Capacity configuration method for photovoltaic hybrid energy storage system

Abstract

The invention provides a capacity configuration method for a photovoltaic hybrid energy storage system, and belongs to the technical field of power system optimization and energy management automation, and the method comprises the steps: constructing a whole life cycle cost model of the photovoltaic hybrid energy storage system in an engineering cycle and a power loss rate model of a typical day; capacity configuration schemes are initialized, corresponding costs are calculated through a full life cycle cost model for different capacity configuration schemes, and corresponding power loss rates are calculated through a power loss rate model; and carrying out capacity configuration on the photovoltaic hybrid energy storage system by taking the capacity configuration scheme with the lowest cost and the power loss rate meeting the requirement of the power loss rate range of the region to be configured as a target capacity configuration scheme. According to the method, the power supply reliability of the photovoltaic hybrid energy storage system can be improved under the condition that the economic performance requirement is met.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization and energy management automation, and in particular relates to a capacity configuration method for a photovoltaic hybrid energy storage system. Background Art

[0002] Photovoltaic power generation is a clean energy that has been increasingly widely used and developed at an accelerated pace in recent years. It has received more and more attention due to its low pollution and large installed capacity. Although photovoltaic power generation has significant advantages, the output of photovoltaic systems is affected by weather, seasons, and day and night changes, and its output has obvious intermittent and volatile characteristics. In order to solve the problem of intermittent and volatile photovoltaic power generation, the introduction of energy storage technology is particularly important. Energy storage technology stores excess electricity when electricity demand is low and releases electricity when demand is high, making the output curve of photovoltaic power generation smooth, which can effectively and steadily output power and reduce energy waste, thereby improving the overall utilization efficiency of photovoltaic power generation systems. At present, according to technical characteristics, energy storage technology can be divided into electrochemical energy storage, electromagnetic energy storage, and mechanical energy storage. In addition, hydrogen energy, as an emerging clean energy, has a very broad application prospect.

[0003] In the process of configuring a multi-energy storage system for a photovoltaic system, the initial configuration of the energy storage capacity plays a decisive role in the overall investment benefits of the system and the power supply reliability during operation.

[0004] In traditional hybrid energy storage capacity optimization configuration research, usually only the optimal economic cost of the energy storage system operation is considered, and a single goal is used as the basis for optimization, which cannot guarantee the power supply reliability of the photovoltaic hybrid energy storage system. Summary of the invention

[0005] In order to solve the problem of low power supply reliability of photovoltaic hybrid energy storage systems in the prior art, the present invention provides a capacity configuration method for photovoltaic hybrid energy storage systems.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A capacity configuration method for a photovoltaic hybrid energy storage system, wherein the photovoltaic hybrid energy storage system includes at least two energy storage devices, including:

[0008] The energy storage capacity and rated power of each energy storage device in the photovoltaic hybrid energy storage system are used as variables, and the full life cycle cost of the photovoltaic hybrid energy storage system during the project period is used as output to construct a full life cycle cost model, and the power failure rate model is constructed with the power failure rate on a typical day as output;

[0009] Initializing the energy storage capacity and the rated power within the set energy storage capacity range and the rated power range to obtain different capacity configuration schemes, wherein the capacity configuration schemes include the energy storage capacity size and the rated power size;

[0010] For different capacity configuration schemes, the corresponding costs are calculated through the life cycle cost model, and the corresponding power failure rate is calculated through the power failure rate model;

[0011] The capacity configuration scheme with the lowest cost and a power failure rate that meets the power failure rate range requirements of the area to be configured is used as the target capacity configuration scheme, and the capacity of the photovoltaic hybrid energy storage system is configured.

[0012] Optionally, the full life cycle cost model of the photovoltaic hybrid energy storage system is:

[0013] C AL =C OC +C REP +C REC +C YW ×365×y;

[0014] Among them, C AL represents the life cycle cost of the photovoltaic hybrid energy storage system, C OC represents the one-time investment cost, C REP represents the replacement cost of the energy storage device during the project cycle, C REC Represents the residual value recovery cost, C YW represents the operation and maintenance cost of the energy storage device on a typical day, and y represents the project cycle in years.

[0015] Optionally, the power failure rate model is:

[0016]

[0017] Where LER represents the load power failure rate, P sp (t) is the power deficit of the photovoltaic hybrid energy storage system at time t, P sp (t) = ∑P E (t)-P load (t); P E (t) is the output power of each energy storage device at time t, P load (t) represents the required power of the photovoltaic hybrid energy storage system at time t.

[0018] Optionally, after calculating the corresponding costs through the life cycle cost model and the corresponding power failure rate through the power failure rate model for different capacity configuration solutions, the method further includes:

[0019] The initialized capacity configuration scheme is regarded as the population, and different capacity configuration schemes are regarded as different individuals. The individuals are non-dominated and sorted based on the corresponding costs and power failure rates of different individuals, and different Pareto levels are assigned to each individual.

[0020] Select target individuals from different individuals according to Pareto rank and crowding distance, perform crossover and mutation on target individuals to generate a new population;

[0021] Iterate the process of calculating cost and power failure rate, assigning Pareto levels and generating new populations until the preset number of iterations is reached;

[0022] Extract the Pareto frontier from the new population and select one or more individuals on the Pareto frontier as the target capacity configuration solution.

[0023] Optionally, for different capacity configuration schemes, the corresponding power failure rate is calculated through a power failure rate model, including:

[0024] Obtain the historical photovoltaic output data set of the area to be configured, cluster the historical photovoltaic output data set using the mean shift clustering algorithm, and obtain the required power on a typical day;

[0025] The VMD algorithm is used to allocate the required power to each energy storage device. Based on the allocated power and different capacity configuration schemes, the working status of each energy storage device in a typical day is simulated to obtain the power deficit of the photovoltaic hybrid energy storage system.

[0026] Substitute the deficit power and demand power into the power failure rate model to obtain the power failure rates corresponding to different capacity configuration schemes.

[0027] Optionally, the historical photovoltaic output data set includes photovoltaic output data under different weather conditions. The historical photovoltaic output data set is clustered by a mean shift clustering algorithm to obtain a demand power of a typical day, including:

[0028] Calculate the centroid of all data points in the window in the historical photovoltaic output data set;

[0029] Move the data points representing different weather conditions to the centroid of the window, and recalculate the centroid based on the moved position; iterate the moving process until the position of the data point no longer changes significantly, and obtain multiple clusters;

[0030] The clusters containing data points greater than the preset threshold are selected, and the mean curve of the cluster is calculated as the required power on a typical day.

[0031] Optionally, the photovoltaic power generation power is distributed to each energy storage device through a VMD algorithm, including:

[0032] The required power is input into the VDM algorithm, and the VMD algorithm is used to decompose the required power into multiple mode functions;

[0033] The modal function is divided into high-frequency components and low-frequency components according to the frequency range;

[0034] Based on the characteristics of the energy storage devices, the high-frequency components and the low-frequency components are allocated to the respective energy storage devices.

[0035] Optionally, the energy storage device includes a lithium ion capacitor, a flywheel, a lithium iron phosphate battery, an all-vanadium liquid flow battery, and a hydrogen fuel cell, and based on the characteristics of the energy storage device, the high-frequency component and the low-frequency component are allocated to each energy storage device, including:

[0036] High-frequency components are distributed to lithium-ion capacitors and flywheels, and low-frequency components are distributed to lithium iron phosphate batteries, all-vanadium flow batteries, and hydrogen fuel cells

[0037] The photovoltaic hybrid energy storage system capacity configuration method provided by the present invention has the following beneficial effects:

[0038] The present invention uses the energy storage capacity and rated power of each energy storage device in the photovoltaic hybrid energy storage system as variables to construct a full life cycle cost model and a power failure rate model; the cost and power failure rate corresponding to different capacity configuration schemes are calculated through the full life cycle cost model and the power failure rate model, and finally the capacity configuration scheme with the lowest cost and a power failure rate that meets the power failure rate range requirements of the area to be configured is selected to perform capacity configuration of the photovoltaic hybrid energy storage system, so that the configuration scheme can meet the requirements of both economic performance and power failure rate, thereby ensuring the reliability of the photovoltaic hybrid energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiment of the present invention and its design scheme, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A structural diagram of a photovoltaic hybrid energy storage system provided by an embodiment of the present invention;

[0041] Figure 2 A schematic flow chart of a photovoltaic hybrid energy storage system capacity configuration method provided in an embodiment of the present invention;

[0042] Figure 3 A structural diagram of a two-layer optimization model provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the technical solution of the present invention and implement it, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.

[0044] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the technical solutions of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0045] In addition, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance. In the description of the present invention, it should be noted that, unless otherwise clearly specified or limited, the terms "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. In the description of the present invention, unless otherwise specified, "plurality" means two or more, which will not be described in detail here.

[0046] Before introducing the method for optimizing the configuration of the capacity of the photovoltaic hybrid energy storage system provided in the embodiment of the present invention, the photovoltaic hybrid energy storage system involved in the present invention is first introduced.

[0047] Figure 1 A structural diagram of a photovoltaic hybrid energy storage system provided in an embodiment of the present invention.

[0048] For example, Figure 1 As shown, the photovoltaic hybrid energy storage system includes five energy storage methods, namely, hydrogen fuel cells, all-vanadium liquid flow batteries, lithium iron phosphate batteries, flywheel energy storage and lithium ion capacitors. Optionally, at least two of the above five energy storage methods can be selected to form a photovoltaic hybrid energy storage system.

[0049] The energy storage configuration of the photovoltaic hybrid energy storage system adopts a common method of AC side coupling and DC side coupling. The DC side energy storage selections are lithium iron phosphate batteries, all-vanadium liquid flow batteries and hydrogen fuel cells, while the AC side energy storage uses lithium ion capacitors and flywheel energy storage with faster response speed and smaller capacity. Among them, the DC coupling method is to connect the energy storage unit to the DC bus through a bidirectional DC / DC converter, and the AC coupling method is to directly couple the energy storage module to the AC power grid through a DC / DC converter and a DC / AC converter, and the AC and DC buses are connected through a DC / AC converter.

[0050] Based on the above photovoltaic hybrid energy storage system, an embodiment of the present invention provides a photovoltaic hybrid energy storage system capacity configuration method applied to the system, such as Figure 2 As shown, the method mainly includes the following steps:

[0051] S1, takes the energy storage capacity and rated power of each energy storage device in the photovoltaic hybrid energy storage system as variables, takes the full life cycle cost of the photovoltaic hybrid energy storage system within the engineering period as output, constructs a full life cycle cost model, and takes the power failure rate on a typical day as output to construct a power failure rate model.

[0052] Among them, the full life cycle cost model of the photovoltaic hybrid energy storage system can be composed of a typical day operation and maintenance cost model, a one-time investment cost model, an equipment replacement cost model within the project cycle, and a residual value recovery cost model. It should be understood that the input of the full life cycle cost model is the energy storage capacity and rated power of each energy storage device, and the output is the full life cycle cost.

[0053] The process of establishing each model is introduced below.

[0054] Establishment of operation and maintenance cost model:

[0055] It should be understood that in a photovoltaic hybrid energy storage system, the operation and maintenance costs of energy storage devices in a typical day include fixed costs and variable costs. Among them, fixed costs are related to the capacity of the device and also include the labor costs of staff; variable costs mainly include the equipment maintenance costs caused by the normal use of the system and environmental impact. Based on this, the operation and maintenance costs can be divided into capacity maintenance costs and power maintenance costs. The operation and maintenance cost model can be expressed as:

[0056] C YW =C EO E rate +C PO P rate ;

[0057] Among them, C YW is the operation and maintenance cost of a typical daily energy storage device, C EO is the unit energy storage capacity operation and maintenance cost of the energy storage device, C PO is the unit rated power operation and maintenance cost of the energy storage device, E rate is the energy storage capacity of the energy storage device, P rate is the rated power of the energy storage device.

[0058] One-time investment cost model establishment:

[0059] The one-time investment cost model includes a one-time investment cost model without hydrogen fuel cells and a one-time investment cost model with hydrogen fuel cells.

[0060] Modeling of one-time investment costs without hydrogen fuel cells:

[0061] One-time investment cost refers to the cost of project startup, equipment purchase, and infrastructure construction. In a hybrid energy storage system, the one-time investment cost is mainly related to the energy storage capacity and rated power of the energy storage device, as well as the total cycle of the project. Its expression is as follows:

[0062]

[0063] Among them, C OC1 is the one-time investment cost of the energy storage system without hydrogen fuel cells, r is the discount rate, and y is the project period.

[0064] One-time investment cost model of hydrogen fuel cells is established:

[0065] Since the raw materials for power generation of hydrogen fuel cells are composed of hydrogen, oxygen, etc., the capacity unit of hydrogen fuel cells is the volume unit of the gas tank (cubic meter), and the calculation formula for its one-time investment cost is:

[0066] C OC_FC =C E E FC +C P P FC ;

[0067] Among them, C OC_FC is the one-time investment cost of hydrogen fuel cell, C E is the unit energy storage volume cost of hydrogen fuel cell gas tank, C P is the unit rated power cost of hydrogen fuel cells, E FC is the energy storage capacity of the hydrogen fuel cell gas tank, P FC is the rated power of the hydrogen fuel cell.

[0068] Establishment of equipment replacement cost model within the project cycle:

[0069] During the entire project cycle, energy storage devices with shorter service life need to be replaced, which is the replacement cost of the equipment. This part of the cost should also be calculated into the full life cycle cost. In the present invention, the project cycle is set to 20 years. Only lithium iron phosphate batteries and all-vanadium flow batteries have a service life shorter than 20 years. Therefore, the replacement cost part can only calculate the two energy storage methods of lithium iron phosphate batteries and all-vanadium flow batteries. The calculation formula is as follows:

[0070]

[0071] Among them, C REP represents the equipment replacement cost, and m is the number of times the energy storage device is replaced during the project cycle.

[0072] Establishment of residual value recovery cost model:

[0073] The residual value recovery cost is to take into account the residual value or scrap value that may be realized after the equipment's service life ends in the capital budget. Since only lithium iron phosphate batteries and all-vanadium flow batteries need to be replaced during the project cycle, the residual value recovery cost is only calculated for these two types of energy storage. The calculation formula is as follows:

[0074]

[0075] Among them, C REC is the residual value recovery cost, σ REC is the residual value recovery rate.

[0076] Combining the above calculation costs, the full life cycle cost model of the photovoltaic hybrid energy storage system can be obtained as follows:

[0077] C AL =C OC +C REP -C REC +C YW ×365×y;

[0078] Among them, C AL represents the life cycle cost of the photovoltaic hybrid energy storage system, C OC Represents the one-time investment cost. When the energy storage system includes hydrogen fuel cells, C OC =C OC1 +C OC_FC ; When the energy storage system does not include hydrogen fuel cells, C OC =C OC1 ; C REP represents the replacement cost of the energy storage device during the project cycle, C REC Represents the residual value recovery cost, C YW represents the operation and maintenance cost of the energy storage device on a typical day, and y represents the project cycle in years.

[0079] Next, the establishment of the power failure rate model is introduced:

[0080] The load power failure rate refers to the situation where the power system cannot meet the load demand within a period of time. This indicator has important reference value for evaluating the power supply reliability of the power system. The load power failure rate is the ratio of the energy storage system's power shortage to the actual load demand power, and its expression is shown in the following formula:

[0081]

[0082] Where LER represents the load power failure rate, P sp (t) represents the power deficit of the photovoltaic hybrid energy storage system at time t, P sp(t) = ∑P E (t)-P load (t), P E (t) is the output power of different energy storage at time t, P load (t) is the required power of the photovoltaic hybrid energy storage system at time t.

[0083] After establishing the life cycle cost model and the typical day power failure rate model, further constraints are set.

[0084] It should be understood that in order to avoid overcharging and over-discharging of each energy storage device and to increase the normal service life of the energy storage device, it is necessary to constrain the state of charge at each moment:

[0085] SOC min ≤SOC ES ≤SOC max ;

[0086] Among them, SOC ES Indicates the state of charge of the energy storage device, SOC min Indicates the lower limit of the state of charge of the energy storage device, SOC max Indicates the upper limit of the state of charge of the energy storage device.

[0087] Secondly, in order to ensure the power supply reliability of the system, it is necessary to set a load power failure rate constraint. The load power failure rate constraint can be set according to the power failure rate range requirements of the area to be configured, for example, within 2%.

[0088] Then it is necessary to set the energy storage capacity constraints of the energy storage device. Through the system load demand and energy storage capacity experience configuration, a range of initial capacity configuration sizes for various energy storage methods is given to facilitate the subsequent algorithm optimization calculation.

[0089] E min ≤E ES ≤E max ;

[0090] Among them, E ES is the energy storage capacity of the energy storage device, E min is the lower limit of energy storage capacity, E max is the upper limit of energy storage capacity.

[0091] Finally, in order to avoid drastic fluctuations in grid-connected power and ensure the continuous and stable operation of the energy storage system, it is necessary to set the charging and discharging power constraints of the energy storage device. The charging and discharging power of different energy storage methods should be limited to a certain power range:

[0092] 0≤P ES ≤P cmax ;

[0093] 0≤P ES ≤Pdmax ;

[0094] Among them, P ES is the discharge power of the energy storage device, P cmax is the upper limit of the charging power of the energy storage device, P dmax It is the upper limit of the discharge power of the energy storage device.

[0095] S2, initializing the energy storage capacity and the rated power within the set energy storage capacity range and the rated power range to obtain different capacity configuration schemes.

[0096] Among them, the capacity configuration plan includes the energy storage capacity and rated power of each energy storage device.

[0097] It should be understood that in the early stage, a certain range will be set for the energy storage capacity and the rated power for constraint. Therefore, by initializing within the set energy storage capacity range and the rated power range, different capacity configuration schemes can be obtained.

[0098] S3, for different capacity configuration schemes, the corresponding cost is calculated through the life cycle cost model, and the corresponding power failure rate is calculated through the power failure rate model.

[0099] Exemplarily, the energy storage capacity and rated power in the capacity configuration scheme are substituted into the above-mentioned life cycle cost model for calculation, and the life cycle cost is output.

[0100] It should be understood that since the energy storage capacity and rated function of the energy storage device will affect the performance of the photovoltaic hybrid energy storage system, thereby affecting the power failure rate of the system, the power failure rate cannot be directly calculated through the rated power size and the energy storage capacity size. Therefore, the VMD algorithm can be used to divide the required power of the photovoltaic hybrid energy storage system and allocate power to each energy storage device to simulate the actual working state of the photovoltaic hybrid energy storage system during the day to obtain the power failure rate.

[0101] It should be understood that in order to take into account the photovoltaic power generation characteristics of the area to be configured, the photovoltaic output data of the local typical day can be combined and clustered using the mean shift clustering algorithm to obtain the required power of the photovoltaic hybrid energy storage system on a typical day.

[0102] The mean shift clustering algorithm does not require the number of clusters to be specified in advance, but automatically determines the cluster center by the density distribution of data points in the feature space. The algorithm forms cluster centers by continuously moving data points in the direction of maximum density and eventually converges to the local density maximum. The locations of these cluster centers represent the dense areas of data points, i.e., clusters. The calculation steps are as follows:

[0103] B1: Construct a PV output dataset, treat all data points as a cluster, and initialize the points as all points within the “window”.

[0104] B2: For each weather condition represented by a data point, calculate its centroid within the window; the centroid is the average position of all points within the window.

[0105] B3: Move the data points representing different weather conditions to the centroid, repeat this step, and recalculate the centroid position until the position of the data points no longer changes significantly, that is, converges to a local density maximum.

[0106] B4: All data points that converge to the same density maximum are classified into the same cluster. At the same time, each density maximum position represents a cluster center.

[0107] B5: If the distance between some clusters is less than the specified threshold, these clusters are merged into one, and the final clustering result includes all cluster centers and their corresponding clusters.

[0108] B6: Select the mean curve of the cluster with a larger number of samples as the power demand of the hybrid energy storage system on a typical day.

[0109] Furthermore, the VMD algorithm is used to allocate the required power to each energy storage device. The working status of each energy storage device in a typical day is simulated based on the allocated power and different capacity configuration schemes to obtain the power deficit of the photovoltaic hybrid energy storage system. The power deficit and required power are brought into the power failure rate model to obtain the power failure rate of different capacity configuration schemes.

[0110] The process of allocating photovoltaic power to each energy storage device through the VMD algorithm is as follows: input the required power into the VDM algorithm, and use the VMD algorithm to decompose the required power into multiple modal functions; divide the modal function into high-frequency components and low-frequency components according to the frequency range; and allocate the high-frequency components and low-frequency components to each energy storage device based on the characteristics of the energy storage device.

[0111] Since the frequencies of each selected energy storage device are different, the VMD algorithm decomposition result decomposes the required power into multiple components from high frequency to low frequency. Combined with the different operating characteristics of DC side energy storage and AC side energy storage, the high-frequency components are distributed to lithium-ion capacitors and flywheel energy storage in turn, while the three relatively low-frequency components correspond to the power input and output of lithium iron phosphate batteries, all-vanadium liquid flow batteries and hydrogen fuel cells respectively.

[0112] In the above technical solution, the mean shift clustering algorithm is used to cluster the historical photovoltaic power generation output data of the configuration area to obtain the photovoltaic output data on typical days, so that the capacity can be configured for different typical days, so that the capacity configuration plan can support randomized scenarios; the allocation strategy obtained by the VMD algorithm can avoid the frequent charging and discharging of lithium iron phosphate batteries and all-vanadium liquid flow batteries, thereby extending the service life of the energy storage device, and make full use of the fast response speed of the AC side energy storage method to make the overall output power of the system more stable.

[0113] S4, taking the capacity configuration scheme with the lowest cost and a power failure rate that meets the power failure rate range requirements of the area to be configured as the target capacity configuration scheme, and configuring the capacity of the photovoltaic hybrid energy storage system.

[0114] The costs and power failure rates corresponding to different capacity configuration schemes are calculated through the S3 process, and then a comprehensive comparison is made to select the capacity configuration scheme whose power failure rate meets the power failure rate range requirements of the area to be configured and has the lowest cost for capacity configuration.

[0115] Alternatively, since the life cycle cost and the power failure rate of the energy storage system are two mutually constrained objectives that constitute a multi-objective problem, the NSGA-Ⅱ algorithm can be used to solve them simultaneously. The process is as follows:

[0116] (1) Randomly generate an initialized population to obtain different capacity configuration schemes, namely, the energy storage capacity E and rated power P of each energy storage device.

[0117] (2) For different capacity configuration schemes, the corresponding costs are calculated using the life cycle cost model, and the corresponding power outage rates are calculated using the power outage rate model. Based on the costs and power outage rates corresponding to different individuals, the individuals are sorted in a non-dominated manner. If the energy storage capacity E and rated power P of a certain capacity configuration scheme in the population are better than those of other capacity configuration schemes, it is a non-dominated solution. Otherwise, it is a dominated solution. Different Pareto levels are assigned to each individual according to the non-dominated sorting.

[0118] (3) The crowding distance of different capacity configuration schemes is calculated. The solutions with high crowding degree are retained through crowding calculation, so that different capacity configuration schemes in the population are evenly distributed in the target space, avoiding falling into the optimal solution, thereby ensuring the diversity of the population.

[0119] (4) According to the non-dominated sorting and crowding distance, target individuals are selected from different individuals to form the next generation population.

[0120] (5) Perform crossover and mutation on the selected target individuals to generate a new capacity configuration plan, and merge the newly generated energy storage configuration plan with the previous generation population into a new population.

[0121] (6) Repeat steps (2) to (5) until the preset number of iterations is reached, extract the Pareto frontier from the new population, and select one or more individuals on the Pareto frontier as the target capacity configuration plan.

[0122] The above optimization problem of energy storage capacity configuration can be solved by establishing a two-level optimization model, such as Figure 3 As shown, Figure 3 A structural diagram of a two-layer optimization model provided in an embodiment of the present invention.

[0123] For example, Figure 3 As shown in the figure, the power failure rate is calculated through the inner model. First, the typical day photovoltaic output data is obtained according to the mean shift clustering algorithm, and then the demand power on a typical day is obtained. The VMD decomposition algorithm is used to decompose the demand power, determine the operation of different energy storage modes in each time period on a typical day of the photovoltaic hybrid energy storage system, and then the power failure rate on a typical day is obtained, and the power failure rate is passed to the outer model. The constraints of the inner model include the SOC limit of each energy storage device, the operating power limit on a typical day, the operating capacity limit, and the power failure rate constraint on a typical day.

[0124] The outer model aims to minimize the full life cycle cost of the energy storage system and ensure that the power outage rate on a typical day meets the power outage rate requirements. The energy storage capacity configuration and rated power configuration of the photovoltaic hybrid energy storage system are used as variables, and the limits of the energy storage capacity and rated power are set as constraints. The power outage rate calculated by the inner model is combined to solve the problem and obtain the optimal capacity configuration plan with the lowest full life cycle cost and the power outage rate on a typical day meeting the power outage rate requirements.

[0125] In the above embodiment, the power failure rate is calculated through the inner model, and the outer model is constructed with the lowest life cycle cost and the power failure rate meeting the power failure rate requirement as the optimization goal. At the same time, the output characteristics of different photovoltaic and energy storage technologies, the life of the energy storage system, and multiple objective constraints such as energy storage power / energy balance and energy storage charge state limitation are comprehensively considered, and then the optimal capacity configuration plan is obtained, so that the overall model considerations are more comprehensive and close to reality.

[0126] It should be pointed out that the specific implementation methods described above can enable those skilled in the art to understand the invention more comprehensively, but do not limit the invention in any way. Therefore, although the invention has been described in detail in this specification and embodiments, those skilled in the art should understand that the invention can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the invention are included in the protection scope of the patent for the invention. Any figure mark in the claims should not be regarded as limiting the claims involved. Any simple change or equivalent replacement of the technical solution that can be obviously obtained by any technician familiar with the field within the technical scope disclosed in the present invention belongs to the protection scope of the present invention.

Claims

1. A photovoltaic hybrid energy storage system capacity configuration method, characterized in that: The photovoltaic hybrid energy storage system includes at least two energy storage devices, including: The energy storage capacity and rated power of each energy storage device in the photovoltaic hybrid energy storage system are used as variables, and the full life cycle cost of the photovoltaic hybrid energy storage system during the project period is used as output to construct a full life cycle cost model, and the power failure rate model is constructed with the power failure rate on a typical day as output; Initializing the energy storage capacity and the rated power within the set energy storage capacity range and the rated power range to obtain different capacity configuration schemes, wherein the capacity configuration schemes include the energy storage capacity size and the rated power size; For different capacity configuration schemes, the corresponding costs are calculated through the life cycle cost model, and the corresponding power failure rate is calculated through the power failure rate model; The capacity configuration scheme with the lowest cost and a power failure rate that meets the power failure rate range requirements of the area to be configured is used as the target capacity configuration scheme, and the capacity of the photovoltaic hybrid energy storage system is configured.

2. The photovoltaic hybrid energy storage system capacity configuration method according to claim 1, characterized in that: The full life cycle cost model of the photovoltaic hybrid energy storage system is: C AL =C OC +C REP +C REC +C YW ×365×y; Among them, C AL represents the life cycle cost of the photovoltaic hybrid energy storage system, C OC represents the one-time investment cost, C REP represents the replacement cost of the energy storage device during the project cycle, C REC Represents the residual value recovery cost, C YW represents the operation and maintenance cost of the energy storage device on a typical day, and y represents the project cycle in years.

3. The photovoltaic hybrid energy storage system capacity configuration method according to claim 1 or 2, characterized in that: The power failure rate model is: Where LER represents the load power failure rate, P sp (t) is the power deficit of the photovoltaic hybrid energy storage system at time t, P sp (t) = ∑P E (t)-P load (t); P E (t) is the output power of each energy storage device at time t, P load (t) represents the required power of the photovoltaic hybrid energy storage system at time t.

4. The photovoltaic hybrid energy storage system capacity configuration method according to claim 1 or 2, characterized in that: After calculating the corresponding costs for different capacity configuration schemes through the life cycle cost model and the corresponding power failure rate through the power failure rate model, it also includes: The initialized capacity configuration scheme is regarded as the population, and different capacity configuration schemes are regarded as different individuals. The individuals are non-dominated and sorted based on the corresponding costs and power failure rates of different individuals, and different Pareto levels are assigned to each individual. Select target individuals from different individuals according to Pareto rank and crowding distance, perform crossover and mutation on target individuals to generate a new population; Iterate the process of calculating cost and power failure rate, assigning Pareto levels and generating new populations until the preset number of iterations is reached; Extract the Pareto frontier from the new population and select one or more individuals on the Pareto frontier as the target capacity configuration solution.

5. The photovoltaic hybrid energy storage system capacity configuration method according to claim 1 or 2, characterized in that: For different capacity configuration schemes, the corresponding power failure rate is calculated through the power failure rate model, including: Obtain the historical photovoltaic output data set of the area to be configured, cluster the historical photovoltaic output data set using the mean shift clustering algorithm, and obtain the required power on a typical day; The VMD algorithm is used to allocate the required power to each energy storage device. Based on the allocated power and different capacity configuration schemes, the working status of each energy storage device in a typical day is simulated to obtain the power deficit of the photovoltaic hybrid energy storage system. Substitute the deficit power and demand power into the power failure rate model to obtain the power failure rates corresponding to different capacity configuration schemes.

6. The photovoltaic hybrid energy storage system capacity configuration method according to claim 5, characterized in that: The historical photovoltaic output data set includes photovoltaic output data under different weather conditions. The historical photovoltaic output data set is clustered by the mean shift clustering algorithm to obtain the power demand on a typical day, including: Calculate the centroid of all data points in the window in the historical photovoltaic output data set; Move the data points representing different weather conditions to the centroid of the window, and recalculate the centroid based on the moved position; iterate the moving process until the position of the data point no longer changes significantly, and obtain multiple clusters; The clusters containing data points greater than the preset threshold are selected, and the mean curve of the cluster is calculated as the required power on a typical day.

7. The photovoltaic hybrid energy storage system capacity configuration method according to claim 5 or 6, characterized in that: The VMD algorithm is used to allocate the required power to each energy storage device, including: The required power is input into the VDM algorithm, and the VMD algorithm is used to decompose the required power into multiple mode functions; The modal function is divided into high-frequency components and low-frequency components according to the frequency range; Based on the characteristics of the energy storage devices, the high-frequency components and the low-frequency components are allocated to the respective energy storage devices.

8. The photovoltaic hybrid energy storage system capacity configuration method according to claim 7, characterized in that: Energy storage devices include lithium-ion capacitors, flywheels, lithium iron phosphate batteries, all-vanadium flow batteries and hydrogen fuel cells. Based on the characteristics of the energy storage devices, high-frequency components and low-frequency components are allocated to each energy storage device, including: The high-frequency components are allocated to lithium-ion capacitors and flywheels, and the low-frequency components are allocated to lithium iron phosphate batteries, all-vanadium liquid flow batteries and hydrogen fuel cells.

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