Electric vehicle demand response scheduling method based on economic model predictive control

Abstract

The invention provides an electric vehicle demand response scheduling method based on economic model predictive control, and relates to the technical field of power systems. Discrete electric vehicle individual behaviors can be mapped into group schedulable dynamic power responses through a load aggregation model. On the basis, a collaborative response model corresponding to the electric vehicle and the energy storage system is constructed, and the collaborative response model can integrate the dynamic response capabilities of the electric vehicle and the energy storage system. Updating a first income model in which the electric vehicle and the energy storage system participate in the demand response, and updating a second income model and a cost model in which the electric vehicle aggregator participates in the demand response; therefore, the economic benefits of all parties can be reflected more accurately. And then solving a target economic model generated by the above models based on a preset constraint condition set so as to perform charging and discharging scheduling on the electric vehicle and the energy storage system. A closed-loop link with precise modeling, economic optimization and cooperative control is formed, and the win-win situation of the economic benefits of the electric vehicle aggregator and the power grid operation benefits is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method for demand response scheduling of electric vehicles based on economic model predictive control. Background Art

[0002] With the adjustment of the national economic industrial structure and the acceleration of the large-scale application of new energy, electric vehicles have become an important carrier for demand-side resource coordination due to their dual attributes of load consumption and energy storage regulation, as well as their charging characteristics that are highly coupled with the user's daily electricity consumption period. Through the coordinated dispatch of large-scale electric vehicles and energy storage systems by electric vehicle aggregators, it can not only alleviate the problems of increased grid control complexity, power quality fluctuations and reduced system reliability caused by large-scale disordered charging behavior, but also achieve the goal of peak-shaving and valley-filling of the grid through the demand response mechanism, forming a win-win energy regulation ecology for all parties.

[0003] Currently, electric vehicles generally adopt contractual constraints and fixed response modes to respond to demand. These methods can guide the charging and discharging behavior of electric vehicles to a certain extent, but they do not conduct observable and controllable modeling of the charging and discharging of electric vehicles. There is a lack of accurate analysis of vehicle status, user behavior preferences and battery dynamic characteristics, and it is difficult to take into account the economy of the demand response scheduling process, resulting in the inability to maximize the benefits of electric vehicles participating in demand response while ensuring the power quality and grid operation efficiency. Summary of the invention

[0004] The purpose of this application is to solve at least one of the above-mentioned technical defects, especially the lack of observable and controllable modeling of the charging and discharging of electric vehicles in the prior art, the lack of accurate analysis of vehicle status, user behavior preferences and battery dynamic characteristics, and the difficulty in taking into account the economy of the demand response scheduling process, resulting in the inability to maximize the benefits of electric vehicles participating in demand response while ensuring the power quality and the operating efficiency of the power grid.

[0005] In a first aspect, the present application provides an electric vehicle demand response scheduling method based on economic model predictive control, the method comprising:

[0006] In a scheduling period, a load aggregation model of the electric vehicle is obtained, and a coordinated response model corresponding to the electric vehicle and the energy storage system based on the state space is constructed according to the load aggregation model;

[0007] Update the first revenue model for electric vehicles and energy storage systems to participate in demand response, and update the second revenue model and cost model for electric vehicle aggregators to participate in demand response;

[0008] Determining state variables and control variables according to the collaborative response model, and generating a target economic model by combining the first benefit model, the second benefit model and the cost model, and solving the target economic model based on a preset set of constraints;

[0009] The charging and discharging of electric vehicles and energy storage systems are scheduled according to the optimal control sequence obtained.

[0010] In one embodiment, the step of obtaining a load aggregation model of an electric vehicle includes:

[0011] Obtain the state of charge of the battery of the electric vehicle during the current scheduling period;

[0012] Determine a first load transfer equation of the electric vehicle in a charging state and a second load transfer equation in a discharging state according to the state of charge;

[0013] The load amount of the electric vehicle is used as a state variable, and the first load transfer equation and the second load transfer equation are combined to generate a load aggregation model of the electric vehicle.

[0014] In one embodiment, the step of constructing a coordinated response model corresponding to the electric vehicle and the energy storage system based on the state space according to the load aggregation model includes:

[0015] extracting a state transfer matrix from the load aggregation model;

[0016] Acquire a state space model of the energy storage system, and extract a state transfer matrix from the state space model;

[0017] Determine a target state matrix according to the state transfer matrix extracted in the load aggregation model and the state space model, and determine a target control matrix according to the rated capacities of the electric vehicle and the energy storage system;

[0018] The charge states of the electric vehicle and the energy storage system are used as state variables, the power changes of the electric vehicle and the energy storage system are used as control variables, and the target state matrix and the target control matrix are combined to generate a coordinated response model corresponding to the electric vehicle and the energy storage system.

[0019] In one embodiment, updating the first revenue model of the electric vehicle and the energy storage system participating in demand response includes:

[0020] Obtain the charging power and discharging power of the electric vehicle and the energy storage system respectively during the current scheduling period;

[0021] Obtain the charging electricity price and discharging electricity price during the current dispatch period, and determine the degradation loss costs of the electric vehicle and the energy storage system during the current dispatch period respectively;

[0022] The charging power, discharging power, degradation loss cost of the electric vehicle and the energy storage system in the current scheduling period, and the charging electricity price and discharging electricity price in the current scheduling period are substituted into the preset first model to obtain a new first profit model.

[0023] In one embodiment, updating the second revenue model and cost model of the electric vehicle aggregator participating in demand response includes:

[0024] Obtain the benchmark load of the distribution network during the current dispatch period, and obtain the total load of the distribution network when electric vehicles are charging in an unordered manner without energy storage coordination;

[0025] Substituting the reference load and the total load of the distribution network into a preset second model to obtain a new second revenue model;

[0026] Calculate the aggregator's operating costs and fixed contracted electric vehicle costs during the current dispatch period based on the aggregated output power of the electric vehicle aggregator during the current dispatch period;

[0027] A cost model for the electric vehicle aggregator is updated based on the aggregator operating cost and the fixed contracted electric vehicle cost.

[0028] In one embodiment, after determining the state variables and the control variables according to the collaborative response model and generating the target economic model in combination with the first revenue model, the second revenue model and the cost model, solving the target economic model based on a preset set of constraints includes:

[0029] Determine an economic function within a current scheduling period according to the first revenue model, the second revenue model and the cost model;

[0030] Extracting state variables and control variables from the collaborative response model, and constructing a target economic model based on the economic function, the state variables and the control variables; the target economic model aims to solve the control variables when the economic function is maximized;

[0031] A preset set of constraints is obtained, and based on the set of constraints, the target economic model is solved using an economic model predictive control algorithm to obtain a control variable sequence when the economic function is maximized under the constraints, and the control variable sequence is determined as the optimal control sequence.

[0032] In one embodiment, the charging and discharging scheduling of the electric vehicle and the energy storage system according to the optimal control sequence obtained by solving the problem includes:

[0033] Obtaining a first control variable in the optimal control sequence, and determining a power change of the electric vehicle and the energy storage system according to the first control variable in the optimal control sequence;

[0034] The power of the electric vehicle and the energy storage system are adjusted respectively based on the power changes of the electric vehicle and the energy storage system to perform charging and discharging scheduling for the electric vehicle and the energy storage system.

[0035] In a second aspect, the present application provides an electric vehicle demand response scheduling device based on economic model predictive control, the device comprising:

[0036] A model building module, used to obtain a load aggregation model of electric vehicles in a scheduling period, and to build a coordinated response model corresponding to the electric vehicles and the energy storage system based on the state space according to the load aggregation model;

[0037] A model updating module, used to update a first revenue model of electric vehicles and energy storage systems participating in demand response, and to update a second revenue model and cost model of electric vehicle aggregators participating in demand response;

[0038] A model solving module, configured to determine state variables and control variables according to the collaborative response model, generate a target economic model by combining the first benefit model, the second benefit model and the cost model, and solve the target economic model based on a preset set of constraints;

[0039] The charging and discharging scheduling module is used to schedule the charging and discharging of electric vehicles and energy storage systems according to the optimal control sequence obtained by solving.

[0040] In a third aspect, the present application provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the electric vehicle demand response scheduling method based on economic model predictive control as described in any of the above embodiments.

[0041] In a fourth aspect, the present application provides a computer device, comprising: one or more processors, and a memory;

[0042] The memory stores computer-readable instructions, and when the one or more processors execute the computer-readable instructions, the steps of the electric vehicle demand response scheduling method based on economic model predictive control as described in any of the above embodiments are performed.

[0043] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0044] The electric vehicle demand response scheduling method based on economic model predictive control provided in the present application obtains the load aggregation model of the electric vehicle in the process of electric vehicle participation in demand response scheduling. Through the load aggregation model, the discrete individual behavior of the electric vehicle can be mapped into a group-dispatched dynamic power response, thereby realizing the centralized management and optimized scheduling of the electric vehicle load. On this basis, a coordinated response model corresponding to the electric vehicle and the energy storage system is further constructed. The coordinated response model can integrate the dynamic response capabilities of the electric vehicle and the energy storage system, realize the dynamic complementarity and response capability integration of the two types of resources, and improve the scheduling flexibility. Then update the first revenue model of the electric vehicle and the energy storage system participating in the demand response, and update the second revenue model and cost model of the electric vehicle aggregator participating in the demand response. Through the update of these models, the economic benefits and cost expenditures of all parties in the demand response process can be more accurately reflected to maximize the economic benefits of all parties. Then, based on the preset constraint set, the target economic model generated by the coordinated response model, the first revenue model, the second revenue model and the cost model is solved, so that the economy and scheduling effect can be taken into account under the constraint set. Finally, the optimal control sequence obtained by the solution is used for charging and discharging scheduling of electric vehicles and energy storage systems. A closed-loop link of precise modeling, economic optimization, and coordinated control is formed, which can maximize the benefits of electric vehicles participating in demand response while also improving power quality and ensuring grid operation efficiency, achieving a win-win situation for economic benefits and grid operation benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0046] Figure 1 A flow chart of an electric vehicle demand response scheduling method based on economic model predictive control provided in an embodiment of the present application;

[0047] Figure 2 A schematic diagram of a flow chart for obtaining a load aggregation model of an electric vehicle provided in an embodiment of the present application;

[0048] Figure 3 A schematic diagram of a process for constructing a coordinated response model corresponding to an electric vehicle and an energy storage system based on a state space according to a load aggregation model provided in an embodiment of the present application;

[0049] Figure 4A schematic diagram of a flow chart for scheduling charging and discharging of electric vehicles and energy storage systems according to an optimal control sequence obtained by solving an embodiment of the present application;

[0050] Figure 5 An example diagram of the overall scheduling framework for electric vehicle aggregators provided in an embodiment of the present application;

[0051] Figure 6 A diagram showing the change of charging and discharging load of an electric vehicle over time provided in an embodiment of the present application;

[0052] Figure 7 A comparative example diagram of an electric vehicle demand response scheduling method based on economic model predictive control provided in an embodiment of the present application;

[0053] Figure 8 A schematic diagram of the structure of an electric vehicle demand response scheduling device based on economic model predictive control provided in an embodiment of the present application;

[0054] Fig. 9 An internal structure diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] Specifically, in the process of participating in demand response, electric vehicles currently generally adopt contractual constraints and fixed response modes to respond to demand. These methods can guide the charging and discharging behavior of electric vehicles to a certain extent, but they do not conduct observable and controllable modeling of the charging and discharging of electric vehicles, lack accurate analysis of vehicle status, user behavior preferences and battery dynamic characteristics, and it is difficult to take into account the economic efficiency of the demand response scheduling process, resulting in the inability to maximize the benefits of electric vehicles participating in demand response while ensuring the quality of power and the operating efficiency of the power grid. Based on this, Figure 1 As shown, the present application provides an electric vehicle demand response scheduling method based on economic model predictive control, the method comprising:

[0057] S101: In a scheduling period, a load aggregation model of electric vehicles is obtained, and a coordinated response model corresponding to electric vehicles and energy storage systems based on a state space is constructed according to the load aggregation model.

[0058] Among them, the load aggregation model is a mathematical model built based on the state space equation. The load aggregation model can be used to map the individual behaviors of decentralized electric vehicles into a group-dispatched dynamic power response. The coordinated response model is a joint model that integrates the dynamic response capabilities of electric vehicles and energy storage systems. The system response model can be used to coordinate the charging and discharging behaviors of the two.

[0059] In this step, when entering a scheduling period, the load aggregation model of electric vehicles can be obtained based on the relevant parameters of electric vehicles included in the electric vehicle aggregator. Then, a coordinated response model corresponding to electric vehicles and energy storage systems based on state space is constructed according to the load aggregation model.

[0060] Furthermore, when the current moment triggers a scheduling period, the relevant parameters of the electric vehicles included in the electric vehicle aggregator are obtained, and the load aggregation model of the electric vehicles is determined and obtained based on this, so that the individual behaviors of the electric vehicles dispersed in the electric vehicle aggregator can be mapped into a group-dispatched dynamic power response, forming a model that can reflect the dynamic changes in the load of the electric vehicle group. Then, according to the load aggregation model and combined with the state space model of the energy storage system, a coordinated response model corresponding to the electric vehicle and the energy storage system is further constructed. The coordinated response model uses the charge state of the electric vehicle and the energy storage system as the state variable, and the power change of the electric vehicle and the energy storage system as the control variable.

[0061] Specifically, regarding the determination of the scheduling period, each scheduling period can be determined by setting the scheduling duration and the scheduling interval, or each scheduling period can be determined according to preset rules. When the rolling scheduling method is adopted, the scheduling interval can be set to be smaller than the scheduling period. For example, when the scheduling interval is 1 day and the scheduling duration is 2 days, if the demand response scheduling starts at 9:00 on the 25th, then the sequence time obtained by the first demand response scheduling is from 9:00 on the 25th to 9:00 on the 27th. At this time, scheduling can be performed based on the sequence result of the first moment. The time for the second demand scheduling is 9:00 on the 26th, and the sequence time obtained by the second demand response scheduling is from 9:00 on the 26th to 9:00 on the 28th, and so on. The use of rolling scheduling can achieve dynamic scheduling, ensuring that the grid load is balanced to the greatest extent while taking into account economy and ensuring stable operation of the grid.

[0062] It can be understood that the dispatch period refers to the period of demand response dispatch. During this period, rolling optimization dispatch can be adopted to achieve dynamic dispatch. The state space refers to the set of all possible values ​​that describe the system state. The system here refers to the system composed of electric vehicles and energy storage systems. For example, the state space can include the value range of variables such as the charging state of electric vehicles, the remaining power of energy storage systems, and the real-time load demand of the power grid.

[0063] S102: Update the first revenue model for electric vehicles and energy storage systems to participate in demand response, and update the second revenue model and cost model for electric vehicle aggregators to participate in demand response.

[0064] Among them, the first benefit model refers to a model that reflects the economic benefits obtained by adjusting charging and discharging behaviors when electric vehicles and energy storage systems participate in demand response. The second benefit model refers to a model that reflects the economic benefits obtained by electric vehicle aggregators through coordinating and dispatching electric vehicle groups when they participate in demand response. The cost model refers to a model that reflects the various costs faced by electric vehicles and energy storage systems when they participate in demand response. The cost model mainly includes aggregator operating costs and fixed contracted electric vehicle costs.

[0065] In this step, the required variables can be determined by the preset first model, and then these variables are obtained, and the first profit model of electric vehicles and energy storage systems participating in demand response is updated based on the obtained variables. Similarly, the required variables can be determined by the preset second model, and then these variables are obtained, and the second profit model of electric vehicle aggregators participating in demand response is updated based on the obtained variables, and then the aggregator operating costs and fixed contracted electric vehicle costs are recalculated to update the cost model of electric vehicle aggregators participating in demand response.

[0066] In one example, the first model can be expressed as:

[0067]

[0068] In the formula, Indicates the total price of charging and discharging, Indicates the scheduling period, represents the discharge power of the electric vehicle at time t, represents the discharge power of the energy storage system at time t, represents the charging power of the electric vehicle at time t, represents the charging power of the energy storage system at time t, represents the discharge electricity price at time t, represents the charging electricity price at time t, represents the degradation loss cost of the battery and energy storage system of the electric vehicle at time t, which can be calculated according to the following expression:

[0069]

[0070] Where k is the linear relationship coefficient between battery life and cycle number, represents the cycle charge and discharge amount of the electric vehicle at time t, represents the cyclic charge and discharge amount of the energy storage system at time t, represents the battery replacement cost of electric vehicles, represents the battery replacement cost of the energy storage system, Indicates the rated capacity of the battery of an electric vehicle. Indicates the rated capacity of the energy storage system.

[0071] In one example, the rated capacities of the batteries of electric vehicles and energy storage systems are 75kWh and 5000kWh respectively, and the replacement costs of the batteries of electric vehicles and energy storage systems are 58,000 yuan and 330,000 yuan respectively. Based on the actual time-of-use electricity price setting of a certain place, the price is 0.232 yuan / kWh during the valley period from 0:00 to 8:00, 0.61 yuan / kWh during the normal period from 13:00 to 14:00 and 20:00 to 24:00, and 1.04 yuan / kWh during the peak period from 9:00 to 12:00 and 15:00 to 19:00. The peak demand period can be set to 16:00 to 20:00, and the valley demand period can be set to 0:00 to 7:00.

[0072] In one example, the second model can be expressed as:

[0073]

[0074] In the formula, represents the benefits of EV aggregators participating in demand response, represents the economic benefit coefficient of electric vehicle aggregators, Indicates the load adjustment percentage after participating in demand response, represents the base load, represents the load reduction of the distribution network before and after demand response, represents the total load of the distribution network when electric vehicles are charged disorderly without energy storage coordination, represents the base load of the distribution network at time t, Represents the load of the distribution network after participating in demand response.

[0075] In one example, the cost model can be expressed as:

[0076]

[0077] In the formula, represents the cost model, , , represents the operating cost coefficient, which is 2, 0.5, and 5 in one example. represents the economic compensation coefficient of fixed-contract electric vehicles, which is 2 yuan / kW in an example. is the aggregate output power of the electric vehicle aggregator at time t.

[0078] S103: After determining the state variables and the control variables according to the coordinated response model, and generating the target economic model by combining the first benefit model, the second benefit model and the cost model, the target economic model is solved based on a preset set of constraints.

[0079] In this step, the state variables and control variables are determined according to the coordinated response model, and then the state variables are used as the constituent elements of the predicted state sequence, and the control variables are used as the constituent elements of the predicted control sequence, and then the target economic model is constructed by combining the first profit model, the second profit model and the cost. After the target economic model is determined, the preset constraint set is obtained, and the target economic model is optimized and solved based on the preset constraint set, so as to obtain the control variables at each moment when the target economic model is maximized in the current scheduling period under the condition of satisfying the preset constraint set, and form the optimal control sequence.

[0080] Among them, state variables refer to the information set that describes the state of the collaborative response model at a specific point in time, control variables refer to the set of variables to be adjusted and controlled in demand response scheduling, predicted state sequence refers to the set of state variables that may be achieved in a predicted future period of time, and predicted control sequence refers to the set of control variables predicted in a predicted future period of time.

[0081] Furthermore, according to the characteristics of different optimization algorithms and actual needs, a suitable optimization algorithm can be adopted to solve the target economic model. For example, particle swarm optimization algorithm, economic model predictive control, linear rule, etc. This application does not make specific restrictions on this.

[0082] In one example, the set of constraints can be expressed as follows:

[0083] (1) The electric vehicle and the energy storage system cannot be in the charging and discharging state at the same time, which can be expressed as:

[0084]

[0085] (2) The upper and lower limit constraints of the state of charge of electric vehicles and energy storage systems are expressed as:

[0086]

[0087] (3) The upper and lower limits of the charging and discharging power of electric vehicles and energy storage systems are expressed as:

[0088]

[0089] (4) The charging and discharging equation constraints of electric vehicles and energy storage systems are expressed as:

[0090]

[0091] (5) The electric vehicle charging demand constraint is expressed as:

[0092]

[0093] In the above formula, represents the discharge power of the electric vehicle, represents the charging power of the electric vehicle, represents the discharge power of the energy storage system, represents the charging power of the energy storage system, , They represent the lower and upper limits of the state of charge of electric vehicles, respectively. represents the state of charge of the electric vehicle at time t, , They represent the lower and upper limits of the state of charge of the energy storage system, respectively. represents the charge state of the energy storage system at time t, represents the discharge power of the electric vehicle at time t, represents the charging power of the electric vehicle at time t, represents the discharge power of the energy storage system at time t, represents the charging power of the energy storage system at time t, , They represent the lower and upper limits of the discharge power of electric vehicles, respectively. , They represent the lower and upper limits of the charging power of electric vehicles, respectively. , They represent the lower and upper limits of the discharge power of the energy storage system, , They represent the lower and upper limits of the charging power of the energy storage system, respectively. represents the charge state of the electric vehicle and the energy storage system at time t, Indicates the rated battery capacity of electric vehicles and energy storage systems, Indicates the charging efficiency, represents the discharge efficiency, represents the charging power of the electric vehicle and the energy storage system at time t, Indicates charging time. represents the charging demand of electric vehicles, Indicates the target state of charge of the electric vehicle when it completes charging and goes off the grid.

[0094] In one example, the maximum charging and discharging power of an electric vehicle, i.e., the rated power, is set to ±20kW, and the maximum charging and discharging power of the energy storage system, i.e., the rated power, is set to ±300kW. , Both are set to 0.95. To protect battery life and reduce losses, the upper and lower limits of the state of charge of the electric vehicle and the energy storage system are set to 0.9 and 0.2. The power demand of the electric vehicle before disconnecting the charging pile Set to 0.8.

[0095] S104: Scheduling charging and discharging of electric vehicles and energy storage systems according to the optimal control sequence obtained.

[0096] The optimal control sequence includes control variables corresponding to multiple consecutive moments.

[0097] In this step, when the target economic model is solved to obtain the optimal control sequence, since the control variables are composed of the power changes of the electric vehicle and the energy storage system, the charging and discharging power of the electric vehicle and the energy storage system can be adjusted based on the optimal control sequence, thereby realizing the charging and discharging scheduling of the electric vehicle and the energy storage system.

[0098] In the above embodiment, during the process of electric vehicles participating in demand response scheduling, the load aggregation model of electric vehicles is obtained. Through the load aggregation model, the discrete individual behaviors of electric vehicles can be mapped into dynamic power responses that can be dispatched by the group, thereby realizing centralized management and optimized scheduling of electric vehicle loads. On this basis, a coordinated response model corresponding to electric vehicles and energy storage systems is further constructed. The coordinated response model can integrate the dynamic response capabilities of electric vehicles and energy storage systems, realize dynamic complementarity and response capability integration of the two types of resources, and improve scheduling flexibility. Then, the first revenue model of electric vehicles and energy storage systems participating in demand response is updated, and the second revenue model and cost model of electric vehicle aggregators participating in demand response are updated. Through the update of these models, the economic benefits and cost expenditures of all parties in the demand response process can be more accurately reflected to maximize the economic benefits of all parties. Then, based on the preset constraint set, the target economic model generated by the coordinated response model, the first revenue model, the second revenue model and the cost model is solved, so that both economic performance and scheduling effect can be taken into account under the constraint set. Finally, the optimal control sequence obtained by the solution is used for charging and discharging scheduling of electric vehicles and energy storage systems. A closed-loop link of precise modeling, economic optimization, and coordinated control is formed, which can maximize the benefits of electric vehicles participating in demand response while also improving power quality and ensuring grid operation efficiency, achieving a win-win situation for economic benefits and grid operation benefits.

[0099] like Figure 2 As shown, in one embodiment, obtaining a load aggregation model of an electric vehicle includes:

[0100] S201: Obtain the charge state of the battery of the electric vehicle during the current scheduling period.

[0101] S202: Determine a first load transfer equation of the electric vehicle in a charging state and a second load transfer equation of the electric vehicle in a discharging state according to the state of charge.

[0102] S203: Using the load of the electric vehicle as a state variable and combining the first load transfer equation and the second load transfer equation to generate a load aggregation model of the electric vehicle.

[0103] The first load transfer equation is used to describe the dynamic change of the battery state of charge of the electric vehicle when it is in the charging state. The second load transfer equation is used to describe the dynamic change of the battery state of charge of the electric vehicle when it is in the discharging state.

[0104] In this step, the first load transfer equation of the electric vehicle in the charging state and the second load transfer equation in the discharging state can be determined by the charge state of the battery in the current scheduling period, so that the change law of the battery load over time can be determined. Then, the load of the electric vehicle is used as the state variable of the load aggregation model to be constructed, and the load aggregation model of the electric vehicle is constructed by combining the first load transfer equation and the second load transfer equation.

[0105] Specifically, in one example, the state of charge at the kth moment can be represented as S(k), and the upper and lower limits of the state of charge are set as , , the state of charge of the charging process is discretized into N state intervals. The charging process can be described as a process of state transition between different state of charge intervals. The transition from the low state of charge interval to the state of charge interval is represented as the charging state, and vice versa as the discharging state. According to the Markov chain theory, the probability of transitioning from interval i to interval i+1 at time t can be expressed as:

[0106]

[0107] In the formula, represents the probability of the charging process transferring from interval i to interval i+1, Indicates the charging time. represents the charging power at time t, Indicates the charging efficiency, , Respectively represent the maximum and minimum values ​​of the battery capacity, represents the average transition probability, which can be calculated using the following expression:

[0108]

[0109] In the formula, , They respectively represent the boundary value of the region that will definitely not be transferred to the i+1th interval after a time interval in the i-th state of charge interval under the charging state and the boundary value of the region that will definitely be transferred to the i+1th interval, is the differential of the state of charge S(k), The transition probability distribution function of the battery interval can be calculated by the following expression:

[0110]

[0111] In the formula, Indicates the lower boundary value of the state of charge interval at time i+1, Indicates the battery capacity. The transition probability density function representing the battery state of charge interval can be calculated by the following expression:

[0112]

[0113] In the formula, is the scale parameter of the gamma distribution, are the shape parameters of the gamma distribution, which are 6.5 and 3 in one example. is the gamma function, expressed as .

[0114] in, It can be calculated according to the following expression:

[0115]

[0116] In summary, the first load transfer equation of the electric vehicle in the charging state can be expressed by the following state space:

[0117]

[0118] In the formula, represents the load in the first state of charge interval at time k+1, represents the load in the first state of charge interval at time k. When no charging instruction is applied to the electric vehicle, the load in each state of charge interval does not change. This process is an idle state, which can be represented by the idle transfer matrix Describe, is an N-dimensional unit matrix. When charging power is applied to an electric vehicle, the load in each state of charge interval will be forced to transfer, which is determined by the charging forced transfer matrix To describe, The expression is as follows:

[0119]

[0120] In the formula, It indicates the probability of transferring from interval 1 to interval 2 during charging, that is, the proportion of load transfer. Indicates the proportion of the charging completed load to the load in interval N.

[0121] According to the above idle transfer matrix and the charge-force transfer matrix The state transfer matrix A during the charging process can be determined c , the expression is as follows:

[0122] A c = A I + P c

[0123] Similarly, the dynamic transfer process of the electric vehicle idle state can be described by the state space as:

[0124]

[0125] Similarly, the second load transfer equation of the electric vehicle in the discharge state can be expressed by the following state space:

[0126]

[0127] In the formula, represents the load in the first state of charge interval at time k+1, represents the load in the first state of charge interval at time k, is the discharge forced transfer matrix, which can be expressed as:

[0128]

[0129] In the formula, It represents the probability of the interval i transferring to the interval i+1 during the discharge process, that is, the proportion of load transfer, Represents the probability that the discharge state remains in interval N.

[0130] According to the above idle transfer matrix and the discharge forced transfer matrix The state transfer matrix A during the discharge process can be determined d , the expression is as follows:

[0131] A d = A I + P d

[0132] In summary, the load aggregation model of electric vehicle charging and discharging can be expressed as:

[0133]

[0134] In the formula, represents the state variable of the load aggregation model at time k+1, represents the aggregated power output variable of the load matrix model at time k, represents the state transfer matrix, which can be expressed as:

[0135]

[0136] represents the system output matrix, which can be expressed as:

[0137]

[0138] In the formula, Indicates the maximum value of the electric vehicle charging power, Indicates the maximum value of the electric vehicle discharge power.

[0139] State variables It can be expressed as follows:

[0140]

[0141] In the formula, Indicates the load in each state of charge interval during the charging process. Indicates the load of each state of charge interval during the idle state transition process. Indicates the load in each state of charge interval during the discharge process.

[0142] In this embodiment, when entering each scheduling period, the required data is obtained according to the preset model structure, and then the load aggregation model of the electric vehicle is updated. In this way, the discrete individual behavior of the electric vehicle can be mapped into a group-schedulable dynamic power response, thereby realizing centralized management and optimized scheduling of the electric vehicle load.

[0143] like Figure 3 As shown, in one embodiment, a coordinated response model corresponding to an electric vehicle and an energy storage system based on a state space is constructed according to a load aggregation model, including:

[0144] S301: extracting a state transfer matrix from a load aggregation model.

[0145] S302: Acquire a state space model of the energy storage system, and extract a state transfer matrix from the state space model.

[0146] S303: Determine a target state matrix according to the state transfer matrix extracted from the load aggregation model and the state space model, and determine a target control matrix according to the rated capacities of the electric vehicle and the energy storage system.

[0147] S304: Using the charge states of the electric vehicle and the energy storage system as state variables, using the power changes of the electric vehicle and the energy storage system as control variables, and combining the target state matrix and the target control matrix to generate a coordinated response model corresponding to the electric vehicle and the energy storage system.

[0148] Among them, the state space model is a mathematical model used to describe the behavior of the energy storage system. It consists of two sets of equations: state equations and output equations. The state equations are used to describe how the internal state of the energy storage system changes over time, while the output equations are used to describe the dependency between the output of the energy storage system and the internal state and input. The state transfer matrix is ​​a matrix that describes how the system state transfers from one time step to the next time step. The system output matrix is ​​a matrix that maps the system state to the system output.

[0149] In this embodiment, a state transfer matrix is ​​extracted from the load aggregation of the electric vehicle, and a state space model of the energy storage system is obtained, and a state transfer matrix is ​​extracted therefrom, and then a target state matrix is ​​generated based on the state transfer matrices extracted from the two, and a target control matrix is ​​determined based on the rated capacity of the electric vehicle and the energy storage system. Then, the state of charge of the electric vehicle and the energy storage system is used as the state variable, and the power change of the electric vehicle and the energy storage system is used as the control variable, and finally, the target state matrix and the target control matrix are combined to generate a coordinated response model corresponding to the electric vehicle and the energy storage system. It can be understood that the target state matrix is ​​a matrix that describes how the state of the system represented by the coordinated response model is transferred from one time step to the next time step, and the target control matrix is ​​used to describe how the control input of the electric vehicle and the energy storage system affects the state change of the system.

[0150] In one example, the charging and discharging power of the energy storage system is mainly controlled by the dispatching instructions. Therefore, the energy storage system does not need to describe its power load changes by the transfer matrix, and its state space model can be directly expressed as:

[0151]

[0152] In the formula, represents the state variable of the state space model at time k+1, which is composed of the charge state of the energy storage system and can be expressed as , Represents the state transfer matrix in the state space model of the energy storage system, represents the control matrix, represents the system output matrix of the state-space model at time t, represents the system output identity matrix, represents the control variable of the state space model at time t, which is composed of the power of the energy storage system and is expressed as , When it is positive, it means the energy storage system is in charging state. express, When it is negative, it means that the energy storage system is in a discharging state. express.

[0153] Based on this, the state equation It can be expressed by the following expression:

[0154]

[0155] In the formula, represents the charging efficiency of the energy storage system, Represents the discharge efficiency of the energy storage system. Indicates the rated capacity of the energy storage system.

[0156] In summary, combining the load aggregation model of electric vehicles and the state space model of energy storage systems, the coordinated response model of electric vehicles and energy storage systems under continuous time variables can be expressed as:

[0157]

[0158] In the formula, represents the state variables of the collaborative response model at time t, represents the target state matrix, represents the target control matrix, represents the control variable of the coordinated response model at time t, represents the output matrix, which can be expressed as [1 1], represents the system output variable of the coordinated response model at time t. Among them, the target state matrix can be generated according to the state transfer matrix extracted from the load aggregation model and the state space model, which can be expressed as:

[0159]

[0160] In the formula, represents the state transition matrix extracted from the load aggregation model, is the state transfer matrix extracted from the state space model.

[0161] Among them, the target control matrix can be generated according to the rated capacity of the electric vehicle and the energy storage system, which can be expressed as:

[0162]

[0163] In the formula, Indicates the rated capacity of the electric vehicle battery. Indicates the rated capacity of the energy storage system.

[0164] Among them, the state variable x and control variable u of the coordinated response model can be expressed as:

[0165]

[0166] In the formula, Indicates the state of charge of the electric vehicle. Indicates the state of charge of the energy storage system. represents the power change of the electric vehicle, Indicates the power change rate of the energy storage system.

[0167] It can be understood that in this embodiment, a coordinated response model corresponding to electric vehicles and energy storage systems is constructed. The coordinated response model can integrate the dynamic response capabilities of electric vehicles and energy storage systems, ensuring that the model can accurately reflect the changes in the charge state of the electric vehicle group and the energy storage system and their impact on the grid load, thereby optimizing the charging and discharging strategies of electric vehicles and energy storage systems to achieve maximum economic benefits and grid load balance.

[0168] In one embodiment, updating a first revenue model of an electric vehicle and an energy storage system participating in demand response includes:

[0169] S1: Obtain the charging power and discharging power of the electric vehicle and the energy storage system during the current scheduling period respectively.

[0170] S2: Obtain the charging electricity price and discharging electricity price during the current scheduling period, and determine the degradation loss costs of the electric vehicle and the energy storage system during the current scheduling period respectively.

[0171] S3: Substitute the charging power, discharging power, degradation loss cost of the electric vehicle and the energy storage system in the current scheduling period, and the charging electricity price and discharging electricity price in the current scheduling period into the preset first model to obtain a new first profit model.

[0172] Among them, degradation loss cost refers to the degradation of battery performance due to charge and discharge cycles, which leads to shortened battery life and increased maintenance costs.

[0173] Specifically, the first revenue model or the expression of the first model in this embodiment can refer to the description of S102.

[0174] In this embodiment, when determining the charging power, discharging power, degradation loss cost of the electric vehicle and the energy storage system in the current scheduling period, as well as the charging electricity price and discharging electricity price in the current scheduling period, these data are substituted into the preset first model to update a new first profit model, so that the economic benefits of the electric vehicle and the energy storage system in the current scheduling period can be accurately evaluated while taking into account the costs caused by battery degradation.

[0175] In one embodiment, updating a second revenue model and a cost model for an electric vehicle aggregator to participate in demand response includes:

[0176] S1: Obtain the benchmark load of the distribution network during the current dispatch period, and obtain the total load of the distribution network when electric vehicles are charged disorderly without energy storage coordination.

[0177] S2: Substitute the benchmark load and the total load of the distribution network into the preset second model to obtain a new second profit model.

[0178] S3: Calculate the aggregator operating cost and fixed contracted electric vehicle cost during the current dispatch period based on the aggregated output power of the electric vehicle aggregator during the current dispatch period.

[0179] S4: Update the cost model of the EV aggregator based on the aggregator operating costs and the fixed contracted EV costs.

[0180] Among them, the benchmark load refers to the basic power demand of the distribution network during a specific period of time without the influence of electric vehicle charging demand. The total load of the distribution network refers to all power demands that the distribution network needs to supply without the coordination of energy storage, including the benchmark load plus the additional load caused by the disorderly charging of electric vehicles. Aggregate output power refers to the total power output that the electric vehicle aggregator can provide by coordinating the charging and discharging behavior of electric vehicles during the current scheduling period. Aggregator operating costs refer to the costs incurred by electric vehicle aggregators during the operation process, including equipment maintenance, system operation, market transactions and other costs. Fixed contracted electric vehicle costs refer to the costs incurred by electric vehicle aggregators signing fixed service contracts with electric vehicle users.

[0181] Specifically, the cost model and the second revenue model or the expression of the second model in this embodiment can refer to the description of S102.

[0182] In this embodiment, by obtaining the benchmark load and the total load of the distribution network and substituting these data into the second model, the potential benefits of the electric vehicle aggregator participating in the demand response can be evaluated. At the same time, the aggregator operating cost and the fixed contracted electric vehicle cost are calculated according to the aggregated output power to update the cost model, so that the economic benefits of the electric vehicle aggregator can be more accurately evaluated.

[0183] In one embodiment, after determining the state variables and the control variables according to the coordinated response model and generating the target economic model by combining the first benefit model, the second benefit model and the cost model, solving the target economic model based on a preset set of constraints includes:

[0184] S1: Determine the economic function in the current scheduling period according to the first revenue model, the second revenue model and the cost model.

[0185] S2: Extract state variables and control variables in the collaborative response model, and build a target economic model based on economic functions, state variables and control variables.

[0186] Among them, the target economic model aims to solve the control variables when maximizing the economic function.

[0187] S3: Obtain a preset set of constraints, and based on the set of constraints, use an economic model predictive control algorithm to solve the target economic model, obtain a control variable sequence when the economic function is maximized under the constraints, and determine the control variable sequence as the optimal control sequence.

[0188] The economic function is used to evaluate the economic benefits of EV aggregators, EVs and energy storage systems participating in demand response. The Economic Model Predictive Control (EMPC) algorithm is a control strategy that combines the predictive capability of model predictive control (MPC) with the goal of economic optimization, aiming to optimize the economic performance of the controlled system.

[0189] In this embodiment, the sum of the first revenue model and the second revenue model is subtracted from the cost model to obtain the economic function in the current scheduling period, and then the state variables and control variables are extracted in the collaborative response model, and the state variables are used as the composition of the predicted state sequence, and the control variables are used as the composition of the predicted control sequence. The target economic model is constructed based on the economic function, the predicted state sequence, and the predicted control sequence, and then the target economic model is solved based on the constraint set to obtain the predicted control sequence when the economic function is maximized under the constraint set, that is, the control variable sequence, and finally the control variable sequence is determined as the optimal control sequence. In this way, both economic performance and scheduling effect can be taken into account under the constraint set. Finally, the optimal control sequence obtained by solving is used for charging and discharging scheduling of electric vehicles and energy storage systems. A closed-loop link of precise modeling, economic optimization, and collaborative control is formed, which can maximize the benefits of electric vehicles participating in demand response while also improving power quality and ensuring grid operation efficiency.

[0190] In one example, let , the prediction control sequence at time k is defined in the prediction time domain N as , the corresponding predicted state sequence of the state variable is , based on this, the target economic model can be expressed as:

[0191]

[0192] In the formula, represents the optimal solution obtained by solving the target economic model, which is composed of the control variables at each moment in the scheduling period. represents the economic function, which is expressed as:

[0193]

[0194] Where N represents the length of the scheduling period, represents the sub-economic function at time k, and its expression is:

[0195]

[0196] In the formula, represents the output of the first profit model at time k, represents the output of the second revenue model at time k, Represents the output of the cost model at time k.

[0197] like Figure 4 As shown, in one embodiment, charging and discharging scheduling of electric vehicles and energy storage systems is performed according to the optimal control sequence obtained by solving, including:

[0198] S401: Obtain the first control variable in the optimal control sequence, and determine the power change of the electric vehicle and the energy storage system according to the first control variable in the optimal control sequence.

[0199] S402: adjusting the power of the electric vehicle and the energy storage system respectively based on the power changes of the electric vehicle and the energy storage system to perform charging and discharging scheduling for the electric vehicle and the energy storage system.

[0200] In this embodiment, the first control variable in the optimal control sequence is obtained, and this control variable represents the charging and discharging strategy that the electric vehicle and the energy storage system should adopt during the current scheduling period. For example, if the power supply capacity of the power grid needs to be increased, the control variable will indicate to increase the discharge power of the energy storage system or reduce the charging power of the electric vehicle. Therefore, based on the control variable, the power change of the electric vehicle and the energy storage system, that is, the specific charging and discharging power value that needs to be adjusted, can be determined. Then, according to these power changes, the power of the electric vehicle and the energy storage system are adjusted respectively to perform the charging and discharging scheduling of the electric vehicle and the energy storage system. By accurately controlling the charging and discharging behavior of electric vehicles and energy storage systems, the load of the power grid can be effectively balanced and the operating efficiency and reliability of the power grid can be improved. In addition, in this process, the benefits of all parties are taken into consideration, and while improving the quality of power and ensuring the operating efficiency of the power grid, the benefits of electric vehicles participating in demand response can also be maximized, achieving a win-win situation of economic benefits and power grid operating benefits.

[0201] In one example, the number of electric vehicles is 500, the number of energy storage systems is 1, the discrete time step is set to 15 minutes, and the load parameters of a 33-node distribution network in a certain place are used for simulation. The overall dispatch framework of the electric vehicle aggregator is as follows: Figure 5 As shown, under the dispatching instructions of the electric vehicle aggregator, energy storage systems, electric vehicles and power grids complement each other to achieve power balance and load adjustment. The parameters of the first revenue model, the second revenue model and the cost model are input into the system to construct the target economic model of the electric vehicle aggregator. After determining the set of constraints, the target economic model is solved to obtain the dispatching results of the electric vehicle aggregator participating in demand response, that is, the optimal control sequence, and control the charging and discharging of electric vehicles and energy storage systems according to the dispatching results. The change of electric vehicle charging and discharging load over time is shown in the figure. Figure 6 As shown in Figure 2, in order to meet the demand response and electric vehicle charging needs at the same time, after the electric vehicle aggregator dispatches, the charging load of electric vehicles increases during the valley period, and the charging load during the peak period is generally small. The dispatch results are as follows: Figure 7 As shown by Figure 7 It can be seen that the scheduling scheme formed by the scheduling results, compared with the case where only electric vehicles are not scheduled and are charged disorderly, when electric vehicles are not scheduled and are charged disorderly, the load during the peak period of the power grid base load increases significantly. When the electric vehicle aggregator schedules electric vehicles and energy storage systems to jointly participate in demand response, compared with the basic load, the load in the valley period has an obvious valley filling effect, and there is a certain peak shaving effect in the peak period. It can be seen that the demand response scheduling method in the present invention has a better load transfer effect.

[0202] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0203] The electric vehicle demand response scheduling device based on economic model predictive control provided in an embodiment of the present application is described below. The electric vehicle demand response scheduling device based on economic model predictive control described below and the electric vehicle demand response scheduling method based on economic model predictive control described above can be referenced to each other.

[0204] like Figure 8 As shown, the present application provides an electric vehicle demand response scheduling device 500 based on economic model predictive control, the device comprising:

[0205] The model building module 501 is used to obtain a load aggregation model of electric vehicles in a scheduling period, and to build a coordinated response model corresponding to the electric vehicles and the energy storage system based on the state space according to the load aggregation model;

[0206] A model updating module 502 is used to update a first revenue model of electric vehicles and energy storage systems participating in demand response, and to update a second revenue model and cost model of electric vehicle aggregators participating in demand response;

[0207] The model solving module 503 is used to determine the state variables and the control variables according to the coordinated response model, and after generating the target economic model by combining the first benefit model, the second benefit model and the cost model, solve the target economic model based on a preset set of constraint conditions;

[0208] The charge and discharge scheduling module 504 is used to schedule the charge and discharge of the electric vehicle and the energy storage system according to the optimal control sequence obtained by solving.

[0209] In the above embodiment, during the process of electric vehicles participating in demand response scheduling, the load aggregation model of electric vehicles is obtained. Through the load aggregation model, the discrete individual behaviors of electric vehicles can be mapped into dynamic power responses that can be dispatched by the group, thereby realizing centralized management and optimized scheduling of electric vehicle loads. On this basis, a coordinated response model corresponding to electric vehicles and energy storage systems is further constructed. The coordinated response model can integrate the dynamic response capabilities of electric vehicles and energy storage systems, realize dynamic complementarity and response capability integration of the two types of resources, and improve scheduling flexibility. Then, the first revenue model of electric vehicles and energy storage systems participating in demand response is updated, and the second revenue model and cost model of electric vehicle aggregators participating in demand response are updated. Through the update of these models, the economic benefits and cost expenditures of all parties in the demand response process can be more accurately reflected to maximize the economic benefits of all parties. Then, based on the preset constraint set, the target economic model generated by the coordinated response model, the first revenue model, the second revenue model and the cost model is solved, so that both economic performance and scheduling effect can be taken into account under the constraint set. Finally, the optimal control sequence obtained by the solution is used for charging and discharging scheduling of electric vehicles and energy storage systems. A closed-loop link of precise modeling, economic optimization, and coordinated control is formed, which can maximize the benefits of electric vehicles participating in demand response while also improving power quality and ensuring grid operation efficiency, achieving a win-win situation for economic benefits and grid operation benefits.

[0210] In one embodiment, the model building module includes:

[0211] The state acquisition submodule is used to obtain the charge state of the battery of the electric vehicle during the current scheduling period;

[0212] An equation determination submodule, used to determine a first load transfer equation of the electric vehicle in a charging state and a second load transfer equation in a discharging state according to the state of charge;

[0213] The model generation submodule is used to use the load of the electric vehicle as a state variable and combine the first load transfer equation and the second load transfer equation to generate a load aggregation model of the electric vehicle.

[0214] In one embodiment, the model building module includes:

[0215] A first extraction submodule, used to extract a state transfer matrix from a load aggregation model;

[0216] A second extraction submodule is used to obtain a state space model of the energy storage system and extract a state transfer matrix from the state space model;

[0217] A matrix determination submodule, for determining a target state matrix according to a state transfer matrix extracted from a load aggregation model and a state space model, and determining a target control matrix according to rated capacities of an electric vehicle and an energy storage system;

[0218] The model building submodule is used to use the charge state of the electric vehicle and the energy storage system as the state variable, the power change of the electric vehicle and the energy storage system as the control variable, and combine the target state matrix and the target control matrix to generate the coordinated response model corresponding to the electric vehicle and the energy storage system.

[0219] In one embodiment, the model updating module includes:

[0220] The power acquisition submodule is used to respectively obtain the charging power and discharging power of the electric vehicle and the energy storage system during the current scheduling period;

[0221] The information determination submodule is used to obtain the charging electricity price and the discharging electricity price during the current scheduling period, and respectively determine the degradation loss costs of the electric vehicle and the energy storage system during the current scheduling period;

[0222] The first updating submodule is used to substitute the charging power, discharging power, degradation loss cost of the electric vehicle and the energy storage system in the current scheduling period, and the charging electricity price and discharging electricity price in the current scheduling period into the preset first model to obtain a new first profit model.

[0223] In one embodiment, the model updating module includes:

[0224] The load acquisition submodule is used to obtain the benchmark load of the distribution network during the current dispatch period, and to obtain the total load of the distribution network when electric vehicles are charged in an unordered manner without energy storage coordination;

[0225] A second updating submodule is used to substitute the reference load and the total load of the distribution network into a preset second model to obtain a new second revenue model;

[0226] A cost calculation submodule, used to calculate the aggregator operating cost and the fixed contracted electric vehicle cost in the current dispatch period according to the aggregated output power of the electric vehicle aggregator in the current dispatch period;

[0227] The third updating submodule is used to update the cost model of the electric vehicle aggregator based on the aggregator operating cost and the fixed contracted electric vehicle cost.

[0228] In one embodiment, the model solving module includes:

[0229] A function determination submodule, used to determine the economic function in the current scheduling period according to the first revenue model, the second revenue model and the cost model;

[0230] The economic model construction submodule is used to extract state variables and control variables in the collaborative response model, and to construct a target economic model based on the economic function, state variables and control variables; the target economic model aims to solve the control variables when the economic function is maximized;

[0231] The model solving submodule is used to obtain a preset set of constraints, and based on the constraint set, use the economic model predictive control algorithm to solve the target economic model, obtain the control variable sequence when the economic function is maximized under the constraint set, and determine the control variable sequence as the optimal control sequence.

[0232] In one embodiment, the charge and discharge scheduling module includes:

[0233] A variable acquisition submodule is used to acquire the first control variable in the optimal control sequence, and determine the power change of the electric vehicle and the energy storage system according to the first control variable in the optimal control sequence;

[0234] The scheduling submodule is used to adjust the power of the electric vehicle and the energy storage system respectively based on the power changes of the electric vehicle and the energy storage system, so as to perform charging and discharging scheduling for the electric vehicle and the energy storage system.

[0235] The division of each module in the above-mentioned electric vehicle demand response scheduling device based on economic model prediction control is only used for illustration. In other embodiments, the electric vehicle demand response scheduling device based on economic model prediction control can be divided into different modules as needed to complete all or part of the functions of the above-mentioned electric vehicle demand response scheduling device based on economic model prediction control. Each module in the above-mentioned electric vehicle demand response scheduling device based on economic model prediction control can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0236] In one embodiment, the present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the electric vehicle demand response scheduling method based on economic model predictive control as described in any of the above embodiments.

[0237] In one embodiment, the present application also provides a computer device having computer-readable instructions stored therein. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the electric vehicle demand response scheduling method based on economic model predictive control as described in any of the above embodiments.

[0238] Indicatively, Fig. 9 As shown, Fig. 9 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 600 may be provided as a server. Fig. 9 The computer device 600 includes a processing component 602, which further includes one or more processors, and a memory resource represented by a memory 601 for storing instructions executable by the processing component 602, such as an application. The application stored in the memory 601 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 602 is configured to execute instructions to perform the electric vehicle demand response scheduling method based on economic model predictive control of any of the above embodiments.

[0239] The computer device 600 may further include a power supply component 603 configured to perform power management of the computer device 600, a wired or wireless network interface 604 configured to connect the computer device 600 to a network, and an input / output (I / O) interface 605. The computer device 600 may operate based on an operating system stored in the memory 601, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM, or the like.

[0240] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0241] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish an entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only include those elements, but also include other elements that are not clearly listed, or also include elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements limited by the sentence "comprise one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements. Herein, the singular "one", "one" and "described / the" may also include plural forms, unless the context clearly indicates another way. It should also be understood that the terms "include / comprise" or "have" etc. specify the existence of stated features, wholes, steps, operations, components, parts or combinations thereof, but do not exclude the possibility of the existence or addition of one or more other features, wholes, steps, operations, components, parts or combinations thereof. At the same time, the term "and / or" used in this specification includes any and all combinations of the relevant listed items.

[0242] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can refer to each other.

[0243] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for dispatching electric vehicle demand response based on economic model predictive control, characterized in that: The method comprises: In a scheduling period, a load aggregation model of the electric vehicle is obtained, and a coordinated response model corresponding to the electric vehicle and the energy storage system based on the state space is constructed according to the load aggregation model; Update the first revenue model for electric vehicles and energy storage systems to participate in demand response, and update the second revenue model and cost model for electric vehicle aggregators to participate in demand response; Determining state variables and control variables according to the collaborative response model, and generating a target economic model by combining the first benefit model, the second benefit model and the cost model, and solving the target economic model based on a preset set of constraints; The charging and discharging of electric vehicles and energy storage systems are scheduled according to the optimal control sequence obtained.

2. The electric vehicle demand response scheduling method based on economic model predictive control according to claim 1 is characterized in that: The step of obtaining the load aggregation model of the electric vehicle includes: Obtain the state of charge of the battery of the electric vehicle during the current scheduling period; Determine a first load transfer equation of the electric vehicle in a charging state and a second load transfer equation in a discharging state according to the state of charge; The load amount of the electric vehicle is used as a state variable, and the first load transfer equation and the second load transfer equation are combined to generate a load aggregation model of the electric vehicle.

3. The electric vehicle demand response scheduling method based on economic model predictive control according to claim 1 is characterized in that: The method of constructing a coordinated response model corresponding to the electric vehicle and the energy storage system based on the state space according to the load aggregation model includes: extracting a state transfer matrix from the load aggregation model; Acquire a state space model of the energy storage system, and extract a state transfer matrix from the state space model; Determine a target state matrix according to the state transfer matrix extracted in the load aggregation model and the state space model, and determine a target control matrix according to the rated capacities of the electric vehicle and the energy storage system; The charge states of the electric vehicle and the energy storage system are used as state variables, the power changes of the electric vehicle and the energy storage system are used as control variables, and the target state matrix and the target control matrix are combined to generate a coordinated response model corresponding to the electric vehicle and the energy storage system.

4. The electric vehicle demand response scheduling method based on economic model predictive control according to claim 1 is characterized in that: The updating of the first revenue model of electric vehicles and energy storage systems participating in demand response includes: Obtain the charging power and discharging power of the electric vehicle and the energy storage system respectively during the current scheduling period; Obtain the charging electricity price and discharging electricity price during the current dispatch period, and determine the degradation loss costs of the electric vehicle and the energy storage system during the current dispatch period respectively; The charging power, discharging power, degradation loss cost of the electric vehicle and the energy storage system in the current scheduling period, and the charging electricity price and discharging electricity price in the current scheduling period are substituted into the preset first model to obtain a new first profit model.

5. The electric vehicle demand response scheduling method based on economic model predictive control according to claim 1 is characterized in that: The second revenue model and cost model for updating electric vehicle aggregators' participation in demand response include: Obtain the benchmark load of the distribution network during the current dispatch period, and obtain the total load of the distribution network when electric vehicles are charging in an unordered manner without energy storage coordination; Substituting the reference load and the total load of the distribution network into a preset second model to obtain a new second revenue model; Calculate the aggregator's operating costs and fixed contracted electric vehicle costs during the current dispatch period based on the aggregated output power of the electric vehicle aggregator during the current dispatch period; A cost model for the electric vehicle aggregator is updated based on the aggregator operating cost and the fixed contracted electric vehicle cost.

6. The electric vehicle demand response scheduling method based on economic model predictive control according to claim 1 is characterized in that: After determining the state variables and the control variables according to the collaborative response model and generating the target economic model in combination with the first benefit model, the second benefit model and the cost model, solving the target economic model based on a preset set of constraints includes: Determine an economic function within a current scheduling period according to the first revenue model, the second revenue model and the cost model; Extracting state variables and control variables from the collaborative response model, and constructing a target economic model based on the economic function, the state variables and the control variables; the target economic model aims to solve the control variables when the economic function is maximized; A preset set of constraints is obtained, and based on the set of constraints, the target economic model is solved using an economic model predictive control algorithm to obtain a control variable sequence when the economic function is maximized under the constraints, and the control variable sequence is determined as the optimal control sequence.

7. The electric vehicle demand response scheduling method based on economic model predictive control according to any one of claims 1 to 6, characterized in that: The charging and discharging scheduling of the electric vehicle and the energy storage system according to the optimal control sequence obtained by solving the problem includes: Obtaining a first control variable in the optimal control sequence, and determining a power change of the electric vehicle and the energy storage system according to the first control variable in the optimal control sequence; The power of the electric vehicle and the energy storage system are adjusted respectively based on the power changes of the electric vehicle and the energy storage system to perform charging and discharging scheduling for the electric vehicle and the energy storage system.

8. An electric vehicle demand response dispatching device based on economic model predictive control, characterized in that: The device comprises: A model building module, used to obtain a load aggregation model of electric vehicles in a scheduling period, and to build a coordinated response model corresponding to the electric vehicles and the energy storage system based on the state space according to the load aggregation model; A model updating module, used to update a first revenue model of electric vehicles and energy storage systems participating in demand response, and to update a second revenue model and cost model of electric vehicle aggregators participating in demand response; A model solving module, configured to determine state variables and control variables according to the collaborative response model, generate a target economic model by combining the first benefit model, the second benefit model and the cost model, and solve the target economic model based on a preset set of constraints; The charging and discharging scheduling module is used to schedule the charging and discharging of electric vehicles and energy storage systems according to the optimal control sequence obtained by solving.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the electric vehicle demand response scheduling method based on economic model predictive control as described in any one of claims 1 to 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the electric vehicle demand response scheduling method based on economic model predictive control as described in any one of claims 1 to 7 are performed.

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