Transformer dynamic capacity increasing method and device based on existing building light storage integrated design

Abstract

The invention provides a transformer dynamic capacity increasing method and device based on existing building light storage integrated design. The method comprises the following steps: establishing a transformer dynamic capacity-increasing mathematical model of an energy storage system in a control period through a time-of-use electricity price policy of an existing building, charging and discharging power of the energy storage system, basic electrical load of a user and photovoltaic power generation power; and taking the charging and discharging power of the energy storage system at the t moment as a decision variable, and solving the dynamic capacity-increasing mathematical model of the transformer by adopting a particle swarm optimization algorithm improved by a random reverse learning strategy, so that the load fluctuation rate and the charging cost of a user-side net load curve are minimum, and the dynamic capacity increasing of the transformer is realized. According to the method, the load rate of the transformer is accurately controlled to be within the safety interval, the flexible adjustment capacity of the building load is improved, meanwhile, the efficient consumption of photovoltaic energy and the operation economy of an energy storage system are considered, and an innovative solution is provided for flexible upgrading and safe and economical operation of an existing building power system under the dual-carbon target.

Description

Technical Field

[0001] The present invention belongs to the technical field of existing building energy system optimization, and specifically relates to a method and device for dynamically increasing the capacity of a transformer based on an integrated photovoltaic and energy storage design of an existing building. Background Art

[0002] Existing buildings generally face insufficient transformer capacity due to technical limitations during initial construction and increased electricity load. While traditional physical capacity expansion methods, such as replacing or expanding transformers, can improve power supply capacity, they are subject to high investment costs, long implementation cycles, and challenges such as limited space and complex construction in urban built-up areas. These methods struggle to meet the demands for flexible upgrades of existing building power systems. Existing load regulation technologies primarily rely on user-side management strategies, such as time-of-use pricing guidance, direct load control, or intelligent terminal optimization. However, these methods either rely on active user responses, which can easily affect user experience, or have limited adjustment ranges, making it difficult to achieve efficient and flexible load regulation.

[0003] In recent years, the application of integrated photovoltaic and energy storage systems in buildings has provided a new path for dynamic capacity expansion, but existing technologies have significant shortcomings. The independent operation mode of photovoltaic and energy storage lacks a coordination mechanism, static scheduling strategies cannot cope with real-time fluctuations, and single-objective optimization does not take into account transformer load factor control, resulting in low feasibility of scheduling schemes. Its essential flaw is that it does not establish a dynamic coupling model of "photovoltaic-energy storage-load-transformer", ignores the response characteristics of energy storage equipment and the uncertainty of photovoltaic output, and traditional optimization algorithms have difficulty in solving the global optimal solution under multiple constraints. There is an urgent need for a dynamic capacity expansion technology that takes into account distribution safety, photovoltaic and energy storage coordination, and economic efficiency. Summary of the Invention

[0004] To address the limited power supply capacity caused by insufficient transformer capacity in existing buildings, as well as the high investment and long cycle of traditional physical capacity expansion methods and the limited effectiveness of existing load regulation strategies, the present invention provides a method and device for dynamic transformer capacity expansion based on an integrated photovoltaic and energy storage design for existing buildings. By constructing a dynamic capacity expansion model for existing building transformers and combining it with an improved particle swarm optimization algorithm (IPSO) to optimize the photovoltaic and energy storage coordinated scheduling strategy, the transformer load rate can be accurately controlled to a safe range without replacing or expanding the transformer, improving the building's load flexibility. This approach simultaneously takes into account the efficient absorption of photovoltaic energy and the economic operation of the energy storage system, providing an innovative solution for the flexible upgrade and safe and economical operation of existing building power systems under the "dual carbon" goals.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for dynamically increasing the capacity of a transformer based on an integrated photovoltaic and energy storage design of an existing building, the specific steps of which are as follows:

[0006] A mathematical model for the dynamic capacity expansion of the transformer within one control cycle of the energy storage system is established by taking into account the time-of-use electricity price policy of existing buildings, the charging and discharging power of the energy storage system, the user's basic electricity load, and the photovoltaic power generation power.

[0007] Taking the charging and discharging power of the energy storage system at time t as the decision variable, a particle swarm optimization algorithm improved with a random reverse learning strategy is used to solve the mathematical model of transformer dynamic capacity expansion, so as to minimize the load fluctuation rate and charging cost of the user-side net load curve and realize dynamic capacity expansion of the transformer.

[0008] Furthermore, the constraints of the transformer dynamic capacity expansion mathematical model include energy storage system capacity constraint, energy storage system output power constraint and transformer load rate constraint;

[0009] The mathematical model of transformer dynamic capacity increase is expressed as:

[0010] minf cap =f load +f gain

[0011] Among them, f load is the load fluctuation rate; f gain For charging costs;

[0012] Minimizing the load fluctuation rate is expressed as:

[0013]

[0014] Where minf load represents the minimization of load fluctuation rate; T represents a control cycle of the energy storage system; P e (t) is the charging and discharging power of the energy storage system at time t; P load (t) represents the basic electricity load of the user at time t; P pv (t) represents the photovoltaic power generation at time t; P ave Indicates the average value of the user's net load after regulation through photovoltaic energy storage;

[0015] Minimizing the charging cost is expressed as:

[0016]

[0017] Where, E p is the peak electricity price in the time-of-use electricity price; E m is the normal electricity price; E v is the off-peak electricity price; T p is the peak period; T m Normal period; T v is the valley period; P e d (t) and They represent the discharge power and charging power of the energy storage system at time t, respectively. The calculation of the value is shown in the following formula:

[0018]

[0019] Among them, P e (t) is the charging and discharging power of the energy storage system at time t.

[0020] Furthermore, the energy storage system capacity constraint is specifically that the energy storage system's own power at time t is between the maximum and minimum power allowed by the energy storage system.

[0021] Furthermore, the energy storage system output power constraint is specifically: the charging and discharging power of the energy storage system at time t is less than or equal to the maximum output power of the energy storage system, and the maximum output power of the energy storage system does not exceed the rated power value.

[0022] Furthermore, the transformer load rate constraint is specifically as follows: the sum of the net load and the energy storage charging load is between 0% and 90% of the transformer capacity; the net load is the user's basic electricity load minus the photovoltaic power generation power.

[0023] Furthermore, the particle swarm optimization algorithm improved by the random reverse learning strategy specifically introduces the random reverse learning strategy into the particle optimization stage of the particle swarm optimization algorithm.

[0024] The present invention also provides a transformer dynamic capacity expansion system with integrated photovoltaic energy storage design, comprising:

[0025] A model building module is used to establish a mathematical model for the dynamic capacity expansion of the transformer within one control cycle of the energy storage system based on the time-of-use electricity price policy of the existing building, the charge and discharge power of the energy storage system, the user's basic power load, and the photovoltaic power generation power;

[0026] The dynamic capacity expansion module uses the charging and discharging power of the energy storage system at time t as the decision variable and adopts a particle swarm optimization algorithm improved by a random reverse learning strategy to solve the mathematical model of the transformer dynamic capacity expansion, thereby minimizing the load fluctuation rate and charging cost of the user-side net load curve and realizing dynamic capacity expansion of the transformer.

[0027] The present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for dynamically increasing the capacity of a transformer based on the integrated photovoltaic and storage design of an existing building are implemented.

[0028] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the above-mentioned method for dynamically increasing the capacity of a transformer based on the integrated photovoltaic and storage design of an existing building.

[0029] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for dynamically increasing the capacity of a transformer based on the integrated photovoltaic and storage design of an existing building.

[0030] Compared with the prior art, the present invention has at least the following beneficial effects:

[0031] The present invention provides a method for dynamic capacity expansion of transformers based on the integrated photovoltaic and storage design of existing buildings. Through the deep integration of photovoltaic and storage coordinated scheduling and the improved IPSO algorithm, it breaks through the high cost and long cycle bottleneck of traditional physical capacity expansion. Compared with the existing technology, its core advantages are: by establishing a dynamic capacity expansion model for existing building transformers, the transformer load rate is accurately controlled to a safe range, avoiding the impact of rigid load regulation on user experience and achieving a significant reduction in load fluctuation rate; by improving the particle swarm algorithm with the help of random reverse learning strategy, the optimization efficiency under multiple constraints is greatly improved, making the energy storage charging and discharging strategy more in line with real-time fluctuation scenarios; without modifying the distribution facilities, the photovoltaic absorption capacity is effectively improved and the energy storage investment payback period is shortened, providing an efficient, economical and adaptable solution for the flexible upgrade of the existing building power system under the "dual carbon" goal, significantly enhancing the safety, flexibility and sustainability of the building energy system.

[0032] This invention uses a photovoltaic-storage collaborative scheduling strategy and an improved particle swarm optimization algorithm (IPSO) to achieve flexible improvement in distribution capacity without replacing or expanding transformers. It is suitable for upgrading and renovating the power systems of existing buildings such as commercial buildings, office buildings, and industrial plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the specific implementation methods or the description of the prior art.

[0034] Figure 1 This is a flow chart of a method for dynamically increasing the capacity of a transformer based on an integrated photovoltaic and energy storage design of an existing building according to the present invention;

[0035] Figure 2 This is a flow chart of dynamic capacity expansion in the present invention;

[0036] Figure 3The electricity load and photovoltaic power generation data collected in one scheduling cycle of four typical days in an embodiment of the present invention;

[0037] Figure 4 The peak and valley time periods of the location of the existing building in the embodiment of the present invention;

[0038] Figure 5 The dynamic capacity increase result for a typical day, May 1, in an embodiment of the present invention;

[0039] Figure 6 The dynamic capacity increase result for a typical day, August 18, in an embodiment of the present invention;

[0040] Figure 7 The dynamic capacity increase result for a typical day, October 4, in an embodiment of the present invention;

[0041] Figure 8 The dynamic capacity increase result for a typical day, December 9, in an embodiment of the present invention;

[0042] Figure 9 is the transformer load factor on a typical day, August 18, in an embodiment of the present invention;

[0043] Figure 10 are the lowest and highest load rates of each typical transformer under the sun in the embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0045] like Figure 1 As shown, for existing buildings, the present invention provides a method for dynamically increasing the capacity of transformers based on the integrated photovoltaic and energy storage design of existing buildings, including:

[0046] Step 1: Obtain the time-of-use electricity price policy, transformer capacity parameters, energy storage system capacity parameters, photovoltaic power generation data within a scheduling cycle, and power load data for the location of the existing building;

[0047] 1) Obtain the local time-of-use electricity price policy for existing buildings, and calculate the valley, flat, and peak periods and their corresponding electricity prices;

[0048] 2) Obtain the transformer capacity parameters of the existing building and the charge and discharge capacity parameters of the energy storage system;

[0049] 3) Collect and count the electricity load data and distributed photovoltaic output data of existing buildings within a scheduling cycle, and finally obtain the electricity load and photovoltaic power generation power data samples within a scheduling cycle.

[0050] Step 2: Set the constraints for dynamic capacity expansion of the transformer according to the above parameters;

[0051] The energy storage system capacity, output power and transformer load rate of the existing building must meet the corresponding constraints;

[0052] (1) Energy storage system capacity constraints

[0053] Since the energy storage system has its own capacity limitations, the capacity constraints of the energy storage system must be met during the scheduling process, as shown below:

[0054] Soc min ≤Soc(t)≤Soc max

[0055] Where, Soc(t) represents the energy storage system’s own charge at time t; Soc min Indicates the minimum amount of electricity allowed by the energy storage system; Soc max The maximum amount of electricity allowed by the energy storage system.

[0056] (2) Energy storage system output power constraints

[0057] When charging and discharging within a certain time period, the energy storage system must also meet power constraints and cannot exceed its rated power value, as shown below:

[0058] P e (t)≤P e,max

[0059] Where, P e,max Indicates the maximum output power of the energy storage system, which does not exceed the rated power value; P e (t) is the charging and discharging power of the energy storage system at time t. A positive value indicates that the energy storage system is charging, and a negative value indicates that the energy storage system is discharging.

[0060] (3) Transformer load rate constraint

[0061] When controlling the charging and discharging behavior of the energy storage system, the transformer load factor constraint must be met. During energy storage charging, the sum of the net load and the energy storage charging load cannot exceed 90% of the transformer capacity, meaning the transformer load factor cannot exceed 0.9. During energy storage discharging, the net load minus the energy storage discharging load must also be between 0 and 90%, meaning the transformer load factor is less than 0.9. The mathematical description of the above constraints is as follows:

[0062] 0≤Pnload (t)+P e (t)≤E t ×0.9

[0063] Where, P nload (t) is the net load at time t, that is, P nload (t) = P load (t)-P pv (t), where P load (t) represents the basic electricity load of the user at time t; P pv (t) represents the photovoltaic power generation at time t; E t Indicates the capacity (active power) of the transformer.

[0064] Step 3: Based on the constraints and considering the goal of “peak shaving and valley filling” of the electricity load of existing buildings after PV-storage synergy, a mathematical model for dynamic transformer capacity expansion for existing buildings is established;

[0065] Comprehensively consider the factors affecting the capacity of existing building transformers and construct an objective function for dynamic capacity increase of existing building transformers;

[0066] With the continuous growth of electricity load in existing buildings, transformers in the original distribution system are facing the risk of overload operation, which increases the possibility of equipment failure and power outage. To address this problem, a photovoltaic-energy storage integrated system is introduced to achieve a flexible response to the new load by optimizing the charging and discharging strategy of the energy storage system, thereby meeting the demand for load capacity expansion. In essence, the optimization of the energy storage charging and discharging strategy aims to adjust the user's original electricity load curve to make it smoother. That is, under the premise of meeting the user's new load demand, the maximum load is controlled within the rated capacity of the transformer to avoid overload operation caused by excessive load fluctuations. Therefore, one of the optimization objective functions for the dynamic capacity expansion of transformers in existing buildings in the present invention is set to minimize the load fluctuation rate minf load , that is, through the coordinated scheduling of photovoltaic and energy storage systems, the fluctuation of the net load curve on the user side can be reduced.

[0067] This goal can be formally expressed as:

[0068]

[0069] Where, T represents a control cycle of the energy storage system; P e (t) is the charge and discharge power of the energy storage system at time t. A positive value indicates that the energy storage system is charging, and a negative value indicates that the energy storage system is discharging. load (t) represents the basic electricity load of the user at time t; P pv (t) represents the photovoltaic power generation at time t; P aveIt represents the average value of the user’s net load after being regulated by photovoltaic energy storage.

[0070] The energy storage system needs to utilize the difference between peak and valley electricity prices within a scheduling cycle, and realize operating income through reasonable charging and discharging scheduling. Therefore, the second optimization objective function for the dynamic capacity expansion of existing building transformers in the present invention is set to maximize operating income. The energy storage system can obtain electricity price income by discharging to the power grid, and if it is charged from the power grid, it needs to charge corresponding electricity fees. If the charging amount of the energy storage comes from distributed photovoltaics, no charging fee is required. Maximizing operating income is to maximize the difference between the income obtained from discharge and the cost required for charging. In order to unify the goal of minimization, the present invention will equate maximizing operating income with the goal of minimizing charging costs, that is, minimizing the difference between the cost required for charging and the income obtained from discharge. This goal can be formally expressed as:

[0071]

[0072] In the formula, E in the time-of-use electricity price p is the peak electricity price; E m is the normal electricity price; E v is the off-peak electricity price; T p is the peak period; T m Normal period; T v is the valley period; P e d (t) and They represent the discharge power and charging power of the energy storage system at time t, respectively. The calculation of the value is shown in the following formula:

[0073]

[0074] In the formula, when calculating If the photovoltaic power generation power at time t is still redundant when meeting the user's electricity demand, the excess power needs to be stored in the energy storage device. No charging fee is charged for this part of the power. At this time, P e (t) should deduct redundant photovoltaic power. If the photovoltaic power generation at time t cannot meet the user's electricity demand, the energy storage system will need to be fully charged from the grid, which will require charging the corresponding electricity fee. If the photovoltaic output period during the day is off-peak, energy storage can be charged first.

[0075] In summary, the ultimate objective function of the dynamic capacity expansion of existing building transformers in the present invention is to minimize the load fluctuation rate and the charging cost, and its mathematical expression is:

[0076] minf cap =f load +f gain

[0077] The electricity load and photovoltaic power generation of existing buildings are known data, so the decision variables for the dynamic capacity increase problem of transformers in existing buildings established in this invention are P e (t), P e (t) is the charging and discharging power of the energy storage system at time t. A positive value indicates that the energy storage system is charging, and a negative value indicates that the energy storage system is discharging.

[0078] Step 4: Use the improved particle swarm optimization algorithm to solve the mathematical model of transformer dynamic capacity expansion, and obtain the optimal solution for the optimal charging and discharging scheduling strategy of the energy storage system within a scheduling cycle. Based on the optimal solution, the dynamic capacity expansion result of the transformer of the existing building within a scheduling cycle is obtained, as follows:

[0079] 1) First, initialize the IPSO population size, fitness function, maximum number of iterations and other parameters;

[0080] 2) The improved particle swarm algorithm of the present invention introduces a random reverse learning strategy in the particle optimization stage of the standard PSO algorithm. By performing random reverse learning on the global optimal solution, the convergence speed of the optimization algorithm is accelerated. The execution of this strategy can be expressed as:

[0081]

[0082] Where, represents the position of the a+1th iteration after the update; lb represents the decision variable E storage The lower limit of ub represents the decision variable E storage The upper limit of rand represents a random number between 0 and 1.

[0083] The standard PSO algorithm updates the position g of the current global optimal solution after each iteration. best , during the iteration process, all particles will be based on g best The position of the update to update its own position, g best The quality of the location will affect the iteration speed of the basic PSO algorithm.

[0084] Therefore, the improved IPSO algorithm updates the positions and g of all particles in each iteration. best After that, g best Perform a random reverse learning, according to the current g best Generate a random reverse position at the position, and judge the pros and cons of the current position and the generated random reverse position, and choose the better position for g best Updates are made to improve the algorithm's ability to jump out of the local optimal state in the later stages of iteration, so that the PSO algorithm can find a more accurate solution in each iterative optimization process and improve the convergence ability of the PSO algorithm.

[0085] 3) Determine whether the particle optimization phase's stopping conditions have been met. If so, output the optimal solution for the energy storage system's optimal charge and discharge scheduling strategy within a scheduling cycle (optimal energy storage system charge and discharge power). Based on this optimal solution, determine the dynamic capacity increase result (minimum load fluctuation rate) for the existing building's transformer within a scheduling cycle. Otherwise, re-execute the particle optimization phase.

[0086] Example 1

[0087] Taking an existing building in Jiangyin City, Jiangsu Province, China as an example for verification, a method for dynamically increasing the capacity of a transformer based on the integrated photovoltaic and energy storage design of an existing building provided by the present invention is adopted. The specific process is as follows Figure 2 As shown, specifically including:

[0088] Step 1: Basic data acquisition and parameter setting. Set the scheduling cycle to 24 hours a day, that is, T is set to 24. And obtain the user power load data and photovoltaic power generation data in the next scheduling cycle. Collect the historical power load and photovoltaic power generation data of four typical days in 2024 for the case study, such as Figure 3 Then, the historical load data and photovoltaic power generation data of 15 typical days from May 2024 to December 2024 are selected as the basic data for the research case, and the corresponding peak and valley period parameters, peak and valley electricity price parameters and transformer capacity parameters are set, as shown in the figure below. Figure 4 shown.

[0089] Step 2: Based on the above parameters, set the constraints for the dynamic capacity expansion of the existing building transformer. The existing building's energy storage system capacity, output power, and transformer load rate must all meet the corresponding constraints;

[0090] Step 2: Set the constraints for dynamic capacity expansion of the transformer according to the above parameters;

[0091] The energy storage system capacity, output power and transformer load rate of the existing building must meet the corresponding constraints;

[0092] (1) Energy storage system capacity constraints

[0093] Since the energy storage system has its own capacity limitations, the capacity constraints of the energy storage system must be met during the scheduling process, as shown below:

[0094] Soc min ≤Soc(t)≤Soc max

[0095] Where, Soc(t) represents the energy storage system’s own charge at time t; Soc min Indicates the minimum amount of electricity allowed by the energy storage system; Soc max The maximum amount of electricity allowed by the energy storage system.

[0096] (2) Energy storage output power constraints

[0097] When charging and discharging within a certain time period, the energy storage system must also meet power constraints and cannot exceed its rated power value, as shown below:

[0098] P e (t)≤P e,max

[0099] Where, P e,max Indicates the maximum output power of the energy storage system, which does not exceed the rated power value.

[0100] (3) Transformer load rate constraint

[0101] When controlling the charging and discharging behavior of the energy storage system, the transformer load factor constraint must be met. When charging, the net load and the energy storage charging load must not exceed 90% of the transformer capacity. When discharging, the net load minus the energy storage discharging load must also be between 0 and 90%. The corresponding mathematical description is as follows:

[0102] 0≤P nload (t)+P e (t)≤E t ×0.9

[0103] Where, P nload (t) is the net load at time t, that is, P nload (t) = P load (t)-P pv (t).

[0104] Step 3: Comprehensively consider the factors affecting the capacity of existing building transformers and construct an objective function for dynamic capacity increase of existing building transformers;

[0105] For the existing building transformer in the embodiment, the optimization objective function for dynamic capacity expansion is set to minimize the load fluctuation rate. This objective can be formally expressed as:

[0106]

[0107] Wherein, T represents a control cycle of the energy storage system. In this embodiment, the value of T is 24, and the time interval is 1 hour. e (t) is the charge and discharge power of the energy storage system at time t. A positive value indicates that the energy storage system is charging, and a negative value indicates that the energy storage system is discharging. load (t) represents the basic electricity load of the user at time t; P pv (t) represents the photovoltaic power generation at time t; P ave It represents the average value of the user’s net load after being regulated by photovoltaic energy storage.

[0108] Step 4: Use the improved particle swarm optimization algorithm IPSO based on randomized reverse learning to obtain the optimal solution for the optimal charging and discharging scheduling strategy of the energy storage system within a scheduling cycle.

[0109] 1) First, initialize the IPSO population size, fitness function, maximum number of iterations and other parameters;

[0110] 2) Then, the improved particle swarm algorithm of the present invention introduces a random reverse learning strategy in the particle optimization phase of the standard PSO algorithm. By performing random reverse learning on the global optimal solution, the convergence speed of the optimization algorithm is accelerated. The execution of this strategy can be expressed as:

[0111]

[0112] Where, represents the position of the a+1th iteration after the update; lb represents the decision variable E storage The lower limit of ub represents the decision variable E storage The upper limit of rand represents a random number between 0 and 1.

[0113] By introducing a random reverse learning strategy, the algorithm's ability to escape from the local optimal state in the later stages of iteration is improved, enabling the PSO algorithm to explore more accurate solutions in each iterative optimization process and thus improving the convergence ability of the PSO algorithm.

[0114] 3) Determine whether the stopping condition has been met. If so, output the optimal solution for the energy storage system's optimal charge and discharge scheduling strategy within a scheduling cycle, i.e., the optimal energy storage system charge and discharge power. Based on the optimal energy storage system charge and discharge power data, obtain the dynamic capacity increase result of the existing building's transformer within a scheduling cycle, i.e., the load fluctuation rate after the dynamic capacity increase. Otherwise, repeat step 2.

[0115] Finally, the dynamic capacity expansion results obtained by IPSO on four typical days, May 1, August 18, October 4, and December 9, are as follows: Figures 5 to 8 As shown in Figure 1, the energy storage system charging and discharging strategy, derived from the IPSO algorithm for dynamic capacity expansion of existing building transformers, enables the energy storage system and distributed photovoltaic generation to interact with the transformer, thereby achieving the goal of peak load shaving and valley filling, thereby realizing the dynamic capacity expansion of existing building transformers. The green area in Figure (b) represents the transformer capacity expansion at different times.

[0116] In order to further understand the effectiveness of the proposed method of dynamic capacity increase for existing buildings in reducing transformer load rate, the transformer capacity increase after dynamic capacity increase and the transformer load rate before and after capacity increase on each typical day are statistically analyzed. Since the data of 15 typical days is too much, only the data of one typical day, August 18, is listed. Figure 9 As shown in the table, the dynamic capacity increase of the existing building's transformer through the integrated solar-energy storage system reduced the transformer's load factor during certain periods, increasing its available capacity. This demonstrates that the energy storage system can store energy during peak PV output and release it during periods of low PV output or at night, thereby regulating the transformer's load factor. Furthermore, negative transformer load factors can be observed during certain periods. This is because when PV output is high, if the energy storage system is not fully charged, some energy may be fed into the grid, potentially causing the transformer's load factor to appear negative during the day (e.g., the negative load factor during midday on some dates in the table). This may indicate that PV output is greater than the local load and is feeding power back to the grid. If the energy storage system is fully charged, PV output may be limited, and in this case, the transformer's load factor may not be significantly reduced by PV. At night or when PV output is low, discharging the energy storage system can increase the transformer's load factor, improving it during periods of low output.

[0117] This embodiment also collects statistics on the lowest transformer load rate and the highest transformer load rate for each of the 15 days, and obtains the following: Figure 10 The results are shown. It can be seen that by dynamically increasing the capacity of existing building transformers, the load factor during both the peak and lowest load periods of each typical day decreased. This indicates that the capacity of existing building transformers was increased by scheduling the energy storage system's charge and discharge power during these periods, validating the effectiveness of the proposed method for dynamically increasing the capacity of existing building transformers. Analysis shows that in May, the transformer load factor showed a negative value during the midday period, possibly due to high photovoltaic output and low local load. In this case, the power system can utilize dynamic capacity expansion technology to store excess photovoltaic power or transmit it to other areas with higher load demand through the smart grid, avoiding energy waste and negative impacts on the power grid. For example, when the load factor was detected to be -0.39 at 12:00 on May 1, the dynamic capacity expansion system could activate the energy storage device to increase energy storage or allocate some power to other locations. At 01:00, if the load factor was 0.09 and a subsequent load increase trend was predicted, the dynamic capacity expansion system could prepare in advance, such as by activating fast-start power generation equipment, to cope with the potential load increase and ensure the stability of the power supply.

[0118] The following are device embodiments of the present invention, which can be used to perform the method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0119] In another embodiment of the present invention, a system for dynamically increasing the capacity of a transformer with an integrated photovoltaic energy storage design is provided. The system runs the method for dynamically increasing the capacity of a transformer with an integrated photovoltaic energy storage design, and the system includes:

[0120] A model building module is used to establish a mathematical model for the dynamic capacity expansion of the transformer within one control cycle of the energy storage system based on the time-of-use electricity price policy of the existing building, the charge and discharge power of the energy storage system, the user's basic power load, and the photovoltaic power generation power;

[0121] The dynamic capacity expansion module uses the charging and discharging power of the energy storage system at time t as the decision variable and adopts a particle swarm optimization algorithm improved by a random reverse learning strategy to solve the mathematical model of the transformer dynamic capacity expansion, thereby minimizing the load fluctuation rate and charging cost of the user-side net load curve and realizing dynamic capacity expansion of the transformer.

[0122] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can implement the operation of a method for dynamically increasing the capacity of a transformer with an integrated photovoltaic energy storage design.

[0123] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for dynamic capacity increase of a transformer with an integrated photovoltaic energy storage design in the above embodiment.

[0124] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0125] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for dynamically increasing the capacity of a transformer based on an integrated photovoltaic and energy storage design of an existing building, characterized in that: The specific steps are as follows: A mathematical model for the dynamic capacity expansion of the transformer within one control cycle of the energy storage system is established by taking into account the time-of-use electricity price policy of existing buildings, the charging and discharging power of the energy storage system, the user's basic electricity load, and the photovoltaic power generation power. Taking the charging and discharging power of the energy storage system at time t as the decision variable, a particle swarm optimization algorithm improved with a random reverse learning strategy is used to solve the mathematical model of transformer dynamic capacity expansion, so as to minimize the load fluctuation rate and charging cost of the user-side net load curve and realize dynamic capacity expansion of the transformer.

2. The method for dynamically increasing the capacity of a transformer based on an integrated photovoltaic and energy storage system in an existing building according to claim 1, characterized in that: The constraints of the transformer dynamic capacity expansion mathematical model include energy storage system capacity constraint, energy storage system output power constraint and transformer load rate constraint; The mathematical model of transformer dynamic capacity increase is expressed as: minf cap =f load +f gain Among them, f load is the load fluctuation rate; f gain For charging costs; Minimizing the load fluctuation rate is expressed as: Where minf load represents the minimization of load fluctuation rate; T represents a control cycle of the energy storage system; P e (t) is the charging and discharging power of the energy storage system at time t; P load (t) represents the basic electricity load of the user at time t; P pv (t) represents the photovoltaic power generation at time t; P ave Indicates the average value of the user's net load after regulation through photovoltaic energy storage; Minimizing the charging cost is expressed as: Where, E p is the peak electricity price in the time-of-use electricity price; E m is the normal electricity price; E v is the off-peak electricity price; T p is the peak period; T m Normal period; T v It is the valley period; and They represent the discharge power and charging power of the energy storage system at time t, respectively. The calculation of the value is shown in the following formula: Among them, P e (t) is the charging and discharging power of the energy storage system at time t.

3. The method for dynamically increasing the capacity of a transformer based on an integrated photovoltaic and energy storage design of an existing building according to claim 2, characterized in that: The energy storage system capacity constraint specifically means that the energy storage system's own power at time t is between the maximum and minimum power allowed by the energy storage system.

4. The method for dynamically increasing the capacity of a transformer based on an integrated photovoltaic and energy storage system in an existing building according to claim 2, characterized in that: The energy storage system output power constraint is specifically: the charging and discharging power of the energy storage system at time t is less than or equal to the maximum output power of the energy storage system, and the maximum output power of the energy storage system does not exceed the rated power value.

5. The method for dynamically increasing the capacity of a transformer based on an integrated photovoltaic and energy storage design of an existing building according to claim 2, characterized in that: The transformer load rate constraint is specifically: the sum of the net load and the energy storage charging load is between 0% and 90% of the transformer capacity; the net load is the user's basic electricity load minus the photovoltaic power generation power.

6. The method for dynamically increasing the capacity of a transformer based on an integrated photovoltaic and energy storage design of an existing building according to claim 1, characterized in that: The particle swarm optimization algorithm improved by random reverse learning strategy specifically introduces random reverse learning strategy into the particle optimization stage of the particle swarm optimization algorithm.

7. A transformer dynamic capacity expansion system with integrated photovoltaic energy storage design, characterized in that: include: A model building module is used to establish a mathematical model for the dynamic capacity expansion of the transformer within one control cycle of the energy storage system based on the time-of-use electricity price policy of the existing building, the charge and discharge power of the energy storage system, the user's basic power load, and the photovoltaic power generation power; The dynamic capacity expansion module uses the charging and discharging power of the energy storage system at any given moment as a decision variable and adopts a particle swarm optimization algorithm improved by a random reverse learning strategy to solve the mathematical model of the transformer's dynamic capacity expansion, thereby minimizing the load fluctuation rate and charging cost of the user-side net load curve and achieving dynamic capacity expansion of the transformer.

8. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of a method for dynamically increasing the capacity of a transformer based on an integrated photovoltaic and storage design of an existing building are implemented as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a method for dynamically increasing the capacity of a transformer based on an integrated photovoltaic and storage design of an existing building are implemented as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of a method for dynamically increasing the capacity of a transformer based on an integrated photovoltaic and storage design of an existing building are implemented as described in any one of claims 1 to 6.

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