Electric vehicle-charging pile participation demand response strategy adjustment method and system

Abstract

The invention relates to an electric vehicle-charging pile participation demand response strategy adjustment method and system, and the method comprises the following steps: collecting the data of an electric vehicle which accesses a network through a charging pile in a region, and determining the initial state of the electric vehicle when the electric vehicle accesses the network; based on the initial state, analyzing the working state of the vehicle-pile cluster by combining an electric vehicle battery capacity SOC constraint, a user intention constraint and a charging and discharging state limitation so as to determine the maximum response capability; based on the maximum response capability and response duration and response amount required by the demand market, determining a vehicle-pile cluster load adjusting amount, and further determining a constraint condition; and establishing a target function by taking the lowest charging cost of the electric vehicle users in the region as a target, and solving by adopting an optimization algorithm in combination with the constraint conditions to obtain an adjustment scheme and complete the adjustment process. Compared with the prior art, the system has the advantages that balance adjustment of power loads is achieved, the energy utilization efficiency is improved, and meanwhile self economic benefits are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power control, and in particular to a method and system for regulating an electric vehicle-charging pile participation demand response strategy. Background Art

[0002] As an energy-saving and "zero-emission" transportation option, electric vehicles (EVs) have great potential in reducing greenhouse gas emissions and promoting the development of low-carbon transportation. Electric vehicles are connected to the power grid through V2G charging piles and can be used as virtual energy storage systems. With the help of incentives such as electricity prices, electric vehicles can transfer their charging power within a certain time range and can even feed electricity back to the system in an emergency to assist it in operating according to system requirements. Under V2G control, electric vehicles can act as both loads and energy storage and distributed power sources of the system, enabling them to actively participate in assisting the operation of the power system.

[0003] Scholars at home and abroad have been focusing their research attention on electric vehicle clusters because the impact of a single small-power electric vehicle on the power grid is very small. However, the charging behavior of electric vehicle clusters is uncertain, so it is necessary to establish a model for analyzing the charging behavior of electric vehicle clusters. Many studies have been carried out by scholars at home and abroad. Yang Jie et al. established a parameter-random electric vehicle cluster model using a genetic algorithm in the literature "Optimal Scheduling Method for Temperature Control Load Based on Cerebellar Model Neural Network". Although this method is very effective in theory, it does not consider the specific conditions of electric vehicles in practice comprehensively enough, and the practical effect is not good. Currently, the most commonly used method for establishing an electric vehicle charging load model is the Monte Carlo method, which generates the travel distance and charging situation of electric vehicles by randomly extracting data. Another study established the relationship between the driving mileage of electric vehicles and the state of charge of electric vehicle batteries to obtain the charging time and used the Monte Carlo method to predict the charging situation. In a study, considering the constraints of the urban traffic road network, the origin-destination analysis method was adopted, and by simulating the driving time and route of electric vehicles, the distribution pattern of electric vehicle charging load in time and space was analyzed.

[0004] By utilizing V2G orderly charging and discharging, electric vehicles are of great significance in balancing the peak and valley loads of the power system and improving power quality. Foreign scholars have conducted a large number of studies on V2G. First, the V2G mode is modeled to explore the actual application of electric vehicles in peak shaving, and the differences between unordered charging and V2G charging schemes in terms of electric vehicle charging delay and peak load are analyzed. Zeng M et al. predicted the impact of electric vehicles on the power system in the United States in 2020 and 2030 based on historical data in the literature "Power charging and discharging scheduling for V2G networks in the smart grid" and studied the charging and discharging characteristics during off-peak hours. Yao Y et al. modeled the power system network using a heuristic algorithm and proposed corresponding control strategies in the literature "Optimization of PHEV charging schedule for load peak shaving" with the aim of minimizing the input and energy consumption. Saber A Y introduced an intelligent method for scheduling large-scale EVs in the literature "Unit commitment with vehicle-to-Grid using particle swarm optimization" and obtained the optimal charging and discharging times within a day using the particle swarm algorithm. In another study "A research on operating systems of Electric Vehicles", an optimized charging strategy aiming to reduce the peak-valley difference was proposed to reduce the peak-valley load difference and improve the power system condition.

[0005] However, compared with other developed countries, the development of V2G technology in China is relatively slow and is still in the initial stage of exploration, mainly based on theory. Experts and scholars first established a mathematical model for the charging and discharging power of electric vehicles to suppress the load fluctuations of electric vehicles entering the power grid. Some researchers proposed a method using the sub-vector particle swarm algorithm to optimize and solve different objective functions, proving the effectiveness of the application of V2G in the power system. Feng Ai et al. established a mathematical model for collaborative optimization of three objectives with the goal of minimizing the active power loss, load curve fluctuation, and voltage deviation of the power system nodes in the literature "Research on the Coordinated Charging Strategy of Electric Vehicles Based on NSGA-II". Huang Run, Zhou Xin et al. established a charging and discharging model that conforms to the Poisson distribution in the literature "Ordered Charging Scheduling Strategy Considering the Uncertainty of Electric Vehicles", and the optimization goal is to minimize the load fluctuation of the power system. Geng Lijuan proposed a multi-objective optimization model in the literature "Research on the Scheduling Strategy of Electric Vehicles Based on V2G", with the goal of maximizing the economic benefits of the power grid, users, and charging piles. Zhang Na et al. adopted a non-linear programming genetic algorithm in the literature "Multi-objective Optimization Strategy for the Coordinated Scheduling of Electric Vehicles and Wind Power Considering V2G", with the goal of reducing the load, peak-valley difference, and charging cost of users.

[0006] The above research results at home and abroad provide an important theoretical basis and practical guidance for analyzing the characteristics of load-side resources, aggregating, and regulating a large number of small loads, but there are also some deficiencies. Some studies ignore the complexity of small loads, the description of the load is not fine enough, and most of them focus on single resources, lacking a comprehensive study on the impact when load-side resources participate comprehensively. Summary of the Invention

[0007] The purpose of the present invention is to provide a method and system for regulating the demand response strategy participated by electric vehicles and charging piles to achieve a double improvement in energy utilization efficiency and economic benefits.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] A method for regulating the demand response strategy participated by electric vehicles and charging piles includes the following steps:

[0010] Collect the data of electric vehicles connected to the grid through charging piles in the area and determine the initial state when the electric vehicles are connected to the grid;

[0011] Based on the initial state, analyze the working state of the vehicle-pile cluster in combination with the SOC constraint of the electric vehicle battery capacity, the user's willingness constraint, and the charging and discharging state limit to determine the maximum response ability;

[0012] Based on the maximum response ability and the required response duration and response amount in the demand market, determine the load regulation amount of the vehicle-pile cluster, and further determine the constraint conditions;

[0013] Establish an objective function with the goal of minimizing the charging cost of electric vehicle users within the region, and combined with the above-mentioned constraints, use an optimization algorithm to solve it to obtain an adjustment plan and complete the adjustment process.

[0014] Furthermore, the steps of determining the initial state of the electric vehicle when it accesses the grid include:

[0015] Based on the electric vehicle data, calculate the possibility of an electric vehicle traveling in a day, and sample through the Monte Carlo method to obtain the probability density and probability distribution of the driving mileage of the electric vehicle;

[0016] Based on the probability density and probability distribution of the driving mileage of the electric vehicle, combined with the data including the battery energy consumption per kilometer of the sampled electric vehicle and the SOC value before the user travels, determine the initial state of the electric vehicle when it accesses the grid.

[0017] Furthermore, the possibility of an electric vehicle traveling in a day follows a lognormal distribution, expressed as:

[0018]

[0019] In the formula, R d is the daily driving mileage of the electric vehicle, f m (γ) is the probability density function of the daily driving mileage of the electric vehicle, μ m , σ m are the mean and standard deviation of the lognormal distribution respectively.

[0020] Furthermore, the expression of the initial state of the electric vehicle when it accesses the grid is:

[0021]

[0022] In the formula, SOC s is the initial SOC value when accessing the grid, δ is the SOC value of the electric vehicle before the user travels, D is the battery capacity, d is the daily travel distance of the electric vehicle, C e is the battery energy consumption per kilometer.

[0023] Furthermore, the expression of the SOC constraint of the electric vehicle battery capacity is:

[0024]

[0025] In the formula, SOC(i,0) is the battery power of the electric vehicle at the initial moment; SOC(i,t) is the battery power of the electric vehicle at time t; η is the charging efficiency; SOC min is the minimum limit of the battery capacity; SOC max is the maximum limit of the battery capacity; η chais the charging efficiency; x EVcha (i, t) is the variable representing the charging state of the i-th electric vehicle at time t; N is the total time of charging and discharging; P EVcha (i,t) is the charging power of the i-th electric vehicle at time t; Δt is the time interval; η disc is the discharge efficiency; x EVdisc (i, t) is the discharge state variable of the i-th electric vehicle at time t; P EVdisc (i,t) is the discharge power of the i-th electric vehicle at time t.

[0026] The expression of the user willingness constraint is:

[0027] 1) The adjustable time of electric vehicles is the time when the electric vehicles are connected to the grid:

[0028] t start (i)≤t(i)≤t end (i)

[0029] 2) When the electric vehicle leaves for the last time, the expected SOC value is met:

[0030] S end (i)≥S set (i)

[0031] Where, t start is the start time of adjustment for the i-th electric vehicle; t end is the end time of adjustment for the i-th electric vehicle; S end (i) is the power of the i-th electric vehicle when it leaves; S set (i) is the power level that the i-th electric vehicle is expected to reach when leaving;

[0032] The expression of the charge and discharge state limitation is:

[0033] x cha +x disc +x sta =1

[0034] In the formula, x cha 、x dis and x sta It is a 0-1 variable, indicating that the electric vehicle is in charging, discharging and standby states respectively.

[0035] Furthermore, the calculation expression of the maximum response capability is:

[0036]

[0037] In the formula, F maxFor the maximum response capacity, N is the number of electric vehicles available for mobilization, SOC(i) is the SOC value of the i-th electric vehicle, and SOC min is the minimum constraint of the battery capacity SOC, and u is the charge and discharge efficiency of the charging pile.

[0038] Furthermore, the constraint conditions include:

[0039] (1) Meeting the travel needs of users;

[0040] (2) Constraints on the SOC of the electric vehicle battery;

[0041] (3) Constraints on the adjustment cost of electric vehicles, including:

[0042] Charging adjustment cost constraint:

[0043]

[0044] Discharge subsidy cost constraint:

[0045]

[0046]

[0047] In the formula, C ch is the charging adjustment cost; N is the number of electric vehicles available for mobilization; n EV is the number of electric vehicles; p b is the subsidy electricity fee for the charging power after the grid participates in the regulation; s EVcha (i, t) is the switch state coefficient, indicating the charging state of the electric vehicle, 1 means accepting the regulation instruction to reduce the charging power, and 0 means not accepting the regulation instruction and continuing with normal charging; P max (i) is the maximum charging power of the i-th electric vehicle; P EV (i, t) is the actual charging power of the i-th electric vehicle at time t; Δt is the time interval; C disc is the discharge subsidy cost; P EVdisc (i, t) is the discharge power of the i-th electric vehicle at time t; η disc is the discharge efficiency; P disc is the energy storage cost for the electric vehicle to discharge; s EVdisc (i, t) is the switch state coefficient, indicating the discharge state of the electric vehicle, 1 means accepting the regulation instruction to discharge, and 0 means not accepting the regulation instruction and not discharging; C EV is the cost of the electric vehicle battery; N EV is the number of charge and discharge cycles that the electric vehicle can perform during its working life.

[0048] Furthermore, the expression of the objective function is:

[0049]

[0050] In the formula, f is the target value, n is the total number of electric vehicles, T is the total time, P(i,t) is the charging power of the i-th electric vehicle in the t-th period; p 3 (t) is the electricity price for charging through the charging pile at time t; P disc (i,t) is the power of the i-th electric vehicle discharging reversely to the power grid in the t-th period; p 4 (t) is the incentive price for the power grid to supply power reversely to the electric vehicle.

[0051] Furthermore, the optimization algorithm includes one of a genetic algorithm, an ant colony algorithm, and a particle swarm algorithm.

[0052] The present invention also provides an electric vehicle-charging pile participation demand response strategy adjustment system, including:

[0053] Initial state determination module: used to collect data of electric vehicles accessing the network through the charging pile in the area and determine the initial state when the electric vehicle accesses the network;

[0054] Maximum response ability determination module: used to analyze the working state of the vehicle-pile cluster based on the initial state, combined with the SOC constraint of the electric vehicle battery capacity, the user willingness constraint, and the charge and discharge state limit to determine the maximum response ability;

[0055] Constraint condition determination module: used to determine the load regulation amount of the vehicle-pile cluster based on the maximum response ability, the response duration and response amount required by the demand market, and further determine the constraint conditions;

[0056] Solution module: used to establish an objective function with the lowest charging cost of electric vehicle users in the area as the target, and combined with the constraint conditions, use an optimization algorithm to solve and obtain an adjustment plan to complete the adjustment process.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] (1) By considering the initial state when the electric vehicle accesses the network and the constraints affecting the working state of the vehicle-pile cluster, and also considering the response duration and response amount of the demand market, the present invention more precisely describes the load characteristics and influencing factors of the load-side resources, and starting from the actual life scenario, studies the effects and influencing situations of the load-side resources. Then, with the lowest cost as the optimization goal, a regulation strategy is formulated, realizing the dual improvement of energy utilization efficiency and economic benefits.

[0059] (2) In the present invention, electric vehicles within the aggregation area are regarded as load loads, and from the perspective of load loads, the demand response of the electric vehicle cluster participating in the power market is adjusted. Through optimizing the adjustment method, charging is carried out during off-peak hours, and power is supplied to the power grid during peak hours, which alleviates the load pressure on the power grid during peak hours, reduces the charging costs of electric vehicle users, and better realizes the demand response of peak shaving and valley filling and new energy consumption.

[0060] (3) The present invention more precisely describes the load characteristics and influencing factors of load-side resources; and starting from the actual life scenarios, it studies the effects and influencing situations of comprehensively adjusting two types of load-side resources. Under the background of power marketization, the load aggregator can formulate corresponding aggregation strategies and adjustment strategies according to the research results, and while realizing the balanced adjustment of power loads and improving energy utilization efficiency, it can also realize its own economic benefits. Description of the Drawings

[0061] Figure 1 It is a schematic flow chart of the method of the present invention;

[0062] Figure 2 It is a framework diagram for the coordinated optimization of electric vehicle-charging pile loads of the present invention;

[0063] Figure 3 It is a price chart for the use of intelligent charging piles of the present invention;

[0064] Figure 4 It is a comparison chart of the change in the battery capacity of electric vehicles of the present invention;

[0065] Figure 5 It is a chart showing the change in the content of the access time period of electric vehicles of the present invention. Detailed Embodiment

[0066] The present invention will be described in detail below with reference to the drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and gives the detailed implementation manner and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0067] Embodiment 1

[0068] This embodiment provides a method for adjusting the demand response strategy of electric vehicle-charging pile participation, as Figure 1 shown. This method includes the following steps:

[0069] S1. Collect data of electric vehicles accessing the network through V2G charging piles within the area.

[0070] When collecting data of electric vehicles accessing the network through V2G charging piles within the area, the data can be divided into: the state of charge of electric vehicles, the number of electric vehicles available for adjustment, and the equipment parameters of electric vehicles.

[0071] The charging of electric vehicles is affected by the driving range of electric vehicles and the initial state of charge SOC.

[0072] Driving range of electric vehicles:

[0073] The distance that an electric vehicle travels before it needs to be charged is a key factor. In contrast, the mileage of a bus is more or less constant. According to the National Household Travel Survey (NHTS), the probability of a private electric vehicle traveling in a day follows a lognormal distribution, that is:

[0074]

[0075] where μ m = 3.31; σ m = 0.87; R d is the daily driving mileage of the electric vehicle, and the probability density and probability distribution of R can be obtained through sampling and sorting by the Monte Carlo method. d

[0076] Through the above collation and analysis of the driving mileage data of electric vehicles, combined with the battery energy consumption per kilometer of electric vehicles obtained through sampling and sorting, and the SOC values before users travel, etc. It is possible to further determine the initial charge state SOC of the electric vehicle before it enters the power grid, so as to determine its maximum response ability in the initial state.

[0077] Initial state of charge SOC:

[0078] In order to determine the initial state of charge of the electric vehicle after it is connected to the grid, various parameters of the electric vehicle are sampled and sorted. These parameters include the state of charge before travel, the battery capacity of the electric vehicle, the energy consumption situation, and other operating parameters of the electric vehicle, etc.

[0079]

[0080] where D is the battery capacity (kW); d is the daily travel distance of the electric vehicle (km); C e is the battery energy consumption per kilometer (kW / km); SOC s is the initial SOC (%) when connected to the grid; δ is the SOC value (%) before the user travels.

[0081] The Monte Carlo method is used to sample the probability distribution of the battery capacity to determine the battery capacity D of the electric vehicle; in addition, the battery energy consumption C e ​The per-kilometer and daily driving distance d are determined by sampling the probability distributions of battery energy consumption and daily driving distance, respectively, in the same way. Based on the above data, the initial state of the electric vehicle when it enters the power grid can be determined using the above formula.

[0082] Determining the initial state of the electric vehicle when it enters the power grid through the above formula can calculate the maximum response capacity of the vehicle-pile cluster working state more accurately, and regulate the load of electric vehicles-charging piles in the region by combining the response duration, response volume, and transaction situation required by the demand response market.

[0083] S2. Analyze the working state of the vehicle-pile cluster to determine the maximum response capacity.

[0084] Specifically, it includes the following steps:

[0085] (1) The SOC constraint of the electric vehicle battery capacity is expressed as follows:

[0086]

[0087] Among them, SOC(i,0) is the electric quantity (kW) of the electric vehicle at the initial moment; SOC(i,t) is the electric quantity (kW) of the electric vehicle at time t; η is the charging efficiency; SOC min is the minimum limit of the battery capacity; SOC max is the maximum limit of the battery capacity; Considering the travel needs of electric vehicle users and reducing the loss of battery life caused by charging and discharging behaviors, it is required that the battery capacity has a minimum value SOC min , and a maximum value SOC max , so SOC max can be taken as the total battery capacity value of the electric vehicle.

[0088] (2) User willingness constraint:

[0089] The adjustable time of the electric vehicle is the time when the electric vehicle is connected to the power grid:

[0090] t start (i) ≤ t(i) ≤ t end (i)

[0091] When the electric vehicle finally leaves, it should meet the SOC value desired by the vehicle owner:

[0092] S end (i) ≥ S set (i)

[0093] Among them, t start is the start time of adjustment for the i-th vehicle; t end is the end time of adjustment for the i-th vehicle; S end (i) is the electric quantity (kW) of the i-th vehicle when it leaves the building; Sset (i) is the amount of electricity (kW) that the owner of the i-th vehicle hopes to reach when leaving.

[0094] (3) Charge and discharge status restrictions

[0095] Previous studies have simplified the states of electric vehicles (EVs) as charging or discharging, but in reality, an EV can be fully charged in about 6-7 hours even with slow charging. Imposing such a restriction would require EVs to discharge, leading to battery degradation. Therefore, in the context of smart charging technology, this study considers the states of EVs as charging, discharging, and standby (i.e., not charging or not discharging).

[0096] x cha +x disc +x sta =1

[0097] Among them, x cha 、x dis and x sta It is a 0-1 variable, indicating that the electric vehicle is in charging, discharging and standby states respectively.

[0098] The maximum response capacity is determined by analyzing the working state of the vehicle-pile cluster by collecting data such as the state of charge of electric vehicles, the number of electric vehicles available for adjustment, and the parameters of electric vehicle equipment, combined with the electric vehicle battery capacity SOC constraint, user willingness constraint, and charge and discharge state restriction. The formula is as follows:

[0099]

[0100] Among them, F max is the maximum response capacity, N is the number of electric vehicles available for deployment, SOC(i) is the SOC value of the i-th electric vehicle, SOC min is the minimum constraint of battery capacity SOC, and u is the charging and discharging efficiency of the charging pile.

[0101] S3. Based on the determined maximum response capacity and demand response market, the electric vehicle-charging pile load in the region is regulated according to the transaction situation.

[0102] Based on the working status of the vehicle-charging pile cluster, the maximum response capacity and the response time and response volume required by the demand response market are determined, and the electric vehicle-charging pile load in the area is regulated according to the transaction situation.

[0103] Demand response market: Generally, the charging and discharging regulation is carried out according to the charging cost of electric vehicles through smart charging piles within the adjustable time period. Try to fully charge during normal periods and reverse power transmission during peak charging periods. For example, 9:00 - 11:00 and 13:00 - 15:00 are peak charging periods with higher prices, and 11:00 - 13:00 and 15:00 - 17:00 are normal periods with lower prices. Electric vehicles in the area will reduce the charging power during the peak period from 9:00 - 11:00, but still charge a certain amount of electricity during the peak time to keep the battery capacity not less than 20% of the total capacity to avoid loss of battery service life. Subsequently, during the normal period from 11:00 - 13:00, the strategy adjusts the electric vehicle to charge as quickly as possible using the lower normal period electricity price, so that it can reverse power transmission to the power grid when participating in demand response from 13:00 - 15:00.

[0104] The demand response market requires the maximum response capacity to formulate the charging and discharging plan. As in the above example, during the normal period from 11:00 - 13:00, the maximum response capacity is relatively large, and the charging power is regulated to be fully charged. When the maximum response capacity is small, it is charged as full as possible with the maximum charging power. Finally, the electric vehicle - charging pile load is reversely regulated according to the transaction situation.

[0105] S4. Determine the vehicle - pile load regulation amount so that the hourly power reduction amount of the vehicle - pile cluster meets the response requirements.

[0106] This step mainly provides the following conditions to be met during regulation:

[0107] (1) Meet the travel needs of users;

[0108] (2) Electric vehicle battery capacity SOC constraint:

[0109]

[0110] Among them, SOC(i,0) is the electricity quantity (kW) of the electric vehicle at the initial moment; SOC(i,t) is the electricity quantity (kW) of the electric vehicle at time t; η is the charging efficiency; SOC min is the minimum limit of the battery capacity; SOC max is the maximum limit of the battery capacity; Considering the travel needs of electric vehicle users and at the same time reducing the loss of battery life caused by charging and discharging behavior, it is required that the battery capacity has a minimum value SOC min , and a maximum value SSOC max .

[0111] (3) Consider the electric vehicle regulation cost constraint;

[0112] The regulation cost can be divided into: charging regulation cost and discharge subsidy cost.

[0113] The charging regulation cost Cch The expression is as follows:

[0114]

[0115] where n EV is the number of electric vehicles; p b is the subsidy electricity fee for the charging power after participating in the regulation (yuan / kW); s EVcha (i, t) is the switch state coefficient, indicating the charging state of the electric vehicle. 1 means accepting the regulation instruction to reduce the charging power, and 0 means not accepting the regulation instruction and continuing with normal charging; P max (i) is the maximum charging power (kW) of the i-th electric vehicle; P EV (i, t) is the actual charging power (kW) of the i-th electric vehicle at time t.

[0116] The discharge subsidy cost C disc has the following expression:

[0117]

[0118] where s EVdisc (i, t) is the switch state coefficient, indicating the discharge state of the electric vehicle. 1 means accepting the regulation instruction to discharge, and 0 means not accepting the regulation instruction and not discharging; P EVdisc (i, t) is the discharge power (kW) of the i-th electric vehicle at time t; η disc is the discharge efficiency. P disc is the energy storage cost for the discharge of the electric vehicle.

[0119] Among them, the energy storage cost P for the discharge of the electric vehicle disc has the following expression:

[0120]

[0121] where C EV is the cost of the electric vehicle battery (yuan / (kW·h)); N EV is the number of charge and discharge cycles of the electric vehicle during its working life C EV is the cost of the electric vehicle battery (yuan / (kW·h)); N EV is the number of charge and discharge cycles of the electric vehicle during its working life.

[0122] S5. Establish the objective function and calculate the power reduction amount and the reduced charging cost of users during the regulation period.

[0123] The objective function established in this step aims to minimize the charging cost of electric vehicle users in the region. When the load aggregator adjusts the participation of electric vehicles in the demand response market, on the premise of achieving the user's charging goal, through measures such as orderly charging and V2G reverse power transmission to the grid, the difference between the cost generated by users charging through V2G charging piles and the incentive income of the grid for reverse power supply to electric vehicles is minimized. The objective function is as follows:

[0124]

[0125] Where: P(i,t) is the charging power (kW) of the i-th electric vehicle at time t; p 3 (t) is the electricity price (yuan / kW) for charging through the charging pile at time t; P disc (i,t) is the power (kW) of the i-th electric vehicle discharging reversely to the grid in the t-th time period; p 4 (t) is the incentive price (yuan / kW) of the grid for reverse power supply to electric vehicles.

[0126] This step uses an optimization algorithm to solve the objective function, calculates the power reduction amount and the reduced charging cost of users during the adjustment period, and finally obtains the result. The optimization algorithm is a genetic algorithm or an ant colony algorithm or a particle swarm algorithm.

[0127] The vehicle-pile load coordination optimization implementation framework of the present invention is as Figure 2 shown. The vehicle-pile cluster collects parameters such as the SOC state and real-time charge and discharge power and aggregates them to the electric vehicle-charging pile control center. The center determines the load reduction capacity and duration to the grid. The electric vehicle-charging pile control center responds to the grid dispatching requirements to achieve peak shaving and valley filling, formulates control strategies for the vehicle-pile cluster, and issues commands.

[0128] To verify the applicability of this embodiment, the present invention studies the electric vehicle charging load aggregation adjustment method, and takes a large industrial and commercial park in Shanghai as an example to conduct a simulation analysis on electric vehicles charging through charging piles in the park parking lot, and compares and analyzes the results in two cases of non-adjustment and optimized adjustment within a scheduling cycle.

[0129] It is assumed that there are 1000 V2G intelligent charging piles in the park, 30% of which are fast charging piles with a maximum charge and discharge efficiency of 60 kW, and the rest are ordinary charging piles with a maximum charge and discharge efficiency of 7 kW. The charge and discharge efficiency of the intelligent charging pile is 90%; the total number of corresponding electric vehicles is 1000, the maximum value of the battery capacity is 62.5 kW, and the initial power of the electric vehicle is randomly distributed between 20-50%; the expected value S of the SOC state of charge when the electric vehicle leaves set(i) is 80%. Considering the loss of the service life of the vehicle battery caused by over-discharge, the lower limit of the allowable discharge of the SOC of the electric vehicle is set to 20% during the simulation process. Parameters such as the battery capacity of the electric vehicle and the initial state at the time of grid connection are determined by sampling using the Monte Carlo method. Since the working hours of enterprises in industrial and commercial parks are generally from 8:30 to 17:30, the acceptance adjustment time of the electric vehicle during the simulation process is set to 9:00 - 17:00, and the price of the discharge subsidy cost for the load aggregator to users is 2.5 yuan / (kWh). The charging cost of the electric vehicle through the smart charging pile during the adjustable time period is as Figure 3 shown. Assuming that according to the transaction situation of the load merchant in the intraday demand response market, it is required that the electric vehicles in the park achieve an average load reduction of not less than 6 MW during the peak load hours of 13:00 - 15:00 within the day.

[0130] As Figure 3 shown in the smart charging pile usage price chart, during the adjustable time period of the electric vehicle, 9:00 - 11:00 and 13:00 - 15:00 are the peak charging periods, and the usage price is 2.6 yuan / kWh. 11:00 - 13:00 and 15:00 - 17:00 are the normal periods, and the price is 1.15 yuan / kWh.

[0131] Figure 4 The electric vehicle battery capacity comparison change chart and Figure 5 the electric vehicle grid connection time period content change situation chart show that during the acceptable adjustment time, the unadjusted electric vehicle will continue to charge until the battery is full, and will also charge normally during the period with higher electricity costs, which not only increases the charging cost of electric vehicle users but also exacerbates the regulation pressure on the power grid during peak load hours. By optimizing the adjustment strategy, the electric vehicles in the area will reduce the charging power during the peak time period of 9:00 - 11:00. However, according to the set lower limit of the discharge of the electric vehicle of 20%, some electricity will still be charged during the peak time to keep the battery capacity not less than 20% of the total capacity to avoid loss of the battery service life. Subsequently, during the normal period of 11:00 - 13:00, the strategy adjusts the electric vehicle to charge as soon as possible using the lower electricity price during the normal period, and can reverse power transmission to the power grid when participating in demand response from 13:00 to 15:00. According to the simulation results, 1000 electric vehicles can reverse power transmission 7200 kW to the power grid through the V2G smart charging pile during the demand response period. Subsequently, during the normal period of 15:00 - 17:00, the electric vehicle is adjusted to continue charging through the charging pile until the end of the adjustment period at 17:00, reaching the expected off-grid power at the end.

[0132] Compared with direct charging without regulation, by accepting the optimized regulation of load aggregators and participating in demand response behavior, the charging cost for users can be reduced from 93.93 yuan to 26.36 yuan. The simulation results show the effectiveness of the optimized regulation strategy. Electric vehicle load aggregators participate in demand response behavior by adjusting the electric vehicle-charging pile load in the region, and the economic expenditure saved for users is very considerable.

[0133] The present invention adopts the Monte Carlo method to randomly extract parameters such as battery capacity, mileage, and initial state of charge of electric vehicles according to probability density, and calculates the load of electric vehicle groups in combination with the lower limit of discharge allowed by electric vehicles, the expected value of power when leaving, and the charging and discharging mode of charging piles. On this basis, a model is established in which electric vehicles in the aggregation area of ​​electric vehicle load aggregators participate in the power market demand response, and an optimization regulation strategy for electric vehicle clusters participating in the power system demand response in the aggregation regulation area is formulated. By charging in normal periods and transmitting electricity to the power grid during peak periods, the load pressure on the power grid during peak periods is reduced, and the charging expenses of electric vehicle users are reduced. Finally, the effectiveness of the model and regulation strategy are verified through simulation examples.

[0134] In summary, the present invention describes the load characteristics and influencing factors of load-side resources in a more detailed manner; and starting from the actual life scenarios, it studies the effects and impacts of comprehensive regulation of two types of load-side resources. In the context of power marketization, load aggregators can formulate corresponding aggregation strategies and regulation strategies based on research results, and realize their own economic benefits while achieving balanced regulation of power loads and improving energy utilization efficiency.

[0135] Example 2

[0136] This embodiment provides an electric vehicle-charging pile participation demand response strategy adjustment system, including:

[0137] Initial state determination module: used to collect data on electric vehicles connected to the network through charging piles in the area and determine the initial state of electric vehicles when they are connected to the network;

[0138] A maximum response capability determination module is used to analyze the working state of the vehicle-pile cluster based on the initial state, combined with the electric vehicle battery capacity SOC constraint, the user willingness constraint and the charge and discharge state restriction to determine the maximum response capability;

[0139] Constraint determination module: used to determine the load adjustment amount of the vehicle-pile cluster based on the maximum response capability and the response time and response amount required by the demand market, and further determine the constraint conditions;

[0140] Solution module: used to establish an objective function with the goal of minimizing the charging cost of electric vehicle users in the area, and combined with the above constraints, an optimization algorithm is used for solution to obtain an adjustment plan and complete the adjustment process.

[0141] The rest is the same as in Embodiment 1.

[0142] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0143] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for adjusting an electric vehicle-charging pile demand response strategy, characterized in that: The following steps are involved: Collect data on electric vehicles that access the network through charging piles in the area and determine the initial state of electric vehicles when they access the network; Based on the initial state, the working state of the vehicle-pile cluster is analyzed in combination with the electric vehicle battery capacity SOC constraint, the user willingness constraint and the charge and discharge state restriction to determine the maximum response capability; Based on the maximum response capability and the response time and response amount required by the demand market, determine the load adjustment amount of the vehicle-pile cluster, and further determine the constraint conditions; An objective function is established with the goal of minimizing the charging cost for electric vehicle users in the region, and combined with the constraints, an optimization algorithm is used to solve it, an adjustment plan is obtained, and the adjustment process is completed.

2. The method for adjusting the electric vehicle-charging pile demand response strategy according to claim 1, characterized in that: The step of determining the initial state of the electric vehicle when it enters the network includes: Based on the electric vehicle data, the probability of the electric vehicle traveling in one day is calculated, and sampling is performed by the Monte Carlo method to obtain the probability density and probability distribution of the electric vehicle mileage; Based on the probability density and probability distribution of the electric vehicle's mileage, combined with sampled data including the battery energy consumption per kilometer of the electric vehicle and the SOC value of the user before the trip, the initial state of the electric vehicle when it joins the network is determined.

3. The method for adjusting the strategy of electric vehicle-charging pile participation in demand response according to claim 2, characterized in that: The probability of an electric vehicle traveling in one day follows a log-normal distribution, expressed as: In the formula, R d is the daily mileage of electric vehicles, f m (γ) is the probability density function of the daily mileage of electric vehicles, μ m , σ m are the mean and standard deviation of the lognormal distribution, respectively.

4. The method for adjusting the electric vehicle-charging pile demand response strategy according to claim 2, characterized in that: The expression of the initial state of the electric vehicle when it enters the grid is: In the formula, SOC s is the initial SOC value when entering the network, δ is the SOC value of the electric vehicle before the user travels, D is the battery capacity, d is the daily travel distance of the electric vehicle, and C e is the battery energy consumption per kilometer.

5. The method for adjusting the electric vehicle-charging pile demand response strategy according to claim 1, characterized in that: The expression of the electric vehicle battery capacity SOC constraint is: SOC min ≤SOC(i,t)≤SOC max In the formula, SOC(i,0) is the power of the electric vehicle at the initial moment; SOC(i,t) is the power of the electric vehicle at time t; η is the charging efficiency; SOC min The minimum limit of battery capacity; SOC max is the maximum limit of battery capacity; η cha is the charging efficiency; x EVcha (i, t) is the variable representing the charging state of the i-th electric vehicle at time t; N is the total time of charging and discharging; P EVcha (i,t) is the charging power of the i-th electric vehicle at time t; Δt is the time interval; η disc is the discharge efficiency; x EVdisc (i, t) is the discharge state variable of the i-th electric vehicle at time t; P EVdisc (i,t) is the discharge power of the i-th electric vehicle at time t. The expression of the user willingness constraint is: 1) The adjustable time of electric vehicles is the time when the electric vehicles are connected to the grid: t start (i)≤t(i)≤t end (i) 2) When the electric vehicle leaves for the last time, the expected SOC value is met: S end (i)≥S set (i) Where, t start is the start time of adjustment for the i-th electric vehicle; t end is the end time of adjustment for the i-th electric vehicle; S end (i) is the power of the i-th electric vehicle when it leaves; S set (i) is the power level that the i-th electric vehicle is expected to reach when leaving; The expression of the charge and discharge state limitation is: x cha +x disc +x sta =1 In the formula, x cha 、x dis and x sta It is a 0-1 variable, indicating that the electric vehicle is in charging, discharging and standby states respectively.

6. The method for adjusting the electric vehicle-charging pile demand response strategy according to claim 1, characterized in that: The calculation expression of the maximum response capability is: In the formula, F max is the maximum response capacity, N is the number of electric vehicles available for deployment, SOC(i) is the SOC value of the i-th electric vehicle, SOC min is the minimum constraint of battery capacity SOC, and u is the charging and discharging efficiency of the charging pile.

7. The method for adjusting the electric vehicle-charging pile demand response strategy according to claim 1, characterized in that: The constraints include: (1) Meeting users’ travel needs; (2) Electric vehicle battery capacity SOC constraint; (3) Electric vehicle regulation cost constraints, including: Charging regulation cost constraints: Discharge subsidy cost constraint: In the formula, C ch is the charging adjustment cost; N is the number of electric vehicles available for deployment; n EV is the number of electric vehicles; p b Subsidy for the charging power after the grid participates in regulation; EVcha (i, t) is the switch state coefficient, which indicates the charging state of the electric vehicle. 1 indicates that the electric vehicle receives the adjustment instruction and charges at a reduced power, and 0 indicates that the electric vehicle receives no adjustment instruction and continues charging normally. max (i) is the maximum charging power of the i-th electric vehicle; P EV (i, t) is the actual charging power of the i-th electric vehicle at time t; Δt is the time interval; C disc is the discharge subsidy cost; P EVdisc (i,t) is the discharge power of the i-th electric vehicle at time t; η disc is the discharge efficiency; P disc Energy storage costs to discharge electric vehicles; EVdisc (i, t) is the switch state coefficient, which indicates the discharge state of the electric vehicle. 1 indicates that the electric vehicle is discharged after receiving the adjustment command, and 0 indicates that the electric vehicle is not discharged after receiving the adjustment command. C EV is the cost of electric vehicle batteries; N EV It is the number of times an electric vehicle can be charged and discharged during its working life.

8. The method for adjusting the electric vehicle-charging pile demand response strategy according to claim 1, characterized in that: The objective function is expressed as: Where f is the target value, n is the total number of electric vehicles, T is the total time, P(i,t) is the charging power of the i-th electric vehicle in time period t; p3(t) is the electricity price charged by the charging pile at time t; P disc (i, t) is the power of reverse discharge of the i-th electric vehicle to the grid in the t-th period; p4(t) is the incentive price of the grid for reverse power supply to the electric vehicle.

9. The method for adjusting the strategy of electric vehicle-charging pile participation in demand response according to claim 1, characterized in that: The optimization algorithm includes one of a genetic algorithm, an ant colony algorithm and a particle swarm algorithm.

10. An electric vehicle-charging pile participation demand response strategy adjustment system, characterized in that: include: Initial state determination module: used to collect data on electric vehicles connected to the network through charging piles in the area and determine the initial state of electric vehicles when they are connected to the network; A maximum response capability determination module is used to determine the maximum response capability by analyzing the working state of the vehicle-pile cluster based on the initial state, combined with the electric vehicle battery capacity SOC constraint, the user willingness constraint, and the charge and discharge state constraint; Constraint determination module: used to determine the load adjustment amount of the vehicle-pile cluster based on the maximum response capability and the response time and response amount required by the demand market, and further determine the constraint conditions; Solution module: used to establish an objective function with the goal of minimizing the charging cost of electric vehicle users in the region, and to solve it using an optimization algorithm in combination with the constraints, to obtain an adjustment plan, and to complete the adjustment process.

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