Mobile energy storage optimization scheduling method and system considering emergency power supply situation

Abstract

The invention relates to the technical field of mobile energy storage scheduling, in particular to a mobile energy storage optimal scheduling method and system considering an emergency power supply situation, and aims to take optimal user satisfaction as a target function of a mobile energy storage optimal scheduling model, and divide the target function into two dimensions of waiting time satisfaction and load recovery satisfaction. And iteratively solving the mobile energy storage optimization scheduling model by adopting an improved particle swarm algorithm, and finally outputting an optimal scheduling strategy, thereby providing an effective scheduling strategy for a mobile energy storage scheduling department. Compared with the prior art, the emergency power supply quality is more completely described, and the power supply efficiency of mobile energy storage in emergency power supply can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile energy storage scheduling, and in particular to a mobile energy storage optimization scheduling method considering emergency power supply situations. Background Art

[0002] As the proportion of renewable energy power generation increases, it is necessary to build supporting energy storage to help absorb renewable energy power. Mobile energy storage has the characteristics of high flexibility and has certain advantages in the absorption of new energy and emergency power supply of distribution networks. At present, each power supply company is equipped with a certain number of mobile energy storage units, which adopt a modular design of energy storage units and auxiliary circuits, and integrate them through standard containers, using cars and other means of transportation to realize the mobility of energy storage systems. Mobile energy storage equipment has the characteristics of compact size, small footprint and rapid response. It is widely used in temporary power supply for important users and emergency power supply in fault conditions. However, there is currently a lack of scheduling methods for the scheduling of mobile energy storage units during emergency power supply, which makes it difficult for mobile energy storage units to meet emergency power supply needs in extreme cases, causing short-term power outages for some customers. Summary of the invention

[0003] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a mobile energy storage optimization scheduling method and system taking into account emergency power supply situations.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] As a first aspect of the present invention, a mobile energy storage optimization scheduling method considering emergency power supply situations is provided, the steps comprising:

[0006] Construct a mobile energy storage optimization scheduling model that takes into account waiting time satisfaction and load recovery satisfaction, with optimal emergency power supply satisfaction as the objective function;

[0007] The improved particle swarm algorithm is used to iteratively solve the mobile energy storage optimization scheduling model and output the optimal scheduling strategy.

[0008] As a preferred technical solution, the mobile energy storage optimization scheduling model takes the objective function F of the optimal emergency power supply satisfaction as the expression:

[0009]

[0010] Among them, F 1 (x) represents the waiting time satisfaction model, F 2 (x) represents the load recovery satisfaction model.

[0011] As a preferred technical solution, the waiting time satisfaction model is expressed as follows:

[0012]

[0013] Among them, T n represents the time required for node n to access MESS from power outage under x scheduling strategy; q n It represents the risk coefficient of power outage per unit time of n nodes, and its expression is:

[0014] q n =c n g n (p n -p′ n )

[0015] Among them, c n represents the power outage probability of n nodes; g n Indicates power outage loss per unit time and unit power; p n represents the power gap of n nodes; p′ n Indicates the power of the self-provided power generation equipment of n nodes.

[0016] As a preferred technical solution, the load recovery satisfaction model sets different emergency power supply priorities for different types of customers. The load recovery satisfaction model F 2 The expression is:

[0017]

[0018] Among them, μ n Indicates the emergency power supply priority of n nodes, N is the set of nodes in the distribution network system, PR n Represents the discharge power of mobile energy storage at node n.

[0019] As a preferred technical solution, the mobile energy storage optimization scheduling model adopts an improved particle swarm algorithm to iteratively solve the optimal scheduling strategy x, and the iterative expression is:

[0020] v ij (d+1)=yv ij (d)+h 1 rand(pbest i (d)-x ij (d))+h 2 rand(gbest i (d)-x ij (d))

[0021] x ij (d+1)=x ij (d)+v ij (d+1)

[0022] Among them, v represents the particle velocity, i represents the number of particles, j represents the particle vector dimension, d represents the number of iterations, y, h1 、h 2 is the weight coefficient, rand is a random number, pbest represents the global optimal value, and gbest represents the local optimal value;

[0023] The adaptive iterative step size adjustment weight coefficient y is set as follows:

[0024]

[0025] Among them, d max Indicates the maximum number of iterations, y max With y min Represents the maximum and minimum y values ​​respectively.

[0026] As a second aspect of the present invention, a mobile energy storage optimization scheduling system considering emergency power supply situations is provided, comprising:

[0027] The optimization dispatch model construction module constructs a mobile energy storage optimization dispatch model that takes into account waiting time satisfaction and load recovery satisfaction, and takes the optimal emergency power supply satisfaction as the objective function;

[0028] The optimal scheduling strategy solving module adopts an improved particle swarm algorithm to iteratively solve the mobile energy storage optimization scheduling model and output the optimal scheduling strategy.

[0029] As a preferred technical solution, the mobile energy storage optimization scheduling model constructed in the optimization scheduling model construction module takes the objective function F of the optimal emergency power supply satisfaction as the expression:

[0030]

[0031] Among them, F 1 (x) represents the waiting time satisfaction model, F 2 (x) represents the load recovery satisfaction model.

[0032] As a preferred technical solution, the waiting time satisfaction model is expressed as follows:

[0033]

[0034] Among them, T n represents the time required for node n to access MESS from power outage under x scheduling strategy; q n It represents the risk coefficient of power outage per unit time of n nodes, and its expression is:

[0035] q n =c n g n (p n -p′ n )

[0036] Among them, c n represents the power outage probability of n nodes; g n Indicates power outage loss per unit time and unit power; p n represents the power gap of n nodes; p′ n Indicates the power of the self-provided power generation equipment of n nodes.

[0037] As a preferred technical solution, the load recovery satisfaction model sets different emergency power supply priorities for different types of customers. The load recovery satisfaction model F 2 The expression is:

[0038]

[0039] Among them, μ n Indicates the emergency power supply priority of n nodes, N is the set of nodes in the distribution network system, PR n Represents the discharge power of mobile energy storage at node n.

[0040] As a preferred technical solution, in the optimization scheduling model solving module, the iterative expression of the improved particle swarm algorithm is:

[0041] v ij (d+1)=yv ij (d)+h 1 rand(pbest i (d)-x ij (d))+h 2 rand(gbest i (d)-x ij (d))

[0042] x ij (d+1)=x ij (d)+v ij (d+1)

[0043] Among them, v represents the particle velocity, i represents the number of particles, j represents the particle vector dimension, d represents the number of iterations, y, h 1 、h 2 is the weight coefficient, rand is a random number, pbest represents the global optimal value, and gbest represents the local optimal value;

[0044] The adaptive iterative step size adjustment weight coefficient y is set as follows:

[0045]

[0046] Among them, d max Indicates the maximum number of iterations, y max With y minRepresents the maximum and minimum y values ​​respectively.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1) In the solution proposed by the present invention, the constructed mobile energy storage optimization scheduling model determines the objective function with the goal of optimizing user satisfaction. In order to more completely characterize the quality of emergency power supply, the objective function is divided into two dimensions: waiting time satisfaction and load recovery satisfaction. The improved particle swarm algorithm is used to iteratively solve the problem, and finally the optimal scheduling strategy is output, providing an effective scheduling strategy for the mobile energy storage scheduling department.

[0049] 2) The present invention adopts an improved particle swarm algorithm for iterative solution. By adaptively adjusting the step size, the algorithm optimization speed in the early stage of iteration can be accelerated, and the optimization accuracy in the later stage of iteration can be improved, thereby improving the overall algorithm performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of a mobile energy storage optimization scheduling method considering emergency power supply situations of the present invention;

[0051] Figure 2 It is a comparison diagram of optimized scheduling schemes in one embodiment of the present invention. DETAILED DESCRIPTION

[0052] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0053] Example 1

[0054] This invention proposes a mobile energy storage optimization scheduling method considering emergency power supply situations. For emergency power supply situations, an optimization scheduling model considering time satisfaction and load recovery satisfaction is given. Figure 1 As shown in the figure, the model determines the objective function with the goal of optimizing user satisfaction. In order to more completely characterize the quality of emergency power supply, the objective function is divided into two dimensions: waiting time satisfaction and load recovery satisfaction. The model uses an improved particle swarm algorithm to iteratively solve and finally outputs the optimal scheduling strategy, effectively improving the power supply efficiency of mobile energy storage in emergency power supply and providing an effective scheduling strategy for mobile energy storage scheduling departments.

[0055] 1. Waiting time satisfaction model

[0056] The duration of power outage is an important factor affecting the satisfaction of power users. Therefore, it is necessary to optimize the dispatching strategy and improve the user's satisfaction with waiting time. 1 , whose expression is:

[0057]

[0058] Among them, T n represents the time required for node n to access MESS from power outage under x scheduling strategy; q n It represents the risk coefficient of power outage per unit time of n nodes, and its expression is:

[0059] q n =c n g n (p n -p′ n ) (2)

[0060] Among them, c n represents the probability of power outage at n nodes, g n It represents the power outage loss per unit time and unit power (yuan / kW·min), p n represents the power gap of n nodes, p′ n Indicates the power of the self-provided power generation equipment of n nodes.

[0061] 2. Load recovery satisfaction model

[0062] For larger-scale emergency power supply situations, the existing mobile energy storage units are difficult to fully meet the emergency power supply needs of all customers at the same time. Therefore, it is necessary to establish a load restoration satisfaction model to maximize customer needs.

[0063] Different types of customers have different emergency power supply priorities. For example, hospitals, governments, large public places, etc., power outages will cause social stability problems, so they have the highest priority; large factories, power outages will cause large economic losses, so they also have a higher priority; for residential users, short-term power outages generally do not cause large losses, so they have a general priority.

[0064] Based on the above analysis, the load recovery satisfaction model F 2 The expression is:

[0065]

[0066] Among them, μ n Indicates the emergency power supply priority of n nodes, N is the set of nodes in the distribution network system, PR n Represents the discharge power of mobile energy storage at node n.

[0067] 3. Optimal dispatch model considering emergency power supply

[0068] The objective function of the optimization scheduling model considering the emergency power supply situation proposed in the present invention is the emergency power supply satisfaction objective function f, which includes the waiting time satisfaction F 1and load recovery satisfaction model F 2 Two dimensions, the expression is:

[0069]

[0070] 4. Improved particle swarm algorithm model solution

[0071] For the above optimization scheduling model, the present invention uses a particle swarm algorithm to iteratively solve the problem, thereby obtaining the optimal scheduling strategy x. The iterative expression is:

[0072] v ij (d+1)=yv ij (d)+h 1 rand(pbest i (d)-x ij (d))+h 2 rand(gbest i (d)-x ij (d)) (5)

[0073] x ij (d+1)=x ij (d)+v ij (d+1) (6)

[0074] Among them, v represents the particle velocity, i represents the number of particles, j represents the particle vector dimension, d represents the number of iterations, y, h 1 、h 2 is the weight coefficient, rand is a random number, pbest represents the global optimal value, and gbest represents the local optimal value.

[0075] In order to improve the performance of the solution algorithm, the present invention improves the particle swarm algorithm and sets an adaptive iterative step size to adjust the weight coefficient, namely:

[0076]

[0077] Among them, d max Indicates the maximum number of iterations, y max With y min Represents the maximum and minimum y values ​​respectively.

[0078] By adaptively adjusting the step size, the algorithm optimization speed in the early stage of iteration can be accelerated, and the optimization accuracy in the later stage of iteration can be improved, thereby improving the overall algorithm performance.

[0079] Example 2

[0080] As one of the specific implementation examples of the present invention, in order to verify the effectiveness of the optimization scheduling model proposed in the present invention, a 5-node example is used in this embodiment to verify the emergency power supply optimization scheduling model proposed in the present invention. The specific parameter settings of the example are shown in Table 1:

[0081] Table 1 Example parameters

[0082]

[0083] In the example, the power of each mobile energy storage unit is 400kW, and each mobile energy storage unit can only operate at one node in a single emergency power supply task.

[0084] To verify the effectiveness of the method of the present invention, two methods are used for comparison: (1) ordinary scheduling strategy, that is, giving priority to the core load recovery demand. (2) random scheduling, taking the average of 100 random scheduling results. The comparison results are shown in Figure 2. Figure 2 As shown in the figure: With the increase in the number of mobile energy storage system (MESS) units, the emergency power supply satisfaction of the three methods increases accordingly, among which the optimized scheduling method and the ordinary scheduling method can reach the peak when the total power of MESS is 2.4MW. In terms of algorithm performance comparison, when the number of MESS units is relatively insufficient, the optimized scheduling method can improve the satisfaction by 9.2% compared with the ordinary scheduling method and 30.8% compared with the random scheduling method, which can effectively help power grid companies achieve higher user satisfaction with smaller MESS equipment investment.

[0085] Example 3

[0086] As another implementation mode of the present invention, this embodiment provides a mobile energy storage optimization scheduling system considering emergency power supply situations using the method described in the above embodiment, which includes:

[0087] The optimization dispatch model construction module constructs a mobile energy storage optimization dispatch model that takes into account waiting time satisfaction and load recovery satisfaction, and takes the optimal emergency power supply satisfaction as the objective function;

[0088] Specifically, the mobile energy storage optimization dispatch model constructed in the optimization dispatch model construction module takes the objective function F of the optimal emergency power supply satisfaction as follows:

[0089]

[0090] Among them, the waiting time satisfaction model F 1 , whose expression is:

[0091]

[0092] Among them, Tn represents the time required for node n to access MESS from power outage under x scheduling strategy; q n It represents the risk coefficient of power outage per unit time of n nodes, and its expression is:

[0093] q n =c n g n (p n -p′ n )

[0094] Among them, c n represents the power outage probability of n nodes; g n Indicates power outage loss per unit time and unit power; p n represents the power gap of n nodes; p′ n Indicates the power of the self-provided power generation equipment of n nodes.

[0095] Load recovery satisfaction model F 2 , different emergency power supply priorities are set for different types of customers, and the load restoration satisfaction model F 2 The expression is:

[0096]

[0097] Among them, μ n Indicates the emergency power supply priority of n nodes, N is the set of nodes in the distribution network system, PR n Represents the discharge power of mobile energy storage at node n.

[0098] The optimal scheduling strategy solving module adopts an improved particle swarm algorithm to iteratively solve the mobile energy storage optimization scheduling model and output the optimal scheduling strategy.

[0099] Specifically, in the optimization scheduling model solving module, the iterative expression of the improved particle swarm algorithm is:

[0100] v ij (d+1)=yv ij (d)+h 1 rand(pbest i (d)-x ij (d))+h 2 rand(gbest i (d)-x ij (d))

[0101] x ij (d+1)=x ij (d)+v ij (d+1)

[0102] Among them, v represents the particle velocity, i represents the number of particles, j represents the particle vector dimension, d represents the number of iterations, y, h 1 、h 2 is the weight coefficient, rand is a random number, pbest represents the global optimal value, and gbest represents the local optimal value;

[0103] The adaptive iterative step size adjustment weight coefficient y is set as follows:

[0104]

[0105] Among them, d max Indicates the maximum number of iterations, y max With y min Represents the maximum and minimum y values ​​respectively.

[0106] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A mobile energy storage optimization scheduling method considering emergency power supply situations, characterized in that the steps include: Construct a mobile energy storage optimization scheduling model that takes into account waiting time satisfaction and load recovery satisfaction, with optimal emergency power supply satisfaction as the objective function; The improved particle swarm algorithm is used to iteratively solve the mobile energy storage optimization scheduling model and output the optimal scheduling strategy.

2. According to claim 1, a mobile energy storage optimization scheduling method considering emergency power supply situations is characterized in that The mobile energy storage optimization scheduling method considering the emergency power supply situation, the mobile energy storage optimization scheduling model takes the objective function F of the optimal emergency power supply satisfaction as the expression: Among them, F1(x) represents the waiting time satisfaction model, and F2(x) represents the load recovery satisfaction model.

3. A mobile energy storage optimization scheduling method considering emergency power supply situations according to claim 2, characterized in that: The waiting time satisfaction model is expressed as follows: Among them, T n represents the time required for node n to access MESS from power outage under x scheduling strategy; q n It represents the risk coefficient of power outage per unit time of n nodes, and its expression is: q n =c n g n (p n -p′ n ) Among them, c n represents the power outage probability of n nodes; g n Indicates power outage loss per unit time and unit power; p n represents the power gap of n nodes; p′ n Indicates the power of the self-provided power generation equipment of n nodes.

4. A mobile energy storage optimization scheduling method considering emergency power supply conditions according to claim 2, characterized in that: The load restoration satisfaction model sets different emergency power supply priorities for different types of customers. The load restoration satisfaction model F2 is expressed as: Among them, μ n Indicates the emergency power supply priority of n nodes, N is the set of nodes in the distribution network system, PR n Represents the discharge power of mobile energy storage at node n.

5. The mobile energy storage optimization scheduling method considering emergency power supply situations according to claim 1 is characterized in that: The mobile energy storage optimization scheduling model adopts the improved particle swarm algorithm to iteratively solve the optimal scheduling strategy x, and the iterative expression is: v ij (d+1)=yv ij (d)+h1rand(pbest i (d)-x ij (d))+h2rand(gbest i (d)-x ij (d)) x ij (d+1)=x ij (d)+v ij (d+1) Where v represents the particle velocity, i represents the number of particles, j represents the particle vector dimension, d represents the number of iterations, y, h1, h2 are weight coefficients, rand is a random number, pbest represents the global optimal value, and gbest represents the local optimal value; The adaptive iterative step size adjustment weight coefficient y is set as follows: Among them, d max Indicates the maximum number of iterations, y max With y min Represents the maximum and minimum y values ​​respectively.

6. A mobile energy storage optimization scheduling system considering emergency power supply situations, characterized in that: include: The optimization dispatch model construction module constructs a mobile energy storage optimization dispatch model that takes into account waiting time satisfaction and load recovery satisfaction, and takes the optimal emergency power supply satisfaction as the objective function; The optimal scheduling strategy solving module adopts an improved particle swarm algorithm to iteratively solve the mobile energy storage optimization scheduling model and output the optimal scheduling strategy.

7. A mobile energy storage optimization scheduling system considering emergency power supply situations according to claim 6, characterized in that: The mobile energy storage optimization scheduling model constructed in the optimization scheduling model construction module is based on the objective function F of the optimal emergency power supply satisfaction, and its expression is: Among them, F1(x) represents the waiting time satisfaction model, and F2(x) represents the load recovery satisfaction model.

8. A mobile energy storage optimization scheduling system considering emergency power supply situations according to claim 7, characterized in that: The waiting time satisfaction model is expressed as follows: Among them, T n represents the time required for node n to access MESS from power outage under x scheduling strategy; q n It represents the risk coefficient of power outage per unit time of n nodes, and its expression is: q n =c n g n (p n -p′ n ) Among them, c n represents the power outage probability of n nodes; g n Indicates power outage loss per unit time and unit power; p n represents the power gap of n nodes; p′ n Indicates the power of the self-provided power generation equipment of n nodes.

9. A mobile energy storage optimization scheduling system considering emergency power supply situations according to claim 7, characterized in that: The load restoration satisfaction model sets different emergency power supply priorities for different types of customers. The load restoration satisfaction model F2 is expressed as: Among them, μ n Indicates the emergency power supply priority of n nodes, N is the set of nodes in the distribution network system, PR n Represents the discharge power of mobile energy storage at node n.

10. A mobile energy storage optimization dispatching system considering emergency power supply situations according to claim 6, characterized in that: In the optimization scheduling model solving module, the iterative expression of the improved particle swarm algorithm is: ij (d+1)=yv ij (d)+h1rand(pbest i (d)-x ij (d))+h2rand(gbest i (d)-x ij (d)) x ij (d+1)=x ij (d)+v ij (d+1) Where v represents the particle velocity, i represents the number of particles, j represents the particle vector dimension, d represents the number of iterations, y, h1, h2 are weight coefficients, rand is a random number, pbest represents the global optimal value, and gbest represents the local optimal value; The adaptive iterative step size adjustment weight coefficient y is set as follows: Among them, d max Indicates the maximum number of iterations, y max With y min Represents the maximum and minimum y values ​​respectively.

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