New energy field station AGC distribution method for power distribution

Abstract

The invention provides a new energy field station AGC distribution method for power distribution, and the method comprises the steps: obtaining the characteristic information of a target photovoltaic power station, and the characteristic information at least comprises the grid-connected point voltage value of the photovoltaic power station, the environment temperature of the photovoltaic power station, and the solar irradiance task profile data; based on the characteristic information, an improved droop control strategy is adopted to calculate the reactive power of the photovoltaic power station; obtaining a reactive power regulation margin based on the reactive power of the photovoltaic power station; generating an active power distribution instruction based on the reactive power regulation margin, the distribution instruction being used for distributing active power; a photovoltaic power supply fatigue loss model is established, a photovoltaic power supply fatigue loss result is obtained, and photovoltaic power supply loss at least comprises IGBT loss and capacitor loss; based on the photovoltaic power supply fatigue loss result, a photovoltaic reactive power optimal distribution model is established, and based on the photovoltaic reactive power optimal distribution model, a distribution strategy is generated, and the distribution strategy is used for distributing reactive power.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power systems, and in particular to a new energy station AGC distribution method for power distribution. Background Art

[0002] With the rapid development of renewable energy, the proportion of new energy such as wind and solar energy in the power system is increasing, bringing new challenges to the stable operation of the power grid. The traditional automatic generation control (AGC) system is mainly aimed at the dispatching control of conventional generators, and it is difficult to cope with the volatility and intermittent characteristics of new energy stations.

[0003] The power generation characteristics of new energy stations are different from those of traditional thermal power units. The power generation of wind and solar energy is greatly affected by weather conditions and has significant randomness and uncertainty. This characteristic poses challenges to the power balance and frequency stability of the power grid, and also affects the stability of voltage. In order to ensure the safe and stable operation of the power system, the AGC system of the new energy station needs to have efficient prediction capabilities, real-time dispatch response capabilities and precise voltage regulation capabilities.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The main purpose of the present invention is to provide a new energy station AGC allocation method for power allocation to solve the problems of unstable and low precision AGC allocation in uncertain environments in the prior art.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present invention, a new energy station AGC allocation method for power allocation is provided, comprising: step S1: obtaining characteristic information of a target photovoltaic power station, wherein the characteristic information at least includes: a voltage value of a photovoltaic power station grid connection point, an ambient temperature of the photovoltaic power station, and solar irradiance mission profile data; step S2: based on the characteristic information, calculating the reactive power of the photovoltaic power station using an improved droop control strategy; step S3: obtaining a reactive power regulation margin based on the reactive power of the photovoltaic power station; step S4: generating an active power allocation instruction based on the reactive power regulation margin, and the allocation instruction is used to allocate active power; step S5: establishing a photovoltaic power supply fatigue loss model, and obtaining a photovoltaic power supply fatigue loss result, wherein the photovoltaic power supply loss at least includes: an IGBT loss and a capacitor loss; step S6: establishing a photovoltaic reactive power optimal allocation model based on the photovoltaic power supply fatigue loss result, and generating an allocation strategy based on the photovoltaic reactive power optimal allocation model, wherein the allocation strategy is used to allocate reactive power.

[0007] Optionally, in step S2, it includes: step S21: controlling the droop slope m of the node voltage-reactive curve in the grid-connected point to be within a preset range; step S22: based on the overvoltage boundary voltage UOBP, the voltage value of the photovoltaic power station grid-connected point, the overvoltage boundary power, and the droop slope m of the node voltage-reactive curve in the grid-connected point, obtaining the reactive power of the photovoltaic power station at the photovoltaic power station grid-connected point.

[0008] Optionally, step S4 includes: step S31: when it is determined that the reactive power regulation margin is sufficient, outputting photovoltaic active power according to the maximum power tracking control strategy; step S32: when it is determined that the reactive power regulation margin is insufficient, reducing part of the photovoltaic active power according to the variable power tracking control strategy.

[0009] Optionally, step S5 includes: step S51: establishing an IGBT fatigue loss model to obtain an IGBT fatigue loss result; step S52: establishing a capacitor fatigue loss model to obtain a capacitor fatigue loss result.

[0010] Optionally, in step S51, it includes: step S511: obtaining IGBT switching power loss and IGBT conduction power loss; step S512: obtaining IGBT power loss based on IGBT switching power loss and IGBT conduction power loss; step S513: using Foster model to convert IGBT power loss to obtain IGBT thermoelectric loss; step S514: based on IGBT thermoelectric loss, using rain flow counting method to obtain IGBT thermal load information; step S515: based on IGBT thermal load information, using Bayerer model to evaluate IGBT fatigue loss to obtain IGBT fatigue loss model; step S516: based on IGBT fatigue loss model, obtaining IGBT fatigue loss result.

[0011] Optionally, in step S511, the steps include: step S5111: obtaining the IGBT switch power loss based on the inverter output current, the rated on-state voltage drop of the IGBT at 25°C, the rated on-state resistance of the IGBT at 25°C, the IGBT junction temperature, and the temperature influence coefficient of the IGBT on-state resistance; step S5112: obtaining the IGBT switch power loss based on the IGBT module turn-on and turn-off frequency, the temperature influence coefficient of the IGBT switch loss, the voltage coefficient of the DC side voltage of the photovoltaic inverter affecting the IGBT switch power loss, and the gate resistance R g The resistance coefficient that affects the IGBT switching power loss is used to obtain the IGBT conduction power loss.

[0012] Optionally, in step S52, it also includes: step S521: obtaining capacitor power loss; step S522: obtaining capacitor hot spot temperature based on capacitor power loss; step S523: obtaining capacitor thermal load information based on capacitor hot spot temperature by using rain flow counting method; step S524: obtaining capacitor fatigue loss model based on capacitor thermal load information; step S525: obtaining capacitor fatigue loss result based on capacitor fatigue loss model.

[0013] Optionally, step S6 includes: step S61: obtaining an average value of fatigue damage of the photovoltaic power source based on the IGBT fatigue loss results and the capacitor fatigue loss results; step S62: establishing a photovoltaic reactive power distribution model based on the average value of fatigue damage of the photovoltaic power source; step S63: obtaining a reactive power distribution instruction based on the photovoltaic reactive power distribution model.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the new energy station AGC allocation method for power allocation of the above embodiment.

[0015] According to another aspect of the present invention, a processor is provided, and the processor is used to run a program, wherein when the program is run, the new energy station AGC allocation method for power allocation of the above embodiment is executed.

[0016] By applying the technical solution of the present invention, the method can monitor the power generation status, load demand and grid frequency of the new energy station in real time, calculate the reactive power of the photovoltaic power station by adopting an improved droop control strategy, and obtain the reactive control margin based on the reactive power of the photovoltaic power station, so as to more accurately allocate reactive power and improve the reactive power allocation accuracy of the photovoltaic power station. At the same time, in order to cope with the volatility of new energy power generation, the reactive power output is adjusted to maintain the voltage within a safe range, thereby ensuring the voltage stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 A schematic diagram of the process of the AGC allocation method for power allocation in a new energy station according to the present invention is shown;

[0019] Figure 2 A schematic diagram of the ambient temperature of a photovoltaic power station according to the present invention is shown;

[0020] Figure 3A data diagram of solar irradiance mission profile of a photovoltaic power station according to the present invention is shown;

[0021] Figure 4 A schematic diagram showing an improved droop control strategy according to the present invention is shown;

[0022] Figure 5 A schematic diagram of a variable power tracking control strategy according to the present invention is shown;

[0023] Figure 6 A schematic diagram of an IGBT thermoelectric coupling model according to the present invention is shown;

[0024] Figure 7 A schematic diagram of a capacitor thermoelectric coupling model according to the present invention is shown;

[0025] Figure 8 A schematic diagram of the rainflow counting method according to the present invention is shown.

[0026] Fig. 9 A schematic diagram of power allocation of a photovoltaic power station based on deep reinforcement learning according to the present invention is shown. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] According to an embodiment of the present invention, a method embodiment of a new energy station AGC distribution method for power distribution is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] Figure 1 A new energy station AGC allocation method for power allocation according to an embodiment of the present invention is as follows: Figure 1 As shown, the method comprises the following steps:

[0031] Step S1: Acquire characteristic information of a target photovoltaic power station, wherein the characteristic information at least includes: a voltage value of a photovoltaic power station grid connection point, an ambient temperature of the photovoltaic power station, and solar irradiance mission profile data.

[0032] like Figure 2 , Figure 3 As shown, the ambient temperature and solar irradiance of the photovoltaic power station change based on time.

[0033] Step S2: Based on the characteristic information, the reactive power of the photovoltaic power station is calculated using an improved droop control strategy.

[0034] Step S3: obtaining reactive power control margin based on the reactive power of the photovoltaic power station.

[0035] Step S4: Based on the reactive power control margin, an active power allocation instruction is generated, and the allocation instruction is used to allocate active power.

[0036] Step S5: Establishing a photovoltaic power supply fatigue loss model to obtain photovoltaic power supply fatigue loss results, wherein photovoltaic power supply loss at least includes: IGBT loss and capacitor loss.

[0037] Step S6: Based on the photovoltaic power source fatigue loss result, a photovoltaic reactive power optimal allocation model is established, and an allocation strategy is generated based on the photovoltaic reactive power optimal allocation model, wherein the allocation strategy is used to allocate reactive power.

[0038] According to the above steps, the method can monitor the power generation status, load demand and grid frequency of the new energy station in real time, calculate the reactive power of the photovoltaic power station by adopting an improved droop control strategy, and obtain the reactive control margin based on the reactive power of the photovoltaic power station, so as to more accurately distribute the reactive power and improve the reactive power distribution accuracy of the photovoltaic power station. At the same time, in order to cope with the volatility of new energy power generation, the reactive power output is adjusted to maintain the voltage within a safe range to ensure the voltage stability of the power system.

[0039] Optionally, in step S2, it includes: step S21: controlling the droop slope m of the node voltage-reactive curve in the grid-connected point to be within a preset range; step S22: based on the overvoltage boundary voltage UOBP, the voltage value of the photovoltaic power station grid-connected point, the overvoltage boundary power, and the droop slope m of the node voltage-reactive curve in the grid-connected point, obtaining the reactive power of the photovoltaic power station at the photovoltaic power station grid-connected point.

[0040] Specifically, Figure 4 As shown in the figure, in order to overcome the shortcomings of traditional local control, the overvoltage boundary point U is set. OBP i , the reactive power control equation is:

[0041]

[0042] Where U i is the voltage at node i; Q PV,i is the inverter reactive output of node i; the droop slope m of the voltage-reactive curve i should be limited to a reasonable range, that is, m i ∈(m min,i ,m max,i ), on the one hand, the voltage-reactive power curve cannot be too inclined, otherwise the reactive power output will be too sensitive to voltage disturbances, causing voltage stability problems. The lower limit of the slope can be determined by the voltage sensitivity theory:

[0043]

[0044] Where: SVQ i,j represents the sensitivity matrix S VQ The (i,j)th element of means the voltage change of node i caused by the unit reactive power change of node j;

[0045] On the other hand, the slope of the curve cannot be too gentle, otherwise it will lose the ability to suppress undervoltage. It should be ensured that the inverter has sufficient regulation ability when the voltage exceeds the lower limit. The undervoltage boundary point is set to ensure that the inverter can inject sufficient reactive power to suppress the occurrence of voltage exceeding the lower limit in extreme scenarios. i Upper limit of the range of variation:

[0046]

[0047] Optionally, step S4 includes: step S31: when it is determined that the reactive power regulation margin is sufficient, outputting photovoltaic active power according to the maximum power tracking control strategy; step S32: when it is determined that the reactive power regulation margin is insufficient, reducing part of the photovoltaic active power according to the variable power tracking control strategy.

[0048] Specifically, Figure 5As shown in the figure, in order to ensure the effectiveness of voltage control, it is necessary to first ensure the adequacy of reactive power adjustable capacity. OV and δ UV They are the reactive power adequacy verification parameters for the extreme upper limit and lower limit scenarios, respectively. OV As an example, the node voltage under the most serious overvoltage scenario in history is recorded as Umax i, where s represents the node where the maximum voltage appears in the network. At this time, the photovoltaic power is close to or equal to the rated power, and the reactive adjustable capacity at this time is recorded as All inverters are Absorbing reactive power, the adequacy results are as follows:

[0049]

[0050] Where: the numerator represents the degree of node s exceeding the limit; the denominator represents the maximum voltage change of node s that can be caused by all inverters. If node s no longer exceeds the voltage upper limit, then the remaining nodes will not exceed the voltage upper limit. If δ OV >1, the overvoltage risk cannot be eliminated, and the network needs to be equipped with more reactive resources; if δ OV ≤1, the overvoltage risk can be eliminated.

[0051] When the reactive power regulation margin is sufficient, the maximum power tracking control strategy is used to output the photovoltaic active power. When the reactive power regulation margin is insufficient, the variable power tracking control is used to reduce part of the photovoltaic active power. The MPPT / VPPT coordinated control strategy is adopted. When the reactive capacity is sufficient, the MPPT control method is adopted; when the reactive capacity is insufficient, the variable power point tracking (VPPT) control method with variable photovoltaic operation working point tracking direction is adopted to limit the output power of the photovoltaic array to the power limit, such as Figure 4 The MPPT / VPPT coordinated control reference voltage v*pv is shown in equation (5).

[0052]

[0053] Optionally, step S5 includes: step S51: establishing an IGBT fatigue loss model to obtain an IGBT fatigue loss result; step S52: establishing a capacitor fatigue loss model to obtain a capacitor fatigue loss result.

[0054] Specifically, Figure 6 As shown, IGBT electrical loss is divided into switching loss and conduction loss, and the calculation formula is:

[0055]

[0056] In the formula, is the IGBT switching loss, is the IGBT conduction loss; δ(t) is the duty cycle; i a is the output current of the photovoltaic inverter; T j is the IGBT junction temperature; V CE_25℃ is the rated on-state voltage drop of IGBT at 25°C; r CE_25℃ K is the rated on-state resistance of the IGBT at 25°C; V_T K is the temperature influence coefficient of IGBT on-state voltage drop; r_T is the temperature influence coefficient of the on-state resistance of the IGBT; f sw K is the IGBT module turn-on and turn-off frequency; sw K is the temperature influence coefficient of IGBT switching loss; 1 (V dc ) is the voltage coefficient of the DC side voltage of the photovoltaic inverter affecting the IGBT switch power loss; K 2 (R g ) is the gate resistance R g The resistance coefficient that affects the IGBT switching power loss.

[0057] Optionally, in step S51, it includes: step S511: obtaining IGBT switching power loss and IGBT conduction power loss; step S512: obtaining IGBT power loss based on IGBT switching power loss and IGBT conduction power loss; step S513: using Foster model to convert IGBT power loss to obtain IGBT thermoelectric loss; step S514: based on IGBT thermoelectric loss, using rain flow counting method to obtain IGBT thermal load information; step S515: based on IGBT thermal load information, using Bayerer model to evaluate IGBT fatigue loss to obtain IGBT fatigue loss model; step S516: based on IGBT fatigue loss model, obtaining IGBT fatigue loss result.

[0058] Optionally, in step S511, the steps include: step S5111: obtaining the IGBT switch power loss based on the inverter output current, the rated on-state voltage drop of the IGBT at 25°C, the rated on-state resistance of the IGBT at 25°C, the IGBT junction temperature, and the temperature influence coefficient of the IGBT on-state resistance; step S5112: obtaining the IGBT switch power loss based on the IGBT module turn-on and turn-off frequency, the temperature influence coefficient of the IGBT switch loss, the voltage coefficient of the DC side voltage of the photovoltaic inverter affecting the IGBT switch power loss, and the gate resistance R g The resistance coefficient that affects the IGBT switching power loss is used to obtain the IGBT conduction power loss.

[0059] Optionally, in step S52, it also includes: step S521: obtaining capacitor power loss; step S522: obtaining capacitor hot spot temperature based on capacitor power loss; step S523: obtaining capacitor thermal load information based on capacitor hot spot temperature by using rain flow counting method; step S524: obtaining capacitor fatigue loss model based on capacitor thermal load information; step S525: obtaining capacitor fatigue loss result based on capacitor fatigue loss model.

[0060] Specifically, the Foster model and power loss model are used for IGBT thermoelectric conversion, such as Figure 6 As shown, the power loss profile is converted into a thermal stress profile;

[0061]

[0062] Where, T j is the IGBT junction temperature; T a is the ambient temperature; Ploss T and Ploss D are the power losses of IGBT and anti-parallel diode; Z tjh , Z ha is the thermal impedance parameter in the Foster model.

[0063] The capacitor temperature is calculated using the capacitor thermoelectric coupling model, such as Figure 7 As shown, the total power loss of DC capacitor can be expressed as:

[0064]

[0065] In the formula, R ESR is the ESR of the DC bus capacitor; I c is the current harmonic amplitude of the capacitor; then the hot spot temperature of the DC capacitor can be expressed as:

[0066] T h =P c,loss R th +T a (11)

[0067] Where, T h is the hot spot temperature of the capacitor; T a is the ambient temperature; R th is the thermal resistance of the capacitor given in the datasheet.

[0068] The rain flow counting method is used to count the thermal load information of IGBT and capacitor. The rain flow counting method is similar to the falling process of raindrops. The counting principle of the rain flow counting method is single-phase cycle and cycle, such as Figure 8 As shown, until all counts are extracted, the specific steps are as follows:

[0069] Step 1: Start from the valley or peak value, and stop the loop when a valley or peak value is lower or higher than the starting point;

[0070] Step 2: Starting from the valley / peak value, the cycle stops when encountering raindrops flowing down the upper slope;

[0071] Step 3: Complete the operations of step 1 and step 2 and extract all loops;

[0072] Step 4: After completing steps 1, 2, and 3, a diverging-converging wave will be left. At this time, you need to cut the waveform into two segments at the peak or trough, and then connect the leftmost and rightmost ends of the two segments, and repeat processes 1, 2, and 3 until the recording is completed.

[0073] The Bayerer model is selected to evaluate the fatigue loss of IGBT. The model can be expressed as:

[0074]

[0075] Where, ΔT j is the junction temperature fluctuation; T jmin is the minimum junction temperature; t on is the heating time of the power cycle; I is the current passing through the bonding wire; D is the bonding wire diameter; V is the blocking voltage; A, β 1 , β 2 , β 3 , β 4 , β 5 , β 6 is the Bayerer model parameter.

[0076] Calculate capacitor fatigue loss based on capacitor fatigue loss model, capacitor life model, taking into account the influence of thermal load and voltage:

[0077]

[0078] In the formula, L and L 0 are the life of the capacitor under actual working conditions and test conditions respectively; V and V 0 are the voltages under actual working conditions and test conditions respectively; T and T 0 are the temperatures under actual working conditions and test conditions respectively; E a is the activation energy of the capacitor; K B is the Boltzmann constant; n is the voltage stress exponent.

[0079] Optionally, step S6 includes: step S61: obtaining an average value of fatigue damage of the photovoltaic power source based on the IGBT fatigue loss results and the capacitor fatigue loss results; step S62: establishing a photovoltaic reactive power distribution model based on the average value of fatigue damage of the photovoltaic power source; step S63: obtaining a reactive power distribution instruction based on the photovoltaic reactive power distribution model.

[0080] Specifically, a photovoltaic reactive power optimal allocation model taking into account the fatigue loss of photovoltaic power sources is established, with the minimum average value of photovoltaic power source fatigue damage as the optimization goal, while meeting the requirements of power grid dispatching instructions. The optimization strategy problem can be expressed as:

[0081]

[0082] During the optimization process, relevant constraints such as grid dispatch, photovoltaic output and power distribution need to be met:

[0083]

[0084]

[0085] In the formula, in addition, Q t is the reactive power dispatch instruction at time t; is the reactive power reference value of the i-th photovoltaic power source at time t; N is the number of photovoltaics in the photovoltaic field; is the reactive power allocation value of the i-th photovoltaic power source at time t under the result of the average allocation method.

[0086] The deep reinforcement learning method is used to solve the optimal reactive power output command, such as Fig. 9 As shown. First, the agent is based on the state s at the current time t t Take action a t , and acts on the environment, and the environment gets reward r after executing the action t and the state s at the next moment t+1 The agent adjusts the weight parameter θ of the neural network to make the strategy θ π Update in the direction of maximizing the cumulative discounted reward, where the cumulative discounted reward can be expressed as

[0087]

[0088] Assuming that the strategy adopted by the agent at each moment is known, a state value function can be introduced to rewrite formula (X) as:

[0089]

[0090] Where: V π (s) represents the expected total reward that the agent can obtain from the initial state s when adopting strategy π. The solution of the optimal strategy is

[0091]

[0092] When the strategy π is known, its optimal solution π* can be obtained. However, if the strategy π is unknown, the agent does not know the probability distribution of all current optional actions in state s, so the state value function can be restated as

[0093]

[0094] Where: s′ represents the next state to which the agent transfers after taking action a in state s; p(s′,r|s,a) represents the probability of entering state s′ and obtaining reward r after taking action a in state s.

[0095] In order to solve the optimal strategy, we need to know the expected return that the agent can get after taking a certain action in a certain state. This state-action value function is expressed by the Bellman equation:

[0096]

[0097] By solving the state-action value function of all actions of the agent in state s, the optimal strategy solution in this state can be obtained.

[0098]

[0099] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the new energy station AGC allocation method for power allocation of the above embodiment.

[0100] According to another aspect of the present invention, a processor is provided, and the processor is used to run a program, wherein when the program is run, the new energy station AGC allocation method for power allocation of the above embodiment is executed.

[0101] This method can monitor the power generation status, load demand and grid frequency of new energy stations in real time, and optimally distribute power through advanced control algorithms. At the same time, in order to cope with the volatility of new energy power generation, the system can also integrate an intelligent voltage control module to maintain the voltage within a safe range by adjusting the reactive power output, thus ensuring the voltage stability of the power system.

[0102] In addition, the method needs to be able to flexibly respond to unexpected events, such as sudden increases and decreases in load or rapid changes in renewable energy generation. By comprehensively considering the distribution of active and reactive power, the method can maintain a safe level of voltage while maintaining frequency stability, thereby improving the reliability and adaptability of the power system.

[0103] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0104] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0105] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0106] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0107] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0108] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0109] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A new energy station AGC allocation method for power allocation, characterized in that: include: Step S1: Acquire characteristic information of a target photovoltaic power station, wherein the characteristic information at least includes: a voltage value of a photovoltaic power station grid connection point, an ambient temperature of the photovoltaic power station, and solar irradiance mission profile data; Step S2: Based on the characteristic information, the reactive power of the photovoltaic power station is calculated using an improved droop control strategy; Step S3: obtaining reactive power control margin based on the reactive power of the photovoltaic power station; Step S4: generating an active power allocation instruction based on the reactive power regulation margin, wherein the allocation instruction is used to allocate active power; Step S5: Establishing a photovoltaic power source fatigue loss model to obtain photovoltaic power source fatigue loss results; Step S6: Based on the photovoltaic power source fatigue loss result, a photovoltaic reactive power optimal allocation model is established, and an allocation strategy is generated based on the photovoltaic reactive power optimal allocation model, wherein the allocation strategy is used to allocate reactive power.

2. The AGC allocation method for new energy stations for power allocation according to claim 1, characterized in that: In the step S2, it includes: Step S21: controlling the droop slope m of the node voltage-reactive power curve in the grid connection point to be within a preset range; Step S22: Based on the overvoltage boundary voltage U OBP , the voltage value of the photovoltaic power station grid-connected point, the overvoltage boundary power, the droop slope m of the node voltage-reactive power curve at the grid-connected point, and the reactive power of the photovoltaic power station grid-connected point is obtained.

3. The AGC allocation method for new energy stations for power distribution according to claim 1, characterized in that: In the step S4, it includes: Step S31: when it is determined that the reactive power regulation margin is sufficient, outputting photovoltaic active power according to the maximum power tracking control strategy; Step S32: when it is determined that the reactive power regulation margin is insufficient, a portion of the photovoltaic active power is reduced according to a variable power tracking control strategy.

4. The AGC allocation method for new energy stations for power distribution according to claim 1, characterized in that: In the step S5, it includes: Step S51: Establish an IGBT fatigue loss model to obtain an IGBT fatigue loss result; Step S52: Establishing a capacitor fatigue loss model to obtain capacitor fatigue loss results.

5. The AGC allocation method for new energy stations for power allocation according to claim 4, characterized in that: In the step S51, it includes: Step S511: Obtaining IGBT switching power loss and IGBT conduction power loss; Step S512: obtaining the IGBT power loss based on the IGBT switch power loss and the IGBT conduction power loss; Step S513: converting the IGBT power loss using the Foster model to obtain the IGBT thermoelectric loss; Step S514: Based on the IGBT thermal loss, a rain flow counting method is used to obtain IGBT thermal load information; Step S515: Based on the IGBT thermal load information, the Bayerer model is used to evaluate the IGBT fatigue loss to obtain an IGBT fatigue loss model; Step S516: Based on the IGBT fatigue loss model, obtain the IGBT fatigue loss result.

6. The AGC allocation method for new energy stations for power allocation according to claim 5, characterized in that: In the step S511, it includes: Step S5111: obtaining the IGBT switch power loss based on the inverter output current, the rated on-state voltage drop of the IGBT at 25° C., the rated on-state resistance of the IGBT at 25° C., the IGBT junction temperature, and the temperature influence coefficient of the IGBT on-state resistance; Step S5112: Based on the IGBT module on and off frequency, the temperature influence coefficient of the IGBT switch loss, the voltage coefficient of the photovoltaic inverter DC side voltage affecting the IGBT switch power loss, the gate resistance R g The resistance coefficient that affects the IGBT switching power loss is used to obtain the IGBT conduction power loss.

7. The AGC allocation method for new energy stations for power allocation according to claim 4, characterized in that: In the step S52, it also includes: Step S521: obtaining capacitor power loss; Step S522: obtaining the hot spot temperature of the capacitor based on the capacitor power loss; Step S523: Based on the hot spot temperature of the capacitor, a rain flow counting method is used to obtain the thermal load information of the capacitor; Step S524: obtaining a capacitor fatigue loss model based on the capacitor thermal load information; Step S525: Based on the capacitor fatigue loss model, a capacitor fatigue loss result is obtained.

8. The AGC allocation method for new energy stations for power allocation according to claim 4, characterized in that: In the step S6, it includes: Step S61: based on the IGBT fatigue loss results and the capacitor fatigue loss results, obtaining the average value of photovoltaic power supply fatigue damage; Step S62: establishing a photovoltaic reactive power distribution model based on the average value of photovoltaic power source fatigue damage; Step S63: obtaining reactive power distribution instructions based on the photovoltaic reactive power distribution model.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the new energy station AGC allocation method for power allocation as described in any one of claims 1 to 8.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the new energy station AGC allocation method for power allocation as described in any one of claims 1 to 8.

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