Active distribution network scheduling optimization method, medium and system including microgrid group

Abstract

The present invention provides an active distribution network scheduling optimization method, medium and system including a microgrid group, belonging to the field of active distribution network scheduling technology, comprising: obtaining active distribution network parameters and microgrid parameters of each microgrid in the active distribution network; using a preset microgrid similarity function to calculate the similarity between any two microgrids; establishing a group of stable scheduling equations for the active distribution network considering the distribution network parameters and a group of stable scheduling equations for the microgrid considering the microgrid parameters; constructing a two-layer optimization model based on a gray wolf hunting optimization algorithm and a genetic algorithm, wherein the gray wolf hunting optimization algorithm is used for optimizing the active distribution network and the genetic algorithm is used for optimizing each microgrid; while initializing the gray wolf population, an initial population of the genetic algorithm is set for each microgrid to represent various possible scheduling schemes within the microgrid; and by not performing outer layer optimization and inner layer optimization, the problem that the existing technology is difficult to take into account the characteristics of the active distribution network as a whole and the characteristics within the microgrid is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of active distribution network scheduling, and in particular, relates to an active distribution network scheduling optimization method, medium and system including a microgrid group. Background Art

[0002] With the rapid development of renewable energy and the increasing load on the user side, traditional passive distribution networks are no longer able to meet the demands of today's power systems. Active distribution networks, a new type of power transmission and distribution system, have emerged in recent years. They can proactively sense and respond to system changes, improving system flexibility and reliability by coordinating the dispatch of resources such as distributed generation, energy storage, and controllable loads. Compared to traditional distribution networks, active distribution networks have more complex topologies and operational characteristics. Within active distribution networks, microgrids, as an important distributed power supply unit, can improve power quality, reduce transmission and distribution losses, and promote the local consumption of renewable energy. Microgrids consist of distributed generation units, energy storage equipment, and controllable loads, and can operate independently or interconnected with the main grid. Microgrids are autonomous and can make proactive decisions and dispatch based on their own load, generation, and energy storage conditions. Currently, the coordinated optimization and dispatch of active distribution networks and microgrids has become a hot area of ​​power system research. Existing research mainly focuses on two aspects: one is the optimization and scheduling at the overall level of the active distribution network, which uses various intelligent algorithms to optimize the distribution network parameters (such as voltage, line capacity, etc.) to improve the reliability and economy of the system; the other is the optimization and scheduling within the microgrid, which coordinates the distributed generation, energy storage and controllable loads within the microgrid to meet the operation requirements of the microgrid itself.

[0003] However, existing research mostly focuses on optimization at a single level, failing to balance the characteristics of both the active distribution network as a whole and the microgrid itself. The complex interactive coupling between the active distribution network and the microgrid requires coordinated optimization at both levels. On the one hand, the overall scheduling scheme of the active distribution network affects the operating status of each microgrid; on the other hand, the scheduling strategy within the microgrid in turn affects the performance of the active distribution network. Therefore, optimization methods that focus solely on a single level often fail to achieve ideal performance. Summary of the Invention

[0004] In view of this, the present invention provides an active distribution network scheduling optimization method, medium and system including a microgrid group, which can solve the technical problem that most existing technologies are optimized at a single level and it is difficult to take into account the characteristics of the active distribution network as a whole and the internal characteristics of the microgrid.

[0005] The present invention is achieved in that:

[0006] A first aspect of the present invention provides a method for optimizing active distribution network scheduling including a microgrid group, comprising the following steps:

[0007] S10, obtaining active distribution network parameters and microgrid parameters of each microgrid in the active distribution network;

[0008] S20, using a preset microgrid similarity function to calculate the similarity between any two microgrids;

[0009] S30, establishing an active distribution network stability dispatch equation group considering distribution network parameters and a microgrid stability dispatch equation group considering microgrid parameters;

[0010] S40, constructing a two-layer optimization model based on a gray wolf hunting optimization algorithm and a genetic algorithm, wherein the gray wolf hunting optimization algorithm is used for active distribution network optimization, and the genetic algorithm is used for each microgrid optimization; while initializing the gray wolf population, an initial population of the genetic algorithm is set for each microgrid, representing various possible scheduling schemes within the microgrid;

[0011] S50, perform outer optimization, update the positions of α, β and δ wolves in the gray wolf hunting optimization algorithm, representing the optimization of the active distribution network scheduling plan. At the same time, for each active distribution network scheduling plan determined by α, β and δ wolves, perform inner optimization, iteratively execute, and finally obtain an optimal complete active distribution network scheduling plan as the target scheduling plan.

[0012] On the basis of the above technical solution, the active distribution network scheduling optimization method including a microgrid group of the present invention can also be improved as follows:

[0013] The distribution network parameters include network topology, line impedance, transformer parameters, voltage limit, and power limit.

[0014] The microgrid parameters include the capacity of distributed power generation equipment, the capacity of energy storage equipment, the controllable load capacity, and the parameters of the interconnection lines between microgrids.

[0015] The active distribution network stability scheduling equation group includes the distribution network power balance equation, the distribution network node voltage equation and the distribution network line power flow equation. Specifically, the active distribution network stability scheduling equation group includes the following equations:

[0016] 1. Distribution network power balance equation:

[0017] ;

[0018] ;

[0019] Where, and Node At the moment Active and reactive power injection; and Node At the moment Active and reactive loads; and Node At the moment The voltage amplitude and phase angle; and are the real and imaginary parts of the node admittance matrix, respectively.

[0020] 2. Distribution network node voltage equation:

[0021] ;

[0022] ;

[0023] Where, and are the lower and upper limits of the voltage amplitude respectively; and are the lower and upper limits of the phase angle respectively.

[0024] 3. Distribution network line power flow equation:

[0025] ;

[0026] ;

[0027] ;

[0028] Where, and Line At the moment Active and reactive power flows; For the line At the moment The apparent power of For the line The maximum allowed transmission capacity.

[0029] Parameter acquisition method:

[0030] 1. Network topology and line parameters ( and ) is obtained through design documents and actual measurements of the distribution network.

[0031] 2. Load data ( and ) is obtained through load forecasting methods or historical data statistics.

[0032] 3. Voltage and power limits ( , , etc.) are determined according to the power system operation standards and equipment specifications.

[0033] The microgrid stability scheduling equations include the microgrid power balance equation, the microgrid node voltage equation, the microgrid power generation equipment output equation, the microgrid energy storage equipment charge and discharge equation, and the microgrid controllable load regulation equation. Specifically, the microgrid stability scheduling equations include the following equations:

[0034] 1. Microgrid power balance equation:

[0035] ;

[0036] ;

[0037] Where, and Microgrid At the moment Active and reactive output of distributed generation; and are the active and reactive power of the energy storage system respectively; and are the active and reactive power of the load respectively; and are the active and reactive powers exchanged with the outside respectively; For microgrids The internal node collection.

[0038] 2. Microgrid node voltage equation:

[0039] ;

[0040] ;

[0041] Where, For microgrids At the moment The voltage amplitude; For microgrids At the moment frequency; and are the lower and upper limits of the voltage amplitude respectively; and are the lower and upper limits of the frequency respectively.

[0042] 3. Microgrid power generation equipment output equation:

[0043] ;

[0044] ;

[0045] ;

[0046] ;

[0047] Where, and are the minimum and maximum active outputs of distributed generation, respectively; and are the minimum and maximum reactive output respectively; and They are respectively the climbing rate limits for ascent and descent.

[0048] 4. Microgrid energy storage device charging and discharging equations:

[0049] ;

[0050] ;

[0051] , when ;

[0052] , when ;

[0053] Where, and are the minimum and maximum charge and discharge power of the energy storage device, respectively; For energy storage equipment at all times State of charge; and are the lower and upper limits of the state of charge, respectively; and are the charge and discharge efficiencies, respectively; is the energy storage capacity; is the time step.

[0054] 5. Microgrid controllable load regulation equation:

[0055] ;

[0056] ;

[0057] Where, and are the minimum and maximum powers of the controllable load respectively; is the total energy demand of the controllable load during the entire scheduling period.

[0058] Parameter acquisition method:

[0059] 1. Distributed power generation equipment parameters ( , , , etc.) through equipment specifications and historical operating data.

[0060] 2. Energy storage equipment parameters ( , , , , etc.) are obtained through equipment specifications and actual testing.

[0061] 3. Controllable load parameters ( , , ) is determined through load characteristic analysis and user needs.

[0062] 4. Voltage and frequency limits ( , , , ) is determined according to the microgrid operation standards.

[0063] 5. Power exchange limit ( , ) is determined based on the capacity of the interconnection lines between microgrids and the agreement.

[0064] Among them, the position of wolf α in the gray wolf hunting optimization algorithm represents the initial scheduling plan of the active distribution network, wolf β represents the second best scheduling plan of the active distribution network, and wolf δ represents the third best scheduling plan of the active distribution network.

[0065] Furthermore, the fitness function of the genetic algorithm is specifically a comprehensive performance index within the microgrid, which is used to represent the quality of the microgrid scheduling plan, including the weighted sum of economic, reliability and environmental protection factors.

[0066] Furthermore, the inner layer optimization steps are specifically as follows: synchronously executing genetic algorithm iterations on each microgrid, including roulette wheel selection operation, single point crossover operation, and uniform mutation operation; if a microgrid produces a better individual, it is used as a seed individual, and the seed individual is introduced into the population of microgrids whose similarity with the microgrid is greater than a preset similarity threshold to replace the individual with the lowest fitness; updating the scheduling plan of each microgrid through iteration until a preset number of iterations or convergence conditions is reached; for each microgrid, selecting the individual with the highest fitness as the optimal scheduling plan for the microgrid; and combining the optimal scheduling plans of all microgrids to form a complete active distribution network scheduling plan.

[0067] Furthermore, the scheduling plan specifically refers to the operation strategy of the active distribution network and each microgrid within a specific time period, including the following aspects:

[0068] 1. Active distribution network level:

[0069] : No. A distribution network node at time Active power injection;

[0070] : No. A distribution network node at time Reactive power injection;

[0071] : No. A distribution network node at time The voltage amplitude;

[0072] : No. A distribution network node at time The phase angle.

[0073] 2. Microgrid level:

[0074] : No. The distributed generation equipment in a microgrid is contribution;

[0075] : No. The energy storage equipment in the microgrid is The charge and discharge power;

[0076] : No. The controllable load in a microgrid is Power consumption;

[0077] : No. The first microgrid and the Between microgrids at time of switching power.

[0078] The mathematical expression of the scheduling scheme is:

[0079] ;

[0080] in, Indicates the distribution network node number, Indicates the microgrid number, Represents a time series.

[0081] Furthermore, the microgrid similarity function is used to quantify the similarity between any two microgrids, taking into account the structural characteristics and operating characteristics of the microgrids. The specific expression is as follows:

[0082] ;

[0083] Where, For microgrids and microgrids The overall similarity of is the structural similarity; is the similarity of operating characteristics; and is the weight coefficient, and .

[0084] The structural similarity is calculated as follows:

[0085] ;

[0086] Where, and Represents microgrid and microgrids No. capacity parameters (distributed generation equipment capacity, energy storage equipment capacity, controllable load capacity, interconnection line capacity between microgrids), Among all microgrids The maximum value of the capacity parameter.

[0087] The running characteristic similarity is calculated as follows:

[0088] ;

[0089] Where, and Represents microgrid and microgrids At the moment The net load curve, is the standard deviation parameter, is the total time step.

[0090] Parameter acquisition method:

[0091] 1. Capacity parameters Obtained through microgrid design documents and equipment specifications.

[0092] 2. Net load curve Obtained through historical operating data statistics or load forecasting methods.

[0093] 3. Weight coefficient and It is determined by expert experience or using the AHP method.

[0094] 4. Standard deviation parameter Determined through statistical analysis of historical data.

[0095] 3. Objective function of the gray wolf optimization algorithm

[0096] Furthermore, the objective function of the Gray Wolf optimization algorithm comprehensively considers economy, reliability, and environmental protection, which can be expressed as follows:

[0097] ;

[0098] Where, is the objective function value; is the total operating cost; is the reliability index; It is an environmental indicator; 、 and are the weight coefficients of economy, reliability and environmental protection respectively, and .

[0099] Total operating costs The calculation is as follows:

[0100] ;

[0101] Where, For the main network Nodes at time The power generation cost function; For the Cost function of distributed generation in a microgrid; For the The operating cost function of each microgrid energy storage device; For the The regulation cost function of the controllable load of a microgrid is given.

[0102] Reliability Index The calculation is as follows:

[0103] ;

[0104] Where, is the nominal voltage; For the Lines at time The apparent power of For the line Rated capacity; is the total number of lines.

[0105] Environmental indicators The calculation is as follows:

[0106] ;

[0107] Where, For the main network Nodes at time Carbon emission function; For the A microgrid distributed generation carbon emission function.

[0108] Parameter acquisition method:

[0109] 1. The power generation cost function and carbon emission function are obtained by fitting historical operating data.

[0110] 2. The operating costs of energy storage equipment and the controllable load regulation costs are determined by equipment specifications and market prices.

[0111] 3. Weight coefficient 、 and Determined by multi-objective decision-making methods (such as AHP).

[0112] 4. Fitness function of genetic algorithm

[0113] Furthermore, the fitness function of the genetic algorithm is used to evaluate the quality of the internal scheduling scheme of the microgrid, which is specifically expressed as follows:

[0114] ;

[0115] Where, For the The fitness function value of a microgrid; The operating cost of the microgrid; is the microgrid reliability index; It is the environmental protection index of microgrid; 、 and is the weight coefficient, and ; A small positive number to prevent the denominator from being zero.

[0116] Microgrid operating costs The calculation is as follows:

[0117] ;

[0118] Where, For the The energy exchange cost of a microgrid with other microgrids or the main grid.

[0119] Microgrid reliability indicators The calculation is as follows:

[0120] ;

[0121] Where, For microgrids The voltage amplitude; For microgrids frequency; and are the nominal voltage and nominal frequency respectively; For microgrids Upper limit of power exchange with external devices.

[0122] Microgrid environmental performance indicators The calculation is as follows:

[0123] ;

[0124] Where, For microgrids Distributed generation at the moment carbon emission function.

[0125] Parameter acquisition method:

[0126] 1. The distributed generation cost function and carbon emission function are obtained by fitting the equipment performance curve and historical operation data.

[0127] 2. The operating costs of energy storage equipment and the controllable load regulation costs are determined by equipment specifications and market prices.

[0128] 3. The cost of energy exchange is determined by energy market prices and trading rules.

[0129] 4. Weight coefficient 、 and Determined by analytic hierarchy process (AHP).

[0130] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned active distribution network scheduling optimization method involving a microgrid group.

[0131] A third aspect of the present invention provides an active distribution network scheduling optimization system including a microgrid group, which includes the above-mentioned computer-readable storage medium.

[0132] Compared with the existing technology, the beneficial effect of the active distribution network scheduling optimization method, medium and system provided by the present invention, which includes a microgrid group, is that it adopts a two-layer optimization approach, which not only considers the optimization of the active distribution network at the overall level, but also takes into account the characteristics of each microgrid, and ultimately obtains a relatively optimal active distribution network scheduling strategy.

[0133] Specifically, the technical effects of the method of the present invention are mainly reflected in the following aspects:

[0134] 1) This approach leverages the interactive coupling between the active distribution network and microgrids, achieving two-level collaborative optimization. At the outer level, an improved Grey Wolf Optimization algorithm is used to optimize the overall dispatching scheme of the active distribution network, taking into account economic efficiency, reliability, and environmental performance. At the inner level, a genetic algorithm is employed to optimize the dispatching within each microgrid, meeting the microgrid's own operational requirements. This two-level optimization strategy balances both global and local requirements, resulting in a more coordinated solution.

[0135] 2) A new microgrid similarity evaluation method is proposed, which not only considers the structural characteristics of microgrids but also their dynamic operating characteristics. This evaluation method can more accurately quantify the degree of difference between microgrids and provide a basis for microgrid clustering and internal optimization.

[0136] 3) Based on the characteristics of active distribution networks and microgrids, corresponding stable dispatch equations were established. The active distribution network equations describe constraints such as power balance, voltage limits, and line capacity at the distribution network level; the microgrid equations describe power balance, voltage and frequency limits, and the operating characteristics of various devices within the microgrid. These equations provide the mathematical foundation for subsequent optimized dispatch.

[0137] 4) At the optimization algorithm level, this invention innovatively integrates the Gray Wolf Optimization Algorithm (GWA) and the Genetic Algorithm (GA), leveraging the strengths of both algorithms. The GWA, as the outer optimization algorithm, effectively explores the global optimal solution for the overall dispatching plan of the active distribution network; while the GA, as the inner optimization algorithm, tailors the characteristics of each microgrid to find the optimal solution that meets its specific needs. The synergistic effect of these two algorithms enhances overall optimization effectiveness.

[0138] Therefore, the solution of the present invention solves the technical problem that most existing technologies are optimized at a single level and it is difficult to take into account the characteristics of the active distribution network as a whole and the internal characteristics of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0139] Figure 1 A flow chart of the method provided by the present invention;

[0140] Figure 2It is the voltage characteristic diagram of active distribution network;

[0141] Figure 3 This is the power exchange characteristic diagram of the microgrid. DETAILED DESCRIPTION

[0142] 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 with reference to the accompanying drawings in the embodiments of the present invention.

[0143] like Figure 1 FIG. 1 is a flow chart of a method for optimizing active distribution network scheduling including a microgrid group provided by the first aspect of the present invention. The method comprises the following steps:

[0144] S10, obtaining active distribution network parameters and microgrid parameters of each microgrid in the active distribution network, wherein the distribution network parameters include network topology, line impedance, transformer parameters, voltage limit, and power limit; the microgrid parameters include distributed generation equipment capacity, energy storage equipment capacity, controllable load capacity, and interconnection line parameters between microgrids;

[0145] S20, using a preset microgrid similarity function to calculate the similarity between any two microgrids;

[0146] S30, establishing an active distribution network stability scheduling equation group considering distribution network parameters and a microgrid stability scheduling equation group considering microgrid parameters; the active distribution network stability scheduling equation group includes a distribution network power balance equation, a distribution network node voltage equation, and a distribution network line power flow equation; the microgrid stability scheduling equation group includes a microgrid power balance equation, a microgrid node voltage equation, a microgrid power generation equipment output equation, a microgrid energy storage equipment charge and discharge equation, and a microgrid controllable load regulation equation;

[0147] S40. Construct a two-layer optimization model based on an improved gray wolf optimization algorithm and a genetic algorithm, wherein the gray wolf algorithm is used for active distribution network optimization and the genetic algorithm is used for each microgrid optimization; initialize the gray wolf population, wherein the position of the α wolf represents the initial scheduling plan of the active distribution network, the β wolf represents the second-best active distribution network scheduling plan, and the δ wolf represents the third-best active distribution network scheduling plan; at the same time, set the initial population of the genetic algorithm for each microgrid to represent various possible scheduling plans within the microgrid; the fitness function of the genetic algorithm is specifically a comprehensive performance index within the microgrid, which is used to represent the quality of the microgrid scheduling plan, including the weighted sum of economic, reliability and environmental factors;

[0148] S50, perform outer optimization, update the positions of α, β and δ wolves, representing the optimization of the active distribution network scheduling plan, and at the same time, for each active distribution network scheduling plan determined by α, β and δ wolves, perform inner optimization, and the steps of the inner optimization are specifically: synchronously execute genetic algorithm iterations on each microgrid, including roulette selection operation, single point crossover operation, and uniform mutation operation; if a microgrid produces a better individual, it is used as a seed individual, and the seed individual is introduced into the population of microgrids whose similarity with the microgrid is greater than a preset similarity threshold, replacing the individual with the lowest fitness; update the scheduling plan of each microgrid through iteration until the preset number of iterations or convergence conditions are reached; for each microgrid, select the individual with the highest fitness as the optimal scheduling plan of the microgrid; combine the optimal scheduling plans of all microgrids to form a complete active distribution network scheduling plan; iteratively execute, and finally obtain an optimal complete active distribution network scheduling plan as the target scheduling plan.

[0149] The specific implementation of the above steps is described in detail below:

[0150] First, in step S10, it is necessary to obtain the key parameters of the active distribution network and each microgrid. For the active distribution network, the parameters that need to be obtained include: network topology ,in is a node set, is the line set; line impedance , indicating line Resistance and reactance; transformer parameters , respectively represent the Transformer ratio and capacity upper limit; voltage limit ;Power limit , representing nodes For each microgrid The parameters that need to be obtained include: distributed generation capacity Energy storage equipment capacity , respectively represent the maximum charge and discharge power, minimum and maximum state of charge; controllable load capacity ;Inter-microgrid interconnection line capacity , represents the microgrid and The maximum exchange power between them.

[0151] In step S20, it is necessary to use the preset microgrid similarity function to calculate the similarity between any two microgrids. The microgrid similarity function includes structural similarity Similarity to operating characteristics Two aspects, the overall expression is:

[0152] ;

[0153] in, and is the weight coefficient, .

[0154] Structural similarity The calculation formula is:

[0155] ;

[0156] in, and Represents microgrid and No. capacity parameters (distributed generation capacity, energy storage capacity, controllable load capacity, interconnection line capacity between microgrids), Among all microgrids The maximum value of the capacity parameter. This formula represents the microgrid and The smaller the difference, the higher the similarity.

[0157] Operating characteristics similarity The calculation formula is:

[0158] ;

[0159] in, and Represents microgrid and At the moment The net load curve, is the standard deviation parameter, is the total time step. This formula represents the microgrid and The closer the curves are, the higher the similarity is.

[0160] The overall similarity between microgrids is obtained by calculating the structural similarity and operating characteristic similarity and taking weighted sum. ,used for microgrid clustering in subsequent optimal scheduling.

[0161] In step S30, it is necessary to establish an active distribution network stability scheduling equation group considering distribution network parameters, and a microgrid stability scheduling equation group considering microgrid parameters.

[0162] The active distribution network stability dispatch equations include the following equations:

[0163] 1. Distribution network power balance equation:

[0164] ;

[0165] ;

[0166] in, and Node At the moment Active and reactive power injection; and Node At the moment Active and reactive loads; and Node At the moment The voltage amplitude and phase angle; and are the real and imaginary parts of the node admittance matrix respectively. These equations describe the power balance relationship of each node.

[0167] 2. Distribution network node voltage equation:

[0168] ;

[0169] ;

[0170] in, and are the lower and upper limits of the voltage amplitude respectively; and These equations constrain the feasible region of node voltages.

[0171] 3. Distribution network line power flow equation:

[0172] ;

[0173] ;

[0174] ;

[0175] in, and Line At the moment The active and reactive power flows; For the line At the moment The apparent power; For the line These equations describe the mathematical relationships for power flow along the line and limit the line capacity.

[0176] The microgrid stability dispatch equations include the following equations:

[0177] 1. Microgrid power balance equation:

[0178] ;

[0179] ;

[0180] in, and Microgrid At the moment Active and reactive output of distributed generation; and are the active and reactive power of the energy storage system respectively; and are the active and reactive power of the load respectively; and are the active and reactive powers exchanged with the outside respectively; For microgrids Internal node set. These equations describe the power balance relationship within the microgrid.

[0181] 2. Microgrid node voltage equation:

[0182] ;

[0183] ;

[0184] in, For microgrids At the moment The voltage amplitude; For microgrids At the moment frequency; and are the lower and upper limits of the voltage amplitude respectively; and These equations constrain the voltage and frequency of the microgrid.

[0185] 3. Microgrid power generation equipment output equation:

[0186] ;

[0187] ;

[0188] ;

[0189] ;

[0190] in, and are the minimum and maximum active outputs of distributed generation, respectively; and are the minimum and maximum reactive output respectively; and These equations constrain the output characteristics of distributed generation equipment within the microgrid.

[0191] 4. Microgrid energy storage device charging and discharging equations:

[0192] ;

[0193] ;

[0194] , when ;

[0195] , when ;

[0196] in, and are the minimum and maximum charge and discharge power of the energy storage device, respectively; For energy storage equipment at all times State of charge; and are the lower and upper limits of the state of charge, respectively; and are the charge and discharge efficiencies, respectively; is the energy storage capacity; These equations describe the charging and discharging characteristics of the energy storage devices within the microgrid.

[0197] 5. Microgrid controllable load regulation equation:

[0198] ;

[0199] ;

[0200] in, and are the minimum and maximum powers of the controllable load respectively; is the total energy demand of the controllable load in the entire dispatch period. These equations constrain the regulation range of the controllable load within the microgrid.

[0201] By establishing these equations, the constraints at the active distribution network and microgrid levels are described, laying a mathematical foundation for subsequent optimal scheduling.

[0202] In step S40, a two-layer optimization model based on the improved grey wolf optimization algorithm and the genetic algorithm is constructed. The outer grey wolf optimization algorithm is used for active distribution network optimization scheduling, and the inner genetic algorithm is used for internal optimization of each microgrid.

[0203] Specifically, the objective function of the gray wolf optimization algorithm comprehensively considers economy, reliability and environmental protection, and can be expressed as:

[0204] ;

[0205] in, is the objective function value; is the total operating cost; is the reliability index; It is an environmental indicator; 、 and are the weight coefficients of economy, reliability and environmental protection respectively, and .

[0206] Total operating costs The calculation is as follows:

[0207] ;

[0208] in, For the main network Nodes at time The power generation cost function; For the Cost function of distributed generation in a microgrid; For the The operating cost function of each microgrid energy storage device; For the The regulation cost function of the controllable load of a microgrid is given.

[0209] Reliability Index The calculation is as follows:

[0210] ;

[0211] in, is the nominal voltage; For the Lines at time The apparent power; For the line Rated capacity; is the total number of lines.

[0212] Environmental indicators The calculation is as follows:

[0213] ;

[0214] in, For the main network Nodes at time Carbon emission function; For the A microgrid distributed generation carbon emission function.

[0215] For the inner genetic algorithm, its fitness function can be expressed as:

[0216] ;

[0217] in, For the The fitness function value of each microgrid; Cost of operating the microgrid; is the microgrid reliability index; It is the environmental protection index of microgrid; 、 and is the weight coefficient, and ; A small positive number to prevent the denominator from being zero.

[0218] Microgrid operating costs The calculation is as follows:

[0219] ;

[0220] in, For the The energy exchange cost of a microgrid with other microgrids or the main grid.

[0221] Microgrid reliability indicators The calculation is as follows:

[0222] ;

[0223] in, For microgrids The voltage amplitude; For microgrids frequency; and are the nominal voltage and nominal frequency respectively; For microgrids Upper limit of power exchange with external devices.

[0224] Microgrid environmental performance indicators The calculation is as follows:

[0225] ;

[0226] in, For microgrids Distributed generation at the moment carbon emission function.

[0227] In the process of two-layer optimization, the outer layer gray wolf optimization algorithm is constantly updated Wolf, Wolf and The position of the wolf represents the optimization of the overall dispatching plan of the active distribution network. 、 and The wolf determines the active distribution network solution, while the inner genetic algorithm simultaneously optimizes the scheduling schemes of each microgrid. During the genetic algorithm iteration, if a microgrid produces a superior individual, it is introduced as a seed to other microgrids with similarities, replacing the individuals with the lowest fitness, thus enabling the propagation of excellent solutions among microgrids.

[0228] Through this two-layer optimization approach, we not only consider the optimization of the active distribution network at the overall level, but also take into account the internal characteristics of each microgrid, and finally obtain a relatively optimal active distribution network scheduling strategy.

[0229] In step S50, the specific optimization process is performed. First, the gray wolf optimization algorithm is executed in the outer layer, and the gray wolf optimization algorithm is continuously updated. Wolf, Wolf and The position of the wolf represents the optimization of the overall dispatching plan of the active distribution network.

[0230] For each 、 and The active distribution network scheduling scheme determined by the wolf is optimized by the genetic algorithm in the inner layer. The specific iterative steps of the genetic algorithm are as follows:

[0231] 1. For each microgrid Perform genetic algorithm selection, crossover, and mutation operations, including:

[0232] The roulette wheel method is used for selection, and individuals with higher fitness are selected to enter the next generation; the single-point crossover operation is used to cross the selected individuals with a certain crossover probability to generate new individuals; the uniform mutation operation is used to perturb some of the individual's decision variables with a certain mutation probability to generate new individuals.

[0233] 2. If a microgrid During the iterative process, better individuals are generated and used as seed individuals to introduce into the population of other microgrids whose similarity to the microgrid is greater than the preset similarity threshold (such as 0.8) to replace the individuals with the lowest fitness.

[0234] 3. Continue iterating the optimization until the preset maximum number of iterations (such as 100 times) or the convergence condition is reached.

[0235] 4. For each microgrid ,The individual with the highest fitness is selected as the optimal ,scheduling scheme of the microgrid.

[0236] 5. Integrate the optimal scheduling plans of all microgrids to form a complete active distribution network scheduling plan.

[0237] Through the coordinated optimization of the outer layer Grey Wolf Algorithm and the inner layer Genetic Algorithm, a relatively optimal active distribution network scheduling strategy was ultimately obtained, which not only considered the overall economic efficiency, reliability, and environmental protection, but also took into account the internal characteristics of each microgrid. This two-layer optimization method fully utilized the advantages of the Grey Wolf Algorithm and the Genetic Algorithm, providing an effective solution for the coordinated optimization of active distribution networks and microgrids.

[0238] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned active distribution network scheduling optimization method involving a microgrid group.

[0239] A third aspect of the present invention provides an active distribution network scheduling optimization system including a microgrid group, which includes the above-mentioned computer-readable storage medium.

[0240] Specifically, the principle of the present invention is:

[0241] First, key parameter information for the active distribution network and each microgrid must be obtained. For the active distribution network, this requires information on the network topology, line parameters, transformer parameters, and voltage and power limits. For each microgrid, information on distributed generation capacity, energy storage capacity, controllable load capacity, and inter-microgrid tie line parameters is required. These parameters lay the foundation for subsequent optimized scheduling.

[0242] Secondly, a new method for evaluating microgrid similarity is proposed. This method not only considers the structural characteristics of microgrids, such as differences in device capacity, but also their dynamic operating characteristics, such as the similarity of net load curves. This comprehensive similarity evaluation can more accurately quantify the differences between microgrids, providing a basis for microgrid clustering and internal optimization.

[0243] Next, a set of equations for the stable dispatch of the active distribution network, which considers distribution network parameters, and a set of equations for the stable dispatch of the microgrid, which considers internal microgrid parameters, were established. The former describes constraints such as power balance, voltage limits, and line capacity at the distribution network level; the latter describes power balance, voltage and frequency limits, and the operating characteristics of various devices within the microgrid. These two sets of equations mathematically quantify the constraints at the active distribution network and microgrid levels, providing a foundation for subsequent optimal dispatch.

[0244] Finally, a two-layer optimization model based on an improved Grey Wolf optimization algorithm and a genetic algorithm was constructed. At the outer layer, the Grey Wolf algorithm optimizes the overall dispatch plan for the active distribution network, taking into account economic efficiency, reliability, and environmental performance. At the inner layer, the genetic algorithm optimizes the dispatch plan within each microgrid to meet its own operational requirements. The synergistic effect of the two algorithms fully leverages their respective strengths.

[0245] Specifically, the outer optimization layer of the Gray Wolf Algorithm effectively explores the global optimal solution for the overall dispatching plan of the active distribution network, while the inner optimization layer of the Genetic Algorithm finds the optimal solution that meets the needs of each microgrid based on its characteristics. During the iterative process of the inner genetic algorithm, if a microgrid produces a superior individual, it will be introduced as a seed to other microgrids with similarities. This enables the propagation of excellent solutions among microgrids and accelerates overall convergence.

[0246] This two-tiered optimization strategy takes into account both the overall performance of the active distribution network and the specific characteristics of each microgrid, resulting in a more coordinated solution. This not only improves the overall operational efficiency and flexibility of the active distribution network, but also meets the needs of the microgrids themselves, achieving coordinated optimization of the active distribution network and microgrids.

[0247] To better understand and implement the present invention, the following provides an example of a specific application scenario: a power company in a certain region is promoting the construction of an active distribution network and plans to connect multiple microgrids to the active distribution network for coordinated optimized scheduling. Using this specific situation as an example, the following illustrates how the method proposed in this invention can be used to optimize the scheduling of the active distribution network and microgrids.

[0248] The active distribution network in this area has 10 nodes and a total line length of 50 kilometers. The main parameters are shown in Table 1:

[0249] Table 1 Active distribution network parameters

[0250]

[0251] There are five microgrids MG1 to MG5 connected to the region. The main parameters of each microgrid are shown in Table 2:

[0252] Table 2 Microgrid parameters

[0253]

[0254] The relevant functions of the microgrid are as follows:

[0255] Power generation cost function: ;

[0256] Energy storage cost function: ;

[0257] Controllable load adjustment cost: ;

[0258] Carbon emission function: ;

[0259] Based on the above parameters, the method proposed in this invention is used to coordinate and optimize the scheduling of the active distribution network and the microgrid. The specific steps are as follows:

[0260] 1. Obtain active distribution network and microgrid parameters

[0261] First, we obtained key parameters for the active distribution network and five microgrids, as shown in Tables 1 and 2. These parameters cover network topology, line parameters, transformer parameters, voltage and power limits, as well as each microgrid's distributed generation capacity, energy storage capacity, controllable load capacity, and tie-line capacity. This data laid the foundation for subsequent optimized scheduling.

[0262] 2. Calculate microgrid similarity

[0263] Next, the similarity between any two microgrids is calculated using the microgrid similarity function proposed in this invention. The similarity function includes two aspects: structural similarity and operational characteristic similarity.

[0264] Structural similarity The calculation formula is:

[0265] ;

[0266] in, and Represents microgrid and No. capacity parameters (distributed generation capacity, energy storage capacity, controllable load capacity, interconnection line capacity between microgrids), Among all microgrids The maximum value of the capacity parameter.

[0267] Operating characteristics similarity The calculation formula is:

[0268] ;

[0269] in, and Represents microgrid and At the moment The net load curve, is the standard deviation parameter, is the total time step.

[0270] Weight coefficient and According to the actual situation, it is set to 0.6 and 0.4, indicating that more emphasis is placed on the structural characteristics of the microgrid. The calculated microgrid similarity matrix is ​​shown in Table 3:

[0271] Table 3 Microgrid similarity matrix

[0272]

[0273] As can be seen from Table 3, MG1 and MG5 have the highest similarity, reaching 0.902, while MG2 and MG3 have the lowest similarity, which is 0.692. This result provides a basis for subsequent microgrid clustering and internal optimization.

[0274] 3. Establishing the optimization equation system

[0275] Based on the acquired parameter information, a group of active distribution network stability dispatching equations considering distribution network parameters and a group of microgrid stability dispatching equations considering microgrid internal parameters were constructed.

[0276] The active distribution network stability dispatch equations include:

[0277] (1) Distribution network power balance equation:

[0278] ;

[0279] ;

[0280] (2) Distribution network node voltage equation:

[0281] ;

[0282] ;

[0283] (3) Distribution network line power flow equation:

[0284] ;

[0285] ;

[0286] ;

[0287] The microgrid stability dispatch equations include:

[0288] (1) Microgrid power balance equation:

[0289] ;

[0290] ;

[0291] (2) Microgrid node voltage and frequency equations:

[0292] ;

[0293] ;

[0294] (3) Microgrid equipment output equation:

[0295] ;

[0296] ;

[0297] ;

[0298] ;

[0299] These equations describe the constraints at the active distribution network and microgrid levels, providing a mathematical basis for subsequent optimal scheduling.

[0300] 4. Perform two-layer optimization

[0301] Based on the above set of equations, a two-level optimization model based on the improved grey wolf optimization algorithm and genetic algorithm was constructed.

[0302] The objective function of the outer gray wolf optimization algorithm is:

[0303] ;

[0304] in, is the total operating cost, is the reliability index, It is an environmental indicator.

[0305] The fitness function of the inner genetic algorithm is:

[0306] ;

[0307] in, For microgrids operating costs, is the microgrid reliability index, It is the environmental protection index of microgrid.

[0308] The specific steps of optimization are as follows:

[0309] 1) The outer gray wolf optimization algorithm initializes the population, where Wolf represents the initial dispatching plan of the active distribution network, Wolf and The wolves represent the second-best and third-best solutions, respectively. At the same time, the initial population of the genetic algorithm is set for each microgrid.

[0310] 2) Outer Grey Wolf Algorithm Iterative Update 、 and The location of the wolf optimizes the overall dispatch of the active distribution network.

[0311] 3) For each 、 and The wolf determines the active distribution network plan, and the inner genetic algorithm simultaneously optimizes the scheduling plan of each microgrid. During the genetic algorithm iteration, if a microgrid produces a better individual, it is introduced as a seed into other microgrids with a similarity greater than 0.8, replacing the individual with the lowest fitness.

[0312] 4) The inner genetic algorithm continues to iterate until it reaches 100 times or converges. Each microgrid selects the individual with the highest fitness as the optimal scheduling solution.

[0313] 5) Integrate the optimal scheduling plans of all microgrids to form a complete active distribution network scheduling plan.

[0314] 6) The outer gray wolf algorithm continues to iterate until the termination condition is met.

[0315] After multiple iterations of optimization, a relatively optimal coordinated dispatching scheme for the active distribution network and the microgrid is finally obtained. The dispatching results at the active distribution network level are shown in Table 4, and the dispatching results at the microgrid level are shown in Table 5.

[0316] Table 4 Active distribution network dispatch results

[0317]

[0318] Table 5 Microgrid dispatch results

[0319]

[0320] As can be seen from Table 4, at the active distribution network level, the active and reactive power injection, voltage amplitude, and phase angle of each node are optimized. As can be seen from Table 5, at the microgrid level, the distributed generation output, energy storage charging and discharging, controllable loads within each microgrid, and energy exchange between microgrids are optimized. Figure 2 The voltage variation characteristics of each node in the active distribution network within 24 hours are shown. It can be seen that the voltage is basically controlled within the range of 0.95~1.05pu, meeting the voltage limit requirements. Figure 3 The results show the power exchanged between the five microgrids and the main grid over a 24-hour period. The energy exchange between the microgrids ranged from -2MW to 2MW, which is consistent with the capacity constraints of the tie lines. Through coordinated optimization at the active distribution network and microgrid levels, a scheduling solution with superior overall performance was ultimately achieved.

[0321] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A method for optimizing active distribution network scheduling involving a microgrid group, characterized in that: The following steps are involved: S10, obtaining distribution network parameters and microgrid parameters of each microgrid in the active distribution network; S20, using a preset microgrid similarity function to calculate the similarity between any two microgrids; S30, establishing an active distribution network stability dispatch equation group considering distribution network parameters and a microgrid stability dispatch equation group considering microgrid parameters; S40, constructing a two-layer optimization model based on a gray wolf hunting optimization algorithm and a genetic algorithm, wherein the gray wolf hunting optimization algorithm is used for active distribution network optimization, and the genetic algorithm is used for each microgrid optimization; while initializing the gray wolf population, an initial population of the genetic algorithm is set for each microgrid, representing various possible scheduling schemes within the microgrid; S50, performing outer optimization, updating the positions of α, β, and δ wolves in the gray wolf hunting optimization algorithm, representing the optimization of the active distribution network scheduling plan, and at the same time, performing inner optimization for each active distribution network scheduling plan determined by α, β, and δ wolves, iteratively executing, and finally obtaining an optimal complete active distribution network scheduling plan as the target scheduling plan; The position of the α wolf in the gray wolf hunting optimization algorithm represents the initial scheduling plan of the active distribution network, the β wolf represents the second best scheduling plan of the active distribution network, and the δ wolf represents the third best scheduling plan of the active distribution network; The fitness function of the genetic algorithm is specifically a comprehensive performance indicator within the microgrid, which is used to represent the quality of the microgrid scheduling plan, including the weighted sum of economic, reliability and environmental factors. The inner layer optimization steps are specifically: synchronously executing the genetic algorithm iteration on each microgrid, including a selection operation using the roulette method, a single point crossover operation, and a uniform mutation operation; if a microgrid produces a better individual, it is used as a seed individual, and the seed individual is introduced into the population of microgrids whose similarity with the microgrid is greater than a preset similarity threshold, replacing the individual with the lowest fitness; The scheduling scheme of each microgrid is updated iteratively until the preset number of iterations or convergence conditions are reached; for each microgrid, the individual with the highest fitness is selected as the optimal scheduling scheme for the microgrid; the optimal scheduling schemes of all microgrids are combined to form a complete active distribution network scheduling scheme.

2. The method for optimizing active distribution network scheduling including a microgrid group according to claim 1, characterized in that: The distribution network parameters include network topology, line impedance, transformer parameters, voltage limit, and power limit.

3. The method for optimizing active distribution network scheduling including a microgrid group according to claim 1, characterized in that: The microgrid parameters include the capacity of distributed power generation equipment, the capacity of energy storage equipment, the controllable load capacity, and the parameters of the interconnection lines between microgrids.

4. The method for optimizing active distribution network scheduling including a microgrid group according to claim 1, characterized in that: The active distribution network stable dispatch equation group includes a distribution network power balance equation, a distribution network node voltage equation and a distribution network line power flow equation.

5. The method for optimizing active distribution network scheduling including a microgrid group according to claim 1, characterized in that: The microgrid stable scheduling equation group includes a microgrid power balance equation, a microgrid node voltage equation, a microgrid power generation equipment output equation, a microgrid energy storage equipment charge and discharge equation, and a microgrid controllable load regulation equation.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, they are used to execute the active distribution network scheduling optimization method including a microgrid group according to any one of claims 1 to 5.

7. An active distribution network scheduling optimization system including a microgrid group, characterized in that: Contains the computer-readable storage medium of claim 6.

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