An advanced adiabatic compressed air energy storage capacity planning method

Abstract

The present application relates to the technical field of energy storage system, and particularly relates to an advanced adiabatic compressed air energy storage capacity planning method, comprising: establishing a capacity configuration model of a combined heat and power regional integrated energy system containing advanced adiabatic compressed air energy storage; adopting a box set robust optimization to process photovoltaic uncertain output, so as to establish a photovoltaic uncertain output robust optimization model; constructing a mixed integer linear programming model according to a pre-constructed objective function, the capacity configuration model and the photovoltaic uncertain output robust optimization model; and solving the mixed integer linear programming model to obtain capacity configuration parameters of the target regional integrated energy system. Thus, the problem that the reliability of the capacity configuration strategy cannot be guaranteed by the renewable energy uncertainty processing method in the prior art, thereby affecting the operation stability of the system, is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage systems, in particular to an advanced adiabatic compressed air energy storage capacity planning method based on box robust optimization. BACKGROUND

[0002] With the progress of society and the development of science, the demand for energy in the world gradually increases, and at the same time, renewable energy such as solar energy and wind energy has become an important means for countries to solve energy crisis and environmental problems. However, due to the periodicity and volatility of renewable energy generation, it is difficult to use directly, so the energy storage system becomes an important device to smooth the output of renewable energy. Advanced adiabatic compressed air energy storage can smooth the output of renewable energy while realizing combined heat and power supply. In complex environments such as high-cold regions, it can still operate stably and safely. Combining compressed air energy storage system with combined heat and power system, by adjusting the output of electricity and heat, the utilization of renewable energy is optimized. In the case of unstable wind and light, different output modes can balance the load of the power grid. Therefore, establishing a regional integrated energy system containing advanced adiabatic compressed air energy storage is an important scheme to improve resource utilization and absorb renewable energy output.

[0003] The output of renewable energy will directly affect the energy storage capacity planning strategy of the regional integrated energy system. If the energy storage capacity is too large, it will reduce the economic efficiency of the integrated energy system, and if the energy storage capacity is too small, it will not meet the operation requirements of the system. At present, the main methods for handling the uncertainty of renewable energy such as solar energy are robust optimization and other methods. The existing robust optimization method uses the target photovoltaic uncertain output scene generated by the multi-scene random planning method to construct the operation constraints based on the combined heat and power unit model and the preset configuration constraints to construct the capacity configuration model, in order to reduce the calculation amount and improve the calculation efficiency. However, this method cannot construct the actual target photovoltaic uncertain output scene, and cannot guarantee the reliability of the capacity configuration strategy.

[0004] In view of this, the present application provides an advanced adiabatic compressed air energy storage capacity planning method and device based on box robust optimization, to solve the problem that the existing renewable energy uncertainty handling method cannot guarantee the reliability of the capacity configuration strategy. SUMMARY

[0005] The present application provides an advanced adiabatic compressed air energy storage capacity planning method and device to solve the problem that the existing renewable energy uncertainty handling method cannot guarantee the reliability of the capacity configuration strategy, which in turn affects the stability of the system operation.

[0006] The embodiment of the first aspect of the application provides a capacity planning method for advanced adiabatic compressed air energy storage, comprising the following steps: establishing a capacity configuration model of a combined heat and power regional integrated energy system containing advanced adiabatic compressed air energy storage; processing uncertain photovoltaic output by using box set robust optimization to establish a robust optimization model of uncertain photovoltaic output; constructing a mixed integer linear programming model according to a pre-constructed objective function, the capacity configuration model and the robust optimization model of uncertain photovoltaic output; and solving the mixed integer linear programming model to obtain capacity configuration parameters of the target regional integrated energy system.

[0007] Optionally, the capacity configuration model of the combined heat and power regional integrated energy system containing advanced adiabatic compressed air energy storage comprises:

[0008] establishing an operation model of the combined heat and power regional integrated energy system containing advanced adiabatic compressed air energy storage; and establishing the capacity configuration model of the target regional integrated energy system according to target operation constraints and the operation model.

[0009] Optionally, the processing of the uncertain photovoltaic output by using box set robust optimization to obtain the robust optimization model of uncertain photovoltaic output comprises:

[0010] establishing a box-type uncertain set of photovoltaic output; setting a photovoltaic output disturbance variable set by using the box-type uncertain set to construct a robust optimization model according to the photovoltaic output disturbance variable set; and linearizing the robust optimization model according to optimization duality theory to obtain the robust optimization model of uncertain photovoltaic output.

[0011] Optionally, the robust optimization model of uncertain photovoltaic output has the following expression:

[0012]

[0013] wherein ω is a disturbance variable, ω min is a maximum disturbance variable, ω max is a minimum disturbance variable, U is a box-type uncertain set, W pv is a photovoltaic power generation system capacity, S d is an illumination intensity under standard conditions, is an inverter conversion efficiency, σ, τ and v are Lagrange coefficients, and e is a unit vector.

[0014] The second aspect embodiment of the present application provides an advanced adiabatic compressed air energy storage capacity planning device, comprising: a first construction module, configured to establish a capacity configuration model of a combined heat and power regional integrated energy system containing advanced adiabatic compressed air energy storage; a processing module, configured to process uncertain photovoltaic output by using a box set robust optimization, so as to establish a robust optimization model of uncertain photovoltaic output; a second construction module, configured to construct a mixed integer linear programming model according to a pre-constructed objective function, the capacity configuration model and the robust optimization model of uncertain photovoltaic output; and a solving module, configured to solve the mixed integer linear programming model, so as to obtain capacity configuration parameters of the target regional integrated energy system.

[0015] Optionally, the first construction module comprises:

[0016] a first construction unit, configured to establish an operation model of the combined heat and power regional integrated energy system containing advanced adiabatic compressed air energy storage; and a second construction unit, configured to establish the capacity configuration model of the target regional integrated energy system according to a target operation constraint condition and the operation model.

[0017] Optionally, the processing module comprises:

[0018] a third construction unit, configured to establish a box uncertain set of photovoltaic output; a setting unit, configured to set a photovoltaic output disturbance variable set by using the box uncertain set, so as to construct a robust optimization model according to the photovoltaic output disturbance variable set; and a linearization unit, configured to linearize the robust optimization model according to optimization duality theory, so as to obtain the robust optimization model of uncertain photovoltaic output.

[0019] Optionally, an expression of the robust optimization model of uncertain photovoltaic output is as follows:

[0020]

[0021] wherein ω is a disturbance variable, ω min is a maximum disturbance variable, ω max is a minimum disturbance variable, U is a box uncertain set, W pv is a photovoltaic power generation system capacity, S d is an illumination intensity under standard conditions, is an inverter conversion efficiency, σ, τ and v are all Lagrange coefficients, and e is a unit vector.

[0022] The third aspect embodiment of the present application provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor executes the program to implement the advanced adiabatic compressed air energy storage capacity planning method as described in the above embodiments.

[0023] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-mentioned advanced adiabatic compressed air energy storage capacity planning method.

[0024] The advanced adiabatic compressed air energy storage capacity planning method and device proposed in the embodiments of the present invention utilize the characteristics of advanced adiabatic compressed air energy storage that can achieve combined generation of cooling, heating and power to establish a regional integrated energy system to improve resource utilization in complex environmental areas. It also adopts box-type aggregate robust optimization to handle uncertain photovoltaic output, ensure the reliability of the capacity configuration strategy, and enhance the ability of the regional integrated energy system to cope with complex environments.

[0025] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0027] Figure 1 A schematic flow chart of an advanced adiabatic compressed air energy storage capacity planning method provided by an embodiment of the present invention;

[0028] Figure 2 A block diagram of an advanced adiabatic compressed air energy storage capacity planning device provided by an embodiment of the present invention;

[0029] Figure 3 This is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0031] The following describes an advanced adiabatic compressed air energy storage capacity planning method and apparatus according to an embodiment of the present invention with reference to the accompanying drawings.

[0032] Figure 1 A schematic flow chart of an advanced adiabatic compressed air energy storage capacity planning method provided in an embodiment of the present invention.

[0033] like Figure 1 As shown, the advanced adiabatic compressed air energy storage capacity planning method includes the following steps:

[0034] In step S101, a capacity configuration model of a combined heat and power regional integrated energy system with advanced adiabatic compressed air energy storage is established.

[0035] In some embodiments, the capacity configuration model of the combined heat and power regional integrated energy system with advanced adiabatic compressed air energy storage is established, comprising:

[0036] An operation model of the combined heat and power regional integrated energy system with advanced adiabatic compressed air energy storage is established.

[0037] The capacity configuration model of the target regional integrated energy system is established according to the target operation constraint condition and the operation model.

[0038] In actual implementation, the regional integrated energy system mainly comprises an advanced adiabatic compressed air energy storage system, a combined heat and power unit, a new energy power station and a heat and power load, so the embodiment of the present application first establishes a regional integrated energy system capacity configuration model comprising an advanced adiabatic compressed air energy storage system and a combined heat and power unit.

[0039] The operation model of the compressor is:

[0040]

[0041] P t is the compression power of the compressor at time t, r is the adiabatic index of air, m t is the air mass flow rate of the compressor at time t, R is the ideal gas constant, η is the efficiency of the compressor in the advanced adiabatic compressed air energy storage system, T k is the inlet air temperature of the kth stage compressor, n is the number of stages of the compressor, β k is the compression ratio of the kth stage compressor. CAESC,t c,t g c CAESc,k,in c c,k

[0042] The operation model of the expander is:

[0043]

[0044] P t is the expansion power of the expander at time t, m t is the air mass flow rate flowing into the expander at time t, η is the efficiency of the expander, T j is the inlet air temperature of the jth stage expander, n is the number of stages of the expander, β j is the expansion ratio of the jth stage expander. CAESG,t g,t g CAESg,j,in g g,j

[0045] The operation model of the gas storage chamber is:

[0046] ​​​​​​​​​​​​​

[0047] wherein P st,τ is the change of air pressure in the gas tank at time τ, T st,in is the rated inlet air temperature of the gas tank, T st,t is the air temperature in the gas tank at time t, P st,0 is the initial air pressure of the gas tank, P st,t is the air pressure in the tank at time t of the gas tank, P st,min ,P st,max are the upper and lower limits of the air pressure in the gas tank.

[0048] wherein the heat storage tank operation model is:

[0049]

[0050] wherein Q qc,k,t is the heat storage power when the kth compressor is running, Q qc,t is the total heat storage amount when the compressor is running, c p,a represents the specific heat capacity of air, T CAESc,k,out,t , T CAESC,k+1,in,t respectively represent the outlet air temperature of the kth compressor and the inlet air temperature of the k+1th compressor, Q qg,j,t is the heat consumption power when the jth expander is running, Q qg,t is the total heat consumption amount when the expander is running, Q HS,t is the heat storage amount of the heat storage tank at time t, Q HS,0 is the initial heat storage amount of the heat storage tank, Q ST,t is the external heat supply amount of the heat storage tank at time t, k st is the energy consumption coefficient, Q STOUT,t is the actual external heat supply amount considering heat loss, Q STmax is the upper limit value of the external heat supply power of the heat storage tank, Q HSmin , Q HSmax are respectively the upper and lower limits of the heat storage in the heat storage tank.

[0051] wherein the combined heat and power unit operation model is:

[0052]

[0053] wherein Q chp,t , η chp,t are respectively the exhaust heat and power generation efficiency of the combined heat and power unit at time t, Q chph,t is the heat production amount of the combined heat and power unit at time t, η l is the heat dissipation loss rate of the combined heat and power unit, η h , C oph are respectively the heating efficiency and the flue gas recovery rate.

[0054] Next, the embodiment of the application will establish a target function with the target of minimizing the investment and operation cost of the target regional comprehensive energy system according to the target operation constraint condition and the established operation model, and establish a capacity configuration model of the target regional comprehensive energy system, wherein the target operation constraint condition is that when the new energy power generation exceeds the load demand of the system, the excess electric energy is stored in the advanced adiabatic compressed air energy storage system; when the new energy power generation cannot meet the load demand of the system, the advanced adiabatic compressed air energy storage system releases the stored electric energy to meet the load demand of the system, and at the same time, the advanced adiabatic compressed air energy storage system can sell electric energy to the power grid to reduce the operation cost; the operation constraints of different devices and the capacity planning constraints of the advanced adiabatic compressed air energy storage system, and the heat and power balance constraints of the regional comprehensive energy system are considered.

[0055] In step S102, the box set robust optimization is adopted to process the uncertain photovoltaic output to obtain a robust optimization model of the uncertain photovoltaic output.

[0056] In some embodiments, the box set robust optimization is adopted to process the uncertain photovoltaic output to obtain a robust optimization model of the uncertain photovoltaic output, including:

[0057] A box-type uncertain set of photovoltaic output is established;

[0058] The box-type uncertain set is used to set a photovoltaic output disturbance variable set to construct a robust optimization model according to the photovoltaic output disturbance variable set;

[0059] The robust optimization model is linearized according to the optimization duality theory to obtain the robust optimization model of the uncertain photovoltaic output.

[0060] In actual execution process, the embodiment of the application considers the uncertainty of the photovoltaic output, and the capacity configuration model based on the box set robust optimization can ensure that the system can still be stably and safely operated under the worst conditions. First, a box-type uncertain set of photovoltaic output is established, and a robust optimization model of the uncertain photovoltaic output is considered. Second, the optimization duality theory, i.e., the KKT condition, is used to linearize the complex robust optimization model to obtain a linear model.

[0061] Specifically, the uncertain photovoltaic output variable is processed, and the actual solar irradiance is represented as S * +ω, and the output power P pv is represented as:

[0062]

[0063] In the formula, ω is a disturbance variable, U is a box-type uncertain set, W pv is the capacity of the photovoltaic power generation system, S *To predict the light intensity, S d is the light intensity under standard conditions, is the inverter conversion efficiency.

[0064] wherein the ω perturbation variable adopts a box uncertainty set and can be expressed as:

[0065] U = { ω | e T ω = 0, ω min ≤ ω ≤ ω max}(7)

[0066] Since there are uncertain parameters in formula (7) and it is a nonlinear model difficult to solve, it is necessary to convert it into a mixed integer linear programming model through optimization of dual theory, i.e. KKT condition.

[0067] According to the optimization dual theory, the photovoltaic uncertain output can be converted into a robust optimization model, and the expression is:

[0068]

[0069] The uncertain part in it is The Lagrange function is constructed as follows:

[0070]

[0071] Let wherein σ, τ, v are all Lagrange coefficients.

[0072] According to the optimization dual theory, it can be obtained that:

[0073]

[0074] Therefore, it can be deduced that the expression of the photovoltaic uncertain output robust optimization model is:

[0075]

[0076] wherein ω is a perturbation variable, ω min is a maximum perturbation variable, ω max is a minimum perturbation variable, U is a box uncertainty set, W pv is a photovoltaic power generation system capacity, S d is a light intensity under standard conditions, is an inverter conversion efficiency, σ, τ, v are all Lagrange coefficients, and e is a unit vector.

[0077] In step S103, a mixed integer linear programming model is constructed according to the pre-constructed objective function, capacity configuration model and photovoltaic uncertain output robust optimization model.

[0078] In actual execution, first, based on the physical characteristics of each device in the regional integrated energy system and the stable and safe operation requirements, the corresponding device operation constraints, device capacity constraints and energy balance constraints are added;

[0079] Among them, the capacity constraints of each module of the advanced adiabatic compressed air energy storage include the upper and lower limits of the rated operating power of the compressor, the upper and lower limits of the operating power of the expander, the upper and lower limits of the volume of the gas storage chamber, and the upper and lower limits of the heat storage capacity of the heat storage tank:

[0080]

[0081] Among them, P CAESC,rmin , P CAESC,rmax and P CAESG,rmin , P CAESG,rmax are the upper and lower limits of the output of the compressor and the turbine, V ST,rmin , V ST,rmax and Q tes,rmin , Q tes,rmax are the upper and lower limits of the volume of the gas storage chamber and the heat storage capacity of the heat storage tank.

[0082] Among them, the advanced adiabatic compressed air energy storage system cannot be charged and discharged at the same time:

[0083] u c +u g ≤1(13)

[0084] Among them, the compression and expansion output of the advanced adiabatic compressed air energy storage system at any time cannot exceed the rated value and the minimum value:

[0085]

[0086] Among them, u c and u g are binary variables of the compression and expansion conditions of the advanced adiabatic compressed air energy storage power station at time t, and their values are 1 and 0 respectively, indicating that the power station is in the compression and expansion conditions, and their values are 0, indicating that the compression and expansion conditions of the power station are in the idle state.

[0087] Among them, for the climbing constraint of the combined heat and power unit, the power change between different times cannot exceed the specified value:

[0088]

[0089] Among them, P chp,min , P chp,max are the upper and lower limits of the power generation of the combined heat and power unit, and R chp,down , R chp,up are the upper and lower climbing rates of the combined heat and power unit.

[0090] Based on the system operation requirements of the combined heat and power microgrid, the internal electrical and thermal balance constraints and the abandoned wind and light constraints must be met.

[0091] Wherein, in order to ensure the normal operation of the microgrid, the system electrical and thermal load must be balanced:

[0092]

[0093] Wherein, P load,t P pv,l is a random variable, and the uncertainty thereof is considered by using a box set robust optimization method, Q load,t is the system thermal load at t.

[0094] Wherein, according to the aforementioned photovoltaic uncertain output robust optimization model, the electrical balance needs to add the following constraints:

[0095]

[0096] Wherein, the system abandoned wind and light must be less than the specified value:

[0097]

[0098] Further, the values to be solved, such as the capacity planning parameters, are set as decision variables, a corresponding objective function is established, and a mixed integer linear programming model of the advanced adiabatic compressed air energy storage capacity in the regional comprehensive energy system is obtained. Specifically, the embodiment of the present application considers the system investment and operation cost, and establishes an advanced adiabatic compressed air energy storage capacity planning model based on box set robust optimization.

[0099] Wherein, the objective function considering the minimum system investment and operation cost is established:

[0100] min C=C inv +C m +C pv +C loss +C grid +C MT (19)

[0101] Wherein, C inv is the daily average investment cost of the advanced adiabatic compressed air energy storage power station, C m is the operation and maintenance cost of the advanced adiabatic compressed air energy storage and the combined heat and power unit, C loss is the abandoned wind and light cost of the system, C grid is the cost of electric energy interaction with the power grid, C MT is the fuel cost of the combined heat and power unit.

[0102] Wherein, the calculation expressions of the costs are as follows:

[0103]

[0104] where f caes,c , f caes,g , f caes,st , f caes,tes represent the investment cost per unit of compression power, per unit of expansion power, per unit of gas storage tank volume, and per unit of heat storage capacity in the advanced adiabatic compressed air energy storage power station, respectively, P CAESC,r , P CAESG,r , V ST,r , and Q tes,r are the rated compression power, rated expansion power, gas storage tank volume, and heat storage tank capacity in the advanced adiabatic compressed air energy storage system, respectively, a is the discount rate, T is the project cycle length, C m,caes , C m,chp are the unit operation and maintenance costs of the advanced adiabatic compressed air energy storage power station and the combined heat and power unit, respectively, e pv , e wt are the unit abandoned light cost and unit abandoned wind cost, respectively, P pv , P pv,l are the predicted photovoltaic power output and actual photovoltaic power output, respectively, P wt , P wt,l are the predicted wind power output and actual wind power output, respectively, P grid,t , P chp,t are the interactive power of the system with the power grid and the electric power of the combined heat and power unit at time t, C rb,t , C rs,t are the electricity purchase cost and electricity sale cost between the system and the power grid at time t, C gas is the natural gas unit price, and L HVNG is the natural gas calorific value.

[0105] In step S104, a mixed integer linear programming model is solved to obtain the capacity configuration parameters of the target regional integrated energy system.

[0106] In actual execution, the mixed integer linear programming model can be solved using MATLAB combined with a commercial solver CPLEX to obtain the capacity configuration parameters of the target regional integrated energy system.

[0107] In summary, according to the advanced adiabatic compressed air energy storage capacity planning method provided by the embodiment of the present application, the characteristics of combined heat and power supply of the advanced adiabatic compressed air energy storage can be used to establish a regional integrated energy system to improve the resource utilization rate in complex environment areas, and the box set robust optimization is used to process the uncertain output of photovoltaic to ensure the reliability of the capacity configuration strategy and improve the ability of the regional integrated energy system to cope with complex environment. In addition, compared with the stochastic programming method, the box set robust optimization method does not need the historical output probability function of the photovoltaic output, only needs to consider the optimal value under the worst condition in the uncertain set, and only requires that the fluctuation range of the uncertain variable does not exceed the uncertain set, and the optimal solution is a feasible solution.

[0108] Secondly, the advanced adiabatic compressed air energy storage capacity planning device provided by the embodiment of the present application is described with reference to the accompanying drawings.

[0109] Figure 2 is a block schematic diagram of the advanced adiabatic compressed air energy storage capacity planning device of the embodiment of the present application.

[0110] As shown in Figure 2 , the advanced adiabatic compressed air energy storage capacity planning device 20 comprises a first construction module 201, a processing module 202, a second construction module 203 and a solving module 204.

[0111] The first construction module 201 is configured to establish a capacity configuration model of a combined heat and power supply regional integrated energy system containing advanced adiabatic compressed air energy storage. The processing module 202 is configured to process the uncertain output of photovoltaic by using box set robust optimization to obtain a photovoltaic uncertain output robust optimization model. The second construction module 203 is configured to construct a mixed integer linear programming model according to the pre-constructed objective function, capacity configuration model and photovoltaic uncertain output robust optimization model. The solving module 204 is configured to solve the mixed integer linear programming model to obtain the capacity configuration parameters of the target regional integrated energy system.

[0112] In some embodiments, the first construction module 201 comprises:

[0113] The first construction unit is configured to establish an operation model of a combined heat and power supply regional integrated energy system containing advanced adiabatic compressed air energy storage.

[0114] The second construction unit is configured to establish a capacity configuration model of a target regional integrated energy system according to the target operation constraint condition and the operation model.

[0115] In some embodiments, the processing module 202 comprises:

[0116] The third construction unit is configured to establish a box-type uncertain set of photovoltaic output.

[0117] The setting unit is configured to set a photovoltaic output disturbance variable set by using a box-type uncertain set, so as to construct a robust optimization model according to the photovoltaic output disturbance variable set;

[0118] The linearization unit is configured to linearize the robust optimization model according to an optimization dual theory, so as to obtain a photovoltaic uncertain output robust optimization model.

[0119] In some embodiments, an expression of the photovoltaic uncertain output robust optimization model is as follows:

[0120]

[0121] Wherein, ω is a disturbance variable, ω min is a maximum disturbance variable, ω max is a minimum disturbance variable, U is a box-type uncertain set, W pv is a photovoltaic power generation system capacity, S d is an illumination intensity under standard conditions, is an inverter conversion efficiency, σ, τ, v are all Lagrange coefficients, and e is a unit vector.

[0122] It should be noted that the foregoing description of the advanced adiabatic compressed air energy storage capacity planning method embodiments also applies to the advanced adiabatic compressed air energy storage capacity planning device of this embodiment, which will not be described here.

[0123] The advanced adiabatic compressed air energy storage capacity planning device provided by the embodiment of the present application utilizes the characteristics of advanced adiabatic compressed air energy storage that can realize combined heat and power supply, establishes a regional integrated energy system to improve the resource utilization rate in complex environment areas, and uses a box-type set robust optimization to process photovoltaic uncertain output, so as to ensure the reliability of the capacity configuration strategy and improve the ability of the regional integrated energy system to cope with complex environments. In addition, compared with the random programming method, the box-type set robust optimization method does not require a historical output probability function of the photovoltaic output, only needs to consider the optimal value under the worst condition in the uncertain set, and only requires that the fluctuation range of the uncertain variable does not exceed the uncertain set, and the optimal solution is a feasible solution.

[0124] Figure 3 The electronic device provided by the embodiment of the present application is shown in a structural schematic diagram. The electronic device can include:

[0125] The memory 301, the processor 302, and the computer program stored in the memory 301 and executable on the processor 302.

[0126] The processor 302 implements the advanced adiabatic compressed air energy storage capacity planning method provided in the above embodiments when executing the program.

[0127] Further, the electronic device further includes:

[0128] A communication interface 303 is configured to communicate between the memory 301 and the processor 302.

[0129] The memory 301 is configured to store a computer program executable in the processor 302.

[0130] The memory 301 can include a high-speed RAM memory, and can also include a non-volatile memory, such as at least one disk memory.

[0131] If the memory 301, the processor 302 and the communication interface 303 are implemented independently, the communication interface 303, the memory 301 and the processor 302 can be connected to each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 3 In the figure, only one thick line is used to represent, but it does not mean that there is only one bus or one type of bus.

[0132] Optionally, in a specific implementation, if the memory 301, the processor 302 and the communication interface 303 are integrated on a chip, the memory 301, the processor 302 and the communication interface 303 can complete communication between each other through an internal interface.

[0133] The processor 302 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present application.

[0134] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the advanced adiabatic compressed air energy storage capacity planning method as above.

[0135] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. In addition, the usage of "first", "second" and the like does not indicate any order but rather serves merely to name various components. Moreover, the usage of "top", "bottom", and the like is made for the purpose of illustration only and does not indicate any orientation. The terms "coupled" and "connected", along with their derivatives, can be used. It should be understood that these terms are not intended as synonyms for each other. Rather, particular features are described as being coupled or connected where the feature is in some way present, for example through shared use of one or more components, and can be communicatively, electrically, structurally, and / or mechanically connected, for example. Similarly, "coupled" or "connected" can be used to indicate that two or more members are either directly in contact or indirectly in contact through one or more intermediate members.

[0136] Furthermore, the terms "first", "second", and the like, merely denote different categories, and do not imply a relative importance or a specific order. Thus, features defined with "first", "second" and the like can include at least one of the features, either explicitly or implicitly. In the description of the application, the term "N" means at least two, for example two, three, etc., unless explicitly specified otherwise.

[0137] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments of modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps, and alternate implementations are possible. In some embodiments, the processes or methods described can be accomplished with one or more hardware items, for example, hardwired circuits, memory, logic circuits, look-up tables, microcode or the like, software programs, firmware programs, microcode routines, embedded logic, embedded software, or any combination thereof, which work together to cause a general purpose computer, a special purpose computer, or both, to perform the processes or methods described. The various embodiments further can interact with a user through one or more computer programs, software applications, firmware applications, operating systems, or the like, which interact with a user. Such software can be written in any of a variety of suitable programming languages and can be executed using a variety of suitable hardware and software configurations. It will be appreciated that computer programs, software applications, firmware applications, operating systems, or the like, can be written in any combination of one or more suitable programming languages, and that such software can be executed using one or more computing devices capable of netlist generation as described herein.

[0138] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of executable instructions stored in a computer readable medium, which can be executed by an instruction execution system, apparatus or device, such as a computer-based system, a processor-based system, or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or a combination of the above. For the purposes of this specification, a "computer readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus or device. The computer readable medium can be a computer readable storage medium or a computer readable signal medium. The computer readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a propagation medium. The computer readable signal medium can include, but is not limited to, a computer readable medium that facilitates transfer of the program from one place to another. A specific example of a computer readable medium is a non-transitory computer-readable storage medium. A specific example of a computer readable signal medium is a source or destination of the computer readable medium. Another specific example of a computer readable signal medium is a computer readable signal travelling through space. Thus, a computer readable medium can take many forms of hardware to carry out the program for use by or in connection with the instruction execution system, apparatus or device.

[0139] It should be understood that aspects of the application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware and in another embodiment, the hardware can be implemented with any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc.

[0140] Those of skill in the art would understand that the steps carried out in the above-mentioned embodiment methods can be carried out by program instructions to relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0141] In addition, each function unit in each embodiment of the present application can be integrated in one processing module, or each unit can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software function module. When the integrated module is realized in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0142] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. An advanced adiabatic compressed air energy storage capacity planning method, characterized by, The method comprises the following steps: A capacity configuration model of a combined heat and power regional integrated energy system with advanced adiabatic compressed air energy storage is established; Box set robust optimization is used to process uncertain photovoltaic output to establish a robust optimization model of uncertain photovoltaic output, and the method specifically comprises the following steps: A box set of uncertain photovoltaic output is established; A set of photovoltaic output disturbance variables is set by using the box set of uncertain photovoltaic output, and a robust optimization model is constructed according to the set of photovoltaic output disturbance variables; The robust optimization model is linearized according to optimization duality theory to obtain the robust optimization model of uncertain photovoltaic output, and the expression of the robust optimization model of uncertain photovoltaic output is as follows: wherein, is the disturbance variable, is the maximum disturbance variable, is the minimum disturbance variable, is the box uncertainty set, is the PV system capacity, is the solar irradiance under standard conditions, is the inverter conversion efficiency, , , are all Lagrange coefficients, is the unit vector; A mixed integer linear programming model is constructed according to a pre-constructed objective function, the capacity configuration model and the robust optimization model of uncertain photovoltaic output; The mixed integer linear programming model is solved to obtain the capacity configuration parameters of the combined heat and power regional integrated energy system with advanced adiabatic compressed air energy storage.

2. The advanced adiabatic compressed air energy storage capacity planning method according to claim 1, characterized in that, The capacity configuration model of the combined heat and power regional integrated energy system with advanced adiabatic compressed air energy storage comprises the following steps: An operation model of the combined heat and power regional integrated energy system with advanced adiabatic compressed air energy storage is established; A capacity configuration model of the target regional integrated energy system is established according to target operation constraints and the operation model.

3. An advanced adiabatic compressed air energy storage capacity planning device, characterized by, The method comprises the following steps: A first construction module is configured to establish a capacity configuration model of a combined heat and power regional integrated energy system with advanced adiabatic compressed air energy storage; A processing module is configured to use box set robust optimization to process uncertain photovoltaic output to establish a robust optimization model of uncertain photovoltaic output, and the processing module comprises the following steps: A third construction unit is configured to establish a box set of uncertain photovoltaic output; A setting unit is configured to set a set of photovoltaic output disturbance variables by using the box set of uncertain photovoltaic output, and a robust optimization model is constructed according to the set of photovoltaic output disturbance variables; A linearization unit is configured to linearize the robust optimization model according to optimization duality theory to obtain the robust optimization model of uncertain photovoltaic output, and the expression of the robust optimization model of uncertain photovoltaic output is as follows: wherein, is the disturbance variable, is the maximum disturbance variable, is the minimum disturbance variable, is the box uncertainty set, is the PV system capacity, is the solar irradiance under standard conditions, is the inverter conversion efficiency, , , are Lagrange coefficients, is the unit vector; A second construction module is configured to construct a mixed integer linear programming model according to a pre-constructed objective function, the capacity configuration model and the robust optimization model of uncertain photovoltaic output; A solving module is configured to solve the mixed integer linear programming model to obtain the capacity configuration parameters of the combined heat and power regional integrated energy system with advanced adiabatic compressed air energy storage.

4. An advanced adiabatic compressed air energy storage capacity planning apparatus according to claim 3, characterised in that, The first construction module comprises the following steps: A first construction unit is configured to establish an operation model of the combined heat and power regional integrated energy system with advanced adiabatic compressed air energy storage; A second construction unit is configured to establish a capacity configuration model of the target regional integrated energy system according to target operation constraints and the operation model.

5. An electronic device, comprising: The method comprises the following steps: A memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the advanced adiabatic compressed air energy storage capacity planning method according to any one of claims 1-2.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by a processor for implementing the advanced adiabatic compressed air energy storage capacity planning method as claimed in any of claims 1-2.

Images