Distributed photovoltaic energy storage scheduling method, system, equipment and medium

Abstract

The invention discloses a distributed photovoltaic energy storage scheduling method, system, equipment and medium, and the method comprises the steps: constructing an energy storage model comprising two mobile energy storage regions and a plurality of photovoltaic power generation regions according to the power transmission requirements, and then constructing an optimization model which takes a current distribution factor as an optimization variable and takes the minimum overall line loss as a target in combination with the energy storage model, and finally, solving the optimization model to obtain an optimal current distribution scheme, and performing distributed photovoltaic energy storage scheduling according to the optimal current distribution scheme. According to the invention, the overall line loss of the transmission line is effectively reduced while the energy storage demand of the energy storage area is ensured.

Description

Technical Field

[0001] The present invention belongs to the field of power grid energy storage scheduling, and specifically relates to a distributed photovoltaic energy storage scheduling method, system, equipment and medium. Background Art

[0002] New clean energy sources such as photovoltaic power generation with renewable properties will become the main power source in the future. Distributed photovoltaic power generation grid-connected energy storage technology has been proposed. With its advantages of flexibility, renewable energy, and low power generation cost, it has become an important energy storage technology to cope with the growing demand for electricity in the future. Therefore, the optimal scheduling of distributed photovoltaic energy storage is of great significance.

[0003] Most existing scheduling methods adopt a fixed current distribution strategy without considering the overall line loss. However, in the current distributed photovoltaic energy storage process, there is a problem of high overall line loss in the power grid transmission lines, and the fixed current distribution method may lead to a situation where the power demand cannot be met. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed photovoltaic energy storage scheduling method, system, device and medium in view of the above-mentioned problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention provides a distributed photovoltaic energy storage scheduling method, comprising:

[0007] S1. Construct an energy storage model according to the requirements of power transmission, wherein the energy storage model includes two mobile energy storage areas, each of which is equipped with a trunk line for power transmission, and a plurality of photovoltaic power generation areas are set up between the two trunk lines to supply power to the trunk line through branch lines;

[0008] S2. Combined with the energy storage model, an optimization model is constructed with the current distribution factor as the optimization variable and the overall line loss as the minimum target;

[0009] S3. Solve the optimization model to obtain the optimal current distribution plan, and perform distributed photovoltaic energy storage scheduling based on the optimal current distribution plan.

[0010] In S2, the objective function of the optimization model includes:

[0011]

[0012]

[0013] In the above formula, loss tot is the overall line loss, loss o 、lossm are the total line losses of the branch line and trunk line, respectively, k is the current distribution factor of the kth photovoltaic power generation area, η k ∈[0,1], is the power generation of the kth photovoltaic power generation area, are the resistances from the kth photovoltaic power generation area to trunk line 1 and trunk line 2 respectively, K is the number of photovoltaic power generation areas set up between the two trunk lines, are the line losses of trunk line 1 and trunk line 2 respectively, are the initial currents of trunk line 1 and trunk line 2 respectively, is the resistance of the trunk line 1 from the kth photovoltaic power generation area access point to the k+1th photovoltaic power generation area access point, are the resistances of the trunk line 2 from the kth photovoltaic power generation area access point to the k+1th photovoltaic power generation area access point;

[0014] The constraints include:

[0015]

[0016] In the above formula, They are the current requirements of mobile energy storage area 1 and mobile energy storage area 2 respectively. They are the maximum current values ​​that mobile energy storage area 1 and mobile energy storage area 2 can withstand respectively.

[0017] The S3 uses an improved particle swarm optimization algorithm to solve the optimization model, including:

[0018] S31, initializing a particle group, wherein the particle group is composed of K current distribution factors;

[0019] S32, calculating the fitness value of each particle, determining the individual extreme value of each particle based on the fitness value, and performing a global search to determine the global extreme value of the particle group;

[0020] S33, updating the speed and position of each particle, wherein the speed of the particle is updated according to the following formula:

[0021]

[0022] In the above formula, is the value of the kth particle in the i-th iteration, σ is the learning rate, R is a random number in (0,1), is the individual extreme value of the kth particle in the i-1th iteration process, x k,i is the global search step size in the i-th iteration, η k is the current distribution factor of the kth photovoltaic power generation area;

[0023] S34, calculating a new fitness value of each particle according to the updated speed and position, and updating the individual extreme value and the global extreme value of each particle based on the new fitness value;

[0024] S35, judging whether the termination condition is met, if so, stopping the iteration and outputting the optimal current distribution scheme; if not, returning to S32 for the next iteration.

[0025] In the S31, The initial value of η k / 2;

[0026] In S35, the optimal current distribution scheme is {v opt,1 ,…,v opt,K}, where v opt,k is the individual extreme value of the kth particle.

[0027] In a second aspect, the present invention proposes a distributed photovoltaic energy storage scheduling system, including an energy storage model construction module, an optimization model construction module, and an optimization model solving and scheduling module;

[0028] The energy storage model construction module is used to construct an energy storage model according to the requirements of power transmission. The energy storage model includes two mobile energy storage areas. The two mobile energy storage areas are respectively equipped with a trunk line for power transmission. Between the two trunk lines, multiple photovoltaic power generation areas are set up to supply power to the trunk line through branch lines.

[0029] The optimization model building module is used to combine the energy storage model to build an optimization model with the current distribution factor as the optimization variable and the minimum overall line loss as the goal;

[0030] The optimization model solving and scheduling module is used to solve the optimization model, obtain the optimal current distribution plan, and perform distributed photovoltaic energy storage scheduling according to the optimal current distribution plan.

[0031] The objective function of the optimization model includes:

[0032]

[0033]

[0034] In the above formula, loss tot is the overall line loss, loss o 、loss m are the total line losses of the branch line and trunk line, respectively, k is the current distribution factor of the kth photovoltaic power generation area, η k ∈[0,1], is the power generation of the kth photovoltaic power generation area, are the resistances from the kth photovoltaic power generation area to trunk line 1 and trunk line 2 respectively, K is the number of photovoltaic power generation areas set up between the two trunk lines, are the line losses of trunk line 1 and trunk line 2 respectively, are the initial currents of trunk line 1 and trunk line 2 respectively, is the resistance of the trunk line 1 from the kth photovoltaic power generation area access point to the k+1th photovoltaic power generation area access point, are the resistances of the trunk line 2 from the kth photovoltaic power generation area access point to the k+1th photovoltaic power generation area access point;

[0035] The constraints include:

[0036]

[0037] In the above formula, They are the current requirements of mobile energy storage area 1 and mobile energy storage area 2 respectively. They are the maximum current values ​​that mobile energy storage area 1 and mobile energy storage area 2 can withstand respectively.

[0038] The optimization model solving and scheduling module uses an improved particle swarm optimization algorithm to solve the optimization model, and its solution process includes:

[0039] A. Initialize a particle swarm, wherein the particle swarm is composed of K current distribution factors;

[0040] B. Calculate the fitness value of each particle, determine the individual extreme value of each particle based on the fitness value, and perform a global search to determine the global extreme value of the particle swarm;

[0041] C. Update the speed and position of each particle, where the speed of the particle is updated according to the following formula:

[0042]

[0043] In the above formula, is the value of the kth particle in the i-th iteration, σ is the learning rate, R is a random number in (0,1), is the individual extreme value of the kth particle in the i-1th iteration process, x k,i is the global search step size in the i-th iteration, η k is the current distribution factor of the kth photovoltaic power generation area;

[0044] D. Calculate the new fitness value of each particle according to the updated speed and position, and update the individual extreme value and global extreme value of each particle based on the new fitness value;

[0045] E. Determine whether the termination condition is met. If so, stop the iteration and output the optimal current distribution solution; if not, return to step B for the next iteration.

[0046] In the step A, The initial value of η k / 2;

[0047] In step E, the optimal current distribution scheme is {v opt,1 ,…,v opt,K}, where v opt,k is the individual extreme value of the kth particle.

[0048] In a third aspect, the present invention provides a distributed photovoltaic energy storage scheduling device, including a memory and a processor;

[0049] The memory is used to store computer program code and transmit the computer program code to the processor;

[0050] The processor is used to execute the aforementioned method according to the instructions in the computer program code.

[0051] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the aforementioned method when executed by a processor.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. A distributed photovoltaic energy storage scheduling method proposed in the present invention first constructs an energy storage model including two mobile energy storage areas and multiple photovoltaic power generation areas according to the requirements of power transmission, and then combines the energy storage model to construct an optimization model with the current distribution factor as the optimization variable and the minimum overall line loss as the goal. Finally, the optimization model is solved to obtain the optimal current distribution plan, and distributed photovoltaic energy storage scheduling is performed according to the optimal current distribution plan. Starting from the perspective of the overall line loss of the transmission line, this method effectively reduces the overall line loss of the transmission line by scheduling the current distribution factors of different photovoltaic power generation areas while ensuring the energy storage demand of the energy storage area.

[0054] 2. A distributed photovoltaic energy storage scheduling method proposed in the present invention adopts an improved particle swarm optimization algorithm to solve the optimization model. The improved particle swarm optimization algorithm introduces the average value of the difference between the optimal value of the previous iteration and the iterative value in the iterative update process, so that the iterative process can converge faster and be closer to the optimal value. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of the method described in Example 1.

[0056] Figure 2 This is a schematic diagram of the structure of the energy storage model in Example 1.

[0057] Figure 3 This is a diagram showing the performance improvement of the method described in Example 1 relative to the average current allocation strategy.

[0058] Figure 4 This is a schematic diagram of the structure of the system described in Example 2.

[0059] Figure 5 This is a structural diagram of the device described in Example 3. DETAILED DESCRIPTION

[0060] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0061] Embodiment 1:

[0062] A distributed photovoltaic energy storage scheduling method, such as Figure 1 As shown, the specific implementation steps are as follows:

[0063] 1. Construct an energy storage model according to the actual power transmission requirements. The energy storage model is as follows: Figure 2 As shown, it includes two mobile energy storage areas and multiple photovoltaic power generation areas. The two mobile energy storage areas (i.e., mobile energy storage area 1 and mobile energy storage area 2) are each equipped with a trunk line for power transmission (i.e., transmission line 1 and transmission line 2). K photovoltaic power generation areas are set up between the two trunk lines to supply power to the trunk lines through branch lines. Trunk line 1 transmits power to mobile energy storage area 1, and trunk line 2 transmits power to mobile energy storage area 2. During the transmission process, K photovoltaic power generation areas supply power to trunk line 1 and trunk line 2 respectively. The initial line capacity of the two trunk lines is 30A, and the transmission capacity of the K photovoltaic power generation areas is 5A.

[0064] 2. Construct the line loss expression under the entire energy storage model.

[0065] For the kth photovoltaic power generation area, the current distribution factor η is introduced k , η k ∈[0,1], The current is distributed to the main line 1, The current is allocated to trunk line 2. Therefore, the line loss from the photovoltaic power generation area to the trunk line 1 and the trunk line 2 is and The calculation formula is as follows:

[0066]

[0067] In the above formula, is the power generation of the kth photovoltaic power generation area, are the resistances from the kth photovoltaic power generation area to the trunk line 1 and the trunk line 2, respectively, and K is the number of photovoltaic power generation areas built between the two trunk lines;

[0068] The total line loss of the branch line is o for:

[0069]

[0070] For the trunk line, the current of each section of the line increases with the amount of electricity collected by the branch line. Therefore, its line loss is the sum of the losses of multiple sections of the line. The line losses of trunk lines 1 and 2 are derived as follows:

[0071]

[0072]

[0073] In the above formula, are the line losses of trunk line 1 and trunk line 2 respectively, are the initial currents of trunk line 1 and trunk line 2 respectively, is the resistance of the trunk line 1 from the kth photovoltaic power generation area access point to the k+1th photovoltaic power generation area access point, are respectively the resistance of the trunk line 2 from the kth photovoltaic power generation area access point to the k+1th photovoltaic power generation area access point.

[0074] Therefore, the total line loss of the trunk line is m for:

[0075]

[0076] The overall line loss in the entire energy storage model tot It consists of a backbone network and branch networks, so there are:

[0077] loss tot =loss o +loss m

[0079] The current demand of mobile energy storage area 1 and mobile energy storage area 2 is and By adjusting the current distribution factor η k The distribution of current can be changed to meet the energy storage demand while reducing the overall line transmission loss. Therefore, a current distribution factor {η1,…,η K} is the optimization variable and the overall line loss is minimized as the goal.

[0080]

[0081]

[0082] In the above formula, They are the maximum current values ​​that mobile energy storage area 1 and mobile energy storage area 2 can withstand respectively.

[0083] 3. Use the improved particle swarm optimization algorithm to solve the optimization model and obtain the optimal current distribution solution, including:

[0084] S31, initialize the particle swarm, the particle swarm is composed of K current distribution factors, and the random values ​​of K particles are given respectively. In order to ensure the accuracy of the particle search process and the optimality of the results, The initial value of η is set to k / 2;

[0085] S32, calculating the fitness value of each particle, that is, the objective function value, determining the individual extreme value of each particle based on the fitness value, and performing a global search to determine the global extreme value of the particle swarm;

[0086] S33, updating the speed and position of each particle, wherein the speed of the particle is updated according to the following formula:

[0087]

[0088] In the above formula, is the value of the kth particle in the i-th iteration, σ is the learning rate, R is a random number in (0,1), is the individual extreme value of the kth particle in the i-1th iteration process, and its update method is: when the current iteration process When the value reaches an extreme value, it also passes renew, is the extreme value of the kth particle in the current iteration, x k,i is the global search step size in the i-th iteration, η k is the current distribution factor of the kth photovoltaic power generation area;

[0089] S34, calculating the new fitness value of each particle according to the updated speed and position, and if the new fitness value is better, updating the individual extreme value and the global extreme value of the particle;

[0090] S35, determine whether the termination condition is met, if so, stop the iteration and output the optimal current distribution solution {v opt,1 ,…,v opt,K}, where v opt,kis the individual extreme value of the kth particle; if it is not reached, return to S32 for the next iteration.

[0091] 4. Distributed photovoltaic energy storage dispatching based on the optimal current distribution plan.

[0092] The performance improvement effect of the method described in this embodiment compared with the average current distribution strategy is as follows: Figure 3 shown.

[0093] Embodiment 2:

[0094] A distributed photovoltaic energy storage dispatching system, such as Figure 4 As shown, it includes an energy storage model building module, an optimization model building module, and an optimization model solving and scheduling module.

[0095] The energy storage model construction module is used to construct an energy storage model according to the requirements of power transmission. The energy storage model includes two mobile energy storage areas. The two mobile energy storage areas are respectively equipped with a trunk line for power transmission. Between the two trunk lines, multiple photovoltaic power generation areas are set up to supply power to the trunk line through branch lines, such as Figure 2 shown.

[0096] The optimization model building module is used to combine the energy storage model to build an optimization model with the current distribution factor as the optimization variable and the minimum overall line loss as the goal. The objective function of the optimization model includes:

[0097]

[0098] In the above formula, loss tot is the overall line loss, loss o 、loss m are the total line losses of the branch line and trunk line, respectively, k is the current distribution factor of the kth photovoltaic power generation area, η k ∈[0,1], is the power generation of the kth photovoltaic power generation area, are the resistances from the kth photovoltaic power generation area to trunk line 1 and trunk line 2 respectively, K is the number of photovoltaic power generation areas set up between the two trunk lines, are the line losses of trunk line 1 and trunk line 2 respectively, are the initial currents of trunk line 1 and trunk line 2 respectively, is the resistance of the trunk line 1 from the kth photovoltaic power generation area access point to the k+1th photovoltaic power generation area access point, are the resistances of the trunk line 2 from the kth photovoltaic power generation area access point to the k+1th photovoltaic power generation area access point;

[0099] The constraints include:

[0100]

[0101] In the above formula, They are the current requirements of mobile energy storage area 1 and mobile energy storage area 2 respectively. They are the maximum current values ​​that mobile energy storage area 1 and mobile energy storage area 2 can withstand respectively.

[0102] The optimization model solving and scheduling module is used to solve the optimization model using an improved particle swarm optimization algorithm to obtain an optimal current distribution scheme, and perform distributed photovoltaic energy storage scheduling according to the optimal current distribution scheme, wherein the improved particle swarm optimization algorithm includes:

[0103] A. Initialize a particle swarm, wherein the particle swarm is composed of K current distribution factors;

[0104] B. Calculate the fitness value of each particle, determine the individual extreme value of each particle based on the fitness value, and perform a global search to determine the global extreme value of the particle swarm;

[0105] C. Update the speed and position of each particle, where the speed of the particle is updated according to the following formula:

[0106]

[0107] In the above formula, is the value of the kth particle in the i-th iteration, σ is the learning rate, R is a random number in (0,1), is the individual extreme value of the kth particle in the i-1th iteration process, x k,i is the global search step size in the i-th iteration, η k is the current distribution factor of the kth photovoltaic power generation area;

[0108] D. Calculate the new fitness value of each particle according to the updated speed and position, and update the individual extreme value and global extreme value of each particle based on the new fitness value;

[0109] E. Determine whether the termination condition is met. If so, stop the iteration and output the optimal current distribution solution; if not, return to step B for the next iteration.

[0110] Embodiment 3:

[0111] A distributed photovoltaic energy storage dispatching device, such as Figure 5 As shown, it includes a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the method as described in Example 1 according to the instructions in the computer program code.

[0112] Embodiment 4:

[0113] A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method described in Embodiment 1 when executed by a processor.

Claims

1. A distributed photovoltaic energy storage scheduling method, characterized in that: The method comprises: S1. Construct an energy storage model according to the requirements of power transmission, wherein the energy storage model includes two mobile energy storage areas, each of which is equipped with a trunk line for power transmission, and a plurality of photovoltaic power generation areas are set up between the two trunk lines to supply power to the trunk line through branch lines; S2. Combined with the energy storage model, an optimization model is constructed with the current distribution factor as the optimization variable and the overall line loss as the goal; S3. Solve the optimization model to obtain the optimal current distribution plan, and perform distributed photovoltaic energy storage scheduling based on the optimal current distribution plan.

2. A distributed photovoltaic energy storage scheduling method according to claim 1, characterized in that: In S2, the objective function of the optimization model includes: In the above formula, loss tot is the overall line loss, loss o 、loss m are the total line losses of the branch line and trunk line, respectively, k is the current distribution factor of the kth photovoltaic power generation area, η k ∈[0,1], is the power generation of the kth photovoltaic power generation area, are the resistances from the kth photovoltaic power generation area to the trunk line 1 and the trunk line 2 respectively, K is the number of photovoltaic power generation areas set up between the two trunk lines, are the line losses of trunk line 1 and trunk line 2 respectively, are the initial currents of trunk line 1 and trunk line 2 respectively, is the resistance of the trunk line 1 from the kth photovoltaic power generation area access point to the k+1th photovoltaic power generation area access point, are the resistances of the trunk line 2 from the kth photovoltaic power generation area access point to the k+1th photovoltaic power generation area access point; The constraints include: In the above formula, They are the current requirements of mobile energy storage area 1 and mobile energy storage area 2 respectively. They are the maximum current values ​​that mobile energy storage area 1 and mobile energy storage area 2 can withstand respectively.

3. A distributed photovoltaic energy storage scheduling method according to claim 1 or 2, characterized in that: The S3 uses an improved particle swarm optimization algorithm to solve the optimization model, including: S31, initializing a particle group, wherein the particle group is composed of K current distribution factors; S32, calculating the fitness value of each particle, determining the individual extreme value of each particle based on the fitness value, and performing a global search to determine the global extreme value of the particle group; S33, updating the speed and position of each particle, wherein the speed of the particle is updated according to the following formula: In the above formula, is the value of the kth particle in the i-th iteration, σ is the learning rate, R is a random number in (0,1), is the individual extreme value of the kth particle in the i-1th iteration process, x k,i is the global search step size in the i-th iteration, η k is the current allocation factor of the kth photovoltaic power generation area; S34, calculating a new fitness value of each particle according to the updated speed and position, and updating the individual extreme value and the global extreme value of each particle based on the new fitness value; S35, judging whether the termination condition is met, if so, stopping the iteration and outputting the optimal current distribution scheme; if not, returning to S32 for the next iteration.

4. A distributed photovoltaic energy storage scheduling method according to claim 3, characterized in that: In the S31, The initial value of η k / 2; In S35, the optimal current distribution scheme is {v opt,1 ,…,v opt,K }, where v opt,k is the individual extreme value of the kth particle.

5. A distributed photovoltaic energy storage dispatching system, characterized in that: The system includes an energy storage model building module, an optimization model building module, and an optimization model solving and scheduling module; The energy storage model construction module is used to construct an energy storage model according to the requirements of power transmission. The energy storage model includes two mobile energy storage areas. The two mobile energy storage areas are respectively equipped with a trunk line for power transmission. Between the two trunk lines, multiple photovoltaic power generation areas are set up to supply power to the trunk line through branch lines. The optimization model building module is used to combine the energy storage model to build an optimization model with the current distribution factor as the optimization variable and the minimum overall line loss as the goal; The optimization model solving and scheduling module is used to solve the optimization model, obtain the optimal current distribution plan, and perform distributed photovoltaic energy storage scheduling according to the optimal current distribution plan.

6. A distributed photovoltaic energy storage dispatching system according to claim 5, characterized in that: The objective function of the optimization model includes: In the above formula, loss tot is the overall line loss, loss o 、loss m are the total line losses of the branch line and trunk line, respectively, k is the current distribution factor of the kth photovoltaic power generation area, η k ∈[0,1], is the power generation of the kth photovoltaic power generation area, are the resistances from the kth photovoltaic power generation area to the trunk line 1 and the trunk line 2 respectively, K is the number of photovoltaic power generation areas set up between the two trunk lines, are the line losses of trunk line 1 and trunk line 2 respectively, are the initial currents of trunk line 1 and trunk line 2 respectively, is the resistance of the trunk line 1 from the kth photovoltaic power generation area access point to the k+1th photovoltaic power generation area access point, are the resistances of the trunk line 2 from the kth photovoltaic power generation area access point to the k+1th photovoltaic power generation area access point; The constraints include: In the above formula, They are the current requirements of mobile energy storage area 1 and mobile energy storage area 2 respectively. They are the maximum current values ​​that mobile energy storage area 1 and mobile energy storage area 2 can withstand respectively.

7. A distributed photovoltaic energy storage dispatching system according to claim 5 or 6, characterized in that: The optimization model solving and scheduling module uses an improved particle swarm optimization algorithm to solve the optimization model, and its solution process includes: A. Initialize a particle swarm, wherein the particle swarm is composed of K current distribution factors; B. Calculate the fitness value of each particle, determine the individual extreme value of each particle based on the fitness value, and perform a global search to determine the global extreme value of the particle swarm; C. Update the speed and position of each particle, where the speed of the particle is updated according to the following formula: In the above formula, is the value of the kth particle in the i-th iteration, σ is the learning rate, R is a random number in (0,1), is the individual extreme value of the kth particle in the i-1th iteration process, x k,i is the global search step size in the i-th iteration, η k is the current allocation factor of the kth photovoltaic power generation area; D. Calculate the new fitness value of each particle according to the updated speed and position, and update the individual extreme value and global extreme value of each particle based on the new fitness value; E. Determine whether the termination condition is met. If so, stop the iteration and output the optimal current distribution solution; if not, return to step B for the next iteration.

8. A dynamic dispatching system for mobile energy storage vehicles for multi-regional electricity consumption according to claim 7, characterized in that: In the step A, The initial value of η k / 2; In step E, the optimal current distribution scheme is {v opt,1 ,…,v opt,K }, where v opt,k is the individual extreme value of the kth particle.

9. A distributed photovoltaic energy storage dispatching device, characterized in that: The device comprises a memory and a processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to any one of claims 1 to 4 according to instructions in the computer program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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