Micro-grid optimization scheduling method based on distributed bee foraging algorithm

Abstract

The invention discloses a micro-grid optimization scheduling method based on a distributed bee foraging algorithm. The method comprises the following steps: establishing a micro-grid optimization scheduling objective function; determining micro-grid optimization scheduling related constraint conditions; performing initialization setting on parameters of the distributed bee foraging algorithm; according to the optimization scheduling related constraint conditions, randomly generating an optimization scheduling scheme; evaluating and sorting nectar sources searched by the investigation bees; carrying out local search by the bees and updating the selected nectar source; carrying out local search by worker bees and updating the selected nectar source; the reconnaissance bees generate optional optimal scheduling schemes in a random search mode; the nectar sources searched by the investigation bees and the selected nectar sources are sorted together, and the selected nectar sources used for next iteration are screened out; and outputting a globally optimal solution and a fitness function value of the target function. According to the method, the bee foraging algorithm is improved to adapt to the optimization requirements of the distributed micro-grid, and the efficient solution of the distributed micro-grid optimization scheduling scheme is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrid operation, and specifically is a microgrid optimization scheduling method based on a distributed bee foraging algorithm. Background Art

[0002] With the rapid economic development and the progress of society, energy depletion and environmental pollution are becoming increasingly serious. Distributed power generation systems make full use of renewable energy sources such as wind and solar energy to alleviate the pressure of energy demand and environmental pollution in traditional centralized power supply systems. Microgrids can connect distributed power sources to the main power grid, thereby making full use of the efficiency of distributed power generation systems. With the increasing proportion of microgrids in the power system, improving the economic performance of micro-power sources in the power grid by properly dispatching them has become an important topic in power system research. How to optimize the dispatching to make the microgrid run more economically and environmentally friendly has important practical significance.

[0003] At present, the mainstream method of microgrid optimization dispatching mostly adopts centralized dispatching method, in which the microgrid control center must obtain information from each micro power source and load, otherwise the dispatching cannot be implemented. Therefore, the centralized method requires powerful computing power and reliable communication between micro power sources and microgrid control center, which is contrary to the distributed characteristics of microgrids. There are few distributed optimization dispatching schemes for microgrid characteristics, and the optimization dispatching efficiency is low. Summary of the invention

[0004] In view of the above problems, the present invention is proposed to provide a microgrid optimization scheduling method based on a distributed bee foraging algorithm to overcome the above problems or at least partially solve the above problems.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A microgrid optimization scheduling method based on a distributed bee foraging algorithm, the method comprising the following steps:

[0007] S1. Establish the objective function of microgrid optimization dispatch;

[0008] S2. Determine the constraints related to the optimal dispatch of the microgrid;

[0009] S3, initializing and setting the parameters of the distributed bee foraging algorithm, where the bee colony includes scout bees, foraging bees and worker bees;

[0010] S4. According to the constraints related to the optimization scheduling, randomly generate the optimization scheduling plan and form a scout bee population;

[0011] S5, evaluating and ranking the nectar sources found by the scout bees;

[0012] S6, honey bees conduct local search and update the selected nectar sources;

[0013] S7, waiting for the worker bees to conduct local search and update the selected nectar source;

[0014] S8, the scout bee uses random search to generate optional optimal scheduling solutions;

[0015] S9. Sort the nectar sources found by the scout bees and the selected nectar sources together according to the quality of the nectar sources, select the selected nectar sources for the next round of iteration, and record the best nectar sources at present;

[0016] S10. Output the global optimal solution of the objective function and its fitness function value.

[0017] Optionally, in step S1, the objective function expression of microgrid optimization scheduling is:

[0018]

[0019] Where, f represents the total cost of microgrid power generation; M represents the total number of micro-power sources in the microgrid; T represents the optimization period; C i (t) represents the power generation cost of the i-th micro power source in the microgrid;

[0020] The power generation cost types of micro power sources include wind power generation cost C WT (t), photovoltaic power generation cost C PV (t), fuel engine power generation cost C FE (t) and battery operating cost C BT (t);

[0021] The cost of wind power generation can be calculated according to the following formula:

[0022]

[0023] In the formula, C WT (t) represents the cost of wind power generation; α wt represents the unit cost of wind power generation; P WT represents wind power generation; Δt represents time variation; T represents the optimization period;

[0024] The cost of photovoltaic power generation can be calculated according to the following formula:

[0025]

[0026] In the formula, C PV (t) represents the photovoltaic power generation cost α pv Represents the unit cost of photovoltaic power generation; P PV represents photovoltaic power generation; Δt represents the time variation; T represents the optimization period;

[0027] The cost of fuel engine power generation can be calculated according to the following formula:

[0028]

[0029] In the formula, C FE (t) represents the cost of power generation from a fuel engine; α fe represents the unit cost of fuel engine power generation; P FE represents the power generated by the fuel engine; Δt represents the time change; T represents the optimization call cycle;

[0030] The battery operating cost can be calculated according to the following formula:

[0031]

[0032] In the formula, C BT (t) represents the battery operating cost; α bt Represents the unit cost of battery discharge; P BT Indicates the battery charge / discharge power; Δt indicates the time change; T indicates the optimization period.

[0033] Optionally, in step S2, the constraints related to the microgrid optimization scheduling include:

[0034] Power balance constraints:

[0035] P WT +P PV +P FE +P BT =P LD +P LS

[0036] Where P WT Indicates wind power generation power; P PV Represents photovoltaic power generation; P FE Indicates the power generated by the fuel engine; P BT Indicates the battery charge / discharge power; P LD is the power load of the microgrid; P LS is the microgrid loss;

[0037] Battery constraints:

[0038]

[0039] Where P BT Indicates the battery charge / discharge power, P out_max and P in_max They represent the maximum discharge power and maximum charge power of the battery respectively; SOC(t) represents the charge stored in the battery at time t; SOC maxand SOC min They represent the upper and lower limits of the battery's storage charge.

[0040] Optionally, in step S3, the parameters of the distributed bee foraging algorithm are initialized and set. The parameters of the distributed bee foraging algorithm mainly include the number of bee colonies, the algorithm stop condition, the neighborhood shrinkage coefficient, and the upper limit of the number of stagnation times of the bee search. st It can be calculated according to the following formula;

[0041]

[0042] Where n C is the neighborhood shrinkage coefficient of the bee search; f ix Indicates rounding.

[0043] Optionally, in step S5, the searched honey source is evaluated by adopting the local cooperative game strategy of each distributed micro-power source, and each micro-power source interacts with its neighboring micro-power sources locally. Then the potential function of the i-th micro-power source can be expressed as:

[0044] F i =C i (t)+C ni (t)+β k (P LD +P H )

[0045] In the formula, F i represents the potential function corresponding to the i-th micro-power scheduling scheme; C i (t) represents the power generation cost of the i-th micro power source in the microgrid; C ni (t) represents the cost of the neighboring power source to provide power to the micro power node; P LD is the power load of the microgrid; P H Indicates that it is provided to the local load; β k is the penalty coefficient, and k is the number of iterations.

[0046] Optionally, in step S6, the steps of local search performed by bees are as follows:

[0047] S61, honey bees are evenly distributed to search near the selected nectar sources;

[0048] S62: For each selected nectar source, the bees assigned to the selected nectar source conduct a random search near the nectar source and update the location of the bees:

[0049] fr i =nl j +rnd·(nu j -nl j)

[0050] In the formula, fr i Indicates the location information of the i-th bee collector; nu j and nl j They represent the upper and lower limits of the neighborhood search for the jth selected nectar source; rnd represents a random number between [0,1];

[0051] S63, evaluating the updated honey bees, if the honey bees search for a nectar source with better quality, then replacing the current selected nectar source with the better nectar source as the new selected nectar source, resetting the number of search stagnations of the current selected nectar source, and restoring the neighborhood range to the original size;

[0052] S64. If the honey bees do not find a nectar source with better quality, the search stagnation counter of the selected nectar source is increased by 1, and the search neighborhood of the selected nectar source is shrunk according to the following formula;

[0053] S nh (n+1)=n sc ·S nh (n)

[0054] Among them, S nh (n) is the current search range; S nh (n+1) is the search range for the next iteration; n sc is the neighborhood shrinkage coefficient;

[0055] S65. Sort all selected nectar sources according to their quality.

[0056] Optionally, in step S7, the steps for the worker bees to perform local search are as follows:

[0057] S71, allocating worker bees to each selected nectar source according to the following formula;

[0058]

[0059] Among them, N j is the number of worker bees allocated to the jth selected nectar source; fitness j is the fitness function value of the jth selected nectar source; k is the fitness function value of the kth selected nectar source; n 0 represents the total number of worker bees; K represents the number of selected nectar sources; fix represents rounding;

[0060] S72, the worker bee randomly selects a dimension from the selected nectar source where it is located, and performs a search update according to the following formula;

[0061]

[0062] Where d represents a randomly selected single dimension to be updated; Represents the position of the worker bee to be updated this time; and They represent the upper and lower limits of the j-th selected honey source neighborhood search d-dimensional space; rnd represents a random number between [0,1];

[0063] S73, evaluating the updated worker bees, if the worker bees search for a nectar source with better quality, the better nectar source is used to replace the current selected nectar source as the new selected nectar source, and the number of search stagnation of the current selected nectar source is reset, and the neighborhood range is restored to the original size;

[0064] Step S74: If the worker bee fails to find a nectar source with better quality, the search stagnation counter of the selected nectar source is increased by 1;

[0065] Step S75, determine whether the number of search stagnation times is greater than the upper limit of the number of stagnation times. If the number of search stagnation times is greater than the upper limit of the number of stagnation times, the corresponding selected nectar source will be abandoned in the global search stage.

[0066] Optionally, scout bees account for 10% of the bee colony, and the number of honey bees and worker bees each account for 45% of the bee colony.

[0067] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0068] Aiming at the demand of distributed power system microgrid optimization scheduling, the present invention takes into account the distributed characteristics of different types of micro power sources, takes the economic efficiency of microgrid system operation as the main goal, introduces microgrid local information interaction and potential game strategy, and constructs the potential function corresponding to the micro power scheduling scheme; by improving the bee foraging algorithm to adapt to the optimization needs of distributed microgrids, the search efficiency is greatly improved, and the efficient solution of distributed microgrid optimization scheduling scheme is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A schematic diagram of a microgrid optimization scheduling method based on a distributed bee foraging algorithm provided in an embodiment of the present application. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0071] See also Figure 1, this embodiment provides a microgrid optimization scheduling method based on a distributed bee foraging algorithm, the method comprising the following steps:

[0072] S1. Establish the objective function of microgrid optimization scheduling.

[0073] In step S1, the objective function expression of microgrid optimization scheduling is:

[0074]

[0075] Where, f represents the total cost of microgrid power generation; M represents the total number of micro-power sources in the microgrid; T represents the optimization period; C i (t) represents the power generation cost of the i-th micro power source in the microgrid.

[0076] The power generation cost types of micro power sources include wind power generation cost C WT (t), photovoltaic power generation cost C PV (t), fuel engine power generation cost C FE (t) and battery operating cost C BT (t).

[0077] The cost of wind power generation can be calculated according to the following formula:

[0078]

[0079] In the formula, C WT (t) represents the cost of wind power generation; α wt represents the unit cost of wind power generation; P WT represents wind power generation; Δt represents time variation; T represents the optimization period.

[0080] The cost of photovoltaic power generation can be calculated according to the following formula:

[0081]

[0082] In the formula, C PV (t) represents the photovoltaic power generation cost α pv Represents the unit cost of photovoltaic power generation; P PV represents photovoltaic power generation; Δt represents the time change; T represents the optimization period.

[0083] The cost of fuel engine power generation can be calculated according to the following formula:

[0084]

[0085] In the formula, C FE (t) represents the cost of power generation from a fuel engine; α fe represents the unit cost of fuel engine power generation; P FErepresents the power generated by the fuel engine; Δt represents the time change; T represents the optimization call cycle.

[0086] The battery operating cost can be calculated according to the following formula:

[0087]

[0088] In the formula, C BT (t) represents the battery operating cost; α bt Represents the unit cost of battery discharge; P BT Indicates the battery charge / discharge power; Δt indicates the time change; T indicates the optimization period.

[0089] S2. Determine the constraints related to the optimal dispatch of the microgrid.

[0090] In step S2, the constraints related to the microgrid optimization scheduling include:

[0091] Power balance constraints:

[0092] P WT +P PV +P FE +P BT =P LD +P LS Where P WT Indicates wind power generation power; P PV Represents photovoltaic power generation; P FE Indicates the power generated by the fuel engine; P BT Indicates the battery charge / discharge power; P LD is the power load of the microgrid; P LS is the microgrid loss.

[0093] Battery constraints:

[0094]

[0095] Where P BT Indicates the battery charge / discharge power, P out_max and P in_max They represent the maximum discharge power and maximum charge power of the battery respectively; SOC(t) represents the charge stored in the battery at time t; SOC max and SOC min They represent the upper and lower limits of the battery's storage charge.

[0096] S3. Initialize and set the parameters of the distributed bee foraging algorithm. The bee colony includes scout bees, honey bees and worker bees. The scout bees account for 10% of the bee colony, and the number of honey bees and worker bees each accounts for 45% of the bee colony.

[0097] In step S3, the parameters of the distributed bee foraging algorithm are initialized and set. The parameters of the distributed bee foraging algorithm mainly include the number of bee colonies, the algorithm stop condition, the neighborhood shrinkage coefficient, and the upper limit of the number of stagnation times of the bee search. st It can be calculated according to the following formula.

[0098]

[0099] Where n c is the neighborhood shrinkage coefficient of the bee search; fix means rounding.

[0100] S4. According to the constraints related to the optimal scheduling, an optimal scheduling plan is randomly generated and a scout bee population is formed.

[0101] S5. Evaluate and sort the nectar sources found by the scout bees.

[0102] In step S5, the searched honey sources are evaluated using the local cooperative game strategy of each distributed micro-power source. Each micro-power source interacts with its neighboring micro-power sources locally. The potential function of the i-th micro-power source can be expressed as:

[0103] F i =C i (t)+C ni (t)+β k (P LD +P H ) (1)

[0104] In the formula, F i represents the potential function corresponding to the i-th micro-power scheduling scheme; C i (t) represents the power generation cost of the i-th micro power source in the microgrid; C ni (t) represents the cost of the neighboring power source to provide power to the micro power node; P LD is the power load of the microgrid; P H Indicates that it is provided to the local load; β k is the penalty coefficient, and k is the number of iterations.

[0105] S6. Honey bees conduct local searches and update selected nectar sources.

[0106] In step S6, the steps of local search performed by bees are as follows:

[0107] S61. Honey bees are evenly distributed to search near the selected nectar sources.

[0108] S62: For each selected nectar source, the bees assigned to the selected nectar source conduct a random search near the nectar source and update the positions of the bees.

[0109] fr i =nl j +rnd·(nu j -nl j )

[0110] In the formula, fr i Indicates the location information of the i-th bee collector; nu j and nl j They represent the upper and lower limits of the neighborhood search for the j-th selected nectar source; rnd represents a random number between [0,1].

[0111] S63. Evaluate the updated honey bees according to formula (1). If the honey bees find a nectar source with better quality, replace the current selected nectar source with the better nectar source as the new selected nectar source, reset the search stagnation times of the current selected nectar source, and restore the neighborhood range to its original size.

[0112] S64. If the honey bees do not find a nectar source with better quality, the search stagnation counter of the selected nectar source is increased by 1, and the search neighborhood of the selected nectar source is shrunk according to the following formula.

[0113] S nh (n+1)=n sc ·S nh (n)

[0114] Among them, S nh (n) is the current search range; S nh (n+1) is the search range for the next iteration; n sc is the neighborhood shrinkage coefficient.

[0115] S65. Sort all selected nectar sources according to their quality.

[0116] S7: Wait for the worker bees to conduct local search and update the selected nectar sources.

[0117] In step S7, the steps for the worker bees to perform local search are as follows:

[0118] S71. Allocate worker bees to each selected nectar source according to the following formula.

[0119]

[0120] Among them, N j is the number of worker bees allocated to the jth selected nectar source; fitness j is the fitness function value of the jth selected nectar source; k is the fitness function value of the kth selected nectar source; n 0 represents the total number of worker bees; K represents the number of selected nectar sources; fix represents rounding.

[0121] S72. The worker bee randomly selects a dimension from the selected nectar source where it is located, and performs search and update according to the following formula.

[0122]

[0123] Where d represents a randomly selected single dimension to be updated; Represents the position of the worker bee to be updated this time; and They represent the upper and lower limits of the d-dimensional space search for the j-th selected nectar source neighborhood; rnd represents a random number between [0,1].

[0124] S73. Evaluate the updated worker bees according to formula (1). If the worker bees find a nectar source with better quality, replace the current selected nectar source with the better nectar source as the new selected nectar source, reset the number of search stagnation times of the current selected nectar source, and restore the neighborhood range to its original size.

[0125] Step S74: If the worker bee fails to find a nectar source with better quality, the search stagnation counter of the selected nectar source is increased by 1.

[0126] Step S75, determine whether the number of search stagnation times is greater than the upper limit of the number of stagnation times. If the number of search stagnation times is greater than the upper limit of the number of stagnation times, the corresponding selected nectar source will be abandoned in the global search stage.

[0127] S8. The scout bee uses a random search method to generate optional optimal scheduling solutions.

[0128] S9. According to the quality of the nectar sources, the nectar sources searched by the scout bees and the selected nectar sources are sorted together, the selected nectar sources for the next round of iteration are screened out, and the best nectar source at present is recorded.

[0129] S10. Output the global optimal solution of the objective function and its fitness function value.

[0130] This embodiment targets the needs of distributed power system microgrid optimization scheduling, takes into account the distributed characteristics of different types of micropower sources, takes the economic operation of the microgrid system as the main goal, introduces the microgrid local information interaction and potential game strategy, and constructs the potential function corresponding to the micropower scheduling scheme. The bee foraging algorithm is improved to meet the needs of distributed microgrid optimization, with high search efficiency, and realizes the efficient solution of distributed microgrid optimization scheduling scheme.

[0131] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A microgrid optimization scheduling method based on a distributed bee foraging algorithm, characterized in that: The method comprises the following steps: S1. Establish the objective function of microgrid optimization dispatch; S2. Determine the constraints related to the optimal dispatch of the microgrid; S3, initializing and setting the parameters of the distributed bee foraging algorithm, where the bee colony includes scout bees, foraging bees and worker bees; S4. According to the constraints related to the optimization scheduling, randomly generate the optimization scheduling plan and form a scout bee population; S5, evaluating and ranking the nectar sources found by the scout bees; S6, honey bees conduct local search and update the selected nectar sources; S7, waiting for the worker bees to conduct local search and update the selected nectar source; S8, the scout bee uses random search to generate optional optimal scheduling solutions; S9. Sort the nectar sources found by the scout bees and the selected nectar sources together according to the quality of the nectar sources, select the selected nectar sources for the next round of iteration, and record the best nectar sources at present; S10. Output the global optimal solution of the objective function and its fitness function value.

2. A microgrid optimization scheduling method based on a distributed bee foraging algorithm as claimed in claim 1, characterized in that: In step S1, the objective function expression of microgrid optimization scheduling is: Where, f represents the total cost of microgrid power generation; M represents the total number of micro-power sources in the microgrid; T represents the optimization period; C i (t) represents the power generation cost of the i-th micro power source in the microgrid; The power generation cost types of micro power sources include wind power generation cost C WT (t), photovoltaic power generation cost C PV (t), fuel engine power generation cost C FE (t) and battery operating cost C BT (t); The cost of wind power generation can be calculated according to the following formula: In the formula, C WT (t) represents the cost of wind power generation; α wt represents the unit cost of wind power generation; P WT represents wind power generation; Δt represents time variation; T represents the optimization period; The cost of photovoltaic power generation can be calculated according to the following formula: In the formula, C PV (t) represents the photovoltaic power generation cost α pv Represents the unit cost of photovoltaic power generation; P PV represents photovoltaic power generation; Δt represents the time variation; T represents the optimization period; The cost of fuel engine power generation can be calculated according to the following formula: In the formula, C FE (t) represents the cost of power generation from a fuel engine; α fe represents the unit cost of fuel engine power generation; P FE represents the power generated by the fuel engine; Δt represents the time change; T represents the optimization call cycle; The battery operating cost can be calculated according to the following formula: In the formula, C BT (t) represents the battery operating cost; α bt Represents the unit cost of battery discharge; P BT Indicates the battery charging / discharging power; Δt indicates the time change; T indicates the optimization period.

3. A microgrid optimization scheduling method based on a distributed bee foraging algorithm as claimed in claim 1, characterized in that: In step S2, the constraints related to the microgrid optimization scheduling include: Power balance constraints: P WT +P PV +P FE +P BT =P LD +P LS Where P WT Indicates wind power generation power; P PV Represents photovoltaic power generation; P FE Indicates the power generated by the fuel engine; P BT Indicates the battery charge / discharge power; P LD is the power load of the microgrid; P LS is the microgrid loss; Battery constraints: Where P BT Indicates the battery charge / discharge power, P out_max and P in_max They represent the maximum discharge power and maximum charge power of the battery respectively; SOC(t) represents the charge stored in the battery at time t; SOC max and SOC min They represent the upper and lower limits of the battery's storage charge.

4. A microgrid optimization scheduling method based on a distributed bee foraging algorithm as claimed in claim 1, characterized in that: In step S3, the parameters of the distributed bee foraging algorithm are initialized and set. The parameters of the distributed bee foraging algorithm mainly include the number of bee colonies, algorithm stop conditions, neighborhood shrinkage coefficient, and the upper limit of the number of stagnation times of the bee search. st It can be calculated according to the following formula; Where n C is the neighborhood shrinkage coefficient of the bee search; fix means rounding.

5. A microgrid optimization scheduling method based on a distributed bee foraging algorithm as claimed in claim 1, characterized in that: In step S5, the searched honey sources are evaluated using the local cooperative game strategy of each distributed micro-power source. Each micro-power source interacts with its neighboring micro-power sources locally. The potential function of the i-th micro-power source can be expressed as: F i =C i (t)+C ni (t)+β k (P LD +P H ) In the formula, F i represents the potential function corresponding to the i-th micro-power scheduling scheme; C i (t) represents the power generation cost of the i-th micro power source in the microgrid; C ni (t) represents the cost of the neighboring power source to provide power to the micro power node; P LD is the power load of the microgrid; P H Indicates that it is provided to the local load; β k is the penalty coefficient, and k is the number of iterations.

6. A microgrid optimization scheduling method based on a distributed bee foraging algorithm as claimed in claim 1, characterized in that: In step S6, the steps of local search performed by bees are as follows: S61, honey bees are evenly distributed to search near the selected nectar sources; S62: For each selected nectar source, the bees assigned to the selected nectar source conduct a random search near the nectar source and update the location of the bees: from i =nl j +rnd·(now j -nl j ) In the formula, fr i Indicates the location information of the i-th bee collector; nu j and nl j They represent the upper and lower limits of the neighborhood search for the jth selected nectar source; rnd represents a random number between [0,1]; S63, evaluating the updated honey bees, if the honey bees search for a nectar source with better quality, then replacing the current selected nectar source with the better nectar source as the new selected nectar source, resetting the number of search stagnations of the current selected nectar source, and restoring the neighborhood range to the original size; S64. If the honey bees do not find a nectar source with better quality, the search stagnation counter of the selected nectar source is increased by 1, and the search neighborhood of the selected nectar source is shrunk according to the following formula; S nh (n+1)=n sc ·S nh (n) Among them, S nh (n) is the current search range; S nh (n+1) is the search range for the next iteration; n sc is the neighborhood shrinkage coefficient; S65. Sort all selected nectar sources according to their quality.

7. A microgrid optimization scheduling method based on a distributed bee foraging algorithm as claimed in claim 1, characterized in that: In step S7, the steps for the worker bees to perform local search are as follows: S71, allocating worker bees to each selected nectar source according to the following formula; Among them, N j is the number of worker bees allocated to the jth selected nectar source; fitness j is the fitness function value of the jth selected nectar source; k is the fitness function value of the kth selected nectar source; n0 represents the total number of bees waiting to work; K represents the number of selected nectar sources; fix represents rounding; S72, the worker bee randomly selects a dimension from the selected nectar source where it is located, and performs a search update according to the following formula; Where d represents a randomly selected single dimension to be updated; Represents the position of the worker bee to be updated this time; and They represent the upper and lower limits of the j-th selected honey source neighborhood search d-dimensional space respectively; rnd represents a random number between [0,1]; S73, evaluating the updated worker bees, if the worker bees search for a nectar source with better quality, the better nectar source is used to replace the current selected nectar source as the new selected nectar source, and the number of search stagnation of the current selected nectar source is reset, and the neighborhood range is restored to the original size; Step S74: If the worker bee fails to find a nectar source with better quality, the search stagnation counter of the selected nectar source is increased by 1; Step S75, determine whether the number of search stagnation times is greater than the upper limit of the number of stagnation times. If the number of search stagnation times is greater than the upper limit of the number of stagnation times, the corresponding selected nectar source will be abandoned in the global search stage.

8. A microgrid optimization scheduling method based on a distributed bee foraging algorithm as claimed in claim 1, characterized in that: Scout bees make up 10% of the bee colony, while harvesting bees and worker bees each make up 45% of the colony.

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