Main-distribution-use cooperative intelligent regulation and control method for improving large-scale photovoltaic consumption of power distribution network

Abstract

The invention discloses a main-distribution-application cooperative intelligent regulation and control method for improving large-scale photovoltaic consumption of a power distribution network, and relates to the field of power distribution networks. The method comprises the following steps: (1) constructing a main network model; (2) constructing a power distribution network model containing'three micro '; (3) constructing a user-side electric vehicle constraint model; (4) establishing a new energy bearing capacity improving model; (5) establishing a new energy bearing capacity evaluation system; and (6) outputting a photovoltaic bearing capacity improvement result through cooperative regulation and control of the main network, the power distribution network and the user side. The influence of photovoltaic access on the operation of the power distribution network is reduced by coordinating the flexible resources of the power transmission network, the power distribution network and the user side, so that the photovoltaic bearing capacity of the power distribution network is improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution networks, specifically a method for coordinated intelligent control of main, distribution, and consumption systems to improve the large-scale photovoltaic consumption of power distribution networks. Background Technology

[0002] Dense distributed photovoltaic (PV) grid integration represents a new structural form for future distribution networks. The large-scale integration of distributed PV alters the power flow distribution of the distribution network, increasing the risk of voltage exceeding limits. Furthermore, the uncertainty of distributed PV output presents new challenges to distribution network planning and operation. Therefore, it is urgent to conduct research on the distributed PV carrying capacity of distribution networks considering safe operation constraints under uncertainty.

[0003] Current research largely focuses on assessing the carrying capacity of distributed photovoltaic (PV) systems, neglecting the issue of capacity enhancement. In fact, from the perspective of distributed PV investment planning, researching effective methods to enhance the carrying capacity of distributed PV is more practically significant. Enhancement methods include additional reactive power compensation, PV inverter power factor adjustment, energy storage configuration, and demand-side response. However, the enhancement capacity of a single method is limited; therefore, it is necessary to study the synergistic combination of different enhancement methods to maximize and more economically improve the carrying capacity of distributed PV. Summary of the Invention

[0004] The purpose of this invention is to provide a method for coordinated intelligent control of main-distribution-consumption in large-scale photovoltaic power grids to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for improving the coordinated intelligent control of main-distribution-consumption in large-scale photovoltaic power grids includes the following steps:

[0007] (1) Construct a main grid model, which includes the output constraints, ramp rate constraints and start-up and shutdown time constraints of thermal power units;

[0008] (2) Construct a distribution network model containing "three micro" micro-grids. The distribution network model adopts a linearized power flow branch model, including: active and reactive power balance constraints at nodes; Ohm's law constraints on branches; and power, heat, and gas load balance constraints of micro-grids.

[0009] (3) Construct a user-side electric vehicle constraint model, including: upper and lower limits of battery capacity constraints; DC fast charging power constraints; and energy balance constraints when leaving the charging station.

[0010] (4) Establish a new energy carrying capacity enhancement model with the objective function of maximizing photovoltaic access capacity;

[0011] (5) Establish a new energy carrying capacity evaluation system and calculate the following indicators: photovoltaic penetration rate, photovoltaic power generation ratio, photovoltaic utilization rate, photovoltaic back-feed probability, and photovoltaic back-feed power ratio;

[0012] (6) Through coordinated regulation of the main grid, distribution network and user side, the photovoltaic carrying capacity is improved.

[0013] As a further preferred embodiment of the present invention: the main grid model consists of multiple nodes distributed in various regions, and thermal power units are connected to the system to supply the power load in the region. The main grid power flow constraints are as follows:

[0014]

[0015] In the formula: B l θ represents the admittance of transmission line l; o(l),t,s θ r(l),t,s F represents the phase angles of transmission line l at the start and end points of time period t in scenario s, respectively; l max P represents the maximum capacity of line l; L,l,t,s P represents the power flow of line l in scenario s during time period t; load,n,t This represents the load of node n during time period t;

[0016] Coal-fired power units must meet the following constraints:

[0017]

[0018] In the formula: u i，s，t This indicates the start-stop state of thermal power unit i in scenario s during time period t, u i，s，t =0 indicates that the unit is stopped, u i，s，t =1 indicates that the unit has started; P Th，i，max P Th，i，min These represent the maximum and minimum output of thermal power unit i, respectively;

[0019]

[0020] In the formula, R u,i R d,i These represent the uphill and downhill ramp rates of unit i, respectively.

[0021] The start-stop constraints for thermal power units are:

[0022]

[0023] In the formula, T s T0 and T0 represent the minimum shutdown and startup times of the unit, respectively.

[0024] As a further preferred embodiment of the present invention: the distribution network model containing "three micros":

[0025] A linearized power flow branch model is used to model the distribution network to adapt to dynamic changes in the system topology.

[0026]

[0027] P i and Q i These are the sums of active and reactive power injected at node i; P ij and Q ij It represents the active and reactive power on branch ij; Ω l It is a set of branches; r ij and x ij These are the resistance and reactance of branch ij; U i It is the voltage at node i; z ij It is a binary variable; if branch (i, j) is closed, it is set to 1; otherwise, it is set to 0. L,i / Q L,i It represents the active and reactive power load consumption of node i; P LL,i / Q LL,i It represents the active and reactive load losses at node i; P DG,i / Q DG,i These are the active and reactive power outputs of the DG at node i, respectively; P sub,i / Q sub,i It refers to the active and reactive power injected from the substation; Ω G It is a group of substations;

[0028] Equations (3a) and (3b) represent the active / reactive power balance of node i. Ohm's law on branch (i,j) is expressed as (3c). The total active power and reactive power injection of node i are determined by (3d)-(3e), respectively.

[0029] As a further preferred embodiment of the present invention: the electricity, heat, and gas load requirements of the microgrid should be equal to the corresponding energy provided, satisfying the following constraints:

[0030] P L,t +P P2G,t =P buy,t -P sell,t +P BESdis,t -P BESch,t +P PVT,t +P WT,t +P CHP,t (4a)

[0031]

[0032] V P2G,t =P CHP,t / η e,CHP / Q H2 +V HS,t (4c)

[0033] Among them, constraints (4a)-(4c) are the energy balance of electricity, heat, and gas load demands, respectively; P L,t P represents the power load at time t; P2G,t P represents the electrical power consumed in the electro-gas conversion at time t. buy,t P represents the amount of electricity purchased at time t; sell,t P represents the amount of electricity sold at time t; PVT,t P represents the photovoltaic power generation at time t; WT,t P represents the wind power generation capacity at time t; CHP,t η represents the combined heat and power output at time t; h,CHP / η e,CHP For electrothermal conversion efficiency; f h,PVT H is the energy conversion coefficient of the photovoltaic system. L,t For heat load; c a T is the specific heat capacity of air. Si,t and T Zi,t These are the air conditioning temperature and the indoor temperature; V P2G,t V represents the amount of H2 produced at time t; HS,t Let η be the gas storage capacity of the gas storage tank at time t; e,CHP For cogeneration conversion efficiency; Q H2 The calorific value of H2;

[0034] Electro-gas conversion model:

[0035] The principle of electro-gas conversion is: CO2 + 4H2 → CH4 + 2H2O, and the amount of H2 produced is calculated as follows:

[0036]

[0037] In the formula, V P2G,t Q represents the amount of H2 produced by electro-gas conversion at time t; H2 The calorific value of H2; η P2G The conversion rate of H2;

[0038] Constraints (5b) and (5c) limit the ramping power and start / stop capability of the electro-pneumatic conversion equipment:

[0039] μ P2G ·P P2G,min ≤P P2G,t ≤μ P2G ·P P2G,max (5b)

[0040] |P P2G,t -P P2G,,t-1 |≤P P2G,ramp (5c)

[0041] Where, μ P2G For electro-gas conversion efficiency; P P2G,min and P P2G,max Power limitations for electro-gas conversion equipment; P P2G,ramp This represents the maximum ramping power of the electro-gas conversion equipment.

[0042] Cogeneration model: Cogeneration equipment simultaneously generates electricity and usable heat, and its power constraint is expressed as:

[0043] P CHP,min ≤P CHP,t ≤P CHP,max (6a)

[0044] In the formula, P CHP,min and P CHP,max Power limitations for combined heat and power (CHP) equipment;

[0045] Hydrogen storage model:

[0046] V H2,t =V H2,t-1 -V HS,t-1 (7a) 0 ≤ V H2,t ≤V H2,r (7b)

[0047] V HS,min ≤V HS,t ≤V HS,max (7c)

[0048] In the formula V H2,t V represents the output flow rate of hydrogen at time t; HS,t V represents the amount of gas stored in the gas storage tank at time t; HS,max and V HS,min Due to capacity limitations of the gas storage tank;

[0049] HVAC model: The physical building structure based on HVAC includes outdoor space, indoor space and walls. The heat storage and heat transfer of the building components based on HVAC are represented by heat capacity and thermal resistance to describe the thermal behavior of their spatial distribution.

[0050] The energy balance equation for region i and wall i is expressed as:

[0051]

[0052] Among them, T out ,T Z ,T W These are the building's exterior, interior, and wall nodes; C Z C W It is the heat capacity of the building and walls; R in ,R out ,R WIt is the thermal resistance between nodes; Q rad It refers to the absorption of solar radiation by walls and interior spaces; Q HVAC It is heat from HVAC; Q int It is random internal heat;

[0053] Micro-energy storage model:

[0054] E BES,t =E BES,t-1 +η ch P BESch,t-1 -P BESdis,t-1 / η dis (9a)0.1*E BES,r ≤E BES,t ≤0.9*E BES,r (9b)

[0055] 0≤P BESch,t ≤P BESch,max (9c)

[0056] 0≤P BESdis,t ≤P BESdis,max (9d)

[0057] In the formula, P BESch,t and P BESdis,t These represent the battery's energy storage charging and discharging power, respectively; P BESch,max and P BESdis,max Battery energy storage charging and discharging power limitations; E BES,r This is a limitation based on the rated capacity of the battery energy storage.

[0058] As a further preferred embodiment of the present invention: the user-side electric vehicle constraint:

[0059] 0.1*E EVB,i,r ≤E EVB,i,t ≤0.9*E EVB,i,r (10a)

[0060] Among them, E EVB,i,t E represents the battery charge of the i-th car at time t; EVB,i,r This represents the rated capacity of the battery of the i-th car.

[0061] Plug-in fast charging model: The DC fast charging power of the vehicle must meet the following constraints:

[0062] 0≤P EVBch,i,t ≤P EVBch,max (10b)

[0063] E EVBch,i,t =P EVBch,i,t Δt (10c)

[0064] E EVB,i,t =EEVB,i,t-1 +η ch E EVBch,i,t-1 (10d)

[0065] Among them, P EVBch,i,t P represents the DC fast charging power of the i-th car at time t; EVBch,max Indicates the maximum power of DC fast charging; E EVBch,i,t η represents the charge level of the i-th car at time t; ch Indicates charging efficiency;

[0066] For each vehicle, the energy it carries when leaving the charging station satisfies the following constraints:

[0067]

[0068] In the formula, E EVB,i,intial and E EVB,i,dep These represent the battery charge of the i-th vehicle when it enters and exits the charging station, respectively.

[0069] As a further preferred embodiment of the present invention: step (4) specifically comprises:

[0070] After distributed photovoltaic (PV) installation, the power flow and voltage distribution of the distribution network lines are altered. Assuming there are n nodes on the distribution network feeder, calculate the voltage U at node k. k for:

[0071]

[0072] In the formula: U0 is the voltage at the connection point between the distribution network and the upstream power grid; U i-1 Let P be the voltage at node i-1, where i = 1, 2, ..., n; DG,j and Q DG,j These represent the active and reactive power of the distributed photovoltaic system at node j, where j = 1, 2, ..., n; P L,j and Q L,j These represent the active and reactive power of the load at node j, respectively; i Let be the line length between node i and node i-1; r and x are the resistance and reactance per unit length of line, respectively;

[0073] When the output of distributed photovoltaic (PV) power sources connected to a node exceeds the load power, the node experiences reverse power flow and is considered a power source node injecting power into the grid. As shown in equation (11a), the greater the output of distributed PV, the higher the node voltage, and the closer to the end of the line, the more significant the voltage rise. When the capacity of distributed PV connected to the grid reaches a certain level, the line power flow and node voltage may exceed the allowable values.

[0074]

[0075] Among them, U min and U max I represents the upper and lower limits of the voltage at distribution network nodes. max U is the upper limit of the current in the distribution network line. i,t and I ij,t Let be the voltage of each node and the current of each line in the distribution network at time t, respectively. Equations (12a) and (12b) are the constraints for safe operation of the distribution network.

[0076] Micro-energy storage model considering reactive power coordination of energy storage system:

[0077]

[0078] 0.1*E BES,r ≤E BES,t ≤0.9*E BES,r (13b)

[0079]

[0080] 0≤P BESch,t ≤S DC-DC (13e)

[0081] 0≤P BESdis,t ≤S DC-DC (13f)

[0082] -S DC-DC ≤Q BESch,t ≤S DC-DC (13g)

[0083] -S DC-DC ≤Q BESdis,t ≤S DC-DC (13h)

[0084] In the formula, P BESch,t and P BESdis,t Q represents the active power of battery charging and discharging, respectively. BESch,t and Q BESdis,t These represent the reactive power of battery energy storage charging and discharging; S DC-DC E represents the capacity of the DC-DC converter between the energy storage battery and the power distribution network. BES,r Limited by the rated capacity of battery energy storage;

[0085] The SOP coordination control constraints are as follows:

[0086] P SOP,i,t +P SOP,j,t =0 (14a)

[0087] -S SOP,i ≤P SOP,i,t ≤S SOP,i (14b)

[0088] -Q SOP,i,l ≤Q SOP,i,t ≤Q SOP,i,u (14c)

[0089]

[0090] Among them, P sop,i,t Q sop,i,t S represents the input active power and input reactive power of SOP at node i at time t. sop,i Q sop,i,l Q sop,i,u , which represent the SOP capacity, lower limit, and upper limit of reactive power accessed by node i, respectively.

[0091] As a further preferred embodiment of the present invention: in the photovoltaic carrying capacity enhancement model, the objective function is to maximize the photovoltaic capacity connected to the distribution network.

[0092]

[0093] Among them, Ω PV For photovoltaic generator sets connected to the distribution network, C PV,i This represents the maximum capacity that photovoltaic generator i is allowed to connect to;

[0094] In addition, distributed photovoltaic power meets the following constraints:

[0095] 0≤P PV,i,t ≤P PV,i,max (16a)

[0096] -Q PV,i,max ≤Q PV,i,t ≤Q PV,i,max (16b)

[0097]

[0098] In the formula, P PV,i,t Q PV,i,t P represents the input active power and input reactive power of PV at node i at time t. PV,i,max Q PV,i,max C represents the upper limit of the active and reactive power of the PV connected to node i, respectively; PV,i Let be the PV capacity connected to node i.

[0099] As a further preferred embodiment of the present invention: the new energy carrying capacity evaluation system established in step (5) is as follows:

[0100] (1) Photovoltaic penetration rate:

[0101]

[0102] In the formula: n represents the number of distributed photovoltaic systems; D i Where i is the installed capacity of distributed photovoltaic system; m is the number of loads; P j Let j be the power of load j;

[0103] (2) Percentage of photovoltaic power generation:

[0104]

[0105] In the formula: t is time; D i (t) represents the power of distributed photovoltaic i at time t; P j (t) represents the power of load j at time t;

[0106] (3) Photovoltaic utilization rate:

[0107]

[0108] In the formula: P ri (t) represents the available power generation of distributed photovoltaic i at time t;

[0109] (4) Probability of photovoltaic reverse transmission:

[0110]

[0111] Where: Δt a t is the duration of the a-th reverse transmission period; b The duration of the b-th time period to be evaluated;

[0112] (5) Percentage of photovoltaic power fed back to the grid:

[0113]

[0114] In the formula: ΔP(t) is the power flow reverse transmission power at time t.

[0115] Compared with the prior art, the beneficial effects of the present invention are:

[0116] 1. A smart control method for coordinating main-distribution-consumption is proposed to improve the carrying capacity of large-scale distributed photovoltaic power generation. This method reduces the impact of photovoltaic access on the operation of the distribution network by coordinating the flexibility resources of the transmission network, distribution network and user side, thereby improving the photovoltaic carrying capacity of the distribution network.

[0117] 2. This paper analyzes the impact of distributed power sources such as photovoltaic, wind, and energy storage on the operation of the distribution network under different scenarios, and proposes an assessment method for the distributed power source absorption capacity of the "three micro" integrated distribution network, providing a basis for high penetration rate access and absorption. Attached Figure Description

[0118] Figure 1This is a schematic diagram of the method flow of the present invention.

[0119] Figure 2 This is a schematic diagram of the distributed photovoltaic carrying capacity of each node in this invention. Detailed Implementation

[0120] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0121] Please see Figure 1 In this embodiment of the invention, a method for coordinated intelligent control of main-distribution-consumption systems to improve the large-scale photovoltaic power consumption of distribution networks includes the following steps:

[0122] Step 1: Construct a main-distributor-user collaborative intelligent control model

[0123] Coordinated regulation of main grid, distribution network, and user-side flexible resources refers to a new cross-level, multi-entity coordinated operation mode of the distribution system, achieved through deep integration and dynamic interaction between the main grid, distribution network, and user-side flexible resources. The main grid relies on a higher voltage level backbone network to achieve long-distance power transmission and interaction with the distribution network. The distribution network, through medium- and low-voltage networks, coordinates and optimizes "micro" resources (such as solar power, micro-power, and micro-electricity) and resources such as power generation, grid, and energy storage, while coordinating with adjustable loads on the user side, such as electric vehicles, to achieve large-scale distributed photovoltaic power consumption and economical system operation. Ultimately, through information sharing and coordinated dispatch, the three parties jointly ensure the economical, safe, and efficient operation of the distribution system. Specific coordination mechanisms are as follows: Figure 1 As shown.

[0124] 3.1.1 Mainnet Model

[0125] The main grid system consists of multiple nodes distributed across various regions, with thermal power units connected to the system to supply the regional power load. The main grid power flow constraints are as follows.

[0126]

[0127] In the formula: B l θ represents the admittance of transmission line l; o(l),t,s θ r(l),t,s F represents the phase angles of transmission line l at the start and end points of time period t in scenario s, respectively; l max P represents the maximum capacity of line l; L,l,t,s P represents the power flow of line l in scenario s during time period t; load,n,t This represents the load of node n during time period t.

[0128] Coal-fired power units, as traditional power generation systems, convert chemical energy into electrical energy by burning fossil fuels, and must meet the following constraints.

[0129]

[0130] In the formula: u i,s,t This indicates the start-stop state of thermal power unit i in scenario s during time period t, u i,s,t =0 indicates that the unit is stopped, u i,s,t =1 indicates that the unit has started; P Th,i,max P Th,i,min These represent the maximum and minimum output of thermal power unit i, respectively.

[0131]

[0132] In the formula, R u,i R d,i These represent the uphill and downhill ramp rates of unit i, respectively.

[0133] The start-up and shutdown of thermal power units must be maintained for a certain period of time to prevent damage caused by frequent start-ups and shutdowns. The start-up and shutdown constraints are as follows:

[0134]

[0135] In the formula, T S T0 and T0 represent the minimum shutdown and startup times of the unit, respectively.

[0136] 3.1.2 Distribution network model including "three micro"

[0137] A linearized power flow branch model is used to model the distribution network to adapt to dynamic changes in the system topology.

[0138]

[0139] Among them, P i and Q i These are the sums of active and reactive power injected at node i; P ij and Q ij It represents the active and reactive power on branch ij; Ω l It is a set of branches; r ij and x ij These are the resistance and reactance of branch ij; U i It is the voltage at node i; z ij It is a binary variable; if branch (i, j) is closed, it is set to 1; otherwise, it is set to 0. L,i / Q L,i It represents the active and reactive power load consumption of node i; P LL,i / Q LL,iIt represents the active and reactive load losses at node i; P DG,i / Q DG,i These are the active and reactive power outputs of the DG at node i, respectively; P sub,i / Q sub,i It refers to the active and reactive power injected from the substation; Ω G It is a group of substations.

[0140] Specifically, equations (3a) and (3b) represent the active / reactive power balance at node i, and Ohm's law on branch (i,j) is expressed as (3c). The total active power and reactive power injection at node i are determined by (3d)-(3e), respectively.

[0141] The electricity, heat, and gas load demand of a microgrid should equal the corresponding energy supplied, and the following constraints must be met:

[0142] P L,t +P P2G,t =P buy,t -P sell,t +P BESdis,t -P BESch,t +P PVT,t +P WT,t +P CHP,t (4a)

[0143]

[0144] V P2G,t =P CHP,t / η e,CHP / Q H2 +V HS,t (4c)

[0145] Among them, constraints (4a)-(4c) are the energy balance of electricity, heat, and gas load demands, respectively; P L,t P represents the power load at time t; P2G,t P represents the electrical power consumed in the electro-gas conversion at time t. buy,t P represents the amount of electricity purchased at time t; sell,t P represents the amount of electricity sold at time t; PVT,t P represents the photovoltaic power generation at time t; WT,t P represents the wind power generation capacity at time t; CHP,t η represents the combined heat and power output at time t; h,CHP / η e,CHP For electrothermal conversion efficiency; f h,PVT H is the energy conversion coefficient of the photovoltaic system. L,t For heat load; c a T is the specific heat capacity of air. Si,t and T Zi,t These are the air conditioning temperature and the indoor temperature; VP2G,t V represents the amount of H2 produced at time t; HS,t Let η be the gas storage capacity of the gas storage tank at time t; e,CHP For cogeneration conversion efficiency; Q H2 The calorific value of H2.

[0146] Electro-to-gas model: The principle of electro-to-gas conversion is: CO2 + 4H2 → CH4 + 2H2O. The generated H2 is calculated as follows:

[0147]

[0148] In the formula, V P2G,t Q represents the amount of H2 produced by electro-gas conversion at time t; H2 The calorific value of H2; η P2G The conversion rate of H2.

[0149] Constraints (5.18) and (5.19) limit the ramping power and start-up / shutdown of the electro-gas conversion equipment.

[0150] μ P2G ·P P2G,min ≤P P2G,t ≤μ P2G ·P P2G,max (5b)

[0151] |P P2G,t -P P2G,,t-1 |≤P P2G,ramp (5c)

[0152] Where, μ P2G For electro-gas conversion efficiency; P P2G,min and P P2G,max Power limitations for electro-gas conversion equipment; P P2G,ramp This is the maximum ramping power of the electro-gas conversion equipment.

[0153] Cogeneration model: Cogeneration equipment can simultaneously generate electricity and usable heat, and its power constraint is expressed as:

[0154] P CHP,min ≤P CHP,t ≤P CHP,max (6a)

[0155] In the formula, P CHP,min and P CHP,max Power limitations for combined heat and power (CHP) equipment.

[0156] Hydrogen storage model: Hydrogen storage requires consideration of factors such as inlet and outlet flow rates, pressure inside the storage tank, and temperature. The model is represented as follows:

[0157] V H2,t =V H2,t-1 -VHS,t-1 (7a)

[0158] 0≤V H2,t ≤V H2,r (7b)

[0159] V HS,min ≤V HS,t ≤V HS,max (7c)

[0160] In the formula V H2,t V represents the output flow rate of hydrogen at time t; HS,t V represents the amount of gas stored in the gas storage tank at time t; HS,max and V HS,min Due to capacity limitations of the gas storage tank.

[0161] HVAC model: The physical building structure based on HVAC includes outdoor space, indoor space, and walls. The heat storage and heat transfer of HVAC-based building components are usually represented by heat capacity and thermal resistance to describe their spatial thermal behavior.

[0162] The energy balance equation for region i and wall i can be expressed as:

[0163]

[0164] Among them, T out ,T Z ,T W These are the building's exterior, interior, and wall nodes; C Z C W It is the heat capacity of the building and walls; R in ,R out ,R W It is the thermal resistance between nodes; Q rad It refers to the absorption of solar radiation by walls and interior spaces; Q HVAC It is heat from HVAC; Q int It is random internal heat.

[0165] Micro-energy storage model: Since overcharging and over-discharging reduce battery life, upper and lower limits of battery capacity and charge / discharge power should be limited.

[0166] E BES,t =E BES,t-1 +η ch P BESch,t-1 -P BESdis,t-1 / η dis (9a)

[0167] 0.1*E BES,r ≤E BES,t ≤0.9*E BES,r (9b)

[0168] 0≤P BESch,t ≤P BESch,max (9c)

[0169] 0≤P BESdis,t ≤P BESdis,max (9d)

[0170] In the formula, P BESch,t and P BESdis,t These represent the battery's energy storage charging and discharging power, respectively; P BESch,max and P BESdis,max Battery energy storage charging and discharging power limitations; E BES,r This is a limitation based on the rated capacity of the battery energy storage.

[0171] 3.1.3 User-side constraints on electric vehicles

[0172] Because overcharging and over-discharging reduce battery life, the upper and lower limits of electric vehicle battery capacity are limited, as shown in the following constraints:

[0173] 0.1*E EVB,i,r ≤E EVB,i,t ≤0.9*E EVB,i,r (10a)

[0174] Among them, E EVB,i,t E represents the battery charge of the i-th car at time t; EVB,i,r This represents the rated capacity of the battery of the i-th car.

[0175] Plug-in fast charging model: The DC fast charging power of the car needs to meet the following constraints:

[0176] 0≤P EVBch,i,t ≤P EVBch,max (10b)

[0177] E EVBch,i,t =P EVBch,i,t Δt (10c)

[0178] E EVB,i,t =E EVB,i,t-1 +η ch E EVBch,i,t-1 (10d)

[0179] Among them, P EVBch,i,t P represents the DC fast charging power of the i-th car at time t; EVBch,max Indicates the maximum power of DC fast charging; E EVBch,i,t η represents the charge level of the i-th car at time t; ch This indicates charging efficiency.

[0180] For each vehicle, the energy it carries when leaving the charging station satisfies the following constraints:

[0181]

[0182] In the formula, E EVB,i,intial and E EVB,i,dep These represent the battery charge of the i-th vehicle when it enters and exits the charging station, respectively.

[0183] Step 2: Construct a model and evaluation system for enhancing the carrying capacity of new energy sources.

[0184] 3.2.1 Factors affecting the carrying capacity of distributed photovoltaic power grids

[0185] The typical structure of a distribution network with dense distributed photovoltaic (PV) integration is shown below. The integration of distributed PV alters the power flow and voltage distribution of the distribution network lines. Assuming there are n nodes on the distribution network feeder, calculate the voltage U at node k. k for:

[0186]

[0187] In the formula: U0 is the voltage at the connection point between the distribution network and the upstream power grid; U i-1 Let P be the voltage at node i-1, where i = 1, 2, ..., n; DG,j and Q DG,j These represent the active and reactive power of the distributed photovoltaic system at node j, where j = 1, 2, ..., n; P L,j and Q L,j These represent the active and reactive power of the load at node j, respectively; i Let be the line length between node i and node i-1; r and x are the resistance and reactance per unit length of line, respectively.

[0188] When the output of distributed photovoltaic (PV) power sources connected to a node exceeds the load power, the node experiences reverse power flow and can be considered a power source node injecting power into the grid. As shown in equation (11a), the greater the output of distributed PV, the higher the node voltage, and the closer to the end of the line, the more significant the voltage rise. When the capacity of distributed PV connected to the grid reaches a certain level, the power flow and node voltage may exceed allowable values.

[0189]

[0190] Among them, U min and U max I represents the upper and lower limits of the voltage at distribution network nodes. max U is the upper limit of the current in the distribution network line. i,t and I ij,t Let be the voltage of each node in the distribution network and the current of each line at time t, respectively. Equations (12a) and (12b) are the constraints for the safe operation of the distribution network.

[0191] 3.2.2 Measures to Improve the Carrying Capacity of Distributed Photovoltaic Power Generation in Distribution Networks

[0192] The output of distributed photovoltaic (PV) power at the source end of the distribution network is random and fluctuating; at the load end, different types of loads, such as residential and commercial loads, change frequently, with large peak-to-valley differences and uncertainties. These uncertainties on both the source and load sides cause probabilistic characteristics in operating indicators such as power flow and node voltage in the distribution network. When the output of distributed PV power connected to a node exceeds the load power, the node experiences reverse power flow and can be considered a power source node injecting power into the grid. The higher the distributed PV output, the higher the node voltage, and the closer to the end of the line, the more pronounced the voltage rise. When the capacity of distributed PV connected reaches a certain level, the power flow and node voltage may exceed permissible values.

[0193] The deployment location of distributed photovoltaic (PV) systems is typically determined by load distribution and regional resource characteristics. Given the location of distributed PV grid connection, it is necessary to fully explore various controllable methods to improve the carrying capacity of distributed PV in the distribution network. This study investigates the regulation of distributed PV carrying capacity in the distribution network from the perspectives of reactive power coordination of energy storage and soft switching collaborative optimization.

[0194] 1) Energy storage reactive power coordination

[0195] With the increasing penetration rate of distributed photovoltaic (PV) power, the problem of voltage exceeding limits in distribution networks is becoming increasingly prominent. The reactive power flow of the system can be dynamically adjusted through the reactive power coordination capability of energy storage systems, effectively suppressing voltage deviations, thereby improving the distributed PV carrying capacity of the distribution network and enhancing the safety and stability of the distribution network operation.

[0196] Micro-energy storage model considering reactive power coordination in energy storage system: Since overcharging and over-discharging will reduce battery life, upper and lower limits of battery capacity and charge / discharge power should be limited:

[0197]

[0198] 0.1*E BES,r ≤E BES,t ≤0.9*E BES,r (13b)

[0199]

[0200] 0≤P BESch,t ≤S DC-DC (13e)

[0201] 0≤P BESdis,t ≤S DC-DC (13f)

[0202] -S DC-DC ≤Q BESch,t ≤S DC-DC (13g)

[0203] -S DC-DC ≤Q BESdis,t ≤S DC-DC (13h)

[0204] In the formula, P BESch,t and P BESdis,t Q represents the active power of battery charging and discharging, respectively. BESch,t and Q BESdis,t These represent the reactive power of battery energy storage charging and discharging; S DC-DC E represents the capacity of the DC-DC converter between the energy storage battery and the power distribution network. BES,r This is a limitation based on the rated capacity of the battery energy storage.

[0205] 2) SOP Coordination and Control

[0206] When photovoltaic (PV) power is integrated into the distribution network, it causes an increase in the voltage at the connected nodes, which in turn affects the power flow and reduces the stability of the distribution network. Standard Operating Procedures (SOPs), as advanced power electronic devices, monitor the distribution network's operating status in real time, analyze load demand and real-time PV output, dynamically adjust switching parameters and the distribution network topology, optimize power flow, and reduce node voltage fluctuations caused by PV integration, thereby improving the distribution network's ability to absorb PV power. The relevant constraints of SOPs are as follows:

[0207] P SOP,i,t +P SOP,j,t =0 (14a)

[0208] -S SOP,i ≤P SOP,i,t ≤S SOP,i (14b)

[0209] -Q SOP,i,l ≤Q SOP,i,t ≤Q SOP,i,u (14c)

[0210]

[0211] Among them, P sop,i,t Q sop,i,t S represents the input active power and input reactive power of SOP at node i at time t, respectively. sop,i Q sop,i,l Q sop,i,u , which represent the SOP capacity, lower limit, and upper limit of reactive power accessed by node i, respectively.

[0212] 3.2.3 Photovoltaic Carrying Capacity Enhancement Model

[0213] This section uses the maximum photovoltaic capacity connected to the distribution network as the objective function.

[0214]

[0215] Among them, Ω PV For photovoltaic generator sets connected to the distribution network, C PV,i This represents the maximum capacity that photovoltaic generator i is allowed to connect to.

[0216] In addition, distributed photovoltaic power must also meet the following constraints:

[0217] 0≤P PV,i,t ≤P PV,i,max (16a)

[0218] -Q PV,i,max ≤Q PV,i,t ≤Q PV,i,max (16b)

[0219]

[0220] In the formula, P PV,i,t Q PV,i,t Let P be the input active power and input reactive power of PV at node i at time t, respectively. PV,i,max Q PV,i,max C represents the upper limit of the active and reactive power of the PV connected to node i, respectively; PV,i Let be the PV capacity connected to node i.

[0221] Step 3: Construct a new energy carrying capacity evaluation system

[0222] 3.3.1 Evaluation Indicators

[0223] (1) Photovoltaic penetration rate: This indicator describes the proportion of distributed photovoltaic (PV) installed capacity to the regional load. Photovoltaic penetration rate is...

[0224]

[0225] In the formula: n represents the number of distributed photovoltaic systems; D i Where i is the installed capacity of distributed photovoltaic system; m is the number of loads; P j Let be the power of load j.

[0226] (2) Photovoltaic power generation share: This indicator describes the proportion of distributed photovoltaic power generation to the regional load power generation over a certain period of time. The photovoltaic power generation share is...

[0227]

[0228] In the formula: t is time; D i (t) represents the power of distributed photovoltaic i at time t; P j (t) represents the power of load j at time t.

[0229] (3) Photovoltaic Utilization Rate: This indicator describes the overall situation of distributed photovoltaic power generation consumption. The distributed photovoltaic power generation utilization rate is...

[0230]

[0231] In the formula: P ri (t) represents the available power generation of distributed photovoltaic i at time t.

[0232] (4) Photovoltaic backfeed probability: This indicator describes the proportion of the total time that the distribution network connected to distributed photovoltaics feeds back to the main grid to the effective power generation time of distributed photovoltaics.

[0233]

[0234] Where: Δt a t is the duration of the a-th reverse transmission period; b The duration of the b-th time period to be evaluated.

[0235] (5) Proportion of photovoltaic backfeeding power: This indicator describes the proportion of power backfeeding from the distribution network connected to distributed photovoltaic to the main grid to the total power generation of distributed photovoltaic.

[0236]

[0237] In the formula: ΔP(t) is the power flow reverse transmission power at time t.

[0238] This invention verifies the effectiveness of the proposed main-distribution-consumption intelligent control technology in improving photovoltaic (PV) grid absorption capacity by comparing three schemes: Scheme 1: considers main grid unit output coordination, SOP (Start of Operation), reactive power control of energy storage, and flexible allocation of electric vehicles for main-distribution-consumption coordinated control; Scheme 2: only considers main grid unit output coordination and flexible allocation of electric vehicles for main-consumption coordinated control; Scheme 3: only considers SOP coordination, reactive power control of energy storage, and flexible allocation of electric vehicles for distribution-consumption coordinated control. The maximum PV capacity and various indicators of each scheme are as follows: Figure 2 As shown in Table 1:

[0239] Compared to Scheme 2, Scheme 1, by incorporating reactive power regulation such as SOP and energy storage, optimizes the power flow distribution of the distribution network and absorbs some reactive power, significantly improving its photovoltaic carrying capacity. Its maximum absorbable photovoltaic capacity increases to approximately 25.56MW, an increase of 11.21MW compared to Scheme 2's approximately 14.35MW. Compared to Scheme 3, Scheme 1, by incorporating main grid unit output coordination, can coordinate power transmission between the main and distribution networks, which is beneficial for photovoltaic absorption in the distribution network. Its maximum absorbable photovoltaic capacity increases by 11.03% compared to Scheme 3's approximately 23.02MW.

[0240] Table 1 Evaluation Indicators for Schemes 1-3

[0241]

[0242]

[0243] Based on the data in Table 1, compared to Scheme 2, Scheme 1 incorporates reactive power regulation through SOPs and energy storage. Optimizing power flow in the distribution network via SOPs effectively suppresses voltage and current fluctuations, thus reducing the impact of photovoltaic (PV) grid integration. Therefore, its PV penetration rate and PV power generation share both increase. The energy storage system absorbs some of the excess reactive power, reducing the demand for power backflow from the distribution network to the main grid. Therefore, its PV backflow probability and backflow power share both decrease. Compared to Scheme 3, Scheme 1 incorporates main grid unit output coordination. By reducing main grid unit output during peak PV output periods in the distribution network, the main grid can absorb more power. During peak PV output periods, the distribution network supplies more power to the main grid, thereby increasing the PV penetration rate and PV power generation share of the distribution network. However, due to the increased power supply from the distribution network to the main grid, its PV backflow power share increases slightly.

[0244] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0245] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for coordinated intelligent control of main-distribution-consumption systems in a power distribution network to improve large-scale photovoltaic power consumption, characterized in that, Includes the following steps: (1) Construct a main grid model, which includes the output constraints, ramp rate constraints and start-up and shutdown time constraints of thermal power units; (2) Construct a distribution network model containing "three micro" elements. The distribution network model adopts a linearized power flow branch model, including: active and reactive power balance constraints at nodes; Ohm's law constraints at branches; and power, heat, and gas load balance constraints of the micro energy network. (3) Construct a user-side electric vehicle constraint model, including: upper and lower limits of battery capacity constraints; DC fast charging power constraints; and energy balance constraints when leaving the charging station. (4) Establish a new energy carrying capacity enhancement model with the objective function of maximizing photovoltaic access capacity; (5) Establish a new energy carrying capacity evaluation system and calculate the following indicators: photovoltaic penetration rate, photovoltaic power generation ratio, photovoltaic utilization rate, photovoltaic back-feed probability, and photovoltaic back-feed power ratio; (6) Through coordinated regulation of the main grid, distribution network and user side, the photovoltaic carrying capacity is improved.

2. The method for coordinated intelligent control of main-distribution-consumption systems to improve the large-scale photovoltaic consumption of distribution networks according to claim 1, characterized in that, The main grid model consists of multiple nodes distributed across various regions. Thermal power units are connected to the system to supply the power load within the region. The main grid power flow constraints are as follows: In the formula: B l θ represents the admittance of transmission line l; o(l),t,s θ r(l),t,s F represents the phase angles of transmission line l at the start and end points of time period t in scenario s, respectively; l max This indicates the maximum capacity of line l; P L,l,t,s P represents the power flow of line l in scenario s during time period t; load,n,t This represents the load of node n during time period t; Coal-fired power units must meet the following constraints: In the formula: u i,s,t This indicates the start-stop state of thermal power unit i in scenario s during time period t, u i,s,t =0 indicates that the unit is stopped, u i,s,t =1 indicates that the unit has started; P Th,i,max P Th,i,min These represent the maximum and minimum output of thermal power unit i, respectively; In the formula, R u,i R d,i These represent the uphill and downhill ramp rates of unit i, respectively. The start-stop constraints for thermal power units are: In the formula, T s T0 and T0 represent the minimum shutdown and startup times of the unit, respectively.

3. The method for coordinated intelligent control of main-distribution-consumption systems to improve the large-scale photovoltaic consumption of distribution networks according to claim 2, characterized in that, The distribution network model containing "three micros": A linearized power flow branch model is used to model the distribution network to adapt to dynamic changes in the system topology. P i and Q i These are the sums of active and reactive power injected at node i; P ij and Q ij It represents the active and reactive power on branch ij; Ω l It is a set of branches; r ij and x ij These are the resistance and reactance of branch ij; U i It is the voltage at node i; z ij It is a binary variable; if branch (i, j) is closed, it is set to 1; otherwise, it is set to 0. L,i / Q L,i It represents the active and reactive power load consumption of node i; P LL,i / Q LL,i It represents the active and reactive load losses at node i; P DG,i / Q DG,i These are the active and reactive power outputs of the DG at node i, respectively; P sub,i / Q sub,i It refers to the active and reactive power injected from the substation; Ω G It is a group of substations; Equations (3a) and (3b) represent the active / reactive power balance of node i. Ohm's law on branch (i,j) is expressed as (3c). The total active power and reactive power injection of node i are determined by (3d)-(3e), respectively.

4. The method for coordinated intelligent control of main-distribution-consumption systems to improve the large-scale photovoltaic consumption of distribution networks according to claim 3, characterized in that, The electricity, heat, and gas load demands of the microgrid should be equal to the corresponding energy provided, satisfying the following constraints: P L,t +P P2G,t =P buy,t -P sell,t +P BESdis,t -P BESch,t +P PVT,t +P WT,t +P CHP,t (4a) V P2G,t =P CHP,t / η e,CHP / Q H2 +V HS,t (4c) Among them, constraints (4a)-(4c) are the energy balance of electricity, heat, and gas load demands, respectively; P L,t P represents the power load at time t; P2G,t P represents the electrical power consumption for the electro-gas conversion at time t. buy,t P represents the amount of electricity purchased at time t; sell,t P represents the amount of electricity sold at time t; PVT,t P represents the photovoltaic power generation at time t; WT,t P represents the wind power generation capacity at time t; CHP,t η represents the combined heat and power output at time t; h,CHP / η e,CHP For electrothermal conversion efficiency; f h,PVT H is the energy conversion coefficient of the photovoltaic system. L,t For heat load; c a T is the specific heat capacity of air. Si,t and T Zi,t These are the air conditioning temperature and the indoor temperature; V P2G,t V represents the amount of H2 produced at time t; HS,t Let η be the gas storage capacity of the gas storage tank at time t; e,CHP For cogeneration conversion efficiency; Q H2 The calorific value of H2; Electro-gas conversion model: The principle of electro-gas conversion is: CO2 + 4H2 → CH4 + 2H2O, and the amount of H2 produced is calculated as follows: In the formula, V P2G,t Q represents the amount of H2 produced by electro-gas conversion at time t; H2 The calorific value of H2; η P2G The conversion rate of H2; Constraints (5b) and (5c) limit the ramping power and start / stop capability of the electro-pneumatic conversion equipment: μ P2G ·P P2G,min ≤P P2G,t ≤μ P2G ·P P2G,max (5b) |P P2G,t -P P2G,,t-1 |≤P P2G,ramp (5c) Where μP₂G is the electro-gas conversion efficiency; P P2G,min and P P2G,max Power limitations for electro-gas conversion equipment; P P2G,ramp This represents the maximum ramping power of the electro-gas conversion equipment. Cogeneration model: Cogeneration equipment simultaneously generates electricity and usable heat, and its power constraint is expressed as: P CHP,min ≤P CHP,t ≤P CHP,max (6a) In the formula, P CHP,min and P CHP,max Power limitations for combined heat and power (CHP) equipment; Hydrogen storage model: V H2,t =V H2,t-1 -V HS,t-1 (7a) 0≤V H2,t ≤V H2,r (7b) V HS,min ≤V HS,t ≤V HS,max (7c) In the formula V H2,t V represents the output flow rate of hydrogen at time t; HS,t V represents the amount of gas stored in the gas storage tank at time t; HS,max and V HS,min Due to capacity limitations of the gas storage tank; HVAC model: The physical building structure based on HVAC includes outdoor space, indoor space and walls. The heat storage and heat transfer of the building components based on HVAC are represented by heat capacity and thermal resistance to describe the thermal behavior of their spatial distribution. The energy balance equation for region i and wall i is expressed as: Among them, T out ,T Z ,T W These are the building's exterior, interior, and wall nodes; C Z C W It is the heat capacity of the building and walls; R in ,R out ,R W It is the thermal resistance between nodes; Q rad It refers to the absorption of solar radiation by walls and interior spaces; Q HVAC It is heat from HVAC; Q int It is random internal heat; Micro-energy storage model: E BES,t =E BES,t-1 +n ch P BESch,t-1 -P BESdis,t-1 / or dis (9a) 0.1*E BES,r ≤E BES,t ≤0.9*E BES,r (9b) 0≤P BESch,t ≤P BESch,max (9c) 0≤P BESdis,t ≤P BESdis,max (9d) In the formula, P BESch,t and P BESdis,t These represent the battery's energy storage charging and discharging power, respectively; P BESch,max and P BESdis,max Battery energy storage charging and discharging power limitations; E BES,r This is a limitation based on the rated capacity of the battery energy storage.

5. The method for coordinated intelligent control of main-distribution-consumption systems to improve the large-scale photovoltaic consumption of distribution networks according to claim 4, characterized in that, The user-side electric vehicle constraints: 0.1*E EVB,i,r ≤E EVB,i,t ≤0.9*E EVB,i,r (10a) Among them, E EVB,i,t E represents the battery charge of the i-th car at time t; EVB,i,r This represents the rated capacity of the battery of the i-th car. Plug-in fast charging model: The DC fast charging power of the vehicle must meet the following constraints: 0 ≤ P EVBch,i,t ≤P EVBch,max (10b) E EVBch,i,t =P EVBch,i,t Δt (10c) THAT EVB,i,t =E EVB,i,t-1 +η ch THAT EVBch,i,t-1 (10d) Among them, P EVBch,i,t P represents the DC fast charging power of the i-th car at time t; EVBch,max Indicates the maximum power of DC fast charging; E EVBch,i,t η represents the charge level of the i-th car at time t; ch Indicates charging efficiency; For each vehicle, the energy it carries when leaving the charging station satisfies the following constraints: In the formula, E EVB,i,intial and E EVB,i,dep These represent the battery charge of the i-th vehicle when it enters and exits the charging station, respectively.

6. The method for coordinated intelligent control of main-distribution-consumption systems to improve the large-scale photovoltaic consumption of distribution networks according to claim 5, characterized in that, Step (4) specifically involves: After distributed photovoltaic (PV) installation, the power flow and voltage distribution of the distribution network lines are altered. Assuming there are n nodes on the distribution network feeder, calculate the voltage U at node k. k for: In the formula: U0 is the voltage at the connection point between the distribution network and the upstream power grid; U i-1 Let P be the voltage at node i-1, where i = 1, 2, ..., n; DG,j and Q DG,j These represent the active and reactive power of the distributed photovoltaic system at node j, where j = 1, 2, ..., n; P L,j and Q L,j These represent the active and reactive power of the load at node j, respectively; i Let be the line length between node i and node i-1; r and x are the resistance and reactance per unit length of line, respectively; When the output of distributed photovoltaic (PV) power sources connected to a node exceeds the load power, the node experiences reverse power flow and is considered a power source node injecting power into the grid. As shown in equation (11a), the greater the output of distributed PV, the higher the node voltage, and the closer to the end of the line, the more significant the voltage rise. When the capacity of distributed PV connected to the grid reaches a certain level, the line power flow and node voltage may exceed the allowable values. Among them, U min and U max I represents the upper and lower limits of the voltage at distribution network nodes. max U is the upper limit of the current in the distribution network lines. i,t and I ij,t Let be the voltage of each node and the current of each line in the distribution network at time t, respectively. Equations (12a) and (12b) are the constraints for safe operation of the distribution network. Micro-energy storage model considering reactive power coordination of energy storage system: 0.1*E BES,r ≤E BES,t ≤0.9*E BES,r (13b) 0≤P BESch,t ≤S DC-DC (13e) 0≤P BESdis,t ≤S DC-DC (13f) -S DC-DC ≤Q BESch,t ≤S DC-DC (13g) -S DC-DC ≤Q BESdis,t ≤S DC-DC (13h) In the formula, P BESch,t and P BESdis,t These represent the active power of battery charging and discharging, respectively; Q BESch,t and Q BESdis,t These represent the reactive power of battery energy storage charging and discharging; S DC-DC E represents the capacity of the DC-DC converter between the energy storage battery and the power distribution network. BES,r Limited by the rated capacity of battery energy storage; The SOP coordination control constraints are as follows: P SOP,i,t +P SOP,j,t =0 (14a) -S SOP,i ≤P SOP,i,t ≤S SOP,i (14b) -Q SOP,i,l ≤Q SOP,i,t ≤Q SOP,i,u (14c) Among them, P sop,i,t Q sop,i,t S represents the input active power and input reactive power of SOP at node i at time t. sop,i Q sop,i,l Q sop,i,u , which represent the SOP capacity, lower limit, and upper limit of reactive power accessed by node i, respectively.

7. The method for coordinated intelligent control of main-distribution-consumption systems to improve the large-scale photovoltaic consumption of distribution networks according to claim 6, characterized in that, In the photovoltaic carrying capacity enhancement model, maximizing the photovoltaic capacity connected to the distribution network is the objective function: Among them, Ω PV For photovoltaic generator sets connected to the distribution network, C PV,i This represents the maximum capacity that photovoltaic generator i is allowed to connect to; In addition, distributed photovoltaic power meets the following constraints: 0≤P PV,i,t ≤P PV,i,max (16a) -Q PV,i,max ≤Q PV,i,t ≤Q PV,i,max (16b) In the formula, P PV,i,t Q PV,i,t P represents the input active power and input reactive power of PV at node i at time t. PV,i,max Q PV,i,max C represents the upper limit of the active and reactive power of the PV connected to node i, respectively; PV,i Let be the PV capacity accessed by node i.

8. The method for coordinated intelligent control of main-distribution-consumption systems to improve the large-scale photovoltaic consumption of distribution networks according to claim 6, characterized in that, The new energy carrying capacity evaluation system established in step (5) is as follows: (1) Photovoltaic penetration rate: In the formula: n represents the number of distributed photovoltaic systems; D i Where i is the installed capacity of distributed photovoltaic system; m is the number of loads; P j Let j be the power of load j; (2) Percentage of photovoltaic power generation: In the formula: t is time; D i (t) represents the power of distributed photovoltaic i at time t; P j (t) represents the power of load j at time t; (3) Photovoltaic utilization rate: In the formula: P ri (t) represents the available power generation of distributed photovoltaic i at time t; (4) Probability of photovoltaic reverse transmission: Where: Δt a The duration of the a-th reverse transmission period; t b The duration of the b-th time period to be evaluated; (5) Percentage of photovoltaic power fed back to the grid: In the formula: ΔP(t) is the power flow reverse transmission power at time t.

Images