Microgrid capacity optimization configuration method based on improved grey wolf algorithm

Abstract

The invention discloses a micro-grid capacity optimization configuration method based on an improved grey wolf algorithm. The method comprises the following steps: S1, constructing a capacity optimization configuration model of a micro-grid based on each distributed power supply of the micro-grid; s2, setting a micro-grid operation scheduling strategy; s3, under the microgrid operation scheduling strategy, solving the capacity optimization configuration model through an improved grey wolf algorithm to obtain an optimal configuration scheme; wherein the solving process comprises the following steps of: initializing a population by adopting a good point set, and confirming an initial configuration scheme; self-adaptive weight coefficients in different advancing directions are determined according to gray wolf positions, and population updating is carried out in combination with convergence factors; using a lens reverse learning strategy to update an optimal individual, and introducing a greedy strategy to carry out individual screening; the optimal configuration scheme is obtained through iteration; the method has higher optimization precision and better optimization stability, and can effectively reduce the annual value comprehensive cost and the power deviation rate of the micro-grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid capacity optimization configuration, and more particularly to a micro-grid capacity optimization configuration method based on an improved grey wolf algorithm. BACKGROUND

[0002] In recent years, with the large-scale access of wind turbines, photovoltaic and other distributed power sources to micro-grids, the intermittent power output has caused increasingly prominent problems, posing a serious challenge to the safe and stable operation of micro-grids. To address this contradiction, source-grid-load-storage collaboration needs to be achieved through capacity optimization configuration. Scientific capacity configuration not only improves the operational resilience of micro-grids, but also reduces the demand for backup capacity, promotes efficient consumption of distributed energy, and provides key technical support for the construction of new power systems.

[0003] Current researches mostly use intelligent optimization algorithms to construct and solve micro-grid capacity optimization configuration models, and through dynamic balance of economic, reliability and renewable energy consumption rate constraints, the optimal configuration strategy is generated, which takes into account both technical performance and investment benefits.

[0004] In the prior art, in the 2023 4th issue of "Smart Power", "Optimal Configuration of Hybrid Energy Storage in Micro-Grid Based on ISSA" constructs a micro-grid hybrid energy storage optimization configuration model with economic efficiency as the target, and uses an improved sparrow search algorithm to solve the model; in the 2022 1st issue of "Solar Energy", "Optimal Configuration of Independent Micro-Grid Capacity Based on Improved Grey Wolf Algorithm" takes the minimum annual cost as the objective function and establishes a system optimization configuration model, and improves the grey wolf algorithm for solving the optimal configuration scheme of the system; in the 2023 4th issue of "Thermal Power Engineering", "Multi-objective Capacity Optimization Configuration of Wind-solar-hydrogen Micro-grid System" constructs a wind-solar-hydrogen energy coupling system model, and combines genetic algorithm (GA) and bionic algorithm (BAS) to propose an improved BAS-GA algorithm to solve the system model; in the 2022 11th issue of "Power Grid and Clean Energy", "Capacity Configuration of Wind-solar-hydrogen System Based on Improved DEC Algorithm" constructs a wind-solar-hydrogen dual-layer optimization configuration model, uses the mutual coupling and mutual adjustment of the inner and outer layers, and proposes an improved DEC algorithm to solve it.

[0005] The above prior art still has the problems of slow convergence speed and easy falling into local optimum of the grey wolf algorithm, and the capacity optimization configuration of the micro-grid under the condition of ensuring the stable operation of the micro-grid is a problem that needs to be solved by those skilled in the art. SUMMARY

[0006] Therefore, the application provides a micro-grid capacity optimization configuration method based on an improved grey wolf algorithm.

[0007] In order to achieve the above object, the application adopts the following technical scheme:

[0008] A micro-grid capacity optimization configuration method based on an improved grey wolf algorithm comprises the following steps:

[0009] S1: constructing a micro-grid capacity optimization configuration model based on each distributed power supply of the micro-grid.

[0010] S2: setting a micro-grid operation scheduling strategy.

[0011] S3: solving the capacity optimization configuration model under the micro-grid operation scheduling strategy by using the improved grey wolf algorithm to obtain an optimal configuration scheme, wherein the solving process comprises:

[0012] initializing a population based on a good point set to confirm an initial configuration scheme; confirming adaptive weight coefficients in different forward directions according to the position of a grey wolf, updating the population in combination with a convergence factor; updating an optimal individual by using a lens reverse learning strategy and screening individuals by using a greedy strategy; and obtaining the optimal configuration scheme through iteration.

[0013] Preferably, the convergence factor is also used for balancing global search and local development, and the convergence factor is:

[0014]

[0015] wherein a is a loss convergence factor, t max is the maximum number of iterations.

[0016] Preferably, S1 is specifically: according to the power generation model and the demand response model of each distributed power supply of the micro-grid, taking the equal annual comprehensive cost and the power deviation rate as objective functions, establishing the capacity optimization configuration model of the micro-grid.

[0017] Preferably, the capacity optimization configuration model comprises:

[0018] Objective function: min f = η1C ACC + η2C STRAF C PDR

[0019] wherein f is the total cost of the system; C ACC is the equal annual comprehensive cost; C PDRis a power deviation rate; C STRAF is a power deviation penalty coefficient; η1, η1 are weight coefficients.

[0020] Constraint conditions:

[0021]

[0022] wherein, N i is the configuration number of the i-th power supply; N imax is the maximum configuration number of the i-th power supply; k SOC is the state of charge of the battery; k SOC,max , k SOC,min are the upper and lower limits of the state of charge of the battery, respectively; P c,max , P c,min are the upper and lower limits of the charging power of the battery, respectively; P d,max , P d,min are the upper and lower limits of the discharging power of the battery, respectively; C LPSP,max is the maximum load power loss rate; C EWR,max is the maximum energy surplus rate; P LR,max is the maximum transmission power of the tie line.

[0023] Preferably, the S2 is specifically:

[0024] S21: obtaining the output power of each power generation model and the required power of the load.

[0025] S22: confirming the unbalanced power according to the power generation output power and the required power of the load.

[0026] S23: scheduling according to the unbalanced power, and calculating the energy surplus rate or the load power loss rate.

[0027] Preferably, the energy surplus rate and the load power loss rate are used to calculate the objective function value of the capacity optimization configuration model.

[0028] Preferably, the population is initialized by using the optimal point set to confirm the initial configuration scheme, and specifically includes:

[0029] Confirming the optimal point set:

[0030] Mapping the optimal point set into the search space can obtain the expression for initializing the population:

[0031]

[0032] In the above formula, x i (j) is the j-th dimensional position of the i-th grey wolf individual, ub j and lb j are the upper and lower bounds of the j-th dimension of the solution space, respectively.

[0033] Preferably, the adaptive weight coefficient under different advancing directions is confirmed according to the position of the gray wolf, and the population is updated in combination with a convergence factor, and specifically includes the following steps.

[0034] Confirming the adaptive weight:

[0035]

[0036] Position updating of the gray wolf individual:

[0037]

[0038]

[0039] Wherein, r1 and r2 are random numbers in [0, 1]; n takes 1, 2, and 3; i takes α, β, and δ; X α , X β , X δ represents the position of the α, β, and δ wolves; X1, X2, and X3 represent the direction and distance of the ω wolf advancing towards the α, β, and δ wolves; ω1, ω2, and ω3 are weight coefficients; and ω is an adaptive factor.

[0040] Preferably, the optimal individual is updated by using a lens reverse learning strategy, and specifically includes the following steps.

[0041]

[0042] Wherein, X T (t+1) represents the position of the gray wolf individual after the lens reverse learning strategy is taken; ub represents the upper limit of the solution space; lb represents the lower limit of the solution space; and n is a reverse learning factor.

[0043] Preferably, the individual is screened by introducing a greedy strategy, and specifically includes the following steps.

[0044]

[0045] Wherein, f(x) is a fitness function, X α (t+1) is the final position of the optimal individual α wolf after updating.

[0046] According to the above technical solution, compared with the prior art, the micro-grid capacity optimization configuration method based on the improved gray wolf algorithm has the following beneficial effects:

[0047] 1. The application establishes a capacity optimization configuration model of the micro-grid according to the power generation model and demand response model of each distributed power supply of the micro-grid, with the equal annual value comprehensive cost and power deviation rate as the objective function. In view of the slow convergence speed and the problem of being easy to fall into local optimum of the grey wolf algorithm, the improved grey wolf algorithm is proposed. First, the good point set is used for initialization to enhance the diversity of the initial population. Then, the nonlinear convergence factor is set to adjust the balance between global search and local development. Next, the adaptive weight factor is introduced to enhance the search ability of the algorithm in the early stage and to speed up the convergence speed in the later stage. Finally, the lens reverse learning strategy and the greedy strategy are used to jump out of the local optimum. The algorithm has higher optimization precision and better optimization stability, and can effectively reduce the equal annual value comprehensive cost and power deviation rate of the micro-grid, so it is of great significance to improve the economy and stability of the micro-grid operation to optimize the capacity configuration under the premise of ensuring the stable operation of the micro-grid.

[0048] 2. The IGWO proposed in the application is compared with the grey wolf algorithm, the whale algorithm and the firefly algorithm for test and performance verification, and the actual data of a certain area are taken as objects for capacity optimization configuration solution to verify the practicability of the model and the IGWO. DETAILED DESCRIPTION

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description only the embodiments of the application, and for those skilled in the art, without creative labor, other drawings can also be obtained according to the provided drawings.

[0050] Figure 1 The flowchart of the micro-grid capacity optimization configuration method based on the improved grey wolf algorithm in the embodiment of the application is shown.

[0051] Figure 2 The micro-grid operation scheduling strategy in the embodiment of the application is shown.

[0052] Figure 3 The function F1 convergence curve in the embodiment of the application is shown.

[0053] Figure 4 The function F2 convergence curve in the embodiment of the application is shown.

[0054] Figure 5 The function F3 convergence curve in the embodiment of the application is shown.

[0055] Figure 6 The function F4 convergence curve in the embodiment of the application is shown.

[0056] Figure 7Fig. 1 is a schematic diagram of annual hourly wind speed data in an embodiment of the present application;

[0057] Figure 8 Fig. 2 is a schematic diagram of annual hourly solar radiation data in an embodiment of the present application;

[0058] Figure 9 Fig. 3 is a schematic diagram of annual hourly electricity load data in an embodiment of the present application;

[0059] Figure 10 Fig. 4 is a load optimization curve in an embodiment of the present application;

[0060] Figure 11 Fig. 5 is an optimization configuration convergence curve in an embodiment of the present application. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0062] As Figure 1 disclosed in the embodiments of the present application is a micro-grid capacity optimization configuration method based on an improved grey wolf algorithm, which comprises the following steps:

[0063] S1: constructing a capacity optimization configuration model of the micro-grid based on each distributed power supply of the micro-grid.

[0064] S2: setting a micro-grid operation scheduling strategy.

[0065] S3: solving the capacity optimization configuration model by the improved grey wolf algorithm under the micro-grid operation scheduling strategy to obtain an optimal configuration scheme. The solving process comprises:

[0066] adopting a good point set to initialize a population and confirm an initial configuration scheme; confirming adaptive weight coefficients under different advancing directions according to the position of the grey wolf, combining a convergence factor to update the population; updating an optimal individual by using a lens reverse learning strategy and introducing a greedy strategy to select individuals; and finally, obtaining the optimal configuration scheme through iteration.

[0067] Specifically, input annual hourly meteorological and load data, generate an initial grey wolf population by using a good point set, and each individual represents all variables required for calculating a target function, such as the number of wind turbines, photovoltaic components and batteries. Different individuals represent different variable configuration situations, i.e., a population.

[0068] Then the fitness of the gray wolf individual is calculated. According to the variable condition of the gray wolf population, power supply is carried out according to the micro-grid operation scheduling strategy, and the objective function is calculated as the fitness of the individual according to the number of power sources and the power supply condition.

[0069] According to the fitness of the individual, the optimal three individuals are selected as alpha wolf, beta wolf and delta wolf. The position of the gray wolf individual and the optimal individual position are updated under the leadership of the alpha wolf, the beta wolf and the delta wolf until the iteration number is reached, and the optimal solution is output.

[0070] In one embodiment, in S1, according to the power generation model and the demand response model of each distributed power source of the micro-grid, the capacity optimization configuration model of the micro-grid is established with the equal annual comprehensive cost and the power deviation rate as the objective function.

[0071] Specifically, the objective function expression of the capacity optimization configuration model of the micro-grid is:

[0072] minf=η1C ACC +η2C STRAF C PDR

[0073] In the above formula, f is the total cost of the system; C ACC is the equal annual comprehensive cost; C PDR is the power deviation rate; C STRAF is the power deviation penalty coefficient; η1 and η2 are weight coefficients.

[0074] The equal annual comprehensive cost expression is:

[0075] C ACC =C ini +C om +C r +C buy -C sell

[0076] In the above formula, f is the total cost of the system; C ACC is the equal annual comprehensive cost; C PDR is the power deviation rate; C STRAF is the power deviation penalty coefficient; η1 and η2 are weight coefficients.

[0077] The power deviation rate expression is:

[0078]

[0079] In the above formula, C LPSP is the load outage rate; C EWR is the energy surplus rate.

[0080] Constraint condition:

[0081]

[0082] In the above formula, N i is the configuration number of the i-th power supply; N imax is the maximum configuration number of the i-th power supply; k SOC is the state of charge of the battery; k SOC,max , k SOC,min are the upper and lower limits of the state of charge of the battery, respectively; P c,max , P c,min are the upper and lower limits of the charging power of the battery, respectively; P d,max , P d,min are the upper and lower limits of the discharging power of the battery, respectively; C LPSP,max is the maximum load power loss rate; C EWR,max is the maximum energy surplus rate; P LR,max is the maximum transmission power of the tie line.

[0083] In an embodiment, the present application obtains a micro-grid operation scheduling strategy by judging the state of charge of the battery, controls the operation of the micro-grid by analyzing whether the battery can perform charging and discharging behavior at this moment, as shown in FIG. 2, the micro-grid operation scheduling strategy in S2 includes: Figure 2

[0084] S21: Obtain the output power of each power generation model and the required power of the load, i.e., micro-grid data. For example, obtain the output power P WT (t) of the wind turbine, the output power P PV (t) of the photovoltaic module, and the required power P L (t) of the load at this moment.

[0085] S22: Confirm the unbalanced power according to the output power of power generation and the required power of the load, and the expression is:

[0086] ΔP(t) = P WT (t) + P PV (t) - P L (t)

[0087] S23: Schedule according to the unbalanced power and calculate the energy surplus rate or the load power loss rate.

[0088] For example, when ΔP(t) > 0, it means that the wind and light power generation can meet the load demand, and there is excess power. When the state of charge of the battery meets the requirement, the battery is preferentially charged, and if there is still excess power, it is sold to the grid. At the same time, it is judged whether there is still energy waste, and if so, the system energy surplus rate is calculated. When the state of charge of the battery does not meet the requirement, the excess power is directly sold to the grid. At the same time, it is judged whether there is still energy waste, and if so, the system energy surplus rate is calculated.

[0089] ​When ΔP(t) < 0, it represents that the wind and light power generation cannot meet the load demand. When the battery SOC meets the requirement, the battery is preferentially discharged, and if the system load demand still cannot be met, power is purchased through the power grid. At the same time, it is judged whether there is still an energy shortage, and if so, the system load power loss rate is calculated. When the battery SOC does not meet the requirement, power is directly purchased through the power grid. At the same time, it is judged whether there is still an energy shortage, and if so, the system load power loss rate is calculated.

[0090] S24: judge whether the time t reaches 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.

[0091] In an embodiment, in S3, the principle of the good point set strategy to initialize the population is: first, a good point set is constructed, which can be expressed as: let G S be a unit cube in a S-dimensional Euclidean space, if r G S , there exists a point set:

[0092]

[0093] The deviation of the point set is satisfies: where C(r, epsilon) is a constant related only to epsilon and r, and epsilon is any positive number. At this time, P n (k) is called a good point set. Take r i (n) = 2cos(2pi / p), p is the smallest prime number satisfying (p-3) / 2 >= s. At this time, the mapping of the good point set to the search space can obtain the expression of the population initialization as:

[0094]

[0095] In the above formula, x i (j) is the jth decision variable in the ith candidate solution, ub j and lb j are the upper bound and lower bound of the jth dimension of the solution space, respectively.

[0096] In this embodiment, compared with randomly generating an initial population, the initial population generated by the good point set can more uniformly spread throughout the search space, which is conducive to improving the optimization performance of the algorithm.

[0097] In order to further implement the above technical solutions, in order to solve the problem that the linear convergence factor in the traditional grey wolf algorithm cannot balance the global search and local development process, this embodiment proposes a nonlinear convergence factor, and its expression is:

[0098]

[0099] where t is the iteration number; tmax is the maximum number of iterations.

[0100] In this embodiment, the improved nonlinear convergence factor decays slowly at the beginning of iteration, which is conducive to enhancing the global search ability of the algorithm; and decays rapidly at the later stage of iteration, which is conducive to enhancing the local development ability of the algorithm.

[0101] Further, in the traditional grey wolf algorithm, the guiding weights of the alpha, beta and delta wolves to the omega wolf are the same, which may cause the reduction of the convergence speed of the algorithm. In order to improve the optimization performance of the algorithm, the weight coefficient is improved on the basis of the original position updating formula, and a coefficient factor changing with the iteration number is introduced. At the initial stage, the front half of the formula occupies a large weight, so that the alpha, beta and delta wolves mainly guide the omega wolf, and at the same time, due to the improved weight coefficient, the alpha, beta and delta wolves have different guiding abilities to the omega wolf, thereby improving the global optimization performance of the algorithm; at the later stage, the rear half of the formula occupies a large weight, so that the alpha wolf mainly guides, thereby improving the convergence speed of the algorithm, and the specific expression is:

[0102]

[0103] In the above formula, r1 and r2 are random numbers in [0, 1]; n takes 1, 2 and 3; i takes alpha, beta and delta; X α , X β , X δ represent the positions of the alpha, beta and delta wolves; X1, X2 and X3 represent the direction and distance of the omega wolf advancing towards the alpha, beta and delta wolves; ω1, ω2 and ω3 are weight coefficients, representing the different guiding abilities of the alpha, beta and delta wolves to the omega wolf; ω is an adaptive factor, which strengthens the guiding ability of the alpha wolf with the increase of the iteration number; A and C are coefficients used in the algorithm to control the position updating, and the two are used to simulate the random disturbance of the surrounding and attack behaviors of the grey wolves in the search.

[0104] Here ends, although convergence can also be achieved, the optimal solution is obtained, but there is also the possibility of falling into local optimum. Therefore, the lens reverse learning and the greedy strategy are introduced, which is conducive to the algorithm jumping out of the local optimum.

[0105] In the traditional grey wolf algorithm, the position of the alpha wolf represents the current optimal solution. With the increase of the iteration number, the population will gradually converge to the position of the alpha wolf, thereby causing the population to lose diversity and increasing the possibility of the algorithm falling into local optimum. The lens reverse learning strategy is to generate a reverse solution on the basis of the original solution by using the principle of convex lens imaging, which is conducive to the algorithm jumping out of the local optimum. Therefore, in each iteration process, after the positions of all grey wolves are updated, the lens reverse learning strategy is used to update the position of the optimal individual alpha wolf, and the expression is:

[0106]

[0107] In the formula, X T (t+1) represents the position of the individual after adopting the lens reverse learning strategy, ub represents the upper bound of the solution space, lb represents the lower bound of the solution space, and n is a reverse learning factor;

[0108] In order to ensure that the population advances in the direction of the optimal solution, a greedy strategy is introduced to screen the solution after the lens reverse learning strategy, so as to ensure that the solution after each iteration is better, and the expression is:

[0109]

[0110] In the formula, f(x) is the fitness function, X α (t+1) is the final position of the alpha wolf update;

[0111] In order to further illustrate the present application, the effectiveness and superiority of the improved algorithm and strategy can be verified by combining simulation experiments. In the verification process, four test functions are used, and the improved grey wolf algorithm (IGWO), the grey wolf algorithm (GWO), the whale optimization algorithm (WOA) and the firefly algorithm (FA) are tested 30 times independently, the population size is set to 30, the iteration number is set to 500, and the test function information is shown in Table 1.

[0112] Table 1 test function table

[0113]

[0114] Table 2 is a function test result table, from which it can be known that, in terms of the average value, the result obtained by the IGWO algorithm is better than that of GWO, WOA and FA, proving that the IGWO algorithm has higher convergence accuracy and the result is closer to the optimal solution. In terms of the standard deviation, the IGWO algorithm is also smaller than GWO, WOA and FA, proving that the IGWO algorithm has better stability.

[0115] Table 2 function test result table

[0116]

[0117] Figures 3-6 The convergence curves of different test functions correspond to F1, F2, F3 and F4 respectively, and from the convergence curves of each test function, it can be seen that, compared with GWO, WOA and FA, the IGWO algorithm can converge to the optimal solution faster, has higher optimization accuracy and convergence speed.

[0118] The present application considers a demand response model based on time-of-use electricity price, which can regulate the power grid load and achieve the effect of peak load shifting. The local meteorological data and load data are shown in Figures 7-9 , the time-of-use electricity price is shown in Table 3, and the load optimization curve is shown in Figure 10The original load power difference is about 197kW, and the load power difference after optimization is reduced to about 148kW, and the load power difference is reduced by 24.9%.

[0119] Table 3 Time-of-use electricity price table

[0120]

[0121] The related parameters of the selected wind turbine, photovoltaic module and battery are shown in Table 4. The rated wind speed of the wind turbine in the micro-grid system is 11m / s, the cut-in wind speed is 3m / s, and the cut-out wind speed is 25m / 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 the discharging efficiency are both 0.85, the SOC normal working range is 20% to 90%; the discount rate r is 4.75%, the maximum load power loss rate C LPSP,max is 3%, the maximum energy surplus rate C EWR,max is 5%, the population and the number of iterations are both 100.

[0122] Table 4 Equipment cost parameter table

[0123] Power supply Wind turbine Photovoltaic module Battery Parameter 30 kW 1 2 V, 1000 Ah Investment cost / ten thousand yuan 27 0.2 0.16 Maintenance cost / ten thousand yuan 0.3 0.002 0.002 Replacement cost / ten thousand yuan 27 0.2 0.16 Service life / a 20 20 5

[0124] In order to further study the influence of introducing the demand response model based on time-of-use electricity price on capacity optimization configuration, the algorithm is used to simulate the situation with and without demand response, and the comparison results with and without demand response are shown in Table 5. As shown in Table 5, after introducing the demand response model, the total cost of the system is reduced by 16.50%, and the power deviation rate is reduced by 26.16%, which proves that it is more beneficial to capacity optimization configuration to introduce the demand response model to carry out peak clipping and valley filling on the load.

[0125] Table 5 Comparison results with and without demand response

[0126] Whether there is demand response Yes No Wind turbine / tai 4 4 Photovoltaic module / each 225 282 Battery block 206 266 System total cost / ten thousand yuan 44.94 53.82 Power deviation rate / percent 4.63 6.27

[0127] In order to further verify the superiority of the micro-grid capacity optimization configuration method of the application, four kinds of algorithms, IGWO, GWO, WOA and FA, are used to solve the capacity optimization configuration problem of the wind-solar-storage micro-grid, and the optimization configuration results are shown in Table 6, and the convergence curves are shown in Figure 11 .

[0128] Table 6 Optimization configuration result table

[0129] Algorithm IGWO GWO WOA FA Wind turbine / tai 4 3 3 6 Photovoltaic module / each 225 295 291 141 Battery block 206 189 247 179 System total cost / ten thousand yuan 44.94 50.35 49.98 53.61 Power deviation rate / percent 4.63 5.97 5.49 6.40

[0130] From Table 6 and Figure 11It can be seen that the total cost of the system calculated by IGWO algorithm is reduced by 10.74%, 10.08% and 16.17% compared with GWO, WOA and FA respectively. At the same time, the power deviation rate is reduced by 22.45%, 15.66% and 27.66% compared with GWO, WOA and FA respectively. It is proved that IGWO algorithm has higher solving accuracy and can improve the economy and stability of the system in solving the capacity optimization configuration problem.

[0131] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant part can be referred to the method part.

[0132] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A microgrid capacity optimization configuration method based on an improved grey wolf algorithm, characterized in that: The following steps are involved: S1: Construct a capacity optimization configuration model of the microgrid based on the distributed power sources of the microgrid; S2: Set the microgrid operation and dispatching strategy; S3: Under the microgrid operation scheduling strategy, the capacity optimization configuration model is solved by using an improved grey wolf algorithm to obtain an optimal configuration solution; wherein the solution process includes: Use the best point set to initialize the population and confirm the initial configuration plan; According to the position of the gray wolf, the adaptive weight coefficient under different moving directions is determined, and the population is updated in combination with the convergence factor; The lens reverse learning strategy is used to update the optimal individual, and a greedy strategy is introduced for individual screening; The optimal configuration solution is obtained through iteration.

2. A microgrid capacity optimization configuration method based on an improved grey wolf algorithm according to claim 1, characterized in that: The convergence factor is also used to balance global search and local development. The convergence factor is: Where a is the loss convergence factor, t max is the maximum number of iterations.

3. The microgrid capacity optimization configuration method based on the improved grey wolf algorithm according to claim 1 is characterized in that: Specifically, S1 includes establishing the capacity optimization configuration model of the microgrid based on the power generation model and demand response model of each distributed power source in the microgrid and taking the equal annual value comprehensive cost and power deviation rate as the objective function.

4. The microgrid capacity optimization configuration method based on the improved grey wolf algorithm according to claim 3 is characterized in that: The capacity optimization configuration model includes: Objective function: minf = η1C ACC +η2C STRAF C PDR Where, f is the total system cost; C ACC is the comprehensive cost of equal annual value; C PDR is the power deviation rate; C STRAF is the power deviation penalty coefficient; η1 and η1 are weight coefficients; Constraints: Among them, N i is the number of configurations of the i-th power supply; N imax is the maximum configuration quantity of the i-th power supply; k SOC is the battery charge state; k SOC,max 、k SOC,min are the upper and lower limits of battery state of charge respectively; P c,max 、P c,min They are the upper and lower limits of battery charging power respectively; P d,max 、P d,min are the upper and lower limits of battery discharge power respectively; C LPSP,max is the maximum load power failure rate; C EWR,max is the maximum energy excess rate; P LR,max is the maximum transmission power of the tie line.

5. A microgrid capacity optimization configuration method based on an improved grey wolf algorithm according to claim 1 or 4, characterized in that: The S2 is specifically: S21: Obtain the output power of each power generation model and the power required by the load; S22: confirm the unbalanced power according to the power output and the load required power; S23: Dispatch according to the unbalanced power and calculate the energy surplus rate or load power failure rate.

6. A microgrid capacity optimization configuration method based on improved grey wolf algorithm according to claim 5, characterized in that: The energy surplus rate and the load power failure rate are used to calculate the objective function value of the capacity optimization configuration model.

7. The microgrid capacity optimization configuration method based on the improved grey wolf algorithm according to claim 1 is characterized in that: Initializing the population using the good point set and confirming the initial configuration scheme specifically include: Confirm the good point set: Among them, n represents the population size, k is the cardinality of good points, and r is the good point; Mapping the good point set to the search space, the expression for population initialization can be obtained as follows: In the above formula, x i (j) is the j-th dimension position of the i-th gray wolf individual, ub j and lb j are the upper and lower bounds of the j-th dimension of the solution space, respectively.

8. The microgrid capacity optimization configuration method based on the improved grey wolf algorithm according to claim 1 is characterized in that: The adaptive weight coefficients under different advancing directions are determined according to the position of the gray wolf, and the population is updated in combination with the convergence factor, specifically including: Confirm adaptive weights: Gray wolf individual location update: Among them, r1 and r2 are random numbers in [0,1]; n is 1, 2, 3; i is α, β, δ; X α 、X β 、X δ represents the position of α, β, and δ wolves; X1, X2, and X3 represent the direction and distance of ω wolf moving toward α, β, and δ wolves; ω1, ω2, and ω3 are weight coefficients; and ω is an adaptive factor.

9. The microgrid capacity optimization configuration method based on the improved grey wolf algorithm according to claim 8 is characterized in that: The method of updating the optimal individual by using the lens reverse learning strategy specifically includes: Among them, X T (t+1) represents the individual position after adopting the lens reverse learning strategy; ub represents the upper bound of the solution space; lb represents the lower bound of the solution space; and n is the reverse learning factor.

10. A microgrid capacity optimization configuration method based on an improved grey wolf algorithm according to claim 1 or 9, characterized in that: The introduction of the greedy strategy for individual screening specifically includes: Among them, f(x) is the fitness function, X α (t+1) is the final position of the optimal individual wolf α after updating.

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