Multistage regulation and control method based on electric vehicle demand response potential

Abstract

The invention discloses a multi-stage regulation and control method based on electric vehicle demand response potential. The method comprises the following steps: firstly, collecting an electric vehicle load demand response potential evaluation value and an area peak regulation instruction in a research area; based on the collected data, a peak regulation instruction is issued to each power distribution network in the region; after each power distribution network receives the peak regulation instruction, establishing a regulation and control model by taking reduction of the network loss of the power distribution network as a target, and issuing the peak regulation instruction to a charging station in the power distribution network; after each charging station receives a peak regulation instruction, establishing an optimization model by taking improvement of the income of the charging station and improvement of the new energy consumption rate as targets, and formulating a time-of-use electricity price and a peak regulation subsidy electricity price for the charging station; and finally, the charging pile in the charging station regulates and controls the charging power of the electric vehicle by taking improvement of the charging cost satisfaction degree and the charging duration satisfaction degree as a target. According to the method, the electricity price and power reduction plan can be reasonably formulated, and the multi-party benefits of the power distribution network, the charging station and the vehicle owner are ensured while the peak regulation task is completed.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system peak regulation, and in particular relates to a multi-level regulation method based on the demand response potential of electric vehicles. Background Art

[0002] The proportion of renewable energy power generation in my country is increasing year by year. Due to the volatility, intermittency, unpredictability and uncontrollability of wind and solar power generation, the power system is facing huge peak-shaving pressure. The peak-shaving resources mainly based on hydropower and thermal power on the traditional power supply side are difficult to fill the peak-shaving gap of the current power system. At the same time, due to the cost and safety issues of large-scale energy storage, as well as the site selection and construction cycle issues of the country's vigorous development of pumped storage, it is difficult to solve the peak-shaving gap problem from the power supply side in the short term. Load demand response is an important source of peak-shaving, and the urgent need for peak-shaving can be solved by tapping the load-side regulation potential.

[0003] Electric vehicle load has huge regulation potential. With the national policies and carbon dioxide emission requirements, the electric vehicle industry has developed rapidly, and charging stations and charging piles have shown a trend of large-scale development. Since electric vehicle companies have the "natural" characteristics of load aggregators, large-scale charging loads have unique advantages in demand response. Therefore, in the face of the problem of how electric vehicle loads should respond to demand response, a scientific regulation method is urgently needed to determine. The existing peak-shaving strategy is to formulate charging electricity prices to guide electric vehicles to charge in an orderly manner, but this method fails to ensure the balance of interests between regional power grids and electric vehicle users, and cannot guarantee the willingness of electric vehicle users to participate in scheduling, which leads to deviations between peak-shaving tasks and actual peak-shaving amounts. Therefore, there is a lack of a multi-level optimization regulation technology that takes into account the interests of both the power grid and users, and can formulate electric vehicle response strategies on the day before, and formulate electric vehicle real-time charging strategies based on peak-shaving deviations within the day. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-level control method based on the demand response potential of electric vehicles, which can reasonably formulate electricity prices and power reduction plans, and ensure the interests of distribution networks, charging stations and car owners while completing peak load regulation tasks.

[0005] The technical solution adopted by the present invention is a multi-level control method based on the demand response potential of electric vehicles, which specifically includes the following steps:

[0006] Step 1: Collect the electric vehicle load demand response potential assessment value and regional peak load regulation instructions in the study area;

[0007] Step 2: Based on the data obtained in step 1, a peak load regulation instruction is issued to each distribution network in the region;

[0008] Step 3: After receiving the peak load regulation instruction, each distribution network establishes a regulation model with the goal of reducing the network loss of the distribution network, uses the sparrow search algorithm to solve, and issues the peak load regulation instruction to the charging stations in the distribution network;

[0009] Step 4: After receiving the peak load-shaving instruction, each charging station establishes an optimization model with the goal of increasing the revenue of the charging station and the consumption rate of new energy, and uses the sparrow search algorithm to solve and formulate time-of-use electricity prices and peak load-shaving subsidy electricity prices for the charging station;

[0010] Step 5: The charging piles in the charging station regulate the charging power of the electric vehicle with the goal of improving satisfaction with the charging cost and charging time.

[0011] The present invention is also characterized in that

[0012] In step 1, the regional peak load shaving instructions are formulated by the regional dispatching center based on the daily load curve.

[0013] In step 2, the peak load regulation instructions are issued to the charging stations under each distribution network. Specifically, a "region-network" day-ahead demand response quantity allocation model is constructed. The "region-network" day-ahead demand response quantity allocation model allocates the regional peak load regulation instructions according to the distribution network potential evaluation value ratio based on the electric vehicle load demand response potential evaluation value obtained in step 1, as shown in the following formula (1):

[0014]

[0015] In the formula, m is the number of the distribution network in the region. is the load peak load allocated by the distribution network m in period t; is the load demand response potential assessment value of distribution network m in period t; It is the electric vehicle load peak-shaving instruction issued by the dispatching center to the study area.

[0016] The control model in step 3 is an inter-station demand response allocation model, and the constraints of the inter-station demand response allocation model include distribution network response constraints, power balance constraints, operation constraints, and electric vehicle load response constraints;

[0017] The expression for reducing the distribution network loss in the inter-station demand response allocation model is shown in formula (2):

[0018]

[0019] In the formula, is the active power of node i in period t; is the reactive power of node i in period t; R ij is the line resistance between node i and node j; V i t is the voltage at node i.

[0020] The optimization model in step 4 is an orderly charging regulation model, which includes a common charging station regulation model, a charging station regulation model containing energy storage, and a photovoltaic and energy storage integrated charging station regulation model.

[0021] The ordinary charging station control model is used to improve the operating income of ordinary charging stations. Its expression is shown in formula (7):

[0022]

[0023] In the formula, C pr1 is the total charging income of the charging station in period t; C pr2 Peak load subsidy for users within the station; N EV , are the total number of electric vehicles in the station and the number of electric vehicles participating in orderly charging during period t; is the charging power of the i-th electric vehicle; is the reduction in charging power of electric vehicle i participating in orderly charging during period t; is the time-of-use electricity price within the time interval t; is the peak load subsidy in period t;

[0024] The constraints of the common charging station control model include load response quantity constraints, power balance constraints, electric vehicle charging constraints and demand response control constraints.

[0025] The regulation model of the energy storage charging station is used to improve the operating income of the energy storage charging station. Its expression is shown in formula (12):

[0026]

[0027] In the formula, C pr1 is the total revenue of the charging station; C EQ C is the energy storage equipment loss and maintenance cost; pr2 is the subsidy cost; F ESS .P ESS (t) is the power loss cost of energy storage system charging and discharging; K ESS .P ESS (t) is the average daily maintenance cost of the energy storage system; K ESS is the operation equivalent coefficient of daily energy storage equipment; F ESS is the loss coefficient of energy storage equipment; y ESS The investment life of the energy storage system;

[0028] The constraints of the energy storage charging station control model include power balance constraints and energy storage system power constraints.

[0029] The control model of the photovoltaic and storage integrated charging station is used to improve the operating income of the photovoltaic and storage integrated charging station and the new energy consumption rate. The expression for improving the operating income of the photovoltaic and storage integrated charging station is shown in formula (15):

[0030]

[0031] In the formula, C pr1 is the total revenue of the charging station; C EQ C is the energy storage equipment loss and maintenance cost; pr2 is the subsidy cost; F PV .P PV (t) is the cost of photovoltaic power generation; K PV .P PV (t) is the cost of photovoltaic maintenance; F ESS .P ESS (t) is the power loss cost of energy storage system charging and discharging; K ESS .P ESS (t) is the average daily maintenance cost of the energy storage system; K ESS , K PV are the operation equivalent coefficients of energy storage and photovoltaic equipment respectively; F ESS 、F PV are the loss coefficients of energy storage and photovoltaic equipment respectively; y ESS is the investment life of the energy storage system; PV The investment life of the photovoltaic system;

[0032] The expression for improving the new energy consumption rate is shown in formula (16):

[0033]

[0034] Where P PV.t is the photovoltaic power consumed by the charging station during period t; is the maximum output of the photovoltaic power generation equipment during period t; C f It is the penalty indicator for abandoning light;

[0035] The constraints of the photovoltaic-storage integrated charging station control model are power balance constraints and photovoltaic equipment constraints.

[0036] In step 5, the charging power of electric vehicles is regulated by constructing a “station-pile” daily charging power optimization regulation model, which is solved by using the sparrow search algorithm. The constraints of the “station-pile” daily charging power optimization regulation model are the charging power constraint and the charging power response constraint. The expression of the “station-pile” daily charging power optimization regulation model for improving the satisfaction with charging costs is shown in formula (19):

[0037]

[0038] In the formula, is the time-of-use electricity price during period t; is the subsidy price for period t; is the power reduction value of the i-th vehicle in period t; P i is the rated capacity of the battery of the i-th vehicle; The battery power ratio when the vehicle starts charging;

[0039] The expression of the charging time satisfaction of the “station-pile” intraday charging power optimization control model is shown in formula (20):

[0040]

[0041] Where, T i , T i pr are the charging time of the i-th vehicle before and after peak load regulation.

[0042] The beneficial effects of the present invention are:

[0043] (1) The multi-level control method based on the electric vehicle demand response potential of the present invention starts from the perspective of the distribution network, so that the electric vehicle load response amount is coordinated with the impedance of each branch of the distribution network in the dispatching area and the conventional power load at each node, so as to minimize the network loss and improve the economy of the grid operation.

[0044] (2) The multi-level control method of the present invention based on the demand response potential of electric vehicles starts from the perspective of charging stations and formulates reasonable time-of-use electricity prices and peak-shaving subsidy electricity prices for different types of charging stations, thereby ensuring the completion of the peak-shaving plan and increasing the revenue of the charging stations.

[0045] (3) The multi-level control method of the present invention based on the demand response potential of electric vehicles starts from the user's perspective, flexibly controls the charging power of each charging pile according to the status information of each electric vehicle, and provides a specific power adjustment plan, so that the charging station can improve the user's satisfaction with the charging cost and charging time while meeting the response quantity requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a multi-stage control method based on the demand response potential of electric vehicles according to the present invention. DETAILED DESCRIPTION

[0047] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] The present invention is based on a multi-level control method for the electric vehicle demand response potential, such as Figure 1 As shown, the specific steps include:

[0049] Step 1: Collect the electric vehicle load demand response potential assessment value and regional peak load regulation instructions in the study area;

[0050] Step 2: Based on the data obtained in step 1, a peak load regulation instruction is issued to each distribution network in the region;

[0051] Step 3: After receiving the peak load regulation instruction, each distribution network establishes a regulation model with the goal of reducing the network loss of the distribution network, uses the sparrow search algorithm to solve, and issues the peak load regulation instruction to the charging stations in the distribution network;

[0052] From the perspective of the distribution network, the load response of electric vehicles is coordinated with the impedance of each branch of the distribution network in the dispatching area and the conventional power load at each node to minimize network losses and improve the economy of grid operation.

[0053] Step 4: After receiving the peak load-shaving instruction, each charging station establishes an optimization model with the goal of increasing the revenue of the charging station and the consumption rate of new energy, and uses the sparrow search algorithm to solve and formulate time-of-use electricity prices and peak load-shaving subsidy electricity prices for the charging station;

[0054] From the perspective of charging stations, reasonable time-of-use electricity prices and peak-shaving subsidy electricity prices should be formulated for different types of charging stations to ensure the completion of peak-shaving plans while increasing the revenue of charging stations.

[0055] Step 5: The charging piles in the charging station regulate the charging power of the electric vehicle with the goal of improving satisfaction with the charging cost and charging time.

[0056] From the user's perspective, according to the status information of each electric vehicle, the charging power of each charging pile can be flexibly controlled, and a specific power adjustment plan can be given, so that the charging station can improve the user's satisfaction with charging costs and charging time while meeting the response volume requirements.

[0057] Example 1

[0058] The multi-level control method based on the demand response potential of electric vehicles of the present invention is implemented in the following steps:

[0059] Step 1: Collect the electric vehicle load demand response potential assessment value and regional peak load regulation instructions in the study area;

[0060] Specifically, the electric vehicle load demand response potential assessment value is calculated using existing technology, and the general calculation process includes collecting electric vehicle related data, extracting data features, and evaluating the demand response potential assessment; the regional peak-shaving instructions in step 1 are formulated by the regional dispatching center based on the daily load curve.

[0061] Step 2: Based on the data obtained in step 1, a peak load regulation instruction is issued to each distribution network in the region;

[0062] Specifically, the peak load regulation instructions are issued to each distribution network in the region in step 2. Specifically, a "region-network" day-ahead demand response quantity allocation model is constructed. The "region-network" day-ahead demand response quantity allocation model allocates the regional peak load regulation instructions according to the distribution network potential evaluation value ratio based on the electric vehicle load demand response potential evaluation value obtained in step 1, as shown in the following formula (1):

[0063]

[0064] In the formula, m is the number of the distribution network in the region. is the load peak load allocated by the distribution network m in period t; is the load demand response potential assessment value of distribution network m in period t; It is the electric vehicle load peak-shaving instruction issued by the dispatching center to the study area.

[0065] Step 3: After receiving the peak load regulation instruction, each distribution network establishes a regulation model with the goal of reducing the network loss of the distribution network, uses the sparrow search algorithm to solve, and issues the peak load regulation instruction to the charging stations in the distribution network;

[0066] Specifically, after each distribution network receives the peak load regulation instruction in step 3, an inter-station demand response quantity allocation model is established with the goal of reducing the network loss of the distribution network, and a sparrow search algorithm is used to solve it, and a peak load regulation instruction is issued to the charging stations in the distribution network.

[0067] The constraints of the inter-station demand response allocation model include distribution network response constraints, power balance constraints, operation constraints and electric vehicle load response constraints;

[0068] Specifically, the expression for reducing the distribution network loss in the inter-station demand response allocation model is shown in formula (2):

[0069]

[0070] In the formula, is the active power of node i in period t; is the reactive power of node i in period t; R ij is the line resistance between node i and node j; V i t is the voltage at node i;

[0071] Specifically, the expression of the distribution network response constraint is shown in the following formula (3):

[0072]

[0073] Where: ΔP i t is the load response value of the i-th charging station in the t-th period; N is the total number of charging stations; It is the peak load-shaving task assigned to this distribution network by the “district-network” dispatching model in the tth period.

[0074] The expression of power balance constraint is shown in the following formula (4):

[0075]

[0076] Where P j,t is the active load at node j in period t; For normal load; is the active power of the electric vehicle load.

[0077] The expression of the running constraint is shown in the following formula (5):

[0078]

[0079] Where P i,t , Q i,t are the active and reactive power of node i at time t; U i,,t , U j,t is the voltage amplitude of node i and node j; θ ij is the phase angle difference between node i and node j.

[0080] The expression of the electric vehicle load response constraint is shown in the following formula (6):

[0081] P c_i,t -P pg_i,t ≤P i,t ≤P c_i,t (6);

[0082] Where P c_i,t is the predicted value of electric vehicle load at the charging station at node i at time t; P pg_i,t is the evaluation value of the electric vehicle load response potential of the charging station at node i at time t; P i,t is the load of node i at time t.

[0083] Step 4: After receiving the peak load regulation instruction, each charging station establishes an orderly charging regulation model with the goal of increasing the revenue of the charging station and increasing the new energy consumption rate. The model is solved using the sparrow search algorithm to formulate a time-of-use electricity price and a peak load subsidy electricity price for the charging station.

[0084] Step 5: The charging piles in the charging station regulate the charging power of the electric vehicle with the goal of improving satisfaction with the charging cost and charging time.

[0085] Example 2

[0086] Based on Example 1, the orderly charging control model in step 4 of the multi-level control method based on the demand response potential of electric vehicles of the present invention includes a common charging station control model, a charging station control model with energy storage, and a photovoltaic and energy storage integrated charging station control model.

[0087] Furthermore, the constraints of the common charging station control model include load response quantity constraints, power balance constraints, electric vehicle charging constraints and demand response control constraints.

[0088] Specifically, the common charging station control model is used to improve the operating income of common charging stations, and its expression is shown in formula (7):

[0089]

[0090] In the formula, C pr1 is the total charging income of the charging station in period t; C pr2 Peak load subsidy for users within the station; N EV , are the total number of electric vehicles in the station and the number of electric vehicles participating in orderly charging during period t; is the charging power of the i-th electric vehicle; is the reduction in charging power of electric vehicle i participating in orderly charging during period t; is the time-of-use electricity price within the time interval t; is the peak load subsidy during period t.

[0091] The expression of load response constraint is shown in formula (8):

[0092]

[0093] Where ΔP i t is the load peak load allocation value of the i-th charging station in the t-th period; is the load demand response of the i-th charging station in period t.

[0094] The expression of power balance constraint is shown in formula (9):

[0095]

[0096] Where P grid The power provided by the distribution network to the charging station; P load For electric vehicle load;

[0097] The expression of electric vehicle charging constraint is shown in formula (10):

[0098]

[0099] In the formula, The amount of electricity stored in the vth electric vehicle during period t; are the upper and lower limits of the storage capacity of the vth electric vehicle respectively; is the charging power of the vth electric vehicle in period t; The charging power limit for the vth electric vehicle; Whether the vth electric vehicle in period t is in charging state, if yes, set to 1, otherwise set to 0; are the initial and final storage capacities of the vth electric vehicle respectively; is the charging efficiency of the vth electric car.

[0100] The expression of demand response regulation constraint is shown in formula (11):

[0101]

[0102] Where P t load As the basic electric vehicle load; The maximum load ratio is reduced by guiding users to shift their charging time through time-of-use electricity prices during period t; is the load amount reduced by the time-of-use electricity price during period t; For users who still choose to charge during the time-of-use electricity price period t, the charging fee is compensated to attract users to participate in orderly charging, reduce their charging power and thus reduce the maximum load ratio; The load reduced by the peak load subsidy during period t; It is the maximum load reduction ratio that the excitation load responds to in a continuous period.

[0103] Specifically, the constraints of the regulation model of the charging station with energy storage include power balance constraints and energy storage system power constraints;

[0104] The regulation model of the energy storage charging station is used to improve the operating income of the energy storage charging station. Its expression is shown in formula (12):

[0105]

[0106] In the formula, C pr1 is the total revenue of the charging station; C EQ C is the energy storage equipment loss and maintenance cost; pr2 is the subsidy cost; F ESS .P ESS (t) is the power loss cost of energy storage system charging and discharging; K ESS .P ESS (t) is the average daily maintenance cost of the energy storage system; K ESS is the operation equivalent coefficient of daily energy storage equipment; F ESS is the loss coefficient of energy storage equipment; y ESSThe investment life of the energy storage system.

[0107] The expression of power balance constraint is shown in (13):

[0108]

[0109] Where P ESS To provide energy storage equipment, P ESS >0, the energy storage discharges to the charging station, otherwise it absorbs electricity from the distribution network.

[0110] The expression of the energy storage system power constraint is shown in formula (14):

[0111]

[0112] In the formula, are the lower and upper limits of the charging power of energy storage device i respectively; are the lower and upper limits of the discharge power of energy storage device i respectively; is the energy storage capacity of energy storage device i in time period t; is the charging efficiency coefficient of energy storage device i; Discharge efficiency coefficient of energy storage device i; is the maximum energy storage capacity of energy storage device i, and the battery energy storage capacity is set to 10% of the total daily load of the distribution network.

[0113] Specifically, the constraints of the photovoltaic and storage integrated charging station control model are power balance constraints and photovoltaic equipment constraints. The photovoltaic and storage integrated charging station control model is used to improve the operating income of the photovoltaic and storage integrated charging station and the new energy consumption rate. The expression for improving the operating income of the photovoltaic and storage integrated charging station is shown in formula (15):

[0114]

[0115] In the formula, C pr1 is the total revenue of the charging station; C EQ C is the energy storage equipment loss and maintenance cost; pr2 is the subsidy cost; F PV .P PV (t) is the cost of photovoltaic power generation; K PV .P PV (t) is the cost of photovoltaic maintenance; F ESS .P ESS (t) is the power loss cost of energy storage system charging and discharging; K ESS .P ESS (t) is the average daily maintenance cost of the energy storage system; K ESS , K PV are the operation equivalent coefficients of energy storage and photovoltaic equipment respectively; F ESS、F PV are the loss coefficients of energy storage and photovoltaic equipment respectively; y ESS is the investment life of the energy storage system; PV The investment life of the photovoltaic system;

[0116] The expression for improving the new energy consumption rate is shown in formula (16):

[0117]

[0118] Where P PV.t is the photovoltaic power consumed by the charging station during period t; is the maximum output of the photovoltaic power generation equipment during period t; C f It is the penalty indicator for abandoning light.

[0119] The expression of power balance constraint is shown in formula (17):

[0120]

[0121] Where P ESS To provide energy storage equipment, P ESS >0, the energy storage discharges to the charging station, otherwise it absorbs electric energy from the distribution network; PV Provide power for photovoltaic equipment; load For load.

[0122] The expression of photovoltaic equipment constraints is shown in formula (18):

[0123] Where pPV(t) is the photovoltaic power coefficient, which is a function of light intensity and temperature; The maximum output of photovoltaic at time t; is the access capacity of photovoltaic distributed power sources; P PV (t) is the active power of photovoltaic power consumed by the charging station, which does not exceed the maximum output range of photovoltaic power.

[0124] Furthermore, in step 5, the charging power of electric vehicles is regulated by constructing a “station-pile” intraday charging power optimization regulation model, which is solved by using the sparrow search algorithm. The constraints of the “station-pile” intraday charging power optimization regulation model are the charging power constraint and the charging power response constraint. The expression of the “station-pile” intraday charging power optimization regulation model for improving the charging cost satisfaction is shown in formula (19):

[0125]

[0126] In the formula, is the time-of-use electricity price during period t; is the subsidy price for period t; is the power reduction value of the i-th vehicle in period t; Pi is the rated capacity of the battery of the i-th vehicle; The battery power ratio when the vehicle starts charging;

[0127] The expression of the charging time satisfaction of the “station-pile” intraday charging power optimization control model is shown in formula (20):

[0128]

[0129] Where, T i , T i pr are the charging time of the i-th vehicle before and after peak load regulation.

[0130] Specifically, the expression of the charging power constraint is shown in formula (21):

[0131]

[0132] In the formula, p i is the charging power of the original charging pile; Δp i is the charging power adjustment amount;

[0133] Specifically, the expression of the charging power response constraint is shown in formula (22):

[0134]

[0135] In the formula, The peak load subsidy response plan obtained from day-ahead dispatch; is the intraday peak regulation error value; It is the load response amount that actually needs to be reduced by the daily charging power control model.

[0136] Example 3

[0137] The Sparrow Search Algorithm (SSA) is a swarm intelligence optimization algorithm proposed based on the behavior of sparrows foraging and escaping predators. The Sparrow Search Algorithm mainly simulates the process of sparrow flocks foraging. Each sparrow has three possible behaviors: 1) as a finder, continue to search for food, 2) as a follower, follow a finder to forage, 3) alert reconnaissance, and give up food if there is danger.

[0138] On the basis of Example 1, in step 3 of the multi-level control method based on the demand response potential of electric vehicles of the present invention, a sparrow search algorithm is used to solve the problem. Specifically, the position of a population composed of N sparrows in the d-dimensional space is shown in formula (23):

[0139]

[0140] Among them, N is the number of input populations, d is the dimension of the problem to be optimized, and the elements in the first row of X represent a set of initialized solutions, that is, the peak load of different distribution networks.

[0141] The fitness value F of all sparrows x As shown in formula (24):

[0142]

[0143] In the formula, F X The elements in represent the objective function, i.e., the network loss of the distribution network.

[0144] There are two types of sparrows in the sparrow search algorithm, namely, finders and joiners. The finder is responsible for finding food for the entire population and providing foraging directions for the joiner. Therefore, the foraging search range of the finder is larger than that of the joiner. In each iteration, the finder iterates according to the following formula. The position update formula for each generation is shown in formula (25):

[0145]

[0146] Among them, t represents the current iteration number, x i,d represents the position information of the i-th sparrow population in the d-th dimension, a∈(0,1] random number, iter max represents the maximum number of iterations, Q represents a random number that follows a normal distribution, L is a d-dimensional row vector whose elements are all 1, R2∈[0,1] represents the warning value of the sparrow population location, and ST∈[0.5,1.0] represents the safety value of the sparrow population location.

[0147] The follower position update formula is shown in formula (26):

[0148]

[0149] Among them, xw is the worst position of sparrows in the current population, xb t is the optimal position of the sparrow in the population. A represents a 1×d matrix whose elements are randomly assigned 1 or -1, and A + =A T (AA T ) -1 .when When , it indicates that the i-th participant has not obtained food and is in a hungry state. At this time, it needs to fly to other places to forage for more energy.

[0150] While the sparrows are foraging, some of them will be responsible for vigilance. When danger approaches, they will give up the current food. That is, no matter whether the sparrow is a finder or a follower, it will give up the current food and move to a new location. In each generation, SD individuals will be randomly selected from the population to perform warning behavior. The position update formula is shown in formula (27):

[0151]

[0152] Among them, β is a random number that conforms to the standard normal distribution, K∈[-1,1] is a uniform random number, ε is a small number to prevent the denominator from being 0, and f w is the fitness value of the sparrow in the worst position.

[0153] Through iterative updates, when the end condition is met, the optimal distribution network outputs the peak load regulation instructions to each charging station. For example, the end condition is 500 iterations, and when the number of iterations is 501, the iteration cycle ends.

[0154] The same is true when the sparrow search algorithm is used to solve step 4 and step 5. When the sparrow search algorithm is used to solve step 4, X in formula (23) is replaced with the time-of-use electricity price and peak-load electricity price of the charging station, and F in formula (24) is replaced with X Replace with the charging station revenue and new energy consumption rate. Step 5 Replace X in formula (23) with the charging station power response, and replace in formula (24) with the user charging cost and time satisfaction.

Claims

1. A multi-level control method based on the demand response potential of electric vehicles, characterized in that: The specific steps include: Step 1: Collect the electric vehicle load demand response potential assessment value and regional peak load regulation instructions in the study area; Step 2: Based on the data obtained in step 1, a peak load regulation instruction is issued to each distribution network in the region; Step 3: After receiving the peak load regulation instruction, each distribution network establishes a regulation model with the goal of reducing the network loss of the distribution network, uses the sparrow search algorithm to solve, and issues the peak load regulation instruction to the charging stations in the distribution network; Step 4: After receiving the peak load-shaving instruction, each charging station establishes an optimization model with the goal of increasing the revenue of the charging station and the consumption rate of new energy, and uses the sparrow search algorithm to solve and formulate time-of-use electricity prices and peak load-shaving subsidy electricity prices for the charging station; Step 5: The charging piles in the charging station regulate the charging power of the electric vehicle with the goal of improving satisfaction with the charging cost and charging time.

2. The multi-level control method based on electric vehicle demand response potential according to claim 1 is characterized in that: In step 1, the regional peak load shaving instruction is formulated by the regional dispatching center according to the daily load curve.

3. The multi-level control method based on electric vehicle demand response potential according to claim 1 is characterized in that: The specific method of issuing peak load regulation instructions to charging stations under each distribution network in step 2 is to construct a "region-network" day-ahead demand response quantity allocation model. The "region-network" day-ahead demand response quantity allocation model allocates regional peak load regulation instructions according to the distribution network potential evaluation value ratio based on the electric vehicle load demand response potential evaluation value obtained in step 1, as shown in the following formula (1): In the formula, m is the serial number of the distribution network in the region. is the load peak load allocated by the distribution network m in period t; is the load demand response potential assessment value of distribution network m in period t; It is the electric vehicle load peak-shaving instruction issued by the dispatching center to the study area.

4. The multi-level control method based on electric vehicle demand response potential according to claim 1 is characterized in that: The control model in step 3 is an inter-station demand response allocation model, and the constraints of the inter-station demand response allocation model include distribution network response constraints, power balance constraints, operation constraints and electric vehicle load response constraints; The expression for reducing the distribution network loss in the inter-station demand response quantity allocation model is shown in formula (2): In the formula, is the active power of node i in period t; is the reactive power of node i in period t; R ij is the line resistance between node i and node j; V i t is the voltage at node i.

5. The multi-level control method based on electric vehicle demand response potential according to claim 1 is characterized in that: The optimization model in step 4 is an orderly charging control model, which includes a common charging station control model, a charging station control model with energy storage, and a photovoltaic and energy storage integrated charging station control model.

6. The multi-level control method based on electric vehicle demand response potential according to claim 5 is characterized in that: The ordinary charging station control model is used to improve the operating income of ordinary charging stations, and its expression is shown in formula (7): In the formula, C pr1 is the total charging income of the charging station in period t; C pr2 Peak load subsidy for users within the station; N EV , are the total number of electric vehicles in the station and the number of electric vehicles participating in orderly charging during period t; is the charging power of the i-th electric vehicle; is the reduction in charging power of electric vehicle i participating in orderly charging during period t; is the time-of-use electricity price within the time interval t; is the peak load subsidy in period t; The constraints of the common charging station control model include load response quantity constraints, power balance constraints, electric vehicle charging constraints and demand response control constraints.

7. The multi-level control method based on electric vehicle demand response potential according to claim 5 is characterized in that: The energy storage charging station control model is used to improve the operating income of the energy storage charging station, and its expression is shown in formula (12): In the formula, C pr1 is the total revenue of the charging station; C EQ C is the energy storage equipment loss and maintenance cost; pr2 is the subsidy cost; F ESS .P ESS (t) is the power loss cost of energy storage system charging and discharging; K ESS .P ESS (t) is the average daily maintenance cost of the energy storage system; K ESS is the operation equivalent coefficient of daily energy storage equipment; F ESS is the loss coefficient of energy storage equipment; y ESS The investment life of the energy storage system; The constraints of the energy storage charging station control model include power balance constraints and energy storage system power constraints.

8. The multi-level control method based on electric vehicle demand response potential according to claim 5 is characterized in that: The photovoltaic and storage integrated charging station control model is used to improve the operating income and new energy consumption rate of the photovoltaic and storage integrated charging station. The expression for improving the operating income of the photovoltaic and storage integrated charging station is shown in formula (15): In the formula, C pr1 is the total revenue of the charging station; C EQ C is the energy storage equipment loss and maintenance cost; pr2 is the subsidy cost; F PV .P PV (t) is the cost of photovoltaic power generation; K PV .P PV (t) is the cost of photovoltaic maintenance; F ESS .P ESS (t) is the power loss cost of energy storage system charging and discharging; K ESS .P ESS (t) is the average daily maintenance cost of the energy storage system; K ESS , K PV are the operation equivalent coefficients of energy storage and photovoltaic equipment respectively; F ESS 、F PV are the loss coefficients of energy storage and photovoltaic equipment respectively; y ESS is the investment life of the energy storage system; PV The investment life of the photovoltaic system; The expression for improving the new energy consumption rate is shown in formula (16): Where P PV.t is the photovoltaic power consumed by the charging station during period t; is the maximum output of the photovoltaic power generation equipment during period t; C f It is the penalty indicator for abandoning light; The constraints of the photovoltaic-storage integrated charging station control model are power balance constraints and photovoltaic equipment constraints.

9. The multi-level control method based on electric vehicle demand response potential according to claim 1 is characterized in that: In step 5, the electric vehicle charging power is regulated by constructing a "station-pile" daily charging power optimization regulation model, which is solved by using a sparrow search algorithm. The constraints of the "station-pile" daily charging power optimization regulation model are charging power constraints and charging power response constraints. The expression of the "station-pile" daily charging power optimization regulation model for improving charging cost satisfaction is shown in formula (19): In the formula, is the time-of-use electricity price during period t; is the subsidy price for period t; is the power reduction value of the i-th vehicle in period t; P i is the rated capacity of the battery of the i-th vehicle; The battery power ratio when the vehicle starts charging; The expression of the charging time satisfaction of the "station-pile" intra-day charging power optimization control model is shown in formula (20): Where, T i , T i pr are the charging time of the i-th vehicle before and after peak load regulation.

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