Photovoltaic energy storage system configuration optimization and market participation method and system

Abstract

The invention discloses a photovoltaic energy storage system configuration optimization and market participation method and system. The method comprises the following steps: acquiring photovoltaic energy storage system data; based on the photovoltaic energy storage system data, determining an objective function of the PSO algorithm optimization model, designing boundary conditions of the objective optimization model according to the objective function, and constructing to obtain the objective optimization model; designing an adjustment scene with or without energy storage based on the electric energy market and the auxiliary service market; and based on the adjustment scene, solving the target optimization model by adopting a particle swarm optimization algorithm to obtain an optimization solving result. According to the method, the absorption rate of the distributed photovoltaic power generation system is effectively improved through the target optimization model, the absorption problem in the photovoltaic power generation grid-connected process is solved, and the light abandoning phenomenon caused by insufficient power grid absorption capacity is reduced; by optimizing the operation strategy of the energy storage system, the stability and reliability of the power grid in emergency situations are improved. In the aspect of economic benefits, a particle swarm optimization algorithm is combined with a linear decline weight strategy, and energy storage configuration is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation optimization, and in particular to a photovoltaic energy storage system configuration optimization and market participation method and system. Background Art

[0002] With the rapid development of the global economy and the continuous growth of population, the consumption of fossil energy is accelerating, and the crisis of its depletion is becoming more and more serious. At the same time, the use of fossil energy has also brought serious environmental pollution problems, such as air pollution, water pollution and soil pollution, which pose a huge threat to human health and the ecological environment. Therefore, the development of a clean, low-carbon, safe and efficient energy system has become a global consensus. As an inexhaustible clean energy, solar energy has great development potential. In recent years, distributed photovoltaic power generation systems have been widely used in the field of solar energy utilization due to their flexibility and high efficiency.

[0003] Although distributed photovoltaic power generation systems have played an important role in energy transformation, they also have some problems that need to be solved. First, the output power of photovoltaic power generation is random and intermittent, which is mainly affected by various factors such as light intensity and weather conditions. Therefore, the output power of photovoltaic power generation systems fluctuates greatly, making it difficult to ensure a continuous and stable power supply, posing certain challenges to the stable operation of the power grid. Secondly, with the continuous increase in electricity demand, especially during peak hours, the power supply problem has become more prominent. It is difficult for photovoltaic power generation systems to provide enough electricity to meet the electricity demand during peak hours in a short period of time, which may lead to an imbalance in the load of the power grid and even cause serious problems such as power outages. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of the present invention to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a photovoltaic energy storage system configuration optimization and market participation method to solve the problem of how to optimize the configuration of photovoltaic energy storage systems and participate in the market in a complex power market environment.

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

[0007] In a first aspect, the present invention provides a photovoltaic energy storage system configuration optimization and market participation method, comprising:

[0008] Obtaining photovoltaic energy storage system data;

[0009] Based on the photovoltaic energy storage system data, determine the objective function of the PSO algorithm optimization model, design the boundary conditions of the target optimization model according to the objective function, and construct the target optimization model;

[0010] Design regulation scenarios with or without energy storage based on the electric energy market and ancillary service market;

[0011] Based on the adjustment scenario, a particle swarm optimization algorithm is used to solve the target optimization model to obtain an optimization solution result.

[0012] As a preferred solution of the photovoltaic energy storage system configuration optimization and market participation method described in the present invention, the objective function includes:

[0013] The annual average cost of the photovoltaic energy storage system is used as the objective function. The annual average cost of the photovoltaic energy storage system includes the total revenue of the photovoltaic energy storage system participating in the market, the annual maintenance cost of the photovoltaic system, the annual maintenance cost of the energy storage system, the depreciation cost of the photovoltaic system and the total depreciation cost of the energy storage system.

[0014] As an optimal solution for the photovoltaic energy storage system configuration optimization and market participation method described in the present invention, the boundary conditions include energy storage charge state constraints, energy storage device power constraints, energy storage device charging and discharging power constraints, and energy storage system upper and lower backup capacity constraints.

[0015] As a preferred solution of the photovoltaic energy storage system configuration optimization and market participation method described in the present invention, wherein: the regulation scenarios with or without energy storage are designed based on the electric energy market and the ancillary service market, including:

[0016] Scenario 1: Only consider the photovoltaic entities participating in the electric energy market and the backup auxiliary service market to purchase and sell electricity and obtain benefits;

[0017] Scenario 2: Consider PV with energy storage only participating in the backup ancillary service market, providing backup services in the backup ancillary service market to receive backup service fees;

[0018] Scenario three: Consider configuring photovoltaic energy storage to participate in the electric energy and backup ancillary service markets, conduct electricity purchase and sales transactions in the electric energy market to obtain revenue, and provide backup services in the backup ancillary service market to obtain backup service fees.

[0019] As a preferred solution of the photovoltaic energy storage system configuration optimization and market participation method described in the present invention, wherein: using a particle swarm optimization algorithm to solve the target optimization model includes:

[0020] Writing the objective function based on the adjustment scenario setting;

[0021] The particle swarm optimization algorithm is used to update and iterate the objective function. In each iteration, it is determined whether the convergence condition is met. If so, the calculation is stopped and the optimization solution result is output.

[0022] As a preferred solution of the photovoltaic energy storage system configuration optimization and market participation method described in the present invention, the objective function is expressed as follows:

[0023]

[0024] Among them, R ave is the annual average cost of the photovoltaic energy storage system, y is the number of years of the project cycle, Z is the total revenue of the photovoltaic energy storage system participating in the market, C1 is the total maintenance cost of the photovoltaic system in y years, C2 is the total maintenance cost of the energy storage system in y years, C3 is the total depreciation cost of the photovoltaic system in y years, and C4 is the total depreciation cost of the energy storage system in y years.

[0025] As a preferred solution of the photovoltaic energy storage system configuration optimization and market participation method described in the present invention, the optimization solution results include photovoltaic output, energy storage rated power, energy storage rated capacity and energy storage charging and discharging power at each moment within 24 moments.

[0026] In a second aspect, the present invention provides a system for photovoltaic energy storage system configuration optimization and market participation, comprising:

[0027] A data acquisition module, used to acquire photovoltaic energy storage system data;

[0028] A function module, used to determine the objective function of the PSO algorithm optimization model based on the photovoltaic energy storage system data, design the boundary conditions of the target optimization model according to the objective function, and construct the target optimization model;

[0029] Scenario module, used to design regulation scenarios with or without energy storage based on the electric energy market and ancillary service market;

[0030] An output module, used to solve the target optimization model based on the adjustment scenario by using a particle swarm optimization algorithm to obtain an optimization solution result;

[0031] The function module specifically includes: taking the annual average cost of the photovoltaic energy storage system as the objective function, wherein the annual average cost of the photovoltaic energy storage system includes the total revenue of the photovoltaic energy storage system participating in the market, the annual maintenance cost of the photovoltaic system, the annual maintenance cost of the energy storage system, the depreciation cost of the photovoltaic system and the total depreciation cost of the energy storage system;

[0032] The objective function is expressed as:

[0033]

[0034] Among them, Rave is the annual average cost of the photovoltaic energy storage system, y is the number of years of the project cycle, Z is the total revenue of the photovoltaic energy storage system participating in the market, C1 is the total maintenance cost of the photovoltaic system in y years, C2 is the total maintenance cost of the energy storage system in y years, C3 is the total depreciation cost of the photovoltaic system in y years, and C4 is the total depreciation cost of the energy storage system in y years;

[0035] Boundary conditions include energy storage state of charge constraints, energy storage device power constraints, energy storage device charge and discharge power constraints, and energy storage system upper and lower reserve capacity constraints;

[0036] The scenario modules specifically include: Scenario 1: Only consider the photovoltaic entities participating in the electric energy market and the backup auxiliary service market to purchase and sell electricity and obtain income;

[0037] Scenario 2: Consider PV with energy storage only participating in the backup ancillary service market, providing backup services in the backup ancillary service market to receive backup service fees;

[0038] Scenario 3: Consider PV with energy storage to participate in the electric energy and backup ancillary service markets, earn revenue from electricity purchase and sales transactions in the electric energy market, and earn backup service fees by providing backup services in the backup ancillary service market.

[0039] The output module specifically includes: writing the objective function based on the adjustment scenario setting;

[0040] The particle swarm optimization algorithm is used to update and iterate the objective function. In each iteration, it is determined whether the convergence condition is met. If so, the calculation is stopped and the optimization solution result is output.

[0041] In a third aspect, the present invention provides a computing device, comprising:

[0042] Memory and processor;

[0043] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the photovoltaic energy storage system configuration optimization and market participation method are implemented.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the photovoltaic energy storage system configuration optimization and market participation method.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: the absorption rate of distributed photovoltaic power generation systems is effectively improved through the target optimization model, the absorption problem in the process of photovoltaic power generation grid connection is solved, and the phenomenon of abandoned light caused by insufficient absorption capacity of the power grid is reduced; secondly, the invention significantly enhances the backup capacity of the power grid by optimizing the operation strategy of the energy storage system, and improves the stability and reliability of the power grid in the face of emergencies. In terms of economic benefits, the present invention adopts a particle swarm optimization algorithm combined with a linear decreasing weight strategy to optimize the energy storage configuration, which significantly improves the economic benefits of the system. In addition, the target optimization model of the present invention comprehensively considers the technical parameters of the energy storage system, the power grid operation constraints, the market mechanism and the economic evaluation, and achieves the best performance in the electric energy market and the backup auxiliary service market. The present invention also improves the flexibility of power grid operation, so that it can more effectively cope with the fluctuations of renewable energy generation, and enhances the adaptability and reliability of the power system. Finally, the present invention fills the research gap, provides a theoretical basis and practical guidance for the wide application of photovoltaic energy storage systems in the modern power market, and has important practical application value and broad market prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0047] Figure 1 A schematic diagram of the overall process of a photovoltaic energy storage system configuration optimization and market participation method according to an embodiment of the present invention;

[0048] Figure 2 A schematic diagram of a photovoltaic output curve of a photovoltaic energy storage system configuration optimization and market participation method according to an embodiment of the present invention;

[0049] Figure 3 A schematic diagram of a load variation curve of a photovoltaic energy storage system configuration optimization and market participation method according to an embodiment of the present invention;

[0050] Figure 4 A schematic diagram of an SOC change curve of scenario 2 of a photovoltaic energy storage system configuration optimization and market participation method according to an embodiment of the present invention;

[0051] Figure 5 A schematic diagram of a SOC change curve for scenario three of a photovoltaic energy storage system configuration optimization and market participation method according to an embodiment of the present invention;

[0052] Figure 6A schematic diagram of a total cost adaptability curve of a photovoltaic energy storage system in scenario 2 of a photovoltaic energy storage system configuration optimization and market participation method according to an embodiment of the present invention;

[0053] Figure 7 A schematic diagram of a total cost adaptability curve of a photovoltaic energy storage system in scenario three of a photovoltaic energy storage system configuration optimization and market participation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0056] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0057] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0058] At the same time, in the description of the present invention, it should be noted that the orientations or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0059] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0060] Example 1

[0061] Reference Figure 1 , which is an embodiment of the present invention, provides a photovoltaic energy storage system configuration optimization and market participation method, including:

[0062] S100: Acquiring photovoltaic energy storage system data;

[0063] In the embodiment of the present invention, the photovoltaic energy storage system data includes basic data of photovoltaic power generation, energy storage, and total load level;

[0064] Specifically, the basic data include:

[0065] The total load level is the typical load of a certain place, and the load curve of the photovoltaic energy storage system in 24 time periods is obtained;

[0066] The photovoltaic power generation is a typical sunrise power, and the load curve of photovoltaic power generation in 24 periods is obtained;

[0067] Input basic parameters of energy storage, including upper and lower limits of state of charge, charge and discharge efficiency, etc.

[0068] Basic parameters of photovoltaic units, including unit power generation cost, power, etc.;

[0069] Considering the scenario where only photovoltaic entities participate in the electric energy market and the backup auxiliary service market to purchase and sell electricity, obtain revenue, and obtain the charging and discharging power curves of user-side energy storage in 24 time periods;

[0070] Considering the scenario of photovoltaic energy storage system, it participates in the electric energy market and the backup auxiliary service market to purchase and sell electricity and obtain profits.

[0071] S102: Based on the photovoltaic energy storage system data, determine the objective function of the PSO algorithm optimization model, design the boundary conditions of the target optimization model according to the objective function, and construct the target optimization model;

[0072] Preferably, the annual average cost of the photovoltaic energy storage system is used as the objective function, and the annual average cost of the photovoltaic energy storage system includes the total revenue of the photovoltaic energy storage system participating in the market, the annual maintenance cost of the photovoltaic system, the annual maintenance cost of the energy storage system, the depreciation cost of the photovoltaic system, and the total depreciation cost of the energy storage system;

[0073] Preferably, the objective function is expressed as:

[0074]

[0075] Among them, R ave is the annual average cost of the photovoltaic energy storage system, y is the number of years of the project cycle, Z is the total revenue of the photovoltaic energy storage system participating in the market, C1 is the total maintenance cost of the photovoltaic system in y years, C2 is the total maintenance cost of the energy storage system in y years, C3 is the total depreciation cost of the photovoltaic system in y years, and C4 is the total depreciation cost of the energy storage system in y years;

[0076] In the embodiment of the present invention, each cost is expressed by the formula:

[0077] 1) The specific formula for the total maintenance cost of a photovoltaic system in y years is:

[0078]

[0079] Among them, k pom is the annual operation and maintenance cost ratio of photovoltaic power generation, C u is the unit cost of photovoltaic power, and P is the power of the photovoltaic system.

[0080] 2) The specific formula for the total maintenance cost of the energy storage system in y years is expressed as:

[0081]

[0082] Among them, k som is the annual operation and maintenance cost ratio of the energy storage system, E u is the unit power cost of energy storage, is the rated power of energy storage, Q u is the unit capacity cost of energy storage, is the rated capacity of the energy storage.

[0083] 3) The specific formula for the total depreciation cost of the photovoltaic system in year y is:

[0084]

[0085] Where, σ is the annual depreciation rate of the photovoltaic system, C u is the unit cost of photovoltaic power, and P is the power of the photovoltaic system.

[0086] 4) The specific formula for the total depreciation cost of the energy storage system in year y is:

[0087]

[0088] Among them, σ s is the annual depreciation rate of the energy storage system, Q u is the unit capacity cost of energy storage, is the rated capacity of the energy storage, is the rated power of energy storage, E u is the unit power cost of energy storage.

[0089] 5) The total revenue of the PV energy storage system participating in the market mainly includes the income from energy storage saving electricity purchase, the government subsidy obtained by the energy storage PV system for storing excess electricity, the income and cost of the positive and negative backup capacity provided by energy storage, the income from the participation of excess photovoltaic power generation in the electricity market, and the cost of the PV energy storage system purchasing electricity. The specific formula is expressed as follows:

[0090]

[0091] Among them, R u is the government subsidy for the total excess electricity stored in energy storage in year y, R a and R b are the benefits of total positive and negative reserve capacity provided by energy storage in year y, respectively. a and C b are the total purchase cost of upper and lower reserve capacity for energy storage in year y, R s is the income from the total excess power generation of photovoltaic power generation in year y participating in the electric energy market, C p is the total cost of electricity purchased by the photovoltaic energy storage system in year y, r is the discount rate, and y is the number of years.

[0092] ① The specific formula for government subsidies for storing excess electricity is as follows:

[0093]

[0094] Among them, Q u,t is the excess power stored at time t, C u The electricity price is subsidized by the government.

[0095] ② The specific formula for the benefits of energy storage providing positive and negative backup capacity is:

[0096]

[0097] Among them, Q a,t The positive reserve capacity provided by the energy storage at time t, Q b,t The negative reserve capacity provided by the energy storage at time t, C a,t is the positive reserve price at time t, C b,t is the negative standby price at time t.

[0098] ③ The specific formula for the benefits of excess photovoltaic power generation participating in the electric energy market is:

[0099]

[0100] Among them, Q s,t is the excess power generation of photovoltaic power at time t.

[0101] ④ The specific formula for the cost of purchasing upper and lower reserve capacity for energy storage is as follows:

[0102]

[0103]

[0104] in, is the positive reserve capacity purchased by the energy storage at time t, is the negative reserve capacity purchased for energy storage at time t, C a,t is the positive reserve price at time t, C b,t is the negative standby price at time t.

[0105] ⑤ The specific formula for the cost of purchasing electricity for the photovoltaic energy storage system is:

[0106]

[0107] Among them, Q p,t The amount of electricity purchased for the photovoltaic energy storage system.

[0108] Preferably, the boundary conditions include energy storage charge state constraints, energy storage device power constraints, energy storage device charge and discharge power constraints, and energy storage system upper and lower reserve capacity constraints;

[0109] In the embodiment of the present invention, each constraint is expressed by a formula:

[0110] 1) Energy storage state of charge constraints:

[0111] Energy storage charge state constraint: The formula is as follows:

[0112]

[0113] in, and They represent the minimum and maximum state of charge of the energy storage system respectively.

[0114] 2) Energy storage device power constraints:

[0115] P t = x(P dis,t -P ch,t )

[0116] Among them, P t is the actual charging and discharging power of the energy storage at time t, x is a 0-1 state variable, 1 means the grid is connected to the energy storage, otherwise it is not connected; P dis,t is the discharge power of energy storage at time t, P ch,tis the charging power of the energy storage at time t.

[0117] 3) Energy storage device charging and discharging power constraints:

[0118]

[0119] in, is the rated power of energy storage, P dis,t is the discharge power of energy storage at time t, P ch,t is the charging power of the energy storage at time t.

[0120] 4) The energy storage system provides upper and lower reserve capacity constraints:

[0121]

[0122] in, and They are the positive and negative standby bid capacities of energy storage at time t, is the rated power of the energy storage.

[0123] S104: Design regulation scenarios with or without energy storage based on the electric energy market and ancillary service market;

[0124] In the embodiment of the present invention, the adjustment scenarios mainly include three:

[0125] Scenario 1: Only consider the photovoltaic entities participating in the electric energy market and the backup auxiliary service market to purchase and sell electricity and obtain benefits;

[0126] Scenario 2: Consider PV with energy storage only participating in the backup ancillary service market, providing backup services in the backup ancillary service market to obtain backup service fees, and storing excess electricity under the background of government subsidies to obtain government subsidies;

[0127] Scenario three: Consider configuring photovoltaic energy storage to participate in the electric energy and backup ancillary service markets, conduct electricity purchase and sale transactions in the electric energy market to obtain revenue, provide backup services in the backup ancillary service market to obtain backup service fees, and store excess electricity under the background of government subsidies to obtain government subsidies.

[0128] S106: Based on the adjustment scenario, a particle swarm optimization algorithm is used to solve the target optimization model and obtain the optimization solution result;

[0129] Preferably, a particle swarm optimization algorithm is used to solve the target optimization model, and the specific steps are as follows:

[0130] S106-1. Input the data of the photovoltaic energy storage system, including the total load level; photovoltaic power load curve; basic parameters of energy storage, including upper and lower limits of state of charge, charging and discharging efficiency, etc.; basic parameters of photovoltaic units, including unit power generation cost, power, etc.;

[0131] S106-2. Write the objective function according to the relevant scenario settings;

[0132] S106-3, initialize the particle swarm, randomly generate the initial population that meets the constraints, where each particle in the particle swarm represents a possible potential solution, that is, each particle contains all the decision variables that need to be solved in this paper - photovoltaic output, energy storage rated power, energy storage rated capacity, energy storage charging and discharging power and other data at each moment within 24 moments;

[0133] S106-4, continuously updating the individual best position and the global best position in the particle swarm, and updating the corresponding fitness value of the objective function;

[0134] S106-5. Determine whether the convergence condition is met. If so, stop the operation and output the optimization solution. If not, regenerate a new population and return to step S106-3 to continue the optimization.

[0135] In the embodiment of the present invention, the optimization solution results include the photovoltaic output, energy storage rated power, energy storage rated capacity and energy storage charging and discharging power at each moment in 24 moments.

[0136] It should be noted that the present invention, based on the collected basic data of the photovoltaic energy storage system, has constructed a target optimization model with the goal of minimizing the annual average cost of the photovoltaic energy storage system, and has designed in detail key boundary conditions including the energy storage charge state, device power, charge and discharge power, and the provision of upper and lower spare capacity, to ensure the accuracy and practicality of the model; further, a variety of adjustment scenarios with or without energy storage are designed to comprehensively evaluate the economy and benefits of the energy storage system under different market environments; finally, the particle swarm optimization algorithm is used to solve the target optimization model, and the optimal photovoltaic output, energy storage rated power, energy storage rated capacity, energy storage charge and discharge power and other key parameters are obtained, realizing the intelligent optimization of the configuration and operation of the photovoltaic energy storage system. The present invention not only improves the economic benefits and market competitiveness of the photovoltaic energy storage system, but also helps to promote the widespread access to renewable energy and the stable operation of the power system, with significant social and environmental benefits.

[0137] The above is a schematic scheme of a photovoltaic energy storage system configuration optimization and market participation method in this embodiment. It should be noted that the technical scheme of the photovoltaic energy storage system configuration optimization and market participation system and the technical scheme of the photovoltaic energy storage system configuration optimization and market participation method described above belong to the same concept. For details not described in detail in the technical scheme of the photovoltaic energy storage system configuration optimization and market participation system in this embodiment, please refer to the description of the technical scheme of the photovoltaic energy storage system configuration optimization and market participation method described above.

[0138] The photovoltaic energy storage system configuration optimization and market participation system in this embodiment includes:

[0139] A data acquisition module, used to acquire photovoltaic energy storage system data;

[0140] Function module, used to determine the objective function of the PSO algorithm optimization model based on the photovoltaic energy storage system data, design the boundary conditions of the target optimization model according to the objective function, and construct the target optimization model;

[0141] Scenario module, used to design regulation scenarios with or without energy storage based on the electric energy market and ancillary service market;

[0142] The output module is used to solve the target optimization model based on the adjustment scenario using the particle swarm optimization algorithm to obtain the optimization solution result;

[0143] The function module specifically includes: taking the annual average cost of the photovoltaic energy storage system as the objective function, wherein the annual average cost of the photovoltaic energy storage system includes the total revenue of the photovoltaic energy storage system participating in the market, the annual maintenance cost of the photovoltaic system, the annual maintenance cost of the energy storage system, the depreciation cost of the photovoltaic system and the total depreciation cost of the energy storage system;

[0144] The objective function is expressed as:

[0145]

[0146] Among them, R ave is the annual average cost of the photovoltaic energy storage system, y is the number of years of the project cycle, Z is the total revenue of the photovoltaic energy storage system participating in the market, C1 is the total maintenance cost of the photovoltaic system in y years, C2 is the total maintenance cost of the energy storage system in y years, C3 is the total depreciation cost of the photovoltaic system in y years, and C4 is the total depreciation cost of the energy storage system in y years;

[0147] Boundary conditions include energy storage state of charge constraints, energy storage device power constraints, energy storage device charge and discharge power constraints, and energy storage system upper and lower reserve capacity constraints;

[0148] The scenario modules specifically include: Scenario 1: Only consider the photovoltaic entities participating in the electric energy market and the backup auxiliary service market to purchase and sell electricity and obtain income;

[0149] Scenario 2: Consider PV with energy storage only participating in the backup ancillary service market, providing backup services in the backup ancillary service market to receive backup service fees;

[0150] Scenario 3: Consider PV with energy storage to participate in the electric energy and backup ancillary service markets, earn revenue from electricity purchase and sales transactions in the electric energy market, and earn backup service fees by providing backup services in the backup ancillary service market.

[0151] The output module specifically includes: writing the objective function based on the adjustment scenario setting;

[0152] The particle swarm optimization algorithm is used to update and iterate the objective function. In each iteration, it is determined whether the convergence condition is met. If so, the calculation is stopped and the optimization solution result is output.

[0153] This embodiment also provides a computing device suitable for photovoltaic energy storage system configuration optimization and market participation, including:

[0154] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the photovoltaic energy storage system configuration optimization and market participation method proposed in the above embodiment.

[0155] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing photovoltaic energy storage system configuration optimization and market participation as proposed in the above embodiment is implemented.

[0156] The storage medium proposed in this embodiment and the method for realizing photovoltaic energy storage system configuration optimization and market participation proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0157] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0158] Example 2

[0159] Reference Figure 2-7 Table 1-4 is an embodiment of the present invention, which provides a photovoltaic energy storage system configuration optimization and market participation method. In order to verify its beneficial effects, the comparison results of several solutions are provided.

[0160] Input the data of the photovoltaic energy storage system, the energy storage rated capacity range is 0-2000kWh, the rated power range is 0-800kW, the energy storage charge state range is 0.2-0.95, and the energy storage charging and discharging efficiency is 0.95. The government subsidized energy storage charging price is 1 yuan / kWh, the remaining photovoltaic energy storage system related parameters are shown in Table 1, the time-of-use electricity price is shown in Table 2, the standby price is shown in Table 3, and the comparison of the three scenarios is shown in Table 4;

[0161] Table 1 Engineering parameters

[0162] Parameter name Numeric Cu 3400 yuan / kW y 25 years r 0.07 P 500kW Eu 450 yuan / kW <![CDATA[Q u ]]> 1200 yuan / kWh

[0163] Table 2 Time-of-use electricity prices

[0164]

[0165] Table 3 Reserve Electricity Price

[0166]

[0167]

[0168] Table 4 Comparison of different scenarios

[0169] Scene 1 Scene 2 Scene 3 Electricity purchase cost (10,000 yuan) 22.34 4.63 10.05 Savings in power generation income (10,000 yuan) 17.13 34.84 29.42 Electricity volume participating in the electricity market (MWh) 48.16 / 27.42 Electricity market income (10,000 yuan) 33.18 / 19.78 Government subsidy income (10,000 yuan) / 34.13 20.74 Backup market purchase cost (10,000 yuan) 1.01 0.13 0.29 Reserve market income (10,000 yuan) / 0.44 0.36 Energy storage rated capacity (kWh) / 1270 680 Energy storage rated power (kW) / 236 174 Total cash flow income (10,000 yuan) 114.38 352.43 360.21 Annual average cost (ten thousand yuan) 8.71 11.60 5.68

[0170] like Figure 2 As shown, Figure 2 This is a typical daily output curve of a photovoltaic power generation system, where the sampling time interval is 1h. Figure 3 is the total load level corresponding to the photovoltaic power generation system, Figure 4 is the energy storage SOC curve, Figure 5 Fitness curve of the net present value of photovoltaic energy storage system.

[0171] According to the optimization model solution process, when the particle swarm algorithm has a maximum iteration number of 150 times and a population number of 80, the optimal energy storage configuration solution is obtained by running the program: the rated capacity of the energy storage is 680kWh and the rated power of the energy storage is 174kW.

[0172] Figure 4 , Figure 5 The state of charge curve of the energy storage when the system has the maximum net present value. Figure 6 , Figure 7 The curve of the annual average cost of the energy storage photovoltaic system changing with the number of iterations shows that the target converges when the number of iterations reaches 55. At this time, the total revenue of the system participating in the market in scenario 2 is 3.5243 million yuan, and the minimum annual average cost is 116,000 yuan / year. The total revenue of the system participating in the market in scenario 3 is 3.6021 million yuan, and the minimum annual average cost is 56,800 yuan / year. If photovoltaics do not introduce energy storage, the total revenue of the system participating in the market is 1.1438 million yuan.

[0173] from Figure 4 and Figure 5 It can be seen that energy storage is mainly charged during the period of 12:00-18:00, because the photovoltaic power generation is greater than the load at this time. Charging at this time can obtain a large amount of government subsidies, and it can also prepare to make up for the huge gap between the load and photovoltaic output at night.

[0174] According to Table 4, we first compare scenario 1 with scenarios 2 and 3:

[0175] It can be seen that in scenario 1 without energy storage, even if the photovoltaic system participates in the electric energy market and the backup market at the same time, the benefits are much lower than scenarios 2 and 3 with energy storage. This is because in the absence of energy storage, when the photovoltaic power generation is not enough to match the load, the power gap can only be made up by purchasing electricity at a high price, which will cause a lot of costs, even if the electric energy market benefits will be higher in scenario 1.

[0176] Compare Scenario 2 and Scenario 3:

[0177] In both cases, the rated capacity and rated power of scenario 2 will be larger, but the total benefit and optimal fitness of its photovoltaic energy storage system are not as good as scenario 3. This is because in scenario 3, energy storage can also participate in the electric energy market to obtain huge profits, with higher utilization of capacity, which can reduce the investment cost of energy storage - even if this will lead to higher purchase costs, saving power generation, government subsidies and lower benefits from the reserve market.

[0178] In summary, this article describes an invention patent on photovoltaic energy storage system configuration optimization and market participation strategy. The invention aims to optimize the configuration of photovoltaic energy storage system and formulate its best participation strategy in the electricity market through a multi-standard optimization modeling method combined with particle swarm optimization algorithm (PSO).

[0179] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A photovoltaic energy storage system configuration optimization and market participation method, characterized in that: include: Obtaining photovoltaic energy storage system data; Based on the photovoltaic energy storage system data, determine the objective function of the PSO algorithm optimization model, design the boundary conditions of the target optimization model according to the objective function, and construct the target optimization model; Design regulation scenarios with or without energy storage based on the electric energy market and ancillary service market; Based on the adjustment scenario, a particle swarm optimization algorithm is used to solve the target optimization model to obtain an optimization solution result.

2. The photovoltaic energy storage system configuration optimization and market participation method according to claim 1, characterized in that: The objective function includes, The annual average cost of the photovoltaic energy storage system is used as the objective function. The annual average cost of the photovoltaic energy storage system includes the total revenue of the photovoltaic energy storage system participating in the market, the annual maintenance cost of the photovoltaic system, the annual maintenance cost of the energy storage system, the depreciation cost of the photovoltaic system and the total depreciation cost of the energy storage system.

3. The photovoltaic energy storage system configuration optimization and market participation method according to claim 1 or 2, characterized in that: The boundary conditions include energy storage state of charge constraints, energy storage device power constraints, energy storage device charging and discharging power constraints, and energy storage system upper and lower backup capacity constraints.

4. The photovoltaic energy storage system configuration optimization and market participation method according to claim 3, characterized in that: The regulation scenarios with or without energy storage designed based on the electric energy market and ancillary service market include: Scenario 1: Only consider the photovoltaic entities participating in the electric energy market and the backup auxiliary service market to purchase and sell electricity and obtain benefits; Scenario 2: Consider PV with energy storage only participating in the backup ancillary service market, providing backup services in the backup ancillary service market to receive backup service fees; Scenario three: Consider configuring photovoltaic energy storage to participate in the electric energy and backup ancillary service markets, conduct electricity purchase and sales transactions in the electric energy market to obtain revenue, and provide backup services in the backup ancillary service market to obtain backup service fees.

5. The photovoltaic energy storage system configuration optimization and market participation method according to claim 4, characterized in that: The particle swarm optimization algorithm is used to solve the target optimization model, including: Writing the objective function based on the adjustment scenario setting; The particle swarm optimization algorithm is used to update and iterate the objective function. In each iteration, it is determined whether the convergence condition is met. If so, the calculation is stopped and the optimization solution result is output.

6. The photovoltaic energy storage system configuration optimization and market participation method according to claim 5, characterized in that: The objective function is expressed as: Among them, R ave is the annual average cost of the photovoltaic energy storage system, y is the number of years of the project cycle, Z is the total revenue of the photovoltaic energy storage system participating in the market, C1 is the total maintenance cost of the photovoltaic system in y years, C2 is the total maintenance cost of the energy storage system in y years, C3 is the total depreciation cost of the photovoltaic system in y years, and C4 is the total depreciation cost of the energy storage system in y years.

7. The photovoltaic energy storage system configuration optimization and market participation method according to claim 6, characterized in that: The optimization solution results include photovoltaic output, energy storage rated power, energy storage rated capacity, and energy storage charging and discharging power at each of the 24 moments.

8. A system for photovoltaic energy storage system configuration optimization and market participation, characterized in that: include, A data acquisition module, used to acquire photovoltaic energy storage system data; A function module, used to determine the objective function of the PSO algorithm optimization model based on the photovoltaic energy storage system data, design the boundary conditions of the target optimization model according to the objective function, and construct the target optimization model; Scenario module, used to design regulation scenarios with or without energy storage based on the electric energy market and ancillary service market; The output module is used to solve the target optimization model based on the adjustment scenario by using a particle swarm optimization algorithm to obtain an optimization solution result.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the photovoltaic energy storage system configuration optimization and market participation method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the photovoltaic energy storage system configuration optimization and market participation method as described in any one of claims 1 to 7.

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