Vehicle network interaction type orderly charging scheduling method

Abstract

The invention provides a vehicle network interaction type ordered charging scheduling method. The method comprises the following steps: acquiring photovoltaic, energy storage and charging pile configuration parameters, electric vehicle charging requirements, system topology, operation data of each device and meteorological data; regression analysis is carried out on charging behaviors and energy storage operation modes of various charging pile electric vehicles, and controllable load and energy storage response capability of each response time period are evaluated; generating power and load power are predicted, and the overall response capability is evaluated; according to the issued instruction, determining a response time period, a response task type and a planned operation load; based on the response demand, the operation mode of each device, the response subsidy profit sharing scheme and the demand constraint, an optimization scheduling model is established; solving an optimal planning problem, outputting a day-ahead response scheme, and declaring and confirming a clearing response demand; and correcting the scheduling strategy in real time. According to the invention, an effective resource control scheme is provided for the vehicle network interaction system, the electric vehicle and the power grid are decoupled, and the utilization rate and the economical efficiency of resources are improved.

Description

Technical Field

[0001] The present invention relates to the field of demand response in the power market, and in particular to a vehicle-grid interactive orderly charging scheduling method for a V2B2G system. Technical Background

[0002] The V2B2G (Vehicle to Building to Grid) system consists of one-way charging piles, V2G charging piles, other uncontrollable loads, photovoltaic power generation systems, energy storage systems, etc. In the destination charging scenario, electric vehicles can provide additional charging and discharging services when they have sufficient power reserves, parking time, and prices exceeding a certain threshold; service vehicles can choose appropriate charging and swapping locations based on price, time period, and distance to provide flexible resources. Without affecting the user's demand for electricity and the demand for electric vehicles, through V2G technology, electric vehicles can be used as a special energy storage device. When electricity prices are high and peak demand response is required, electric vehicles can feed electricity. When electricity prices are low and valley filling demand response is required, orderly charging can be used to increase the microgrid load and efficiently utilize new energy electricity.

[0003] Load aggregators participate in the virtual power plant demand response by aggregating distributed resources, building a platform with digital means, obtaining transaction demand from demand response, electric energy market, and auxiliary service market in the north, and issuing regulation demand in the south. According to the forecast and actual power demand, they configure the dispatch plan and modify the dispatch instructions in real time, control and manage the energy input and output, coordinate the allocation of various resources in different operation scenarios such as daily and demand response, and promote the efficient operation of the system. The virtual power plant system improves the flexibility of energy supply and demand and reduces the operating cost of the power system.

[0004] At present, V2G charging piles participate in the virtual power plant demand response and auxiliary control demand as controllable loads. Due to the disorder of charging, the immediacy of demand response, and the hardware constraints on charging and discharging power, the execution layer needs a reliable system control strategy. It is necessary to combine expert experience and artificial intelligence, fully consider the goals and constraints of electric vehicles in demand response, balance the interests of all parties, and achieve rapid response and effective participation of new energy in the power market. The system needs to promote the coordinated development of new energy vehicles and new power systems in a fully market-oriented manner, and improve the stability and economy of microgrid operation in application scenarios. Summary of the invention

[0005] The present invention aims to propose a vehicle-grid interactive orderly charging scheduling method to achieve at least one of the following objectives:

[0006] First, based on the response needs released by the virtual power plant, the operating parameters and status parameters of each device are extracted through charging piles, energy storage response capability assessment, system load forecasting and power generation forecasting, and appropriate response plans and control strategies are output.

[0007] The second is to increase user participation by converting the control demands issued by the virtual power plant into value-added services for electric vehicle charging and discharging behaviors in destination scenarios.

[0008] The third is to meet the VPP response needs by achieving economic power balance and enhance the ability of new energy to participate in market transactions.

[0009] To achieve the above objectives, the vehicle-grid interactive orderly charging scheduling method provided by the present invention comprises the following steps:

[0010] Step 1, data analysis: Through intelligent sensor monitoring, information collection and communication technology, obtain photovoltaic, energy storage, charging pile configuration parameters, electric vehicle charging requirements, equipment operation data, meteorological data, and system topology;

[0011] Step 2: Resource responsiveness assessment: Through regression analysis of various types of charging piles, electric vehicle charging behaviors and energy storage operation modes, the controllable load and energy storage response capabilities of each response period are assessed; by predicting the power generation and load power, the overall response capability is assessed;

[0012] Step 3, receiving the response demand: the virtual power plant management center issues the demand response type and specific content, and determines the response period, response task type, and planned operating load according to the issued instructions;

[0013] Step 4: Establish a scheduling model: Establish an optimization scheduling model based on response requirements, operation modes of various devices, response subsidy revenue sharing plan, and demand constraints;

[0014] Step 5: Output and confirm the response plan: solve the optimal planning problem, output the day-ahead response plan, declare and confirm the clearing response demand;

[0015] Step 6: Real-time response strategy modification: Obtain the operating data of each device and modify the scheduling strategy in real time according to the objective function and constraints of the scheduling model.

[0016] The following technical solutions are further optimized solutions:

[0017] In step 1, the system includes a device layer and a scheduling layer; the device layer includes a unidirectional DC charging pile, a V2G DC charging pile, a photovoltaic power generation system and an energy storage system, wherein the photovoltaic power generation system supplies power to the bus, the unidirectional DC charging pile obtains power from the bus, and the V2G DC charging pile and the energy storage system are a bidirectional energy regulation system; the scheduling layer includes a charging pile management system, an energy management system and a demand response management system, the charging pile management system obtains user charging needs and manages charging and discharging order details; the demand response management system connects to the virtual power plant management center, receives response needs, and declares responses; the energy management system comprehensively analyzes device layer data, external data, and revenue sharing plans, and formulates and executes scheduling strategies.

[0018] In step 1, the parameters and data obtained by data analysis include: installed capacity, photovoltaic power cost p PV ; Working mode of energy storage system, energy storage cost per kWh BS , Rated charge and discharge power Charge and discharge conversion efficiency μ BS,c , μ BS,d 、Battery capacity Q BS , battery state of charge SOC, charging cut-off state of charge SOC max , discharge end state of charge SOC min ;Charging power of various types of charging guns Discharge power Charging period P ci , discharge period T di , Maximum discharge power of charging stack Maximum demand of microgrid distribution transformer Ambient temperature, humidity, radiation intensity, wind speed, precipitation intensity, cloud cover; microgrid purchased electricity price The electricity price corresponding to the time-sharing charging and discharging period T of the charging station

[0019] In step 2, based on the charging gun data, regression analysis is performed to obtain the characteristic power of each type of charging gun as P EVi ; According to meteorological data and photovoltaic power generation power P PV , train the prediction model and perform photovoltaic output prediction According to meteorological data, date type and total load P of microgrid Load 、Charging load P EV , train the prediction model and predict the total load of the microgrid Charging load Based on the historical data before the response period, the energy storage benchmark power is calculated according to the demand response specifications of the region. Positive is discharging, negative is charging.

[0020] In step 2, the modeling parameters of the prediction model include the electric vehicle battery capacity C EV , battery state of charge SOC, battery discharge end state of charge SOC min 、User's expected state of charge (SOC) when leaving the station end , Rated power of charging pile Maximum discharge power of charging stack Electric vehicle charging efficiency Electric vehicle discharge efficiency Start charging time t start , expected departure time t end ;

[0021] Charging Model:

[0022]

[0023] Charging power State of Charge SOC EV ≤SOC end .

[0024] Discharge set model:

[0025]

[0026] Dischargeable electric vehicle total discharge capacity C EVS =∑ V2G放电电车 C EV , SOC EVS =∑ V2G放电电车 SOC, discharge power State of Charge SOC min ≤SOC EVS .

[0027] Expected charging power for electric vehicles:

[0028]

[0029] t max It is the latest start time of full-power charging, that is, the latest time when the electric vehicle can maintain low-power charging after entering the grid.

[0030] In step 4, the scheduling model takes the maximum economic benefit as the objective function:

[0031]

[0032] T is 288 time periods with a granularity of 5 minutes per day, C grid is the electricity purchase fee between the destination microgrid system and the power grid, P grid is the planned operating power of the microgrid, C PV is the photovoltaic electricity cost, CBS The electricity cost of energy storage is is the charge and discharge cost, C DR is the demand response fee, Declare electricity prices for demand response;

[0033]

[0034]

[0035] Taking peak shaving demand response as an example, the microgrid system constraints are:

[0036]

[0037] P DR =P base -P grid

[0038]

[0039] Energy storage system constraints:

[0040] SOC min ≤SOC BS ≤SOC max

[0041]

[0042] Electric vehicle charging and discharging power constraints:

[0043]

[0044] SOC EVi ≤SOC end

[0045] SOC min ≤SOC EVS

[0046]

[0047] Electric vehicle non-dispatch period constraints:

[0048] t=t start Or t = t end or t ≥ t max

[0049]

[0050] In step 5, the output day-ahead response plan includes: time period T, demand response amount P DR , energy storage control instructions Control instructions for each unidirectional DC charging pile V2G DC charging stack control instructions

[0051] The step 6, real-time modification of the scheduling strategy refers to modifying the instructions through real-time data feedback, specifically, modifying the day-ahead forecast in the scheduling model described in step 4 Replaced with real-time monitoring data P PV , P EV , P Load , solve the scheduling model and output the real-time scheduling strategy.

[0052] The implementation of the present invention has the following beneficial effects:

[0053] Based on the energy flow generated by "vehicle-grid interaction", the present invention uses a digital platform to achieve cross-time and cross-regional resource aggregation capabilities, participate in power market interaction and local resource scheduling, realize the value distribution of "power market-terminal distribution network-end user", and activate the multi-factor collaborative operation under the complex resource combination on the demand side. With the goal of power market transactions and resource collaborative operation, the present invention hierarchically schedules electric vehicles and energy storage resources to charge and discharge in an orderly manner, balances the interests of all parties, participates in power transactions without affecting travel needs, and improves economic efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the overall process of the present invention.

[0055] Figure 2 It is a schematic structural diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to examples.

[0057] The vehicle-grid interactive orderly charging scheduling method of the present invention comprises the following steps:

[0058] Step 1: Data analysis: Through intelligent sensor monitoring, information collection and communication technology, obtain photovoltaic, energy storage, charging pile, transformer configuration parameters, electric vehicle charging requirements, system topology, equipment operation data, meteorological data, and time-of-use electricity price information.

[0059] System topology Figure 2As shown in the figure, it includes the equipment layer composed of unidirectional DC charging piles, V2G DC charging piles, photovoltaic power generation systems and energy storage systems, among which the photovoltaic power generation system supplies power to the bus, the unidirectional DC charging piles obtain power from the bus, and the V2G DC charging piles and energy storage systems are bidirectional energy regulation systems. The scheduling layer includes the charging pile management system, the energy management system, and the demand response management system. The charging pile management system obtains user charging needs, manages charging and discharging order details, etc. The demand response management system connects to the virtual power plant management center, receives response needs, declares responses, etc. The energy management system comprehensively analyzes the equipment layer data, external data, and revenue sharing plan, and formulates and executes scheduling strategies.

[0060] The technical parameters of the photovoltaic and storage system include installed capacity, photovoltaic power cost p PV , the working mode of the energy storage system, the energy storage cost p BS , Rated charge and discharge power Charge and discharge conversion efficiency μ BS,c , μ BS,d 、Battery capacity Q BS , battery state of charge SOC, charging cut-off state of charge SOC max , discharge end state of charge SOC min The load data includes one-way DC charging piles and V2G DC charging piles, among which the charging pile data includes the charging power of each type of charging gun Discharge power Charging period T ci , discharge period T di , Maximum discharge power of charging stack Maximum demand of microgrid distribution transformer Meteorological data include ambient temperature, humidity, radiation intensity, wind speed, precipitation intensity, and cloud cover. The charging station time-sharing charging and discharging price data includes the corresponding price of each time period T

[0061] Step 2: Resource responsiveness assessment. Through regression analysis of various types of charging piles, electric vehicle charging behaviors, and energy storage operation modes, the controllable load and energy storage responsiveness of each response period are assessed. By predicting the power generation and load power, the overall responsiveness is assessed.

[0062] According to the charging gun data, regression analysis can be performed to obtain the characteristic power of each type of charging gun in the embodiment of the present invention as P EVi According to meteorological data and photovoltaic power generation power P PV , train the prediction model and perform photovoltaic output prediction According to meteorological data, date type and total load P of microgrid Load 、Charging load P EV , train the prediction model and predict the total load of the microgrid Charging load Based on the historical data before the response period, the energy storage benchmark power is calculated according to the demand response specification of the area where the embodiment is located. (Positive means discharging, negative means charging).

[0063] Among them, the dispatchability of electric vehicles during demand response evaluation requires behavioral modeling of electric vehicles. The interaction between electric vehicles and power grids is divided into three modes: disordered charging, orderly charging, and bidirectional charging and discharging. In this embodiment, electric vehicles include public vehicles and private cars. It is necessary to adopt economic measures such as peak and valley electricity prices and demand response revenue sharing to encourage the implementation of intelligent control measures without affecting the charging needs of electric vehicles themselves, so as to guide the charging of electric vehicles and realize the transition from disordered charging to orderly charging. V2G can also treat electric vehicles as special energy storage devices without affecting the demand for the use of electric vehicles. At present, V2G charging piles are costly, the power trading market is immature, the vehicle-grid balance is difficult, and users have concerns about overdraft of battery cycles. These problems need to be solved urgently. Electric vehicles are mainly used as transportation tools rather than mobile energy storage. Under the current market conditions, the embodiment of the present invention installs a certain proportion of V2G charging piles at the photovoltaic storage charging station to realize bidirectional charging and discharging, and the rest are unidirectional DC charging piles, which participate in market transactions in a comprehensive response.

[0064] Modeling the charging and discharging behavior of electric vehicles, the main parameters are: electric vehicle battery capacity C EV , battery state of charge SOC, battery discharge end state of charge SOC min 、User's expected state of charge (SOC) when leaving the station end , Rated power of charging pile Maximum discharge power of charging stack Electric vehicle charging and discharging efficiency Start charging time t start , expected departure time t end .

[0065] Charging Model:

[0066]

[0067] Charging power State of Charge SOC EV ≤SOC end .

[0068] Discharge set model:

[0069]

[0070] Dischargeable electric vehicle total discharge capacity C EVS =∑ V2G放电电车 C EV , SOCEVS =∑ V2G放电电车 SOC, discharge power State of Charge SOC min ≤SOC EVS .

[0071] Expected charging power for electric vehicles:

[0072]

[0073] t max It is the latest time to start full-power charging, that is, the latest time at which the electric vehicle can maintain low-power charging after entering the grid. Failure to start full-power charging after this time will result in the electric vehicle's power level not meeting user expectations when it leaves the station.

[0074] The adjustable capacity of electric vehicles is defined as the dispatchable space of electric vehicles under the coordination of charging and discharging capacity and charging and discharging time, which is the sum of the adjustable capacity of electric vehicles participating in the dispatch in the station. In the demand response day-ahead dispatch, load aggregators can predict the charging behavior of electric vehicle users, evaluate their electric vehicle dispatch potential for the next day, and form a reportable next-day load curve.

[0075] Step 3: Receive response demand. The virtual power plant management center issues the demand response type and specific content, and determines the response period, response task type, baseline load P according to the issued instructions. base .

[0076] Step 4: Establish a dispatch model. Establish an optimal dispatch model based on response requirements, equipment operation modes, and demand constraints. Take the maximum economic benefit as the objective function:

[0077]

[0078] T is 288 time periods with a granularity of 5 minutes per day, C grid is the electricity purchase fee between the destination microgrid system and the power grid, P grid is the planned operating power of the microgrid, C PV is the photovoltaic electricity cost, C BS The electricity cost of energy storage is is the charge and discharge cost, C DR is the demand response fee, Declare electricity prices for demand response.

[0079]

[0080] Taking peak shaving demand response as an example, the microgrid system constraints are:

[0081]

[0082] PDR =P base -P grid

[0083]

[0084] Energy storage system constraints:

[0085] SOC min ≤SOC BS ≤SOC max

[0086]

[0087] Electric vehicle charging and discharging power constraints:

[0088]

[0089] SOC EVi ≤SOC end

[0090] SOC min ≤SOC EVS

[0091]

[0092] Electric vehicle non-dispatch period constraints:

[0093] t=t start Or t = t end or t ≥ t max

[0094]

[0095] Step 5: Output and confirm the response plan. Solve the optimal planning problem, output the day-ahead response plan, declare and confirm the clearing response demand. The plan includes time period T, demand response quantity P DR , energy storage control instructions Control instructions for each unidirectional DC charging pile V2G DC charging stack control instructions

[0096] Step 6: Real-time response strategy modification. Obtain the operating data of each device and modify the scheduling strategy in real time according to the objective function and constraints of the scheduling model.

[0097] When declaring on the day-ahead, the load and power generation forecasted on the day-ahead will deviate from the real-time data during the day, and the instructions need to be corrected through real-time data feedback. Replaced with real-time monitoring data P PV, P EV , P Load , solve the scheduling model and output the real-time scheduling strategy.

[0098] The execution effect is as follows:

[0099] Before demand response: Energy storage stores electricity in advance. Based on the microgrid electricity purchase price and under the constraint of the microgrid's maximum demand, energy storage stores electricity to the maximum discharge amount during the demand response period based on the original charging and discharging plan.

[0100] During demand response: unidirectional DC charging piles can dispatch electric vehicles, reducing the charging load; V2G DC charging piles can dispatch electric vehicles to discharge appropriately; energy storage can correct the charging and discharging power in real time based on the charging and discharging plan to meet the microgrid demand response load requirements.

[0101] After the response is completed: considering the user's charging and discharging needs, based on the optimal economic benefits, jointly dispatch energy storage and charging piles, and the destination scenario microgrid system operates stably.

Claims

1. A vehicle-grid interactive orderly charging scheduling method, characterized in that: The following steps are involved: Step 1, data analysis: Through intelligent sensor monitoring, information collection and communication technology, obtain photovoltaic, energy storage, charging pile configuration parameters, electric vehicle charging requirements, equipment operation data, meteorological data, and system topology; Step 2: Resource responsiveness assessment: Through regression analysis of various types of charging piles, electric vehicle charging behaviors and energy storage operation modes, the controllable load and energy storage response capabilities of each response period are assessed; by predicting the power generation and load power, the overall response capability is assessed; Step 3, receiving the response demand: the virtual power plant management center issues the demand response type and specific content, and determines the response period, response task type, and planned operating load according to the issued instructions; Step 4: Establish a scheduling model: Establish an optimization scheduling model based on response requirements, operation modes of various devices, response subsidy revenue sharing plan, and demand constraints; Step 5: Output and confirm the response plan: solve the optimal planning problem, output the day-ahead response plan, declare and confirm the clearing response demand; Step 6: Real-time response strategy modification: Obtain the operating data of each device and modify the scheduling strategy in real time according to the objective function and constraints of the scheduling model.

2. The vehicle-grid interactive orderly charging scheduling method of claim 1, characterized in that: The system described in step 1 includes a device layer and a scheduling layer; the device layer includes a unidirectional DC charging pile, a V2G DC charging pile, a photovoltaic power generation system and an energy storage system, wherein the photovoltaic power generation system supplies power to the bus, the unidirectional DC charging pile obtains electricity from the bus, and the V2G DC charging pile and the energy storage system are a bidirectional energy regulation system; the scheduling layer includes a charging pile management system, an energy management system and a demand response management system, the charging pile management system obtains user charging needs and manages charging and discharging order details; the demand response management system connects to the virtual power plant management center, receives response needs, and declares responses; the energy management system comprehensively analyzes device layer data, external data, and revenue sharing plans, and formulates and executes scheduling strategies.

3. The vehicle-grid interactive orderly charging scheduling method of claim 1, characterized in that: In step 1, the parameters and data obtained by data analysis include: installed capacity, photovoltaic power cost p PV ; Working mode of energy storage system, energy storage cost per kWh BS , Rated charge and discharge power Charging conversion efficiency μ BS,c , discharge conversion efficiency μ BS,d 、Battery capacity Q BS , battery state of charge SOC, charging cut-off state of charge SOC max , Discharge cut-off state of charge SOC min ;Charging power of various types of charging guns Discharge power Charging period T ci , discharge period T di , Maximum discharge power of charging stack Maximum demand of microgrid distribution transformer Ambient temperature, humidity, radiation intensity, wind speed, precipitation intensity, cloud cover; microgrid purchased electricity price The electricity price corresponding to the time-sharing charging period T of the charging station The electricity price corresponding to the time-sharing discharge period T of the charging station 4. The vehicle-grid interactive orderly charging scheduling method of claim 1, characterized in that: In step 2, based on the charging gun data, regression analysis is performed to obtain the characteristic power of each type of charging gun as P EVi ; According to meteorological data and photovoltaic power generation power P PV , train the prediction model and perform photovoltaic output prediction According to meteorological data, date type and total load P of microgrid Load 、Charging load P EV , train the prediction model and predict the total load of the microgrid Charging load Based on the historical data before the response period, the energy storage benchmark power is calculated according to the demand response specifications of the region. Positive is discharging, negative is charging.

5. The vehicle-grid interactive orderly charging scheduling method of claim 4, characterized in that: In step 2, the modeling parameters of the prediction model include the electric vehicle battery capacity C EV , battery state of charge SOC, battery discharge end state of charge SOC min 、User's expected state of charge SOC when leaving the station end 、Rated power of charging pile Maximum discharge power of charging stack Electric vehicle charging efficiency Electric vehicle discharge efficiency Start charging time t start , expected departure time t end ; Charging Model: Charging power State of Charge SOC EV ≤SOC end Discharge set model: Dischargeable electric vehicle total discharge capacity C EVS =∑ V2G放电电车 C EV , SOC EVS =∑ V2G放电电车 SOC, discharge power State of Charge SOC min ≤SOC EVS Expected charging power for electric vehicles: t max It is the latest start time of full-power charging, that is, the latest time when the electric vehicle can maintain low-power charging after entering the grid.

6. The vehicle-grid interactive orderly charging scheduling method of claim 5, characterized in that: In step 4, the scheduling model takes the maximum economic benefit as the objective function: T is 288 time periods with a granularity of 5 minutes per day, C grid is the electricity purchase fee between the destination microgrid system and the power grid, P grid is the planned operating power of the microgrid, C PV is the photovoltaic electricity cost, C BS The electricity cost of energy storage is is the charge and discharge cost, C DR is the demand response fee, Declare electricity prices for demand response; Peak shaving demand response microgrid system constraints: P DR =P base -P grid Energy storage system constraints: SOC min ≤SOC BS ≤SOC max Electric vehicle charging and discharging power constraints: SOC EVi ≤SOC end SOC min ≤SOC EVS Electric vehicle non-dispatch period constraints: t=t start Or t = t end or t≥t max 7. The vehicle-grid interactive orderly charging scheduling method of claim 6, characterized in that: In step 5, the output day-ahead response plan includes: time period T, demand response amount P DR , energy storage control instructions Control instructions for each unidirectional DC charging pile V2G DC charging stack control instructions 8. The vehicle-grid interactive orderly charging scheduling method of claim 7, characterized in that: The step 6, real-time modification of the scheduling strategy refers to modifying the instructions through real-time data feedback, specifically, modifying the day-ahead forecast in the scheduling model described in step 4 Replaced with real-time monitoring data P PV , P EV , P Load , solve the scheduling model and output the real-time scheduling strategy.

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