A method, device and terminal equipment for optimizing scheduling of frequency modulation

Abstract

The application is suitable for the technical field of power system frequency modulation control, and provides a secondary frequency modulation optimization scheduling method, device and terminal equipment, which comprises the following steps: a virtual power plant model is constructed, and supply side data and demand side data are obtained; a long time scale day-ahead optimization scheduling plan with a target function of maximum secondary frequency modulation benefit is constructed based on the supply side data and the demand side data; a short time scale intra-day rolling scheduling plan with a target function of highest comprehensive frequency modulation performance is constructed based on the long time scale day-ahead optimization scheduling plan; a first target function with minimum secondary frequency modulation error penalty is constructed based on the short time scale intra-day rolling scheduling plan and real-time supply side data; and the short time scale intra-day rolling scheduling plan is feedback corrected based on the first target function, intra-day supply side data and intra-day demand side data. The application not only improves the frequency modulation benefit and effect of the virtual power plant secondary frequency modulation, but also reduces the wind / generation curtailment rate and improves the new energy consumption rate.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system frequency modulation control, and particularly relates to a secondary frequency modulation optimization scheduling method and device and a terminal equipment. BACKGROUND

[0002] In recent years, more and more new energy units such as photovoltaic and wind power are integrated into the power grid system, but the output of the new energy units is greatly affected by environmental factors such as climate and illumination, and the intermittent and volatile output characteristics are particularly prominent, which reduces the ability of the power grid to maintain frequency quality and threatens the safe and stable operation of the power grid.

[0003] As a new type of power energy aggregation technology in the future energy market, a virtual power plant (VPP) can aggregate various types of energy distributed in different areas into a stable and controllable energy set, and reasonably optimize and utilize various adjustable resources, which not only plays an important role in balancing the supply and demand of the power grid, but also better provides auxiliary frequency modulation services for the power grid.

[0004] However, most of the existing methods for evaluating the benefits of virtual power plants evaluate the benefits from internal costs and life cycle capital flows, and do not comprehensively consider frequency modulation benefits and frequency modulation performance. SUMMARY

[0005] To overcome the problems in the related art, the embodiments of the application provide a secondary frequency modulation optimization scheduling method, device and terminal equipment to improve the frequency modulation benefits and effects of secondary frequency modulation of the virtual power plant, and also effectively reduce the impact of the deviation between the day-ahead optimization scheduling plan and the actual situation, reduce the wind / photovoltaic curtailment rate, and improve the consumption rate of new energy.

[0006] The application is implemented by the following technical solutions:

[0007] In a first aspect, the embodiments of the application provide a secondary frequency modulation optimization scheduling method, which includes:

[0008] A virtual power plant model is constructed to obtain supply side data and demand side data; a long time scale day-ahead optimization scheduling plan is constructed with the maximum secondary frequency modulation benefit as an objective function based on the supply side data and the demand side data; a short time scale intra-day rolling scheduling plan is constructed with the highest comprehensive frequency modulation performance as an objective function based on the long time scale day-ahead optimization scheduling plan; a first objective function with the minimum secondary frequency modulation error penalty is constructed based on the short time scale intra-day rolling scheduling plan and real-time supply side data; and the short time scale intra-day rolling scheduling plan is feedback corrected based on the first objective function, intra-day supply side data and intra-day demand side data.

[0009] In a possible implementation form of the first aspect, the virtual power plant model comprises a supply side and a demand side, the supply side comprises conventional frequency modulation units, new energy frequency modulation units and energy storage systems, the demand side comprises flexible loads and controllable loads, the new energy frequency modulation units comprise wind power frequency modulation units and photovoltaic frequency modulation units, and the flexible loads comprise electric vehicles. The supply side data comprises historical supply side data and predicted supply side data, and the demand side data comprises historical demand side data and predicted demand data.

[0010] In a possible implementation form of the first aspect, the supply side data and the demand side data are acquired, comprising:

[0011] The wind power frequency modulation unit output power is acquired, and an expression of the wind power frequency modulation unit output power is:

[0012]

[0013] wherein,

[0014]

[0015] In the formula, P WT is the wind power frequency modulation unit output power, P r is a rated maximum power of the wind power frequency modulation unit, v r is a rated wind speed of the wind turbine, v ci is a cut-in wind speed of the wind turbine, v co is a cut-out wind speed of the wind turbine, and v is a real-time wind speed.

[0016] The photovoltaic frequency modulation unit output power is acquired, and an expression of the photovoltaic frequency modulation unit output power is:

[0017] P PV = P ST K AC [1+k W (T c -T ST )] / K ST

[0018] In the formula, P PV is the photovoltaic frequency modulation unit output power, P ST is a photovoltaic frequency modulation unit output power under standard test conditions, K AC is an illumination intensity, k W is a power temperature coefficient, T c is a working temperature of a photovoltaic array, T ST is a standard test environment temperature, K ST is an illumination intensity under standard test conditions.

[0019] The electric vehicle demand power is acquired, and an expression of the electric vehicle demand power is:

[0020]

[0021] wherein N is the number of electric vehicles, i is the i-th electric vehicle, M is the number of simulation times, j is the j-th simulation, and t is the 24 time points in the scheduling period, is the charging power of each electric vehicle at t, is the discharging power of each electric vehicle at t, is the total demand power of electric vehicles after M times of simulation at t.

[0022] In a possible implementation manner of the first aspect, the long-time-scale day-ahead optimization scheduling plan with the maximum frequency modulation benefit as the objective function is constructed, including:

[0023] Based on the supply-side data and the demand-side data, the long-time-scale day-ahead scheduling plan is constructed.

[0024] A second objective function with the maximum frequency modulation benefit is constructed, and the expression of the second objective function is:

[0025] MaxC pro =C in -C cost

[0026] wherein C pro is the net profit obtained by the frequency modulation, C in is the total compensation benefit of the frequency modulation, and C cost is the total expenditure of the frequency modulation. The total compensation benefit of the frequency modulation is determined based on the total frequency modulation mileage compensation at t and the total frequency modulation capacity compensation at t, and the total expenditure of the frequency modulation is determined based on the cost of the traditional frequency modulation unit participating in the frequency modulation, the cost of the new energy frequency modulation unit participating in the frequency modulation, the cost of the energy storage system participating in the frequency modulation, and the cost of the flexible load participating in the frequency modulation.

[0027] Based on the second objective function and the long-time-scale day-ahead scheduling plan, the long-time-scale day-ahead optimization scheduling plan is constructed.

[0028] In a possible implementation manner of the first aspect, the long-time-scale day-ahead optimization scheduling plan is further determined based on a supply-side output value constraint, a maximum frequency modulation power allowable deviation constraint, and an energy storage power constraint. The supply-side output value constraint is determined based on the output value of the traditional energy frequency modulation unit, the output value of the new energy frequency modulation unit, the output value of the energy storage system, and the output value of the flexible load. The output value of the traditional energy frequency modulation unit, the output value of the new energy frequency modulation unit, the output value of the energy storage system, and the output value of the flexible load are respectively subject to their own physical property constraints. The maximum frequency modulation power operation deviation constraint value is determined based on the frequency modulation demand power reference value and the frequency modulation power allowable deviation value. The energy storage power constraint is determined based on the state of charge of the energy storage unit, the property of the energy storage unit, and the charging and discharging rules of the energy storage unit.

[0029] In a possible implementation manner of the first aspect, the short-time-scale intra-day rolling scheduling plan with the highest comprehensive frequency modulation performance as an objective function is constructed, including:

[0030] The long-time-scale day-ahead optimal scheduling plan is divided into a plurality of short-time-scale intra-day scheduling plans.

[0031] A third objective function with the highest comprehensive frequency modulation performance is constructed, and an expression of the third objective function is as follows:

[0032]

[0033] In the expression, k z is a comprehensive frequency modulation performance index, n is a number of frequency modulation units participating in secondary frequency modulation, k 1.i is a regulation rate index, k 2.i is a response time index, and k 3.i is a regulation accuracy index.

[0034] In the expression, the regulation rate index is a rate at which the frequency modulation unit responds to a secondary frequency modulation control instruction, and an expression of the regulation rate index is as follows: k 1.i = k v.i / k av , k v.i is a measured regulation rate of each frequency modulation unit participating in frequency modulation, and k av is an average regulation rate of each frequency modulation unit participating in frequency modulation. The response time index is a time delay of the frequency modulation unit responding to the secondary frequency modulation control instruction, and an expression of the response time index is as follows: k 2.i = 1-(T d.i / 5×60), T d.i is a response delay time of each frequency modulation unit participating in frequency modulation, and the unit is second. The regulation accuracy index is an accuracy of the frequency modulation unit responding to the secondary frequency modulation control instruction, and an expression of the regulation accuracy index is as follows: ξ i is a regulation error of the frequency modulation unit, and ξ al is an allowed error of the regulation of the frequency modulation unit.

[0035] Based on the third objective function and the short-time-scale intra-day scheduling plan, a short-time-scale intra-day rolling scheduling plan is constructed.

[0036] In a possible implementation manner of the first aspect, an expression of the first objective function with the minimum secondary frequency modulation error penalty is as follows:

[0037]

[0038] In the expression, f3 is a secondary frequency modulation error penalty, is real-time supply-side data, and λ is a penalty factor.

[0039] In a possible implementation manner of the first aspect, the Monte Carlo method is adopted to obtain the demand power of the electric vehicle through M times of simulation; and the quantum genetic algorithm is adopted to calculate the frequency modulation optimization scheduling method.

[0040] In the second aspect, the embodiments of the present application provide a frequency modulation optimization scheduling device, comprising:

[0041] A model construction module is configured to construct a virtual power plant model and obtain supply side data and demand side data. A scheduling plan construction module is configured to construct, based on the supply side data and the demand side data, a long time scale day-ahead optimization scheduling plan with a maximum frequency modulation benefit as an objective function; and to construct, based on the long time scale day-ahead optimization scheduling plan, a short time scale intra-day rolling scheduling plan with a highest comprehensive frequency modulation performance as an objective function; and to construct, based on the short time scale intra-day rolling scheduling plan and real-time supply side data, a first objective function with a minimum frequency modulation error penalty. A feedback correction module is configured to feedback correct the short time scale intra-day rolling scheduling plan based on the first objective function, intra-day supply side data and intra-day demand side data.

[0042] In the third aspect, the embodiments of the present application provide a terminal device, comprising a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor implements the frequency modulation optimization scheduling method of any one of the first aspect when executing the computer program.

[0043] In the fourth aspect, the embodiments of the present application provide a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the frequency modulation optimization scheduling method of any one of the first aspect.

[0044] In the fifth aspect, the embodiments of the present application provide a computer program product, when the computer program product runs on the terminal device, the terminal device executes the frequency modulation optimization scheduling method of any one of the first aspect.

[0045] It can be understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and will not be repeated here.

[0046] The embodiment of the application constructs a virtual power plant model, obtains supply side data and demand side data, constructs a long time scale day-ahead optimal scheduling plan with a secondary frequency modulation benefit as a target function based on the supply side data and the demand side data, combines the day-ahead optimal scheduling plan with a comprehensive frequency modulation performance index to generate a short time scale intra-day rolling scheduling plan, constructs a first target function according to real-time supply side data, and finally corrects the short time scale intra-day rolling scheduling plan based on the first target function. The embodiment of the application discloses a secondary frequency modulation optimal scheduling method based on a frequency modulation benefit and a comprehensive frequency modulation performance index, which can not only adjust the intra-day rolling scheduling plan in real time, improve the frequency modulation benefit and frequency modulation effect of the secondary frequency modulation of the virtual power plant, but also effectively reduce the influence of the deviation between the day-ahead optimal scheduling plan and the actual situation, reduce the wind / photovoltaic curtailment rate, and improve the consumption rate of new energy.

[0047] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present specification. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0049] Figure 1 is a schematic flow chart of a secondary frequency modulation optimal scheduling method provided by an embodiment of the present application;

[0050] Figure 2 is a schematic block diagram of a virtual power plant model provided by an embodiment of the present application;

[0051] Figure 3 is a flowchart of a Monte Carlo method for simulating demand power of an electric vehicle provided by an embodiment of the present application;

[0052] Figure 4 is a flowchart of constructing a long time scale day-ahead optimal scheduling plan provided by an embodiment of the present application;

[0053] Figure 5 is a flowchart of constructing a short time scale intra-day rolling scheduling plan provided by an embodiment of the present application;

[0054] Figure 6 is a structural schematic diagram of a secondary frequency modulation optimal scheduling device provided by an embodiment of the present application;

[0055] Figure 7 is a structural schematic diagram of a terminal device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0056] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0057] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0058] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0059] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0060] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0061] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0062] In recent years, the rapid development of distributed power generation makes the current power system develop towards the direction of diversified energy supply resources, decentralized generator units and clean resource types, and also brings severe challenges to the operation of the power grid, such as the reduction of system rotational inertia and the instability of frequency security. As a new type of power energy aggregation technology in the future energy market, the virtual power plant (VPP) can aggregate and uniformly dispatch and control traditional generator units, distributed new energy power sources, energy storage systems and demand side response, and provide auxiliary frequency modulation services for the power grid.

[0063] The secondary frequency modulation of the virtual power plant refers to that each frequency modulation unit in the virtual power plant provides sufficient adjustable frequency capacity and adjustment rate, and adjusts the output of each frequency modulation unit in real time within the allowable deviation to track the frequency and meet the requirements of system frequency stability.

[0064] However, the construction of the virtual power plant requires a large amount of equipment modification and technical and economic investment, therefore, most of the existing methods for evaluating the benefits of the virtual power plant evaluate the benefits from the internal cost, the whole life cycle of the fund flow, and fail to fully combine the frequency modulation benefits and the frequency modulation performance indicators, and cannot comprehensively evaluate the economic and technical values brought by the operation of the virtual power plant.

[0065] Based on the above problems, the secondary frequency modulation optimization dispatching method provided in an embodiment of the present application provides a feasible solution to the above problems.

[0066] Figure 1 is a schematic flowchart of a secondary frequency modulation optimization dispatching method provided by an embodiment of the present application, referring to Figure 1 The method can not include steps 101 to 105, and the details of the method are as follows:

[0067] In step 101, a virtual power plant model is constructed, and supply side data and demand side data are obtained.

[0068] The virtual power plant is a complex integrated system, which includes an energy management system, a communication control system, a power generation system, an energy storage system and an auxiliary system. In order to facilitate the description, Figure 2 only the parts related to the embodiments of the present application are shown. Referring to Figure 2 , the constructed virtual power plant model is described.

[0069] After the virtual power plant is incorporated into the power grid system, the power grid system will issue the calculated virtual power plant output value to the virtual power plant through the automatic generation control (AGC) instruction, and the virtual power plant will adjust the output of each frequency modulation unit to assist in stabilizing the inertia level and frequency characteristics of the power grid system.

[0070] In some embodiments, the constructed virtual power plant model can be divided into a supply side and a demand side. The supply side can include traditional units, new energy units and energy storage systems, and the demand side can include flexible loads and controllable loads. Because both traditional units and new energy units can balance the supply and demand loads of the power grid by adjusting their own output, the purpose of secondary frequency modulation is achieved, so in this application, the traditional unit is also called a traditional frequency modulation unit, and the new energy unit is called a new energy frequency modulation unit.

[0071] For example, the traditional frequency modulation unit can be a frequency modulation unit using traditional energy such as diesel, gas or coal, or a water conservancy frequency modulation unit using renewable energy. The new energy frequency modulation unit can be a wind power frequency modulation unit and a photovoltaic frequency modulation unit. Because it does not affect the scheme disclosed in this application, this application further limits the frequency modulation unit.

[0072] The energy storage system mainly includes an energy storage unit and a converter device. Optionally, the energy storage unit can be a lithium battery, a fuel cell or other type of storage battery, and this application does not further limit the energy storage.

[0073] Flexible load refers to a load that can actively participate in power grid operation control, can interact with the power grid, and has flexible characteristics, such as an electric vehicle. Controllable load refers to a specific user's load that can be limited for a period of time according to the contract under the requirement of the power supply department.

[0074] In some embodiments, after constructing the virtual power plant model, the supply side data and the demand side data need to be mastered. Among them, the supply side data includes historical supply side data and predicted supply side data, and the demand side data includes historical demand side data and predicted demand data.

[0075] The output of the traditional frequency modulation unit is more controllable, while the output of the new energy frequency modulation unit is greatly affected by environmental factors such as climate and temperature, and has obvious intermittency and volatility. The supply side data obtained in this application is mainly the output value of the new energy frequency modulation unit.

[0076] For example, the output power of the wind power frequency modulation unit is obtained, and the expression of the output power of the wind power frequency modulation unit is:

[0077]

[0078] Among them,

[0079]

[0080] In the formula, P WT is the output power of the wind power frequency modulation unit, P r is the rated maximum power of the wind power frequency modulation unit, v r is the rated wind speed of the wind turbine, and v ci is the cut-in wind speed of the wind turbine.co The wind speed is cut out for the fan, and v is the real-time wind speed.

[0081] For example, the output power of the photovoltaic frequency modulation unit is obtained, and the expression of the output power of the photovoltaic frequency modulation unit is:

[0082] P PV = P ST K AC [1+k w (T c -T ST )] / K ST

[0083] In the formula, P PV is the output power of the photovoltaic frequency modulation unit, P ST is the output power of the photovoltaic frequency modulation unit under standard test conditions, K AC is the illumination intensity, k W is the power temperature coefficient, T c is the working temperature of the photovoltaic array, T ST is the standard test environment temperature, and K ST is the illumination intensity under standard test conditions.

[0084] Because the flexible load can interact with the power grid system, it can be both a power supplier and a power user, so the influence of the flexible load on the secondary tuning in the supply side also needs to be considered.

[0085] The charging and discharging process of the electric vehicle as a flexible load is a process with strong spatial and temporal randomness, and the Monte Carlo method is a good solution to simulate spatial and temporal randomness. Therefore, the Monte Carlo method can be used to simulate the probability function of the charging and discharging characteristics of the electric vehicle to realize the prediction of the demand power of the electric vehicle.

[0086] For example, the demand power of the electric vehicle is obtained, and the expression of the demand power of the electric vehicle is:

[0087]

[0088] In the formula, N is the number of electric vehicles, i is the i-th electric vehicle, M is the number of simulations, j is the j-th simulation, t is the 24 time points in the scheduling period, is the charging power of each electric vehicle at t, is the discharging power of each electric vehicle at t, is the total demand power of the electric vehicle after M simulations at t.

[0089] Referring to Figure 3 , the specific steps of simulating the demand power of the electric vehicle by the Monte Carlo method are as follows:

[0090] A1, initialize data. Set the number of simulation times as M times, set the number of electric vehicles as N, set the initial state of charge of the electric vehicle as SOC1, set the end state of charge of the electric vehicle as SOC2, and set the charging and discharging time as T1.

[0091] A2, based on the initial state of charge SOC1, the end state of charge SOC2 and the charging and discharging time T1, calculate the charging and discharging time T cha and the time T1 of ending charging and discharging.

[0092] A3, superimpose the charging and discharging power P of the electric vehicle to obtain the demand power curve of each electric vehicle.

[0093] A4, determine whether all electric vehicle demand power curves are obtained, if not, repeat A3 until all electric vehicle demand power curves are obtained, and if yes, execute the next step.

[0094] A5, simulate all obtained electric vehicle demand power curves, and superimpose to obtain the total demand power curve of the electric vehicle.

[0095] A6, determine whether M times of simulation are performed. If not, return to step A3 until M times of simulation are performed, and if yes, execute the next step.

[0096] A7, calculate the mean value of the total power of the electric vehicle after M times of simulation.

[0097] Step 101 establishes a virtual power plant model, and discloses the power output values of the wind power frequency modulation unit, the photovoltaic frequency modulation unit and the flexible load represented by the electric vehicle. The method provides basic data for subsequent execution of the method.

[0098] In step 102, based on the supply side data and the demand side data, a long time scale day-ahead optimization scheduling plan is constructed with the maximum secondary frequency modulation benefit as the objective function.

[0099] The day-ahead scheduling plan is to coordinate and schedule the active power of the supply side in the virtual power plant in advance for 1 day according to the output value of the frequency modulation unit in the virtual power plant, the load demand and the power frequency modulation market clearing price and other messages, and to obtain the optimal economic operation scheme in day-ahead under the constraints of meeting the secondary frequency modulation instruction and various safety operation indexes issued by the superior. Figure 4 The flowchart of constructing a long time scale day-ahead optimization scheduling plan provided by an embodiment of the application is shown, referring to Figure 4 .

[0100] In step 1021, based on the supply side data and the demand side data, a long time scale day-ahead scheduling plan is constructed.

[0101] In some embodiments, a long-time-scale day-ahead scheduling plan is constructed according to various objective constraints such as output values of supply sides in the virtual power plant, user load demand, and clearing price of frequency modulation market. The output values of the supply sides can include frequency modulation units, state of charge (SOC) of energy storage systems, and flexible loads.

[0102] For example, the long-time-scale day-ahead scheduling plan divides 24 hours of the day-ahead scheduling plan into multiple long-time scales, which can be adaptively modified according to actual conditions or superior requirements. In this application, 1 hour is taken as a long-time scale to control the output values of the supply sides in the virtual power plant.

[0103] In step 1022, a second target function of maximum secondary frequency modulation benefit is constructed.

[0104] In some embodiments, the second target function of maximum secondary frequency modulation benefit is constructed on the premise of meeting superior requirements and safe operation.

[0105] For example, the expression of the second target function is as follows:

[0106] MaxC pro = C in -C cost

[0107] In the expression, C is net profit obtained by secondary frequency modulation, C is total compensation benefit of secondary frequency modulation, and C is total expenditure of secondary frequency modulation. pro in cost

[0108] Optionally, the total compensation benefit of secondary frequency modulation is determined based on total frequency modulation mileage compensation at time t and total frequency modulation capacity compensation at time t.

[0109] For example, the expression of the total compensation benefit of secondary frequency modulation is as follows:

[0110]

[0111] In the expression, C is total frequency modulation mileage compensation at time t, C is total frequency modulation capacity compensation at time t, C is frequency modulation mileage provided by the i th frequency modulation unit at time t, C is mileage settlement price of the i th frequency modulation unit at time t, C is average value of comprehensive frequency modulation performance indexes of the i th frequency modulation unit at time t, m is the number of frequency modulation units participating in secondary frequency modulation, C is frequency modulation capacity of the j th frequency modulation unit at time t, and C is frequency modulation service duration of the j th frequency modulation unit at time t. inA inB ​​​​​​​​​​The secondary frequency modulation capacity compensation standard price is determined based on the total secondary frequency modulation cost, the traditional frequency modulation unit cost, the new energy frequency modulation unit cost, the energy storage system cost, and the flexible load cost.

[0112] Optionally, the total secondary frequency modulation cost is determined based on the cost of the traditional frequency modulation unit participating in frequency modulation, the cost of the new energy frequency modulation unit participating in frequency modulation, the cost of the energy storage system participating in frequency modulation, and the cost of the flexible load participating in frequency modulation.

[0113] For example, the expression of the total secondary frequency modulation cost is as follows:

[0114]

[0115] In the formula, C TU is the cost of the traditional frequency modulation unit participating in frequency modulation, C NEU is the cost of the new energy frequency modulation unit participating in frequency modulation, C ESS is the cost of the energy storage system participating in frequency modulation, and C DSR is the cost of the flexible load participating in frequency modulation. is the output value of the traditional frequency modulation unit at time t; a i , b i , and c i are the coefficients of the quadratic function of the economic dispatch generation cost of the traditional frequency modulation unit, respectively. is the output value of the new energy frequency modulation unit at time t, c op is the operation and maintenance coefficient of the new energy frequency modulation unit, c q is the penalty coefficient of the abandoned wind or light of the new energy frequency modulation unit. is the output value of the energy storage system at time t, c om is the operation and maintenance coefficient of the energy storage system. is the load variation at time t, is the demand power value of the electric vehicle at time t. is the compensation price of the unit load participating in secondary frequency modulation at time t, is the compensation price of the electric vehicle participating in secondary frequency modulation at time t.

[0116] In some embodiments, the long-time scale day-ahead optimization scheduling plan is determined based on the supply-side output value constraint, the maximum allowable deviation constraint of the secondary frequency modulation power, and the energy storage power constraint.

[0117] Optionally, the supply-side output value constraint is determined based on the output value of the traditional energy frequency modulation unit, the output value of the new energy frequency modulation unit, the output value of the energy storage system, and the output value of the flexible load.

[0118] For example, the expression of the supply-side output value constraint is as follows:

[0119]

[0120] In the formula, a planned output value of a traditional energy frequency modulation unit at time t in a long-time scale day-ahead optimization scheduling plan, a planned output value of a traditional energy frequency modulation unit, a planned output value of a small hydropower frequency modulation unit, a planned output value of a wind power frequency modulation unit, a planned output value of a photovoltaic frequency modulation unit, a planned output value of an energy storage system, a planned output value of an electric vehicle participating in secondary frequency modulation.

[0121] Further, the planned output value of the traditional energy frequency modulation unit, the planned output value of the new energy frequency modulation unit, the planned output value of the energy storage system, and the planned output value of the flexible load are respectively constrained by their own physical properties.

[0122] For example, the expression of the supply side output unit constrained by its own physical properties is as follows:

[0123]

[0124] In the formula, is the minimum value of the output of the traditional energy frequency modulation unit, is the maximum value of the output of the traditional energy frequency modulation unit, is the minimum value of the output of the wind power frequency modulation unit, is the maximum value of the output of the wind power frequency modulation unit, is the minimum value of the output of the photovoltaic frequency modulation unit, is the maximum value of the output of the photovoltaic frequency modulation unit, is the minimum value of the output of the energy storage system, is the maximum value of the output of the energy storage system, is the minimum value of the output of the electric vehicle participating in secondary frequency modulation, is the maximum value of the output of the electric vehicle participating in secondary frequency modulation.

[0125] Optionally, the maximum constraint value of the secondary frequency modulation power operation deviation is determined based on the secondary frequency modulation demand power reference value and the maximum allowable deviation value of the secondary frequency modulation power.

[0126] For example, the expression of the maximum allowable deviation value constraint of the secondary frequency modulation power is as follows:

[0127]

[0128] In the formula, σ is the maximum allowable deviation value of the secondary frequency modulation power, is the reference value of the secondary frequency modulation demand power at time t.

[0129] Optionally, the energy storage power constraint is determined based on the state of charge of the energy storage unit, the properties of the energy storage unit itself, and the charging and discharging rules of the energy storage unit.

[0130] An exemplary expression of the energy storage power constraint is:

[0131]

[0132]

[0133] wherein,

[0134]

[0135] wherein, is the state of charge of the energy storage unit at time t, ε is the self-discharge rate of the energy storage unit, ε ∈ [0, 1], E is the capacity of the energy storage unit, is the charging power of the energy storage unit at time t, is the discharging power of the energy storage unit at time t, η is the charging and discharging efficiency of the energy storage unit, is the charging state of the energy storage unit, takes the value 0 or 1, indicates that the energy storage unit is in the charging state, is the discharging state of the energy storage unit, takes the value 0 or 1, indicates that the energy storage unit is in the discharging state, SOC min is the minimum value of the state of charge of the energy storage unit, SOC max is the maximum value of the state of charge of the energy storage unit.

[0136] It should be noted that the expression of the energy storage power constraint ensures that the energy storage unit cannot simultaneously perform charging and discharging operations, which conforms to the actual situation, and also indicates that the SOC of the energy storage unit at the start time and the end time of the scheduling period is equal, which can continue to circulate and schedule in the next period.

[0137] In step 1023, a long-time-scale day-ahead optimization scheduling plan is constructed based on the second objective function and the long-time-scale day-ahead scheduling plan.

[0138] Based on the goal orientation of the second objective function and the long-time-scale day-ahead scheduling plan, a long-time-scale day-ahead optimization scheduling plan with the maximum secondary frequency modulation benefit as the objective function is constructed.

[0139] Step 102 constructs a long-time-scale day-ahead optimization scheduling plan with the maximum secondary frequency modulation benefit as the objective function from the actual situation, which ensures the economic efficiency of the virtual power plant, takes into account the superior command and the safe operation of itself, and fully prepares for the subsequent construction of the day-ahead scheduling plan.

[0140] In step 103, a short-time-scale day-ahead rolling scheduling plan with the highest comprehensive frequency modulation performance as the objective function is constructed based on the long-time-scale day-ahead optimization scheduling plan.

[0141] Exemplarily, Figure 5 A flowchart of constructing a short-time-scale intra-day rolling scheduling plan provided by an embodiment of the present application is shown, and the flow of constructing a short-time-scale intra-day rolling scheduling plan can specifically include steps 1031 to 1033. Figure 5 , The flow of constructing a short-time-scale intra-day rolling scheduling plan can specifically include steps 1031 to 1033.

[0142] In step 1031, the long-time-scale day-ahead optimal scheduling plan is divided into a plurality of short-time-scale intra-day scheduling plans.

[0143] The intra-day scheduling plan takes the long-time-scale day-ahead optimal scheduling plan as a benchmark, focuses on the secondary frequency modulation effect of the virtual power plant on the power grid, and takes the best frequency modulation effect as the optimization target while taking into account the frequency modulation safety.

[0144] Exemplarily, a 1-hour long-time scale is divided into 4 short-time scales of 15 minutes. Taking 15 minutes as the short-time scale, the short-term prediction information such as the active power output of the supply side, the load demand, and the real-time market electricity price is updated in real time.

[0145] Optionally, when the long-time-scale day-ahead optimal scheduling plan is divided into a plurality of short-time-scale intra-day scheduling plans, a large-scale sampling can also be performed according to the error probability density function to obtain a series of deterministic sample sets reflecting the error distribution characteristics, and typical scenarios are obtained according to the similarity between samples. Under the constraints of the day-ahead optimal scheduling plan, the active power output of the supply side in the virtual power plant is more carefully coordinated and optimized in the short-time scale, the small-range active regulation of the secondary frequency modulation instruction is completed, and the optimal short-time-scale intra-day scheduling plan is obtained.

[0146] In step 1032, a third target function with the highest comprehensive frequency modulation performance is constructed.

[0147] In some embodiments, each frequency modulation unit participating in frequency modulation in the virtual power plant is very different. Different frequency modulation units are limited by their own characteristics, have different response times, and have different adjustment accuracies. In order to quickly find suitable frequency modulation units to participate in secondary frequency modulation, the present application creatively introduces the concept of a comprehensive frequency modulation performance index, and establishes a third target function with a high comprehensive frequency modulation performance index as the target.

[0148] Exemplarily, the expression of the third target function is:

[0149]

[0150] In the formula, k z is the comprehensive frequency modulation performance index, n is the number of frequency modulation units participating in secondary frequency modulation, k 1.i is the adjustment rate index, k 2.i is the response time index, and k3.i The adjustment accuracy index is adjusted.

[0151] Optionally, the adjustment rate index is the rate at which the frequency modulation unit responds to the secondary frequency modulation control instruction, and the expression of the adjustment index is:

[0152] k 1.i = k v.i / k av

[0153] In the formula, k v.i is the measured adjustment rate of each frequency modulation unit participating in frequency modulation; and k av is the average adjustment rate of each frequency modulation unit participating in frequency modulation.

[0154] Optionally, the response time index is the time delay of the frequency modulation unit responding to the secondary frequency modulation control instruction, and the expression of the response time index is:

[0155] k 2.i = 1-(T d.i / 5×60)

[0156] In the formula, T d.i is the response delay time of each frequency modulation unit participating in frequency modulation, in seconds, which is the time difference from when the frequency modulation unit receives the frequency modulation control instruction to when the corresponding response action is started.

[0157] Optionally, the adjustment accuracy index is the accuracy of the frequency modulation unit responding to the secondary frequency modulation control instruction, and the expression of the adjustment accuracy index is:

[0158]

[0159] In the formula, ξ i is the adjustment error of the frequency modulation unit, which is the deviation value between the actual output value of the frequency modulation unit after responding to the secondary frequency modulation instruction and the instruction, and ξ al is the allowed error of the frequency modulation unit adjustment, which can be 1.5% of the rated output value.

[0160] In step 1033, a short-time-scale intra-day rolling scheduling plan is constructed based on the third objective function and the short-time-scale intra-day scheduling plan.

[0161] Based on the target orientation of the third objective function and the short-time-scale intra-day scheduling plan, a short-time-scale intra-day rolling scheduling plan is constructed, with the highest comprehensive frequency modulation performance as the objective function.

[0162] Step 103 introduces a comprehensive frequency modulation performance index considering the actual situation, which can schedule the most suitable frequency modulation unit participating in secondary frequency modulation through the comprehensive frequency modulation performance index. At the same time, by selecting the most suitable new energy frequency modulation unit, the wind / photovoltaic curtailment rate is reduced, and the new energy consumption rate is improved.

[0163] In step 104, a first target function of minimum penalty of secondary frequency modulation error is constructed based on the short time scale intra-day rolling scheduling plan and real-time supply side data.

[0164] It is known that there is certainly some difference between the perfect fine intra-day rolling scheduling plan and the actual power grid operation. In order to eliminate the error between the intra-day rolling scheduling plan and the real-time power grid operation as much as possible, the first target function of minimum penalty of secondary frequency modulation error is introduced in the application.

[0165] Exemplarily, the expression of the first target function of minimum penalty of secondary frequency modulation error is as follows:

[0166]

[0167] In the formula, f3 is the penalty of secondary frequency modulation error, is the real-time supply side data, and λ is the penalty factor.

[0168] Step 104 introduces the penalty term of secondary frequency modulation error and aims to minimize the penalty. It has practical significance to eliminate the error between the intra-day rolling scheduling plan and the real-time power grid operation as much as possible.

[0169] In step 105, the short time scale intra-day rolling scheduling plan is feedback corrected based on the first target function, the intra-day supply side data and the intra-day demand side data.

[0170] In some embodiments, the short time scale intra-day rolling scheduling plan is feedback corrected based on the real-time intra-day supply side data and the real-time intra-day demand side data, in combination with the first target function of minimum penalty of secondary frequency modulation error.

[0171] Exemplarily, the quantum genetic algorithm can be used to calculate the secondary frequency optimization scheduling method provided by the application. The quantum genetic algorithm is a genetic algorithm based on the principle of quantum calculation. It is based on the representation of quantum state vector, and the probability amplitude of quantum bit is applied to the coding of chromosome. A chromosome can express the superposition of multiple states, and the updating operation of chromosome is realized by using quantum logic gate, thereby realizing the genetic optimization solution of the target and avoiding the phenomena of multiple selection times, slow convergence speed and easy falling into local extreme value of the conventional genetic algorithm.

[0172] The application provides a secondary frequency modulation optimization scheduling method, which establishes a virtual power plant model containing four parts of traditional frequency modulation units, new energy frequency modulation units, energy storage systems and demand side response by analyzing the random output characteristics of adjustable frequency resources. The four parts of the virtual power plant model are coordinated and optimized in multiple time scales: in the day-ahead optimization scheduling plan, the virtual power plant as a whole participates in the secondary frequency modulation to maximize the income as the target orientation; in the intra-day rolling scheduling plan, the output characteristics of the new energy frequency modulation unit are fully considered, and the highest comprehensive frequency modulation index is taken as the target orientation to realize real-time rolling optimization of the automatic generation control (AGC) scheduling curve; finally, the minimum secondary frequency modulation error penalty is taken as the target orientation to feedback and correct the intra-day rolling optimization plan, so that the virtual power plant can not only track the AGC scheduling curve in real time, but also improve the frequency modulation income and frequency modulation effect of the virtual power plant in secondary frequency modulation of the power grid, and the consumption rate of new energy is also improved.

[0173] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the application.

[0174] Corresponding to the secondary frequency modulation optimization scheduling method described in the above embodiment, Figure 6 The structure block diagram of the secondary frequency modulation optimization scheduling device provided by the embodiments of the application is shown, and only the parts related to the embodiments of the application are shown for ease of illustration.

[0175] Referring to Figure 6 The secondary frequency modulation optimization scheduling device in the embodiments of the application can include a model construction module 201, a scheduling plan construction module 202 and a feedback correction module 203.

[0176] The model construction module 201 is configured to construct a virtual power plant model and acquire supply side data and demand side data.

[0177] Optionally, the supply side data and the demand side data are acquired, including:

[0178] The wind power frequency modulation unit output power is acquired, and the expression of the wind power frequency modulation unit output power is:

[0179]

[0180] Among them,

[0181]

[0182] In the formula, P WT is the wind power frequency modulation unit output power, P r is the rated maximum power of the wind power frequency modulation unit, and v rVr is the rated wind speed of the wind turbine, v ci Vci is the cut-in wind speed of the wind turbine, v co Vco is the cut-out wind speed of the wind turbine, v

[0183] The output power of the photovoltaic frequency modulation unit is obtained, and an expression of the output power of the photovoltaic frequency modulation unit is:

[0184] P PV = P ST K AC [1+k W (T c -T ST )] / K ST

[0185] In the formula, P is the output power of the photovoltaic frequency modulation unit, P is the output power of the photovoltaic frequency modulation unit under standard test conditions, K is the light intensity, k is the power temperature coefficient, T is the working temperature of the photovoltaic array, T is the standard test environment temperature, K is the light intensity under standard test conditions. PV ST AC W c ST ST

[0186] The demand power of the electric vehicle is obtained, and an expression of the demand power of the electric vehicle is:

[0187]

[0188] In the formula, N is the number of electric vehicles, i is the i-th electric vehicle, M is the simulation number, j is the j-th simulation, t is the 24 time points in the scheduling period, is the charging power of each electric vehicle at t, is the discharging power of each electric vehicle at t, is the total demand power of the electric vehicle after M simulations at t.

[0189] The scheduling plan construction module 202 is configured to construct a long-time scale day-ahead optimization scheduling plan with a target function of maximum secondary frequency modulation benefit based on supply side data and demand side data.

[0190] Optionally, the long-time scale day-ahead optimization scheduling plan with the target function of maximum secondary frequency modulation benefit comprises:

[0191] The long-time scale day-ahead scheduling plan is constructed based on the supply side data and the demand side data.

[0192] A second target function of maximum secondary frequency modulation benefit is constructed, and an expression of the second target function is:

[0193] Max C​​​​​​​pro = C in - C cost

[0194] In the formula, C pro is the net profit obtained by the secondary frequency modulation, C in is the total compensation income of the secondary frequency modulation, and C cost is the total expenditure of the secondary frequency modulation. The total compensation income of the secondary frequency modulation is determined based on the total frequency modulation mileage compensation at time t and the total frequency modulation capacity compensation at time t, and the total expenditure of the secondary frequency modulation is determined based on the cost of the traditional frequency modulation unit participating in frequency modulation, the cost of the new energy frequency modulation unit participating in frequency modulation, the cost of the energy storage system participating in frequency modulation, and the cost of the flexible load participating in frequency modulation.

[0195] Based on the second objective function and the long-time-scale day-ahead scheduling plan, a long-time-scale day-ahead optimal scheduling plan is constructed.

[0196] In the scheduling plan construction module 202, the time-scale day-ahead optimal scheduling plan constructed is also determined based on the supply-side output value constraint, the maximum secondary frequency modulation power allowable deviation constraint, and the energy storage power constraint.

[0197] Optionally, the supply-side output value constraint is determined based on the traditional energy frequency modulation unit output value, the new energy frequency modulation unit output value, the energy storage system output value, and the flexible load output value. The traditional energy frequency modulation unit output value, the new energy frequency modulation unit output value, the energy storage system output value, and the flexible load output value are respectively constrained by their own physical properties.

[0198] Optionally, the maximum secondary frequency modulation power operation deviation constraint value is determined based on the secondary frequency modulation demand power reference value and the secondary frequency modulation power allowable deviation value.

[0199] Optionally, the energy storage power constraint is determined based on the energy storage unit state of charge, the energy storage unit own attribute, and the energy storage unit charging and discharging rule.

[0200] The scheduling plan construction module 202 is also used to construct a short-time-scale intra-day rolling scheduling plan with the highest comprehensive frequency modulation performance as an objective function based on the long-time-scale day-ahead optimal scheduling plan.

[0201] Optionally, constructing the short-time-scale intra-day rolling scheduling plan with the highest comprehensive frequency modulation performance as an objective function includes:

[0202] The long-time-scale day-ahead optimal scheduling plan is divided into a plurality of short-time-scale intra-day scheduling plans.

[0203] A third objective function with the highest comprehensive frequency modulation performance is constructed, and the expression of the third objective function is:

[0204]

[0205] wherein k z is a comprehensive frequency modulation performance index, n is the number of frequency modulation units participating in secondary frequency modulation, k 1.i is a regulation rate index, k 2.i is a response time index, k 3.i is a regulation accuracy index.

[0206] wherein the regulation rate index is the rate at which the frequency modulation unit responds to the secondary frequency modulation control instruction, and the expression of the regulation index is: k 1.i = k v.i / k av , k v.i is the measured regulation rate of each frequency modulation unit participating in frequency modulation, k av is the average regulation rate of each frequency modulation unit participating in frequency modulation. The response time index is the time delay of the frequency modulation unit responding to the secondary frequency modulation control instruction, and the expression of the response time index is: k 2.i = 1-(T d.i / 5x60), T d.i is the response delay time of each frequency modulation unit participating in frequency modulation, in seconds. The regulation accuracy index is the accuracy of the frequency modulation unit responding to the secondary frequency modulation control instruction, and the expression of the regulation accuracy index is: ξ i is the regulation error of the frequency modulation unit, and ξ al is the allowed error of the regulation of the frequency modulation unit.

[0207] Based on the third objective function and the short-time scale intraday scheduling plan, a short-time scale intraday rolling scheduling plan is constructed.

[0208] The scheduling plan construction module 202 is further configured to construct a first objective function with minimum secondary frequency modulation error penalty based on the short-time scale intraday rolling scheduling plan and real-time supply side data.

[0209] Optionally, the expression of the first objective function with minimum secondary frequency modulation error penalty is:

[0210]

[0211] wherein f3 is the secondary frequency modulation error penalty, is the real-time supply side data, and λ is a penalty factor.

[0212] The feedback correction module 203 is configured to feedback correct the short-time scale intraday rolling scheduling plan based on the first objective function, the intraday supply side data and the intraday demand side data.

[0213] It should be noted that the information interaction, execution process and the like between the above apparatus / units are based on the same concept as the method embodiments of the present application, and specific functions and brought technical effects can be referred to the method embodiments part, which will not be repeated here.

[0214] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software. In addition, the specific name of each functional unit and module is only for easy distinction, and does not limit the protection scope of the present application. The specific working process of the unit and module in the system can be referred to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0215] The embodiment of the present application also provides a terminal device, referring to Figure 7 The terminal device 300 can include at least one processor 310, a memory 320, the memory 320 stores a computer program 321 executable on the at least one processor 310, and the processor 310 implements the steps in any of the above method embodiments when executing the computer program 321, for example Figure 1 The steps 101 to 105 in the embodiment shown. Alternatively, the processor 310 implements the functions of each module / unit in the above apparatus embodiments when executing the computer program 321, for example Figure 6 The functions of the modules 201 to 203 shown.

[0216] For example, the computer program 321 can be divided into one or more modules / units, one or more modules / units are stored in the memory 320 and executed by the processor 310 to complete the present application. The one or more modules / units can be a series of computer program segments capable of completing a specific function, which is used to describe the execution process of the computer program in the terminal device 300.

[0217] Those skilled in the art can understand that Figure 7 It is only an example of a terminal device and does not constitute a limitation on the terminal device, which can include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0218] The processor 310 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0219] The memory 320 can be an internal storage unit of the terminal device, and can also be an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory 320 is used to store the computer program and other programs and data required by the terminal device. The memory 320 can also be used to temporarily store data that has been output or will be output.

[0220] The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus, an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0221] The frequency modulation optimization scheduling method provided by the embodiments of the present application can be applied to a terminal device such as a computer, a wearable device, a vehicle-mounted device, a tablet computer, a notebook computer, a netbook, a personal digital assistant (PDA), an augmented reality (AR) / virtual reality (VR) device, a mobile phone, etc. The embodiments of the present application do not make any limitation on the specific type of the terminal device.

[0222] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps in each of the embodiments of the secondary frequency modulation optimization scheduling method.

[0223] The embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal is caused to execute the steps in each of the embodiments of the secondary frequency modulation optimization scheduling method.

[0224] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the present application can implement all or part of the processes in the above-mentioned embodiments by a computer program to instruct related hardware to complete, and the computer program can be stored in a computer readable storage medium. The computer program is executed by a processor to implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0225] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0226] In the embodiments provided by the present application, it should be understood that the disclosed apparatus / network device and method can be implemented in other manners. For example, the embodiments of the apparatus / network device described above are merely illustrative. For example, the division of the modules or units is merely logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0227] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0228] The above-described embodiments are merely used to illustrate the technical solutions of the present application, but not limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A frequency modulation optimization scheduling method applied to a virtual power plant, characterized in that, The method comprises the following steps: constructing a virtual power plant model to obtain supply side data and demand side data; the virtual power plant model comprises a supply side and a demand side, the supply side comprises traditional frequency modulation units, new energy frequency modulation units and energy storage systems, the demand side comprises flexible loads and controllable loads, the new energy frequency modulation units comprise wind power frequency modulation units and photovoltaic frequency modulation units, and the flexible loads comprise electric vehicles; the supply side data comprises historical supply side data and predicted supply side data, and the demand side data comprises historical demand side data and predicted demand data; based on the supply side data and the demand side data, a long time scale day-ahead optimization scheduling plan is constructed with a maximum secondary frequency modulation benefit as a target function; based on the long time scale day-ahead optimization scheduling plan, a short time scale day-ahead rolling scheduling plan is constructed with a highest comprehensive frequency modulation performance as a target function; based on the short time scale day-ahead rolling scheduling plan and real-time supply side data, a first target function with a minimum secondary frequency modulation error penalty is constructed; based on the first target function, day-ahead supply side data and day-ahead demand side data, the short time scale day-ahead rolling scheduling plan is feedback corrected; the short time scale day-ahead rolling scheduling plan with the highest comprehensive frequency modulation performance as a target function comprises the following steps: the long time scale day-ahead optimization scheduling plan is divided into a plurality of short time scale day-ahead scheduling plans; a third target function with the highest comprehensive frequency modulation performance is constructed, and an expression of the third target function is as follows: In the formula, is a comprehensive frequency modulation performance index, n is the number of frequency modulation units participating in secondary frequency modulation, is a regulation rate index, is a response time index, is a regulation accuracy index; the adjustment rate index is an index of a rate at which a frequency modulation unit responds to a secondary frequency modulation control instruction, and an expression of the adjustment rate index is as follows: In the formula, is the measured regulating speed of each frequency modulation unit participating in frequency modulation, is the average regulating speed of each frequency modulation unit participating in frequency modulation; the response time index is an index of a time delay at which the frequency modulation unit responds to the secondary frequency modulation control instruction, and an expression of the response time index is as follows: In the formula, is the response delay time of each frequency modulation unit participating in frequency modulation, in seconds; the adjustment accuracy index is an index of accuracy at which the frequency modulation unit responds to the secondary frequency modulation control instruction, and an expression of the adjustment accuracy index is as follows: wherein is the frequency regulation error of the frequency-regulating unit, is the allowed error of the frequency regulation of the frequency-regulating unit; based on the third target function and the short time scale day-ahead scheduling plan, the short time scale day-ahead rolling scheduling plan is constructed.

2. The method of claim 1, wherein, the supply side data and the demand side data are obtained, comprising the following steps: wind power frequency modulation unit output power is obtained, and an expression of the wind power frequency modulation unit output power is as follows: wherein, In the formula, is the wind power frequency modulation unit output power, is the rated maximum power of the wind power frequency modulation unit, is the rated wind speed of the wind turbine, is the cut-in wind speed of the wind turbine, is the cut-out wind speed of the wind turbine, is the real-time wind speed; photovoltaic frequency modulation unit output power is obtained, and an expression of the photovoltaic frequency modulation unit output power is as follows: wherein Ppv is the photovoltaic frequency-regulated unit output power, Ppvstd is the photovoltaic frequency-regulated unit output power under standard test conditions, I is the light intensity, β is the power temperature coefficient, Tpv is the operating temperature of the photovoltaic array, Tstd is the standard test ambient temperature, Istd is the light intensity under standard test conditions; electric vehicle demand power is obtained, and an expression of the electric vehicle demand power is as follows: wherein, N is the number of electric vehicles, i is the first i electric vehicle, M is the number of simulations, j is the first j simulation, t is the 24 time instants within the scheduling period, is the t charging power of each electric vehicle at time instant, is the t discharging power of each electric vehicle at time instant, is the t total demand power of electric vehicles after M simulations at time instant.

3. The method of claim 1, wherein, the long time scale day-ahead optimization scheduling plan with the maximum secondary frequency modulation benefit as a target function comprises the following steps: based on the supply side data and the demand side data, a long time scale day-ahead scheduling plan is constructed; a second target function with the maximum secondary frequency modulation benefit is constructed, and an expression of the second target function is as follows: wherein is the net profit from the secondary frequency modulation, is the total compensation for the secondary frequency modulation, is the total expenditure for the secondary frequency modulation; The secondary frequency modulation compensation total income is determined based on t The time total frequency modulation mileage compensation is determined based on t The time total frequency modulation capacity compensation is determined, and the secondary frequency modulation total expenditure is determined based on the cost of the conventional frequency modulation unit participating in frequency modulation, the cost of the new energy frequency modulation unit participating in frequency modulation, the cost of the energy storage system participating in frequency modulation, and the cost of the flexible load participating in frequency modulation. based on the second target function and the long time scale day-ahead scheduling plan, the long time scale day-ahead optimization scheduling plan is constructed.

4. The method of claim 3, wherein, the long time scale day-ahead optimization scheduling plan is further determined based on supply side output value constraints, maximum secondary frequency modulation power allowable deviation constraints and energy storage power constraints; The supply side output value constraint is determined based on a traditional energy frequency modulation unit output value, a new energy frequency modulation unit output value, a storage energy system output value and a flexible load output value, wherein the traditional energy frequency modulation unit output value, the new energy frequency modulation unit output value, the storage energy system output value and the flexible load output value are respectively constrained by their own physical properties; The secondary frequency modulation power operation deviation maximum constraint value is determined based on a secondary frequency modulation demand power reference value and a secondary frequency modulation power allowable deviation value; The storage energy power constraint is determined based on a storage energy unit state of charge, a storage energy unit own property and a storage energy unit charging and discharging rule.

5. The method of claim 1, wherein, The expression of the first target function with the minimum secondary frequency modulation error penalty is: wherein is a quadratic frequency modulation error penalty, is real-time supply-side data, is t is a reference value for the momentary quadratic frequency modulation demand power, is a penalty factor.

6. The method of claim 2, wherein, Using the Monte Carlo method M The electric vehicle's required power is obtained by a second simulation; and the secondary frequency modulation optimization scheduling method is calculated using a quantum genetic algorithm.

7. A frequency-modulated optimization scheduling device, applied to a virtual power plant, characterized in that, It comprises: A model construction module is configured to construct a virtual power plant model and acquire supply side data and demand side data; the virtual power plant model comprises a supply side and a demand side, the supply side comprises traditional frequency modulation units, new energy frequency modulation units and storage energy systems, the demand side comprises flexible loads and controllable loads, the new energy frequency modulation units comprise wind power frequency modulation units and photovoltaic frequency modulation units, and the flexible loads comprise electric vehicles; The supply side data comprises historical supply side data and predicted supply side data, and the demand side data comprises historical demand side data and predicted demand data; A scheduling plan construction module is configured to construct a long time scale day-ahead optimization scheduling plan with a maximum secondary frequency modulation benefit as a target function based on the supply side data and the demand side data, construct a short time scale intra-day rolling scheduling plan with the highest comprehensive frequency modulation performance as a target function based on the long time scale day-ahead optimization scheduling plan, and construct a first target function with the minimum secondary frequency modulation error penalty based on the short time scale intra-day rolling scheduling plan and real-time supply side data; A feedback correction module is configured to feedback correct the short time scale intra-day rolling scheduling plan based on the first target function, intra-day supply side data and intra-day demand side data. The scheduling plan construction module is specifically configured to: divide the long time scale day-ahead optimization scheduling plan into a plurality of short time scale intra-day scheduling plans; construct a third target function with the highest comprehensive frequency modulation performance, and the expression of the third target function is: In the formula, is a comprehensive frequency modulation performance index, n is the number of frequency modulation units participating in secondary frequency modulation, is a regulation rate index, is a response time index, is a regulation accuracy index; The regulation rate index is a rate at which a frequency modulation unit responds to a secondary frequency modulation control instruction, and the expression of the regulation rate index is: In the formula, is the measured regulating speed of each frequency modulation unit participating in frequency modulation, is the average regulating speed of each frequency modulation unit participating in frequency modulation; The response time index is a time delay at which a frequency modulation unit responds to a secondary frequency modulation control instruction, and the expression of the response time index is: In the formula, is the response delay time of each frequency modulation unit participating in frequency modulation, in seconds; The regulation accuracy index is an accuracy at which a frequency modulation unit responds to a secondary frequency modulation control instruction, and the expression of the regulation accuracy index is: wherein is the frequency regulation error of the frequency-regulating unit, is the allowed error of the frequency regulation of the frequency-regulating unit; The short time scale intra-day rolling scheduling plan is constructed based on the third target function and the short time scale intra-day scheduling plans. 8.A terminal device, comprising a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the computer program comprises the following steps of: The processor executes the computer program to implement the method in any one of claims 1 to 6. The processor executes the computer program to implement the method in any one of claims 1 to 6.

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