Wind and light storage micro-grid capacity optimization configuration method based on PGWO algorithm

Abstract

The invention relates to the technical field of micro-grid configuration, in particular to a wind and light storage micro-grid capacity optimization configuration method based on a PGWO algorithm, and the method comprises the following steps: building a wind and light storage micro-grid capacity optimization configuration model according to the output power model of each distributed power supply in a micro-grid; setting a micro-grid operation strategy; under the micro-grid operation strategy, solving the capacity optimization configuration model through a PGWO algorithm to obtain a micro-grid optimal configuration scheme; according to the method, the PGWO algorithm combining Bernoulli chaotic mapping initialization, individual historical memory and a border crossing processing strategy is provided, the method has higher global optimization capacity, capacity configuration optimization can be carried out on the premise of stable operation of the microgrid, and the operation cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of microgrid configuration technology, and more specifically to a method for optimizing the capacity configuration of wind-solar-storage microgrids based on the PGWO algorithm. Background Technology

[0002] With increasing global emphasis on environmental protection and sustainable development, the energy structure is shifting from traditional fossil fuels to renewable energy. Solar and wind power, among other renewable energy sources, are clean and renewable, but they also suffer from intermittency and volatility. Microgrids, by constructing a collaborative "source-grid-load-storage" architecture, provide a systematic solution for the efficient integration and flexible dispatch of distributed energy resources. Capacity optimization, as a core element of microgrid planning and design, scientifically coordinates the synergistic operation of distributed energy resources and energy storage systems, controlling investment and maintenance costs while ensuring power supply reliability, achieving a win-win situation of economy and stability.

[0003] Intelligent algorithms are a class of heuristic search algorithms based on natural phenomena, biological behavior, or human intelligence. They have the characteristics of self-organization, self-adaptation, self-learning, and global search capabilities, and can quickly find near-optimal solutions in complex search spaces. Therefore, they are widely used in the problem of microgrid capacity optimization.

[0004] In existing technologies, the paper "Capacity Optimization Configuration of Clean Microgrids Considering Hybrid Pumped Storage Retrofit" published in the 7th issue of the 2025 *Journal of Electrical Engineering* takes an off-grid wind-solar-hydro-storage multi-energy complementary system as the research object, constructs a multi-source collaborative capacity optimization configuration model, and uses the parallel-solvable C&CG algorithm to solve it; the paper "Capacity Optimization Configuration of Grid-connected Wind Power-Photovoltaic-Pumped Storage-Battery System" published in the 9th issue of the 2023 *Journal of Power Engineering* constructs a joint operation system of wind power-photovoltaic-pumped storage-battery and uses the NSGA-II algorithm and TOPSIS to solve the optimal capacity configuration scheme of the system; the paper "Capacity Optimization Configuration of DC Microgrid Based on Improved Sparrow Search Algorithm" published in the 8th issue of the 2023 *Journal of Solar Energy* constructs a grid-connected wind-solar-storage DC microgrid model and proposes an improved sparrow search algorithm to solve the capacity optimal configuration problem.

[0005] The aforementioned existing technologies still suffer from slow convergence speed and insufficient optimization ability of the gray wolf algorithm. Therefore, obtaining a more economical microgrid capacity configuration while ensuring stable operation of the microgrid is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a method for optimizing the capacity configuration of wind-solar-storage microgrids based on the PGWO algorithm, which can optimize the capacity configuration under the premise of stable operation of the microgrid.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A method for optimizing the capacity configuration of a wind-solar-storage microgrid based on the PGWO algorithm includes the following steps:

[0009] S1. Based on the output power model of each distributed power source in the microgrid, build a capacity optimization configuration model for the wind-solar-storage microgrid;

[0010] S2. Set the microgrid operation strategy;

[0011] S3. Under the microgrid operation strategy, the capacity optimization configuration model is solved using the PGWO algorithm to obtain the optimal configuration scheme for the microgrid; wherein the solution process includes:

[0012] Bernoulli chaotic mapping is used to generate an initial gray wolf population and determine the initial configuration scheme; each gray wolf individual contains all the variables required to solve the capacity optimization configuration model, and different gray wolf individuals represent different variable configurations;

[0013] The weight coefficients for different search directions are determined based on the location of the gray wolf, and the historical best position of the individual gray wolf is introduced for population update.

[0014] Update out-of-bounds individuals using preset mapping rules;

[0015] After a preset number of iterations, the optimal configuration scheme of the microgrid is obtained.

[0016] Furthermore, in S1, the capacity optimization configuration model is constructed using the equivalent annual comprehensive cost as the objective function. The objective function expression of the capacity optimization configuration model is:

[0017] minC ACC =C ini +C om +C r +C buy -C sell

[0018] Among them, C ACC C is the annual comprehensive cost of the microgrid system; ini C represents the annual initial investment cost of the microgrid system. om The annual operation and maintenance cost of a microgrid system; C r C is the equivalent annual equipment replacement cost for a microgrid system. buy The cost of electricity for a microgrid system; C sell This refers to the revenue generated from the sale of electricity by the microgrid system.

[0019] Furthermore, in S1, the constraints of the capacity optimization configuration model are:

[0020]

[0021] Where, N i N represents the number of configurations for the i-th type of power supply; imax The maximum number of power sources of type i can be configured; SOC(t) is the state of charge of the battery at time t; SOC max SOC min These are the upper and lower limits of the state of charge, respectively; P c (t) represents the charging power of the battery at time t, P c (t) represents the discharge power of the battery at time t, P c,max P d,max These are the upper limits for charging power and discharging power of the battery, respectively; P buy (t) represents the power purchased at time t, P buy,max P represents the maximum power purchase capacity. sell (t) represents the electricity sold at time t, P sell,max P is the maximum power output; WT (t) represents the wind power output at time t; P PV (t) represents the photovoltaic power output at time t; P X (t) represents the battery charging and discharging power at time t, with discharging being positive and charging being negative; P D (t) represents the grid interaction power at time t, with purchasing electricity from the grid being positive and selling electricity to the grid being negative; P L (t) represents the load power at time t.

[0022] Furthermore, S2 includes:

[0023] S21: For any given time t, obtain the output power of wind and solar power generation and the power required by the load;

[0024] S22: Determine the unbalanced power based on the output power of wind and solar power generation and the load power;

[0025] S23: Energy dispatch is carried out according to the preset microgrid operation strategy based on unbalanced power.

[0026] Furthermore, in S23, when the unbalanced power ΔP(t)>0, it means that the wind and solar power output can meet the load demand. At this time, if the battery is fully charged, the excess power will be sold to the grid; if the battery is not fully charged, the battery will be charged first, and the excess power after being fully charged will be sold to the grid.

[0027] When the unbalanced power ΔP(t) < 0, it means that the wind and solar power output cannot meet the load demand. At this time, if the battery can meet the remaining load, the battery will be discharged to meet the remaining load. If the battery cannot meet the remaining load, the battery will be discharged first, and then the power will be purchased from the grid to meet the load that the battery cannot meet.

[0028] Furthermore, in S3, Bernoulli chaotic mapping is used to generate an initial gray wolf population and determine the initial configuration scheme, including:

[0029] Randomly generate a j-dimensional vector x0, where each component of the vector is between 0 and 1;

[0030] The Bernoulli chaotic map is used to generate i vectors, as follows:

[0031]

[0032] Where λ is the control parameter; x i This represents the i-th point generated after Bernoulli chaotic mapping, i.e., the initialized gray wolf population, and its position represents the initial configuration scheme.

[0033] Mapping Bernoulli chaos onto the range of variable values, we obtain the expression for population initialization as follows:

[0034] X i =(ub-lb)gx i +lb

[0035] In the above formula, X i Let be the position of the i-th gray wolf individual, and ub and lb be the upper and lower bounds of the value range, respectively.

[0036] Furthermore, in S3, weight coefficients for different search directions are determined based on the gray wolf's position, and the historical best position of individual gray wolves is introduced for population updates, including:

[0037] X(t+1)=c1r3K1+(G1K2+G2K3) / 2+c2r4(X ibest -X(t))

[0038]

[0039] Where X(t+1) represents the current position of the gray wolf after the update, and X(t) represents the current position of the gray wolf before the update. ibest Let C1 be the optimal position experienced by the current individual gray wolf; c1 be the optimal influence coefficient of the population; c2 be the individual memory coefficient; G1 and G2 be the weight coefficients; r1, r2, r3, and r4 be random numbers in the range [0,1]; m takes values ​​of α, β, and δ, and X...α X β X δ Let represent the positions of α, β, and δ wolves, which are the three gray wolf individuals with the best fitness. n takes values ​​of 1, 2, and 3. K1, K2, and K3 represent the direction and distance of the current gray wolf individual moving towards α, β, and δ wolves, respectively. A and C are intermediate parameters; a represents the convergence factor; t represents the number of iterations. max This indicates the maximum number of iterations.

[0040] Furthermore, in S3, the weighting coefficients G1 and G2 are determined as follows:

[0041]

[0042] Furthermore, in S3, the preset mapping rules are as follows:

[0043]

[0044] Among them, X new σ represents the update position of the out-of-bounds individual; s is a random number between [0,1]; σ is the out-of-bounds handling factor between [0,1]; ub and lb are the upper and lower bounds of the value interval, respectively; and x represents the out-of-bounds individual.

[0045] As can be seen from the above technical solution, compared with the prior art, the present invention has the following beneficial effects:

[0046] This invention establishes a microgrid capacity optimization configuration model based on the distributed power sources within the microgrid. Addressing the slow convergence speed and insufficient optimization capability of the Grey Wolf algorithm, the PGWO algorithm is proposed. First, Bernoulli chaotic mapping is used for initialization to achieve a more uniform initial population distribution. Next, the concept of individual historical memory is introduced, incorporating the historical optimal positions experienced by individual Grey wolves into the position update decision. Finally, a specific mapping rule is used to perform a secondary update on the positions of individuals that have exceeded their limits. This algorithm possesses stronger global optimization capabilities and can effectively reduce the overall annual cost of the microgrid. Therefore, optimizing capacity configuration while ensuring stable operation of the microgrid is of great significance for reducing its operating costs. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0048] Figure 1A flowchart of the capacity optimization configuration method for wind-solar-storage microgrids based on the PGWO algorithm provided by the present invention;

[0049] Figure 2 This is a schematic diagram of the microgrid operation strategy provided by the present invention;

[0050] Figure 3 This is a schematic diagram of the annual hourly wind speed data provided by the present invention;

[0051] Figure 4 This is a schematic diagram of the annual hourly solar radiance data provided by the present invention;

[0052] Figure 5 This is a schematic diagram of the annual hourly electricity load data provided by the present invention;

[0053] Figure 6 The optimized configuration convergence curve provided by this invention;

[0054] Figure 7 Convergence curves under different operating modes provided by this invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] like Figure 1 As shown in the figure, this invention discloses a method for optimizing the capacity configuration of a wind-solar-storage microgrid based on the PGWO algorithm, comprising the following steps:

[0057] S1. Based on the output power model of each distributed power source in the microgrid, build a capacity optimization configuration model for the wind-solar-storage microgrid;

[0058] S2. Set the microgrid operation strategy;

[0059] S3. Under the microgrid operation strategy, the capacity optimization configuration model is solved using the PGWO algorithm to obtain the optimal configuration scheme for the microgrid; wherein, the process of solving using the PGWO algorithm includes:

[0060] Bernoulli chaotic mapping is used to generate an initial gray wolf population and determine the initial configuration scheme; each gray wolf individual contains all the variables required to solve the capacity optimization configuration model, and different gray wolf individuals represent different variable configurations;

[0061] The weight coefficients for different search directions are determined based on the location of the gray wolf, and the historical best position of the individual gray wolf is introduced for population update.

[0062] Update out-of-bounds individuals using preset mapping rules;

[0063] After a preset number of iterations, the optimal configuration scheme of the microgrid is obtained.

[0064] The following is a further explanation of each of the above steps.

[0065] S1. Based on the output power model of each distributed power source in the microgrid, and with the equivalent annual comprehensive cost as the objective function, the capacity optimization configuration model is constructed. The objective function expression of the capacity optimization configuration model is:

[0066] minC ACC =C ini +C om +C r +C buy -C sell

[0067] Among them, C ACC C is the annual comprehensive cost of the microgrid system; ini C represents the annual initial investment cost of the microgrid system. om The annual operation and maintenance cost of a microgrid system; C r C is the equivalent annual equipment replacement cost for a microgrid system. buy The cost of electricity for a microgrid system; C sell This refers to the revenue generated from the sale of electricity by the microgrid system.

[0068] The constraints of the capacity optimization configuration model are:

[0069]

[0070] Where, N i N represents the number of configurations for the i-th type of power supply; imax The maximum number of power sources of type i can be configured; SOC(t) is the state of charge of the battery at time t; SOC max SOC min These are the upper and lower limits of the state of charge, respectively; P c (t) represents the charging power of the battery at time t, P c (t) represents the discharge power of the battery at time t, P c,max P d,max These are the upper limits for charging power and discharging power of the battery, respectively; P buy (t) represents the power purchased at time t, P buy,max P represents the maximum power purchase capacity. sell(t) represents the electricity sold at time t, P sell,max P is the maximum power output; WT (t) represents the wind power output at time t; P PV (t) represents the photovoltaic power output at time t; P X (t) represents the battery charging and discharging power at time t, with discharging being positive and charging being negative; P D (t) represents the grid interaction power at time t, with purchasing electricity from the grid being positive and selling electricity to the grid being negative; P L (t) represents the load power at time t.

[0071] S2. By determining the unbalanced power, the microgrid operation strategy is obtained, thereby controlling the microgrid for energy dispatch, such as... Figure 2 As shown, the microgrid operation strategy is as follows:

[0072] S21: For any given time t, obtain the output power of wind and solar power generation and the power required by the load.

[0073] S22: Determine the unbalanced power based on the output power of wind and solar power generation and the load power:

[0074] ΔP(t)=P WT (t)+P PV (t)-P L (t)

[0075] Where ΔP(t) is the unbalanced power, P WT (t) represents the output power of the wind turbine, P PV (t) represents the output power of the photovoltaic module, P L (t) represents the power required by the load.

[0076] S23: Energy dispatch is carried out according to the preset microgrid operation strategy based on unbalanced power.

[0077] When the unbalanced power ΔP(t)>0, it means that the wind and solar power output can meet the load demand. At this time, if the battery is fully charged, the excess power will be sold to the grid; if the battery is not fully charged, the battery will be charged first, and the excess power after being fully charged will be sold to the grid.

[0078] When the unbalanced power ΔP(t) < 0, it means that the wind and solar power output cannot meet the load demand. At this time, if the battery can meet the remaining load, the battery will be discharged to meet the remaining load. If the battery cannot meet the remaining load, the battery will be discharged first, and then the power will be purchased from the grid to meet the load that the battery cannot meet.

[0079] S24. Determine if the time t has reached the set value (i.e., the number of hours in a year). If t is less than the set value, return to S21. If t reaches the set value, end the process.

[0080] S3. Under the microgrid operation strategy, the capacity optimization configuration model is solved by the PGWO algorithm to obtain the optimal configuration scheme of the microgrid.

[0081] Specifically, firstly, annual hourly meteorological and load data are input, and an initial gray wolf population is generated using Bernoulli chaotic mapping. Each gray wolf individual contains all the variables needed to calculate the objective function, such as the number of wind turbines, photovoltaic modules, and batteries. Different individuals represent different variable configurations, i.e., the population.

[0082] Then, the fitness of all individual gray wolves is calculated. Based on the variables of the gray wolf population, power is supplied according to the microgrid operation strategy, and the fitness of individuals is obtained by calculating the optimization objective function.

[0083] The gray wolves are sorted according to their fitness, and the three with the best fitness are identified as α wolf, β wolf, and δ wolf. Under the leadership of α wolf, β wolf, and δ wolf, the positions of the gray wolves and any out-of-bounds individuals are updated until the required number of iterations is reached, at which point the optimal solution is output.

[0084] The Bernoulli chaotic mapping is used to generate an initial gray wolf population, where the location information of the initial gray wolf population represents the initial configuration scheme. The specific process includes:

[0085] 1) Randomly generate a j-dimensional vector x0, where each component of the vector is between 0 and 1;

[0086] 2) Generate i vectors using the Bernoulli chaotic mapping, which is as follows:

[0087]

[0088] Where λ is the control parameter, x i This represents the i-th point generated after Bernoulli chaotic mapping, i.e., the initialized gray wolf population, and its position represents the initial configuration scheme.

[0089] 3) Mapping Bernoulli chaos onto the range of variable values ​​yields the expression for population initialization:

[0090] X i =(ub-lb)gx i +lb

[0091] In the above formula, X i Let be the position of the i-th gray wolf individual, and ub and lb be the upper and lower bounds of the value range, respectively.

[0092] In this embodiment, compared to randomly generating the initial population, the initial population generated by the Bernoulli chaotic mapping is more ergodic, which helps the algorithm to perform efficient searches globally.

[0093] While the traditional gray wolf algorithm establishes a correlation mechanism between an individual's current position and the group's historical best positions, it neglects the effective utilization of the individual's own search experience, potentially leading to the loss of local information and decreased search efficiency. To improve the algorithm's global optimization capability, the historical best positions experienced by each gray wolf are incorporated into the position update decision. The formula for updating the gray wolf's position is as follows:

[0094] X(t+1)=c1r3K1+(G1K2+G2K3) / 2+c2r4(X ibest -X(t))

[0095]

[0096] Where X(t+1) represents the current position of the gray wolf after the update, and X(t) represents the current position of the gray wolf before the update. ibest Let C1 be the optimal position experienced by the current individual gray wolf; c1 be the optimal influence coefficient of the population; c2 be the individual memory coefficient; G1 and G2 be the weight coefficients; r1, r2, r3, and r4 be random numbers in the range [0,1]; m takes values ​​of α, β, and δ, and X... α X β X δ Let represent the positions of α, β, and δ wolves, which are the three gray wolf individuals with the best fitness. n takes values ​​of 1, 2, and 3. K1, K2, and K3 represent the direction and distance of the current gray wolf individual moving towards α, β, and δ wolves, respectively. A and C are intermediate parameters; a represents the convergence factor; t represents the number of iterations. max This indicates the maximum number of iterations.

[0097] The weighting coefficients G1 and G2 are determined as follows:

[0098]

[0099] Next, this embodiment proposes a specific mapping rule to perform a secondary update on individuals that cross the boundary, avoiding the clustering of individuals near the boundary, which weakens population diversity and limits the algorithm's global exploration capability. Its expression is:

[0100]

[0101] Among them, X newσ represents the update position of the out-of-bounds individual; s is a random number between [0,1]; σ is the out-of-bounds handling factor between [0,1]; ub and lb are the upper and lower bounds of the value interval, respectively; and x represents the out-of-bounds individual.

[0102] When a gray wolf goes out of bounds, that is, when the configuration information it represents exceeds the set upper and lower limits, its position is corrected according to the mapping rule, thereby updating the configuration information it represents.

[0103] The performance of the method of the present invention will be further verified below.

[0104] The present invention employs a time-of-use pricing system for electricity transactions between the microgrid and the upper-level power grid. The time-of-use pricing is shown in Table 1, and local meteorological and load data are available in [reference needed]. Figures 3-5 .

[0105] Table 1 Time-of-use Electricity Price Table

[0106]

[0107] The relevant parameters of the wind turbine, photovoltaic module, and battery selected in this invention are shown in Table 2. In the microgrid system, the rated wind speed of the wind turbine is 11 m / s, the cut-in wind speed is 3 m / s, and the cut-out wind speed is 25 m / s; the temperature difference adjustment factor λ of the photovoltaic module is -0.0047; the self-discharge coefficient ξ of the battery is 0.01, the charging efficiency and discharging efficiency are both 0.85, and the normal operating range of SOC is 20%–90%; the discount rate r is 4.75%, and the population and iteration number are both 100.

[0108] Table 2 Equipment Cost Parameters

[0109] power supply Wind turbine photovoltaic modules storage battery parameter 30kW 1 2V, 1000Ah Investment cost / 10,000 yuan 27 0.2 0.16 Maintenance cost / 10,000 yuan 0.3 0.002 0.002 Replacement cost / 10,000 yuan 27 0.2 0.16 Service life / a 20 20 5

[0110] To verify the superiority of the wind-solar-storage microgrid capacity optimization configuration method of the present invention, the improved PGWO algorithm and GWO algorithm were used to solve the capacity optimization configuration problem of the wind-solar-storage microgrid, respectively. The optimization configuration results are shown in Table 3, and the convergence curves are shown in Table 3. Figure 6 .

[0111] Table 3 Optimization Configuration Results

[0112] algorithm Wind turbine / unit Photovoltaic module / unit Battery / block Equivalent annual comprehensive cost / 10,000 yuan PGWO 7 383 399 48.93 GWO 7 363 478 54.28

[0113] From Table 3 and Figure 6 It can be seen that the PGWO algorithm can achieve convergence with fewer iterations and obtain a better solution. The comprehensive cost of the annual value obtained by the PGWO algorithm is 9.86% lower than that of the GWO algorithm, proving that the PGWO algorithm has a stronger global optimization ability. When solving the microgrid capacity optimization configuration problem, it can obtain better configuration results and improve the economic performance of the microgrid.

[0114] To analyze the synergistic gain effect of wind-solar-storage microgrids in multi-energy complementarity, the capacity optimization configuration problems of wind-storage, solar-storage, and wind-solar-storage microgrids were solved and compared under the same parameter settings, constraints, and operating strategies. The optimization configuration results under different operating modes are shown in Table 4, and their convergence curves are shown in... Figure 7 As shown.

[0115] Table 4 Optimization Configuration Results under Different Operating Modes

[0116] type Wind turbine / unit Photovoltaic module / unit Battery / block Equivalent annual comprehensive cost / 10,000 yuan Wind storage 13 0 846 89.09 Photovoltaic storage 0 743 1182 99.08 Wind and solar energy storage 7 383 399 48.93

[0117] From Table 4 and Figure 7 It can be seen that the annual comprehensive cost of the wind-solar-storage microgrid model is lower than that of the wind-storage microgrid and solar-storage microgrid models. The main reason is that the wind-solar-storage microgrid integrates the advantages of multiple renewable energy sources, and its spatiotemporal complementarity of wind and solar resources can significantly improve the economic efficiency of system capacity optimization.

[0118] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0119] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the capacity configuration of a wind-solar-storage microgrid based on the PGWO algorithm, characterized in that, Includes the following steps: S1. Based on the output power model of each distributed power source in the microgrid, build a capacity optimization configuration model for the wind-solar-storage microgrid; S2. Set the microgrid operation strategy; S3. Under the microgrid operation strategy, the capacity optimization configuration model is solved using the PGWO algorithm to obtain the optimal configuration scheme for the microgrid; wherein the solution process includes: Bernoulli chaotic mapping is used to generate an initial gray wolf population and determine the initial configuration scheme; each gray wolf individual contains all the variables required to solve the capacity optimization configuration model, and different gray wolf individuals represent different variable configurations; The weight coefficients for different search directions are determined based on the location of the gray wolf, and the historical best position of the individual gray wolf is introduced for population update. Update out-of-bounds individuals using preset mapping rules; After a preset number of iterations, the optimal configuration scheme of the microgrid is obtained.

2. The method for optimizing the capacity configuration of wind-solar-storage microgrids based on the PGWO algorithm according to claim 1, characterized in that, In S1, the capacity optimization configuration model is constructed using the equivalent annual comprehensive cost as the objective function. The objective function expression of the capacity optimization configuration model is: minC ACC =C ini +C om +C r +C buy -C sell Among them, C ACC C is the annual comprehensive cost of the microgrid system; ini C represents the annual initial investment cost of the microgrid system. om The annual operation and maintenance cost of a microgrid system; C r C is the equivalent annual equipment replacement cost for a microgrid system. buy The cost of electricity for a microgrid system; C sell This refers to the revenue generated from the sale of electricity by the microgrid system.

3. The method for optimizing the capacity configuration of wind-solar-storage microgrids based on the PGWO algorithm according to claim 1, characterized in that, In S1, the constraints of the capacity optimization configuration model are: Where, N i N represents the number of configurations for the i-th type of power supply; imax The maximum number of power sources of type i can be configured; SOC(t) is the state of charge of the battery at time t; SOC max SOC min These are the upper and lower limits of the state of charge, respectively; P c (t) represents the charging power of the battery at time t, P c (t) represents the discharge power of the battery at time t, P c,max P d,max These are the upper limits for charging power and discharging power of the battery, respectively; P buy (t) represents the power purchased at time t, P buy,max P represents the maximum power purchase capacity. sell (t) represents the electricity sold at time t, P sell,max P is the maximum power output; WT (t) represents the wind power output at time t; P PV (t) represents the photovoltaic power output at time t; P X (t) represents the battery charging and discharging power at time t, with discharging being positive and charging being negative; P D (t) represents the grid interaction power at time t, with purchasing electricity from the grid being positive and selling electricity to the grid being negative; P L (t) represents the load power at time t.

4. The method for optimizing the capacity configuration of wind-solar-storage microgrids based on the PGWO algorithm according to claim 1, characterized in that, S2 include: S21: For any given time t, obtain the output power of wind and solar power generation and the power required by the load; S22: Determine the unbalanced power based on the output power of wind and solar power generation and the load power; S23: Energy dispatch is carried out according to the preset microgrid operation strategy based on unbalanced power.

5. The method for optimizing the capacity configuration of wind-solar-storage microgrids based on the PGWO algorithm according to claim 4, characterized in that, In S23, when the unbalanced power ΔP(t)>0, it means that the wind and solar power output can meet the load demand. At this time, if the battery is fully charged, the excess power will be sold to the grid; if the battery is not fully charged, the battery will be charged first, and the excess power after being fully charged will be sold to the grid. When the unbalanced power ΔP(t) < 0, it means that the wind and solar power output cannot meet the load demand. At this time, if the battery can meet the remaining load, the battery will be discharged to meet the remaining load. If the battery cannot meet the remaining load, the battery will be discharged first, and then the power will be purchased from the grid to meet the load that the battery cannot meet.

6. The method for optimizing the capacity configuration of wind-solar-storage microgrids based on the PGWO algorithm according to claim 1, characterized in that, In S3, Bernoulli chaotic mapping is used to generate an initial gray wolf population and determine the initial configuration scheme, including: Randomly generate a j-dimensional vector x0, where each component of the vector is between 0 and 1; The Bernoulli chaotic map is used to generate i vectors, as follows: Where λ is the control parameter; x i This represents the i-th point generated after Bernoulli chaotic mapping, i.e., the initialized gray wolf population, and its position represents the initial configuration scheme. Mapping Bernoulli chaos onto the range of variable values, we obtain the expression for population initialization as follows: X i =(ub-lb)gx i +lb In the above formula, X i Let be the position of the i-th gray wolf individual, and ub and lb be the upper and lower bounds of the value range, respectively.

7. The method for optimizing the capacity configuration of wind-solar-storage microgrids based on the PGWO algorithm according to claim 1, characterized in that, In S3, weight coefficients for different search directions are determined based on the gray wolf's position, and the historical best position of each gray wolf is used for population updates, including: X(t+1)=c1r3K1+(G1K2+G2K3) / 2+c2r4(X ibest -X(t)) Where X(t+1) represents the current position of the gray wolf after the update, and X(t) represents the current position of the gray wolf before the update. ibest Let C be the optimal position experienced by the current individual gray wolf; c1 is the optimal influence coefficient of the population; c2 is the individual memory coefficient; G1 and G2 are weight coefficients; r1, r2, r3, and r4 are random numbers in the range [0,1]; m takes values ​​of α, β, and δ, and X... α X β X δ Let represent the positions of α, β, and δ wolves, which are the three gray wolf individuals with the best fitness. n takes values ​​of 1, 2, and 3. K1, K2, and K3 represent the direction and distance of the current gray wolf individual moving towards α, β, and δ wolves, respectively. A and C are intermediate parameters; a represents the convergence factor; t represents the number of iterations. max This indicates the maximum number of iterations.

8. The method for optimizing the capacity configuration of wind-solar-storage microgrids based on the PGWO algorithm according to claim 7, characterized in that, In S3, the weighting coefficients G1 and G2 are determined as follows:

9. The method for optimizing the capacity configuration of wind-solar-storage microgrids based on the PGWO algorithm according to claim 1, characterized in that, In S3, the preset mapping rules are as follows: Among them, X new σ represents the update position of the out-of-bounds individual; s is a random number between [0,1]; σ is the out-of-bounds handling factor between [0,1]; ub and lb are the upper and lower bounds of the value interval, respectively; and x represents the out-of-bounds individual.

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