Multi - microgrid group planning method, device and electronic equipment

Abstract

The present invention provides a multi-microgrid group planning method, device, and electronic device. The method includes: obtaining multiple typical scenarios of the target power system based on a pre-established wind-solar combined output model of the target power system; establishing a microgrid configuration model of the target power system under the multiple typical scenarios based on a first preset objective function and a first preset constraint; obtaining capacity configuration information of each microgrid in the target power system based on the microgrid configuration model; aggregating the microgrids in the target power system based on the capacity configuration information of each microgrid and a preset aggregation method to obtain multiple microgrid groups; establishing a microgrid group configuration model based on a second preset objective function and a second preset constraint; obtaining capacity configuration information of the target microgrid group based on the microgrid group configuration model; and performing capacity configuration on the target microgrid group based on the capacity configuration information of the target microgrid group. The present invention can reduce the operating cost of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrids, and in particular, to a multi-microgrid group planning method, device and electronic device. Background Art

[0002] In recent years, microgrids have been regarded as an effective solution for renewable energy and have become an essential part of the energy Internet, playing an increasingly important role in improving power supply reliability, security, and accommodating renewable energy.

[0003] With the continuous intensification of the promotion of high-proportion new energy, the microgrid side will also experience a new development process, gradually transitioning from the independent operation mode of "single microgrid" to the operation mode of "microgrid group", and the new development path also brings new technical challenges. Currently, the existing microgrid groups do not consider the accommodation of new energy during networking and configuration, resulting in poor economy of the power system. Summary of the Invention

[0004] Embodiments of the present invention provide a multi-microgrid group planning method, device and electronic device to solve the problem of poor economy existing in the power system adopting the microgrid group operation mode in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a multi-microgrid group planning method, device and electronic device, including:

[0006] Obtain multiple typical scenarios of the target power system according to the pre-established wind-solar combined output model of the target power system;

[0007] Establish a microgrid configuration model of the target power system under multiple typical scenarios according to the first preset objective function and the first preset constraint conditions;

[0008] Obtain the capacity configuration information of each microgrid in the target power system according to the microgrid configuration model;

[0009] Aggregate the microgrids in the target power system according to the capacity configuration information of each microgrid and the preset aggregation method to obtain multiple microgrid groups;

[0010] Establish a microgrid group configuration model of the target microgrid group according to the second preset objective function and the second preset constraint conditions, where the target microgrid group is any one of the multiple microgrid groups;

[0011] Obtain the capacity configuration information of the target microgrid group according to the microgrid group configuration model;

[0012] Configure the photovoltaic installed capacity, controllable power source installed power, and energy storage device capacity in the target microgrid group according to the capacity configuration information of the target microgrid group.

[0013] In a second aspect, an embodiment of the present invention provides a multi-microgrid group planning device, including:

[0014] A first acquisition module, configured to obtain multiple typical scenarios of the target power system according to a pre-established wind-solar combined output model of the target power system;

[0015] A first establishment module, configured to establish a microgrid configuration model of the target power system under multiple typical scenarios according to a first preset objective function and a first preset constraint condition;

[0016] A second acquisition module, configured to obtain capacity configuration information of each microgrid in the target power system according to the microgrid configuration model;

[0017] An aggregation module, configured to aggregate the microgrids in the target power system according to the capacity configuration information of each microgrid and a preset aggregation method to obtain multiple microgrid groups;

[0018] A second establishment module, configured to establish a microgrid group configuration model of the target microgrid group according to a second preset objective function and a second preset constraint condition, where the target microgrid group is any one of the multiple microgrid groups;

[0019] A third acquisition module, configured to obtain capacity configuration information of the target microgrid group according to the microgrid group configuration model;

[0020] A configuration module, configured to configure the photovoltaic installed capacity, controllable power supply installed power, and energy storage device capacity in the target microgrid group according to the capacity configuration information of the target microgrid group.

[0021] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation manner of the first aspect above are implemented.

[0022] An embodiment of the present invention provides a multi - microgrid group planning method, device and electronic device. First, according to the wind - solar combined power output model of the target power system established in advance, multiple typical scenarios of the target power system are obtained; then, according to the first preset objective function and the first preset constraint conditions, a microgrid configuration model of the target power system under multiple typical scenarios is established; next, according to the microgrid configuration model, the capacity configuration information of each microgrid in the target power system is obtained; after that, according to the capacity configuration information of each microgrid and the preset aggregation method, the microgrids in the target power system are aggregated to obtain multiple microgrid groups; then, according to the second preset objective function and the second preset constraint conditions, a microgrid group configuration model of the target microgrid group is established, where the target microgrid group is any one of the multiple microgrid groups; then, according to the microgrid group configuration model, the capacity configuration information of the target microgrid group is obtained; finally, according to the capacity configuration information of the target microgrid group, the photovoltaic installed capacity, the controllable power source installed power and the energy storage device capacity in the target microgrid group are configured. In this way, a multi - microgrid group planning method considering new - energy consumption is provided, which can reduce the investment cost of the power system and improve the economic benefits of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0024] Figure 1 is the implementation flowchart of the multi - microgrid group planning method provided by the embodiment of the present invention;

[0025] Figure 2 is the flowchart of obtaining the typical scenario corresponding to the target power system provided by the embodiment of the present invention;

[0026] Figure 3 is the schematic flowchart of aggregating microgrids into microgrid groups provided by the embodiment of the present invention;

[0027] Figure 4 is the schematic structural diagram of the multi - microgrid group planning device provided by the embodiment of the present invention;

[0028] Figure 5 is the schematic diagram of the electronic device provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from hindering the description of the present invention.

[0030] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will be described through specific embodiments in conjunction with the accompanying drawings.

[0031] As described in the related art, in recent years, microgrids have been regarded as an effective solution for renewable energy and have become an essential part of the energy Internet, playing an increasingly important role in improving power supply reliability, security, and accommodating renewable energy.

[0032] With the continuous intensification of the promotion of a high proportion of new energy, the microgrid side will also undergo a new development process. It will gradually transition from the independent operation mode of "single microgrid" to the operation mode of "microgrid cluster", and the new development path also brings new technical challenges. Currently, the existing microgrid clusters do not consider the accommodation of renewable energy during networking and configuration, resulting in poor economy of the power system.

[0033] To solve the problems of the existing technology, embodiments of the present invention provide a multi-microgrid clustering planning method, device, and electronic device. First, the multi-microgrid clustering planning method provided by the embodiments of the present invention will be introduced below.

[0034] As Figure 1 shown, the multi-microgrid clustering planning method provided by the embodiments of the present invention includes the following steps:

[0035] S101. Obtain multiple typical scenarios of the target power system according to the pre-established combined wind and solar power output model of the target power system.

[0036] In some embodiments, the specific processing of step S101 can be as follows:

[0037] Obtain the weather load data of the preset time period in the area where the target power system is located;

[0038] Construct a combined wind, solar, and load power output probability distribution model corresponding to the target power system according to the weather load data;

[0039] Establish a combined wind and solar power output probability distribution model according to the combined wind, solar, and load power output probability distribution model;

[0040] Obtain multiple typical scenarios of the target power system according to the combined wind and solar power output probability distribution model, the preset sampling algorithm, and the preset clustering algorithm.

[0041] In some embodiments, according to the weather load data of the past three years in the region where the target power system is located, a non-parametric kernel density estimation method is used to construct the probability distribution model of the output of wind, light, and load at each moment. By calculating the Kendall rank correlation coefficient as the correlation measure of the output of wind power plants and photovoltaic power generation, and using the Frank Copula function to depict the joint probability distribution curve of the output of wind farms and photovoltaic power plants, a combined wind and light output model is established.

[0042] Specifically, the kernel density estimation formula is:

[0043]

[0044] Where is the kernel density function of x; x j represents any sample in the random variable x; n is the sample size; h is the smoothing coefficient; is the kernel function, and usually a symmetric unimodal probability density function centered at 0 is selected.

[0045] Referring to the above kernel density estimation formula, the actually measured output data is converted into an output rate, normalized, and the output rates of wind farms and photovoltaic power plants are selected as random variables.

[0046] Specifically, the output rate of the wind farm is x1, and its corresponding probability density ; the output rate of the photovoltaic power plant is x2, and its corresponding probability density function ; the output rate of the load is x3, and its corresponding probability density function f L (x3). 、 and are the sample spaces of x1, x2, and x3 respectively, and n is the sample size. Respectively, 、 and are substituted into the kernel density estimation formula, and kernel density estimation is performed to obtain 、 and f L (x3) probability distribution models, that is, the wind-light-load probability distribution models are obtained.

[0047] Specifically, based on the established wind-light-load probability distribution model, according to the Copula theory, the combined wind and light output probability distribution model can be:

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] Among them, and are the cumulative probability distributions of the outputs of the wind farm and the PV power station respectively, τ is the Kendall rank correlation coefficient of the outputs of the wind farm and the PV power station, and the set is the sample space composed of n groups of observation values of the random vector . and are two groups of observation values randomly selected from the set ϕ, and , θ is the relevant parameter of the Frank Copula function, P is the probability of the occurrence of an event, , p is the integration variable, , ; f1(x) and f2(y) represent and respectively.

[0054] Specifically, if , then and are consistent; if , then and are inconsistent.

[0055] In some embodiments, the specific processing of the above step 101 may be as follows: Based on the Monte Carlo sampling method, a plurality of sample points are randomly selected from the combined wind-solar power output probability distribution model to generate a plurality of scenario samples; based on a preset clustering algorithm, the plurality of scenario samples are clustered to obtain a plurality of typical scenarios.

[0056] As Figure 2 shows, it shows a processing flow for obtaining typical scenarios corresponding to a target power system from a combined wind-solar power output probability distribution model.

[0057] In some embodiments, according to the combined wind-solar probability density curve, a certain number of representative planning typical scenarios are obtained through sampling and reduction of scenarios.

[0058] Specifically, when sampling scenarios, the Monte Carlo sampling method is used to randomly select several sample points to generate scenarios.

[0059] In some embodiments, for two-dimensional continuous random variables, assuming that the random variables A and B follow a certain distribution, is the joint probability distribution function of A and B, is the cumulative distribution function of A and B, and are the inverse functions of A and B with respect to the cumulative distribution function respectively.

[0060] The specific processing according to the Monte Carlo sampling method can be as follows:

[0061] At on the z-axis from 0 to 1, n sample values are randomly selected, which are respectively , , …, ;

[0062] According to the obtained random values c k , the corresponding n sample values A k and B k are respectively obtained by using the characteristics of the inverse function, ; , where ;

[0063] If there are m random variables A and B, the above two steps are repeated, and n sample values are respectively sampled for each random variable as a row in two matrices. After all random variables are sampled, two m*n matrices will be obtained;

[0064] Taking the two matrices as the input for subsequent calculations, 2n outputs will be obtained. The two variables respectively correspond to n outputs, and the two sets of new output sample values are used as two sets of sample points generated by sampling.

[0065] Specifically, in the sample points of the sampling-generated scenario, the K-means clustering method is used to extract the scenario features of the sample points and divide the scenarios to form typical scenarios for planning.

[0066] S102. According to the first preset objective function and the first preset constraint conditions, establish a microgrid configuration model of the target power system under multiple typical scenarios.

[0067] It should be noted that the first preset objective function is constructed with the goal of minimizing the total life-cycle cost of the microgrid.

[0068] In some embodiments, the first preset objective function includes:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] Among them, is the total cost of the whole life cycle of the microgrid, is the annual average construction cost of the microgrid, is the annual operation cost of the microgrid, C PV is the investment and maintenance cost of the photovoltaic power station in the microgrid, C F is the investment and maintenance cost of the controllable power source in the microgrid, C ESS is the investment and maintenance cost of the energy storage device in the microgrid, P PV is the total installed capacity of photovoltaic in the microgrid, N PV is the number of photovoltaic panels to be planned, P PV_unit is the rated power of a single photovoltaic panel, P F is the total power of the controllable power source in the microgrid, B ESS is the rated power of the energy storage device in the microgrid, P ESS is the total power of the energy storage device in the microgrid, η PV is the unit capacity cost of photovoltaic, η f is the unit capacity cost of the controllable power source, η B is the unit energy cost of energy storage, η P is the unit capacity cost, η inv is the cost coefficient of the inverter, η D is the equal-valued day factor, d is the discount rate, y is the service life of the equipment, C f (t) is the operation cost of the controllable power source in the microgrid, C ESS (t) is the operation cost of the energy storage in the microgrid, C L (t) is the operation cost of the ordinary load in the microgrid, C DR (t) is the operation cost of the controllable load in the microgrid, a f 、b f 、c fis the operating cost coefficient of the controllable power supply in the microgrid, P F (t) is the output power of the controllable power supply in the microgrid at time t, T is the expected operating period, P ESS (t) is the charging and discharging power of the energy storage in the microgrid at time t, a ess 、b ess 、c ess are the operating cost coefficients of the energy storage in the microgrid, p(t) is the electricity purchase price of the load in the microgrid at time t, q(t) is the total electricity consumption of the users in the microgrid at time t, ΔP DR (t) is the total adjustment amount of the controllable load in the microgrid at time t, a DR is the cost coefficient of the controllable load in the microgrid.

[0082] In some embodiments, the first preset constraint condition includes the microgrid penetration rate constraint, the ramp rate constraint of the distributed power supply in the microgrid, and the peak-valley difference constraint of the microgrid exchange power;

[0083] Specifically, the microgrid penetration rate constraint is:

[0084]

[0085]

[0086]

[0087] Among them, P PV (t) is the photovoltaic output at time t, P F (t) is the output of the controllable power supply at time t, P L (t) is the electricity consumption power of the ordinary charge at time t, P DR (t) is the electricity consumption power of the controllable load at time t, T is the scheduling period, λ is the microgrid penetration rate, P m is the power of the microgrid penetrating into the large power grid, P s is the total power of the power system of the distribution network connected to the microgrid;

[0088] The ramp rate constraint of the distributed power supply in the microgrid is:

[0089]

[0090] Among them, and are the rising ramp rate and the falling ramp rate of the output of the controllable power supply in the microgrid respectively, and are the output power of the controllable power supply in the microgrid at time t-1 and the output power at time t respectively;

[0091]

[0092] Among them, and are the rising ramp rate and the falling ramp rate of the output of the energy storage device in the microgrid respectively, and represent the output power of the controllable power source in the microgrid at time t-1 and time t respectively;

[0093]

[0094] Among them, and represent the rising ramp rate and the falling ramp rate of the output of the PCC node of the microgrid connected to the grid respectively, and represent the output power of the PCC node of the microgrid connected to the grid at time t-1 and time t respectively;

[0095] The peak-valley difference constraint of the microgrid exchange power is:

[0096]

[0097]

[0098] Among them, is the maximum power inverted from the microgrid to the distribution network; is the maximum power absorbed by the microgrid from the distribution network, is the maximum load in the microgrid; The direction of the power transmitted by the microgrid is selected as the positive direction, and the direction of the absorbed power is the negative direction.

[0099] In some embodiments, the first preset constraint condition further includes: the limit constraint of the investment cost of the microgrid system, the constraint of the maximum number of installed photovoltaic units, the power balance constraint, the load power outage rate constraint, the minimum output constraint of the controllable power source, the operation constraint of the energy storage system, the PCC power constraint, the microgrid light curtailment rate constraint.

[0100] Specifically, the limit constraint of the construction cost of the microgrid system is:

[0101]

[0102] Among them, is the annual average construction cost of the microgrid, is the maximum value allowed for the annual average construction cost of the microgrid system.

[0103] Specifically, the constraint of the maximum number of installed photovoltaic units is:

[0104]

[0105] Among them, It represents the total number of photovoltaic investment panels, which is mainly restricted by factors such as the actual site and the investment amount of the microgrid.

[0106] Specifically, the power balance constraint is:

[0107]

[0108] Among them, 、 、 represent the power outputs of photovoltaic, controllable power source, and energy storage device at time t respectively; is the power consumption of ordinary load at time t; represents the power consumption of controllable load within time t; is the missing power at time t.

[0109] Specifically, the load power shortage rate constraint is:

[0110]

[0111]

[0112]

[0113] Among them, T takes the whole year, 8760h; the sampling interval is 1h.

[0114] It should be noted that for the above load power shortage rate constraint, it is set for the power supply reliability of the microgrid. The load power shortage means that the system cannot meet the load power demand within a certain period of time.

[0115] Specifically, the minimum power output constraint of the controllable power source is:

[0116]

[0117]

[0118]

[0119] Among them, is the minimum power output of the controllable power source in the microgrid.

[0120] Specifically, the operation constraint of the energy storage system is:

[0121]

[0122] Among them, represents the power output of the energy storage device at time t; is the maximum power of the energy storage device output.

[0123] Specifically, the PCC power constraint is:

[0124]

[0125] Among them, is the exchanged active power of the PCC at time t, is the lower limit of the exchanged active power of the PCC, is the upper limit of the exchanged active power of the PCC. It should be noted that when , it means that the distribution network injects power into the microgrid; , it means that the microgrid provides power to the distribution network.

[0126] Specifically, the curtailment rate constraint of the microgrid is:

[0127]

[0128] Among them, is the total power generation of the photovoltaic during the operation period T (8760 hours a year), where represents the predicted maximum output of the photovoltaic at time t, measured as a percentage of the rated power of the photovoltaic, is the number of photovoltaic panels to be planned, is the rated power of a single photovoltaic panel; represents the total power generation of the controllable power source during the operation period T; represents the total electricity consumption of the ordinary load during the operation period T, represents the total electricity consumption of the controllable load during the operation period T. Since the energy storage system has equal electricity at the beginning and end of each operation period, the power generation / consumption of the energy storage system is not considered in the above formula.

[0129] To absorb as much renewable energy as possible, the curtailment rate of the microgrid system needs to meet the constraint condition:

[0130]

[0131] Among them, is the upper limit value of the allowable curtailment rate of the microgrid. For example, the curtailment rate can be limited within 5%.

[0132] It should be noted that for the above curtailment rate constraint, it is set for the existing curtailment phenomenon. The curtailment phenomenon refers to the phenomenon that the electricity generated by the photovoltaic in the microgrid is higher than the electricity consumption of its internal load, and when the energy storage battery and the distribution network cannot absorb the remaining electricity, the remaining electricity needs to be discarded.

[0133] S103. According to the microgrid configuration model, obtain the capacity configuration information of each microgrid in the target power system.

[0134] In some embodiments, the capacity configuration information of the microgrid includes: the total installed capacity of photovoltaic power in the microgrid, the total installed power of controllable power sources, and the total capacity of energy storage devices.

[0135] Specifically, the specific process of solving the microgrid configuration model based on the particle swarm algorithm can be as follows:

[0136] Set the time interval of two periods to 1h, and the scheduling period T = 8760;

[0137] Select the total output power P of photovoltaic panels in the microgrid PV , the total power P of controllable power sources in the microgrid F , and the rated capacity B of energy storage devices in the microgrid ESS as particles, and set their initial values to 0;

[0138] Set the initial iteration number k = 1, and the maximum iteration number k max = 80;

[0139] Solve the above cost function for P PV , P F , B ESS , and calculate the particle values at each moment to make them meet the above constraint conditions;

[0140] Update the velocity and position of the particles:

[0141]

[0142]

[0143]

[0144] where k is the iteration number, x k represents the spatial position of the particle at the iteration number k, x k represents the velocity of the particle at the iteration number k, c1 and c2 are learning factors, with values of 1 and 0.8 respectively, r1 and r2 are random numbers uniformly distributed between (0, 1), the inertia weight is set to 0.6, p bestk is the self-optimal solution of the particle at the k-th iteration, and g bestk is the global optimal solution of the particle at the k-th iteration;

[0145] If k > k max , then output the total output power of photovoltaic panels in the microgrid, the total power of controllable power sources in the microgrid, and the rated capacity of energy storage devices in the microgrid, that is, output the final result of the microgrid optimal configuration; otherwise, let k = k + 1, and return to the above solution for P PV , P F , B ESSThe cost function, and calculate the particle values at each moment to satisfy the above constraint conditions.

[0146] Specifically, the capacity configuration information of each microgrid in the target power system includes: the total installed capacity of photovoltaic in the microgrid, the total installed power of controllable power sources, and the total capacity of energy storage devices.

[0147] S104. Aggregate the microgrids in the target power system according to the capacity configuration information of each microgrid and a preset aggregation method to obtain multiple microgrid groups.

[0148] As Figure 3 shown, it shows a processing flow of aggregating microgrids to obtain microgrid groups.

[0149] In some embodiments, the specific processing of the above step 104 can be as follows:

[0150] Based on the capacity configuration information of each microgrid, calculate the node coupling degree of the distribution network connected to each microgrid;

[0151] According to the node coupling degree, divide the microgrids in the target power system into multiple microgrid areas;

[0152] Obtain the total value of the improvement degree of the preset index corresponding to different microgrid combination methods in the same microgrid area;

[0153] Determine the microgrid combination under the microgrid combination method with the largest total improvement degree value as multiple microgrid groups.

[0154] In some embodiments, the specific processing of calculating the node coupling degree of the distribution network connected to each microgrid based on the capacity configuration information of each microgrid can be as follows:

[0155]

[0156] Among them, S VQ is the mathematical matrix of sensitivity, ΔV is the voltage difference between two nodes of the distribution network, and ΔQ is the difference in reactive power injection between two nodes of the distribution network. The ratio of the injected node reactive power change amount to the node voltage change amount represents the coupling relationship between two nodes. If the ratio is larger, the interaction relationship between the two nodes is less close, and then the electrical distance between the two nodes is farther; conversely, if the ratio is smaller, the influence of reactive power on voltage is larger, the electrical distance between the two nodes is smaller, and their relationship is closer.

[0157]

[0158]

[0159] Among them, L ijis the electrical distance between node i and node j, which is used to characterize the closeness between connection points in a power line.

[0160]

[0161] Among them, ωL is the node coupling degree, i and j are node numbers in the distribution network; Lij is the electrical distance between node i and j; µ is a constant, taking 1 when the load / power source types between node i and j are the same, and taking 0 otherwise.

[0162] In some embodiments, the specific process of dividing the microgrid in the target power system into multiple microgrid areas according to the node coupling degree can be as follows:

[0163] S1. Calculate the electrical distance L between each pair of nodes according to the above formula ij ;

[0164] S2. Find the maximum electrical distance and the minimum electrical distance L min , and take the step size ;

[0165] S3. Calculate the node coupling degree of the load nodes according to the electrical distance L ij and sort them from largest to smallest;

[0166] S4. Take the PV node (the node with reactive power reserve) as the central point i;

[0167] S5. Through continuous search and iteration, merge the nodes in the system whose electrical distance is greater than the partition reference value r until a partition is formed where the electrical distance between the merged nodes is less than the partition reference value r;

[0168] S6. Repeat S4 and S5 to form n pv partitions centered on the PV node;

[0169] S7. Take the node with the largest node coupling degree among the remaining load nodes as the central node, and repeat S5 to obtain the partition centered on this load node;

[0170] S8. Repeat S7 to obtain the partition of the power system;

[0171] S9. Analyze the specific problems that occur during the partitioning process, adjust and determine the partitions so that the number of partitions is equal to the sum of the number of PV nodes and the number of coupling nodes. The node with the largest node coupling degree in each load partition is the coupling node, and its value is equal to the number of all partitions that do not contain PV nodes.

[0172] In some embodiments, before obtaining the total improvement value of the preset indicators corresponding to different microgrid combination modes in the same microgrid area, it further includes: calculating the node coupling degree threshold ω th , and taking the node coupling degree ω ij between node i and node j in the same area being greater than the threshold ω th as a necessary condition for aggregation.

[0173] In some embodiments, the specific processing of obtaining the total improvement value of the preset indicators corresponding to different microgrid combination modes in the same microgrid area may be as follows:

[0174] First, perform a joint operation simulation on any two microgrids in the same area, and calculate the corresponding first preset objective function value and constraint condition value when the two microgrids are operating jointly. Then, classify the first preset objective function indicators and constraint condition indicators, which can be divided into benefit type indicators b ij and cost type indicators . The benefit type indicators refer to the indicators that are expected to be improved after the joint optimization of the microgrid, and the cost type indicators refer to the indicators that are expected to be reduced after the joint optimization of the microgrid. Next, select two microgrids to be jointly operated in a distribution network area, and the cooperative income increment criterion is described as follows: when at least one benefit type indicator b ij satisfies , and the remaining benefit type indicators and all cost type indicators satisfy ; or when at least one cost type indicator , and the remaining cost type indicators and all benefit type indicators satisfy , at this time, it is considered that forming a microgrid group by the two microgrids will bring cooperative incremental income, and these two microgrids can be networked into a group.

[0175] In this way, for the microgrids in other areas of the distribution network, repeat the above steps of judging whether the cooperative income increment criterion is satisfied, and finally, all the microgrids that can form groups in the distribution network can be aggregated together to form a microgrid group.

[0176] S105. According to the second preset objective function and the second preset constraint conditions, establish a microgrid group configuration model for the target microgrid group, where the target microgrid group is any one of the multiple microgrid groups.

[0177] It should be noted that the second preset objective function is constructed with the goal of minimizing the total life cycle cost of the microgrid group.

[0178] In some embodiments, the second preset objective function includes:

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188]

[0189]

[0190]

[0191] Among them, is the total life cycle cost of the microgrid cluster, is the annual average construction cost of the microgrid cluster, is the annual operation cost of the microgrid cluster, is the investment and maintenance cost of photovoltaic in the microgrid cluster, is the investment and maintenance cost of controllable power sources in the microgrid cluster, is the investment and maintenance cost of energy storage devices in the microgrid cluster, is the total installed capacity of photovoltaic in the microgrid cluster, is the number of photovoltaic panels to be planned in the microgrid cluster, P PV_unit is the rated power of a single photovoltaic panel, is the total power of controllable power sources in the microgrid cluster, is the rated power of energy storage devices in the microgrid cluster, is the total power of energy storage devices in the microgrid cluster, η PV is the unit capacity cost of photovoltaic, η f is the unit capacity cost of controllable power sources, η B is the unit energy cost of energy storage, η P is the unit capacity cost, η inv is the inverter cost coefficient, η D is the equal-valued day factor, d is the discount rate, y is the service life of the equipment, is the operation cost of controllable power sources in the microgrid cluster, is the operating cost of energy storage within the microgrid cluster, is the operating cost of ordinary loads within the microgrid cluster, is the operating cost of controllable loads within the microgrid cluster, 、 、 are the operating cost coefficients of controllable power sources within the microgrid cluster, is the output power of the controllable power source within the microgrid cluster at time t, T is the expected operating period, P ESS (t) is the charging and discharging power of the energy storage within the microgrid cluster at time t, 、 、 are the operating cost coefficients of the energy storage within the microgrid cluster, is the electricity purchase price of the load within the microgrid at time t, is the total electricity consumption of users within the microgrid cluster at time t, is the total adjustment amount of controllable loads within the microgrid cluster at time t, is the controllable load cost coefficient within the microgrid cluster.

[0192] In some embodiments, the second preset constraint condition includes: the microgrid cluster penetration rate constraint, the distributed power source ramp rate constraint within the microgrid cluster, and the peak-valley difference constraint of the microgrid cluster exchange power;

[0193] The microgrid cluster penetration rate constraint is:

[0194]

[0195]

[0196]

[0197] Among them, is the power of the microgrid cluster penetrating into the distribution network as a whole, 、 respectively represent the output of the photovoltaic and controllable power sources within the microgrid cluster at time t; is the power consumption of the ordinary load within the microgrid cluster at time t; represents the power consumption of the controllable load within the microgrid cluster at time t; T is the scheduling period, λ cluster is the microgrid cluster penetration rate, is the power of the microgrid cluster penetrating into the distribution network as a whole, is the total power of the distribution network electricity connected to the microgrid cluster;

[0198] The distributed power source ramp rate constraint within the microgrid cluster is:

[0199]

[0200] Among them, and are the upward and downward ramp rates of the output of controllable power sources in the microgrid cluster, respectively; and represent the output power of the controllable power source at times t-1 and t, respectively;

[0201]

[0202] wherein, and are the upward and downward ramp rates of the output of energy storage devices in the microgrid cluster, respectively; and represent the output power of the energy storage device at times t-1 and t, respectively;

[0203]

[0204] wherein, and are the upward and downward ramp rates of the output of the PCC node where the microgrid cluster is connected to the distribution network, respectively; and represent the output power of the PCC node at times t-1 and t, respectively;

[0205] It should be noted that for the above-mentioned ramp rate constraint of the output power of the controllable power source in the microgrid cluster, the ramp rate of the output power of the controllable power source in the microgrid cluster is used to characterize the change in the output of the controllable power source in the microgrid cluster within two consecutive time intervals.

[0206] The peak-valley difference constraint of the exchange power of the microgrid cluster is:

[0207]

[0208]

[0209] wherein, is the maximum power fed back from the microgrid cluster to the distribution network; is the maximum power absorbed by the microgrid cluster from the distribution network, is the maximum load in the microgrid cluster; the direction of the transmission power of the microgrid is selected as the positive direction, and the direction of the absorbed power is the negative direction.

[0210] It should be noted that for the above-mentioned peak-valley difference rate constraint of the exchange power, the peak-valley difference rate of the exchange power refers to the ratio of the annual maximum peak-valley difference of the exchange power between the microgrid cluster and the distribution network during grid connection to the annual maximum electricity load in the microgrid cluster.

[0211] In some embodiments, the second preset constraint conditions further include: the constraint on the limit of the investment cost of the microgrid group system, the constraint on the maximum number of installed photovoltaic units in the microgrid group, the constraint on the power balance of the microgrid group, the constraint on the load power outage rate, the constraint on the minimum output of the controllable power source, the operation constraint of the energy storage system, the PCC power constraint, and the constraint on the photovoltaic curtailment rate of the microgrid group.

[0212] Specifically, the constraint on the limit of the construction cost of the microgrid group system is:

[0213]

[0214] Wherein, is the maximum allowable annual average construction cost of the microgrid group system.

[0215] Specifically, the constraint on the maximum number of installed photovoltaic units in the microgrid group is:

[0216]

[0217] Wherein, represents the total number of investment photovoltaic panels in the microgrid group, which is mainly restricted by factors such as the actual site and the investment amount of the microgrid group.

[0218] Specifically, the constraint on the power balance of the microgrid group is:

[0219]

[0220] Wherein, and and respectively represent the output of photovoltaic, controllable power source, and energy storage equipment in the microgrid group at time t; is the power consumption of ordinary loads in the microgrid group at time t; represents the power consumption of controllable loads in the microgrid group at time t; is the load loss power in the microgrid group at time t.

[0221] Specifically, the constraint on the load power outage rate is:

[0222]

[0223]

[0224]

[0225]

[0226]

[0227]

[0228] Wherein, denotes the power shortage rate of the microgrid group load, where \(T\) takes the whole year, \(8760h\); the sampling interval is \(1h\). and and respectively denote the output powers of the photovoltaic power, controllable power supply, and energy storage device in a microgrid \(i\) within the microgrid group at time \(t\). is the power consumption of the ordinary load in microgrid \(i\) at time \(t\). denotes the power consumption of the controllable load in microgrid \(i\) at time \(t\). is the power shortage of microgrid \(i\) at time \(t\). denotes the power shortage rate of microgrid \(i\).

[0229] Specifically, the minimum output power constraint of the controllable power supply is:

[0230]

[0231]

[0232]

[0233]

[0234]

[0235] Among them, is the minimum output power of the controllable power supply within the microgrid group. is the minimum output power of the controllable power supply in microgrid \(i\).

[0236] Specifically, the operation constraint of the energy storage system is:

[0237]

[0238]

[0239] Among them, denotes the output power of the energy storage device within the microgrid group at time \(t\). is the output power of the energy storage device in microgrid \(i\) at time \(t\). is the lower limit of the active power output of the energy storage device in the microgrid group. is the upper limit of the active power output of the energy storage device in the microgrid group. is the lower limit of the active power output of the energy storage device in microgrid \(i\). is the upper limit of the active power output of the energy storage device in microgrid \(i\).

[0240] Specifically, the PCC power constraint is:

[0241]

[0242]

[0243] in, is the exchange active power of the microgrid group integrated into the distribution network PCC node at time t, The lower limit of active power exchange for microgrid group to be incorporated into distribution network PCC node, Exchange active power cap for microgrid clusters incorporated into distribution network PCC nodes. is the exchange active power of microgrid i incorporated into the PCC node of the microgrid group at time t, The lower limit of active power exchanged between microgrid i and PCC node of microgrid group, The upper limit of active power exchange for microgrid i incorporated into the PCC node of microgrid group.

[0244] Specifically, the curtailment rate constraint of the microgrid group is:

[0245] in, It represents the total power generation of photovoltaic power in the microgrid group during the operation period T (8760 hours a year), where It represents the maximum output prediction of photovoltaic power in the microgrid group within time t, measured as a percentage of the photovoltaic rated power. The number of photovoltaic panels to be planned in the microgrid group, is the rated power of a single photovoltaic panel; It represents the total power generation of the controllable power sources in the microgrid group during the operation period T; It represents the total power consumption of common loads in the microgrid group during the operation period T. It represents the total power consumption of the controllable loads in the microgrid group during the operation period T.

[0246]

[0247] Since the energy storage system has the same amount of electricity at the beginning and end of each operating cycle, the above formula does not consider the power generation / consumption of the energy storage system. The upper limit of the allowed curtailment rate within the microgrid cluster. For example, the curtailment rate is limited to 5%.

[0248] S106. Acquire capacity configuration information of the target microgrid group according to the microgrid group configuration model.

[0249] In some embodiments, the microgrid group configuration model is solved based on the particle swarm algorithm. The specific process is as follows:

[0250] Set the time interval between the two periods to 1 hour, and the scheduling period T=8760;

[0251] Select the total output power of the photovoltaic panels in the microgrid cluster , the total power of the controllable power sources in the microgrid , and the rated capacity of the energy storage device in the microgrid as particles, and set their initial values to 0;

[0252] Set the initial iteration number k = 1, and the maximum iteration number k max = 80;

[0253] Solve the above cost function for , , , and calculate the particle values at each moment to satisfy the above constraint conditions;

[0254] Update the velocity and position of the particles:

[0255]

[0256]

[0257]

[0258] where k is the iteration number, x k represents the spatial position of the particle at the iteration number k, and x k represents the velocity of the particle at the iteration number k. c1 and c2 are learning factors, with values of 1 and 0.8 respectively. r1 and r2 are random numbers uniformly distributed between (0, 1), The inertia weight is set to 0.6, p bestk is the self-optimal solution of the particle at the k-th iteration, and g bestk is the global optimal solution of the particle at the k-th iteration;

[0259] If k > k max , then output the total output power of the photovoltaic panels in the microgrid, the total power of the controllable power sources in the microgrid, and the rated capacity of the energy storage device in the microgrid, that is, output the final result of the microgrid optimal configuration; otherwise, let k = k + 1, and return the above cost function for , , , and calculate the particle values at each moment to satisfy the above constraint conditions.

[0260] In some embodiments, the capacity configuration information includes the total installed capacity of the photovoltaic in the microgrid, the total installed power of the controllable power sources, and the total capacity of the energy storage device.

[0261] S107. Configure the photovoltaic installed capacity, the controllable power source installed power, and the energy storage device capacity in the target microgrid cluster according to the capacity configuration information of the target microgrid cluster.

[0262] An embodiment of the present invention provides a multi - microgrid group planning method. First, according to the wind - solar combined power output model of the target power system established in advance, multiple typical scenarios of the target power system are obtained; then, according to the first preset objective function and the first preset constraint conditions, a microgrid configuration model of the target power system under multiple typical scenarios is established; next, according to the microgrid configuration model, the capacity configuration information of each microgrid in the target power system is obtained; after that, according to the capacity configuration information of each microgrid and the preset aggregation method, the microgrids in the target power system are aggregated to obtain multiple microgrid groups; then, according to the second preset objective function and the second preset constraint conditions, a microgrid group configuration model of the target microgrid group is established, where the target microgrid group is any one of the multiple microgrid groups; then, according to the microgrid group configuration model, the capacity configuration information of the target microgrid group is obtained; finally, according to the capacity configuration information of the target microgrid group, the photovoltaic installed capacity, the controllable power source installed power, and the energy storage device capacity in the target microgrid group are configured. In this way, a multi - microgrid group planning method considering new - energy consumption is provided, which can reduce the investment cost of the power system and improve the economic benefits of the power system.

[0263] It should be understood that the magnitudes of the sequence numbers of the steps in the above - mentioned embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0264] The following is the device embodiment of the present invention. For the details not described in detail, reference can be made to the corresponding method embodiments above.

[0265] [[ID=~10]] Figure 4 The structural schematic diagram of a multi - microgrid group planning device provided by an embodiment of the present invention is shown. For the sake of convenience of description, only the parts related to the embodiment of the present invention are shown and are described in detail as follows:

[0266] As Figure 4 shown, the multi - microgrid group planning device includes:

[0267] A first acquisition module 401, configured to obtain multiple typical scenarios of the target power system according to the wind - solar combined power output model of the target power system established in advance;

[0268] A first establishment module 402, configured to establish a microgrid configuration model of the target power system under multiple typical scenarios according to the first preset objective function and the first preset constraint conditions;

[0269] A second acquisition module 403, configured to obtain the capacity configuration information of each microgrid in the target power system according to the microgrid configuration model;

[0270] An aggregation module 404, configured to aggregate the microgrids in the target power system according to the capacity configuration information of each microgrid and a preset aggregation method, so as to obtain multiple microgrid groups;

[0271] A second establishment module 405, configured to establish a microgrid group configuration model for a target microgrid group according to a second preset objective function and second preset constraint conditions, where the target microgrid group is any one of the multiple microgrid groups;

[0272] A third acquisition module 406, configured to acquire the capacity configuration information of the target microgrid group according to the microgrid group configuration model;

[0273] A configuration module 407, configured to configure the photovoltaic installed capacity, controllable power source installed power, and energy storage device capacity in the target microgrid group according to the capacity configuration information of the target microgrid group.

[0274] In some embodiments, the first acquisition module is further configured to:

[0275] Acquire weather load data for a preset time period in the region where the target power system is located;

[0276] Construct a wind-solar-load output probability distribution model corresponding to the target power system according to the weather load data;

[0277] Establish a wind-solar combined output probability distribution model according to the wind-solar-load output probability distribution model;

[0278] Acquire multiple typical scenarios of the target power system according to the wind-solar combined output probability distribution model, a preset sampling algorithm, and a preset clustering algorithm.

[0279] In some embodiments, the first acquisition module is further configured to:

[0280] Randomly select multiple sample points from the wind-solar combined output probability distribution model based on the Monte Carlo sampling method to generate multiple scenario samples;

[0281] Cluster the multiple scenario samples based on a preset clustering algorithm to obtain multiple typical scenarios.

[0282] In some embodiments, the first preset objective function of the first establishment module includes:

[0283]

[0284]

[0285]

[0286]

[0287]

[0288]

[0289]

[0290]

[0291]

[0292]

[0293]

[0294]

[0295] Among them, is the total cost of the whole life cycle of the microgrid, is the annual average construction cost of the microgrid, is the annual operating cost of the microgrid, C PV is the investment and maintenance cost of the photovoltaic power station in the microgrid, C F is the investment and maintenance cost of the controllable power source in the microgrid, C ESS is the investment and maintenance cost of the energy storage device in the microgrid, P PV is the total installed capacity of photovoltaic in the microgrid, N PV is the number of photovoltaic panels to be planned, P PV_unit is the rated power of a single photovoltaic panel, P F is the total power of the controllable power source in the microgrid, B ESS is the rated power of the energy storage device in the microgrid, P ESS is the total power of the energy storage device in the microgrid, η PV is the unit capacity cost of photovoltaic, η f is the unit capacity cost of the controllable power source, η B is the unit energy cost of energy storage, η P is the unit capacity cost, η inv is the inverter cost coefficient, η D is the equal-valued day coefficient, d is the discount rate, y is the service life of the equipment, C f (t) is the operating cost of the controllable power source in the microgrid, C ESS (t) is the operating cost of the energy storage in the microgrid, C L (t) is the operating cost of the ordinary load in the microgrid, C DR (t) is the operating cost of the controllable load in the microgrid, a f 、b f 、c f are the operating cost coefficients of the controllable power source in the microgrid, P F(t) is the output power of the controllable power source in the microgrid at time t, T is the desired operation period, P ESS (t) is the charge and discharge power of the energy storage in the microgrid at time t, a ess , b ess , c ess are the operation cost coefficients of the energy storage in the microgrid, p(t) is the electricity purchase price of the load in the microgrid at time t, q(t) is the total electricity consumption of the users in the microgrid at time t, ΔP DR (t) is the total regulation amount of the controllable load in the microgrid at time t, a DR is the cost coefficient of the controllable load in the microgrid.

[0296] In some embodiments, the first preset constraint condition includes the microgrid penetration constraint, the ramp rate constraint of the distributed power source in the microgrid, and the peak-valley difference constraint of the microgrid exchange power;

[0297] Specifically, the microgrid penetration constraint is:

[0298]

[0299]

[0300]

[0301] Among them, P PV (t) is the photovoltaic output at time t, P F (t) is the output of the controllable power source at time t, P L (t) is the electricity consumption power of the ordinary charge at time t, P DR (t) is the electricity consumption power of the controllable load at time t, T is the scheduling period, λ is the microgrid penetration rate, P m is the power of the microgrid penetrating into the large power grid, P s is the total power of the power system of the distribution network connected to the microgrid;

[0302] The ramp rate constraint of the distributed power source in the microgrid is:

[0303]

[0304] and are the rising ramp rate and the falling ramp rate of the output of the controllable power source in the microgrid respectively, and are the output power of the controllable power source in the microgrid at time t - 1 and the output power at time t respectively;

[0305]

[0306] Among them, and are the rising ramp rate and the falling ramp rate of the energy storage device output in the microgrid, respectively, and represent the output power of the controllable power source in the microgrid at the (t - 1)th moment and the tth moment, respectively;

[0307]

[0308] wherein, and represent the rising ramp rate and the falling ramp rate of the output of the microgrid grid-connected PCC node, respectively, and represent the output power of the microgrid grid-connected PCC node at the (t - 1)th moment and the tth moment, respectively;

[0309] The peak-valley difference constraint of the microgrid exchange power is:

[0310]

[0311]

[0312] wherein, is the maximum power inverted from the microgrid to the distribution network; is the maximum power absorbed by the microgrid from the distribution network, is the maximum load in the microgrid.

[0313] The aggregation module is further configured to:

[0314] Calculate the node coupling degree of the distribution network connected to each microgrid based on the capacity configuration information of each microgrid;

[0315] Divide the microgrids in the target power system into multiple microgrid areas according to the node coupling degree;

[0316] Obtain the total improvement value of the preset index corresponding to different microgrid combination modes in the same microgrid area;

[0317] Determine the microgrid combination under the microgrid combination mode with the maximum total improvement value as multiple microgrid groups.

[0318] In some embodiments, the second preset objective function includes:

[0319]

[0320]

[0321]

[0322]

[0323]

[0324]

[0325]

[0326]

[0327]

[0328]

[0329]

[0330]

[0331] Among them, is the total life cycle cost of the microgrid cluster, is the annual average construction cost of the microgrid cluster, is the annual operation cost of the microgrid cluster, is the investment and maintenance cost of photovoltaic in the microgrid cluster, is the investment and maintenance cost of controllable power sources in the microgrid cluster, is the investment and maintenance cost of energy storage devices in the microgrid cluster, is the total installed capacity of photovoltaic in the microgrid cluster, is the number of photovoltaic panels to be planned in the microgrid cluster, P PV_unit is the rated power of a single photovoltaic panel, is the total power of controllable power sources in the microgrid cluster, is the rated power of energy storage devices in the microgrid cluster, is the total power of energy storage devices in the microgrid cluster, η PV is the unit capacity cost of photovoltaic, η f is the unit capacity cost of controllable power sources, η B is the unit energy cost of energy storage, η P is the unit capacity cost, η inv is the inverter cost coefficient, η D is the equal-valued day factor, d is the discount rate, and y is the service life of the equipment. is the operation cost of controllable power sources in the microgrid cluster, is the operation cost of energy storage in the microgrid cluster, is the operation cost of ordinary loads in the microgrid cluster, is the operation cost of controllable loads in the microgrid cluster, , , are the operation cost coefficients of controllable power sources in the microgrid cluster, is the output power of the controllable power sources in the microgrid cluster at time t, T is the expected operation period, P ESS (t) is the charging and discharging power of the energy storage in the microgrid cluster at time t, 、 、 are the operating cost coefficients of the energy storage in the microgrid cluster, is the electricity purchase price of the load in the microgrid at time t, is the total electricity consumption of the users in the microgrid cluster at time t, is the total regulation amount of the controllable load in the microgrid cluster at time t, is the cost coefficient of the controllable load in the microgrid cluster.

[0332] In some embodiments, the second preset constraint conditions include: the microgrid cluster penetration rate constraint, the distributed power source ramp rate constraint in the microgrid cluster, and the peak-valley difference constraint of the exchange power in the microgrid cluster;

[0333] The microgrid cluster penetration rate constraint is:

[0334]

[0335]

[0336]

[0337] Among them, is the power of the microgrid cluster penetrating into the distribution network as a whole, 、 respectively represent the outputs of the photovoltaic and controllable power sources in the microgrid cluster at time t; is the power consumption of the ordinary load in the microgrid cluster at time t; represents the power consumption of the controllable load in the microgrid cluster at time t; T is the scheduling period, λ cluster is the microgrid cluster penetration rate, is the power of the microgrid cluster penetrating into the distribution network as a whole, is the total power of the distribution network electricity connected to the microgrid cluster;

[0338] The distributed power source ramp rate constraint in the microgrid cluster is:

[0339]

[0340] Among them, and are the rising ramp rate and falling ramp rate of the output of the controllable power source in the microgrid cluster respectively; and respectively represent the output power of the controllable power source at time t - 1 and time t;

[0341]

[0342] Among them, and are respectively the rising ramp rate and the falling ramp rate of the output of the energy storage device in the microgrid cluster; and respectively represent the output power of the energy storage device at the (t - 1)th moment and the tth moment;

[0343]

[0344] Among them, and are respectively the rising ramp rate and the falling ramp rate of the output of the PCC node where the microgrid cluster is connected to the distribution network; and respectively represent the output power of the PCC node at the (t - 1)th moment and the tth moment;

[0345] It should be noted that for the above-mentioned ramp rate constraint of the output power of the controllable power source in the microgrid cluster, the ramp rate of the output power of the controllable power source in the microgrid cluster is used to characterize the change in the output of the controllable power source within the microgrid cluster in two consecutive time intervals.

[0346] The peak - valley difference constraint of the exchange power of the microgrid cluster is:

[0347]

[0348]

[0349] Among them, is the maximum power fed back from the microgrid cluster to the distribution network; is the maximum power absorbed by the microgrid cluster from the distribution network, is the maximum load within the microgrid cluster.

[0350] It should be noted that for the above-mentioned peak - valley difference rate constraint of the exchange power, the peak - valley difference rate of the exchange power refers to the ratio of the annual maximum peak - valley difference of the exchange power between the microgrid cluster and the distribution network during grid connection to the annual maximum power consumption load within the microgrid cluster.

[0351] In an embodiment of the present invention, a multi-microgrid group planning device is provided. First, according to the wind-solar combined output model of the target power system established in advance, multiple typical scenarios of the target power system are obtained; then, according to the first preset objective function and the first preset constraint conditions, a microgrid configuration model of the target power system under multiple typical scenarios is established; then, according to the microgrid configuration model, the capacity configuration information of each microgrid in the target power system is obtained; after that, according to the capacity configuration information of each microgrid and the preset aggregation method, the microgrids in the target power system are aggregated to obtain multiple microgrid groups; then, according to the second preset objective function and the second preset constraint conditions, a microgrid group configuration model of the target microgrid group is established, where the target microgrid group is any one of the multiple microgrid groups; then, according to the microgrid group configuration model, the capacity configuration information of the target microgrid group is obtained; finally, according to the capacity configuration information of the target microgrid group, the photovoltaic installed capacity, the controllable power supply installed power, and the energy storage device capacity in the target microgrid group are configured. In this way, a multi-microgrid group planning method considering new energy consumption is provided, which can reduce the investment cost of the power system and improve the economic benefits of the power system.

[0352] Figure 5 is a schematic diagram of the electronic device provided by the embodiment of the present invention. As Figure 5 shown, the electronic device 5 of this embodiment includes: a processor 50, a memory 51, and a computer program 52 stored in the memory 51 and executable on the processor 50. When the processor 50 executes the computer program 52, the steps in the method embodiments of the above various distribution network topology identifications are implemented, such as Figure 1 the steps 101 to 107 shown. Alternatively, when the processor 50 executes the computer program 52, the functions of each module in the above device embodiments are implemented, such as Figure 4 the functions of the modules 401 to 407 shown.

[0353] Exemplarily, the computer program 52 can be divided into one or more modules. One or more modules are stored in the memory 51 and executed by the processor 50 to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 52 in the electronic device 5. For example, the computer program 52 can be divided into Figure 4 the modules 401 to 407 shown.

[0354] The electronic device 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art can understand, Figure 5This is only an example of the electronic device 5 and does not limit the electronic device 5. It may include more or fewer components than those shown in the figure, or combine some components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0355] The so-called processor 50 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0356] The memory 51 may be an internal storage unit of the electronic device 5, such as the hard disk or memory of the electronic device 5. The memory 51 may also be an external storage device of the electronic device 5, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 5. Further, the memory 51 may also include both the internal storage unit and the external storage device of the electronic device 5. The memory 51 is used to store the computer program and other programs and data required by the electronic device. The memory 51 may also be used to temporarily store data that has been output or will be output.

[0357] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0358] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0359] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0360] In the embodiments provided by the present invention, it should be understood that the disclosed device / electronic device and method can be implemented in other ways. For example, the device / electronic device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0361] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0362] In addition, in each embodiment of the present invention, each functional unit can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0363] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned method embodiments for identifying the topology of each distribution network can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0364] The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A multi - microgrid group planning method, characterized in that, Including: Obtain multiple typical scenarios of the target power system according to the established combined wind and solar power output model of the target power system; Establish a microgrid configuration model of the target power system under the multiple typical scenarios according to the first preset objective function and the first preset constraint conditions; Obtain the capacity configuration information of each microgrid in the target power system according to the microgrid configuration model; Aggregate the microgrids in the target power system according to the capacity configuration information of each microgrid and the preset aggregation method to obtain multiple microgrid groups; Establish a microgrid group configuration model of the target microgrid group according to the second preset objective function and the second preset constraint conditions, where the target microgrid group is any one of the multiple microgrid groups; Obtain the capacity configuration information of the target microgrid group according to the microgrid group configuration model; Configure the photovoltaic installed capacity, controllable power supply installed power, and energy storage device capacity in the target microgrid group according to the capacity configuration information of the target microgrid group.

2. The multi-microgrid group planning method according to claim 1, characterized in that Before obtaining multiple typical scenarios of the target power system according to the established combined wind and solar power output model of the target power system, the method further includes: Obtain the weather load data of the preset time period in the area where the target power system is located; Construct a combined wind, solar, and load output probability distribution model corresponding to the target power system according to the weather load data; Establish a combined wind and solar power output probability distribution model according to the combined wind, solar, and load output probability distribution model; Obtain multiple typical scenarios of the target power system according to the combined wind and solar power output probability distribution model, the preset sampling algorithm, and the preset clustering algorithm.

3. The multi-microgrid group planning method according to claim 2, wherein The obtaining multiple typical scenarios of the target power system according to the combined wind and solar power output probability distribution model, the preset sampling algorithm, and the preset clustering algorithm includes: Randomly select multiple sample points from the combined wind and solar power output probability distribution model based on the Monte Carlo sampling method to generate multiple scenario samples; Cluster the multiple scenario samples based on the preset clustering algorithm to obtain multiple typical scenarios.

4. The multi - micro - grid group planning method according to claim 1, wherein The first preset objective function includes: minC A&O = C investemetn + C operation C investement = 365 × η D (C PV + C F + C ESS ) C PV = η PV P PV P PV = N PV P PV_unit C F = η f P F C ESS = η B B ESS + η P P ESS + η inv P ESS C operation = C f (t) + C ESS (t) + C L (t) + C DR (t) Among them, C A&O is the total cost of the microgrid's entire life cycle, C investement is the annual average construction cost of the microgrid, C operation is the annual operating cost of the microgrid, C PV is the investment and maintenance cost of the photovoltaic power station in the microgrid, C F is the investment and maintenance cost of the controllable power source in the microgrid, C ESS is the investment and maintenance cost of the energy storage device in the microgrid, P PV is the total installed capacity of photovoltaic in the microgrid, N PV is the number of photovoltaic panels to be planned, P PV_unit is the rated power of a single photovoltaic panel, P F is the total power of the controllable power source in the microgrid, B ESS is the rated power of the energy storage device in the microgrid, P ESS is the total power of the energy storage device in the microgrid, η PV is the unit capacity cost of photovoltaic, η f is the unit capacity cost of the controllable power source, η B is the unit energy cost of energy storage, η P is the unit capacity cost, η inv is the inverter cost coefficient, η D is the equal-valued day coefficient, d is the discount rate, y is the equipment service life, C f (t) is the operating cost of the controllable power source in the microgrid, C ESS (t) is the operating cost of the energy storage in the microgrid, C L (t) is the operating cost of the ordinary load in the microgrid, C DR (t) is the operating cost of the controllable load in the microgrid, a f 、b f 、c f are the operating cost coefficients of the controllable power source in the microgrid, P F (t) is the output power of the controllable power source in the microgrid at time t, T is the expected operating period, P ESS (t) is the charge and discharge power of the energy storage in the microgrid at time t, a ess 、b ess 、c ess are the operating cost coefficients of the energy storage in the microgrid, p(t) is the electricity purchase price of the load in the microgrid at time t, q(t) is the total electricity consumption of users in the microgrid at time t, ΔP DR (t) is the total regulation amount of the controllable load in the microgrid at time t, a DR is the controllable load cost coefficient in the microgrid.

5. The multi-microgrid group planning method according to claim 1, wherein The first preset constraint conditions include microgrid penetration rate constraint, distributed power source ramp rate constraint within the microgrid, and microgrid exchange power peak-valley difference constraint; Among them, the microgrid penetration rate constraint is: λ min <λ<λ max Among them, P PV (t) is the photovoltaic output at time t, P F (t) is the output of the controllable power source at time t, P L (t) is the power consumption of ordinary charges at time t, P DR (t) is the power consumption of the controllable load at time t, T d is the scheduling period, λ is the microgrid penetration rate, P m is the power of the microgrid penetrating into the large power grid, P s is the total power of the power system of the distribution network connected to the microgrid, λ min is the lower limit of the microgrid penetration rate, λ max is the upper limit of the microgrid penetration rate; The distributed power source ramp rate constraint within the microgrid is: -RD Fi ≤P F (t)-P F (t - 1)≤RU Fi RU Fi and RD Fi are the rising ramp rate and the falling ramp rate of the controllable power source output in the microgrid, respectively. P F (t - 1) and P F (t) are the output power of the controllable power source in the microgrid at the moment of t - 1 and the output power at the moment of t, respectively; -RD ESSi ≤P ESS (t)-P ESS (t - 1)≤RU ESSi Among them, RU ESSi and RD ESSi are the rising ramp rate and the falling ramp rate of the energy storage device output in the microgrid, respectively. P ESS (t - 1) and P ESS (t) represent the output powers of the controllable power sources in the microgrid at the (t - 1)-th moment and the t-th moment, respectively; -RD pcci ≤P pcc (t)-P pcc (t - 1)≤RU pcci Among them, RU pcci and RD pcci respectively represent the upward and downward ramping rates of the output of the PCC node of the microgrid connected to the grid, and P pcc (t - 1) and P pcc (t) respectively represent the output power of the PCC node of the microgrid connected to the grid at the (t - 1)th moment and the tth moment; The microgrid exchange power peak-valley difference constraint is: D<D max Among them, P s，max is the maximum power fed back from the microgrid to the distribution network; P i，max is the maximum power absorbed by the microgrid from the distribution network, and P L，max is the maximum load in the microgrid. D is the peak-valley difference of the microgrid exchange power, and D max is the upper limit of the peak-valley difference of the microgrid exchange power.

6. The multi-microgrid group planning method according to claim 1, characterized in that The aggregating the microgrids in the target power system according to the capacity configuration information of each microgrid and the preset aggregation method to obtain multiple microgrid groups includes: Calculate the node coupling degree of the distribution network connected to each microgrid based on the capacity configuration information of each microgrid; Divide the microgrids in the target power system into multiple microgrid areas according to the node coupling degree; Obtain the total improvement value of the preset index corresponding to different microgrid combination methods within the same microgrid area; Determine the microgrid combination under the microgrid combination method with the maximum total improvement value as the multiple microgrid groups.

7. The multi-microgrid group planning method according to claim 1, characterized in that The second preset objective function includes: Among them, is the total life cycle cost of the microgrid cluster, is the annual average construction cost of the microgrid cluster, is the annual operation cost of the microgrid cluster, is the investment and maintenance cost of the photovoltaic in the microgrid cluster, is the investment and maintenance cost of the controllable power source in the microgrid cluster, is the investment and maintenance cost of the energy storage equipment in the microgrid cluster, is the total installed capacity of the photovoltaic in the microgrid cluster, is the number of photovoltaic panels to be planned in the microgrid cluster, P PV_unit is the rated power of a single photovoltaic panel, is the total power of the controllable power source in the microgrid cluster, is the rated power of the energy storage equipment in the microgrid cluster, is the total power of the energy storage equipment in the microgrid cluster, η PV is the unit capacity cost of the photovoltaic, η f is the unit capacity cost of the controllable power source, η B is the unit energy cost of the energy storage, η P is the unit capacity cost, η inv is the inverter cost coefficient, η D is the equal-valued day coefficient, d is the discount rate, y is the equipment service life, is the operation cost of the controllable power source in the microgrid cluster, is the operation cost of the energy storage in the microgrid cluster, is the operation cost of the ordinary load in the microgrid cluster, is the operation cost of the controllable load in the microgrid cluster, is the operation cost coefficient of the controllable power source in the microgrid cluster, is the output power of the controllable power source in the microgrid cluster at time t, T is the expected operation period, P ESS (t) is the charge and discharge power of the energy storage in the microgrid cluster at time t, is the operation cost coefficient of the energy storage in the microgrid cluster, p cluster (t) is the electricity purchase price of the load in the microgrid at time t, q cluster (t) is the total electricity consumption of the users in the microgrid cluster at time t, is the total regulation amount of the controllable load in the microgrid cluster at time t, is the cost coefficient of the controllable load in the microgrid cluster.

8. The multi-microgrid group planning method according to claim 1, characterized in that The second preset constraint conditions include: the penetration rate constraint of the microgrid group, the ramp rate constraint of distributed power sources within the microgrid group, and the peak-valley difference constraint of the exchange power of the microgrid group; The penetration rate constraint of the microgrid group is: Among them, is the power of the microgrid group integrally penetrating into the distribution network, respectively represent the power outputs of the photovoltaic and controllable power sources in the microgrid group at time t; is the power consumption of the ordinary load in the microgrid group at time t; represents the power consumption of the controllable load in the microgrid group within time t; T d is the scheduling period, λ cluster is the penetration rate of the microgrid group, is the power of the microgrid group integrally penetrating into the distribution network, is the total power of the distribution network power connected to the microgrid group, is the lower limit of the penetration rate of the microgrid group, is the upper limit of the penetration rate of the microgrid group; The ramp rate constraint of distributed power sources within the microgrid group is: Among them, and are the rising ramp rate and the falling ramp rate of the controllable power supply output in the microgrid cluster, respectively; and represent the output power of the controllable power supply at the (t - 1)th moment and the tth moment, respectively. Wherein, and are respectively the rising ramp rate and the falling ramp rate of the output of the energy storage device in the microgrid cluster; and respectively represent the output power of the energy storage device at the (t - 1)th moment and the tth moment; Among them, and are the rising ramp rate and the falling ramp rate of the PCC node output when the microgrid cluster is integrated into the distribution network, respectively; and represent the output power of the PCC node at the (t - 1)th moment and the tth moment, respectively. The peak-valley difference constraint of the exchange power of the microgrid group is: Among them, is the maximum power fed back from the microgrid cluster to the distribution network; is the maximum power absorbed by the microgrid cluster from the distribution network, is the maximum load within the microgrid cluster, D cluster is the peak-valley difference of the exchange power of the microgrid cluster, is the upper limit of the peak-valley difference of the exchange power of the microgrid cluster.

9. A multi - microgrid group planning device, characterized in that, including: A first acquisition module, configured to acquire multiple typical scenarios of the target power system according to the pre-established wind-solar combined output model of the target power system; A first establishment module, configured to establish a microgrid configuration model of the target power system under the multiple typical scenarios according to a first preset objective function and first preset constraint conditions; A second acquisition module, configured to acquire the capacity configuration information of each microgrid in the target power system according to the microgrid configuration model; An aggregation module, configured to aggregate the microgrids in the target power system according to the capacity configuration information of each microgrid and a preset aggregation method to obtain multiple microgrid groups; A second establishment module, configured to establish a microgrid group configuration model of the target microgrid group according to a second preset objective function and second preset constraint conditions, where the target microgrid group is any one of the multiple microgrid groups; A third acquisition module, configured to acquire the capacity configuration information of the target microgrid group according to the microgrid group configuration model; A configuration module, configured to configure the photovoltaic installed capacity, the controllable power source installed power, and the energy storage device capacity in the target microgrid group according to the capacity configuration information of the target microgrid group.

10. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the multi-microgrid grouping planning method according to any one of claims 1 to 8 above are implemented.

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