Active distribution network scheduling optimization method comprising micro-grid group, medium and system

Abstract

The invention provides an active distribution network scheduling optimization method comprising a micro-grid group, a medium and a system, and belongs to the technical field of active distribution network scheduling, and the method comprises the steps: obtaining an active distribution network parameter and a micro-grid parameter of each micro-grid in an active distribution network; calculating the similarity between any two micro-grids by using a preset micro-grid similarity function; establishing an active power distribution network stable scheduling equation set considering the power distribution network parameters and a micro-grid stable scheduling equation set considering the micro-grid parameters; a double-layer optimization model based on a grey wolf hunting optimization algorithm and a genetic algorithm is constructed, the grey wolf hunting optimization algorithm is used for active power distribution network optimization, and the genetic algorithm is used for microgrid optimization; when the grey wolf population is initialized, setting an initial population of a genetic algorithm for each micro-grid to represent various possible scheduling schemes in the micro-grid; by not executing outer-layer optimization and inner-layer optimization, the problem that the characteristics of the whole active power distribution network and the interior of the micro-grid are difficult to consider in the prior art 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 continuous increase of user-side loads, the traditional passive distribution network can no longer meet the needs of the current power system. Active distribution network is a new type of power transmission and distribution system that has emerged in recent years. It can actively sense and respond to changes in the system and improve the flexibility and reliability of the system by coordinating and dispatching distributed generation, energy storage and controllable loads. Compared with traditional distribution networks, active distribution networks have more complex topological structures and operating characteristics. In active distribution networks, microgrids, as an important distributed power supply unit, can improve the quality of electric energy, reduce transmission and distribution losses, and promote the local consumption of renewable energy. Microgrids are composed of distributed generation units, energy storage equipment and controllable loads, and can operate independently or be interconnected with the main grid. Microgrids have the characteristics of autonomous operation and can make active decisions and dispatch according to their own loads, power generation and energy storage. At present, the coordinated optimization 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 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 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, most of the existing research is based on a single level of optimization, which makes it difficult to take into account the characteristics of the active distribution network as a whole and the internal characteristics of the microgrid. There is a complex interactive coupling relationship between the active distribution network and the microgrid, which requires coordinated optimization at two levels. On the one hand, the overall scheduling plan of the active distribution network will affect the operating status of each microgrid; on the other hand, the scheduling strategy within the microgrid will in turn affect the performance of the active distribution network. Therefore, the optimization method based on only a single level is often difficult 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 of the 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: A first aspect of the present invention provides an active distribution network scheduling optimization method including a microgrid group, which includes the following steps: S10, obtaining active 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 a group of stable dispatch equations for an active distribution network taking into account distribution network parameters and a group of stable dispatch equations for a microgrid taking into account 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, setting an initial population of the genetic algorithm for each microgrid, representing various possible scheduling schemes within the microgrid; S50, perform outer optimization, update the positions of α, β and δ wolves in the gray wolf hunting optimization algorithm, which represents 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.

[0006] 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: The distribution network parameters include network topology, line impedance, transformer parameters, voltage limit, and power limit.

[0007] The microgrid parameters include the capacity of distributed power generation equipment, the capacity of energy storage equipment, the capacity of controllable loads, and the parameters of interconnection lines between microgrids.

[0008] The active distribution network stability dispatch 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 dispatch equation group includes the following equations: 1. Distribution network power balance equation: ; ; In the formula, 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.

[0009] 2. Distribution network node voltage equation: ; ; In the formula, and are the lower and upper limits of the voltage amplitude respectively; and are the lower and upper limits of the phase angle respectively.

[0010] 3. Distribution network line power flow equation: ; ; ; In the formula, and Line At the moment Active and reactive power flows; For line At the moment The apparent power of For line The maximum allowed transmission capacity.

[0011] Parameter acquisition method: 1. Network topology and line parameters ( and ) is obtained through design documents and actual measurements of the distribution network.

[0012] 2. Load data ( and ) is obtained through load forecasting methods or historical data statistics.

[0013] 3. Voltage and power limits ( , , etc.) is determined according to the power system operation standards and equipment specifications.

[0014] The microgrid stable dispatch equation group includes 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 stable dispatch equation group includes the following equations: 1. Microgrid power balance equation: ; ; In the formula, 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 respectively the active and reactive power exchanged with the outside; For microgrid The internal node collection.

[0015] 2. Microgrid node voltage equation: ; ; In the formula, For microgrid At the moment The voltage amplitude of For microgrid 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.

[0016] 3. Output equation of microgrid power generation equipment: ; ; ; ; In the formula, and are the minimum and maximum active output of distributed generation, respectively; and are the minimum and maximum reactive power output respectively; and They are respectively the ascending and descending climbing rate limits.

[0017] 4. Microgrid energy storage equipment charging and discharging equations: ; ; , when ; , when ; In the formula, and are the minimum and maximum charge and discharge power of the energy storage device, respectively; For energy storage equipment at all times The state of charge; and are the lower and upper limits of the state of charge, respectively; and are the charging and discharging efficiencies, respectively; is the energy storage capacity; is the time step.

[0018] 5. Microgrid controllable load regulation equation: ; ; In the formula, 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.

[0019] Parameter acquisition method: 1. Distributed power generation equipment parameters ( , , , etc.) through equipment specifications and historical operating data.

[0020] 2. Energy storage equipment parameters ( , , , , etc.) are obtained through equipment specifications and actual testing.

[0021] 3. Controllable load parameters ( , , ) is determined through load characteristic analysis and user demand.

[0022] 4. Voltage and frequency limits ( , , , ) is determined according to the microgrid operation standards.

[0023] 5. Power exchange limit ( , ) is determined according to the capacity and agreement of the interconnection lines between microgrids.

[0024] Among them, the position of wolf α in the grey wolf hunting optimization algorithm represents the initial scheduling plan of the active distribution network, wolf β represents the suboptimal scheduling plan of the active distribution network, and wolf δ represents the third-optimal scheduling plan of the active distribution network.

[0025] Furthermore, the fitness function of the genetic algorithm is specifically a comprehensive performance index within the microgrid, which is used to indicate the quality of the microgrid scheduling scheme, including the weighted sum of economic, reliability and environmental protection factors.

[0026] Furthermore, the steps of the inner layer optimization are specifically: synchronously executing 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; updating the scheduling plan of each microgrid through iteration until a preset number of iterations or convergence conditions are reached; for each microgrid, selecting the individual with the highest fitness as the optimal scheduling plan for the microgrid; combining the optimal scheduling plans of all microgrids to form a complete active distribution network scheduling plan.

[0027] Furthermore, the dispatching scheme specifically refers to the operation strategy of the active distribution network and each microgrid in a specific time period, including the following aspects: 1. Active distribution network level: : No. A distribution network node at time Active power injection; : No. A distribution network node at time Reactive power injection; : No. A distribution network node at time The voltage amplitude of : No. A distribution network node at time The phase angle.

[0028] 2. Microgrid level: : No. The distributed generation equipment in a microgrid is contribution; : No. The energy storage device in the microgrid is The charge and discharge power; : No. The controllable load in a microgrid is Power consumption; : No. The microgrid and The microgrids are of switching power.

[0029] The mathematical expression of the scheduling scheme is: ; in, Indicates the node number of the distribution network. Indicates the microgrid number, Represents a time series.

[0030] 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 microgrid. The specific expression is as follows: ; In the formula, For microgrid and microgrids The overall similarity of is the structural similarity; is the similarity of operating characteristics; and is the weight coefficient, and .

[0031] The structural similarity is calculated as follows: ; In the formula, 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 parameters.

[0032] The running characteristic similarity is calculated as follows: ; In the formula, and Represents microgrid and microgrids At the moment The net load curve, is the standard deviation parameter, is the total time step.

[0033] Parameter acquisition method: 1. Capacity parameters Obtained through microgrid design documents and equipment specifications.

[0034] 2. Net load curve Obtained through historical operating data statistics or through load forecasting methods.

[0035] 3. Weight coefficient and It is set by expert experience or determined using the AHP method.

[0036] 4. Standard Deviation Parameter Determined through statistical analysis of historical data.

[0037] 3. Objective function of the gray wolf optimization algorithm Furthermore, the objective function of the Gray Wolf optimization algorithm comprehensively considers economy, reliability and environmental protection, which is specifically expressed as follows: ; In the formula, is the objective function value; is the total operating cost; is a reliability indicator; It is an environmental indicator; , and are the weight coefficients of economy, reliability and environmental protection respectively, and .

[0038] Total operating cost The calculation is as follows: ; In the formula, 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 a microgrid energy storage device; For the The regulation cost function of the controllable load of a microgrid.

[0039] Reliability Index The calculation is as follows: ; In the formula, is the nominal voltage; For the Lines at time The apparent power of For line Rated capacity; is the total number of lines.

[0040] Environmental performance index The calculation is as follows: ; In the formula, For the main network Nodes at time Carbon emission function; For the The distributed generation of microgrids is The carbon emission function.

[0041] Parameter acquisition method: 1. The power generation cost function and carbon emission function are obtained by fitting the historical operation data.

[0042] 2. The operating costs of energy storage equipment and controllable load regulation costs are determined by equipment specifications and market prices.

[0043] 3. Weight coefficient , and Determined by multi-objective decision-making methods (such as AHP).

[0044] 4. Fitness function of genetic algorithm Furthermore, the fitness function of the genetic algorithm is used to evaluate the quality of the internal dispatching scheme of the microgrid, which is specifically expressed as follows: ; In the formula, 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.

[0045] Microgrid operating costs The calculation is as follows: ; In the formula, For the The energy exchange cost of a microgrid with other microgrids or the main grid.

[0046] Microgrid reliability indicators The calculation is as follows: ; In the formula, For microgrid The voltage amplitude of For microgrid frequency; and are the nominal voltage and nominal frequency respectively; For microgrid The upper limit of the power exchange with the outside.

[0047] Microgrid environmental performance indicators The calculation is as follows: ; In the formula, For microgrid Distributed generation at the time The carbon emission function.

[0048] Parameter acquisition method: 1. The distributed generation cost function and carbon emission function are obtained by fitting the equipment performance curve and historical operation data.

[0049] 2. The operating costs of energy storage equipment and controllable load regulation costs are determined by equipment specifications and market prices.

[0050] 3. The cost of energy exchange is determined by energy market prices and trading rules.

[0051] 4. Weight coefficient , and Determined by analytic hierarchy process (AHP).

[0052] 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.

[0053] 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.

[0054] Compared with the prior art, the beneficial effects of the active distribution network scheduling optimization method, medium and system including a microgrid group provided by the present invention are: a double-layer optimization idea is adopted, 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 finally obtains a relatively optimal active distribution network scheduling strategy.

[0055] Specifically, the technical effects of the method of the present invention are mainly reflected in the following aspects: 1) The interactive coupling relationship between the active distribution network and the microgrid is fully utilized to achieve collaborative optimization at two levels. In the outer layer, the improved gray wolf optimization algorithm is used to optimize the overall dispatching scheme of the active distribution network, taking into account indicators such as economy, reliability and environmental protection; in the inner layer, the genetic algorithm is used to optimize the dispatching within each microgrid to meet the operation needs of the microgrid itself. Through this two-layer optimization strategy, both overall and local needs are taken into account, and a more coordinated solution is achieved.

[0056] 2) A new microgrid similarity evaluation method is proposed, which not only considers the structural characteristics of the microgrid, but also its dynamic operation characteristics. This evaluation method can more accurately quantify the degree of difference between microgrids and provide a basis for the clustering and internal optimization of microgrids.

[0057] 3) According to the characteristics of active distribution network and microgrid, the corresponding stable dispatch equations are established respectively. The active distribution network equations describe the constraints such as power balance, voltage limit and line capacity at the distribution network level; the microgrid equations describe the power balance, voltage and frequency limit, and the operating characteristics of various equipment within the microgrid. These equations lay a mathematical foundation for the subsequent optimization dispatch.

[0058] 4) At the optimization algorithm level, the present invention innovatively integrates the gray wolf optimization algorithm and the genetic algorithm, giving full play to the advantages of the two algorithms. The gray wolf algorithm, as the outer optimization algorithm, can effectively explore the global optimal solution of the overall dispatching scheme of the active distribution network; while the genetic algorithm, as the inner optimization algorithm, can find the optimal solution that meets its own needs according to the characteristics of each microgrid. The synergy of the two algorithms improves the overall optimization effect.

[0059] Therefore, the solution of the present invention solves the technical problem that most of the 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

[0060] Figure 1 A flow chart of the method provided by the present invention; Figure 2 It is the voltage characteristic diagram of active distribution network; Figure 3 This is the power exchange characteristic diagram of the microgrid. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0062] like Figure 1 As shown, it is a flow chart of an active distribution network scheduling optimization method including a microgrid group provided by the first aspect of the present invention, and the method comprises the following steps: 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 power generation equipment capacity, energy storage equipment capacity, controllable load capacity, and interconnection line parameters between microgrids; S20, using a preset microgrid similarity function to calculate the similarity between any two microgrids; S30, establishing an active distribution network stable dispatching equation group considering distribution network parameters and a microgrid stable dispatching equation group considering microgrid parameters; the active distribution network stable dispatching 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 stable dispatching 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 charging and discharging equation and a microgrid controllable load regulation equation; S40, constructing 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; initializing 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 suboptimal active distribution network scheduling plan, and the δ wolf represents the third-optimal active distribution network scheduling plan; at the same time, setting the initial population of the genetic algorithm for each microgrid, representing 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 pros and cons of the microgrid scheduling plan, including the weighted sum of economic, reliability and environmental protection factors; 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, the steps of the inner optimization are: synchronously perform genetic algorithm iteration on each microgrid, including selection operation, single point crossover operation and uniform mutation operation using roulette method; 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.

[0063] The specific implementation methods of the above steps are described in detail below: First, in step S10, the key parameters of the active distribution network and each microgrid need to be obtained. For the active distribution network, the parameters that need to be obtained include: network topology ,in is a node set, is the line collection; line impedance , indicating line Resistance and reactance; Transformer parameters , respectively represent the Transformer ratio and capacity upper limit; voltage limit ;Power limit , respectively represent nodes For each microgrid For example, 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 Maximum exchange power between.

[0064] In step S20, it is necessary to use a 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: ; in, and is the weight coefficient, .

[0065] Structural similarity The calculation formula is: ; 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.

[0066] Similarity of operating characteristics The calculation formula is: ; 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.

[0067] The overall similarity between microgrids is obtained by calculating the structural similarity and operating characteristic similarity and taking weighted sum. , used for clustering of microgrids in subsequent optimal scheduling.

[0068] In step S30, it is necessary to establish an active distribution network stability dispatching equation group considering distribution network parameters and a microgrid stability dispatching equation group considering microgrid parameters.

[0069] The active distribution network stable dispatch equations include the following equations: 1. Distribution network power balance equation: ; ; 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.

[0070] 2. Distribution network node voltage equation: ; ; in, and are the lower and upper limits of the voltage amplitude respectively; and are the lower and upper limits of the phase angle, respectively. These equations constrain the feasible domain of node voltages.

[0071] 3. Distribution network line power flow equation: ; ; ; in, and Line At the moment The active and reactive power flows; For line At the moment The apparent power; For line These equations describe the mathematical relationships for line power flow and place limits on line capacity.

[0072] The microgrid stable dispatch equations include the following equations: 1. Microgrid power balance equation: ; ; 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 respectively the active and reactive power exchanged with the outside; For microgrid Internal node set. These equations describe the power balance relationship inside the microgrid.

[0073] 2. Microgrid node voltage equation: ; ; in, For microgrid At the moment The voltage amplitude; For microgrid 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. These equations constrain the voltage and frequency of the microgrid.

[0074] 3. Microgrid power generation equipment output equation: ; ; ; ; in, and are the minimum and maximum active output of distributed generation, respectively; and are the minimum and maximum reactive power output respectively; and These equations constrain the output characteristics of distributed generation equipment within the microgrid.

[0075] 4. Microgrid energy storage equipment charging and discharging equations: ; ; , when ; , when ; in, and are the minimum and maximum charging and discharging power of the energy storage device, respectively; For energy storage equipment at all times The state of charge; and are the lower and upper limits of the state of charge, respectively; and are the charging and discharging efficiencies, respectively; is the energy storage capacity; These equations describe the charging and discharging characteristics of the energy storage devices inside the microgrid.

[0076] 5. Microgrid controllable load regulation equation: ; ; 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.

[0077] By establishing these sets of equations, the constraints at the active distribution network and microgrid levels are described, laying a mathematical foundation for subsequent optimal scheduling.

[0078] 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 the active distribution network optimization scheduling, and the inner genetic algorithm is used for the internal optimization of each microgrid.

[0079] Specifically, the objective function of the gray wolf optimization algorithm comprehensively considers economy, reliability and environmental protection, and can be expressed as: ; 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 .

[0080] Total operating cost The calculation is as follows: ; 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 a microgrid energy storage device; For the The regulation cost function of the controllable load of a microgrid.

[0081] Reliability Index The calculation is as follows: ; in, is the nominal voltage; For the Lines at time The apparent power; For line Rated capacity; is the total number of lines.

[0082] Environmental performance index The calculation is as follows: ; in, For the main network Nodes at time Carbon emission function; For the The distributed generation of microgrids is The carbon emission function.

[0083] For the inner genetic algorithm, its fitness function can be expressed as: ; in, For the The fitness function value of a microgrid; Cost of running 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.

[0084] Microgrid operating costs The calculation is as follows: ; in, For the The energy exchange cost of a microgrid with other microgrids or the main grid.

[0085] Microgrid reliability indicators The calculation is as follows: ; in, For microgrid The voltage amplitude; For microgrid frequency; and are the nominal voltage and nominal frequency respectively; For microgrid The upper limit of the power exchange with the outside.

[0086] Microgrid environmental performance indicators The calculation is as follows: ; in, For microgrid Distributed generation at the time The carbon emission function.

[0087] 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 plan, and the inner genetic algorithm synchronously optimizes the dispatching plan of each microgrid. When the genetic algorithm iterates, if a microgrid produces a better individual, it will be introduced as a seed to other microgrids with greater similarity, replacing the individual with the lowest fitness, so as to achieve the propagation of excellent solutions among microgrids.

[0088] Through this two-layer optimization approach, we not only consider the overall optimization of the active distribution network, but also take into account the characteristics of each microgrid, and finally obtain a relatively optimal active distribution network scheduling strategy.

[0089] In step S50, a 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.

[0090] For each , and The active distribution network dispatching scheme determined by the wolf synchronously executes the optimization of the genetic algorithm in the inner layer. The specific iterative steps of the genetic algorithm are as follows: 1. For each microgrid Perform genetic algorithm selection, crossover, and mutation operations, including: The roulette 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.

[0091] 2. If a microgrid In the iterative process, better individuals are generated and used as seed individuals. They are introduced 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.

[0092] 3. Continue iterating the optimization until the preset maximum number of iterations (such as 100 times) or convergence condition is reached.

[0093] 4. For each microgrid ,The individual with the highest fitness is selected as the optimal scheduling scheme of the microgrid.

[0094] 5. Integrate the optimal dispatching plans of all microgrids to form a complete active distribution network dispatching plan.

[0095] After the coordinated optimization of the outer layer grey wolf algorithm and the inner layer genetic algorithm, a relatively optimal active distribution network dispatching strategy is finally obtained, which not only takes into account the overall economy, reliability and environmental protection, but also takes into account the characteristics of each microgrid. This two-layer optimization method gives full play to the advantages of the grey wolf algorithm and the genetic algorithm, and provides an effective solution for the coordinated optimization of active distribution networks and microgrids.

[0096] 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.

[0097] 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.

[0098] Specifically, the principle of the present invention is: First, it is necessary to obtain the key parameter information of the active distribution network and each microgrid. For the active distribution network, it is necessary to obtain the network topology, line parameters, transformer parameters, and voltage and power limits; for each microgrid, it is necessary to obtain the distributed generation capacity, energy storage capacity, controllable load capacity, and interconnection line parameters between microgrids. These parameters lay the foundation for subsequent optimization and scheduling.

[0099] Secondly, a new method for evaluating the similarity of microgrids is proposed. This method not only takes into account the structural characteristics of microgrids, such as the differences in the capacity of various types of equipment, but also the dynamic operating characteristics of microgrids, such as the similarity of net load curves. This comprehensive similarity evaluation can more accurately quantify the differences between microgrids and provide a basis for the clustering and internal optimization of microgrids.

[0100] Next, the stable dispatching equations of the active distribution network considering the distribution network parameters and the stable dispatching equations of the microgrid considering the internal parameters of the microgrid were established. The former describes the constraints such as power balance, voltage limit and line capacity at the distribution network level; the latter describes the power balance, voltage and frequency limit inside the microgrid, as well as the operating characteristics of various equipment. Through these two sets of equations, the constraints at the active distribution network and microgrid levels are mathematically transformed, providing a basis for subsequent optimization scheduling.

[0101] Finally, a two-layer optimization model based on the improved gray wolf optimization algorithm and genetic algorithm was constructed. In the outer layer, the gray wolf algorithm optimizes the overall dispatching scheme of the active distribution network, taking into account indicators such as economy, reliability and environmental protection; in the inner layer, the genetic algorithm optimizes the dispatching scheme within each microgrid to meet the operation requirements of the microgrid itself. The synergy of the two algorithms gives full play to their respective advantages.

[0102] Specifically, the outer optimization of the gray wolf algorithm can effectively explore the global optimal solution of the overall dispatching plan of the active distribution network, while the inner optimization of the genetic algorithm can find the optimal solution that meets its own needs according to the characteristics of each microgrid. During the iteration process of the inner genetic algorithm, if a microgrid produces a better individual, it will be introduced as a seed into other microgrids with greater similarity, realizing the propagation of excellent solutions among microgrids and accelerating the overall convergence.

[0103] Through this double-layer optimization strategy, the overall performance indicators of the active distribution network are taken into account, while the characteristics of each microgrid are taken into account, and a more coordinated solution is achieved. This not only improves the overall operating efficiency and flexibility of the active distribution network, but also meets the needs of the microgrid itself, and realizes the coordinated optimization of the active distribution network and the microgrid.

[0104] In order to better understand and implement the present invention, an embodiment of a specific application scenario of the present invention is provided below: 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 optimization scheduling. The following takes the specific situation of the region as an example to illustrate how to use the method proposed in the present invention to optimize the scheduling of active distribution networks and microgrids.

[0105] The active distribution network in this area has 10 nodes and the total length of the line is 50 kilometers. The main parameters are shown in Table 1: Table 1 Active distribution network parameters

[0106] There are five microgrids MG1~MG5 connected to the area, and the main parameters of each microgrid are shown in Table 2: Table 2 Microgrid parameters

[0107] The relevant functions of the microgrid are as follows: Power generation cost function: ; Energy storage cost function: ; Controllable load adjustment cost: ; Carbon emission function: ; According to the above parameters, the method proposed by the present invention is used to perform coordinated optimization scheduling of active distribution network and microgrid. The specific steps are as follows: 1. Obtain active distribution network and microgrid parameters First, the key parameters of the active distribution network and the five microgrids were obtained, as shown in Tables 1 and 2. These parameters cover the network topology, line parameters, transformer parameters, voltage and power limits, as well as the distributed generation capacity, energy storage capacity, controllable load capacity and tie line capacity of each microgrid. These data lay the foundation for subsequent optimization and dispatching.

[0108] 2. Calculate microgrid similarity Next, the microgrid similarity function proposed in the present invention is used to calculate the similarity between any two microgrids. The similarity function includes two aspects: structural similarity and operating characteristic similarity.

[0109] Structural similarity The calculation formula is: ; 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 parameters.

[0110] Similarity of operating characteristics The calculation formula is: ; in, and Represents microgrid and At the moment The net load curve, is the standard deviation parameter, is the total time step.

[0111] Weight coefficient and According to the actual situation, it is set to 0.6 and 0.4, indicating that more attention is paid to the structural characteristics of the microgrid. The calculated microgrid similarity matrix is ​​shown in Table 3: Table 3 Microgrid similarity matrix

[0112] 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 the subsequent microgrid clustering and internal optimization.

[0113] 3. Establish the optimization equation set Based on the acquired parameter information, a group of stable dispatching equations for active distribution network considering distribution network parameters and a group of stable dispatching equations for microgrid considering internal parameters of microgrid were constructed.

[0114] The active distribution network stable dispatch equations include: (1) Distribution network power balance equation: ; ; (2) Distribution network node voltage equation: ; ; (3) Distribution network line power flow equation: ; ; ; The microgrid stable dispatch equations include: (1) Microgrid power balance equation: ; ; (2) Microgrid node voltage and frequency equations: ; ; (3) Microgrid equipment output equation: ; ; ; ; These equations describe the constraints at the active distribution network and microgrid levels, providing a mathematical basis for subsequent optimal scheduling.

[0115] 4. Perform two-layer optimization 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.

[0116] The objective function of the outer gray wolf optimization algorithm is: ; in, is the total operating cost, is the reliability index, It is an environmental indicator.

[0117] The fitness function of the inner genetic algorithm is: ; in, For microgrid The operating cost, is the microgrid reliability index, It is the environmental protection index of microgrid.

[0118] The specific steps of optimization are as follows: 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.

[0119] 2) Outer Grey Wolf Algorithm Iterative Update , and The location of the wolf optimizes the overall dispatching of the active distribution network.

[0120] 3) For each , and The wolf determines the active distribution network plan, and the inner genetic algorithm synchronously optimizes the scheduling plan of each microgrid. During the iteration of the genetic algorithm, if a microgrid produces a better individual, it will be introduced as a seed into other microgrids with a similarity greater than 0.8 to replace the individual with the lowest fitness.

[0121] 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.

[0122] 5) Integrate the optimal dispatching plans of all microgrids to form a complete active distribution network dispatching plan.

[0123] 6) The outer grey wolf algorithm continues to iterate until the termination condition is met.

[0124] After multiple iterations of optimization, a relatively optimal coordinated dispatching scheme for active distribution network and 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.

[0125] Table 4 Active distribution network dispatch results

[0126] Table 5 Microgrid dispatch results

[0127] As can be seen from Table 4, at the level of active distribution network, 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 level of microgrid, the distributed generation output, energy storage charging and discharging, controllable load and energy exchange between microgrids within each microgrid 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 power changes of the five microgrids exchanged with the main grid within 24 hours are shown. The energy exchange between the microgrids is in the range of -2MW~2MW, which meets the capacity limit of the interconnection line. Through the coordinated optimization of the active distribution network and the microgrid level, a dispatching scheme with better comprehensive performance is finally obtained.

[0128] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An active distribution network scheduling optimization method including a microgrid group, characterized in that: The following steps are involved: S10, obtaining active 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 a group of stable dispatch equations for an active distribution network taking into account distribution network parameters and a group of stable dispatch equations for a microgrid taking into account 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, setting an initial population of the genetic algorithm for each microgrid, representing various possible scheduling schemes within the microgrid; S50, perform outer optimization, update the positions of α, β and δ wolves in the gray wolf hunting optimization algorithm, which represents 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.

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 capacity of controllable loads, and the parameters of 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 dispatch 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. The method for optimizing active distribution network scheduling including a microgrid group according to claim 1, characterized in that: The position of the α wolf in the grey wolf hunting optimization algorithm represents the initial dispatching plan of the active distribution network, the β wolf represents the suboptimal dispatching plan of the active distribution network, and the δ wolf represents the third-optimal dispatching plan of the active distribution network.

7. The method for optimizing active distribution network scheduling including a microgrid group according to claim 6, characterized in that: The fitness function of the genetic algorithm is specifically a comprehensive performance index within the microgrid, which is used to indicate the quality of the microgrid dispatching scheme, including the weighted sum of economic, reliability and environmental protection factors.

8. The method for optimizing active distribution network scheduling involving a microgrid group according to claim 7, characterized in that: The inner layer optimization steps are specifically: synchronously executing genetic algorithm iterations on each microgrid, including a selection operation using a roulette wheel 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 a microgrid whose similarity with the microgrid is greater than a preset similarity threshold, replacing the individual with the lowest fitness; The scheduling plan 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 plan for the microgrid; the optimal scheduling plans of all microgrids are combined to form a complete active distribution network scheduling plan.

9. 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 involving a microgrid group as described in any one of claims 1 to 8.

10. An active distribution network scheduling optimization system including a microgrid group, characterized in that: A computer-readable storage medium comprising the computer-readable storage medium of claim 9.

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