Scheduling method and apparatus for mobile hybrid energy storage system

Abstract

Disclosed in the present application are a scheduling method and apparatus for a mobile hybrid energy storage system. The method in the present application comprises: acquiring path planning parameters, and, on the basis of the path planning parameters, initializing and solving a preset first mathematical model so as to acquire a first path, the first mathematical model being constructed and acquired with the goal of minimizing transportation time of mobile energy storage in hybrid energy storage; acquiring operation parameters of a power grid system, and incorporating the first path to initialize and solve a preset second mathematical model, so as to acquire a first hybrid energy storage operation solution, the second mathematical model being constructed and acquired with the goal of minimizing operation costs of hybrid energy storage and the amount of wind curtailment; and, on the basis of the first path and the first hybrid energy storage operation solution, issuing a scheduling instruction to a hybrid energy storage system in each time period. The present application can improve the utilization rate of mobile energy storage, and reduce the operation costs of hybrid energy storage systems while ensuring safe operation of power grid systems.

Description

A mobile hybrid energy storage system scheduling method and device TECHNICAL FIELD

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

[0002] With the development of new energy technology, more and more new energy power generation devices are connected to the power generation system. The new energy power generation devices have strong volatility and high dispersion. The high dispersion causes the power grid system to be divided into multiple microgrids, and the strong volatility causes the microgrids to have problems such as sudden changes in load, uncontrolled voltage or power output.

[0003] The prior art often uses 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 regulation and control response time to seconds, effectively improving the safety of the power grid system. However, when facing a distributed new energy power generation system, it is often necessary to establish an independent hybrid energy storage regulation and control system in each microgrid for energy storage regulation and control. The above method can effectively ensure the safe operation of each microgrid, but wastes the mobile and convenient transportation characteristics of mobile energy storage, resulting in high overall hybrid energy storage operation costs.

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

[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 scheme and ensuring the safe operation of the power grid system under the mobile energy storage scheduling scheme.

[0006] To solve the above technical problem, in a first aspect, the present application embodiment provides a mobile hybrid energy storage system scheduling method, comprising:

[0007] Obtaining path planning parameters, and initializing and solving a preset first mathematical model according to the path planning parameters to obtain a first path; the first mathematical model is built to obtain the minimum transportation time of the mobile energy storage in the hybrid energy storage as the target;

[0008] Obtaining power grid system operation parameters, and initializing and solving a preset second mathematical model in combination with the first path to obtain a first hybrid energy storage operation scheme; the second mathematical model is built to obtain the minimum hybrid energy storage operation cost and wind curtailment as the target;

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

[0010] Compared with the prior art, the embodiments of the application have the following beneficial effects: by establishing the path planning problem of the mobile energy storage between the microgrids (i.e., between the nodes), the optimal first path of the mobile energy storage in the transportation between the grids is first obtained, the time spent by the mobile energy storage in the transportation process is reduced, thereby the time of the mobile energy storage actually participating in the wind turbine energy storage regulation in the entire mixed energy storage system scheduling interval is improved, and the safety of the grid system is further improved. In addition, based on the first path with the optimal transportation time, the operation scheme of each mobile energy storage and the super capacitor in the mixed energy storage system is optimized, the mobile characteristics of the mobile energy storage are reasonably utilized under the premise of ensuring the safety of the grid, fixed mobile energy storages do not need to be arranged at each node, and the operation cost of the mixed energy storage system is reduced.

[0011] In some embodiments of the first aspect of the application, the path planning parameters are obtained, and a preset first mathematical model is initialized and solved according to the path planning parameters, including:

[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; and the second parameter matrix is composed of the distance between any two nodes.

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

[0014] The first constraint condition of the first mathematical model is initialized according to the second parameter matrix; and 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 embodiments have the following beneficial effects: because the traffic congestion conditions of different road sections in different time periods are different, the unit distance traffic congestion time is also different, the first objective function for evaluating the transportation time of the first path is established according to the unit distance traffic congestion time of different road sections in different time periods, the transportation time of the first path in the real situation is reduced, and the safety of the grid system is effectively ensured.

[0017] In some embodiments of the first aspect of the 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] wherein, is the unit distance traffic jam time of the path corresponding to the i-th path decision at t i 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 driving speed of the vehicle; and T is the transportation time corresponding to the first path.

[0020] Compared with the prior art, the above embodiment has the beneficial effects that by calculating the time cost corresponding to each path decision in the first path, the total transportation time of the first path is accurately evaluated, so that the subsequent hybrid energy storage operation decision can effectively ensure the safe operation of the power grid system.

[0021] In some embodiments of the first aspect of the application, the first constraint condition is used to restrict the connectivity between any two destinations in combination with the second parameter matrix, and specifically comprises:

[0022] The second parameter matrix is specifically:

[0023] The first constraint condition is specifically:

[0024] wherein, 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}; and N is the total number of nodes.

[0025] Compared with the prior art, the above embodiment has the beneficial effects that since some nodes cannot directly communicate with each other, the distance between the nodes that cannot communicate with each other needs to be limited to prevent repeated driving of the path when planning the path, and the efficiency of path planning can be improved.

[0026] In some embodiments of the first aspect of the application, the power grid system operation parameter is obtained, and a preset second mathematical model is initialized and solved in combination with the first path to obtain a first hybrid energy storage operation scheme, and the method comprises:

[0027] According to the arrival order of each node in the first path, the second objective function of the second mathematical model is initialized in combination with the power grid system operation parameter;

[0028] According to the power grid system operation parameter, the second constraint condition of the second mathematical model is initialized;

[0029] The initialized second mathematical model is solved to obtain the first hybrid energy storage operation scheme.

[0030] Compared with the prior art, the above embodiment has the following beneficial effects: when the first path is known, the time of reaching each node can be known, at this time, according to the arrival time and the arrival order of each node, the configuration and operation decision of the hybrid energy storage under different arrival time nodes can be further accurately evaluated, and the amount of abandoned wind and the cost of the hybrid energy storage in operation are effectively reduced.

[0031] In some embodiments of the first aspect of the application, initializing the 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 comprises:

[0032] The second objective function comprises an abandoned wind cost calculation function and a hybrid energy storage operation cost function.

[0033] The abandoned wind cost calculation function is specifically:

[0034] The hybrid energy storage operation cost function is specifically:

[0035] Wherein, C gu is the abandoned wind cost; c gu is the abandoned wind cost coefficient; P WT (t i ,j) is the active power output of the wind turbine at node j in time period t i ; P g (t i ,j) is the active power delivered to the grid by node j in time period t i ; P L (t i ) is the active power required by the load in time period t i ; P mb (t i ) is the charge and discharge power of the mobile energy storage in time period t i ; P sc (t i ,j) is the charge and discharge power of the super capacitor at node j in time period t i ; k mbp and k scp are the unit time power maintenance cost coefficients of the mobile energy storage and the super capacitor, respectively; k sce and k mbe are the unit capacity average prices of the mobile energy storage and the super capacitor, respectively; E sc (t i ,j) is the capacity of the super capacitor at node j in time period t i ; E mb (t i ) is the capacity of the mobile energy storage in time period t i ; T sc and Tmb respectively, the service life of the super capacitor and the mobile energy storage; r is the discount rate.

[0036] Compared with the prior art, the above-mentioned embodiments have the following beneficial effects: since the super capacitor corresponding to each node of the micro-grid needs to be independently executed before the mobile energy storage reaches, the wind curtailment caused by the independent regulation of each node during the movement of the mobile energy storage needs to be considered when evaluating the wind curtailment, so as to accurately evaluate the total wind curtailment of the final obtained hybrid energy storage system operation scheme. At the same time, since the capacity and power of the super capacitor or the mobile energy storage required during each regulation are different, the capacity allocation and power allocation strategy with the lowest operation cost need to be accurately evaluated to reduce the cost of the final obtained hybrid energy storage operation scheme.

[0037] In some embodiments of the first aspect of the application, the second constraint condition comprises a hybrid energy storage capacity constraint, a hybrid energy storage power scheduling constraint, and a hybrid energy storage state of charge constraint.

[0038] The hybrid energy storage capacity constraint is obtained by constraining the capacity of the mobile energy storage and the super capacitor that can be set in each time period.

[0039] The hybrid energy storage power scheduling constraint is obtained by constraining the sum of the charging and discharging power of the mobile energy storage and the super capacitor in each time period to meet the hybrid energy storage scheduling instruction.

[0040] The hybrid energy storage state of charge constraint is obtained by constraining the state of charge of the mobile energy storage and the super capacitor in each time period to meet the safe operation state.

[0041] Compared with the prior art, the above-mentioned embodiments have the following beneficial effects: in order to avoid the final obtained hybrid energy storage operation scheme from causing the power grid system to be in an unsafe operation state due to unreasonable allocation of the capacity and power ratio of the super capacitor and the mobile energy storage, the regulation range of each energy storage device in the entire regulation period is constrained, thereby improving the safety of the power grid system.

[0042] In a second aspect, the embodiments of the application also provide 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.

[0043] The first mathematical model solving module is configured to obtain path planning parameters, initialize and solve a preset first mathematical model according to the path planning parameters, and obtain a first path. The first mathematical model is built to minimize the transportation time of the mobile energy storage in the hybrid energy storage.

[0044] The second mathematical model solving module is configured to acquire power grid system operation parameters, and initialize and solve a preset second mathematical model in combination with the first path to acquire a first hybrid energy storage operation scheme; the second mathematical model is built to minimize hybrid energy storage operation cost and wind curtailment;

[0045] The dispatching instruction issuing module is configured to issue a dispatching instruction to the hybrid energy storage system in each time period according to the first path and the first hybrid energy storage operation scheme.

[0046] In some embodiments of the second aspect of the application, the first mathematical model solving module is configured to acquire path planning parameters, and initialize and solve a preset first mathematical model according to the path planning parameters, including:

[0047] The path planning parameters include a first parameter matrix and a second parameter matrix; the first parameter matrix is composed of unit distance traffic congestion time of a road section between any two destinations in each time period; and the second parameter matrix is composed of distances between any two destinations.

[0048] The first objective function of the first mathematical model is initialized according to the first parameter matrix; and the first objective function is configured to calculate transportation time corresponding to the first path in combination with the first parameter matrix.

[0049] The first constraint condition of the first mathematical model is initialized according to the second parameter matrix; and the first constraint condition is configured to constrain connectivity between any two destinations in combination with the second parameter matrix.

[0050] The first mathematical model is solved by a path planning algorithm to acquire the first path.

[0051] In some embodiments of the second aspect of the application, the first objective function is configured to calculate transportation time corresponding to the first path in combination with the first parameter matrix, including:

[0052] The first objective function is specifically:

[0053] wherein, is unit distance traffic congestion time of a path corresponding to the i i time path decision in the t i is straight line distance of a path corresponding to the i time path decision; v is average vehicle driving speed; and T is transportation time corresponding to the first path.

[0054] In some embodiments of the second aspect of the application, the first constraint condition, used in combination with the second parameter matrix, restricts the connectivity between any two destinations, and includes:

[0055] The second parameter matrix, specifically:

[0056] The first constraint condition, specifically:

[0057] Wherein, 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.

[0058] In some embodiments of the second aspect of the application, the second mathematical model solving module is configured to obtain power grid system operation parameters, and initialize and solve a preset second mathematical model in combination with the first path to obtain a first hybrid energy storage operation scheme, including:

[0059] According to the arrival order of each node in the first path, in combination with the power grid system operation parameters, a second objective function of the second mathematical model is initialized;

[0060] According to the power grid system operation parameters, a second constraint condition of the second mathematical model is initialized;

[0061] Solving the initialized second mathematical model obtains the first hybrid energy storage operation scheme.

[0062] In some embodiments of the second aspect of the application, the second objective function of the second mathematical model is initialized according to the arrival order of each node in the first path in combination with the power grid system operation parameters, including:

[0063] The second objective function includes a wind curtailment cost calculation function and a hybrid energy storage operation cost function;

[0064] The wind curtailment cost calculation function, specifically:

[0065] The hybrid energy storage operation cost function, specifically:

[0066] Wherein, C gu is the wind curtailment cost; c gu is the wind curtailment cost coefficient; P WT (t i ,j) is the active power output of the wind turbine at node j in time period t i ; P g (t i ,j) is the active power output of the hybrid energy storage at node j in time period t iActive power delivered by node j to the grid; P L (t i ) is the charging and discharging power of the mobile energy storage at time period t i Active power required by the load; P mb (t i ) is the charging and discharging power of the mobile energy storage at time period t i Active power required by the load; P sc (t i ,j) is the charging and discharging power of the super capacitor at node j at time period t i k mbe and k sce are the unit time power maintenance cost coefficients of the mobile energy storage and the super capacitor respectively; k scp and k mbp are the unit capacity average prices of the mobile energy storage and the super capacitor respectively; E sc (t i ,j) is the capacity of the super capacitor at node j at time period t i E mb (t i ) is the capacity of the mobile energy storage at time period t i T sc and T mb are the specified service life of the super capacitor and the mobile energy storage respectively; r is the discount rate.

[0067] In some embodiments of the second aspect of the application, the second constraint condition comprises a hybrid energy storage capacity constraint, a hybrid energy storage power scheduling constraint, and a hybrid energy storage state of charge constraint.

[0068] The hybrid energy storage capacity constraint is obtained by constraining the settable capacity of the mobile energy storage and the super capacitor at each time period.

[0069] The hybrid energy storage power scheduling constraint is obtained by constraining the sum of the charging and discharging power of the mobile energy storage and the super capacitor at each time period to meet the hybrid energy storage scheduling instruction.

[0070] The hybrid energy storage state of charge constraint is obtained by constraining the state of charge of the mobile energy storage and the super capacitor at each time period to meet the safe operation state. BRIEF DESCRIPTION OF DRAWINGS

[0071] FIG. 1 is a flow diagram of a mobile hybrid energy storage system scheduling method according to some embodiments of the application;

[0072] FIG. 2 is a structural diagram of a mobile hybrid energy storage system scheduling device according to some embodiments of the application. DETAILED DESCRIPTION

[0073] With reference to the drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work belong to the scope of protection of the present application.

[0074] Embodiment one

[0075] Please refer to FIG. 1, a mobile hybrid energy storage system scheduling method provided by the embodiments of the present application, comprising S10 to S30, specifically:

[0076] 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 built to minimize the transportation time of the mobile energy storage in the hybrid energy storage.

[0077] Further, in some embodiments of the present application, the obtaining path planning parameters and initializing and solving the preset first mathematical model according to the path planning parameters comprises:

[0078] The path planning parameters comprise a first parameter matrix and a second parameter matrix; the first parameter matrix is composed of the unit distance traffic jam 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;

[0079] According to the first parameter matrix, a first objective function of the first mathematical model is initialized; the first objective function is used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix;

[0080] According to the second parameter matrix, a first constraint condition of the first mathematical model is initialized; the first constraint condition is used to constrain the connectivity between any two nodes in combination with the second parameter matrix;

[0081] The first mathematical model is solved by a path planning algorithm to obtain the first path.

[0082] Preferably, in some embodiments of the present application, the hybrid energy storage comprises a mobile energy storage and a super capacitor, wherein the super capacitor can be replaced by any fixed energy storage device.

[0083] Preferably, in some embodiments of the present application, the first mathematical model can be built and initialized in the following preferred manner:

[0084] S11: The first objective function of the first mathematical model is built by the following formula:

[0085] wherein, is the unit distance traffic congestion time of the path corresponding to the ith path decision in the time period t i is the straight line distance of the path corresponding to the ith path decision; v is the average driving speed of the vehicle; T is the transport time corresponding to the first path; T i is the transport time corresponding to the first path; T ti is the unit distance traffic congestion time of the path corresponding to the ith path decision in the time period t i is the unit distance traffic congestion time of the path corresponding to the ith path decision in the time period t i is the unit distance traffic congestion time of the path corresponding to the ith path decision in the time period t is the unit distance traffic congestion time of the path corresponding to the ith path decision in the time period t

[0086] S12: The first constraint condition of the first mathematical model is constructed by the following formula:

[0087] wherein, 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.

[0088] It can be seen from S11 to S12 that when the path selected by the ith path decision is the path from node j to node h, l i = l j,h In addition, in the second parameter matrix, if node j and node h are not interconnected or j = h, l j,h in the second parameter matrix can be set as a very large real number, otherwise, l j,h in the second parameter matrix is the real straight line distance from node j to node h.

[0089] Preferably, due to the introduction of the hybrid energy storage system, the regulation time can be shortened to seconds, therefore, after determining the time period corresponding to two adjacent path decisions, the hybrid energy storage is started to regulate the power system, at this time, the time spent for regulation (i.e. ) can be ignored, or a fixed regulation time (i.e. wherein t is the set fixed regulation time) is set.

[0090] Preferably, after the first mathematical model is constructed, the first mathematical model can be solved by using any path planning algorithm, and the following is an example of detailed steps for obtaining the first path based on the Floyd algorithm:

[0091] S13: Assuming that the second parameter matrix is initialized by the method described in S12, the second parameter matrix is further updated: assuming that the transit point from node j to node h is k, all node combinations are traversed, and it is checked whether there is a path passing through node k, such that l j,h >l j,k +l k,h ; if there is, update l j,h as 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.

[0092] S14: Further combining the final second parameter matrix obtained in S13, update each Traverse all node combinations, and check whether there is a path passing through node k, such that If there is, 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 the unit distance traffic congestion time between all node combinations in each reaches the minimum.

[0093] 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.

[0094] Since the traffic congestion of different road sections is different in different periods, the unit distance traffic congestion time is also different. According to the unit distance traffic congestion time of different road sections in different periods, a first objective function for evaluating the transportation time of the first path is established, which reduces the transportation time of the first path in the real situation and effectively guarantees the safety of the power grid system.

[0095] Preferably, the first path can also be planned by any intelligent optimization algorithm with the first objective function in S11 as the optimization target, which will not be described here.

[0096] S20: Obtain the power grid system operation parameters, and initialize and solve a preset second mathematical model to obtain a first hybrid energy storage operation scheme in combination with the first path; the second mathematical model is built to minimize the hybrid energy storage operation cost and wind curtailment.

[0097] Further, in some embodiments of the present application, the power grid system operation parameters are acquired, and a preset second mathematical model is initialized and solved in combination with the first path to acquire a first hybrid energy storage operation scheme, including:

[0098] According to the arrival order of each node in the first path, the second objective function of the second mathematical model is initialized in combination with the power grid system operation parameters;

[0099] According to the power grid system operation parameters, the second constraint condition of the second mathematical model is initialized;

[0100] The initialized second mathematical model is solved to acquire the first hybrid energy storage operation scheme.

[0101] When the first path is known, the time of arrival at each node can be known, and at this time, according to the arrival time and the arrival order of each node, the configuration and operation decision of the hybrid energy storage under different arrival time nodes can be further accurately evaluated, and the amount of abandoned wind power and the cost of the hybrid energy storage in operation are effectively reduced.

[0102] Further, in some embodiments of the present application, the second constraint condition includes a hybrid energy storage capacity constraint, a hybrid energy storage power scheduling constraint, and a hybrid energy storage state of charge constraint;

[0103] The hybrid energy storage capacity constraint is acquired by constraining the capacity of the mobile energy storage and the super capacitor that can be set in each period;

[0104] The hybrid energy storage power scheduling constraint is acquired by constraining the sum of the charge and discharge power of the mobile energy storage and the super capacitor in each period to meet the hybrid energy storage scheduling instruction;

[0105] The hybrid energy storage state of charge constraint is acquired by constraining the state of charge of the mobile energy storage and the super capacitor in each period to meet the safe operation state.

[0106] In order to avoid that the finally acquired hybrid energy storage operation scheme causes the power grid system to be in an unsafe operation state due to unreasonable allocation of the capacity and power ratio of the super capacitor and the mobile energy storage, the control range of each energy storage device in the entire control period is constrained to improve the safety of the power grid system.

[0107] Preferably, in some embodiments of the present application, the second mathematical model can be constructed by the following preferred embodiments:

[0108] S21: The second objective function of the second mathematical model is constructed by the following formula:

[0109] The second objective function includes an abandoned wind power cost calculation function and a hybrid energy storage operation cost function;

[0110] The wind abandonment cost calculation function, in particular:

[0111] The hybrid energy storage operation cost function, in particular:

[0112] The second objective function, in particular: C = C gu + C s

[0113] Wherein, C is the total optimization target; C gu is the wind abandonment cost; c gu is the wind abandonment cost coefficient; P WT (t i ,j) is the active power output of the wind turbine at time period t i Node j; P g (t i ,j) is the active power delivered to the grid by node j at time period t i ; P L (t i ) is the active power required by the load at time period t i ; P mb (t i ) is the charge and discharge power of the mobile energy storage at time period t i ; P sc (t i ,j) is the charge and discharge power of the super capacitor at node j at time period t i ; k mbe and k sce are the unit time power maintenance cost coefficients of the mobile energy storage and the super capacitor, respectively; k scp and k mbp are the unit capacity average prices of the mobile energy storage and the super capacitor, respectively; E sc (t i ,j) is the capacity of the super capacitor at node j at time period t i ; E mb (t i ) is the capacity of the mobile energy storage at time period t i ; T sc and T mb are the specified service life of the super capacitor and the mobile energy storage, respectively; and r is the discount rate.

[0114] S22: Construct the second constraint condition of the second mathematical model by the following formula:

[0115] S221: Construct the hybrid energy storage capacity constraint:

[0116] S222: Construct the hybrid energy storage power scheduling constraint: Pmb (t i )+P sc (t i ,j)=P HESS (t i ,j),i∈{1,2,…,N-1},j∈{1,2,…,N}

[0117] S223:Constructing the hybrid energy storage state of charge constraints:

[0118] wherein, and are the maximum capacity settings of the mobile energy storage and the super capacitor respectively; P HESS (t i ,j) is the hybrid energy storage power scheduling instruction required by node j at t i period to meet; and are the minimum and maximum state of charge of the mobile energy storage respectively; and are the minimum and maximum state of charge of the super capacitor respectively; SOC mb (t i ) is the state of charge of the mobile energy storage at t i period; SOC sc (t i ,j) is the state of charge of the super capacitor at node j at t i period.

[0119] Preferably, when the second mathematical model is constructed, the second mathematical model is solved by the following preferred implementation method:

[0120] S23: According to the node arrival order determined by the first path, determine the period of arrival of each node, i.e. t i , according to the arrival period of each node, determine the grid system operation parameters, and initialize the constraint conditions in S22 and the parameters in the wind curtailment cost calculation function in S21 according to the grid system operation parameters.

[0121] S24: Solve the second mathematical model by any commercial solver to obtain the first hybrid energy storage operation scheme.

[0122] S30: According to the first path and the first hybrid energy storage operation scheme, issue scheduling instructions to the hybrid energy storage system at each period.

[0123] In summary, the mobile hybrid energy storage system scheduling method provided by the embodiments has the following beneficial effects: by establishing the path planning problem of the mobile energy storage between the microgrids (i.e., between the nodes), the optimal first path of the mobile energy storage during transportation between the grids is first obtained, the time spent by the mobile energy storage during transportation is reduced, thereby improving the time of the mobile energy storage actually participating in the wind turbine energy storage regulation and control in the entire hybrid energy storage system scheduling interval, and further improving the safety of the grid system. In addition, based on the first path with the optimal transportation time, the operation scheme of each mobile energy storage and the super capacitor in the hybrid energy storage system is optimized, the mobile characteristics of the mobile energy storage are reasonably utilized under the premise of ensuring the safety of the grid, without the need to set fixed mobile energy storages at each node, and the operation cost of the hybrid energy storage system is reduced.

[0124] Embodiment Two

[0125] Referring to FIG. 2, the mobile hybrid energy storage system scheduling device provided by the embodiments includes a first mathematical model solving module 11, a second mathematical model solving module 12, and a scheduling instruction issuing module 13.

[0126] The first mathematical model solving module 11 is configured to obtain path planning parameters, initialize and solve a preset first mathematical model according to the path planning parameters, and obtain a first path. The first mathematical model is built to minimize the transportation time of the mobile energy storage in the hybrid energy storage system.

[0127] The second mathematical model solving module 12 is configured to obtain grid system operation parameters, initialize and solve a preset second mathematical model in combination with the first path, and obtain a first hybrid energy storage operation scheme. The second mathematical model is built to minimize the hybrid energy storage operation cost and the amount of abandoned wind power.

[0128] The scheduling instruction issuing module 13 is configured to issue scheduling instructions to the hybrid energy storage system at each time interval according to the first path and the first hybrid energy storage operation scheme.

[0129] In some embodiments of the present application, the first mathematical model solving module 11 is configured to obtain path planning parameters and initialize and solve a preset first mathematical model according to the path planning parameters, including:

[0130] 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 at each time interval. The second parameter matrix is composed of the distance between any two nodes.

[0131] initialize a first objective function of the first mathematical model according to the first parameter matrix; the first objective function is used to calculate a transportation time corresponding to the first path in combination with the first parameter matrix;

[0132] initialize a first constraint condition of the first mathematical model according to the second parameter matrix; the first constraint condition is used to constrain a connection between any two nodes in combination with the second parameter matrix;

[0133] obtain the first path by solving the first mathematical model through a path planning algorithm.

[0134] 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:

[0135] The first objective function is specifically:

[0136] wherein, is a unit distance traffic jam time corresponding to the path of the i-th path decision at t i is a total number of nodes; l i is a straight line distance corresponding to the path of the i-th path decision; v is an average driving speed of the vehicle; and T is the transportation time corresponding to the first path.

[0137] In some embodiments of the present application, the first constraint condition is used to constrain the connection between any two nodes in combination with the second parameter matrix, including:

[0138] The second parameter matrix is specifically:

[0139] The first constraint condition is specifically:

[0140] wherein, L is the second parameter matrix; l j,h represents a distance from a node j to a node h, j, h ∈ {1, 2, …, N}; N is a total number of nodes.

[0141] In some embodiments of the present application, the second mathematical model solving module 12 is used to obtain power grid system operation parameters, initialize and solve a preset second mathematical model in combination with the first path, and obtain a first hybrid energy storage operation scheme, including:

[0142] initialize a second objective function of the second mathematical model according to an arrival order of each node in the first path in combination with the power grid system operation parameters;

[0143] initialize a second constraint condition of the second mathematical model according to the power grid system operation parameter;

[0144] solve the initialized second mathematical model to obtain the first hybrid energy storage operation scheme.

[0145] In some embodiments of the present application, 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 parameter comprises:

[0146] The second objective function comprises a wind curtailment cost calculation function and a hybrid energy storage operation cost function.

[0147] The wind curtailment cost calculation function is specifically:

[0148] The hybrid energy storage operation cost function is specifically:

[0149] wherein C gu is the wind curtailment cost; c gu is a wind curtailment cost coefficient; P WT (t i ,j) is the active power output of the wind turbine at node j in time period t i ; P g (t i ,j) is the active power delivered to the grid by node j in time period t i ; P L (t i ) is the active power required by the load in time period t i ; P mb (t i ) is the charge and discharge power of the mobile energy storage in time period t i ; P sc (t i ,j) is the charge and discharge power of the super capacitor at node j in time period t i ; k mbe and k sce are respectively the unit time power maintenance cost coefficients of the mobile energy storage and the super capacitor; k scp and k mbp are respectively the unit capacity average prices of the mobile energy storage and the super capacitor; E sc (t i ,j) is the capacity of the super capacitor at node j in time period t i ; E mb (t i ) is the capacity of the mobile energy storage in time period t i ; T sc and T mbThe service life of the super capacitor and the mobile energy storage is respectively regulated; and r is a discount rate.

[0150] In some embodiments of the present application, the second constraint condition comprises a hybrid energy storage capacity constraint, a hybrid energy storage power scheduling constraint, and a hybrid energy storage state of charge constraint.

[0151] The hybrid energy storage capacity constraint is obtained by constraining the settable capacity of the mobile energy storage and the super capacitor in each time period.

[0152] The hybrid energy storage power scheduling constraint is obtained by constraining the sum of the charging and discharging power of the mobile energy storage and the super capacitor in each time period to meet the hybrid energy storage scheduling instruction.

[0153] The hybrid energy storage state of charge constraint is obtained by constraining the state of charge of the mobile energy storage and the super capacitor in each time period to meet the safe operation state.

[0154] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present application. The mobile hybrid energy storage system scheduling device provided by the embodiments of the present application can implement any one of the method item embodiments of the present application, i.e., the mobile hybrid energy storage system scheduling method provided by embodiment one.

[0155] In summary, the mobile hybrid energy storage system scheduling device provided by the embodiments of the present application has the following beneficial effects: by establishing a path planning problem of the mobile energy storage between each micro-grid (i.e., between nodes), the optimal first path of the mobile energy storage in the transportation between each grid is first obtained, the time spent by the mobile energy storage in the transportation process is reduced, thereby improving the time of the mobile energy storage actually participating in the wind turbine energy storage regulation and control in the entire hybrid energy storage system scheduling interval, and further improving the safety of the grid system. In addition, based on the first path with the optimal transportation time, the operation scheme of each mobile energy storage and the super capacitor in the hybrid energy storage system is optimized, the mobile characteristics of the mobile energy storage are reasonably utilized under the premise of ensuring the safety of the grid, fixed mobile energy storages do not need to be arranged at each node, and the operation cost of the hybrid energy storage system is reduced.

[0156] Embodiment three

[0157] On the basis of 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. The mobile hybrid energy storage system scheduling terminal device comprises 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 one embodiment of the present application is implemented.

[0158] For example, in this embodiment, the computer program can 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 can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the mobile hybrid energy storage system scheduling device.

[0159] The mobile hybrid energy storage system scheduling device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The mobile hybrid energy storage system scheduling terminal device can include, but is not limited to, a processor and a memory.

[0160] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The processor is the control center of the mobile hybrid energy storage system scheduling device, and connects various parts of the mobile hybrid energy storage system scheduling device through various interfaces and lines. The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the mobile hybrid energy storage system scheduling device by running or executing the computer program and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0161] Embodiment Four

[0162] On the basis of the above-mentioned embodiment of the mobile hybrid energy storage system scheduling method, another embodiment of the present application provides a storage medium, which comprises a stored computer program, wherein when the computer program runs, the device where the storage medium is located executes the mobile hybrid energy storage system scheduling method of any one of the embodiments of the present application.

[0163] In this embodiment, the storage medium is a computer readable storage medium, the computer program comprises computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to the legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0164] The above-mentioned specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application, and it should be understood that the above-mentioned is only for the specific embodiments of the present application and is not used to limit the protection scope 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 principles of the present application shall be included in the protection scope of the present application.

Claims

1. A mobile hybrid energy storage system dispatching method, characterized in that, The method comprises the following steps: obtaining path planning parameters, and initializing and solving a preset first mathematical model according to the path planning parameters to obtain a first path; the first mathematical model is built to minimize the transportation time of mobile energy storage in the hybrid energy storage; obtaining grid system operation parameters, and initializing and solving a preset second mathematical model in combination with the first path to obtain a first hybrid energy storage operation scheme; the second mathematical model is built to minimize the hybrid energy storage operation cost and wind curtailment; according to the first path and the first hybrid energy storage operation scheme, issuing a dispatch instruction to the hybrid energy storage system at each time period.

2. The dispatch method of claim 1, wherein, The method comprises the following steps: wherein the path planning parameters comprise 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 at each time period; and 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; solving the first mathematical model by a path planning algorithm to obtain the first path. The first objective function is used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix, and comprises:

3. The dispatch method of claim 2, wherein, The first constraint condition is used to constrain the connectivity between any two nodes in combination with the second parameter matrix, and comprises: The first objective function, in particular is: wherein For the period of t i , the unit distance traffic congestion time of the path corresponding to the ith path decision; N is the total number of nodes; l i is the straight line distance of the path corresponding to the ith path decision; v is the average driving speed of the vehicle; and T is the transportation time corresponding to the first path.

4. The dispatch method of claim 2, wherein, The method comprises the following steps: The second parameter matrix, in particular: The first constraint condition is specifically: where L is a 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. The dispatch method of claim 1, wherein, initializing a second objective function of the second mathematical model according to the arrival order of each node in the first path in combination with the grid system operation parameters; initializing a second constraint condition of the second mathematical model according to the grid system operation parameters; solving the initialized second mathematical model to obtain the first hybrid energy storage operation scheme. The method comprises the following steps:

6. A mobile hybrid energy storage system dispatching method according to claim 5, wherein, The second objective function comprises a wind curtailment cost calculation function and a hybrid energy storage operation cost function; The second constraint condition comprises a hybrid energy storage capacity constraint, a hybrid energy storage power dispatch constraint and a hybrid energy storage state of charge constraint; The abandoned wind cost calculation function is specifically: The hybrid energy storage operation cost function is specifically: wherein C gu is the curtailment cost; c gu is the curtailment cost coefficient; P WT (t i ,j) is the active power output of the wind turbine at node j in time period t i ; P g (t i ,j) is the active power delivered to the grid by node j in time period t i ; P L (t i ) is the active power required by the load in time period t i ; P mb (t i ) is the charge / discharge power of the mobile energy storage in time period t i ; P sc (t i ,j) is the charge / discharge power of the supercapacitor at node j in time period t i ; k mbe and k sce are the unit time power maintenance cost coefficients of the mobile energy storage and the supercapacitor, respectively; k scp and k mbp are the unit capacity average prices of the mobile energy storage and the supercapacitor, respectively; E sc (t i ,j) is the capacity of the supercapacitor at node j in time period t i ; E mb (t i ) is the capacity of the mobile energy storage in time period t i ; T sc and T mb are the specified service life of the supercapacitor and the mobile energy storage, respectively; and r is the discount rate.

7. The dispatch method of claim 5, wherein, The hybrid energy storage capacity constraint is obtained by constraining the capacity of the mobile energy storage and the super capacitor at each time period; The hybrid energy storage power dispatch constraint is obtained by constraining the sum of the charging and discharging power of the mobile energy storage and the super capacitor at each time period to meet the hybrid energy storage dispatch instruction; and ​ The mixed energy storage state of charge constraint is built to satisfy a safe operation state by constraining the state of charge of the mobile energy storage and the super capacitor in each time period. 8.A mobile hybrid energy storage system scheduling device, characterized in that, It comprises: 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 configured to obtain path planning parameters, initialize and solve a preset first mathematical model according to the path planning parameters, and obtain a first path. The first mathematical model is built to minimize the transportation time of the mobile energy storage in the mixed energy storage. The second mathematical model solving module is configured to obtain power grid system operation parameters, initialize and solve a preset second mathematical model in combination with the first path, and obtain a first mixed energy storage operation scheme.

9. A mobile hybrid energy storage system dispatching device according to claim 8, wherein, The second mathematical model is built to minimize the mixed energy storage operation cost and the amount of abandoned wind power. The scheduling instruction issuing module is configured to issue scheduling instructions to the mixed energy storage system in each time period according to the first path and the first mixed energy storage operation scheme. The first mathematical model solving module is configured to obtain path planning parameters, initialize and solve a preset first mathematical model according to the path planning parameters, It comprises: The path planning parameters comprise 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.

10. A mobile hybrid energy storage system dispatching device according to claim 9, wherein, The second parameter matrix is composed of the distance between any two nodes. The first objective function, in particular is: wherein, The unit distance traffic jam time corresponding to the path of the ith path decision in the time period t i N is the total number of nodes; l i is the straight line distance of the ith path decision; v is the average driving speed of the vehicle; and T is the transportation time corresponding to the first path.

11. A mobile hybrid energy storage system dispatching device according to claim 9, wherein, The first objective function of the first mathematical model is initialized according to the first parameter matrix. The second parameter matrix, in particular: The first constraint condition, in particular is: where L is a 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 dispatch device according to claim 8, wherein, The first objective function is used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix. The first constraint condition of the first mathematical model is initialized 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 path is obtained by solving the first mathematical model through a path planning algorithm.

13. A mobile hybrid energy storage system dispatch device according to claim 12, wherein, The first objective function is used to calculate the transportation time corresponding to the first path in combination with the first parameter matrix, which comprises: The first constraint condition is used to constrain the connectivity between any two nodes in combination with the second parameter matrix, which comprises: The second mathematical model solving module is configured to obtain power grid system operation parameters, initialize and solve a preset second mathematical model in combination with the first path, and obtain a first mixed energy storage operation scheme, which comprises: The second objective function of the second mathematical model is initialized according to the arrival order of each node in the first path in combination with the power grid system operation parameters. The second constraint condition of the second mathematical model is initialized according to the power grid system operation parameters. The first mixed energy storage operation scheme is obtained by solving the initialized second mathematical model. The second objective function of the second mathematical model is initialized according to the arrival order of each node in the first path in combination with the power grid system operation parameters, which comprises: The second objective function comprises an abandoned wind power cost calculation function and a mixed energy storage operation cost function. The abandoned wind cost calculation function is specifically: The hybrid energy storage operation cost function is specifically: Among them, C gu Cost of wind curtailment; c gu P represents the cost coefficient for wind curtailment. WT (t i (j) represents the time period t i The active power output of the wind turbine at node j; P g (t i (j) represents the time period t i The active power supplied by node j to the grid; P L (t i ) represents the time period t i The active power required by the load; P mb (t i For mobile energy storage during period t i The charging and discharging power; P sc (t i (j) represents the supercapacitor at node j during time period t. i The charging and discharging power; k mbe and k sce These are the unit-time power maintenance cost coefficients for mobile energy storage and supercapacitors, respectively; k scp and k mbp These are the average unit capacity prices for mobile energy storage and supercapacitors, respectively; E sc (t i (j) represents the supercapacitor at node j during time period t. i capacity; E mb (t i For mobile energy storage during period t i The capacity; T sc and T mb These represent the specified service life of supercapacitors and mobile energy storage, respectively; r is the discount rate.

14. A mobile hybrid energy storage system dispatch device according to claim 12, wherein, The second constraint condition comprises a hybrid energy storage capacity constraint, a hybrid energy storage power scheduling constraint and a hybrid energy storage state of charge constraint. The hybrid energy storage capacity constraint is obtained by constraining the settable capacity of the mobile energy storage and the super capacitor in each time period. The hybrid energy storage power scheduling constraint is obtained by constraining the sum of the charging and discharging power of the mobile energy storage and the super capacitor in each time period to meet the hybrid energy storage scheduling instruction. The hybrid energy storage state of charge constraint is obtained by constraining the state of charge of the mobile energy storage and the super capacitor in each time period to meet the safe operation state.

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