Electric Vehicle Demand Response Scheduling Method Based on Economic Model Predictive Control

Abstract

The present application provides an electric vehicle demand response scheduling method based on economic model predictive control, which relates to the technical field of power systems. Through the load aggregation model, the discrete individual behaviors of electric vehicles can be mapped into a group-schedulable dynamic power response. On this basis, a collaborative response model corresponding to the electric vehicle and the energy storage system is constructed, and this collaborative response model can integrate the dynamic response capabilities of the two. Then, the first revenue model for the electric vehicle and the energy storage system participating in the demand response is updated, as well as the second revenue model and cost model for the electric vehicle aggregator participating in the demand response. In this way, the economic benefits of all parties can be more accurately reflected. Then, based on a preset set of constraint conditions, the target economic model generated by the above models is solved to perform charge and discharge scheduling for the electric vehicle and the energy storage system. A closed-loop link of accurate modeling, economic optimization, and collaborative control is formed to achieve a win-win situation for the economic benefits of the electric vehicle aggregator and the grid operation benefits.

Description

Technical Field

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

[0002] With the acceleration of the adjustment of the national economic industrial structure and the large-scale application process of new energy, electric vehicles, with their dual attributes of load consumption and energy storage regulation, and charging characteristics highly coupled with the user's domestic power consumption period, have become an important carrier for coordinating resources on the demand side. Through the coordinated scheduling of large-scale electric vehicles and energy storage systems by electric vehicle aggregators, not only can problems such as the increased complexity of grid control, power quality fluctuations, and reduced system reliability caused by large-scale unordered charging behavior be alleviated, but also the goal of peak shaving and valley filling of the power grid can be achieved through the demand response mechanism, forming an energy regulation ecosystem that benefits all parties.

[0003] Currently, during the process of electric vehicles participating in demand response, contract constraints and fixed response modes are generally adopted for demand response. To a certain extent, these methods can guide the charging and discharging behavior of electric vehicles, but they do not perform observable and controllable modeling on 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 economy of the demand response scheduling process, resulting in the inability to maximize the benefits of electric vehicles participating in demand response while ensuring power quality and the operating efficiency of the power grid. Summary of the Invention

[0004] The purpose of this application aims to at least solve one of the above 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 power quality and the operating efficiency of the power grid.

[0005] In a first aspect, this application provides a demand response scheduling method for electric vehicles based on economic model predictive control, and the method includes:

[0006] In a scheduling period, obtain the load aggregation model of electric vehicles, and construct a corresponding collaborative response model of electric vehicles and energy storage systems based on the state space according to the load aggregation model;

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

[0008] Determine the state variables and control variables according to the collaborative response model, and generate a target economic model by combining the first revenue model, the second revenue model, and the cost model, and then solve the target economic model based on a preset set of constraint conditions;

[0009] Perform charge and discharge scheduling on the electric vehicle and the energy storage system according to the optimal control sequence obtained by the solution.

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

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

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

[0013] Use the load of the electric vehicle as the state variable, and generate a load aggregation model of the electric vehicle by combining the first load transfer equation and the second load transfer equation.

[0014] In one embodiment, the constructing the collaborative 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] Extract the state transition matrix from the load aggregation model;

[0016] Obtain the state space model of the energy storage system, and extract the state transition matrix from the state space model;

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

[0018] Use the state of charge of the electric vehicle and the energy storage system as the state variable, use the power change of the electric vehicle and the energy storage system as the control variable, and generate a collaborative response model corresponding to the electric vehicle and the energy storage system by combining the target state matrix and the target control matrix.

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

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

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

[0022] Substitute the charging power, discharging power, degradation loss cost of the electric vehicle and energy storage system during the current scheduling period, and the charging price and discharging price during the current scheduling period into the preset first model to obtain a new first revenue model.

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

[0024] Obtain the reference load of the distribution network during the current scheduling period, and obtain the total load of the distribution network during disorderly charging of electric vehicles without energy storage coordination;

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

[0026] Calculate the operating cost of the aggregator and the cost of fixed-contracted electric vehicles during the current scheduling period according to the aggregated output power of the electric vehicle aggregator during the current scheduling period;

[0027] Update the cost model of the electric vehicle aggregator based on the aggregator operating cost and the cost of fixed-contracted electric vehicles.

[0028] In one embodiment, after determining the state variables and control variables according to the coordinated response model, and generating a target economic model by combining the first revenue model, the second revenue model, and the cost model, solving the target economic model based on a preset set of constraint conditions includes:

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

[0030] Extract the state variables and control variables from the coordinated response model, and construct 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] Obtain a preset set of constraint conditions, and based on the set of constraint conditions, use an economic model predictive control algorithm to solve the target economic model to obtain a sequence of control variables when the economic function is maximized under the satisfaction of the set of constraint conditions, and determine this sequence of control variables as the optimal control sequence.

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

[0033] Obtain the first control variable in the optimal control sequence, and determine the power change amounts of the electric vehicle and the energy storage system according to the first control variable in the optimal control sequence;

[0034] Based on the power change amounts of the electric vehicle and the energy storage system, adjust the powers of the electric vehicle and the energy storage system respectively to perform charge and discharge 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 includes:

[0036] A model construction module, configured to obtain a load aggregation model of an electric vehicle in a scheduling period, and construct a cooperative response model corresponding to the electric vehicle and the energy storage system based on the state space according to the load aggregation model;

[0037] A model update module, configured to update a first benefit model for the electric vehicle and the energy storage system to participate in demand response, and update a second benefit model and a cost model for the electric vehicle aggregator to participate in demand response;

[0038] A model solving module, configured to determine state variables and control variables according to the cooperative response model, generate a target economic model in combination with the first benefit model, the second benefit model, and the cost model, and solve the target economic model based on a preset constraint condition set;

[0039] A charge and discharge scheduling module, configured to perform charge and discharge scheduling for the electric vehicle and the energy storage system according to the obtained optimal control sequence.

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

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

[0042] The memory stores computer-readable instructions. 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 one of the above embodiments are executed.

[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 by this application, during the process of electric vehicles participating in demand response scheduling, obtains the load aggregation model of electric vehicles. Through the load aggregation model, the discrete individual behaviors of electric vehicles can be mapped into the dynamically schedulable power response of the group, thereby realizing the centralized management and optimal scheduling of electric vehicle loads. On this basis, a collaborative response model corresponding to the electric vehicle and the energy storage system is further constructed. This collaborative response model can integrate the dynamic response capabilities of the electric vehicle and the energy storage system, realize the dynamic complementarity of the two types of resources and the integration of response capabilities, and improve scheduling flexibility. Then, the first revenue model for the electric vehicle and the energy storage system participating in demand response is updated, as well as the second revenue model and cost model for the electric vehicle aggregator participating in demand response. Through the update of these models, the economic interests 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, the target economic model generated by the collaborative response model, the first revenue model, the second revenue model, and the cost model is solved based on a preset set of constraint conditions, so that both economy and scheduling effect can be taken into account under the premise of meeting the set of constraint conditions. Finally, the obtained optimal control sequence is used to perform charge and discharge scheduling on the electric vehicle and the energy storage system. A closed-loop link of accurate modeling, economic optimization, and collaborative control is formed, which can maximize the benefits of electric vehicles participating in demand response while improving the power quality and ensuring the grid operation efficiency, achieving a win-win situation between economic benefits and grid operation benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following-described drawings are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0046] Figure 1 It is a schematic flowchart of a method for scheduling electric vehicle demand response based on economic model predictive control provided by an embodiment of this application;

[0047] Figure 2 It is a schematic flowchart of obtaining the load aggregation model of electric vehicles provided by an embodiment of this application;

[0048] Figure 3 It is a schematic flowchart of constructing a collaborative response model corresponding to an electric vehicle and an energy storage system based on the state space according to the load aggregation model provided by an embodiment of this application;

[0049] Figure 4Schematic diagram of the charging and discharging scheduling of an electric vehicle and an energy storage system according to the optimal control sequence obtained by solving in an embodiment of the present application;

[0050] Figure 5 Example diagram of the overall scheduling framework of an electric vehicle aggregator provided in an embodiment of the present application;

[0051] Figure 6 Display diagram of the charging and discharging load of an electric vehicle changing with time provided in an embodiment of the present application;

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

[0053] Figure 8 Schematic structural diagram of a demand response scheduling device for an electric vehicle based on economic model predictive control provided in an embodiment of the present application;

[0054] Figure 9 Internal structure diagram of a computer device provided in an embodiment of the present application. Specific embodiments

[0055] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0056] Specifically, in the process of current electric vehicles participating in demand response, contract constraints and fixed response modes are generally used for demand response. To a certain extent, these methods can guide the charging and discharging behaviors of electric vehicles, but they do not perform observable and controllable modeling on the charging and discharging of electric vehicles, lack accurate analysis of vehicle states, 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 power quality and the operating efficiency of the power grid. Based on this, as Figure 1 shown, the present application provides a demand response scheduling method for an electric vehicle based on economic model predictive control, and the method includes:

[0057] S101: In a scheduling period, obtain the load aggregation model of the electric vehicle, and construct a collaborative response model corresponding to the electric vehicle and the energy storage system based on the state space according to the load aggregation model.

[0058] Among them, the load aggregation model is a mathematical model constructed based on the state space equation. The load aggregation model can be used to map the individual behaviors of dispersed electric vehicles into a dynamically schedulable group power response. The collaborative 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 their charging and discharging behaviors.

[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 the electric vehicles included in the electric vehicle aggregator. Then, a collaborative 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.

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

[0061] Specifically, for 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 less 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 o'clock on the 25th, then the sequence time obtained from the first demand response scheduling is from 9 o'clock on the 25th to 9 o'clock on the 27th. At this time, scheduling can be based on the sequence result of the first moment. The time of the second demand scheduling is 9 o'clock on the 26th, and the sequence time obtained from the second demand response scheduling is from 9 o'clock on the 26th to 9 o'clock on the 28th, and so on. Adopting the rolling scheduling method can achieve dynamic scheduling, ensuring the maximum balance of the grid load while taking into account economy and guaranteeing the stable operation of the power grid.

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

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

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

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

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

[0067]

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

[0069]

[0070] In the formula, k is the linear relationship coefficient between battery life and the number of cycles, represents the cyclic 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 the electric vehicle, represents the battery replacement cost of the energy storage system represents the rated capacity of the battery of the electric vehicle represents the rated capacity of the energy storage system

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

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

[0073]

[0074] where represents the revenue of the electric vehicle aggregator participating in demand response represents the economic benefit coefficient of the electric vehicle aggregator represents the percentage of load adjustment after participating in demand response represents the baseline load represents the load reduction of the distribution network before and after demand response represents the total load of the distribution network during the unordered charging of electric vehicles without energy storage coordination represents the baseline 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] where represents the cost model , , represent the operation cost coefficients, which are taken as 2, 0.5, and 5 respectively in one example represents the economic compensation coefficient for the fixed contracted electric vehicles, which is taken as 2 yuan / kW in one example is the aggregated output power of the electric vehicle aggregator at time t

[0078] S103: Determine state variables and control variables according to the collaborative response model, generate a target economic model by combining the first revenue model, the second revenue model, and the cost model, and then solve the target economic model based on a preset set of constraint conditions.

[0079] In this step, determine state variables and control variables according to the collaborative response model. Then, use the state variables as the constituent elements of the predicted state sequence and the control variables as the constituent elements of the predicted control sequence. Next, combine the first revenue model, the second revenue model, and the cost to construct the target economic model. After determining the target economic model, obtain the preset set of constraint conditions and perform an optimization solution on the target economic model based on the preset condition set, so as to obtain the control variables at each moment that maximize the target economic model within the current scheduling period under the condition of satisfying the preset condition set, forming an optimal control sequence.

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

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

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

[0083] (1) The electric vehicle and the energy storage system cannot be in the charging and discharging states simultaneously, which is expressed as:

[0084]

[0085] (2) The upper and lower limits of the state of charge constraints for the electric vehicle and the energy storage system are expressed as:

[0086]

[0087] (3) The upper and lower limits of the charging and discharging power constraints for the electric vehicle and the energy storage system are expressed as:

[0088]

[0089] (4) The charging and discharging equality constraints for the electric vehicle and the energy storage system are expressed as:

[0090]

[0091] The charging demand constraint of the electric vehicle 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, and respectively represent the lower and upper limits of the state of charge of the electric vehicle, represents the state of charge of the electric vehicle at time t, and respectively represent the lower and upper limits of the state of charge of the energy storage system, represents the state of charge 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, and respectively represent the lower and upper limits of the discharge power of the electric vehicle, and respectively represent the lower and upper limits of the charging power of the electric vehicle, and respectively represent the lower and upper limits of the discharge power of the energy storage system, and respectively represent the lower and upper limits of the charging power of the energy storage system, represents the state of charge of the electric vehicle and the energy storage system at time t, represents the rated battery capacity of the electric vehicle and the energy storage system, represents the charging efficiency, represents the discharge efficiency, represents the charging power of the electric vehicle and the energy storage system at time t, represents the charging time, represents the charging demand of the electric vehicle, represents the target state of charge when the electric vehicle completes charging and disconnects from the grid.

[0094] In an example, the maximum charge and discharge power of the electric vehicle, i.e., the rated power, is set to ±20 kW, and the maximum charge and discharge power of the energy storage system, i.e., the rated power, is set to ±300 kW, and the charge and discharge efficiency and Both are set to 0.95. To protect the 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 respectively. The power demand of the electric vehicle before unplugging the charging pile is set to 0.8.

[0095] S104: Perform charge and discharge scheduling on the electric vehicle and the energy storage system according to the obtained optimal control sequence.

[0096] Among them, the optimal control sequence includes control variables corresponding to multiple consecutive moments.

[0097] In this step, when the optimal control sequence is obtained by solving the target economic model, since the control variables are composed of the power change amounts of the electric vehicle and the energy storage system, the charge and discharge powers of the electric vehicle and the energy storage system can be adjusted based on the optimal control sequence, thereby realizing the charge and discharge scheduling of the electric vehicle and the energy storage system.

[0098] In the above embodiments, during the process of the electric vehicle participating in the demand response scheduling, the load aggregation model of the electric vehicle is obtained. Through the load aggregation model, the discrete individual behaviors of the electric vehicles can be mapped into the group-schedulable dynamic power response, so as to realize the centralized management and optimal scheduling of the electric vehicle load. On this basis, a collaborative response model corresponding to the electric vehicle and the energy storage system is further constructed. This collaborative response model can integrate the dynamic response capabilities of the electric vehicle and the energy storage system, realize the dynamic complementarity of the two types of resources and the integration of the response capabilities, and improve the scheduling flexibility. Then, the first revenue model for the electric vehicle and the energy storage system participating in the demand response is updated, and the second revenue model and cost model for the electric vehicle aggregator participating in the demand response are updated. Through the update of these models, the economic interests 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, the target economic model generated by the collaborative response model, the first revenue model, the second revenue model, and the cost model is solved based on the preset constraint set, so as to balance the economy and the scheduling effect under the satisfaction of the constraint set. Finally, the obtained optimal control sequence is used for the charge and discharge scheduling of the electric vehicle and the energy storage system. A closed-loop link of accurate modeling, economic optimization, and collaborative control is formed, which can maximize the benefits of the electric vehicle participating in the demand response while improving the power quality and ensuring the grid operation efficiency, achieving a win-win situation of economic benefits and grid operation benefits.

[0099] As Figure 2 shown, in one of the embodiments, obtaining the load aggregation model of the electric vehicle includes:

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

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

[0102] S203: Use the load of the electric vehicle as the state variable, and generate a load aggregation model of the electric vehicle in combination with the first load transfer equation and the second load transfer equation.

[0103] Among them, the first load transfer equation is used to describe the dynamic change of the state of charge of the battery when the electric vehicle is in the charging state. The second load transfer equation is used to describe the dynamic change of the state of charge of the battery when the electric vehicle 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 through the state of charge of the battery in the current scheduling period, so that the change law of the battery load over time can be determined. Then, use the load of the electric vehicle as the state variable of the load aggregation model to be constructed, and construct the load aggregation model of the electric vehicle in combination with the first load transfer equation and the second load transfer equation.

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

[0106]

[0107] In the formula, represents the probability of transferring from interval i to interval i + 1 in the charging process, represents the charging duration, represents the charging power at time t, represents the charging efficiency, 、 represent the maximum and minimum values of the battery capacity respectively, represents the average transfer probability, and the average transfer probability can be calculated according to the following expression:

[0108]

[0109] In the formula, 、 respectively represent the boundary value of the region that will surely not transfer to the (i + 1)-th 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 surely transfer to the (i + 1)-th interval, is the differential of the state of charge S(k), represents the transfer probability distribution function of the battery interval and can be calculated by the following expression:

[0110]

[0111] In the formula, represents the lower boundary value of the state of charge interval at the (i + 1)-th moment, represents the battery capacity, represents the transfer probability density function of the battery state of charge interval and can be calculated by the following expression:

[0112]

[0113] In the formula, is the scale parameter of the gamma distribution, is the shape parameter of the gamma distribution and takes 6.5 and 3 respectively in an example, is the gamma function, expressed as .

[0114] Among them, can be calculated according to the following expression:

[0115]

[0116] To sum up, the first load transfer equation of the electric vehicle under the charging state can be represented by the following state space:

[0117]

[0118] In the formula, represents the load in the first state of charge interval at the (k + 1)-th moment, represents the load in the first state of charge interval at the k-th moment. When no charging instruction is applied to the electric vehicle, the load in each state of charge interval does not change at all, and this process is the idle state, which can be described by the idle transfer matrix for description, is the N-dimensional identity matrix. When charging power is applied to the electric vehicle, the load in each state of charge interval will undergo forced transfer, which is described by the charging forced transfer matrix for description, The expression of

[0119]

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

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

[0122] A c = A I + P c

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

[0124]

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

[0126]

[0127] In the formula, represents the load in the first state of charge interval at the (k + 1)-th moment, represents the load in the first state of charge interval at the k-th moment, is the discharging forced transfer matrix, which can be expressed as:

[0128]

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

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

[0131] A d = A I + P d

[0132] To sum up, the load aggregation model of the electric vehicle charging and discharging can be expressed as:

[0133]

[0134] In the formula, represents the state variable of the load aggregation model at the (k + 1)-th moment, represents the aggregated power output variable of the load matrix model at the k-th moment, represents the state transition matrix, which can be expressed as:

[0135]

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

[0137]

[0138] In the formula, represents the maximum value of the charging power of the electric vehicle, represents the maximum value of the discharging power of the electric vehicle.

[0139] The state variable can be expressed as follows:

[0140]

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

[0142] In this embodiment, when entering each scheduling period, the required data is obtained according to the pre-set model structure, and then the load aggregation model of the electric vehicle is updated, so that the discrete individual behaviors of the electric vehicles can be mapped into the group-schedulable dynamic power response, thereby realizing the centralized management and optimal scheduling of the electric vehicle load.

[0143] As Figure 3 shown, in one of the embodiments, a collaborative 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, including:

[0144] S301: Extract the state transition matrix from the load aggregation model.

[0145] S302: Obtain the state space model of the energy storage system, and extract the state transition matrix from the state space model.

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

[0147] S304: Use the state of charge of the electric vehicle and the energy storage system as state variables, the power change of the electric vehicle and the energy storage system as control variables, and combine the target state matrix and the target control matrix to generate a corresponding cooperative response model for 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: the state equation and the output equation. The state equation is used to describe how the internal state of the energy storage system changes over time, and the output equation is used to describe the dependence relationship between the output of the energy storage system and the internal state and input. The state transition matrix is a matrix that describes how the system state transfers from one time step to the next. The system output matrix is a matrix that maps the system state to the system output.

[0149] In this embodiment, extract the state transition matrix from the load aggregator of the electric vehicle, and obtain the state space model of the energy storage system and extract the state transition matrix from it. Then generate the target state matrix according to the state transition matrices extracted from both, and determine the target control matrix according to the rated capacities of the electric vehicle and the energy storage system. Then use the state of charge of the electric vehicle and the energy storage system as state variables, the power change of the electric vehicle and the energy storage system as control variables, and finally combine the target state matrix and the target control matrix to generate a corresponding cooperative response model for 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 cooperative response model transfers from one time step to the next, and the target control matrix is used to describe how the control inputs of the electric vehicle and the energy storage system affect the state change of the system.

[0150] In an example, the charging and discharging power of the energy storage system is mainly controlled by the scheduling instruction. Therefore, the energy storage system does not need the transfer matrix to describe its power load change, 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 the k + 1 moment, which is composed of the state of charge of the energy storage system and can be expressed as , represents the state transition 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 the t moment, represents the system output identity matrix, represents the control variable of the state space model at the t moment, which is composed of the power of the energy storage system and is expressed as , When it is positive, it indicates that the energy storage system is in the charging state, denoted by denoted by When it is negative, it indicates that the energy storage system is in the discharging state, denoted by denoted by

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

[0154]

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

[0156] In summary, by combining the load aggregation model of the electric vehicle and the state space model of the energy storage system, the collaborative response model corresponding to the electric vehicle and the energy storage system under continuous time variables can be expressed as:

[0157]

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

[0159]

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

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

[0162]

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

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

[0165]

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

[0167] It can be understood that in this embodiment, a collaborative response model corresponding to the electric vehicle and the energy storage system is constructed. This collaborative response model can integrate the dynamic response capabilities of the electric vehicle and the energy storage system to ensure that the model can accurately reflect the changes in the state of charge of the electric vehicle group and the energy storage system and their impact on the grid load, so as to optimize the charging and discharging strategies of the electric vehicle and the energy storage system to achieve maximum economic benefits and grid load balance.

[0168] In one of the embodiments, updating the first revenue model for the electric vehicle and the energy storage system to participate in demand response includes:

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

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

[0171] S3: Substitute the charging power, discharging power, degradation loss costs 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 revenue model.

[0172] Among them, the degradation loss cost refers to the cost caused by the decline in battery performance due to charge and discharge cycles, resulting in a shortened battery life and increased maintenance costs.

[0173] Specifically, the expression of the first revenue model or 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 costs 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, substitute these data into the preset first model to update and obtain a new first revenue model, so as to accurately evaluate the economic benefits of the electric vehicle and the energy storage system in the current scheduling period, and at the same time consider the costs brought by battery degradation.

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

[0176] S1: Obtain the reference load of the distribution network in the current scheduling period, and obtain the total load of the distribution network during uncoordinated charging of electric vehicles without energy storage.

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

[0178] S3: Calculate the operating cost of the aggregator and the cost of fixed-contract electric vehicles during the current scheduling period according to the aggregated output power of the electric vehicle aggregator in the current scheduling period.

[0179] S4: Update the cost model of the electric vehicle aggregator based on the operating cost of the aggregator and the cost of fixed-contract electric vehicles.

[0180] Wherein, the reference load refers to the basic power demand of the distribution network in a specific period 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 energy storage coordination, including the reference load plus the additional load brought by uncoordinated charging of electric vehicles. The aggregated output power refers to the total power output that the electric vehicle aggregator can provide by coordinating the charging and discharging behaviors of electric vehicles in the current scheduling period. The operating cost of the aggregator refers to the costs generated during the operation of the electric vehicle aggregator, including equipment maintenance, system operation, market transactions, etc. The cost of fixed-contract electric vehicles refers to the costs generated by the electric vehicle aggregator signing fixed service contracts with electric vehicle users.

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

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

[0183] In one embodiment, after determining the state variables and control variables according to the coordinated response model and generating a target economic model by combining the first revenue model, the second revenue model and the cost model, solving the target economic model based on a preset set of constraint conditions 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 the state variables and control variables in the collaborative response model, and construct an objective economic model based on the economic function, state variables, and control variables.

[0186] Among them, the objective of the objective economic model is to solve for the control variables when the economic function is maximized.

[0187] S3: Obtain a preset set of constraint conditions, and based on the set of constraint conditions, use the economic model predictive control algorithm to solve the objective economic model, obtaining a sequence of control variables when the economic function is maximized under the satisfaction of the set of constraint conditions, and determining this sequence of control variables as the optimal control sequence.

[0188] Among them, the economic function is used to evaluate the economic benefits of the electric vehicle aggregator, electric vehicles, and energy storage systems participating in demand response. The economic model predictive control algorithm (Economic Model Predictive Control, abbreviated as EMPC) is a control strategy that combines the predictive ability 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 difference between the sum of the first revenue model and the second revenue model and the cost model is calculated to obtain the economic function within the current scheduling period. Then, the state variables and control variables are extracted in the collaborative response model. 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. An objective economic model is constructed based on the economic function, predicted state sequence, and predicted control sequence. Furthermore, the objective economic model is solved based on the set of constraint conditions to obtain the predicted control sequence, that is, the sequence of control variables, when the economic function is maximized under the satisfaction of the set of constraint conditions. Finally, this sequence of control variables is determined as the optimal control sequence. This can take into account both economy and scheduling effect under the satisfaction of the set of constraint conditions. Finally, the obtained optimal control sequence is used for the charge and discharge scheduling of electric vehicles and energy storage systems. A closed-loop link of precise modeling, economic optimization, and collaborative control is formed, maximizing the benefits of electric vehicles participating in demand response while also improving power quality and ensuring the operation efficiency of the power grid.

[0190] In an example, let , define the predicted control sequence at time k in the prediction horizon N as , and the predicted state sequence of the corresponding state variables as . Based on this, the objective economic model can be expressed as:

[0191]

[0192] In the formula, represents the optimal solution obtained by solving the objective economic model, which is composed of the control variables at each moment within the scheduling period. Represents an economic function, and its expression is:

[0193]

[0194] In the formula, 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 revenue 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] Such as Figure 4 As shown, in one embodiment, the charging and discharging scheduling of the electric vehicle and the energy storage system is performed according to the obtained optimal control sequence, including:

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

[0199] S402: Adjust the power of the electric vehicle and the energy storage system respectively based on the power change amount of the electric vehicle and the energy storage system, so as to perform the charging and discharging scheduling of 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 it is necessary to increase the power supply capacity of the power grid, this 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 this control variable, the power change amount of the electric vehicle and the energy storage system can be determined, that is, the specific numerical value of the charging and discharging power to be adjusted. Then, the power of the electric vehicle and the energy storage system is adjusted respectively according to these power change amounts to perform the charging and discharging scheduling of the electric vehicle and the energy storage system. By precisely controlling the charging and discharging behavior of the electric vehicle and the energy storage system, the power grid load can be effectively balanced, and the operation efficiency and reliability of the power grid can be improved. In addition, considering the revenue situation of all parties in this process, while improving the power quality and ensuring the operation efficiency of the power grid, the benefits of the electric vehicle participating in demand response can also be maximized, achieving a win-win situation between economic benefits and power grid operation 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 scheduling framework of the electric vehicle aggregator is as follows Figure 5 shown. Under the scheduling instructions of the electric vehicle aggregator, energy is mutually supplied among the energy storage system, electric vehicles, and the power grid 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 constraint conditions, the target economic model is solved to obtain the scheduling result of the electric vehicle aggregator's participation in demand response, that is, the optimal control sequence, and the charging and discharging of electric vehicles and the energy storage system are controlled according to the scheduling result. The variation of the charging and discharging load of electric vehicles with time is as follows Figure 6 shown. To simultaneously meet the demand response and the energy replenishment requirements of electric vehicles, after the electric vehicle aggregator's scheduling, the charging load of electric vehicles increases during the valley period, and the charging load during the peak period is generally small. The scheduling result is as follows Figure 7 shown. As can be seen from Figure 7 , compared with the case where electric vehicles are only charged disorderly without scheduling, when electric vehicles are charged disorderly without scheduling, the load during the peak period on the basic load of the power grid increases significantly. When the electric vehicle aggregator schedules electric vehicles and the energy storage system to participate in demand response synergistically, compared with the basic load, there is an obvious valley filling effect during the valley period and a certain peak shaving effect during the peak period. Thus, it can be seen that the demand response scheduling method in the present invention has a good load transfer effect.

[0202] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps do not necessarily need to be executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps does not have a strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily need to be executed at the same moment, but can be executed at different moments. The execution order of these steps or stages does not necessarily need to be sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0203] Next, the electric vehicle demand response scheduling device based on economic model predictive control provided by the embodiments of the present application will be described. The electric vehicle demand response scheduling device based on economic model predictive control described below can be correspondingly referred to the electric vehicle demand response scheduling method based on economic model predictive control described above.

[0204] As shown inFigure 8 As shown in Figure 8 , the present application provides an electric vehicle demand response scheduling device 500 based on economic model predictive control. The device includes:

[0205] A model construction module 501, configured to obtain a load aggregation model of electric vehicles during a scheduling period, and construct a corresponding collaborative response model of electric vehicles and energy storage systems based on the state space according to the load aggregation model;

[0206] A model update module 502, configured to update a first revenue model for electric vehicles and energy storage systems to participate in demand response, and update a second revenue model and a cost model for electric vehicle aggregators to participate in demand response;

[0207] A model solving module 503, configured to determine state variables and control variables according to the collaborative response model, generate a target economic model by combining the first revenue model, the second revenue model, and the cost model, and solve the target economic model based on a preset set of constraint conditions;

[0208] A charge and discharge scheduling module 504, configured to perform charge and discharge scheduling on electric vehicles and energy storage systems according to the obtained optimal control sequence.

[0209] In the above embodiment, during the process of electric vehicles participating in demand response scheduling, a load aggregation model of electric vehicles is obtained. Through the load aggregation model, discrete individual behaviors of electric vehicles can be mapped into group-schedulable dynamic power responses, so as to realize centralized management and optimal scheduling of electric vehicle loads. On this basis, a corresponding collaborative response model of electric vehicles and energy storage systems is further constructed. This collaborative 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 for electric vehicles and energy storage systems to participate in demand response is updated, and the second revenue model and the cost model for electric vehicle aggregators to participate in demand response are updated. Through the update of these models, the economic interests and cost expenditures of all parties during the demand response process can be more accurately reflected to maximize the economic benefits of all parties. Then, the target economic model generated by the collaborative response model, the first revenue model, the second revenue model, and the cost model is solved based on a preset set of constraint conditions, so as to take into account economy and scheduling effect under the premise of meeting the set of constraint conditions. Finally, the obtained optimal control sequence is used to perform charge and discharge scheduling on electric vehicles and energy storage systems. A closed-loop link of accurate modeling, economic optimization, and collaborative control is formed, which can maximize the benefits of electric vehicles participating in demand response while improving power quality and ensuring the operation efficiency of the power grid, achieving a win-win situation of economic benefits and power grid operation benefits.

[0210] In one of the embodiments, the model construction module includes:

[0211] A state acquisition sub-module, configured to acquire the state of charge of the battery of the electric vehicle during the current scheduling period;

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

[0213] A model generation sub-module, configured to use the load of the electric vehicle as a state variable, and generate a load aggregation model of the electric vehicle in combination with the first load transfer equation and the second load transfer equation.

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

[0215] A first extraction sub-module, configured to extract a state transition matrix from the load aggregation model;

[0216] A second extraction sub-module, configured to obtain a state space model of the energy storage system, and extract a state transition matrix from the state space model;

[0217] A matrix determination sub-module, configured to determine a target state matrix according to the state transition matrices 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;

[0218] A model construction sub-module, configured to use the states of charge of the electric vehicle and the energy storage system as state variables, use the power change amounts of the electric vehicle and the energy storage system as control variables, and generate a collaborative response model corresponding to the electric vehicle and the energy storage system in combination with the target state matrix and the target control matrix.

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

[0220] A power acquisition sub-module, configured to acquire the charging power and discharging power of the electric vehicle and the energy storage system during the current scheduling period respectively;

[0221] An information determination sub-module, configured to acquire 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;

[0222] A first update sub-module, configured to substitute the charging power, discharging power, degradation loss costs of the electric vehicle and the energy storage system during the current scheduling period, and the charging electricity price and discharging electricity price during the current scheduling period into a preset first model to obtain a new first revenue model.

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

[0224] A load acquisition sub-module, configured to acquire the reference load of the distribution network during the current scheduling period, and acquire the total load of the distribution network during uncoordinated charging of electric vehicles without energy storage;

[0225] A second update sub-module, configured 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 sub-module, configured to calculate the operating cost of the aggregator and the fixed contracted electric vehicle cost during the current scheduling period according to the aggregated output power of the electric vehicle aggregator during the current scheduling period;

[0227] A third update sub-module, configured to update the cost model of the electric vehicle aggregator based on the operating cost of the aggregator and the fixed contracted electric vehicle cost.

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

[0229] A function determination sub-module, configured to determine the economic function during the current scheduling period according to the first revenue model, the second revenue model and the cost model;

[0230] An economic model construction sub-module, configured to extract state variables and control variables from the coordinated response model, and construct a target economic model based on the economic function, state variables and control variables; the target economic model aims at the control variables when maximizing the economic function;

[0231] A model solving sub-module, configured to obtain a preset set of constraint conditions, and based on the set of constraint conditions, solve the target economic model by using an economic model predictive control algorithm to obtain a sequence of control variables when maximizing the economic function under the satisfaction of the set of constraint conditions, and determine the sequence of control variables as the optimal control sequence.

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

[0233] A variable acquisition sub-module, configured to acquire the first control variable in the optimal control sequence, and determine the power change amounts of the electric vehicle and the energy storage system according to the first control variable in the optimal control sequence;

[0234] A scheduling sub-module, configured to adjust the power of the electric vehicle and the energy storage system respectively based on the power change amounts of the electric vehicle and the energy storage system to perform charge and discharge scheduling on 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 predictive control is only for illustrative purposes. In other embodiments, the electric vehicle demand response scheduling device based on economic model predictive 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 predictive control. Each module in the above-mentioned electric vehicle demand response scheduling device based on economic model predictive control can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules.

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

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

[0238] Schematically, as Figure 9 shown, Figure 9 is an internal structure schematic diagram of a computer device provided by an embodiment of the present application. The computer device 600 can be provided as a server. Referring to Figure 9 , the computer device 600 includes a processing component 602, which further includes one or more processors, and memory resources represented by a memory 601 for storing instructions executable by the processing component 602, such as application programs. The application programs stored in the memory 601 can 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 in any of the above embodiments.

[0239] The computer device 600 may also 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 XTM, Unix TM, Linux TM, Free BSDTM, or the like.

[0240] Those skilled in the art can understand that Figure 9 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this 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 component arrangement.

[0241] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element. In this article, the singular forms "a", "an" and "the" may also include the plural form unless the context clearly dictates otherwise. It should also be understood that the terms "including / containing" or "having" etc. specify the existence of the 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 related listed items.

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

[0243] The foregoing 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 readily apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A demand response scheduling method for electric vehicles based on economic model predictive control, characterized in that, The method includes: In a scheduling period, obtaining a load aggregation model of an electric vehicle, extracting a state transition matrix from the load aggregation model, obtaining a state space model of an energy storage system, and extracting a state transition matrix from the state space model; Determining a target state matrix according to the state transition matrices extracted from the load aggregation model and the state space model, and determining a target control matrix according to the rated capacities of the electric vehicle and the energy storage system; Using the state of charge of the electric vehicle and the energy storage system as state variables, using the power change amounts of the electric vehicle and the energy storage system as control variables, and generating a collaborative response model corresponding to the electric vehicle and the energy storage system in combination with the target state matrix and the target control matrix; Updating a first revenue model for the electric vehicle and the energy storage system to participate in demand response, and updating a second revenue model and a cost model for the electric vehicle aggregator to participate in demand response; Determining state variables and control variables according to the collaborative response model, generating a target economic model in combination with the first revenue model, the second revenue model and the cost model, and solving the target economic model based on a preset set of constraint conditions; Performing charge and discharge scheduling on the electric vehicle and the energy storage system according to the obtained optimal control sequence.

2. The demand response scheduling method for electric vehicles based on economic model predictive control according to claim 1, wherein The obtaining of the load aggregation model of the electric vehicle includes: Obtaining the state of charge of the battery of the electric vehicle in the current scheduling period; Determining a first load transfer equation for the electric vehicle in the charging state and a second load transfer equation for the electric vehicle in the discharging state according to the state of charge; Using the load amount of the electric vehicle as a state variable, and generating a load aggregation model of the electric vehicle in combination with the first load transfer equation and the second load transfer equation.

3. The demand response scheduling method for electric vehicles based on economic model predictive control according to claim 1, wherein The updating of the first revenue model for the electric vehicle and the energy storage system to participate in demand response includes: Respectively obtaining the charging power and discharging power of the electric vehicle and the energy storage system in the current scheduling period; Obtaining the charging electricity price and discharging electricity price in the current scheduling period, and respectively determining the degradation loss costs of the electric vehicle and the energy storage system in the current scheduling period; Substituting the charging power, discharging power, degradation loss costs 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 a preset first model to obtain a new first revenue model.

4. The demand response scheduling method for electric vehicles based on economic model predictive control according to claim 1, wherein The updating of the second revenue model and the cost model for the electric vehicle aggregator to participate in demand response includes: Obtaining the reference load of the distribution network in the current scheduling period, and obtaining the total load of the distribution network when the electric vehicles charge disorderly without energy storage collaboration; Substituting the reference load and the total load of the distribution network into a preset second model to obtain a new second revenue model; Calculating the operating cost of the aggregator and the fixed contracted electric vehicle cost in the current scheduling period according to the aggregated output power of the electric vehicle aggregator in the current scheduling period; Updating the cost model of the electric vehicle aggregator based on the aggregator operating cost and the fixed contracted electric vehicle cost.

5. The demand response scheduling method for electric vehicles based on economic model predictive control according to claim 1, characterized in that After determining the state variables and control variables according to the collaborative response model, and generating a target economic model by combining the first revenue model, the second revenue model, and the cost model, solving the target economic model based on a preset set of constraint conditions includes: Determining an economic function within the current scheduling period according to the first revenue model, the second revenue model, and the cost model; Extracting state variables and control variables in 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 for the control variables when the economic function is maximized; Obtaining a preset set of constraint conditions, and based on the set of constraint conditions, using an economic model predictive control algorithm to solve the target economic model, obtaining a sequence of control variables when the economic function is maximized under the satisfaction of the set of constraint conditions, and determining this sequence of control variables as the optimal control sequence.

6. The demand response scheduling method for electric vehicles based on economic model predictive control according to any one of claims 1 to 5, characterized in that The charging and discharging scheduling of the electric vehicle and the energy storage system according to the solved optimal control sequence includes: Obtaining the first control variable in the optimal control sequence, and determining the power change amounts of the electric vehicle and the energy storage system according to the first control variable in the optimal control sequence; Based on the power change amounts of the electric vehicle and the energy storage system, respectively adjusting the powers of the electric vehicle and the energy storage system to perform charging and discharging scheduling on the electric vehicle and the energy storage system.

7. An electric vehicle demand response scheduling device based on economic model predictive control, characterized in that, The device includes: A model construction module, configured to, within a scheduling period, obtain a load aggregation model of an electric vehicle, extract a state transition matrix from the load aggregation model, and obtain a state space model of an energy storage system, extract a state transition matrix from the state space model, determine a target state matrix according to the state transition matrices 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, using the state of charge of the electric vehicle and the energy storage system as state variables and the power change amounts of the electric vehicle and the energy storage system as control variables, and generating a corresponding collaborative response model for the electric vehicle and the energy storage system by combining the target state matrix and the target control matrix; A model update module, configured to update the first revenue model for the electric vehicle and the energy storage system participating in demand response, and update the second revenue model and the cost model for the electric vehicle aggregator 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 revenue model, the second revenue model, and the cost model, and then solve the target economic model based on a preset set of constraint conditions; A charging and discharging scheduling module, configured to perform charging and discharging scheduling on the electric vehicle and the energy storage system according to the solved optimal control sequence.

8. A storage medium, characterized in that: The computer-readable instructions are stored in the storage medium, and when executed by one or more processors, cause the one or more processors to execute the steps of the method for scheduling electric vehicle demand response based on economic model predictive control according to any one of claims 1 to 6.

9. A computer device, characterized in that, Including: One or more processors, and a memory; Computer-readable instructions are stored in the memory, 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 according to any one of claims 1 to 6 are executed.

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