Reactive compensation method for cooperative mobile energy storage and conventional energy storage configuration

Abstract

The invention discloses a reactive compensation method for cooperative mobile energy storage and conventional energy storage configuration, and belongs to the field of energy storage. The method comprises the following three core steps: firstly, constructing an MESS motorcade driving model which covers a road condition matrix, position constraint, operation constraint and power output constraint, and ensuring the moving and power scheduling feasibility of the MESS; and secondly, establishing a coordination power grid reactive power optimization model, integrating power limitation and load flow calculation of the DG, the static reactive power compensator and the fixed energy storage system, and realizing multi-resource coordination. And finally, considering load and DG prediction errors, and constructing a robust variant of a coordination network recovery model so as to enhance the recovery capability of the active power distribution network. The problem that a traditional fixed reactive power compensation device is flexible when dealing with load fluctuation and output uncertainty of a distributed power supply is solved. According to the method, dynamic reactive power optimization is realized by integrating space-time maneuverability of a mobile energy storage system and continuity of fixed energy storage.

Description

Technical Field

[0001] This invention relates to the field of energy storage, specifically to a reactive power compensation method for collaborative mobile energy storage and conventional energy storage configurations. Background Technology

[0002] In distribution networks and areas with high penetration of new energy sources, load fluctuations and distributed power output exhibit significant spatiotemporal uncertainties, leading to dynamic changes in reactive power demand points and quantities. Existing technologies mainly rely on devices such as static var compensators (SVCs) and capacitor banks installed in substations or at fixed nodes. These devices have fixed response speeds, compensation capacities, and locations, making it difficult to cope with rapid and dispersed reactive power deficits. Especially in weak links of the grid or temporary heavy load scenarios, conventional fixed reactive power compensation devices lack flexibility and adaptability, easily leading to over-compensation or under-compensation problems, resulting in voltage exceeding limits and increased network losses.

[0003] Mobile energy storage systems, due to their spatial and temporal flexibility, have played a role in active power services such as load shaving and valley filling, emergency power supply, and improving power supply reliability. However, current optimization of mobile energy storage operations mostly focuses on active power dispatch and energy transfer. While the power electronic converters they are equipped with possess rapid and continuous reactive power regulation capabilities, this potential is often overlooked or treated as an auxiliary function in existing application models. This lack of systematic and coordinated planning and dispatch with the dynamic reactive power demand of the power grid has resulted in the idleness of high-quality regulation resources.

[0004] Existing reactive power compensation configuration methods typically consider various compensation devices independently. On the one hand, the configuration of conventional stationary energy storage may not fully consider the possibility of reactive power synergy with mobile energy storage; on the other hand, there is a lack of an effective coordinated scheduling strategy to unify and optimize the "spatial mobility" of mobile energy storage, the "temporal continuity" of stationary energy storage, and the compensation characteristics of traditional reactive power equipment. This results in the overall system efficiency failing to reach its optimal level in terms of global voltage control, minimizing network losses, and maximizing equipment utilization. Therefore, there is an urgent need for a configuration and operation method that can comprehensively plan and coordinate the reactive power output of both energy storage forms in real time. Summary of the Invention

[0005] To address the aforementioned shortcomings in the existing technology, this invention provides a reactive power compensation method for coordinated mobile energy storage and conventional energy storage configurations.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A reactive power compensation method for coordinated mobile energy storage and conventional energy storage configurations includes the following steps: S1. Construct a MESS fleet driving model and establish constraints; S2. Construct a coordinated power grid reactive power optimization model based on the MESS fleet driving model, including power constraints and power flow calculations for distributed power sources, static var compensators, and stationary energy storage systems. S3. Construct a coordinated network recovery model that considers the uncertainty of load and DG output prediction, and obtain a robust variant of the coordinated network recovery model to restore the reactive power deficit of the active distribution network.

[0007] Furthermore, the feature is that the expression for the MESS fleet transportation road conditions and relative mileage model is constructed in S1:

[0008]

[0009]

[0010] In the formula, express t The road network state matrix at any given time. express t The relative distance traveled between time node m and node n This represents the distance between node m and node n. This indicates the degree of congestion between node m and node n at time t. This represents the number of vehicles at node m and node n at time t. This represents the number of vehicles that started blocking between node m and node n at time t.

[0011] Furthermore, the constraints in S1 include fleet position constraints, operational constraints, and power output constraints, wherein: The vehicle position constraints are represented as follows:

[0012]

[0013]

[0014]

[0015] Among them, when When = 1, the m-th MESS is located at node i at time t; when When = 0, the m-th MESS is not located at node i at time t; This represents the initial state of MESS; The total number of MESS; The total number of nodes; This indicates that the m-th MESS does not move at the initial time. This indicates that the m-th MESS does not move at the final moment; Total time; Let m be the movement state of the m-th MESS at time t; The operational constraints are expressed as follows:

[0016]

[0017]

[0018]

[0019]

[0020] in, =1 indicates that the m-th MESS begins its journey at time t; =0 indicates that the m-th MESS stops moving at time t; =1 indicates that the m-th MESS stops moving at time t; =0 indicates that the m-th MESS begins its journey at time t; This represents the maximum travel frequency of the MESS. The energy provided by the m-th MESS at time point t; Let T be the driving state of MESS at time point t'; T is the set of all time points t. The output power constraint is expressed as:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] in, This represents the upper limit of the power of the m-th MESS; Let be the power of the m-th MESS at time t; Let be the total output power of MESS at node i at time t; Let m be the state of charge of the battery at the initial moment; The state of charge of the battery for the m-th MESS at a set time; Let m be the state of charge of the battery at time t; The charge / discharge efficiency of the m-th MESS; Let m be the battery capacity of the m-th MESS. Δt For time intervals; This is the state-of-charge limit for the m-th MESS battery; This represents the upper limit of the state of charge of the m-th MESS battery.

[0028] Furthermore, step S2 specifically includes the following steps: S21. According to the formula:

[0029] Calculate the maximum sum of predicted power at each node in the active distribution network, where, It is the maximum value of the sum of the predicted power of the i-th node; This represents the total number of nodes in an active distribution network. bi Let be the weight factor of the i-th node; Let be the active power consumed by the actual load of the i-th node at time t; S22. Construct DG power output constraints, including active power limits:

[0030]

[0031] Reactive power range and complex power constraints:

[0032]

[0033]

[0034]

[0035]

[0036] In the formula, Let be the active power of the i1th DG at time t; Let be the power generation efficiency of the i1th DG at time t; Ψ is the rated complex power of the i1th DG; DG Ψ represents the total number of DG. T φ is the total time; φ is the minimum power factor angle of the DG. Let be the reactive power of the i1th DG at time t; The maximum power factor angle of the DG; S23. Construct the reactive power output constraint for SVC, expressed as:

[0037] in, Let be the reactive power of the i2th static var compensator at time t; This is the lower limit of the reactive power of the i2th static var compensator at time t; Ψ is the upper limit of the reactive power of the i2th static var compensator at time t; SVC This represents the total number of static var compensators. S24. Construct the power output constraint for a stationary energy storage system, expressed as:

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] in, This indicates whether the i-th stationary energy storage system is charging at time t. =1 indicates a charging state. =0 indicates a non-charging state; This indicates whether the i-th stationary energy storage system is in a discharging state at time t. =1 indicates a discharge state. =0 indicates a non-discharge state; This refers to the total number of stationary energy storage systems. Let be the actual charging power of the i-th stationary energy storage system at time t; This is the lower limit of the charging power of the i-th stationary energy storage system at time t; The upper limit of the charging power of the i3rd stationary energy storage system at time i; Let be the actual discharge power of the i-th stationary energy storage system at time t; This is the lower limit of the discharge power of the i-th stationary energy storage system at time t; Let be the upper limit of the discharge power of the i3rd stationary energy storage system at time t; The initial energy storage state of the i3rd fixed energy storage system; The preset energy storage state for the i3rd fixed energy storage system at the initial moment; Let t represent the energy storage state of the i-th fixed energy storage system at time t. Let be the charging efficiency of the i3rd stationary energy storage system; Δt is the time interval between charging and discharging of the stationary energy storage system. Let be the discharge efficiency of the i3rd stationary energy storage system; The rated energy storage for the i3rd stationary energy storage system; This is the lower limit of the energy storage state of the i3rd stationary energy storage system; This represents the upper limit of the energy storage state of the i3rd stationary energy storage system; S25. Construct power flow constraints, represented as:

[0044]

[0045] in, Let be the active power flowing from node k to node i at time t; bi is the weighting factor of node i. Let be the active power flowing from node i to node j at time t; and Each represents the set of nodes connected to the i-th node; ΨE is the total number of branches from node i to node j. Let be the reactive power flowing from node i to node j at time t; Let be the reactive power flowing from node k to node i at time t; The closed or open state of the branch formed between the i-th node and the j-th node; =1 indicates that the branch is closed; =0 indicates that the branch is open; Let be the voltage of the i-th node at time t; Let be the voltage of the j-th node at time t; Let be the resistance between the i-th node and the j-th node; Let M be the reactance between the i-th node and the j-th node; M is a constant. Let be the power factor of the load at node i; S26. Construct network security constraints, represented as follows:

[0046]

[0047]

[0048] in, Vi represents the maximum complex power capacity from node i to node j; Vi is the lower voltage limit of the i-th node; Vi is the upper voltage limit of the i-th node; S27. Construct topological constraints, represented as follows:

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] in, For line k i The active power; Let be the active power of line ij; This represents the net load from DG to Sub node i; This is the set of sub-station nodes; For the number of microgrids, when When the value is 1, there are no microgrids in the distribution network; Let be the capacity between lines ij; |·| represents the cardinality of the set.

[0058] Furthermore, step S3 specifically includes the following steps: S31. Calculate the load demand and DG output forecast values; S32. Construct the ellipsoidal uncertainty set of load forecasting error and DG forecasting error; S33. Construct a robust variant expression for the coordination network recovery model.

[0059] Furthermore, the specific calculation method for calculating the load demand and DG output forecast values ​​in S31 is as follows:

[0060]

[0061] in, Let be the average active power consumed by the actual load of the i-th node at time t; The prediction error is the actual active power consumed by the i-th node at time t. Let be the average active power of the i1th DG at time t; Let be the prediction error of the active power of the i1th DG at time t.

[0062] Furthermore, the ellipsoidal uncertainty sets of the load prediction error and the DG prediction error in S32 are expressed as follows:

[0063]

[0064] in, The ellipsoidal uncertainty set of the load forecast error; Let be the ellipsoidal uncertainty set of the DG prediction error; Uncertain budget for the load; For DG's uncertain budget; Let be the covariance matrix of the load forecasting error; Let be the covariance matrix of the i1th DG prediction error; The radial uncertainty of the load; Let be the radial uncertainty of DG.

[0065] Furthermore, the robust variant expression of the coordination network recovery model in S33 is:

[0066]

[0067] .

[0068] The present invention has the following beneficial effects: This invention significantly enhances the spatiotemporal flexibility and dynamic response capability of reactive power compensation in the power grid. By incorporating mobile energy storage with "spatial mobility" into the reactive power compensation system and coordinating its configuration and scheduling with fixed-location conventional energy storage, it can proactively track and respond to dynamically changing reactive power demand hotspots in the power grid. This allows compensation resources to "flow on demand," effectively solving the problem of insufficient adaptability of traditional fixed reactive power compensation devices in dealing with distributed energy sources and fluctuating loads, and achieving precise and rapid voltage support for the entire power grid, especially in weak links and temporary heavy-load areas.

[0069] This invention deeply explores and efficiently utilizes the reactive power regulation potential of mobile energy storage, improving the overall efficiency of resource utilization. Breaking away from the traditional model where mobile energy storage primarily provides active power services, this invention, through a collaborative optimization model, actively and systematically schedules its grid-connected converter to participate in reactive power regulation while performing tasks such as energy transfer and emergency power supply. This transforms mobile energy storage into a "mobile multi-functional power regulation unit" without increasing additional hardware investment, significantly enhancing the technical and economic value of mobile energy storage assets and avoiding the idleness of its reactive power regulation capabilities.

[0070] This invention achieves global optimization through multi-resource collaboration, enhancing the safety and economy of power grid operation. The configuration and operation method proposed in this invention does not simply superimpose mobile energy storage, conventional energy storage, and traditional reactive power equipment. Instead, it establishes a unified optimization model to coordinate the complementary characteristics of various resources on both temporal (e.g., the continuity of conventional energy storage versus the temporary nature of mobile energy storage) and spatial scales. This collaborative mechanism can minimize active power losses and optimize voltage quality across the entire network while meeting system voltage safety constraints. Simultaneously, by optimizing the output allocation of each device, it extends equipment lifespan, reduces overall system operation and investment costs, and achieves a comprehensive operational goal of safety, economy, and efficiency. Attached Figure Description

[0071] Figure 1 This is a schematic diagram of the reactive power compensation method for the coordinated mobile energy storage and conventional energy storage configuration of the present invention.

[0072] Figure 2 This is a schematic diagram of reactive power optimization configuration of a stationary energy storage system according to an embodiment of the present invention.

[0073] Figure 3 This is a schematic diagram of the reactive power optimization configuration of mobile energy storage according to an embodiment of the present invention.

[0074] Figure 4 This is a voltage timing distribution diagram of IEEE 33 nodes according to an embodiment of the present invention. Detailed Implementation

[0075] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0076] A reactive power compensation method for coordinated mobile energy storage and conventional energy storage configurations, such as... Figure 1 As shown, it includes: Construct a MESS fleet driving model; Construct a coordinated power grid reactive power optimization model based on the MESS fleet driving model; A coordinated network recovery model considering the uncertainties of load and DG output forecasts is constructed, resulting in a robust variant of the coordinated network recovery model to recover reactive power deficits in active distribution networks.

[0077] A reactive power compensation method for coordinated mobile energy storage and conventional energy storage configurations, characterized in that the construction of the MESS fleet driving model includes: Constructing the model expression for MESS fleet transportation road conditions and relative mileage:

[0078]

[0079]

[0080] in, express t The road network state matrix at any given time. express t The relative distance traveled between time node m and node n This represents the distance between node m and node n. This indicates the degree of congestion between node m and node n at time t. This represents the number of vehicles at node m and node n at time t. This represents the number of vehicles that started blocking between node m and node n at time t.

[0081] Constructing the MESS fleet position constraints:

[0082]

[0083]

[0084]

[0085] Among them, when When = 1, the m-th MESS is located at node i at time t; when When = 0, the m-th MESS is not located at node i at time t; This represents the initial state of MESS; The total number of MESS; The total number of nodes; This indicates that the m-th MESS does not move at the initial time. This indicates that the m-th MESS does not move at the final moment; Total time; Let m be the movement state of the m-th MESS at time t; Constructing MESS runtime constraints:

[0086]

[0087]

[0088]

[0089]

[0090] in, =1 indicates that the m-th MESS begins its journey at time t; =0 indicates that the m-th MESS stops moving at time t; =1 indicates that the m-th MESS stops moving at time t; =0 indicates that the m-th MESS begins its journey at time t; This represents the maximum travel frequency of the MESS. The energy provided by the m-th MESS at time point t; Let T be the driving state of MESS at time point t'; T is the set of all time points t. Constructing MESS power output constraints:

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097] in, This represents the upper limit of the power of the m-th MESS; Let be the power of the m-th MESS at time t; Let be the total output power of MESS at node i at time t; Let m be the state of charge of the battery at the initial moment; The state of charge of the battery for the m-th MESS at a set time; Let m be the state of charge of the battery at time t; The charge / discharge efficiency of the m-th MESS; Let m be the battery capacity of the m-th MESS. Δt For time intervals; This is the state-of-charge limit for the m-th MESS battery; This represents the upper limit of the state of charge of the m-th MESS battery.

[0098] A coordinated network recovery model is constructed based on the MESS fleet driving model, including: The maximum sum of predicted power at each node in an active distribution network is calculated using the following formula:

[0099] in, It is the maximum value of the sum of the predicted power of the i-th node; This represents the total number of nodes in an active distribution network. bi Let be the weight factor of the i-th node; Let be the active power consumed by the actual load of the i-th node at time t; Constructing DG power output constraints:

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106] in, Let be the active power of the i1th DG at time t; Let be the power generation efficiency of the i1th DG at time t; Ψ is the rated complex power of the i1th DG; ΨDG is the total number of DGs; ΨT is the total time; φ is the minimum power factor angle of the DG; Let be the reactive power of the i1th DG at time t; The maximum power factor angle of the DG; Constructing reactive power output constraints for SVC:

[0107] in, Let be the reactive power of the i2th static var compensator at time t; This is the lower limit of the reactive power of the i2th static var compensator at time t; ΨSVC represents the upper limit of reactive power of the i2th static var compensator at time t; ΨSVC represents the total number of static var compensators. Power output constraints for stationary energy storage systems:

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] in, This indicates whether the i-th stationary energy storage system is charging at time t. =1 indicates a charging state. =0 indicates a non-charging state; This indicates whether the i-th stationary energy storage system is in a discharging state at time t. =1 indicates a discharge state. =0 indicates a non-discharge state; This refers to the total number of stationary energy storage systems. Let be the actual charging power of the i-th stationary energy storage system at time t; This is the lower limit of the charging power of the i-th stationary energy storage system at time t; The upper limit of the charging power of the i3rd stationary energy storage system at time i; Let be the actual discharge power of the i-th stationary energy storage system at time t; This is the lower limit of the discharge power of the i-th stationary energy storage system at time t; Let be the upper limit of the discharge power of the i3rd stationary energy storage system at time t; The initial energy storage state of the i3rd fixed energy storage system; The preset energy storage state for the i3rd fixed energy storage system at the initial moment; Let t represent the energy storage state of the i-th fixed energy storage system at time t. Let be the charging efficiency of the i3rd stationary energy storage system; Δt is the time interval between charging and discharging of the stationary energy storage system. Let be the discharge efficiency of the i3rd stationary energy storage system; The rated energy storage for the i3rd stationary energy storage system; This is the lower limit of the energy storage state of the i3rd stationary energy storage system; This represents the upper limit of the energy storage state of the i3rd stationary energy storage system; Constructing power flow constraints:

[0114]

[0115] in, Let be the active power flowing from node k to node i at time t; bi is the weighting factor of node i. Let be the active power flowing from node i to node j at time t; and Each represents the set of nodes connected to the i-th node; ΨE is the total number of branches from node i to node j. Let be the reactive power flowing from node i to node j at time t; Let be the reactive power flowing from node k to node i at time t; The closed or open state of the branch formed between the i-th node and the j-th node; =1 indicates that the branch is closed; =0 indicates that the branch is open; Let be the voltage of the i-th node at time t; Let be the voltage of the j-th node at time t; Let be the resistance between the i-th node and the j-th node; Let M be the reactance between the i-th node and the j-th node; M is a constant. Let be the power factor of the load at node i; Establishing network security constraints:

[0116]

[0117]

[0118] in, Vi represents the maximum complex power capacity from node i to node j; Vi is the lower voltage limit of the i-th node; Vi is the upper voltage limit of the i-th node; Constructing topological constraints:

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] in, Let ki be the active power of line ki; Let be the active power of line ij; This represents the net load from DG to Sub node i; This is the set of sub-station nodes; For the number of microgrids, when When the value is 1, there are no microgrids in the distribution network; Let be the capacity between lines ij; |·| represents the cardinality of the set.

[0128] A coordinated network recovery model considering load and DG output forecast uncertainties is constructed, resulting in a robust variant of the coordinated network recovery model to restore network power supply in active distribution networks, including: Calculate the load demand and DG output forecast values ​​using the following formulas:

[0129]

[0130] in, Let be the average active power consumed by the actual load of the i-th node at time t; The prediction error is the actual active power consumed by the i-th node at time t. Let be the average active power of the i1th DG at time t; Let be the prediction error of the active power of the i1th DG at time t; Construct the ellipsoidal uncertainty sets for load forecasting error and DG forecasting error:

[0131]

[0132] in, The ellipsoidal uncertainty set of the load forecast error; Let be the ellipsoidal uncertainty set of the DG prediction error; Uncertain budget for the load; For DG's uncertain budget; Let be the covariance matrix of the load forecasting error; Let be the covariance matrix of the i1th DG prediction error; The radial uncertainty of the load; The radial uncertainty of DG; Constructing a robust variant expression for the coordination network recovery model:

[0133]

[0134]

[0135] After determining the optimal layout and capacity of the energy storage equipment, the stationary energy storage system is positioned at the optimal location. Subsequently, reactive power optimization is implemented to obtain a 24-hour operation plan for the stationary energy storage system, such as... Figure 2 As shown in the diagram, all load operating states meet the constraints, with a minimum state of charge of 0.2. ESS1, located at node 15, experiences frequent fluctuations due to the influence of WT2; ESS2, located at node 29, exhibits some fluctuations under the influence of PV2, but these are less pronounced than those of ESS1. This indicates that wind energy has a greater impact on the traditional power grid than photovoltaics. ESS1, ESS3, and ESS4, being on the backbone, exhibit similar charging and discharging modes, and the overall system correlation shows the same power flow trend. Each energy storage charging and discharging strategy follows the principle of peak shaving and valley filling to improve the absorption capacity of new energy sources.

[0136] Configuration and state of charge constraints of mobile energy storage systems, such as Figure 3 As shown in the figure, mobile energy storage devices X, Z, and W exhibit frequent switching during charging and discharging, reflecting the sensitive response characteristics of the mobile energy storage system.

[0137] The node voltage change curve over time is shown in the figure. Figure 4 As shown, voltage levels improved under both stationary and mobile energy storage optimization conditions. Although stationary energy storage can operate without reactive power compensation gaps and its output power is higher than that of mobile energy storage (which has a positive effect on output efficiency), both stationary and mobile energy storage optimization significantly impacted system stability. The two optimization methods reduced voltage deviation to 28.07% and 45.52% of the original values, respectively, with the mean absolute error and root mean square error also decreasing simultaneously. Due to the location-invariant nature of the parameters, stationary energy storage has a slight advantage over mobile energy storage.

[0138] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0141] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0142] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A reactive power compensation method for a coordinated mobile energy storage and conventional energy storage configuration, characterized in that, Includes the following steps: S1. Construct the MESS fleet driving model and establish constraints; S2. Construct a coordinated power grid reactive power optimization model based on the MESS fleet driving model, including power constraints and power flow calculations for distributed power sources, static var compensators, and stationary energy storage systems. S3. Construct a coordinated network recovery model that considers the uncertainty of load and DG output prediction, and obtain a robust variant of the coordinated network recovery model to restore the reactive power deficit of the active distribution network.

2. The reactive power compensation method for a collaborative mobile energy storage and conventional energy storage configuration according to claim 1, characterized in that, The expression for the MESS fleet transportation road conditions and relative mileage model is constructed in S1 as follows: In the formula, express t The road network state matrix at any given time. express t The relative distance traveled between time node m and node n This represents the distance between node m and node n. This indicates the degree of congestion between node m and node n at time t. This represents the number of vehicles at node m and node n at time t. This represents the number of vehicles that started blocking between node m and node n at time t.

3. The reactive power compensation method for a collaborative mobile energy storage and conventional energy storage configuration according to claim 2, wherein the constraints in S1 include vehicle fleet position constraints, operational constraints, and power output constraints, wherein: The vehicle position constraints are represented as follows: Among them, when When = 1, the m-th MESS is located at node i at time t; when When = 0, the m-th MESS is not located at node i at time t; This represents the initial state of MESS; The total number of MESS; The total number of nodes; This indicates that the m-th MESS does not move at the initial time. This indicates that the m-th MESS does not move at the final moment; Total time; Let m be the movement state of the m-th MESS at time t; The operational constraints are expressed as follows: in, =1 indicates that the m-th MESS begins its journey at time t; =0 indicates that the m-th MESS stops moving at time t; =1 indicates that the m-th MESS stops moving at time t; =0 indicates that the m-th MESS begins its journey at time t; This represents the maximum travel frequency of the MESS. The energy provided by the m-th MESS at time point t; Let T be the driving state of MESS at time point t'; T is the set of all time points t. The output power constraint is expressed as: in, This represents the upper limit of the power of the m-th MESS; Let be the power of the m-th MESS at time t; Let be the total output power of MESS at node i at time t; Let m be the state of charge of the battery at the initial moment; The state of charge of the battery for the m-th MESS at a set time; Let m be the state of charge of the battery at time t; The charge / discharge efficiency of the m-th MESS; Let m be the battery capacity of the m-th MESS. Δt For time intervals; This is the state-of-charge limit for the m-th MESS battery; This represents the upper limit of the state of charge of the m-th MESS battery.

4. The reactive power compensation method for a coordinated mobile energy storage and conventional energy storage configuration according to claim 3, wherein step S2 specifically includes the following steps: S21. According to the formula: Calculate the maximum sum of predicted power at each node in the active distribution network, where, It is the maximum value of the sum of the predicted power of the i-th node; This represents the total number of nodes in an active distribution network. bi Let be the weight factor of the i-th node; Let be the active power consumed by the actual load of the i-th node at time t; S22. Construct DG power output constraints, including active power limits: Reactive power range and complex power constraints: In the formula, Let be the active power of the i1th DG at time t; Let be the power generation efficiency of the i1th DG at time t; Ψ is the rated complex power of the i1th DG; DG Ψ represents the total number of DG. T φ is the total time; φ is the minimum power factor angle of the DG. Let be the reactive power of the i1th DG at time t; The maximum power factor angle of the DG; S23. Construct the reactive power output constraint for SVC, expressed as: in, Let be the reactive power of the i2th static var compensator at time t; This is the lower limit of the reactive power of the i2th static var compensator at time t; Let be the upper limit of the reactive power of the i2th static var compensator at time t; Ψ SVC This represents the total number of static var compensators. S24. Construct the power output constraint for a stationary energy storage system, expressed as: in, This indicates whether the i-th stationary energy storage system is charging at time t. =1 indicates a charging state. =0 indicates a non-charging state; This indicates whether the i-th stationary energy storage system is in a discharging state at time t. =1 indicates a discharge state. =0 indicates a non-discharge state; This refers to the total number of stationary energy storage systems. Let be the actual charging power of the i-th stationary energy storage system at time t; This is the lower limit of the charging power of the i-th stationary energy storage system at time t; The upper limit of the charging power of the i3rd stationary energy storage system at time i; Let be the actual discharge power of the i-th stationary energy storage system at time t; This is the lower limit of the discharge power of the i-th stationary energy storage system at time t; Let be the upper limit of the discharge power of the i3rd stationary energy storage system at time t; The initial energy storage state of the i3rd fixed energy storage system; The preset energy storage state for the i3rd fixed energy storage system at the initial moment; Let t represent the energy storage state of the i-th fixed energy storage system at time t. Let be the charging efficiency of the i3rd stationary energy storage system; Δt is the time interval between charging and discharging of the stationary energy storage system. Let be the discharge efficiency of the i3rd stationary energy storage system; The rated energy storage for the i3rd stationary energy storage system; This is the lower limit of the energy storage state of the i3rd stationary energy storage system; This represents the upper limit of the energy storage state of the i3rd stationary energy storage system; S25. Construct power flow constraints, represented as: in, Let be the active power flowing from node k to node i at time t; bi is the weighting factor of node i. Let be the active power flowing from node i to node j at time t; and Each represents the set of nodes connected to the i-th node; ΨE is the total number of branches from node i to node j. Let be the reactive power flowing from node i to node j at time t; Let be the reactive power flowing from node k to node i at time t; The closed or open state of the branch formed between the i-th node and the j-th node; =1 indicates that the branch is closed; =0 indicates that the branch is open; Let be the voltage of the i-th node at time t; Let be the voltage of the j-th node at time t; Let be the resistance between the i-th node and the j-th node; Let M be the reactance between the i-th node and the j-th node; M is a constant. Let be the power factor of the load at node i; S26. Construct network security constraints, represented as follows: in, Vi represents the maximum complex power capacity from node i to node j; Vi is the lower voltage limit of the i-th node; Vi is the upper voltage limit of the i-th node; S27. Construct topological constraints, represented as follows: in, For line k i The active power; Let be the active power of line ij; This represents the net load from DG to Sub node i; This is the set of sub-station nodes; For the number of microgrids, when When the value is 1, there are no microgrids in the distribution network; Let be the capacity between lines ij; |·| represents the cardinality of the set.

5. The reactive power compensation method for a coordinated mobile energy storage and conventional energy storage configuration according to claim 4, wherein step S3 specifically includes the following steps: S31. Calculate the load demand and DG output forecast values; S32. Construct the ellipsoidal uncertainty set of load forecasting error and DG forecasting error; S33. Construct a robust variant expression for the coordination network recovery model.

6. The reactive power compensation method for a coordinated mobile energy storage and conventional energy storage configuration according to claim 5, wherein the specific calculation method for calculating the load demand and DG output prediction value in step S31 is as follows: in, Let be the average active power consumed by the actual load of the i-th node at time t; The prediction error is the actual active power consumed by the i-th node at time t. Let be the average active power of the i1th DG at time t; Let be the prediction error of the active power of the i1th DG at time t.

7. The reactive power compensation method for a coordinated mobile energy storage and conventional energy storage configuration according to claim 6, wherein the ellipsoidal uncertainty set of the load forecasting error and DG forecasting error in S32 is expressed as: in, The ellipsoidal uncertainty set of the load forecast error; Let be the ellipsoidal uncertainty set of the DG prediction error; Uncertain budget for the load; For DG's uncertain budget; Let be the covariance matrix of the load forecasting error; Let be the covariance matrix of the i1th DG prediction error; The radial uncertainty of the load; Let be the radial uncertainty of DG.

8. The reactive power compensation method for a coordinated mobile energy storage and conventional energy storage configuration according to claim 7, wherein the robust variant expression of the coordinated network recovery model in S33 is: 。

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