Energy storage planning method and device of household photovoltaic system, electronic equipment and medium

Abstract

The invention discloses an energy storage planning method and device for a household photovoltaic system, electronic equipment and a medium, and the method comprises the steps: determining a photovoltaic power generation capacity and an energy storage capacity according to the historical power utilization data and meteorological data of each household photovoltaic system in a community in a preset time period, and calculating a self-sufficiency rate according to the photovoltaic power generation capacity; determining a priority sequence of each household according to the self-sufficiency rate and the energy storage capacity of each household, and determining an energy storage scheduling strategy according to the capacity of the shared energy storage system and the priority sequence; the method comprises the steps of determining the photovoltaic conversion efficiency, the energy storage conversion efficiency and the charging and discharging efficiency of a photovoltaic system of each household, calculating the total efficiency and the charging and discharging income of the photovoltaic system of each household, establishing a multi-objective optimization function and constraint conditions, and searching a global optimal solution by adopting a simulated annealing algorithm so as to update the capacity of a shared energy storage system and adjust an energy storage scheduling strategy. According to the invention, the problems of poor stability, high cost, unreasonable resource allocation and the like of a traditional household photovoltaic system are solved, and the overall performance of the household photovoltaic system is improved.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic energy storage technology, and in particular to an energy storage planning method, device, electronic equipment and medium for a household photovoltaic system. Background Art

[0002] With the development of renewable energy, household photovoltaic systems have been widely used as an important form of distributed energy. However, traditional household photovoltaic systems face many challenges in actual operation. First, photovoltaic power generation is greatly affected by natural factors such as weather, and the power generation is unstable, which means that users may not be able to fully rely on photovoltaic power generation to meet their needs during certain periods of time. Secondly, installing energy storage equipment separately will increase the user's initial investment cost, and due to the differences in electricity consumption patterns and photovoltaic output of each household, the utilization rate of energy storage equipment may be low. In addition, due to the lack of an effective coordination mechanism, the energy storage system of a single household often finds it difficult to achieve optimal allocation of resources in the region, resulting in energy waste. In summary, there is an urgent need for an energy storage planning method for household photovoltaic systems to improve the overall performance of the system. Summary of the invention

[0003] In order to solve at least one of the technical problems mentioned above, the present invention provides a method, device, electronic device and medium for energy storage planning of a household photovoltaic system.

[0004] In a first aspect, the present invention provides a method for energy storage planning of a household photovoltaic system, the method comprising:

[0005] Obtain the historical electricity consumption data and meteorological data of each household photovoltaic system in the community during the preset period, determine the photovoltaic power generation capacity and energy storage capacity based on the historical electricity consumption data and meteorological data, and calculate the self-sufficiency rate based on the photovoltaic power generation capacity;

[0006] Determine the priority order of each household based on the self-sufficiency rate and energy storage capacity of each household, analyze the supply and demand matching relationship of each household to determine the capacity of the shared energy storage system, and determine the energy storage scheduling strategy based on the shared energy storage system capacity and priority order;

[0007] Determine the photovoltaic conversion efficiency, energy storage conversion efficiency and charging and discharging efficiency of each household photovoltaic system, calculate the total efficiency of each household photovoltaic system based on the photovoltaic conversion efficiency and energy storage conversion efficiency, and calculate the charging and discharging benefits of each household based on the charging and discharging efficiency;

[0008] A multi-objective optimization function is established based on the total system efficiency and charging and discharging benefits of each household. Constraints are established based on the capacity of the shared energy storage system. The simulated annealing algorithm is used to find the global optimal solution. The capacity of the shared energy storage system is updated according to the solution results, and the energy storage scheduling strategy is adjusted using the updated shared energy storage system capacity.

[0009] Preferably, determining the photovoltaic power generation capacity and energy storage capacity according to historical electricity consumption data and meteorological data includes:

[0010]

[0011] In the formula, Q 光伏 Represents photovoltaic power generation capacity, E 用电 represents the total daily electricity consumption of M households, E 辐射 represents the average daily effective radiation, τ 板 Represents the photovoltaic panel conversion efficiency, δ 损耗 Represents the photovoltaic system loss factor;

[0012] Q 储能 =(E 用电 -E 输出 )×N 无光 ;

[0013] In the formula, Q 储能 Represents energy storage capacity, E 输出 represents the average daily photovoltaic output, N 无光 Indicates the number of consecutive days without sunlight.

[0014] Preferably, the average daily effective radiation is:

[0015]

[0016] In the formula, G min , G max represents the minimum and maximum values ​​of solar radiation intensity in a given period of time, k1 and k2 represent constants, μ and σ represent the mean and variance of solar radiation intensity in a given period of time, Indicates the integral from 0 to 24 points.

[0017] Preferably, the calculating the self-sufficiency rate according to the photovoltaic power generation capacity includes:

[0018]

[0019] In the formula, S 自给-i represents the photovoltaic power generation self-sufficiency rate of the i-th household, Q 光伏-i represents the photovoltaic power generation capacity of the i-th household, E 用电-i Represents the daily electricity consumption of the i-th household, 1≤i≤M.

[0020] Preferably, the determining of the photovoltaic conversion efficiency, energy storage conversion efficiency and charging and discharging efficiency of each household photovoltaic system includes:

[0021] τ 光转 =τ 板 ×τ 逆变 ×δ 损耗 ;

[0022] τ 储转 =τ 充放 (1-τ 自放 );

[0023]

[0024] In the formula, τ 光转 , τ 储转 , τ 充放 , τ 自放 Respectively represent the photovoltaic conversion efficiency, energy storage conversion efficiency, charging and discharging efficiency and self-discharging efficiency of each household photovoltaic system; τ 逆变 Indicates the inverter conversion efficiency, E 放 Represents the total energy released by the energy storage system during discharge, E 充 represents the total energy input to the energy storage system during charging; ΔE is the total energy lost by the energy storage system during the T period, E 初始 is the total energy of the energy storage system at the beginning of period H.

[0025] Preferably, the total efficiency of the photovoltaic system of each household is calculated according to the photovoltaic conversion efficiency and the energy storage conversion efficiency, and the charging and discharging income of each household is calculated according to the charging and discharging efficiency, including:

[0026] τ 光总 =τ 光转 ×τ 储转 ×Z 匹配 ;

[0027] C 充放 =(P 高峰 -P 低谷 )×Q 储能 ×L 充放 ;

[0028] In the formula, τ 光总 is the total efficiency of the photovoltaic system of each household, Z 匹配 is the load matching degree of each household, C 充放 is the charging and discharging income of each household, P 高峰 , P 低谷 is the electricity price during peak and off-peak periods, L 充放 Is the number of charge and discharge times.

[0029] In a second aspect, the present invention further provides an energy storage planning device for a household photovoltaic system, the device comprising:

[0030] An electricity consumption data acquisition unit is used to acquire the historical electricity consumption data and meteorological data of each household photovoltaic system in the community within a preset period of time, determine the photovoltaic power generation capacity and energy storage capacity based on the historical electricity consumption data and meteorological data, and calculate the self-sufficiency rate based on the photovoltaic power generation capacity;

[0031] An energy storage strategy determination unit, used to determine the priority order of each household according to the self-sufficiency rate and energy storage capacity of each household, analyze the supply and demand matching relationship of each household to determine the capacity of the shared energy storage system, and determine the energy storage scheduling strategy according to the shared energy storage system capacity and priority order;

[0032] The efficiency benefit analysis unit is used to determine the photovoltaic conversion efficiency, energy storage conversion efficiency and charging and discharging efficiency of each household photovoltaic system, calculate the total efficiency of each household photovoltaic system based on the photovoltaic conversion efficiency and energy storage conversion efficiency, and calculate the charging and discharging benefits of each household based on the charging and discharging efficiency;

[0033] The energy storage scheduling optimization unit establishes a multi-objective optimization function based on the total system efficiency and charging and discharging benefits of each household, establishes constraints based on the capacity of the shared energy storage system, uses a simulated annealing algorithm to find the global optimal solution, updates the capacity of the shared energy storage system based on the solution results, and adjusts the energy storage scheduling strategy using the updated shared energy storage system capacity.

[0034] In a third aspect, the present invention further provides an electronic device comprising a processor and a memory, wherein the memory is used to store computer program code, and the computer program code comprises computer instructions. When the processor executes the computer instructions, the electronic device executes the method as described in the first aspect above and any possible implementation thereof.

[0035] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the method as described in the first aspect above and any possible implementation thereof.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention provides an energy storage planning method for a household photovoltaic system, comprising obtaining historical electricity consumption data and meteorological data of each household photovoltaic system in a community within a preset time period, determining photovoltaic power generation capacity and energy storage capacity according to the historical electricity consumption data and meteorological data, and calculating the self-sufficiency rate according to the photovoltaic power generation capacity; determining the priority order of each household according to the self-sufficiency rate and energy storage capacity of each household, analyzing the supply and demand matching relationship of each household to determine the capacity of a shared energy storage system, and determining an energy storage scheduling strategy according to the capacity and priority order of the shared energy storage system; determining the photovoltaic conversion efficiency, energy storage conversion efficiency and charging and discharging efficiency of each household photovoltaic system, calculating the total efficiency of the photovoltaic system of each household according to the photovoltaic conversion efficiency and the energy storage conversion efficiency, and calculating the charging and discharging benefit of each household according to the charging and discharging efficiency; establishing a multi-objective optimization function according to the total system efficiency and charging and discharging benefit of each household, establishing constraint conditions according to the capacity of the shared energy storage system, using a simulated annealing algorithm to find a global optimal solution, updating the capacity of the shared energy storage system according to the solution result, and adjusting the energy storage scheduling strategy using the updated shared energy storage system capacity.

[0038] The present invention accurately predicts photovoltaic power generation by acquiring historical electricity consumption data and meteorological data of each household in the community, and rationally plans the energy storage capacity based on this, thereby improving the self-sufficiency rate of the system. By analyzing the supply and demand matching relationship of each household to determine the capacity of the shared energy storage system, and formulating the energy storage scheduling strategy based on the priority order, the utilization efficiency of energy storage facilities can be effectively improved, redundant construction can be reduced, and the initial investment cost of users can be reduced. Through comprehensive consideration of photovoltaic conversion efficiency, energy storage conversion efficiency, and charging and discharging efficiency, a multi-objective optimization function is established, so that the system can maximize economic benefits while meeting power supply reliability. The present invention not only solves the problems of poor stability, high cost, and unreasonable resource allocation in traditional household photovoltaic systems, but also further improves system efficiency and ensures the continuity and economy of power supply. By using the simulated annealing algorithm to find the global optimal solution, the system can still maintain good adaptability and flexibility in a dynamically changing environment, thereby achieving the purpose of energy saving and emission reduction and optimizing resource allocation.

[0039] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0041] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0042] Figure 1A schematic diagram of a flow chart of an energy storage planning method for a household photovoltaic system provided by an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of the structure of an energy storage planning device for a household photovoltaic system provided by an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 creative work are within the scope of protection of the present invention.

[0046] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0047] See also Figure 1 , Figure 1 The following is a flow chart of a method for energy storage planning of a household photovoltaic system provided by an embodiment of the present invention. Figure 1 As shown, the method includes:

[0048] S10. Obtain historical electricity consumption data and meteorological data of each household photovoltaic system in the community during a preset period of time, determine photovoltaic power generation capacity and energy storage capacity based on the historical electricity consumption data and meteorological data, and calculate the self-sufficiency rate based on the photovoltaic power generation capacity.

[0049] First, it is necessary to collect historical electricity consumption data from each household photovoltaic system in the community. The preset period can be the daily electricity consumption data for the past year or the first quarter. Meteorological data includes sunshine hours, temperature and humidity, etc. At the same time, electricity price structure data should also be obtained to understand peak and valley electricity price information. These data can be automatically recorded and uploaded to the data center or cloud platform by smart meters or photovoltaic system monitoring equipment. Then, the collected historical electricity consumption data is used to analyze the electricity demand, and the household electricity consumption pattern is identified through time series analysis methods to distinguish the electricity demand in different time periods, such as peak hours and flat valley hours. At the same time, critical loads should be determined, that is, equipment that must always be powered on, such as refrigerators, medical equipment, etc. Among them, the average daily electricity consumption is the total electricity consumption in the preset period divided by the total number of days, and the peak electricity consumption is the maximum value of the daily electricity consumption.

[0050] Furthermore, the photovoltaic power generation capacity and energy storage capacity are determined based on historical electricity consumption data and meteorological data, including:

[0051]

[0052] In the formula, Q 光伏 Represents photovoltaic power generation capacity, E 用电 represents the total daily electricity consumption of M households, E 辐射 represents the average daily effective radiation, τ 板 Represents the photovoltaic panel conversion efficiency, δ 损耗 Represents the photovoltaic system loss factor;

[0053] Q 储能 =(E 用电 -E 输出 )×N 无光 ;

[0054] In the formula, Q 储能 Represents energy storage capacity, E 输出 represents the average daily photovoltaic output, N 无光 Indicates the number of consecutive days without sunlight.

[0055] Preferably, the average daily effective radiation is:

[0056]

[0057]

[0058] In the formula, G min , G max represents the minimum and maximum values ​​of solar radiation intensity in a given period of time, k1 and k2 represent constants, μ and σ represent the mean and variance of solar radiation intensity in a given period of time, Indicates the integral from 0 to 24 points.

[0059] In this embodiment, the photovoltaic power generation capacity is calculated based on the user's power consumption, solar radiation and photovoltaic panel performance parameters. The average daily effective radiation is determined through historical data, and the required photovoltaic panel area or power is estimated in combination with the actual conversion efficiency of the photovoltaic panel and the system loss. The energy storage capacity represents the additional energy that needs to be stored to meet the power demand during the period without sunlight. Here, E 输出 is the average daily PV output calculated based on historical data, and N 无光 It is the number of consecutive days without sunlight calculated based on historical meteorological data. 辐射 By introducing the mean and variance in statistics and using integral operations, the actual changes in solar radiation can be simulated more accurately.

[0060] Furthermore, the calculation of the self-sufficiency rate according to the photovoltaic power generation capacity includes:

[0061]

[0062] In the formula, S 自给-i represents the photovoltaic power generation self-sufficiency rate of the i-th household, Q 光伏-i represents the photovoltaic power generation capacity of the i-th household, E 用电-i Represents the daily electricity consumption of the i-th household, 1≤i≤M.

[0063] S20. Determine the priority order of each household according to the self-sufficiency rate and energy storage capacity of each household, analyze the supply and demand matching relationship of each household to determine the capacity of the shared energy storage system, and determine the energy storage scheduling strategy according to the shared energy storage system capacity and priority order.

[0064] Generally, users with low self-sufficiency and insufficient energy storage have higher priority because they need more external energy support, while users with high self-sufficiency and nearly full energy storage have lower priority. Assuming M users, according to the above formula, the self-sufficiency rate of photovoltaic power generation of the i-th household can be obtained as S 自给-i The net demand of each household is D 需求-i , then the relationship is: D 需求-i =E 用电-i -Q 光伏-i , judge D 需求-i Is it less than 0? If so, it proves that the demand is less than the photovoltaic power generation, which means that the household has excess energy to share. Otherwise, it means that the household needs energy replenishment.

[0065] After analyzing the supply and demand matching relationship of each household, the capacity Q of the shared energy storage system can be determined. 共享 for:

[0066] Q 共享 =β×D 需求-max ;

[0067] In the formula, β is a constant greater than 1, D 需求-max Represents the maximum net electricity demand among all users.

[0068] When determining the energy storage dispatch strategy based on the capacity and priority order of the shared energy storage system, first create an energy pool of the shared energy storage system capacity, aggregate the remaining energy of all users to form a dispatchable energy pool within the community, and then allocate energy to users with unmet needs in descending order of priority of different users in the community. If the total energy storage in the energy pool is not enough to fully meet the needs of all users, the remaining energy is allocated proportionally. Finally, update the user's energy storage status and demand satisfaction flag according to the allocation result. Check whether the needs of all users are met or the energy pool has been exhausted. If so, the algorithm ends; otherwise, return to recalculate the net demand of each household until all needs are met or the energy pool is empty and stops the energy storage dispatch process.

[0069] S30. Determine the photovoltaic conversion efficiency, energy storage conversion efficiency and charging and discharging efficiency of each household photovoltaic system, calculate the total efficiency of each household photovoltaic system based on the photovoltaic conversion efficiency and the energy storage conversion efficiency, and calculate the charging and discharging income of each household based on the charging and discharging efficiency.

[0070] Specifically, the determination of the photovoltaic conversion efficiency, energy storage conversion efficiency and charging and discharging efficiency of each household photovoltaic system includes:

[0071] τ 光转 =τ 板 ×τ 逆变 ×δ 损耗 ;

[0072] τ 储转 =τ 充放 (1-τ 自放 );

[0073]

[0074] In the formula, τ 光转 , τ 储转 , τ 充放 , τ 自放 Respectively represent the photovoltaic conversion efficiency, energy storage conversion efficiency, charging and discharging efficiency and self-discharging efficiency of each household photovoltaic system; τ 逆变 Indicates the inverter conversion efficiency, E 放 Represents the total energy released by the energy storage system during discharge, E 充 represents the total energy input to the energy storage system during charging; ΔE is the total energy lost by the energy storage system during the T period, E 初始 is the total energy of the energy storage system at the beginning of period H.

[0075] Furthermore, the total efficiency of the photovoltaic system of each household is calculated according to the photovoltaic conversion efficiency and the energy storage conversion efficiency, and the charging and discharging income of each household is calculated according to the charging and discharging efficiency, including:

[0076] τ 光总 =τ 光转 ×τ 储转 ×Z 匹配 ;

[0077] C 充放 =(P 高峰 -P 低谷 )×Q 储能 ×L 充放 ;

[0078] In the formula, τ 光总 is the total efficiency of the photovoltaic system of each household, Z 匹配 is the load matching degree of each household, C 充放 is the charging and discharging income of each household, P 高峰 , P 低谷 is the electricity price during peak and off-peak periods, L 充放 Is the number of charge and discharge times.

[0079] In the above formula, the efficiency loss that may be introduced by photovoltaic panels, inverters and other components during the conversion process is taken into account when calculating the total efficiency of the photovoltaic system to evaluate the ability of the entire photovoltaic system to convert solar energy into usable electrical energy. When calculating the charging and discharging benefits, it is used to measure the economic benefits obtained by transferring energy between different electricity price periods through the energy storage system. Load matching usually refers to the degree of match between power output and load demand. In solar photovoltaic systems, it reflects the degree of match between the system output power and the actual power consumed by the load. If the system can effectively meet the load demand, that is, provide sufficient power when the load needs power, and minimize waste, then the load matching degree is high.

[0080] This embodiment can more reasonably plan the configuration of the photovoltaic system and the energy storage system and realize the effective use of resources by comprehensively considering the photovoltaic conversion efficiency and the energy storage conversion efficiency, as well as evaluating the load matching degree. The calculation of the self-discharge efficiency can help users understand the status of the energy storage equipment, maintain or replace it in time, and ensure the long-term stable operation of the system. Through meticulous management and scheduling, users can obtain a more stable power supply, and can adjust their electricity consumption behavior according to actual conditions to improve their quality of life.

[0081] S40. Establish a multi-objective optimization function based on the total system efficiency and charging and discharging benefits of each household, establish constraints based on the capacity of the shared energy storage system, use a simulated annealing algorithm to find the global optimal solution, update the capacity of the shared energy storage system based on the solution results, and use the updated shared energy storage system capacity to adjust the energy storage scheduling strategy.

[0082] Specifically, this embodiment includes the following steps:

[0083] 1) Establish the objective function:

[0084] f = {f1, f2};

[0085] f1=max(τ 光总 );

[0086] f2=max(C 充放 );

[0087] 2) Establish constraints:

[0088] Q 光伏-i ≥Q 光伏-min ;

[0089] Q 储能-i ≥Q 储能-min ;

[0090] Q 共享 ≥Q 共享-min ;

[0091] In the formula, Q 光伏-min , Q 储能-min and Q 共享-min The minimum value of photovoltaic power generation capacity, energy storage capacity and shared energy storage system capacity, that is, the photovoltaic power generation capacity, energy storage capacity of each household and the shared energy storage system capacity in the community must meet a minimum capacity requirement;

[0092] Furthermore, it is ensured that at any time, the energy generated by the energy storage system and the photovoltaic system is sufficient to cover the demand:

[0093] ∑ i Q 光伏-i (t)+ΔQ 共享 (t)≥∑ i E 用电-i (t);

[0094] Where ΔQ 共享 (t) represents the change in the capacity of the energy storage system at time t.

[0095] 3) Simulated annealing algorithm solution:

[0096] 3.1) Set the initial temperature T0, cooling rate α, and maximum number of iterations X;

[0097] 3.2) Randomly generate a set of initial solutions {Q 光伏-i , Q 共享};

[0098] 3.3) Calculate the objective function value {f1, f2} of the current solution;

[0099] 3.4) Randomly perturb the current solution and generate a new solution {Q 光伏-i * , Q 共享 *};

[0100] 3.5) Calculate the objective function value of the new solution {f1 * , f2 *};

[0101] If f1 * >f1 or f2 * >f2, accept the new solution; if f1 * ≤f1 and f2 * ≤f2, with probability Accept the new solution, where ΔF = |f1 * -f1|+|f2 * -f2|;

[0102] 3.6) Cooling: Update temperature T←αT;

[0103] 3.7) Iterate steps 3.3)-3.6) until the maximum number of iterations is reached or the convergence condition is met;

[0104] 4) Update the capacity of the shared energy storage system according to the optimization results. The updated shared energy storage system capacity network is Q that maximizes {f1, f2} 共享 As the capacity of the shared energy storage system changes, the energy storage dispatch strategy will also be adjusted accordingly. Specifically, the allocation ratio can be adjusted to meet the electricity needs of energy users with different priorities.

[0105] Through the above process, the optimal photovoltaic system capacity and energy storage system capacity that maximize the total system efficiency and charging and discharging benefits can be found. The shared energy storage system capacity is updated according to the solution results, and the energy storage scheduling strategy is adjusted. This makes the energy storage utilization rate higher, optimizes the photovoltaic configuration cost of the community, and improves the overall system performance.

[0106] In summary, the method provided by the present invention accurately predicts photovoltaic power generation by obtaining historical electricity consumption data and meteorological data of each household in the community, and rationally plans the energy storage capacity based on this, thereby improving the self-sufficiency rate of the system. By analyzing the supply and demand matching relationship of each household to determine the capacity of the shared energy storage system, and formulating the energy storage scheduling strategy based on the priority order, the utilization efficiency of energy storage facilities can be effectively improved, redundant construction can be reduced, and the initial investment cost of users can be reduced. Through comprehensive consideration of photovoltaic conversion efficiency, energy storage conversion efficiency, and charging and discharging efficiency, a multi-objective optimization function is established, so that the system can maximize economic benefits while meeting power supply reliability. The present invention not only solves the problems of poor stability, high cost, and unreasonable resource allocation in traditional household photovoltaic systems, but also further improves system efficiency and ensures the continuity and economy of power supply. By adopting the simulated annealing algorithm to find the global optimal solution, the system can still maintain good adaptability and flexibility in a dynamically changing environment, thereby achieving the purpose of energy saving and emission reduction and optimizing resource allocation.

[0107] See also Figure 2 In one embodiment, the present invention further provides an energy storage planning device for a household photovoltaic system, the device comprising:

[0108] The power consumption data acquisition unit 100 is used to acquire the historical power consumption data and meteorological data of each household photovoltaic system in the community within a preset period of time, determine the photovoltaic power generation capacity and energy storage capacity according to the historical power consumption data and meteorological data, and calculate the self-sufficiency rate according to the photovoltaic power generation capacity;

[0109] The energy storage strategy determination unit 200 is used to determine the priority order of each household according to the self-sufficiency rate and energy storage capacity of each household, analyze the supply and demand matching relationship of each household to determine the capacity of the shared energy storage system, and determine the energy storage scheduling strategy according to the shared energy storage system capacity and the priority order;

[0110] The efficiency benefit analysis unit 300 is used to determine the photovoltaic conversion efficiency, energy storage conversion efficiency and charging and discharging efficiency of each household photovoltaic system, calculate the total efficiency of each household photovoltaic system according to the photovoltaic conversion efficiency and the energy storage conversion efficiency, and calculate the charging and discharging benefit of each household according to the charging and discharging efficiency;

[0111] The energy storage scheduling optimization unit 400 establishes a multi-objective optimization function according to the total system efficiency and charging and discharging benefits of each household, establishes constraint conditions according to the capacity of the shared energy storage system, uses a simulated annealing algorithm to find the global optimal solution, updates the capacity of the shared energy storage system according to the solution result, and adjusts the energy storage scheduling strategy using the updated shared energy storage system capacity.

[0112] It can be understood that the functions or modules included in the device provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0113] The present invention also provides an electronic device, including a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any possible implementation manner.

[0114] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any possible implementation manner.

[0115] See also Figure 3 , Figure 3 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention.

[0116] The electronic device 2 includes a processor 21, a memory 22, an input device 23, and an output device 24. The processor 21, the memory 22, the input device 23, and the output device 24 are coupled via a connector, and the connector includes various interfaces, transmission lines, or buses, etc., which are not limited in the embodiments of the present invention. It should be understood that in various embodiments of the present invention, coupling refers to mutual connection in a specific manner, including direct connection or indirect connection through other devices, for example, through various interfaces, transmission lines, buses, etc.

[0117] The processor 21 may be one or more graphics processing units (GPUs). When the processor 21 is a GPU, the GPU may be a single-core GPU or a multi-core GPU. Optionally, the processor 21 may be a processor group consisting of multiple GPUs, and the multiple processors are coupled to each other via one or more buses. Optionally, the processor may also be other types of processors, etc., which are not limited in the embodiments of the present invention.

[0118] The memory 22 can be used to store computer program instructions and various computer program codes including program codes for executing the scheme of the present invention. Optionally, the memory includes but is not limited to random access memory (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM), or portable read only memory (CD-ROM), which is used for related instructions and data.

[0119] The input device 23 is used to input data and / or signals, and the output device 24 is used to output data and / or signals. The input device 23 and the output device 24 can be independent devices or an integrated device.

[0120] It is understandable that in the embodiment of the present invention, the memory 22 is not only used to store related instructions, and the embodiment of the present invention does not limit the specific data stored in the memory.

[0121] Understandably, Figure 3 Only a simplified design of an electronic device is shown. In practical applications, the electronic device may also include other necessary components, including but not limited to any number of input / output devices, processors, memories, etc., and all video analysis devices that can implement the embodiments of the present invention are within the protection scope of the present invention.

[0122] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0123] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

[0124] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0125] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0126] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0127] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0128] A person skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by a computer program to instruct the relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. The aforementioned storage medium includes: a read-only memory (ROM) or a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

Claims

1. A method for energy storage planning of a household photovoltaic system, characterized in that: The method comprises: Obtain the historical electricity consumption data and meteorological data of each household photovoltaic system in the community during the preset period, determine the photovoltaic power generation capacity and energy storage capacity based on the historical electricity consumption data and meteorological data, and calculate the self-sufficiency rate based on the photovoltaic power generation capacity; Determine the priority order of each household based on the self-sufficiency rate and energy storage capacity of each household, analyze the supply and demand matching relationship of each household to determine the capacity of the shared energy storage system, and determine the energy storage scheduling strategy based on the shared energy storage system capacity and priority order; Determine the photovoltaic conversion efficiency, energy storage conversion efficiency and charging and discharging efficiency of each household photovoltaic system, calculate the total efficiency of each household photovoltaic system based on the photovoltaic conversion efficiency and energy storage conversion efficiency, and calculate the charging and discharging benefits of each household based on the charging and discharging efficiency; A multi-objective optimization function is established based on the total system efficiency and charging and discharging benefits of each household. Constraints are established based on the capacity of the shared energy storage system. The simulated annealing algorithm is used to find the global optimal solution. The capacity of the shared energy storage system is updated according to the solution results, and the energy storage scheduling strategy is adjusted using the updated shared energy storage system capacity.

2. The energy storage planning method for household photovoltaic systems according to claim 1, characterized in that: Determining photovoltaic power generation capacity and energy storage capacity based on historical electricity consumption data and meteorological data includes: In the formula, Q 光伏 Represents photovoltaic power generation capacity, E 用电 represents the total daily electricity consumption of M households, E 辐射 represents the average daily effective radiation, τ 板 Represents the photovoltaic panel conversion efficiency, δ 损耗 Represents the photovoltaic system loss factor; Q 储能 =(And 用电 -AND 输出 )×N 无光 ; In the formula, Q 储能 Represents energy storage capacity, E 输出 represents the average daily photovoltaic output, N 无光 Indicates the number of consecutive days without sunlight.

3. The energy storage planning method for household photovoltaic systems according to claim 2, characterized in that: The average daily effective radiation is: In the formula, G min , G max represents the minimum and maximum values ​​of solar radiation intensity in a given period of time, k1 and k2 represent constants, μ and σ represent the mean and variance of solar radiation intensity in a given period of time, Indicates the integral from 0 to 24 points.

4. The energy storage planning method for household photovoltaic systems according to claim 1, characterized in that: The self-sufficiency rate is calculated based on photovoltaic power generation capacity, including: In the formula, S 自给-i represents the photovoltaic power generation self-sufficiency rate of the i-th household, Q 光伏-i represents the photovoltaic power generation capacity of the i-th household, E 用电-i Represents the daily electricity consumption of the i-th household, 1≤i≤M.

5. The energy storage planning method for household photovoltaic systems according to claim 2, characterized in that: The determination of the photovoltaic conversion efficiency, energy storage conversion efficiency and charging and discharging efficiency of each household photovoltaic system includes: t 光转 =t 板 ×t 逆变 ×d 损耗 ; t 储转 =t 充放 (1-t 自放 ); In the formula, τ 光转 , τ 储转 , τ 充放 , τ 自放 Respectively represent the photovoltaic conversion efficiency, energy storage conversion efficiency, charging and discharging efficiency and self-discharging efficiency of each household photovoltaic system; τ 逆变 Indicates the inverter conversion efficiency, E 放 Represents the total energy released by the energy storage system during discharge, E 充 represents the total energy input to the energy storage system during charging; ΔE is the total energy lost by the energy storage system during the T period, E 初始 is the total energy of the energy storage system at the beginning of period H.

6. The energy storage planning method for household photovoltaic systems according to claim 5, characterized in that: The total efficiency of each household's photovoltaic system is calculated based on the photovoltaic conversion efficiency and the energy storage conversion efficiency, and the charging and discharging income of each household is calculated based on the charging and discharging efficiency, including: t 光总 =t 光转 ×t 储转 ×Z 匹配 ; C 充放 =(P 高峰 -P 低谷 )×Q 储能 ×L 充放 ; In the formula, τ 光总 is the total efficiency of the photovoltaic system of each household, Z 匹配 is the load matching degree of each household, C 充放 is the charging and discharging income of each household, P 高峰 , P 低谷 is the electricity price during peak and off-peak periods, L 充放 Is the number of charge and discharge times.

7. An energy storage planning device for a household photovoltaic system, characterized in that: The device comprises: An electricity consumption data acquisition unit is used to acquire the historical electricity consumption data and meteorological data of each household photovoltaic system in the community within a preset period of time, determine the photovoltaic power generation capacity and energy storage capacity based on the historical electricity consumption data and meteorological data, and calculate the self-sufficiency rate based on the photovoltaic power generation capacity; An energy storage strategy determination unit, used to determine the priority order of each household according to the self-sufficiency rate and energy storage capacity of each household, analyze the supply and demand matching relationship of each household to determine the capacity of the shared energy storage system, and determine the energy storage scheduling strategy according to the shared energy storage system capacity and priority order; The efficiency benefit analysis unit is used to determine the photovoltaic conversion efficiency, energy storage conversion efficiency and charging and discharging efficiency of each household photovoltaic system, calculate the total efficiency of each household photovoltaic system based on the photovoltaic conversion efficiency and energy storage conversion efficiency, and calculate the charging and discharging benefits of each household based on the charging and discharging efficiency; The energy storage scheduling optimization unit establishes a multi-objective optimization function based on the total system efficiency and charging and discharging benefits of each household, establishes constraints based on the capacity of the shared energy storage system, uses a simulated annealing algorithm to find the global optimal solution, updates the capacity of the shared energy storage system based on the solution results, and adjusts the energy storage scheduling strategy using the updated shared energy storage system capacity.

8. An electronic device, characterized in that: The electronic device comprises a processor and a memory, wherein the memory is used to store computer program codes, and the computer program codes comprise computer instructions. When the processor executes the computer instructions, the electronic device executes the energy storage planning method for a household photovoltaic system as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the energy storage planning method for a household photovoltaic system according to any one of claims 1 to 6.

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