Mobile hybrid energy storage system scheduling method and device

Abstract

The invention discloses a mobile hybrid energy storage system scheduling method and device. The method comprises the following steps: acquiring path planning parameters, initializing and solving a preset first mathematical model according to the path planning parameters, and acquiring a first path; the first mathematical model is established and obtained by taking minimization of the transportation time of mobile energy storage in hybrid energy storage as a target; acquiring operation parameters of a power grid system, initializing and solving a preset second mathematical model in combination with the first path, and acquiring a first hybrid energy storage operation scheme; the second mathematical model is established and obtained by taking the minimization of the hybrid energy storage operation cost and the wind curtailment amount as targets; and according to the first path and the first hybrid energy storage operation scheme, issuing a scheduling instruction to the hybrid energy storage system in each time period. According to the invention, the utilization rate of mobile energy storage can be improved, the operation cost of the hybrid energy storage system is reduced, and safe operation of a power grid system is ensured.

Description

Technical Field

[0001] The present application relates to the field of energy storage scheduling, and in particular to a method and device for scheduling a mobile hybrid energy storage system. Background Art

[0002] With the development of new energy technologies, more and more new energy power generation equipment are connected to the power generation system. New energy power generation equipment has strong volatility and high dispersion. The high dispersion makes the power grid system divided into multiple microgrids, while the strong volatility can cause problems such as sudden load changes, voltage or power output out of control in the microgrid.

[0003] Existing technologies often use a hybrid energy storage system of supercapacitors and mobile energy storage for comprehensive control. The method of using a hybrid energy storage system for comprehensive control can shorten the control response time to seconds, effectively improving the safety of the power grid system. However, when faced with a distributed new energy power generation system, it is often necessary to establish an independent hybrid energy storage control system in each microgrid to perform energy storage control. Although the above method can effectively ensure the safe operation of each microgrid, it will waste the mobile and convenient transportation characteristics of mobile energy storage, resulting in excessively high operating costs for the overall hybrid energy storage.

[0004] Therefore, how to reduce the operating cost of the hybrid energy storage system and ensure the safe operation of the entire power grid system is a technical problem that needs to be solved at present. Summary of the invention

[0005] The present application provides a mobile hybrid energy storage system scheduling method and device to solve the technical problem of obtaining a mobile energy storage scheduling plan and ensuring the safe operation of a power grid system under the mobile energy storage scheduling plan.

[0006] In order to solve the above technical problems, in a first aspect, an embodiment of the present application provides a mobile hybrid energy storage system scheduling method, comprising:

[0007] Acquire path planning parameters, and initialize and solve a preset first mathematical model according to the path planning parameters to acquire a first path; the first mathematical model is constructed and acquired with the goal of minimizing the transportation time of mobile energy storage in hybrid energy storage;

[0008] Obtaining the operating parameters of the power grid system, and in combination with the first path, initializing and solving the preset second mathematical model to obtain a first hybrid energy storage operation plan; the second mathematical model is constructed and obtained with the goal of minimizing the hybrid energy storage operation cost and the amount of wind abandoned;

[0009] According to the first path and the first hybrid energy storage operation plan, a scheduling instruction is issued to the hybrid energy storage system in each time period.

[0010] Compared with the prior art, the embodiments of the present application have the following beneficial effects: by establishing the path planning problem of mobile energy storage between microgrids (i.e., between nodes), the optimal first path of mobile energy storage when transporting between power grids is first obtained, and the time spent by mobile energy storage during transportation is reduced, thereby increasing the time that mobile energy storage actually participates in wind turbine energy storage regulation within the entire hybrid energy storage system scheduling interval, and further improving the safety of the power grid system. In addition, based on the first path for obtaining the optimal transportation time, the operation schemes of each mobile energy storage and supercapacitor in the hybrid energy storage system are optimized, and the movable characteristics of mobile energy storage are reasonably utilized under the premise of ensuring the safety of the power grid, and there is no need to set up fixed mobile energy storage at each node, thereby reducing the operating cost of the hybrid energy storage system.

[0011] In some embodiments of the first aspect of the present application, the obtaining of path planning parameters, and initializing and solving a preset first mathematical model according to the path planning parameters, includes:

[0012] The path planning parameters include: a first parameter matrix and a second parameter matrix; the first parameter matrix is ​​composed of the unit distance traffic congestion time of the road section between any two nodes in each time period; the second parameter matrix is ​​composed of the distance between any two nodes;

[0013] Initializing a first objective function of the first mathematical model according to the first parameter matrix; the first objective function is used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix;

[0014] Initializing a first constraint condition of the first mathematical model according to the second parameter matrix; the first constraint condition is used to constrain the connectivity between any two nodes in combination with the second parameter matrix;

[0015] The first mathematical model is solved by a path planning algorithm to obtain the first path.

[0016] Compared with the prior art, the above embodiment has the following beneficial effects: since the traffic congestion conditions of different sections in different time periods are different, the traffic congestion time per unit distance is also different. According to the traffic congestion time per unit distance of different sections in different time periods, a first objective function for evaluating the transportation time of the first path is established, thereby reducing the transportation time of the first path in actual situations and effectively ensuring the safety of the power grid system.

[0017] In some embodiments of the first aspect of the present application, the first objective function is used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix, including:

[0018] The first objective function is specifically:

[0019]

[0020] in, For i The time period is the unit distance traffic congestion time of the path corresponding to the i-th path decision; N is the total number of nodes; l i is the straight-line distance of the path corresponding to the i-th path decision; v is the average speed of the vehicle; T is the transportation time corresponding to the first path.

[0021] Compared with the prior art, the above embodiment has the following beneficial effects: by calculating the time consumed corresponding to each path decision in the first path, the total transportation time of the first path is accurately evaluated, so that the hybrid energy storage operation decision obtained subsequently can effectively ensure the safe operation of the power grid system.

[0022] In some embodiments of the first aspect of the present application, the first constraint condition, used to constrain the connectivity between any two destinations in combination with the second parameter matrix, includes:

[0023] The second parameter matrix is ​​specifically:

[0024]

[0025] The first constraint condition is specifically:

[0026]

[0027] Where L is the second parameter matrix; l j,h Represents the straight-line distance from node j to node h, j,h∈{1,2,…,N}; N is the total number of nodes.

[0028] Compared with the prior art, the above embodiment has the following beneficial effects: since some nodes cannot communicate directly with each other, it is necessary to limit the distance between non-interconnected nodes to prevent repeating the path during path planning, and at the same time improve the efficiency of path planning.

[0029] In some embodiments of the first aspect of the present application, the obtaining of the grid system operating parameters, and initializing and solving a preset second mathematical model in combination with the first path to obtain a first hybrid energy storage operating scheme includes:

[0030] Initializing a second objective function of the second mathematical model according to the arrival order of each node in the first path and in combination with the power grid system operation parameters;

[0031] Initializing a second constraint condition of the second mathematical model according to the power grid system operating parameters;

[0032] Solve the initialized second mathematical model to obtain the first hybrid energy storage operation plan.

[0033] Compared with the prior art, the above embodiment has the following beneficial effects: after knowing the first path, the arrival time at each node can be known. At this time, according to the arrival time and arrival order of each node, the configuration and operation decision of the hybrid energy storage at different arrival time nodes can be further accurately evaluated, thereby effectively reducing the amount of wind abandonment and the cost of hybrid energy storage during operation.

[0034] In some embodiments of the first aspect of the present application, the initializing the second objective function of the second mathematical model according to the arrival order of each node in the first path in combination with the power grid system operation parameters includes:

[0035] The second objective function includes: a wind curtailment cost calculation function and a hybrid energy storage operation cost function;

[0036] The wind abandonment cost calculation function is specifically:

[0037]

[0038] The hybrid energy storage operation cost function is specifically:

[0039]

[0040] Among them, C gu is the wind curtailment cost; c gu is the wind abandonment cost coefficient; P WT (t i ,j) is the time period t i Active power output of the wind turbine at node j; P g (t i ,j) is the time period t i The active power transmitted by node j to the grid; P L (t i ) is the time period t i Active power required by the load; P mb (t i ) is the mobile energy storage in time period t i The charge and discharge power; P sc (t i ,j) is the supercapacitor of node j in time period t i The charge and discharge power; k mbp and k scp are the unit time power maintenance cost coefficients of mobile energy storage and supercapacitor respectively; k sce and k mbe are the average unit capacity prices of mobile energy storage and supercapacitors respectively; E sc (t i,j) is the supercapacitor of node j in time period t i Capacity; E mb (t i ) is the mobile energy storage in time period t i Capacity; T sc and T mb are the specified service life of supercapacitor and mobile energy storage respectively; r is the discount rate.

[0041] Compared with the prior art, the above embodiment has the following beneficial effects: since the supercapacitors of the microgrid corresponding to each node need to independently perform the control task before the mobile energy storage arrives, when evaluating the wind abandonment, it is necessary to consider the wind abandonment caused by the independent control of each node during the mobile energy storage, so as to accurately evaluate the total wind abandonment of the hybrid energy storage system operation plan. At the same time, since the capacity and power of the supercapacitor or mobile energy storage required are different each time the control is performed, it is necessary to accurately evaluate the capacity allocation and power allocation strategy with the lowest operating cost, so as to reduce the cost of the hybrid energy storage operation plan finally obtained.

[0042] In some embodiments of the first aspect of the present application, the second constraint condition includes: a hybrid energy storage capacity constraint, a hybrid energy storage power dispatch constraint, and a hybrid energy storage charge state constraint;

[0043] The hybrid energy storage capacity constraint is constructed and obtained by constraining the capacity that can be set for the mobile energy storage and the supercapacitor in each time period;

[0044] The hybrid energy storage power dispatch constraint is constructed and obtained by constraining the sum of the charging and discharging power of the mobile energy storage and the supercapacitor in each time period to meet the hybrid energy storage dispatch instruction;

[0045] The hybrid energy storage charge state constraint is established and obtained by constraining the charge state of the mobile energy storage and the supercapacitor in each time period to meet the safe operation state.

[0046] Compared with the prior art, the above embodiment has the following beneficial effects: in order to avoid the hybrid energy storage operation scheme finally obtained, which causes the power grid system to be in an unsafe operation state due to unreasonable distribution of the capacity and power ratio of supercapacitors and mobile energy storage, the safety of the power grid system is improved by constraining the control range of each energy storage device in the entire control cycle.

[0047] In a second aspect, an embodiment of the present application further provides a mobile hybrid energy storage system scheduling device, comprising: a first mathematical model solving module, a second mathematical model solving module and a scheduling instruction issuing module;

[0048] The first mathematical model solving module is used to obtain path planning parameters, and initialize and solve a preset first mathematical model according to the path planning parameters to obtain a first path; the first mathematical model is constructed and obtained with the goal of minimizing the transportation time of mobile energy storage in hybrid energy storage;

[0049] The second mathematical model solving module is used to obtain the operating parameters of the power grid system, and initialize and solve the preset second mathematical model in combination with the first path to obtain the first hybrid energy storage operation plan; the second mathematical model is constructed and obtained with the goal of minimizing the hybrid energy storage operation cost and the amount of wind abandoned;

[0050] The scheduling instruction issuing module is used to issue scheduling instructions to the hybrid energy storage system in each time period according to the first path and the first hybrid energy storage operation plan.

[0051] In some embodiments of the second aspect of the present application, the first mathematical model solving module is used to obtain path planning parameters, and initialize and solve a preset first mathematical model according to the path planning parameters, including:

[0052] The path planning parameters include: a first parameter matrix and a second parameter matrix; the first parameter matrix is ​​composed of the unit distance traffic congestion time of the road section between any two destinations in each time period; the second parameter matrix is ​​composed of the distance between any two destinations;

[0053] Initializing a first objective function of the first mathematical model according to the first parameter matrix; the first objective function is used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix;

[0054] Initializing a first constraint condition of the first mathematical model according to the second parameter matrix; the first constraint condition is used to constrain the connectivity between any two destinations in combination with the second parameter matrix;

[0055] The first mathematical model is solved by a path planning algorithm to obtain the first path.

[0056] In some embodiments of the second aspect of the present application, the first objective function is used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix, including:

[0057] The first objective function is specifically:

[0058]

[0059] in, For iThe time period is the unit distance traffic congestion time of the path corresponding to the i-th path decision; N is the total number of nodes; l i is the straight-line distance of the path corresponding to the i-th path decision; v is the average speed of the vehicle; T is the transportation time corresponding to the first path.

[0060] In some embodiments of the second aspect of the present application, the first constraint condition, used to constrain the connectivity between any two destinations in combination with the second parameter matrix, includes:

[0061] The second parameter matrix is ​​specifically:

[0062]

[0063] The first constraint condition is specifically:

[0064]

[0065] Where L is the second parameter matrix; l j,h Represents the straight-line distance from node j to node h, j,h∈{1,2,…,N}; N is the total number of nodes.

[0066] In some embodiments of the second aspect of the present application, the second mathematical model solving module is used to obtain the operating parameters of the power grid system, and initialize and solve the preset second mathematical model in combination with the first path to obtain the first hybrid energy storage operation plan, including:

[0067] Initializing a second objective function of the second mathematical model according to the arrival order of each node in the first path and in combination with the power grid system operation parameters;

[0068] Initializing a second constraint condition of the second mathematical model according to the power grid system operating parameters;

[0069] Solve the initialized second mathematical model to obtain the first hybrid energy storage operation plan.

[0070] In some embodiments of the second aspect of the present application, the initializing the second objective function of the second mathematical model according to the arrival order of each node in the first path in combination with the power grid system operation parameters includes:

[0071] The second objective function includes: a wind curtailment cost calculation function and a hybrid energy storage operation cost function;

[0072] The wind abandonment cost calculation function is specifically:

[0073]

[0074] The hybrid energy storage operation cost function is specifically:

[0075]

[0076] Among them, C gu is the wind curtailment cost; c gu is the wind abandonment cost coefficient; P WT (t i ,j) is the time period t i Active power output of the wind turbine at node j; P g (t i ,j) is the time period t i The active power transmitted by node j to the grid; P L (t i ) is the time period t i Active power required by the load; P mb (t i ) is the mobile energy storage in time period t i The charge and discharge power; P sc (t i ,j) is the supercapacitor of node j in time period t i The charge and discharge power; k mbe and k sce are the unit time power maintenance cost coefficients of mobile energy storage and supercapacitor respectively; k scp and k mbp are the average unit capacity prices of mobile energy storage and supercapacitors respectively; E sc (t i ,j) is the supercapacitor of node j in time period t i Capacity; E mb (t i ) is the mobile energy storage in time period t i Capacity; T sc and T mb are the specified service life of supercapacitor and mobile energy storage respectively; r is the discount rate.

[0077] In some embodiments of the second aspect of the present application, the second constraint condition includes: a hybrid energy storage capacity constraint, a hybrid energy storage power dispatch constraint, and a hybrid energy storage charge state constraint;

[0078] The hybrid energy storage capacity constraint is constructed and obtained by constraining the capacity that can be set for the mobile energy storage and the supercapacitor in each time period;

[0079] The hybrid energy storage power dispatch constraint is constructed and obtained by constraining the sum of the charging and discharging power of the mobile energy storage and the supercapacitor in each time period to meet the hybrid energy storage dispatch instruction;

[0080] The hybrid energy storage charge state constraint is established and obtained by constraining the charge state of the mobile energy storage and the supercapacitor in each time period to meet the safe operation state. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A schematic flow chart of a mobile hybrid energy storage system scheduling method provided in some embodiments of the present application;

[0082] Figure 2 A schematic structural diagram of a mobile hybrid energy storage system scheduling device provided for some embodiments of the present application. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0084] Embodiment 1

[0085] Please refer to Figure 1 , a mobile hybrid energy storage system scheduling method provided in an embodiment of the present application, including S10 to S30, specifically:

[0086] S10: Obtain path planning parameters, and initialize and solve a preset first mathematical model according to the path planning parameters to obtain a first path; the first mathematical model is constructed and obtained with the goal of minimizing the transportation time of mobile energy storage in hybrid energy storage.

[0087] Further, in some embodiments of the present application, obtaining the path planning parameters, and initializing and solving the preset first mathematical model according to the path planning parameters, includes:

[0088] The path planning parameters include: a first parameter matrix and a second parameter matrix; the first parameter matrix is ​​composed of the unit distance traffic congestion time of the road section between any two nodes in each time period; the second parameter matrix is ​​composed of the distance between any two nodes;

[0089] Initializing a first objective function of the first mathematical model according to the first parameter matrix; the first objective function is used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix;

[0090] Initializing a first constraint condition of the first mathematical model according to the second parameter matrix; the first constraint condition is used to constrain the connectivity between any two nodes in combination with the second parameter matrix;

[0091] The first mathematical model is solved by a path planning algorithm to obtain the first path.

[0092] Preferably, in some embodiments of the present application, hybrid energy storage includes: mobile energy storage and supercapacitors, wherein the supercapacitors can be replaced by any fixed energy storage device.

[0093] Preferably, in some embodiments of the present application, the first mathematical model can be constructed and initialized in the following preferred manner:

[0094] S11: Construct the first objective function of the first mathematical model by the following formula:

[0095]

[0096] in, For i The time period is the unit distance traffic congestion time of the path corresponding to the i-th path decision; N is the total number of nodes; l i is the straight-line distance of the path corresponding to the i-th path decision; v is the average speed of the vehicle; T is the transportation time corresponding to the first path; t i During the time period, the unit distance traffic jam time of the corresponding path between each node. If two nodes are not connected, the unit distance traffic jam time is set to an infinite real number; T is the first parameter matrix; t i During the time period, the unit distance traffic congestion time corresponding to the path between node j and node h, when the path of the i-th path decision is the path between node j and node h,

[0097] S12: Construct the first constraint condition of the first mathematical model through the following formula:

[0098]

[0099] Where L is the second parameter matrix; l j,h Represents the straight-line distance from node j to node h, j,h∈{1,2,…,N}; N is the total number of nodes.

[0100] From S11 to S12, we can see that when the path selected by the i-th path decision is the path from node j to node h, then l i = l j,h In addition, in the second parameter matrix, if node j and node h are not interoperable or j = h, then the l in the second parameter matrix can be j,h is set to a very large real number, otherwise, l in the second parameter matrix j,his the true straight-line distance from node j to node h.

[0101] Preferably, due to the introduction of the hybrid energy storage system, the control time can be shortened to seconds. Therefore, after determining the time periods corresponding to two adjacent path decisions, the power system is controlled by the hybrid energy storage. At this time, the time spent on the control can be ignored (i.e. ), or set a fixed control time (i.e. Where t is the fixed control time set).

[0102] Preferably, after the first mathematical model is constructed, the first mathematical model can be solved by any path planning algorithm. For example, the following are detailed steps for obtaining the first path based on the Floyd algorithm:

[0103] S13: Assuming that the second parameter matrix is ​​initialized by the method described in S12, the second parameter matrix is ​​further updated: let the transit point from node j to node h be k, traverse all node combinations, and check whether there is a path passing through node k such that l j,h > j,k +l k,h ; If it exists, update l j,h for l j,k +l k,h , even if l j,h =∞, and record the path information; otherwise, continue to traverse all node combinations until all node combinations are traversed; repeat the above steps until the distance between all node combinations in the second parameter matrix reaches the shortest.

[0104] S14: Further combine the final second parameter matrix obtained in S13 to update each Traverse all node combinations and check whether there is a path passing through node k such that If it exists, update And record the path information, even if Otherwise, continue to traverse all node combinations until all node combinations are traversed; repeat the above steps until all node combinations are traversed. The traffic congestion time per unit distance between all node combinations is minimized.

[0105] S15: Combine the second parameter matrix and the first parameter matrix finally obtained in S13 and S14 to obtain the path corresponding to the shortest transportation time between any two nodes.

[0106] Since the traffic congestion conditions on different sections of roads are different in different time periods, the traffic congestion time per unit distance is also different. According to the traffic congestion time per unit distance on different sections of roads in different time periods, a first objective function for evaluating the transportation time of the first path is established to reduce the transportation time of the first path under actual circumstances and effectively ensure the safety of the power grid system.

[0107] Preferably, any intelligent optimization algorithm may be used to plan the first path with the first objective function in S11 as the optimization target, which will not be described in detail here.

[0108] S20: Acquire the operating parameters of the power grid system, and in combination with the first path, initialize and solve the preset second mathematical model to obtain a first hybrid energy storage operation plan; the second mathematical model is constructed and obtained with the goal of minimizing the hybrid energy storage operation cost and the amount of wind abandoned.

[0109] Further, in some embodiments of the present application, the obtaining of the grid system operating parameters and, in combination with the first path, initializing and solving a preset second mathematical model to obtain a first hybrid energy storage operating scheme include:

[0110] Initializing a second objective function of the second mathematical model according to the arrival order of each node in the first path and in combination with the power grid system operation parameters;

[0111] Initializing a second constraint condition of the second mathematical model according to the power grid system operating parameters;

[0112] Solve the initialized second mathematical model to obtain the first hybrid energy storage operation plan.

[0113] After knowing the first path, the arrival time of each node can be known. At this time, according to the arrival time and arrival order of each node, the configuration and operation decision of the hybrid energy storage at different arrival time nodes can be further accurately evaluated, effectively reducing the amount of wind curtailment and the cost of hybrid energy storage during operation.

[0114] Further, in some embodiments of the present application, the second constraint condition includes: hybrid energy storage capacity constraint, hybrid energy storage power dispatch constraint and hybrid energy storage charge state constraint;

[0115] The hybrid energy storage capacity constraint is constructed and obtained by constraining the capacity that can be set for the mobile energy storage and the supercapacitor in each time period;

[0116] The hybrid energy storage power dispatch constraint is constructed and obtained by constraining the sum of the charging and discharging power of the mobile energy storage and the supercapacitor in each time period to meet the hybrid energy storage dispatch instruction;

[0117] The hybrid energy storage charge state constraint is established and obtained by constraining the charge state of the mobile energy storage and the supercapacitor in each time period to meet the safe operation state.

[0118] In order to avoid the hybrid energy storage operation scheme that is finally obtained, which causes the power grid system to be in an unsafe operating state due to the unreasonable distribution of the capacity and power ratio of supercapacitors and mobile energy storage, the safety of the power grid system is improved by constraining the control range of each energy storage device within the entire control cycle.

[0119] Preferably, in some embodiments of the present application, the second mathematical model can be constructed by the following preferred embodiments:

[0120] S21: Construct the second objective function of the second mathematical model by the following formula:

[0121] The second objective function includes: a wind curtailment cost calculation function and a hybrid energy storage operation cost function;

[0122] The wind abandonment cost calculation function is specifically:

[0123]

[0124] The hybrid energy storage operation cost function is specifically:

[0125]

[0126] The second objective function is specifically:

[0127] C=C gu +C s

[0128] Among them, C is the overall optimization goal; C gu is the wind curtailment cost; c gu is the wind abandonment cost coefficient; P WT (t i ,j) is the time period t i Active power output of the wind turbine at node j; P g (t i ,j) is the time period t i The active power transmitted by node j to the grid; P L (t i ) is the time period t i Active power required by the load; P mb (t i ) is the mobile energy storage in time period t i The charge and discharge power; P sc (t i ,j) is the supercapacitor of node j in time period t i The charge and discharge power; k mbeand k sce are the unit time power maintenance cost coefficients of mobile energy storage and supercapacitor respectively; k scp and k mbp are the average unit capacity prices of mobile energy storage and supercapacitors respectively; E sc (t i ,j) is the supercapacitor of node j in time period t i Capacity; E mb (t i ) is the mobile energy storage in time period t i Capacity; T sc and T mb are the specified service life of supercapacitor and mobile energy storage respectively; r is the discount rate.

[0129] S22: Construct the second constraint condition of the second mathematical model by the following formula:

[0130] S221: Constructing hybrid energy storage capacity constraints:

[0131]

[0132] S222: Construct hybrid energy storage power dispatch constraints:

[0133] P mb (t i )+P sc (t i ,j)=P HESS (t i ,j),i∈{1,2,…,N-1},j∈{1,2,…,N}

[0134] S223: Constructing hybrid energy storage state of charge constraints:

[0135]

[0136] in, and are the maximum capacity settings for mobile energy storage and supercapacitors respectively; P HESS (t i ,j) is the node j at t i Hybrid energy storage power dispatch instructions that meet the needs of the time period; and are the minimum and maximum charge states of mobile energy storage, respectively; and They are the minimum and maximum state of charge of the supercapacitor; SOC mb (t i ) is the mobile energy storage at t i State of charge during a period; SOC sc (t i,j) is the supercapacitor at node j at t i The state of charge during the time period.

[0137] Preferably, after the second mathematical model is constructed, the second mathematical model is solved by the following preferred implementation method:

[0138] S23: According to the node arrival order determined by the first path, determine the time period for each node to arrive, i.e., t i , according to the arrival time period of each node, the power grid system operating parameters are determined, and according to the power grid system operating parameters, the constraint conditions in S22 and the parameters in the wind abandonment cost calculation function in S21 are initialized.

[0139] S24: Solve the second mathematical model through any commercial solver to obtain the first hybrid energy storage operation plan.

[0140] S30: According to the first path and the first hybrid energy storage operation plan, a scheduling instruction is issued to the hybrid energy storage system in each time period.

[0141] In summary, the mobile hybrid energy storage system scheduling method provided by the embodiment of the present application has the following beneficial effects: by establishing the path planning problem of mobile energy storage between microgrids (i.e., between nodes), the optimal first path of mobile energy storage when transporting between power grids is first obtained, and the time spent by mobile energy storage during transportation is reduced, thereby increasing the time that mobile energy storage actually participates in wind turbine energy storage regulation within the entire hybrid energy storage system scheduling interval, and further improving the safety of the power grid system. In addition, based on the first path for obtaining the optimal transportation time, the operation schemes of each mobile energy storage and supercapacitor in the hybrid energy storage system are optimized, and the movable characteristics of mobile energy storage are reasonably utilized under the premise of ensuring the safety of the power grid, and there is no need to set up fixed mobile energy storage at each node, thereby reducing the operating cost of the hybrid energy storage system.

[0142] Embodiment 2

[0143] refer to Figure 2 , a mobile hybrid energy storage system scheduling device provided in an embodiment of the present application, comprising: a first mathematical model solving module 11, a second mathematical model solving module 12 and a scheduling instruction issuing module 13;

[0144] The first mathematical model solving module 11 is used to obtain path planning parameters, and initialize and solve a preset first mathematical model according to the path planning parameters to obtain a first path; the first mathematical model is constructed and obtained with the goal of minimizing the transportation time of mobile energy storage in hybrid energy storage;

[0145] The second mathematical model solving module 12 is used to obtain the operating parameters of the power grid system, and initialize and solve the preset second mathematical model in combination with the first path to obtain the first hybrid energy storage operation plan; the second mathematical model is constructed and obtained with the goal of minimizing the hybrid energy storage operation cost and the amount of wind abandoned;

[0146] The dispatch instruction issuing module 13 is used to issue dispatch instructions to the hybrid energy storage system in each time period according to the first path and the first hybrid energy storage operation plan.

[0147] In some embodiments of the present application, the first mathematical model solving module 11 is used to obtain path planning parameters, and initialize and solve a preset first mathematical model according to the path planning parameters, including:

[0148] The path planning parameters include: a first parameter matrix and a second parameter matrix; the first parameter matrix is ​​composed of the unit distance traffic congestion time of the road section between any two nodes in each time period; the second parameter matrix is ​​composed of the distance between any two nodes;

[0149] Initializing a first objective function of the first mathematical model according to the first parameter matrix; the first objective function is used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix;

[0150] Initializing a first constraint condition of the first mathematical model according to the second parameter matrix; the first constraint condition is used to constrain the connectivity between any two nodes in combination with the second parameter matrix;

[0151] The first mathematical model is solved by a path planning algorithm to obtain the first path.

[0152] In some embodiments of the present application, the first objective function is used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix, including:

[0153] The first objective function is specifically:

[0154]

[0155] in, For i The time period is the unit distance traffic congestion time of the path corresponding to the i-th path decision; N is the total number of nodes; l i is the straight-line distance of the path corresponding to the i-th path decision; v is the average speed of the vehicle; T is the transportation time corresponding to the first path.

[0156] In some embodiments of the present application, the first constraint condition, used to constrain the connectivity between any two nodes in combination with the second parameter matrix, includes:

[0157] The second parameter matrix is ​​specifically:

[0158]

[0159] The first constraint condition is specifically:

[0160]

[0161] Where L is the second parameter matrix; l j,h Represents the distance from node j to node h, j,h∈{1,2,…,N}; N is the total number of nodes.

[0162] In some embodiments of the present application, the second mathematical model solving module 12 is used to obtain the operating parameters of the power grid system, and initialize and solve the preset second mathematical model in combination with the first path to obtain the first hybrid energy storage operation plan, including:

[0163] Initializing a second objective function of the second mathematical model according to the arrival order of each node in the first path and in combination with the power grid system operation parameters;

[0164] Initializing a second constraint condition of the second mathematical model according to the power grid system operating parameters;

[0165] Solve the initialized second mathematical model to obtain the first hybrid energy storage operation plan.

[0166] In some embodiments of the present application, the step of initializing the second objective function of the second mathematical model according to the arrival order of each node in the first path in combination with the power grid system operation parameters includes:

[0167] The second objective function includes: a wind curtailment cost calculation function and a hybrid energy storage operation cost function;

[0168] The wind abandonment cost calculation function is specifically:

[0169]

[0170] The hybrid energy storage operation cost function is specifically:

[0171]

[0172] Among them, C gu is the wind curtailment cost; c gu is the wind abandonment cost coefficient; P WT (ti ,j) is the time period t i Active power output of the wind turbine at node j; P g (t i ,j) is the time period t i The active power transmitted by node j to the grid; P L (t i ) is the time period t i Active power required by the load; P mb (t i ) is the mobile energy storage in time period t i The charge and discharge power; P sc (t i ,j) is the supercapacitor of node j in time period t i The charge and discharge power; k mbe and k sce are the unit time power maintenance cost coefficients of mobile energy storage and supercapacitor respectively; k scp and k mbp are the average unit capacity prices of mobile energy storage and supercapacitors respectively; E sc (t i ,j) is the supercapacitor of node j in time period t i Capacity; E mb (t i ) is the mobile energy storage in time period t i Capacity; T sc and T mb are the specified service life of supercapacitor and mobile energy storage respectively; r is the discount rate.

[0173] In some embodiments of the present application, the second constraint condition includes: hybrid energy storage capacity constraint, hybrid energy storage power dispatch constraint and hybrid energy storage charge state constraint;

[0174] The hybrid energy storage capacity constraint is constructed and obtained by constraining the capacity that can be set for the mobile energy storage and the supercapacitor in each time period;

[0175] The hybrid energy storage power dispatch constraint is constructed and obtained by constraining the sum of the charging and discharging power of the mobile energy storage and the supercapacitor in each time period to meet the hybrid energy storage dispatch instruction;

[0176] The hybrid energy storage charge state constraint is established and obtained by constraining the charge state of the mobile energy storage and the supercapacitor in each time period to meet the safe operation state.

[0177] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present invention. A mobile hybrid energy storage system scheduling device provided by the embodiment of the present invention can implement any method item embodiment of the present invention, that is, the mobile hybrid energy storage system scheduling method provided in embodiment one.

[0178] In summary, the mobile hybrid energy storage system scheduling device provided by the embodiment of the present application has the following beneficial effects: by establishing the path planning problem of mobile energy storage between microgrids (i.e., between nodes), the optimal first path of mobile energy storage when transporting between power grids is first obtained, and the time spent by mobile energy storage during transportation is reduced, thereby increasing the time that mobile energy storage actually participates in wind turbine energy storage regulation within the entire hybrid energy storage system scheduling interval, and further improving the safety of the power grid system. In addition, based on the first path for obtaining the optimal transportation time, the operation schemes of each mobile energy storage and supercapacitor in the hybrid energy storage system are optimized, and the movable characteristics of mobile energy storage are reasonably utilized under the premise of ensuring the safety of the power grid, and there is no need to set up fixed mobile energy storage at each node, thereby reducing the operating cost of the hybrid energy storage system.

[0179] Embodiment 3

[0180] Based on the above-mentioned embodiment of the mobile hybrid energy storage system scheduling method, another embodiment of the present application provides a mobile hybrid energy storage system scheduling terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the mobile hybrid energy storage system scheduling method of any embodiment of the present application is implemented.

[0181] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the mobile hybrid energy storage system scheduling device.

[0182] The mobile hybrid energy storage system dispatching device may be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The mobile hybrid energy storage system dispatching terminal device may include, but is not limited to, a processor and a memory.

[0183] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the mobile hybrid energy storage system dispatching device, and uses various interfaces and lines to connect the various parts of the entire mobile hybrid energy storage system dispatching device. The memory may be used to store the computer program and / or module, and the processor implements various functions of the mobile hybrid energy storage system dispatching device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the mobile phone, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMed i aCard, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0184] Embodiment 4

[0185] Based on the above-mentioned embodiment of the mobile hybrid energy storage system scheduling method, another embodiment of the present application provides a storage medium, wherein the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the mobile hybrid energy storage system scheduling method of any embodiment of the present application.

[0186] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0187] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for dispatching a mobile hybrid energy storage system, characterized in that: include: Acquire path planning parameters, and initialize and solve a preset first mathematical model according to the path planning parameters to acquire a first path; The first mathematical model is constructed and obtained with the goal of minimizing the transportation time of mobile energy storage in hybrid energy storage; Obtaining the operating parameters of the power grid system, and in combination with the first path, initializing and solving the preset second mathematical model to obtain a first hybrid energy storage operation plan; the second mathematical model is constructed and obtained with the goal of minimizing the hybrid energy storage operation cost and the amount of wind abandoned; According to the first path and the first hybrid energy storage operation plan, a scheduling instruction is issued to the hybrid energy storage system in each time period.

2. A mobile hybrid energy storage system scheduling method according to claim 1, characterized in that: The path planning parameters are obtained, and according to the path planning parameters, a preset first mathematical model is initialized and solved, include: The path planning parameters include: a first parameter matrix and a second parameter matrix; the first parameter matrix is ​​composed of the unit distance traffic congestion time of the road section between any two nodes in each time period; the second parameter matrix is ​​composed of the distance between any two nodes; Initializing a first objective function of the first mathematical model according to the first parameter matrix; the first objective function is used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix; Initializing a first constraint condition of the first mathematical model according to the second parameter matrix; the first constraint condition is used to constrain the connectivity between any two nodes in combination with the second parameter matrix; The first mathematical model is solved by a path planning algorithm to obtain the first path.

3. A mobile hybrid energy storage system scheduling method according to claim 2, characterized in that: The first objective function, used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix, includes: The first objective function is specifically: in, For i The time period is the unit distance traffic congestion time of the path corresponding to the i-th path decision; N is the total number of nodes; l i is the straight-line distance of the path corresponding to the i-th path decision; v is the average speed of the vehicle; T is the transportation time corresponding to the first path.

4. A mobile hybrid energy storage system scheduling method according to claim 2, characterized in that: The first constraint condition, used to constrain the connectivity between any two nodes in combination with the second parameter matrix, includes: The second parameter matrix is ​​specifically: The first constraint condition is specifically: Where L is the second parameter matrix; l j,h Represents the straight-line distance from node j to node h, j,h∈{1,2,…,N}; N is the total number of nodes.

5. A mobile hybrid energy storage system scheduling method according to claim 1, characterized in that: The obtaining of the grid system operating parameters, and combining the first path, initializing and solving a preset second mathematical model, and obtaining a first hybrid energy storage operating scheme, includes: Initializing a second objective function of the second mathematical model according to the arrival order of each node in the first path and in combination with the power grid system operation parameters; Initializing a second constraint condition of the second mathematical model according to the power grid system operating parameters; Solve the initialized second mathematical model to obtain the first hybrid energy storage operation plan.

6. A method for dispatching a mobile hybrid energy storage system according to claim 5, characterized in that: Initializing the second objective function of the second mathematical model according to the arrival order of each node in the first path in combination with the power grid system operation parameters includes: The second objective function includes: a wind curtailment cost calculation function and a hybrid energy storage operation cost function; The wind abandonment cost calculation function is specifically: The hybrid energy storage operation cost function is specifically: Among them, C gu is the wind curtailment cost; c gu is the wind abandonment cost coefficient; P WT (t i ,j) is the time period t i The active power output of the wind turbine at node j; P g (t i ,j) is the time period t i The active power transmitted by node j to the grid; P L (t i ) is the time period t i Active power required by the load; P mb (t i ) is the mobile energy storage in time period t i The charge and discharge power; P sc (t i ,j) is the supercapacitor of node j in time period t i The charge and discharge power; k mbe and k sce are the unit time power maintenance cost coefficients of mobile energy storage and supercapacitor respectively; k scp and k mbp are the average unit capacity prices of mobile energy storage and supercapacitors respectively; E sc (t i ,j) is the supercapacitor of node j in time period t i Capacity; E mb (t i ) is the mobile energy storage in time period t i Capacity; T sc and T mb are the specified service life of supercapacitor and mobile energy storage respectively; r is the discount rate.

7. A method for dispatching a mobile hybrid energy storage system according to claim 5, characterized in that: The second constraint condition includes: hybrid energy storage capacity constraint, hybrid energy storage power dispatch constraint and hybrid energy storage charge state constraint; The hybrid energy storage capacity constraint is constructed and obtained by constraining the capacity that can be set for the mobile energy storage and the supercapacitor in each time period; The hybrid energy storage power dispatch constraint is constructed and obtained by constraining the sum of the charging and discharging power of the mobile energy storage and the supercapacitor in each time period to meet the hybrid energy storage dispatch instruction; The hybrid energy storage charge state constraint is established and obtained by constraining the charge state of the mobile energy storage and the supercapacitor in each time period to meet the safe operation state.

8. A mobile hybrid energy storage system scheduling device, characterized in that: include: A first mathematical model solving module, a second mathematical model solving module and a scheduling instruction issuing module; The first mathematical model solving module is used to obtain path planning parameters, and initialize and solve a preset first mathematical model according to the path planning parameters to obtain a first path; the first mathematical model is constructed and obtained with the goal of minimizing the transportation time of mobile energy storage in hybrid energy storage; The second mathematical model solving module is used to obtain the operating parameters of the power grid system, and initialize and solve the preset second mathematical model in combination with the first path to obtain the first hybrid energy storage operation plan; the second mathematical model is constructed and obtained with the goal of minimizing the hybrid energy storage operation cost and the amount of wind abandoned; The scheduling instruction issuing module is used to issue scheduling instructions to the hybrid energy storage system in each time period according to the first path and the first hybrid energy storage operation plan.

9. A mobile hybrid energy storage system dispatching device according to claim 8, characterized in that: The first mathematical model solving module is used to obtain path planning parameters, and initialize and solve a preset first mathematical model according to the path planning parameters. include: The path planning parameters include: a first parameter matrix and a second parameter matrix; the first parameter matrix is ​​composed of the unit distance traffic congestion time of the road section between any two nodes in each time period; the second parameter matrix is ​​composed of the distance between any two nodes; Initializing a first objective function of the first mathematical model according to the first parameter matrix; the first objective function is used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix; Initializing a first constraint condition of the first mathematical model according to the second parameter matrix; the first constraint condition is used to constrain the connectivity between any two nodes in combination with the second parameter matrix; The first mathematical model is solved by a path planning algorithm to obtain the first path.

10. A mobile hybrid energy storage system dispatching device according to claim 9, characterized in that: The first objective function, used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix, includes: The first objective function is specifically: in, For i The time period is the unit distance traffic congestion time of the path corresponding to the i-th path decision; N is the total number of nodes; l i is the straight-line distance of the path corresponding to the i-th path decision; v is the average speed of the vehicle; T is the transportation time corresponding to the first path.

11. A mobile hybrid energy storage system dispatching device according to claim 9, characterized in that: The first constraint condition, used to constrain the connectivity between any two nodes in combination with the second parameter matrix, includes: The second parameter matrix is ​​specifically: The first constraint condition is specifically: Where L is the second parameter matrix; l j,h Represents the straight-line distance from node j to node h, j,h∈{1,2,…,N}; N is the total number of nodes.

12. A mobile hybrid energy storage system dispatching device according to claim 8, characterized in that: The second mathematical model solving module is used to obtain the operating parameters of the power grid system, and initialize and solve the preset second mathematical model in combination with the first path to obtain the first hybrid energy storage operation plan, including: Initializing a second objective function of the second mathematical model according to the arrival order of each node in the first path and in combination with the power grid system operation parameters; Initializing a second constraint condition of the second mathematical model according to the power grid system operating parameters; Solve the initialized second mathematical model to obtain the first hybrid energy storage operation plan.

13. A mobile hybrid energy storage system dispatching device according to claim 12, characterized in that: Initializing the second objective function of the second mathematical model according to the arrival order of each node in the first path in combination with the power grid system operation parameters includes: The second objective function includes: a wind curtailment cost calculation function and a hybrid energy storage operation cost function; The wind abandonment cost calculation function is specifically: The hybrid energy storage operation cost function is specifically: Among them, C gu is the wind curtailment cost; c gu is the wind abandonment cost coefficient; P WT (t i ,j) is the time period t i The active power output of the wind turbine at node j; P g (t i ,j) is the time period t i The active power transmitted by node j to the grid; P L (t i ) is the time period t i Active power required by the load; P mb (t i ) is the mobile energy storage in time period t i The charge and discharge power; P sc (t i ,j) is the supercapacitor of node j in time period t i The charge and discharge power; k mbe and k sce are the unit time power maintenance cost coefficients of mobile energy storage and supercapacitor respectively; k scp and k mbp are the average unit capacity prices of mobile energy storage and supercapacitors respectively; E sc (t i ,j) is the supercapacitor of node j in time period t i Capacity; E mb (t i ) is the mobile energy storage in time period t i Capacity; T sc and T mb are the specified service life of supercapacitor and mobile energy storage respectively; r is the discount rate.

14. A mobile hybrid energy storage system dispatching device according to claim 12, characterized in that: The second constraint condition includes: hybrid energy storage capacity constraint, hybrid energy storage power dispatch constraint and hybrid energy storage charge state constraint; The hybrid energy storage capacity constraint is constructed and obtained by constraining the capacity that can be set for the mobile energy storage and the supercapacitor in each time period; The hybrid energy storage power dispatch constraint is constructed and obtained by constraining the sum of the charging and discharging power of the mobile energy storage and the supercapacitor in each time period to meet the hybrid energy storage dispatch instruction; The hybrid energy storage charge state constraint is established and obtained by constraining the charge state of the mobile energy storage and the supercapacitor in each time period to meet the safe operation state.

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