A method, system, device and medium for dispatching distributed photovoltaic energy storage

Abstract

A distributed photovoltaic (PV) energy storage scheduling method, system, equipment, and medium are disclosed. The method first constructs an energy storage model comprising two mobile energy storage areas and multiple PV power generation areas based on power transmission requirements. Then, combining the energy storage model, an optimization model is constructed with current allocation factors as optimization variables and the goal of minimizing overall line losses. Finally, the optimization model is solved to obtain the optimal current allocation scheme, and distributed PV energy storage is scheduled according to this scheme. This invention effectively reduces the overall line losses of transmission lines while ensuring the energy storage needs of the energy storage areas are met.

Description

Technical Field

[0001] This invention belongs to the field of power grid energy storage dispatch, specifically relating to a distributed photovoltaic energy storage dispatch method, system, equipment and medium. Background Technology

[0002] New clean energy sources, such as renewable photovoltaic power generation, will become the main power source in the future. Distributed photovoltaic power generation and grid-connected energy storage technology has been proposed, and with its advantages of flexibility, renewability, and low power generation costs, it has become an important energy storage technology to meet the ever-increasing electricity demand. Therefore, the optimized scheduling of distributed photovoltaic energy storage is of great significance.

[0003] Most existing scheduling methods adopt fixed current allocation strategies without taking into account overall line losses. However, the current distributed photovoltaic energy storage process suffers from high overall line losses in the power grid transmission lines, and the fixed current allocation method may lead to situations where power demand cannot be met. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the existing technology by providing a distributed photovoltaic energy storage scheduling method, system, device, and medium.

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

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

[0007] S1. Construct an energy storage model according to the power transmission requirements. The energy storage model includes two mobile energy storage areas. Each of the two mobile energy storage areas is equipped with a main line for power transmission. Multiple photovoltaic power generation areas are set up between the two main lines to replenish the main lines with power through branch lines.

[0008] S2. Combining the energy storage model, construct an optimization model with the current distribution factor as the optimization variable and the overall line loss as the objective.

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

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

[0011]

[0012]

[0013] In the above formula, loss tot For the overall line loss, loss o lossm η represents the total line loss of branch lines and main lines, respectively. k Let η be the current distribution factor for the k-th photovoltaic power generation area. k ∈[0,1], Let k be the power generation of the photovoltaic power generation area. These represent the resistances from the k-th photovoltaic power generation area to main line 1 and main line 2, respectively, where K is the number of photovoltaic power generation areas connected between the two main lines. These are the line losses for main line 1 and main line 2, respectively. These are the initial currents of main line 1 and main line 2, respectively. The resistance of the main line 1 from the k-th photovoltaic power generation area access point to the (k+1)-th photovoltaic power generation area access point. These are the resistances of the main line 2 from the kth photovoltaic power generation area access point to the (k+1)th photovoltaic power generation area access point;

[0014] The constraints include:

[0015]

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

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

[0018] S31. Initialize the particle swarm, which consists of K current allocation factors;

[0019] S32. 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.

[0020] S33. Update the velocity and position of each particle, where the particle velocity is updated according to the following formula:

[0021]

[0022] In the above formula, Let be the value of the k-th particle in the i-th iteration, σ be the learning rate, and R be a random number within (0,1). Let x be the individual extreme value of the k-th particle during the (i-1)-th iteration. k,i Let η be the global search step size in the i-th iteration. k Let be the current distribution factor for the k-th photovoltaic power generation region;

[0023] S34. Calculate the new fitness value of each particle based on the updated speed and position, and update the individual extreme value and global extreme value of each particle based on the new fitness value;

[0024] S35. Determine if the termination condition has been met. If it has, stop the iteration and output the optimal current allocation scheme. If not, return to S32 for the next iteration.

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

[0026] In S35, the optimal current allocation scheme is {v opt,1 ,…,v opt,K}, where v opt,k Let be the individual extreme value of the k-th particle.

[0027] Secondly, this 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 power transmission requirements. The energy storage model includes two mobile energy storage areas, each equipped with a main line for power transmission. Between the two main lines, multiple photovoltaic power generation areas are erected to replenish the main lines with power through branch lines.

[0029] The optimization model construction module is used to combine the energy storage model to construct an optimization model with the current distribution factor as the optimization variable and the overall line loss as the objective.

[0030] The optimization model solving and scheduling module is used to solve the optimization model, obtain the optimal current allocation scheme, and perform distributed photovoltaic energy storage scheduling based on the optimal current allocation scheme.

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

[0032]

[0033]

[0034] In the above formula, loss tot For the overall line loss, loss o loss m η represents the total line loss of branch lines and main lines, respectively. k Let η be the current distribution factor for the k-th photovoltaic power generation area. k ∈[0,1], Let k be the power generation of the photovoltaic power generation area. These represent the resistances from the k-th photovoltaic power generation area to main line 1 and main line 2, respectively, where K is the number of photovoltaic power generation areas connected between the two main lines. These are the line losses for main line 1 and main line 2, respectively. These are the initial currents of main line 1 and main line 2, respectively. The resistance of the main line 1 from the k-th photovoltaic power generation area access point to the (k+1)-th photovoltaic power generation area access point. These are the resistances of the main line 2 from the kth photovoltaic power generation area access point to the (k+1)th photovoltaic power generation area access point;

[0035] The constraints include:

[0036]

[0037] In the above formula, These are the current requirements for mobile energy storage area 1 and mobile energy storage area 2, respectively. These 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 the particle swarm, which consists of K current allocation 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 velocity and position of each particle, where the particle velocity is updated according to the following formula:

[0042]

[0043] In the above formula, Let be the value of the k-th particle in the i-th iteration, σ be the learning rate, and R be a random number within (0,1). Let x be the individual extreme value of the k-th particle during the (i-1)-th iteration. k,i Let η be the global search step size in the i-th iteration. k Let be the current distribution factor for the k-th photovoltaic power generation region;

[0044] D. Calculate the new fitness value of each particle based on 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 if the termination condition has been met. If it has, stop the iteration and output the optimal current allocation scheme. If it has not been met, return to step B for the next iteration.

[0046] In step A The initial value is η k / 2;

[0047] In step E, the optimal current allocation scheme is {v opt,1 ,…,v opt,K}, where v opt,k Let be the individual extreme value of the k-th particle.

[0048] Thirdly, the present invention proposes 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 configured to execute the aforementioned method according to instructions in the computer program code.

[0051] Fourthly, the present invention provides a computer-readable storage medium on which a computer program is stored, and which, when executed by a processor, implements the aforementioned method.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] 1. This invention proposes a distributed photovoltaic energy storage scheduling method. First, it constructs an energy storage model comprising two mobile energy storage regions and multiple photovoltaic power generation regions based on power transmission requirements. Then, it combines the energy storage model to construct an optimization model with current allocation factors as optimization variables and minimizing overall line loss as the objective. Finally, it solves the optimization model to obtain the optimal current allocation scheme and performs distributed photovoltaic energy storage scheduling based on this scheme. This method, from the perspective of overall transmission line loss, effectively reduces the overall line loss of transmission lines while ensuring the energy storage needs of the energy storage regions by scheduling the current allocation factors of different photovoltaic power generation regions.

[0054] 2. The distributed photovoltaic energy storage scheduling method proposed in this invention uses an improved particle swarm optimization algorithm to solve the optimization model. This improved particle swarm optimization algorithm introduces the average value of the difference between the optimal value of the previous iteration and the iteration value during the iterative update process, so that the iterative process can converge faster and get closer to the optimal value. Attached Figure Description

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

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

[0057] Figure 3 The diagram shows the performance improvement of the method described in Example 1 compared to the average current distribution strategy.

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

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

[0060] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0061] Example 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 based on actual power transmission requirements, as shown in the figure below. Figure 2 As shown, the system comprises two mobile energy storage areas and multiple photovoltaic (PV) power generation areas. Each of the two mobile energy storage areas (Mobile Energy Storage Area 1 and Mobile Energy Storage Area 2) is equipped with a main power transmission line (Transmission Line 1 and Transmission Line 2). K PV power generation areas are connected between the two main power lines and supply power to the main power lines via branch lines. Main power line 1 transmits power to mobile energy storage area 1, and main power line 2 transmits power to mobile energy storage area 2. During transmission, the K PV power generation areas supply power to both main power lines 1 and 2. The initial line capacity of the two main power lines is 30A, and the transmission capacity of the K PV power generation areas is 5A.

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

[0065] For the k-th photovoltaic power generation region, a current allocation factor η is introduced. k η k ∈[0,1], will The current is allocated to the main line 1. The current is allocated to main line 2. Therefore, the line losses from the photovoltaic power generation area to main line 1 and main line 2 are... and The calculation formula is as follows:

[0066]

[0067] In the above formula, Let k be the power generation of the photovoltaic power generation area. These are the resistances from the k-th photovoltaic power generation area to main line 1 and main line 2, respectively, where K is the number of photovoltaic power generation areas erected between the two main lines.

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

[0069]

[0070] For the main line, the current in each segment increases with the amount of electricity brought in by the branch lines. Therefore, its line loss is the sum of the losses of multiple segments. The line losses of main lines 1 and 2 are derived as follows:

[0071]

[0072] In the above formula, These are the line losses for main line 1 and main line 2, respectively. These are the initial currents of main line 1 and main line 2, respectively. The resistance of the main line 1 from the k-th photovoltaic power generation area access point to the (k+1)-th photovoltaic power generation area access point. These are the resistances of the main line 2 from the kth photovoltaic power generation area access point to the (k+1)th photovoltaic power generation area access point.

[0073] Therefore, the total line loss of the main line m for:

[0074]

[0075] Overall line loss in the entire energy storage model tot It consists of a trunk network and branch networks, therefore:

[0076] loss tot =loss o +loss m .

[0077] 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 It can modify the current distribution, reducing overall line transmission losses while meeting energy storage requirements. Therefore, a current distribution factor {η1,…,η} is constructed. K The following optimization problem model is used to optimize variables and minimize overall line loss:

[0078]

[0079]

[0080] In the above formula, These are the maximum current values ​​that mobile energy storage area 1 and mobile energy storage area 2 can withstand, respectively.

[0081] 3. An improved particle swarm optimization algorithm is used to solve the optimization model and obtain the optimal current allocation scheme, specifically including:

[0082] S31. Initialize the particle swarm, which consists of K current allocation factors, each assigned a random value to one of the K particles. To ensure accuracy and optimal results during the particle search process, the following steps are taken: The initial value is set to η k / 2;

[0083] S32. Calculate the fitness value of each particle, i.e. the objective function value, 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.

[0084] S33. Update the velocity and position of each particle, where the particle velocity is updated according to the following formula:

[0085]

[0086] In the above formula, Let be the value of the k-th particle in the i-th iteration, σ be the learning rate, and R be a random number within (0,1). Let $\frac{k}{k}$ be the individual extreme value of the $k$-th particle during the $i-1$-th iteration, and its update method is: when the current iteration is in the... When the extreme value is reached, the value also passes through renew, Let x be the extreme value of the k-th particle in the current iteration. k,i Let η be the global search step size in the i-th iteration. k Let be the current distribution factor for the k-th photovoltaic power generation region;

[0087] S34. Calculate the new fitness value of each particle based on the update speed and position. If the new fitness value is better, update the individual extreme value and global extreme value of the particle.

[0088] S35. Determine if the termination condition has been met. If it has, stop the iteration and output the optimal current allocation scheme {v}. opt,1 ,…,v opt,K}, where v opt,k This represents the individual extreme value of the k-th particle; if it is not reached, return to S32 for the next iteration.

[0089] 4. Distributed photovoltaic energy storage scheduling is carried out according to the optimal current allocation scheme.

[0090] The performance improvement of the method described in this embodiment compared to the average current distribution strategy is as follows: Figure 3 As shown.

[0091] Example 2:

[0092] A distributed photovoltaic energy storage dispatch system, such as Figure 4 As shown, it includes an energy storage model construction module, an optimization model construction module, and an optimization model solving and scheduling module.

[0093] The energy storage model construction module is used to construct an energy storage model according to power transmission requirements. The energy storage model includes two mobile energy storage areas, each equipped with a main power line for power transmission. Multiple photovoltaic power generation areas are constructed between the two main power lines, supplying power to the main power lines via branch lines. Figure 2 As shown.

[0094] The optimization model construction module is used to combine the energy storage model to construct an optimization model with the current allocation factor as the optimization variable and the overall line loss as the objective. The objective function of the optimization model includes:

[0095]

[0096] In the above formula, loss tot For the overall line loss, loss o loss m η represents the total line loss of branch lines and main lines, respectively. k Let η be the current distribution factor for the k-th photovoltaic power generation area. k ∈[0,1], Let k be the power generation of the photovoltaic power generation area. These represent the resistances from the k-th photovoltaic power generation area to main line 1 and main line 2, respectively, where K is the number of photovoltaic power generation areas connected between the two main lines. These are the line losses for main line 1 and main line 2, respectively. These are the initial currents of main line 1 and main line 2, respectively. The resistance of the main line 1 from the k-th photovoltaic power generation area access point to the (k+1)-th photovoltaic power generation area access point. These are the resistances of the main line 2 from the kth photovoltaic power generation area access point to the (k+1)th photovoltaic power generation area access point;

[0097] The constraints include:

[0098]

[0099] In the above formula, These are the current requirements for mobile energy storage area 1 and mobile energy storage area 2, respectively. These are the maximum current values ​​that mobile energy storage area 1 and mobile energy storage area 2 can withstand, respectively.

[0100] The optimization model solving and scheduling module is used to solve the optimization model using an improved particle swarm optimization algorithm to obtain the optimal current allocation scheme, and to perform distributed photovoltaic energy storage scheduling based on the optimal current allocation scheme. The improved particle swarm optimization algorithm includes:

[0101] A. Initialize the particle swarm, which consists of K current allocation factors;

[0102] 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.

[0103] C. Update the velocity and position of each particle, where the particle velocity is updated according to the following formula:

[0104]

[0105] In the above formula, Let be the value of the k-th particle in the i-th iteration, σ be the learning rate, and R be a random number within (0,1). Let x be the individual extreme value of the k-th particle during the (i-1)-th iteration. k,i Let η be the global search step size in the i-th iteration. k Let be the current distribution factor for the k-th photovoltaic power generation region;

[0106] D. Calculate the new fitness value of each particle based on the updated speed and position, and update the individual extreme value and global extreme value of each particle based on the new fitness value;

[0107] E. Determine if the termination condition has been met. If it has, stop the iteration and output the optimal current allocation scheme. If it has not been met, return to step B for the next iteration.

[0108] Example 3:

[0109] 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 transfer the computer program code to the processor; the processor is used to execute the method as described in Embodiment 1 according to the instructions in the computer program code.

[0110] Example 4:

[0111] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in Embodiment 1.

Claims

1. A distributed photovoltaic energy storage dispatching method, characterized in that, The method includes: S1. Construct an energy storage model according to the power transmission requirements. The energy storage model includes two mobile energy storage areas. Each of the two mobile energy storage areas is equipped with a main line for power transmission. Multiple photovoltaic power generation areas are set up between the two main lines to replenish the main lines with power through branch lines. S2. Combining the energy storage model, an optimization model is constructed with the current allocation factor as the optimization variable and the overall line loss as the objective. The objective function of this optimization model includes: In the above formula, loss tot For the overall line loss, loss o loss m η represents the total line loss of branch lines and main lines, respectively. k Let η be the current distribution factor for the k-th photovoltaic power generation area. k ∈[0,1], Let k be the power generation of the photovoltaic power generation area. These represent the resistances from the k-th photovoltaic power generation area to main line 1 and main line 2, respectively, where K is the number of photovoltaic power generation areas connected between the two main lines. These are the line losses for main line 1 and main line 2, respectively. These are the initial currents of main line 1 and main line 2, respectively. The resistance of the main line 1 from the k-th photovoltaic power generation area access point to the (k+1)-th photovoltaic power generation area access point. These are the resistances of the main line 2 from the kth photovoltaic power generation area access point to the (k+1)th photovoltaic power generation area access point; The constraints include: In the above formula, These are the current requirements for mobile energy storage area 1 and mobile energy storage area 2, respectively. These are the maximum current values ​​that mobile energy storage area 1 and mobile energy storage area 2 can withstand, respectively. S3. Solve the optimization model to obtain the optimal current allocation scheme, and perform distributed photovoltaic energy storage scheduling based on the optimal current allocation scheme.

2. The distributed photovoltaic energy storage scheduling method according to claim 1, characterized in that, The S3 algorithm employs an improved particle swarm optimization algorithm to solve the optimization model, including: S31. Initialize the particle swarm, which consists of K current allocation factors; S32. 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. S33. Update the velocity and position of each particle, where the particle velocity is updated according to the following formula: In the above formula, Let be the value of the k-th particle in the i-th iteration, σ be the learning rate, and R be a random number within (0,1). Let x be the individual extreme value of the k-th particle during the (i-1)-th iteration. k,i Let η be the global search step size in the i-th iteration. k Let be the current distribution factor for the k-th photovoltaic power generation region; S34. Calculate the new fitness value of each particle based on the updated speed and position, and update the individual extreme value and global extreme value of each particle based on the new fitness value; S35. Determine if the termination condition has been met. If it has, stop the iteration and output the optimal current allocation scheme. If not, return to S32 for the next iteration.

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

4. A distributed photovoltaic energy storage dispatch system, characterized in that, The system includes an energy storage model construction module, an optimization model construction module, an optimization model solving and scheduling module; The energy storage model construction module is used to construct an energy storage model according to the power transmission requirements. The energy storage model includes two mobile energy storage areas, each equipped with a main line for power transmission. Between the two main lines, multiple photovoltaic power generation areas are erected to replenish the main lines with power through branch lines. The optimization model construction module is used to combine the energy storage model to construct an optimization model with the current allocation factor as the optimization variable and the overall line loss as the objective. The objective function of the optimization model includes: In the above formula, loss tot For the overall line loss, loss o loss m η represents the total line loss of branch lines and main lines, respectively. k Let η be the current distribution factor for the k-th photovoltaic power generation area. k ∈[0,1], Let k be the power generation of the photovoltaic power generation area. These represent the resistances from the k-th photovoltaic power generation area to main line 1 and main line 2, respectively, where K is the number of photovoltaic power generation areas connected between the two main lines. These are the line losses for main line 1 and main line 2, respectively. These are the initial currents of main line 1 and main line 2, respectively. The resistance of the main line 1 from the k-th photovoltaic power generation area access point to the (k+1)-th photovoltaic power generation area access point. These are the resistances of the main line 2 from the kth photovoltaic power generation area access point to the (k+1)th photovoltaic power generation area access point; The constraints include: In the above formula, These are the current requirements for mobile energy storage area 1 and mobile energy storage area 2, respectively. These are the maximum current values ​​that mobile energy storage area 1 and mobile energy storage area 2 can withstand, respectively. The optimization model solving and scheduling module is used to solve the optimization model, obtain the optimal current allocation scheme, and perform distributed photovoltaic energy storage scheduling based on the optimal current allocation scheme.

5. A distributed photovoltaic energy storage dispatching system according to claim 4, 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 the particle swarm, which consists of K current allocation 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 velocity and position of each particle, where the particle velocity is updated according to the following formula: In the above formula, Let be the value of the k-th particle in the i-th iteration, σ be the learning rate, and R be a random number within (0,1). Let x be the individual extreme value of the k-th particle during the (i-1)-th iteration. k,i Let η be the global search step size in the i-th iteration. k Let be the current distribution factor for the k-th photovoltaic power generation region; D. Calculate the new fitness value of each particle based on 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 if the termination condition has been met. If it has, stop the iteration and output the optimal current allocation scheme. If it has not been met, return to step B for the next iteration.

6. A dynamic dispatching system for mobile energy storage vehicles targeting multi-regional power consumption, as described in claim 5, is characterized in that... In step A The initial value is η k / 2; In step E, the optimal current allocation scheme is {v opt,1 ,…,v opt,K }, where v opt,k Let be the individual extreme value of the k-th particle.

7. A distributed photovoltaic energy storage dispatching device, characterized in that, The device 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 configured to execute the method as described in any one of claims 1-3 according to instructions in the computer program code.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-3.

Images