Distributed photovoltaic energy storage regulation voltage deviation collaborative planning method

Abstract

The invention relates to a distributed photovoltaic energy storage regulation voltage deviation collaborative planning method, which comprises the following steps: step 1, collecting adjustable point injection power, distributed photovoltaic energy storage active output power and photovoltaic energy storage reactive output power of a power distribution network, and establishing a three-layer model of a power distribution network system; 2, optimizing the three-layer model established in the step 1 by taking the lowest annual cost of the power distribution network, the optimal planning configuration of distributed photovoltaic energy storage and the minimum voltage deviation of the power distribution network as targets; and step 3, solving the three-layer model by using an improved multi-objective litsea rotundifolia optimization algorithm to obtain a distributed photovoltaic energy storage regulation voltage deviation collaborative planning result. According to the method, the power flowing into the power distribution network from the superior power grid can be effectively reduced, the influence on the main network caused by reverse power transmission generated when the high-proportion distributed power supply is connected can be eliminated, the capacity expansion of the transformer is slowed down to a certain extent, and the utilization rate of existing equipment is improved.

Description

Technical Field

[0001] The present invention relates to the field of distributed energy storage technology, and in particular to a method for coordinated planning of distributed photovoltaic energy storage to adjust voltage deviation. Background Art

[0002] A large number of distributed power sources are connected to the distribution network. The randomness of their access points and the uncertainty of their output have brought new problems to the planning and operation of the distribution network. At the same time, large-scale distributed new energy and energy storage development models will pose severe challenges to the efficiency and benefits of the original distribution network planning and operation. With the rapid growth of loads and the increasing peak-to-valley difference, the problems of "low standards, weak connections, and low voltage" in urban and rural distribution networks have become increasingly prominent. Load demand response, as an effective means of regulation, can alleviate the above problems to a certain extent, but to fundamentally solve them, energy storage technology needs to be introduced. The consumption of new energy urgently needs supporting energy storage to alleviate the impact of the short-term insufficient capacity of the local distribution network.

[0003] At the same time, many areas have been affected by voltage deviations due to the large-scale access of distributed photovoltaics to local distribution networks. On the one hand, with the increase in the penetration rate of distributed power sources, voltage deviations continue to increase or even exceed the limit, which may cause the distributed power sources to fail to be sent up, seriously affecting the power supply quality; on the other hand, low voltage problems in remote areas lead to slow growth in electricity consumption, and users' electricity demand cannot be met. Under this situation, it is urgent to study various technologies to solve the voltage deviation problem. Therefore, how to find the best solution from both technical and economic perspectives is the key to reasonably solving the voltage deviation problem. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a distributed photovoltaic energy storage voltage deviation coordinated planning method that can solve the voltage deviation problem in response to the above-mentioned prior art.

[0005] The technical solution adopted by the present invention to solve the above technical problems is: a method for coordinated planning of voltage deviation adjustment of distributed photovoltaic energy storage, characterized by comprising the following steps:

[0006] Step 1: Collect the injection power of the adjustable point of the distribution network, the active output power of the distributed photovoltaic energy storage, and the reactive output power of the photovoltaic energy storage, and establish a three-layer model of the distribution network system;

[0007] Step 2: Optimize the three-layer model established in step 1 with the goal of minimizing the annual cost of the distribution network, optimizing the planning and configuration of distributed photovoltaic energy storage, and minimizing the voltage deviation of the distribution network;

[0008] Step 3: Use the improved multi-objective golden jackal optimization algorithm to solve the three-layer model and obtain the coordinated planning results of distributed photovoltaic energy storage to regulate voltage deviation.

[0009] The improved multi-objective golden jackal optimization algorithm in step 3 includes an initialization phase, a prey search phase, and a prey encirclement and attack phase. The improved multi-objective golden jackal optimization algorithm is based on the existing golden jackal optimization algorithm. A sine-cosine algorithm of a position update process is introduced into the encirclement and attack phase to obtain the position update formula of the golden jackal; then the Cauchy-Gaussian mutation strategy is introduced into the position update formula of the golden jackal to obtain the optimized position update formula of the golden jackal; finally, Gaussian perturbation is added to the optimized position update formula of the golden jackal.

[0010] In the initialization stage, the number of distributed photovoltaic energy storage connected to the distribution network is set to Y0, and the calculation formula of Y0 is:

[0011] Y0=Y min +rand*(Y max -Y min )

[0012] Among them, Y min is the minimum value of Y0, Y max is the maximum value of Y0, and rand is a function for generating random values ​​in the range [0, 1];

[0013] And initialize the overall distribution configuration matrix Prey(1) of the distribution network as:

[0014]

[0015] Among them, Y 1，1 , Y 1，2 and Y 1，d They represent the first voltage deviation position on the first transmission network of the distribution network, the second voltage deviation position on the first transmission network of the distribution network and the dth voltage deviation position on the first transmission network of the distribution network respectively; Y 2，1 , Y 2，2 and Y 2，d They represent the first voltage deviation position on the second transmission network of the distribution network, the second voltage deviation position on the second transmission network of the distribution network, and the dth voltage deviation position on the second transmission network of the distribution network; Y n，1 , Y n，2 and Y n，d They respectively represent the first voltage deviation position on the nth transmission network of the distribution network, the second voltage deviation position on the nth transmission network of the distribution network, and the dth voltage deviation position on the nth transmission network of the distribution network.

[0016] In the prey search phase, two distributed photovoltaic energy storages are connected to the initialized distribution network, which is recorded as completing one iteration. The number of iterations t is set to 1 and substituted into the following two calculation formulas to obtain the relative position Y1(1) of the distributed photovoltaic energy storage after one iteration and the total voltage deviation position Y2(1) of the distribution network after one iteration.

[0017] Y1(t)=Y M (t)-E*|Y M (t)-rl*Prey(t)|

[0018] Y2(t)=Y FM (t)-E*|Y FM (t)-rl*Prey(t)

[0019] Among them, Y1(t) and Y2(t) are the relative position of distributed photovoltaic energy storage after t iterations and the total voltage deviation position of the distribution network after t iterations, respectively. M (t) and Y FM (t) are the locations of the two distributed photovoltaic energy storages newly connected to the distribution network in the tth iteration, E is the voltage deviation, and rl represents a random number based on the Levy distribution;

[0020] The following calculation formula is used to update the position of the distributed photovoltaic energy storage to obtain the distributed photovoltaic energy storage position Y(t+1) after the t+1th iteration. The calculation formula of Y(t+1) is:

[0021]

[0022] The calculation formula of the voltage deviation E is:

[0023] E=E1*E0

[0024] Among them, E1 represents the decreasing process of voltage deviation, and the calculation formula of E1 is: E1=c1*(1-(t / T)), c1 is the set constant, and T is the maximum number of iterations; E0 represents the initial state of voltage deviation, and the calculation formula of E0 is: E0=2*r-1, r is a random number in the range of [0,1].

[0025] When the voltage deviation |E|>1, the prey search phase is executed; when the voltage deviation |E|<1, the prey encirclement and attack phase is executed.

[0026] In the stage of encircling and attacking the prey, the relative position Y1(t) of the distributed photovoltaic energy storage after t iterations and the total voltage deviation position Y2(t) of the distribution network after t iterations are calculated according to the following formula;

[0027] Y1(t)=Y M (t)-E*|rl*YM (t)-Prey(t)|

[0028] Y2(t)=Y M (t)-E*|rl*Y M (t)-Prey(t)|

[0029] Introducing the above Y1(t) formula into the sine-cosine algorithm of the position update process, we get the position update formula of the golden jackal;

[0030]

[0031] Among them, Y′1(t) is the updated position of Y1(t), a is a constant, r2 is a random number between [0, 2π], r3 is a random number between [-2, 2], and r4 is a random number between [0, 1];

[0032] Then the Cauchy-Gauss mutation strategy is introduced into the position update formula of the golden jackal to obtain the optimized position update formula of the golden jackal, Y M1 (t)=Y′1(t)+Y′1(t)*Cauchy(0,1);

[0033] Among them, Cauchy (0,1) is the standard Cauchy distribution function;

[0034] Finally, Gaussian perturbation is added to the optimized position update formula of the golden jackal to obtain the distributed photovoltaic energy storage position Y after the t+1th iteration after adding Gaussian perturbation. M * (t+1), Y M * The calculation formula for (t+1) is:

[0035]

[0036] Among them, Y M1 * (t) is the value of Y M1 (t) Add Gaussian perturbation, Y M2 * (t) is the value of Y M2 (t) Add Gaussian perturbation, Y M2 (t) = d min +(d max +d min )*z G , d min is the minimum value of the distributed photovoltaic energy storage location, d max is the maximum value of the distributed photovoltaic energy storage location, z G is the solution space under Gaussian perturbation, η is the standard Gaussian distribution operator, N T is the number of particles in the chaotic sequence.

[0037] In the prey search phase or the prey encirclement and attack phase, after each N iterations, the minimum values ​​of Y1(t) and Y2(t) are taken out and recorded as Y 1min (t) and Y 2min (t), i and j are both positive integers;

[0038] If Y1(t+1)>Y 1min (t) and Y2(t+1)>Y 2min (t), then confirm the two distributed photovoltaic energy storage locations connected to the distribution network, and connect the two distributed photovoltaic energy storage to the distribution network; in this way, the cycle is repeated, and finally Y1(t) and Y2(t) both tend to 0, then the voltage deviation of the distribution network is the smallest.

[0039] Compared with the prior art, the advantages of the present invention are: the improved multi-objective golden jackal optimization algorithm is used to perform cluster optimization control on the access of distributed photovoltaic energy storage, so that it can meet part of the load demand during the peak period of the distribution network, and can effectively reduce the power flowing into the distribution network from the upper power grid, and can eliminate the power backflow generated when a high proportion of distributed power sources is connected. The impact on the main grid can be eliminated, which can slow down the expansion of transformers to a certain extent and improve the utilization rate of existing equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Flow chart of the method for coordinated planning of voltage deviation adjustment by distributed photovoltaic energy storage in an embodiment of the present invention DETAILED DESCRIPTION

[0041] The present invention is further described in detail below with reference to the accompanying drawings.

[0042] like Figure 1 As shown, the method for coordinated planning of voltage deviation adjustment of distributed photovoltaic energy storage in this embodiment includes the following steps:

[0043] Step 1: Collect the injection power of the adjustable point of the distribution network, the active output power of the distributed photovoltaic energy storage, and the reactive output power of the photovoltaic energy storage, and establish a three-layer model of the distribution network system;

[0044] Step 2: Optimize the three-layer model established in step 1 with the goal of minimizing the annual cost of the distribution network, optimizing the planning and configuration of distributed photovoltaic energy storage, and minimizing the voltage deviation of the distribution network;

[0045] Step 3: Use the improved multi-objective golden jackal optimization algorithm to solve the three-layer model and obtain the coordinated planning results of distributed photovoltaic energy storage to regulate voltage deviation.

[0046] The improved multi-objective golden jackal optimization algorithm in step 3 of the present embodiment includes an initialization phase, a prey search phase, and a prey encirclement and attack phase. The improved multi-objective golden jackal optimization algorithm is based on the existing golden jackal optimization algorithm. A sine-cosine algorithm of a position update process is introduced into the prey encirclement and attack phase to obtain the position update formula of the golden jackal. The Cauchy-Gauss mutation strategy is then introduced into the position update formula of the golden jackal to obtain the optimized position update formula of the golden jackal. Finally, Gaussian perturbation is added to the optimized position update formula of the golden jackal.

[0047] In the initialization stage, the number of distributed photovoltaic energy storage connected to the distribution network is set to Y0, and the calculation formula of Y0 is:

[0048] Y0=Y min +rand*(Y max -Y min )

[0049] Among them, Y min is the minimum value of Y0, Y max is the maximum value of Y0, and rand is a function for generating random values ​​in the range [0, 1];

[0050] And initialize the overall distribution configuration matrix Pre y(1) of the distribution network as:

[0051]

[0052] Among them, Y 1,1 , Y 1,2 and Y 1,d They represent the first voltage deviation position on the first transmission network of the distribution network, the second voltage deviation position on the first transmission network of the distribution network and the dth voltage deviation position on the first transmission network of the distribution network respectively; Y 2，1 , Y 2，2 and Y 2，d They represent the first voltage deviation position on the second transmission network of the distribution network, the second voltage deviation position on the second transmission network of the distribution network, and the dth voltage deviation position on the second transmission network of the distribution network; Y n，1 , Y n，2 and Y n，d They respectively represent the first voltage deviation position on the nth transmission network of the distribution network, the second voltage deviation position on the nth transmission network of the distribution network, and the dth voltage deviation position on the nth transmission network of the distribution network.

[0053] In the prey search phase, two distributed photovoltaic energy storages are connected to the initialized distribution network, which is recorded as completing one iteration. The number of iterations t is set to 1 and substituted into the following two calculation formulas to obtain the relative position Y1(1) of the distributed photovoltaic energy storage after one iteration and the total voltage deviation position Y2(1) of the distribution network after one iteration.

[0054] Y1(t)=Y M (t)-E*|Y M (t)-rl*Prey(t)|

[0055] Y2(t)=Y FM (t)-E*|Y FM (t)-rl*Prey(t)|

[0056] Among them, Y1(t) and Y2(t) are the relative position of distributed photovoltaic energy storage after t iterations and the total voltage deviation position of the distribution network after t iterations, respectively. M (t) and Y FM (t) are the locations of the two distributed photovoltaic energy storages newly connected to the distribution network in the tth iteration, E is the voltage deviation, and rl represents a random number based on the Levy distribution;

[0057] The following calculation formula is used to update the position of the distributed photovoltaic energy storage to obtain the distributed photovoltaic energy storage position Y(t+1) after the t+1th iteration. The calculation formula of Y(t+1) is:

[0058]

[0059] The calculation formula of voltage deviation E is:

[0060] E=E1*E0

[0061] Among them, E1 represents the decreasing process of voltage deviation, and the calculation formula of E1 is: E1=c1*(1-(t / T)), c1 is the set constant, and T is the maximum number of iterations; E0 represents the initial state of voltage deviation, and the calculation formula of E0 is: E0=2*r-1, r is a random number in the range of [0,1].

[0062] When the voltage deviation |E|>1, the prey search phase is executed; when the voltage deviation |E|<1, the prey encirclement and attack phase is executed.

[0063] In the stage of encircling and attacking the prey, the relative position Y1(t) of the distributed photovoltaic energy storage after t iterations and the total voltage deviation position Y2(t) of the distribution network after t iterations are calculated according to the following formula;

[0064] Y1(t)=Y M (t)-E*|rl*YM (t)-Prey(t)|

[0065] Y2(t)=Y M (t)-E*|rl*Y M (t)-Prey(t)|

[0066] Introducing the above Y1(t) formula into the sine-cosine algorithm of the position update process, we get the position update formula of the golden jackal;

[0067]

[0068] Among them, Y′1(t) is the updated position of Y1(t), a is a constant, r2 is a random number between [0, 2π], r3 is a random number between [-2, 2], and r4 is a random number between [0, 1];

[0069] Then the Cauchy-Gauss mutation strategy is introduced into the position update formula of the golden jackal to obtain the optimized position update formula of the golden jackal, Y M1 (t)=Y′1(t)+Y′1(t)*Cauchy(0,1);

[0070] Among them, Cauchy (0,1) is the standard Cauchy distribution function;

[0071] Finally, Gaussian perturbation is added to the optimized position update formula of the golden jackal to obtain the distributed photovoltaic energy storage position Y after the t+1th iteration after adding Gaussian perturbation. M * (t+1), Y M * The calculation formula for (t+1) is:

[0072]

[0073] Among them, Y M1 * (t) is the value of Y M1 (t) Add Gaussian perturbation, Y M2 * (t) is the value of Y M2 (t) Add Gaussian perturbation, Y M2 (t) = d min +(d max +d min )*Z G , d min is the minimum value of the distributed photovoltaic energy storage location, d max is the maximum value of the distributed photovoltaic energy storage location, Z G is the solution space under Gaussian perturbation, η is the standard Gaussian distribution operator, N T is the number of particles in the chaotic sequence.

[0074] In the prey search phase or the prey encirclement and attack phase, after each N iterations, the minimum values ​​of Y1(t) and Y2(t) are taken out and recorded as Y 1min (t) and Y 2min (t), i and j are both positive integers;

[0075] If Y1(t+1)>Y 1min (t) and Y2(t+1)>Y 2min (t), then confirm the two distributed photovoltaic energy storage locations connected to the distribution network, and connect the two distributed photovoltaic energy storage to the distribution network; in this way, the cycle is repeated, and finally Y1(t) and Y2(t) both tend to 0, then the voltage deviation of the distribution network is the smallest.

[0076] In this embodiment, the voltage deviation is adjusted in combination with the annual cost of the distribution network and the planning configuration of distributed photovoltaic energy storage to ensure that the voltage deviation is minimized as much as possible.

[0077] The multi-objective golden jackal optimization algorithm is used to perform cluster optimization control on the access of distributed photovoltaic energy storage, so that it can meet part of the load demand during the peak period of the distribution network, effectively reduce the power flowing into the distribution network from the upper power grid, and eliminate the power backflow caused by the access of a high proportion of distributed power sources. The impact on the main grid can be eliminated, which can slow down the expansion of transformers to a certain extent and improve the utilization rate of existing equipment.

[0078] Through the improved use of distributed photovoltaic energy storage, the economic interests of power grid companies can be protected, and at the same time the economic cost paid by the power grid to cope with high-penetration distributed photovoltaic access can be reduced (compared with traditional reactive voltage regulation equipment, distributed energy storage can reduce supporting costs by about 10%). It can reduce the cost of energy development and utilization in the entire society, and can enhance the adaptability of distribution networks in remote areas to distributed photovoltaic access. The operating efficiency of the photovoltaic and storage collaborative operation system is improved by 10-15%, and the peak-valley time-space energy transfer capability is enhanced, peak shaving and valley filling can be achieved, the balance of supply and demand in the power grid is promoted, and the reliability and stability of power supply in remote areas are improved.

[0079] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A distributed photovoltaic energy storage voltage deviation adjustment collaborative planning method, characterized in that The steps include: Step 1: Collect the injection power of the adjustable point of the distribution network, the active output power of the distributed photovoltaic energy storage, and the reactive output power of the photovoltaic energy storage, and establish a three-layer model of the distribution network system; Step 2: Optimize the three-layer model established in step 1 with the goal of minimizing the annual cost of the distribution network, optimizing the planning and configuration of distributed photovoltaic energy storage, and minimizing the voltage deviation of the distribution network; Step 3: Use the improved multi-objective golden jackal optimization algorithm to solve the three-layer model and obtain the coordinated planning results of distributed photovoltaic energy storage to regulate voltage deviation.

2. The method for coordinated planning of voltage deviation adjustment for distributed photovoltaic energy storage according to claim 1 is characterized in that: The improved multi-objective golden jackal optimization algorithm in step 3 includes an initialization phase, a prey search phase, and a prey encirclement and attack phase. The improved multi-objective golden jackal optimization algorithm is based on the existing golden jackal optimization algorithm. A sine-cosine algorithm of a position update process is introduced into the encirclement and attack phase to obtain the position update formula of the golden jackal; then the Cauchy-Gaussian mutation strategy is introduced into the position update formula of the golden jackal to obtain the optimized position update formula of the golden jackal; finally, Gaussian perturbation is added to the optimized position update formula of the golden jackal.

3. The method for coordinated planning of voltage deviation adjustment for distributed photovoltaic energy storage according to claim 2 is characterized in that: In the initialization stage, the number of distributed photovoltaic energy storage connected to the distribution network is set to Y0, and the calculation formula of Y0 is: Y0=Y min +rand*(Y max -AND min ) Among them, Y min is the minimum value of Y0, Y max is the maximum value of Y0, and rand is a function for generating random values ​​in the range [0, 1]; And initialize the overall distribution configuration matrix Prey(1) of the distribution network as: Among them, Y 1，1 , Y 1，2 and Y 1，d They represent the first voltage deviation position on the first transmission network of the distribution network, the second voltage deviation position on the first transmission network of the distribution network and the dth voltage deviation position on the first transmission network of the distribution network respectively; Y 2，1 , Y 2，2 and Y 2，d They represent the first voltage deviation position on the second transmission network of the distribution network, the second voltage deviation position on the second transmission network of the distribution network, and the dth voltage deviation position on the second transmission network of the distribution network; Y n，1 , Y n，2 and Y n，d They respectively represent the first voltage deviation position on the nth transmission network of the distribution network, the second voltage deviation position on the nth transmission network of the distribution network, and the dth voltage deviation position on the nth transmission network of the distribution network.

4. The method for coordinated planning of voltage deviation adjustment for distributed photovoltaic energy storage according to claim 3 is characterized in that: In the prey search phase, two distributed photovoltaic energy storages are connected to the initialized distribution network, which is recorded as completing one iteration. The number of iterations t is set to 1 and substituted into the following two calculation formulas to obtain the relative position Y1(1) of the distributed photovoltaic energy storage after one iteration and the total voltage deviation position Y2(1) of the distribution network after one iteration. Y1(t)=Y M (t)-E*|Y M (t)-rl*Prey(t)| Y2(t)=Y FM (t)-E*|Y FM (t)-rl*Prey(t)| Among them, Y1(t) and Y2(t) are the relative position of distributed photovoltaic energy storage after t iterations and the total voltage deviation position of the distribution network after t iterations, respectively. M (t) and Y FM (t) are the locations of the two distributed photovoltaic energy storages newly connected to the distribution network in the tth iteration, E is the voltage deviation, and rl represents a random number based on the Levy distribution; The following calculation formula is used to update the position of the distributed photovoltaic energy storage to obtain the distributed photovoltaic energy storage position Y(t+1) after the t+1th iteration. The calculation formula of Y(t+1) is:

5. The method for coordinated planning of voltage deviation adjustment for distributed photovoltaic energy storage according to claim 4 is characterized in that: The calculation formula of the voltage deviation E is: E=E1*E0 Among them, E1 represents the decreasing process of voltage deviation, and the calculation formula of E1 is: E1=c1*(1-(t / T)), c1 is the set constant, and T is the maximum number of iterations; E0 represents the initial state of voltage deviation, and the calculation formula of E0 is: E0=2*r-1, r is a random number in the range of [0, 1].

6. The method for coordinated planning of voltage deviation adjustment for distributed photovoltaic energy storage according to claim 5 is characterized in that: When the voltage deviation |E|>1, the prey search phase is executed; when the voltage deviation |E|<1, the prey encirclement and attack phase is executed.

7. The method for coordinated planning of voltage deviation adjustment for distributed photovoltaic energy storage according to claim 6 is characterized in that: In the stage of encircling and attacking the prey, the relative position Y1(t) of the distributed photovoltaic energy storage after t iterations and the total voltage deviation position Y2(t) of the distribution network after t iterations are calculated according to the following formula; Y1(t)=Y M (t)-E*|rl*Y M (t)-Prey(t)| Y2(t)=Y M (t)-E*|rl*Y M (t)-Prey(t)| Introducing the above Y1(t) formula into the sine-cosine algorithm of the position update process, we get the position update formula of the golden jackal; Among them, Y′1(t) is the updated position of Y1(t), a is a constant, r2 is a random number between [0, 2π], r3 is a random number between [-2, 2], and r4 is a random number between [0, 1]; Then the Cauchy-Gauss mutation strategy is introduced into the position update formula of the golden jackal to obtain the optimized position update formula of the golden jackal, Y M1 (t)=Y′1(t)+Y′1(t)*Cauchy(0,1); Wherein, Cauchy(0,1) is the standard Cauchy distribution function; Finally, Gaussian perturbation is added to the optimized position update formula of the golden jackal to obtain the distributed photovoltaic energy storage position Y after the t+1th iteration after adding Gaussian perturbation. M * (t+1), Y M * The calculation formula for (t+1) is: Among them, Y M1 * (t) is the value of Y M1 (t) Add Gaussian perturbation, Y M2 * (t) is the value of Y M2 (t) Add Gaussian perturbation, Y M2 (t) = d min +(d max +d min )*z G , d min is the minimum value of the distributed photovoltaic energy storage location, d max is the maximum value of the distributed photovoltaic energy storage location, z G is the solution space under Gaussian perturbation, η is the standard Gaussian distribution operator, N T is the number of particles in the chaotic sequence.

8. The method for coordinated planning of voltage deviation adjustment for distributed photovoltaic energy storage according to claim 7 is characterized in that: In the prey search phase or the prey encirclement and attack phase, after each N iterations, the minimum values ​​of Y1(t) and Y2(t) are taken out and recorded as Y 1min (t) and Y 2min (t), i and j are both positive integers; If Y1(t+1)>Y 1min (t) and Y2(t+1)>Y 2min (t), then confirm the locations of the two distributed photovoltaic energy storage devices connected to the distribution network, and connect the two distributed photovoltaic energy storage devices to the distribution network; In this way, the cycle eventually results in Y1(t) and Y2(t) both tending to 0, and the voltage deviation of the distribution network is minimal at this time.

Images