Distributed Energy Emergency Scheduling Method and System for Distribution Network Considering Mobile Energy Storage

Abstract

The present invention relates to a method and system for distributed energy emergency scheduling of a distribution network considering mobile energy storage. The method includes constructing a constraint model for mobile energy storage; constructing a constraint model for the distribution network; taking the minimum operation cost of the distribution network as the objective and using the distribution network constraint model and the mobile energy storage constraint model as constraint conditions, constructing a distributed energy emergency scheduling model for the distribution network considering mobile energy storage resources; and using the adaptive penalty coefficient - alternating direction method of multipliers to iteratively solve the distributed energy emergency scheduling model for the distribution network to obtain an optimal emergency scheduling plan for the distribution network. The present invention takes into account the participation of mobile energy storage resources, can effectively improve the flexibility, economy, and reliability of emergency power supply in urban distribution networks; at the same time, it uses a distributed optimization framework and the adaptive penalty coefficient - alternating direction method of multipliers, taking into account both economic optimality and algorithm convergence, and promoting the healthy development of urban distribution networks.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation and energy planning, and particularly to a distribution network distributed energy emergency scheduling method and system considering mobile energy storage. Background Art

[0002] Due to the occurrence of small-probability high-risk extreme disaster events, it may lead to large-scale and long-term power outages in urban distribution networks, causing each microgrid in the urban distribution network to enter an islanded operation state. In this scenario, limited by the energy storage system and the shortage of diesel engines or stored electricity and fuel, relying solely on distributed generation and energy storage systems may not be able to meet the power supply requirements of some critical loads. Therefore, the flexible application of mobile energy storage for emergency power supply to all electrical loads in the distribution network and the compensation for the shortage of power resources in each microgrid have become research hotspots.

[0003] Currently, domestic and foreign scholars have carried out a large number of studies on the problem of mobile energy storage resources participating in the energy emergency scheduling of urban distribution networks. However, there are two deficiencies in the existing research. One is to conduct centralized optimization by treating the studied distribution network as a whole, ignoring the objective conditions of different operating entities in each microgrid and the need to protect the privacy information of important electrical loads. In existing research, this problem is often not improved and a distributed optimization scheduling framework is not designed, resulting in the difficulty of practical application of the proposed methods and models. The other is that in the process of applying the traditional alternating direction method of multipliers (ADMM) for distributed optimization scheduling, the selection of the penalty coefficient has a great influence on the convergence of the algorithm. A smaller penalty coefficient usually leads to slow convergence of the penalty coefficient, and a larger penalty coefficient is likely to lead to slow convergence of the decision variables. Thus, it can be seen that if the penalty coefficient is not properly selected, it will seriously affect the convergence of the standard ADMM algorithm. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a distribution network distributed energy emergency scheduling method and system considering mobile energy storage, which can overcome the deficiencies of traditional models, provide a more flexible emergency power supply plan for urban distribution networks, and at the same time take into account the economic optimality and algorithm convergence, promoting the healthy development of urban distribution networks.

[0005] The technical solution of the present invention to solve the above technical problem is as follows: A distribution network distributed energy emergency scheduling method considering mobile energy storage includes the following steps,

[0006] S1. Construct a mobile energy storage constraint model according to the discrete energy flow characteristics and traffic flow characteristics of mobile energy storage;

[0007] S2. Construct a distribution network constraint model composed of diesel engines, wind power, photovoltaic power, and energy storage systems and including the access of mobile energy storage according to the IEEE distribution network topology;

[0008] S3. With the goal of minimizing the operation cost of the distribution network and taking the distribution network constraint model and the mobile energy storage constraint model as constraints, construct a distribution network distributed energy emergency scheduling model considering mobile energy storage resources;

[0009] S4. Use the adaptive penalty coefficient - alternating direction multiplier method to iteratively solve the distribution network distributed energy emergency scheduling model to obtain the optimal emergency scheduling plan for the distribution network.

[0010] Based on the above - mentioned distribution network distributed energy emergency scheduling method considering mobile energy storage, the present invention also provides a distribution network distributed energy emergency scheduling system considering mobile energy storage.

[0011] A distribution network distributed energy emergency scheduling system considering mobile energy storage includes the following modules.

[0012] A mobile energy storage constraint model construction module, which is used to construct a mobile energy storage constraint model according to the discrete energy flow characteristics and traffic flow characteristics of mobile energy storage;

[0013] A distribution network constraint model construction module, which is used to construct a distribution network constraint model composed of diesel engines, wind power, photovoltaic and energy storage systems and including the access of mobile energy storage according to the IEEE distribution network topology structure;

[0014] A distribution network distributed energy emergency scheduling model construction module, which is used to take the minimization of the distribution network operation cost as the goal and take the distribution network constraint model and the mobile energy storage constraint model as constraints to construct a distribution network distributed energy emergency scheduling model considering mobile energy storage resources;

[0015] An iterative solution module, which is used to use the adaptive penalty coefficient - alternating direction multiplier method to iteratively solve the distribution network distributed energy emergency scheduling model to obtain the optimal emergency scheduling plan for the distribution network.

[0016] Based on the above - mentioned distribution network distributed energy emergency scheduling method considering mobile energy storage, the present invention also provides a computer storage medium.

[0017] A computer storage medium includes a memory and a computer program stored in the memory. When the computer program is executed by a processor, it implements the distribution network distributed energy emergency scheduling method considering mobile energy storage as described above.

[0018] The beneficial effects of the present invention are as follows: A distribution network distributed energy emergency scheduling method, system and computer storage medium considering mobile energy storage in the present invention, in the scenario of mobile energy storage resources participating in the energy emergency scheduling of urban distribution networks, constructs a mobile energy storage constraint model by combining discrete energy flow and traffic flow models; based on the IEEE distribution network topology structure, constructs a distribution network constraint model composed of diesel engines, wind power, photovoltaic and energy storage systems and including the access of mobile energy storage; thus, with the goal of minimizing the operation cost of the distribution network and taking the distribution network constraint model and the mobile energy storage constraint model as constraint conditions, establishes a distributed energy emergency scheduling model for urban distribution networks considering mobile energy storage resources; considering that the convergence characteristics of the traditional alternating direction multiplier method are seriously affected by the iteration coefficient, adopts the adaptive penalty coefficient - alternating direction multiplier method to adaptively adjust the penalty coefficient during the iteration process, and uses the gurobi commercial solver to iteratively solve the distributed energy emergency scheduling model of the urban distribution network to obtain the optimal emergency scheduling plan for the urban distribution network. Therefore, compared with the traditional energy emergency scheduling plan for urban distribution networks, the present invention takes into account the participation of mobile energy storage resources, can effectively improve the flexibility, economy and reliability of emergency power supply in urban distribution networks, and at the same time adopts a distributed optimization framework to effectively protect the privacy information of important loads in urban distribution networks, provides an optimal strategy for the energy emergency scheduling of urban distribution networks, better conforms to the development direction of future urban power distribution systems, and promotes the healthy development of urban distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flowchart of a distribution network distributed energy emergency scheduling method considering mobile energy storage according to the present invention;

[0020] Figure 2 is a topological schematic diagram of an improved IEEE 33-node distribution network system adopted in the embodiment;

[0021] Figure 3 is a curve graph of wind power output, photovoltaic output and load prediction adopted in the embodiment;

[0022] Figure 4 is a schematic diagram of the optimal scheduling path of the simulated mobile energy storage;

[0023] Figure 5 is a schematic diagram of the microgrid load supply strategy of the simulation;

[0024] Figure 6 is a schematic diagram of the node voltage change situation under different simulation scenarios;

[0025] Figure 7 is a schematic diagram of the iterative convergence situation under different algorithms;

[0026] Figure 8This is the structural block diagram of a distribution network distributed energy emergency dispatch system considering mobile energy storage according to the present invention. Detailed implementation manners

[0027] The principles and features of the present invention will be described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0028] As Figure 1 shown, a distribution network distributed energy emergency dispatch method considering mobile energy storage includes the following steps:

[0029] S1. Construct a mobile energy storage constraint model according to the discrete energy flow characteristics and traffic flow characteristics of mobile energy storage.

[0030] S2. Construct a distribution network constraint model composed of diesel engines, wind power, photovoltaic power, and energy storage systems and including the access of mobile energy storage according to the IEEE distribution network topology.

[0031] S3. With the goal of minimizing the operation cost of the distribution network and using the distribution network constraint model and the mobile energy storage constraint model as constraint conditions, construct a distribution network distributed energy emergency dispatch model considering mobile energy storage resources.

[0032] S4. Use the adaptive penalty coefficient - alternating direction method of multipliers to iteratively solve the distribution network distributed energy emergency dispatch model to obtain the optimal emergency dispatch plan for the distribution network.

[0033] The following is a specific description of S1 - S4:

[0034] S1. Construct a mobile energy storage constraint model according to the discrete energy flow characteristics and traffic flow characteristics of mobile energy storage.

[0035] After a power outage accident occurs, after receiving a fault warning, the urban distribution network emergency command center needs to quickly conduct an emergency response, make a decision, and allocate local controllable emergency resources to generate the current optimal urban distribution network energy emergency dispatch plan. Among them, for the local controllable mobile energy storage resources, considering their dual characteristics of discrete energy flow and traffic flow, the mobile energy storage constraint model composed of discrete energy flow and traffic flow is shown in the following formulas (1) - (4); specifically, the mobile energy storage constraint model includes a mobile energy storage traffic flow constraint model and a mobile energy storage discrete energy flow constraint model.

[0036] The mobile energy storage traffic flow constraint model is

[0037]

[0038] Among them, let the initial moment when the power outage accident occurs be the 0 period. For all mobile energy storage aggregations, n is the mobile energy storage variable; For the node set in the distribution network, i and j are node variables; For the time period set of emergency dispatching after a power outage accident, h is the time period variable; For the 0-1 position flag variable of the mobile energy storage, specifically indicating whether the mobile energy storage n has reached node i in the h time period. If it has reached, then If it has not reached, then θ ji Indicates the actual distance between node j and node i, and ΔH represents the length of each time period;

[0039] The discrete energy flow constraint model of the mobile energy storage is as follows,

[0040]

[0041]

[0042]

[0043] Among them, Are all constants, Are respectively the maximum active discharge power, maximum active charge power, and maximum reactive power of the power battery in the mobile energy storage n, Are respectively the maximum active discharge power, maximum active charge power, and maximum reactive power of the energy storage battery in the mobile energy storage n, Represents the active discharge power of the power battery in the mobile energy storage n in the h time period, Respectively represent the active power absorbed and reactive power absorbed by the power battery in the mobile energy storage n through node i in the h time period, Respectively represent the active power absorbed, active power released, and reactive power absorbed by the energy storage battery in the mobile energy storage n through node i in the h time period, Are all constants and respectively represent the capacities of the power battery and energy storage battery in the mobile energy storage n, Are all constants, Respectively represent the initial state of charge, minimum state of charge, and maximum state of charge of the power battery in the mobile energy storage n, Respectively represent the initial state of charge, minimum state of charge, and maximum state of charge of the energy storage battery in the mobile energy storage n, Are all constants, Respectively represent the charge-discharge efficiency coefficient and active capacity of the power battery in the mobile energy storage n, Respectively represent the charge-discharge efficiency coefficient and active capacity of the energy storage battery in the mobile energy storage n, are the state of charge of the power battery in the mobile energy storage n at time h and time H, respectively, are the state of charge of the energy storage battery in the mobile energy storage n at time h and time H, respectively.

[0044] Specifically, Equation (1) represents the traffic flow constraint of the mobile energy storage, including the location uniqueness constraint and the transfer time constraint. Equations (2)-(4) represent the discrete energy flow constraint of the mobile energy storage, which is restricted by the 0-1 location flag variable of the mobile energy storage Among them, Equation (2) represents the active power and reactive power constraints of the power battery and the energy storage battery of the mobile energy storage. Equation (3) represents the capacity constraints of the power battery and the energy storage battery of the mobile energy storage. Equation (4) represents the state of charge constraint of the mobile energy storage.

[0045] S2. According to the IEEE distribution network topology, construct a distribution network constraint model composed of diesel engines, wind power, photovoltaic power, and energy storage systems and including the access of mobile energy storage.

[0046] In the optimal urban distribution network energy emergency dispatch plan after a power outage accident, for the access of locally controllable diesel engines, wind power, photovoltaic power, energy storage system resources, and mobile energy storage, based on the IEEE distribution network topology, the distribution network constraint model composed of diesel engines, wind power, photovoltaic power, and energy storage systems and including the access of mobile energy storage is as shown in the following equations (5)-(8); specifically, the distribution network constraint model includes a diesel engine operation constraint model, a wind power and photovoltaic operation constraint model, an energy storage system operation constraint model, and a distribution network power flow constraint model;

[0047] The diesel engine operation constraint model is

[0048]

[0049] Among them, is the output operation state variable of the diesel engine configured at node i in the distribution network at time h. If the diesel engine configured at node i in the distribution network is in the operating state at time h, then If the diesel engine configured at node i in the distribution network is in the shutdown state at time h, then is the start-stop state variable of the diesel engine configured at node i in the distribution network at time h-1. If the diesel engine configured at node i in the distribution network is in the starting action at time h-1, then If the diesel engine configured at node i in the distribution network is in the stopping action at time h-1, then are all constants and respectively represent the minimum active power, maximum active power, minimum reactive power, maximum reactive power, and maximum active power change rate of the diesel engine configured at node i, represents the active power and reactive power injected by the diesel engine configured at node i at time h;

[0050] The wind power and PV operation constraint model is as follows:

[0051]

[0052] Wherein, are respectively the maximum outputs of the wind power and PV configured at node i in hour h, are respectively the actual outputs of the wind power and PV configured at node i in hour h;

[0053] The energy storage system operation constraint model is as follows:

[0054]

[0055] Wherein, are all constants and respectively represent the initial state of charge, minimum state of charge, maximum state of charge, minimum charging power, maximum charging power, minimum discharging power, maximum discharging power, capacity, charge-discharge efficiency coefficient, and active capacity of the energy storage system configured at node i, respectively represent the active power absorbed, active power released, and reactive power released by the energy storage system through node i in hour h, respectively represent the state of charge of the energy storage system configured at node i in hour h and hour H;

[0056] The distribution network power flow constraint model is as follows:

[0057]

[0058] Wherein, represents the set of lines in the distribution network, and (i, m) represents the line composed of nodes i and m r ji 、x ji 、 are all constants and respectively represent the resistance, reactance, maximum transmission capacity, and square value of the maximum transmission complex current of the line ; respectively represent the square value of the minimum complex voltage and the square value of the maximum complex voltage of node i, is the active power demand and reactive power demand of the load configured at node i in hour h, P ji,h 、Q ji,h 、l ji,h respectively represent the active power transmission, reactive power transmission, and square value of the transmission current of the line in hour h, v i,h represents the square value of the complex voltage v of node i in hour h j,h represents the square value of the complex voltage of node j in hour h.

[0059] Specifically, Equation (5) represents the operation constraints of the diesel engine configured at node i, Equation (6) represents the operation constraints of the wind power and photovoltaic power configured at node i, and Equation (7) represents the operation constraints of the energy storage system configured at node i, including state of charge constraints, power constraints, and capacity constraints. Equation (8) represents the operation constraints based on the Distflow power flow equation, including node power balance constraints, voltage magnitude constraints, branch transmission capacity constraints, transmission current constraints, and transmission power constraints.

[0060] S3. With the goal of minimizing the operation cost of the distribution network and using the distribution network constraint model and the mobile energy storage constraint model as constraints, a distribution network distributed energy emergency scheduling model considering mobile energy storage resources is constructed.

[0061] The objective function with the goal of minimizing the operation cost of the distribution network is

[0062]

[0063]

[0064] Among them, is the inherent configuration operation cost of node i at hour h, including the operation cost of the energy storage system and the operation cost of the diesel engine is the operation cost of mobile energy storage n at hour h, including the operation cost of the power battery and the operation cost of the energy storage battery p G 、a i 、b i 、c i 、 are all constants and respectively represent the unit fuel price, diesel engine start-stop cost coefficient, diesel engine operation cost coefficient, diesel engine power generation cost coefficient, energy storage system depreciation cost coefficient, power battery depreciation cost coefficient, and energy storage battery depreciation cost coefficient;

[0065] Then the distribution network distributed energy emergency scheduling model is

[0066]

[0067]

[0068] s.t. (1)-(8)

[0069] S4. The adaptive penalty coefficient - alternating direction multiplier method is used to iteratively solve the distribution network distributed energy emergency scheduling model to obtain the optimal emergency scheduling plan for the distribution network.

[0070] S4 specifically includes the following S41 - S43,

[0071] S41. Transform the distribution network distributed energy emergency scheduling model into the augmented Lagrangian form.

[0072] The augmented Lagrangian form of the distribution network distributed energy emergency scheduling model is

[0073]

[0074]

[0075] where is the augmented Lagrangian function, and x and y respectively represent the optimization decision variables of node i and mobile energy storage n in the distribution network; α and β are both given penalty coefficients; are the actual active power interaction variable and the virtual active power interaction variable respectively, and respectively represent the active power injected by mobile energy storage n into node i at time h and the active power collected by node i from mobile energy storage n at time h; are the actual reactive power interaction variable and the virtual reactive power interaction variable respectively, and respectively represent the reactive power injected by mobile energy storage n into node i at time h and the reactive power collected by node i from mobile energy storage n at time h; δ is the Lagrangian multiplier vector between the actual active power interaction variable and the virtual active power interaction variable, is the Lagrangian multiplier vector between the actual reactive power interaction variable and the virtual reactive power interaction variable. Specifically, δ n,i,h represents the Lagrangian multiplier vector between the reactive power injected by mobile energy storage n into node i at time h and the reactive power collected by node i from mobile energy storage n at time h, represents the Lagrangian multiplier vector between the reactive power injected by mobile energy storage n into node i at time h and the reactive power collected by node i from mobile energy storage n at time h.

[0076] S42. Based on the distribution network distributed energy emergency scheduling model in the augmented Lagrangian form, decompose the energy emergency scheduling problem into a node optimization sub - problem and a mobile energy storage optimization sub - problem.

[0077] The expression of the node optimization sub - problem is

[0078]

[0079] The expression of the mobile energy storage optimization sub - problem is

[0080]

[0081] where k ∈ {1, 2,..., K} is the number of iterations. is the set of optimized decision variables for node i in the distribution network, is the set of optimized decision variables for mobile energy storage n, x i (k + 1) is the optimized decision variable of node i in the distribution network at the (k + 1)-th iteration, x i is the optimized decision variable of node i in the distribution network, y n (k + 1) is the optimized decision variable of mobile energy storage n at the (k + 1)-th iteration, y n is the optimized decision variable of mobile energy storage n, δ(k), both are the penalty coefficients at the (k + 1)-th iteration, is the actual active power interaction variable at the k-th iteration, is the virtual active power interaction variable at the (k + 1)-th iteration, is the actual reactive power interaction variable at the k-th iteration, is the virtual reactive power interaction variable at the (k + 1)-th iteration, is problem P 1 The augmented Lagrangian function when α and β are given.

[0082] S43. Use the adaptive penalty coefficient - alternating direction multiplier method to iteratively solve the node optimization sub-problem and the mobile energy storage optimization sub-problem to obtain the optimal emergency scheduling plan for the distribution network.

[0083] The specific content of S43 is as follows,

[0084] S431. Initialize the node optimization sub-problem and the mobile energy storage optimization sub-problem: k = 1, α(k), β(k) > 0, K > 1; Define three constants λ 1 、λ 2 、M, and λ 1 , λ 2 > 0, M > 1; where, α(k) and β(k) are the penalty coefficients at the k-th iteration;

[0085] S432. Collect the power demand configured at each node in the h-hour period after a power outage accident through the distribution network The maximum output values of wind power and photovoltaic power configured at each node The state of charge of the energy storage system configured at each node The diesel engine state variables configured at each node The initial positions of each mobile energy storage And the state of charge of the power battery and the energy storage battery

[0086] S433. All nodes collect Solve the node optimization decision variable \(x\) according to the expression of the node optimization sub - problem by combining the parameters collected in S432 i (k + 1) and the interaction variable And send the interaction variable To the mobile energy storage;

[0087] S434, all mobile energy storages collect the interaction variables sent Solve the mobile energy storage optimization decision variable \(y\) according to the expression of the mobile energy storage optimization sub - problem by combining the parameters collected in S432 n (k + 1) and the interaction variable

[0088] S435, judge whether the interaction variable obtained by solving in S433 And the interaction variable obtained by solving in S434 Satisfy the convergence of the following formula (15);

[0089]

[0090] Wherein, Is the original residual between the interaction variables , Is the original residual between the interaction variables , φ 1 (k + 1) is the dual residual between the interaction variables , φ 2 (k + 1) is the dual residual between the interaction variables ;

[0091] If the interaction variable obtained by solving in S433 And the interaction variable obtained by solving in S434 Satisfy the convergence of formula (15), then the node optimization decision variable \(x\) obtained by solving in S433 i (k + 1) and the mobile energy storage optimization decision variable \(y\) obtained by solving in S434 n (k + 1) are the optimal emergency dispatching schemes of the distribution network in the \(h\) - hour after the power outage accident;

[0092] If the interaction variable obtained by solving in S433 And the interaction variable obtained by solving in S434 Do not satisfy the convergence of formula (15), then update the penalty coefficients \(\alpha\), \(\beta\) and the Lagrange multiplier vector \(\delta\), And based on the updated penalty coefficients \(\alpha\), \(\beta\) and the Lagrange multiplier vector \(\delta\), Return to S433, continue with distributed iteration until the convergence of formula (15) is satisfied or the maximum number of iterations K is reached;

[0093] Among them, the formulas for updating the penalty coefficients α and β are

[0094]

[0095] The formula for updating the Lagrange multiplier vector δ, is

[0096]

[0097] S4 can be solved using the Gurobi solver. After solving the model, what is obtained is the optimal emergency dispatch plan for the urban distribution network considering mobile energy storage resources within H hours after a power outage fault, including the output of diesel engines, wind power, photovoltaic power, and energy storage systems, the traffic transfer and output of mobile energy storage, and the node voltages, branch currents, transmission powers, etc. of the distribution network.

[0098] The following are specific embodiments of the present invention:

[0099] This embodiment adopts Figure 2 the improved IEEE 33-node distribution network topology shown in the figure. Among them, nodes #1-13 and #19-28 form Microgrid 1, nodes #14-18 form Microgrid 2, and nodes #29-33 form Microgrid 3. The access situations of diesel engines, wind power, photovoltaic power, energy storage power station systems, and mobile energy storage are as Figure 2 shown in the figure. Considering that the time scale of the urban distribution network emergency dispatch is 4 hours and the unit dispatch time is 10 minutes. The test scenario is that after a power outage accident occurs, the connection between the distribution network represented by the test system and the large power grid is interrupted, and at the same time, each microgrid enters the island operation state.

[0100] Set the charge and discharge efficiency of the energy storage system and mobile energy storage to 90%. Use ternary lithium batteries and lead-acid batteries as the power battery and energy storage battery of the mobile energy storage respectively, with specifications of 200 kWh (capacity) / 120 kW (rated charging power) / 50 kW (rated discharge power) and 3000 kWh (capacity) / 800 kW (rated charging power) / 800 kW (rated discharge power) respectively. The relevant coefficients of the diesel engine are designed as a = 0.0083, b = 0.05, c = 0.2, and the diesel price is set at 9 yuan / L. Further, use Figure 3The typical data of the electricity load, wind power, and photovoltaic power shown are used as the prediction data for the three microgrids after a power outage accident. The distances between the mobile energy storage at Node #28 (Microgrid 1), Node #18 (Microgrid 2), and Node #33 (Microgrid 3) are set to 10 km, 10 km, and 20 km respectively, and the driving speed is set to 60 km / h. Therefore, the driving times of the mobile energy storage at the access points of the three microgrids are: 10 minutes, 10 minutes, and 20 minutes. The optimal scheduling path with 2 mobile energy storages as an example is as Figure 4 shown. The operating costs of the urban distribution network with and without considering the participation of mobile energy storage resources are calculated respectively as shown in Table 1. Therefore, the flexible use of mobile energy storage can save about 22.22% of the operating cost. The load supply situations of the three microgrids are as Figure 5 shown. It can be seen that the flexible use of mobile energy storage resources can effectively reduce the dependence on diesel engines and energy storage resources in each microgrid and effectively improve the power supply demand for critical loads. The nodes of Microgrid 1 are renumbered according to the power flow direction, and the images of the voltage of each node in Microgrid 1 changing with time with and without considering the participation of mobile energy storage resources are respectively made as Figure 6 shown. Compared with not considering the participation of mobile energy storage resources, the participation of mobile energy storage resources can effectively supplement electric energy and stabilize the node voltage. Based on the algorithm parameters λ 1 =λ 2 =0.05, α(1)=β(1)=0.0002, M=K=3, the iterative convergence images of the traditional ADMM algorithm and the adaptive penalty coefficient - ADMM algorithm are made as Figure 7 shown. The adaptive penalty coefficient - ADMM algorithm can effectively overcome the non - convergence problem caused by improper selection of the penalty coefficient.

[0101] Table 1

[0102]

[0103] Based on the above - mentioned distribution network distributed energy emergency scheduling method considering mobile energy storage, the present invention also provides a distribution network distributed energy emergency scheduling system considering mobile energy storage.

[0104] As Figure 8 shown, a distribution network distributed energy emergency scheduling system considering mobile energy storage includes the following modules,

[0105] A mobile energy storage constraint model construction module, which is used to construct a mobile energy storage constraint model according to the discrete energy flow characteristics and traffic flow characteristics of the mobile energy storage;

[0106] A distribution network constraint model construction module, which is used to construct a distribution network constraint model composed of a diesel engine, a wind power generation unit, a photovoltaic power generation unit and an energy storage system and including the access of mobile energy storage according to the IEEE distribution network topology;

[0107] A distribution network distributed energy emergency dispatch model construction module, which is used to construct a distribution network distributed energy emergency dispatch model considering mobile energy storage resources with the goal of minimizing the operation cost of the distribution network and with the distribution network constraint model and the mobile energy storage constraint model as the constraint conditions;

[0108] An iterative solution module, which is used to iteratively solve the distribution network distributed energy emergency dispatch model by using the adaptive penalty coefficient - alternating direction method of multipliers to obtain the optimal emergency dispatch plan for the distribution network.

[0109] Based on the above distribution network distributed energy emergency dispatch method considering mobile energy storage, the present invention also provides a computer storage medium.

[0110] A computer storage medium, including a memory and a computer program stored in the memory, where the computer program, when executed by a processor, implements the distribution network distributed energy emergency dispatch method considering mobile energy storage as described above.

[0111] In the scenario of the distribution network distributed energy emergency dispatch method, system and computer storage medium considering mobile energy storage of the present invention, a mobile energy storage constraint model is constructed by combining the discrete energy flow and traffic flow models when mobile energy storage resources participate in the energy emergency dispatch of the urban distribution network; based on the IEEE distribution network topology, a distribution network constraint model composed of a diesel engine, a wind power generation unit, a photovoltaic power generation unit and an energy storage system and including the access of mobile energy storage is constructed; thus, with the goal of minimizing the operation cost of the distribution network and with the distribution network constraint model and the mobile energy storage constraint model as the constraint conditions, a distributed energy emergency dispatch model for the urban distribution network considering mobile energy storage resources is established; considering that the convergence characteristics of the traditional alternating direction method of multipliers are seriously affected by the iteration coefficient, the adaptive penalty coefficient - alternating direction method of multipliers is used to adaptively adjust the penalty coefficient during the iteration process, and the urban distribution network distributed energy emergency dispatch model is iteratively solved by means of the gurobi commercial solver to obtain the optimal emergency dispatch plan for the urban distribution network. Therefore, compared with the traditional energy emergency dispatch plan for the urban distribution network, the present invention takes into account the participation of mobile energy storage resources, can effectively improve the flexibility, economy and reliability of the emergency power supply of the urban distribution network, and at the same time adopts a distributed optimization framework to effectively protect the privacy information of the important loads of the urban distribution network, provides an optimal strategy for the energy emergency dispatch of the urban distribution network, is more in line with the development direction of the future urban power distribution system, and promotes the healthy development of the urban distribution network.

[0112] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A distributed energy emergency scheduling method for a distribution network considering mobile energy storage, characterized in that: It includes the following steps, S1. According to the discrete energy flow characteristics and traffic flow characteristics of mobile energy storage, construct a mobile energy storage constraint model; S2. According to the IEEE distribution network topology, construct a distribution network constraint model composed of diesel engines, wind power, photovoltaic power, and energy storage systems and including the access of mobile energy storage; S3. With the goal of minimizing the operation cost of the distribution network and using the distribution network constraint model and the mobile energy storage constraint model as constraint conditions, construct a distributed energy emergency scheduling model for the distribution network considering mobile energy storage resources; S4. Use the adaptive penalty coefficient - alternating direction multiplier method to iteratively solve the distributed energy emergency scheduling model of the distribution network to obtain the optimal emergency scheduling plan for the distribution network; In the S1, the mobile energy storage constraint model includes a mobile energy storage traffic flow constraint model and a mobile energy storage discrete energy flow constraint model; The mobile energy storage traffic flow constraint model is, Among them, is the set of all mobile energy storages, and n is the mobile energy storage variable; is the set of nodes in the distribution network, and i, j are node variables; is the set of time periods for emergency dispatching after a power outage accident, and h is the time period variable; is the 0-1 position flag variable of the mobile energy storage, specifically indicating whether the mobile energy storage n has reached node i at time period h. If it has reached, then If it has not reached, then θ ji represents the actual distance between node j and node i, and ΔH represents the length of each time period; The mobile energy storage discrete energy flow constraint model is, Among them, are respectively the maximum active discharge power, maximum active charge power, and maximum reactive power of the power battery in mobile energy storage n, are respectively the maximum active discharge power, maximum active charge power, and maximum reactive power of the energy storage battery in mobile energy storage n, represents the active discharge power of the power battery in mobile energy storage n during the h period, respectively represent the active power and reactive power absorbed by the power battery in mobile energy storage n through node i during the h period, respectively represent the active power absorbed, active power released, and reactive power absorbed by the energy storage battery in mobile energy storage n through node i during the h period, respectively represent the capacities of the power battery and energy storage battery in mobile energy storage n, respectively represent the initial state of charge, minimum state of charge, and maximum state of charge of the power battery in mobile energy storage n, respectively represent the initial state of charge, minimum state of charge, and maximum state of charge of the energy storage battery in mobile energy storage n, respectively represent the charge-discharge efficiency coefficient and active capacity of the power battery in mobile energy storage n, respectively represent the charge-discharge efficiency coefficient and active capacity of the energy storage battery in mobile energy storage n, are respectively the states of charge of the power battery in mobile energy storage n during the h period and H period, are respectively the states of charge of the energy storage battery in mobile energy storage n during the h period and H period; In the S2, the distribution network constraint model includes a diesel engine operation constraint model, a wind power and photovoltaic operation constraint model, an energy storage system operation constraint model, and a distribution network power flow constraint model; The diesel engine operation constraint model is, wherein, is the output operation state variable of the diesel engine configured for node i in the distribution network at hour h. If the diesel engine configured for node i in the distribution network is in the operating state at hour h, then If the diesel engine configured for node i in the distribution network is in the shutdown state at hour h, then is the start-stop state variable of the diesel engine configured for node i in the distribution network at hour h-1. If the diesel engine configured for node i in the distribution network is in the starting action at hour h-1, then If the diesel engine configured for node i in the distribution network is in the stopping action at hour h-1, then respectively represent the minimum active power, maximum active power, minimum reactive power, maximum reactive power, and maximum active power change rate of the diesel engine configured for node i; represents the active power and reactive power injected by the diesel engine configured for node i at hour h; The wind power and photovoltaic operation constraint model is, Among them, The maximum output powers of wind power and photovoltaic power configured for node i at hour h, The actual output powers of wind power and photovoltaic power configured for node i at hour h; The energy storage system operation constraint model is, Among them, respectively represent the initial state of charge, minimum state of charge, maximum state of charge, minimum charging power, maximum charging power, minimum discharging power, maximum discharging power, capacity, charge-discharge efficiency coefficient, and active capacity of the energy storage system configured at node i. respectively represent the active power absorbed, active power released, and reactive power released by the energy storage system through node i during hour h. respectively represent the state of charge of the energy storage system configured at node i during hour h and hour H. The distribution network power flow constraint model is, Among them, represents the set of lines in the distribution network, and (i, m) represents the line composed of nodes i and m r ji , x ji , respectively represent the resistance, reactance, maximum transmission capacity, and square value of the maximum transmission complex current of the line . respectively represent the square value of the minimum complex voltage and the square value of the maximum complex voltage of node i The active power demand and reactive power demand of the load configured for node i in the h-th period, P ji,h , Q ji,h , l ji,h respectively represent the active power transmission power, reactive power transmission power, and square value of the transmission current of the line in the h-th period, v i,h represents the square value of the complex voltage v of node i in the h-th period j,h represents the square value of the complex voltage of node j in the h-th period; In the S3, the objective function with the goal of minimizing the operation cost of the distribution network is, Among them, is the inherent configuration operation cost of node i in hour h, including the operation cost of the energy storage system and the operation cost of the diesel engine is the operation cost of mobile energy storage n in hour h, including the operation cost of the power battery and the operation cost of the energy storage battery p G 、a i 、b i 、c i 、 respectively represent the unit fuel price, the start-stop cost coefficient of the diesel engine, the operation cost coefficient of the diesel engine, the power generation cost coefficient of the diesel engine, the depreciation cost coefficient of the energy storage system, the depreciation cost coefficient of the power battery, and the depreciation cost coefficient of the energy storage battery; Then the distributed energy emergency scheduling model of the distribution network is, s.t. (1)-(8).

2. The distributed energy emergency scheduling method for a distribution network considering mobile energy storage according to claim 1, characterized in that: The S4 is specifically, S41. Transform the distributed energy emergency scheduling model of the distribution network into an augmented Lagrangian form; S42. Based on the distributed energy emergency scheduling model of the distribution network in the augmented Lagrangian form, decompose the energy emergency scheduling problem into a node optimization sub-problem and a mobile energy storage optimization sub-problem; S43. Use the adaptive penalty coefficient - alternating direction multiplier method to iteratively solve the node optimization sub-problem and the mobile energy storage optimization sub-problem to obtain the optimal emergency scheduling plan for the distribution network.

3. The distributed energy emergency scheduling method for a distribution network considering mobile energy storage according to claim 2, characterized in that: The augmented Lagrangian form of the distributed energy emergency scheduling model of the distribution network is, Among them, is the augmented Lagrangian function, where x and y respectively represent the optimization decision variables of node i and mobile energy storage n in the distribution network; α and β are both given penalty coefficients; are the actual active power interaction variable and the virtual active power interaction variable respectively, and represent the active power injected by mobile energy storage n into node i at time h and the active power collected by node i from mobile energy storage n at time h respectively; are the actual reactive power interaction variable and the virtual reactive power interaction variable respectively, and represent the reactive power injected by mobile energy storage n into node i at time h and the reactive power collected by node i from mobile energy storage n at time h respectively; δ is the Lagrangian multiplier vector between the actual active power interaction variable and the virtual active power interaction variable, is the Lagrangian multiplier vector between the actual reactive power interaction variable and the virtual reactive power interaction variable. Specifically, δ n,i,h represents the Lagrangian multiplier vector between the reactive power injected by mobile energy storage n into node i at time h and the reactive power collected by node i from mobile energy storage n at time h, represents the Lagrangian multiplier vector between the reactive power injected by mobile energy storage n into node i at time h and the reactive power collected by node i from mobile energy storage n at time h.

4. The distributed energy emergency scheduling method for a distribution network considering mobile energy storage according to claim 3, characterized in that: The expression of the node optimization sub-problem is, The expression of the mobile energy storage optimization sub-problem is, where \(k\in\{1,2,\cdots,K\}\) is the iteration number, \(X\) i is the set of optimized decision variables of node \(i\) in the distribution network, is the set of optimized decision variables of mobile energy storage \(n\), \(x\) i (k + 1) is the optimized decision variable of node \(i\) in the distribution network at the \((k + 1)\)-th iteration, \(x\) i is the optimized decision variable of node \(i\) in the distribution network, \(y\) n (k + 1) is the optimized decision variable of mobile energy storage \(n\) at the \((k + 1)\)-th iteration, \(y\) n is the optimized decision variable of mobile energy storage \(n\), \(\delta(k)\), both are penalty coefficients at the \((k + 1)\)-th iteration, is the actual active power interaction variable at the \(k\)-th iteration, is the virtual active power interaction variable at the \((k + 1)\)-th iteration, is the actual reactive power interaction variable at the \(k\)-th iteration, is the virtual reactive power interaction variable at the \((k + 1)\)-th iteration, is problem \(P\) 1 is the augmented Lagrangian function when \(\alpha\) and \(\beta\) are given.

5. The distributed energy emergency scheduling method for a distribution network considering mobile energy storage according to claim 4, characterized in that: The S43 is specifically, S431, Initialize the node optimization sub-problem and the mobile energy storage optimization sub-problem: k = 1, α(k), β(k) > 0, K > 1; Define three constants λ 1 , λ 2 , M, and λ 1 , λ 2 > 0, M > 1; where, α(k) and β(k) are the penalty coefficients at the k-th iteration; S432, collecting the power demands configured at each node during the h hours after a power outage accident through the distribution network The maximum output values of wind power and photovoltaic power configured at each node The state of charge of the energy storage system configured at each node The diesel engine state variables configured at each node The initial positions of each mobile energy storage And the state of charge of the power battery and the energy storage battery S433, all nodes collect and, in combination with the parameters collected in the said S432, solve for the node optimization decision variable x according to the expression of the node optimization sub-problem i (k + 1) and the interaction variable and send the interaction variable to the mobile energy storage; S434, all the interactive variables sent by the mobile energy storage are collected Combined with the parameters collected in the above S432, solve the mobile energy storage optimization decision variable y according to the expression of the mobile energy storage optimization sub-problem n (k + 1) and the interactive variables S435, determine whether the interaction variable obtained by solving the said S433 and the interaction variable obtained by solving the said S434 satisfy the convergence of the following formula (15); Among them, is the raw residual between the interaction variables ; is the raw residual between the interaction variables ; φ 1 (k + 1) is the dual residual between the interaction variables ; φ 2 (k + 1) is the dual residual between the interaction variables ; If the interaction variable obtained by solving S433 and the interaction variable obtained by solving S434 satisfy the convergence of formula (15), then the node optimization decision variable x i (k + 1) obtained by solving S433 and the mobile energy storage optimization decision variable y n (k + 1) are the optimal emergency dispatching schemes for the distribution network in the h hours after the power outage accident; If the interaction variables obtained by solving S433 and the interaction variables obtained by solving S434 do not satisfy the convergence of formula (15), then update the penalty coefficients α, β and the Lagrange multiplier vector δ, and based on the updated penalty coefficients α, β and the Lagrange multiplier vector δ, return to S433 and continue with the distributed iteration until the convergence of formula (15) is satisfied or the maximum number of iterations K is reached; Among them, the formulas for updating the penalty coefficients α and β are, Update the Lagrange multiplier vector δ, The formula for which is 6. A distributed energy emergency scheduling system for a distribution network considering mobile energy storage, characterized in that: Applied to the distribution network distributed energy emergency scheduling method considering mobile energy storage as described in any one of claims 1 to 5, it includes the following modules, A mobile energy storage constraint model construction module, which is used to construct a mobile energy storage constraint model according to the discrete energy flow characteristics and traffic flow characteristics of mobile energy storage; A distribution network constraint model construction module, which is used to construct a distribution network constraint model composed of diesel engines, wind power, photovoltaic and energy storage systems and including the access of mobile energy storage according to the IEEE distribution network topology; A distribution network distributed energy emergency scheduling model construction module, which is used to construct a distribution network distributed energy emergency scheduling model considering mobile energy storage resources with the goal of minimizing the operation cost of the distribution network and taking the distribution network constraint model and the mobile energy storage constraint model as constraint conditions; An iterative solution module, which is used to iteratively solve the distribution network distributed energy emergency scheduling model by using the adaptive penalty coefficient-alternating direction multiplier method to obtain the optimal emergency scheduling plan for the distribution network.

7. A computer storage medium, characterized in that: It includes a memory and a computer program stored in the memory. When the computer program is executed by a processor, it realizes the distribution network distributed energy emergency scheduling method considering mobile energy storage as described in any one of claims 1 to 5.

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