CN114421460A

CN114421460A - Multifunctional power grid dispatching system and method containing electric automobile aggregators

Info

Publication number: CN114421460A
Application number: CN202210031607.0A
Authority: CN
Inventors: 韩伟; 戴欣; 梁嘉诚
Original assignee: HuaiAn Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
Current assignee: HuaiAn Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
Priority date: 2022-01-12
Filing date: 2022-01-12
Publication date: 2022-04-29

Abstract

A multifunctional power grid dispatching system and method with electric automobile aggregators are provided, the system comprises: a power grid regulation center, a thermal power plant, a renewable energy power plant, an electric vehicle aggregator, and a user load; the multifunctional power grid dispatching system realizes multifunctional dispatching of a power grid by utilizing an electric automobile cluster under an electric automobile aggregator flag, and comprises the following steps: peak clipping and valley filling, renewable energy consumption and power grid emergency dispatching; the electric vehicle aggregator comprises: the device comprises a data processing module, a prediction module, a central decision module and a charge-discharge control module; the data processing module receives a scheduling instruction of a power grid regulation and control center and transmits the instruction to the central decision module; the central decision module receives the scheduling instruction of the data processing module and the electric automobile state information and quotation of the electric automobile cluster, executes charge and discharge calculation of the electric automobile cluster, and transmits data to the charge and discharge control module to control the electric automobile cluster.

Description

Multifunctional power grid dispatching system and method containing electric automobile aggregators

Technical Field

The invention belongs to the field of power systems, and particularly relates to a multifunctional power grid dispatching system and method containing electric automobile aggregators.

Background

As a novel power load, an electric vehicle has the characteristics of strong randomness and large fluctuation in charging behavior, extra burden is often caused to a power grid, and it is expected that under the condition of future large-scale electric vehicle access, the disordered grid-connected charging of the electric vehicle can affect the economical, stable and reliable operation of the power grid, so that a reasonable and ordered optimization regulation and control method needs to be adopted for the charging behavior of the electric vehicle.

In order to achieve the aim of 'double carbon', the wind power solar energy total installation is improved to 12 hundred million kilowatts in 2030 years, the non-fossil energy consumption percentage reaches 25%, the renewable energy proportion in the energy consumption is further improved to more than 60% in 2050 years, intermittent renewable energy such as wind power and photovoltaic energy is gradually changed from secondary energy in a power system to main energy, but the output of the renewable energy such as the wind power and the photovoltaic energy in a power grid has the characteristics of strong intermittency and large fluctuation, the grid connection consumption is difficult, meanwhile, impact can be generated on the stability of the power grid, and the long-term development of the renewable energy industry in China is seriously influenced.

Meanwhile, with the continuous improvement of the proportion of renewable energy sources in a power grid, a synchronous generator on the power supply side is gradually replaced by power electronic equipment, traditional frequency modulation resources in the system are gradually scarce, and the continuous reduction of inertia becomes a common problem faced by most national power systems.

As a flexible load, an electric vehicle is in a stop state 90% of the time all day, has great regulation potential under the support of the V2G technology, and a single electric vehicle has small charging load and limited battery capacity, so that researchers put forward the concept of an electric vehicle aggregator, namely, a certain number of electric vehicles are aggregated into a whole, so that the schedulable load and the energy storage capacity of a certain scale are obtained.

The characteristic that the electric automobile cluster is used as a load and can be dispatched for a long time is utilized, and the peak clipping and valley filling of the power grid load and the timely consumption of new energy can be completed. Meanwhile, compared with the frequency modulation resources such as a frequency modulation hydroelectric generating set and a gas generating set which are used in the past, the electric automobile cluster has the characteristics of high active power response speed and strong climbing capability as an emergency frequency modulation resource, so that the electric automobile cluster under the aggregator can be used as an object for quick frequency response under a certain specific situation.

Disclosure of Invention

In order to solve the defects in the prior art, the invention aims to provide a multifunctional power grid dispatching system and method containing electric automobile aggregators.

The invention adopts the following technical scheme. The invention provides a multifunctional power grid dispatching system comprising an electric automobile aggregator, which comprises: a power grid regulation center, a thermal power plant, a renewable energy power plant, an electric vehicle aggregator, and a user load; the multifunctional power grid dispatching system realizes multifunctional dispatching of a power grid by utilizing an electric automobile cluster under an electric automobile aggregator flag, and comprises the following steps: peak clipping and valley filling, renewable energy consumption and power grid emergency dispatching; the electric vehicle aggregator comprises: the device comprises a data processing module, a prediction module, a central decision module and a charge-discharge control module; the data processing module receives a scheduling instruction of a power grid regulation and control center and transmits the instruction to the central decision module; the central decision module receives the scheduling instruction of the data processing module and the electric automobile state information and quotation of the electric automobile cluster, executes charge and discharge calculation of the electric automobile cluster, and transmits data to the charge and discharge control module to control the electric automobile cluster.

Preferably, the power grid regulation and control center is used as a core of the scheduling system, receives the capacities and quotations reported by the thermal power plant, the renewable energy power plant and the electric vehicle aggregator, sends scheduling instructions to the thermal power plant, the renewable energy power plant and the electric vehicle aggregator, and sends load shedding instructions to users under the condition of emergency scheduling.

Preferably, the thermal power plant and the renewable energy power plant receive a dispatching instruction of a power grid regulation and control center according to a protocol and a clearing price and output electric energy;

the renewable energy power plant includes a wind power plant and a photovoltaic power plant.

Preferably, the electric vehicle aggregator further comprises an emergency control module; the emergency control module is a module which has priority control over the charge and discharge management module in an electric vehicle aggregator, and under the condition of power grid failure, the power grid regulation and control center can cross over a central decision module of the aggregator, receive an emergency scheduling instruction through the emergency control module, generate an overcharge and discharge scheduling instruction, and complete the rapid control over the electric vehicle cluster through the charge and discharge scheduling instruction.

Preferably, the multifunctional power grid dispatching system further comprises an analysis and statistics module, wherein the analysis and statistics module is used for collecting power generation information of a renewable energy power plant in real time under the condition of implementing renewable energy consumption, comparing and predicting data, decomposing the output deviation through an EMD empirical mode decomposition method, dividing the output deviation into a high-frequency part and a low-frequency part, not performing new energy output optimization on small-amount deviation occurring at high frequency, and sending difference information to a regulation and control center on large-amount deviation occurring at low frequency.

The invention provides a multifunctional power grid dispatching method comprising an electric automobile aggregator, which is operated on the multifunctional power grid dispatching system comprising the electric automobile aggregator, and comprises the following steps:

step 1, setting a scheduling priority, setting emergency scheduling as a first priority, setting peak clipping and valley filling as a second priority, and setting available renewable energy consumption as a third priority; if entering the emergency scheduling, executing the step 5;

step 2, collecting electric automobile data under the electric automobile aggregator flag, and predicting the charge and discharge available capacity of the electric automobile cluster under the electric automobile aggregator flag at each moment of the next day; respectively predicting the output condition of each time of the next day by a thermal power plant and a renewable energy power plant;

step 3, performing optimization calculation based on the peak clipping and valley filling scheduling optimization model of the electric automobile aggregator according to the prediction result in the step 2, obtaining output configurations of the electric automobile aggregator, the thermal power plant and the renewable energy power plant at the next day, generating peak clipping and valley filling instructions, issuing the peak clipping and valley filling instructions and performing the peak clipping and valley filling instructions;

step 4, collecting the output of the renewable energy power plant at each moment of the day, calculating the deviation of the output configuration at each moment agreed with the peak clipping and valley filling instruction, performing optimization calculation by using a renewable energy consumption scheduling optimization model based on an electric vehicle aggregator on the basis of finishing peak clipping and valley filling, obtaining the available charging capacity of the electric vehicle cluster under the electric vehicle aggregator for renewable energy consumption and the usage amount of the system renewable energy, generating a renewable energy consumption instruction, issuing the renewable energy consumption instruction to the electric vehicle aggregator and the renewable energy power plant, and performing the renewable energy consumption instruction;

and 5, when the power grid enters an emergency state due to a fault, immediately entering an emergency dispatching function according to a dispatching instruction, simultaneously stopping peak clipping, valley filling and renewable energy consumption functions, executing optimization calculation by using a power grid emergency dispatching model based on an electric automobile aggregator, obtaining the output of the electric automobile aggregator, the output of a thermal power plant, the output of a renewable energy power plant and load shedding amount, generating an emergency dispatching instruction, and issuing and executing the emergency dispatching instruction.

Preferably, in step 3, based on the peak clipping and valley filling scheduling optimization model of the electric vehicle aggregator, the output configuration of the electric vehicle aggregator, the thermal power plant and the renewable energy power plant at each time of the next day is solved under the constraint condition with the minimum standard deviation of the total power generation cost and the load curve of the system as the optimization target.

Preferably, in step 3, the peak clipping and valley filling optimization objective function including the electric vehicle aggregator is expressed by the following formula,

in the formula:

F_plsrepresenting a peak clipping and valley filling optimization objective function containing electric vehicle aggregators, namely minimizing the standard deviation of the system operation cost and the load curve according to set weight;

λ₁represents the system running cost weight coefficient, lambda₂Representing a system load curve standard deviation weight coefficient;

C_costrepresents the system operating cost, C_costRepresenting the maximum value of the system operation cost;

σ represents the standard deviation of the system load curve, σ_maxAnd the maximum value of the standard deviation of the system load curve is shown.

Preferably, in step 3, the total system power generation cost includes a total system power generation cost of a fuel cost of the thermal power generating unit, a starting and stopping cost of the thermal power generating unit, an environmental pollution cost of the thermal power generating unit, a power generation cost of renewable energy, a capital cost of an electric vehicle aggregator, a cluster charging and discharging cost of electric vehicles, and an aging cost of electric vehicle batteries;

the constraint conditions comprise system power balance constraint, thermal power unit output constraint, rotation standby constraint, climbing constraint, wind power unit output constraint, photovoltaic output constraint and electric vehicle charging and discharging constraint.

Preferably, in step 3, the system total power generation cost objective function is expressed by the following formula,

C_cost＝C_Gi+C_s-s+C_pollution-i+C_wind+C_solar+C_V2G+C_veh+C_battery (2)

in the formula:

C_Girepresenting the fuel cost of the thermal power generating unit i; c_s-sRepresenting the starting and stopping cost of the thermal power generating unit; c_pollution-iRepresenting the environmental pollution cost of the thermal power generating unit i; c_windRepresenting the direct cost of wind power; c_solarRepresents a direct cost of the photovoltaic; c_V2GRepresents the capital cost resulting from the electric vehicle aggregator's upfront investment; c_vehRepresents the charge and discharge cost of the electric vehicle;C_batteryrepresents the cost of charging and discharging the battery of the electric automobile.

Preferably, the step of the electric vehicle aggregator participating in the peak clipping and valley filling scheduling includes:

and 3.1, the electric vehicle aggregator receives the scheduling information of the regulation and control center, the scheduling information is transmitted to the central decision module through the data processing module, and the central decision module calculates the specific charging and discharging scheduling deployment of all vehicles under the flag according to the real-time vehicle information uploaded by the charging and discharging control module, including but not limited to the charging state, the charging power and the discharging power of the electric vehicle.

And 3.2, transmitting the charging and discharging scheduling information of all vehicles participating in scheduling to each subordinate electric vehicle cluster through the charging and discharging management module, and finishing charging and discharging control of all electric vehicles.

Preferably, in step 4, the renewable energy consumption optimization scheduling model optimizes the charging power of the electric vehicle cluster and the usage amount of the renewable energy of the system under the constraint condition by taking the lowest consumption cost of the new energy of the system as an optimization target.

Preferably, the system new energy consumption cost comprises: wind and light abandonment cost, electric vehicle aggregator capital cost, electric vehicle cluster extra charging service cost and electric vehicle battery aging cost;

the constraint conditions include: the system comprises a system power balance constraint, a wind turbine generator output constraint, a photovoltaic output constraint and an electric automobile charging power constraint.

Preferably, the step of participating in the new energy consumption scheduling optimization by the new energy power plant and the electric vehicle aggregator comprises:

step 4.1, decomposing the output deviation by an analysis and statistics module of the power grid through an EMD empirical mode decomposition method according to the deviation between the actual output of the wind power and the photovoltaic power in the day and the output appointed before reporting to the power grid regulation center, dividing the output deviation into a high-frequency part and a low-frequency part, not performing new energy output optimization on small-amount deviation occurring at high frequency, and sending difference information to the regulation center on large-amount deviation occurring at low frequency;

4.2, on the basis of finishing the peak clipping and valley filling functions, the power grid regulation and control center considers the schedulable electric automobile capacity of grid connection at the current time period, takes the lowest system new energy consumption cost as an optimization target, regulates the network access electric quantity of the renewable energy power plant, and calls a certain amount of electric automobile clusters to finish new energy consumption configuration by controlling charging power;

4.3, the wind power photovoltaic power plant receives the scheduling information of the regulation center to complete real-time output adjustment;

4.4, the electric automobile aggregator receives the scheduling information of the regulation and control center, transmits the scheduling information to the central decision module through the data processing module, and the central decision module calculates the specific charging and discharging deployment of all vehicles according to the real-time vehicle information uploaded by the charging and discharging control module;

and 4.5, transmitting the charging and discharging scheduling information of all vehicles participating in scheduling to each subordinate electric vehicle cluster through the charging and discharging management module, and finishing charging and discharging control of all electric vehicles.

And 4.6, according to the dispatching capacity, the power grid regulation and control center settles the auxiliary service cost to the electric automobile aggregator.

Preferably, in step 5, the electric vehicle aggregator output, the thermal power plant output, the renewable energy power plant output and the load shedding amount are optimized under the constraint condition based on the electric grid emergency scheduling model of the electric vehicle aggregator with the minimum total system frequency modulation cost as the optimization target.

Preferably, the power grid emergency dispatching comprises the following steps:

step 5.1, when a power grid has a serious fault, a monitoring module in the system transmits data such as load increment, system tie line power deviation, system power deviation and the like to a regulation and control center, and the regulation and control center can convert the system into an emergency control operation mode;

step 5.2, under the operation mode, the control center directly passes over a central decision module of an electric vehicle aggregator according to a protocol to acquire the control right of an emergency scheduling module in the aggregator system;

step 5.3, determining the called frequency modulation resource in the emergency scheduling module according to a preset system frequency deviation partition diagram;

step 5.4, the frequency modulation power plant receives the emergency scheduling information of the regulation center to complete real-time output adjustment;

step 5.5, an emergency dispatching module of an electric automobile aggregator receives dispatching information of a regulation center, and calculates specific charging and discharging deployment of all vehicles according to real-time vehicle information uploaded by a charging and discharging control module;

and 5.6, transmitting the charging and discharging scheduling information of all vehicles participating in scheduling to each subordinate electric vehicle cluster through the charging and discharging management module, and finishing charging and discharging control of all electric vehicles.

And 5.7, according to the dispatching capacity, the power grid regulation and control center settles the auxiliary service cost to the electric automobile aggregator.

Preferably, step 5.3 comprises:

(1) when the absolute value of the system frequency deviation | delta f | ∈ (0, f)_return]The system power deviation belongs to the frequency modulation dead zone range of a conventional unit, and in order to control the power deviation in the dead zone range and prevent the power deviation from diffusing to a normal regulation zone, the electric automobile cluster is used as a flexible load of the system to participate in frequency modulation.

(2) When the absolute value of the system frequency deviation is | delta f | ∈ (f)_return，f_alert]In order to ensure that the frequency is in the normal regulation area and is not overlapped into an early warning area, charging vehicles in the electric vehicle cluster are used as flexible loads of the system, idle vehicles are used as energy storage equipment of the system to participate in frequency modulation, and output configuration of various frequency modulation resources is optimized by matching with a frequency modulation power plant in the system and aiming at the lowest frequency modulation cost.

(3) When the absolute value of the system frequency deviation is | delta f | ∈ (f)_alert，f_limit]And the system power deviation belongs to an early warning area, and all vehicles in the electric automobile cluster are used as energy storage equipment to participate in frequency modulation in order to ensure that the frequency is in the early warning area and is not overlapped into an emergency regulation area, and are matched with a frequency modulation power plant in the system to optimize the output configuration of various frequency modulation resources by taking the lowest frequency modulation cost as a target.

(4) When the absolute value of the frequency deviation delta f of the system is larger than f_limitThe system stability has been impaired byAnd (4) preventing the accident from expanding, and separating the out-of-step power grid by a third defense line of the system.

Compared with the prior art, the invention has the beneficial effects that at least: the invention provides a multifunctional scheduling system model, which comprises a power grid regulation and control center, a thermal power plant, a renewable energy power plant, an electric automobile aggregator and other modules. The multifunctional dispatching of the power grid is realized by controlling charging and discharging of the electric automobile cluster, and comprises a peak clipping and valley filling function, a renewable energy consumption function and a power grid emergency dispatching function.

The invention provides a functional model of an electric vehicle aggregator, which comprises a data processing module, a prediction module, a central decision module, a charge and discharge control module and an analysis and statistics module, and elaborates functions and transmission flows of the modules in detail.

The invention combines the V2G technology, and on the basis of regarding the electric automobile as a power supply-load, the charging and discharging of the electric automobile are scheduled by considering the three-party income of the system, so that the peak clipping and valley filling of a load curve, the real-time consumption of renewable energy and the emergency scheduling under the condition of power grid failure are realized.

The invention regulates the control steps of the peak clipping and valley filling function, the renewable energy resource consumption function and the power grid emergency scheduling function, thereby forming a complete electric vehicle charging and discharging scheduling strategy, being suitable for scheduling large-scale electric vehicle clusters under the electric vehicle aggregator, and simultaneously being suitable for output scheduling of other power supplies in a scheduling system.

Drawings

FIG. 1 is a block diagram of a multifunctional power grid dispatching system including an electric vehicle aggregator according to the present invention;

fig. 2 is a schematic diagram of a multifunctional power grid dispatching system including an electric vehicle aggregator for implementing peak clipping and valley filling dispatching;

FIG. 3 is a schematic diagram of a multifunctional power grid dispatching system including an electric vehicle aggregator implementing renewable energy consumption dispatching provided by the invention;

fig. 4 is a schematic diagram of an emergency dispatching implemented by the multifunctional power grid dispatching system including the electric vehicle aggregator according to the present invention.

Detailed Description

The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.

As shown in fig. 1, embodiment 1 of the present invention provides a multifunctional power grid dispatching system including an electric vehicle aggregator, including: a grid regulation center, a thermal power plant, a renewable energy power plant, an electric vehicle aggregator, and a user load. The multifunctional power grid dispatching system realizes multifunctional dispatching of a power grid by utilizing an electric automobile cluster under an electric automobile aggregator flag, and the multifunctional dispatching system comprises but is not limited to a peak clipping and valley filling function, a renewable energy consumption function and a power grid emergency dispatching function.

The multifunctional power grid dispatching system operates all functions according to a preset priority sequence, wherein the power grid emergency dispatching is a first priority, the peak load shifting is a second priority, and the renewable energy consumption is a third priority. The peak clipping and valley filling functions are preferentially met in daily operation, the aggregator can realize the renewable energy consumption function according to the remaining schedulable capacity after meeting the peak clipping and valley filling function configuration, when the power grid enters an emergency state due to a fault, the system immediately enters the emergency scheduling function according to the scheduling instruction, and meanwhile, the peak clipping and valley filling and the renewable energy consumption function are stopped.

The power grid regulation and control center is used as a core of a dispatching system, receives the capacities and quotations reported by a thermal power plant, a renewable energy power plant and an electric vehicle aggregator, sends dispatching instructions to the thermal power plant, the renewable energy power plant and the electric vehicle aggregator, and sends load shedding instructions to users under the condition of emergency dispatching.

And the thermal power plant and the renewable energy power plant receive a dispatching instruction of a power grid regulation and control center according to the protocol and the clearing price and output electric energy. The renewable energy power plant includes a wind power plant and a photovoltaic power plant.

The electric automobile aggregator is used as a middle layer to collect flag-off electric automobile cluster state information and quotation, and transmits a control instruction of the power grid regulation and control center on the electric automobile cluster.

In a preferred embodiment of the present invention, the electric vehicle aggregator comprises: the device comprises a data processing module, a prediction module, a central decision module and a charge-discharge control module.

Furthermore, the data processing module receives a dispatching instruction of the power grid regulation and control center, transmits the instruction to the central decision module, serves as a connection module of the electric vehicle aggregator and the regulation and control center, and records the charging and discharging power output by the aggregator to the power grid in real time.

Furthermore, the prediction module predicts the available charge and discharge capacity of each time node in the next day by collecting the state information of the electric vehicle, the travel habits of the vehicle owner, the quotation information, the weather forecast and other information in multiple time scales, and uploads the prediction information to the regulation and control center.

Further, the central decision module receives the scheduling instruction of the data processing module and the electric vehicle state information and quotation of the electric vehicle cluster, completes the optimal calculation of charging and discharging of the electric vehicle cluster under the electric vehicle aggregator according to a preset algorithm condition after information is summarized, and transmits data to the charging and discharging control module for control.

Further, the charge and discharge control module is used as a module connected with the electric automobile cluster, collects vehicle information and quotation uploaded by the electric automobile cluster under the flag, wherein the vehicle information comprises conventional data such as historical travel data and vehicle usage habits of an owner, real-time electric automobile parameters, uploads the information to the central decision module, and is used as an instruction executor to complete specific execution of a downlink scheduling instruction.

In a further preferred embodiment of the invention, the electric vehicle aggregator further comprises an emergency control module. The emergency control module is a module which has priority control over the charge and discharge management module in an electric vehicle aggregator, and under the condition of power grid failure, the power grid regulation and control center can cross over a central decision module of the aggregator, receive an emergency scheduling instruction through the emergency control module, generate an overcharge and discharge scheduling instruction, and complete the rapid control over the electric vehicle cluster through the charge and discharge scheduling instruction.

In a preferred but non-limiting embodiment of the present invention, the multifunctional power grid dispatching system further includes an analysis and statistics module, configured to collect power generation information of a renewable energy power plant in real time under a condition of implementing renewable energy consumption, compare predicted data, decompose an output deviation by an EMD empirical mode decomposition method, divide the output deviation into a high-frequency part and a low-frequency part, perform no new energy output optimization on a small deviation occurring at a high frequency, and send difference information to a control center on a large deviation occurring at a low frequency.

The multifunctional power grid dispatching system comprising the electric automobile aggregator further comprises: the frequency modulation unit is different from a thermal power plant and is specially used for participating in power grid fault frequency modulation.

It is noted that, the user load may be in a case of a power grid fault, and the power grid control center may cut off a corresponding part of the load according to a fault risk level according to a protocol convention.

As shown in fig. 2, 3 and 4, embodiment 2 of the present invention provides a multifunctional power grid dispatching method including an electric vehicle aggregator, including the following steps:

step 1, setting a scheduling priority, setting emergency scheduling as a first priority, setting peak clipping and valley filling as a second priority, and setting available renewable energy consumption as a third priority. And if the emergency scheduling is entered, executing the step 5.

It can be understood that in daily operation, namely normal operation of the power grid, the peak clipping and valley filling functions are preferably met without entering an emergency state due to faults, electric vehicle aggregators can realize the renewable energy consumption function according to the remaining schedulable capacity after meeting the configuration of the peak clipping and valley filling functions, and when the power grid enters the emergency state due to faults, the system immediately enters the emergency scheduling function according to the scheduling instruction and stops the peak clipping and valley filling and the renewable energy consumption function.

Step 2, collecting electric automobile data under the electric automobile aggregator flag, and predicting the charge and discharge available capacity of the electric automobile cluster under the electric automobile aggregator flag at each moment of the next day; the thermal power plant and the renewable energy power plant respectively predict the output situation at each moment of the next day.

In a preferred embodiment of the present invention, the electric vehicle data includes, but is not limited to: the system comprises electric vehicle state information, vehicle owner travel habits and quotation information in multiple time scales, and real-time charge state, charging power, discharging power and the like of the electric vehicle.

In a further preferred embodiment of the invention, the available charge and discharge capacity of the electric vehicle cluster under the electric vehicle aggregator at each time of the next day is predicted by using information such as electric vehicle state information, vehicle owner travel habits, quotation information and weather forecast in multiple time scales. The person skilled in the art can use any prediction method to measure the available charge and discharge capacity of the electric vehicle cluster under the electric vehicle aggregator at each time of the next day, and the method falls into the scope of the invention.

And 3, performing optimization calculation by using the charge and discharge available capacity of the electric automobile cluster under the electric automobile aggregator, the prediction results of the thermal power plant and the renewable energy power plant at the next day and the system load prediction results and a peak clipping and valley filling scheduling optimization model based on the electric automobile aggregator to obtain the output configuration of the electric automobile aggregator, the thermal power plant and the renewable energy power plant at the next day, and generating peak clipping and valley filling instructions to be issued to the electric automobile aggregator, the thermal power plant and the renewable energy power plant.

In a preferred embodiment of the present invention, the electric vehicle aggregator-based peak clipping and valley filling scheduling optimization model takes the minimum standard deviation of the total power generation cost and the load curve of the system as an optimization target, and solves the output configuration of the electric vehicle aggregator, the thermal power plant, and the renewable energy power plant at each time of the next day under the constraint condition.

It is noted that more, fewer, or other optimization objectives may be selected by one skilled in the art, and the present invention is a preferred but non-limiting embodiment with the objective of minimizing the total system generation cost and the standard deviation of the load curve.

In a preferred embodiment of the present invention, the peak clipping and valley filling optimization objective function including the electric vehicle aggregator is expressed by the following formula,

in the formula:

It is worth noting that the target functions of the system operation cost and the system load curve standard deviation are different in dimension, and the above formulas are respectively subjected to normalization processing to form a final peak clipping and valley filling optimization target function containing the electric vehicle aggregators.

In a further preferred embodiment of the present invention, the total system power generation cost includes a total system power generation cost including a thermal power unit fuel cost, a thermal power unit startup and shutdown cost, a thermal power unit environmental pollution cost, a renewable energy power generation cost, an electric vehicle aggregator capital cost, an electric vehicle cluster charging and discharging cost, and an electric vehicle battery aging cost. The constraint conditions comprise system power balance constraint, thermal power unit output constraint, rotation standby constraint, climbing constraint, wind power unit output constraint, photovoltaic output constraint and electric vehicle charging and discharging constraint.

It is noted that one skilled in the art can select more, fewer, or other cost components to form the total system power cost, and the use of eight cost components to form the total system power cost is a preferred, but non-limiting embodiment of the present invention.

In a further preferred embodiment of the invention, the system operating cost objective function is expressed by the following formula,

C_cost＝C_Gi+C_s-s+C_pollution-i+C_wind+C_solar+C_V2G+C_veh+C_battery (2)

in the formula:

C_Girepresenting the fuel cost of the thermal power generating unit i; c_s-sRepresenting the starting and stopping cost of the thermal power generating unit; c_pollution-iRepresenting the environmental pollution cost of the thermal power generating unit i; c_windRepresenting the direct cost of wind power; c_solarRepresents a direct cost of the photovoltaic; c_V2GRepresents the capital cost resulting from the electric vehicle aggregator's upfront investment; c_vehRepresents the charge and discharge cost of the electric vehicle; c_batteryRepresents the cost of charging and discharging the battery of the electric automobile.

In a more preferred embodiment of the present invention, the fuel cost C of the thermal power generating unit i_GiAs expressed in the following formula,

in the formula:

a_irepresenting a first fuel cost factor, b, of the thermal power unit i_iRepresenting a second fuel cost coefficient, c, of the thermal power unit i_iRepresenting a third fuel cost coefficient of the thermal power generating unit i;

P_G(i，t)and the output power of the thermal power generating unit i at the moment t is shown.

In a further preferred embodiment of the invention, the start-stop cost C of the thermal power plant is_s-sAs expressed in the following formula,

in the formula:

HSC_(i)represents the hot start cost, CSC, of the thermal power plant i_(i)Representing the cold start cost of the thermal power generating unit i;

CSH_(i，t)indicating the cold start time, MD, of the thermal power unit i_(i，t)Represents the minimum fall time of the thermal power generating unit i,

and representing the continuous shutdown time of the thermal power generating unit i.

In a more preferred embodiment of the present invention, the cost C of environmental pollution of the thermal power generating unit i_pollution-iAs expressed in the following formula,

in the formula:

α_iexpressing the first greenhouse gas emission coefficient, beta, of the thermal power generating unit i_iExpressing the second greenhouse gas emission coefficient, gamma, of the thermal power generating unit i_iRepresenting a third greenhouse gas emission coefficient of the thermal power generating unit i;

C_pollutionand the punishment unit price of greenhouse gas emission of the thermal power generating unit is represented.

In a more preferred embodiment of the invention, the direct cost of wind power refers to the cost paid by the grid to purchase power from the wind power plant, expressed in the following formula,

C_wind＝C_wi(t)·P_wind(t) (6)

in the formula:

C_wi(t) represents the unit price of electricity purchased to the wind power plant at time t,

P_wind(t) represents the amount of electricity purchased to the wind power plant at time t.

In a more preferred embodiment of the invention, the direct cost of the photovoltaic means the cost paid by the grid for purchasing electric energy from the photovoltaic plant, expressed in the following formula,

C_solar＝C_so(t)·P_solar(t) (7)

in the formula:

C_wi(t) represents the unit price of electricity purchased to the photovoltaic power station at time t,

P_solarand (t) represents the amount of electricity purchased to the photovoltaic power station at the time t.

In a more preferred embodiment of the present invention, the capital cost incurred by the electric vehicle aggregator's upfront investment is the annual capital cost of the electric vehicle aggregator's participation in the V2G service, expressed in the following formula,

in the formula:

d represents the rate of depreciation,

n^*indicating the useful life of the V2G system,

C_crepresenting the capital cost invested by the electric vehicle aggregator in the early part of the project.

In a more preferred embodiment of the present invention, the charge/discharge cost C of the electric vehicle_vehAs expressed in the following formula,

in the formula:

C_charge(t) represents the charge price at time t after negotiation, C_discharge(t) represents a discharge price at time t after negotiation;

η_crepresents the charging efficiency at time t, η_dRepresents the discharge efficiency at the time t;

P_charge(t) represents the charging power at time t, P_discharge(t) represents discharge power at time t.

In a more preferred embodiment of the present invention, the charge/discharge battery wear cost C of the electric vehicle_batteryAs expressed in the following formula,

in the formula:

C_brepresenting the capital cost of the battery including the cost of replacement labor,

L_bthe life of the battery cycle is indicated,

E_bindicating the battery capacity.

In a further preferred embodiment of the invention, the system load curve standard deviation objective function, σ, is expressed as the following formula,

in the formula:

P_L(t) represents the conventional load capacity except the electric automobile cluster in the system at the moment t,

represents the average value of the total load of the system,

r represents the number of scheduling instant segments.

It is to be noted that, regarding the constraint conditions used for calculating the peak clipping and valley filling scheduling optimization model based on the electric vehicle aggregator, those skilled in the art may select more, fewer or other constraints, and the system power balance constraint, the thermal power unit output constraint, the rotational standby constraint, the climbing constraint, the wind power unit output constraint, the photovoltaic output constraint and the electric vehicle charging and discharging constraint used in the present invention are preferred but non-limiting embodiments.

In a further preferred embodiment of the invention, the system power balance constraint is expressed in the following formula,

in a further preferred embodiment of the present invention, the thermal power unit output constraint is expressed by the following formula,

in the formula:

representing the minimum output of the thermal power generating unit i at the moment t,

and the maximum output of the thermal power generating unit i at the moment t is shown.

In a further preferred embodiment of the invention, the rotational back-up constraint is expressed by the following formula,

in the formula:

sr (t) represents the rotation reserve capacity at time t.

In a further preferred embodiment of the invention, the hill climbing constraint is expressed by the following formula,

P_G(i，t+1)-P_G(i，t)≤RU_i (15)

P_G(i，t)-P_G(i，t+1)≤RD_i (16)

in the formula:

RU_irepresenting the upward climbing limit of the thermal power generating unit i,

RD_iand indicating the downward climbing limit of the thermal power generating unit i.

In a further preferred embodiment of the invention, the wind turbine output constraint, expressed in the following formula,

in the formula:

the minimum output of the wind turbine at the moment t is shown,

and the maximum output of the wind turbine generator at the moment t is shown.

In a further preferred embodiment of the invention, the photovoltaic power plant output constraints are expressed in the following formula,

in the formula:

represents the minimum output of the photovoltaic power station at the moment t,

and the maximum output of the photovoltaic power station at the moment t is shown.

In a further preferred embodiment of the present invention, the charge and discharge constraints of the electric vehicle are expressed by the following formula,

in the formula:

represents the maximum charging power of the electric vehicle,

and represents the maximum discharge power of the electric automobile.

Still further preferably, the step of the electric vehicle aggregator participating in the peak clipping and valley filling scheduling comprises:

And 4, collecting the output of the renewable energy power plant at each moment of the day, calculating the deviation of the output configuration at each moment agreed with the peak clipping and valley filling instruction, performing optimization calculation by using a renewable energy consumption scheduling optimization model based on an electric vehicle aggregator on the basis of finishing peak clipping and valley filling, obtaining the available charging capacity of the electric vehicle cluster under the electric vehicle aggregator for renewable energy consumption and the usage amount of the system renewable energy, and generating a renewable energy consumption instruction to be issued to the electric vehicle aggregator and the renewable energy power plant.

In a preferred embodiment of the invention, the renewable energy consumption optimization scheduling model optimizes the charging power of the electric vehicle cluster and the usage amount of the renewable energy of the system under the constraint condition by taking the lowest consumption cost of the new energy of the system as an optimization target.

In a further preferred embodiment of the present invention, the system new energy consumption cost comprises: wind curtailment cost, electric vehicle aggregator capital cost, electric vehicle cluster additional charging service cost, and electric vehicle battery aging cost. The constraint conditions include: the system comprises a system power balance constraint, a wind turbine generator output constraint, a photovoltaic output constraint and an electric automobile charging power constraint.

In a preferred embodiment of the present invention, the new energy consumption cost objective function of the electric vehicle aggregator-based system is expressed by the following formula,

F_nec＝min{C_abandon，C_V2G+C_battery+C_sp-veh} (21)

in the formula:

F_necand (3) representing a new energy consumption optimization objective function comprising the electric automobile aggregator, namely comparing the sum of the capital cost caused by the early investment of the electric automobile aggregator, the extra charging service cost of the electric automobile cluster and the aging cost of the electric automobile battery with the system wind and light abandoning cost, and taking the minimum value of the two as the optimization objective.

C_abandonThe cost of wind and light abandoning of the system is represented;

C_V2Grepresents the capital cost, C, due to the electric vehicle aggregator's upfront investment_batteryRepresents the charge and discharge battery wear cost of the electric vehicle, C_sp-vehAnd the charging cost of the electric automobile in the system wind and light abandoning mode is shown.

In a more preferred embodiment of the invention, the system wind and light abandonment cost C_abandonAs expressed in the following formula,

C_abandon＝C_ab.wind·P_ab.wind(t)+C_ab.solar·P_ab.solar(t) (22)

in the formula:

C_ab.windpenalty price representing wind curtailment of the system, C_ab.solarRepresents the penalty price of the system for abandoning the light,

P_ab.wind(t) air flow rate at time t, P_ab.solar(t) represents the amount of light lost by the system at time t.

In a more preferred embodiment of the present invention, the charging cost C of the electric vehicle in the system wind and light abandoning mode_sp-vehAs expressed in the following formula,

in the formula:

C_sp-chargerepresenting the charging compensation electricity price set for the electric automobile cluster at the moment t;

P_charge(t) represents the charging power at time t;

η_cindicating the charging efficiency at time t.

In a more preferred embodiment of the present invention, the air flow rate P of the system at time t_ab.wind(t) sum of waste light amount P_ab.solar(t) is expressed by the following formula,

in the formula:

P_wind(t) represents the power generation amount of the wind turbine at the time t,

P_con.wind(t) represents the actual wind power generation usage amount of the system after the optimization at the moment t,

P_solar(t) represents the power generation of the photovoltaic power plant at time t,

P_con.solarand (t) represents the actual photovoltaic power generation usage amount of the optimized system at the time t.

In a preferred but non-limiting embodiment of the invention, constraints may include, but are not limited to, system power balance constraints, wind turbine output constraints, photovoltaic output constraints, and electric vehicle charging power constraints.

In a further preferred embodiment of the invention, the system power balance constraint is expressed as P_con.wind(t)+P_con.solar(t)-P_charge(t)＝P_wind.set(t)+P_solar.set(t) (25)

In the formula:

P_wind.set(t) represents the generated energy of the power grid regulation and control center and the wind power plant appointed according to the predicted value at the moment t,

P_solar.setand (t) representing the power generation amount agreed by the power grid regulation and control center and the photovoltaic power plant according to the predicted value at the moment t.

In a further preferred embodiment of the invention, the wind turbine output constraint is expressed by the following formula,

P_con.wind(t)≤P_wind(t) (26)

in the formula:

P_windand (t) represents the power generation amount of the wind turbine at the time t.

P_con.solar(t)≤P_solar(t) (27)

in the formula:

P_solarand (t) represents the power generation amount of the photovoltaic power station at the moment t.

In a further preferred embodiment of the present invention, the electric vehicle charging power constraint is expressed by the following formula,

in the formula:

and the upper limit of the charging capacity uploaded to the system by the electric automobile aggregator at the moment t is shown.

Still further preferably, the step of the new energy power plant and the electric vehicle aggregator participating in the new energy consumption scheduling optimization comprises:

4.4, the electric vehicle aggregator receives the scheduling information of the regulation and control center, the scheduling information is transmitted to the central decision module through the data processing module, and the central decision module calculates the specific charging and discharging deployment of all vehicles according to the real-time vehicle information (the electric vehicle parameters related to the time t in the formula) uploaded by the charging and discharging control module;

And 5, when the power grid enters an emergency state due to a fault, the system immediately enters an emergency scheduling function according to the scheduling instruction, simultaneously stops the functions of peak clipping, valley filling and renewable energy consumption, executes optimization calculation by using a power grid emergency scheduling model based on the electric automobile aggregator, obtains the output of the electric automobile aggregator, the output of the thermal power plant, the output of the renewable energy power plant and the load shedding amount, generates an emergency scheduling instruction and sends the emergency scheduling instruction to the electric automobile aggregator, the thermal power plant, the renewable energy power plant and the user load.

In a preferred embodiment of the present invention, the electric grid emergency scheduling model based on the electric vehicle aggregator optimizes the electric vehicle aggregator output, the thermal power plant output, the renewable energy power plant output and the load shedding amount under the constraint condition with the minimum total system frequency modulation cost as an optimization target.

In a further preferred embodiment of the present invention, the total system frequency modulation cost includes a thermal power unit fuel cost, a thermal power unit environmental pollution cost, a thermal power unit loss cost, a renewable energy emergency scheduling cost, an electric vehicle aggregator capital cost, an electric vehicle cluster emergency scheduling cost, an electric vehicle battery aging cost, and a user load shedding loss cost. The constraint conditions comprise system power balance constraint, thermal power unit output constraint, climbing constraint, wind power unit output constraint, photovoltaic output constraint, electric vehicle charging and discharging power constraint and electric vehicle charging and discharging quantity.

In a preferred embodiment of the present invention, the total frequency modulation cost objective function as the emergency scheduling objective function including the electric vehicle aggregator is expressed by the following formula,

F_is＝min(C_Gi+C_loss+C_pollution-i+C_new+C_V2G+C_urgent-veh+C_battery+C_compensation)

in the formula:

F_isexpressing a total frequency modulation cost objective function, namely taking the minimum total frequency modulation cost of the system comprising the fuel cost of the thermal power generating unit, the environmental pollution cost of the thermal power generating unit, the loss cost of the thermal power generating unit, the emergency scheduling cost of renewable energy, the capital cost of an electric automobile aggregator, the emergency scheduling cost of an electric automobile cluster, the aging cost of an electric automobile battery and the load shedding loss cost of a user as an optimization objective,

C_Girepresenting the fuel cost, C, of the thermal power unit i_lossRepresenting loss costs of frequency modulation units, C_pollution-iRepresenting the environmental pollution cost, C, of the thermal power generating unit i_newRepresenting the scheduling costs involving wind and photovoltaic participation in frequency modulation, C_V2GRepresents the capital cost, C, due to the electric vehicle aggregator's upfront investment_urgent-vehCost of calling electric vehicle to participate in frequency modulation in emergency, C_batteryRepresents the charge and discharge battery wear cost of the electric vehicle, C_compensationRepresenting the cost of the compensation to the user due to the load shedding.

In a preferred embodiment of the invention, the loss cost C of the frequency modulation unit_lossAs expressed in the following formula,

in the formula:

C_SPrepresenting the additional cost generated by frequent climbing of the thermal power generating unit.

In the preferred embodiment of the invention, the scheduling cost C including participation of wind power and photovoltaic in frequency modulation_newAs expressed in the following formula,

C_new＝C_urgent-windP_wind(t)+C_urgent-solarP_solar(t) (31)

in the formula:

C_urgent-windrepresents the compensation electricity price of the fan participating in the emergency control,

C_urgent-solarand the compensation electricity price of the photovoltaic power station participating in emergency control is represented.

In a preferred embodiment of the invention, the cost C of calling the electric vehicle to participate in the frequency modulation in an emergency situation_urgent-vehAs expressed in the following formula,

in the formula:

C_urgent-crepresents the charging price of the electric automobile in emergency dispatch,

C_urgent-dand the discharge price of the electric automobile in emergency dispatching is shown.

In a preferred embodiment of the invention, the reimbursement cost C for the user due to the load shedding_compensationAs expressed in the following formula,

in the formula:

C_lossrepresents a compensation price of electricity that the load cut causes loss to the user,

P_loss(t) represents the user load at time t.

The power grid emergency dispatching method comprises the following steps:

and 5.3, determining the called frequency modulation resources in the emergency scheduling module according to a preset system frequency deviation partition diagram. More specifically, step 5.3 comprises:

(4) When the absolute value of the frequency deviation delta f of the system is larger than f_limitAnd the stability of the system is destroyed, and in order to prevent the expansion of accidents, the out-of-step power grid is disconnected by a third defense line of the system.

step 5.5, an emergency dispatching module of an electric vehicle aggregator receives dispatching information of a regulation center, and calculates (here, electric vehicle parameters related to t moment in a formula) according to real-time vehicle information uploaded by a charging and discharging control module to obtain specific charging and discharging deployment of all vehicles;

The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.

Claims

1. A multifunctional power grid dispatching system comprising electric vehicle aggregators, comprising: a power grid regulation center, a thermal power plant, a renewable energy power plant, an electric vehicle aggregator, and a user load; it is characterized in that the preparation method is characterized in that,

the multifunctional power grid dispatching system realizes multifunctional dispatching of a power grid by utilizing an electric automobile cluster under an electric automobile aggregator flag, and comprises the following steps: peak clipping and valley filling, renewable energy consumption and power grid emergency dispatching;

the electric vehicle aggregator comprises: the device comprises a data processing module, a prediction module, a central decision module and a charge-discharge control module; the data processing module receives a scheduling instruction of a power grid regulation and control center and transmits the instruction to the central decision module; the central decision module receives the scheduling instruction of the data processing module and the electric automobile state information and quotation of the electric automobile cluster, executes charge and discharge calculation of the electric automobile cluster, and transmits data to the charge and discharge control module to control the electric automobile cluster.

2. The multifunctional power grid dispatching system comprising electric automobile aggregators, according to claim 1, is characterized in that:

3. The multifunctional power grid dispatching system comprising electric automobile aggregators, according to claim 1, is characterized in that:

the thermal power plant and the renewable energy power plant receive a dispatching instruction of a power grid regulation and control center according to a protocol and a clearing price and output electric energy;

4. The multifunctional power grid dispatching system comprising electric automobile aggregators according to any one of claims 1 to 3, characterized in that:

the electric vehicle aggregator also comprises an emergency control module; the emergency control module is a module which has priority control over the charge and discharge management module in an electric vehicle aggregator, and under the condition of power grid failure, the power grid regulation and control center can cross over a central decision module of the aggregator, receive an emergency scheduling instruction through the emergency control module, generate an overcharge and discharge scheduling instruction, and complete the rapid control over the electric vehicle cluster through the charge and discharge scheduling instruction.

5. The multifunctional power grid dispatching system comprising electric automobile aggregators according to any one of claims 1 to 3, characterized in that:

the multifunctional power grid dispatching system further comprises an analysis and statistics module, wherein the analysis and statistics module is used for collecting power generation information of a renewable energy power plant in real time under the condition of implementing renewable energy consumption, comparing and predicting data, decomposing output deviation through an EMD empirical mode decomposition method, dividing the output deviation into a high-frequency part and a low-frequency part, not performing new energy output optimization on small-amount deviation occurring at high frequency, and sending difference information to a regulation and control center on large-amount deviation occurring at low frequency.

6. A multifunctional power grid dispatching method including an electric vehicle aggregator, which is operated on the multifunctional power grid dispatching system including the electric vehicle aggregator as claimed in any one of claims 1 to 5, and is characterized by comprising the following steps:

7. The multifunctional power grid dispatching method comprising electric automobile aggregators according to claim 6, characterized in that:

in step 3, solving the output configuration of the electric vehicle aggregator, the thermal power plant and the renewable energy power plant at each moment of the next day under the constraint condition by taking the minimum standard deviation of the total power generation cost and the load curve of the system as an optimization target based on the peak clipping and valley filling scheduling optimization model of the electric vehicle aggregator.

8. The multifunctional power grid dispatching method comprising electric automobile aggregators according to claim 6, characterized in that:

in step 3, the peak clipping and valley filling optimization objective function including the electric vehicle aggregators is expressed by the following formula,

in the formula:

σ represents the standard deviation of the system load curve, σ_maxPresentation systemMaximum standard deviation of load curve.

9. The multifunctional power grid dispatching method comprising electric automobile aggregators according to claim 7 or 8, characterized in that:

in step 3, the total system power generation cost comprises the total system power generation cost of the fuel cost of the thermal power generating unit, the starting and stopping cost of the thermal power generating unit, the environmental pollution cost of the thermal power generating unit, the power generation cost of renewable energy sources, the capital cost of electric automobile aggregators, the cluster charging and discharging cost of electric automobiles and the aging cost of electric automobile batteries;

10. The multifunctional power grid dispatching method comprising electric automobile aggregators according to claim 9, characterized in that:

in step 3, the system total power generation cost objective function is expressed by the following formula,

C_cost＝C_Gi+C_s-s+C_pollution-i+C_wind+C_solar+C_V2G+C_veh+C_battery (2)

in the formula:

11. The multifunctional power grid dispatching method comprising electric automobile aggregators according to claim 9, characterized in that:

the method for the electric vehicle aggregator to participate in peak clipping and valley filling scheduling comprises the following steps:

12. The multifunctional power grid dispatching method comprising electric automobile aggregators according to claim 6, characterized in that:

in the step 4, the renewable energy consumption optimization scheduling model takes the lowest system new energy consumption cost as an optimization target, and optimizes the charging power of the electric automobile cluster and the use amount of the renewable energy of the system under the constraint condition.

13. The multifunctional power grid dispatching method comprising electric vehicle aggregators according to claim 12, characterized in that:

the system new energy consumption cost comprises the following steps: wind and light abandonment cost, electric vehicle aggregator capital cost, electric vehicle cluster extra charging service cost and electric vehicle battery aging cost;

14. The multifunctional power grid dispatching method comprising electric automobile aggregators, according to claim 12 or 13, is characterized in that:

the method for the new energy power plant and the electric automobile aggregator to participate in the new energy consumption scheduling optimization comprises the following steps:

15. The multifunctional power grid dispatching method comprising electric automobile aggregators according to claim 6, characterized in that:

in the step 5, the electric network emergency dispatching model based on the electric automobile aggregator optimizes the output of the electric automobile aggregator, the output of the thermal power plant, the output of the renewable energy power plant and the load shedding amount under the constraint condition by taking the minimum total frequency modulation cost of the system as an optimization target.

16. The multifunctional power grid dispatching method comprising electric vehicle aggregators according to claim 15, characterized in that:

the power grid emergency dispatching method comprises the following steps:

17. The multifunctional power grid dispatching method comprising electric vehicle aggregators according to claim 16, wherein:

step 5.3 comprises the following steps:

(2) When the absolute value of the system frequency deviation is | delta f | ∈ (f)_return,f_alert]In order to ensure that the frequency is in the normal regulation area and is not overlapped into an early warning area, charging vehicles in the electric vehicle cluster are used as flexible loads of the system, idle vehicles are used as energy storage equipment of the system to participate in frequency modulation, and output configuration of various frequency modulation resources is optimized by matching with a frequency modulation power plant in the system and aiming at the lowest frequency modulation cost.

(3) When the absolute value of the system frequency deviation is | delta f | ∈ (f)_alert,f_limit]And the system power deviation belongs to an early warning area, and all vehicles in the electric automobile cluster are used as energy storage equipment to participate in frequency modulation in order to ensure that the frequency is in the early warning area and is not overlapped into an emergency regulation area, and are matched with a frequency modulation power plant in the system to optimize the output configuration of various frequency modulation resources by taking the lowest frequency modulation cost as a target.

(4) When the absolute value of the system frequency deviation delta f>f_limitAnd the stability of the system is destroyed, and in order to prevent the expansion of accidents, the out-of-step power grid is disconnected by a third defense line of the system.