Supply deployment space-time optimization method, device and equipment for mobile energy storage vehicle

Abstract

The invention discloses a mobile energy storage vehicle-oriented supply deployment space-time optimization method, device and equipment. The method comprises the following steps: realizing short-term electricity price prediction by using an autoregression integral moving average model; constructing a multi-layer space-time network model representing space-time movement characteristics and charging and discharging states of the mobile energy storage vehicle; the charging and discharging power constraint and the electric quantity state constraint of the mobile energy storage vehicle are constructed, and the total cost generated in the operation process of the mobile energy storage vehicle system is calculated; and the total income of the mobile energy storage vehicles is calculated, and a supply deployment space-time optimization model with the goal of maximizing the net income of supply deployment of the mobile energy storage vehicles is constructed in combination with the total cost. According to the invention, through short-term electricity price prediction data, the charging and discharging scheduling of a plurality of mobile energy storage vehicles is associated, the total revenue generated by mobile deployment is obtained, and in combination with the total cost generated in the operation process of the energy storage vehicles, the optimization model for maximizing the supply deployment net revenue of the mobile energy storage vehicles is constructed. Therefore, optimal position planning and scheduling of the mobile energy storage vehicle system are realized.

Description

Technical Field

[0001] The present invention relates to the field of mobile energy storage, and in particular to a method, device and equipment for spatiotemporal optimization of supply deployment for mobile energy storage vehicles. Background Art

[0002] In recent years, with the continuous growth of power load and the rapid increase in the proportion of new energy power generation, the tension of power resources has become increasingly severe, and the peak-to-valley difference has further widened. The introduction of energy storage technology in the power system can effectively alleviate the demand pressure of peak power consumption and increase the daily load rate of the system, thereby greatly improving the utilization efficiency of power generation equipment. At present, the scale of large-scale centralized battery energy storage is still relatively limited, which is mainly attributed to the high initial investment cost and the lack of flexible and efficient utilization solutions, resulting in low return on investment. However, with the growing demand for energy storage flexibility, the application of energy storage will become more and more common. However, general fixed energy storage can only be deployed in a specific location. Once installed, it cannot be moved at will. If the demand in the area where the energy storage system is located is lower than expected, the return on investment will decrease, resulting in waste of resources and inability to adapt to demand fluctuations. In addition, in the event of emergencies such as natural disasters and sudden power outages, fixed energy storage can only provide power support for the installation point and cannot temporarily provide power to other areas. In addition to the intermittent profit problem caused by low utilization, long-term idleness will also accelerate the attenuation of battery capacity, further shortening the revenue-generating life of the energy storage system, thereby affecting its overall economic efficiency. In contrast, mobile energy storage vehicles have great advantages in flexibility, cost efficiency, emergency response, and the ability to coordinate with distributed energy, and are more suitable for application scenarios with dynamic changes in demand and rapid development of distributed energy.

[0003] At the same time, with the promotion of relevant national policies, each region needs to establish corresponding price mechanisms in combination with different application scenarios, scientifically divide peak and valley periods, introduce peak electricity price mechanisms, and reasonably expand the gap between peak and valley electricity prices, so as to create favorable conditions for the development of new energy storage on the user side and further promote the participation of new energy storage in the power market and dispatching. Under the new electricity price mechanism, regional electricity prices are closely related to the load level of the line. For some regions, the line has been in a low-load state for two months, and the load rate of the distribution transformer is low, which may make the original peak and valley electricity price evolve to a lower and more volatile one; on the contrary, some lines have a high load level, a high load rate of distribution transformers and limited transfer capacity, so the power department suppresses peak electricity consumption by raising electricity prices. In this environment where the difference in peak and valley electricity prices is significant, mobile energy storage vehicles can achieve more arbitrage opportunities with their flexible deployment capabilities, while improving the absorption capacity of renewable energy.

[0004] Therefore, optimizing the supply and deployment model of mobile energy storage vehicles will be the key to enhancing their economic value and promoting sustainable development. Summary of the invention

[0005] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art, and to provide a method, device and equipment for spatiotemporal optimization of supply deployment of mobile energy storage vehicles. By using short-term electricity price forecast data, the charging and discharging scheduling of multiple mobile energy storage vehicles is associated to obtain the total revenue generated by mobile deployment, and combined with the total cost generated during the operation of the energy storage vehicle, a model for maximizing the net revenue of mobile energy storage vehicle supply deployment is constructed to achieve the optimal location planning and scheduling of the mobile energy storage vehicle system.

[0006] To achieve the above objectives, the technical solution of the present invention is: a method for spatiotemporal optimization of supply deployment for mobile energy storage vehicles, comprising:

[0007] Based on historical electricity price data, the autoregressive integrated moving average model is used to capture the historical fluctuation pattern of electricity prices and realize short-term electricity price forecasting;

[0008] A multi-layer spatiotemporal network model is constructed to represent the spatiotemporal mobility characteristics and charging and discharging status of mobile energy storage vehicles. The mobile arc represents the movement of mobile energy storage vehicles between grid nodes, and the fixed arc represents the energy exchange between mobile energy storage vehicles and the distribution network.

[0009] Constructing the charging and discharging power constraints and power state constraints of the mobile energy storage vehicle, and calculating the total cost generated by the mobile energy storage vehicle system during operation, the total cost including the moving path cost, battery loss cost and operation and maintenance cost;

[0010] The total revenue of mobile energy storage vehicles is calculated by predicting short-term electricity prices. Combined with the total cost, a spatiotemporal optimization model for supply deployment is constructed with the goal of maximizing the net revenue of mobile energy storage vehicle supply deployment. The optimal supply deployment plan for mobile energy storage vehicles is solved.

[0011] Based on the historical electricity price data, the autoregressive integrated moving average model is used to capture the historical fluctuation pattern of electricity prices and realize short-term electricity price forecasting, including:

[0012] The autoregressive model is:

[0013]

[0014] In the formula, y t is the current electricity price; p is the order; μ is the constant term; γ i is the autocorrelation coefficient; ∈ t for noise;

[0015] The moving average model is:

[0016]

[0017] Where q is the order; θ iis the coefficient of the moving average term;

[0018] The autoregressive moving average model is:

[0019]

[0020] The d-order difference of the non-stationary historical electricity price time series is performed, and its own correlation coefficient and partial autocorrelation coefficient are calculated. The optimal p and q orders of the autoregressive moving average model are obtained through AIC / BIC search, and a differential autoregressive moving average model is constructed to predict future short-term electricity prices.

[0021] The constraints of the multi-layer spatiotemporal network model include:

[0022] The spatiotemporal mobility constraints of mobile energy storage vehicles are:

[0023]

[0024] In the formula, K m，ab，t is whether the mobile energy storage vehicle m chooses to move from node a to node b at time t; M is the number of mobile energy storage vehicles; T is the total movement time; R is the number of grid nodes;

[0025] The flow balance constraint of mobile energy storage vehicles is:

[0026]

[0027] In the formula, R + is the departure node of the mobile energy storage vehicle; R - It is the arrival node of the mobile energy storage vehicle;

[0028] The initial and terminal state constraints of the mobile energy storage vehicle are:

[0029]

[0030] In the formula, K m，ab，0 is the initial position of the mobile energy storage vehicle; K m，ab，1 is the position state of the mobile energy storage vehicle at time t = 1; K m，ab，T is the final position of the mobile energy storage vehicle;

[0031] The path selection constraints of the mobile energy storage vehicle are:

[0032]

[0033] In the formula, K m，ab，t+1 Whether the mobile energy storage vehicle m chooses to move from node a to node b at time t+1.

[0034] The charging and discharging power constraints of the mobile energy storage vehicle are:

[0035]

[0036] In the formula, is the charging power of mobile energy storage vehicle m at node a at time t; is the discharge power of mobile energy storage vehicle m at node a at time t; K m，aa，t Whether the mobile energy storage vehicle m chooses to stay at node a at time t; and are the maximum charging power and maximum discharging power of the mobile energy storage vehicle m respectively; and They are whether the mobile energy storage vehicle m can perform charging and discharging at node a at time t; T is the total movement time; M is the number of mobile energy storage vehicles; and R is the number of grid nodes.

[0037] The power state constraint of the mobile energy storage vehicle is:

[0038]

[0039] In the formula, E m，t+1 is the power state of the mobile energy storage vehicle m at time t+1; E m，t is the power state of the mobile energy storage vehicle m at time t; is the charging power of mobile energy storage vehicle m at node a at time t; is the discharge power of mobile energy storage vehicle m at node a at time t; and is the charging efficiency and discharging efficiency of the mobile energy storage vehicle m; Δt is the length of time for charging and discharging between time t and time t+1; The lower limit of battery power for mobile energy storage vehicles; It is the upper limit of the battery power of mobile energy storage vehicles.

[0040] The moving path cost C move for:

[0041]

[0042] In the formula, is the moving cost of the mobile energy storage vehicle m from node a to node b at time t; d ab is the distance from node a to node b; c move is the unit distance cost; K m，ab，t is whether the mobile energy storage vehicle m chooses to move from node a to node b at time t; T is the total movement time; M is the number of mobile energy storage vehicles; R is the number of grid nodes;

[0043] The battery loss cost C loss for:

[0044]

[0045] In the formula, l m，a，t is the capacity decay caused by the charging and discharging of the mobile energy storage vehicle m at node a at time t; c loss is the unit battery capacity attenuation cost; q is a fixed attenuation capacity of the battery per day; α and β are the battery capacity attenuation coefficients during charging and discharging, respectively; is the charging power of mobile energy storage vehicle m at node a at time t; is the discharge power of mobile energy storage vehicle m at node a at time t;

[0046] The operation and maintenance cost C op for:

[0047] C op =γ E c E E total +γ P C P P total ;

[0048] In the formula, γ E and γ P are the energy storage energy operation and maintenance coefficient and the energy storage power operation and maintenance coefficient respectively; c E and c P are energy storage energy price and energy storage power price respectively; E total is the total energy capacity of the mobile energy storage vehicle system; P total is the total power capacity of the mobile energy storage vehicle system.

[0049] The objective function of the supply deployment spatiotemporal optimization model is:

[0050]

[0051] Where T is the total travel time; M is the number of mobile energy storage vehicles; R is the number of grid nodes; σ a，t is the electricity price at node a at time t; is the discharge power of mobile energy storage vehicle m at node a at time t; is the charging power of mobile energy storage vehicle m at node a at time t; C move is the moving path cost; C loss is the battery loss cost; C op For operation and maintenance costs.

[0052] A device for optimizing the time and space of supply deployment of mobile energy storage vehicles, which is applied to the above-mentioned method, comprises:

[0053] The short-term electricity price forecasting model building module is used to capture the historical fluctuation pattern of electricity prices based on historical electricity price data using the autoregressive integrated moving average model to achieve short-term electricity price forecasting;

[0054] A multi-layer spatiotemporal network model construction module is used to construct a multi-layer spatiotemporal network model representing the spatiotemporal mobility characteristics and charging and discharging status of mobile energy storage vehicles, where the mobile arc represents the movement of the mobile energy storage vehicle between grid nodes, and the fixed arc represents the energy exchange between the mobile energy storage vehicle and the distribution network;

[0055] A cost model building module is used to build the charging and discharging power constraints and power state constraints of the mobile energy storage vehicle, and calculate the total cost generated by the mobile energy storage vehicle system during operation, the total cost including the moving path cost, battery loss cost and operation and maintenance cost;

[0056] The optimal supply deployment solution module is used to calculate the total revenue of mobile energy storage vehicles through the predicted short-term electricity price, and to build a supply deployment spatiotemporal optimization model with the goal of maximizing the net revenue of mobile energy storage vehicle supply deployment in combination with the total cost, and to solve the optimal supply deployment plan for mobile energy storage vehicles.

[0057] A device for optimizing the time and space of supply deployment for mobile energy storage vehicles, comprising a memory and a processor;

[0058] The memory is used to store computer program code and transmit the computer program code to the processor;

[0059] The processor is used to execute the method according to the instructions in the computer program code.

[0060] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] In a method, device and equipment for spatiotemporal optimization of supply deployment for mobile energy storage vehicles of the present invention, the electricity prices of different nodes on the same day are predicted through a time prediction sequence, and the charging and discharging benefits of the mobile energy storage vehicle are obtained by using the electricity price difference. At the same time, a spatiotemporal mobility characteristic model of the mobile energy storage vehicle is constructed, and a mobile path cost, battery loss cost and operation and maintenance cost are combined to construct an optimization model for maximizing the net benefit of supply deployment of the mobile energy storage vehicle, so as to realize the optimal location planning and scheduling of the mobile energy storage vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of a method for spatiotemporal optimization of supply deployment for mobile energy storage vehicles of the present invention.

[0064] Figure 2 It is a schematic diagram of the space-time transmission network model of a mobile energy storage vehicle in an embodiment of the present invention.

[0065] Figure 3 It is a structural block diagram of a supply deployment spatiotemporal optimization device for mobile energy storage vehicles according to the present invention.

[0066] Figure 4 It is a structural block diagram of a supply deployment time-space optimization device for mobile energy storage vehicles of the present invention. DETAILED DESCRIPTION

[0067] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0068] See also Figure 1 , a method for spatiotemporal optimization of supply deployment for mobile energy storage vehicles, comprising:

[0069] S1. Based on historical electricity price data, the autoregressive integral moving average model is used to capture the historical fluctuations of electricity prices and realize short-term electricity price forecasts. Short-term electricity forecasts require the collection of historical electricity prices in different regions for continuous time periods, and the analysis of electricity price time series data with seasonality, trend and autocorrelation through the autoregressive integral moving average model to capture the historical fluctuations of electricity prices and realize short-term electricity price forecasts to assist in revenue analysis and optimize the charging and discharging scheduling of mobile energy storage vehicle systems. Among them, the autoregressive integral moving average model is divided into three parts: autoregressive model, moving average model and difference method.

[0070] S2. Construct a multi-layer spatiotemporal network model that represents the spatiotemporal mobility characteristics and charging and discharging status of mobile energy storage vehicles. The movement behavior of mobile energy storage vehicles is represented by arcs, which are divided into two categories: mobile arcs and fixed arcs. Among them, mobile arcs represent the movement of mobile energy storage vehicles between power grid nodes, and fixed arcs represent the energy exchange between mobile energy storage vehicles and distribution networks. Each layer of the schematic model represents the spatiotemporal mobility characteristics of a mobile energy storage vehicle, and the superposition of multi-layer models is the spatiotemporal mobility characteristics of the entire mobile energy storage vehicle system.

[0071] S3, constructing the charging and discharging power constraints and power state constraints of the mobile energy storage vehicle, and calculating the total cost generated by the mobile energy storage vehicle system during operation, the total cost including the moving path cost, battery loss cost and operation and maintenance cost;

[0072] S4. Calculate the total revenue of mobile energy storage vehicles through the predicted short-term electricity price, and build a spatiotemporal optimization model for supply deployment with the goal of maximizing the net revenue of mobile energy storage vehicle supply deployment based on the total cost, and solve the optimal supply deployment plan for mobile energy storage vehicles.

[0073] The present invention aims to maximize net benefits through flexible and efficient scheduling and path planning. Considering the spatiotemporal deployment scenario of mobile energy storage vehicle systems, firstly, the historical electricity price fluctuations are captured based on the autoregressive integral moving average model to predict future short-term electricity prices. Secondly, the path selection and charging and discharging behavior selection of energy storage vehicles are modeled using a multi-layer spatiotemporal network model to ensure efficient operation of the vehicle in time and space dimensions. Thirdly, battery energy constraints, power constraints, and liquidity constraints are established to ensure the safety and stability of the energy storage system, and mobile path costs, battery loss costs, and operating and maintenance costs are constructed. Finally, the present invention constructs an optimization model that maximizes the net benefits of mobile energy storage vehicle supply deployment by associating the constructed multidimensional cost model with the electricity price benefits obtained from scheduling. This method comprehensively considers the spatiotemporal mobility characteristics and charging and discharging strategies of each energy storage vehicle to ensure that the vehicle completes the task according to the optimal path and is efficiently deployed between different nodes. It has extremely high scalability and broad practical application scenarios.

[0074] Furthermore, based on the historical electricity price data, the autoregressive integrated moving average model is used to capture the historical fluctuation pattern of electricity prices and realize short-term electricity price forecasting, including:

[0075] The autoregressive model is used to describe the relationship between the current value and the historical value, and uses the historical data of the variable itself to predict itself. The p-order autoregressive model is expressed as:

[0076]

[0077] In the formula, y t is the current electricity price value; p is the order; μ is a constant term used to capture the mean trend of the data; γ i is the autocorrelation coefficient; ∈ t for noise;

[0078] The moving average model focuses on the accumulation of error terms in the autoregressive model. The q-order moving average is expressed as:

[0079]

[0080] Where q is the order; θ i is the coefficient of the moving average term, indicating the current value y t The relationship between the error and the errors at the past q time points;

[0081] Utilizes linear combinations of historical noise to eliminate random fluctuations in forecasts.

[0082] The autoregressive moving average model is:

[0083]

[0084] If the historical electricity price time series is stable, the above model can be used to predict short-term electricity prices. However, electricity prices are affected by different time periods and have obvious seasonal trends. Therefore, it is necessary to combine the autoregressive moving average model and the difference method to test the stability of historical electricity prices. Perform d-order differences on the non-stationary historical electricity price time series, calculate its own correlation coefficient ACF and partial autocorrelation coefficient PACF, and use AIC / BIC search to obtain the optimal p and q orders of the autoregressive moving average model, construct a differential autoregressive moving average model, predict future short-term electricity prices, and obtain the electricity price of the day.

[0085] AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) are statistical indicators for model selection, and are often used in time series modeling to determine the optimal model parameters (p, d, q). They balance the explanatory power of the model with the complexity of the model to prevent the model from overfitting.

[0086] The AIC is expressed as:

[0087] AIC=-2ln(L)+2K:

[0088] Where L is the maximum likelihood function of the model; K is the number of model parameters. When the sample size is large, the AIC criterion model may have non-convergence problems, and BIC makes up for the shortcomings of AIC.

[0089] BIC is expressed as:

[0090] BIC = -2ln(L) + Lln(n);

[0091] Where L is the maximum likelihood function of the model and n is the sample size.

[0092] The smaller the above two evaluation indicators are, the better the model prediction effect is, so as to determine the optimal parameters.

[0093] Further, such as Figure 2 The mobile space-time transmission network model shown in Figure 1 represents the time and space dimensions of mobile energy storage vehicles moving between grid nodes in different time periods. The space-time constraints of mobile energy storage vehicles are the core part of optimal scheduling. By controlling the path selection, flow continuity, and starting and terminal states of mobile energy storage vehicles, we can ensure that mobile energy storage vehicles complete their tasks along the optimal path. The constraints of the following multi-layer space-time network model are constructed:

[0094] Mobile energy storage vehicles can only choose one path to move or stay at a certain node in each time period. Each energy storage vehicle will not cross multiple nodes or perform multiple tasks at the same time in the same time period, that is, at any time, the same mobile energy storage vehicle can only move on one arc. The binary variable K represents the movement selection of the energy storage vehicle, and the spatiotemporal mobility constraints of the mobile energy storage vehicle are constructed:

[0095]

[0096] In the formula, K m，ab，t is whether the mobile energy storage vehicle m chooses to move from node a to node b at time t; M is the number of mobile energy storage vehicles; T is the total movement time; R is the number of grid nodes.

[0097] In order to avoid random jumping between different nodes of the mobile energy storage vehicle and ensure the physical feasibility of path planning, after the mobile energy storage vehicle starts from a node, it must reach the target node in the next time period, that is, the end position of any mobile energy storage vehicle at time t must be the starting position of the vehicle at time t+1, ensuring that the path movement of the vehicle is continuous, thereby constructing the flow balance constraint of the mobile energy storage vehicle:

[0098]

[0099] In the formula, R + is the departure node of the mobile energy storage vehicle; R - It is the arrival node of the mobile energy storage vehicle.

[0100] To ensure the integrity of the scheduling, each energy storage vehicle must start from the starting node as planned and eventually reach the target node to ensure the completion of the scheduling task. At the same time, in order to make the scheduling more targeted, it is necessary to restrict the initial and terminal states. The initial and terminal state constraints of the mobile energy storage vehicle are constructed as follows:

[0101]

[0102] In the formula, K m，ab，0 is the initial position of the mobile energy storage vehicle; K m，ab，1 is the position state of the mobile energy storage vehicle at time t=1; K m，ab，T is the final position of the mobile energy storage vehicle.

[0103] In order to ensure the effectiveness of charging and discharging behavior and improve mobility efficiency, the mobile energy storage vehicle cannot frequently travel between two nodes. The path selection constraint of the mobile energy storage vehicle is:

[0104]

[0105] In the formula, K m，ab，t+1Whether the mobile energy storage vehicle m chooses to move from node a to node b at time t+1.

[0106] Furthermore, in order to ensure efficient, safe and economical operation of mobile energy storage vehicles, there are limitations on battery energy and power during the charging and discharging process of mobile energy storage vehicles.

[0107] When a mobile energy storage vehicle is located at a grid node for charging and discharging, the charging and discharging power must be limited within the rated range to avoid damaging the energy storage equipment. The following constraints are introduced:

[0108] In addition to the maximum charging and discharging power demand, it is necessary to ensure that the mobile energy storage vehicle remains at the grid node a, that is, charging and discharging can only be performed when the position of the mobile energy storage vehicle remains unchanged between two adjacent moments. Otherwise, the mobile energy storage vehicle cannot be charged or discharged while in motion:

[0109]

[0110] The state of the battery in the mobile energy storage vehicle and the line conditions and voltage conditions at the grid node will also affect the charging and discharging effects:

[0111]

[0112] Any energy storage vehicle can only be charged or discharged at the same time, but not both at the same time:

[0113]

[0114] In the formula, is the charging power of mobile energy storage vehicle m at node a at time t; is the discharge power of mobile energy storage vehicle m at node a at time t; K m，aa，t Whether the mobile energy storage vehicle m chooses to stay at node a at time t; and are the maximum charging power and maximum discharging power of the mobile energy storage vehicle m respectively; and They are whether the mobile energy storage vehicle m can charge and discharge at node a at time t. The value depends on the battery status of the mobile energy storage vehicle at this time and whether the grid node can successfully connect to the mobile energy storage device. If it is charging or discharging, the value is 1, otherwise it is 0; T is the total movement time; M is the number of mobile energy storage vehicles; R is the number of grid nodes.

[0115] Furthermore, based on the electrochemical properties of the energy storage battery, the state of charge SOC of the mobile energy storage vehicle is affected by the power and efficiency of charging and discharging at the previous moment, so the state of charge constraint of the mobile energy storage vehicle is:

[0116]

[0117] The above formula represents the energy loss generated during the charging and discharging process of mobile energy storage vehicles. Similarly, in order to avoid overcharging or over-discharging, ensure the stable operation of the battery system, avoid damaging the energy storage equipment and reducing the battery life, the power state has a rated range constraint:

[0118]

[0119] In the formula, E m，t+1 is the power state of the mobile energy storage vehicle m at time t+1; E m，t is the power state of the mobile energy storage vehicle m at time t; is the charging power of mobile energy storage vehicle m at node a at time t; is the discharge power of mobile energy storage vehicle m at node a at time t; and is the charging efficiency and discharging efficiency of the mobile energy storage vehicle m; Δt is the length of time for charging and discharging between time t and time t+1; The lower limit of battery power for mobile energy storage vehicles; It is the upper limit of the battery power of mobile energy storage vehicles.

[0120] Furthermore, during the operation of the mobile energy storage vehicle system, non-negligible costs will be generated, usually including moving path costs, battery loss costs and operation and maintenance costs.

[0121] The mobile energy storage vehicle needs to consume energy and incur corresponding costs when moving between different nodes. At the same time, long-distance and long-term movement will increase the time cost and may miss the charging and discharging opportunities. The moving path cost C move for:

[0122]

[0123] In the formula, is the moving cost of the mobile energy storage vehicle m from node a to node b at time t; d ab is the distance from node a to node b; c move is the unit distance cost; K m，ab，t is whether the mobile energy storage vehicle m chooses to move from node a to node b at time t. If so, the value is 1, otherwise it is 0; T is the total movement time; m is the number of mobile energy storage vehicles; R is the number of grid nodes.

[0124] The battery will experience capacity degradation during the charge and discharge cycle, that is, the available capacity of the battery will gradually decay with use. Long-term capacity decay will shorten the battery life cycle and increase the frequency of battery replacement. Natural aging is also related to the charge and discharge power. The battery loss cost C loss for:

[0125]

[0126] In the formula, l m，a，t is the capacity decay caused by the charging and discharging of the mobile energy storage vehicle m at node a at time t; c loss is the unit battery capacity attenuation cost; q is a fixed attenuation capacity of the battery per day; α and β are the battery capacity attenuation coefficients during charging and discharging, respectively; is the charging power of mobile energy storage vehicle m at node a at time t; is the discharge power of mobile energy storage vehicle m at node a at time t.

[0127] The daily maintenance of mobile energy storage vehicles includes regular inspections, replacement of parts, system upgrades, and operation and maintenance of the battery management system. These operations and maintenance will generate additional operation and maintenance costs. The operation and maintenance cost C op for:

[0128] C op =γ E c E E totat +γ P c P P total ;

[0129] In the formula, γ E and γ P are the energy storage energy operation and maintenance coefficient and the energy storage power operation and maintenance coefficient respectively; c E and c P are energy storage energy price and energy storage power price respectively; E total is the total energy capacity of the mobile energy storage vehicle system, which is the sum of the total battery power of all mobile energy storage vehicles in the entire system; P total It is the total power capacity of the mobile energy storage vehicle system, which is the sum of the total power required by all mobile energy storage vehicles in the entire system.

[0130] Furthermore, the mobile path cost, battery loss cost and operation and maintenance cost generated by the spatiotemporal deployment of the mobile energy storage system are summarized to obtain the total cost of the system. Combined with the electricity price benefits obtained from the mobile deployment, the objective function of the spatiotemporal optimization model for supply deployment is constructed as follows:

[0131]

[0132] Where T is the total travel time; M is the number of mobile energy storage vehicles; R is the number of grid nodes; σ a，t is the electricity price at node a at time t; is the discharge power of mobile energy storage vehicle m at node a at time t; is the charging power of mobile energy storage vehicle m at node a at time t; Cmove is the moving path cost; C loss is the battery loss cost; C op For operation and maintenance costs.

[0133] See also Figure 3 The present invention also provides a device for spatiotemporal optimization of supply deployment for mobile energy storage vehicles, which is applied to the above-mentioned method for spatiotemporal optimization of supply deployment for mobile energy storage vehicles, and the device comprises:

[0134] The short-term electricity price forecasting model building module is used to capture the historical fluctuation pattern of electricity prices based on historical electricity price data using the autoregressive integrated moving average model to achieve short-term electricity price forecasting;

[0135] A multi-layer spatiotemporal network model construction module is used to construct a multi-layer spatiotemporal network model representing the spatiotemporal mobility characteristics and charging and discharging status of mobile energy storage vehicles, where the mobile arc represents the movement of the mobile energy storage vehicle between grid nodes, and the fixed arc represents the energy exchange between the mobile energy storage vehicle and the distribution network;

[0136] A cost model building module is used to build the charging and discharging power constraints and power state constraints of the mobile energy storage vehicle, and calculate the total cost generated by the mobile energy storage vehicle system during operation, the total cost including the moving path cost, battery loss cost and operation and maintenance cost;

[0137] The optimal supply deployment solution module is used to calculate the total revenue of mobile energy storage vehicles through the predicted short-term electricity price, and to build a supply deployment spatiotemporal optimization model with the goal of maximizing the net revenue of mobile energy storage vehicle supply deployment in combination with the total cost, and to solve the optimal supply deployment plan for mobile energy storage vehicles.

[0138] See also Figure 4 , the present invention also provides a supply deployment time-space optimization device for mobile energy storage vehicles, including a memory and a processor;

[0139] The memory is used to store computer program code and transmit the computer program code to the processor;

[0140] The processor is used to execute the above-mentioned method for spatiotemporal optimization of supply deployment for mobile energy storage vehicles according to the instructions in the computer program code.

[0141] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for spatiotemporal optimization of supply deployment for mobile energy storage vehicles described above is implemented.

[0142] Generally speaking, the computer instructions for implementing the method of the present invention may be carried in any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media, except for the signal itself that is temporarily propagating.

[0143] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0144] Computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer or to an external computer (for example, using an Internet service provider to connect via the Internet) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0145] The above-mentioned device and non-temporary computer-readable storage medium can refer to the specific description of a supply deployment spatiotemporal optimization method for mobile energy storage vehicles and its beneficial effects, which will not be repeated here.

[0146] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for spatiotemporal optimization of supply deployment for mobile energy storage vehicles, characterized in that: include: Based on historical electricity price data, the autoregressive integrated moving average model is used to capture the historical fluctuation pattern of electricity prices and realize short-term electricity price forecasting; A multi-layer spatiotemporal network model is constructed to represent the spatiotemporal mobility characteristics and charging and discharging status of mobile energy storage vehicles. The mobile arc represents the movement of mobile energy storage vehicles between grid nodes, and the fixed arc represents the energy exchange between mobile energy storage vehicles and the distribution network. Constructing the charging and discharging power constraints and power state constraints of the mobile energy storage vehicle, and calculating the total cost generated by the mobile energy storage vehicle system during operation, the total cost including the moving path cost, battery loss cost and operation and maintenance cost; The total revenue of mobile energy storage vehicles is calculated by predicting short-term electricity prices. Combined with the total cost, a spatiotemporal optimization model for supply deployment is constructed with the goal of maximizing the net revenue of mobile energy storage vehicle supply deployment. The optimal supply deployment plan for mobile energy storage vehicles is solved.

2. A method for spatiotemporal optimization of supply deployment for mobile energy storage vehicles according to claim 1, characterized in that: Based on the historical electricity price data, the autoregressive integrated moving average model is used to capture the historical fluctuation pattern of electricity prices and realize short-term electricity price forecasting, including: The autoregressive model is: In the formula, y t is the current electricity price; p is the order; μ is the constant term; γ i is the autocorrelation coefficient; ∈ t for noise; The moving average model is: Where q is the order; θ i is the coefficient of the moving average term; The autoregressive moving average model is: The d-order difference of the non-stationary historical electricity price time series is performed, and its own correlation coefficient and partial autocorrelation coefficient are calculated. The optimal p and q orders of the autoregressive moving average model are obtained through AIC / BIC search, and a differential autoregressive moving average model is constructed to predict future short-term electricity prices.

3. A method for spatiotemporal optimization of supply deployment for mobile energy storage vehicles according to claim 1, characterized in that: The constraints of the multi-layer spatiotemporal network model include: The spatiotemporal mobility constraints of mobile energy storage vehicles are: In the formula, K m，ab，t is whether the mobile energy storage vehicle m chooses to move from node a to node b at time t; M is the number of mobile energy storage vehicles; T is the total movement time; R is the number of grid nodes; The flow balance constraint of mobile energy storage vehicles is: In the formula, R + is the departure node of the mobile energy storage vehicle; R - It is the arrival node of the mobile energy storage vehicle; The initial and terminal state constraints of the mobile energy storage vehicle are: In the formula, K m，ab，0 is the initial position of the mobile energy storage vehicle; K m，ab，1 is the position state of the mobile energy storage vehicle at time t = 1; K m，ab，T is the final position of the mobile energy storage vehicle; The path selection constraints of the mobile energy storage vehicle are: In the formula, K m，ab，t+1 Whether the mobile energy storage vehicle m chooses to move from node a to node b at time t+1.

4. A method for spatiotemporal optimization of supply deployment for mobile energy storage vehicles according to claim 1, characterized in that: The charging and discharging power constraints of the mobile energy storage vehicle are: In the formula, is the charging power of mobile energy storage vehicle m at node a at time t; is the discharge power of mobile energy storage vehicle m at node a at time t; K m，aa，t Whether the mobile energy storage vehicle m chooses to stay at node a at time t; and are the maximum charging power and maximum discharging power of the mobile energy storage vehicle m respectively; and They are whether the mobile energy storage vehicle m can perform charging and discharging at node a at time t; T is the total movement time; M is the number of mobile energy storage vehicles; and R is the number of grid nodes.

5. A method for spatiotemporal optimization of supply deployment for mobile energy storage vehicles according to claim 1, characterized in that: The power state constraint of the mobile energy storage vehicle is: In the formula, E m，t+1 is the power state of the mobile energy storage vehicle m at time t+1; E m，t is the power state of the mobile energy storage vehicle m at time t; is the charging power of mobile energy storage vehicle m at node a at time t; is the discharge power of mobile energy storage vehicle m at node a at time t; and is the charging efficiency and discharging efficiency of the mobile energy storage vehicle m; Δt is the length of time for charging and discharging between time t and time t+1; The lower limit of battery power for mobile energy storage vehicles; It is the upper limit of the battery power of mobile energy storage vehicles.

6. A method for spatiotemporal optimization of supply deployment for mobile energy storage vehicles according to claim 1, characterized in that: The moving path cost C move for: In the formula, is the moving cost of the mobile energy storage vehicle m from node a to node b at time t; d ab is the distance from node a to node b; c move is the unit distance cost; K m，ab，t is whether the mobile energy storage vehicle m chooses to move from node a to node b at time t; T is the total movement time; M is the number of mobile energy storage vehicles; R is the number of grid nodes; The battery loss cost C loss for: In the formula, l m，a，t is the capacity decay caused by the charging and discharging of the mobile energy storage vehicle m at node a at time t; c loss is the unit battery capacity attenuation cost; q is a fixed attenuation capacity of the battery per day; α and β are the battery capacity attenuation coefficients during charging and discharging, respectively; is the charging power of mobile energy storage vehicle m at node a at time t; is the discharge power of mobile energy storage vehicle m at node a at time t; The operation and maintenance cost C op for: C op =c E c E E total +g P c P P total ; In the formula, γ E and γ P are the energy storage energy operation and maintenance coefficient and the energy storage power operation and maintenance coefficient respectively; c E and c P are energy storage energy price and energy storage power price respectively; E total is the total energy capacity of the mobile energy storage vehicle system; P total is the total power capacity of the mobile energy storage vehicle system.

7. A method for spatiotemporal optimization of supply deployment for mobile energy storage vehicles according to claim 1, characterized in that: The objective function of the supply deployment spatiotemporal optimization model is: Where T is the total travel time; M is the number of mobile energy storage vehicles; R is the number of grid nodes; σ a，t is the electricity price at node a at time t; is the discharge power of mobile energy storage vehicle m at node a at time t; is the charging power of mobile energy storage vehicle m at node a at time t; C move is the moving path cost; C loss is the battery loss cost; C op For operation and maintenance costs.

8. A device for optimizing the supply deployment time and space of mobile energy storage vehicles, characterized in that: The device is applied to the method described in any one of claims 1 to 7, and the device comprises: The short-term electricity price forecasting model building module is used to capture the historical fluctuation pattern of electricity prices based on historical electricity price data using the autoregressive integrated moving average model to achieve short-term electricity price forecasting; A multi-layer spatiotemporal network model construction module is used to construct a multi-layer spatiotemporal network model representing the spatiotemporal mobility characteristics and charging and discharging status of mobile energy storage vehicles, where the mobile arc represents the movement of the mobile energy storage vehicle between grid nodes, and the fixed arc represents the energy exchange between the mobile energy storage vehicle and the distribution network; A cost model building module is used to build the charging and discharging power constraints and power state constraints of the mobile energy storage vehicle, and calculate the total cost generated by the mobile energy storage vehicle system during operation, the total cost including the moving path cost, battery loss cost and operation and maintenance cost; The optimal supply deployment solution module is used to calculate the total revenue of mobile energy storage vehicles through the predicted short-term electricity price, and to build a supply deployment spatiotemporal optimization model with the goal of maximizing the net revenue of mobile energy storage vehicle supply deployment in combination with the total cost, and to solve the optimal supply deployment plan for mobile energy storage vehicles.

9. A time-space optimization device for supply deployment of mobile energy storage vehicles, characterized in that: including memory and processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to any one of claims 1 to 7 according to instructions in the computer program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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