Advanced adiabatic compressed air energy storage capacity planning method

Abstract

The invention relates to the technical field of energy storage systems, in particular to an advanced adiabatic compressed air energy storage capacity planning method, which comprises the following steps of: establishing a capacity configuration model of a combined cooling heating and power regional comprehensive energy system containing advanced adiabatic compressed air energy storage; processing the photovoltaic uncertain output by adopting box type set robust optimization 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 integrated energy system in the target area. Therefore, the problems that in the prior art, a renewable energy uncertainty processing method cannot guarantee the reliability of a capacity configuration strategy, and then the operation stability of a system is affected are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage systems, and in particular to an advanced adiabatic compressed air energy storage capacity planning method based on box-type robust optimization. Background Art

[0002] With the progress of society and the development of science, the demand for energy in countries around the world is gradually increasing. At the same time, renewable energy such as solar energy and wind energy have become important means for countries to solve energy crises and environmental problems. However, due to the periodicity and volatility of renewable energy generation, it is difficult to use it directly. Therefore, the energy storage system has become an important device for smoothing the output of renewable energy. Advanced adiabatic compressed air energy storage can realize combined heat and power generation while smoothing the output of renewable energy. It can still operate stably and safely in complex environmental areas such as high-altitude cold. Combining the compressed air energy storage system with the combined heat and power system, by adjusting the output of electricity and heat, optimize the use of renewable energy. In the case of unstable wind and light, different output modes can balance the load of the power grid. Therefore, establishing a regional comprehensive energy system containing advanced adiabatic compressed air energy storage is an important solution to improve resource utilization and absorb renewable energy output.

[0003] The output of renewable energy will directly affect the energy storage capacity planning strategy in the regional integrated energy system. Excessive energy storage capacity will reduce the operating economy of the integrated energy system, while too small energy storage capacity cannot meet the operating requirements of the system. At present, there are mainly robust optimization methods to deal with the uncertainty of renewable energy such as solar energy. The existing robust optimization method uses the multi-scenario random planning method to pre-generate the target photovoltaic uncertain output scenario in order to reduce the amount of calculation and improve the calculation efficiency, and then constructs a capacity configuration model based on the operating constraints and preset configuration constraints of the combined heat and power unit model. However, this method cannot construct the actual target photovoltaic uncertain output scenario and cannot guarantee the reliability of the capacity configuration strategy.

[0004] In view of this, the present invention provides an advanced adiabatic compressed air energy storage capacity planning method and device based on box-type robust optimization to solve the problem that the renewable energy uncertainty processing method in the prior art cannot guarantee the reliability of the capacity configuration strategy. Summary of the invention

[0005] The present invention provides an advanced adiabatic compressed air energy storage capacity planning method and device to solve the problem that the uncertainty processing method of renewable energy in the prior art cannot guarantee the reliability of the capacity configuration strategy, thereby affecting the operating stability of the system.

[0006] The first aspect of the present invention provides an advanced adiabatic compressed air energy storage capacity planning method, comprising the following steps: establishing a capacity configuration model for a combined cooling, heating and power regional integrated energy system containing advanced adiabatic compressed air energy storage; using box-type collective robust optimization to process photovoltaic uncertain output to establish a photovoltaic uncertain output robust optimization model; constructing a mixed integer linear programming model based on a pre-constructed objective function, the capacity configuration model and the photovoltaic uncertain output robust optimization model; solving the mixed integer linear programming model to obtain the capacity configuration parameters of the target regional integrated energy system.

[0007] Optionally, the method of establishing a capacity configuration model for a regional integrated energy system for combined cooling, heating and power generation including advanced adiabatic compressed air energy storage includes:

[0008] An operation model of the regional integrated energy system for combined cooling, heating and power with advanced adiabatic compressed air energy storage is established; and a capacity configuration model of the target regional integrated energy system is established based on target operation constraints and the operation model.

[0009] Optionally, the adopting of box-type aggregate robust optimization to process the photovoltaic uncertain output to obtain a photovoltaic uncertain output robust optimization model includes:

[0010] A box-type uncertainty set of photovoltaic output is established; a photovoltaic output disturbance variable set is set using the box-type uncertainty set to construct a robust optimization model based on the photovoltaic output disturbance variable set; and the robust optimization model is linearized according to the optimization duality theory to obtain the photovoltaic uncertain output robust optimization model.

[0011] Optionally, the photovoltaic uncertain output robust optimization model is expressed as:

[0012]

[0013] Among them, ω is the disturbance variable, ω min is the maximum disturbance variable, ω max is the minimum disturbance variable, U is the box uncertainty set, W pv is the capacity of photovoltaic power generation system, S d is the light intensity under standard conditions, is the inverter conversion efficiency, σ, τ, v are all Lagrange coefficients, and e is a unit vector.

[0014] The second aspect of the present invention provides an advanced adiabatic compressed air energy storage capacity planning device, including: a first construction module, used to establish a capacity configuration model of a regional integrated energy system for combined heat and power supply containing advanced adiabatic compressed air energy storage; a processing module, used to process photovoltaic uncertain output using box-type collective robust optimization to establish a photovoltaic uncertain output robust optimization model; a second construction module, used to construct a mixed integer linear programming model based on a pre-constructed objective function, the capacity configuration model and the photovoltaic uncertain output robust optimization model; a solution module, used to solve the mixed integer linear programming model to obtain the capacity configuration parameters of the target regional integrated energy system.

[0015] Optionally, the first building block includes:

[0016] The first construction unit is used to establish an operation model of the regional integrated energy system for combined cooling, heating and power containing advanced adiabatic compressed air energy storage; the second construction unit is used to establish a capacity configuration model of the target regional integrated energy system based on the target operation constraints and the operation model.

[0017] Optionally, the processing module includes:

[0018] The third construction unit is used to establish a box-type uncertainty set of photovoltaic output; the setting unit is used to set the photovoltaic output disturbance variable set using the box-type uncertainty set to construct a robust optimization model based on the photovoltaic output disturbance variable set; the linearization unit is used to linearize the robust optimization model according to the optimization duality theory to obtain the photovoltaic uncertain output robust optimization model.

[0019] Optionally, the photovoltaic uncertain output robust optimization model is expressed as:

[0020]

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

[0022] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in 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 as described in the above embodiment.

[0023] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, 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 embodiment of the present invention utilizes the characteristics of advanced adiabatic compressed air energy storage that can achieve cogeneration of cooling, heating and power to establish a regional integrated energy system to improve resource utilization in complex environmental areas, and adopts box-type aggregate robust optimization to process uncertain photovoltaic output, ensure the reliability of capacity configuration strategy, and enhance the ability of regional integrated energy systems to cope with complex environments.

[0025] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will 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 easily 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 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] Embodiments of the present invention are described in detail below, 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 should not be construed as limiting the present invention.

[0031] The following describes an advanced adiabatic compressed air energy storage capacity planning method and device 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 regional integrated energy system for combined cooling, heating and power generation including advanced adiabatic compressed air energy storage is established.

[0035] In some embodiments, a capacity configuration model of a regional integrated energy system for combined cooling, heating and power generation including advanced adiabatic compressed air energy storage is established, including:

[0036] Establish an operational model of a regional integrated energy system with combined cooling, heating and power including advanced adiabatic compressed air energy storage;

[0037] A capacity configuration model of the integrated energy system in the target area is established based on the target operation constraints and operation model.

[0038] In the actual implementation process, the regional integrated energy system mainly includes an advanced adiabatic compressed air energy storage system, a cogeneration unit, a new energy power station and a thermal power load. Therefore, the embodiment of the present invention first establishes a regional integrated energy system capacity configuration model including an advanced adiabatic compressed air energy storage system and a cogeneration unit.

[0039] Among them, the compressor operation model is:

[0040]

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

[0042] Among them, the operation model of the expander is:

[0043]

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

[0045] Among them, the operation model of the gas storage chamber is:

[0046]

[0047] Among them, P st,τ is the change in air pressure in the air storage chamber at time τ, T st,in is the rated inlet air temperature of the air storage chamber, T st,t is the air temperature in the air storage room at time t, P st,0 is the initial air pressure in the air storage chamber, P st,t is the air pressure in the tank at time t in the air storage chamber, P st,min ,P st,max The upper and lower limits of air pressure in the air storage chamber.

[0048] Among them, the operation model of the heat storage tank is:

[0049]

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

[0051] Among them, the operation model of the combined heat and power unit is:

[0052]

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

[0054] Next, the embodiment of the present invention will establish an objective function based on the target operating constraints and the above-established operating model, with the goal of minimizing the investment and operating cost of the regional integrated energy system, and establish a capacity configuration model for the target regional integrated energy system, wherein the target operating constraints are: when renewable energy power generation exceeds the load demand of the system, the excess electricity is stored in the advanced adiabatic compressed air energy storage system; when renewable energy power generation cannot meet the load demand of the system, the advanced adiabatic compressed air energy storage system releases the stored electricity to meet the load demand of the system, and at the same time it can sell electricity to the power grid to reduce operating costs; considering the operating constraints of different equipment and the capacity planning constraints of the advanced adiabatic compressed air energy storage system, the thermal and electric balance constraints of the regional integrated energy system.

[0055] In step S102, the photovoltaic uncertain output is processed by using the box-type collective robust optimization to obtain a photovoltaic uncertain output robust optimization model.

[0056] In some embodiments, the photovoltaic uncertain output is processed by using box-type aggregate robust optimization to obtain a photovoltaic uncertain output robust optimization model, including:

[0057] Establish a box-type uncertainty set of PV output;

[0058] The box-type uncertainty set is used to set the photovoltaic output disturbance variable set, so as to construct a robust optimization model based on the photovoltaic output disturbance variable set.

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

[0060] In the actual implementation process, the embodiment of the present invention takes into account the uncertainty of photovoltaic output and adopts a capacity configuration model based on box-type set robust optimization to ensure that the system can still operate stably and safely under the worst conditions. First, a box-type uncertain set of photovoltaic output is established, and its photovoltaic uncertain output robust optimization model is considered; secondly, the optimization duality theory, namely the KKT condition, is used to linearize the complex robust optimization model to obtain its linearized model.

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

[0062]

[0063] In the formula, ω is the disturbance variable, U is the box uncertainty set, and W is pv is the capacity of 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] Among them, the ω disturbance 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 equation (7) and it is a nonlinear model that is difficult to solve, it is necessary to convert it into a mixed integer linear programming model through the optimization duality theory, namely the KKT condition.

[0067] According to the optimization duality theory, the uncertain photovoltaic output can be converted into a robust optimization model, expressed as:

[0068]

[0069] Regarding the uncertain parts Construct the Lagrangian function for it:

[0070]

[0071] make Among them, σ, τ, and v are all Lagrange coefficients.

[0072] According to the optimization duality theory, we can get:

[0073]

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

[0075]

[0076] Among them, ω is the disturbance variable, ω min is the maximum disturbance variable, ω max is the minimum disturbance variable, U is the box uncertainty set, W pv is the capacity of photovoltaic power generation system, S d is the light intensity under standard conditions, is the 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 the actual implementation process, firstly, based on the physical characteristics and stable and safe operation requirements of each device in the regional integrated energy system, the corresponding equipment operation constraints, equipment 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 air 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 compressor and turbine output, respectively, V ST,rmin 、V ST,rmax and Q tes,rmin , Q tes,rmax They are the upper and lower limits of the volume of the air storage chamber and the heat storage capacity of the heat storage tank respectively.

[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, it is required that 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 They are binary variables of the compression condition and expansion condition of the advanced adiabatic compressed air energy storage power station at time t. When the value is 1, it means that the power station is in the compression condition and expansion condition respectively. When the value is 0, it means that the compression condition and expansion condition of the power station are in idle state respectively.

[0087] Among them, for the ramp constraint of the combined heat and power unit, the power change at 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 capacity of the combined heat and power unit, R chp,down , R chp,up are the up and down ramp rates of the cogeneration unit respectively.

[0090] System operation requirements based on the combined heat and power microgrid, which must meet the system's internal electricity and heat balance constraints and wind and solar power curtailment constraints.

[0091] Among them, in order to ensure the normal operation of the microgrid, the system electric and thermal loads must be balanced:

[0092]

[0093] Among them, P load,t is the system load at time t, P pv,l is a random variable, and its uncertainty is considered using a box-set robust optimization method. load,t is the system heat load at time t.

[0094] Among them, according to the above-mentioned photovoltaic uncertain output robust optimization model, the following constraints need to be added to the power balance:

[0095]

[0096] Among them, the system's wind and solar curtailment must be less than the specified value:

[0097]

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

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

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

[0101] Among them, C inv is the average daily investment cost of an advanced adiabatic compressed air energy storage power station, C m is the operation and maintenance cost of advanced adiabatic compressed air energy storage and combined heat and power units, C loss is the system’s wind and solar curtailment cost, C grid is the cost of interacting with the grid, C MT is the fuel cost of the CHP unit.

[0102] The calculation expressions of various costs are as follows:

[0103]

[0104] Among them, f caes,c 、f caes,g 、f caes,st 、f caes,tes They represent the investment cost per unit compression power, per unit expansion power, per unit volume of gas storage tank, and per unit heat storage in the advanced adiabatic compressed air energy storage power station, respectively. 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, a is the discount rate, T is the project cycle, 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, e pv 、e wt are the unit solar curtailment cost and the unit wind curtailment cost respectively, P pv , P pv,l are the predicted photovoltaic output and the actual photovoltaic output, P wt , P wt,l are the predicted wind power output and the actual wind power output, P grid,t , P chp,t are the interaction power between the system and the 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 sales cost between the system and the grid at time t, C gas is the unit price of natural gas, L HVNG is the calorific value of natural gas.

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

[0106] In the actual implementation process, MATLAB combined with the commercial solver CPLEX can be used to solve the mixed integer linear programming model to obtain the capacity configuration parameters of the integrated energy system in the target area.

[0107] In summary, according to the advanced adiabatic compressed air energy storage capacity planning method proposed in the embodiment of the present invention, a regional integrated energy system is established by utilizing the characteristics of advanced adiabatic compressed air energy storage that can realize cogeneration of cooling, heating and power, so as to improve the resource utilization rate in complex environmental areas, and the box-type set robust optimization is used to handle the uncertain photovoltaic output, so as to ensure the reliability of the capacity configuration strategy and enhance 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 the historical output probability function of the photovoltaic output, but only needs to consider the optimal value in the worst case in its uncertain set, and only requires that the fluctuation range of the uncertain variable does not exceed its uncertain set, and the optimal solution is the feasible solution.

[0108] Next, the advanced adiabatic compressed air energy storage capacity planning device proposed according to an embodiment of the present invention is described with reference to the accompanying drawings.

[0109] Figure 2 It is a block diagram of an advanced adiabatic compressed air energy storage capacity planning device according to an embodiment of the present invention.

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

[0111] Among them, the first construction module 201 is used to establish a capacity configuration model for a regional integrated energy system of combined cooling, heating and power with advanced adiabatic compressed air energy storage. The processing module 202 is used to process the photovoltaic uncertain output using box-type set robust optimization to obtain a photovoltaic uncertain output robust optimization model. The second construction module 203 is used to construct a mixed integer linear programming model based on the pre-constructed objective function, capacity configuration model and photovoltaic uncertain output robust optimization model. The solution module 204 is used 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 building block 201 includes:

[0113] The first building block is used to establish an operation model of a regional integrated energy system for combined cooling, heating and power generation with advanced adiabatic compressed air energy storage;

[0114] The second construction unit is used to establish a capacity configuration model of the integrated energy system in the target area according to the target operation constraints and the operation model.

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

[0116] A third construction unit is used to establish a box-type uncertain set of photovoltaic output;

[0117] A setting unit, used for setting a photovoltaic output disturbance variable set by adopting a box-type uncertainty set, so as to construct a robust optimization model according to the photovoltaic output disturbance variable set;

[0118] The linearization unit is used to linearize the robust optimization model according to the optimization duality theory to obtain a robust optimization model for photovoltaic uncertain output.

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

[0120]

[0121] Among them, ω is the disturbance variable, ω min is the maximum disturbance variable, ω max is the minimum disturbance variable, U is the box uncertainty set, W pv is the capacity of photovoltaic power generation system, S d is the light intensity under standard conditions, is the inverter conversion efficiency, σ, τ, v are all Lagrange coefficients, and e is a unit vector.

[0122] It should be noted that the aforementioned explanation of the embodiment of the advanced adiabatic compressed air energy storage capacity planning method is also applicable to the advanced adiabatic compressed air energy storage capacity planning device of this embodiment, and will not be repeated here.

[0123] According to the advanced adiabatic compressed air energy storage capacity planning device proposed in the embodiment of the present invention, a regional integrated energy system is established by utilizing the characteristics of advanced adiabatic compressed air energy storage that can realize cogeneration of cooling, heating and power, so as to improve the resource utilization rate in complex environmental areas, and adopt box-type set robust optimization to handle the uncertain photovoltaic output, so as to ensure the reliability of the capacity configuration strategy and enhance 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 the historical output probability function of the photovoltaic output, but only needs to consider the optimal value in the worst case in its uncertain set, and only requires that the fluctuation range of the uncertain variable does not exceed its uncertain set, and the optimal solution is the feasible solution.

[0124] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

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

[0126] When the processor 302 executes the program, the advanced adiabatic compressed air energy storage capacity planning method provided in the above embodiment is implemented.

[0127] Furthermore, the electronic device further comprises:

[0128] The communication interface 303 is used for communication between the memory 301 and the processor 302 .

[0129] The memory 301 is used to store computer programs that can be run on the processor 302 .

[0130] The memory 301 may include a high-speed RAM memory, and may also include a non-volatile memory (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 communicate with 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. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only 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 communicate with each other through an internal interface.

[0133] The processor 302 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0134] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned advanced adiabatic compressed air energy storage capacity planning method.

[0135] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0136] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0137] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0138] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0139] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0140] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0141] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0142] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An advanced adiabatic compressed air energy storage capacity planning method, characterized in that: The following steps are involved: Establish a capacity configuration model for a regional integrated energy system with combined cooling, heating and power including advanced adiabatic compressed air energy storage; The box-type ensemble robust optimization is used to process the photovoltaic uncertain output, so as to establish the 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; Solve the mixed integer linear programming model to obtain the capacity configuration parameters of the target area integrated energy system.

2. The advanced adiabatic compressed air energy storage capacity planning method according to claim 1, characterized in that: The capacity configuration model of the regional integrated energy system for combined cooling, heating and power generation with advanced adiabatic compressed air energy storage is established, including: Establishing an operation model of the regional integrated energy system for combined cooling, heating and power generation containing advanced adiabatic compressed air energy storage; A capacity configuration model of the target area integrated energy system is established according to the target operation constraints and the operation model.

3. The advanced adiabatic compressed air energy storage capacity planning method according to claim 1, characterized in that: The box-type aggregate robust optimization is used to process the photovoltaic uncertain output to establish a photovoltaic uncertain output robust optimization model, including: Establish a box-type uncertainty set of PV output; The box-type uncertainty set is used to set a photovoltaic output disturbance variable set, so as to construct a robust optimization model according to the photovoltaic output disturbance variable set; The robust optimization model is linearized according to the optimization duality theory to obtain the photovoltaic uncertain output robust optimization model.

4. The advanced adiabatic compressed air energy storage capacity planning method according to claim 3, characterized in that: The expression of the photovoltaic uncertain output robust optimization model is: Among them, ω is the disturbance variable, ω min is the maximum disturbance variable, ω max is the minimum disturbance variable, U is the box uncertainty set, W pv is the capacity of photovoltaic power generation system, S d is the light intensity under standard conditions, is the inverter conversion efficiency, σ, τ, v are all Lagrange coefficients, and e is a unit vector.

5. An advanced adiabatic compressed air energy storage capacity planning device, characterized in that: include: The first building block is used to establish a capacity configuration model for a regional integrated energy system with combined cooling, heating and power including advanced adiabatic compressed air energy storage; A processing module, used for processing the photovoltaic uncertain output by adopting box-type set robust optimization to establish a photovoltaic uncertain output robust optimization model; A second construction module is used to construct 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; A solution module is used to solve the mixed integer linear programming model to obtain the capacity configuration parameters of the target area integrated energy system.

6. The advanced adiabatic compressed air energy storage capacity planning device according to claim 5, characterized in that: The first building block comprises: The first construction unit is used to establish an operation model of the regional integrated energy system for combined cooling, heating and power generation containing advanced adiabatic compressed air energy storage; The second construction unit is used to establish a capacity configuration model of the target area integrated energy system according to the target operation constraint conditions and the operation model.

7. The advanced adiabatic compressed air energy storage capacity planning device according to claim 5, characterized in that: The processing module comprises: A third construction unit is used to establish a box-type uncertain set of photovoltaic output; A setting unit, configured to set a photovoltaic output disturbance variable set by using the box-type uncertainty set, so as to construct a robust optimization model according to the photovoltaic output disturbance variable set; A linearization unit is used to linearize the robust optimization model according to the optimization duality theory to obtain the photovoltaic uncertain output robust optimization model.

8. The advanced adiabatic compressed air energy storage capacity planning device according to claim 7, characterized in that: The expression of the photovoltaic uncertain output robust optimization model is: Among them, ω is the disturbance variable, ω min is the maximum disturbance variable, ω max is the minimum disturbance variable, U is the box uncertainty set, W pv is the capacity of photovoltaic power generation system, S d is the light intensity under standard conditions, is the inverter conversion efficiency, σ, τ, v are all Lagrange coefficients, and e is a unit vector.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in 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 as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the advanced adiabatic compressed air energy storage capacity planning method as described in any one of claims 1 to 4.

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