A power distribution network dispatching optimization method based on energy storage system

Abstract

This invention discloses a distribution network scheduling optimization method based on an energy storage system, belonging to the field of power system optimization and scheduling. With the goal of minimizing photovoltaic curtailment, load shedding, and the operating costs of mobile energy storage (MESS), a traffic-distribution network coupled optimization model is constructed. Based on the traffic uncertainty at each time period on the road, a constraint relationship is established between path state variables and driving state variables to simulate the traffic congestion delay of the MESS, while ensuring sufficient charging and discharging time for the MESS at each node. The coupling between the transportation network and the distribution network is achieved through the path state variables, driving state variables, and output power of the MESS. Traffic uncertainty is transformed into an opportunity constraint on congestion delay. By simulating the congestion delay of the MESS through path state variables and driving state variables, the driving behavior and power scheduling of the MESS are optimized, thereby improving the distribution network's capacity to support photovoltaic power generation and residential loads.

Description

Technical Field

[0001] This invention relates to the field of power system optimization scheduling, specifically a distribution network scheduling optimization method based on energy storage systems. Background Technology

[0002] Renewable energy sources, such as photovoltaic and wind power, pose significant challenges to power system dispatching due to their inherent randomness and undispatchability. Energy storage systems, with their load-shifting capabilities, can effectively alleviate these challenges. However, rapid economic and social development has driven a sharp increase in electricity demand, particularly in urban areas. While stationary energy storage systems are now widely used, during peak electricity demand periods, power injection from stationary energy storage systems and the main grid can lead to transmission congestion, thereby affecting power supply reliability. Therefore, to maintain operational stability, distribution system operators have to implement measures such as abandoning renewable energy and reducing load, resulting in economic losses. Thus, the core challenge facing distribution network dispatching remains the mismatch between renewable energy generation and load demand, as well as the limitations of the distribution network's own carrying capacity.

[0003] Against this backdrop, Mobile Energy Storage Systems (MESS), as an integrated new energy storage solution, have attracted widespread attention from the industry. This system integrates a transportation platform, containerized batteries, and a power electronic converter with four-quadrant operation capabilities. It can be controlled throughout the entire process by distribution system operators, with the converter acting as the interface between the batteries and the distribution network to enable bidirectional energy exchange. Compared to stationary energy storage systems, MESS offers flexible mobility, dynamically adjusting its deployment location based on the distribution network's operating status, renewable energy output, and load distribution. This provides greater flexibility for distribution network dispatching and demonstrates significant potential in enhancing the distribution network's carrying capacity.

[0004] However, the operation of MESS in urban environments is inevitably affected by traffic uncertainty. Traffic congestion-induced travel delays directly disrupt the charging and discharging schedules and dispatch rhythms of MESS, thus affecting its support for the distribution network. Most existing studies on MESS optimal dispatch do not fully consider this key factor of traffic uncertainty, making the dispatch schemes difficult to implement in practice. However, the few studies that do consider this factor fail to establish an effective coupling model between traffic delays and MESS dispatch, resulting in overly complex models with insufficient practicality, making them unsuitable for guiding engineering practice. Therefore, we propose a distribution network dispatch optimization method based on energy storage systems to alleviate or solve the above problems.

[0005] The information disclosed above in this background section is only for enhancing the understanding of the background section of this invention, and therefore may include prior art that is not known to those skilled in the art. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a distribution network scheduling optimization method based on an energy storage system. This method solves several issues in existing technologies, such as insufficient consideration of traffic uncertainty in MESS optimization scheduling, model complexity caused by uncertain traffic delays in a few studies that do consider this factor, and the inability to effectively alleviate the spatiotemporal mismatch between renewable energy and load, as well as the limited carrying capacity of the distribution network.

[0007] To achieve the above objectives, this invention provides a distribution network dispatch optimization method based on an energy storage system, comprising the following steps:

[0008] S1. Construct an objective function to minimize the costs of photovoltaic curtailment, load shedding, and mobile energy storage system operation. The objective function θ is:

[0009]

[0010] In the formula, C mess C pv and C load These represent the operating cost coefficients for the mobile energy storage system, the cost coefficients for forgoing photovoltaic power generation, and the cost coefficients for interrupting residential power supply; T represents the total dispatch period; Ω mess Ω road Ω sta and Ω bus These represent the sets of mobile energy storage systems, transportation network roads, transportation network nodes, and distribution network nodes, respectively; υω nm,t is the path state variable of the mobile energy storage system; σω m,t is the driving state variable of the mobile energy storage system; ppred pv,j,t and ppv,j,t are the maximum active power that photovoltaic power generation can generate and the actual photovoltaic active power absorbed by the distribution network, respectively; ppredd,j,t and pd,j,t are the active power demand of residential load and the active power actually provided to the load, respectively.

[0011] S2. Establish a distribution network dispatch model, including node power balance equations, voltage drop equations, and voltage and power amplitude constraints.

[0012] The power injected into each node of the distribution network includes:

[0013]

[0014] In the formula, p gen,j,t and q gen,j,t The active and reactive power injected by the generator into the distribution network; ppv,j,t Active power injected into the power distribution network by photovoltaic power generation; p mess,j,t and q mess,j,t The active and reactive power injected into the distribution network by the mobile energy storage system; p load,j,t and q load,j,t The active and reactive power absorbed by the load from the distribution network;

[0015] The power balance equations and voltage drop equations of the distribution network include:

[0016]

[0017] In the formula, r ij and x ij Represent the resistance and reactance of each branch respectively; i→j or (i, j) represents the set Ω branch One of the connections.

[0018] Constraints on bus voltage, generator power, and photovoltaic power output include:

[0019] .

[0020] S3. Establish a traffic network scheduling model for the mobile energy storage system. Based on traffic uncertainties, simulate the traffic congestion delay of the mobile energy storage system through path state variables υω nm,t and driving state variables σω m,t, and ensure the charging and discharging time of the mobile energy storage system at the nodes.

[0021] The simulation of traffic congestion delay includes:

[0022] The traffic delay data for each road at different times were fitted using a normal distribution, and the resulting distribution function is shown below:

[0023]

[0024] In the formula, the mean is tmean td,t, and the standard deviation is δtd,t. td,t And tmean td, where t is a non-negative integer;

[0025] The aforementioned traffic uncertainty can be transformed into an opportunity constraint:

[0026]

[0027]

[0028] In the formula, t td,t α represents the maximum permissible traffic delay, and α is the confidence level.

[0029] Based on traffic delay t td,tThe constraints for the established path state variable υω nm,t and driving state variable σω m,t are as follows:

[0030]

[0031] The above formula represents the future value of MESS vehicle ω when it chooses road (n, m) at time t. td,t You cannot choose a new road during this time period;

[0032]

[0033] The above formula indicates that when the ωth MESS vehicle selects road (n,m) in time period t, from time period t to the future t... td,t It was in motion during a certain period of time.

[0034] The operation of MESS in the transportation network also meets the following constraints:

[0035] Existence constraint: That is, at any time period t, an energy storage vehicle must and can only travel on one road or stop at one station; if m=n, it means that the energy storage vehicle stays at station n during time period t.

[0036] Flow conservation constraint:

[0037] That is, when an energy storage vehicle arrives at station m in time period t, it will inevitably depart from station m at time t+1 to go to other stations or stay at this station.

[0038] Journey closed-loop constraint:

[0039]

[0040]

[0041] An energy storage vehicle must depart from a garage during the day and return to the garage after completing its task. Let the garage station be m. init During the first and last time slots of each day, T, one must depart from and arrive at the garage.

[0042] S4. Construct coupling constraints between the transportation network and the distribution network. Constrain the energy exchange between the mobile energy storage system and the distribution network by using the path state variable υωnm,t and the travel state variable σωm,t of the mobile energy storage system.

[0043] The coupling constraints include the grid connection state constraints of MESS:

[0044]

[0045]

[0046] In the formula, pωmes,m,t and qωmes,m,t are the active and reactive power injected into the distribution network by the ω-th MESS from node m in the t-th time period; pωch,m,t and pωdch,m,t are the active power of the MESS during charging and discharging, respectively; and qωch / dch,m,t is the reactive power of the MESS.

[0047] It also includes MESS charge / discharge state constraints, through the auxiliary variable β t accomplish:

[0048]

[0049]

[0050]

[0051] In the formula, β t β is an auxiliary variable for charge / discharge state. t When β = 0, MESS is in a charging state. t When =1, MESS is in a discharging state; p m,max q m,max These are the maximum active power and maximum reactive power of MESS, respectively.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] This invention introduces path state variables and driving state variables, and constructs a constraint relationship between the two, transforming traffic uncertainty into an opportunity constraint on congestion delay. It can simulate traffic congestion delay in MESS without the need for complex uncertainty solving algorithms, thus solving the problem of model complexity and lack of practicality caused by considering traffic uncertainty, making the model easier to implement in engineering.

[0054] This invention aims to minimize photovoltaic curtailment, load reduction, and MESS operating costs. By optimizing the MESS's operating path and charging / discharging status, it achieves energy matching between photovoltaic power generation and residential loads in time and space.

[0055] This invention establishes coupling constraints between the path state variables, driving state variables, and output power of the MESS (Mechanical Energy Storage System), achieving deep collaboration between the transportation network and the distribution network. This allows the MESS scheduling to adapt to actual traffic operations while accurately responding to changes in power supply and demand in the distribution network. Furthermore, the coupling mechanism effectively alleviates transmission congestion caused by the joint injection of stationary energy storage with the main grid, enhances the distribution network's capacity to support photovoltaic power generation and residential loads, and ensures the stability of distribution network operation and the reliability of power supply.

[0056] This invention clarifies the travel and stopping states of the MESS by constraining path state variables and travel state variables, ensuring that the MESS has sufficient charging and discharging time after arriving at the node, thus avoiding the problem of insufficient charging and discharging due to traffic delays. Simultaneously, by constraining the charging and discharging states of the MESS through auxiliary variables, charging and discharging conflicts are avoided, improving the accuracy and effectiveness of MESS scheduling.

[0057] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0058] Figure 1 This is a flowchart of a power distribution network scheduling optimization method based on an energy storage system according to the present invention.

[0059] Figure 2 This is a schematic diagram of the 33-node distribution network topology and charging / discharging station distribution in Embodiment 2 of the present invention.

[0060] Figure 3 This is a voltage diagram of a photovoltaic power station in scenario one of embodiment two of the present invention.

[0061] Figure 4 This is a residential load voltage diagram for scenario one in embodiment two of the present invention.

[0062] Figure 5 This is a diagram of abandoned photovoltaic power and load shedding power in scenario one of embodiment two of the present invention.

[0063] Figure 6 This is a diagram showing the traffic delay result in scenario two of embodiment two of the present invention.

[0064] Figure 7 This is a MESS operation route diagram for scenario two in embodiment two of the present invention.

[0065] Figure 8 This is a diagram of abandoned photovoltaic power, load shedding, and MESS power in scenario two of embodiment two of the present invention.

[0066] Figure 9 This is a MESS state-of-charge diagram for scenario two in embodiment two of the present invention.

[0067] Figure 10 This is a diagram showing the traffic delay result in scenario three of embodiment two of the present invention.

[0068] Figure 11 This is a MESS operation route diagram for scenario three in embodiment two of the present invention.

[0069] Figure 12 This is a diagram of abandoned photovoltaic power, load shedding, and MESS power in scenario three of embodiment two of the present invention.

[0070] Figure 13 This is a MESS state-of-charge diagram for scenario three in embodiment two of the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. It should be noted that the drawings are schematic and not illustrated to scale. For clarity and convenience, the relative sizes and proportions of the parts shown in the drawings have been exaggerated or reduced in size. Any size is only illustrative and not limiting.

[0072] Example 1

[0073] A distribution network dispatch optimization method based on energy storage system, as shown in the appendix. Figure 1 As shown, it includes the following steps:

[0074] S1. Construct the objective function. The objective function θ is defined as minimizing the costs of photovoltaic curtailment, load shedding, and the operation of mobile energy storage systems.

[0075]

[0076] In the formula, C mess C pv and C load These represent the operating cost coefficients for the mobile energy storage system, the cost coefficients for forgoing photovoltaic power generation, and the cost coefficients for interrupting residential power supply; T represents the total dispatch period; Ω mess Ω road Ω sta and Ω bus These represent the sets of mobile energy storage systems, transportation network roads, transportation network nodes, and distribution network nodes, respectively; υω nm,t is the path state variable of the mobile energy storage system; σω m,t is the driving state variable of the mobile energy storage system; ppred pv,j,t and ppv,j,t are the maximum active power that photovoltaic power generation can generate and the actual photovoltaic active power absorbed by the distribution network, respectively; ppredd,j,t and pd,j,t are the active power demand of residential load and the active power actually provided to the load, respectively.

[0077] S2. Establish a distribution network dispatching model. Among them,

[0078] The power injected into each node of the distribution network includes:

[0079]

[0080] In the formula, p gen,j,t and q gen,j,t The active and reactive power injected by the generator into the distribution network; p pv,j,t Active power injected into the power distribution network by photovoltaic power generation; p mess,j,t and q mess,j,t The active and reactive power injected into the distribution network by the mobile energy storage system; p load,j,t and q load,j,t The active and reactive power absorbed by the load from the distribution network;

[0081] The power balance equations and voltage drop equations of the distribution network include:

[0082]

[0083] In the formula, r ij and x ij Represent the resistance and reactance of each branch respectively; i→j or (i, j) represents the set Ω branch One of the connections. That is, from bus i to bus j.

[0084] Constraints on bus voltage, generator power, and photovoltaic power output include:

[0085] .

[0086] S3. Establish a transportation network scheduling model for mobile energy storage systems (MESS). The operation of MESS within the transportation network must satisfy three basic constraints:

[0087] Existence constraint: That is, at any time period t, an energy storage vehicle must and can only travel on one road or stop at one station; if m=n, it means that the energy storage vehicle stays at station n during time period t.

[0088] Flow conservation constraint:

[0089] That is, when an energy storage vehicle arrives at station m in time period t, it will inevitably depart from station m at time t+1 to go to other stations or stay at this station.

[0090] Journey closed-loop constraint:

[0091]

[0092]

[0093] In the above formula, an energy storage vehicle must depart from the garage during the day and return to the garage after completing its task for the day. Let the garage station be m. initDuring the first and last time slots of each day, T, one must depart from and arrive at the garage.

[0094] Subsequently, path state variable υω nm,t and driving state variable σω m,t are added to represent the current road selection and traffic congestion status of MESS, respectively. A normal distribution is then used to fit the time-segmented traffic delay data for each road, and the resulting distribution function is shown below:

[0095]

[0096] In the formula, the mean is tmean td,t, and the standard deviation is δtd,t. td,t And tmean td, where t is a non-negative integer;

[0097] The aforementioned traffic uncertainty can be transformed into an opportunity constraint:

[0098]

[0099]

[0100] In the formula, t td,t α represents the maximum permissible traffic delay, and α is the confidence level.

[0101] According to traffic delay t td,t Establish constraints between the path state variable υω nm,t and the travel state variable σω m,t. Assume that under congestion-free conditions, the MESS travels on all roads in only one time period. When congestion occurs, if the ω-th MESS chooses to travel from node n to node m in time period t, then this MESS will... td,t No new roads can be selected during a given time period, and the resulting constraints are as follows:

[0102]

[0103] When there is a traffic jam, if the ωth MESS vehicle chooses to travel from node n to node m in time period t, then in time period t and in the future t td,t The constraints are as follows, given that the vehicle is in a driving state during a given time period:

[0104]

[0105] S4. Construct coupling constraints between the transportation network and the distribution network to achieve coupling between them. The grid connection state of the MESS is constrained by the path state variable υω nm,t and the travel state variable σω m,t. The constructed constraints are as follows:

[0106]

[0107]

[0108] In the formula, pωmes,m,t and qωmes,m,t represent the active and reactive power injected into the distribution network by the ω-th MESS from node m in time period t; pωch,m,t and pωdch,m,t represent the active power of the MESS during charging and discharging, respectively; and qωch / dch,m,t represents the reactive power of the MESS. According to the formula, when υωmm,t=1 and σωm,t=0, the MESS is in a driving state, and the power injected into the distribution network by the MESS is forcibly constrained to 0. When υωmm,t=1 and σωm,t=1, the MESS has reached node m, and at this time, the MESS is connected to the grid and can exchange energy with the distribution network.

[0109] Add auxiliary variable β t The constraints for the charging and discharging states of MESS are as follows:

[0110]

[0111]

[0112]

[0113] According to the above formula, the maximum active power and maximum reactive power of each MESS vehicle are respectively p m,max and q m,max When β t When β = 1, MESS is in a discharging state, and the active power of charging is forcibly constrained to 0. t When the value is 0, the MESS is in a charging state, and the active power of discharge is forcibly constrained to 0.

[0114] Example 2

[0115] This embodiment uses a 33-node distribution network as the test system, and its topology and charging / discharging station distribution are as follows: Figure 2 As shown in the figure, this distribution network is connected to the main grid through one node. The system includes photovoltaic power generation units and three types of loads: industrial load, agricultural load, and residential load. Among them, the power demand of industrial load remains stable throughout the day without significant peak-valley fluctuations; agricultural load only has power demand during the day and no load consumption at night; residential load has low power demand during the day and significantly higher power demand at night, exhibiting a typical daytime low and nighttime high power demand characteristic.

[0116] There are a total of 4 charging and discharging stations in the distribution network. Station No. 1 is the main station and also serves as a MESS parking garage. Stations No. 2-4 are distributed charging and discharging stations, corresponding to the residential load concentration area and the photovoltaic power generation concentration area, respectively.

[0117] The day is divided into 96 time periods. Under congestion-free conditions, a MESS can travel between any two stations in just one time period. Each MESS has a rated active power of 500 kW, a rated reactive power of 500 kVar, and a rated capacity of 8 MWh. The maximum and minimum state of charge (SOC) of the MESS are 0.9 and 0.1, respectively.

[0118] The cost of abandoning solar power is $500 / MWh, the cost of load shedding is $1000 / MWh, and the operating cost of each MESS vehicle is $25 / per period.

[0119] This embodiment sets up three scenarios: Scenario 1, the distribution network is not equipped with MESS; Scenario 2, the distribution network is equipped with MESS but traffic uncertainty is not considered; Scenario 3, the distribution network is equipped with MESS and traffic uncertainty is considered. All scenarios have the same photovoltaic output and the same load demand. Scenario 2 and Scenario 3 are each equipped with 5 MESS vehicles. Based on each scenario, a scheduling scheme is generated using the traffic distribution network coupling optimization model constructed in this invention.

[0120] Scenario 1: Without MESS, the bus voltage and power dispatch results of the distribution network are shown in the attached figure. Figure 3 - Appendix Figure 5 As shown, photovoltaic (PV) power generation peaks at midday, while residential load demand is highest in the evening. Due to bus voltage constraints, some PV output and load demand are either curtailed or shelved. The PV power absorbed by the distribution network equals the available PV output minus the curtailed PV output. The load supplied by the distribution network equals the load demand minus the shelved load. The costs of curtailment and load shelving are $3604.5 and $5098.7 respectively, totaling $8703.2.

[0121] Scenario 2: With MESS deployed but traffic uncertainties not considered, all MESS are scheduled according to predetermined traffic delay times. The scheduling results are shown in the attached figure. Figure 6 - Appendix Figure 9 As shown. Without considering traffic uncertainties, the attached... Figure 6 Traffic delay for each time period is the mean of the normal distribution corresponding to that time period. (See attached image.) Figure 7As shown, since there is no photovoltaic output before 5:00 AM, all MESS (Mobile Energy Storage System) systems depart at 5:00 AM, having previously been waiting in the garage. MESS system 1 shuttles between the garage and station 4, while MESS systems 3 and 4 shuttle between the garage and station 3. Mobile energy storage system 2 first travels to station 3, then proceeds to station 2, and finally returns to the garage. Mobile energy storage system 5 first arrives at station 4, then moves to station 2, and finally returns to the garage. As traffic uncertainties are not considered, traffic delays from noon to evening are evenly distributed. Therefore, MESS systems tend to depart from the photovoltaic power station as late as possible to absorb more photovoltaic energy. (See attached diagram) Figure 8 As shown, the five MESS vehicles, with their ample capacity, fully utilized the photovoltaic power and ensured that all loads were supplied. The resulting curtailment cost, load shedding cost, and MESS transportation cost were $0, $0, and $125 respectively, totaling $125. According to the attached... Figure 5 With appendix Figure 8 In comparison, the total discharge power of MESS from 16:45 to 17:00 was 0.8 MW, lower than the 1 MW required by the distribution network without MESS. This phenomenon is due to the voltage regulation capability provided by the reactive power compensation of MESS; the missing power is provided by the substation at node 1. Therefore, MESS can transport surplus photovoltaic power back to the garage as an emergency reserve. (See attached...) Figure 9 As shown, MESS No. 3 eventually returned to the garage with all the stored energy, while MESS No. 1 and No. 4 only brought back a small amount of remaining energy.

[0122] Scenario 3: When considering traffic uncertainty, traffic delays fluctuate drastically. MESS scheduling must weigh transportation costs against the penalty costs of curtailing light and load shedding. The scheduling results are attached. Figure 10 - Appendix Figure 13 As shown. (Attached) Figure 10 The traffic delay for each time period is obtained by randomly generating values ​​from a normal distribution and rounding them. Severe congestion occurs from 14:15-15:00 (4 time periods) and 15:15-16:00 (4 time periods). (See attached image) Figure 11 As shown, MESS devices 2 and 5 avoided these times, while mobile energy storage systems 1 and 4 chose to depart during these periods; MESS device 3 remained charged. Ultimately, MESS devices 2 and 4 arrived at site 2 and began supplying power at 16:15. MESS devices 1 and 5 supplied power from site 1 (the garage). (See attached image.) Figure 12As shown, although all MESS vehicles provided reactive power support, a load shedding of 3.2 kW still occurred within 15 minutes. The costs were: curtailment cost $0, load shedding cost $0.8, transportation cost $156.3, totaling $157.1. If three MESS vehicles need to arrive at station 1 or 2 before 16:15, MESS vehicle 1 or 5 must depart earlier, but the increased transportation costs will exceed the $0.8 load shedding penalty. (See attached...) Figure 13 As shown, in this scenario, two MESS vehicles return to the garage with remaining energy. Which vehicle has remaining energy depends on the traffic congestion situation that day.

[0123] In summary, this invention transforms traffic uncertainty into an opportunity constraint on congestion delay. By simulating the congestion delay of MESS through path state variables and driving state variables, it optimizes the driving behavior and power scheduling of MESS, thereby improving the distribution network's capacity to support photovoltaic power generation and residential loads.

[0124] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0125] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A distribution network dispatch optimization method based on an energy storage system, characterized in that, Includes the following steps: S1. Construct an objective function to minimize the costs of photovoltaic curtailment, load shedding, and mobile energy storage system operation. The objective function θ is: In the formula, C mess C pv and C load These represent the operating cost coefficients for the mobile energy storage system, the cost coefficients for forgoing photovoltaic power generation, and the cost coefficients for interrupting residential power supply; T represents the total dispatch period; Ω mess Ω road Ω sta and Ω bus These represent the sets of mobile energy storage systems, transportation network roads, transportation network nodes, and distribution network nodes, respectively; υω nm,t is the path state variable of the mobile energy storage system; σω m,t is the driving state variable of the mobile energy storage system; ppred pv,j,t and p pv,j,t are the maximum active power that photovoltaic power generation can generate and the actual photovoltaic active power absorbed by the distribution network, respectively; ppred d,j,t and pd,j,t are the active power demand of residential load and the active power actually provided to the load, respectively. S2. Establish a distribution network dispatch model, including node power balance equations, voltage drop equations, and voltage and power amplitude constraints; S3. Establish a traffic network scheduling model for the mobile energy storage system. Based on traffic uncertainty, simulate the traffic congestion delay of the mobile energy storage system through path state variable υωnm,t and driving state variable σωm,t, and ensure the charging and discharging time of the mobile energy storage system at the nodes. S4. Construct coupling constraints between the transportation network and the distribution network. Constrain the energy exchange between the mobile energy storage system and the distribution network by using the path state variable υω nm,t and the driving state variable σω m,t of the mobile energy storage system.

2. The distribution network dispatch optimization method based on energy storage system according to claim 1, characterized in that, The power injected into each node of the distribution network includes: In the formula, p gen,j,t and q gen,j,t The active and reactive power injected by the generator into the distribution network; p pv,j,t Active power injected into the power distribution network by photovoltaic power generation; p mess,j,t and q mess,j,t The active and reactive power injected into the distribution network by the mobile energy storage system; p load,j,t and q load,j,t The active and reactive power absorbed by the load from the distribution network; The power balance equations and voltage drop equations of the distribution network include: In the formula, r ij and x ij Represent the resistance and reactance of each branch respectively; i→j or (i, j) represents the set Ω branch One of the connections.

3. The distribution network dispatch optimization method based on energy storage system according to claim 1, characterized in that: In S2, the constraints on bus voltage, generator power, and photovoltaic power output include: 。 4. The distribution network dispatch optimization method based on energy storage system according to claim 1, characterized in that: In S3, the path state variable υω nm,t and the driving state variable σω m,t are defined as follows: When υω nm,t=1 and n≠m, it means that the ωth mobile energy storage system chooses to take the road (n,m) in the time period t, that is, it leaves node n and drives towards node m; When υω nm,t=1 and n=m, it means that the ωth MESS did not select a road in the time period t, that is, the ωth MESS is related to node m in the time period t. Whether it is in motion or has arrived is determined by the value of the driving state variable σω m,t. When υω nm,t=0, it means that the mobile energy storage system does not exist on the road (n,m) or at node m; When σωm,t is 0, it means that the ωth mobile energy storage system is heading towards node m in time period t; When σωm,t is 1, it means that node m has been reached.

5. The distribution network dispatch optimization method based on an energy storage system according to claim 1, characterized in that, In S3, the simulation of traffic congestion delay includes: The traffic delay data for each road at different times were fitted using a normal distribution, and the resulting distribution function is shown below: In the formula, the mean is tmean td,t, and the standard deviation is δtd,t. td,t And tmean td, where t is a non-negative integer; The aforementioned traffic uncertainty can be transformed into an opportunity constraint: In the formula, t td,t α represents the maximum permissible traffic delay, and α is the confidence level.

6. The distribution network dispatch optimization method based on an energy storage system according to claim 1, characterized in that, In S3, based on traffic delay t td,t The constraints for the established path state variable υω nm,t and driving state variable σω m,t are as follows: The above formula indicates that when the ωth mobile energy storage system selects road (n, m) in time period t, the future t... td,t You cannot choose a new road during this time period; The above formula indicates that when the ωth MESS vehicle selects road (n,m) in time period t, from time period t to the future t... td,t It was in motion during a certain period of time.

7. The distribution network dispatch optimization method based on energy storage system according to claim 1, characterized in that, In S4, the coupling constraints include grid connection state constraints for the mobile energy storage system: In the formula, pωmes,m,t and qωmes,m,t are the active and reactive power injected into the distribution network from node m by the ωth mobile energy storage system in the t-th time period; pωch,m,t and pωdch,m,t are the active power of MESS charging and discharging, respectively; and qωch / dch,m,t is the reactive power of the mobile energy storage system.

8. The distribution network dispatch optimization method based on energy storage system according to claim 1, characterized in that, S4 also includes constraints on the charge and discharge states of mobile energy storage systems, using the auxiliary variable β. t accomplish: In the formula, β t β is an auxiliary variable for charge / discharge state. t When β = 0, the mobile energy storage system is in a charging state. t When p = 1, the mobile energy storage system is in a discharging state; m,max q m,max These represent the maximum active power and maximum reactive power of the mobile energy storage system, respectively.

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