Distributed optical heat storage coupling system configuration method and device

Abstract

The invention is suitable for the technical field of energy planning and configuration, and provides a distributed light heat storage coupling system configuration method and device, and the method comprises the steps: obtaining the heat load data of a non-central heating target region in a heating period and the equipment data of a distributed light heat storage coupling system; based on the equipment data of the distributed optical heat storage coupling system, constructing an electric-thermal coupling model; and based on the heat load data in the heating period, taking the minimum annual comprehensive cost of the distributed optical heat storage coupling system in the whole life cycle as an upper-layer target and the minimum annual operation cost as a lower-layer target, and solving the electric-thermal coupling model by using a particle swarm algorithm to obtain an optimal configuration result of the capacity of each device in the distributed optical heat storage coupling system. According to the method, the heat load data in the heating period can be fully considered in the configuration process, it is ensured that the system can meet the actual heating requirement of a user, and the capacity configuration optimal scheme of the distributed light heat storage coupling system in different scenes is determined through the bilevel programming model.

Description

Technical Field

[0001] The present application belongs to the technical field of energy planning and configuration, and in particular, relates to a distributed photovoltaic thermal storage coupling system configuration method and device. Background Art

[0002] In recent years, as the concept of "ecological priority and green development" has become increasingly popular, and under the trend of actively promoting the development and utilization of renewable energy, there is an urgent need to develop clean heating based on renewable energy to adjust the existing energy structure, control the total energy consumption, respond to climate change and other issues, and plan and configure distributed solar-heat storage coupling systems.

[0003] Since most of the houses in rural areas are scattered, the centralized heating solution is no longer applicable. How to further realize the clean heating transformation in rural areas has always been a difficult point on the road to clean heating. In related technologies, the two-level planning model is modeled from two aspects: configuration optimization and operation optimization of the distributed solar-heat storage coupling system. Multi-level planning was first proposed by Bracken J and McGill JT. Bi-level optimization is a classic and common type of multi-level optimization system. Its core idea is to supervise and control the lower model through the upper model to achieve the optimization of the overall goal of the system. Specifically, the upper model can affect the operation and decision-making process of the lower model through decision variables, while the lower model can independently solve the optimal decision result and feed the result back to the upper model, thereby further affecting its decision-making process. Through continuous iteration, updating and coordination, the optimization effect of the overall goal can be finally achieved. By constructing a multi-level optimization structure, the system can be decomposed into several sub-problems, and the optimal effect of the overall goal can be achieved through the flow of data and information between the upper and lower layers.

[0004] However, in the process of promoting clean heating in rural areas, the general two-tier planning model is no longer applicable. It is necessary to consider building a distributed heating system based on the two-tier planning model and adapting it to local conditions. Summary of the invention

[0005] The embodiments of the present application provide a distributed photovoltaic-heat storage coupling system configuration method and device, which can fully consider the heat load data during the heating period during the configuration process, ensure that the system can meet the actual heating needs of users, and determine the optimal capacity configuration solution of the distributed photovoltaic-heat storage coupling system in different scenarios by using a two-layer planning model.

[0006] This application is implemented through the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a method for configuring a distributed solar-thermal energy storage coupling system, including:

[0008] Obtain the heat load data of the target area during the heating period and the equipment data of the distributed solar-thermal energy storage coupling system in non-central heating areas;

[0009] Based on the equipment data of the distributed solar-thermal storage coupling system, an electric-thermal coupling model is constructed;

[0010] Based on the heat load data of the heating period, with the minimum annual comprehensive cost of the distributed solar-thermal storage coupling system over its entire life cycle as the upper-level goal and the minimum annual operating cost as the lower-level goal, the particle swarm algorithm is used to solve the electric-thermal coupling model to obtain the optimal configuration results of the capacity of each device in the distributed solar-thermal storage coupling system.

[0011] In a second aspect, an embodiment of the present application provides a distributed photovoltaic thermal storage coupling system configuration device, which executes the distributed photovoltaic thermal storage coupling system configuration method as in the first aspect, including:

[0012] A data acquisition module, used to obtain the heat load data of the target area for non-central heating during the heating period and the equipment data of the distributed solar-thermal storage coupling system;

[0013] A model building module is used to build an electric-thermal coupling model based on the equipment data of the distributed solar-thermal storage coupling system;

[0014] The model solving module is used to solve the electric-thermal coupling model based on the heat load data during the heating period, with the minimum annual comprehensive cost of the distributed solar-heat storage coupling system over its entire life cycle as the upper-level goal and the minimum annual operating cost as the lower-level goal, to obtain the optimal configuration results of the capacity of each device in the distributed solar-heat storage coupling system.

[0015] Compared with the related art, the embodiments of the present application have the following beneficial effects:

[0016] The distributed photovoltaic thermal storage coupling system configuration method and device of the embodiment of the present application fully considers the heat load data of the heating period during the configuration process, ensuring that the system can meet the actual heating needs of users. On the basis of the two-layer planning model, the construction of a distributed heating system is considered according to local conditions. When optimizing the equipment capacity configuration, two levels of decision-making are considered. The upper-level optimization aims to minimize the annual comprehensive cost, which is a capacity optimization configuration problem. The lower-level model aims to minimize the annual operating cost, which is a simple optimization operation problem. The particle swarm algorithm is used to solve the decision variables of the upper and lower models, and the optimal capacity configuration solution of the distributed photovoltaic thermal storage coupling system under different scenarios is determined concisely and efficiently.

[0017] The beneficial effects of the above-mentioned second aspect embodiment refer to the beneficial effects of the first aspect embodiment, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 This is a general architecture diagram of a distributed solar-thermal storage coupling system provided in one embodiment of the present application;

[0020] Figure 2 It is a flow chart of a method for configuring a distributed solar-thermal storage coupling system provided in an embodiment of the present application;

[0021] Figure 3 It is a schematic diagram of a process of constructing an electric-thermal coupling model provided by an embodiment of the present application;

[0022] Figure 4 This is a schematic diagram of temperature rise control of an air source heat pump provided in one embodiment of the present application;

[0023] Figure 5 This is a diagram of an electric-thermal coupling model of a distributed solar thermal storage system provided in an embodiment of the present application;

[0024] Figure 6 This is a schematic diagram of variables transferred between upper and lower layers of a distributed solar-thermal storage coupling system provided in an embodiment of the present application;

[0025] Figure 7 It is a heat load power curve diagram of each region during the heating period provided by an embodiment of the present application;

[0026] Figure 8 It is a typical daily unit capacity photovoltaic power generation curve for each region provided in an embodiment of the present application;

[0027] Fig. 9 is the typical daily equipment operating power of region 1 provided in an embodiment of the present application;

[0028] Fig.10 is the typical daily equipment operating power of region 2 provided in an embodiment of the present application;

[0029] Fig.11 is the typical daily equipment operating power of region 4 provided in an embodiment of the present application;

[0030] Fig.12 is the typical daily equipment operating power of region 3 provided in an embodiment of the present application;

[0031] Fig.13 It is a structural schematic diagram of a distributed solar-thermal storage coupling system configuration device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0032] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0033] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0034] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0035] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0036] References to "an embodiment", "one embodiment" or "some embodiments" described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0037] 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 application will be clearly and completely described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0038] Reference Figure 1, which is a general architecture diagram of a distributed photovoltaic thermal storage coupling system of an embodiment of a distributed photovoltaic thermal storage coupling system configuration method and device of the present invention. The prediction method and device are run in a distributed control center 101. The control center 101 communicates with each intelligent circuit breaker 102, 103 and 104 through the Internet, and controls the interaction with the external power grid, the energy storage system and the distributed photovoltaic system to meet the power and heating needs within the system.

[0039] Figure 2 is a flow chart of a distributed solar thermal storage coupling system configuration method based on the overall architecture diagram of the above-mentioned distributed solar thermal storage coupling system provided by an embodiment of the present application, with reference to Figure 2 The configuration method of the distributed solar thermal energy storage coupling system comprises:

[0040] Step 201, obtaining the heat load data of the target area for non-central heating during the heating period and the equipment data of the distributed solar-heat storage coupling system.

[0041] For example, the heat load required for the building in different geographical scenarios, such as different latitudes or whether it is a mountainous area, etc., can be selected, and the date with the maximum heat load value can be selected as the typical day.

[0042] Distributed solar-thermal storage coupling system can include distributed photovoltaic system, energy storage system and heat pump system, such as Figure 1 Then, the equipment data of the distributed photovoltaic heat storage coupling system includes the output data of the distributed photovoltaic system, the energy storage data of the energy storage system, and the operation data of the air source heat pump.

[0043] Step 202: construct an electric-thermal coupling model based on the equipment data of the distributed solar-thermal energy storage coupling system.

[0044] Based on the overall architecture of the distributed photovoltaic thermal energy storage coupling system, the energy conversion relationship of the system is described from the two levels of the system's electrical energy network and thermal energy network, and an electrical-thermal coupling model of the overall system is established. Among them, the following "system" refers to the "distributed photovoltaic thermal energy storage coupling system".

[0045] Step 203, based on the heat load data during the heating period, with the annual comprehensive cost of the distributed solar-heat storage coupling system within its entire life cycle minimized as the upper-level goal and the annual operating cost minimized as the lower-level goal, the particle swarm algorithm is used to solve the electric-thermal coupling model to obtain the optimal configuration result of the capacity of each device in the distributed solar-heat storage coupling system.

[0046] In the capacity configuration problem of distributed photovoltaic and thermal energy storage coupling systems, the double-layer optimization structure can better take into account the coupling relationship between different links and optimize the transmission efficiency, reduce energy waste and loss, and at the same time, combine the heat load data of the heating period under different regional scenarios and the targeted electric-thermal coupling model to obtain the optimal configuration results of the capacity of each device in the distributed photovoltaic and thermal energy storage coupling system.

[0047] Among them, the particle swarm algorithm (PSO) is derived from simulating the collective behavior of bird flocks. It regards the solution to the problem as a particle in space and allows these particles to find the optimal solution to the overall problem through cooperation and information sharing. This optimization idea has broad application prospects and can be applied to optimization problems in various fields.

[0048] This embodiment fully considers the heat load data of the heating period during the configuration process to ensure that the system can meet the actual heating needs of users. On the basis of the two-layer planning model, the construction of a distributed heating system is considered according to local conditions. The distributed photovoltaic thermal storage coupling system, two-layer optimization, multi-cost planning, and particle swarm solving algorithm are specifically applied. It is based on the electric-thermal coupling model of the distributed photovoltaic thermal storage coupling system, introduces the full life cycle when calculating the investment cost of the system, and considers the whole process of equipment operation and maintenance in the later stage in the planning stage. Under the two-layer planning model, the annual comprehensive cost of the system is taken as the upper target, and the annual operating cost is taken as the lower target. The equipment capacity in the distributed photovoltaic thermal storage system is optimized, thereby achieving the optimal daily operation and maintenance cost and investment cost of the distributed photovoltaic thermal storage coupling system, providing a basis for the construction planning of the distributed photovoltaic thermal storage coupling system.

[0049] In one possible implementation, see Figure 3 , step 202 comprises:

[0050] Step 2021: construct a physical output model of the photovoltaic system based on the output data of the distributed photovoltaic system.

[0051] The output power of the distributed photovoltaic system is related to the solar irradiance, the photoelectric conversion efficiency of photovoltaic solar cells, the area of ​​photovoltaic panels, etc. According to the main factors affecting the output of the distributed photovoltaic system, the physical output model of the photovoltaic system is constructed, as shown in formula (1).

[0052] P PV =ζη m Aηcosθ (1)

[0053] Among them, P PV is the actual photovoltaic power output, in kW; ζ is the solar irradiance, in W / m 2 ; η m is the photoelectric conversion efficiency; A is the photovoltaic panel area, in m 2; η is the efficiency of the photovoltaic module; θ is the incident angle of sunlight.

[0054] At the same time, in order to maximize the energy output of the photovoltaic system and improve the efficiency of the system, the photovoltaic system operates in the Maximum Power Point Tracking (MPPT) mode.

[0055] When the photovoltaic system is connected to the distributed solar-thermal storage coupling system, the following constraints must be met:

[0056] 0≤P PV ≤P MAX (2)

[0057] Among them, P MAX It is the maximum photovoltaic output power, in kW, which is mainly related to the photovoltaic installed capacity.

[0058] Step 2022: construct an energy storage model of the energy storage system based on the energy storage data of the energy storage system.

[0059] Under the control of the photovoltaic storage inverter, the energy storage system can track the changes in photovoltaic output and adjust the charging and discharging power. While providing adjustable space for photovoltaic consumption in the system, it plays an important role in suppressing the thermal load in the grid and the randomness and intermittency of photovoltaics, and buffering the uncertainty of photovoltaic output. The access of the energy storage system provides a guarantee for improving the reliability and continuity of the system power supply. The energy storage system not only has the advantages of strong short-term power throughput capacity, easy change of adjustment direction, and fast response, but is also particularly outstanding in responding to load regulation and peak shaving.

[0060] In the distributed solar-thermal energy storage coupling system of this embodiment, the energy storage device uses a lithium battery, and its mathematical model is shown in formula (3). The energy storage model of the energy storage system is expressed as:

[0061] E b,t =E b,t-1 +P c,t ηΔt (3)

[0062] Among them, E b,t E is the amount of electricity stored in the energy storage battery at time t, in kW·h; b，t-1 is the amount of electricity stored in the energy storage battery at time t-1, in kW·h; P c,t is the charging and discharging power of the energy storage battery at time t, in kW. c,t When the discharge is positive, P c,t is negative; η is the energy conversion efficiency of the energy storage battery; Δt is the time interval between time t and time t-1.

[0063] The state of charge (SOC) of an energy storage system refers to the ratio of the energy currently stored in the energy storage device to its rated capacity, as shown in formula (4). The state of charge of an energy storage system directly reflects the current energy storage status of the energy storage device and is crucial to the operation and management of the system.

[0064]

[0065] Among them, SOC t is the state of charge of the energy storage system at time t; E t is the energy stored in the energy storage system at time t, in kW·h; if the energy storage device is a lithium battery, then E t Equal to E b,t ; E ESS is the rated capacity of the energy storage system, in kW·h.

[0066] When the energy storage system participates in system control, it must meet the charging and discharging power constraints, charge constraints, capacity constraints, etc., namely:

[0067] -P in,max ≤P in,t ≤P in,max (5)

[0068] -P o,max ≤P o,t ≤P o,max (6)

[0069] SOC min ≤SOC t ≤SOC max (7)

[0070]

[0071] E t +(P in,t -P o,t )λ1Δt≥E min (9)

[0072] E t +(P in,t -P o,t )λ2Δt≤E max (10)

[0073] Among them, P in,t is the charging power of the energy storage system at time t, in kW; P o,t is the discharge power of the energy storage system at time t, in kW; P in,max is the maximum charging power of the energy storage system, in kW; P o,max is the maximum discharge power of the energy storage system, in kW; SOCmax is the maximum state of charge of the energy storage system, SOC min SOC is the minimum state of charge of the energy storage system. t is the state of charge of the energy storage system at time t; SOC0 is the state of charge of the energy storage system at time 0; T is the time period; E max is the upper limit of energy storage capacity, E min is the lower limit of the energy storage capacity; Δt is the time interval; λ1 is the energy storage charging efficiency, and λ2 is the energy storage discharging efficiency.

[0074] Step 2023: construct an operation model of the air source heat pump based on the operation data of the air source heat pump.

[0075] Describe the working characteristics of the heat pump in heating or cooling mode, explore the main factors affecting the optimization of the heat pump, and the operation model of the air source heat pump is expressed as:

[0076]

[0077]

[0078] COP EC =a·(T w -T n )+b·(T w -T k )+c (13)

[0079] in, is the heating / cooling power output by the air source heat pump, in kW; The electric power input to the air source heat pump, in kW; COP EC is the heating / cooling coefficient of the air source heat pump; P Y P is the electric power consumed by the air source heat pump compressor, in kW; F The electric power consumed by auxiliary equipment; T w is the outdoor temperature in °C; T n is the indoor temperature in °C; T k is the operating benchmark temperature of the air source heat pump, in °C; a is the indoor temperature deviation coefficient, b is the outdoor temperature difference coefficient, and c is the heat load coefficient. These coefficients can be adjusted and optimized according to the characteristics of the heat pump system, equipment parameters, operating conditions, and environmental factors.

[0080] When the air source heat pump is operating under rated conditions, it must meet output constraints, environmental condition constraints, COP (heating / cooling coefficient) constraints, controllable time constraints, etc. Generally speaking, the COP in heating mode will change with the change of outdoor temperature, but when the outdoor ambient temperature drops to a certain value, the heating efficiency of the air source heat pump begins to drop significantly. If the external ambient temperature meets the operating conditions of the heat pump, it can be considered that the heating coefficient COP of the air source heat pump remains basically unchanged.

[0081] The constraints of the operation model of the air source heat pump are expressed as:

[0082] Q min ≤Q≤Q max (14)

[0083] T min ≤T wnk ≤T max (15)

[0084] COP min ≤COP EC ≤COP max (16)

[0085] h n,t ∈h' n,t (17)

[0086] Among them, Q min The rated minimum heating capacity of the air source heat pump, in kW; Q max T is the rated maximum heating capacity of the air source heat pump, in kW; min T is the minimum ambient temperature at which the air source heat pump can maintain rated working conditions, in °C; wnk The ambient temperature at which the air source heat pump can maintain rated working conditions, including T w 、T n and T k ; T max COP is the maximum ambient temperature at which the air source heat pump can maintain rated working conditions, in °C; min COP is the minimum coefficient of performance of air source heat pump max is the maximum performance coefficient of the air source heat pump; h n,t is the actual controlled period of the user, h' n,t Allow controlled time periods for users.

[0087] Since the heating power of the heat pump is fixed under rated conditions, the change of heating capacity is achieved by controlling the start and stop frequency. The specific heating operation process is as follows: Figure 4 shown. Figure 4This is a curve showing the indoor temperature and the electric power consumed by a single air source heat pump over time when it is running. Under normal operating conditions, the air source heat pump starts and stops periodically within the temperature range. When the room temperature T rises to the set upper temperature limit T max The heat pump stops. As the indoor heat is dissipated and consumed, the indoor temperature drops. During this process, the air source heat pump remains stopped. When the room temperature T drops to the set lower temperature limit T min When the air source heat pump starts, the heating process is carried out again, and the cycle is repeated.

[0088] Step 2024, constructing an electric-thermal coupling model based on the physical output model of the photovoltaic system, the energy storage model of the energy storage system and the operation model of the air source heat pump.

[0089] The distributed photovoltaic and thermal storage coupling system is a hub connecting users and the distribution network. It mainly includes distributed photovoltaic, energy storage system, power load, electric heating load and controller, etc. The system guarantees the heating needs of users through electric heating equipment. Due to the high energy efficiency, low cost, comfort, environmental protection and stability of air source heat pumps, the modeling and optimized operation of electric heating equipment in this embodiment are based on air source heat pumps. The composition architecture of the entire system is as follows: Figure 1 shown.

[0090] There are two operating modes for distributed photovoltaic and thermal storage coupling systems. One is grid-connected operation, in which the system is connected to the external power grid through an off-grid / grid-connected switch, and the two support each other. The photovoltaic system outputs at maximum power or operates at limit power. While suppressing photovoltaic output fluctuations, the energy storage system can interact with the main grid to achieve balance and optimization of the system's internal power demand; the other is off-grid operation. In the event of external power grid failure or power outage, the system can be completely separated from the main grid and operate independently. The energy storage system and distributed photovoltaic system can temporarily meet the system's internal power and heating needs.

[0091] Since the energy exchange network of the distributed photovoltaic thermal storage coupling system consists of two parts: the electric energy network and the thermal energy network, in order to achieve the coordination and mutual assistance of the two systems, it is necessary to start from the system electrical balance model and thermal balance model respectively, and study the economic problems of the system at the electrical network level and the heating comfort problems at the thermal network level. The model analysis of the electric-thermal coupling network is the basis for optimizing the operation of the entire system. The electric-thermal coupling model of the system, such as Figure 5 shown.

[0092] Based on the mathematical models of the aforementioned devices, a mathematical coupling model of the distributed photovoltaic heat storage coupling system is established, in which the heat pump is a unidirectional flow unit as the medium for coupling the two energies; there is a bidirectional flow of electric energy between the system and the external power grid and the energy storage system; the distributed photovoltaic system is a single output unit, but the electricity it generates can be directly supplied to the heat pump, and it can also sell electricity to the external power grid when there is surplus output.

[0093] Exemplarily, the electro-thermal coupling model is expressed as:

[0094]

[0095] Among them, P pv,t is the photovoltaic output power at time t, in kW; P b,t is the power purchased and sold between the distributed solar-thermal energy storage coupling system and the external power grid system at time t, in kW. b,t When the electricity is sold, P b,t is positive; P LE,t is the power load power at time t, in kW; P LH,t is the heating load power of the air source heat pump at time t, in kW; P c,t is the charging and discharging power of the energy storage system at time t, in kW. c,t When the discharge is positive, P c,t Negative; Q EC The air source heat pump compressor is at input P LH,t The heating power generated by the action of is in kW; Q4 is the heat power dissipated by the air source heat pump during heat exchange in the circulation system, in kW; Q5 is the heat power dissipated by indoor air convection of the heating user, in kW; E is the heat power required to adjust the indoor temperature to the temperature set by the air source heat pump, in kW.

[0096] In this embodiment, the photovoltaic system output model, the energy storage model of the energy storage system and the operation model of the air source heat pump are respectively constructed, and then the electric-thermal coupling model of the distributed photovoltaic and thermal storage coupling system is constructed. The energy conversion relationship of the system is described from the two levels of the system's electric energy network and thermal energy network, and the electric-thermal coupling model of the overall system is established. The power constraints of related equipment are established, and the controllable quantities in the system optimization scheduling are further clarified.

[0097] In one possible implementation, see Figure 6 In step 203, the upper optimization goal of the distributed photovoltaic heat storage coupling system is to achieve the lowest annual comprehensive cost within the life cycle of the system. The capacity of the photovoltaic power generation system, energy storage equipment and air source heat pump is used as a decision variable and passed to the lower optimization model; the lower optimization model takes the minimum annual operating cost of the system as the goal, and the decision variables are the energy storage charging and discharging power and the system sales and purchase power. The lower model feeds back the equipment power operation status to the upper model to influence its decision-making process.

[0098] The upper level objective of the electro-thermal coupling model is expressed as:

[0099] minC tot =C1+C2+C3+C4+C5 (19)

[0100] Among them, C tot is the annual comprehensive cost of the system during its entire life cycle; C1 is the annual investment cost of the distributed solar-thermal storage coupling system; C2 is the annual grid interaction cost; C3 is the annual carbon emission cost; C4 is the annual depreciation cost of energy storage; and C5 is the annual maintenance cost of the equipment.

[0101] The decision variable for the upper configuration of the electric-thermal coupling model is the energy storage capacity K bat , Photovoltaic capacity K pv and the capacity of the air source heat pump K AH .

[0102] The investment cost of the system includes the investment cost of the photovoltaic power generation system, air source heat pump and energy storage equipment. Different equipment has different service life, so when calculating the investment cost, it is necessary to use the method of calculating the equal annual value investment cost, that is, to convert the one-time investment cost at the initial planning stage into each year in the equipment life cycle, eliminating the impact of the service life on the investment cost. The equal annual value initial investment cost is related to the capacity and service life of the equipment, and can more accurately reflect the actual level of investment cost and cost-effectiveness.

[0103] Exemplarily, the equivalent annual investment cost C1 of the distributed solar-thermal energy storage coupling system is expressed as:

[0104] C1=C PV +C bat +C AH (20)

[0105] Among them, C PV is the annual investment cost of the photovoltaic system; C bat is the annual investment cost of the energy storage system; C AH It is the equivalent annual investment cost of the air source heat pump.

[0106] The equivalent annual investment costs of each equipment are:

[0107]

[0108] in, Represents the photovoltaic investment cost per unit capacity; Represents the energy storage investment cost per unit capacity; represents the investment cost of air source heat pump per unit capacity; r is the discount rate; t PV Indicates the service life of photovoltaic equipment; t bat Indicates the service life of the energy storage equipment; t AH Indicates the service life of the air source heat pump.

[0109] For example, the annual grid interaction cost refers to the cost of buying electricity from the grid in a year, which is specifically expressed as the difference between the cost of buying electricity and the cost of selling electricity.

[0110] C2=C buy -C sell (twenty four)

[0111] Among them, C buy Indicates the electricity cost of purchasing electricity from the power grid in a year; C sell Represents the revenue from selling electricity to the grid in a year.

[0112] When the system is running, the required energy comes from photovoltaic power generation equipment and the power grid. Photovoltaic power generation does not require fuel, so its operating cost is mainly reflected in the cost of selling electricity to the power grid, that is, the interaction cost. The interaction cost is one of the important factors affecting the economic benefits of the system. In actual operation, it is necessary to reasonably adopt the strategy of buying and selling electricity according to the needs and actual situation of system operation, and make fine adjustments in combination with the rules and policy environment of the power market.

[0113]

[0114] Among them, C buy,t represents the electricity purchase price at time t; C sell,t represents the electricity price at time t; P buy,t represents the power purchased from the grid at time t; P sell,t Represents the power sold to the grid at time t.

[0115] For example, the annual carbon emission fee is a fee levied on carbon dioxide emissions. In the context of "dual carbon", it is hoped that carbon emissions can be reduced and environmental protection can be promoted by charging according to the carbon content in energy.

[0116]

[0117] Among them, β is the cost of unit carbon dioxide emissions; μ is the carbon emissions per unit of electricity. The electricity purchased from the power grid is generated by thermal power plants. The production of 1kWh of electricity consumes 0.34kg of coal. Based on the carbon content of 70%, it will produce about 0.87kg of carbon dioxide, so μ can be set to 0.87.

[0118] For example, the annual depreciation cost of energy storage refers to the annual depreciation cost caused by the discharge of energy storage during one year of use.

[0119]

[0120] Among them, K DE P is the depreciation cost of each 1kWh of energy storage discharged; bat,dis,t Represents the discharge power of the energy storage system at time t.

[0121]

[0122] Among them, K rep,bat Z is the single reset cost of energy storage per unit capacity; max It is the maximum number of charge and discharge cycles that the energy storage can undergo during its life cycle. k1 is the energy storage depreciation coefficient.

[0123] Exemplarily, the annual maintenance costs of the equipment generated by the use of the equipment, because the depreciation costs of energy storage have been discussed separately, so only the maintenance costs of photovoltaics and air source heat pumps are considered here.

[0124]

[0125] Among them, K DP is the maintenance cost of photovoltaic; K DA is the maintenance cost of the air source heat pump; P AH The electrical power consumed by the air source heat pump.

[0126] For example, in the upper-level optimization configuration of the system, the main constraint is the capacity limit of each device to ensure the stable operation and economic benefits of the solar thermal energy storage system. Equipment capacity constraint is an important constraint in the operation of the solar thermal energy storage system, and its specific constraint method can be optimized according to the characteristics, functional requirements and operating environment of different devices. In the configuration stage, it is necessary to select the appropriate equipment capacity according to the system requirements and budget, and make comprehensive considerations and adjustments.

[0127]

[0128]

[0129] in, is the maximum investment capacity of photovoltaic equipment, is the maximum investment capacity of energy storage equipment, It is the maximum investment capacity of the air source heat pump, which is generally determined based on the maximum household capacity of the equipment on the market.

[0130] In the upper-level optimization configuration model of the solar thermal storage system of this embodiment, the annual grid interaction cost, annual carbon emission cost and annual depreciation cost of energy storage are all related to the system's power sales and purchases and the charging and discharging power of energy storage in each period of the year. The calculation of these costs is closely related to the actual operation of the system. In order to facilitate calculation and comparison, these costs are combined into the annual operating cost, which is used to evaluate the economic and environmental benefits of the system. To achieve the lowest annual comprehensive cost within the life cycle of the system, the capacity of the photovoltaic power generation system, energy storage equipment and air source heat pump is used as a decision variable and passed to the lower optimization model.

[0131] In one possible implementation, the objective function of the lower-level optimization operation is the annual operating cost of the system, which includes annual grid interaction costs, annual energy storage depreciation costs, annual carbon emission costs, and annual maintenance costs. The decision variables of the lower-level optimization operation are the power sold to the grid and the charging and discharging power of energy storage.

[0132] The lower level objective of the electro-thermal coupling model is expressed as:

[0133] minC op =C2+C3+C4+C5 (33)

[0134] Among them, C op is the annual operating cost of the distributed solar-thermal energy storage coupling system.

[0135] The first thing that the solar thermal storage system must satisfy is the electric power balance of each device in each scheduling period, that is, the photovoltaic power generation and the interaction between the power grid must meet the power demand of the air source heat pump. Because there is a time asynchrony between the power curve of photovoltaic power generation and the load curve of the air source heat pump, energy storage is used as a device to temporarily store photovoltaic power generation.

[0136] The constraints of the lower-level objectives of the electric-thermal coupling model include electric power balance constraints, thermal power balance constraints, grid interaction power constraints, and energy storage charging and discharging constraints.

[0137] Exemplarily, the electric power balance constraint is expressed as:

[0138] P buy,t +P PV,t +P bat,dis,t =P bat,ch,t +P AH,t +P sell,t (34)

[0139] Among them, P buy,t P is the power purchased from the grid at time t; pv,t is the photovoltaic output power at time t; P bat,dis,t represents the discharge power of the energy storage system at time t; P bat,ch,t is the charging power of the energy storage system at time t; P AH,t P sell,t is the power sold to the grid at time t.

[0140] The thermal power balance constraint is expressed as:

[0141] Q AH =P heat (35)

[0142] Among them, Q AH is the heating power of the air source heat pump, P heat The heating power required by the house.

[0143] Exemplarily, there is an upper limit on the power of interaction between the building energy system and the grid, and the grid interaction power constraint is expressed as:

[0144]

[0145] A buy,t +A sell,t ≤1 (38)

[0146] in, Indicates the upper limit of the power purchased from the power grid; Indicates the upper limit of power sold to the grid; A sell,t A is the electricity sales status at time t; buy,t is the electricity purchasing status at time t, A sell,t When A is 0, buy,t is 1, A sell,t When A is 1, buy,t If it is 0, you cannot buy or sell electricity at the same time.

[0147] For example, in the actual operation of the solar thermal storage system, the charging and discharging power of the energy storage part will be subject to the maximum power limit; and in each scheduling period, the energy storage cannot be charged and discharged at the same time; in addition, in order to protect the service life of the energy storage equipment, the charge state of the energy storage needs to be constrained during the scheduling process. The charge state of the energy storage should be controlled within a certain range, and over-discharge or over-charging should be avoided as much as possible; in addition, in order to ensure the continuity of scheduling, the charge state of the energy storage should remain the same at the start and end of the scheduling cycle, so as to ensure the efficiency and reliability of the operation of the entire solar thermal storage system.

[0148] The energy storage charging and discharging constraints are expressed as:

[0149]

[0150] B bat,ch,t +B bat,dis,t ≤1 (41)

[0151] SOC t ∈[SOC min ,SOC max ](42)

[0152] SOC(t1)=SOC(t1+T f ) (43)

[0153] in, Indicates the minimum charging power of the energy storage system; Indicates the maximum charging power of the energy storage system; Indicates the minimum discharge power of the energy storage system; Indicates the maximum discharge power of the energy storage system; B bat,ch,t and B bat,dis,t Represents the charge and discharge state variable of the energy storage system at time t, which is a 0 or 1 variable. The storage system cannot be charged and discharged at the same time; SOC max and SOC min Indicates the upper and lower limits of the remaining power of the energy storage; the scheduling period of the operation optimization is T f hours, and t1 is the first moment of each scheduling cycle.

[0154] Through the synergy of the upper and lower optimization models, the solar thermal storage system can obtain the best capacity configuration and operation strategy, minimize the total cost of the system, and improve the environmental protection and energy efficiency of the system. At the same time, the information transmission and feedback mechanism of the upper and lower models can better coordinate the coupling relationship between the various components of the system and optimize and improve the overall performance of the system.

[0155] In order to verify the effectiveness of the above-mentioned distributed solar thermal energy storage coupling system configuration method, the HVACT ideal air conditioning system is used in EnergyPlus to analyze the heat of the house. This embodiment does not consider the impact of people's daily life on the heat load. The heat pump works all day long. The files of the EnergyPlus meteorological database are used to simulate the heat load data of the heating period in different regions. The heat load power of the heating period in each region can be obtained, such as Figure 7 shown.

[0156] exist Figure 7 It can be seen that the highest value of winter heat load in region 1 occurs in mid-to-late January. The maximum value of heat load power in region 2 occurs in mid-February. The heat load data of region 2 is relatively smooth overall, and the value is the smallest among the four regions. The maximum and minimum values ​​of heat load power in region 3 are also the maximum and minimum values ​​of all four regions. Since region 3 is at a lower latitude, the heat load is relatively small overall. Due to the influence of the mountains, the heat load power reaches its maximum value in mid-to-late January, and the value is very large. The change trend of heat load power in region 4 is basically the same as that in region 1. The peak of heat load in region 4 occurs in mid-to-early January. In order to ensure that the heating plan can meet the heating demand throughout the winter, the day with the highest heat load power in each region is selected as the typical day.

[0157] Based on the temperature and solar irradiance, the photovoltaic power generation per unit capacity in each region on a typical day can be calculated, such as Figure 8 The double-level programming model based on particle swarm algorithm was written using the matlab simulation platform. The photovoltaic power generation power of each region and the house heat demand data obtained by EnergyPlus simulation were input respectively. The photovoltaic, energy storage capacity and air source heat pump capacity of each region can be obtained, as shown in Table 1, which are all determined by the maximum heat load of the house.

[0158] Table 1 Equipment capacity in each region

[0159]

[0160] It can be seen from the results that the configuration scheme proposed in this embodiment has the best effect in Handan, because region 4 has a lower latitude, higher temperature, higher solar irradiance, and the smallest house heat load, so the configured photovoltaic and energy storage capacity are the least; the system has the least effect in region 3, because region 3 has a higher latitude, lower temperature, lower solar irradiance, and the largest house heat load. Increasing the capacity of photovoltaic equipment will increase the investment cost of the system, and it is impossible to make up for this cost by reducing operating costs, so the capacity of photovoltaic and energy storage is reduced instead; the difference in the photovoltaic storage capacity between region 1 and region 2 is not large, because region 1 and region 2 have similar latitudes and temperatures. Region 2 is affected by the ocean, so the power generation efficiency of photovoltaic equipment is not as high as that of region 1, so the capacity of photovoltaic equipment is reduced, and the capacity of energy storage equipment is increased in order to transfer and store electricity. The following is a detailed analysis of the optimized operation of typical days during the heating period in each region.

[0161] like Fig. 9 As shown in the figure, it shows the hourly equipment operating power of Shijiazhuang in a typical day. The scheduling time is 1 hour and the scheduling cycle is 24 hours. Fig. 9 It can be clearly observed that the time-of-use electricity price mechanism has a certain impact on energy storage behavior. When the electricity price is low (1:00 and 22:00) and the photovoltaic power generation power is high, the energy storage system will perform charging operations to release electricity when the electricity price is high and supply it to the heat pump for use in order to obtain better economic benefits. In addition, when the photovoltaic power generation is large and the demand for equipment such as air source heat pumps is met, the excess electricity will be sold to the power grid. Through calculation, it can be obtained that the system can emit 38.98kg less carbon dioxide on a typical day.

[0162] like Fig.10 As shown, the hourly equipment operating power of Region 2 in a typical day is shown. It can be clearly seen from the figure that compared with Region 1, the air source heat pump power curve in Region 2 shows a more gentle trend, because Region 2 is located near the ocean, the temperature difference between day and night is small, and the temperature changes smoothly. The energy storage system will be charged for later use during the electricity price valley (1:00 and 22:00) and when the photovoltaic power generation power is high. After the photovoltaic power generation meets the demand of the air source heat pump during the day, the excess power will be sold. Calculation shows that the system can emit 37.07kg less carbon dioxide on a typical day.

[0163] like Fig.11The figure shows the equipment operating power of a typical day in region 4. It can be clearly seen that due to the influence of the terrain, the temperature difference between day and night in region 4 is large, and the power curve of the air source heat pump changes most significantly. At the same time, since the latitude of region 4 is the lowest among the four regions, the power value of the air source heat pump is the smallest. The energy storage will still be charged when the electricity price is low (3:00 and 22:00), and the excess electricity generated by photovoltaic power during the day will be sold to the power grid. Through calculation, it can be obtained that the system can emit 23.54kg less carbon dioxide on a typical day.

[0164] like Fig.12 As shown in the figure, the typical day equipment operation power of region 3 is shown. It can be seen that the load power of the air source heat pump in region 3 is large and the change is relatively gentle. Because region 3 is located at a high latitude, more heat is required for heating in winter. The energy storage will be charged when the electricity price is low (6:00 and 24:00) and the photovoltaic power generation power is high. When the photovoltaic power generation power is high, the excess electricity will be sold to the grid. Through calculation, it can be obtained that the system can emit 33.48kg less carbon dioxide on a typical day.

[0165] In summary, the distributed photovoltaic and thermal storage coupling system configuration method provided in the embodiment of the present application takes the lowest annual comprehensive cost and the lowest annual operation and maintenance cost as the optimization goals. When optimizing the equipment capacity configuration, two levels of decision-making are considered. The upper-level optimization takes the lowest annual comprehensive cost as the goal, which is a capacity optimization configuration problem. The decision variables are the capacity of photovoltaics, energy storage and heat pumps. The lower-level model takes the lowest annual operating cost as the goal, which is a simple optimization operation problem. The decision variables are the power sold and purchased and the charging and discharging power of energy storage. The particle swarm algorithm is used to solve the decision variables of the upper and lower models. The capacity of the distributed photovoltaic and thermal storage system equipment is optimized using a two-level planning model, thereby providing a basis for the construction of a distributed photovoltaic and thermal storage coupling system, further realizing the coordinated optimization of photovoltaics, energy storage and energy use on the household side, effectively improving the economy of residents' "electric heating", and promoting the implementation of the national "coal to electricity" project, and contributing to the country's realization of intelligent, efficient and information-based electricity.

[0166] We conducted an in-depth study of the equipment characteristics of the photovoltaic heat storage system and established a mathematical model for the core equipment of photovoltaics, energy storage and air source heat pumps, so as to more accurately describe and explain the operating laws and performance characteristics of the system. This not only makes the basic structure of the photovoltaic heat storage system and its mutual relationships clear, but also provides strong support for its performance optimization.

[0167] In different regional scenarios, the heat load required by the building is simulated, and the date with the maximum heat load value is selected as a typical day. The daily operation optimization model of that day is solved, and the annual operation cost is obtained by looping. The objective function of the lower model is to minimize the annual operation cost. The energy storage charging and discharging power and the power sold and purchased of the lower model are brought back to the upper model to solve the equipment capacity under the condition of the minimum annual comprehensive cost. The calculation results show that the scheme proposed in this embodiment can give the optimal capacity to adapt to various climates, which has the effect of energy saving and emission reduction.

[0168] See also Fig.13 The embodiment of the present application provides a distributed photovoltaic thermal storage coupling system configuration device, which executes the distributed photovoltaic thermal storage coupling system configuration method as described in the above embodiment, including a data acquisition module 301, a model building module 302 and a model solving module 303.

[0169] The data acquisition module 301 is used to acquire the heat load data of the target area for non-central heating during the heating period and the equipment data of the distributed solar-heat storage coupling system;

[0170] A model building module 302 is used to build an electric-thermal coupling model based on the equipment data of the distributed solar-thermal storage coupling system;

[0171] The model solving module 303 is used to solve the electric-thermal coupling model based on the heat load data during the heating period, with the annual comprehensive cost of the distributed solar-heat storage coupling system over its entire life cycle as the upper-level goal and the annual operating cost as the lower-level goal, to obtain the optimal configuration result of the capacity of each device in the distributed solar-heat storage coupling system.

[0172] In a possible implementation, the equipment data of the distributed photovoltaic heat storage coupling system includes output data of the distributed photovoltaic system, energy storage data of the energy storage system, and operation data of the air source heat pump.

[0173] The model building module 302 is specifically used for:

[0174] Based on the output data of distributed photovoltaic systems, a physical output model of photovoltaic systems is constructed;

[0175] Based on the energy storage data of the energy storage system, an energy storage model of the energy storage system is constructed;

[0176] Based on the operation data of the air source heat pump, an operation model of the air source heat pump is constructed;

[0177] An electric-thermal coupling model is constructed based on the physical output model of the photovoltaic system, the energy storage model of the energy storage system and the operation model of the air source heat pump.

[0178] In a possible implementation, the physical output model of the photovoltaic system is expressed as:

[0180] P PV =ζη m Aηcosθ (1)

[0182] Among them, P PV is the actual photovoltaic output power; ζ is the solar irradiance; η m is the photoelectric conversion efficiency; A is the photovoltaic panel area; η is the photovoltaic module efficiency; θ is the incident angle of sunlight.

[0183] In a possible implementation, the energy storage model of the energy storage system is expressed as:

[0184] E b,t =E b,t-1 +P c,t ηΔt (3)

[0185] Among them, E b,t E is the amount of electricity stored in the energy storage battery at time t; b，t-1 P is the amount of electricity stored in the energy storage battery at time t-1; c,t is the charging and discharging power of the energy storage battery at time t, and P c,t When the discharge is positive, P c,t is negative; η is the energy conversion efficiency of the energy storage battery; Δt is the time interval between time t and time t-1.

[0186] In a possible implementation, the operation model of the air source heat pump is expressed as:

[0187]

[0188] COP EC =a·(T w -T n )+b·(T w -T k )+c (13)

[0189] in, is the heating\cooling power output by the air source heat pump; The electrical power input to the air source heat pump; COP EC is the heating / cooling coefficient of the air source heat pump; P Y P is the electric power consumed by the air source heat pump compressor; F The electric power consumed by auxiliary equipment; T w is the outdoor temperature; T n is the indoor temperature; T k is the operating reference temperature of the air source heat pump; a is the indoor temperature deviation coefficient, b is the outdoor temperature difference coefficient, and c is the heat load coefficient.

[0190] In one possible implementation, the electro-thermal coupling model is expressed as:

[0191]

[0192] Among them, P pv,i is the photovoltaic output power at time i; P b,i is the power purchased and sold between the distributed solar-thermal energy storage coupling system and the external power grid system at time i. b,i When the electricity is sold, P b,i is positive; P LE,i is the power load power at time i; P LH,i is the heating load power of the air source heat pump at time i; P c,i is the charging and discharging power of the energy storage system at time i, and P c,i When the discharge is positive, P c,i Negative; Q EC The air source heat pump compressor is at input P LH,i The heating power generated by the action of ; Q4 is the heat power dissipated by the air source heat pump during heat exchange in the circulation system; Q5 is the heat power dissipated by indoor air convection of the heating user; E is the heat power required to adjust the indoor temperature to the temperature set by the air source heat pump.

[0193] In a possible implementation, the upper level objective of the electric-thermal coupling model is expressed as:

[0194] minC tot =C1+C2+C3+C4+C5 (19)

[0195] Among them, C tot is the annual comprehensive cost of the system during its entire life cycle; C1 is the annual investment cost of the distributed solar-thermal storage coupling system; C2 is the annual grid interaction cost; C3 is the annual carbon emission cost; C4 is the annual depreciation cost of energy storage; and C5 is the annual maintenance cost of the equipment.

[0196] In a possible implementation, the equivalent annual investment cost C1 of the distributed solar-thermal energy storage coupling system is expressed as:

[0197] C1=C PV +C bat +C AH (20)

[0198] Among them, C PV is the annual investment cost of the photovoltaic system; C bat is the annual investment cost of the energy storage system; C AH It is the equivalent annual investment cost of the air source heat pump.

[0199] In one possible implementation, the lower level objective of the electric-thermal coupling model is expressed as:

[0200] minC op =C2+C3+C4+C5 (33)

[0201] Among them, C op is the annual operating cost of the distributed solar-thermal energy storage coupling system;

[0202] The constraints of the lower-level objectives of the electric-thermal coupling model include electric power balance constraints, thermal power balance constraints, grid interaction power constraints, and energy storage charging and discharging constraints.

[0203] It should be noted that although several units / modules or sub-units / modules of the distributed solar thermal energy storage coupling system configuration device are mentioned in the above detailed description, this division is only exemplary and not mandatory. In fact, according to the embodiment of the present application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules for embodiment.

[0204] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0205] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the distributed solar-thermal storage coupling system configuration method provided in the above embodiment of the present application is implemented.

[0206] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0207] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A distributed solar-thermal energy storage coupling system configuration method, characterized in that: include: Obtain the heat load data of the target area during the heating period and the equipment data of the distributed solar-thermal energy storage coupling system in non-central heating areas; Based on the equipment data of the distributed solar thermal storage coupling system, an electric-thermal coupling model is constructed; Based on the heat load data of the heating period, with the minimum annual comprehensive cost of the distributed solar-thermal storage coupling system over its entire life cycle as the upper-level goal and the minimum annual operating cost as the lower-level goal, the particle swarm algorithm is used to solve the electric-thermal coupling model to obtain the optimal configuration result of the capacity of each device in the distributed solar-thermal storage coupling system.

2. The distributed solar thermal energy storage coupling system configuration method according to claim 1, characterized in that: The equipment data of the distributed photovoltaic heat storage coupling system includes the output data of the distributed photovoltaic system, the energy storage data of the energy storage system and the operation data of the air source heat pump; The step of constructing an electric-thermal coupling model based on the equipment data of the distributed solar-thermal storage coupling system includes: Based on the output data of the distributed photovoltaic system, a physical output model of the photovoltaic system is constructed; Based on the energy storage data of the energy storage system, construct an energy storage model of the energy storage system; Based on the operation data of the air source heat pump, construct an operation model of the air source heat pump; The electric-thermal coupling model is constructed based on the physical output model of the photovoltaic system, the energy storage model of the energy storage system and the operation model of the air source heat pump.

3. The distributed solar thermal energy storage coupling system configuration method according to claim 2, characterized in that: The physical output model of the photovoltaic system is expressed as: P PV =live m Ancosθ Among them, P PV is the actual photovoltaic output power; ζ is the solar irradiance; η m is the photoelectric conversion efficiency; A is the photovoltaic panel area; η is the photovoltaic module efficiency; θ is the incident angle of sunlight.

4. The distributed solar thermal energy storage coupling system configuration method according to claim 2, characterized in that: The energy storage model of the energy storage system is expressed as: And b,t =E b,t-1 +P c,t ηΔt Among them, E b,t E is the amount of electricity stored in the energy storage battery at time t; b，t-1 P is the amount of electricity stored in the energy storage battery at time t-1; c,t is the charging and discharging power of the energy storage battery at time t, and P c,t When the discharge is positive, P c,t is negative; η is the energy conversion efficiency of the energy storage battery; Δt is the time interval between time t and time t-1.

5. The distributed solar thermal energy storage coupling system configuration method according to claim 2, characterized in that: The operation model of the air source heat pump is expressed as: COP EC =a·(T w -T n )+b·(T w -T k )+c in, is the heating\cooling power output by the air source heat pump; The electrical power input to the air source heat pump; COP EC is the heating / cooling coefficient of the air source heat pump; P Y P is the electric power consumed by the air source heat pump compressor; F The electric power consumed by auxiliary equipment; T w is the outdoor temperature; T n is the indoor temperature; T k is the operating reference temperature of the air source heat pump; a is the indoor temperature deviation coefficient, b is the outdoor temperature difference coefficient, and c is the heat load coefficient.

6. The distributed solar thermal energy storage coupling system configuration method according to claim 2, characterized in that: The electro-thermal coupling model is expressed as: Among them, P pv,i is the photovoltaic output power at time i; P b,i is the power purchased and sold between the distributed solar-thermal energy storage coupling system and the external power grid system at time i. b,i When the electricity is sold, P b,i is positive; P LE,i is the power load power at time i; P LH,i P is the heating load power of the air source heat pump at time i; c,i is the charging and discharging power of the energy storage system at time i, and P c,i When the discharge is positive, P c,i Negative; Q EC The air source heat pump compressor is at input P LH,i The heating power generated by the action of ; Q4 is the heat power dissipated by the air source heat pump during heat exchange in the circulation system; Q5 is the heat power dissipated by indoor air convection of the heating user; E is the heat power required to adjust the indoor temperature to the temperature set by the air source heat pump.

7. The distributed solar thermal energy storage coupling system configuration method according to claim 6, characterized in that: The upper level objective of the electro-thermal coupling model is expressed as: minC tot =C1+C2+C3+C4+C5 C tot is the annual comprehensive cost of the system during its entire life cycle; C1 is the annual investment cost of the distributed solar-thermal storage coupling system; C2 is the annual grid interaction cost; C3 is the annual carbon emission cost; C4 is the annual depreciation cost of energy storage; and C5 is the annual maintenance cost of the equipment.

8. The distributed solar thermal energy storage coupling system configuration method according to claim 7, characterized in that: The equivalent annual investment cost C1 of the distributed solar-thermal energy storage coupling system is expressed as: C1=C PV +C bat +C AH Among them, C PV is the annual investment cost of the photovoltaic system; C bat is the annual investment cost of the energy storage system; C AH It is the equivalent annual investment cost of the air source heat pump.

9. The distributed solar thermal energy storage coupling system configuration method according to claim 7, characterized in that: The lower level objective of the electro-thermal coupling model is expressed as: minC op =C2+C3+C4+C5 Among them, C op is the annual operating cost of the distributed solar-thermal energy storage coupling system; The constraints of the lower level objectives of the electric-thermal coupling model include electric power balance constraints, thermal power balance constraints, grid interaction power constraints and energy storage charging and discharging constraints.

10. A distributed photovoltaic thermal energy storage coupling system configuration device, using the distributed photovoltaic thermal energy storage coupling system configuration method according to any one of claims 1 to 9, comprising: A data acquisition module, used to obtain the heat load data of the target area for non-central heating during the heating period and the equipment data of the distributed solar-thermal storage coupling system; A model building module, used to build an electric-thermal coupling model based on the equipment data of the distributed solar-thermal storage coupling system; The model solving module is used to solve the electric-thermal coupling model based on the heat load data of the heating period, with the minimum annual comprehensive cost of the distributed solar-thermal storage coupling system over its entire life cycle as the upper-level goal and the minimum annual operating cost as the lower-level goal, to obtain the optimal configuration result of the capacity of each device in the distributed solar-thermal storage coupling system.

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