the n is the total node number of the system; e is a unit vector, e= [ 11..1.)] ^T The method comprises the steps of carrying out a first treatment on the surface of the X is a viable set of X; y is the node voltage and line current system state vector; A. b is the coefficient matrix of x, y separately; k is a system parameter vector; on the basis, an improved model considering the on-load tap adjustment, reactive compensation and inverter power factor control DG receiving capacity improvement technology is as follows: />

Where a is a coefficient vector of x, a= [ a ] ₁ a ₂ ...a _n ] ^T The method comprises the steps of carrying out a first treatment on the surface of the z is a state vector corresponding to the transformer transformation ratio, reactive compensation quantity and inverter power factor lifting technology; z is a viable set of Z; c is a coefficient matrix of z; when the uncertainty of DG and load is considered, the opportunity planning model is built as: / >

Wherein P {.cndot. } is a probability vector; beta is an opportunity constraint vector and can reflect the qualification rate requirements of various operation indexes of the power distribution network.

In this embodiment, the promotion policy specifically includes the following aspects: (1) tap adjustment of on-load tap-changing transformer: the tap position of the on-load voltage regulating transformer is flexibly adjusted, the system voltage level can be quickly adjusted, the overvoltage problem during large-scale DG access can be effectively prevented, and the DG receiving capacity is further improved; (2) reactive compensation: when the high-permeability DG is connected into the power distribution network to cause overvoltage problem, inductive reactive power can be compensated by the static reactive power compensator to quickly reduce the voltage level, so that the DG receiving capacity can be improved; (3) inverter power factor control: the power factor control is carried out on the DG grid-connected inverter, reactive power is absorbed in the output peak period of the DG grid-connected inverter, namely, the DG grid-connected inverter operates at a lagged power factor, so that the overall voltage level is reduced, and the DG receiving capacity can be improved; (4) network reconstruction: the tide distribution is regulated through network topology optimization, so that the safe operation index of the system can be ensured to be maintained in a reasonable Fan Tong, and the DG admittance capacity is improved; (5) demand case response: the requirement side response based on electricity price or excitation can meet the requirements of translational property and broad width of the flexibility of the power distribution network, can effectively increase the net load of DG output peak time, reduces the out-of-limit risk of operation constraint, and is beneficial to improving the DG admittance capability; (6) energy storage technology: the energy storage participates in the power supply and demand balance adjustment of the power distribution network, so that the power permeability of the DG output peak time is effectively reduced, and the operation constraint is ensured not to exceed the limit; (7) voltage coordination control: the voltage coordination control can further optimize the voltage distribution of the system, wherein the cluster voltage coordination control effectively combines the advantages of centralized control and distributed control, can realize intra-cluster autonomous optimization and inter-cluster coordination optimization based on cluster division, has better voltage regulation performance, and can remarkably improve DG receiving capacity; (8) and (3) each transformation: the primary equipment of the power distribution network is transformed and upgraded, so that the current tolerance capacity of the primary equipment of the power distribution network can be effectively improved, and the DG receiving capacity is improved by relaxing short circuit current constraint.

The intermittent energy power generation acceptance assessment model in the step 2 specifically comprises the following steps: two optimization targets for intermittent energy power generation acceptance assessment are provided, namely, the intermittent energy power generation acceptance is maximum, the maximum power generation capacity of intermittent energy power generation is set to be the predicted power generation capacity, the minimum technical output is set to be 0, and accordingly, an objective function can be expressed as follows:

wherein NT is the number of time periods in the evaluation period; NR is the number of intermittent energy generator sets; p (P) _i,t Generating a power generation plan for the unit i in a t period; secondly, setting the intermittent energy source power generation cost to be a numerical value lower than 0 by taking the lowest total power generation cost of the system as a target, and representing the environmental protection benefit of the intermittent energy source power generation, wherein the objective function is as follows:

wherein: NI is the number of units participating in scheduling in the system; c (C) _i,t Generating cost of the unit i in the period t; ST (ST) _i,t The starting cost of the unit i at the time t is set; when the maximum intermittent energy power generation acceptance is adopted as an evaluation target, the conventional energy is not considered under the condition of meeting various constraint conditionsSource power generation cost; by adopting the system with the lowest overall power generation cost as a daily standard, the cost balance in the process of receiving intermittent energy power generation can be fully considered in evaluation, and the auxiliary service compensation mechanism with reasonable design is facilitated to mobilize enthusiasm of all parties, so that the intermittent energy receiving capacity is improved.

The new energy acceptance daily evaluation model in the step 2 specifically comprises the following steps:

step 2.1: determining a new energy admission capacity assessment area: according to the topological structure of the power grid, the new energy access area and the related new energy electric field information to be evaluated are automatically determined, and the main process is as follows: (1) reading new energy unit information from the power grid model; (2) according to the topological structure of the power grid, determining a station and a line from a new energy unit to a power grid load center or an outgoing tie line gateway; (3) recording the relation between the new energy unit and the plant station and the line; (4) repeating the step (1), and determining the relation between all new energy units of the power grid and the plant stations and the line; (5) determining a new energy unit and a relevant new energy unit according to the relationship between the new energy unit and the plant stations, the line relationship and the power grid partition information;

step 2.2: partition day-ahead new energy acceptance assessment model: the method based on safety constraint economic dispatch, namely SCED model, is adopted to evaluate the day-ahead new energy admitting capability of the wind power and photovoltaic new energy access region, namely, on the basis of meeting the running conditions of a power grid and a conventional unit, the conventional unit cooperates with the new energy unit output adjustment of the new energy access region, and the safety limit of the evaluated new energy access region for admitting new energy is analyzed; the method comprises the following steps: 1) Evaluation target: the method is characterized in that wind power and photovoltaic new energy are accessed into a region or a system, the aim of the daily new energy admittance capability assessment is to enable the assessment region or the system to accept the maximum new energy generating capacity in the future day on the basis of meeting the power grid load, the power grid safety and the unit operation constraint conditions, and the objective function is as follows:

Wherein G is _w A new energy unit collection platform in the evaluation area; g is a set of all new energy units in the system; n (N) _T For an evaluation period; p is p _w (gT) is the maximum power output of the new energy unit g in the period t; 2) Constraint conditions: 2.1 New energy output constraint): in order to obtain the limit capability of safely receiving new energy in the evaluation area, the new energy unit output in the evaluation area should meet the maximum technical output constraint, namely: />

In the method, in the process of the invention,

maximum technical output of the new energy unit g at the time t; for the new energy unit outside the evaluation area, the output force should meet the constraint of the new energy power predicted value, namely: />

In (1) the->

The new energy power predicted value of the new energy unit g at the time t is obtained; 2.2 System load balancing constraints): />

Wherein I is a conventional unit set except a new energy unit set; the system power generation caliber payload when L (t) is t; 2.3 Rotational reserve constraint:

in (1) the->

The upper rotation provided for the conventional unit i in the period t is reserved; />

A spare requirement for the system to rotate on the t period;r(i, t) rotating for standby under the supply of the conventional unit i in the period t;R(t) is the system's rotational standby demand at time t; 2.4 Conventional unit output constraint): p is p ^min (i,t)·U(i,t)≤p(i,t)≤p ^max (i,t) U (i, t) (10) where p ^min (i,t)、p ^max (i, t) are respectively the minimum and maximum technical output of the conventional unit i at the time t; u (i, t) is a running sign of the conventional unit i at the time t; 2.5 Conventional unit adjustment rate constraints: -delta _i ≤p(i,t)-p(i,t-1)≤Δ _i (11) Wherein, delta _i Maximum value of climbing rate per period for the conventional sail group i; 2.6 Branch and section flow constraints):

in (1) the->

The upper and lower limit values of the tidal current power of the branch or the section l are respectively; p is p _f (l, t) is l planning power flow at time t; 2.7 Partition backup constraint): />

Wherein Ar is a spare partition; _Ar R、R′ _Ar respectively performing upward rotation and downward rotation for the area;

in this embodiment, the daily new energy admission capacity assessment is to assess the maximum output of new energy received by each new energy access region and system according to the new energy access region, and provide reference basis for daily, daily planning and scheduling operation, and the regional daily new energy admission capacity assessment flow specifically includes: (1) determining a wind power maximum access analysis area object, and determining a wind power access area to be evaluated and associated wind turbines according to a power grid topology model and a wind power plant position; (2) calling a static security check sensitivity service module to calculate sensitivity data of the monitoring element; (3) according to wind power prediction data, the wind power output wind receiving power prediction value of the wind power outside the evaluation area is fixed for each evaluation analysis area or system, the wind power output in the evaluation area is adjustable, and a conventional unit in the system is matched with wind power output adjustment; evaluating the maximum new energy admission electric quantity and the maximum admission curve of the wind power access area of the model according to the regional day-ahead new energy admission capacity; (4) sequentially repeating the step (3) for each wind power access area to be evaluated; (5) outputting and displaying the maximum new energy receiving electric quantity and the maximum receiving curve of each wind power access area and system.

The construction in the step 3 is based on a multi-region new energy admission capacity evaluation model, and specifically comprises the following steps:

step 3.1: objective function: the model takes the optimal system operation economy as a target, and the objective function comprises the fuel cost I of the thermal power unit, the start-stop cost H of the thermal power unit, the variable operation cost V of various units and circuits, the electricity-lack cost N and the pollutant emission cost E, namely: minZ=I+H+V+N+E (14), wherein,

wherein j is a thermal power generating unit; a, a _j Rated fuel consumption rate for the thermal power unit j; c _j The unit price of the fuel for the thermal power unit j; p (P) _i,j,k,t The continuous decision variable represents the output of the region i type j unit at the moment t of the typical scene k; />

Wherein d _j The method is the single start-stop cost of the thermal power generating unit j; u (U) _i,j,k,t As an integer Ce variable, representing the start-stop state of the power supply of the region i type j at the moment t of the typical scene k; />

In the formula, v _j 、vl _m The method comprises the steps of (1) changing operation cost coefficients of a unit j and a line m, wherein the type j comprises thermal power, hydroelectric power, pumped storage, energy storage, wind power and solar power generation, and when j is the thermal power unit, 0 represents unit shutdown and 1 represents unit startup; j is pumping energy storage and energy storage unit, 0 represents pumping/charging working condition, 1 represents charging/generating 1 working condition; l (L) _m,k,t The continuous decision variable represents the power flow of the mth power line at the moment t of the typical scene k;

wherein n is _i The unit electricity-shortage cost of the area i; y is Y _i,j,t As a continuous decision variable, representing the power shortage of the region i in the typical scene k; />

In the formula e _j Pollutant emission coefficients for unit power generation of unit j; z _j Cost per unit pollutant emission;

step 3.2: constraint conditions: 3.21 Resource constraint): the new energy acceptance capacity of each region is not more than the constraint of the available wind and light resources in the region:

wherein C is _wind,i The wind power acceptance capacity of the region i is represented by a continuous decision variable; c (C) _solar,i Representing the solar power generation acceptance of region i; c (C) _max,wind,i 、C _max,solar,i The maximum loadable installed scale of the wind and light resources of the region i is respectively; 3.22 Power balance constraint): to reduce the calculation scale, the power level is updated according to the typical scene of the horizontal year, and from time of the typical scene k to time of t in the area i, the following are: />

Wherein P is _i,j,k,t The output of each unit in the region i is generated; l (L) _m,k,t Line output for a drop point or a start point located in region i; y is Y _i,j,t The electricity shortage condition of the area i is shown; 3.23 Upper and lower limit constraints of unit and line output: 3.231 For thermal power generating units): u (U) _i,j,k,t P _i,j,min ≤P _i,j,k,t ≤U _i,j,k,t P _i,j,max (22) Wherein j is a thermal power unit; p (P) _i,j,min 、P _i,j,max The lower limit and the upper limit of the output of the thermal power unit j are respectively; 3.232 For energy storage and pumped storage):

Wherein j is a pumped storage and energy storage unit; p (P) _i,j,genmin 、P _i,j,genmax The lower limit and the upper limit of the output of the power generation/discharge working condition of the unit j are respectively set; p (P) _i,j,pumpmin 、P _i,j,pumpmax The lower limit and the upper limit of the output of the pumping/charging working condition of the unit j are respectively set; 3.233 Wind power and solar power generation): against windThe maximum output of each moment of electricity and solar energy generation is the product of the predicted available output per unit value and the acceptable installation scale, namely: p is more than or equal to 0 _i,j,k,t ≤C _i P _{i,j,k,t,available} (24) Wherein j is a wind power and solar generator set; p (P) _{i,j,k,t,available} The available output per unit value at time t; c (C) _i Acceptable installation scale for region i; 3.234 Line(s): p (P) _lineminm ≤L _m,k,t ≤P _linemaxm (25) Wherein P is _lineminm 、P _linemaxm The upper and lower limits of the line output are respectively; 3.24 Continuous start-stop constraint of thermal power generating unit): for thermal power generating units, the minimum continuous opening and minimum continuous closing time constraint needs to be met, namely: />

Wherein T is _j,on 、T _j,off The continuous opening time and the minimum continuous closing time of the dust pan of the thermal power generating unit j are respectively; 3.25 Unit and line climbing constraints): for various units and lines, climbing constraint needs to be met, namely, the power change cannot exceed the climbing rate in the normal running state, and the climbing rate limit can be broken through when the machine is started and stopped: RD (RD) _j ≤P _i,j,k,t -P _i,j,k,t-1 ≤RU _j (27) In the formula, RD _j 、RU _j The maximum climbing speed of the unit or the line j is respectively the maximum climbing speed of the unit or the line j; 3.26 Pumped storage and energy storage unit related constraints): when the pumped storage unit is operated, the water level of the upper and lower reservoirs needs to be maintained between the highest water level and the dead water level: / >

In which W is _u 、W _d Representing the water quantity of the upper reservoir and the lower reservoir respectively; w (W) _u,max 、W _u,min The maximum allowable water quantity and the minimum allowable water quantity of the upper reservoir are respectively; w (W) _d,max 、W _d,min The maximum allowable water quantity and the minimum allowable water quantity of the lower reservoir are respectively; at different moments, the water volumes of the upper reservoir and the lower reservoir have a dynamic coupling relationship, namely: />

Wherein lambda is _g 、λ _p Respectively generating and pumping conversion coefficients; for a daily/weekly cycle type unit, assuming that the day/week is divided into s time periods, the upper reservoir levels of the initial time period and the final time period should be kept consistent, i.e. +.>

In (1) the->

The initial and final time period water levels; 3.27 Hydroelectric generating set related constraints: the hydroelectric generating set can be divided into two types, namely radial flow type water output is determined by natural water supply and is not adjustable, and the modeling mode is similar to that of new energy; for reservoir-contained adjustable hydroelectric power, it can be equivalent to a combination of radial hydroelectric power and a pump-storage unit with reservoir on model, in which the equivalent radial hydroelectric power is determined by natural water supply, and P is used _i,j,k,t,hyo1 The equivalent pumped storage output is determined by the system requirement and the current water level of the reservoir, and is determined by P _i,j,k,t,hyo2 Expressed, the overall force of the adjustable hydropower can be expressed as: p (P) _i,j,k,t,hyo ＝P _i,j,k,t,hyo1 +P _i,j,k,t,hyo2 (31) There are the following operational constraints: p is more than or equal to 0 _i,j,k,t,hyo1 ≤P _i,j,k,t,exp (32) Wherein P is _i,j,k,t,exp Is a natural water supply condition; formula (32) represents that the equivalent radial flow hydroelectric power fraction needs to be less than the maximum expected power of natural running water; p is more than or equal to 0 _i,j,k,t,hyo1 +P _i,j,k,t,hyo2 ≤P _i,j,max (33) Formula (33) represents that the overall output force of the hydropower is greater than zero and less than the maximum rated output force of the unit; the equivalent pumped storage output part can be positive or negative, a positive value represents that water discharged from a reservoir participates in regulation, a negative value represents that natural water can be stored in the reservoir when the natural water needs to be discarded due to peak regulation, and the absolute value of the output of the part cannot exceed the rated capacity of the unit: -P _i,j,max ≤P _i,j,k,t,hyo2 ≤P _i,j,max (34) Meanwhile, the water level of the reservoir meets dynamic balance constraint, and cannot exceed the maximum and minimum water levels, which are the same as the formulas (28) to (30); 3.28 System standbyConstraint: rotational redundancy generally includes rotational redundancy required for the load and rotational redundancy to account for wind and solar renewable energy uncertainty: />

Wherein R is _i,j,k,t Spare capacity available at time t for each unit; beta _load Rotating the standby proportion for the load; beta _vg Rotating the standby proportion for new energy; 3.29 New energy electricity limit ratio constraint): the ratio of the wind and the light must be smaller than a given value:

wherein j is a wind-light unit; alpha is the maximum limit proportion;

in this embodiment, the evaluation flow for constructing the multi-region new energy admission capacity-based evaluation model is as follows: (1) dig the input: inputting system power supply and power grid planning information except wind power and solar power generation installation in the horizontal year: the system comprises load prediction information, various power installation and layout, cross-region power flow direction and scale, technical operation parameters of each unit, line and section operation parameters, new energy output characteristics and maximum electricity limiting proportion information; (2) new energy admission capacity calculation: the method comprises the steps of comprehensively considering uncertainty of new energy output, operation characteristics of a service type power supply, optimization configuration of peak regulation capacity of each region, cross-regional power exchange optimization, unit combination and economic scheduling constraint factors, constructing a multi-region new energy consumption capacity assessment model, and calling CPLEX to perform optimization solving calculation to obtain new energy acceptance capacity of each region and a system operation simulation result; (3) load tracking capability and frequency stability checking: further checking the system optimization result through frequency stability analysis, determining the influence of wind power and other randomness and intermittent power supply development and digestion schemes on the system frequency stability, and evaluating the adaptability of the power system frequency stability; if the check does not pass, the upper limit of the new energy admittance capacity of the area is reduced according to a certain step length, and the step (2) is returned to, and the new energy admittance capacity of each area is recalculated; (4) and (3) result statistics: outputting wind power and solar power generation receiving capacity of each region in the system; calculating and outputting new energy out-feed and local digestion proportions in the transmitting end region, and new energy in-feed and local digestion proportions in the receiving end region; the output system corresponds to the operation condition under the optimal admission capacity, and comprises the following steps: and the new energy power limiting proportion of each area and the running condition of the Grignard machine set.

The new energy maximum admitting ability evaluation model constructed in the step 4 based on regional power balance specifically comprises the following steps:

step 4.1: modeling by a new energy maximum admitting capacity assessment method: the built model aims at the maximum value of new energy acceptable by the system, the whole area is divided into 13 areas, the wind power, the photovoltaic power, the thermal power and the hydropower installed capacity of each area are comprehensively considered by taking the single area as a unit, various types of power output are taken as a variable to participate in the power balance of the areas, meanwhile, the transmission power of the connecting lines of each ground box and surrounding areas is taken as a variable to participate in the power balance of each area, so that each area is connected together through the connecting lines between the areas, the power balance of the whole area is realized, and as shown in fig. 8, the objective function of the model is as follows:

wherein p is _{i_pv} 、p _{i_wind} Photovoltaic and wind power output values of the region i are respectively; n is the number of divided areas;

step 4.2: model solving based on particle swarm optimization algorithm: and solving a model by adopting a particle swarm algorithm and combining a parallelizable adjustment mechanism, and obtaining an acceptable new energy maximum value through iterative optimization for a certain number of times.

The new energy maximum admittance capacity assessment method in the step 4.1 is used for modeling: constraints considered by the model in the calculation process include: (1) power balance constraints for each region:

Wherein N is the number of divided regions; p is p _{i_fire} 、p _{i_water} 、p _{i_pv} 、p _{i_wind} The thermal power, hydroelectric power, photovoltaic power and wind power output values of the region i are respectively; p is p _{i_load} Is the load of region i; />

Exchanging values for power of the region i and surrounding regions; k (k) _i The number of transmission lines related to the region i and surrounding regions; (2) rotating spare capacity constraints: the system spare capacity comprises load spare, maintenance spare and accident spare, in addition, because of the uncertainty of new energy output, after the new energy output is added into the system power balance, the requirement on the system spare capacity is necessarily increased, namely: s is S _i ≥p _{imax_load} ×(l％+s％)+p _{i_pre} ×ω％+p _r (39) Wherein S is _i The spare capacity of the system needed by the region i; p is p _{imax_load} Predicting a maximum load for region i; l% is a load standby proportion, and the general value is 2% -5%; s% is the accident standby proportion, and the general value is 5% -10%; p is p _{i_pre} Predicting power for new energy; omega% is the requirement of predicted new energy output errors on various scenes; p is p _r The spare capacity is used for maintenance; (3) power supply output constraint in each region: the power output comprises thermal power, wind power, photovoltaic and hydroelectric power output of each region, wherein the thermal power output is output constraint after rotation standby is considered, and the hydroelectric power output is output according to a certain proportion of the installed capacity of the hydroelectric power of each region according to a calculation period: p is p _{i_down} ≤p _i ≤p _{i_up} (40) Wherein p is _i ＝[p _{i_fire} p _{i_water} p _{i_pv} p _{i_wind} ] ^T Power output vector for the ith area, p _{i_fire} 、p _{i_water} 、p _{i_pv} 、p _{i_wind} Respectively obtaining thermal power, hydroelectric power, photovoltaic power and wind power mountain power values of the region; p is p _{i_down} ＝[p _{id_fire} p _{id_water} p _{id_pv} p _{id_wind} ] ^T Lower limit of power supply output, p _{id_fire} 、p _{id_water} 、p _{id_pv} 、p _{id_wind} Respectively lower limit values of thermal power, hydroelectric power, optical power and wind power output; p is p _{i_up} ＝[p _{iu_ fire} p _{iu_water} p _{iu_pv} p _{iu_wind} ] ^T Is the peak power upper limit vector, p _{iu_fire} 、p _{iu_water} 、p _{iu_pv} 、p _{iu_wind} The upper limit values of thermal power, hydroelectric power, photovoltaic power and wind power output are respectively set; (4) transmission line capacity constraints between regions: the transmission line comprises 38 lines between l3 ground-matched regions, and the transmission capacity of all the lines meets the following constraint: p is p _{id_line} ≤p _{i_line} ≤p _{iu_line} (41) In which, in the process,

transmission line power delivery value vector, k, for region i and surrounding related regions _i The number of transmission lines related to the region i and the surrounding regions is i=n; p is p _{id_line} 、≤p _{iu_line} The lower limit and the upper limit vector of the transmission line capacity related to the region i and the surrounding region are respectively, and the total transmission line number is +.>

In this embodiment, the model solving based on the particle swarm optimization algorithm in step 4.2 specifically includes the following steps:

step 4.21: particle swarm optimization algorithm: the particle swarm optimization algorithm is put forward by the prey behavior of the bird swarm, and the particle speed update formula is as follows:

the particle location update formula is:

wherein V is _i ＝[V _i1 ,V _i2 ,...,V _in ]Is the velocity of particle i; x is X _i ＝[X _i1 ,X _i2 ,...,X _in ]Is the position of particle i; omega is an inertial weight, and the searching capacity of the particles in a local and global range is controlled by changing the omega value; c ₁ 、c ₂ Is a learning factor; r is (r) ₁ 、r ₂ A random number between 0 and 1, p _besti Historically best location for particle i; g _best The best location for the global particle history;

step 4.22: parallelizable adjustment mechanism handles equality constraintsConditions are as follows: the power balance between regions relates to the equality constraint problem, and a parallelizable adjustment mechanism is introduced to solve the equality constraint problem in a model, specifically: floating elements of the solution vector which are found by the particles in the iterative process and do not meet the equality constraint in a search range, wherein the floating value is determined by the degree of violating the equality constraint and the looseness of each dimensional variable, and the looseness is the difference value between the current value and the upper/lower limit value Fan Dun of the current value; the specific description is as follows: for (k) _i +4) dimensional variable

The difference between the mountain and the upper/lower limit is calculated according to the formula (44): />

When->

When not feasible solution vector H _i The degree of violation of the equality constraint is +.>

The floating value +.>

The solution after parallelizable adjustment is obtained as: />

This solution necessarily satisfies the constraint of the equation, and a flow chart for solving the model using the particle swarm algorithm is shown in fig. 9.

The new energy scheduling method based on the admission capacity in the step 5 specifically comprises the following steps:

Step 5.1: medium-long term scheduling: 5.11 Power generation capacity prediction: a statistical analysis method is adopted, namely, a correlation is established according to actual generated energy, abandoned wind/photoelectric quantity and installed capacity of different new energy electric fields/stations in different histories, namely, the correlation is as follows: y is _t ＝ax _t +b (46), where y _t Generating power for a new energy electric field; x is x _t The electric field installed capacity is the new energy; a. b is an expression constant term; 5.12 Power generation schedule planning): according to the prediction result of the electric field generating capacity of the new energy, comprehensively considering the absorbing capacity of the power grid, if the receiving capacity of the power grid is insufficient, if the power grid is too serious, the conventional power supply is required to be rearranged to receive more new energy power, and the receiving capacity of the power grid is re-estimated according to a formula (46), and if the power grid is unavoidable, the receiving capacity of the power grid is distributed among the new energy electric fields according to a certain power limiting mode; 5.13 Electricity limit distribution mode): the new energy output limit is distributed in the following mode: pulling a loading capacity proportion equal ratio principle, an average allocation limit capacity principle, a limit principle from high to low according to electricity price, a limit principle from high to low according to power forecast accuracy, and a limit principle from high to low according to a test edge node;

step 5.2: short-term scheduling: 5.21 Power generation capacity prediction: the short-term power generation capacity is derived from short-term new energy power prediction, and a common power prediction method comprises a prediction based on digital weather prediction, a statistical prediction method represented by a time sequence method and a learning prediction technology mainly based on a neural network; 5.22 Grid acceptance capability estimation: the conventional power supply power generation plan is extracted, the tie line plan and the power limit are comprehensively considered, and the acceptance of the power grid can be estimated and obtained; 5.23 Short-term day schedule planning): analyzing and comparing the total network new energy admittance capacity according to the current power prediction result of the new energy electric field, carrying out new energy electricity limiting analysis, and obtaining a scheduling plan of each new energy electric field according to a certain electricity limiting distribution mode;

Step 5.3: ultra-short term scheduling: the scheduling flow and the calculation method are the same as short-term scheduling, and a certain electricity limiting distribution mode is adopted according to the real-time maximum admittance curve of the power grid and the power prediction result of the new energy source of the power grid, so that the ultra-short-term power generation scheduling control curve of the new energy source is adjusted in real time.

In use, the invention considers various constraint and differentiation scenes in the actual operation of the distribution network, adopts the combination form of different lifting means to conduct targeted optimization control, and optimally configures and schedules flexible resources such as flexible adjustment power supply, energy storage and the like in the distribution network based on flexibility indexes, thereby effectively making up weak links of the system and maximally improving DG receiving capacity; the new energy acceptance day-ahead evaluation model comprehensively considers the topology structure and the running state of the distribution network, evaluates the maximum capability of the new energy acceptance day-ahead of the distribution network in the new energy access area one by one, provides references for day-ahead and day-ahead planning and scheduling operation, effectively solves the problem of new energy acceptance fairness in the day-ahead power generation plan, can effectively avoid the contradiction of the factory network generated in the new energy electricity limiting process under the condition that the new energy cannot be completely consumed, improves the practicability and the reliability of the day-ahead scheduling plan, and provides reliable guarantee for safe and stable operation of the distribution network; according to the multi-region new energy acceptance ability evaluation model, repeated iteration production simulation optimizing calculation is not needed; the installation scale of each region which can receive new energy is not a fixed value, but is simultaneously taken into optimization as a decision variable, namely, operation simulation and the joint optimization of each region which can bear the installation scale of the new energy are carried out, so that the system can obtain the maximum installation scale of the new energy which can be received by the system, the system running state and the new energy consumption condition under the condition of corresponding maximum receiving capacity under the condition of considering a series of operation constraints such as new energy heat, unit combination and the like; after each time of obtaining a planning result, checking the system frequency stability; the new energy maximum admittance capacity assessment model based on regional power balance is based on the regional power balance, and takes the transmission power constraint of the connecting lines among the regions into consideration, so that the new energy admittance capacity of each region is obtained, and it is a line for limiting the improvement of the new energy admittance capacity in the transmission lines among the regions when the maximum new energy admittance capacity is obtained, and is used for referencing the future power grid planning construction and the installation scale of the new energy; the invention has the advantages of reducing the wind and light discarding phenomenon, accurately evaluating the receiving level, avoiding repeated iterative optimization and realizing new energy and operation control.

Claims (10)

1. An evaluation method for improving new energy receiving capacity of a distribution network is characterized by comprising the following steps of: the method comprises the following steps:

step 1: constructing a mathematical model of the receiving capacity of the distributed power supply, adopting a lifting strategy, and planning and operating a power distribution network containing a high-proportion distributed power supply;

step 2: constructing an intermittent energy power generation admitting capacity evaluation model and a new energy admitting capacity day-ahead evaluation model, and solving the new energy admitting fairness problem;

step 3: constructing a new energy acceptance capacity assessment model based on multiple areas, and effectively assessing the installation scale of new energy acceptable under the requirement of a certain limit proportion;

step 4: constructing a new energy maximum admitting capacity evaluation model based on regional power balance, and providing references for the installation scale and the construction time sequence of new energy in each region;

step 5: and a new energy scheduling method based on admission capacity is adopted to provide reference for the power grid to absorb and schedule new energy.

2. The evaluation method for improving new energy receiving capability of distribution network according to claim 1, wherein the evaluation method is characterized by comprising the following steps: the mathematical model of the distributed power supply receiving capacity in the step 1 is specifically as follows: the mathematical model of the distributed power supply receiving capacity is as follows:

wherein x is a distributed power supply, i.e. DG access capacity decision vector, and x= [ x ] ₁ x ₂ ...x _n ] ^T The method comprises the steps of carrying out a first treatment on the surface of the n is the total node number of the system; e is a unit vector, e= [ 11..1.)] ^T The method comprises the steps of carrying out a first treatment on the surface of the X is a viable set of X; y is the node voltage and line current system state vector; A. b is the coefficient matrix of x, y separately; k is a system parameter vector; on the basis, an improved model considering the on-load tap adjustment, reactive compensation and inverter power factor control DG receiving capacity improvement technology is as follows:

where a is a coefficient vector of x, a= [ a ] ₁ a ₂ ...a _n ] ^T The method comprises the steps of carrying out a first treatment on the surface of the z is transformer transformation ratio, reactive compensation quantity and inverterA state vector corresponding to the power factor boosting technology; z is a viable set of Z; c is a coefficient matrix of z; when the uncertainty of DG and load is considered, the opportunity planning model is built as: />

Wherein P {.cndot. } is a probability vector; beta is an opportunity constraint vector and can reflect the qualification rate requirements of various operation indexes of the power distribution network.

3. The evaluation method for improving the new energy receiving capability of the distribution network according to claim 2, wherein the evaluation method is characterized by comprising the following steps: the lifting strategy in the step 1 specifically comprises the following aspects: (1) tap adjustment of on-load tap-changing transformer: the tap position of the on-load voltage regulating transformer is flexibly adjusted, the system voltage level can be quickly adjusted, the overvoltage problem during large-scale DG access can be effectively prevented, and the DG receiving capacity is further improved; (2) reactive compensation: when the high-permeability DG is connected into the power distribution network to cause overvoltage problem, inductive reactive power can be compensated by the static reactive power compensator to quickly reduce the voltage level, so that the DG receiving capacity can be improved; (3) inverter power factor control: the power factor control is carried out on the DG grid-connected inverter, reactive power is absorbed in the output peak period of the DG grid-connected inverter, namely, the DG grid-connected inverter operates at a lagged power factor, so that the overall voltage level is reduced, and the DG receiving capacity can be improved; (4) network reconstruction: the tide distribution is regulated through network topology optimization, so that the safe operation index of the system can be ensured to be maintained in a reasonable Fan Tong, and the DG admittance capacity is improved; (5) demand case response: the requirement side response based on electricity price or excitation can meet the requirements of translational property and broad width of the flexibility of the power distribution network, can effectively increase the net load of DG output peak time, reduces the out-of-limit risk of operation constraint, and is beneficial to improving the DG admittance capability; (6) energy storage technology: the energy storage participates in the power supply and demand balance adjustment of the power distribution network, so that the power permeability of the DG output peak time is effectively reduced, and the operation constraint is ensured not to exceed the limit; (7) voltage coordination control: the voltage coordination control can further optimize the voltage distribution of the system, wherein the cluster voltage coordination control effectively combines the advantages of centralized control and distributed control, can realize intra-cluster autonomous optimization and inter-cluster coordination optimization based on cluster division, has better voltage regulation performance, and can remarkably improve DG receiving capacity; (8) and (3) each transformation: the primary equipment of the power distribution network is transformed and upgraded, so that the current tolerance capacity of the primary equipment of the power distribution network can be effectively improved, and the DG receiving capacity is improved by relaxing short circuit current constraint.

4. The evaluation method for improving new energy receiving capability of distribution network according to claim 1, wherein the evaluation method is characterized by comprising the following steps: the intermittent energy power generation acceptance assessment model in the step 2 specifically comprises the following steps: two optimization targets for intermittent energy power generation acceptance assessment are provided, namely, the intermittent energy power generation acceptance is maximum, the maximum power generation capacity of intermittent energy power generation is set to be the predicted power generation capacity, the minimum technical output is set to be 0, and accordingly, an objective function can be expressed as follows:

wherein NT is the number of time periods in the evaluation period; NR is the number of intermittent energy generator sets; p (P) _i,t Generating a power generation plan for the unit i in a t period; secondly, setting the intermittent energy source power generation cost to be a numerical value lower than 0 by taking the lowest total power generation cost of the system as a target, and representing the environmental protection benefit of the intermittent energy source power generation, wherein the objective function is as follows: />

Wherein: NI is the number of units participating in scheduling in the system; c (C) _i,t Generating cost of the unit i in the period t; ST (ST) _i,t The starting cost of the unit i at the time t is set; when the maximum intermittent energy power generation acceptance is adopted as an evaluation target, the conventional energy power generation cost is not considered under the condition of meeting various constraint conditions; by adopting the system with the lowest overall power generation cost as a daily standard, the cost balance in the process of receiving intermittent energy power generation can be fully considered in evaluation, and the auxiliary service compensation mechanism with reasonable design is facilitated to mobilize enthusiasm of all parties, so that the intermittent energy receiving capacity is improved.

5. The evaluation method for improving the new energy receiving capability of the distribution network according to claim 4, wherein the evaluation method comprises the following steps: the new energy acceptance daily evaluation model in the step 2 specifically comprises the following steps:

step 2.1: determining a new energy admission capacity assessment area: according to the topological structure of the power grid, the new energy access area and the related new energy electric field information to be evaluated are automatically determined, and the main process is as follows: (1) reading new energy unit information from the power grid model; (2) according to the topological structure of the power grid, determining a station and a line from a new energy unit to a power grid load center or an outgoing tie line gateway; (3) recording the relation between the new energy unit and the plant station and the line; (4) repeating the step (1), and determining the relation between all new energy units of the power grid and the plant stations and the line; (5) determining a new energy unit and a relevant new energy unit according to the relationship between the new energy unit and the plant stations, the line relationship and the power grid partition information;

step 2.2: partition day-ahead new energy acceptance assessment model: the method based on safety constraint economic dispatch, namely SCED model, is adopted to evaluate the day-ahead new energy admitting capability of the wind power and photovoltaic new energy access region, namely, on the basis of meeting the running conditions of a power grid and a conventional unit, the conventional unit cooperates with the new energy unit output adjustment of the new energy access region, and the safety limit of the evaluated new energy access region for admitting new energy is analyzed; the method comprises the following steps: 1) Evaluation target: the method is characterized in that wind power and photovoltaic new energy are accessed into a region or a system, the aim of the daily new energy admittance capability assessment is to enable the assessment region or the system to accept the maximum new energy generating capacity in the future day on the basis of meeting the power grid load, the power grid safety and the unit operation constraint conditions, and the objective function is as follows:

Wherein G is _w A new energy unit collection platform in the evaluation area; g is a set of all new energy units in the system; n (N) _T For an evaluation period; p is p _w (g, t) is the maximum power output of the new energy unit g in the t period; 2) Constraint conditions: 2.1 New energy output constraint): in order to obtain the limit capability of safely receiving new energy in the evaluation area, the new energy unit in the evaluation area has the output of meeting the maximum technologyThe surgical force constraints, namely: />

In the method, in the process of the invention,

maximum technical output of the new energy unit g at the time t; for the new energy unit outside the evaluation area, the output force should meet the constraint of the new energy power predicted value, namely: />

In (1) the->

The new energy power predicted value of the new energy unit g at the time t is obtained; 2.2 System load balancing constraints): />

Wherein I is a conventional unit set except a new energy unit set; the system power generation caliber payload when L (t) is t; 2.3 Rotational reserve constraint:

in (1) the->

The upper rotation provided for the conventional unit i in the period t is reserved; />

A spare requirement for the system to rotate on the t period; r (i, t) is the lower rotation reserve provided by the conventional unit i in the period t; r (t) is the rotation standby requirement of the system in the t period; 2.4 Conventional unit output constraint): p is p ^min (i,t)·U(i,t)≤p(i,t)≤p ^max (i, t). U (i, t) (10) wherein p ^min (i,t)、p ^max (i, t) are respectively the minimum and maximum technical output of the conventional unit i at the time t; u (i, t) is a conventional unit iWhether a sign is operated at the time t; 2.5 Conventional unit adjustment rate constraints: -delta _i ≤p(i,t)-p(i,t-1)≤Δ _i (11) Wherein, delta _i Maximum value of climbing rate per period for the conventional sail group i; 2.6 Branch and section flow constraints):

wherein p is _f (l,t)、p _f (l, t) are upper and lower limit values of tidal current power of the branch or the section l respectively; p is p _f (l, t) is l planning power flow at time t; 2.7 Partition backup constraint): />

Wherein Ar is a spare partition; r is R _Ar 、R′ _Ar Respectively performing upward rotation and downward rotation for the area;

step 2.3: evaluation analysis: the evaluation of the daily new energy admission capacity is to evaluate the maximum output of each new energy access area and the system for receiving new energy according to the new energy access area, and provide reference basis for daily planning and scheduling operation, and the process of the evaluation of the regional daily new energy admission capacity is specifically as follows: (1) determining a wind power maximum access analysis area object, and determining a wind power access area to be evaluated and associated wind turbines according to a power grid topology model and a wind power plant position; (2) calling a static security check sensitivity service module to calculate sensitivity data of the monitoring element; (3) according to wind power prediction data, the wind power output wind receiving power prediction value of the wind power outside the evaluation area is fixed for each evaluation analysis area or system, the wind power output in the evaluation area is adjustable, and a conventional unit in the system is matched with wind power output adjustment; evaluating the maximum new energy admission electric quantity and the maximum admission curve of the wind power access area of the model according to the regional day-ahead new energy admission capacity; (4) sequentially repeating the step (3) for each wind power access area to be evaluated; (5) outputting and displaying the maximum new energy receiving electric quantity and the maximum receiving curve of each wind power access area and system.

6. The evaluation method for improving new energy receiving capability of distribution network according to claim 1, wherein the evaluation method is characterized by comprising the following steps: the construction in the step 3 is based on a multi-region new energy admission capacity evaluation model, and specifically comprises the following steps:

step 3.1: objective function: the model takes the optimal system operation economy as a target, and the objective function comprises the fuel cost I of the thermal power unit, the start-stop cost H of the thermal power unit, the variable operation cost V of various units and circuits, the electricity-lack cost N and the pollutant emission cost E, namely: minZ=I+H+V+N+E (14), wherein,

wherein j is a thermal power generating unit; a, a _j Rated fuel consumption rate for the thermal power unit j; c _j The unit price of the fuel for the thermal power unit j; p (P) _i,j,k,t The continuous decision variable represents the output of the region i type j unit at the moment t of the typical scene k; />

Wherein d _j The method is the single start-stop cost of the thermal power generating unit j; u (U) _i,j,k,t As an integer Ce variable, representing the start-stop state of the power supply of the region i type j at the moment t of the typical scene k; />

In the formula, v _j 、vl _m The method comprises the steps of (1) changing operation cost coefficients of a unit j and a line m, wherein the type j comprises thermal power, hydroelectric power, pumped storage, energy storage, wind power and solar power generation, and when j is the thermal power unit, 0 represents unit shutdown and 1 represents unit startup; j is pumping energy storage and energy storage unit, 0 represents pumping/charging working condition, 1 represents charging/generating 1 working condition; l (L) _m,k,t The continuous decision variable represents the power flow of the mth power line at the moment t of the typical scene k; />

Wherein n is _i The unit electricity-shortage cost of the area i; y is Y _i,j,t As a continuous decision variable, representing the power shortage of the region i in the typical scene k; />

In the formula e _j Pollutant emission coefficients for unit power generation of unit j; z _j Cost per unit pollutant emission;

step 3.2: constraint conditions: 3.21 Resource constraint): the new energy acceptance capacity of each region is not more than the constraint of the available wind and light resources in the region:

wherein C is _wind,i The wind power acceptance capacity of the region i is represented by a continuous decision variable; c (C) _solar,i Representing the solar power generation acceptance of region i; c (C) _max,wind,i 、C _max,solar,i The maximum loadable installed scale of the wind and light resources of the region i is respectively; 3.22 Power balance constraint): to reduce the calculation scale, the power level is updated according to the typical scene of the horizontal year, and from time of the typical scene k to time of t in the area i, the following are: />

Wherein P is _i,j,k,t The output of each unit in the region i is generated; l (L) _m,k,t Line output for a drop point or a start point located in region i; y is Y _i,j,t The electricity shortage condition of the area i is shown; 3.23 Upper and lower limit constraints of unit and line output: 3.231 For thermal power generating units): u (U) _i,j,k,t P _i,j,min ≤P _i,j,k,t ≤U _i,j,k,t P _i,j,max (22) Wherein j is a thermal power unit; p (P) _i,j,min 、P _i,j,max The lower limit and the upper limit of the output of the thermal power unit j are respectively; 3.232 For energy storage and pumped storage): / >

Wherein j is a pumped storage and energy storage unit; p (P) _i,j,genmin 、P _i,j,genmax The lower limit and the upper limit of the output of the power generation/discharge working condition of the unit j are respectively set; p (P) _i,j,pumpmin 、P _i,j,pumpmax The lower limit and the upper limit of the output of the pumping/charging working condition of the unit j are respectively set; 3.233 For wind powerAnd solar power generation: for wind power and solar power generation, the maximum output at each moment is the product of the predicted available output per unit value at the moment and the scale of the nano-installation, namely: p is more than or equal to 0 _i,j,k,t ≤C _i P _{i,j,k,t,available} (24) Wherein j is a wind power and solar generator set; p (P) _{i,j,k,t,available} The available output per unit value at time t; c (C) _i Acceptable installation scale for region i; 3.234 Line(s): p (P) _lineminm ≤L _m,k,t ≤P _linemaxm (25) Wherein P is _lineminm 、P _linemaxm The upper and lower limits of the line output are respectively; 3.24 Continuous start-stop constraint of thermal power generating unit): for thermal power generating units, the minimum continuous opening and minimum continuous closing time constraint needs to be met, namely:

wherein T is _j,on 、T _j,off The continuous opening time and the minimum continuous closing time of the dust pan of the thermal power generating unit j are respectively; 3.25 Unit and line climbing constraints): for various units and lines, climbing constraint needs to be met, namely, the power change cannot exceed the climbing rate in the normal running state, and the climbing rate limit can be broken through when the machine is started and stopped: RD (RD) _j ≤P _i,j,k,t -P _i,j,k,t-1 ≤RU _j (27) In the formula, RD _j 、RU _j The maximum climbing speed of the unit or the line j is respectively the maximum climbing speed of the unit or the line j; 3.26 Pumped storage and energy storage unit related constraints): when the pumped storage unit is operated, the water level of the upper and lower reservoirs needs to be maintained between the highest water level and the dead water level: / >

In which W is _u 、W _d Representing the water quantity of the upper reservoir and the lower reservoir respectively; w (W) _u,max 、W _u,min The maximum allowable water quantity and the minimum allowable water quantity of the upper reservoir are respectively; w (W) _d,max 、W _d,min The maximum allowable water quantity and the minimum allowable water quantity of the lower reservoir are respectively; at different moments, the water volumes of the upper reservoir and the lower reservoir have a dynamic coupling relationship, namely: />

Wherein lambda is _g 、λ _p Respectively generating and pumping conversion coefficients; for a daily/weekly cycle type unit, assuming that the day/week is divided into s time periods, the upper reservoir levels of the initial time period and the final time period should be kept consistent, i.e. +.>

In (1) the->

The initial and final time period water levels; 3.27 Hydroelectric generating set related constraints: the hydroelectric generating set can be divided into two types, namely radial flow type water output is determined by natural water supply and is not adjustable, and the modeling mode is similar to that of new energy; for reservoir-contained adjustable hydroelectric power, it can be equivalent to a combination of radial hydroelectric power and a pump-storage unit with reservoir on model, in which the equivalent radial hydroelectric power is determined by natural water supply, and P is used _i,j,k,t,hyo1 The equivalent pumped storage output is determined by the system requirement and the current water level of the reservoir, and is determined by P _i,j,k,t,hyo2 Expressed, the overall force of the adjustable hydropower can be expressed as: p (P) _i,j,k,t,hyo ＝P _i,j,k,t,hyo1 +P _i,j,k,t,hyo2 (31) There are the following operational constraints: p is more than or equal to 0 _i,j,k,t,hyo1 ≤P _i,j,k,t,exp (32) Wherein P is _i,j,k,t,exp Is a natural water supply condition; formula (32) represents that the equivalent radial flow hydroelectric power fraction needs to be less than the maximum expected power of natural running water; p is more than or equal to 0 _i,j,k,t,hyo1 +P _i,j,k,t,hyo2 ≤P _i,j,max (33) Formula (33) represents that the overall output force of the hydropower is greater than zero and less than the maximum rated output force of the unit; the equivalent pumped storage output part can be positive or negative, a positive value represents that water discharged from a reservoir participates in regulation, a negative value represents that natural water can be stored in the reservoir when the natural water needs to be discarded due to peak regulation, and the absolute value of the output of the part cannot exceed the rated capacity of the unit: -P _i,j,max ≤P _i,j,k,t,hyo2 ≤P _i,j,max (34) At the same time, the water level of the reservoir should be dynamic levelThe balance constraint is that the maximum and minimum water levels cannot be exceeded, and the balance constraint is the same as formulas (28) to (30); 3.28 System standby constraints): rotational redundancy generally includes rotational redundancy required for the load and rotational redundancy to account for wind and solar renewable energy uncertainty: />

Wherein R is _i,j,k,t Spare capacity available at time t for each unit; beta _load Rotating the standby proportion for the load; beta _vg Rotating the standby proportion for new energy; 3.29 New energy electricity limit ratio constraint): the ratio of the wind and the light must be smaller than a given value: />

Wherein j is a wind-light unit; alpha is the maximum limit electric proportion.

7. The evaluation method for improving new energy receiving capability of distribution network according to claim 1, wherein the evaluation method is characterized by comprising the following steps: the new energy maximum admitting ability evaluation model constructed in the step 4 based on regional power balance specifically comprises the following steps:

Step 4.1: modeling by a new energy maximum admitting capacity assessment method: the built model aims at the maximum value of new energy acceptable by the system, the whole area is divided into 13 areas, the wind power, photovoltaic, thermal power and hydropower installed capacity of each area are comprehensively considered by taking the single area as a unit, various types of power output are taken as a variable to participate in the power balance of the area, meanwhile, the transmission power of the connecting lines of each ground box and surrounding areas is taken as a variable to participate in the power balance of each area, so that each area is connected together through the connecting lines between the areas, the power balance of the whole area is realized, and the objective function of the model is as follows:

wherein p is _{i_pv} 、p _{i_wind} Photovoltaic and wind power output values of the region i are respectively; n is the number of divided areas; />

Step 4.2: model solving based on particle swarm optimization algorithm: and solving a model by adopting a particle swarm algorithm and combining a parallelizable adjustment mechanism, and obtaining an acceptable new energy maximum value through iterative optimization for a certain number of times.

8. The evaluation method for improving the new energy receiving capability of the distribution network according to claim 7, wherein the evaluation method comprises the following steps: the new energy maximum admittance capacity assessment method in the step 4.1 is used for modeling: constraints considered by the model in the calculation process include: (1) power balance constraints for each region:

Wherein N is the number of divided regions; p is p _{i_fire} 、p _{i_water} 、p _{i_pv} 、p _{i_wind} The thermal power, hydroelectric power, photovoltaic power and wind power output values of the region i are respectively; p is p _{i_load} Is the load of region i; />

Exchanging values for power of the region i and surrounding regions; k (k) _i The number of transmission lines related to the region i and surrounding regions; (2) rotating spare capacity constraints: the system spare capacity comprises load spare, maintenance spare and accident spare, in addition, because of the uncertainty of new energy output, after the new energy output is added into the system power balance, the requirement on the system spare capacity is necessarily increased, namely: s is S _i ≥p _{imax_load} ×(l％+s％)+p _{i_pre} ×ω％+p _r (39) Wherein S is _i The spare capacity of the system needed by the region i; p is p _{imax_load} Predicting a maximum load for region i; l% is the load standby proportion; s% is the accident standby proportion; p is p _{i_pre} Predicting power for new energy; omega% is the requirement of predicted new energy output errors on various scenes; p is p _r The spare capacity is used for maintenance; (3) power supply output constraint in each region: the power output comprises thermal power, wind power, photovoltaic power and hydroelectric power output of each region, wherein the thermal power output is output constraint after rotation standby is considered, and the hydroelectric power output is calculated according to each time periodA certain proportion of the installed capacity of regional water is output: p is p _{i_down} ≤p _i ≤p _{i_up} (40) Wherein p is _i ＝[p _{i_fire} p _{i_water} p _{i_pv} p _{i_wind} ] ^T Power output vector for the ith area, p _{i_fire} 、p _{i_water} 、p _{i_pv} 、p _{i_wind} Respectively obtaining thermal power, hydroelectric power, photovoltaic power and wind power mountain power values of the region; p is p _{i_down} ＝[p _{id_fire} p _{id_water} p _{id_pv} p _{id_wind} ] ^T Lower limit of power supply output, p _{id_fire} 、p _{id_water} 、p _{id_pv} 、p _{id_wind} Respectively lower limit values of thermal power, hydroelectric power, optical power and wind power output; p is p _{i_up} ＝[p _{iu_fire} p _{iu_water} p _{iu_pv} p _{iu_wind} ] ^T Is the peak power upper limit vector, p _{iu_fire} 、p _{iu_water} 、p _{iu_pv} 、p _{iu_wind} The upper limit values of thermal power, hydroelectric power, photovoltaic power and wind power output are respectively set; (4) transmission line capacity constraints between regions: the transmission line comprises 38 lines between l3 ground-matched regions, and the transmission capacity of all the lines meets the following constraint: p is p _{id_line} ≤p _{i_line} ≤p _{iu_line} (41) Wherein p is _{i_line} ＝[p _{i1_line} p _{i2_ line} ...p _{iki_line} ] ^T Transmission line power delivery value vector, k, for region i and surrounding related regions _i The number of transmission lines related to the region i and the surrounding regions is i=n; p is p _{id_line} 、≤p _{iu_line} The lower limit and the upper limit vector of the transmission line capacity related to the region i and the surrounding region are respectively, and the total transmission line number is +.>

9. The evaluation method for improving the new energy receiving capability of the distribution network according to claim 7, wherein the evaluation method comprises the following steps: the model solving based on the particle swarm optimization algorithm in the step 4.2 specifically comprises the following steps:

step (a)4.21: particle swarm optimization algorithm: the particle swarm optimization algorithm is put forward by the prey behavior of the bird swarm, and the particle speed update formula is as follows:

the particle location update formula is:

wherein V is _i ＝[V _i1 ,V _i2 ,...,V _in ]Is the velocity of particle i; x is X _i ＝[X _i1 ,X _i2 ,...,X _in ]Is the position of particle i; omega is an inertial weight, and the searching capacity of the particles in a local and global range is controlled by changing the omega value; c ₁ 、c ₂ Is a learning factor; r is (r) ₁ 、r ₂ A random number between 0 and 1, p _besti Historically best location for particle i; g _best The best location for the global particle history;

step 4.22: the parallelizable adjustment mechanism handles the equality constraint: the power balance between regions relates to the equality constraint problem, and a parallelizable adjustment mechanism is introduced to solve the equality constraint problem in a model, specifically: floating elements of the solution vector which are found by the particles in the iterative process and do not meet the equality constraint in a search range, wherein the floating value is determined by the degree of violating the equality constraint and the looseness of each dimensional variable, and the looseness is the difference value between the current value and the upper/lower limit value Fan Dun of the current value; the specific description is as follows: for (k) _i +4) dimensional variable p _i ＝[p _i1 p _i2 p _i3 ...p _i(ki+4) ] ^T The difference between the mountain and the upper/lower limit is calculated according to the formula (44):

when->

When not feasible solution vector H _i The degree of violation of the equality constraint is +.>

The floating value +.>

The solution after parallelizable adjustment is obtained as: />

This solution necessarily satisfies the equality constraint.

10. The evaluation method for improving new energy receiving capability of distribution network according to claim 1, wherein the evaluation method is characterized by comprising the following steps: the new energy scheduling method based on the admission capacity in the step 5 specifically comprises the following steps:

step 5.1: medium-long term scheduling: 5.11 Power generation capacity prediction: a statistical analysis method is adopted, namely, a correlation is established according to actual generated energy, abandoned wind/photoelectric quantity and installed capacity of different new energy electric fields/stations in different histories, namely, the correlation is as follows: y is _t ＝ax _t +b (46), where y _t Generating power for a new energy electric field; x is x _t The electric field installed capacity is the new energy; a. b is an expression constant term; 5.12 Power generation schedule planning): according to the prediction result of the electric field generating capacity of the new energy, comprehensively considering the absorbing capacity of the power grid, if the receiving capacity of the power grid is insufficient, if the power grid is too serious, the conventional power supply is required to be rearranged to receive more new energy power, and the receiving capacity of the power grid is re-estimated according to a formula (46), and if the power grid is unavoidable, the receiving capacity of the power grid is distributed among the new energy electric fields according to a certain power limiting mode; 5.13 Electricity limit distribution mode): the new energy output limit is distributed in the following mode: pulling a loading capacity proportion equal ratio principle, an average allocation limit capacity principle, a limit principle from high to low according to electricity price, a limit principle from high to low according to power forecast accuracy, and a limit principle from high to low according to a test edge node;

Step 5.2: short-term scheduling: 5.21 Power generation capacity prediction: the short-term power generation capacity is derived from short-term new energy power prediction, and a common power prediction method comprises a prediction based on digital weather prediction, a statistical prediction method represented by a time sequence method and a learning prediction technology mainly based on a neural network; 5.22 Grid acceptance capability estimation: the conventional power supply power generation plan is extracted, the tie line plan and the power limit are comprehensively considered, and the acceptance of the power grid can be estimated and obtained; 5.23 Short-term day schedule planning): analyzing and comparing the total network new energy admittance capacity according to the current power prediction result of the new energy electric field, carrying out new energy electricity limiting analysis, and obtaining a scheduling plan of each new energy electric field according to a certain electricity limiting distribution mode;

step 5.3: ultra-short term scheduling: the scheduling flow and the calculation method are the same as short-term scheduling, and a certain electricity limiting distribution mode is adopted according to the real-time maximum admittance curve of the power grid and the power prediction result of the new energy source of the power grid, so that the ultra-short-term power generation scheduling control curve of the new energy source is adjusted in real time.

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